Full disclosure: I am a Gen-X-er myself. So yes, I might be slightly biased. But looking at the current state of our industry, I genuinely believe the Gen-X crew is about to make a massive comeback. We are the engineering team humanity needs right now to bring tech back down to earth…

The “Specs” of a Transition Generation

In the tech world, we love a good architecture diagram. If you map out the history of modern computing, Gen-X represents the ultimate bridge. We are the only generation in the current workforce that boasts these specific system requirements:

• The Analog Bootstrapping (Before Tech): We grew up troubleshooting life without a search engine. If your bicycle chain broke on the way to school, you fixed it with oily hands and a rock. That bred a 100% “can-do” mentality.

• The Architecture Phase (Building Tech): We didn’t just inherit the digital world; we literally crimped the cat-5 cables and configured the early servers. We saw the transition from 56k dial-up modems to gigabit fiber. We build it.

• The Modern Bloat (Now): We are currently watching the infrastructure we built get smothered by endless layers of optimization, micro-services and automated hand-holding.

Right now, the industry is over-engineering everything to an absurd degree. We have offloaded about 95,5% of our critical thinking to algorithms. If a system goes down today, the immediate reaction is to open a ticket or ask an AI tool. The Gen-X reaction? Open the terminal, look at the raw logs and find the broken line of code.

Craving the Analogue Baseline

This comeback isn’t just about how we write code or manage IT infrastructure; it is a total lifestyle recalibration. On my socials, I see more and more tech professionals actively stepping away from the glowing rectangles during their down time. We are craving experiences that tech cannot flatten or optimize.

There is a massive resurgence in analogue music, with vinyl sales hitting records because people want to physically drop a needle onto a groove and listen to an entire album from start to finish, no skipping, no algorithm-generated playlists. We are seeing a return to physical books, fountain pens, and paper notebooks.

Even our hobbies are shifting back to the physical world. People want to fix old motorcycles, restore vintage audio gear or bake sourdough bread from scratch. Why? Because you cannot “undo” a physical mistake with Ctrl+Z. It requires patience, presence, and tactile logic. Gen-X understands that a life lived entirely through a 6,7-inch screen is a deeply impoverished one…

Why we Needs the Gen-X Pragmatic Specs

The reason this is trending on socials right now is simple: people are tired of tech that feels like a chore. We have flattened the human experience with software. Gen-X-ers are pragmatic tech nerds. We love solid performance and tight code, but we have exactly 0,0% patience for fluff.

Here is how the Gen-X mindset contrasts with the current “hype cycle” era of tech:

Pro-tip for the modern stack: Just because you can micro-optimize a feature using a complex cloud network doesn’t mean you should. Sometimes, a single, elegant script running locally is 1.000 times more reliable.

Lets Make Tech Human Again

We need to inject that level-headed, “seen-it-all” Gen-X attitude back into product design and corporate leadership. Having lived through the dot-com crash of the year 2.000 and the financial collapse of 2.008, this generation doesn’t panic when the hype cycle resets. They stay steady.

They know what life looked like before the glowing rectangle took over our attention spans. They don’t want to optimize every single second of human existence; they want to build efficient tools so they can shut the laptop down at 18.00 and actually enjoy a real conversation. It is time to let the pragmatists back into the driver’s seat to clean up the bloat and make tech serve humans again, not the other way around.

Are you noticing this same urge to strip away the digital noise on your own feeds or do you think we’ve gone too far down the rabbit hole to actually simplify things again?

It got me thinking about what the hell “modern marketing” is actually doing lately. We’ve entered this weird era where the entire discipline seems to have traded process, system, rigor and precision for the superficial glory of social media reach. It’s all about volume over authenticity… And frankly, it’s getting a bit ridiculous/silly…

The AI Firehose: Infinite Noise, Zero Signal

Lets look at the specs of this problem. Now that we are firmly in 2026, AI adoption among marketers has crossed the 90,0% mark. On paper, productivity is through the roof. But if you look at the actual performance data, a striking paradox emerges: only about 13,0% of organizations report any meaningful improvement in their actual ROI or pipeline…

Why? Because instead of using AI to build better infrastructure or deeply analyze customer intent, marketing has used it to build an automated trash-generator.

• The Content Tsunami: Need a blog post? Punch a three-word prompt into a chatbot and boom, out comes a 2.000-word essay that says absolutely nothing new.

• The Social Media Mirage: Every platform is flooded with hyper-polished, AI-optimized “thought leadership” that feels like it was written by a committee of robots trying to mimic human emotion.

• The Illusion of Scale: Marketing teams are celebrating a 40,0% increase in website traffic, completely ignoring the fact that it’s just other AI bots scraping their page or low-intent clicks that will never turn into a single cent of revenue.

It’s a classic case of prioritizing activity over actual impact. They are treating AI as a quick productivity hack to flood channels, rather than an invisible layer of operational infrastructure.

Whatever Happened to Process and Rigor?

True tech geeks know that a system is only as good as its architecture. If you build a chaotic data pipeline, your application crashes. Yet, marketing seems perfectly content running a chaotic pipeline as long as the top-of-funnel graph goes up and to the right.

Marketing has largely abandoned the disciplined art of creating an actual, rigorous system to onboard tangible prospects. They’ve swapped pipeline velocity for vanity metrics.

Think about the standard B2B enterprise journey right now. A company will spend thousands of euros on targeted ads to get an enterprise prospect to download a whitepaper. The moment the form is filled, the system considers it a win. But what happens next?

The prospect is dumped into a rigid, soul-crushing automated email sequence that fires off every Tuesday-morning at 09:00 like clockwork. There is no real intent detection, no dynamic rebalancing based on actual behavior and no human checking to see if the messaging even matches the buyer’s actual pain points. It’s just volume, volume and more volume…

Broken Telemetry: The “Algorithm Change” Scapegoat

This brings us to the biggest bug in the entire marketing stack: how we measure success, and why leadership completely ignores the data…

In engineering, telemetry is absolute. If your server response time spikes by 150,0%, you don’t write a narrative about how the server was “feeling creative” that day. You trace the error, look at the logs, and deploy a fix.

In marketing, analytics have become completely decoupled from business reality. When marketing presents data to the board, they bring a deck full of abstract metrics: click-through rates, follower growth, and “share of voice”. Management’s eyes immediately glaze over. Why? Because none of these numbers have a direct line to net revenue or EBITDA. It’s noise…

Worse yet, whenever the data actually looks bad, say, a 45,0% drop in organic traffic or a complete freeze in inbound leads, marketing deploys the ultimate get-out-of-jail-free card:

“LinkedIn changed its algorithm, so the current drop doesn’t really mean anything. We are adjusting our hashtag strategy”.

Imagine a DevOps engineer telling the CEO: “AWS changed its dashboard UI, so the fact that our checkout page is down 30,0% of the time doesn’t really mean anything”. You’d be fired before your next coffee break.

By blaming the black-box algorithms of big tech corporations for every dip in performance, marketing avoids accountability. Leadership, in turn, stops treating marketing as a serious revenue engine and starts viewing it as an expensive, unpredictable coloring department. Everyone tolerates the lack of precision because the excuse is baked right into the culture…

Moving From Reach to Revenue Architecture

The smart players (I call them the “1%”), the ones pulling ahead without burning through their budgets, are experiencing severe “AI fatigue” from this noise. They are dialing back the volume and focusing heavily on precision…

Instead of chasing 1.000.000 superficial impressions on a viral video, they are building robust systems that look at micro-signals, like actual documentation depth reads, repeat visits and CRM activity to score a prospect’s true purchase probability.

They are treating marketing like an engineering problem: optimizing the conversion funnel, cleaning up the data governance and ensuring that when a sales-ready lead arrives, it actually means something.

It turns out that a single, deeply researched, human-guided case study that solves a specific problem for 10 high-value accounts is worth infinitely more than 10.000 generic, AI-generated blog posts. Who would have thought? Uh?

The Pro-Tip: Debug the Huddle, Ignore the Feed

If we want to fix this, we have to look at the internal huddle, the real, raw product-led data sitting inside our own perimeter.

Why is this absolutely critical? Because when you optimize for a social media feed, you are building your entire business pipeline on rented land. You don’t own the algorithm, you don’t own the audience, and you sure as hell don’t own the data integrity.

When you strip away the automated AI fluff and focus on raw, unfiltered authenticity, the metrics transform. Real buyers can spot an AI-generated template from a mile away. True authenticity acts like a cryptographic handshake; it establishes trust because it proves a human actually spent time understanding a specific, complex problem.

By tracking how real people interact with your actual system, analyzing database logs of user onboarding, measuring time-on-page for technical documentation and watching closed-loop revenue metrics, you build a predictable engine. Even if your conversion rate drops by a minor 0,5%, you can isolate the precise variable in your funnel and patch it, rather than throwing your hands up because some third-party algorithm had a bad day. Precise data plus authentic messaging equals an unshakeable pipeline…

Please: We need to stop glorifying the megaphone, stop blaming the algorithms, and start fixing the plumbing.

Are you ready to mute the social noise and start auditing the actual database logs of your marketing funnel, or is the allure of the viral graph still too strong to resist?

If you have been tracking the feeds, May/June 2026 is officially turning into the most absurd months in public market history. We have a triple-header of mega-cap tech giants racing to list:

• SpaceX has filed its S-1, looking to raise a casual $75 billion at an eye-watering valuation of roughly $1,8 trillion.

• Anthropic quietly dropped its confidential paperwork, riding high on Claude’s enterprise dominance and a massive $965 billion valuation.

• OpenAI followed suit days later, pitching its own confidential S-1 to the SEC while aiming for that sweet $1,0 trillion benchmark despite burning cash on server infrastructure like a broken furnace.

It is a total geek carnival. But as I sat here trying to parse SpaceX post-merger xAI metrics while squinting through the Riviera glare, a server brought me a crisp, cold India Pale Ale. And I started to think: given the choice between buying into a historic IPO or sinking my capital into a premium IPA, the beer is structurally the superior architecture.

The Spec Sheet: IPO vs IPA

Lets do a quick breakdown of the system architecture for both options.

Why the IPA Outperforms the Tech Giants

Pro-tip: Never invest in a speculative machine learning cluster when you can invest in human happiness infrastructure.

Let’s look at the logical reasoning behind why the IPA takes the win over Sam Altman’s or Elon Musk’s latest financial moonshots:

• Cooling Efficiency: OpenAI and Anthropic are spending billions of dollars on data centers just to keep their LLMs from melting down. My IPA? It requires a simple ice bucket. The thermodynamic efficiency of a cold beer on the Riviera is completely unmatched by any Nvidia-backed server farm.

• User Base Stability: OpenAI’s weekly active users reportedly stalled at 900.000.000 earlier this year because competition is brutal. The global user base for beer? It has been growing steadily for about 5.000 years without a single software patch or forced rebrand. It is an incredibly stable ecosystem.

• Zero Dilution: When SpaceX issues 555.000.000 new shares to raise capital, your slice of the pie shrinks. When you order another pint of a solid Dutch IPA (even out here in France, I managed to find a decent import), your volume increases by exactly 100,00%. No dilution, just pure asset accumulation.

• The Reality Check: Wall Street analysts are calling these AI listings a “reality check” for the tech sector. Markets are driven by future expectations and massive data center dependencies. An IPA, however, provides immediate, deterministic feedback. You take a sip, the bitter notes hit your palate at a precise 8,5% ABV, and the value is realized instantly. No speculative bubbles here.

Final Verdict

Look, I love bleeding-edge tech as much as any proper nerd. Tracking the enterprise run rates of Claude Code or the launch frequency of the Falcon 9 gets my adrenaline going. But watching these tech oligarchs chase a combined $3,5 trillion in market cap while navigating the SEC makes me exhausted.

Out here under the Mediterranean sun, the smart money is on the IPA. It is decentralized, highly scalable (you can always order another round), and completely immune to algorithmic hallucinations.

I am going to close my laptop now before the Riviera heat thermal-throttles my CPU. Enjoy the market madness, folks, and remember to keep your assets liquid…

Disclaimer: This is not financial advice!

Lets compile this list and break down the code behind these idioms, step by logical step.

When it rains it pours

You know the drill. Your primary server goes down, and exactly 1,5 seconds later, your automated backup fails, your ISP decides to do ‘routine maintenance’, and your smart coffee machine throws an Error 404. In the tech world, system errors do not queue politely. They DDoS your sanity. This idiom is just the universe reminding us that redundancy isn’t just a best practice; it’s a survival tactic.

When you piss against the wind, you get wet

It’s a crude saying, but it operates on solid logic. In our world, this is the equivalent of insisting on building your entire enterprise backend in an obsolete, unsupported framework from 2008 just because you want to be “different”. The wind is developer consensus and industry standards, my friend. Fight it, and you’re just going to end up soaking in 10.000 unresolvable compiler errors.

A wet blanket

Every dev team or IT department has one. They are the human firewall. You pitch a brilliant, revolutionary app feature, and they instantly hit you with the latency specs, the API rate limits, and the harsh reality that your UI animations will drain a smartphone battery in 12,5 minutes. Annoying? Yes. Absolutely necessary to keep the project from crashing and burning? Also yes.

Water under the bridge

Ah, legacy code. You wrote it three years ago at 3:00 AM, fueled by cheap energy drinks and pure panic. It works, nobody knows how it works, but the data flows over it smoothly. We don’t look at it, we don’t touch it, and we certainly don’t try to refactor it. We just let it flow.

Cold water on an idea

This is what happens during a Q3 budget review when you request 2.500 euros for a new workstation to “compile code faster” (read: play heavy games on ultra settings). Management pours the cold water. Your dreams of 128GB of RAM and a top-tier GPU are suddenly shivering in the corner. It’s the ultimate thermal throttling of ambition.

Water is wet

This is the ultimate baseline of truth. In the digital universe, this idiom translates to the irrefutable laws of physics: “Chrome eats RAM”, “Printers are inherently evil”, and “Users will always ignore the documentation”. You don’t argue with it; you just accept it and buy more memory.

So there you have it, the fluid dynamics of tech life. Keep your servers dry, your cables managed, and try not to spill your espresso on your mechanical keyboard…

Let’s talk about the elephant in the server room…

What if AI is actually just shit?

I know, I know. Total blasphemy in the tech world. As geeks, we are supposed to be bowing at the altar of Large Language Models and neural networks. But let’s take a calm, hard look at the reality. We are feeding 1.000s of terabytes of data into models with 175.000.000.000 parameters, and half the time, it hallucinates facts like a drunk uncle at a family barbecue.

Yet, we are bending over backwards to fit this square peg into our daily round holes. We want it to work so badly that we are willing to forgive a staggering amount of technical incompetence. Why?

The Hope, The Hustle and The Laziness

The harsh truth is that we are all exhausted. The average modern worker is managing 1.500 different digital touchpoints a week. We are so overwhelmingly busy that we are absolutely desperate to offload anything.

AI isn’t really selling us productivity; it’s selling us hope. The hope that we can claw back 2,5 hours of our day. We are ready to throw our workflows at a black box and just accept the collateral damage because, honestly, we are lazy AF.

If AI writes a slightly robotic email and gets a critical nuance wrong 5,5% of the time, we just shrug, hit send, and deal with the fallout later. “Good enough” has become the new gold standard, and we are willingly trading quality for the sheer convenience of not having to type.

Gullibility and The Unspoken Fear

Then there is the gullibility factor. And man, I absolutely hate that I have to name-drop this again, but here we are, repeating the same historical tech mistakes. We’ve hit the absolute high-tide moment of this hype cycle. We don’t call it FOMO anymore, that sounds too teenager-ish. Now, we dress it up in corporate-speak like “maintaining competitive agility” or “embracing the paradigm shift”.

WTF!

If anything else, t is just the fear of missing out… Again… Still…

Companies are burning through 10.000s of euros on AI integrations that essentially function as overpriced autocompletes. No one wants to be the one dev, or the one startup, left behind. So, we nod along, pretending the emperor is fully clothed, while the AI still struggles to count how many Rs are in the word “strawberry”.

The Self-Fulfilling Crap-Shoot

At this point, I think a lot of this is driven by pure fatigue. We are tired of the hype, but we’re locked in. We train these models on our own rushed, mediocre human output and the AI synthesizes it, dilutes it, and spits it back out. Then, we publish that AI-generated mediocrity back onto the web, where the next generation of models will scrape it for training data… It is like audio-cassette copies (I stop here showing my age)…

It is a completely self-fulfilling crap-shoot of diminishing returns…

But we accept it, because we just want to move on. We are already scanning the horizon, exhausted by prompt engineering, wondering, “What’s the next thing?”. Maybe Quantum Computing will come along and fix the mess we made with AI.

Pro-tip: it won’t…

So, we might just be settling for artificial stupidity because it saves our tired fingers a few keystrokes, but at least we can still enjoy a good, locally brewed Amsterdam IPA while the data centers slowly melt down…

When we look at GenAI and Large Language Models (LLMs), aren’t we just looking backward? Is this tech actually capable of building a new future, or is it merely making the past cheaper, faster and more redundant?

The short answer is: Technically, yes, it is a rearview mirror. But practically, it’s a bit more complicated than that. Lets check how these models actually work under the hood and why our skepticism is entirely justified (kind of), before we also look at the loophole.

The Code of the Past: Why You’re 100% Correct

From a pure computer science perspective, LLMs are, by definition, historical archives. They don’t experience the world; they ingest it after the fact.

• The Historical Snapshot: An LLM is trained on a frozen dataset, petabytes of text scraped from the internet up to a specific cutoff date. It creates a mathematical distribution of human history.

• The Next-Token Trap: When you type a prompt, the model calculates the statistical probability of the next word. It wants to give you the most plausible answer based on what humans have already written.

• The Optimization Bias: Right now, 90% of commercial AI tools are used for compression. We use them to summarize 50-page documents, generate standard boilerplate code, or write generic marketing copy. It’s making the status quo 10 times faster, but it isn’t inventing a new paradigm.

If a model tries to output something truly radical that exists outside its training data, its internal algorithm flags it as an error. It aims for a high confidence score, lets say, 99,9% certainty, that its output aligns with established human knowledge.

The Latent Space: Where Hidden Innovation Lives

So, if AI is stuck in the past, how can it possibly help us innovate? The answer lies in something called latent space.

When a model trains on millions of documents, it maps concepts into a massive, multi-dimensional mathematical grid. It calculates relationships between ideas that no single human brain could ever map out simply because we lack the bandwidth to read 1.000.000 books for example...

To understand how this leads to new discoveries, we have to look at the difference between how humans and machines find new ideas:

AI doesn’t need to invent a new law of physics to innovate. By connecting a dot from 18th-century botany with a dot from modern metallurgy, it can highlight a solid breakthrough that humans missed because those two fields never talk to each other.

The “Polder Model” of Modern Tech

In the Netherlands, we have the polder model, a traditional concept of fierce collaboration to achieve the impossible (like keeping a country that is mostly below sea level perfectly dry).

I see the future of AI in exactly the same way.

Don’t look at GenAI as the creator of the future. Look at it as the ultimate clean-up crew for the past.

By automating, optimizing, and rendering the mundane parts of our work redundant, AI strips away the cognitive heavy lifting. It handles the history, leaving the chaotic, unpredictable 1% of true human genius free to actually build what comes next. It’s not going to invent the future for us, but it is handing us the bricks…

How are you balancing this in your own workflow, are you using AI strictly to speed up the old stuff, or have you managed to use it to spark a completely new idea?

Below are a few classic day-to-day hardware and software moments in the real world that will make you realize it was a OSI Level 8 (user) error all along.

1. The Rental Car Fuel Roulette

The Glitch

You’re driving a shared Greenwheels car or a rental. You pull into the gas station, and a wave of pure panic hits you. Which side is the fuel cap on? You stretch your neck out the window like a confused giraffe, trying to see the reflection in the station window. You curse the automotive industry for not standardizing this one simple feature across all platforms.

The Patch

You sit at the pump and look closely at the dashboard instrument cluster. It turns out, right next to the little gas pump icon, there is a tiny, blatant arrow. It points either left or right. It has been there your entire life, occupying about 2 mm of screen real estate, telling you exactly where the tank is.

The Verdict

You’ve been driving for over 10 years and the vehicle’s UI has been whispering the answer to you the entire time. Solid engineering, user blindness.

2. The Pot Handle Hole

The Glitch

You’re making a solid pasta sauce in your apartment. You need to put the wooden spoon down, but you don’t want to get red sauce all over your clean countertop. You look at the hole at the end of the pot’s handle and think, “Why do they put holes in these? I don’t live in a rustic tavern; I don’t hang my pans from the ceiling. This is just a waste of material.”

The Patch

You look up the design blueprint. The hole isn’t just for storage optimization. It is perfectly sized to angle your cooking spoon over the pot so the sauce drips right back into the food instead of onto your stove.

The Verdict

You stare at the spoon resting perfectly in the handle like a sword in a stone. You have used a paper towel as a “spoon shield” for a decade for absolutely no reason.

3. Your Voice on an Audio Recording

The Glitch

You hear a video of yourself talking or listen to a voice note you just sent on WhatsApp. Your soul immediately leaves your body. Why do you sound like a nasal, prepubescent cartoon character? You immediately assume the microphone hardware is cheap or corrupted by some terrible compression algorithm.

The Patch

Time for a quick physics lesson. When you speak, you hear your own voice through bone conduction. The sound waves travel through your skull bones, which act like a natural bass booster, making your voice sound deep and rich to your own ears. A microphone captures what everyone else hears: just the raw sound waves traveling through the air.

The Verdict

The microphone isn’t broken; its calibration is actually perfectly accurate. Your skull has just been lying to you your entire life to protect your self-esteem.

4. The Toothpaste + Orange Juice Betrayal

The Glitch

You do the responsible thing and brush your teeth. Then, you pour a glass of fresh juice. You take a sip and your mouth is instantly flooded with the taste of battery acid and despair. You wonder why toothpaste turns a sweet fruit into a biochemical weapon.

The Patch

This is a classic chemical UI override. Toothpaste contains a foaming agent called SLS (Sodium Laureth Sulfate). SLS temporarily numbs your sweet taste buds and destroys the phospholipids that usually block bitter tastes. When you drink the juice, your brain processes the pure citric acid with zero percent sweetness to balance it out.

The Verdict

It’s not a magical chemical reaction; you literally just temporarily disabled your own tongue’s ability to decode joy.

5. The Microwave Turntable Dance

The Glitch

You’re heating up a leftover burrito. It’s spinning around on the glass plate inside the microwave, and you’re growing increasingly impatient. Why does it need to spin? It’s inside an enclosed box. Just sit still and get hot, you absolute drama queen of an appliance.

The Patch

Microwaves don’t actually use hot air; they use electromagnetic waves (usually operating at around 2,4 GHz) that bounce around the metal interior. Because of wave physics, these reflections create fixed “hot spots” and “cold spots” inside the chamber. If the burrito didn’t rotate, one half would turn into volcanic magma while the center remained a solid block of ice.

The Verdict

The rotating plate is a brilliant hardware workaround for a fundamental physics limitation. It’s the only thing standing between you and a trip to the emergency dentist.

The Final Verdict…

It’s funny how often we blame the product design when we just haven’t read the manual. Catch you in the next post, geeks. Let me know if you’ve been fooled by any of these hardware hacks yourself…

For us geeks, optimizing a system is second nature. But what happens when the system we are trying to optimize is ourselves?

The Optimization Hustle and the ATS Dragon

In the good old days, your CV was a simple, factual document. You wrote down what you did, where you studied and maybe threw in a line about your hobbies just to prove you weren’t a robot. It showed you were up for the job, plain and simple…

But nowadays? It’s a completely different ballgame. You might be competing against 1.500 other applicants for a single role, so you feel the pressure to become a 99,9% perfect match for the job description. If the old Applicant Tracking System (ATS) was a dumb dragon that just hoarded shiny keywords, the updated beast you are facing today is likely ATS version 2.0 or even 3.0. It doesn’t just scan for exact phrases anymore; it tries to infer context, grade your experience and match your semantic profile against 1.000s of other candidates. It is an algorithmic gatekeeper and honestly, trying to beat it by its own rules can feel like trying to bike against a brutal headwind on the Keizersgracht…

The Ghost in the Machine

We have to tweak our profiles continuously just to get “reach” and to be “seen”. And look, gaming the system is an old profession. I get it. We use the right keywords, we format for the parsers and we make sure the algorithms feed our names to the recruiters…

But here is the thought-provoking part: these modern profiles are correct, factual and in most cases entirely truthful... but they completely erase the person... Kind off…

The actual humanity, the quirks, the humor, the unique traits that make you an interesting colleague to sit next to, gets boiled down to sterile business facts... We strip away our personality and replace it with HR mumbo-jumbo, catch-phrases and buzzwords that only a recruiter’s scanning software responds to... We are essentially turning ourselves into walking SEO articles...

We are left with this bizarre Catch-22. You either play the game, strip away your humanity to appease the keyword scanners and maybe get an interview where you then have to desperately prove you actually have a pulse... or you write an authentic, human-centric profile and risk never being found at all. Apply and get ignored, or refuse to play and remain invisible…

The Trojan Horse Strategy

You cannot ignore the dragon, but you can kind of trick it into letting the real you inside the castle walls. We need a solid middle ground. Below a little sum-up of my personal experiences, tips-and-tricks I got from people that know this sh*t, and distilled it into 3 points:

• Feed the Machine its Data: Dedicate a specific, highly structured section of your CV purely to the hard specs. List your skills, software and certifications exactly as the job description phrases them. If they want a “Data Analyst” do not call yourself a “Numbers Whisperer”. Give the algorithm the 10.000 data points it craves so it checks its little boxes.

• Contextualize the Impact: The new dragon likes context. Instead of just saying “managed a team”, say “managed a team of 5, delivering the project 2 weeks early”. Numbers and clear outcomes satisfy the parsing logic while showing a human reader that you actually get things done.

• Keep the Summary Human: This is your Trojan inside the horse. Your professional summary is where you drop the corporate-speak. Once the bot flags you as a match and passes your CV to an actual human HR rep, this is what they will read. Make it punchy, let your personality bleed through and explain why you do what you do. Pro-tip: you can be a professional without being a robot. For reals!

The Ultimate Bypass

Honestly? The absolute best way to beat the ATS dragon is to not fight it at all. In the tech world, and pretty much everywhere else, the “backdoor” (or actually “frontdoor”), is networking. A solid referral bypasses the algorithm completely. If you find a job you like, find the team lead or the internal recruiter on LinkedIn and send a direct, polite message. People hire people; algorithms just filter them...

It is incredibly frustrating to feel like you have to compress your entire human experience into a machine-readable data file just to get a paycheck… But keep your chin up. Feed the machine the strict facts it needs, but keep your tone authentic enough so that when a real person finally reads it, they actually want to grab a coffee with you…

I think I need a fresh stroopwafel to process all this corporate dystopia… Do you think the system will ever swing back to valuing the human element first, or are we permanently stuck optimizing ourselves for the machines? …

We have been dangerously dependent on our devices for over a decade, that isn't exactly news. But the way AI is quietly weaving itself, disguised under a blanket of anthropomorphism, into this existing dependency is turning a bad habit into a massive, silent mental health crisis...

For years, we let our smartphones feed us cheap dopamine. We relied on them to distract us from boredom, using social media and endless feeds to numb out. It made us distracted, restless, and anxious...

That said, AI is fundamentally different, and significantly more dangerous. It isn't just hijacking our attention span anymore; it is hijacking our deep, evolutionary need to be heard and understood…

When you start relying on your tech not just for entertainment, but for comfort, therapy, and emotional validation, your mental health is squarely on the line… We are already living in a deeply lonely society…

Historically, that painful feeling of loneliness is what forced us out of our comfort zones to seek out real human contact…

Now, we are being offered a perfectly tailored, synthetic substitute. If you can get 100% unconditional support from a piece of software in your pocket whenever you feel a little sad, why risk the vulnerability and effort of talking to a real, flawed person?

The consequence is a profound and creeping isolation. We are slowly losing the psychological resilience required to deal with actual human beings. If you outsource your emotional regulation to a program that never challenges you, your own coping mechanisms will atrophy…

It is a slow-motion disaster…

We are creating a world where people are constantly connected to servers, yet entirely detached from humanity, unable to handle the slightest bit of real-world friction…

Rember: You are “talking” to a tool, not your best buddy…

We need to treat this as the mental health hazard it is. In my opinion, critical thinking is a very good way to stay “real" (this is NOT medical advice):

• Acknowledge the escalation: Accept that conversing with an AI is not the same as just scrolling through a news feed. It targets a much deeper, more vulnerable part of your psyche. Treat it with the caution it deserves.

• Stop outsourcing your emotions: If you are feeling anxious, stressed, or incredibly lonely, resist the urge to type it into a prompt box. Sit with the discomfort. Process it yourself, or bring it to a real human being.

• Reclaim the empty space: We have forgotten how to just exist. Put the phone in your pocket. Take a walk along the canals, listen to the bikes rattling by, and let your mind wander without a digital crutch. You need that disconnected silence to stay sane.

We cannot let our dependency on convenience cost us our minds…

Anyway, sorry for the “heavy” piece, I am just worried…

I think I am off to stare at some ducks in the Vondelpark instead of my screen for a bit to practice what I “preach” here.

What is the one digital habit or app you know you need to drop right now for the sake of your own sanity?

Grab a fresh coffee, because we need to dig a little deeper into this whole “AI discount” nonsense… Or in short:

“Gimme a discount because YOU use AI”…

Let’s maintain our calm, but also call a spade a spade: the people demanding these price cuts fundamentally do not know what they are talking about (duh!)… It’s a maddening display of tech illiteracy disguised as shrewd business negotiation...

Let me touch onto this a bit using some famous Dutch pragmatism and break down the absolute stupidity of the “AI discount” mentality…

The “Magic Wand” Delusion

There is this bizarre assumption floating around boardrooms that AI is an “easy button”. Clients read a headline about an LLM passing a bar exam, and suddenly they think my job consists of typing “make me a secure, scalable SaaS platform” into a prompt box, hitting enter, and taking a nap...

Here is the pragmatic reality: AI is a very fast, very confident idiot… Yes, it can generate 10.000 lines of boilerplate code in seconds. But it will also invent a nonexistent JavaScript library, misconfigure your API endpoints and casually introduce a security vulnerability that could leak your entire customer database…

The stupidity of the discount request lies in thinking the generation of the work is the hard part. It’s not… The hard part is the auditing, the critical thinking and the domain expertise required to clean up the AI’s mess and turn it into a functional, production-ready product…

You aren’t paying me to type; you’re paying me to know what to type and why the AI is lying to us…

The Economics of Ignorance: Sack a Colleague?

Let’s explore the dark, unspoken logic behind these discount requests. What exactly is the client’s endgame here? Do they expect tech agencies to sack their junior developers, replace them with a monthly AI subscription, and pass those “savings” down the line? Are we supposed to give a discount so the client can afford to implement their own AI tools and fire their own colleagues?

It’s completely backward. Implementing AI professionally isn’t free. Between enterprise-grade API access, secure cloud infrastructure to host local models (because we aren’t feeding your proprietary data into a public chatbot), and the sheer computational overhead, running a modern tech stack is expensive. An enterprise AI license can easily generate massive costs for a small team, not to mention the token costs and the variance of unpredictability around that… All risks the profesional takes of your hands...

We aren’t cutting costs to give you a bargain; we are investing heavily so we can build more complex, ambitious things for you…

You Pay for the Cheese, Not the Cow’s Chewing Speed

In the Netherlands, we have a saying: Doe normaal, dan doe je al gek genoeg (Act normal, that’s crazy enough). So let’s act normal and look at basic commerce…

If you take your bicycle to a mechanic in the Jordaan because the gears are jammed, you don’t care what tools they use. If they use a sophisticated pneumatic wrench to fix it in ten minutes instead of wrestling with a rusty spanner for an hour, you don’t ask for a 50% discount… You gladly pay the 85,50 invoice because your bike works, you didn’t have to fix it yourself, and the wheels won’t fall off when you hit a tram track…

Why do we treat software and digital strategy differently? The client wants a Michelin-star digital product at fast-food prices just because the chef bought a better food processor…

Raising the Ceiling, Not Lowering the Floor

The most frustrating part of this whole debate is the complete lack of vision. AI isn’t about doing the same boring, mundane tasks for less money. It’s about raising the ceiling of what is possible…

Because I use AI to handle the tedious data formatting or basic CI/CD pipeline setups, I now have the bandwidth to build you a predictive machine-learning integration that we never would have had the budget or time for three years ago… We are delivering exponentially more value, doing far more complex things in shorter timeframes. And somehow, the response to getting a vastly superior product is, “Can I get 20% off?”…

If you really think it’s that easy, and that the expertise doesn’t matter anymore, my advice remains the same: go do it yourself. See how far you get when your payment gateway goes down at 2:00 AM and you have to ask a chatbot how to fix a server logic error you don’t even understand…

Anyway, I need to go gently explain to a client why they can’t just host their entire e-commerce backend on a free tier of ChatGPT…

Have you had to deal with clients trying to fundamentally devalue your expertise lately, and how did you shut them down?

We are in mid-2026, and the tech landscape is undeniably… well… Solid? The models we have access to now are absolute beasts (both in a positive and negative way). We were promised that this era of AI would make us all 1.000x more productive. And sure, it did. But nobody really warned us about the architectural flaw in our new daily workflow: the crushing, anxiety-inducing boredom of waiting…

The reality of the modern AI-driven workday. It is no longer a steady stream of productivity; it is an extreme, bursty workload that is practically designed to fry our internal CPUs (brain).

The 100% CPU Spike

When you are working with an advanced model, it requires absolute, deep concentration… Really deep, even for any uncompleted stuff… You are constructing the perfect prompt context, setting up system parameters, loading in the right data sets, and doing serious mental gymnastics to ensure the machine understands your exact intent… For those 10,0 or 15,0 minutes, your brain is operating at 100% capacity. You are the conductor of a massive digital orchestra…

And then, you hit ‘Enter’…

The Void of the Progress Bar

Suddenly, your mental RPMs drops to absolute zero. You are plunged into a void…

The unpredictable latency of AI is a massive UX problem... You are waiting for the output, but it’s a black box… Will this complex data synthesis take 5,5 seconds, or will it queue up and take 4,0 minutes? You just don’t know what it’s doing on the backend…

You sit there staring at a blinking cursor or a spinning geometric shape, completely paralyzed... You can’t start a new deep-thought process because the AI might finish at any millisecond now and demand your attention back…

The Haunting Reiteration

When the output finally hits, the peace is instantly shattered... We all know AI rarely nails complex logic perfectly on the very first try. What follows is the haunting reiteration. You spot a hallucinated variable or a flawed structural loop. You tweak the prompt. You hit ‘Enter’. You wait again…

This cycle of high-intensity focus followed by forced, unproductive nothingness creates bizarre emotional spikes... High highs when the code runs flawlessly; frustratingly low lows when you are just staring at a screen waiting for a server cluster thousands of kilometers away to spit back a text file…

The ‘Legacy Processor’ Bottleneck

You finally get the perfect output… You just accomplished a solid 40,0 hours of deep analytical work in exactly 2,5 hours… You ship it off to your manager or another department…

Now you hit the ultimate bottleneck: human colleagues… Waiting again…

You are operating at AI speed, but the rest of the company is still running on legacy hardware (i.e., human reading speed and corporate bureaucracy)… You are forced to sit on your hands waiting for the business to digest the massive wave of productivity you just generated. The asymmetry between your output generation and their input absorption is staggering…

The Double-Stress Burnout Trap

Here is where the real danger lies…

Pro-tip: Human brains hate a vacuum.

During those low-lows, waiting for the AI to generate, or waiting for Steve from Accounting to read your 10.000-word automated report, anxiety creeps in. To alleviate the boredom, you spin up a secondary task. Maybe you fire up another AI instance to tackle a totally different project… And maybe another one… etc…

But then, the first AI finishes its job. Suddenly, you have two massive cognitive loads colliding and to take core of. You are context-switching between two deep-focus tasks, trying to keep up with machines that process information millions of times faster than you do... You fill every second of downtime with more workload anxiety…

It is a double stress factor. We aren’t working less; we are just packing 1.000 percent more context-switching into the same 8-hour window. It is an absolute fast-track to burnout, masked as hyper-efficiency…

Pace Yourself

We need to rethink how we pace ourselves with these tools before our own internal hardware crashes… The boredom of waiting should be turned into solid reflection, not to start another AI task/agent/etc…

Anyway, my current prompt just finished rendering after a solid 3,0 minutes of me staring blankly at the wall, so I’ve got to get back to the “grind”...

Try to remember to blink today, you geeks…

There is a weird psychological quirk we’ve all experienced… Usually, we care about quality… Lets take beer: We look at the brewing method, the flavor profile, the exact temperature of the tap. On a normal Tuesday evening in a corner pub, a standard Heineken is, frankly, garbage. A solid 3 out of 10. But… if you take that exact same beer and drink it in a highly specific situation, your brain completely ignores the taste. The environment takes over, turning absolute swill into nectar from the heavens…

Since my work involves dragging a suitcase across various timezones and enduring endless meetings, I’ve spent (forces) a lot of time “analyzing” this phenomenon… And discovered that the same thing applies in several situations…

Let me share my findings…

The 06:30 Airport Pint

This is the classic example. You are at Schiphol Airport, Terminal D, and the sun hasn’t even come up yet. Society’s normal rules don’t apply here. In this weird twilight zone, paying €10,50 for a watery, mass-produced lager makes perfect sense. You aren’t a person anymore; you’re just luggage waiting to be boarded. That overpriced beer hits exactly right and makes the chaos tolerable. It is 5 o’clock in the afternoon somewhere, right?

The Delayed NS Train Can

You are on the NS Intercity train out of Amsterdam, and the train grinds to a halt in the middle of nowhere due to “leaves on the tracks”. You’ve been stuck for 45 minutes, and despair is setting in. Then… you remember the lukewarm can of generic pilsner you grabbed at the AH To Go for €1,20. Normally, this stuff is used to unclog drains, but on a delayed train, it becomes a liquid ticket to inner peace…

The Midnight Minibar Rescue

Imagine a work trip where you’ve spent 15 hours in fluorescent-lit conference rooms. You finally swipe your keycard at the hotel room, completely exhausted. Everything outside is closed. You open the humming, wildly inefficient minibar to find a tiny €8,00 bottle of basic lager. It’s cold, but barely. Drinking it while sitting on the edge of the bed staring at the wall isn’t just a drink; it’s a mental reset button…

The Trade Show Plastic Cup

You’ve just walked 25.000 steps on thin carpets over concrete floors at a massive convention. Your feet and back hurts, and you’re carrying a bag full of flyers, goodies and other “merc” you’ll never read or use. Suddenly, some random company hosts a “networking hour” with a keg of the cheapest IPA legally allowed. Served in a flimsy plastic cup that bends in your hand, it tastes vaguely of soap. Yet, in that exact moment, it’s the most refreshing thing you could ever ask for…

The Post-Deadline Office Relic

You and your team just finished a massive, grueling project late on a Friday night. Everyone is running on fumes. You raid the office fridge and, tucked behind a jar of moldy sambal, you find a single, forgotten bottle of Amstel, past its expiration date by at least six months. You split it three ways in paper coffee cups. It tastes incredibly stale, but the feeling of victory makes it taste like vintage champagne…

The Amsterdam Staircase Moving Beer

If you live in this city, you know the absolute horror of moving. You’ve just spent four hours pivoting a heavy, oversized sofa up three flights of famously steep, narrow Dutch stairs. Your knees are shaking, and you are covered in a very specific type of dust mixed with sweat. The fridge was unplugged hours ago, so the generic supermarket brand beer your friend hands you is a questionable 17 degrees Celsius. Under normal circumstances, warm pilsner is a crime. But while you are slumped on a taped-up cardboard box, questioning your life choices, that lukewarm can tastes like pure, unfiltered victory…

The Summer Festival Token Pint

You are standing in a field somewhere outside the city. It’s been raining, your sneakers are coated in mud, and you just traded a colored plastic token, which somehow cost you €3,80 for a drink. The bartender hands you a squishy plastic cup filled mostly with foam and a splash of watery, flat lager. Someone bumps into you and spills a quarter of it on your jacket. But the bass is vibrating in your chest, the sun is finally breaking through the clouds, and your favorite band is walking onto the stage. In that exact second, that overpriced, flat beer is the greatest beverage ever crafted by human hands…

The Post-Long-Haul Shower Beer

This is the ultimate reset. You’ve just returned from a grueling 12-hour work flight. You feel grimy, jetlagged, and you can still smell the recycled airplane cabin air on your clothes. You walk into your apartment, ignore the unpacked suitcase, and head straight for the bathroom. The only thing left in your fridge from before your trip is a random, uninspired export lager. You take it into a scalding hot shower. The contrast between the freezing cold aluminum can and the hot steam creates a sensory overload that completely masks the metallic taste of the beer. It doesn’t just hydrate you; it washes away the travel fatigue…

The Craftbeer Snob’s System Failure

You are at a specialty pub in Amsterdam with your usual crew of beer aficionados… We’re talking about the kind of friends who debate yeast strains and refuse a perfectly fine tripel if it’s served in the wrong shape of glass… Honestly, we can be absolute snobs… Then, someone brings a round of something completely out of place to the table, a flashy, aggressively marketed can that goes against every rule of traditional brewing. It doesn’t fit the narrative, so by default, we all agree to hate it upfront… You take a sip, fully prepared to roast it with a scathing review. But here comes the massive anti-climax: it actually tastes fantastic. The cognitive dissonance is real... You look around the table, and everyone is silently, secretly, enjoying it. Surrounded by good company and the same tired, pretentious debates, this supposedly “crap” beer acts as a complete palette and arrogance reset. It forces you to drop the act and just enjoy a surprisingly great drink…

Context is everything!

Context really is everything… It changes how we taste things, proving that sometimes the experience relies entirely on the situation, not the actual quality of the product..

I’m going to sign off for the evening and grab something properly brewed, though honestly, a part of me misses that delayed-train can right now…

At the moment, a lot of boardrooms are panicking. They see the risks, they don’t know how to handle the tech and they default to slamming the big red ‘BLOCK’ button on the corporate firewall. But the truth is:

Blocking AI doesn’t stop AI. It just turns it into Shadow IT.

The Shadow IT Amplifier

When you block a tool that genuinely/assumingly makes an employee’s job easier, they don’t suddenly revert to the stone age. They just switch off the corporate WiFi, pull out their personal devices, and get the job done anyway…

Suddenly, you have 1.000s of unvetted AI interactions happening daily. That proprietary code your developer was struggling with? It just got pasted into a public web interface on a home network. I recently looked at an internal audit for a local firm that discovered an estimated 10s of 1.000s unauthorized API calls to AI services in a single month after a strict ban.. That’s not a security policy; that’s an amplifier for massive, unknown tech debt and data leaks.

Why the Boardroom is Terrified (And They Aren’t Wrong)

Let’s be fair to management for a second. We need to cut through the marketing hype and criticize the tech itself. Companies default to blocking because AI is not a magical, plug-and-play intern. It is a wildly complex, volatile power tool that practically requires a 10.000-page manual that nobody ever wrote…

The most frustrating part for us geeks? It’s non-deterministic. If you query a standard SQL database 1.000 times, you get the exact same answer 1.000 times. You ask a Large Language Model the same question twice, and it might hallucinate a completely different variable. When dealing with critical business logic where a 0,50% margin of error can tank a project, that unpredictability is a massive liability…

The Subject Matter Expert Rule

Because of this volatility:

AI should only be operated by subject matter experts. Period.

The biggest fear companies have, and the main thing they are trying to prevent, is a blind copy-paste culture. An experienced developer or data analyst can look at AI-generated output, instantly spot the 15,25% that is absolute garbage, and correct it. A novice who blindly copies that same output just introduced a critical vulnerability into your pipeline. AI doesn’t replace expertise; it strictly requires it to function safely. If you amplify a lack of knowledge, things get catastrophic fast.

And no, replacing “expensive” matter experts with a “Junior + AI” for costs, scale and be more profitable… Just no…

The Subsidized Token Trap: Cost Savings vs. Reality

And then there is the financial delusion. Right now, executives are foaming at the mouth to introduce AI purely as a “cost-saving” measure. This is a trap…

API tokens might look dirt cheap today, but remember that they are heavily subsidized by the tech giants to build a market and hook you in. We are building up a massive, systemic dependency across the corporate world. Usage is rocketing sky-high, and the inevitable reality is that token prices will eventually become very expensive... Good luck with those “cost savings” when your entire operational workflow relies on an API that just hiked its prices by 400%…

Introducing AI to save costs is entirely the wrong angle. It has to be a calculation of costs versus productivity and outcome (I might have shown my age with that sentence). It’s bottom-line stuff. If you find yourself in a position where you are considering replacing personnel with AI just to afford your skyrocketing token costs... well, that’s a whole other nightmare…

And speaking of financial traps… Let’s look at the absolute absurdity of tracking and gamifying AI token usage to measure or even motivate employee productivity… (I hope you heard my massive deep sigh)…

Management is trying to justify their massive AI budgets by monitoring how many tokens a team burns through, without measurable outcomes or map the costs to revenue, productivity increase etc… Together with a ridiculous trend of “tokkenmaxxing”, invented by the AI industry themselfs…

If you tie an employee’s value or budget to their AI usage, you aren’t measuring productivity, you’re just incentivizing them to pump the costs even more. People will waste 10.000s of tokens on useless prompts just to hit an arbitrary KPI, completely ruining their attitude toward actual work…

Gamifying AI usage to justify a bloated tech spend... Let’s save that headache for another time/blog.

The Overlap: Illiteracy and the Impossible Task

This is where the financial, technical, and human problems collide. Because of widespread AI illiteracy at the management level, companies don’t understand their own operational problems well enough to deploy AI safely or economically. They see the volatility of the tool, the dependency risks, the complex incoming laws (like the EU AI Act), and they freeze. That said, they probably were clueless already and not only with/for AI, it says more about the company then the tech…

We have an old Dutch saying: “Voorkomen is beter dan genezen” (prevention is better than healing). When it comes to the legal, ethical, and financial tech debt generated by rogue or ignorant AI usage, preventing the damage is infinitely cheaper than trying to fix it. But blindly blocking it makes prevention an impossible task.

How to Fix It: Facilitate, Don’t Frustrate

You have to facilitate AI to control/manage it. Here is a pragmatic playbook:

• Build the Sandbox: Give your teams a safe, ring-fenced, enterprise-secured environment where data isn’t used to train public models and token usage can be monitored.

• Gatekeep by Competence: Distribute these tools based on expertise. Give access to the professionals who actually have the knowledge to assess and validate the AI’s output.

• Establish Total Clarity: A vague “use responsibly” in the handbook is useless. You need crystal clear do’s and don’ts tailored to specific departments.

• Focus on Outcome, Not Just Cost: Evaluate AI tools based on net productivity gains, factoring in the future risk of token price hikes.

• Combat Illiteracy: Invest heavily in training. An employee who understands why an AI hallucination is dangerous is the best firewall you can buy.

• Embrace Dutch Pragmatism: Look at the actual workflow. If an AI tool realistically saves an expert 12,5 hours a week, blocking it is just bad business. Your job is to figure out how to facilitate that efficiency gain securely.

It’s time to stop treating AI like a virus, or a cheap replacement for human expertise, and start treating it like the extremely costly and high-maintenance infrastructure it is. If you don’t build secure, sensible roads for your experts, people are just going to drive on the grass and wreck your lawn…

Now, if you’ll excuse me, I’m going to go reboot my router and pretend that solves all my administrative problems…

The Myth of the “Deciding” AI

We are living in an era where every piece of software is suddenly branded as “smart”…

From your smart thermostat to massive enterprise LLMs, the marketing pitch is constantly implying that these tools are making decisions for us. But here is the hard, uncompromising truth:

Tools don’t make decisions.

Going to steal a popular meme here, but here it goes: Back in 1979, an internal IBM training slide stated something profound: “A computer can never be held accountable, therefore a computer must never make a management decision”.

Nearly fifty years later, that logic is still completely solid. Yet, I see companies acting like this foundational rule suddenly expired just because we have better GPUs. We need to untangle two concepts that get thrown into the blender way too often: responsibility and accountability.

Responsibility vs. Accountability

In the engineering world, we like clean definitions. Here is the breakdown:

• Responsibility is about execution. It’s the engine doing the manual labor. If you need to parse 1.500.000 rows of log data in 0,2 seconds, you give that responsibility to a script or an AI model. It does the heavy lifting.

• Accountability is about ownership. It’s about who takes the hit when the server crashes, the data gets leaked or the company loses money.

A piece of code cannot be fired. An algorithm cannot go to jail. An AI model doesn’t care if a project tanks or if it outputs a completely fabricated legal precedent. Because a tool cannot be accountable, it inherently cannot make a decision. A decision carries weight, context and consequence. What the AI actually does is compute a statistical probability and generate an output based on its training. The decision is what you, the operator, choose to do with that output (including to automate that).

The Ultimate Cover-Up

Lately, “The algorithm did it” has become the modern corporate equivalent of “The dog ate my homework” (I tend to say this a lot lately for a lot of things)…

When an automated HR system filters out perfectly good candidates or a dynamic pricing algorithm artificially inflates the cost of a flight to 2.500,00 euros, the immediate PR response is often to blame the tech. It’s a highly convenient smokescreen...

Blaming the tool is just a “polite” way to cover up:

• Mismanagement: Throwing shiny tech at a fundamentally broken business process and hoping it fixes the culture.

• Operational Deficiency: Failing to implement proper safeguards, error-handling or hooman-in-the-loop protocols.

• Lack of Knowledge: Buying an expensive enterprise AI suite without understanding its parameters, biases or technical limitations.

If your system fails, it’s not because the AI was “bad”. It’s because the operator deployed a tool they didn’t fully understand, in an environment that wasn’t ready for it, without bothering to check the output.

The “Scale” Trap: When the Hooman Drowns

The very first step of the process: selecting the tool itself. Accountability doesn’t just start when the AI spits out a result; it starts the moment you sign the procurement contract for that shiny new system…

Here is the classic scenario. Management buys a high-throughput AI tool to handle, let’s say, network security alerts or customer support triage. They boldly implement a “Hooman in the Loop” policy to feel secure. In theory, perfectly responsible…

But in practice? The machine is churning out 15.000 flags or drafts a day, and there are exactly three hoomans assigned to review them…

You don’t need a PhD in queueing theory to see the bottleneck here. It is like trying to bike through the Leidseplein on a Friday night, complete gridlock. When the hooman in the loop cannot physically keep up with the load the machine generates, something dangerous happens: automation complacency…

Those reviewers get exhausted. They get alert fatigue. Instead of rigorously checking the AI’s math, they start mindlessly clicking “Approve” just to clear the queue. They wave goodbye to their responsibility and accountability altogether, simply because the sheer volume makes critical thinking impossible...

And this is where the situation gets infinitely worse than having no AI at all. Now, you don’t just have a machine making unsupervised decisions; you have a machine making unsupervised decisions with the official rubber-stamp of hooman approval. It gives a massive illusion of safety while actively destroying it.

If the system approves a fraudulent transaction of 50.000,00 euros because the reviewer was too swamped to actually look at it, you can’t blame the reviewer for being slow, and you definitely can’t blame the AI for doing exactly what you bought it to do…

The accountability failure happened on day one. Selecting a tool that operates at a scale of 1.000, while your hooman oversight capacity maxes out at 10,0, is a fundamental architectural failure. If you buy a Formula 1 car but only hire a bicycle mechanic to maintain it, don’t act shocked when the wheels fall off at 300 km/h…

You have to scale your hooman oversight logically with your automated throughput. If you can’t afford the hooman bandwidth to properly review the machine’s output, you simply can’t afford the machine. It is a solid rule of thumb…

Who (not what) is responsible and accountable in this context? Do you feel it yet? :-).

Doing It Right: Enter Critical Thinking

I know… I am repeating myself a lot on this… But, how do we navigate this without becoming “luddites” who refuse to use modern tools? It requires a level-headed approach, a healthy dose of common sense and, dare I say it: critical thinking…

Here are a few examples on how to keep the accountability exactly where it belongs:

• Treat AI like a brilliant junior developer: If you hire a smart but inexperienced junior coder, you let them write the boilerplate code, but you always review their pull requests before pushing anything to production. You don’t blindly trust their output because they lack architectural wisdom. Apply the exact same logic to your tech stack. It’s a tool to accelerate your workflow, not a replacement for your senior expertise.

• Define the “Hooman in the Loop”: For any automated system that impacts real people or real money, there must be a defined hooman checkpoint. Let’s say you’re using an AI tool to draft medical diagnostic summaries. The AI can highlight anomalies in a dataset in 0,5 seconds, which is incredibly efficient. But the doctor signs off on the final diagnosis. If the diagnosis is wrong, the doctor is accountable, not the software vendor.

• Audit your inputs and outputs: If your dataset is garbage, your output is garbage. If you use an algorithm to optimize supply chain routes and it accidentally reroutes 5.000 delivery trucks into a flooded zone, the failure wasn’t the AI being malicious. The failure was the operator not validating real-time environmental constraints within the model’s parameters.

Wild and Powerful

Tools are getting wildly powerful and as a geek, I think that’s a beautiful thing. I love pushing the limits of what my hardware and software can do. But the more powerful the tool, the heavier the accountability on the operator holding it. Keep your system specs high, but keep your standards for hooman oversight higher.

Anyway, my coffee has officially gone cold… Keep building smart things, folks, just remember who is actually steering the ship… The captain… You…

If you’ve spent any time pair-programming with modern LLMs, you’ve noticed the paradox... AI is terrifyingly good at writing that first draft of a script. It is equally brilliant at fixing the script when you paste the error logs back into the chat. So, the million-dollar question is: Why doesn’t it just write it flawlessly in the first place? Why can’t we just tell the AI, “Write this app, and keep iterating internally until you fix all the dead code and regressions, then give me the final product”?

It feels like the ultimate example of Hooman-In-The-Loop (HITL), but to understand why we are stuck in this loop, we need to look under the hood. It boils down to a mix of token mechanics, architectural blindspots, and the fundamental nature of how these models “think”…

1. The Probabilistic Trap

First, we have to remember what an LLM actually is. It is not a compiler. It is not a deterministic logic engine. It is a highly advanced, probabilistic text predictor. When it generates code, it isn’t “building” a mental map of your software architecture; it is guessing the next most likely sequence of tokens based on its training data…

When you ask it for a complex feature, it plots a statistical path. But code requires 100,0% deterministic accuracy. A single missed semicolon or a misaligned variable scope ruins the whole execution. The AI can guess right 99,9% of the time, but that 0,1% margin of error is where your application crashes. It can’t catch it beforehand because it doesn’t run the code before outputting it…

2. The Context Window and “Attention Dilution”

You might think, “Well, just give it a bigger context window!” Modern models can hold upwards of 1.000.000 or even 2.000.000 tokens. That sounds like enough to grasp the “big picture,” right?

Not exactly. Even with massive context windows, models suffer from attention dilution. They are great at understanding the immediate syntax of the function they are writing right now, but they start to forget the intricate downstream dependencies of a module they wrote 500 lines ago.

When you ask an AI to iterate on its own without hooman intervention, it easily gets lost in the weeds. It will fix one regression, but in doing so, it statistically drifts away from the original architectural constraints, creating two new bugs somewhere else. It simply cannot hold the entire “big picture” in crisp, absolute focus the way a strict static analyzer does…

3. The Lack of a Ground Truth

Why does the AI suddenly become a genius again when you point out the error?

Because you are providing the Ground Truth. You are the runtime environment.

When you run the code, compile it, and get an error stack trace, you are introducing hard, deterministic facts back into the AI’s probabilistic brain. You are essentially saying, “Your statistical guess failed at line 42 with this exact memory leak.” The AI then uses that highly specific constraint to recalculate a new probability path, which is usually right on the money.

If you tell an AI to “keep iterating until it works,” it has no way to verify if it works. It would have to write its own test suites, but because it is generating the tests using the same probabilistic logic it used to write the code, the tests will likely validate the flawed logic. It becomes a hallucination loop…

The Ultimate HITL

So, yes, AI coding is the prime example of HITL, and it probably will be for the foreseeable future. We are not just prompt engineers; we are the deterministic anchors for probabilistic engines. We provide the compilation, the architectural vision, and the boundary conditions. The AI provides the raw, brute-force syntax generation.

It’s a solid partnership, honestly. We get to skip the tedious boilerplate, and the AI gets a safety net so it doesn’t accidentally delete the production database while trying to center a div… ;-).

How are you currently managing your AI workflow, do you write your own tests before feeding them to the AI, or do you let the AI write the tests and just act as the hooman compiler yourself? ….

Let’s unpack this, because fundamentally, this entire concept is a massive contradictio in terminis…

The Paradox of “Responsible Vibes”

For the uninitiated, “vibe-coding” is the act of using AI to generate software purely through natural language prompts. You don’t write syntax; you just throw ideas at the wall, tell the AI the “vibe” of what you want, and copy-paste whatever it spits out until the app magically compiles…

But trying to bolt the word “responsible” onto that process completely misses the point of why the process exists in the first place.

Here is the reality of the tech stack when you are vibe-coding:

• Zero Architecture: You aren’t planning out a scalable database schema; you’re just asking for a login page.

• Performance Ignorance: There is absolutely zero thought given to Big O time complexity or memory leaks.

• Spaghetti Output: You usually end up with about 1.500 lines of heavily abstracted, unmaintainable code.

The whole point of vibe-coding is to give a sledgehammer to non-developers, the “noobs”, if we’re being honest, so they can smash out a prototype that normally would require a dedicated dev-force and a budget of 50.000,00 euros. It is inherently chaotic, fast, and completely irresponsible by traditional engineering standards. That is its actual superpower… I think… Maybe…

If You Want It Responsible, Just “Code”

The idea of teaching someone to vibe-code “responsibly” implies that you can somehow maintain strict adherence to security protocols, data compliance, and clean architecture while still just talking to a chatbot…

You can’t. If you want your application to be secure, maintainable, and hit a 99,9% uptime standard, you don’t try to “vibe” your way into strict compliance. You drop the vibes and you actually code.

To fix software responsibly, you need to understand exactly what the machine is doing at a structural level. You need to know why a specific dependency is vulnerable, or why your API calls are bottlenecking. If you are reliant on a course to teach you how to prompt an AI into not making a mess, you are essentially trying to learn software engineering without learning the engineering part.

Vibe-coding is a fantastic tool for rapid prototyping, personal scripts or getting a minimum viable product off the ground over a weekend. But trying to institutionalize it with “responsible” courses feels like trying to put a speed limit on a rollercoaster, it just ruins the ride without actually making it a functional method of transportation.

Let the noobs vibe, and let the engineers code. Trying to merge the two just leaves everyone frustrated.,,

Anyway, that’s my rant for the afternoon. Have any of you actually tried building a serious, production-ready project purely through vibe-coding, and if so, how quickly did it turn into a complete dumpster fire?

I was browsing through my feeds this morning while sipping a… lets call it an “espresso”, and I stumbled upon a piece from The Pragmatic Engineer about a new Silicon Valley trend called “Tokenmaxxing”. And I have to confess something: every time I see the word “token” anywhere in tech, my deformed, aging tech mind immediately screams: “Token-Ring”…

I know this because I am old, and back in the day, I actually deployed Token-Ring networks in Fortune 500 companies. For the younger folks who never had the pleasure of dealing with thick cables and Multistation Access Units (MAU), Token-Ring was a local area network protocol where computers physically passed an electronic “token” around a loop. You only had one token. If your machine had the token, it had permission to transmit data. It was orderly, it was polite, and it was agonizingly slow…

You respected the token. You cherished the token…

Now, let’s fast forward to 2026 and look at “Tokenmaxxing”. According to the article, developers at giants like nVidia, Meta, Microsoft, and Salesforce are actively trying to burn as many AI tokens (the units of data processed by LLMs like Claude or Copilot) as humanly possible. Why? Because management set up internal leaderboards and minimum spend quotas to track who is the most “AI-native”… Sigh…

Devs are literally asking their AI agents to read massive, irrelevant documentation to answer basic questions 10x slower, or prompting complex code generation for projects they have zero intention of building, just to throw the results in the digital trash. Over at Meta, employees reportedly burned through 60.200.000.000.000 tokens in a single month. Yes, 60,2 trillion. If paying retail, that’s roughly a $900.000.000,00 API bill… WTF!…

It is incredibly silly, there is absolutely no point to it, and it is clearly just a craze. But let’s be honest with ourselves: tokenmaxxing is nothing new. It’s just Goodhart’s Law dressed up in a shiny new AI wrapper. When a measure becomes a target, it ceases to be a good measure…

Think about the classic analogue cases that map perfectly to this nonsense:

• Lines of Code (LOC) Maxxing: The oldest trick in the book. “Oh, the managers are tracking productivity by how many lines of code I write? Watch me deploy the most verbose, unoptimized, 1.000-line switch statement known to humanity instead of a simple array lookup.”

• Toner-maxxing: The 90s office classic. You needed to look incredibly busy for the director walking the floor, so you sent a 500-page blank document to the heavy-duty LaserJet. The relentless mechanical whirring of the printer made you look like a highly critical asset doing very important corporate things.

• Green-status Maxxing: Strapping a desk fan or an electric toothbrush to your mouse so your Microsoft Teams status stays a productive, healthy green while you’re out buying stroopwafels at the Albert Heijn.

• Health Maxxing: That corporate FitBit you got to see if you are actually contribute to your health. Healthy-body, healthy-mind and ready to put the new-found energy and mindset into the job… If you owned a dog, he got used wearing it on its tail.

Optimizing for an input rather than an output is fundamentally broken. It’s like measuring the skill of a cyclist here in Amsterdam by how many bicycle chains they snap in a month. “Wow, Maarten snapped 4 chains this week, he must be pedaling incredibly hard!” No, Maarten just doesn’t know how to use grease, and he’s costing us a fortune in repairs…

The irony is beautiful, though. In the Token-Ring days, we waited patiently for one single token to do real, tangible work. Today, developers are hoarding and setting millions of tokens on fire to do absolutely nothing…

Eventually, the CFOs are going to look at these massive AI bills, realize they are paying $170,00 a month per developer for generated garbage, and the leaderboards will vanish overnight...

Pro-Tip: Ignore the gamified noise, keep a calm head, and just focus on shipping solid software. Please.

Now, I’m going to grab another coffee and maybe stare at a picture of an old Token-Ring adapter just to calm my nerves… Maybe relive the IBM vs Madge game that was going on at that time… God, I miss those days…

What’s the most ridiculous vanity metric you’ve ever had to game at work? Let me know below!

The Cult of the Algorithm

A strange phenomenon is taking over our digital backyard right now. Artificial intelligence is rapidly mutating from a genuinely solid piece of tech into a weird blend of pseudo-science and pseudo-religion. I would honestly prefer not to drink this specific cocktail. But I will admit to taking a sip on occasion when I am desperately trying to parse a messy block of code at 3 AM…

We actually have a problem on our hands: The gullible outnumber the skeptics. Millions of users treat Large Language Models like omniscient oracles rather than the predictive text engines they actually are.

Specs, Not Spells

Break this down logically for a second. Your favorite AI is not channeling the cosmos. It is a massive statistical engine. We are talking about neural networks often packing around 1.500.000.000.000 parameters that process inputs to guess the next logical token. Run a prompt with a temperature setting of 0,7 and you get a creative but highly mathematical output. It is math, not magic…

Point out that a model has a factual hallucination rate of 8,5% or that its logic tree completely collapses under complex reasoning, and people get incredibly defensive for some reason…

The Anti-AI Fallacy

Any valid criticism of AI is currently being shouted down as being “Anti-AI”. That is factually incorrect. Demanding transparency, questioning training data, and pointing out structural flaws is actual, very necessary critical thinking…

People refuse to be told off. They love the tool too much. It gives them instant gratification, and nobody wants to hear that their new favorite productivity hack might just be feeding them confidently delivered nonsense.

• Pro-tip: A tool is only as reliable as your own ability to verify its output. Outsource your baseline logic to an algorithm, and you are not upgrading your workflow. You are downgrading your brain…

A Dangerous Divide

This blind faith is incredibly powerful, but it is undeniably dangerous. It divides communities in a way that is weird but entirely understandable. Absolute zealots stand on one side and hardcore doomers on the other. Very few geeks are left in the middle who actually care about the architecture, the limits of the math, and the ethical guardrails…

Hoomans love a silver bullet. We love the idea of a machine that can do the heavy lifting for us. The reality is that we are losing minds here… We trade intellectual rigor for convenience and build a strange tech-based dogma in the process…

My smart thermostat just decided to drop my apartment ambient temperature to 15,5 degrees because it learned I like to be cool. I clearly need to go have a stern manual chat with an algorithm before I freeze to death…

Keep your wits about you, and stay skeptical…

Every startup pitches it as a magical x10 multiplier. You write code? Now you’re a 10x developer. You write copy? Boom, you’re churning out 10x more articles a day.

But from where I’m sitting, looking at the actual day-to-day deployment of these large language models... the “A” is starting to look an awful lot like “Attenuation” instead. Why does our promised warp speed feels more like a slow crawl?

The Myth of x10 Amplification

We are sold this vision of unbridled efficiency. The pitch is solid: plug the API into your workflow, let the neural nets do the heavy lifting, and watch your output scale logarithmically. But here on the ground, the reality of deploying generative models is significantly messier than the slick keynote presentations suggest.

The x0,8 Attenuation Reality

Let’s do the math on this alleged efficiency. You want to automate a task that usually takes 1 hour of focused work. You hand it off to your shiny new AI sidekick.

• The Prompt Tax: First, you spend roughly 25 minutes wrestling with prompt engineering because the bot does not completely understood the context or decided to format the data as a haiku. They call this “reiteration”, which you normally do, but now it is more of a back-n-forth then really getting somewhere.

• The Hallucination Tax: Then, you spend roughly 30 mins meticulously fact-checking the output, because the AI confidently hallucinated a completely fake software library or cited a non-existent metric.

• The Integration Tax: Finally, you spend another 20 minutes (re)formatting the output so it actually fits into your company’s existing rigid templates.

NOTE: Not even taking into consideration the “waiting” on the response/output which varies so much it is completely unpredictable, plus the disconnects, halting/stalling for no reason, run out of tokens when almost finished, and sometimes AI goes into verbal diarrhea mode, making you redo-from-start.

But… Congrats! Your x10 efficiency just netted you x0,8 productivity. The AI didn’t give you less work; it gave you different, slightly more annoying work. We’ve effectively become middle managers for very confident, very fast, digitally intoxicated interns. It’s incredibly difficult to trust a system that is fundamentally probabilistic when your daily operations require strictly deterministic results.

The Meatware Bottleneck

Then there’s the human element. General AI illiteracy is incredibly high. Most organizations are trying to strap a jet engine onto a rusty omafiets (grandma bike).

Good luck prompting…

They have legacy processes designed around human verification, human pacing, and human error. Now, they are trying to inject non-deterministic AI outputs into the middle of these rigid structures. You can’t just slap a chatbot onto a corporate bureaucracy from 2015 and expect miracles. The processes aren’t up to speed, the compliance teams are having collective panic attacks, and the average user still thinks the AI “knows” things rather than just mathematically predicting the next most logical token…

Do We Actually Need (or Want) x10?

Think about it. If everyone starts pumping out 10.000 emails, Slack messages, and Jira tickets a day using AI, the only way to process all that incoming noise is to use more AI to read and summarize it. It becomes a closed, infinite feedback loop of synthetic noise. We are generating digital garbage just so our machines have something to parse, while the humans sit on the sidelines wondering why they feel so exhausted and become more clueless by the day…

Maybe x0.8 is exactly what we need right now. A slight attenuation to force us to slow down, verify the facts, and actually apply critical thought to the digital artifacts we are putting out into the world… Not just using the tool because of the tool…

Anyway, I’m off to manually fix a script my “intelligent” code-buddy decided to rewrite using deprecated Python 2.7 libraries. Keep your code clean and your expectations grounded, folks…

Let’s talk about the new VC. And no, I don’t mean Venture Capital throwing 2.500.000 euros at a startup whose only product is a thin wrapper around someone else’s API. I mean Vibe-Coding…

The Era of Vibe-Coding (VC)

Historically, we were digital blacksmiths. We hammered out memory allocations, forged our own sorting algorithms, and bled over missing semicolons. Now? We are transitioning into something resembling ancient oracles or alchemists…

Vibe-coding is essentially programming by intent, mood, and natural language. You don’t write the boilerplate; you whisper an incantation to your AI, and it conjures up 1.000 lines of Python. You are no longer explicitly defining logic; you are casting spells. And like any ancient magic, the results depend entirely on the purity of your vibes and how well the spirits (LLMs) interpret your prompt…

The New CI/CD Pipeline

In this mystical age of VC, our trusty CI/CD pipeline (Continuous Integration and Continuous Deployment) has quietly evolved into something far more psychological. I like to call it Continuous Intuition and Continuous Delusion.

Let’s break down this modern alchemy:

• Continuous Intuition (The Spark): This is the vibe-coding phase. You don’t draft a rigid 50-page architecture document; you just feel the app into existence. You prompt the engine with, “Build me a sleek dashboard that feels like a warm breeze but handles 10.000 requests per second”. You are channeling your pure intuition. The AI parses your aura and outputs an entire React frontend… Boom!

• Continuous Delusion (The Trap): This is where the ancient lens gets a bit dark. Once the code generates, we enter a state of collective delusion. We assume the black box understood our vibes perfectly. We merge it. We deploy version 1.0 directly to production, fully delusional that the AI handled edge cases, memory leaks, security, and any race conditions. It is the modern equivalent of dancing for rain, seeing a drizzle, and assuming you are a weather god…

The Ancient Lens on Modern Tech

Think about it logically. We are treating modern AI agents like the Oracle of Delphi. We bring them offerings (context windows), we receive cryptic but seemingly functional outputs (code blocks), and we implement them without fully understanding the underlying mechanics.

We have traded the heavy hammer of explicit syntax for the mysterious wand of semantic intent.

This isn’t inherently bad. Being an alchemist is arguably less tedious than being a mechanic. But if you don’t understand the fundamental laws of digital physics, your vibe-coded castle will eventually crumble under the weight of its own technical debt.

The abstraction layer is getting thicker, and at some point, you still need to know how to read the raw runes (the actual source code) when the spell inevitably misfires.

Alright, my coffee is cold and I need to go unlock my bike before it rusts into the pavement…

Before I log off: How much of your current codebase would you say is built on solid, understandable engineering, and how much is just you successfully vibing with a black box?