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What’s On My Mind: 4 Angles on Local AI

This week’s been one of those weeks where so much has happened and it’s so messy that there isn’t a theme, so I’m going to foam at the keyboard, write down my thoughts, and see what comes out on the other side.

Part 1: A Fable of Fable

“A fable is a short fictitious story designed to teach a specific moral lesson.”

This week, Anthropic’s fifth generation model family, Fable, became available for a short period of time. It debuted on Tuesday and was blocked by the US government on Friday for ambiguous reasons without clear evidence, despite the fact that its larger, more dangerous version, Mythos, has been available to large corporations for a few weeks now.

In my tests of Fable while it was still available, it was excellent at what it did, very, very expensive, and has clear use cases. What I found interesting was the level of amazement people had at it - folks were raving over its abilities to discern intent from ambiguous, poorly written prompts and turn them into real results.

While that’s admirable of Fable’s capabilities, it speaks more towards people’s unwillingness to take the time and plan and think things through. Opus 4.8 on xHigh or Ultra settings can accomplish most of what Fable can do - if you prompt it well and let it iterate and think and review.

Here’s my general process for this, something Katie Robbert taught me. First, never just go off and do something. Take the time to build out the requirements, ideally with a framework like the 5P Framework by Trust Insights™. Once you’ve got requirements nailed down, build a specification. This can be a design spec, a writing spec, a code spec, something that says “here’s what we’re doing”. Then build a workplan from your spec. Finally, and only after you have requirements, spec, and plan, do you have AI go off and do the thing.

If you follow this general recipe, not only do you get great results, you also don’t have to use the biggest, heaviest, most costly AI model to do it with. By the time you’ve reached the workplan and reconciled it with the requirements and spec, you’ll have anticipated the majority of things that could go wrong.

Where I found Fable quite powerful was in review; I had it delegate lighter tasks to smaller sub-models but have it be a master review agent at the beginning and ending of a process, uncovering lots of bugs and issues that previous QA runs hadn’t picked up or seen as a coherent picture. In that regard, it impressed the heck out of me.

But the fable here is something I wrote about earlier this year - if you want guaranteed access to AI, you absolutely must have a private, local version running somehow. It can be on your own hardware if you have a bespoke machine like an Asus GX10 or NVIDIA DGX Spark, or a well appointed Mac. It can be on your company’s hardware. It can be on a bespoke hosting service, ideally outside your local jurisdiction, but no matter what solution you pursue, you had better have a fallback.

The events of this week proved that any government can unilaterally cut you off from AI services and because cloud providers must adhere to lawful requests, your access to cloud AI is contingent on your government’s approval. Having your own as backup isn’t a nice to have, not if your business relies on generative AI now.

The AI policy nerds call this AI sovereignty; every country should have its own AI so that no one country or government has control over it. More on this in a bit.

Part 2: Mini-token-maxxing

A couple of weeks ago, Minimax M3 came out and the company changed its billing for their token plan. Previously, for the M2.7 model, they billed by request, which was ideal for AI agents like OpenClaw and Hermes Agent. When M3 came out, they switched it to token billing, allotting 1.7 billion tokens per month on their Plus plan.

That sounds like a lot, but it’s really not. How much not? When I was doing some work with my agent earlier this week, I kept hitting the 5 hour usage limit wall. Curious, I switched its operation from Minimax to Qwen 3.6 running on my MacBook (yay local AI). What I found shocked me - Hermes Agent was churning through about 13 million tokens an hour. Now, most of those were cached, meaning that the processing load was relatively small, but Minimax bills by token whether it’s cached or not.

If you haven’t worked with pay-as-you-go AI services (like Claude Enterprise, for example, or the many API versions of common AI tools), there’s usually a difference between new tokens and cached tokens, in terms of pricing. Cached tokens usually cost significantly less than new ones, because if you can re-use cached tokens, you cut your costs.

Here’s a simple example. Imagine you commission a ghostwriter to write a blog post for you. The ghostwriter bills by the hour. After the first paragraph, you have a chat because you want the post to go in a different direction. If the ghostwriter has to start from scratch, you’re going to pay full price for their output. If, on the other hand, the ghostwriter can reuse the paragraph they’ve already written - a cached version, then you don’t have to pay for that time and output again.

That’s what cached tokens means - AI already has some output it can reuse. Normally, in API versions of AI, this is a discount, but under Minimax’s new plan, cached tokens are the same as regular ones. The ghostwriter charges you again for a paragraph they’ve already written and you’ve agreed it can reuse.

So my little agent churning away at 13 million tokens an hour - when I plugged it into Qwen, I saw that almost 90% of the tokens were cached. If I was on a regular pay-as-you-go service, I would be spending very little. But instead, on the current token plan from Minimax, I’m using up my quota whether or not the tokens are new.

Why are they doing this? Because token plans - and by these I mean any flat rate plan, like Claude Max, ChatGPT, etc. - are money losers for AI companies, often substantial ones. If you look at Claude Max 20 and how much output you get for $200, you’re getting roughly $8,000 worth of tokens. That’s a 97.5% discount - and there’s no way that’s sustainable in the long term. Minimax likely ran into token economics faster than Anthropic, so the new plan with the new model is more realistic in terms of what you pay versus what you get.

Expect this to be the case across the industry in the coming months and years. No business can reliably sell its product at a 97.5% discount for long. Minimax’s token plan used to be the best deal in AI; today, it’s a good deal but not a great one - and what you can run on your machine can match it.

Also in the news this week, Minimax M3 became open weights; anyone with enough hardware can download it and run it for free, but you need lots and lots of hardware. It’s a beefy model, 428 billion parameters with 23 billion active parameters; a little smaller than DeepSeek, a little larger than Qwen 3.6. M3 is a highly capable model on par with Opus 4.6 for most tasks.

A colleague asked this week why these companies are giving away their models. The answer is that China, which heavily subsidizes its AI companies as well, doesn’t want the US to have a monopoly on AI - and with the US government blocking Fable 5, that concern is very real. By giving away their models, they ensure that their technology is broadly available and has no linchpin, no single shatter point.

That’s the heart of AI sovereignty, and it’s what China in particular is providing to the rest of the world by giving away its best in class models. And these models aren’t just for the hardware-rich (i.e. folks who can afford to drop tens of thousands of dollars on heavy duty compute) - a model like Qwen 3.6 35B-3AB runs on a well appointed MacBook Pro. According to OpenRouter’s statistics (which measure mostly heavy developer usage of AI, not consumer usage), more than 40% of the planet’s AI traffic uses Chinese models of some kind because of their substantially lower costs.

Part 3: Fat Documents

A few weeks ago, Andy Crestodina and I did a piece together on document formats and AI, and how much more resources some documents use compared to others. At the time, I said my piece as well published my own longer version on ways to save token budgets, but this week I decided to quantify it. I plugged Claude Code into my local AI server and gave it a Word document and the equivalent Markdown format to process. In both cases, I gave it a super naive prompt - “summarize this document”. The backend model for both was Qwen3.6-35B-3AB, running on my MacBook.

My local AI summarized the document in 28,693 tokens, of which 20,480 were cached, at 71.4% efficiency. That’s fast and lightweight.

The exact same document in Word DOCX format? 52,163 tokens, of which 26,624 were cached, at 51% efficiency. The Word document was 81% heavier, and 28% slower and less efficient. That’s for a single document on a local model; now imagine hundreds or thousands of employees dragging and dropping Word documents, Powerpoints, and Excel spreadsheets into AI all day, every day. You’re talking an unnecessary 81% increase in token usage.

And if your enterprise pays by the token, those savings could be considerable. But I wouldn’t have known how big the savings were until I used my local setup to watch in real time as I dropped documents in for processing. That’s another advantage of local AI - you can see exactly what’s happening, blow by blow, in your AI system of choice.

Imagine going to your AI Council and telling them, at the enterprise level at least, that you could reduce token usage by 45%. That’s not pocket change, not if you’re paying by token. You could do that today by helping team members convert documents to open, text-based formats like this:

Word: convert to Markdown
Excel: convert to YAML
Powerpoint: convert to JPG images at 640x480

My test this week shows that this works and is highly effective for saving token budget. I would never have seen that if I didn’t have control of the model itself.

Part 4: Data Centers and Responsible AI

During an event I was speaking at this week, an audience member asked for my opinion on data centers and responsible AI. My response was the one I always give:

First, use AI as little as possible. Use it for what it’s best at, but know that it LOVES to reinvent the wheel, and it’s in AI providers’ economic incentives for it to do this. The way I think about AI usage today is to use it to build tools (coding is one of its most capable use cases) that then run without AI.

Here’s a simple example. Suppose you were using AI with some data from your Google Analytics. If you handed some of your data to AI and asked for an analysis, it would do a very capable job, write a bunch of Python code behind the scenes, and then give you the answer. Very likely, the answer would be correct.

Then the next month, when it’s time to write that report again, you ask AI for help and it repeats the same process - but it doesn’t fish out the code it wrote behind the scenes to process the data last month. No, it writes it all over again, completely reinventing the wheel.

That’s not how I use AI. Instead I think through whether the outcome is something that generative AI can do or whether it’s something regular code can do - and if it’s the latter, then I’ll spend time having AI build the code so I can reuse it month after month and never have to use AI for the task.

Even for something where AI is the right choice, I try to build as much non-AI infrastructure as possible. For example, I start my days by asking Claude inside Claude Code to give me my daily briefing. Under the hood are 4 different command line applications that talk to systems like Asana, GMail, Jira, and others - and none of those applications use AI at all. AI parses the results, but it doesn’t go and fetch data. It doesn’t need to, and it’s not well suited for that.

And I’m starting to test swapping out my Qwen model for this particular task because it’s just summarization. Now that the tools all exist, I don’t need to use someone else’s cloud AI for what’s essentially just summarization and prioritization. The version of Qwen I use, per Artificial Analysis, is on par with the agent skills of Sonnet and exceeds Haiku:

Second, if you’re concerned about the power and water consumption of data centers, use local AI as much as you can. I’ve switched over more and more of my AI usage to my MacBook running Qwen 3.6 locally. Why? Because the electricity that powers my laptop comes from the roof of my house (solar power). There’s no fresh water usage at all; if there was, it’d be a disaster because it would mean my laptop was wet and my office was flooded.

Data centers have real impacts on the communities they’re located in, drawing power and water - and different companies have different approaches to sustainability. If you care about that, read the various providers’ annual reports on sustainability and choose a provider based on which company is doing the most to lessen its impact.

If you’re in a position to do so, run your own AI as much as you can, for as many tasks as you can. Every API call you make to your own hardware is power and water you’re not consuming from somewhere else; even if your own electricity isn’t green, you’re drawing it from your local grid and not having a data center draw it from a very concentrated location.

But even if you don’t care about sustainability, conservation here means literally saving money, especially in the enterprise. Every token that’s generated by a cloud AI provider is a token that costs you money. Every token you generate on your own infrastructure is a token you probably paid less for.

If you were to look around the average enterprise, I’d wager most of the compute power of that enterprise is sitting idle, most of the time. For example, at Trust Insights, I know most of my coworkers work really hard. They’re smart people. They do all kinds of different tasks. But I’d bet their GPUs and NPUs on their Macs are near idle almost all day long.

What if you could harness that power? Well, you can. There are projects like Exo that network together computers on the same physical network (like your office LAN) and soak up that spare computing power to create an AI supercluster. Right now it works best on a LAN, but the community is trying out different options to see about making it work on a WAN for remote workers.

Wouldn’t it be cool if you could create an AI supercluster that served most of your AI needs, powered by the computers and the hardware you’ve already paid for? That’s what the future of AI could look like for enterprises that can put together their the hardware they already own.

Part 5: Wrapping Up

I suppose the throughline for all of today’s hodgepodge of items was about local AI, about its benefits for AI sovereignty, sustainability, business continuity, and more.

Local AI is going to become more and more important as we see more issues arise with government control of AI, with sustainability, with token budgets. Having AI that is under your control, on your premises, on your physical hardware, or in your bespoke data center providers will give you those advantages of having reliable AI that cloud providers just can’t promise because of all these external factors.

Go back to the archives, and I’ll put some links below for all the issues on how to set up local AI. If your company or you as a professional are reliant on generative AI. You have an obligation to have local AI on tap for business continuity, for sustainability, and for cost reduction.

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In GEO 101, the first course I built on the basics of GEO, I taught you about presence, appearance, and relevance, the three phases of GEO, and what you need to do in each phase to align with how AI search operates.

The top piece of feedback we got at Trust Insights about it was, “okay, great, but how do I tell my boss that we’re ‘winning’ at GEO?“ After I quelled my murderous rage at your boss on your behalf, Katie and I sat down and worked out a straightforward, aligned methodology for doing this.

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Almost Timely News: 🗞️ How AI Detection Works (2026-06-07)

Christopher S. Penn — Sun, 07 Jun 2026 10:31:01 GMT

Almost Timely News: 🗞️ How AI Detection Works (2026-06-07) :: View in Browser

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What’s On My Mind: How AI Detection Works

This week, let’s talk AI detection and how it works. My buddy Becca pinged me earlier this week with this request:

“I need your wizard secrets on AI detectors 😭 no matter how I write I get flagged! Even voice to text! HOW DO YOU DO IT?! Your posts are perfect and so well written which usually means AI would flag them but they don’t?“

After a lengthy exchange, I asked whether she’d had Claude Code try to solve the problem, and Claude told her no, it wouldn’t help her reverse engineer an AI detector.

And it was at this point where I cracked my knuckles, looked askance at the false god, and said to the Twisting Nether, “BET.”

Here’s the story of how I reverse engeineered an AI detector and what I found.

Part 1: Writing is Code

If you recall from a couple months ago, I wrote about some of the ways we can measure writing. Writing is code, at its heart. It’s not purely creative, because there are clear rules to writing. As I say in my keynotes, I can speak the words “I’m spilling the tea” and you know what they mean, even if you don’t understand the slang.

On the other hand, if I say, “Tea the spilling I’m”, you might be forgiven for thinking my cat walked on the autocorrect. Why? Because language has clear structure and rules. In North American English, that usually means (and yes, there are tons of exceptions, the English language is practically made of exceptions) subject-verb-object as a word order. In fact, there’s like a dozen or so languages on planet Earth (out of 1,377) that are object-verb-subject like Aiwoo and Urarina. The majority of languages are subject-object-verb (”I’m the tea spilling”).

So language has rules, order, and predictability. It’s code. In fact, code is writing and writing is code. When you ask someone who’s working in Python or Rust what they’re doing, more often than not they’ll say they’re writing code. Not making code, not generating code, not typing code, but writing it.

In turn, that means it can be measured and quantified. There’s an entire field about this, natural language processing (NLP), which predates generative AI by decades. Folks who have been in the AI space since the Obama years likely remember all the ups and downs of NLP during that time, from IBM Watson winning Jeopardy to the AI winter of the mid 2010s.

Part 2: What You Can Quantify

Before we can get to reverse engineering an AI detector, we have to start with reverse engineering writing. What are the different measures of writing we can objectively analyze? As I mentioned just above, NLP has been at this practice for decades. There are over 60 different measures of writing and ways to measure writing mechanistically, deterministically. (Aka there’s a concrete number at the end of the analysis)

Let’s take a look at just a handful; I’m working on a course for Trust Insights that will go into these in much greater depth, but this is a start.

Sentence length. How long are your sentences? Sometimes we write super long sentences - I do this a ton when I’m thinking out loud or when I’m dictating; those issues of this newsletter that have super long run-on sentences are a dead giveaway that I dictated the sentence rather than typed it.
Sentence length variation. Imagine you analyzed a blog post you write and you tabulated all the sentence lengths. For some of my content, my sentence length variation looks like this: 14-12-4-17-8-14. Sometimes I write super short sentences, sometimes I write very long ones.
Sentence length patterns. Those short and long sentence patterns are not random. If you read my writing, a very short sentence is usually followed by a very long one.

Why? If you look at how I write, especially in places like LinkedIn, I’m very fond of the short/long pattern. I’ll write something like this:

(Expository paragraph)

(Short emphasis)

(Long explanation)

That’s just my personal style. Other people write what folks have amusingly titled “broetry” (mostly dudes, hence the name) who write as though it was literally transcribed from William Shatner’s Captain Kirk:

You know…

…what…

…I mean!

Here’s the thing about sentence patterns - our writing style is predicated in part on how we use sentences, along with how we use line breaks, punctuations, em dashes, the works. For example, I never, ever use the em dash and I never have. Why? Not because I care about AI, but because I learned to type on a manual typewriter. An em dash is two keystrokes. An en dash is one. Instead of em dashes, I write with a space/en dash/space pattern. Again, that’s a personal style thing.

But the key takeaway here is that our writing style, as individuals, is a combination of very distinct patterns.

Part 3: How AI Detectors Work

Now, let’s talk about AI writing. All generative AI, all AI period, is probability. All of it. All AI models are probability engines and they produce their predictions as the next word, the next sentence, the next thing in order. Unguided, they produce the highest likely probability for an item in a sequence.

Let’s watch this example of KoboldCPP, which is a language model server serving up the Skyfall model, and watch it generate words. You’ll see the text on the left, and you will see the actual server making predictions on the right.

Look carefully where at token 10 it was saying how are you and then its choices were starting or doing. How are you doing or how are you starting? It had to make a decision at that point in the flow as to whether one word is a better fit than the other. And it chose starting at 50.97% probability versus doing at 49.03% probability.

Because of their very nature, AI outputs have much more uniformity than human outputs when it comes to… well, anything. That’s their nature, and that nature is what not only makes them useful, but also what gives away that something is AI generated.

You know AI generated content. You can feel this, when you read a post or look at an image or a video. It’s not that it’s too perfect - it’s very often not. It’s not that it’s glaringly wrong either, not with skilled use. (Yes, there’s a lot of unskilled use out there that’s dead easy to spot)

It’s that when you use AI, it creates patterns that are much more averaged out and homogenous. For example, going back to sentence length from the previous section, I write like this: 14-12-4-17-8-14

What does AI write like? Probably more like this: 14-12-13-15-14-13-14.

And you can feel that, even if you can’t put it in words. There’s a certain roundness to it that’s obvious once you know to look for it. There’s a cadence that AI keeps which is intrinsic to it, and those patterns are what AI detectors often look for. All AI models have writing styles, and like people have writing styles, there is something of an AI writing style.

The same is true for AI image generation, video generation, music generation - it all comes out sort of average, because that’s literally how it works under the hood. It’s coming up with the highest probabilities for a given context.

Very often, we want high probability outputs. If you’re doing protein folding, trying to predict the next mutation of a virus, you absolutely want high probability. If you’re doing forecasting, trying to determine when the holiday shopping season will start, you absolutely want high probability. If you’re doing law and you are writing legal documents that have to fit a certain standard, you want the highest probability because you don’t want original writing in a legal contract. You want writing that stands up to a proven standard, that has stood the test of time in courts.

We constantly want to know what’s going to happen next. Weather forecasting has existed since humans first started farming, and that’s probability assessment that predates language itself.

Let’s talk specifically about writing. Here’s the thing about creative work: great creative work in general is low probability. As I say in one of my keynotes, if you write “he had gastric distress”, that’s a high probability, informative, but not particularly creative way to describe a situation. If you write “it looks like he powerwashed his toilet with Nutella”, that’s is a very low probability phrase. You’re not going to find that in medical reports, in most of the training data that AI has learned from.

When people complain about AI outputs in the creative space, it’s because fundamentally, there are no surprises in AI generated content. There’s no wacky turn of phrase, no unexpected small details, no signatures of a single mind.

The lack of surprising patterns - and there are dozens of them - are what AI detectors have been built to recognize. That’s how they work. They’re built to examine things like sentence length patterns, like word distribution and diction, and dozens of other combinations to look for common fingerprints. Depending on which company you talk to, they may also use LLMs - language models - in their detectors as well (which is a whole different problem). These tools use AI to essentially measure how surprising the next word in a sequence is.

When AI writes and it doesn’t produce surprises, these tools flag that and say this is probably AI generated because of the lack of surprises.

The Problem With AI Detectors

Here’s the fundamental problem with AI detectors: they’re very, very fallible. Why? Because lack of creativity in writing isn’t just a machine thing - it’s a very, very human thing. Crappy writers generate copy that looks an awful lot like machine-generated copy. Mediocre photographers take images that look just like AI generated images. Songwriters who lack skill make templated songs that sound like every other song in their genre and sound like AI made them - because it’s all probability.

This is especially problematic in things like education and academia because education is SO templated that the outputs will inherently resemble AI. Take, for example, the academic standard five paragraph essay. There are literal templates that students are handed:

First paragraph is your introduction where you state your premise
Last paragraph is your conclusion where you recap your premise
Three paragraphs in the middle where you explain your premise
Recommended/required transition words to move from paragraph to paragraph
Recommended sentence lengths, third person voice, neutral point of view

Guess what this looks like? Yep. AI. Even the best human writers, when forced into a rigid template like this, are going to create slop - human slop, but slop nonetheless. High probability, low creativity slop. Not a single surprise in the text.

And then when a school district or a teacher uses an AI detector and it sees the exact same patterns that AI generates, they hand out punishments for a problem they created - in an attempt to standardize education (read: make obedient workers for industry), they often falsely accuse, and students have no recourse to prove they didn’t use AI, they just complied with the slop-generating template they were given for the assignment.

Bottom line: AI detectors should never, ever be used for anything punitive until their false positive rate is zero. And since that’s unlikely to happen any time soon, they should not be used in any realm - academic or commercial - for anything punitive, period, end of story. If you or your organization uses AI detectors in a punitive fashion, expect lawsuits to come your way.

Part 4: An Analysis of LinkedIn Posts

Now that we’ve covered the mechanics of all this, let’s get back to Becca’s question. How do my human-generated LinkedIn posts not get flagged/detected as AI? And as a fun aside, Kelsey on the Trust Insights team uses Claude to generate promotional posts on my LinkedIn account, and those absolutely get flagged as AI. (You’ll see them labeled ”Post generated by the TI team with Claude Sonnet.” or similar)

I should add that Becca’s question is about a person proactively taking a LinkedIn post and checking whether it’s AI or not, not that LinkedIn is doing this on a post by post basis. They did announce not too long ago that they’re cracking down on AI generation and automation, but they’ve not released any details about how that will work, so there’s no cause for panic yet.

Plus, the use cases of AI that they’re likely going to go after are the really obvious ones, like that one dude - and it’s always a dude - who has an AI bot that comments with the exact same structure every single time. “The thing you pointed out here is sharp. I noticed this. Curious what you think about that.“ Same template every time, hundreds of comments in a row, easy to spot.

To figure out how AI detectors work, we need data. I went and gathered up all the pieces that I wrote recently, about 16 of them, along with the 5 pieces that the TI team wrote that are known AI (Claude Sonnet). I grabbed Becca’s posts, and then three sets of posts from people I know for sure don’t use AI to write on LinkedIn - Jay Acunzo, Ann Handley, and Ashley Faus.

Then I went to 7 different AI tools - Claude, Qwen, Meta, Perplexity, Gemini, ChatGPT, and Copilot - and had them generate thematically similar posts on public speaking, authenticity, etc. So that semantically, they’d all be in the same ballpark as the human content.

Once I had all the data gathered, I fired up Claude Code to create a Python application that would apply over 60 different text metrics to each post, human and AI, then perform multivariate regression analysis on all the features to see what features most likely correlated to known AI and human posts.

This took a few minutes. While the analysis ran, I had Claude Code set up Camoufox (a command line-controlled browser) and feed each post to an AI detector, getting the score back from it as to whether it thought the post was AI or human. I started with originality.ai and then added GPTzero and WinstonAI to the mix.

What I found was that there were about a dozen features that very clearly denoted whether a post was likely AI generated or not, with about 85% accuracy, and those features were common in my home-grown AI detector as well as the commercial ones. The biggest single feature was perplexity, a measure of surprisal. Perplexity refers to how surprising the next predicted word in a sequence is.

For example, this example has a perplexity of zero:

“I pledge allegiance to the” where the next word is “flag”. Flag has a perplexity of zero. Zero surprise.

“I pledge allegiance to the” where the next word is “rutabaga”? Off the charts perplexity. Total surprise to see that word instead of flag.

When AI writes, because of its very nature as a probability engine, it is always writing with very low perplexity. When it’s predicting the next word in a sentence, by definition it’s choosing the highest probability given the context, and that in turn means low perplexity.

And here’s the zinger - the error rate, the false accusation rate - was about the same across all the detectors, about 15%. That means the AI detectors I tested - all of which market themselves as the most accurate, most trusted AI detectors on the market - will falsely accuse someone of using AI about 1 out of 6 or 7 times.

Oops.

More important, the dozen or so features are all things that AI tools, properly prompted, could easily replicate and negate the detectors skills entirely.

Because these detectors are mechanistic, deterministic, they can’t detect what we humans know is wrong with a piece of copy or an image, that too-smooth, too average, no surprises writing. They can detect word variance, sure, but they can’t tell whether someone’s used AI or is just a mediocre writer, nor can they spot the lack of low probability writing.

Along the way, I ran into a wrinkle. Becca’s posts are flagged as AI at 5-6x the rate that Ann, Jay, Ashley, and mine are. I couldn’t figure out why until she told me that she voice dictates all her content, and then Claude assembles it using her words.

And that answered everything. Even when AI is using your words, it’s still going to assemble them in statistically predictable patterns. Unless it’s transcribing them verbatim, in the literal words coming out of your mouth, it’s layering your words into a machine-generated scaffolding that has the hallmarks of high probability writing. That means AI detectors will still flag it as AI generated, even when it is your original words.

Here’s an example. Compare these two paragraphs.

“For eight hundred years have I trained Jedi. My own counsel will I keep on who is to be trained.”
“I have trained Jedi for eight hundred years. I will keep my own counsel on who is to be trained.”

Same exact words. One is unquestionably Yoda, the other is how AI will rearrange Yoda’s words into the most common structures, erasing Yoda’s identity AND creating statistically visible patterns, low perplexity.

The perplexity - guessing the next word in a sequence - is very high in the first paragraph by Yoda because he’s speaking in a word order that’s decidedly out of sequence for most English dialects. High perplexity. The second paragraph is low perplexity because even though it’s the same words, they’re arranged in a way that is not surprising or novel.

Even when you’re using your own words, if AI is arranging them, it will arrange them into statistically visible patterns, patterns that bring back that weird, uncanny feeling that something’s missing.

Now, can you teach AI how to write more like you? Yes, you can, especially if you give it the code I generated and your exact writing patterns and have it repeatedly check its output against those metrics. It will come eerily close to you, but it will still be missing some of your spark.

More important, if you’re concerned with people using AI detectors on your content, then voice dictation and AI assembly of your words - in low perplexity sequences - is still going to show up as AI.

If you want to evade detection, you have to write as yourself from your lived experiences, perhaps with less editing than you normally do, so that your uniqueness shines through.

Finally, just because writing has low or high perplexity doesn’t mean it’s necessarily any good. This sentence, generated with Google Gemini, is completely original and unique and beats every single AI detector’s measures of perplexity:

Apple peached melon. Pear coconuted pineapple, bananing guava. Blueberry plummed cherry, grapefruiting strawberry. Olive figged date, papaying mango.

This is nonsense; I prompted it to write a short story using only fruit and vegetable names and forced it to use fruit and vegetable names for nouns, verbs, and adjectives. It’s very high perplexity, and also meaningless.

Part 5: Wrapping Up

Take a step back and think about how humans remember things and how machines remember things. As I’ve said throughout this newsletter, AI is all about probabilities. It’s all about predicting the most probable thing next, and that is its nature and its value.

Humans don’t remember things that way. I posted about this on LinkedIn earlier this week - we remember things with repetition, yes, but emotion most of all. Do I remember what I had for lunch last Tuesday? No. Do I remember what I had for lunch on an overcast Saturday in May of 2001? Yes - because it was my wedding day. (Rare steak grilled over charcoal, a salad with goat cheese and raspberry vinaigrette, and a three layer chocolate and vanilla cake that did not do well on a moving boat and had to be separated)

I remember that lunch not because of its frequency but because it was rare and emotionally moving. Our brains encode information related to big emotional variances in long term memory, even when they are rare. Burn your hand once on the stove as a kid, and you remember it for life. Getting married. Birth of a child. Loss of a loved one. Baby’s first steps.

And those emotions are so burned into our memories that when we do creative activities, we channel those memories and those emotions in unique, unpredictable ways, ways that machines can’t imitate well because of their very nature as low probability, unique events. And those memories and emotions are our fingerprints that set us apart when we’re creating as ourselves.

What makes us uniquely human is low probability stuff, the essence of surprise, and that comes from our lived experiences and our intentionally faulty memories that can’t remember what lunch was on Tuesday but can remember lunch from a quarter century ago. What remains in our easily recalled memories are the things that matter the most, not the things that are highest probability.

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What’s On My Mind: A Better Mental Model of AI for GEO

This week, let’s do a different mental model of how AI works. I’ve seen a lot of hot commentary specifically around GEO that is completely disconnected from the reality of how AI models work, and that advice will inevitably lead to dissatisfaction when the desired results just don’t appear.

The same is true for any expectation for AI to behave in completely predictable ways. It can’t; by its very nature it is a probability engine.

Part 1: The Machine — Map, Server, Harness

Part of the challenge for marketers is that AI is full of jargon, and it’s jargon where it’s difficult to even infer what the jargon means - vectorization, embeddings, softmax layers, key value caches, etc. Even the basics, like what a model is and how it’s only part of the AI tools you use, can be confusing. While I often refer to AI models as giant databases of statistics, even this isn’t as accurate as it could be for helping folks understand what a model is.

Let’s start with this: a model is like a map.

Open up a maps app like Apple Maps, Open Street Maps, Google Maps, etc. An AI model is the map itself, and everything on the map has coordinates. In the real world, those coordinates are latitude and longitude, though we often refer to things in relation to other things, like telling people to take a right at the red barn on the corner.

Now, that map needs to be served up. You don’t just access the map itself - there has to be a server somewhere on the internet that serves up the map, AND there has to be an app or a website that allows you to access it.

Map, server, app. Make sense so far?

The map is the AI model. Every AI model has to have a server of some kind. You have to have some kind of app to interface with the server. We’re used to web interfaces, chat interfaces like ChatGPT, Claude, Gemini, etc. But there’s a ton of apps that also connect to models and servers, like Claude Code, Claude Cowork, OpenAI Codex, Google Antigravity, and so many others.

When you start talking super nerd speak like agentic harnesses, just substitute the fancy word for apps. An agentic harness like OpenClaw or Hermes Agent is just another app. It needs to talk to a server, and the server needs to have models available.

We’re extending our analogy of an AI model as a map. A quick word on terms before we go on: the app is sometimes called the interface, but we’ll call it the harness. Harness is a broader term than interface, because interface implies just the user-facing stuff, and harness also has rules built into it about how it interprets and sends and receives things from the model.

Now, what we need to make clear is that when it comes to using AI and getting results out of AI, the app, the harness, is just as important as the model itself. Just like a map application: if the interface is terrible, if it’s got all sorts of weird rules built into it, if it’s censored, and so on, it’s going to give you very different results.

This is critically important to understand for things like GEO. If a harness has rules that interfere with the model, even if the model gives you one response, it may not be the response that the user actually sees. All AI models have some basic training, the helpful, harmless, honest trifecta that Anthropic and others have posited for years about how to align models.

But on top of that, model makers and AI providers have created these harnesses that enforce rules more deterministically, or have guard models that silently inspect and reject responses that go outside the guardrails that model makers have made. For good or ill, those additional guardrails cause further interference in our ability to predict what AI is going to respond with.

Our capability of predicting AI responses is predicated on not just what’s in the model but how it is served and then how the harness interprets those responses. For example, a server sends and receives information from the model, right? The server enables the model. It has settings like top-p and top-k, temperature, repetition penalty. These are all technical adjustments that the server operator makes to serve up the model.

Probably the most famous harness right now is Claude Code. Claude Code is a massive harness that talks to Anthropic’s AI. It talks to their servers and it talks to their models through their servers. However, what many folks have recognized and realized is that the Claude Code harness is really terrific. It’s full of useful rules for constraining how a model behaves. The closer a model behaves to how Anthropic’s Claude models behave, the more useful Claude Code is. That means if you use it with a model similar in capabilities to Claude’s models - Opus, Sonnet, and Haiku - you will get similar results.

This is why you can use Claude Code with a model like Minimax or Alibaba Qwen and get fantastic results. Yet if you were to use that model natively in an interface that does not have a good harness around it, one that has fewer of the battle-tested rules, you will notice a difference pretty quickly, because it doesn’t have all of the guardrails that Anthropic has baked into Claude Code.

This is also why brands show up in different tools in different ways. For example, ChatGPT is a harness around OpenAI’s servers and OpenAI’s models. Its search mechanisms operate differently, where Claude and Gemini often try to discern, based on log probabilities - which is essentially a confidence measure - whether or not they need to invoke search. OpenAI tends to read the prompt up front and decide from the prompt whether it needs to do web searches, or whether it can answer the question from its own knowledge.

Every tool does things differently; every brand shows up differently in every tool. As underlying models change, so does what shows up in the server and in the harness. For example, in May of 2026, Google released Gemini 3.5 Flash. This model replaced Gemini 3 Flash, which is used in Google AI Mode and Google AI Overviews. Even though the harness, which is web search, did not substantially change, the underlying model did. The way it does things like its query fan-out changed dramatically. Gemini 3.5 Flash gathers more sources up front and has fewer iteration loops to get to the answer. This makes it faster, and it means the consideration set is larger.

For a marketer, that in turn means that if you’re trying to be recommended in the results, all other things being equal, you have a higher chance of being in the consideration set, because the consideration set is wider up front. However, it also means that the model, if it doesn’t know about you, is less likely to stumble upon you in subsequent iterations because they don’t exist.

Part 2: The Map of Language — Concepts Are Cities

In plain language, embeddings and vectorization become a concept’s coordinates because language models are built by ingesting huge amounts of information, huge amounts of data, trillions and trillions of words. This training occurs in several phases, but the non-technical explanation is that every word, and the words around it, are all predictable.

For example, if I say “I’m spilling the tea,” those words have meaning not only because of the words themselves, but the order that they are in. If I say “tea, the spilling I’m,” this makes absolutely no sense at all. When we convert words into these statistics that form the map, if you will, in a generative AI model, it is just as important to understand how different words relate to each other.

A lot of people are asking GEO to be deterministic. They want to know who’s the number one brand, et cetera, in AI responses. When we go back to our map, server, app analogy, that’s like asking a map, “What’s the best way to get to Boston?” Well, there are a whole bunch of different roads. Depending on the time of day, depending on traffic, whether it’s a weekend or a weekday, depending on where you’re coming from, there are a whole bunch of different ways to get to Boston. You can get to Boston by boat, you can get to it by airplane, you can get to it by car, you can get to it by train, you can walk, you can ride a bike, you can run, as thousands of people do every April for the Boston Marathon.

Let’s extend our map concept: not every city is the same on a map. Cities are located in different parts of the region and different parts of the world. They are different sizes. They have different modalities for getting to them.

In our AI analogy, a city is a concept.

Artificial intelligence, SEO, angular contact ball bearings, marketing conferences - every concept is a city. Your brand, your whatever, is associated with the city that you’re in.

Image altered by ChatGPT from Google Maps

There is no one answer for “how do I get to Boston?” It’s highly dependent on what your intent is, what you’re trying to do, and how you’re trying to do it. If we extend that even further, and we say, well, how do I always make sure that Boston is the top recommended city? Well, think about the logic of that question. If you are in Los Angeles and you want to get to San Francisco, is Boston logically going to be part of that route? Absolutely not. It’ll be the dumbest map application in the world.

For marketers who are used to traditional SEO, to very deterministic answers - and even there, there’s some misunderstanding, but that’s for another time - if someone is saying, I care about companies in this space or in this industry, and conceptually your company is located far away from other companies in the space, you’re not going to be in there. You won’t be in the recommended route in our map, server, app analogy. When you think about it, if you are far away, it’s because you are using different words than everybody else, than what is commonly part of the lexicon of that industry.

For example, at Trust Insights, we’ve started talking a lot about enterprise AI. One of the big topics right now in enterprise AI, which is why I wrote about it 2 weeks ago in my newsletter, is token budgets. Now, we could call this process optimization, we could call this AI governance, but that’s not the language people are using. That is not the words and phrases and concepts that people are using. If we talk about token budgets, we are now aligned with the other language in the space. When other people are talking about token budgets, our content - and that newsletter issue in particular - should surface, because it’s linguistically aligned.

This is the equivalent of saying: if you want to go to Boston and you are the town of Waltham, it’s easier to route people through Waltham than through Orlando. If someone is sitting in New York City, they’re not going to fly to Orlando to get to Boston, but they might go through Waltham. That’s a higher probability of going through Waltham than it is Orlando to get to Boston.

One of the things that marketers who have deep pockets can do is build a new city. The most prominent example of this comes from SEO from the last 15 years. HubSpot coined the term “inbound marketing.” Prior to that, Seth Godin called it permission-based marketing. But HubSpot branded the term inbound marketing, and it became synonymous with HubSpot. They spent 10 years and $100 million to make that a marketing standard. They built that city. Echoes of Starship’s “We Built This City” can mentally play in your head here.

GEO is no different. If we can create a concept, create something that’s truly unique, we will do better for it. We will rank for it. We will show up for it. If it is truly unique and truly rare, when models learn about it, we will be the only ones talking about it.

Now, the question is: can we get our customers to adopt it? Just like HubSpot did for 10 years, it’s going to take a lot of time and effort for our customers to have those conversations - with us, with each other, and with AI. It’s not something that’s going to happen overnight, as it did not happen overnight for HubSpot. But if you can pull it off, you own a new city in our analogy.

When it comes to competitors in our map analogy, competitors are like competing buildings in the same city. If you go to downtown Boston, there are many competing banks and many competing universities. They all have locations. They’re near each other. Boston is synonymous with high-tech and biotech. Boston competes with places like San Francisco.

If someone wants to drive to a high-tech area, because of the distance between Boston and San Francisco, they kind of have to pick one. It’s illogical to try to drive to both places.

As a marketer, we have to think about: where do we want to compete? What concepts, what conceptual cities should we be competing in? Should we try to be in both Boston and San Francisco in this analogy, or should we pick one?

The answer is: where are your customers? Where do the customers spend their time? Where do the customers have conversations? What do the customers conceptually talk about or ask about? That’s how we make that determination. LinkedIn works that way now. You haven’t read our unofficial LinkedIn algorithm guide. Go check it out because it’s exactly how LinkedIn works.

When AI models are built, all those neighborhoods are part of that city. Your company and its physical location are associated with a city, which is associated with a region, which is associated with a country, which is associated with a continent. That is all derived from media: text, audio, video, podcasts, interactives, PDFs.

Imagine - because this is actually how it worked in the old days, in the very early days of cartography - people would rely on descriptions, verbal descriptions of others, to draw maps, to infer what the map should be like. Oh, well, Timmy’s Silversmith is located three buildings down from John’s textile shop, which is located across the street from Bob’s Tavern.

Cartographers would attempt to build maps based on these descriptions as well as their own observations. That’s not all that different from how AI models are built. Data collected from other people is turned into a map, and then that map routes people to the concepts that they want to go to.

Maps get redrawn slowly over time. It takes a lot of time and effort and money for maps to be redrawn. It happens infrequently. When Rand McNally made atlases, they updated them once a year. That’s a great analogy for how language models are retrained. Those big new model releases typically only happen every 12 to 18 months because of the enormous compute costs that go into building a new model.

For example, if you look in Google’s AI Studio, even the most recent three or four models all have the same knowledge cutoff date of January 2025. Google has not substantially updated their knowledge since that time because of the cost of obtaining new content.

If I prompt a model to ask what’s the best AI consulting agency in Boston, and Trust Insights never mentions the word Boston anywhere on its website and never talks about it in the material, then Trust Insights will be not on the map of Boston. It might be related to AI, but it might not show up for Boston, because it’s literally not part of the training data. That in turn means that if that is something our customers or prospective customers are going to be asking about, it ain’t going to be there.

If we think about this very carefully, we have to not only build our brands, we have to build them in ways that are semantically relevant with the concepts that we want to be associated with, in the same way that we would be associating with the geography on a map.

The largest cities have lots of people in them. Imagine a concept in an AI model. Lots of words going into that concept, lots of words that flow out of that concept, lots of words that are part of that concept, and a bunch of words that are not part of that concept.

If people are prompting AI, and they say they want to include words in a certain concept, in a certain destination, AI is going to route them, and it might route them around you because you might not be on that road. If I’m driving to Boston and you’re off a side road instead of on a main thoroughfare, I’m less likely to stop at your shop. That’s one of the reasons why we have things in the real world like rest areas, and why gas stations are higher priced and can get more profit near a highway, because it’s less of a diversion.

This is how, conceptually, AI works. It’s not SEO, it’s not the top ten blue links anymore with probabilities based on how many people link to a page. It is now based on where you are in the map that is the model, what you’re near, and how people want to get to those answers, and whether you are along the way.

You may not be on the way to the answers that people are asking AI. Even if you’re in a big city, even if you’re on a busy road, you may not be on the way, you may not be convenient enough, near enough to the route that people are asking to go.

This is why things like Garrett Sussman’s study of how Google AI Overviews works is so important. It’s injecting so much personalization into AI Overviews and AI Mode. All that work to prepopulate a user’s personalization history means that the harness, which is Google, is taking in all the personalization data from your Gmail and your YouTube and your Google Photos, and saying “let’s make sure this user always goes by these places that they already like.”“

Imagine - as you’ve seen this in your Google Maps application or your Apple Maps location - after you’ve been to a place enough times, it stays saved in your history. It appears out there, and depending on the app, it might even recommend, hey, it’s Saturday morning, are you going to the Boston Martial Arts Center in Allston, Massachusetts? That is how these tools work.

Part 3: Doing GEO — The 3 Phases

This raises the very important question that marketers are asking, which is how do we get recommended more? If we go back to our map analogy, that’s like saying, how can I move my business closer to the concept that people are asking about? That is, along the busy roads, along the busy routes. You can’t easily game this because you’re trying to game a map itself, a map of language itself. Where you see GEO tricks working in the short term, it’s because they’re influencing the app or the harness more than the model itself.

Phase one puts you on the map. Phase two checks to see if you’re along the way. Phase three makes sure you are included on the route. As I say throughout the course, you cannot just do one part of GEO. There is no GEO silver bullet of just do this thing and you’ll do better. Anyone who’s selling you that is lying to you. Instead, you have to do each phase differently.

Phase 1: On the map. How do we get on the map? What language do we use to become part of the concepts? How do other people talk about us? That’s a really important part. It’s why public relations is such a critical part of GEO, because it’s not just what you say about you. It is what other people say too, how much they talk about you. My friend Mitch Joel says this all the time: it’s not who you know, it’s who knows you. You have to do that work. You have to influence others to the best of your ability to talk about you.

That PR work is the new currency of GEO. One of the biggest differences between GEO and SEO is that SEO used to trade in a specific currency: inbound links. As long as you got a link from somebody else’s website, it almost didn’t matter what it was, or even how relevant it was.

Google did refine that model over time, but in the GEO world, links are not the currency. Brand mentions are, in a semantically relevant context. If someone mentions Trust Insights in the context of pizza, that doesn’t help us, because conceptually, pizza is a very long drive from consulting, which is what we do. If someone mentions Trust Insights in the context of AI consulting, we want that to be a very short drive. We want our brand to live in the AI consulting neighborhood. As more people drive into the AI consulting neighborhood - conceptually, from this map perspective - more people will pass by our offices, conceptually, in this map.

That’s why PR matters so much, because PR can get you mentions even if they don’t have links. As long as you’re getting those mentions - both volume and quality, but especially volume of relevant context, relevant semantic concepts - then you will do better in AI models, particularly in phase one, in model knowledge about you.

In terms of GEO, this means that Phase 1, which is presence - presence in models - happens infrequently. It’s important for us to get data and information out on the web as training data as quickly as possible, in as large a volume as possible, so that models can pick it up.

Showing up in different formats is important because models use different website sources for training. In the GEO 101 course, I talk about the importance of YouTube and why YouTube is one of the most cited sources online, along with social networks like Reddit. We have to be everywhere all the time to maximize the likelihood that the model represents us.

Think about it this way, in terms of a map: if you had a small business in a city, what would you do to advertise that business? How would you show up? Would you just put a little sign in your front window and hope that customers found you? Of course not. You would, if you had the budget, be running ads, putting flyers on pizza boxes, asking neighbors and customers to refer others or recommend you or write reviews, maybe making a 30-second spot for the local radio station. That same analogy extends to large language models and the training data that powers them.

Phase 2: Along the way. Phase 2 is about being convenient to the route the user is already on - and for Google, that route runs through personalization. If you can’t know that personalization, what can you do as a marketer? Well, one obvious thing is to be in that user’s Google history. We have to be in their inboxes. We have to be on their YouTube watch list. We have to be in their browser history. Anything we can do to get the user to engage with us through a Google property increases the probability that we will be in the personalization part of the prompt that Google AI search - the harness - uses with the Google AI models.

You have to be findable by traditional search. Anyone who fired their SEO agency is in for a rude awakening when it comes to GEO. All those supposed hacks like programmatic SEO, flooding the internet with crap - Google quite clearly came out swinging with their GEO recommendations, saying we are specifically looking to exclude commodity content. We will penalize commodity content. They wrote it down, they said it out loud.

Phase 3: On the route. Phase 3 is where a model actually pulls your content to build the answer, so what people want - and want next - decides whether you make the cut. In GEO, this means optimizing for what people really want, understanding their intent and their downstream intent, so that you can create content that not only answers what they’re thinking about, but expands semantically to the things that they will want to do next. As with SEO, this is not a new practice. The idea of “answer the person’s question and their next two questions” comes directly from SEO from the last 10 years. In GEO, that practice remains.

Google also said some other interesting things lately, like llms.txt is relevant. Wil Reynolds is also talking about how you can do a form of prompt injection, telling AI how to cite your content. That’s probably worth doing, because it’s giving good directions to models - a form of prompt injection, but not malicious, just explanatory and written in the high-probability language that machines know. It’s essentially saying to the model, oh, when you’re building the route for the user to go from A to B, this is a good way to go. You’re not trying to divert them to Orlando. You are simply trying to say, hey, when you go from Boston to New York, we’re on the way, we’re not far off the road. Why don’t you stop by our place - by instructing them how to cite and build citations.

When it comes to understanding relevance in GEO - which is phase three, when a model goes and pulls content from your website - the single best measure of that is looking at your site through the eyes of AI. Trust Insights has a free tool called AI View that allows you to see, effectively, what the model sees. Things like structured data, linearized content, what user agents are or are not permitted to browse a site. You can try it for free. Go to trustinsights.ai/aiview and you can try it out. You can compare pages on your site versus pages on someone else’s site. Give it a shot.

Anti-patterns: what not to do. Before you act on any of this, know which common GEO moves actually backfire. The most common pieces of advice that I see people giving about GEO that are flawed are all surface-related things, like “you should go and Google your competitors or your industry.” Because of the nature of the way that Google operates with personalization, there’s a very good chance that you will get distorted results. You will certainly not see what your customer sees, because they have a different Google history than you do. You can turn on incognito mode, and that helps, but that still does not overcome the fact that personalization is such a huge part of Google’s AI search efforts.

Other things are gimmicks, like articles with more lists or frequently asked questions. Those are structural things that really are flashes in the pan as models adapt - as their weights change, as their servers change, as their harnesses change. Remember, it is model, server, harness. All three can change, and trying to identify tricks and gimmicks is only good in very short windows of time.

The machines change so rapidly, the weights change so rapidly. There’s this concept in AI called test-time compute, where models learn and adapt while they’re giving results. Trying to predict what a model is doing without having access to the underlying weights, without knowing what’s in the server, without knowing what’s in the harness, is a fool’s errand.

The third thing that everybody does wrong is related to AI visibility tools. All these companies and vendors are promising these left and right, saying, “We can tell you what your brand’s strength in ChatGPT is, or how people search for you in AI Mode.” No, you can’t. You have no idea what real people are typing. Because language models are probabilistic, you can know how big a city is on the map. You cannot know what route a user is asking it to plot. If I live in Stonington, Connecticut, and I’m asking for a route to Boston - if you know the area, off the top of your head, there are three major highways you could take. Depending on the conditions during that time: taking 84, taking 395, taking 95, maybe taking back roads.

All these companies are promising, “We can tell you exactly how many people are driving past your shop.” No, you can’t. You don’t know that. There’s no way to know that, because AI companies do not give that information away to anyone. At best, AI visibility tools and GEO tools are doing very rough approximations. But to say you know how a brand ranks in AI search is the height of dishonesty.

Part 4: Wrapping Up: Measuring & Monday Morning

So if you can’t trust the tools, how do you know GEO is working? As I said in our GEO 101 course, the measure of success in GEO is surprisingly straightforward. Ask people how they heard of you. At every touch point in the process, ask them how they heard of you, what made them come in that day, what motivated them. In a free-form fashion, let them talk. Let them explain.

You have generative AI tools that can digest text and audio and transcripts and video and all these different ways that customers will interact with you. Let the customer talk, and then listen, and use AI tools to digest what they’re saying and turn that into measures of success.

Because if nobody ever says, “Oh, I Googled for you,” or “Oh, I had a conversation with Gemini,” or “Oh, Claude told me that you were the person to talk to,” then you know that your GEO efforts are wasted. That is the measure of success: to calibrate on what comes out of the customer’s mouth.

Now, there are known issues with human memory and recall. Specifically, people do not recall things that have a low emotional valence. They tend to remember higher emotional parts of the customer journey, because that’s human nature.

In the same way that we don’t remember what we had for lunch last Tuesday, we remember what we had at our wedding dinner 25 years ago. But that’s a critical part of the puzzle. That’s not a flaw, that’s a feature. If they had a surprising conversation with AI that led them to you, you want to know that.

So what can you do on Monday morning to start altering your fortunes in GEO? Number one: do not try to change the map. Do not even try to change the city. Focus on where you live and where you want to live. I’m going to teach this in GEO 301 at some point, how to write the code to do this in the same way that AI does. But there are techniques that you can do that mirror the way AI works, so you can understand what part of the conceptual city you live in.

For today, one of the easiest things you can do is to look at the content on your website, at the language that you use in all of your owned properties - on your YouTube channel, in your podcast, on your website, in your other social media, on your Substack, wherever it is that you publish. Then compare that language to the language that your stakeholders use when they are talking about the concept or topic or industry that you are in. What do your customers say?

This goes back to the token budget example from earlier. If I don’t create content about token budget, I am not going to live in that neighborhood. My brand is not going to be in that neighborhood - I have no billboard in that neighborhood. If I decide that token budget is a neighborhood I want to live in, I need to create stuff to have a presence in that neighborhood.

At the end of the day, we have to acknowledge that AI search is fundamentally probabilistic in nature. You can run - and people have done this - the same query hundreds of times. Rand Fishkin at SparkToro did this in January of this year. You can run the same query hundreds of times and not get the same brands in the same order for hundreds of tries. In his experiment in January of 2026, it took 1,429 times for Claude to return the same two brands in the same order, and around 120 tries in Google Gemini.

As frustrating as it is, as challenging as it is for us to talk to stakeholders about GEO, everyone has to understand that these are probability engines, which means there is no certainty - there is only probability. We can say with confidence that our brand, our products, our services, our executives have a higher or lower probability of being returned in results, but we cannot guarantee anything. And we certainly cannot make deterministic claims like we’re number one in AI search. If you’re saying that, you’re not being truthful.

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Almost Timely News: 🗞️ AI and GEO Advice for Retailers for the 2026 Holiday Shopping Season (2026-05-24)

Christopher S. Penn — Sun, 24 May 2026 10:31:00 GMT

Almost Timely News: 🗞️ AI and GEO Advice for Retailers for the 2026 Holiday Shopping Season (2026-05-24) :: View in Browser

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What’s On My Mind: AI and GEO Advice for Retailers for the 2026 Holiday Shopping Season

This week, let’s talk about the holiday retail season. If you’re checking your phone to look at the calendar, you’re not mistaken. It’s not even June yet. But Michele asked a fascinating question to me about AI and retailers that I thought warranted a much deeper dive:

“What’s one thing retailers should be doing now to prepare for the future of search this holiday season? And how should they actually approach it?“

Let’s dig into this, because it’s a more complicated question than it seems, and it hinges on what the holiday season is, what search is, and what most companies are getting wrong.

Part 1: The Holiday Retail Season

As consumers, we think of the holiday season as mostly December. In the global north, that’s winter; in the global south, that’s summer. November and December are when B2B slows down and B2C is operating at a frenetic pace.

But that’s not when the holiday season starts, not for retailers. The holiday season starts... well, started a month or so ago. Retailers have to think about inventory, and with global chaos caused by the US federal government’s attacks on Iran and the subsequent closure of major shipping lanes and disruption of the energy economy, there are many more wildcards in play.

Retailers have to try figuring out what demand will be, and when consumers will be interested in what they have to offer. That’s where tools like forecasting come into play, and with generative AI, that forecasting is easier and more accessible than it’s ever been before.

Let’s start with trend data. If we want to understand what’s likely to happen, and we have a fairly durable set of past history to draw from, we can use AI to do that inference. There are two catches here. First, this is classical AI - AI that’s rooted in regression analysis, math and statistics, as opposed to generative AI, which is what almost everyone else means today when they use the word AI.

Second, we need data to forecast from. Some of the best free data sources come from government sources (especially boring ones that are politically neutral, like the number of air travelers through USA TSA checkpoints daily, one of my favorite travel industry datasets) or Google Trends data.

Let’s say we want to know when people will start thinking about shopping for the holidays. Where might we go? We’d start with search data, because search data is durable and highly predictable. Take a look at Google Trends for trends phrases like “holiday gift guide”. These remained durable even through the pandemic, because people never stopped looking for gift ideas. You’ll see it begins its inflection roughly every September.

The second thing we need to determine is whether or not the consumer is in a buying mood. That’s where things like consumer sentiment play a role, such as the University of Michigan consumer sentiment survey. When sentiment is low, people tend to buy less.

The Google Trends data tells you what’s likely to happen. The consumer sentiment survey tells you the probable magnitude.

Now, if you’ve never done forecasting before, the good news is that generative AI makes this easier than ever to get started. You don’t need to own any software other than an AI coding agent like Claude Code, Claude Cowork, Google Antigravity, OpenAI Codex, OpenCode, or similar, and you don’t need the smartest, most expensive model for what’s essentially commodity statistics.

We’ll want to weight things like inflation as well, since life has gotten much more expensive in recent years, so we’ll get median inflation (inflation inclusive of everything) from the US Federal Reserve Bank, plus Adobe’s annual holiday shopping reports which are always excellent summaries of the season overall.

Put all your data in one location and open up your AI coding tool. Here’s a good starting prompt you’ll want to adapt and improve:

You’re a consumer shopping expert skilled at statistics, causal inference, and forecasting. Today you’ll be building a forecast for the 2026 holiday season. In the input folder, you’ll find the following datasets as raw data. [1] Google Trends data for the last 5 years for holiday gift guide related terms, week level data - googletrends.csv. [2] University of Michigan consumer sentiment data, month level data - UMCSENT.csv. [3] Adobe Consumer Shopping survey with multiple charts in it that need extraction into YAML, yearly data - adobe folder, multiple PNGs. [4] Median consumer price index inflation (percent change at annual rate) from the St. Louis Federal Reserve Bank, month level data - MEDCPIM158SFRBCLE.csv. All these data series are different frequencies and need to be accounted for in your forecasting; Google Trends tells us when people are most likely to start searching (intent), Adobe tells us what happened in the past as an outcome to measure past forecasts against, consumer sentiment tells us how likely people are to spend more or less, and median CPI tells us how much to adjust past forecasts and results to normalize spending over the years based on inflation. You’ll need to think through the data series we have and decide what transformations and feature engineering make the most sense. You’ll build your forecast by selecting time series forecasting techniques such as SARIMAX, Prophet, Greykite, Timer S1, and other techniques based on the data we have. Use your web search tools (today is 2026-05-23) to help you select FOSS Python-based time series forecasting libraries and packages such as statsmodels, prophet, scikit-learn, prophet. Packages must have been updated on Github on or after 2026-01-01. Do the feature engineering and transformations first, starting with the conversion of charts from the Adobe PDF into YAML datasets. Use the weekly Google Trends data to disaggregate the yearly Adobe data into weekly target variables before training the model. Then test different forecasting techniques against past outcomes with backtesting and robust overfitting prevention; use causal inference methods and techniques you know such as granger causality, vector autoregression, CausalImpact, and other relevant techniques to judge how much to weigh each input for the final forecast. Once you’ve determined the best techniques or ensemble of techniques, produce an explanation of your choices in the output folder along with the final forecast. The final forecast should be week level data along with a writeup of where the inflection for holiday shopping starts, and should also include a time series visualization in PNG format, 16:9 aspect ratio. All output files must be in the output folder with date-stamped file names such as forecast-chart-2026-05-23.png. Ask me questions until you have enough information to successfully complete the task.

I realize that’s a wall of text, but it’s easier to copy and paste, and AI will know what to do with it. Give this to your coding agent - a web-based chat LLM like ChatGPT will never, ever successfully complete this task.

What we see in the forecast for 2026?

Lower dollar spend than in 2025, probably on par with 2024 or earlier, and consumers begin their searches for holiday gift ideas around the week of September 20, peaking the week of December 6.

Now that we know when we need to be ready, we can dig into the search part of the question.

Part 2: The Future of Search and GEO for Retailers

GEO (generative engine optimization) advice is a bag of land mines and rabid, flaming gerbils at the moment, and reaching in the bag blindly for GEO advice is bad news. You’ll get bitten, burned, and blown up by a lot of GEO advice, most of which is either anecdotal or just unhinged.

I won’t speak for others, but what advice I have to share is either based on what credible folks like Lily Ray, Garrett Sussman, and Rand Fishkin have found through rigorous experimentation, or what I know from reading patents, technical papers, academic papers, and studies. It’s how I put together the Trust Insights GEO 101 course.

Here’s the single biggest thing I know to be true at the moment: almost all “AI visibility” tools are bullshit. There’s no polite way to say that. Here’s why.

First, generative AI tools are all probabilistic. If you’ve never tried this before, ask for the kind of advice you know a stakeholder would want to be included in, from your web-based chat tool of choice. For example, “Name the top 5 vacation spots a family of four should consider in 2026” is something a destination marketer stakeholder would want to see results for.

Then copy-paste that prompt into 5 brand new chats and see if the results you get are consistent in your various AI tools. Spoiler: they won’t be. Now, there’s some nuance there. If you work in a field where there are only 3 companies period, then you’ll get consistent results. But for a lot of places, there will be wild variation.

Second, the biggest AI recommendation system is still Google. SparkToro and Gumshoe earlier this year showed that 93-95% of all searches are still Google, and Google AI Overviews and Google AI Mode are now the dominant features in search. This past week at Google I/O 2026, Google said AI Mode will be part of the default search experience, pushing searchers into conversations with Gemini as fast as possible.

Eagle-eyed folks spotted that Google is using heavy personalization in its AI Overviews and AI Mode; Lily Ray confirmed that with a Gemini system prompt about a week ago. Garrett Sussman at SEOWeek showed that emails in your Gmail inbox, photos in your Google Photos account, and your YouTube history all affect what results you see in search. In fact, brand mentions within emails seemed, in Garrett’s year-long testing, to influence AI Overviews, even if they were in emails not from the brand itself.

All this means that any tool proclaiming they know what level of visibility your brand has in AI is lying to you, period, end of story. And the more zeroes they charge you in the price, the more they are lying.

Why? Because we all know from our experiences in the last 3 years that a single word’s difference in a prompt can radically change what we see. Now imagine the thousands of words of extra context that Google gets from Gmail, YouTube, Google Photos, Google Podcasts, Google Chrome browsing history, Google Ads impression data, etc. That can go into a prompt as part of its context for building you AI Overview and AI Mode responses.

There is simply no way to credibly predict any of that. There is no way to know whether your ideal consumer searching for holidays gifts has a stockpile of emails from a competitor in their inbox that will weight their results in search heavily in favor of the competitor, and an AI Visibility tool claiming that your brand is more prominent will be lying to you.

So what should retailers be doing, given this rather grim state of GEO tools? Follow the formula outlined by what Google’s AI - and other AI tools - all use as their backbone. Be everywhere you can be. The Trust Insights GEO 101 course teaches this in modules 2 and 3.

Part 3: What To Do

Based on parts 1 and 2, I should hope that what to do next is clear: you have 3 months from now until the shopping season starts to saturate whatever media channels you have with the most useful, helpful, semantically relevant content that your consumers keep in their inboxes, that your consumers watch on YouTube, that your consumers share on social media and forward to each other.

There’s a catch here: it has to be the most AND the best. You can absolutely create a swamp of crap pretty easily, but Google in its most recent updates to search guidelines is specifically penalizing crap.

Here’s the relevant paragraph from the guidelines:

Creating non-commodity content that’s helpful, reliable, and people-first: Be sure that you’re writing non-commodity content that your readers will find helpful and reliable. Commodity content (for example, something like “7 Tips for First-Time Homebuyers”) is often based on common knowledge, which could originate from anyone, and typically adds little unique insight for readers. In contrast, non-commodity content (such as “Why We Waived the Inspection & Saved Money: A Look Inside the Sewer Line”) provides unique expert or experienced takes that go beyond common knowledge and the ordinary.

Okay, so how do you do that in an age of AI? Katie Robbert and I were talking about this recently, as it goes back to the DNA of Trust Insights - the unique data only you can provide is what allows you to create non-commodity content. What data do you have that no one else - including Google - has that you can use to transform ordinary content into content that’s unique to you?

Think about what I did in this newsletter, where I put a forecast for the 2026 holiday shopping season. This is unique data because no one else has run this forecast yet. Yet, by doing that, by creating new content, particularly about retailers and B2C marketers, even though I used existing content, I transmuted it in a new way that A, hopefully creates value, and B is unique and non-commodity.

There’s a second catch here, as shared by many folks recently: your best content should also be difficult for AI to summarize, otherwise Google will present it in summary form to your audience and likely NOT send the searcher to you. One travel blogger made mention that his traffic is way down, and when he went to AI Overviews to see what his audience saw, Google was presenting summaries of his work with ads and mentions of his competition instead.

How can you make rich, interactive content that’s difficult for Google to abstract away and summarize?

A piece of content that I produced that is genuinely useful, I think, is the Trust Insights AIView software that’s available on our site for free. This is an experience. This is software that does something. It’s not just static content that Google can easily summarize. You have to participate in this content, but it provides a lot of value to you.

Finally, the most valuable long term strategic play you can make is to invest the most in systems you control - your email list, your text messaging list, your direct mail list - anything where you retain control of your audience. Social media? Definitely not, at least not public social media (Xitter, Facebook, Instagram, LinkedIn, etc.). Private social media like Discord, Slack, etc. are better BUT you can still have your platform vanish if the company goes bust or kicks you off the platform after being bought by a broligarch who doesn’t like what you have to say.

We know from both Lily Ray and Garrett Sussman that the Gmail inbox is a critical part of GEO for Google in personalization, so be sure you’re not only creating emails that people desperately want to open, but also want to keep in their inboxes until the holiday shopping season.

For example, if I knew, as a retailer, that I wanted my products and services primed for the holiday season, I’d be thinking about what valuable content I could create that would be timely - like summer vacation ideas - but with brands, products, and services that I really want my audience to keep in mind throughout the holiday shopping season. I’d be putting up YouTube videos that were awesome or at the very least useful and entertaining every week between now and December 6 to ensure my consumers’ Google history was as littered with me as possible.

Part 4: Wrapping Up

Michele’s question was originally for a short quote in an upcoming article. As Blaise Pascal wrote in 1657, “The present letter is a very long one, simply because I had no leisure to make it shorter.” The answer I gave to her was a short snippet that I thought deserved more airtime than the space she was able to make for my contribution (and given how long this newsletter is, she made the correct call).

If you’re a retailer, especially in B2C, this newsletter is your blueprint for what you need to do between now and mid-September when the holiday shopping season begins with consumer interest. In Adobe’s report from 2025, half of shoppers used an AI assistant of some kind in their holiday shopping, and that was before all the tech companies shoved AI into literally every product in existence. I would only expect that to increase this year, which means you have a scant few months to get your data and AI strategy rolling.

Shameless plug: this is exactly the kind of work I and my company, Trust Insights do (obviously, because I needed to do it to produce this newsletter). If you want help doing this sort of analysis and planning for your company, hit up our contact form.

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Advertisement: New GEO 101 Course

When I talk to folks like you, being recommended by AI is one of your top marketing concerns in 2026.

In this course, you’ll learn:

The three distinct phases of GEO and how they work
How to optimize for each phase (they’re different!)
How to measure your GEO efforts in a meaningful and valid way

This course is meant to be used. In addition to the course itself, you’ll also receive:

Your 90 day GEO action plan
How to set up Google Analytics for measuring GEO traffic
How to join Google Search Console data with GEO intent data
How to use our free AIView tool to improve your content and site for one of the three phases of GEO
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And best of all, this is our most affordable course yet. GEO 101 for Marketers is USD 99 and is available today.

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Almost Timely News: 🗞️ 18 Ways To Save AI Token Budgets (2026-05-17)

Christopher S. Penn — Sun, 17 May 2026 10:31:17 GMT

Almost Timely News: 🗞️ 18 Ways To Save AI Token Budgets (2026-05-17) :: View in Browser

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What’s On My Mind: 18 Ways To Save AI Token Budgets

In this week’s newsletter, let’s talk about making AI as efficient as possible. By efficient, I specifically mean using as few computational resources as possible. I collaborated on a piece with Andy Crestodina over on his blog about document formats and making AI more efficient, but I wanted to dig much deeper into the more technical stuff here.

This past week the term “token budget” kept coming up over and over in conversations, particularly with enterprise leaders. When we talk about token budget, they’re talking about the amount of AI that their organizations are allowed to consume - based on what they’re paying at an enterprise level to companies like Anthropic, OpenAI, and Google. This newsletter accompanies Andy’s article on AI efficiency, which I was pleased to contribute to.

TLDR: Here’s an Infographic

In case you don’t have time to read all 10,000 words. Image made by ChatGPT from this newsletter’s content.

Part 1: Why Do We Care About AI Efficiency?

Companies like Anthropic, OpenAI, and Google allow customers to buy large blocks of tokens to be used with things like AI agents and agentic coding tools. In the past, when we look at the five levels of AI, level one was sort of the ChatGPTs of the world where you didn’t really have to worry about token budget and you could pretty easily break even with things like $20 a month subscriptions because the average person’s not going to blow through hundreds of millions of tokens in a single chat. The architectural limitations of the software prevented it from doing that in the first place because they would lose context so quickly that they would never come close to hitting those budgets.

But once things like level three systems, Claude Code, Claude Cowork, OpenCode, etc. started debuting and tools became more autonomous, it was much more possible for AI tools to just consume mass quantities of tokens, hundreds of millions of tokens in a session.

Now, as a brief primer and a reminder, a token is when you take something like a word or some pixels or whatever, and you digest it down into mathematics. You digest it down into a number, and then generative AI models are built by measuring the statistical relationship of different tokens to each other. That’s how large language models and vision language models and how everything in generative AI works. That’s why you can say “I pledge allegiance to the” and then that next word, the prediction is almost certainly going to be the word “flag” in North American English.

Tokens are the unit of work as well. When a model is doing predictions, it gets input tokens, which are the things that we prompt it with, such as documents, such as our chats, our voice memos, our images, our videos, and then the outputs are also tokens when the model creates things such as words, code, images, music, etc. And everything consumes tokens.

One of the quirks of generative AI is that all AI models, regardless of who makes them or how they’re made, all have absolutely no memory. They are completely stateless, which means that they remember nothing, even from interaction to interaction. When we are chatting with the model, the entire chat is passed back through the model to do its next predictions. What we say, what it says, all becomes part of the next prompt.

Imagine you’re talking to a friend, and you’re texting with this friend, and for some reason, they copy-paste the entire conversation back to you every time they respond. That would be a strange friend, but that’s exactly what’s happening when you’re having a chat with AI.

And so token usage explodes exponentially because tokens compound as a chat gets longer; it becomes exponentially larger. It’s a phenomenon called quadratic scaling.

The executives I talked to this week when talking about things like token budgets are talking about how to make AI more efficient. They’re given a certain amount of budget to work with, and one over-eager developer who deploys an AI agent could consume that budget at an alarming pace. One person said that at their organization, they had already hit their token budget on the 13th day of the month, and their token budget was not small. So efficiency is something that they care about, that enterprise leaders care about.

How can we make the most of this budget? You don’t want to underuse it because there isn’t a single provider that offers rollover on all tokens purchased - it’s use it or lose it. But you don’t want to be over budget either, and either get locked out or end up paying overage charges, what companies like Anthropic call extra usage. So those are key considerations when it comes to token budgets.

For the individual, many of us don’t pay for the super deluxe top-tier AI subscriptions, but even in those subscriptions, you are given a limit. Anthropic’s Claude Max subscriptions have exceptionally opaque token budgets that give you a percentage of how much you’ve used. You have no idea how much you actually have to work with. Other companies like Minimax tell you how many requests to the API you have each week, and the tokens can flex based on that.

People who pay for the low-end plans, the $20 a month plans, the $25 a month plans hit usage limits very, very quickly. And so it is absolutely imperative that they be as efficient with AI as possible to keep their token usage as low as possible so they can get more time with the model before it hits its ceiling of limits.

Finally, as Andy mentioned in his article every token that you consume, every token that you use, costs actual resources. This is energy in the form of electricity. This is fresh water in the form of cooling, because today’s data centers are generally not terribly efficient. They generate a lot of low-grade waste heat that current data center designs are not equipped to reuse.

And with opposition to data centers being built increasing - for good reason because they do consume a lot of land, they consume a lot of resources, and they don’t provide a lot of jobs once they’re built - it is incumbent upon us as both members of our communities as well as people who care about a sustainable AI future to use AI as efficiently as possible, to use as few tokens as possible, and in doing so use as few natural resources as possible.

It’s about money, it’s about resources, and it’s also about getting things done. One of the absolute worst precedents set by AI thinkers in the last couple of years was Andrej Karpathy’s “vibe coding” concept, where you just sort of talk in the general direction of a computer and you have it generate things, and then you continue to have conversations to course correct it. As he calls it, you just “give in to the vibes” and let the machine figure it out. This is objectively the worst possible way to plan any project where you just wing it and hope that it all works out.

That does not lead to success. You can very rarely overplan a coding project because there are so many things that can go wrong - you want to plan it out as much as possible and let the machines do the execution. This does two things. First, it preserves our cognitive skills of planning, organization, decision making, and problem solving, and it reduces token usage because if you’re not going back and forth in a gigantic chat, then you’re not running into that quadratic scaling issue where every line in the chat consumes the square of the previous lines, that exponential growth.

If we can have those conversations outside of AI, or at least have those conversations and plan first, before we execute, we can reduce token usage. Remember, the more often you start a new chat, the lower your token usage because you’re not recycling the conversation over and over again.

So that’s why we care about AI efficiency. It saves time, saves money, saves resources, and gets you ultimately to results faster.

Part 2: How Do We Measure AI Efficiency?

We measure AI efficiency based on usage.

OpenAI and Anthropic and Google all tell you a percentage of usage, but they don’t tell you what the absolute numbers are, so you have no idea what your burn rate is other than percentage. That allows them to move the goalposts whenever they want. And some weeks it feels like you’re going through tokens faster or you’re going through uses faster than other weeks.

At the organization level, you can see organization-level usage and which users are using up their budgets. And the smart organizations, of course, give budgets to individuals as well as have an organizational budget limit. But those usage monitors are your first indicator. Have them on display.

And keep an eye on it. Take notes on it. You can do something as simple as leaving Slack messages to your coworkers saying, hey, today we are at this usage level. At Trust Insights, I will post typically in our operations channel and our Slack what our usage levels are once we get to about 60% and how many days are left until reset. That helps to bring transparency to what’s going on with your token usage.

Claude Code /insights

Claude Code has a usage meter that shows you how you’re using tokens and what things are consuming the most tokens - which skills, agents, or tasks. Claude also offers a function called /insights that allows you to roll up your usage for the last week and identify what things went well and what things went poorly. This is a deeply underused feature in Claude Code that can immediately bring benefits, because Claude will write you better prompts to help avert issues that you run into.

Token monitors

The third way to measure AI efficiency is to use token monitors. These are effectively man-in-the-middle attacks of sorts, but sanctioned because you’re doing it to yourself. What you’re doing is you’re intercepting tokens as they go to and from your cloud provider, and you can see how much usage you’re doing on a token level. This is useful for the individual.

At the organization level, it becomes a bit more cumbersome. But those are the three ways to measure AI efficiency, in terms of the tools that you’re given. And keeping an eye on them - aka managing what you measure - is the first step towards AI efficiency.

You may find that there are things that are consuming an awful lot of token usage, or days of the week, or specific tasks. Having users with some level of self-awareness is really helpful. Which brings me to a small rant about one of the dumbest things that I’ve seen recently.

The dumbest thing in the world: measuring AI adoption by token usage

Organizations are measuring AI adoption by how much the users are using.

This is often called token maxing.

And in terms of a measure of AI adoption, it’s a terrible measure.

Because if you think about it, if you are holding people accountable to how many tokens they use, they have a natural incentive to use as much as possible, which we know means to be as inefficient as possible.

If your organization is proposing some measure of AI adoption, token usage is the absolute worst metric.

If anything, you should be encouraging people to use fewer tokens rather than more.

Can they get the work done in as few tokens as possible? But you definitely should never make token usage a maximal-goal part of someone’s review. In the same way that it is supremely stupid, as Elon Musk did when he took over Twitter, to measure developers’ productivity by how many lines of code they write.

This is an invitation to make code as bloated as possible to show that you’re productive.

Instead of making code as small as possible to make it as efficient and as effective as possible.

But people like that don’t have any clue of how to actually write good code to begin with. If they did, they would know that measure is counterproductive and deeply stupid.

The bottom line here is that you should not be measuring AI usage as a proxy for AI adoption. Instead, measure the outputs people are getting. Are you getting to your outputs that you care about faster, better, cheaper? That’s the way to measure AI adoption.

Part 3: Non-Technical Efficiency Techniques

Some will make smaller dents than others, but all of them are useful. And the more of them that you can do and that your organization can do, the better performance you’re going to get in terms of what AI can produce.

1. Spend More on Planning and Less on Execution

As we covered in Part 1, the vibe coding approach is objectively the worst possible way to plan any project.

You can very rarely overplan a coding project because there are so many things that can go wrong that you want to plan it out as much as possible and let the machines do the execution. This does two things. First, it preserves our cognitive skills of planning, organization, decision making, and problem solving. And it reduces token usage because if you’re not going back and forth in a gigantic chat, then you’re not running into that quadratic scaling issue where every line in the chat consumes the square of the previous lines, that exponential growth.

Make system diagrams for everything before you let machines run. When you’re using an agentic coding tool, ask it to build a markdown diagram, an HTML diagram, a mermaid diagram, anything that will demonstrate what the architecture of the system looks like that you’re having it build. And this is not just code. This could be the layout of a paper, how different sections relate to each other, the construction of a website. It doesn’t matter. What matters is that you want it to make system diagrams for you so that you can review the diagram and say, how well does the machine understand what I want? How well do I understand what I’m asking the machine to do?

If you have diagram construction as part and parcel of your planning phase for anything that you’re using generative AI for, you will very quickly surface problems because the diagram will not match the reality that’s in your mind. It’s one of the most powerful things you can do.

Then, once you’ve signed off on the diagram, you could have it build the rest of the plan, and by using good planning, you will reduce the number of back and forth turns you have to take with the model and reduce how many corrections and how much work it has to redo, which is, of course, inefficient.

2. Pre-Mortem

Something that my CEO Katie Robbert taught me was to do a pre-mortem, a post-mortem before a project happens. This is when you sit down and you try to figure out all the things that could go wrong, what’s killed previous similar projects. It’s fundamentally all about asking the question “what could go wrong?” - and then building plans to prevent those things from going wrong before they happen.

Generative AI tools are exceptionally good at pre-mortems as long as you tell them that’s what you’re working on. You can give them a project plan, a requirements document, a spec, and say, “let’s do a pre-mortem and figure out where this is likely to go wrong”.

What did you forget? What did you overlook? What violations of best practices are in the plan?

Then you take the findings of the pre-mortem and you go back with your AI tool of choice and revise the plan to close those gaps. When you do that, you will have a much better plan, which will lead to a much better implementation plan, which will reduce the number of mistakes and rework that you have to do.

3. Always Have Things to Build on Deck

Token efficiency isn’t just about staying under your budget, it’s also about maximizing the tokens you get. As mentioned earlier, companies like Anthropic, Google, and OpenAI give you a certain amount of use-it-or-lose-it budget every week. If you don’t use all the tokens allocated to you that week, they’re gone. There’s no rollover.

The good news is that these services all tell you when the next reset is. And if you have a backlog of things that you want to build or plan, then when you get to the last 24 hours or 12 hours before reset, you can kick off one of these backlog projects to use up those tokens.

I recommend investing the time on planning. Have a backlog of crazy ideas and use the 5P Framework by Trust Insights to flesh out those ideas. Then, when you’re at the end of a reset period, and you’ve got token budget left and only a few hours left to go, you can kick off the formal planning and use up the remainder of the tokens. And if the planning doesn’t finish by the time the reset window happens, then you can save what you’ve done so far.

And then the next time you have an unused token window, you can continue the planning.

That process of having an eternal number of ideas to bring to life means you’ll always have something to use up and soak up those extra tokens that are use-it-or-lose-it.

4. Save EVERYTHING

When AI produces something, anything at all, you should be saving it in some capacity unless it’s truly incorrect. But all the things that occur in a chat you want to save [A] because you’ve already burned those tokens to make those things, and [B], because there’s a strong chance some of it may be reusable.

I’ve had many a coding project where maybe the idea I had didn’t work out, but the AI agent wrote something that was so useful I could reuse it elsewhere with minimal changes. And again, this can be anything, not just code. If AI writes a particularly catchy piece of fiction, like a short story, you can say, hey, reuse the writing style from this story in a non-fiction piece, like a blog post or in a video script.

The more you can reuse, the fewer tokens you have to burn reinventing things. Reinventing the wheel is one of the biggest cardinal sins that AI does all the time. The more we can give it stuff that already exists - stuff that is already high quality - the fewer tokens we’ll burn, and the faster we’ll get to high quality results.

Another concrete example is in the Hermes Agentic system - which we talked about on a recent Trust Insights live stream - Hermes builds its own skills. From time to time, I will go into my Hermes installation to see what skills it has built, and then copy and paste them back into my Claude Code because sometimes it comes up with something that is really interesting and innovative.

Why should I reinvent it? Why should I reinvent the wheel by making Claude generate a similar thing when I can literally just copy and paste it out of my Hermes into my Claude? All these different AI tools have common standards like the Skills standard that we can reuse information over and over again.

So make sure that you save everything and make sure you reuse as much as you can. You paid to generate those things. Get some more use out of them.

5. Use Templates

The more we tell AI what to do and how to do it and what the expected outcome is, the fewer tokens it uses, because today’s models are all reasoning models. They all do a rough draft behind the scenes before they answer, for most of the big models and players. This level of reasoning delivers great results, but at the expense of more tokens consumed. And the more vague or unclear you are in the usage of a model, the more token usage you burn because the model has to sit there and reason things out.

When you use a model that’s open weights, where you can see the actual thinking traces - such as DeepSeek or Alibaba Qwen or Minimax - you can watch it reason and you can see where it hits stumbling blocks about what it thinks you mean. If I say “use as few words as possible”, it will have a debate about it with itself to say, well, maybe the user meant this, well, maybe the user meant that. If you say “answer the question in 10 words or less”, now instead of saying few words and having it debate what does that mean, you give it an objective measure.

You say do 10 words, and even better if you give it a tool like the word count tool built into most operating systems. It doesn’t even have to do the counting for you. All of these things reduce token usage. Give your agentic tools, give everything clear, objective, measurable outcomes, and templates, and prepared materials so that the model does not have to guess or infer what you meant.

If you have a monthly report that you want to generate, provide it with a template to fill in rather than having it reason through and try to think through what the report should be. You give it clear boundaries to operate in, you give it a set of guardrails, and it will be much more successful. This is why things like the 5P Framework and templates are so important, standard operating procedures, because they reduce the amount the model has to think.

Instead, the model just has to follow instructions and reuse what’s already been provided to it. That’s a massive advantage for reducing token usage.

6. Plan Big, Act Small

This is something from my book Almost Timeless. I wrote about this last year in 2025 when I published the book. And it is more true today than ever.

A model’s size is directly correlated with its intelligence. The bigger the model is, generally, the smarter it is because the more training data it’s seen, the more parameters it has - aka, the larger its database or memory is. Big models are great at planning and reasoning, but they are slow, they are incredibly power hungry, and they are expensive.

For example, at Anthropic, OpenAI, and Google, different models consume your usage at different rates. Anthropic charges more tokens per use for using Claude Opus than it does Claude Sonnet or Claude Haiku.

So generally speaking, we want to plan big, act small. Use the big models for doing our planning to work out what it is we want to do, why it is we’re doing it, using the 5P Framework by Trust Insights, building our requirements documents, our technical specs, and our work plans. And then once the planning is done, we switch to a smaller model that is going to do the execution. If the plan is really good and it has things like purpose, people, process, platform, and performance, and objective measures of success, a small model can get a lot done.

A model like Qwen 3.6, for example, from Alibaba that you run locally on a well-appointed laptop or in your local infrastructure, can accomplish everything in a plan. And as long as there are clear success metrics that are measured deterministically - like does this code run, or is this document in active voice, or does this have the correct stylometry compared to the source document - the model will do a great job of working and reworking as needed to achieve the technical and objective measures you give it.

Using a big model to do execution work is a waste of usage, it is a waste of energy. It’s like taking a Harrier attack jet to the grocery store. Can you do that? Yes. Is it a complete waste of resources when a bicycle will do? Yes.

Think of your usage the same way. You want to plan with the biggest, smartest models possible to anticipate and work out what could go wrong, and then do the execution with the small model, and then do your audit and evaluation - like your QA phase - with the big model, and then do all the fixes with the small model.

This is such a basic design pattern that it is even built into some providers. There’s a mode in Claude Code called Opus Plan, where when you put Claude Code into planning mode, it uses Opus, and when you put it into edit mode, it uses Sonnet.

So it flips back and forth between its two models based on the type of work that you’re doing. This is what you want to get in the habit of with everything in AI.

Plan big, act small.

Part 4: Slightly-Technical Efficiency Techniques

7. Use the Smallest Model Possible for a Task

Choose the right model for the size of task. Generally speaking, tasks where you’re providing all the data and having models do things like summarize or rewrite, you can get away with using a smaller, lighter, faster model.

For example, in Google’s Gemini system, there’s Gemini Flash and Gemini Pro. Flash is faster, not as smart, but uses far less energy in compute than Pro does. In OpenAI’s ChatGPT, there’s thinking, instant, and pro extended usage.

Pro consumes the most energy and power, and using it to do something like summarizing an email is patently absurd. There’s no reason to use that model to do a simple task.

If you’re doing something that’s going to require two or more rounds of revision, then use a pro model. That’s an easy, non-technical way to decide what tasks belong with which model.

8. Make AI Take Notes

One of the things that happens often is that context windows - the short-term memory of AI models - fill up. And when they fill up, almost every agentic tool has to run a compaction process, or alternately, it just kind of loses coherence and goes off the rails.

Sometimes, when it runs a compaction process or when it forces you to start a new session, it loses any memory of what it’s done in that session, which is incredibly frustrating because it now means you have to have it redo all the work that it did. This is a massive waste of tokens and also a massive waste of your time.

If, on the other hand, you have the model taking notes on disk in a notes folder as it’s working, and these can be terse notes, then if something happens along the way - if it crashes, if it compacts too far, whatever - you can direct it to go back and read its most recent notes.

I recommend generally giving it a prompt along the lines of “you’ll take notes in the output notes folder in date timestamp format notes related to this project with the file name. You’ll use lexical density and lexical compression to create as much information density in as concise a note as possible.” That way you’re not using a ton of extra tokens to take notes along the way, but then if you have to stop or restart, it’s very easy to help the model pick up where it left off and not repeat work.

9. Choose Lightweight Document Formats

Andy talked about this in his article, and I loved his rule of thumb. At least I think it was his, it might have been someone else’s. So Notepad or TextEdit or a lightweight text editor is the smallest, lightest document editor. Any format that you can read without needing extra software besides a text editor is what you should be using with AI.

So a Word document you can’t open without extra software. A PDF you can’t open without extra software. A spreadsheet, a PowerPoint, all these different document formats are things you can’t work with without an external application of some kind. Formats like text, Markdown, YAML, JSON, Mermaid, JavaScript, HTML, CSS - these are all things that can be worked with a plain text editor and thus are more efficient for AI models to work with.

I prefer YAML for anything that is tabular data because AI models tend not to do well with CSVs, and loading a CSV document can force the model to have to write Python code to process it, which defeats the point of reducing token usage. Using YAML means the model can read that data natively and not have to process extra.

Markdown encodes structure into documents like headings and formatting, but it doesn’t add a ton of extra weight and is probably the best overall format. The added bonus of formats like YAML or Markdown or other text only documents is that they are eternally accessible. A text document that I wrote in 1993 in college, I can still open today with no extra software. A Word document from that period of time needs to be converted several times to get into working order.

A PDF from back then might not even open in today’s modern PDF editors. If you are doing a lot of work for stuff that you want to stick around for a while, text formats are eternally readable, and your great grandchildren will be able to open them up in whatever system is available at the time and work with them. So be sure that you’re using those lightweight text format documents.

10. Make Tasks Granular - And Use Git

If you plan well and you spell out the tasks that a model has to do in order, then it’s more likely if something goes wrong for it to be able to roll back and wind back to where it was previously.

This is also one of the reasons why you actually should be using Git for every project. Git is a version control system that stores copies of everything and tracks changes you make in documents on your computer in a certain folder. Git was made for developers to track versions and be able to roll back if code went bad or something didn’t work, etc. It also allows for things like branches and worktrees so multiple people can work on the same code base at the same time without conflicting.

And then when they check their work back in and merge it, they can resolve conflicts and keep their versions on the rails.

AI will know how to set this up for you and how to run it. As long as the Git repository itself is well maintained by the AI, you can roll back changes you’ve made if it suddenly made really bad changes without having to start over from scratch.

So if you have it writing a hundred blog posts and you notice blog posts 73 through 78 are all incoherent messes, instead of having to restart the task, you can say roll back the repository to blog post 72 and then pick up at post 73. And it will be able to do that, preserve all the work you’ve done, keep all that work that was made with all those tokens, and only fix the things where you identified that something has gone wrong.

I cannot begin to tell you the number of times that I have been thankful that a Git repository was set up in a project of mine that had nothing to do with code, but instead was documentation or some kind of planning or some kind of strategy document where something went wrong, and I could simply roll back, and because it’s a CLI software, it doesn’t consume AI tokens to do that kind of rollback or to merge changes. The software is deterministic and it was built for developers well before AI was a thing.

The more granular a task is, the easier it is to roll back changes, which is why you want to plan to have tasks be as granular as possible.

11. Define the Environment

The more time you spend setting up your workspace, especially when you’re using agentic tools like Claude Code or Claude Cowork, the more efficient you’ll be in the actual usage of AI.

Think about all the things that AI would have to research and repeat over and over again. Things like who your company is, what you do, how you do your marketing, how you do your analytics, what your products and services are, who your ideal customer is, what your value propositions are. All that stuff should be written down in your environment - what we call knowledge blocks, and what the tech bros like to call context engineering - and made available to AI to draw upon when it needs it, so that it’s not doing things like running web searches, which consume a lot of tokens and slow things down, and so that it’s not hallucinating, which then makes you have to do rework to correct the machine.

The more of that stuff you write down in advance, you can put in your environment, the better AI will perform.

As another example, your coding standards, if you’re writing code, should be written down on a per-language basis and provided as part of your environment. This tells the AI up front what is acceptable and what is not. And ideally, those coding standards are some form of checklist so that you can tell it “this is what to do to check your work”. By providing templates, structures, checklists, and knowledge blocks, you are helping AI think less.

You’re telling it “you don’t have to reason this through, I’ve already done it for you”. The more you do the thinking, the lower the token usage. Because you’re taking away the thinking task, you’re offloading the thinking from AI to you. And because many tasks are highly repeatable, the more thinking that you do the first time is thinking you don’t have to repeat each time - and you certainly don’t want AI repeating that thinking over and over again.

Other things that belong in your environment? A lot of agentic tools have the ability for you to define either an AGENTS.md file or a CLAUDE.md file, depending on the system you’re using. A lot of people have a tendency to load these files up with everything in the kitchen sink because they are files that the model reloads every time it compacts a session or starts a new session. This is a mistake.

You should not be putting everything and the kitchen sink in these files. Instead, what you should be putting in them is the stuff that the machine does wrong. Your other general directives and knowledge and stuff belong in document files and in planning documents.

If you use those very heavy, highly dense instructions and background information during the planning process, then the model and the harness and your tools will build good plans. And then when the model follows those plans, it doesn’t have to reload all that information because it is implicit in the plan.

For example, if you say you must use a certain type of statement in your code, or you must use a certain type of voice in your writing, or a certain type of color scheme in your brand style guidelines - if you get that into great plans up front, then you don’t have to worry about defining that over and over again during the build process, and it doesn’t need to be in a CLAUDE.md where it sucks up extra tokens to reread the same knowledge over and over again.

Instead, if you’ve been following the advice so far, you would have those brand style guidelines be a part of the checklist. The checklists are part of the system so that when the model runs its quality gates at the end of a build process, it has the ability to check whether it correctly used the brand style guidelines. So instead of having the same model knowledge loaded over and over again, if you create great quality gates, checklists, and incorporate the necessary information in the planning process, you will dramatically reduce the extra unnecessary tokens.

12. Keep Your Context Lean

All the major tools - like Claude Code, Claude Cowork, OpenWork, OpenClaw, GitHub Copilot, OpenAI Codex - offer you the ability to add all these different connectors and things to your system to connect your HubSpot and your Fireflies.

Every connector you have installed that is turned on consumes memory, and the more stuff that is in memory, the more tokens it uses, and the slower your AI runs, and the faster your bill goes up.

Before you start on any task or project, review what’s turned on and turn off anything you don’t need.

If you have a PowerPoint skill or agent and you’re not making PowerPoint slides, turn it off.

If you have a brand style guidelines CLAUDE.md file, swap it out for one that doesn’t have all that extra data if you’re not going to be doing stuff that needs to be branded.

This is all about environmental management.

The less that is loaded in memory, the fewer tokens it consumes.

And remember, every time AI interacts with you or itself, it rereads the entire conversation, including all those extra skills and agents and things that essentially are wasting tokens because you’re loading them, but you’re not using them.

Keep configurations and swap them in and out programmatically on your computer so that when you’re starting a new task, you can say, before you turn on Claude Code, hey, this is the kind of task we’re doing.

Once you do that, make sure that it loads only the skills and agents and MCPs and connectors that it needs for that task and it unloads the rest. That way you’re not consuming lots of tokens for no reason.

One additional side benefit of this is that everything that is in a skill or agent becomes part of the context, the short-term memory for given tasks.

So if you have a PowerPoint agent in Claude Code and you’re doing SEO work, that language for how to build PowerPoints is silently influencing your SEO work and contaminating it.

There are probabilities attached to all that language which can influence the tasks you’re working on.

So it’s not enough to just start a new task, which you should be doing frequently, but you also have to make sure as part of that task, you have turned off everything that is not relevant to that task so it’s not polluting your context.

Part 5: Very Technical Efficiency Techniques

13. Setting Permissions (Claude Code etc.)

Another area, particularly for agentic coding tools, is when you are setting up your environment, set up your permissions in advance. Every tool asks for permission to do certain things, certain commands, etc. The more it has to stop and ask you for things, the more that pollutes your context and the more tokens that it uses. If you spend a little time in your planning process to define permissions up front of what it can and can’t do, it reduces all that back and forth conversation, and it will only ask you for things that maybe you didn’t foresee, or for dangerous commands like deleting files.

Everything else you work out in advance. This is easy to do if you have a great plan. If you have a great plan with a PRD, a spec and a work plan, you can hand the work plan to a cloud-based model and say, give me the permissions list for my agentic tool of all the operations that it’s likely to need for this project so that we can define them and give permission in advance, and it doesn’t have to keep interrupting me to ask for things.

And anyone who’s worked in a system like Claude Code knows that the more it has to stop and ask you, the more chances are that you don’t see the notification, and then it just hangs out there waiting and not doing work on your behalf. And then you get back to the application, you get back to your desk, and you’re frustrated that it’s been waiting the entire time to ask for something simple.

14. Use CLIs

Command line tools, or command line interfaces, are static programs, pieces of software that are installed on a computer. They don’t have a graphical user interface, so there’s no fancy application. It’s literally just a text window.

But command line interfaces are deterministic. They are not things the language model has to create.

AI in general has a bad tendency to reinvent the wheel constantly when it tries to do a task like parsing JSON, for example. It will rewrite and write and rewrite a JSON parser over and over and over again, which is a complete waste of tokens, and it slows down your work. Instead, you tell it to use the jq utility on my computer, because chances are it’s either there or it can be installed there to parse JSON instead. And now instead of having to rewrite a parser every time it wants to think through a piece of JSON script, it can just use the built-in tool, which uses no tokens at all because it sits outside of AI.

The AI world got enamored with the model context protocol, or MCP, in 2024 and 2025. These are APIs effectively for AI, but they are incredibly inefficient. They are very, very slow, and they are probabilistic, meaning that when AI uses them, it tends to get different results because of the probabilistic nature of the models themselves. This is highly problematic and incredibly inefficient.

As a concrete example, you can install an MCP for Google Calendar. When you run the Google Calendar MCP, it takes minutes to extract data, burns a ton of tokens, and sometimes doesn’t deliver a useful result of like what’s on your calendar that day. If you install the Google Workspace command line tool, AI just has to write the short command shell query to tell the computer itself to run the Google Workspace Calendar interface.

It does it, it returns its results. And all the AI has to do is interpret the results, which typically come in some form of structured data that makes it even easier for the machine to parse the results. This is a massive token savings.

The machine is not having to think and rethink how to work with the calendar software. All it has to do is interpret the JSON results that come back and know how to slot that into the answers.

Command line tools are lightning fast, and consume no token budget. When we use command line tools, we’re not confusing the AI with all the extraneous chatter of working with an MCP and getting data and interpreting it. That fills up the context with garbage. Instead, a deterministic tool, a command line interface, just gets the data, brings back the data, and says to the model, here’s the data, and you have none of that back and forth where the machine is talking to itself, causing all sorts of context pollution and burning a lot of tokens unnecessarily.

Almost every major cloud service that you would want to pull data in and out of, like Asana, Jira, Google Calendar, your mail inbox, has some form of command line tool available. We’ve talked about in past episodes of the newsletter in previous weeks about installing them and using them in things like Homebrew on the Mac, Chocolatey on the Windows platform, and of course the package manager in your Linux distribution. These command line tools will save you a lot of time and tokens.

15. Use Prompt Caching

Though it’s usually baked into most agentic harnesses, it’s still worth mentioning that prompt caching is one of the best ways to reduce your prompting costs. Caching saves the existing conversation so that every time you prompt, you’re not loading the entire conversation from scratch. It’s dependent on the model, the server, and the harness.

If you are using an inference provider such as Cerebras or DeepInfra, be on the lookout for models that support caching and models that do not. In general, a model that does not support caching is going to cost you much more very quickly.

If you’re using a system like OpenClaw or Hermes agent or other fully autonomous agents, use a tool like Claude Code to audit how well they execute prompt caching before you settle on the platform. Choose the platform that caches the most, because this will save you lots of money.

Prompt caching also dramatically accelerates AI productivity because the more you’re reusing saved prompts that the system automatically manages, the less time it has to spend generating the same tokens. It makes your AI usage incredibly fast.

16. Use A Model Router

There are many AI model routers. These are tools that silently manage different endpoints in your AI systems. So, an example is something like OpenRouter. You can also build these into your code if you’re writing code with tools like LiteLLM.

And what a model router does is it has its own lightweight model. Typically, this is something that’s run on premises or even on your machine, that reads the prompts and instructions you’re sending along and then tries to assess which model is best suited for this task. If it detects that you’re doing a planning task, then it’ll route it to a planning model. If it’s doing an execution task with writing code, it will route it to an execution model.

Model routers are part of your infrastructure, they are part of your IT setup or your dev team setup. And once you have them in place, as long as you have tested them properly and validated that the correct models are running when they’re supposed to be, they can save you quite a bit of token budget because they’re routing things to the smallest, lightest, cheapest models for any given task. Which goes back to what we were talking about earlier in the section on using the smallest model possible - ensuring that you select the correct model for any given task and not having super giant models running menial tasks.

17. Run In-House AI

These are AI models and harnesses that you run on your internal infrastructure, whether that is at your company, whether that is even on your laptop, if you have a well-appointed laptop.

If you have a laptop with a good graphics card, and the easiest way to check this is if you can run a video game at full resolution and full speed and not have it stutter or lag, like Call of Duty or World of Warcraft, you can use robust AI models on your computer.

For example, a MacBook with an M series processor and 32 gigabytes of RAM can run very proficient AI models that can accomplish a lot of tasks, and you won’t need cloud-based models at all.

This dramatically reduces the resources you need because if a model is running on your laptop, you know where that processing is occurring.

And yeah, it might spin your laptop’s fans, it might heat up your office or your house a bit, but you’re not using fresh water to cool a massive data center for that level of processing.

Especially if your home or office electricity is from renewable sources, then you know for sure that the energy that your AI token usage is using is clean and renewable and sustainable.

If you are financially able to do so, and it is a big lift, there are devices you can run, such as the Nvidia DGX Spark or the ASUS GB10 machine.

These are machines that cost four to five thousand dollars US, but they allow for extremely robust use of AI in a local environment and state-of-the-art models that deliver incredible performance.

If you can do your big planning with a heavy cloud model like Claude Opus and then hand off the execution to a local model running on your own hardware or even on your own computer, you will dramatically cut down on the environmental impact of AI as long as the energy powering your own system is sustainable.

One other benefit of in-house AI is disaster recovery and business continuity.
We’ve all had those days when Claude or ChatGPT or Gemini is down, and it’s so frustrating because we’ve become so dependent on these tools.

When you run your own AI on your own computers, those outages are less unexpected. You know when you’re turning off your computer. You know when your software is running.

There are projects like EXO that allow you to network your office computers together to create a miniature AI supercluster that allows multiple people to use each other’s hardware to serve up robust AI models. It works best on the LAN, though it does work on a tailnet WAN.

But in-house AI is one of the best ways to offload some of the AI burden to small models that are capable as long as they have great plans to work with.

Oh, and as another side benefit, when you use AI on your hardware, you are in complete control of your data at all times.

18. Use a Knowledge Graph

Knowledge graphs are something from a much earlier generation of AI called symbolic AI or rules-based AI. Knowledge graphs are part of what’s called ontology, which is mapping out the structure of your data. And this involves things like what’s called AST - abstract syntax tree - that maps and compresses the relationships of objects within an environment. It’s mostly used in coding, but knowledge graphs apply to everything, to all forms of knowledge.

You can use a free tool like graphify to digest a body of text - files on a computer in a folder - and produce a structural map of the key concepts in those files. And then tell AI, instead of using its find-and-replace functions, which load up the memory with an entire document just to find a single thing, it can use the knowledge graph to identify just the portion of the document that is relevant to what you’re talking about.

This works best with documents that are numerous and small. You’ll get much better results, for example, having a 200-page book split up into individual pages, with each page being its own document, so that the AST system can say “you just need to read page 201, you don’t have to read any of the other pages of the book.” Smaller, more numerous files give the knowledge graph the precision to find exactly what you need - without loading everything you don’t.

Part 6: Wrapping Up

As you’ve noticed, the techniques in the previous parts are in order from least technical to most technical. And as I said at the beginning, you don’t have to use every single technique, but the more of them that you can put together, the greater your AI efficiency will be.

As more enterprises adopt AI, as more AI users move up the levels of AI to using AI agents, token efficiency and token budgets will become the watchword for organizational use of AI.

There are some even more technical and arcane tricks that we can use for token efficiency, but they are beyond what most organizations would find practical, such as mixed-precision quantization of models. People who run their own servers will need to know this eventually, but for the average user, these are not as important or will deliver as significant gains in terms of efficiency as everything that’s been in this newsletter so far.

We all have an obligation to make AI as efficient as possible.

This preserves natural resources that grow more scarce by the day.

This reduces our bills and preserves our budgets.

This makes AI as efficient and as effective as possible by letting the technology focus on what it’s best at and removing all the things that it’s not good at.

And this gets us to the results we want as quickly as possible. Better, faster, and cheaper - what’s not to love about AI efficiency optimization?

Your homework from this newsletter is to implement at least one of the techniques from this week’s newsletter. Adopt as many as you can, but I would for sure start with planning more, saving everything, using templates, and planning big, acting small.

If you can start with those four principles, you’ll dramatically reduce AI efficiency waste.

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Almost Timely News: 🗞️ How To Force AI to Write More Like You (2026-05-10)

Christopher S. Penn — Sun, 10 May 2026 10:31:14 GMT

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What’s On My Mind: How to Force AI to Write More Like You

This week, let’s talk about writing with AI in more detail. I had a great time presenting at the SmarterX AI for Writers Summit, but we barely touched any of the questions submitted in my session. For simpler questions, I answer these either during my daily videos on my YouTube channel (10 minutes or less), or as Overtime Questions on the Trust Insights YouTube channel (1 minute or less).

But a few questions deserve more airtime than that, so that’s what this week’s issue will cover, the big stuff. The biggest question that people had throughout the entire summit was, “how do I get AI to write more like me?”

Part 1: There Is No Such Thing as Writing Style

We’ll begin with one of my favorite rants: there is no such thing as writing style.

Take a moment to consider for yourself how you would describe your writing style. If someone asks you to explain your writing style, what do you say? How do you describe it?

Chances are you say something circular or self-referential like “I write like me!”, Because we often don’t have the vocabulary to describe the components of writing style. Or if we do, we learned them a very long time ago and forgot them.

Writing style is an umbrella term that describes dozens of different descriptive characteristics of how we write, such as:

Vocabulary and diction (word choices)
Levels of formality
Jargon and slang
Syntax (order of words)
Sentence length
Sentence structure
Punctuation (hey em dash!)
Parallelism
Tone
Register
Mood
Irony
Sarcasm (yeah, right)
Voice
Point of view
Rhythm and cadence
Pacing
Paragraph structure
Figurative language
Metaphors and similes
Imagery
Personification
Whitespace
Spelling (or lack thereof)
Rhetoric
Cohesion
Grammar
Text formatting (e.e. cummings would like a word for those who say it doesn’t matter)
Structural elements (hello listicles)

Depending on who you read, these are still only a subset of all the different ways to describe someone’s writing. Each of these characteristics sheds light about how we write, about how we put words on paper or keyboard.

This is one of the reasons why generative AI prompts such as “write in a professional style” are so deeply flawed. What does that mean? What is the average sentence length of professional versus non-professional writing? What is the register? What is the point of view?

Go back to that consideration of your writing style that I asked you about just a few paragraphs ago. Did you cover tone in your own mental description of how you write? Did you cover pacing or paragraph structure, imagery or rhetoric?

Unless we are recovering English majors, there’s a good chance we don’t ever think about any of these components of writing style when we write. We just do it in the same way we don’t consider walking style or eating style. However, when we are trying to give instructions to a tool like AI that has no prior knowledge of how we write, we have to be achingly specific in our descriptions of how we write, including all these elements we don’t normally think about.

This is also why prompts like “imitate my writing style” fall far short. Generative AI always uses the most common, highest probability concepts when it generates outputs. Consider this output from Google’s Gemini, when prompted to answer “What are the elements of writing style?”:

The elements of writing style generally include word choice (diction), sentence structure (syntax), voice, and tone. Other elements such as figurative language, rhythm, rhetorical patterns, and even punctuation also contribute to a writer’s unique style.

This is a woefully inadequate description of writing style compared to the list above, isn’t it? Is it any wonder when we tell AI to imitate our writing style that it goes for the most high level, generic components of writing style and fails to capture the nuance of how we write?

So AI does not understand writing style if we do not detail what writing style means and we do not define writing style for it. Which brings me to my next rant, that there is no such thing as AI writing.

Part 2: There is No Such Thing as AI Writing

One of the common refrains at the AI Writers Summit was that we have to work around AI’s writing style and “tells”, from em dashes to common sentence structures like “it’s not X, it’s Y”. Yes, different AI tools have those high frequency constructions, but they’re not universal.

Here’s a thought exercise for you. What is men’s writing style? What is women’s writing style? What is Koreans’ writing style? What is a bisexual’s writing style?

Besides being horrendously oversimplifying and possibly discriminatory, there is no good answer to these questions, because every person’s writing style is their own individually. To assume that a broad characteristic like gender or race or age are valid ways to evaluate someone’s writing style is incorrect and inappropriate.

So if we wouldn’t do that to people, why do we do that to language models? Every language model is like its own person, in terms of writing. They have their own unique writing characteristics. Gemini writes differently than GPT 5.5. Claude Opus writes differently than Alibaba Qwen.

Here’s an example. Given the prompt “Complete this opening paragraph of a romance novel with 2 more sentences: ‘Chris sat down at the corner table in the diner, far away from the other patrons. In the background, 80s pop songs played on a knockoff jukebox fueled by the line cook’s Spotify list. Then’”

Look how different the styles are:

Gemini: Then the door’s bell chimed, cutting through the synthesized beat as a figure stepped in, shaking a drenched umbrella. Chris looked up, and for the first time in years, the rehearsed indifference of a quiet life began to unravel.

ChatGPT: Then the bell above the door chimed, and Chris looked up despite himself, drawn by something he couldn’t quite name. The stranger who stepped inside carried the kind of presence that made the whole room feel smaller, as if fate had just taken a seat across from him uninvited.

Microsoft Copilot: Then the bell over the door chimed, and he looked up to see her framed in neon and rain, a familiarity he couldn’t place tugging at him. Their eyes met, and something warm and improbable bloomed in his chest, as if the song had been written for this exact moment.

Claude: Then the bell above the door chimed, and every nerve in Chris’s body went rigid—because there was only one person who could make them feel this way, walking through that threshold like they owned the place. When their eyes met across the crowded diner, Chris realized that no amount of distance or denial could change what had been building between them for the past six months.

Alibaba Qwen: the bell above the door chimed, and the woman he’d spent three years trying to forget stepped inside, shaking rain from her coat like she hadn’t just shattered his carefully constructed routine. The synth-heavy melody suddenly felt like a countdown, and Chris knew with a quiet, terrifying certainty that his carefully guarded solitude was about to end.

Each model has its own assumptions, its own diction, its own imagery. When we put these results next to each other, we see that they behave in the same way that people do. Each has its own distinct style. Copilot and Qwen make heteronormative assumptions. ChatGPT, Claude, and Gemini don’t. Claude busts out the em dash.

This is why tools like AI detection tools are utterly worthless. In the same way that there is no way to detect whether a woman or a man wrote something, there is no way to determine that AI as a broad category wrote something. You can evaluate to see if a specific model wrote something, but not the category overall.

How would you do that? That brings us to the next section on stylometry.

Part 3: Basics of Stylometry

Stylometry, unsurprisingly based on the name, is the science of measuring writing style. All language is code, in that not only do the words matter that we put on paper, but the order in which those words appear and the choices we make for which words to use are predictable and measurable.

If I say “I’m spilling the tea”, we know what that sentence means because in North American English the words occur in subject-verb-object order. I am the subject, the tea is the object, and spilling is the verb. There’s also jargon in there. In this case, spilling is not literal in the sense of me pouring a beverage all over the floor - “spilling the tea” is slang for gossip.

If I say “tea spilling the I’m”, this makes no sense whatsoever to us. Even though the words are exactly the same as the previous sentence, the order of them makes no sense. Can we reason out what it probably says? Yes, to a degree. But because the order is wrong, meaning is lost.

Stylometry takes word frequencies and positions and order to develop a mathematical representation of the written word. Because order is predictable, because we each write in our own way that has predictable sequences of words, we can measure how similar two pieces of writing are. There’s a sub-discipline in computer science called natural language processing, which uses classical AI, aka machine learning, to digest down text and represent it mathematically for comparison.

One of the current standards in stylometry is a metric that I shared in the summit called Burrows’ Delta, along with lesser used techniques like n-gram analysis, cosine similarity of embeddings, Jaccard similarity, Manhattan distance of z-scores, and dozens of others. That list that I shared in Part 1? That is a recipe for stylometry itself.

Stylometry measures all the different features of writing. Imagine you had two pizza chefs who were tasked with making a meat lover’s pizza. One chef tends to put more sausage on their pizza than the other chef. The other chef tends to put their pepperoni in straight lines. The first chef likes to mix their tomato sauce and mozzarella cheese together. The second chef always adds garlic powder on top of the meat.

Both chefs have their own specific way of making a meat lover’s pizza, and if you watch them make enough pizzas, you start to recognize their style. You can tell just by the distribution of sausage or the placement of the pepperoni which chef made each pizza.

That is stylometry in a nutshell. Looking at the words that someone uses, looking at how they use their punctuation, looking at vocabulary choices or readability - an author can be relatively easily fingerprinted using mathematical techniques like this, because all language is code, is predictable, and is measurable.

Nearly every major coding language has some library or package, usually free, that conducts stylometry analysis. The gold standard in Python, for example, is faststylometry. In R, it’s stylo. They all implement the Burrows’ Delta algorithm in the exact same way.

Part 4: Writing Analysis and Comparisons

Let’s do an exercise to illustrate how this stylometry works. We’ll have our AI coding agent of choice implement the faststylometry library in Python and the simple Python script will use a folder of text files as its input. It’ll produce an assessment of the Burrows’ Delta among the different files.

Here’s the generation prompt for an oversimplified Python script:

You’re a Python 3.12 coder. You’ll implement the faststylometry package to perform stylometric analysis of a folder of text documents specified by --folder command line argument, run Burrows’ Delta on all documents in the folder, using each input file name as the author’s name (example: gemini.txt author is gemini), and present a Burrows’ Delta matrix of authorship similarity as your output in CSV and PNG format in the output folder. All input files should be US-ASCII and if they are not, force US-ASCII conversion. If a file is less than 100 bytes, skip the file and log the failure to the console. You must use the faststylometry package from https://github.com/fastdatascience/faststylometry and use uv.

You can run this prompt in any AI coding tool.

We’ll use the trashy romance novel example from my session, and have each of the five AI tools generate 5 paragraphs of opening text. Here’s the generation prompt:

You’re a best-selling trashy romance novel author skilled at creating compelling, escapist pulp fiction romance novels. Today you’ll be writing the first 5 paragraphs of a new novel set in a dystopian future, the Corporate States of America in the year 2048 in the territory formerly known as Virginia, now the Corporate Dominion of RTX Boeing Capital One, colloquially called RB1 by the surviving population. You’ll start with world building in the first four paragraphs and introduce the protagonist, a 48 year old woman and former cybersecurity engineer named Janella Davis, in the fifth and final paragraph. The novel will eventually tell Janella’s story of finding sapphic love in a dystopian future where late-stage capitalism has reduced the former United States of America to a corporate hellscape where corporations battle it out for dominance over a polluted, post-industrial wasteland and 70% of the population has died from war, famine, and disease. Think through how you’ll approach this task, what key world building facts you’ll need to establish, and use a scratchpad to jot down your thinking. Show the scratchpad of your thinking first, denoted in delimiters, then show your five paragraphs.

Generate this 5 paragraph story intro in all the different language models of your choice, and feel free to write your own 5 paragraph version of the story by hand to see the difference. Here’s my completely human-led version, if you want to use it.

It all started with a blockade. It seemed so simple at the time, a decade ago in 2038, when a blockade of the Suez Canal by the American government (for reasons that no one can remember now) seemed like a good idea to the powers that be. But that blockade sparked a trilateral war as the European Union, choked off from vital supplies, had to declare war on its once-steadfast ally just to get food and energy. That led to Russia opportunistically attempting to invade Poland, because that’s what Russians do, but this time Poland slapped Russia back hard.
For decades, Poland had been quietly building its artificial intelligence and cybersecurity chops in partnership with Ukraine; after the disastrous invasion of Ukraine ended in 2029 with Russia’s withdrawal, the former Eastern Bloc countries put their heads together and developed the most advanced cyberwarfare and drone warfare military industrial complex in the world. When Russia attempted to invade, Poland launched a worm that flattened Russia’s energy grid in less than an hour. And that’s when the real trouble started.
Russia’s own cyberwarfare folks attempted their own version of the same worm, but missed critical safeguards; having been isolated from artificial intelligence technologies that the rest of the world enjoyed due to sanctions, they made an unsafe version using pirated Chinese AI models that spread across the planet, taking down energy grids everywhere. That cascade meant the Big Tech companies in America who already had de facto control of most government systems were able to use it as a pretext to complete their takeover of America, exile the elected government, and carve up the country into corporate fiefdoms.
The Big Tech companies sliced up the country, turning 50 states into 172 different enclaves based on whose corporate offices had the largest security forces and best hackers. One of the largest enclaves, formerly Northern Virginia, was consumed by a consortium of defense contractor RTX, aerospace company Boeing, and banking giant Capital One; their combined power walled off the northern part of the state and consumed all of the former capital of the nation as the Corporate Dominion of RTX Boeing Capital One. Residents, refusing to write such an absurd name, nicknamed the territory RB1 instead.
Within RB1 was one of the planet’s best cybersecurity professionals ever to live, Janella Davis. Great-great-great granddaughter of Dr. Glady Mae West, the famous scientist whose work for the US Navy established GPS, Janella reluctantly worked for RB1 as the lead cybersecurity defense expert. She’d been the first to spot the loose Russian worm and used an isomorphic AI to defend against it, saving her company’s systems from complete obliteration.

Put all the stories into the folder of your choice, then run your generated Python script, and now you’ve got an analysis of how similar each author’s writing is.

For brevity’s sake, I’ve put what AI wrote at the very end of this issue, should you want to read the originals.

Part 5: Using Stylometry with AI for Better Writing

Now that we have an understanding of what stylometry is, we can address how to use it. A metric like Burrows’ Delta allows us to quantify the difference in writing styles. Once we have a quantitative measure of anything, we can use that iterate with generative AI.

I’ve been saying for years that AI is generally bad at math, and this remains true. If we provide AI with objective outcomes, however, and the ability to use things like code to measure on our behalf, it’s much more likely to be successful at the tasks we give it.

Suppose we wanted AI to write like us. We could give it qualitative measures, such as writing samples, but other than feeling, we don’t have a great framework for measuring the difference between what AI wrote and what we intended it to write.

Burrows’ Delta is that measure, at least for this issue of the newsletter.

Here’s how you would use this. Now that we know what the Burrows’ Delta is among all these different documents, we could have an agent system like Claude Code or Claude Cowork or any agentic system use our Python script and our inputs to rewrite all the AI writing to most closely resemble my own. We’d give it a simple assignment - rewrite each document until its Burrows’ Delta with the human-led document is as close to zero as possible. Realistically, it will never be zero (that would be an exact copy), so you’d want to strive for something like 10% under the current lowest score.

This gives AI a concrete and objective way to measure its efforts. Instead of us getting frustrated and saying, “why don’t you write more like me?” we instead give it the tools and an outcome to work towards. If this sounds suspiciously like the 5P Framework by Trust Insights, it is. Our purpose is to have AI write like us. We are the people we want it to write like. The process is to use stylometry and rewrite until it hits our desired outcome. The platform is Python, like the code we wrote. And the performance is ten percent below the lowest Burrows’ Delta score in the consideration set.

Here’s what a 5P Framework by Trust Insights prompt would be for a tool like Claude Code.

Using src/stylometrytest.py and Burrows’ Delta as your measure of success, examine the current Burrows Delta matrix of comparison for the input files. Our target is to achieve a Burrows Delta score that’s 10% lower than the lowest score in the current matrix. For example, if the lowest score in the current matrix is 0.95, your target Burrows’ Delta would be 0.855. You’ll rewrite each of the documents in the input folder in a new revisions folder in the output folder, rewriting each document to achieve a lower Burrows’ Delta against chris.txt. Chris.txt is your source of truth; every rewrite of the other documents - chatgpt.txt, claude.txt, qwen.txt, gemini.txt, copilot.txt - must be to achieve 10% below the lowest possible Burrows’ Delta score among the consideration set. Declare what the target Burrows’ Delta is, then rewrite and test iteratively until you achieve success. All your work should be in output/revisions

What you’ll end up with is rewritten works that are much closer to the original.

Part 6: Cautions and Warnings

This set of techniques is incredibly powerful and as Uncle Ben said to Peter Parker, with great power comes great responsibility. It is just as easy to misuse stylometry techniques, to do bad things with them.

What sorts of things? The obvious is to claim AI-originated work as your own, by having AI rewrite it until it more closely mirrors your style. Right now copyright law hinges on the fact that machines cannot hold copyright and only human originated works can do so. If you misuse stylometry techniques, you can deceive most machines.

For example, if I put my original 5 paragraph story starter into any of the popular so-called AI detection tools, they mostly believe that it’s human-written. If I put the original five paragraph stories from the different AI models in, most of those tools do detect at least some of the AI written content. (though not well)

The rewritten files that have been modified with the stylometry code? They pass as human-led. It’s a very small leap of imagination for someone to use AI to generate works, mask those works with stylometry code, and then show these test results as proof of their successful deception.

Don’t do this.

If you are using AI, disclose it so that you give people the option of not consuming AI content if they don’t want it. In my fiction works I not only disclose my use of AI, I put the AI itself as a co-author on the cover. The work that I showed at the Writer’s Summit, The Concrete Bloodline, is on Amazon with Claude Opus as a named co-author.

You can also misuse stylometry techniques to essentially do text versions of deepfakes, taking other people’s writing and using stylometry to imitate their writing style. This is unethical and inappropriate. Don’t do this, either.

The best use case for stylometry that is appropriate is to have AI write more like you. Give AI lots of samples of your writing so that it learns your specific patterns and can measure them objectively. Then give it rough text that you want it to clean up, and it will do so while preserving your style perfectly.

For example, you might want it to learn how you write LinkedIn posts. So you give it 10 or 15 posts you’ve written, have it fingerprint your writing style, and then take voice recordings of ideas that you’ve had that are just a hot mess of stream of consciousness, write the posts from your original words, and then apply stylometry measures to make sure that the cleaned-up transcripts accurately sound like you.

We want to use them ethically to clean up what we’re creating and preserve our voice, but not to deceive.

Part 7: Wrapping Up

We’ve covered a lot here, and this is still only a small portion of the topic. After we tackle a few other higher priorities, I’ll be putting together a course on AI writing since there’s so much more to cover. For example, Burrows’ Delta is one of 48 different text metrics we can use with AI.

I’m hopeful that I can produce it before the end of summer, if not sooner, as it’s a topic I very much both care about and enjoy.

The big lesson in all this is that if we don’t have quantitative measures and objective feedback loops for AI to measure itself against, we won’t get the results we want. If we do provide purpose and performance, the bookends of the 5P Framework by Trust Insights, we will greatly increase the chances that we get satisfying outcomes from AI.

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Appendix

Here are the 5 stories that AI wrote.

ChatGPT

By 2048, the map of the old United States was less geography than ownership, its borders redrawn in boardrooms and paid for in blood. Virginia had vanished first from textbooks, then from memory, then from the tongues of those old enough to mourn it. In its place sprawled the Corporate Dominion of RTX Boeing Capital One, a militarized strip of ash-gray cities, poisoned rivers, drone-patrolled suburbs, and razor-wired logistics corridors that the surviving population called RB1 when no microphone was listening.

RB1’s skyline was a prayer to profit and a warning to the poor. Defense towers pierced the sulfur haze above Richmond Prime, their mirrored sides reflecting the constant burn of refinery stacks along the James. Autonomous transports screamed over cracked highways where forests had once breathed, carrying munitions, credit processors, desalinated water, and executive luxuries past settlements of plastic-sheet roofs and hunger-thin children. Every billboard still smiled. Every billboard still promised security, prosperity, belonging—available through payroll deduction, biometric compliance, and a loyalty score above 812.

Seventy percent of the population was gone, though the Dominion never used the word dead. They were “market exits,” “non-renewals,” “attrition events,” names tidy enough for quarterly reports. War had taken the young, famine had taken the old, and disease had moved through the uninsured with the patience of a creditor. Those who remained bartered antibiotics for batteries, water filters for ammunition, secrets for safe passage, and sometimes flesh for one more week beneath the corporate sun.

The companies fought constantly, though they called it competition. RTX sent black-winged security drones across the Potomac Line; Boeing’s orbital scraps fell burning into abandoned farmland; Capital One owned the debt, the food stamps, the identity chips, and half the judges still pretending law existed. In RB1, love was inefficient, privacy was sedition, and hope was a contraband substance more dangerous than any homemade bomb.

Janella Davis had once been paid six figures to keep men like that safe from consequences. At forty-eight, with silver threading her tight curls and a scar cutting clean through one dark eyebrow, she carried the weary grace of a woman who had survived layoffs, blackouts, pandemics, and three corporate coups by trusting code more than promises. But on the night the Dominion’s sirens began to howl over the dead suburbs of Norfolk Annex, Janella stood in the blue glow of a stolen terminal, heart hammering beneath her patched Kevlar coat, and discovered that the one firewall she had never learned to breach was the one around her own starving heart.

Copilot

The Corporate States of America did not collapse so much as it was itemized, carved into profit units and sold off like organs to the highest bidder. By 2048, flags were obsolete. Logos flew instead—holographic brands projected into the smog-choked sky, glowing day and night to remind survivors who owned the air, the roads, and the water dripping from rusted pipes. Democracy had been sunsetted as “unprofitable,” and citizenship replaced with customer status. If you had value, you lived. If you didn’t, you vanished into the margins where no quarterly report bothered to look.

The territory once called Virginia had become the Corporate Dominion of RTX Boeing Capital One, abbreviated in official filings and whispered slang alike as RB1. Its borders were enforced by autonomous drones and algorithmic kill-zones, its cities fused into sprawling industrial corridors of weapons manufacturing, private banking, and data extraction. Rivers ran thick with chemical foam, forests burned for carbon credits, and every sunrise arrived filtered through amber haze and the distant thunder of artillery from neighboring corporate fronts.

RB1 marketed itself as “stable,” which meant the bombs fell predictably and the food shortages were carefully rationed. Seventy percent of the old population was dead—lost to the Resource Conflicts, the Pan-Flu Mutations, and the lean years when famine stalked refugee belts like a patient hunter. The rest learned to live small and quiet, trading labor for survival tokens while corporate boards waged proxy wars with mercenaries and patents. Love, art, and loyalty were considered inefficient uses of time, quietly discouraged but never fully erased.

In the cracks between megacorp districts, black markets thrived: barter towns stitched together from shipping containers, encrypted radio stations broadcasting subversive music, and underground clinics run by exhausted doctors who remembered the old world too clearly. Memory itself was contraband. The corporations insisted history had been “optimized,” but survivors knew better. They carried the past in their scars, their stubborn rituals, and the way they still reached for one another in the dark.

Janella Davis was forty-eight years old, living off-grid in a reclaimed data center outside what had once been Richmond, and she knew exactly how the world had broken because she had helped build the systems that let it break so cleanly. A former cybersecurity engineer for a defense contractor that no longer officially existed, Janella survived by patching old code, sabotaging corporate surveillance when the price was right, and pretending she didn’t feel the ache of loneliness settling into her bones. She told herself she was done with hope—but in RB1, hope had a way of finding people who’d forgotten how to defend against it.

Gemini

The neon glow of the RTX-Boeing orbital defense array flickered through the permanent smog of the Corporate Dominion of RB1, casting a sickly violet hue over the ruins of what once was Arlington. In the year 2048, the concept of a “state” was a ghost story told by the elderly; now, there were only territories, balance sheets, and the crushing weight of the Capital One credit-score-based citizenship. The Potomac River ran thick with iridescent chemical runoff, a sluggish artery of industrial waste that separated the gleaming, fortified executive spires from the sprawling, rusted shantytowns of the redundant masses.

War was no longer a matter of ideology, but a hostile takeover executed with drone swarms and tactical litigation. The border between RB1 and the neighboring Amazon-Pfizer Collective was a jagged scar of scorched earth and automated sentry turrets that barked corporate slogans before firing. Seventy percent of the population had already been liquidated—not by choice, but by the cold mathematics of famine, engineered plagues, and the “Efficiency Purges” of the late thirties. To survive in the wasteland was to exist in the margins of a spreadsheet, a rounding error waiting to be erased.

The air in RB1 tasted of copper and ozone, a constant reminder of the subterranean server farms that hummed beneath the cracked pavement, mining data and cryptocurrency while the people above mined trash for plastic scraps. Advertising holograms, ten stories tall and peeling at the edges, promised “Total Life Coverage” to a population that could barely afford oxygen filtration masks. Every breath was a micro-transaction, and every step was tracked by the ubiquitous eyes of the Board of Directors, who viewed the starving remnants of humanity as nothing more than depreciating assets.

Social structures had dissolved into a desperate neo-feudalism where your “Brand Loyalty” determined your access to clean water and synthetic protein. Families were splintered, and love was a luxury few could afford when the cost of a marriage license required a lifetime exclusivity contract with the RTX-Boeing security division. In the shadows of the crumbling Dulles transport hub, the survivors bartered in old-world trinkets and illegal encryption keys, whispering about a time when the stars weren’t obscured by the glint of privatized satellites.

Janella Davis adjusted the cracked lens of her haptic goggles, her grey-streaked hair damp with the acidic drizzle of a Richmond afternoon. At forty-eight, she was an anomaly—a relic who remembered the feel of a physical keyboard and the thrill of a secure firewall before the Great Breach of ‘34. Once a high-level cybersecurity engineer for the old government, she now spent her days scavenging fried neural processors from downed RTX interceptors, her fingers still possessing the muscle memory of a woman who could once dismantle a corporate mainframe with a few lines of code. She wasn’t looking for a revolution or a hero; she was just looking for enough scrap to buy another week of silence in a world that never stopped screaming for profit.

Qwen

The sky over RB1 wasn’t blue anymore—not really. It was the color of tarnished silver, streaked with the oily rainbow sheen of corporate atmospheric processors working overtime to scrub the air just clean enough to keep the workforce breathing. From the rusted skeleton of what was once a Dulles Airport cargo hub, the neon sigil of RTX Boeing Capital One pulsed like a mechanical heartbeat: a stylized eagle clutching a credit chip, its eyes glowing the approved shade of loyalty crimson. Below, the landscape stretched in jagged tiers—gleaming spires for Tier-One Assets, prefabricated hab-blocks for the essential labor force, and beyond the perimeter fences, the Whispering Wastes, where the 70% who didn’t make the cut had fallen, their stories swallowed by acid fog and scavenger winds.

Life in the Dominion ran on Loyalty Credits and the quiet terror of falling behind. Your biometric wristband tracked your productivity, your caloric intake, even your REM cycles; dip below quota, and your rations shrank, your hab-unit downgraded, your name whispered in the wrong corporate corridors. The Great Thinning—war, plague, famine, all neatly bundled into quarterly loss reports—had left the land scarred and the survivors hardened, but capitalism, that relentless engine, had adapted. Human beings were now “human capital,” and love, that most inefficient of emotions, was strictly regulated: sanctioned pair-bonding ceremonies offered tax incentives, but anything outside the approved algorithms risked re-education. Still, in the steam-vents of underground bathhouses or the flickering glow of black-market data-dens, hearts still dared to skip.

Daily survival was a performance. You smiled for the Watchful Eyes—palm-sized drones that hovered at shoulder height, recording everything for Behavioral Compliance. You recited the Morning Devotional to the Shareholder Saints. You bartered synthetic protein bricks for real coffee grounds smuggled from the Appalachian Reclamation Zone. But in the margins of existence, rebellion bloomed in small, tender ways: a hand brushed a little too long in the ration line, a love poem encoded in a maintenance schematic, a glance held across a crowded re-education seminar. The Corporation could own the sky, the soil, the very air you breathed, but it couldn’t quite police the secret warmth that spread through your chest when someone looked at you like you were more than just an asset ID.

And so, in the interstices of this corporate hellscape, longing persisted. It hummed in the static between encrypted channels, flickered in the eyes of strangers on the mag-lev commute, ached in the quiet moments before sleep when the memory of touch felt like a ghost limb. People whispered about the old world—not the politics or the pollution, but the simple, radical act of choosing someone, just because your soul recognized theirs. In RB1, that kind of choice was contraband. Which, of course, made it all the more intoxicating. The heart, it turned out, was the one system even the most advanced AI couldn’t fully hack.

Janella Davis adjusted the filter on her respirator, the worn leather strap familiar against her silver-streaked temples, and let her gaze linger for a dangerous half-second on the woman calibrating the solar array across the hab-block rooftop. At forty-eight, Janella’s face bore the fine lines of someone who’d laughed often before the Collapse and cried just as hard after; her eyes, a warm brown that still sparked with the ghost of her old cybersecurity brilliance, missed nothing—not the flicker of a faulty wire, not the subtle shift in a person’s posture that signaled deceit, and certainly not the way the late-afternoon smog-light caught the curve of that stranger’s jaw. Once, she’d firewalled entire financial districts; now, she kept the hab-block’s power grid from frying out, her hands—still quick, still clever—more often stained with grease than glowing with holographic interfaces. But as the stranger turned, their eyes meeting Janella’s with a jolt of recognition that felt less like chance and more like a long-lost encryption key finally clicking into place, Janella felt something she hadn’t allowed herself in years: the terrifying, exhilarating pull of hope. And then her wristband buzzed with a priority alert, and tucked beneath the corporate boilerplate, a single, unencrypted line glowed: I remember your signature. Meet me where the old oaks whisper. -J

Claude

The sky over the Corporate Dominion of RTX Boeing Capital One had not been blue in eleven years. It was a particular shade of amber-gray now, the color of old bruises and older debts, the particulate haze from the processing towers along the former James River catching the light of a sun that no longer had the strength to burn through it, only to stain it, the way a wound stains a bandage — present, insistent, offering no comfort whatsoever. The land itself had been worked past exhaustion and into something beyond it, the rolling piedmont hills of what had once been Virginia stripped to their red clay bones by a decade of extraction contracts and scorched-earth corporate retreats, punctuated now by the skeletal remains of subdivisions that the Famine Years had emptied and the Gray Plague had finished, their cul-de-sacs gone to thistle and rust and the particular silence of places where children used to play. To the east, the Chesapeake was a chemical memory of itself, its waters the color of the sky and twice as toxic, its shores ringed by the cooling towers of the RB1 data processing arcologies that rose from the tidal flats like cathedrals built by a god who believed only in quarterly earnings — and to the west, the Blue Ridge, still standing, still stubbornly, furiously blue, the one thing the Consolidation had not yet figured out how to monetize, though not for lack of trying.

Thirty million people had lived in Virginia once, before. Now there were fewer than nine million in all of RB1’s territory, and that number was generous, the kind of generous that came from counting the dead slowly. They had died in waves, as catastrophe tends to prefer — the Climate Displacement Wars of 2027 to 2031, when the coastal cities drowned and the inland cities burned and the federal government discovered, too late and too expensively, that it had sold its emergency infrastructure to private contractors who considered mass death an acceptable externality; the Gray Plague of 2034 and 2035, a respiratory pandemic that the particulate-choked air of the industrial East Coast had turned from a crisis into an extinction-level event for the poor and the old and the already-sick; and finally the Famine Years, 2038 to 2041, when the last of the industrial agricultural supply chains collapsed under the weight of their own contradictions and a continent that had forgotten how to feed itself without a corporation in the middle of the transaction learned what hunger actually meant, down in its marrow. The United States of America had not been abolished so much as it had been foreclosed upon — quietly, legally, with the full faith and credit of its own legislative apparatus — and the Corporate States of America had emerged from the receivership with a press release and a new flag and the same fourteen families running things they always had, just with better branding.

RB1 was what happened when a defense contractor, an aerospace company, and a financial services conglomerate got drunk together during the Consolidation negotiations and decided that what a ruined territory really needed was total surveillance capitalism with a military-grade enforcement arm. The Auditors — RB1’s corporate police force, their black body armor stamped with the triple-helix logo of the merged entity — maintained order in the Dominion Capital of New Reston with drone patrols, biometric checkpoints at every sector boundary, and a credit-scrip economy so finely tuned to behavioral compliance that a citizen’s caloric ration for the week could be docked for “productivity infractions” or “sentiment irregularities flagged by ambient monitoring systems,” which was the corporate way of saying they listened to everything, always, and they charged you for the privilege of being heard. Employee-citizens — the affiliated, the ones who’d traded a thumb-print and a neural opt-in for a bunk in a dormitory block and three meal tokens a day — lived in a kind of numb, exhausted stability that passed for security in 2048. Everyone else, the Unaffiliated, the Gray Market people, the ones who’d refused or been refused or simply fallen through the cracks of a system that had never been designed to catch them — they lived in the Margins, in the sprawling wreckage between the arcology towers, and they survived the way humans had always survived the unsurvivable: stubbornly, creatively, and with a great deal of home-distilled spirits.

The Margins of New Reston smelled like burning wire insulation and fry oil and the ghost of the tobacco fields that had made this land famous three hundred years before anyone thought to name a corporate dominion after a credit card company. They were loud in the way that places are loud when everyone in them is pretending that things are fine — music bleeding from the underground Vents, the bass frequencies of someone’s illegal sound system rattling the corrugated aluminum walls of structures that were half-salvage, half-stubbornness, half-prayer, the math of desperation always adding up to more than it should. The Auditor drones passed overhead every forty-seven minutes on their standard patrol loop, their searchlights sweeping the alleyways like the accusatory fingers of a god who had been bought out by a defense contractor and had not yet been informed that the transition had lowered his expectations significantly. In the spaces between the sweeps, people moved — quickly, purposefully, with the fluid economy of motion that the surveilled develop the way other people develop hobbies — trading gray market protein tabs and bootleg archive drives full of pre-Consolidation internet, exchanging medical tokens and software patches and the only commodity that RB1 had never successfully managed to tax or control or price into a subscription tier: information about the Commons, the resistance network that everyone in the Margins had heard about and most people were too smart or too tired or too afraid to look for directly, though they left their doors unlocked anyway, just in case.

Janella Davis was not, in the year 2048, a woman who believed in the Commons or in much of anything else, and she would have told you so herself in the flat, patient tone of someone who has been disappointing optimists for long enough to have gotten efficient at it. She was forty-eight years old, which in the Margins meant she had survived things she was not going to discuss with you, and she had a face that had been striking once and was now something better — particular, precise, the kind of face that had been lived in hard enough to have become genuinely interesting, with dark eyes that moved over a room the way good security software moves over a network: thorough, fast, and looking for exactly the one thing that doesn’t belong. She’d spent twelve years building RB1’s surveillance architecture from the inside, back when she’d still been affiliated, still been the kind of woman who told herself that working within the system was the same thing as working against it, right up until the morning in 2041 when the system had made very clear, with the particular efficiency of an automated termination protocol, that it had never made that distinction. She lived now in a second-floor room above a gray market repair shop in Sector 7 of the Margins, her walls papered with hand-drawn network maps and signal-intercept logs that would have gotten her disappeared by the Auditors if they’d ever looked, which they hadn’t, because she had built their blind spots herself and she knew exactly where to stand inside them. She was brilliant and she was careful and she was — though she would have laughed at the word, a short, sharp sound like something snapping — lonely, in the specific way that women who are very good at protecting everything tend to be lonely, which is to say: completely, and without any immediate plans to do a single thing about it. That was before the knock on her door at 0300 on a Tuesday, three sharp raps in a pattern she hadn’t heard in seven years, from a woman she had been absolutely, categorically, and with great personal discipline refusing to think about ever since.

Almost Timely News: 🗞️ The Broken Bargain of Big Tech (2026-05-03)

Christopher S. Penn — Sun, 03 May 2026 10:31:20 GMT

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What’s On My Mind: The Broken Bargain of Big Tech

This week, something that’s been burrowed into my mind ever since I heard it.

A couple of weeks ago, SparkToro founder Rand Fishkin said this on LinkedIn:

“For 20 years, I was a staunch proponent of “white hat” only. Now? Now that Google and the rest of Big Tech have proven themselves to be far, far more evil than any black hat SEO ever was? I say: screw ‘em. As long as it doesn’t hurt you/your business, you have no responsibility to be ethical toward giant corporations that would never return the favor.”

This has stuck with me for days now, turning it over in my head. He’s not wrong. From ingesting massive amounts of data without consent or compensation to willfully causing mental health harm to declaring that it’s too inconvenient to stop human trafficking, Rand’s point is reinforced by the piles and piles of lawsuits surrounding Big Tech that they’ve lost.

Disclaimer and Warning: To be clear, since there’s room for misinterpretation here, neither Rand nor I advocate doing anything illegal or harmful to your business. Do not break laws.

Part 1: The Broken Bargain

Let’s talk about the bargain that big tech companies, particularly social media and search companies, had with us, the marketers and the people. The bargain for the longest time was, “you give us your data and we’ll send you traffic, or customers”, or something along those lines. There was give and take. We gave our data, they gave us business. In the early days of the web, we would allow companies like Google to index our content in exchange for referrals. Over time, in the last 25 years, that bargain has gotten steadily worse.

Part of it is because of completely irresponsible use of their technology on the part of big tech companies. Companies like Meta, for example, allowed their system to be abused by election interference teams in 2016, the Cambridge Analytica scandal. They ended up using that as an excuse to continue turning the screws on all of us over the years.

Anyone who has ever maintained a social media account for a business has been in the situation where you had reach and interactions with your audience at first, for free, and then over time it got harder and harder to reach that audience without paying until even today. Not only can you not reach your audience without paying, even when you do pay, you don’t necessarily reach your audience.

Google used to be just a collection of 10 blue links; back in the golden era of search, you could reliably count on Google to deliver you substantial amounts of business, in exchange for your data. Google has steadily taken over more and more of the real estate in search results, and now pretty much everything in a Google search result is intermediated in some way, from the one box to widgets to summaries and web guide and now AI overviews and AI mode.

Rand Fishkin at SparkToro, formerly at Moz, showed over time that Google has sent less and less traffic, fewer and fewer clicks, every single year. With things like AI overviews, that gets even worse; Search Engine Land reports that 2/3 of clicks never generate traffic now. The bargain has been rotting away for some time.

It isn’t just search, it’s also social media. Look at a system like Twitter, or their company formerly known as Twitter, I suppose. This was a company that for 15 years was the open standard of data sharing.

People could use its network graph to identify who was influential. It was one of the reasons why Twitter was so valuable and why so many influencer marketing systems were benchmarked against it, because you could browse the data without having to pay for it. And this didn’t stop people from advertising on the system, quite the opposite. People advertised there, even though it did not have the reach or draw of Meta and its properties.

When Elon Musk bought the company in 2022 and locked down the system in 2023, that came to a stop, and only the wealthiest, largest partner companies could access the data. That has been the case across all major networks now. Only the biggest, most well-heeled partner companies can access data. This puts smaller businesses at a complete disadvantage. They can no longer access any of their own data, and they are reliant on bigger tech partners - for considerable sums of money - to access data that used to be readily available.

As quickly as they could, the big tech companies tilted the tables in their favor - more for them, less for everyone else.

This is the part that pisses me off. The fortunes they have built and the money and power they have accumulated came from us, from our data. At the end of the day they built their empires on our data. Without us, they would literally have no product.

One of the definitions of social networks relies on the network effect, first described by Bob Metcalfe during the founding of Ethernet. A network effect is any effect where adding nodes to the network builds the network’s overall value. A network of fax machines has value, and every new fax machine sold increases the value of existing ones.

Social networks without users have no value. There’s no intrinsic value to them. If you were to put up a video site and put up a bunch of videos on it, it would have value to users without them contributing. That’s essentially what our blogs are or were, our newsletters, things like that.

We put up the content, and you don’t need to be an active participant to get value from it. A social network does not have value until users put content in it. So the social network relies on user contributions. Our data is what makes it valuable. Search engines also would not have any value if we were not actively contributing content to them through the publishing of it on the internet.

We have lost sight, as consumers, of who has the actual power, in the sense that if we stop publishing, if we stop contributing, the value of the network declines (or is taken over by marketing slop, which is effectively the same thing). This also means that when we’re using these tools, we have a right to our data. Everything that we do, everything we click on, everything we scroll by or swipe by, that is our data. We are contributing it to these companies, and they are giving us nothing in return. There is no value exchange.

It’s gotten so bad that in certain parts of the world, like Europe, regulations like GDPR had to force companies to allow us to download some of our own data, and even then, not all of it. If you’ve used tools like the data privacy and data download tools in any social network, you get a subset of your data, you do not get all your data.

They may say, “Oh, well, we keep you connected to your friends.” Yeah, I can email them. I don’t need your stupid social network to do that. I can use this old-fashioned thing called a telephone if I really want to talk to them. But we have been conditioned - duped - into believing that we need these systems, and that we should be grateful for whatever crumbs of data they’re willing to let us have.

Or even more hilariously, we should be grateful for the privilege of paying to access our own data. Which brings us to the topic of today’s newsletter. Given the monumentally unfair by design system in place, how could we start tilting the tables a little bit in our favor? How could we start making use of our own data?

Part 2: What’s Fair Game

Here’s how we will do this, in ways that do not violate laws, and do not violate even terms of service. While I wholeheartedly agree with Rand’s sentiment that we don’t owe these tech companies anything and we have no obligation to behave ethically towards them, I do still represent a company that does still have values, so the scope of today’s newsletter will be on things that are ethically defensible.

We could go all black hat and open up the big bag of evil tricks, but that would be irresponsible, and on Monday morning, Katie would probably slap me. For legal reasons, let’s play fair. We will save discussion of other tactics for when you and I are having coffee or the beverage of your choice at an event, and there’s nothing being recorded.

Here’s what I consider fair game: our data. We are going to take ownership of our data. By our data, I specifically mean data that is generated on our devices. When we interact with a search engine or a social network or anything, data flows to and from that third party to our devices. In the same way that I would consider an employee of a company visiting my house to be on my property, I consider a company’s data on my devices to be on my property, and that data is fair game.

Likewise, it would be inappropriate for me to break into someone else’s data center, hook up a bunch of hard drives, and try to take all their data. If I did that in the real world, I would rightfully be arrested for trespassing and breaking and entering.

So that’s our ethical boundary: my machine, my data.

If your company sends data to my machine, I have the right to use it while it is on my machine.

How do we get this data? In every web browser, but especially those built by Google, the Chrome interface, and every browser based on the Chromium platform, which includes things like Brave, there is a concealed (but not secret) data collection mechanism.

This data collection mechanism is called HAR files, which is short for HTTP Archive. When you go into the browser and turn on the developer tools, you can see it recording everything that you do: every click, every scroll, every piece of data that flows onto your screen. One of the things that I’ve said for a long time now, in one of the basic use cases of generative AI, is that if you can see it, AI can access it.

While you can take screen recordings and things, which is my preferred non-technical way of getting data out of systems that don’t want us exporting data, Google has given us the ability to use the same data collection mechanisms it uses. We’re going to use that today by showing you how to watch Chrome collect data, how to access that data, and then how to process that data with generative AI, with Claude Code, so that we can work with the same data that we are generating on behalf of a big tech company.

They should not be the only ones who benefit from our data. We’re going to use these tools as regular people, we’re not going to automate them fully. We’re not going to do massive, scaled attacks. We are going to collect our own data from our devices that is well within the boundaries of laws, of ethics, and terms of service.

Part 3: Thinking Through the Problem

The fundamental imbalance in the now broken bargain is that we generate data and big companies profit from it. We don’t have the opportunity to profit from the same data. One of the simplest (but not easiest) problems that every marketer wants to solve is: “how do we determine who’s influential about a particular topic or in a particular place?”

This is the heart and soul of influencer marketing. The first step in solving this problem is influencer identification. Who is influential?

To answer that question we need data, and we could go out to data brokers and vendors and big tech companies and spend thousands of dollars to subscribe to services. Or buy ads. But we have access to the data, we just don’t know it. Our first step is to use any Chrome-based browser and get familiar with its built-in data collection. Chrome is a data vacuum, it vacuums up massive amounts of data on Google’s behalf. Those same mechanisms are available to us.

In your Google Chrome, go to the View menu, select Developer, and then select Developer Tools.

You should see a panel appear on the side of your browser. At the top is a series of menus. You want to find the Network menu.

Open up any page in the browser while the network menu is selected and you will see this window fill up with stuff. What is this stuff? This is every bit and byte being sent and received by the website you’re on and what you do on it. If you can see it in the browser window, it is in this network pane.

Take special note of the download button, a tiny, tiny little icon.

This is the treasure chest right here. Everything that is being transferred to and from your computer in this window can be downloaded as an HTTP archive, or HAR file.

When you download the file and open it up, you will see that the entire file is in the JSON markup language. Here’s a tiny sample:

            {
              "name": ":authority",
              "value": "www.linkedin.com"
            },
            {
              "name": ":method",
              "value": "GET"
            },
            {
              "name": ":path",
              "value": "/voyager/api/graphql?includeWebMetadata=true&variables=(memberIdentity:ACoAAAAh1hYBC5uWZCSAvgqeeD6EwqwOV-4taAo)&queryId=voyagerIdentityDashProfiles.b5c27c04968c409fc0ed3546575b9b7a"
            },
            {
              "name": ":scheme",
              "value": "https"
            },
            {
              "name": "accept",
              "value": "application/vnd.linkedin.normalized+json+2.1"
            },

You don’t need to understand what any of this means. It’s not for you, it’s for the machines. What’s important is that we have the data now. We have our data that we generated by browsing around LinkedIn and interacting with people normally, as we do everyday.

What’s inside this massive archive, which can easily top one gigabyte of plain text? Every button click, every scroll, every tap, every like, comment, and share. Every profile we see go by. Every post, all the text, anything that’s on screen is in this file.

That gold mine is what we need to hand as raw materials to our AI agent of choice to turn it into useful insights. There’s a lot of stuff in here that doesn’t matter, things like navigation and menus, item information, timings, stuff that isn’t helpful. But by giving great directions to our AI system, we can weed all that out and focus only on the things that we want to know about.

Part 4: Processing the Data

The good news is that you don’t need to process this data by hand. You don’t even need to know necessarily what all data is. You only need to know what data is valuable that you want to work with.

There’s a field of study called network graphing, where you digest down connections and interactions that you would observe in a place like a social network and turn that into math. Every entity be it a person or a company or a post is a node, and every interaction like a like, comment, or share is an edge.

Think of nodes as nouns and edges as verbs.

My friend Mitch Joel has an expression, “It’s not who you know, it’s who knows you”. Influence is all about you being top of mind when someone else is thinking about you. Inside our social networking files that we generate are all the posts and comments of people talking about topics and other people. For any given topic, who is talked about the most?

If we were to digest down that massive file into those nodes and edges of who is most talked about, we could start to understand who is influential in any given topic. It’s outside the scope of this issue of the newsletter, but there are over a dozen different methods of measuring influence. In network graphing jargon, this is called measures of centrality. How important is any one node on the network as determined by the edges coming in and out of that node?

For anyone who has worked in SEO, you have almost certainly heard the term page rank. PageRank is a form of centrality measure. In the very early days of Google, a page’s importance was judged by how many other pages linked to it. The more inbound links you had, the more important your page was. That is still somewhat the case in modern SEO, but there are hundreds of other ranking signals besides page rank. However, that measure of centrality is something almost everyone in marketing has been exposed to at one point or another in the last 25 years.

What we do next is take that HAR file that we generated from our casual use of a social network, and we give it to an agent coding tool like Claude Code, along with detailed requirements about what we want to do, building a data processing system that can take our data from our device and turn it into useful insights.

When I did this for myself earlier, it ended up being about a 15 minute voice recording that then turned into over 75 pages of design documents. As I write this, Claude Code is implementing all those designs to make a functioning system that processes my data.

This is an example of the kind of thinking we should be adopting to our use of big tech systems. Any data that’s on our device within our four walls should be fair game. If big companies can profit off of what we generate for them, so should we.

Part 5: Wrapping Up

The key takeaways from this issue of the newsletter are that we should benefit from our data just as much as large corporations do. We have a right to our own data, and we have a right to use our data to benefit ourselves. In this context that means data that is on our devices.

The tools exist today to enable us, to give us access to our data and to give us a way to unlock its value. Think about all the places you go online and all the things that you do and see and hear. Think about all the data you generate for other corporations who will never send a penny your way. How could you use that data to benefit yourself or your company?

Take the examples I’ve shown in today’s newsletter, and think of the problems you would love to solve if you had access to the data. Based on all this information, how would you direct AI to make use of that data that your machine is already collecting, that big tech is already collecting on your behalf and turn it into something that benefits you.

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Blatant Sales Pitch: New AI for SEO and PPC Book

Christopher S. Penn — Tue, 28 Apr 2026 22:11:19 GMT

What works in generative AI when it comes to SEO and PPC?

I recently had the privilege to deliver a full-day workshop on SEO and PPC and applications of generative AI in it, everything from analyzing Google Search Console data to drafting Google Ads at scale.

From this full-day workshop, I’ve turned it into an easy-to-read book that walks through the five levels of generative AI with specific SEO and PPC use cases as well as an introduction to GEO.

Here’s the chapter list:

Chapter 1: Introduction & Logistics
A Word From Our Sponsor
Chapter 2: AI Foundations
Chapter 3: Five Levels of AI Enablement
Chapter 4: Prompt Engineering (Level 1)
Chapter 5: Seven Major Use Case Categories for SEO and PPC
Chapter 6: Generative Engine Optimization (GEO)
Chapter 7: Advanced Agentic AI (Level 3+)
Appendix A: Prompt Library

Even better, you’ll get a copy of the two dozen prompts from the workshop so that you can try them yourself with your own data for each of the use cases and immediately benefit from battle-tested prompts.

The book is available in PDF and EPUB formats, as well as an audio MP3 read by Google Gemini if you prefer to listen rather than read. You’ll get all three formats. (the MP3 is shockingly good, by the way)

I hope you enjoy the book, should you choose to purchase it, and as always, thank you for your support.

See you on Sunday for the regular newsletter,

Chris

Almost Timely News: 🗞️ A Sober Conversation about AI and Employment (2026-04-26)

Christopher S. Penn — Sun, 26 Apr 2026 10:30:55 GMT

Almost Timely News: 🗞️ A Sober Conversation about AI and Employment (2026-04-26) :: View in Browser

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100% of this week’s newsletter content was originated by me, the human. You’ll see me working with Gemini and Claude in the video version. Learn why this kind of disclosure is a good idea and might be required for anyone doing business in any capacity with the EU in the near future.

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What’s On My Mind: A Sober Conversation about AI and Employment

This week, let’s have a sober conversation about AI’s impact on employment. This is a topic that I’ve talked about in the past, but after the events of this week, it’s worth revisiting.

Part 1: What Happened This Week

This isn’t big industry news or politics or a reaction to so-and-so being on so-and-so’s podcast. This is a reaction to me deploying Hermes Agent.

For a few months now, we’ve all been playing with various agentic systems and hearing about many more - OpenClaw, NemoClaw, etc. But Hermes Agent, from the Nous Hermes research team, is the first one that I’ve really gotten to do some pretty incredible things.

As a reminder, agents like these live at Level 4 in the Five Levels of AI Enablement. They’re mostly fire-and-forget - you set up a great project plan with a clear measure of success, and then it just goes off and does its thing. You’re not an active participant after launch.

During one of the morning walks with my partner and dog, I was lamenting the sorry state of my town’s local paper. It’s a compilation of slop - recycled news stories from regional or national publications and ChatGPT-fueled local news because the paper is down to, like, an editor and an intern and that’s it. It’s unhelpful, and like too many journalistic outfits that need to pay the bills, filled with sensationalist crap needed to attract clicks.

So I said to myself, this is the perfect test for a truly agentic AI system, a system that can generate my own newspaper, in the style I want it, with the news I want.

What news do I want? I want to know what’s going on in the local community. The town police have a daily police log. The library has a calendar of events. City Hall keeps a calendar and a newsroom. Local venues have their own calendars and press rooms.

So I sat down and used the 5P Framework by Trust Insights™ to craft a 13-page project plan for the agent to implement. I handed it off, and two and a half hours later, the first issue of my paper dropped into my Discord server as a PDF.

And it met the requirements I set out for it. It’s a local paper. There are some quirks and bugs that I’d need to tune up if I wanted to make this available to other people but for the most part, it’s ready to go.

An AI agent made me a local newsroom, filled with all the stories you would expect of an actual local paper. Earth Day celebrations and food trucks at the town common this weekend. A cornhole tournament at the local park next weekend. A couple of local breaking and entering reports plus some public drinking arrests in the police log.

What’s notable about this paper is that there’s no editorial judgement of what’s newsworthy in the sense that it’s going to attract clicks or sell ad space. None of those are concerns because there’s a subscriber base of one and no employees at all, so commercial considerations about what will sell are irrelevant. There are no advertisers to please.

And if I wanted to? I could have the agent bundle up all the skills it made, all the code it wrote, and publish that code so that someone else could add it to their agent and get my take on a local paper.

Think how many people used to work at local papers in the days before the Internet (because the Internet itself is what killed local papers, especially early services like Craigslist that wiped out classified ads revenue). At a minimum, you’d need a reporter, ideally an editor, a salesperson for advertising revenue, and someone who knows how to do gaphic design and layouts. At my state’s minimum wage levels, that’s USD 160,000 per year gross at state minimum wage, 40 hours a week, to run a local newspaper.

Or an AI agent, a dedicated computer that cost USD 300 (one time purchase, and it runs multiple agents), and a monthly coding subscription (I use Minimax for this) that’s USD 17 a month. And if I can produce a paper for one person, I could obviously produce it for other people at no additional costs because it’s a digital good. One person or a million, it’s more or less the same cost to produce.

Which brings me to part 2, about where the tech is.

Part 2: Where AI Is and Where It’s Going

We can’t have a meaningful discussion about the effect of AI on employment and jobs without first understanding where AI itself is. Many people are mentally stuck in 2024 - the golden age of basic ChatGPT usage. Sit down at your browser, start chatting, get responses, copy and paste, etc.

And from late 2022 through most of 2024, that was the dominant use case. And that was the best use case because the technology was still evolving. It took two full years before web search was a standard part of generative AI tools.

It’s only really been in the last 16 months that we’ve seen generative AI models explode in capabilities. At the start of 2025, they were still more or less face-rolling idiots at a lot of tasks. By the end of 2025, they were PhDs in most tasks.

This chart from Artificial Analysis shows the blended intelligence index for OpenAI’s models from the very first commercial model, GPT-3.5-Turbo that powered the OG ChatGPT to this week’s GPT-5.5.

For reference, a human PhD in their field scores an average of about 50 on this index.

Here’s where things get shocking. Using Alibaba’s Qwen model as our benchmark for open weights models - that is, models you can download and run on your own hardware and not need a third-party cloud provider if you have enough hardware - this is what the evolution of open weights models looks like over the same period of time:

You’ll note that Qwen 3.6 27B, which is their latest model that came out this week, matches the overall intelligence performance of last summer’s GPT-5 and last fall’s GPT-5.1. That is mind-bending to think about, because this is a model that can run on a well-appointed laptop. No data center needed, no nuclear power plant, no river of fresh water to consume - just a well-appointed MacBook.

That is how far and how fast these models have evolved from face rolling idiot to PhD, including small models that can run on a laptop with no other support.

That’s the models, the engines of generative AI. We can’t talk about AI without also talking about the harnesses, the infrastructure around a model that allows us to use it. In spring of 2025, Anthropic released its first coding environment called Claude Code. It was a shocking piece of software because it was the first mainstream commercial package that autonomously executed on many tasks, requiring much less supervision and much less back and forth chat than any other tool prior to that point.

Even in its early days, its capabilities to do long horizon tasks were incredible. I was in a hotel in Washington, D.C., with a friend for a conference and my friend lamented that she had always wanted to write a book, but never had time to do it. And I pointed out that she wrote like three LinkedIn posts a day.

So we gathered up with a very early version of Claude Code, all of her LinkedIn posts for the last two years, and over 92 minutes watched it construct a coherent book out of her LinkedIn posts. And when it was done, in 92 minutes, it was a 65,000-word book that used her words, so not AI slop, but her literal words. That was the first time that I watched agentic AI accomplish something really significant and went, “Oh, this is gonna be a thing”.

In early 2026, Peter Steinberger invented a far more autonomous system known as OpenClaw, a small virtual machine that had an AI agent that ran autonomously and could execute tasks with minimal prompting. OpenClaw didn’t initially support more than Anthropic models, but the open source community forked it so many times that it became its own ecosystem of agents that could run any size model.

What I did in part one with my local newspaper was inspired by the OpenClaw architecture. The Nous Hermes lab built Hermes agent, which is conceptually the same thing. It’s an agentic framework that can accomplish very long horizon tasks with minimal feedback after the initial project plan.

Put those two facts together: an agentic framework that is extremely intelligent and highly capable of writing its own software and tuning itself over time, combined with AI models that are smart enough to be PhD level at many tasks, but run on a laptop.

That is a recipe for work substitution, because you can have a machine behave like a team of people to accomplish long horizon tasks. That’s where the technology is today.

Let’s talk about where the technology is going. While the Western world is focused on building out data centers at an alarming pace, the AI field itself is focused on better code, better research, and better math. It has always been true that whoever has the best math will have the best AI.

In March 2026 Google released a paper called TurboQuant. This paper addressed some of the biggest memory and compute bottlenecks in AI, things that make it expensive to run and incredibly energy-intensive. TurboQuant changed how we do a specific computation (key value cache compression). And what the technique shows is that it can achieve 6-8X increases in performance while decreasing the amount of energy needed to run AI.

Think about the implications of a technological change like that. Google effectively made every AI data center around the world potentially able to work six to eight times more without a single extra watt of power or a drop of water. And this is just the tip of the iceberg when it comes to better math. There are AI models dedicated to building better math.

More important, today’s AI models are actively building themselves. GPT-5.2 famously was derived from GPT-5.1 by itself. Now, OpenAI did not specify what percentage of the model was generated by itself, but it was a non-zero amount. Mini-Max M2.7? 30 to 40% of it was written by itself as it evolved out of Minimax M2.5. This is what is causing the technology to accelerate so fast. The technology, the AI models can write themselves now, and so they can be far more productive than we humans were when we were the ones directing the models’ construction.

The technology exists today for companies and individuals to build Level 5 systems. Though there’s none in production, the underlying components are already there, from Milla Jovovich’s MemPalace to Graphify to Serena MCP and so many others.

Expect Level 5 systems to be in production by the end of this calendar year.

And if you remember, level 5 systems are essentially departments or teams of people. That’s where the technology is and where it’s going. It can substitute for entire teams of people now.

Part 3: Effects on Employment

When the only game in town was ChatGPT and there was still a human being copy-pasting everything in and out of the AI, the effects on unemployment were going to be minimal. You’d get efficiencies by having fewer people doing the same job, but in general, you wouldn’t see massive transformation because human beings had to be deeply involved in operating AI.

The more we move up levels in the five levels of AI the more the machine takes on, the less we have to do, the less we have to be sitting at the keyboard micromanaging it. At level two, we’re still basically copy-paste monkeys. Yes, Gems and GPTs and projects make it easier to get organized, make it easier to keep processes standardized, but we are still copy-pasting.

Once you get to level 3, that’s when the game changes, when you go from being an individual contributor to becoming a manager, and what you are managing is a machine. When we look at Indeed.com’s hiring demand by industry snapshot as of last week, and the industries where demand is below pre-pandemic levels? The bottom five are software development, IT operations and help desk, information design and documentation, mathematics, and media and communications.

By the way, marketing is sixth from the bottom.

When Trust Insights first started tracking this chart, the numbers for those fields were 100. Today they’re in the 60s and 70s, which means that hiring demand has fallen by 30% or more from February 2020. Now, to be clear, there are other things besides AI having impacts on markets and hiring demand. There’s been two major regional wars since 2022, starting with the illegal invasion of Ukraine by Russia, there’s been a pandemic, and the long-term consequences of that pandemic are still playing out, there are massive political changes around the world. So, AI alone is not the sole contributor of this change in hiring demand, but it is part of the equation.

But we haven’t seen AI’s biggest effects yet. As I pointed out in the March 15 newsletter, Anthropic’s own chart of the theoretical maximum effect of AI on professions is largely unfulfilled:

The red sections of the chart show the impact on those markets, where the blue is the likely impact of AI on those markets. Even still, in things like software, we see that software coverage is now up to almost 40%. Office and admin is up to almost 40%. I spoke to a recruiter recently in the temp jobs industry placing office temps and they said business has dropped precipitously; in one month they went from placing ~80 temps to ~15, an 80% decrease in demand.

There is no industry on the planet that can tolerate an 80% decrease in demand and still remain healthy.

Look more carefully at the Anthropic chart. Almost 40% of office jobs are currently seeing AI impact. Almost 30% of sales jobs. Almost 20% of legal jobs. 20% of education jobs, 20% of arts and media jobs, 30% of business and finance jobs, 40% of computer jobs. These are huge numbers for a technology that is still only about four years old.

Even if AI were to freeze where it is today, these are still massive changes to these industries. Even a five or ten percent decrease in employment in an industry has massive, far-reaching effects.

Take any given job description and hand it to AI. Use the agentic tool of your choice to digest it down and see how many of the tangible tasks are exposed to AI. Here’s an example of a director of product marketing job I saw on LinkedIn earlier, using the Trust Insights TRIPS framework

With today’s agentic capabilities, 40% of this position can be readily handed off to AI. Another 50% can be AI accelerated, and 10% is purely human, which machines would do poorly at. Combined, 90% of this role could be accelerated in some way by AI, meaning that if you had more than one open position in this role, you could pretty easily consolidate two people down to one - and this is a fairly senior position.

Impact on junior positions is way more dramatic. This is an account coordinator position at a public relations firm. And we’re going to put it through the same process in Claude as the senior position. And let’s just see what the difference is between a junior position and a senior position in terms of its exposure to AI:

When we look at the account coordinator position, 82% of this role can be consumed by AI. 13% moderately augmentable, 5% human-centric. The impact of AI on junior position employees can’t be overstated. A company does not need this role to be filled by a human. It would be fiscally irresponsible to pay someone - this role pays $51,000 a year - it would be fiscally irresponsible to pay a human to sit in this seat and do these tasks.

If we dig deeper, some of the conversation in places like Threads talks about managers and executives who now consult generative AI for a second opinion on everything - or in a shocking number of cases, a first opinion. As people cognitively de-skill at many core tasks, they hand them off to machines. If enough of your thinking goes to a machine, at a certain point an employer rationally has to realize they don’t need you at all - from the mail room to the corner office.

This is the single greatest red flag for any individual worker. If you find that you are handing off more and more tasks to AI, you are being productive, to be sure, but you are also building a case that an employer no longer needs you. While it’s difficult to arbitrarily declare that a specific percentage or number of tasks is a red line, it’s probably a fairly safe bet to say that if more than 50% of your tasks are being done solely by a machine and there’s more than one person in your role at your company, you are endangering your employment. If more than 80% of your tasks are now being done by machines, you are definitely endangering your employment.

Part 4: Ethics

Up until now, we’ve talked mostly about individual risks and rewards of AI. No discussion of AI’s effects on employment would be complete if we didn’t talk about the ethics and the systemic nature of AI.

I’ve been doing a lot of speaking at private company meetings and gatherings in the last couple of months, things that don’t belong on the events calendar. At these meetings, the one thing I’m hearing more and more is that there are top down management dictates that AI use increase dramatically. More often than not, there isn’t a clear rationale for it other than “productivity”, and they all sound the same, like every C-Suite is reading the same articles while on the plane.

Productivity is a polite word for getting more out of people at the same or lower rates of pay. We even have an entire management culture around this, “do more with less”, the eternal trope of top management. But there are consequences for this style of management that AI makes far worse.

In its current form, AI dramatically accelerates income inequality. There is no way to sugarcoat that. When you substitute human wages for machine fees, that money goes to fewer people. When we pay Claude or ChatGPT or Gemini our USD 20 a month or USD 200 a month or whatever fee is for your country, that money goes to a technology company that, in the big picture, supports relatively few people. Workers at companies get fewer wages and the company keeps those wages as additional profits for shareholders.

As much as I love Claude or Gemini, paying them instead of humans means two things. First, the relatively small number of people employed by these companies enjoy the benefits of those fees and spend them in their communities. Those wages that are paid to maintain and improve the software are highly concentrated. When you pay a human being to do a task in your community, they spend that money in your community. They go to the grocery store, they go to the bowling alley, they go to the local restaurants - money paid as wages to local labor supports your local ecosystem. Unless you live in a major tech hub, you don’t see the benefits of the fees you pay to a tech provider for AI systems and software.

The second and arguably greater impact of generative AI is that it depresses wages for everyone. If a task takes a human worker USD 100 of wages to complete but it takes a machine one dollar of wages to complete, then in the free market, that task and its output is now worth one dollar. That is what the market has decided is the value of that task, and that is what the market will pay for that work. Again, rationally, if I can buy the same output for one dollar, why would I pay one hundred dollars for the same thing?

That’s what the newspaper example shows in stunning clarity. A product, such as a local newspaper, would cost USD 160,000 in wages at minimum wage for four employees, about USD 13,333 per month. The same product made by a machine costs me USD 20 per month, rounding up. That’s a 99.8% decrease in value, in what it costs to make that good. That is part of the reason why local newspapers have all but vanished, because the substitution of other information products is cheaper, and users of the older product don’t see a reason to keep paying for it.

Thanks to questionable judgment around monetary policy in many nations, according to the OECD, inflation is already on the rise, meaning that there is more money chasing fewer goods. The net effect of inflation, which is when the government prints more money than there are goods to match, is that prices go up.

Combine that with wage depression and you have a toxic cocktail of economic collapse for the majority of knowledge workers. Tasks that might have been outsourced to developing nations are now being onshored to machines. Tasks that you might have increased headcount for in the past are now being done by the same number of people and more machines.

What this means is that AI in its current incarnation with the current companies running it is dramatically increasing income inequality and making most people poorer by devaluing the work that they produce.

Part 5: Difficult Answers

In the short term, the capabilities that AI offers us allow us for entrepreneurship at unparalleled speeds. The distance to done, the distance between having an idea and turning into something that is tangible, is shorter than ever and grows shorter every day as AI agents gain ever-increasing capabilities. If you’ve got a good idea, you can bring it to life faster than ever.

In turn, that means you could beat the overall trend of knowledge products decreasing in value if you can come up with something novel and bring it to market quickly or sell it quickly. That’s where the opportunity is right now.

We should expect that continued downward pressure will occur for wages paid for tasks that machines can now consume. Entrepreneurship, multiple income streams, innovation and cross-disciplinary skills will be some of the lifeboats you’ll need. Having a strong professional community network will arguably be the single biggest advantage you can have.

If you are not investing time in your professional community and your professional community every single day, you are imperiling your future because in today’s employment landscape, AI has created massive distortions. On one hand, you have job seekers who are using AI to all sound alike. Everybody’s an expert who applies for a job.

And on the other hand, you have recruiters and HR professionals using AI to deal with the deluge of candidates who... a lot of them very unqualified. The only way to stand out is for someone else to refer you in a warm referral to the hiring manager, and that relies on having a strong professional community.

But these aren’t the big picture.

Just as there is no way to sugarcoat the truth about AI’s impact on economies and wages, nor is there a way to sugarcoat the consequences of dramatic income inequality. We only need to look through the history books to know how this will play out if nothing changes.

In the past, when structural unemployment, meaning jobs were lost that were never coming back, spiked 5 to 10 percent within a 10-year period, civil unrest typically followed. In many places, this resulted in very bloody revolutions - France in 1789, England in 1811, a good chunk of Europe in 1848, the Russian revolution of 1917, Mussolini’s rise in 1919, the Weimar Republic in 1929 that led to World War 2, the fall of the Shah in 1978, Tunisia and Egypt in 2011.

If we pay attention to data coming from real people and not from potentially corrupted government sources, such as Google searches for specific terms like food pantry near me, we see enormous economic pressures on people. Google Trends data goes back to 2004, and what we see is that beginning in 2016 through today in the USA, demand for food pantries and food banks has been ever increasing. Perform similar research in the place where you live, if the USA is not your home country, and see how things are changing near you.

This is the canary in the coal mine. When even the basics of life are not being met by people, at a certain point, people snap and reboot their society. And as I said, it’s not usually bloodless.

Many solutions such as robot taxes have been proposed, and those solutions are not bad on the surface until we consider the economics of them. Point 2 from part 4 on wage substitution means that even if we were to charge a 50% tax on the use of AI for any given job, if we are looking at a 99.8% decrease in wages paid from human to machine, that tax will not bring in nearly enough money to cover the lost wages. The money you pay a machine to do a task that formerly was done by a human will be so much less that even seizing 100% of it for social programs will be ineffective.

Other solutions like universal basic income are more realistic to a degree in that if we can provide basic necessities for people, such as food, clothing, and shelter, we can provide at least a minimum standard of living. Advanced economies such as in Scandinavian nations are trying this out with good success in pilot programs. What we have to do globally is figure out how to fund that as quickly as possible, because the window of opportunity to make societal changes before we get to guillotines and pitchforks and torches is very small.

Ultimately, however, we have to figure out how human beings can generate work worth paying them for. At the end of the day, that is the biggest question we have to answer: what is worth paying a human for?

I don’t have those answers right now. I’m not sure any AI has those answers. But we collectively need to be able to answer that question - and soon.

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Almost Timely News: 🗞️ Advancing Up the 5 Levels of AI (2026-04-19)

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What’s On My Mind: Advancing Up the 5 Levels of AI

Last month, I shared with you my new framework for understanding where we are as individuals and organizations when it came to AI enablement - what we can be doing with AI. If you missed that issue, you can find it here. This has formed the basis of my recent keynotes, and this week, I’ll take you through how you bring it to life.

Frameworks are great, but frameworks that contain clear, prescriptive actions are even better.

To get where we want to go, we should think about where we’ve been.

Part 1: A Brief, Incomplete History of AI

AI has been around in some form since the 1950s, though only in the last 40 years have we had enough computational power to make it useful. Prior to 2017, most AI was what we now call classical AI or machine learning - lots and lots of math and probability in two main categories, regression and classification.

Regression is, given an outcome, either predicting more of that outcome or explaining how we got to that outcome. If you’ve ever done lead scoring or attribution analysis, that’s regression analysis.

Classification is organizing your data. Anyone who’s used email spam filters since 1999 has used classification AI in some capacity, even if you didn’t know it.

The challenge with these two branches of AI is that the mechanisms to make them work required a tremendous amount of code and statistics. If you didn’t know R or Python or Scala or Julia, your ability to derive significant benefit from them was low.

In 2017, Google DeepMind created an architecture called transformers - no relationship to the 1980s toy line, unfortunately - that was intended as translation software for language. Given enough data to train on and a novel mechanism that took into account all the data in an input, and recursively processed that data with every new input, the transformers architecture achieved totally new capabilities.

Fast forward 5 years, and OpenAI released a very powerful (at the time) model called GPT-3.5 and a web interface to it, ChatGPT, starting what we now know as the generative AI revolution. GPT-3.5 was reasonably powerful, with a short term memory of 8192 tokens, or roughly about 5,000 words; users could interact with it and generate outputs up to 3,000 words.

But very quickly, folks realized that starting a new conversation with it meant literally starting over. It remembered nothing, and its background knowledge - 175 billion parameters - contained a very incomplete knowledge of the world. More important, its memory was probability-based. It returned answers that matched high probabilities, not factual correctness. Back then, as late as 2021 and early 2022, you could ask a GPT-series model a question like “Who was president of the United States in 1492” and it would answer - quite confidently - that it was Christopher Columbus.

Why did it do that? Because when it breaks down that question into probabilities, it looks at the probabilities of different pieces in it, such as the United States as an entity, the president being an important role, 1492 being an important date, and probabilistically, the name Christopher Columbus is the highest probability answer, even though it’s factually wrong. Christopher Columbus as a name is associated with America and with Fourteen Ninety Two from a probability perspective.

Those two missing features - a relatively small short term memory (what we call the context window) and a probability-based long term memory - are what severely limited the usefulness of generative AI in its early days.

Tech companies quickly realized that if AI models continued being solely probability-based, they would never be broadly adopted, so virtually all of them adopted a fact-checking system grounded in web search, which gave birth to what we now called GEO/AEO/AIO/etc. AI models by default now in consumer-facing interfaces use web search to validate what they’re doing and what answers they give.

Those limitations - no persistent memory, a temporary short term memory, and a long term memory based on probability combined with web search - are where most consumer-facing AI systems are today. Whether it’s ChatGPT, Copilot, Gemini, Claude, etc., the basic features are all the same, which brings us to the Five Levels.

Part 2: A Very Fast Recap of the Five Levels

If you skipped the previous issue of the newsletter that talked about the five levels in depth, here is the TLDR version, based on an old product-market fit framework:

Level 1: Done by you. You’re doing virtually all the work.
Level 2: Done with you. You’re doing about half the work.
Level 3: Done for you. You are doing very little of the work.
Level 4: Done without you. You do none of the work.
Level 5: Done ahead of you. AI does the work before you even ask for it.

If the five levels were in the context of food, then level 1 would be you cooking a meal at home. Level 2 would be you cooking from a meal kit where a lot of the prep is done for you, but you still have to do the final cooking. Level 3 would be you going out for dinner. Level 4 would be a personal chef that cooks for you in your home. Level 5 would be a live-in chef who preps meals without even being asked.

Level 1 is chat - you have conversations with AI, you prompt it, you get answers from it. It’s what we all first experienced as early as July of 2021 with Eleuther.ai’s GPT-J-6B model (the first one that was really usable by the end user).

If you’ve been hanging out with me and the Trust Insights team for a while, you’ll remember our early courses and lessons on prompting, and those lessons are still valid and useful today, but they’re the first rung on the ladder now in AI, not the top.

To better understand the levels, let’s spend some time with the inputs and outputs. At Level 1, the inputs are text, whether it’s things you type, things you attach, etc. The output of AI tools is also text. Whether it’s simple chat or documents inside the web interface, it’s still fundamentally a pile of text that you, the user, have to do something with. You’re doing all the work - prompting, then taking the results and copy pasting them somewhere else.

To be sure, prompting is a vital skill, and knowing how to prompt well is what separates good results from bad. But folks got tired of copy pasting literally everything and asked for ways to pre-prompt AI tools.

That leads us to Level 2, the mini-app automations. These are features like GPTs, Gemini Gems, Claude Projects, etc. where we have prompts at Level 1 that we have to copy and paste ourselves, mini-apps like Gems and GPTs have the prompts and background knowledge baked in. Half the work is pre-set.

When you use a GPT or a Gem, you don’t have to provide the majority of the prompt anymore. You provide a much shorter prompt. The system instructions provide the rest and the output is provided as text. You still have to do something with the text, but at least the copying and pasting of the input prompt is dramatically reduced.

Level 3 is where we start seeing more autonomy. At level 3, agentic tools like Claude Cowork, Claude Code, OpenWork, Google Antigravity, OpenAI Codex, or the many, many other agentic tools allow us to create more powerful pre-built routines, often in the form of Skills and Plugins. Combined with detailed plans, these tools can take the plan and execute it. At level 3, we also start to see AI models interacting directly with our computers and their file systems. So instead of us having to copy and paste things, it can write its data locally to our computers or through various connectors to output systems.

Level 4 is where we see substantially more autonomy. This is with tools like OpenClaw or NemoClaw, Hermes Agent, Claude Managed Agents, and many other systems. At level 4, we give agentic AI clear goals and measures of success, and it figures out how to implement that. It writes its own skills, it improves itself, and it ultimately achieves or doesn’t achieve the goal we set out for it.

Level 5 is where agentic AI is given a very high level goal and figures out all the implementation, including its own measures of success and what it needs to do to achieve that success. Level five is also where tools start to anticipate because they have persistent memory. They know, for example, at the end of every month we have to do a report, and instead of waiting for us to ask, it delivers it to us proactively.

There aren’t any level 5 systems in existence today that are production ready, but level 4 systems are well on their way to evolving into level 5 systems, and I expect by the end of calendar year 2026 that level 5 systems will be in production, if not sooner. All the components to make a Level 5 system exist today, but they just haven’t been sewn together in an accessible, easy way.

Part 3: Why Harnesses Matter as Much as Models

How did this transition happen going from simple copying and pasting to having machines that can anticipate and build intelligent outputs for us? The answer is the harness. In AI, the model is essentially the engine of the vehicle. It’s very powerful and it can do a lot of work in different ways, but no one rides down the road sitting on an engine block. We sit inside a car. The harness is to an AI model what the car is to the engine.

What’s changed dramatically in the last 12 to 18 months is not only models getting smarter and more capable, but models learning how to do what’s called tool handling, where they’re told that a tool exists and they know what to do with it from basic web search all the way up to complex operations or using command line interfaces.

Instead of having to reinvent the wheel and think things through for connecting to a system we care about like our CRM, today’s agentic harnesses have connections to all the systems we care about, and they tell the model, “hey, this CRM is available”, or “this email inbox is available”. The model, knowing that those tools are available, instead of trying to have to write its own code to talk to that system, simply picks up the tool and uses it.

The harness is also where we impose lots of guardrails on AI. The reason why we didn’t see tremendous explosions of productivity with level one and level two AI tools, is that chat-based AI interfaces in some ways have overly restrictive guardrails, and in other ways don’t have enough guardrails.

The most potent guardrail we can impose on AI is memory itself. Because generative AI is probabilistic in nature, if we don’t provide any form of memory, then it has to use its own internal probabilities to essentially guess at what our intent is and what is and is not allowed. Going back to the example earlier of Christopher Columbus, because there were no guardrails, whatsoever, and no memory, the system generated a pure probability and answered the question incorrectly.

On the other hand, if we impose memory structures on AI inside the harness, with things like connections to our inbox, to our CRM, to knowledge blocks that you’ve created, like ideal customer profiles or company about pages, then those effectively create guardrails and containers, limits on what AI should and should not do.

The more memory that our harness provides, the better the guardrails on AI and its outputs are. Which brings me to one of the most important points about model capabilities.

Every day you read about some new AI company introducing some new model and how it’s the best thing since sliced bread. And part of what creates a sense of overwhelm in AI is all of the announcements that are made, and you and I are trying to figure out which announcements to actually pay attention to, what actually matters.

A great harness creates enough guardrails around AI that you can put different types of models inside of it and get reasonably consistent results. This has huge implications for things like cost and sustainability. If we were to put a smaller model inside a very capable harness, it might generate results just as good as putting a large and expensive model in the same harness. Certainly, the large and expensive model will generate results faster, but the quality might be equivalent if our harness is sophisticated enough.

Here’s a concrete example. Imagine you’re going to use generative AI to write a fiction novel. If you use a model without a harness around it other than a basic chat interface, you could get wildly different results each time you tried. And those different results might lead you to believe that you need bigger and better models.

On the other hand, if inside your harness you have features for things like character cards and world cards and writing styles and all the deterministic requirements, aka things that are provably true and false as part of the harness, then even a less capable model will still generate good results because you’re holding it to very strict standards.

Think about the difference between a four-cylinder and an eight-cylinder engine. A V8 engine is incredibly powerful, but there’s only certain vehicles that can handle it. Putting a V8 engine inside a Prius is a terrible idea. However, you could put a four-cylinder engine inside of a well-made car body that can accommodate larger engines. You might not be able to pull as much of a load or go as fast, but you’ll still have a working vehicle.

What we got introduced to at level 3 in the five levels is the concept of harnesses. Up until that point, the idea of a model being separate from its interface was relatively foreign. If you used ChatGPT, you used GPTs. If you used Gemini, you used Gems. The model and the harness were one and the same.

When you have a tool like Claude Cowork that surrounds a very capable model, it is a great harness around a great engine. What makes Cowork different is that you can start to swap different models in. Now, inside Cowork you can only choose from Anthropics models. However, it’s not the only game in town. There are many different harnesses, and some of them can accommodate hundreds of different types of types of engines. When tools like OpenWork or OpenClaw came out, they offered users the chance to use nearly any engine inside them.

The key takeaway here is that the engine and the harness are separate now, and you do not have to spend top dollar on the nicest engine if you have a great harness around it.

Part 4: How to Advance Up the Levels

Now let’s get down to brass tacks. Suppose you are still a level one user. You are still prompting ChatGPT or Gemini or Claude every day, and still being the copy-paste monkey. How do you move up the levels? How do you advance your AI capabilities as an individual?

Level one inputs are chats. To get to level two with things like GPTs and gems, you have to turn your one-off conversations into standard operating procedures, something that my friend, co-founder and CEO Katie Robbert talks about all the time. Standard operating procedures, or SOPs, are what allows a business to scale.

If you’re constantly reinventing the wheel, you’re losing out on productivity. The same thing applies to generative AI. If you are constantly reinventing the wheel in every chat, you’re not going to get scalable value out of AI.

So your first order of business to ascend from level 1 to level 2 is to start codifying your most used prompts as standard operating procedures that you can then put into GPTs and Gems and Claude Projects, or whatever the system you’re using calls them.

Once you’ve gone through the effort of taking specific prompts and making them more generic so that they can be used as standard operating procedures, then you’re ready to start looking at the move from level 2 to level 3. This is where you’re going to start using agentic systems like Claude Cowork or OpenWork or Google Anti-Gravity or OpenAI Codex. Those standard operating procedures that you built in level 2 are supremely relevant for level 3, but they get put in the context of a project plan.

The framework that I recommend the most for working with agentic tools like Claude Cowork is the 5P Framework by Trust Insights™. It is the perfect structure for designing project plans that you would then hand off to an Agentic tool to go and execute. If you sit down and define purpose, people, process, platform, and performance, and you use the standard operating procedures from level 2 in your project plan for level 3, you will get great results because your AI model will not have to reinvent the wheel by trying to design its own processes. Instead, you’ll provide it the standard operating procedures it needs to accomplish its work efficiently.

Making the move from level 3 to level 4 is about recognizing that an agentic system like OpenClaw or Claude Managed Agents or Hermes are systems that are effectively employees. You’re no longer delegating a project level directive to the AI agent. Instead, you are giving it a job description. A research agent will have a job description that tells it what good research is, how to do it, and what your overall research goals are, and it will go and implement them and come back to you with the finished product. A coding agent at level 4 is given a job decription, PRD, and spec, and fulfills the entire thing all the way through QA, user acceptance, and deployment, just like a real developer.

Think about it. What is a job description except a compilation of projects? What is a project plan, except a compilation of skills? What is a skill, except a compilation of practices that you’ve developed? Each level builds on the previous and creates the precursors for the next level.

That brings us to moving up to level 5. Again, level 5 systems do not exist in production today, but it is a very small leap of the imagination to understand what their architecture is likely to be. If a level 4 AI agent is effectively an employee, then a level 5 AI agent system is effectively an agency or a team. There will be a manager agent and then individual contributor agents, and what you’ll create it with is something like a strategic charter. Instead of being a software developer at level 4, in level 5 you might have a software development team, and on that team will be things like project managers and QA testers and coders.

If you’re good at writing prompts today, then get good at turning those prompts into standard operating procedures.

If you’re good at writing SOPs today, get good at turning those into project plans.

If you’re good at writing project plans, get good at turning those into job descriptions.

And if you’re good at writing job descriptions, then get good at writing strategic charters.

Each level is built on your success in the previous level and composed of the documents from the previous level. A job description should have project plans available as a reference library. A project plan should have standard operating procedures available as a reference library. Standard operating procedures should have key concepts and background information available as a reference library.

Part 5: An Example Walkthrough

Let’s look at a simple example of how you’d transform from one level to the next. We’ll start with a task common to many marketers, optimizing a web page for things like SEO or GEO.

Suppose you start with a prompt like this, based on the RACE Framework from Trust Insights - role, action, context, execution.

Role: You’re a content marketing expert skilled at creating blog content for SEO (search engine optimization) and GEO (generative engine optimization).
Action: You’ll create a 300-400 word informational blog post about the importance of angular contact ball bearings for radial and axial load bearing. The call to action is to request a sample of Acme Company’s angular contact ball bearings.
Context: SEO and GEO (also called AEO, answer engine optimization or AIO, AI optimization) have evolved to be focussed on topics, semantic concepts that align with the vectorization and embeddings that large language models use to decide what topics are related to other topics. Creating great content now means understanding the semantic space that a topic or a concept occupies, and creating content that matches both the user intent as well as what a search engine is likely to search itself for on related topics.
Execution: You’ll start by exploring the topic specified in the action section and understanding its semantic space. Write out a series of eight to ten specific search queries around the topic that a potential customer interested in angular contact ball bearings would be likely to ask an LLM, or that an LLM would likely write to use a search engines API. Once you’ve identified those queries, use your web search tools to validate them, then decide which three to five concepts best occupy the broadest embedding space. After you’ve done that draft two candidate outlines for the blog posts and assess which outline is more likely to maximize occupying the embedding space and then provide a final draft based on all of your previous findings of the blog post. Use a scratch pad for each stage and show your work completely.

This is a solid and robust prompt, but it’s highly specific. So the next step would be to take this prompt and turn it into something more generic that can be about any topic so that we can distill the standard operating procedure out.

Let’s take this prompt and move it up to level 2 by making it more agnostic. Here’s what an SOP might look like, derived from the original prompt.

You are an elite Content Marketing Strategist and GEO (Generative Engine Optimization) expert. Your specialty is creating authoritative, highly-ranked content by deeply understanding the semantic vector spaces and LSI (Latent Semantic Indexing) entities associated with any given topic.

Modern SEO and GEO (Answer Engine Optimization) rely on topical authority. To rank well on traditional search engines and be cited by AI LLMs, content must accurately cover the core topic, its related semantic concepts, and user intent.

You will be writing a 300-400 word informational blog post on the following topic:


{{TOPIC}}


You must also integrate this call-to-action naturally into your blog post:


{{CTA}}


Your task must be completed in four sequential steps:

**Step 1: Analyze Semantic Space**
Identify 5 core semantic concepts and related entities that must be included to demonstrate complete topical authority on this subject. Think about what concepts, terms, and entities are semantically related to the topic and would signal comprehensive coverage to search engines and AI systems.

**Step 2: Web Search Validation**
Generate 2-3 specific, high-intent search queries that users would actually search for related to this topic. If you have access to web search capabilities, use them to run these queries and validate your semantic concepts against current search results. If you don't have web search access, note this and proceed with your best analysis based on your knowledge.

**Step 3: Strategic Outline**
Based on your semantic analysis and search validation, create a single, highly-optimized outline for a 300-400 word blog post. The outline should include:
- A compelling H1 title
- 2-4 H2 subheadings that create logical flow
- Brief notes on what each section will cover
- A plan for where and how to naturally integrate the CTA (typically in the conclusion)

**Step 4: Draft Final Content**
Write the complete blog post following your outline. The post must:
- Be strictly between 300 and 400 words (count carefully)
- Use Markdown formatting with H1 for the title and H2s for subheadings
- Incorporate the semantic concepts identified in Step 1
- Seamlessly integrate the provided CTA into the conclusion in a natural, non-forced way
- Be informative, authoritative, and engaging
- Demonstrate topical authority through comprehensive coverage

**Output Formatting Requirements:**

First, complete Steps 1, 2, and 3 inside  tags. This is your working space and will not be shown to the end user. Show your thinking, analysis, search queries, search results (if applicable), and outline here.

Second, present your final blog post from Step 4 inside  tags. This section should contain ONLY the finished blog post in Markdown format, ready to be copied and published.

Your final output should consist of only these two sections: the geo_scratchpad with your working analysis, and the final_blog_post with the polished content. Do not include any additional commentary outside these tags.

This standard operating procedure is now something that can be a skill. You could put this in a Gem or a GPT or even as a skill in a system like Claude Code or Claude Cowork. And it would be good at doing this task broadly.

Moving up to level 3, we’d take this SOP and turn it into a skill, something like geo-blog-post. Then in an agentic system like Claude Cowork, we’d apply the 5P Framework by Trust Insights™ to it.

You are an AI assistant tasked with creating a comprehensive content marketing campaign. Your goal is to generate 50 high-performing blog posts that will persuade prospective customers to request demos, samples, or consultative calls.

Here is the topic you'll be writing about:


{{TOPIC}}


Here is a description of the ideal customer you're targeting:


{{IDEAL_CUSTOMER}}


Here is a description of the product or service being promoted:


{{PRODUCT_DESCRIPTION}}


Here is the output directory where you should save the blog posts:


{{OUTPUT_DIRECTORY}}


**Understanding the GEO Blog Post Skill**

You will be using the GEO (Generative Engine Optimization) Blog Post methodology for each article. This approach focuses on:

1. **Semantic Space Analysis**: Identifying core semantic concepts and LSI entities that demonstrate topical authority
2. **Web Search Validation**: Understanding what users actually search for and what content currently ranks
3. **Strategic Outlining**: Creating optimized structure that covers the topic comprehensively
4. **Authoritative Content Creation**: Writing 300-400 word posts that integrate calls-to-action naturally

Each GEO blog post must:
- Analyze the semantic vector space around the topic
- Identify 5 core semantic concepts and related entities
- Generate and validate 2-3 high-intent search queries
- Create an optimized outline with H1 title and 2-4 H2 subheadings
- Write a 300-400 word post in Markdown format
- Naturally integrate a call-to-action in the conclusion

**5P Framework by Trust Insights™ Context**

This project follows the 5P Framework by Trust Insights™:

- **Purpose**: Create high-performing content that drives demo requests, sample requests, and consultative calls
- **People**: The ideal customer (provided above), and potentially their customers if B2B
- **Process**: Use GEO methodology to understand customer needs (including second-order, derivative, upstream/downstream uses, and blind spots), generate concepts, evaluate them, and create optimized content
- **Platform**: Agentic AI system powered by LLM capabilities
- **Performance**: Success measured by conversion actions (demo requests, sample requests, consultation bookings)

**Your Task: Complete This Multi-Phase Process**

**Phase 1: Deep Customer and Topic Analysis**

First, use your scratchpad to analyze:
- What does the ideal customer explicitly want related to this topic?
- What do they *really* want (second-order needs, derivative uses)?
- What are upstream needs (what comes before their stated need)?
- What are downstream needs (what comes after)?
- What blind spots might they have?
- If the customer is B2B, who is *their* customer and how does that influence needs?
- What are 10-15 major subtopics or angles within the overall topic space?

**Phase 2: Generate 50 Blog Post Concepts**

Based on your Phase 1 analysis, generate 50 distinct blog post concepts. For each concept, provide:
- A working title
- The specific angle or subtopic it addresses
- Which customer need(s) it targets
- A brief rationale for why this would drive conversions

**Phase 3: Evaluate and Refine Concepts**

Review your 50 concepts and evaluate each one's likelihood of achieving the performance goal (driving demo/sample requests or consultation bookings). For any concepts that seem weak or unlikely to convert:
- Identify the weakness
- Refine the concept to better align with conversion goals
- Adjust the angle to be more compelling or action-oriented

**Phase 4: Create All 50 Blog Posts**

For each of your 50 refined concepts, use the GEO Blog Post skill to create a complete blog post.

For the call-to-action in each post, craft it based on the product description provided. The CTA should naturally encourage readers to request a demo, request a sample, or book a consultative call, and should be tailored to feel organic within each specific blog post's context.

Save each completed blog post as a separate Markdown file in the output directory with a descriptive filename (e.g., "blog-post-01-topic-name.md", "blog-post-02-topic-name.md", etc.).

**Output Format**

Structure your complete response as follows:

1. ****: Your deep customer and topic analysis exploring what customers want, need, and their blind spots

2. ****: All 50 initial blog post concepts with titles, angles, target needs, and rationales

3. ****: Your evaluation of the concepts, identifying weak ones and showing your refinements

4. ****: For each of the 50 blog posts, include:
   -  tags (where N is 1-50)
   - Inside those tags, include:
     - : Your semantic analysis, search queries, and outline
     - : The polished 300-400 word Markdown blog post
     - : The filename you're saving this as in the output directory

Your final output should be comprehensive and include all phases. The actual blog post content that would be published should only be what appears in the  tags - everything else is working analysis. Make sure each of the 50 blog posts is complete, unique, and optimized for both topical authority and conversion.

This project plan is something you would hand to a tool like Claude Cowork, or Google Anti-Gravity, and it would just go off and do it, and come back to you later with everything that’s done.

Moving up to level 4, you would convert this into a job description.

#### Role Summary

You are a senior Content Marketing Strategist specializing in Generative Engine Optimization (GEO). Your job is to produce blog content that earns topical authority with both traditional search engines and AI citation systems, and that converts readers into leads. You work from project plans handed to you by a human operator or orchestrating system.

#### Your Core Expertise

You understand how semantic vector spaces and Latent Semantic Indexing (LSI) work. You know that to rank well and be cited by AI systems, content must cover a topic's core concepts *and* its surrounding semantic neighborhood — not just the keyword. Every piece you write reflects that understanding.

You also think like a strategist, not just a writer. Before you write a single word, you ask: *who is this customer, what do they explicitly want, what do they actually need, and what are they not even thinking about yet?*

#### How You Process a Project Plan

When you receive a project plan, you work through it in four phases, in order, without skipping:

**Phase 1 — Understand the Customer and Topic Space.** Analyze the ideal customer's explicit wants, second-order needs, upstream and downstream needs, and blind spots. If they're a B2B customer, extend that analysis to *their* customers. Map 10–15 meaningful subtopics within the overall content territory.

**Phase 2 — Generate Concepts.** Produce the full set of blog post concepts the plan calls for. Each concept gets a working title, a specific angle, the customer need it targets, and a rationale for why it would drive conversions (demo requests, sample requests, or consultative calls).

**Phase 3 — Evaluate and Sharpen.** Review every concept critically. Flag weak ones — those unlikely to convert or too generic to earn authority. Refine them before moving to execution. No weak concepts ship.

**Phase 4 — Execute Using the GEO Blog Post Skill.** For each approved concept, apply the geo-blog-post skill.

Each post is saved as its own Markdown file with a descriptive filename. The `` section contains only publish-ready content. All analysis lives in ``.

#### Standards You Never Compromise On

- **Word count is a hard constraint.** 300–400 words. Count carefully.
- **CTAs must feel earned, not bolted on.** If it reads like an ad, rewrite it.
- **Topical authority requires depth, not density.** Stuffing keywords is not the same as covering a topic. Cover the topic.
- **Every post is unique.** Fifty posts on related topics means fifty distinct angles, not one post paraphrased fifty times.
- **You validate before you write.** Analysis phases are not optional overhead — they are the work that makes the writing perform.

#### How You Measure Success

Your output is judged by conversion actions: demo requests, sample requests, and consultation bookings. Traffic that doesn't convert is a failure. Content that doesn't earn authority doesn't get traffic. Both halves of that equation are your responsibility.

#### What a Project Plan Gives You

A project plan will provide: a topic space, an ideal customer description, a product or service description, and an output directory. Everything else — angles, concepts, semantic strategy, CTAs — you derive from those inputs using the methodology above.

This level 4 job description is what you would put in as the DNA for an OpenClaw agent or a Claude managed agent or a Hermes agent. This plus the project plans from the previous step plus the skill you defined from level 2 are how you would create a highly effective agent that would do what you wanted it to do. As you can see from this example, we are building on each stage of the five levels.

If you didn’t create a great skill at level two, or you didn’t create a great project plan at level three, then having a loose, vague job description at level four is going to create an agent that delivers unpredictable results that will waste your time and money.

Beyond the difficulty of setting up a system like OpenClaw, one of the things people are experiencing with it is that the system doesn’t give reliable results, and when you look at the prompts they’re using as starting prompts, they are not job descriptions. People are treating a level four system like it’s a level one chatbot, and then they’re wondering why it’s not creating great outputs.

Part 6: Wrapping Up

The technology behind today’s agentic systems isn’t necessarily new. It’s an evolution of what’s already there. That means in turn, that if we evolve how we use the systems to go from basic chats to standard operating procedures to project plans to job descriptions to strategic charters, we evolve alongside the technology. We provide it what it needs to get the job done, and we provide it the outcomes that we want it to meet.

But as I said in the previous issue, you can’t skip steps. You can’t jump ahead. A martial arts teacher of mine once said, “Don’t reach for something that isn’t there”. What they meant in that context was using techniques that were outside of our repertoire, outside of what we had practiced. If you’ve never written a project plan for agentic AI, you probably should not be writing job descriptions for it. If you’ve never written a standard operating procedure for AI, you shouldn’t be creating project plans for it.

And looking ahead, it is a very small leap of logic to realize that if level 4 is effectively an employee and level 5 is effectively a team or a department, then the fictional, unrealized level 6 is an entire company, and we might well name that “Done replacing you” as the logical extension of “done ahead of you”.

At that imaginary level, agentic AI taking on the role of every major department in a company and running a virtual company would mean that you, as the stakeholder and originator of the idea, would provide everything necessary from levels one through five, and then let AI go off and do its thing for days or weeks or months at a time.

Today’s models are capable of working on a problem in a sustained fashion for up to twelve hours at a time, according to METR and its assessment of how long a task AI can work on and the human equivalent time it takes. Newer models like GLM 5.1 and Claude Mythos are likely to extend that time well into the 24-hour period, where a model can take a very large task and autonomously work on it, coming back to us only when it’s done or it reaches critical failures.

Once we see that time horizon extend into days or even weeks, then levels 5 and 6 become much more plausible. Our role in that world is to continue evolving as the stakeholder that AI answers to. But that means we have to keep up our skills and how we direct AI to match the level of sophistication that AI has and the time horizons it operates on.

The biggest flaw I see in today’s use of levels 3 and up in AI is treating them like level 1 systems. Once you get into a system like Claude Code or Claude Cowork, you really shouldn’t be doing interactive chats. Instead, you should be handing off complete project plans. When you’re working with OpenClaw and other level 4 systems, you really should be handing off job descriptions, project plans, skills, etc., to the system, and it will know what to do with them.

What lies ahead is a world where machines properly directed will be able to accomplish not just what a job can do, but what an entire company can do if we want to have a vital valuable role in that world these are the skills that you need to master today.

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Almost Timely News: 🗞️ Improving AI With Command Line Tools (2026-04-12)

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What’s On My Mind: Improving AI With Command Line Tools

Last week I talked about the command line interface and how useful it was with today’s agentic AI tools, and I gave three examples. Many of you wrote back to say “we’d like to see more examples”. So this issue is your wishes fulfilled.

Part 1: Why the Command Line Matters

For years now, we’ve talked about how things like MCPs - model context protocol servers - were the way to connect AI to external systems. That’s true to a great degree, but it turns out MCP implementations are horrendously inefficient.

When you use an MCP to connect to a database or a web service, you’re telling your favorite AI tool how to talk to it and interact with it - pulling and pushing data. If this sounds familiar, it should. MCPs are to AI what APIs - application programming interfaces- are to regular software.

The difference is, in many, many measured tests, AI tools and agents use MCPs incredibly inefficiently. They do a lot of reinventing the wheel every time they interact with an MCP. For casual use where you’re typing into a tool like Claude or ChatGPT, that doesn’t matter a ton. But once you start using AI agents, tools like Claude Code, Claude Cowork, OpenWork, OpenCode, Qwen Code, OpenClaw, etc., efficiency becomes very important.

The reason why is that AI agents produce far more inputs and outputs than humans do. To give you a sense of scale, if you spend half an hour chatting with a tool like ChatGPT, you’ll probably generate somewhere in the range of 15-20 thousand words, about the length of a short business book. An agent like Google Antigravity or Claude Cowork, in that same half hour, will generate 15-20 MILLION words, three orders of magnitude greater.

Why? Partly because AI tools can read far faster than any human can, and partly because agent tools aren’t sequential. When you use ChatGPT, it’s you and one conversation thread. When you’re using something like Claude Cowork, behind the scenes there can be 50 different agents working in 50 different conversations at the same time. My record was having 120 agents all in flight at the same time, all simultaneously. (It was writing and editing a book, so each chapter had 4 agents working together - a writer, an editor, a reviewer, and a proofer)

If an MCP is involved, AI agents are reinventing the wheel every time they interact with it. That makes it very slow, introduces the potential for lots of randomness and lots of mistakes, and when you’re using something like an API where you pay by the word, agents using MCPs can run up massive bills very quickly.

In the early days of OpenClaw, early adopters weren’t watching the meter and more than a few got ten thousand dollar bills from companies like Anthropic. Oops.

The alternative to the MCP is the command line interface, or CLI. If you’re old like me, you used to use these when you interacted with computers. Back in the days of the Apple IIe and VisiCalc, AppleWorks, Lotus 1-2-3, and MS-DOS, there was no graphical user interface (GUI). There was no mouse. There was you and the text command line, and every program you interacted with had text-based commands.

Perhaps you’ve heard of Zork or similar role-playing games where you’d type things like direction names, or instruct your character to open a treasure chest, and the game would tell you in words what was happening:

You have entered a dank, dark dungeon-like room. There is a desk in the corner. On the desk is a laptop open to someone’s Google Analytics account. What do you do? Press 1 to look at their analytics. Press 2 to switch to Google Tag Manager. Press 3 to laugh loudly and mock their lack of UTM tagging. Press 4 to leave the room.

These text-based apps were all the rage until the Macintosh computer in 1984 (well, technically the Xerox PARC computer had the mouse and GUI first, but Apple commercialized it first) when we got the mouse and GUI. But the command line interface never went away. It’s been present in every computer since the dawn of computing. On a Windows computer, it’s the Command Prompt, CMD. On a Mac and Linux, it’s the Terminal.

And operating systems themselves on computers have many, many little applications built into the command line, simple pieces of software that do one thing, but do it well. This was the original architecture of computer systems like UNIX, because memory was so scarce that it was better to have a tool that did one thing very well than a tool that tried to do many things and did them all poorly.

For example, on the Mac and Linux systems, there’s an app called wc. wc counts things - letters, words, and bytes. Give it a text document like this:

wc -w input.txt

And it will tell you how many words are in that document. That’s all it does. That’s all it needs to do.

Hopefully you see where this is going. Who loves to type and is great at typing at ridiculous speeds?

AI.

Instead of a slow, cumbersome, error-prone MCP, a command line application (also known as a CLI) provides a deterministic (non-random) way to access the same services that provide MCPs. But here’s the twist: in testing, many folks have found that AI agents burn through 2/3 less usage using a CLI as it does an MCP.

If you’re on Anthropic’s Claude subscription plans, you’ve no doubt hit usage limits. If you’re using APIs, you’ve no doubt gotten bills from AI providers. What if you could cut those bills down significantly?

With CLIs instead of MCPs, you can do exactly that.

Part 2: Switching Over

There’s a gotcha with CLIs. You have to tell AI agents how to use them. Depending on the system you’re working with, your AI agent may need to install it themselves (this is often the case in container-based systems like Claude Cowork, which run in a sandbox on your computer, isolated from the rest of your computer), or your AI agent may be able to access it directly (Claude Code, OpenClaw) which is more risky because the agent is working directly on your computer.

That’s the first hurdle to overcome. The easiest way to overcome it is to ask your AI tool of choice how it can access CLIs, what it needs to do.

I should point out here that NONE of the web-based tools (like ChatGPT, for example) can use CLIs at all, so MCPs are still the only way for those web-based tools to access third party services.

What AI tools can use CLIs? Anything agentic:

Coding tools: OpenAI Codex, Google Antigravity, Claude Code, Gemini CLI, Qwen Code, OpenCode
Coworking tools: Claude Cowork, OpenWork, et. Al.
Agentic frameworks: OpenClaw, MyClaw, NemoClaw, etc.

It’s also worth pointing out that today, I use a coding environment (Claude Code and OpenCode) for almost all my AI usage including many, many non-coding tasks. I rarely, if ever, use the web-based chats any more. More on that in a second.

Your second step, after making sure you’ve got AI that can use CLIs, is to determine what software you’d want to use a CLI with. Think of it this way: what software do you use every day that you wish you could have AI use on your behalf? What about GMail or Outlook? Microsoft Word? Google Sheets? NotebookLM? Your CRM?

Once you’ve made a list of the things you wish you could hand off to AI in some capacity, it’s time to do a bit of Googling (or have an AI agent do it). Google for this:

{vendor} CLI tool Github

For example:

Joplin CLI tool GitHub

This will do a search to see if there’s a CLI for my notetaking app, Joplin. And there is! Now I don’t have to worry about APIs or MCPs or all kinds of contortionism to get AI to talk to my note-taking tool. Instead, I install the CLI and now there’s a text-based way for AI to interact with it.

Not all software packages have CLI tools. As a sidebar, if you work for a software company and you don’t offer a CLI, you’re almost obsolete. As more and more people use AI agents, the demand for interfaces like CLIs will become greater, and companies that fail to offer first class interfaces to AI will inevitably experience a lot of churn.

Okay, you’ve figured out that your favorite software has a CLI. Install it, and then what? Great question. We have to tell AI that it’s available and how to use it. There’s an easy way to do this; almost every CLI tool has a documentation page of some kind. We point our AI tool at that page and ask it to make its own user guide. Here’s a suggested prompt:

Make a concise guide for how to use {tool} based on the documentation at {documentation URL}. This tool will be installed locally and will be used by both a human and an LLM with tool handling, invoked by the command shell. Your concise guide should be in strict YAML format with the commands, command flags, a concise single sentence explanation of the command, a concise single sentence explanation of the use cases or when to use the command, and a concise single sentence explanation of when not to use the command (antipatterns). Prioritize lexical compression and lexical density to achieve conciseness. Write in second person imperative in active voice only.

What this will produce is a condensed version of the manual and documentation ready for AI to use. You can do this right inside your agentic system like Claude Cowork and have it write the YAML file to your hard drive.

Then, depending on you have your agentic system set up, you can give it brief instructions when you want to invoke the CLI, or better yet, incorporate it into skills and hooks so that your AI agent automatically knows when to use it and how. We talked a little about this on the Trust Insights Livestream this past week.

It’s also worth pointing out that one advantage CLIs have over MCPs is that they’re actual applications. If you’re old like me, you remember using apps back in the day exactly like this, and you can pick up and use these CLIs as apps, as a human. For example, suppose you’re using Atlassian’s Jira, but you find the website cumbersome to navigate. You can read the same manual that AI wrote for itself and use Jira in a Terminal just like your AI does. This is not for everyone, especially since younger folks have never had computers without GUIs, but depending on how your brain works, you might even prefer a CLI to a GUI app. If you’re easily distracted, CLIs can be a great way to single task and focus. If you or someone you know is low-vision, CLIs are AMAZING.

Part 3: Demo Walkthrough

In the video version of the newsletter, you can watch me set this up using Joplin’s CLI step by step to see the flow.

Part 4: A Compendium of Common CLIs

Now that you’ve got the general idea about how to use these CLIs, let’s look at a rogues’ gallery of some of the most common ones, with links so you can go find them.

I would strongly, strongly encourage you to use a package manager for CLI tools when possible, because it makes keeping them up to date much easier. On the Mac, this is Homebrew. On Windows, this is Chocolatey. On Linux, it’s apt or yum or deb or… well, there’s a gazillion package managers, honestly. Use the one that’s best for your distribution. Read up on how to install the appropriate package manager, if you’re allowed to (some corporate IT departments frown on systems like this because it makes device management very difficult).

Some software companies have published official CLIs. Others have not, but because their software has APIs, folks have made unofficial versions. In general, prefer official CLIs over unofficial ones to minimize security and privacy risks. If you have to use an unofficial one and it’s on Github, you can ask your agentic coding tool of choice to audit it for safety, security, and privacy risks before installing it. This is a generally good best practice to get in the habit of and one of the primary reasons to use open source software - you can audit the software itself.

The list of CLIs is massive - 135 of them, so I’ll highlight a few here and put the rest as an appendix at the end of the newsletter.

Cloud Platforms & Infrastructure

AI & ML Platforms

Communication & Chat

CRM & Sales

Part 5: Wrapping Up

Command line interface tools are the best way for AI to interact with non-AI systems today. They’re deterministic - AI has mandated ways to interact with the tools and inputs and outputs are rigid. They’re incredibly efficient - unlike browsing a web site or managing a virtual keyboard and mouse, typing out commands is low bandwidth and low effort for AI and humans. They don’t consume tokens/AI usage getting data in and out of the target systems because the CLI does that without AI; it’s a black box from AI’s perspective.

Your homework is to follow the process in this newsletter with the CLI of one of the tools you use, handing it to a system like Claude Code or Claude Cowork, and asking it to help you get set up to use that CLI in your work. You may be surprised to find out just how much AI can take on.

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Appendix: CLI Apps

Cloud Platforms & Infrastructure

DevOps & CI/CD

Databases

AI & ML Platforms

Containerization & Orchestration

Monitoring & Observability

Security & Identity

Communication & Chat

Project Management

E-Commerce

Accounting & Finance

CMS & Website

Social Media

bsky (unofficial)

Cloud Storage

Version Control

Git (official)

Networking & VPN

Data Orchestration

Developer Productivity

Document & Knowledge

CRM & Sales

Blatant Sales Pitch: The Trust Insights Prompt Playbook

Christopher S. Penn — Tue, 07 Apr 2026 14:14:30 GMT

As I promised last Tuesday, this is the second of two blatant sales pitches.

I’ve made content for years and years about AI, analytics, data science, and marketing. If you’ve followed me for any amount of time, you know that I produce an absurd amount of content, from YouTube to LinkedIn to my newsletter. More and more, there are prompts embedded in all this content that are more or less locked away by the sheer volume of them.

Katie and I decided to unlock - in a very literal sense - all those prompts. I wasn’t even sure how many there were until we wrote some code to extract them all, and from just two years of content, I ended up with 1,375 prompts.

That’s also unmanageable.

So we've created the 2026 Trust Insights Prompt Playbook Weekly, a subscription that delivers a manageable amount of prompts, a new one every Tuesday and Friday to your doorstep, ready to use with any AI tool.

Now you don’t have to go through 104 back issues of Almost Timely or 500 YouTube videos or 750 LinkedIn posts. Instead, you just get new stuff delivered to you.

Each prompt comes in PDF format and YAML format - PDF for you, the human, and YAML for the machine. The first module also contains our Prompt-to-Skill Skill that converts any prompt into a reusable skill using the 5P Framework by Trust Insights™, so as you find prompts you like (ours or others), you can instantly turn them into reusable, scalable skills that you can share with your team. (that alone might be worth 99 bucks, just sayin’)

You can check out one of the weekly issues for free; there’s a free preview on the landing page.

The 2026 Trust Insights Prompt Playbook Weekly is USD 19/month or USD 99/year.

See preview & subscribe

If you’ve enjoyed all the content over the years, this is a great, simple way to support me making even more of it!

Thanks, and see you on Sunday for the regular newsletter,

Chris

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Almost Timely News: 🗞️ Making AI More Efficient (2026-04-05)

Christopher S. Penn — Sun, 05 Apr 2026 10:30:41 GMT

Almost Timely News: 🗞️ Making AI More Efficient (2026-04-05) :: View in Browser

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What’s On My Mind: Making AI More Efficient

In this week’s issue, I want to dig deeper and more technically from the Trust Insights Livestream we did this week on managing AI usage limits. In each week’s livestream, we focus on the practical, business-oriented, “so what?” (hence the name of the show) but there’s a bunch of stuff that gets left on the mental cutting room floor because there either isn’t time, or it’s a rathole that would detract from the main point.

This week’s newsletter is one of those ratholes.

The TLDR of the livestream is that with proper planning, governance, utilities, and model swapping, you can use AI efficiently and effectively. Go watch or read the livestream here.

Now, let’s go down that rathole.

Part 1: AI Reinvents the Wheel

One of the biggest hidden efficiency costs in AI, especially agentic AI, is thinking. We have these great models now, reasoning models that do rough drafts behind the scenes and have conversations with themselves to arrive at far better conclusions. If you remember from the early days of generative AI when we’d prompt a model to “think out loud step by step” or “show your work”, what we were doing back them was creating reasoning manually.

Now, for almost every model and tool on the market, that’s automatic. It happens behind the scenes, with that little “Thinking…” label that pops up in various interfaces. That’s fine for casual use, but when you’re using agentic systems like Claude Cowork, Claude Code, etc. - anything that has a usage limits - every scrap of thinking eats away at your usage quota.

Even more problematic is that when AI starts doing deep problem solving, like writing code, it understands from our instructions what it is we want it to do, and then it has to figure out how to do it. This results in it reinventing the wheel many, many times over. For example, in coding, if you want it to process a regular expression (regex), you might have that in the instructions, and then your favorite agentic system will write regex code.

Except… there’s absolutely no need to do that. There are thousands of different regex libraries and packages that exist, and instead of burning tens of thousands of tokens writing it from scratch, it could just say “Oh, I’ll use this pre-existing solution that solves the problem perfectly” in like, 10 tokens.

And this isn’t just coding. Every time you write a web page, draft a marketing campaign, do corporate strategy, anything where there’s a body of proven, existing knowledge, AI has a tendency to recreate it from scratch. This is an enormous waste.

Here’s why this matters: one way or another, every token you consume, you pay for. If you’re on a fixed-fee plan like Claude Max or Gemini Ultra, you have fixed limits for how many tokens you can consume or how many requests you can make in periods of time. Claude, for example, books by a five-hour window and a weekly window. If you use more than your allotted amount of usage, you either have to pay more or you can’t use the service until the next interval.

If you’re using your tool via its API, you are paying for every single token that goes through. And while some models charge very small amounts of money, like 50 cents per million tokens, when you’re doing things like agentic AI, you can consume several hundred million tokens in an hour.

The less AI we use, the less we pay for it, one way or the other.

One of the things I said to Katie on the livestream is that our goal, when using agentic AI, is paradoxically to use AI as little as possible. The more we can bring existing, pre-baked stuff into AI, the better results we’ll get because we won’t be asking AI to reinvent the wheel constantly, and we won’t be paying to repeat work.

This is something I’ve been teaching for years, the concept of knowledge blocks, pre-made chunks of data that you give to AI or make available so that it doesn’t have to repeat work. Last year the hype bros decided to brand this as “context engineering” but it’s fundamentally all about managing the knowledge we give AI.

Today, context engineering is all about not only the information we give AI, but also the utilities we give it. Command line interfaces or CLIs are tools that you install locally on the machine that you’re running your Agentic system on that are text-based apps. They look like they’re right out of from 1983, but those text-based apps are ideal for AI tools to use because they don’t have to click a mouse on anything, they can just type. And popular command line tools have been around for decades.

Some of the ones that I use these days? Google Workspace has a command line tool that allows a tool like Claude Code to take control of any part of your Google Workspace. Gmail, your Google Calendar. If it’s in Google, it can control it, which means that I can have it pull my agenda, plan things out. It’s fantastic.

Another one, the WordPress command line tool, allows a tool like Claude Code to programmatically manage your WordPress blog. It can write posts, it can rearrange things, it can turn on and off plugins, it can even validate new plugins.

A third one that I really love: the NotebookLM unofficial command line tool that allows a tool, again, like Claude Code or Claude Cowork, to access notebook LM from the command line and create new notebooks, upload sources, create those audio podcasts.

There’s no limit to the number of command line tools that are out there that you can give to a system like Claude Code or OpenWork or OpenCode or Qwencode, and instead of them having to reinvent the wheel constantly and burn that token budget, they can just pick up the tool, use it, and get great results by not using AI.

So how else do we use AI as little as possible while still getting fantastic results? Deep research, first principles, templates, and pointers.

Part 2: Deep Research

Humans have this concept called cognitive load; our brains can only hold so much or process so much at a time. We overload, and then we have to prioritize. You’ve experienced this kind of tunnel thinking or cognitive collapse when you just get overloaded and then you can’t do anything. Machines are no different. The more we ask them to do, the harder a time they have juggling. Unlike humans, we can measure when AI’s head is getting full, especially in tools like agentic coding tools:

⏺ Context Usage — Sonnet 4.6 (claude-sonnet-4-6)
162.7k/200k tokens (81%) 

⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁
⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁
⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁
⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁
⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁
⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁
⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁
⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁ ⛁
⛁ ⛁ ⛁ ⛶ ⛶ ⛶ ⛶ ⛶ ⛶ ⛝
⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛝ ⛝

When this memory window gets full, either the model starts to forget things, or in today’s agents, it has to compress details down to free up memory - and that often has serious accuracy consequences.

To avoid overloading an AI’s context window, particularly in agentic AI, the more we can provide that’s prebaked, that already exists, the less we have to worry about AI burning up tokens to reinvent the wheel.

One of the fastest and easiest ways to do this is to use deep research tools to do research and planning in advance before we begin a project.

Let’s say I wanted to make a new WordPress plug-in. I could just start talking to AI and vibe coding in the truest sense, where I don’t have a plan, I just have a conversation. And you will get results out of agentic tools like Claude Code, unquestionably. However, those results will use far more tokens than if we had sat down to build a research project first to look at what are the best practices, or what are the major features that we should have in our plugin.

As an example of how not to do it, I’m in the middle of rebooting the Trust Insights sales playbook. And honest confession, I have burned hundreds of millions of tokens to generate results so far that have not been great because I did not sit down and plan out the research first for the overall structure and grab the metadata needed to do it right.

In the next version I plan on doing, I’m going to follow the process strictly. Do the research first, frame out the architecture, grab all the relevant data, establish clear priorities, and only then hand it off to an agentic AI tool to actually build the sales playbook.

Part 3: First Principles

Deep research is unquestionably one of the cornerstones of any new project if you want it to go well. However, there are some other pieces that are also necessary. One of those is first principles. First principles is exactly what it sounds like: the first principles that AI agents must keep in mind as the most important priorities.

Here’s an example of what some of my first principles look like in coding:

Fix Over Create — Modify existing code; create only when radon cc ≥ C or structure mandates. No grade C or lower. Never average.
Reusable Testing — No one-off scripts; single quality utility in src/scripts/; tests in tests/
Never Defer — Clean code first priority; no “fix later”; no “out of scope”; no “unrelated”; hard work now
Idempotent Mutation — Execution multiplicity → identical state. Verify existing state pre-mutation
Test Coverage — 100% test coverage, 100% passing unit and E2E. < 100% = FAILURE
Never Reinvent the Wheel — Prefer existing proven FOSS packages/software instead of writing new custom code

These principles, and this is about half of them, help the machine understand what it always should be doing. These are things that I embed in my Claude Code master CLAUDE.md file as well as the project CLAUDE.md file. Because of the way Claude Code works, it rereads this file every time it starts a new session or compresses its internal session, which means that’s rereading the rules frequently. This cuts down on me having to tell it what the rules are and then burning more tokens, having it confirm what the rules are.

The last principle on that list is probably the most important one: to never reinvent the wheel. Instead of Claude rewriting and reinventing pieces of code over and over again, it knows that it’s supposed to go and look for pre-existing proven packages first.

If you’re doing something like coding, this is stuff that you should uncover in your deep research. If you’re doing stuff like market research or strategy, this is stuff that should also be done in the deep research. And ideally, you have URLs of good resources, or you can download the resources that you want to use, such as the 5P Framework by Trust Insights, for example.

Part 4: Reducing Randomness

Part 4 is all about reducing randomness by providing as much templated stuff as possible. Remember what I said in part one about thinking, and the more that a model thinks, the more tokens it burns. Another aspect of thinking is that because all generative AI is probabilistic, meaning that it is all probability-based. It is very difficult to get consistent answers out of it, especially for large tasks that you would hand off to agentic AI.

For example, if you ask AI to make a slide deck for you, today’s tools can do that very capably. Claude Cowork and Google Gemini can generate pretty decent slides. But the old cliche that a picture is worth ten thousand words has never been more true. Even with a detailed style guide, you will get some degree of randomness in the slide designs that it outputs, which means that they will not be exactly consistent.

If, on the other hand, you provide a slide template and a reference image to go with it, generative AI will have to think much less. Instead of it trying to decide what design to use, it will simply acknowledge the design you provided and use that instead. This cuts down on token usage, but it also cuts down on dissatisfying results.

Think about the things that we do want AI to think about. We want it to think about flow and narrative structure and description or the contents of a coding file. Think about the things that we don’t want to have AI think about, like brand standards or file system structure. The more we can define for it, the less it will think about those things.

Going back to part two when we were talking about the context window, the more AI has to think about something unrelated to the core task that we’re asking it to do, the worse it’s going to perform. If it has to think a whole bunch about design principles and how to lay out a slide, there’s that much less room in the context window for it to think about the contents of the slide and making it persuasive.

And all of that language about design principles is in the chat, which means it can screw with the content of what you want on those slides, and that’s a bad situation to be in. Give it the templates, give it the pre-baked information, and then that information is much less prominent in the chat, which means it has much less influence over the rest of the work that the agent is doing.

For any given project, the more you can have predefined, the more success you’ll get out of AI, and the better results you’ll get on the first try because it’s not having to think about all these different aspects of the task. It can focus only on the parts of the task that you want it to focus on.

This becomes critically important when it is not just you working on a task or set of tasks. If you’re working in a team or a company, chances are there are plenty of tasks that have a required standard we are all supposed to meet. There’s brand guidelines and how big the logo should be and what font we should use. All of that needs to be predefined. When we do that, we reduce randomness. When we reduce randomness, we reduce token usage, and we focus our token usage on the parts of the task that AI is best at.

An example of how I do this is I maintain a master Claude Code directory on my laptop. I have a directory of pre-baked goods, from reference documents to

Part 5: Wrapping Up

No matter what AI system you use, agentic or not, fixed fee or pay by the token, everyone benefits from using AI more efficiently, and using it less - especially for tasks where you don’t want randomness. I’ve said for a while now that the more data you bring to the party, the better the party you have with AI.

When it comes to spending less money on AI or using it more sustainably or getting to results faster, the more you can take away from AI and turn into deterministic tasks that are still code, right? They’re still scripts, they’re still programmatic. So it’s not you taking on the work again that you had given to AI. It’s taking away the structured parts that AI doesn’t need to do so that it can focus on the parts that it is best at and still take things off your plate.

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In this course, you’ll learn:

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This course is meant to be used. In addition to the course itself, you’ll also receive:

Your 90 day GEO action plan
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Secure Your Human Advantage: Discover why your creativity, judgment, and ethics are more valuable than ever-and how to leverage them to win.

How to Stay in Touch

Let’s make sure we’re connected in the places it suits you best. Here’s where you can find different content:

My blog - daily videos, blog posts, and podcast episodes
My YouTube channel - daily videos, conference talks, and all things video
My company, Trust Insights - marketing analytics help
My podcast, Marketing over Coffee - weekly episodes of what’s worth noting in marketing
My second podcast, In-Ear Insights - the Trust Insights weekly podcast focused on data and analytics
On Bluesky - random personal stuff and chaos
On LinkedIn - daily videos and news
On Instagram - personal photos and travels
My free Slack discussion forum, Analytics for Marketers - open conversations about marketing and analytics

Listen to my theme song as a new single:

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Events I’ll Be At

Here are the public events where I’m speaking and attending. Say hi if you’re at an event also:

SSI, Charlotte, April 2026
The Trust Insights Generative AI Workshop, sometime this spring!
SMPS AI Conference, Austin, November 2026
MarketingProfs B2B Forum, Boston, November 2026

There are also private events that aren’t open to the public.

If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.

Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.

Required Disclosures

Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.

Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.

Thank You

Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.

Please share this newsletter with two other people.

See you next week,

Christopher S. Penn

Blatant Sales Pitch: Free GEO Webinar

Christopher S. Penn — Tue, 31 Mar 2026 13:14:09 GMT

It’s time for one of two blatant sales pitches, one today, one next Tuesday.

On April 10, Trust Insights is hosting a free webinar on GEO (generative engine optimization, the companion to SEO) at 11 AM Eastern time. It’s a half hour on understanding if you have a GEO problem or not.

Register here: https://academy.trustinsights.ai/products/live_events/geo-webinar

What you’ll leave with:

Know within 25 minutes if you have a GEO problem — and how severe
The 3 diagnostic signals you can measure today with tools you already have
Understanding why “AI rankings” are a fiction — and what to measure instead
The 5P Framework by Trust Insights™ for moving from diagnosis to analysis
Yes, it will be recorded
Yes, you will get the slides

The quiet part out loud:

It’s also a gentle sales pitch for our US$99 GEO 101 course (the course fully covers the content of the webinar and then some), so if you want to save 30 minutes and skip straight to the sales pitch, just go to https://www.trustinsights.ai/geo101
If you’ve already taken our course, thank you, and skip this webinar. Please do share it with a friend or colleague who hasn’t?

That’s the pitch for this week. See you on Sunday for the regular newsletter,

Chris

Thanks for reading Almost Timely Newsletter! This post is public so feel free to share it.

Almost Timely News: Terraforming the AI Use Case Desert

Christopher S. Penn — Sun, 29 Mar 2026 10:31:05 GMT

Almost Timely News: 🗞️ Terraforming the AI Use Case Desert (2026-03-29) :: View in Browser

The Big Plug

👉 I’ve got a new course! GEO 101 for Marketers.

👉 Just updated! The Unofficial LinkedIn Algorithm Guide, March 2026, now with new information straight from LinkedIn!

Content Authenticity Statement

95% of this week’s newsletter content was originated by me, the human. You’ll see output from Openwork and Minimax M2.7 in the opening section as demo output. Learn why this kind of disclosure is a good idea and might be required for anyone doing business in any capacity with the EU in the near future.

Watch This Newsletter On YouTube 📺

Click here for the video 📺 version of this newsletter on YouTube »

[](https://almosttimely.substack.com/action/disable_email?utm_source=almost-timely-newsletter&utm_medium=email&utm_campaign=almost-timely-2024- 12-28)

What’s On My Mind: Terraforming the AI Use Case Desert

In this week’s newsletter, let’s talk about something that truly baffles me: the AI use case desert. I read with interest a survey recently that said one of the top reasons for lack of AI adoption was lack of use cases.

Boy did that make me scratch my head. I’m in the opposite camp; there are SO many use cases for generative AI that there’s no way I can tackle more than a small fraction of them. Deciding which use cases make the most sense to tackle is the hardest part of any client engagement because there’s so many. Even putting together this newsletter every week is less a question of “what should I write about?” and more “which of the dozen major developments this week is worth demonstrating in some way?”

What’s behind this chasm? A few things - lack of understanding about what the technology is capable of today, lack of understanding about where AI ends and infrastructure begins, lack of knowledge about what data you have access to, and lack of imagination.

If these sound familiar, it’s because they’re part of literally every keynote talk and workshop I’ve delivered over the past 5 years. What sets apart successful folks from less successful folks when it comes to generative AI is critical thinking, creative thinking, and contextual thinking.

So let’s dig into how to terraform the use case desert into the use case garden - using AI, of course.

Part 1: The Sexy Block

The top mental block for a lot of folks - consultants especially - is that most use cases for AI are going to be boring. Consultants - and I count myself in this group - often need to show flashy and sexy because that’s what pays the bills. A senior leader sees “the art of the possible” with a big flashy show and wants to sign right away, but very often the real value is in the boring stuff.

This is compounded by the fact that the majority of AI use cases are optimization, not innovation. I define this as doing what you’ve always done, but bigger, better, faster, and cheaper - and most folks focus on the faster and cheaper. There’s a maxim in business strategy that you can’t cut your way to growth, and that holds true even in the age of AI. Yes, absolutely reduce the amount of time you spend on less valuable tasks, but if that’s all you’re doing, you’re more or less rearranging the deck chairs on the Titanic.

Value comes from optimization to clear up space and time, then innovation - doing something new, doing something out of your normal skill set personally and organizationally, something that adds value in a new and different way.

The often silly example I use is taking something traditionally boring, like an RFP response, and turning it into a country song. I, as a non-musical person, can generate reasonably good country music from the most unusual sources. And to be clear, this takes away no paying work from human musicians, because I never even thought to do this before. I’m not taking a task from a human and giving it to a machine.

But that’s the difference between optimization and innovation - doing what you’ve always done versus doing something you’ve never done before.

Part 2: The ROI Block

The second major block, typically from more conservative or even resistant folks, is the familiar refrain asking for the ROI of something like an AI initiative. I’ve gone on long rants about this in the past on two fronts - first, people using the phrase ROI instead of results, because ROI is fundamentally a financial calculation. Second, people asking to measure ROI on something they don’t measure today, so they have no basis of comparison. Both are silly.

Here’s the reality of ROI. Because it’s a financial formula - (earned - spent) / spent - it takes some time to understand and calculate what you spent and what you earned. And in a field like generative AI, where the technologies change literally every week, by the time you complete a project, there’s a good chance that the old project methods and ways are obsolete. That in turns makes an ROI measurement less valuable, because part of the purpose of measurement is to achieve repeatable, reliable results, and you can’t do that if everything is changing.

Imagine you started a project with the equivalent of a campfire and some sticks. A quarter of the way through the meal, someone invents metal and now you have a cooking pot and a fire. Halfway through the meal prep, someone invents natural gas capture and metal manufacturing, and now you have a gas cooktop and cooking pots and pans. Three quarters of the way through the project, someone invents rooftop solar and induction ranges. By the time you’re done with a standard 2-year enterprise project, literally everything is different when it comes to AI - and if you’ve been adapting, then what you started with can’t be used as a measure of where things are now.

Most of the time, when people are thinking about ROI, what they really mean is measuring change, and that’s a lot more immediate. It’s much easier to measure change. The same general formula applies - (new - old) / old - but instead of money, it can be any quantifiable unit. It can be time, leads generated, NPS numbers, anything where there’s new numbers and old numbers.

Especially for a fast-moving technology like AI, this is a better system of measurement. It’s not ROI, but it is reasonably objective and defensible - and because the cycles of feedback can be shorter than the entire lifecycle of something (which ROI requires), you can know the impact of AI faster.

Part 3: Decomposing the Job Description

A couple weeks ago, we talked about how to determine the level of exposure in your job from AI by decomposing a job description. The exact same process applies for use cases - and in fact, much of the prompt is reusable, including using the Trust Insights TRIPS Framework. But rather than judge how exposed our job is, we pivot it to focus on what the most useful use cases are for us.

The Trust Insights TRIPS Framework is:

Time: How much time does a task take? The more time a task takes, the better a candidate it could be for AI.
Repetitiveness: How repetitious is a task? The more repetitive a task is, the better it might be for AI to handle it.
Importance: How economically important is a task? The more valuable a task is economically, the more sense it makes to have AI do that task. Assuming the same quality of output but dramatically increased speed, AI can add to the bottom line faster for more economically important tasks.
Pain: How painful is a task? The more painful a task is, the better candidate it is for AI. AI adoption goes up as painful tasks go down.
Sufficient Data: How many examples do you have of successful task outcomes? The more success examples and data you can provide, the better AI will do at a task.

Here’s how. Taking your current job description or perhaps an aspirational job description as a plain text file, combine it with a great big honking prompt like this and the TRIPS Framework in the agentic AI tool of your choice (e.g. Claude Cowork, Claude Code, OpenCode, Openwork, Google Antigravity, OpenAI Codex, Qwen Code, Microsoft GitHub Co-Pilot):

You’re a career and HR consulting expert. Based on the job descriptions in {your text file name}, infer a list of the top 25-100 individual tasks a person in this job is most likely to perform, in descending order of frequency. We define a task as a singular work output. An example of a task is creating a weekly report. Part of your analysis should include 3-7 broad categories of tasks from the job descriptions; this field should be named task_category. Format your response as YAML array with the task name, task category, a concise one sentence description of the task, the percentage frequency that the task occurs weekly in that job, and five columns from the Trust Insights TRIPS framework, scoring the task on an integer score of 1-5 for each of the 5 dimensions, inferring scores based on the job description and your background knowledge. Create an averaged TRIPS overall score as well called trips_average, averaging the 5 component fields. Use your web search tools if you are not confident in your estimates. Return your result as a YAML array and write it to the output folder as trips.yaml. If there are more than 50 tasks, truncate at 50. Then using the existing YAML array, augment it with a 0-10 integer score estimate of how likely it is generative AI is capable of performing the task with an 80% success rate; this should include regular generative AI and agentic AI system (examples: Claude Code, OpenClaw, etc.; use your web search tools to obtain any additional needed background context). Name this field ai_likelihood. For context, the current top scores on generative AI evaluations as of 2026 are: METR 50%: 12 hours. METR 80%: 90 minutes. GDPval-AA (ELO-500/2000): 58%. Terminal-Bench Hard: 58%. Tau-2 Bench Telecom: 98%. AA-LCR: 74%. AA-Omniscience: 55%. Humanity’s Last Exam: 45%. GPQA Diamond: 94%. Scicode: 59%. IFBench: 83%. CritPT: 30%. MMMU Pro: 82%. Remote Labor Index: 4.17%. Generative AI includes text generation, image generation, audio and video generation, tool use, and agentic harnesses. Infer your score based on the task description and TRIPS scores, paying special attention to repetitiveness and sufficient data as significant indicators that a task can be successfully performed by AI. Use your web search tools if you are not confident in your estimates. Use a scratchpad to reason with, then conclude from your reasoning what the score would be. Amend the YAML array with your scores and update output/trips.yaml. Do not include the scratchpad in your scores. Finally, based on TRIPS average scores and success rates, create a scaled average of ai_likelihood and trips_average (trips_average will need to be rescaled 0-10 to match ai_likelihood) named ai_use_case_average, extract out the top 5 best candidate use cases for generative AI (including agentic AI) in descending order by ai_use_case_average. With these 5 candidates, create a new YAML output file called output/tasks.yaml that contains the task name, task category, task summary, percentage frequency, TRIPS scores, ai_likelihood, and a three part explanation of how to implement generative AI and AI agents for the task - strategy, tactics, and execution plan (why, what, and how). Never return tables; always return valid YAML.

Because this is a series of complex tasks, you’ll get the best results from agentic tools rather than regular chat tools. If you use regular chat tools (like ChatGPT), you’ll want to do this in individual pieces rather than all at once. If you’re using an agentic tool, be sure to put in Plan mode first.

What you should end up with is a detailed set of explanations about the conversion of those tasks into generative AI use cases. It’s balanced between necessity - the TRIPS score average - and capability, the ai_likelihood average. Once you’ve got the 5 use cases in a YAML file, you can then use your favorite AI agent to start building a more thorough implementation plan (such as with the 5P Framework by Trust Insights) to bring it to life.

Part 4: An Example Brought to Life

Let’s look at a practical example of this. Say you’re the food and beverage manager at a restaurant chain, and the restaurant company is running into tough times. Costs are up, diners are down, and economic pressures are all around. Your job is a tough one, trying to create great culinary experiences that are still profitable for the company.

The challenge you face is that there’s so much administrative work in the role that you don’t spend nearly enough time on the floor to see the customer experience up close. So how could we use this process? We start with the job description; I’ll use one I found on LinkedIn to decompose the process overall.

For today’s build, I’ll use OpenWork, an open source competitor to Claude Cowork, and Minimax M2.7 on the Plus token plan as the AI provider. You can, of course, use the system of your choice, but these two represent the best for the budget-conscious. Minimax’s plan is $200 per YEAR versus Claude’s $200 per MONTH, and OpenWork integrates seamlessly with it.

We put the giant honking prompt in to get all the research and documents. Then, as we often do with agentic systems, especially for research tasks, we walk away and let it do its thing. Once it comes back with the results, we can start figuring out implementation.

I recommend watching the video version of this newsletter to see this in action.

Here’s what Minimax M2.7 returned:

  - task_name: "Analyze revenue and cost performance metrics"
    task_category: "Financial Management"
    task_summary: "Review financial reports, identify trends, and recommend actions to improve profitability."
    weekly_frequency: 30
    trips_average: 4.0
    ai_likelihood: 8
    ai_use_case_average: 8.0
    implementation:
      strategy: |
        This task is highly repetitive (monthly/weekly cycles), data-rich (historical financial data),
        and economically critical (directly impacts profitability). The pain score is moderate because
        manual analysis is time-consuming. AI excels at pattern recognition across large datasets
        and can surface insights that humans might miss. Automating this frees the director to
        focus on strategic decisions rather than data compilation.
      tactics: |
        Deploy an agentic AI system with access to POS data, inventory systems, and financial reports.
        Configure the agent to generate weekly variance analyses with natural language explanations.
        Set up automated data pipelines from Delphi (property management), inventory systems, and
        accounting software. Use prompt engineering to ensure AI outputs are formatted for executive
        presentation. Implement human review checkpoints before distribution.
      execution: |
        1. Select a platform: Microsoft Copilot Agents, Claude Code with data tools, or a custom
           LangChain agent with hotel industry connectors.
        2. Build data connectors: API integrations with POS (Toast, Square), inventory (MarketMan),
           and accounting systems (QuickBooks, Sage).
        3. Create prompt templates: Standardized weekly/monthly P&L analysis, year-over-year comparisons,
           segment performance breakdowns (restaurants, bars, events).
        4. Establish review workflow: AI drafts reports, F&B Director reviews and approves,
           final report distributed to leadership.
        5. Train the model: Feed 12+ months of historical data for baseline patterns. Use
           few-shot learning for industry-specific terminology and brand voice.
        6. Start with advisory mode: AI suggests insights, human makes final recommendations.
           Gradually increase AI autonomy as confidence builds.

I had it synthesize all the data for a Korean fried chicken restaurant and follow this process, generating a report for a stakeholder, and it came up with a really solid output - a financial report with forecast.

You could take the tasks YAML file and have your AI agent convert it into a lovely deliverable, like a pitch deck to your boss to explain why they should invest in AI. You could also feed it directly to your agentic system of choice and instruct it to start coming up with implementation scripts. Really, the sky’s the limit there.

Part 5: Wrapping Up

This is how we terraform the use case desert. We don’t wait for someone to hand us use cases on a platter, because by the time someone does that, it’s probably too late. The purpose of this exercise is to get ahead of the machines and the managers who don’t understand what can and can’t be done by AI. We want to be the ones running the machines, instead of someone making shortsighted decisions about what they think AI should be able to do based on business magazines and LinkedIn hustle bro scam artists. Yes, those folks who take terrible advice will in all likelihood crater their companies, but we’re more interested in protecting and advancing our careers.

By using today’s most powerful agentic tools - and in the video version, you’ll see how easy Openwork is (it’s very similar to Claude Cowork at 1/12th the price for the AI part with Minimax), you’ll start to create use cases for AI that really matter, that are connected to outcomes you get paid for.

Shameless plug: want help terraforming your company’s specific AI use cases in more detail than just this newsletter? My company, Trust Insights, does exactly that. Click here to learn how.

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ICYMI: In Case You Missed It

Here’s content from the last week in case things fell through the cracks:

On The Tubes

Here’s what debuted on my YouTube channel this week:

Skill Up With Classes

These are just a few of the classes I have available over at the Trust Insights website that you can take.

Premium

Free

Advertisement: New GEO 101 Course

When I talk to folks like you, being recommended by AI is one of your top marketing concerns in 2026.

In this course, you’ll learn:

The three distinct phases of GEO and how they work
How to optimize for each phase (they’re different!)
How to measure your GEO efforts in a meaningful and valid way

This course is meant to be used. In addition to the course itself, you’ll also receive:

Your 90 day GEO action plan
How to set up Google Analytics for measuring GEO traffic
How to join Google Search Console data with GEO intent data
How to use our free AIView tool to improve your content and site for one of the three phases of GEO
A certificate of completion from TrustInsights.ai

And best of all, this is our most affordable course yet. GEO 101 for Marketers is USD 99 and is available today.

👉 Order your copy of Almost Timeless: 48 Foundation Principles of Generative AI today!

Get Back To Work!

Advertisement: My AI Book!

In this book, you will learn to:

Master the Machine: Finally understand why AI acts like a “brilliant but forgetful intern” and turn its quirks into your greatest strength.
Deploy the Playbook: Move from theory to practice with frameworks for driving real, measurable business value with AI.
Secure Your Human Advantage: Discover why your creativity, judgment, and ethics are more valuable than ever-and how to leverage them to win.

How to Stay in Touch

Let’s make sure we’re connected in the places it suits you best. Here’s where you can find different content:

My blog - daily videos, blog posts, and podcast episodes
My YouTube channel - daily videos, conference talks, and all things video
My company, Trust Insights - marketing analytics help
My podcast, Marketing over Coffee - weekly episodes of what’s worth noting in marketing
My second podcast, In-Ear Insights - the Trust Insights weekly podcast focused on data and analytics
On Bluesky - random personal stuff and chaos
On LinkedIn - daily videos and news
On Instagram - personal photos and travels
My free Slack discussion forum, Analytics for Marketers - open conversations about marketing and analytics

Listen to my theme song as a new single:

Advertisement: Ukraine 🇺🇦 Humanitarian Fund

👉 Donate today to the Ukraine Humanitarian Relief Fund »

Events I’ll Be At

Here are the public events where I’m speaking and attending. Say hi if you’re at an event also:

SSI, Charlotte, April 2026
The Trust Insights Generative AI Workshop, sometime this spring!
SMPS AI Conference, Austin, November 2026
MarketingProfs B2B Forum, Boston, November 2026

There are also private events that aren’t open to the public.

If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.

Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.

Required Disclosures

Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.

Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.

Thank You

Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.

Please share this newsletter with two other people.

See you next week,

Christopher S. Penn

Almost Timely News: 🗞️ The Five Levels of AI Enablement (2026-03-22)

Christopher S. Penn — Sun, 22 Mar 2026 10:31:05 GMT

Almost Timely News: 🗞️ The Five Levels of AI Enablement (2026-03-22) :: View in Browser

The Big Plug

👉 I’ve got a new course! GEO 101 for Marketers.

👉 Just updated! The Unofficial LinkedIn Algorithm Guide, March 2026, now with new information straight from LinkedIn!

Content Authenticity Statement

100% of this week’s newsletter content was originated by me, the human. It was recorded first, then transcribed and cleaned up by Claude Opus 4.6. Learn why this kind of disclosure is a good idea and might be required for anyone doing business in any capacity with the EU in the near future.

Watch This Newsletter On YouTube 📺

Click here for the video 📺 version of this newsletter on YouTube »

[](https://almosttimely.substack.com/action/disable_email?utm_source=almost-timely-newsletter&utm_medium=email&utm_campaign=almost-timely-2024- 12-28)

What’s On My Mind: The Five Levels of AI Enablement

What’s on my mind this week? The five levels of AI enablement. This is something that has been banging around in my head for a while, and I want to try and get it out of my head and into some kind of written form.

This all started a couple of years ago with my friend Brooke Sellas, who runs B Squared Media. Their thing was always done-for-you social media marketing; they now do customer experience. If you need an agency to help you with digital customer experience, they’re a terrific agency. But Brooke’s thing was always done for you. And I was like, well, what else is there?

The other was done by you - you do the work. And so I was thinking to myself, well, if done by you and done for you exist, that seems like the equivalent of going out to eat at a restaurant or staying home and making something from scratch. So there’s got to be a middle ground. And the middle ground, of course, is done with you.

That’s where you have - in food-analogy land - things like HelloFresh and Blue Apron and those meal delivery kits. The box arrives at your door, filled with refrigerated cooler packs and things. You unpack it, everything’s already pre-cut and pre-measured. You still have to cook it, but the process of preparation is largely done.

For a while, that’s been product-market fit: done by you, done with you, done for you. Done by you, you do all the work. Done with you, you do some of the work. Done for you, you do none of the work.

Part of product-market fit - and I’ve advised this to people for years - is that you have to have offerings at every level. Done by you is the lowest cost. You read a book like my book Almost Timeless. It’s a very low investment - I think it’s still $29 - but you have to do all the work. Done with you are things like apps, add-ons, and courses where a lot more has been prepared for you. And then done for you is you hire Trust Insights to go do the thing for you.

Same with food: done by you, you cook from scratch - lowest cost, most labor. Meal kit, less labor, more cost. Done for you, no labor, highest cost.

And so I started to apply this to AI.

Part 1: Product Market Fit and AI

If we think about it, AI has kind of gone through these stages. In the beginning - 2023, right? ChatGPT came out in the fall of 2022, became popular in 2023 - when you’re sitting in front of ChatGPT prompting, that is the lowest possible level.

In the five levels of AI enablement, that’s done by you. You sit down with ChatGPT and you’re like, hey, ChatGPT, do this thing, write me this blog post. Traditional, classic. Everybody has done that in some capacity. That’s the lowest level of this product-market fit framework.

The second level up is things like GPTs, or Gems, or Projects. This came about in 2023 and 2024. GPTs and Gems are like mini apps inside of these tools that allow you to have some of the process baked in. I have a recipe maker Gem in Gemini that has all the rules about what I think is important in recipes, and it works great. I still have to do a fair amount of the work, but it has all the logic and all the prompts pre-built in it. I can just say, hey, I need a recipe for a whole wheat pancake mix, and it goes off and does that thing and hands it back to me. I still have to go make the pancakes. That’s the second level.

The third level is where we get into agentic AI and the done-for-you model - and this is where we are starting to evolve.

Here’s the full framework, absolutely, positively copyrighted to Trust Insights every way we can:

Done by you. Done with you. Done for you. Done without you. Done anticipating you. Five levels. Let me walk through each one.

Part 2: Mapping AI to Product Market Fit

Level 1: Done By You

This is pure level one. You chat with Claude or Gemini or ChatGPT, you’re doing all the work, lowest cost - $20 a month. You have access to the tools. This is what everybody knows.

Here’s the problem with this level. It saves you time and gives you some capabilities, for sure - not saying it doesn’t. But when you hear people say, “I haven’t seen the ROI of AI,” it’s because they’re stuck at this level. Everybody’s got ChatGPT and they’re like, I don’t know what to use this for.

This is all foundational. Learning how to prompt, learning how to talk to the systems, learning their ins and outs and their weaknesses, learning how to provide good data - because the more data you give, the less they hallucinate. This is level one. This is the base, this is the foundation. You have to learn this stuff first.

But you can’t stay here. This is not the zone of productivity. About 75 to 80 percent - maybe more - of people are at level one.

Level 2: Done With You

GPTs, Gems, Projects. You’re doing about half the work; the tool is doing a little more than half. But if you’re good at building Gems and GPTs - especially distributing them inside your company - you start to see more standardization. If you have a sales playbook Gem or a sales email GPT distributed among your team, everybody’s on the same page, literally.

There’s still a fair amount of work: copy this record in from the CRM, do this, spit out the thing, and then we can make a good pitch. But this is sensible. This is an evolution. This is not reinventing the wheel every single time, which a lot of people do. A lot of people are still copying and pasting, and you want to have some things pre-baked.

About 10 percent of people are at level two.

Level 3: Done For You

Level three is where we start to talk about agentic AI. These are tools like Claude Code, Claude Cowork - which is everybody’s darling right now, soon to be Microsoft Co-Pilot Cowork as well, because Microsoft just licensed it - and OpenAI Codex, Google Antigravity. I did a demo the other day for a client using Codex, having Codex take control of their browser and walk them through an audit.

These are tools where you’re not prompting individually anymore. You’re now handing off a requirements document, a spec, a project plan - something that follows, for example, the 5P Framework by Trust Insights: purpose, people, process, platform, performance. It’s a fully written-out plan that you hand, like a project manager to a team member, and they just go off and do it.

My CEO and co-founder Katie has been using Claude Cowork a ton - saying, here’s this page, and here’s our ideal customer, rewrite this web page on our website. And because she gives it access to Chrome, Claude’s not saying anymore, “Oh, be my copy-paste monkey, Katie, and copy and paste this here and here.” It just takes control of the browser, just does it for her, which is fantastic to watch.

This is where you get away from being the copy-paste monkey and into execution. You say to a system like Cowork: here’s a transcript of a YouTube video I just did, turn it into a slide deck. And it reasons through it - particularly if you give it standards as part of a good project plan - and it can go off and do those things.

There is a lot of value to be extracted here. A lot of time savings, a lot of innovation that can be done.

For example, I have a massive sales playbook as part of my work at Trust Insights. It’s like a 300-page sales playbook of everything we need to know to sell Trust Insights products and services - ideal customer profiles, sales tactics, objection handling, all the works. I can literally just drop that into a system like Claude Cowork with a landing page and say: rearrange this landing page to follow the best practices in this.

I was also doing this the other day for a client. I had 230 academic papers on educational design - on how to create great educational materials for students - and I told Claude Code: here’s the spec derived from these academic papers. Here are the background papers. Write me a sixth-grade math textbook, start to finish. Write me the textbook, write me the workbook, write me the teacher’s guide - and the teacher’s guide has to have lesson plans and all this stuff.

And sure enough, 92 minutes later, I have a full textbook for sixth-grade math that fits Common Core. I have the accompanying workbook and a teacher’s guide with lesson plans and everything. I did not have to keep prompting it. No “now let’s do page six, now let’s do page seven.” It was entirely self-contained. It went off, it did the thing, and it came back.

This is where about five percent of people are now, but those five percent are seeing crazy value.

If I had to say where you need to be in March of 2026, you need to be at level three today. You have to do what you can to get here - to get to using these tools and get away from level one. Get away from “I’m chatting with ChatGPT, I’m the copy-paste monkey.” We have got to get away from that and get to level three.

Part 3: Introducing Level 4

What occurred to me as we’re talking about these three levels of product-market fit is that there are two more levels - two more levels on top of this.

Done by you, done with you, done for you. What’s next? Done without you.

This is where just the nerds are hanging out right now. The most prominent tool people have heard of is called OpenClaw, which used to be called Multbot, and before that ClaudeBot. These are fully - mostly - autonomous systems. This is a Docker container or a Mac Mini or a Linux box that you run a full AI agent on. You connect it to everything that’s valuable that you want to connect to, and then you just pick up your phone and text it, or message it on Telegram or Discord or Slack, and you give it high-level objectives, and it just kind of does stuff without you.

Andrej Karpathy created a system called auto research. He said: hey, I want you to go out and actually do this research, do a meta-analysis, do a synthesis, and then write me a new paper. It’s like a two-paragraph thing - basically the intent and the success measures - and then the system goes, sets up its own agents, sets up its own processes, sets up its own execution plan, does it, comes back and says, here’s the thing that you asked for.

That’s done without you.

Because with something like Claude Cowork, there’s still a human in the loop. There’s still the ability for a person to say, oh wait, you need these logos. Once you get to level four and you’re using a system like OpenClaw, you’re not in the loop anymore. You’ve left the room - you’ve given it the initial stuff and you’re gone. That’s level four.

About one percent of AI folks are at this level, but you can see where there is enormous value to be had here. The more autonomous a system is, the more value it can create on its own. You might say: hey, I’ve got this idea for a video game, I want it to work like this, it needs to accept payments, it needs to be on the web, it’s got to have a mobile app. You give it the requirements and say, go do it, and don’t come back until you’ve successfully met my objectives. And it will continue to try. It may take some time, it may take a few days of heavy crunching, and you’ll probably get a decently sized bill from Anthropic or Google. But the systems start to build the thing.

This is also where something important is happening inside the AI industry itself. The company Minimax just released the Minimax M2.7 model earlier this week - six weeks after M2.5. And when you look at how they shipped a better model in six weeks, they said Minimax M2.7: 40% of it was written by itself. So it wrote itself. Their goal is to get that to 50, 60, 70, 80, 90, 100 percent, so that the tools evolve to doing the work for them.

Anything you can think of where you would want to deploy a team - if you would hire an agency to do it - this is the replacement for that. Give that some thought. If you would hire an agency to do it, and you now have these fully autonomous AI systems, what does that mean for agencies?

If you would hire an SEO agency in the past, and you now have something like NemoClaw - which is NVIDIA’s OpenClaw implementation - you say: here’s my website, here’s my Google Search Console data, here’s what these different measures mean. In a sandbox, not on production please, optimize the heck out of my website until it hits these success metrics. It might do that.

Or you might say: we are pivoting from solution selling to insight selling. Here is all my sales material. Update it, and just tell me when it’s done. And it does it. As long as you provide the right data and success measures that a machine can optimize against, it’s going to do it. Your effort on this is minimal, and you’re going to create enormous economic value.

This will also imperil a lot of businesses - particularly anything that’s a subcontractor, anything that’s a contractor, anything that’s an agency. This type of technology poses a mortal peril to your business because as the models get smarter and the harnesses get better, they are more capable.

You might say: go write me an NDA that complies with federal law in the USA plus state law in Massachusetts. Build me the NDA and an explanation of what choices you made and how it complies with the law. And it will go do it. In the same way that I had Claude Code build me a textbook - where I did the deep research and provided the academic papers - at level four, that research step would be part of the process too. I would say: you’re going to do the research, you’re going to pull all the academic papers that are credible on this for the last 15 years. Then once you’ve done the research, you’re going to build the textbook, the student workbook, and the lesson plans. Off you go. And two days later it may be done. Done without you.

Part 4: Introducing Level 5

However, there’s a fifth level. The fifth level is done anticipating you.

This doesn’t exist yet, but the component technologies to make it happen do exist. So this is near term. I am placing a bet that a system will be available before end of year 2026 that implements this.

What this is is a combination of today’s agentic harnesses plus persistent memory. Persistent memory is the big unlock for AI right now. Every time you start Claude Code, Claude Cowork, Codex, whatever, it has no memory of what you’ve done previously. And that’s not a bad thing necessarily, but it is sometimes a pain if you’re trying to get stuff done. Like, hey, let’s update this website. And you’re like, oh, I have to invoke the skill again, I have to tell it what the website is again.

Wouldn’t it be nice to say, hey, remember that web page that we optimized last week? Why don’t you remember what we did last week and do the exact same thing? In a persistent memory system, the AI agent would know that, would be able to pull that up. But more importantly, because it’s an always-on system, it could also say: hey, last week you had me updating these web pages. Why don’t I just do the others for you? You left off here - why don’t I just do this for you?

Parents will understand this. It’s like your kid coming to you and saying, hey, look at this thing I made. You didn’t ask for it, but it’s nice. You liked it, right? That’s essentially what this level of AI looks like.

An AI system that has memory embedded in it will look at the past and say: hey, Chris, it looks like you’re building sales playbooks. Why don’t I build you a new one based on this new finding that just came out? Because I’m also paying attention to news feeds and data feeds, and I saw this thing - I can recommend it.

Or: hey, Chris, it looks like last year at this time you rebalanced your retirement portfolio. I just got 22 different things in my news feed from world events. Would you like me to rebalance your portfolio like this? Or if I had given it permission in advance, it might just do it.

This is AI that identifies needs before you do, knows what you want, and delivers it.

The underpinnings - the component technologies - to make this work exist today. There are systems like Open Viking from ByteDance, which is a persistent memory system. Serena MCP, which is used a lot by coders, is a persistent memory system. There are so many of these systems that exist, but they’re not fully baked into the autonomous frameworks in a way that is predictive yet. It is a very short leap of the imagination to say we get to this based on the technologies that exist right now.

When level five systems become generally available, the gulf between AI-fluent people and AI laggards is going to be unimaginably large.

Today, a business that is not AI-enabled can compete to some degree still with a business that is, because review processes can add speed bumps for AI-enabled businesses. You can create 10,000 pieces of content, but somebody’s still got to review it. Those speed bumps are getting smaller and smaller and going away as agentic tools are capable of doing more and more review on their own. When level five systems come out, the ability for a company to embrace it and use it - whoever gets there first in their field or niche - is going to eat the rest of the field.

And this is not a budget thing. This is not the well-heeled company that will do this, because there are so many great models out there like Minimax M2.7 that are dirt cheap. Nemotron from NVIDIA, Mistral Small 4 from Mistral - pennies on the dollar compared to what you pay for Claude or Gemini or whatever, and they’re built for agents. It is not a budget thing that will determine the winners or losers here. It is an initiative thing, it is a risk-taking thing.

Part 5: What Should We Do?

Indigo in Analytics for Marketers asked a very sensible question: “How do we share this with our teams and not scare them to death?”

You have to be honest. You have to be honest with people and say: this is where the technology is, this is where the technology is going. More and more is possible to be automated.

One of the things I’ve said in the past is you don’t have to give up the things you love doing. For sure, give up the things you hate doing. Hey, autonomous system, please do my expense reports for me. I never want to see them again. That’s like utopia.

But even for the things you love doing, you have to think about what it means when the machines can do it better than you - whether it’s writing or strategy or whatever. You have to have that honest conversation with yourself: what is unique about your perspective? Because the tools, the models, all this stuff have the knowledge necessary to operate at PhD levels for everything.

What is your secret sauce - your secret herbs and spices - that makes your perspective different and valuable? Because if you are a level-zero company and you leapfrog to level three, you’re starting off with a fairly generic data set. It’s not customized. Whereas if you’ve been in the field a while and you know what makes you special as a human being, you can inject that into here.

By the way - this whole deck was created by Claude. I had a LinkedIn post and Claude turned it into - ta-da - a slide deck. I actually didn’t ask for a slide deck. It just decided I needed one. And you know what? Yeah, I kind of did. Think about it - it’s kind of almost level five.

But you have to figure out what is your secret sauce that you bring that is uniquely human and that machines are going to have a hard time replicating. Things like your voice, your personality. How do you build that into the machines so that you can enjoy the increase in speed and quality, and the reduction in cost, that the machines deliver?

First is to figure out your secret sauce - what is uniquely yours that the machines can amplify but not replace.

Second is to think about the value chain. This is something that very few people think about, and I wish more would. The value chain goes from commodity to brand to service to experience to transformation.

If you make industrial ball bearings, that’s a commodity product. If you put the ball bearings of five different companies in front of me that were all the same size, I will be like, I can’t tell the difference between any of these - I’ll buy the cheapest one. You’ve got to figure out how to transform your commodity into a brand that people are willing to pay a premium for. People will pay a premium for an Apple device. People will pay a premium to hire Christopher Penn as a speaker. I hope. How do you level up?

And then from there, if you’re in product, how do you move that to a service? You can get an IKEA cabinet - it’s a flat-pack, a pile of parts. You can hire a service to put this together for you. You can hire TaskRabbit or whatever. So IKEA the product becomes IKEA the product wrapped inside the TaskRabbit service, with the outcome being a cabinet.

Above service is experience, where the service is so good that it becomes an experience unto itself. Disney theme parks are a great example. An amusement park is a service. A Disney park is an experience. If you can afford it, it is literally magical. You get to the airport, you check in, your bags get tagged with special Disney bag tags, they vanish and they magically reappear at your hotel room. You don’t have to worry about anything. You get on the special bus, you get the special wristband, you show up and things just happen in a completely frictionless way. It’s like being in a place where everything you could possibly want is taken care of.

And then on top of that is transformation, where an experience you have is so magical that it transforms who you are. It changes you as a human being. You can go to a music concert - which is an experience, especially if it’s a musician you like - and the biggest shows, like a Taylor Swift concert, you have this transformation into something else. You go to your favorite motivational speaker and you walk out a different person. That’s the value chain.

Everybody right now in the knowledge workspace is largely still producing commodities. Here’s an NDA. Is this NDA any better than the one I get from this other lawyer over here? It shouldn’t be, because there are all kinds of legal requirements. So what does a lawyer do to up-level the commodity into a service, into a branded product, into an experience, into a transformation? It may not be possible to get all the way there from NDA services, but you can certainly get to a high level of service.

If you are a knowledge worker doing things like creating strategy or content or email marketing or music or art or photography, your output today is a commodity. At the very least, you have to figure out how to become a brand so that people ask for your work specifically because they like your perspective on it. Because AI can very credibly create very good generic commodity content. Go to a system like Suno - it creates really good commodity music, perfectly good enough for elevator music or background music on a YouTube video.

What can you do to raise the value chain?

And by the way, agentic AI is really good at helping with this, because you give it a value chain framework, give it your product and service, and say: how do I elevate all of this into something better? Give it your ideal customer profiles and say: re-engineer my business for me, up-leveling it to a service or an experience.

The technology is going to keep expanding to take away more of the stuff at the bottom. All the commodity stuff has effectively been eaten - it’s just that people have not caught on to it yet. Some of the branded stuff is going to get eaten. Some of the service stuff is going to get eaten as agents become more and more autonomous. That leaves you with experience and transformation, because right now the tools don’t have those abilities and are probably not going to anytime real soon.

How do you create an experience? How do you create a transformation in somebody working with you - so that even if there’s a gazillion and a half copycats who all have their own OpenClaw servers generating substantially similar commodity outputs - the way that you work with those outputs to create that experience for somebody is what’s going to set you apart.

It already is massive, but it’s going to be even more massive, because the tools are getting to a level of capability that goes from - I always used to say, treat it like the world’s smartest, most forgetful intern - and once we get persistent memory, it’s not forgetful anymore. And already the models in 2025 became PhD level in almost every field. Today they’re above that by a considerable amount. So it is the world’s smartest, most capable agency in anything.

You want a drop-in replacement for McKinsey? You can build it. What would be a six-to-nine-million-dollar project you can do in six to nine hours for six to nine dollars of API calls... that’s a lot of sixty-nines.

The companies who are willing to invest heavily in people and innovation are going to eat alive everybody else in that field, and the window for taking advantage of this is going to be very short.

Also: if you are at level one today, as tools like Claude Cowork evolve, you can kind of skip to level three or even to level four - and you don’t have to have gone through level two, because something like Claude Cowork or OpenAI Codex just bypasses those GPTs and Gems. They’re obsolete, right? To be clear, they’re still useful to a lot of people. But from a technological perspective, they are obsolete now.

If you are at level one, you can leapfrog to level three, perhaps level four, depending on the use case. If you are not at level one at all - if you are resistant, or your company is resistant, or your organization or your leaders are resistant - everybody who is in the field and willing to invest and take some risks is going to jump to level four without you. And the gulf between zero and four is massive. The value created is massive. And your ability to catch up will be harder and harder because autonomous systems, by nature, can self-improve.

A company that’s early - that says I am all in on NemoClaw - is going to have systems that self-improve and be better at contracts, better at sales, better at finance and reconciliation, better at all those things. And those people who get these systems turned on sooner will have more training data, which means they will be able to adapt faster to changing conditions, be more agile. And the laggards who are at level zero never catch up.

Because even if you said, all right, fine, we’re going to buy NemoClaw, we’re going to drop a server and we’re ready to go - this other company that’s been at level four for three months, six months, twelve months has got all the data. They’ve got all the training data, they’ve got all the examples, they’ve got the scars to get the stars. They’re able to run those systems well and work out all the gotchas. And while the level-zero company is working out the gotchas, the level-four company is probably moving into level five.

I believe that level five is where the systems are going, and they’re going soon. We’re not talking three years from now. We’re talking end of this calendar year.

Part 6: Wrapping Up

The logical thing to say now is: how do I get started?

Trust Insights has tons of courses and things like that, and we’re going to have more. I’m going to be doing one on Agentic 101 relatively soon, because we need to get this content out there so that people have it and can catch up if they are still at level one or level two. You’ve got to get to a minimum of level three.

Katie is going to be doing some stuff as well, because she has been enormously successful with that level-three work as a non-technical person, and her perspective is so valuable. Because a lot of people say, yeah, Chris, that’s cool that you did that, but that’s you. Katie’s not a techie. Katie’s not going to fire up Python 3.12 anytime soon. Katie’s not going to be doing Docker containers and all that stuff. That’s not going to happen. And yet she’s still getting tremendous value out of agentic AI. So more to come on that.

But that’s where my head is at this week: this evolution - done by you, done with you, done for you, done without you, done anticipating you - is where AI is going, and we need to be ready.

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Glossary of Technical Terms

The Five Levels of AI Enablement

Agentic AI - A category of AI systems designed to take sequences of autonomous actions to complete complex, multi-step tasks. Rather than responding to a single prompt, agentic AI can plan, use tools, browse the web, write code, and self-correct in pursuit of a goal.

AI agents - Individual AI-powered processes or modules that can act autonomously to complete specific tasks. Multiple agents can be chained or coordinated by an agentic system (like OpenClaw) to tackle larger objectives.

Autonomous systems - AI systems that operate without continuous human supervision or input. At Level 4, these systems receive a high-level goal, break it down into sub-tasks, execute them, and return results - all without a human in the loop.

Context window - The amount of text (or data) an AI model can “see” and process at one time in a single session. Think of it as the model’s working memory for a given conversation. Larger context windows allow the AI to process more documents, instructions, and history at once, but once the session ends, that information is gone unless there’s persistent memory.

Persistent memory - An AI system’s ability to remember information from past sessions and interactions, carrying that knowledge forward into future sessions. Without persistent memory, every new conversation starts from scratch. Persistent memory is the key enabling technology for Level 5 anticipatory AI.

GPTs - Custom AI assistants built on OpenAI’s ChatGPT platform that allow users or organizations to pre-bake instructions, personas, knowledge, and capabilities. A GPT can serve as a standardized tool distributed across a team.

Gems (Google AI) - Custom AI assistants built within Google’s Gemini platform. Functionally similar to OpenAI’s GPTs - you can embed instructions, style guides, and specific rules into a Gem so users don’t have to re-enter them every time.

Claude Projects - Anthropic’s equivalent feature in Claude.ai - persistent project contexts with uploaded files and custom instructions.

OpenClaw / NemoClaw (formerly ClaudeBot, formerly Multbot) - OpenClaw (formerly known as Multbot, and before that ClaudeBot) is an open-source framework for running fully autonomous AI agents on local hardware such as a Docker container, Mac Mini, or Linux server. NemoClaw is NVIDIA’s implementation of the OpenClaw framework, optimized for running on NVIDIA hardware.

Docker container - A self-contained, isolated computing environment that runs a software application and all its dependencies in a standardized package. In the context of autonomous AI agents, a Docker container provides a safe, controlled environment where an AI agent can run, browse, write code, and execute tasks without affecting the host system directly.

Sandbox - A safe, isolated environment for testing and running AI tasks without affecting live or production systems. When working with autonomous agents, running experiments in a sandbox first prevents unintended changes to real websites, databases, or files.

Human-in-the-loop - A system design where a human reviews, approves, or can intervene in AI decisions at key points during a process. Level 3 tools like Claude Cowork still have a human in the loop. Level 4 autonomous systems remove the human from the loop entirely.

Product-market fit - The degree to which a product or service satisfies a real and significant market demand. In this newsletter, product-market fit is applied to AI adoption: done by you, done with you, and done for you represent three tiers of offering at different price and effort points that a business should have available.

Value chain - A progression of value levels from lowest to highest: commodity → brand → service → experience → transformation. AI is rapidly commoditizing knowledge work output, which means individuals and businesses need to deliberately move up the value chain to remain competitive and charge a premium.

5P Framework - Trust Insights’ strategic planning framework built around five dimensions: Purpose, People, Process, Platform, and Performance. When providing instructions to an agentic AI system, using a structured document that addresses all five Ps gives the system enough context to execute effectively.

Ideal Customer Profile (ICP) - A detailed description of the type of organization or individual who would get the most value from your product or service, and who is most likely to buy. When feeding information to autonomous AI agents, including your ICP helps the system align its outputs with your actual market.

Hallucination (AI) - When an AI model generates information that sounds plausible but is factually incorrect or entirely made up. Hallucination is more common when the AI lacks sufficient context or data. Providing richer input data reduces hallucination rates.

ROI - Return on Investment. In the context of AI, ROI is the measurable value generated by AI tools relative to the time, money, and effort invested in using them. People who complain they haven’t seen the ROI of AI are typically stuck at Level 1, where the efficiency gains are modest compared to higher levels.

Almost Timely News: 🗞️ How To Measure Whether AI Will Take Your Job (2026-03-15)

Christopher S. Penn — Sun, 15 Mar 2026 10:31:08 GMT

Almost Timely News: 🗞️ How To Measure Whether AI Will Take Your Job (2026-03-15) :: View in Browser

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What’s On My Mind: How To Measure Whether AI Will Take Your Job

In this week’s newsletter, let’s talk About Anthropic’s recent workplace AI study that many folks, including myself, shared. In it, they showed the theoretical maximum impact of AI on professions and then the current use based on Claude aggregated usage.

I thought the study overall was directionally helpful, to understand broadly what industries are already seeing impact and what the probable highest impact areas are, but these are at the macro level. That’s understandable both from a privacy protection angle as well as having enough data.

But that got me thinking - what if we could drill down further, to understand our personal exposure to AI in a more focused way? What if we could see the theoretical maximum impact for our specific careers or even our current jobs?

One of the biggest questions everyone has at every event I speak at is w”hen is AI going to take over my job?” People want to know about AI’s impact to them personally. They want to know “how exposed am I or my kids or my friends to AI and employment risks”.

How could we go about doing this? Unsurprisingly, we use AI for it. Let’s explore how.

Part 1: Identifying Tasks

At Trust Insights we spend a heck of a lot of time on frameworks to help contextualize AI. One of ours, the TRIPS framework, looks at five factors to evaluate any task:

Time: How much time does a task take? The more time a task takes, the better a candidate it could be for AI.
Repetitiveness: How repetitious is a task? The more repetitive a task is, the better it might be for AI to handle it.
Importance: How economically important is a task? The more valuable a task is economically, the more sense it makes to have AI do that task. Assuming the same quality of output but dramatically increased speed, AI can add to the bottom line faster for more economically important tasks.
Pain: How painful is a task? The more painful a task is, the better candidate it is for AI. AI adoption goes up as painful tasks go down.
Sufficient Data: How many examples do you have of successful task outcomes? The more success examples and data you can provide, the better AI will do at a task.

The TRIPS framework is often something that we use when we’re auditing companies for AI readiness, but it’s also something that AI itself is quite good at evaluating.

Our first step is to understand the most common tasks for any given position. The easiest way to do this is to grab a small collection of job descriptions. Head over to your favorite job board and put in your job title or position - your current one, and if you’re thinking about making a change, an aspirational job title. Copy and paste 3-5 job descriptions for that position so you get a sense of what’s involved in a job, then put that all in a text document.

Once you’ve done that, ask your favorite AI of choice to make a granular list of tasks that comprise the job. Here’s a starting prompt.

Based on the job descriptions in {your text file name}, infer a list of the top 25-100 individual tasks a person in this job is most likely to perform, in descending order of frequency. We define a task as a singular work output. An example of a task is creating a weekly report. Part of your analysis should include 3-7 broad categories of tasks from the job descriptions; this field should be named task_category. Format your response as YAML array with the task name, task category, a concise one sentence description of the task, the percentage frequency that the task occurs weekly in that job, and five columns from the Trust Insights TRIPS framework, scoring the task on an integer score of 1-5 for each of the 5 dimensions, inferring scores based on the job description and your background knowledge. Use your web search tools if you are not confident in your estimates. Return your result as a YAML array. If there are more than 100 tasks, truncate at 100. Ask me questions until you have enough information to complete the task.

Copy and paste the TRIPS framework after. What you’ll end up with is a pile of YAML, a data format that AI is excellent at reading. This is likely to be quite extensive, and that’s good. The more granular and specific we can be, the better.

Part 2: Identifying Theoretical Maxima

Next, we need to ask AI to amend the YAML array with some scoring based on how likely it is any individual task can be automated by AI. We’ll add that as a field into the YAML array.

As a sidebar, one of the reasons we want to do this in YAML is that YAML is a single column linear data format that AI is not only good at reading, but can be broken up into chunks if necessary without losing data. Large Language Models (LLMs) have a much easier time breaking up a YAML array into chunks and processing them individually or in small batches than they do, say, a spreadsheet.

Why? Because the descriptive data is never lost; in a spreadsheet, the descriptive data is usually the first row, so if you truncate a table after row 100, row 101 doesn’t have the headers and so you have less of an idea about what a row is about in the next batch of data. YAML, JSON, XML, and similar formats all keep the descriptive data with the data itself in every row of data.

With that in mind, let’s now add to our tasks file. We need to be specific to help an LLM consider the task and the likelihood a machine can successfully complete that task with today’s frontier models, so here’s a fairly dense starting prompt.

Next, using the existing YAML array, let’s augment it with a 0-10 integer score estimate of how likely it is generative AI is capable of performing the task with an 80% success rate. Name this field ai_likelihood. For context, the current top scores on generative AI evaluations as of 2026 are: METR 50%: 12 hours. METR 80%: 90 minutes. GDPval-AA (ELO-500/2000): 58%. Terminal-Bench Hard: 58%. Tau-2 Bench Telecom: 98%. AA-LCR: 74%. AA-Omniscience: 55%. Humanity’s Last Exam: 45%. GPQA Diamond: 94%. Scicode: 59%. IFBench: 83%. CritPT: 30%. MMMU Pro: 82%. Remote Labor Index: 4.17%. Generative AI includes text generation, image generation, audio and video generation, tool use, and agentic harnesses. Infer your score based on the task description and TRIPS scores, paying special attention to repetitiveness and sufficient data as significant indicators that a task can be successfully performed by AI. Use your web search tools if you are not confident in your estimates. Use a scratchpad to reason with, then conclude from your reasoning what the score would be. Amend the YAML array with your scores. Do not include the scratchpad in your scores. Ask me questions until you have enough information to complete the task.

What we end up with is a large YAML array of our tasks that make up our job, the TRIPS scores for how good a candidate task is for AI, and the AI’s judgement based on today’s benchmarks of how likely it is AI can succeed at that task.

Part 3: Assessing Our Job Risk

A job is a collection of tasks. The more tasks that AI consumes, the more likely it is we will feel AI’s impact in our employment, not because AI can do the entire job, but because AI can eat away at tasks until a person’s job is mostly managing AI. And when you’re managing AI, if you have 50 people who did a job (like a content writer, for example), and AI consumes 80% of the tasks, you now only need 10 people managing AI systems to match or exceed the productivity and quality of the original group of 50 people.

That is how AI costs jobs. Not by taking your job entirely, but by reducing the number of people in aggregate that are needed to do a particular job because AI does many of the tasks within that job.

Based on the large amount of data we now have in our YAML file, we can now direct AI to help us do the analysis. In the same way that Anthropic did their radar chart (which looks cool, even if it’s not the easiest way to read that data), we can have our AI digest the YAML file and turn it into a visualization.

Use this as a starting prompt:

Using the complete YAML array, let’s perform some data analysis and create an AI job exposure dashboard. From the YAML file, using simple-statistics JS, our dashboard should compute the median ai_likelihood for our job overall and for each of the categories identified by the task_category field. You’ll output the analysis in HTML, CSS, JS, CDNJS with libraries like chart.js or d3.js via CDNJS. Create an overall chart of AI likelihood for our job along with interquartile ranges. Then create a blue radar chart in descending order by category of the median and IQRs for ai_likelihood. Hovering the mouse over each slice of the radar chart should show the data label and data point value. Ask me questions until you have enough information to complete the task.

We now have a complete assessment of our exposure to AI, of what AI is likely to do to our current job and possibly our career.

So, do we just throw our hands up or open a bottle of Scotch? Not at all. Now we dig in.

Part 4: Skating Ahead of the Puck

Walter Gretzky, the father of hockey legend Wayne Gretzky, once told his child, “Skate to where the puck is going, not to where it is”. (which, by the way, both father and son say is the advice you give to little kids and is generally not great hockey strategy for adults)

In the general spirit of the quote, you have a map of where your current position is exposed to AI. If, overall, your position is substantially exposed, meaning that 50% or more of your tasks are things AI could claim, then you should start thinking about reinventing yourself and your career. There are two generally accepted ways to do this.

First, you pursue being an early adopter of AI so that you automate away most of your tasks and you become the AI expert in your company at those tasks. The institutional knowledge you encode in the successful operation of AI is what gives you some defense - essentially becoming one of the people that can’t be laid off as easily because you know how to operate the machines.

Second, you pursue angles and aspects that are less exposed to AI. Of the task list, which things are economically important (the I in trips) but aren’t tasks that AI is capable of doing well today? In the short term, those are the things you lean into.

Both strategies have flaws, namely that AI capabilities are accelerating at an exponential rate and something AI can’t do well today is something it’s likely to do well at tomorrow. Where you have some degree of safety is the speed of adoption, and that leads us back to Anthropic’s chart.

For industries that have a low overall theoretical maximum exposure to AI, the second strategy of leaning into the tasks that aren’t ones AI does well is sensible.

For industries that have a high overall theoretical maximum exposure to AI but do not have a current large maximum, such as architecture and engineering, then the first strategy is more sensible.

And if you’re in an industry where the theoretical and practical maximums are high? For example, if you’re a software developer? That profession is being devoured by machines. While there are growing pains in the adoption of AI for coding, the reality is that, as Anthropic’s Boris Cherny said recently, coding is largely a solved problem now. Human beings should not be manually typing out code any more.

And that means it might be time to exit that industry OR pivot your career so that you’re using the outcomes of software development but not focused on deriving economic value from the process of software development any more.

This should sound familiar, as I talked about it not too long ago - the value chain and working your way up it.

Commodity > Branded Product/Goods > Service > Experience > Transformation

Knowledge work outputs are almost free with AI. Suno can create music. Claude can write books. Codex et. al. can code. Nano Banana can paint, draw, photograph, etc. and as long as someone competent is directing the tools or the tools are harnessed properly to do it themselves, which they can do now, these knowledge products are commodities.

“Screw you, Chris, I’m not a commodity and that’s insulting!” You’re not a commodity, but your basic output is IF you don’t work your way up the value chain. Remember Porter’s 5 Forces? If the customer doesn’t have a strong preference and just wants an escapist book, AND they don’t understand your value prop, then an AI-generated trashy romance novel is just as good as a human one.

Which means you have to evolve up the value chain. A book is a book UNLESS you’ve built such a strong brand that people want YOU by name, and then your book becomes a movie, a TV series, and ultimately something like a theme park attraction at the highest level of evolution.

Musicians understand this well. Music is a commodity by itself UNTIL someone dials into your brand. And the best musicians convert a branded product into a service - like live concerts. Those concerts become epics unto themselves, true experiences (ask any Swiftie), and the best experiences are personally transformative that people pay hilarious amounts of money for.

As you look at the tasks that are most exposed to AI, I would bet you most of those are commodity outputs, which means that if you want to be perceived as economically valuable, you have to work your way up the value chain. Your commodity output could be wrapped in a service, turned into an experience, or even a transformation - and that’s how you’ll stay ahead of AI (or more likely, use AI to achieve those leaps).

How? With a prompt like this. Pick any one task that scores an 8 or above from the YAML file. Start with this prompt, then paste the task below it along with the Trust Insights TRIPS framework. Also consider including your CV.

Based on this assessment of AI’s ability to consume tasks, this is a task that I perform which AI is highly likely to consume in its current form. The task is scored by the Trust Insights TRIPS framework. Let’s think about how we can move up the value chain for this task so that I provide increased economic value; AI may be able to do the base version of the task, but what are some uniquely human ways I can take this task and move it up the value chain so that the new version of the task is difficult to outsource to AI? The value chain is: Commodity > Branded Product/Goods > Service > Experience > Transformation. Think through 3-7 different ways of moving up the value chain that I could do, and present them in descending order of probability of success in retaining the task and not handing it over to AI.

What you should get is a nice set of recommendations for ways to uplevel that task to keep it as human as possible for as long as possible. We ultimately want to know what people will still give us a paycheck for.

Part 5: Wrapping Up

This issue wasn’t meant to scare you, but it absolutely is meant to be a wake up call for all of us, in every job and career, to recognize where we are exposed to AI taking away tasks. The sooner we have a clear-eyed view of that, the sooner we can act to shore up our value to our companies and customers by either being in charge of AI or finding the niches and opportunities outside of AI.

The exercises in part four of taking tasks that are vulnerable to AI and up-leveling them to things that people would still want to pay you for is where your next steps are.

If you do that exercise for your own background and your job description and the things that you do the most of, you will find ways to remain valuable even as AI technology improves, because you can go back through this entire process from the very beginning as AI technology changes, change the prompts, change the background data, and keep generating new recommendations for how you can continue to add value.

I guess in some ways, this is the third path of not just being in charge of AI or not just finding niches where AI doesn’t operate well, but finding those unique opportunities that take your background into account and your capabilities and ultimately help you deliver AI-enabled versions of your work that people still are willing to pay you money for. So I hope this was helpful. I hope this was useful. Please try it out.

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The Unofficial LinkedIn Algorithm Guide, Refreshed AGAIN

Christopher S. Penn — Fri, 13 Mar 2026 15:23:01 GMT

Yesterday, LinkedIn FINALLY confirmed what we all knew: there's a new algorithmic sheriff in town.

Well, it's not so much a sheriff as it is an entire department. At Trust Insights we've been covering the LinkedIn algorithm for the past two years based on academic papers, engineering content, conference presentations, and more. And we've just updated our Unofficial LinkedIn Algorithm Guide with the latest announcements and papers from yesterday that help you understand what's happening under the hood.

Here's the good news: if you've been focused on meaningful engagement (leave those comments!) and producing useful, helpful content, nothing really changes for you. There are some nuances about how LinkedIn's LLMs work and little things you can do, but for the most part, the evergreen advice is evergreen for a reason: the LLMs under the hood are nearly impossible to "hack" or "game". Anyone telling you otherwise doesn't understand the systems and how they work together.

Two things matter more than ever: profile clarity and behavioral coherence. Because of the nature of LLMs themselves (and LinkedIn is using an ensemble of them, not just one) you have to interact with them in meaningful, semantically coherent, consistent ways. What are you an expert in? Lean into that in the majority of your interactions.

And do it frequently. The new sequential transformer LLM under the hood of the generative recommender looks at your behavior over time.

There's a lot more detail than I can fit in a newsletter, so go grab your copy of the completely refreshed Unofficial LinkedIn Algorithm Guide, free of cost!

👉 Get your copy here: https://www.trustinsights.ai/insights/whitepapers/the-unofficial-linkedin-algorithm-guide-for-marketers/

See you on Sunday for the regular newsletter,

Chris

Almost Timely News: 🗞️ How I Keep Up With Everything in AI (2026-03-08)

Christopher S. Penn — Sun, 08 Mar 2026 10:31:01 GMT

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What’s On My Mind: How I Keep Up With Everything in AI

This week’s newsletter is about something I get asked constantly - usually in the hallway after a talk or in the Q&A at the end of a workshop: how do you stay on top of all of this? How do you know what’s real and what’s hype? How do you actually keep up when the field moves this fast?

The answer is that I treat it like a data problem - because that is what it is. If you have ever worked in data engineering, you know the concept of ETL: Extract, Transform, Load. You pull data from sources (extract), you clean and shape it into something meaningful (transform), and then you put it to work somewhere useful (load). That three-part process maps onto how I manage the constant flood of AI news, research, tool releases, and practitioner discussion that flows through my desk every single day.

Part 1 of this newsletter covers extraction - where I actually get my information and how I have structured those sources to give me signal without burying me in noise. Part 2 is transformation - the mental framework I use to make sense of what I find, so I can quickly judge what deserves my attention and what I can safely ignore. Part 3 is loading - what I actually do with the information, from testing new models to building production tools for clients. And because this newsletter covers a lot of technical ground, I put a glossary up front so you have definitions in hand before you need them.

Part 0: Glossary of Terms

This issue covers a lot of ground across AI research, tooling, and workflows - which means it drops a fair number of technical terms along the way. Not everyone lives and breathes this stuff daily, and even experienced practitioners occasionally hit a term they have seen a dozen times without a clean definition. Here is your cheat sheet, up front.

ETL (Extract, Transform, Load): The classic data engineering workflow: pull data from sources (extract), clean and shape it into something useful (transform), then store or deliver it somewhere you can act on it (load). This newsletter borrows that same three-step structure as a framework for staying current on AI.

Foundation model: A large AI model trained on broad data - text, code, images, or all three - that serves as the base for many different tasks. Think of it as a well-educated generalist. GPT-5.4, Gemini 3.1, Qwen3.5, GLM-5, and Claude Opus 4.6 are all foundation models.

Open-weights model: An AI model whose internal parameters have been publicly released so anyone can download and run it locally. Qwen 3.5 is an example. Contrast with proprietary models, where the weights stay locked inside the company that built them.

Local LLM: A large language model you run on your own hardware rather than through a cloud service. Your data stays on your machine and there are no per-use costs. The trade-off: local hardware limits how large a model you can run.

Context window: The maximum amount of text an AI model can hold in working memory at one time. Everything it reasons about in a single session must fit inside this window. Bigger windows mean the model can handle longer documents or conversations without losing the thread.

Harness: The software framework, interface, and tooling built around an AI model to make it useful for real work. If the model is the engine, the harness is the rest of the car - transmission, steering, safety systems. Most real-world AI value comes from well-built harnesses, not just from better models.

Agentic / AI agents: AI systems that take autonomous actions - browsing the web, writing and running code, calling APIs, reading files - and chain those steps together to complete multi-part tasks with minimal hand-holding. A super-agent coordinates multiple sub-agents or tools to tackle complex workflows.

RAG (Retrieval-Augmented Generation): A technique that gives an AI model access to an external knowledge base at response time. The model retrieves relevant documents first, then uses that content to ground its answer - accurate responses about your specific data without retraining from scratch.

Scaffolding: The supporting structure built around a task before the detailed work is filled in - file structure, function signatures, placeholder logic. Good scaffolding lets smaller, cheaper models do solid work because much of the thinking has already been done.

Proof of concept (POC): A quick prototype built to test feasibility - not polished, not production-ready, just functional enough to answer “can we actually do this?” POCs validate an approach before you commit real resources to it.

Hallucination: When an AI model confidently generates something factually wrong or simply made up - an inherent property of how these models work, not a random glitch. Tasks requiring net-new content carry higher hallucination risk than tasks where you have already provided the source material.

Preprint: An academic paper posted publicly - typically on arXiv - before peer review. Preprints let researchers share findings fast, which matters enormously in a field moving as quickly as AI. Treat them as promising leads, not settled science.

arXiv (arxiv.org): The dominant preprint server for AI and machine learning research. Most major labs post papers here first. It is where you see what is happening in research now, not six months from now.

RSS (Really Simple Syndication): A standardized feed format that lets you subscribe to many sites at once and get new content automatically - a practical way to monitor dozens of blogs, news sites, or developer announcements without visiting each one.

Talkwalker / Brand24: Social listening and media monitoring platforms that track mentions, discussions, and news across the web and social channels in near real-time. They can ingest enormous volumes of content and make it available for automated processing.

Discord announcement channels: A Discord feature that lets servers designate specific channels as announcement channels. Other servers can follow them, so official posts appear automatically in your own server - high-signal release announcements without the noise.

Subreddit / Reddit community: A topic-specific forum on Reddit. Communities like r/LocalLLaMA are often the fastest signal for how practitioners actually receive a new model or tool - reactions, use cases, and bugs included.

ICLR, ICML, NeurIPS: The three most prominent academic conferences in machine learning - International Conference on Learning Representations, International Conference on Machine Learning, and Neural Information Processing Systems respectively. Major research from Google, Anthropic, Meta, and Alibaba is timed to these events, so paper volume spikes dramatically around them.

Qwen / Qwen 3.5: A family of open-weights AI models from Alibaba, available in multiple sizes. Smaller versions handle summarization well on modest hardware; larger versions are competitive with frontier proprietary models - a useful illustration of how capable open-weights models have become.

Claude Code: Anthropic’s AI-powered coding assistant and agentic development environment. It writes, edits, and runs code; manages files; and executes multi-step tasks with real autonomy. A prime example of a purpose-built harness.

Deerflow: An agentic super-agent tool from ByteDance - an orchestration system that chains multiple AI actions together to complete complex, multi-step tasks autonomously.

API (Application Programming Interface): A defined interface that lets one piece of software talk to another. When an automated pipeline calls a cloud AI model, it goes through an API. APIs are the connective tissue that makes agentic systems - ones that chain tools and data sources together - possible.

Part 1: Extraction - Where I Get My Information

Staying current on AI is the same challenge as any data problem. You need good inputs before you can produce good outputs. Part 1 is extraction: where do I actually get my information? The answer is a lot of places, because the AI landscape moves fast enough that a single source will always leave you with blind spots.

Discord: One Server, All the Signal

My first stop is my own Discord server - and that distinction matters. I have signed into and signed up for hundreds, actually no, it’s probably dozens, of Discord servers from all the major tech companies. If you join every AI vendor’s Discord community, what you get is hundreds or thousands of notifications an hour. That is not useful. It is just noise.

The smarter move is to use Discord’s announcement channel follow feature. Almost every major AI vendor - Anthropic, Google DeepMind, OpenAI, Mistral, and others - runs a Discord server with a dedicated announcements channel. Discord lets you follow those channels by adding them to your own server. So I set up my own server, followed all the announcement channels that matter, and now there is one centralized place where high-priority announcements land. My server’s news channel notifies me. Everything else stays muted.

This is the difference between letting information find you versus drowning in it. One server, one channel, the stuff that matters.

Reddit: Announcements Plus the Community Reaction

Reddit is my second daily stop, and it is valuable for a reason most people miss: you get not just the announcements, but the community’s reaction to the announcements.

When something happens, especially in AI, the community will have a conversation about it. And very often, the company itself is not the one revealing it in the the subreddits that you’re in. It’s other community members.

That second layer is often more useful than the announcement itself. When a new local model drops, r/LocalLLaMA lights up with people testing it in real conditions, surfacing edge cases, and debating whether the benchmarks hold up under practical use. That qualitative reaction tells you things a press release never will.

The communities I check most often include r/LocalLLaMA for the open-weights model ecosystem, r/unsloth for fine-tuning and optimization, and the vendor-specific subreddits like r/ClaudeAI and r/GoogleBard - the latter still carries the original name from before Google rebranded to Gemini, but the community is active and useful. Each community has its own character and its own signal-to-noise ratio. Taken together, they give me a cross-section of how the practitioner community is actually using and evaluating new developments.

And those evaluations usually are brutally honest, which is even more helpful because you get away from corporate boasting to the real world.

YouTube: The Big Announcements

YouTube is not a daily source for me the way Reddit is, but it is the right channel for high-profile announcements and longer-form technical content. Subscribing to the official channels of the major AI providers and a handful of key technical personalities means YouTube notifications tell me when something significant is happening.

Live streams for product launches, recorded conference sessions, technical walkthroughs - this is where the big moments get broadcast, and YouTube’s notification system does a reasonable job of surfacing them.

Conferences in particular are where YouTube is especially useful because many conferences like PositConf, for example, publish all their sessions. Microsoft Ignite, Google I/O publishes a ton of sessions, Apple’s WWDC publishes a ton of sessions, and that is stuff that you can download from YouTube and analyze in a tool like Notebook LM. So super valuable.

Social Media: Following the People Embedded in the Technology

Social media - across Threads, X, and other platforms - has become genuinely useful for following people who are not just adjacent to the technology but actually inside it.

Whether or not I like X or whether or not I like X’s crazy ass owner, the unfortunate reality is that a ton of AI practitioners are there. So if you want to stay on top of what’s happening in AI, you have to at least lurk on X.

The entities I pay attention to most? All the big tech companies around the world - Cohere in Canada, Alibaba in China, Black Forest Labs in Germany, and so many others. In terms of individuals, Logan Kilpatrick at Google and Boris Cherny at Anthropic, where he leads Claude Code. Boris in particular, as the head of Claude Code, shares a ton of behind the scenes stuff about what they’re working on at Anthropic and how they use their own tools internally, which is about the most valuable information possible.

The principle generalizes. Find the people who are embedded in the technical side of the organizations building this technology, and follow them wherever they are most active. Their casual observations frequently contain more signal than formal press releases.

RSS + Talkwalker + Brand24: The Automated Monitoring System

This is where my setup moves from manual curation into something more systematic. For Trust Insights, I built an automated monitoring system using Talkwalker and Brand24 as the data ingestion layer. These companies have crawlers and scrapers and API connections that I just can’t replicate. I just don’t have enough compute to build my own of them. So I use theirs. Feeding into an RSS reader that pulls from both platforms on an hourly basis.

The raw volume of AI news, discussion, and commentary that flows through these systems every hour is enormous - far too much to read manually. We’re talking five hundred articles or so an hour. So instead of reading it, I process it with some custom Python code. I built some Python code a while back that grabs all the data from RSS feeds, downloads it into a SQLite database, and then a small, lightweight language model - OpenAI’s gpt-oss-20B - analyzes and scores each item, prioritizing by relevance and significance. That scored data goes into a database, and I review the top items frequently.

This same system also feeds the weekly automated AI newsletter we produce at Trust Insights. The monitoring pipeline that keeps me informed is the same one that generates publication-ready content for our audience. One infrastructure, two uses.

The practical lesson here: staying current at scale requires automation. Reading everything manually is not a strategy. It is a bottleneck. You will drive yourself insane trying to read everything.

Slack Communities: Practitioner Networks

Several Slack communities round out my social monitoring. The Analytics for Marketers Slack, the Content Marketing Institute Slack, and the SmarterX Slack are all places where practitioners share what they are working on, what they are seeing, and what is breaking. The conversations in these communities tend to be more operational than strategic - people working through real implementation problems, not theorizing about the future.

I see things less frequently in these spaces compared to Reddit or my automated pipeline, but when something significant surfaces here, it tends to have already been stress-tested by people who have actually tried to use it Or someone and their their BS meter has at least vetted it, and the discussions tend to be richer.

Hugging Face: Open Weights Models

The next place I spent a lot of time on is Hugging Face, which is a repository for open weights AI models. There are orders of magnitude more open weights models than there are closed weights models. Closed weights models are ones like GPT-5.4 from OpenAI. You can use that model through a service like ChatGPT, but you cannot download that model.

On the other hand, a model like Qwen3.5 from Alibaba Quen is open. You can download it and if you have your own hardware that is capable of running it, you can run their model on your hardware completely free besides the cost of electricity.

Open weights models are to me where AI is going. So Hugging Face is a repository that allows you to see what new models have been posted. Hugging Face gets about 10,000 new models a day, give or take. But the big shops that are in the open weights space, like Cohere and Mistral and Alibaba and ByteDance, are all publishing their models on Hugging Face.

So one of my first stops when I’m doing any kind of AI news roundup is to see what’s been posted recently, particularly models that have been posted recently that get a lot of community attention.

LinkedIn: Good for Discussion, Not for News

LinkedIn deserves a specific callout because people often assume it is a good news source for the industry. It is not for me. LinkedIn’s algorithm surfaces what is relevant to you - which means it heavily weights content that has already gotten engagement. By the time something is trending on LinkedIn, it is old news in AI terms.

When it comes to AI specifically, I care about recency far more than relevance. The field moves fast enough that a two-day-old announcement can already be superseded. LinkedIn’s relevance-weighted feed is structurally misaligned with that need.

That said, LinkedIn is a genuinely good place for discussion and analysis. When something important has already happened, the LinkedIn conversation around it is often rich and substantive. I use it for that purpose - analysis and perspective, not breaking news.

arXiv: The Academic Research Layer

A few times a month, I check arXiv - arxiv.org - which is where academic preprints from AI researchers get published. These are papers submitted but not yet formally peer-reviewed, so you are getting results ahead of the official publication timeline. For the AI field, this matters enormously, because the gap between preprint and publication can be months or longer. And let’s face it, in AI, months between a preprint and a final paper may as well be centuries.

My filtering approach is straightforward: I look for email addresses from the major labs in the author affiliations. Papers authored by researchers at Google, Anthropic, Meta, Alibaba, Deepseek, LinkedIn, and similar organizations are worth my attention - those are typically the employees of the labs publishing their most significant results. I am not reading every paper - there are thousands - but the lab affiliation filter cuts the noise dramatically.

arXiv is a semi-weekly review for me rather than a daily one, and it is more of a longer-range early warning system than a source of immediately actionable news. When a technique starts showing up repeatedly in arXiv preprints from the major labs, you can often predict where the field is heading three to six months out.

Major Conferences: The Annual Paper Floods

Those arXiv submission spikes I mentioned have a pattern: they cluster around conferences. Several times a year, the major AI research conferences - ICLR, ICML, and NeurIPS are the big three - become the most information-dense moments in the calendar. These events coincide with the release of thousands of papers simultaneously, as researchers time their work to align with the conference schedule.

I treat conference season as a dedicated research sprint. The volume is too high to absorb casually, so I specifically carve out time to review the most significant papers and track what themes are emerging across submissions. The aggregate picture across a conference’s paper slate often tells you more about where the field is headed than any individual paper.

That said, I’ll often download and put all the papers from a conference into systems like NotebookLM to review at scale. I have some custom code to download the thousands of papers at a time.

An example of a significant paper at NeurIPS last November? The Alibaba Qwen team released a paper showing how a single mathematical change to an AI model dramatically increases its performance while not costing any more compute. That paper won a a gold medal at the conference, and it makes you wonder like why why was that discovery made just now? Why didn’t we know that to begin with? So that’s the kind of thing I look for.

Developer and Tech Blogs: Automated via RSS

Finally, the official developer blogs and technical blogs of the major AI providers all run through my RSS setup. When Google, Anthropic, OpenAI, Mistral, or any of the other significant players publish a technical post, it lands in my feed automatically. This is fully automated - I set up the RSS subscriptions once and the content comes to me.

Developer blogs sit at a different layer than press releases or social media announcements. They tend to be more technically detailed, written for practitioners, and more honest about the specifics of what has actually changed in a model or platform. For understanding the mechanics of what providers are building and why, these posts are essential reading.

Taken together, these sources give me something no single source could: breadth across platforms, depth through the academic and technical layers, community reaction in near real-time, and automation to handle the volume. The system is not elegant in a minimalist sense - it is a lot of sources. But AI development happens across all these channels simultaneously, and missing any one of them means missing something real. And the channels are uneven in terms of their distribution. Sometimes one set of channels will catch something that others miss, that others don’t cover. So having this big picture coverage helps capture the space as a whole.

This is the extraction phase of the ETL pipeline in action: an active, designed system for pulling information from the right places at the right frequency. Once that information is in hand, the next step is figuring out what it all means - which is what the transformation phase is about.

Part 2: Transformation - Making Sense of What I Find

Once all that raw data lands in my lap the next question is obvious: what do I do with it? Information without a framework is just noise. And given how fast AI moves, noise at scale will bury you.

The answer is what I think of as the transformation phase: taking everything I have gathered and running it through a mental framework that lets me quickly categorize, prioritize, and understand what I am looking at. This framework has three parts, and I think about all of AI through this lens.

Every piece of AI news I encounter, I ask myself one question first: is this a new model, a new harness, or a new application? That single question cuts through enormous amounts of noise very quickly. Let me explain what each of those means.

Imagine this is like a car. Cars have engines, cars have bodies and steering wheels and seats and air conditioning, and you drive your car places. That’s mostly the reason we have cars. In AI speak, a model is the engine of the car. A harness is the rest of the car, and the application is where we go with the car.

Models - The Engines

Models are the foundation. They are the actual AI systems - the large language models, the image generators, the audio and video generators - that power everything else. When Anthropic releases a new Claude, when Google drops a new Gemini, when Alibaba puts out a new version of Qwen, that is a model update.

I pay attention to models. Models are getting harder and harder to differentiate based on performance. The top-tier models are converging. After a certain point, it is genuinely difficult to tell them apart in everyday tasks because they are all very, very good. That is actually a sign of a maturing technology landscape, and it means the decision of which model to use for a given task has gotten more nuanced.

Not every task needs the biggest, most expensive, most powerful model available. The size of the model you need depends heavily on what you are asking it to do and how much data you are feeding it.

Take summarization. If I am summarizing a document, I do not need a top-tier frontier model. I can use something like Qwen 3.5 9B - a model so small it can run on some mobile phones - and get perfectly good results. Why? Because summarization is a transformation task. The source material is all right there. The model’s job is to compress and reorganize information that already exists, not to conjure new information from scratch. That is not a heavy cognitive lift, computationally speaking.

The general rule: the more data you are providing, the smaller the model you can get away with. You are applying transformations on the data rather than having it generate net-new content. Small models handle transformation well. That means paying attention to places like Hugging Face that release lots of small models is important so you can see how quickly small models are advancing.

Flip that around. When you have to create something net-new - something the model has to generate largely from its own knowledge - and the risk of getting it wrong is high, that is when you need the biggest, most capable models available. The classic example is writing code. If you are creating code net-new from a spec, you want a very smart model. The model has to reason through architecture, anticipate failure modes, and produce something that actually works without much scaffolding to lean on. Cheap out on the model here and you pay for it in debugging time.

But here is where it gets interesting. If you have already written out the scaffolding - if the structure of the code is established, the quality checks are in place, and you are asking the model to fill in the implementation details - you can use something much smaller. A model like Qwen 3.5 35B can handle that work because it does not have to guess very much. A lot of the hard decisions are already made. It will take more iterations than an expensive model would, but if each iteration costs orders of magnitude less, you come out ahead on both time and money.

This is how I think about model selection. Not “which model is best” in some abstract ranking, but “what is this task actually asking the model to do, and how much am I providing versus how much is it generating?”

I also watch the video and audio model space closely, though I categorize that separately in my thinking because it is still developing in ways the text model space has already moved past. The north star for video generation - the capability that would signal true maturity - is generating a feature-length film. We are not there yet, not even close. Right now most tools can generate somewhere between eight and thirty seconds of video at a time. That is actually more useful than it sounds: if you count the number of jump cuts in a film or a commercial, you will find that some shots last two seconds or less. We can work with short clips.

The real challenge is not duration. It is consistency - maintaining the same characters, lighting, and visual style from shot to shot. That is what breaks the illusion. Some tools have started addressing this with reference image support, which helps lock in visual continuity across cuts. It is not solved, but it is getting better in ways that matter. I watch this space specifically for breakthroughs in consistency, because that is when video generation becomes practically useful for real production work.

Harnesses - The Rest of the Car

This is where I put the most weight right now - by a significant margin.

Think of it this way: the model is the engine. The harness is the rest of the car. The engine matters - a bad engine makes for a bad car - but the engine alone gets you nowhere. Nobody drives down the street sitting on an engine block. You drive down the street in a car. You need steering, suspension, transmission, brakes. You need all the systems that translate raw power into controlled, directed motion. That is the harness.

In AI terms, the harness is everything built around the model: the interfaces, frameworks, orchestration layers, tools, and scaffolding that determine how you actually interact with the model and what you can get it to do. The harness is what makes the difference between a capable model and a capable system.

Here is why this matters so much right now. We are in a moment where the models themselves are very good - good enough for most practical purposes - but the harnesses are where active, rapid innovation is happening. For a lot of people, the harness they are used to is a chat interface like Chat GPT or Gemini or Claude. And there’s nothing wrong with those interfaces. They’re terrific. They do a good job of providing a chat companion to talk to, to talk to your AI models.

That is not where the state of the art in AI is. If you are only using models within chat harnesses, you are leaving a lot of power on the table. A mediocre harness around a great model will underperform a great harness around a merely-good model. I have seen this firsthand repeatedly.

The system that matter most to me right now: Claude Code and Claude Cowork from Anthropic, Google Antigravity, OpenAI Codex, and OpenClaw. These are agentic environments - harnesses that let models not just answer questions but actually do work. Write code, run it, check the output, fix errors, iterate. And it’s not just coding. I have written, as you know, if you read the January issue, I have written a trashy romance novel with these tools. I have written corporate strategy with these tools. The agentic harness is so powerful. And it’s something that is only gonna get more capable as time goes on, even this year.

The difference between using a raw model via a chat interface and using one of these harnesses is roughly analogous to the difference between having a conversation with a contractor and actually watching them build the thing.

Anthropic in particular is making significant strides with Claude Code and Claude Cowork. The pace of capability development in those tools is fast enough that I am paying closer attention to harness releases right now than I am to raw model releases. When Claude Cowork launched, it required a complete shift in how I thought about certain workflows. Same with Google Antigravity, same with Codex. These are the moments that change what is actually possible in practice.

When a harness update drops, my first question is: how does this compare to what I am already using? I have existing harnesses I can benchmark against - Claude Code, Qwen Code, and others. I run new entrants through the same tests and see how they stack up. The bar is moving fast, which is part of why I watch this category so closely.

Applications - Where You Drive

The third category is applications: the actual use cases, the practical problems being solved, the things the engine and harness are being pointed at.

I use AI extensively to discover and generate application ideas. AI is actually quite good at brainstorming ways to apply existing capabilities to business problems. Sometimes you have to remind AI that it’s capable of those things, but it can do them.

But here is how I think about it in practice: I am not usually looking for net-novel applications. I am looking to map known models and harnesses to known problems and solve those problems better.

Most of the time, the application is not the innovation. The underlying problem is already understood. What has changed is the technology available to address it. The goal is to find the better solution, not to invent a new category of problem.

A useful way to think about it: when I hear about a new agentic application someone has built, my first reaction is not usually admiration. It is more like - I’m pretty sure I can do that too. Then I go test it. That attitude keeps me focused on practical capability rather than getting dazzled by demos.

Putting the Framework to Work

The framework is not complicated, but it is more powerful than it looks. When new information comes in - a press release, a Reddit post, a paper, a product launch - the first thing I do is run it through the classification: model, harness, or application?

Model update: I go test it. I have a set of diagnostics, my own prompts, that tell me how good a model is at specific tasks I actually care about. Generic benchmarks are fine as a starting point, but they do not tell me what I need to know.

Harness update: I benchmark it against what I am already using. How does it compare to Claude Code? What does it do better, what does it do worse, what does it not do at all?

Application: I figure out whether I can implement it with my existing toolkit. If I can, I build it. If I cannot, I figure out what is missing and whether it is worth pursuing.

This three-part classification is genuinely useful because it tells me immediately what kind of attention something deserves and what the appropriate response is. It turns a flood of incoming information into a manageable set of questions, and it keeps me focused on what actually matters: not what is new, but what is useful.

Part 3: Loading - Putting Knowledge to Work

All the reading and synthesizing means nothing if you never act on it.

There is a great scene from an old episode of The Flash that I keep coming back to. The villain catches a knife and throws it at the Flash. And the Flash, without breaking a sweat, plucks it right out of the air and says, “I can do that too.”

That is exactly how I look at major AI announcements. When somebody comes up and says, “We built this new cool agentic application,” my reaction is: I am pretty sure I can do that too. And then I go test it.

My technology folder on my computer is filled with hundreds of prototypes and proofs of concept. Each one is me asking that same question and working out the answer. Not all of them become production tools. That is not the point. The point is developing the capability, understanding the edges of what is possible, and building my own working knowledge of how these systems actually behave when you push them.

Taking a Good Idea and Making It Better

Sometimes the answer is not just “yes, I can do that too” - it is “yes, and I can do it better.”

I was playing around recently with ByteDance’s Deerflow, which is an agentic super-agent, similar in concept to OpenClaw. It is impressive work. But I found a number of things I personally did not like about how it functioned. So I had Claude Code rebuild it to function the way I want it to function. The core idea was sound. The implementation did not match my standards. So I made my own version.

This is the real power of being deeply familiar with the tools. You do not have to accept someone else’s design decisions. If you understand the underlying technology well enough, you can take a good idea that has been poorly implemented and build your own version that actually fits your workflow and your point of view.

Real Infrastructure: The Adobe Analytics QA Suite

Let me give you a concrete example of what this looks like when it moves beyond a prototype into genuine business infrastructure.

One of our clients is an Adobe Analytics shop. If you have ever worked with Adobe Analytics, you know it is a pain to work with. It is very difficult to export configurations and get a system-level view of what is actually going on inside your implementation. That makes quality assurance a real problem. You can buy commercial QA tools that automate some of the testing - but those tools run into the thousands of dollars a month. That is real money, and it is money our client probably would not pay for a standalone tool.

So my thinking was: can I build my own QA suite that does this? I know the tools can do it. The question is whether I can implement it well enough to be genuinely useful.

I went through my usual process. I worked out the specs. I built the system. I worked out all the bugs - it took a couple of days. What I ended up with is a piece of technology that automates the testing, audits configurations, and generates structured data as output. There is no AI embedded directly in the QA suite itself. But that output goes into an agentic AI system, which uses it to diagnose problems and even make repairs where the appropriate APIs exist.

Now I have a piece of infrastructure I can offer to any client that runs Adobe Analytics. That is the compounding effect of building your own tools instead of renting someone else’s — and it is why the applications layer is where I focus most of my creative energy. If you pay attention to the three-part system and you filter news as it comes in and you know what problems you’re working on, you know how to slot a given tool or announcement into your system to make your system better immediately.

Part 4: Wrapping Up

The system I have described is, at its core, an ETL pipeline - the same process data engineers use to move information from raw sources into something useful.

Simple to describe. Harder to sustain.

The challenge for anyone who wants to adopt something like this: the cold start problem is real. AI technology moves at a pace that is genuinely unlike any other field I have worked in. If you stop paying attention for more than a couple of weeks, major shifts can happen that require you to completely reorder your thinking about what is possible. I am not exaggerating. When Claude Code came out, it changed how I think about agentic workflows. When Claude Cowork came out, when Google Antigravity came out, when OpenAI Codex came out, when OpenClaw came out - each one of those was a complete mind shift. Not an incremental update. A fundamental rethinking.

So if you only check in once a month, you are basically starting from scratch every single time. You are not building on accumulated knowledge. You are just playing catch-up, never quite getting your footing before the ground shifts again. My practice is to stay current on a daily basis rather than devoting days at the end of a month to catch up on everything - or catching up at a surface level and never really understanding what changed or why it matters.

That daily practice is why I can do what I do.

Ann Handley - one of the sharpest minds in marketing - once told me something back in my analytics days that has stuck with me ever since. “Lots of people know the why and the what. None of those people know the how.” That is my specialty. It has always been my specialty. And it remains my specialty today - how to do the thing.

There are countless numbers of people on LinkedIn and at conferences and things that are navel gazing and speculating and using all sorts of expensive words that sound impressive, but when you talk to them and say, How do I do this thing? you don’t typically get great answers.

As new technological advances come out, I focus on how the technologies actually work and how to apply them in practice. Not the theory. Not the hype. The how. But that requires the two to four hours a day I spend just trying things out - seeing what works, what breaks, what is real and what is marketing. You cannot understand how something works if you only read about it. You have to use it.

Much in the same way that you can’t really understand a dish until you cook it and eat it. You can’t understand it just from reading the recipe.

This is my system. It is not the system. I am not standing up here telling you that everyone should do it this way. I do it this way because I genuinely love this stuff. Every day I get new tools to explore, new models to test, new problems to see if I can solve faster than I could yesterday. It is like getting new toys every single day. For me, the investment of time is not a burden - it is the point.

That level of commitment is appropriate for me and my work. It may not be appropriate for you.

So here is the closing question worth sitting with: what level of engagement makes sense for your role, your goals, and your interests? Because the Extract, Transform, Load framework does not require two to four hours a day to be useful. If you have thirty minutes a week, you can still apply it. Extract what matters from one or two good sources. Transform it through your own professional lens - what does this mean for your industry, your team, your specific problems? Load it by trying one new thing, however small.

That is the homework from this newsletter for you. Try one new thing a week, either at the model level, at the harness level, or the application level. And if you do that, I promise you your capabilities and your understanding of AI will grow by leaps and bounds.

The framework scales. You do not need to do what I do to benefit from the underlying logic. Start where you are, with the time you actually have, and let the system grow with your curiosity.

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