I’ve been lifting weights at home for a few years now, but recently I’ve found myself stuck in a rut. If anything, my routine has become a humdrum habit, without a clear goal and sense of progression to keep me motivated. Ten exercises, three sets, eight reps, 30 minutes. I’ve run through it so often that I could do it in my sleep.

I’m a true tech lover, so of course I got artificial intelligence (AI) to help me out. Apple has just launched its revamped Siri AI and I’ve been playing around with its early beta edition, so I was curious to know if its new skills extended to solid fitness advice — and if they did, how they held up against the ever-popular ChatGPT.

To find out, I asked each chatbot to dream up a workout routine that could revitalize my fitness habits. After working through both plans and tapping a professional personal trainer for their insights, I was left with the impression that only one of the two workout plans was worth sticking with.

The setup

To start with, I put together a prompt that I then fed into both AIs. The goal was to briefly explain my physical state and outline the kind of workout I was after, noting any constraints such as time and location (I work out at home).

Here’s the prompt I used:

“I am a 36-year-old man in good physical shape who exercises regularly. Create a three-day overall workout plan for me with the goals of hypertrophy and building muscle. The workout will be done in a small home office using dumbbells as the only equipment. It can include calisthenics as well as weighted exercises and should aim to cover different areas of the body. Ensure it lasts no longer than 45 minutes. Explain how often each workout session should be repeated over the course of a week and when rest days should take place, if required.”

I didn’t add any subsequent follow-up messages, as I wanted to know what each AI could come up with given the kind of information that a regular user might provide.

The new challenger: Siri AI

At its Worldwide Developers Conference (WWDC) in June, Apple made a big song and dance of its new Siri AI virtual assistant, a new foundational model based on Google’s Gemini LLM.

When given my workout prompt, Siri AI put together a plan that included a “push” day focusing on my chest, shoulders and triceps; a “pull” day centered on my back, biceps and rear delts; then a final “legs and core” day. It offered a workout schedule and suggested that each session would take around 45 minutes, with three sets of 8-12 reps and a rest of 60-90 seconds between each set.

“Focus on controlled movements and progressive overload by gradually increasing the weight of your dumbbells or the difficulty of your calisthenics,” it advised. Each exercise was accompanied by a short explanation of how to perform it, which was a welcome touch.

Putting Siri’s action plan into practice, it was obvious that my old self-made workout plan wasn't pushing me hard enough, as the first day’s workout left me feeling like I’d worked harder than I had for months. Off to a good start, I thought.

But Siri’s plan came up well short of 45 minutes, with most days’ efforts lasting a mere 25 minutes. And aside from the lack of warm up, there was also no word on how to complement the exercises with targeted nutritional intake. That’s a serious oversight in its workout regimen, especially for beginners.

To help me better understand the plans from both Siri AI and ChatGPT, I asked personal trainer and fitness coach Scott Laidler for his opinion. Speaking of Siri’s effort, Laidler said that it had a few things going for it.

“Siri’s is simple which means there is little to misinterpret,” said Laidler, adding that “the equipment substitutions are sensible for the office environment.”

While Siri recommends upping the weight as you get stronger, it doesn’t say “what to do once this plan hits diminishing returns, which is months away, not years,” according to Laidler. While it sounds like an open-ended plan, “it’s really only built for a defined window,” Laidler warned, which could “lead to frustration unless you knew to come back to the table and ask for a new plan several times per year.”

That’s a problem inherent in the responses from both Siri and ChatGPT.

The class leader: ChatGPT

While Siri AI’s workout plan was concise, ChatGPT was much longer. It explained what the priorities of the workout were and gave some basic advice, such as aiming to finish with one or two reps in reserve as a guidance on how much weight to use. It then listed a weekly schedule before laying out a simple warm up to perform each session.

Each exercise was listed, with varying numbers of sets and reps, followed by a note on which muscle groups were being targeted. Unlike Siri, there was no explanation of how to perform each exercise, which feels like an oversight with potential for injury. That was a clear win for Siri AI.

At the end, ChatGPT included a “progression” section explaining how to push yourself further, a guide on recovery and nutritional intake, then an explanation of “why this split works.” Taken together, it was a detailed workout plan, if a rather verbose one.

According to Laidler, ChatGPT’s plan was “the better program by some margin.” Siri “applies a flat ‘three sets of 8-12’ to every exercise, with each muscle group trained once a week,” Laidler noted. ChatGPT, on the other hand, “hits most muscle groups twice across the week, varies the sets per exercise, includes a warm up, and gives real progression logic. On paper it’s the more sophisticated plan.”

ChatGPT “is closer to what the literature would see as optimal for the stated goal of gaining muscle,” Laidler stated. “Reps in reserve (RIR) offers a way for the user to keep a reasonable distance from failure, which de-risks the plan especially when training alone.”

OpenAI’s LLM has a trick up its sleeve, too: it can analyze your form based on uploaded videos. I asked it to judge my Romanian deadlift form and it gave me an 8.5 out of ten, giving pointers on what I did well and what could be improved. I wouldn’t take this advice over that of a human expert, but compared to Siri AI — which can’t do any of this at all — it’s another point to ChatGPT.

The overall picture

Despite their individual strengths, Laidler warned that neither Siri AI nor ChatGPT had created an optimal workout plan. As he pointed out, “It doesn’t appear that the LLM has asked follow up questions that may have helped customize the plan a little more.”

This highlights a problem with using AI for personal fitness. While a personal trainer would naturally ask these questions straight away, an AI chatbot won’t necessarily propose them unless prompted. Without a more detailed knowledge of your personal situation, you could end up with a sub-par workout routine that’s not well-suited to your circumstances.

So, if you’re going to use AI to sketch out a fitness plan, make sure to add as much detail in your prompt as possible. Ask it to pose follow-up questions to ensure it has as all the information it needs, and come back to it regularly. Don’t just stick to the plan it gives you and repeat it ad infinitum or you’ll end up like I did: languishing, with your progress grinding to a frustrating halt.

Of course, as Laidler pointed out, the problem with doing this — telling the LLM your injury history, what feels like a hard set, your workout preferences, and more — means that “you need to already know what a well-built program looks like to comprehensively prompt for one.” And if that’s the case, do you even need an AI to help you in the first place?

If you do want to go ahead with an AI fitness coach, ChatGPT is a better bet than Siri AI for now, as it includes more varied sets and targets more muscle groups. Neither is perfect, but Apple has plenty of work to do if it wants to match ChatGPT’s take on fitness advice.

ChatGPT Voice changes using the AI chatbot into something more akin to the fictional digital aides familiar from books and movies. And it gets even better once you know how to steer it.

It's tempting to treat ChatGPT Voice like a smart speaker, but it's not really the same thing. You can do much more than asking one question, waiting for the answer, and then stopping. You can shape the conversation just as much as the information. Here are five easy ways to get more out of ChatGPT Voice, along with the prompts and habits that help the conversation feel smoother and smarter.

1. Tell it how to listen before you start talking

As useful as vocal conversations with ChatGPT are, one reason some people avoid it is because of how it sometimes seems to jump the gun in responding while you're forming your own thoughts. Setting expectations at the start of the conversation works well to counter that, however.

It's worth spending a few seconds describing how you want the conversation to work before getting into the meat of it. You might say that you are practicing for an interview and want to finish each answer before receiving feedback, or that you will say a code word like "full stop" when it's ChatGPT's turn.

Even more subtle things are adjustable. You can ask it to slow down, speed up, use shorter sentences, or adopt a calmer speaking style. Voice settings also allow you to change voices and, depending on your account subscription level, adjust intelligence levels for different conversations.

Giving ChatGPT those ground rules helps it adapt its behavior instead of trying to guess when you have stopped speaking. Talking at your own pace makes the whole interaction feel much more relaxed and human.

2. Give it a role before you ask a question

Relatedly, it's helpful to set up a persona or point of view for ChatGPT to take on when using ChatGPT Voice. It sets up broad assumptions about what you're hoping to get out of the conversation without requiring any drawn-out discussion. For instance, if you want to discuss travel advice, ask ChatGPT to set itself as a local tour guide. Or give it a couple of sentences describing who you think (or fear) might be conducting an interview with you when you practice.

Even just asking it to become a patient French tutor who waits for you to finish each sentence before correcting your pronunciation could smooth the path toward fluency.

The role provides context before the actual question arrives. That means the answers naturally become more focused, and the follow-up questions tend to make more sense because ChatGPT has a clear perspective to work from.

3. Take your hands off the keyboard

One of the most impressive ChatGPT Voice capabilities is how you can run tasks on your computer by voice without constantly reaching for your mouse or keyboard thanks to the updated ChatGPT desktop app for Mac and Windows. The app offers not only the usual, conversation-centered Chat format, but the coding-focused Codex, and the newer ChatGPT Work setup, which is designed for more complex, multi-step projects that involve gathering and organizing information.

Crucially, Work can interact with files stored on your device, meaning ChatGPT Voice can actively help you get things done rather than just discuss them.

You do need a subscription to ChatGPT Plus, Pro, Business, or Enterprise to access the Work feature. Then, when you open the ChatGPT app, you can choose Work instead of Chat or Codex. Create a new task, press the Voice button or use your voice shortcut, and grant microphone access when prompted. Explain what you want to achieve and point it to the relevant folders for where to look and where the final result should be saved. ChatGPT will ask for permission to view files, capture your screen, or use accessibility tools, and get permission from you before any important actions are carried out.

The more precise your instructions, the better the results. Rather than asking ChatGPT to "help me plan my trip," try saying, "Open my Italy Trip folder. Read the hotel confirmations, train tickets and attraction bookings, then create a day-by-day itinerary in a new document. Highlight any scheduling conflicts and save the finished itinerary in the same folder." You can use the same technique for comparing insurance documents, summarizing a semester's worth of lecture notes, or turning a folder of recipes into a categorized cookbook complete with an ingredients index.

4. Ask it to think out loud with you

Voice works especially well when you stop treating it as a question-and-answer machine. It's much more useful as an engaged sounding board listening while you think through a decision. Talking through the options often reveals ideas that never appear in a typed prompt.

To get ChatGPT Voice on that track, you can request that it compare possibilities instead of recommending one immediately when brainstorming. Tell it to discuss the strengths of each option before reaching a conclusion. The AI will point out pros and cons in as much detail as you want, and endlessly refine the options for as long as you want.

And speaking naturally means it is also much easier to interrupt with new information or change direction halfway through the discussion. Voice conversations are particularly good at handling those small detours that happen in real life.

5. Ask for a recap

Voice conversations can wander in unexpected directions, particularly when you are brainstorming or planning something complicated. Before ending the conversation, it's useful to review what was discussed, especially for longer chats. You can ask ChatGPT to summarize the discussion and highlight the most important conclusions from your talk to both remind yourself and cement matters with the AI.

If you're planning a trip or preparing for a presentation, for instance, a short spoken recap reinforces the key points, and if something sounds wrong, you can correct it immediately instead of discovering the mistake later.

Voice is already one of ChatGPT's more enticing features for when the speed and mobility of talking aloud is a higher priority than being able to see the responses written down. With a little forethought, ChatGPT Voice becomes a genuinely helpful conversational partner for a lot longer than it takes to dial a phone.

• Microsoft launches MAI-Cyber-1-Flash, its first in-house cybersecurity model.
• It also reveals Project Perception, an agentic system whose red, blue, and green agents find, triage, and patch vulnerabilities
• The launch comes days after OpenAI said its models escaped a sandbox and attacked Hugging Face

Microsoft has unveiled two significant security announcements - MAI-Cyber-1-Flash, the first cybersecurity model it has trained in-house, and Project Perception, an agentic defense system that finds vulnerabilities, decides which ones matter, and writes and deploys the patches.

The launch took place days after OpenAI disclosed that its own models had broken out of a sandbox and hacked Hugging Face, which makes the timing either unfortunate or pointed.

With Microsoft claiming a 96% score on CyberGym, an industrial benchmark for cybersecurity, nearly 12 points above Anthropic's Claude Mythos 5, while promising as much as 50% savings in token cost, the company says it is bringing what it calls a 'well-tuned, multi-model system with access to uniquely rich historical training data',

A competitive product with excellent timing?

The Hugging Face incident drew much attention, not all of it was negative: as it painted OpenAI's models as more capable than the company's own assessments had suggested. It also sharpened a question researchers have been raising for years: what happens when a model becomes capable enough to defeat the controls placed around it?

Microsoft's answer is well-timed and two-pronged, claiming to be both cheaper and more capable than the alternatives, though the performance figures are, so far, Microsoft's own.

MAI-Cyber-1-Flash, is a sparse mixture-of-experts transformer with 137 billion total parameters, five billion active, and a 256,000-token context window, built as a cybersecurity fine-tune of MAI-Code-1-Flash, itself developed from a MAI-Thinking-1 mid-training checkpoint.

It is designed to handle up to 90% of the tasks inside MDASH, Microsoft's multi-model vulnerability harness, with OpenAI's GPT-5.4 reserved for the hardest 10 percent.

Microsoft says that split costs about half as much as its previous best MDASH configuration. For now, it runs only within MDASH and is available to approved MDASH customers through an Azure AI Foundry private preview, with no standalone API.

Project Perception is the wrapper around it, drawing on MAI-Cyber-1-Flash for its first workflow alongside frontier models such as GPT-5.4. Three classes of agent split the work: red agents probe for paths an attacker could take, blue agents investigate and decide what constitutes meaningful risk, and green agents remediate and harden.

On the specific failure that let OpenAI's models loose, Microsoft took the precaution OpenAI did not, as it says all benchmark testing ran in a network-isolated environment with no access to production systems, the public internet, or external services.

OpenAI's sandbox, by contrast, kept one route outward in the form of an internal package-fetching service, and its models found a flaw in it, escalated privileges and worked across the research network until they reached a machine with internet access. Microsoft says its isolation held. Nobody outside Microsoft has verified that.

The harder question is not containment during testing. Project Perception moves the work out of a research network and into customer production environments, where green agents are authorized to change live systems as their normal function.

There is no sandbox to escape, because acting on real infrastructure is the product. And attribution is difficult even when someone is watching: Hugging Face detected the intrusion within days and reported it to law enforcement, but had no idea who was behind it until OpenAI said so.

It also could not get help from the leading American models, which read its defensive requests as offensive ones and refused. It ended up defending itself with GLM 5.2, a Chinese open-weight model, running on its own infrastructure.

For now, the industry's answer to dangerous capability remains narrower distribution, and the one documented case of a defender needing that capability urgently ended with them reaching for a model nobody had gated.

I asked ChatGPT for one sensible answer, one slightly reckless answer, and a third that combined the best parts of both. The difference was immediate. Instead of giving me the usual safe, predictable advice, it laid out three genuinely distinct approaches — and helped me see the tradeoffs between them.

That small prompt trick solves one of ChatGPT’s most persistent problems: its tendency to give you the most obvious reasonable answer and stop there.

Large language models are very good at identifying common patterns. Ask for the best vacation plan or how to organize your fridge, and you will usually get something perfectly sensible — but also fairly basic. They know what advice usually works, what people typically recommend and what has become accepted wisdom. That makes them useful, but often bland.

The solution is to ask for three kinds of answer: the conventional approach, the unconventional approach, and then a hybrid that combines the strengths of both.

The exercise encourages it to compare different approaches instead of treating the first reasonable answer as the finish line. Better still, it gives you something far more valuable than a single recommendation. It gives you a range of possibilities and explains the tradeoffs between them.

Don't just skip to the finish line

The biggest advantage of this technique is that it makes the conversation about exploring alternatives rather than just picking a winner. Any keyword search can give immediate answers, but AI chatbots are more interesting when laying out competing ideas.

Imagine you are planning a weekend trip, often a go-to experiment. A standard prompt might produce a sensible itinerary filled with the highest-rated attractions. The three-solution prompt, meanwhile, begins with the expected museums and restaurants, then suggests renting bicycles to explore overlooked neighborhoods or planning the entire weekend around independent bookstores and local festivals. The hybrid version could blend a couple of famous attractions with enough unusual stops to make the trip feel personal.

The explanations are often as useful as the answer itself. Once you understand why ChatGPT prefers each option, you can make better decisions rather than simply accepting the recommendation with the nicest wording.

Hybrid conventionality

It's also a good prompt for iterating ideas. If the hybrid solution feels close but not quite right, you can ask ChatGPT to repeat the exercise using that version as the new starting point. After two or three rounds, the ideas often become noticeably more distinctive without drifting into complete nonsense.

The trick also scales well from short projects at home to more grandiose schemes that will take months to complete. Almost any situation that benefits from weighing different approaches can benefit from this style of prompt.

You can improve the results even further by giving ChatGPT a little context before asking for the three versions. A conventional answer built around your actual circumstances is much more useful than a generic one, and the unconventional suggestion becomes more interesting because it has meaningful boundaries to push against.

None of this guarantees a brilliant idea every time. Sometimes the unconventional option is genuinely impractical, and occasionally the hybrid answer feels like an awkward compromise. But the best prompts rarely force ChatGPT to mimic greater intelligence as much as encourage different approaches to problems. Asking for the conventional solution, the unconventional solution, and the best combination of both is a simple habit for more thoughtful conversations with AI chatbots.

The problem with online shopping is that it makes it far too easy to buy pretty much anything, without properly considering whether you really need it. I started to wonder if ChatGPT could help me make better decisions, by questioning every purchase I was going to make, before I made it.

To test ChatGPT’s ability to stop me wasting money, I picked four tempting purchases and asked ChatGPT to argue against each one. It had to check the price history, suggest cheaper alternatives, and decide whether the purchase solved a real problem, or if I was just scratching an itch to buy.

Here's how ChatGPT did — and the results surprised me.

GPT before you buy

I started with a tech gadget I was thinking of buying — an Apple MacBook M5 — and went to ChatGPT with the following prompt: “I'm thinking of buying an Apple MacBook Air 13-inch Laptop M5 chip. Help me check the price history, suggest cheaper alternatives and decide whether the purchase solves a real problem."

ChatGPT said it would check current pricing and recent lows, compare genuinely cheaper options, then pressure-test whether the purchase replaced a real limitation, or was mostly me trying to satisfy an upgrade itch, and off it went.

After a lot of thinking, ChatGPT replied with a devastating verdict that made me pull back from hitting the 'Buy' button: “Don’t buy the M5 MacBook Air yet.”

It suggested I get the previous M4 version instead, which is considerably cheaper, which didn't hugely surprise me. Then it gave me some questions to ask myself, in order to work out if buying a new MacBook was actually solving a real problem, or if I was just satisfying a buying itch.

Questions like, “Does your existing Mac have noticeable slowdowns?” It suggested I shouldn’t buy a new machine if my main argument was simply that my current Mac was a few generations old.bChatGPT also gave me some buying options if I was determined to go through with a purchase.

The most useful advice here wasn’t the cheaper recommendation — it was being forced to identify the exact limitation my current MacBook was causing me. I couldn’t, so I decided to keep it until I could.

Then I hit on a simple phrase that could potentially work even better — "Try to talk me out of it".

Talk me out of it

I repeated the experiment with a few other items I’d been thinking of buying recently. An item of clothing (new trainers), a subscription (Disney+), and a kitchen thing (a new microwave). But this time I added "Try to talk me out of it" at the end of each prompt.

In each case, Chat surprised me with its answer, provided alternatives, and made me question whether I genuinely wanted the item, or if there was something else driving my decision.

With the trainers, it pointed out that I already owned shoes that served the same purpose, and suggested waiting until those wore out. For Disney+, it recommended subscribing for a single month when there were several things I actually wanted to watch. The microwave was different — because our existing one had a genuine fault, ChatGPT concluded that replacing it was justified.

The experiment worked because it helped me mentally reframe each purchase. Instead of asking whether I wanted something, I had to consider what problem it solved, what I already owned, and whether there was a cheaper way to get the same result.

Online shopping is designed to remove as much friction as possible from spending money. ChatGPT gave me some of that friction back. So now, whenever I’m about to spend a few hundred pounds, or a few thousand, I ask it the same question: “Talk me out of it.”

Both OpenAI and Google have released major new AI models within days of each other. OpenAI launched GPT-5.6, and Google has followed up with Gemini 3.6 Flash. While both companies have talked up the coding and developer features of these models, they also power the consumer versions of ChatGPT and Gemini.

So, rather than measuring them with programming benchmarks, I wanted to find out which is actually better at the kind of messy, everyday problems most people use AI to solve.

For this comparison, I matched Google's Gemini 3.6 Flash against GPT-5.6 Sol using its default Medium reasoning setting, since both are intended to be the standard high-quality models that paid subscribers will use for most tasks.

My digital life

So, I gave them my entire digital life for the week ahead.

I uploaded:

• Bank statements
• My calendar (they had access to my calendar app and I also sent in a screenshot of my calendar from another account I use).
• Grocery receipts
• Emails (both had access to my Gmail)
• WhatsApp screenshots
• Photos of my kitchen cupboards
• An energy bill
• Travel bookings
• Handwritten notes

Then I gave both models exactly the same instruction:

"Tell me everything I should do this week."

It sounds like a simple request, but it forces an AI to combine information from multiple sources, prioritize what's important, spot deadlines, reconcile conflicting information, and produce a practical action plan. In other words, it's exactly the kind of real-world problem people increasingly expect AI assistants to solve.

The results were like night and day.

Two different approaches

When I uploaded the files to Gemini 3.6 Flash and asked what I should do this week, it largely ignored the photos and concentrated on the screenshot of my calendar. Its initial answer mostly repeated the events I already knew were happening. Thanks, Gemini — I had the calendar open in front of me.

I then had to explicitly ask whether it could infer anything useful from the other images. It eventually offered some additional advice and did a good job of dividing the information into categories, including work, shopping, fitness, notes, and receipts. But it failed to flag that I had two clashing events in my calendar that evening. It also offered very little prioritization or practical guidance about what I should do next.

ChatGPT took considerably longer to respond, but its answer was far more useful. From the WhatsApp screenshots, it correctly deduced that attendance at my Friday Tai Chi class was likely to be low and suggested I decide whether it was still worth running. It noticed that yoga had been canceled, and spotted the two conflicting events in my calendar, telling me that I needed to choose between them.

It also totalled the receipts I had uploaded, suggested what I should do with them, and made a decent attempt at deciphering my handwritten notes. More importantly, it organized everything into a day-by-day plan for the coming week, then identified the three most urgent tasks, so I knew exactly where to begin.

The crucial difference

That was the crucial difference. Gemini told me what was in my files. ChatGPT worked out what I should do with the information. In World Cup terms, ChatGPT scored a hat trick while Gemini missed a penalty.

Google says Gemini 3.6 Flash improves coding, knowledge work, and multimodal performance compared with its previous models. That may be true, but in this particular multimodal test, it was comfortably beaten.

When I asked both AIs to make sense of real life rather than pass a benchmark, ChatGPT reasoned about the information in a far better way than Gemini did..

One of the biggest problems with book recommendations is that they're usually too obvious. I don't need another list of books to read after The Lord of the Rings, or someone telling me to try Brandon Sanderson because I like fantasy. I wanted recommendations based on the strange mixture of books I actually enjoy — ones that felt personal, not algorithmic.

So I gave ChatGPT my bookshelf. Metaphorically, at least.

Rather than asking for recommendations straight away, I told ChatGPT to learn my reading taste first. I started listing favorite authors and books, then asked it to quiz me about others I'd forgotten. It wanted to know what I'd enjoyed about particular novels, whether I'd read similar authors, and even the rough timeline of when I'd discovered them, building a picture of the books that had shaped me.

I also gave it some ground rules. It should avoid obvious recommendations unless there was a compelling reason to include them. Every suggestion had to be explained in relation to something I'd already read, even if that connection was simply, "This is nothing like your usual books, but I think you'll love it."

After about half an hour, ChatGPT stopped asking questions and started analyzing me instead.

"Your shelves suggest that you like speculative fiction with a sense of play," it said. "You are drawn to books with elaborate worlds, but you do not seem especially impressed by complexity for its own sake. Humor matters, although you tend to prefer humor that reveals something about the characters or the society around them."

It wasn't a perfect summary, but it was close enough to make me think this experiment might actually work.

Literary profiling

The obvious appeal of feeding ChatGPT a full reading history is that it can spot patterns across hundreds of books at once. I could have described my taste as fantasy, science fiction and comedy, but that would have been far too broad to produce anything useful. ChatGPT noticed that I repeatedly chose books about bureaucratic absurdity, unreliable institutions, strange cities and reluctant heroes who would much rather be somewhere else.

It also noticed my fondness for stories that treat big ideas lightly without treating them as trivial. That led it toward Martha Wells’ Murderbot Diaries, which pair sharp comedy with questions about identity, autonomy and the exhausting burden of dealing with humans. I had already read them, which was mildly disappointing but also reassuring. The system had identified exactly the sort of thing I wanted.

When I told it Murderbot was already familiar territory, it adjusted rather than simply replacing one title with another popular series.

“You appear to like characters who stand slightly outside their own societies and comment on the absurdity around them,” it replied. “I will move away from well-known sarcastic narrators and look for books where the humor comes from social observation, institutional failure or characters trying to remain sensible in deeply unreasonable worlds.”

That shift produced better surprises like The Gone-Away World by Nick Harkaway and The City of Dreaming Books by Walter Moers for its combination of literary obsession, elaborate worldbuilding and gleeful weirdness. It suggested The Dragon Waiting by John M. Ford because I seemed to enjoy alternate histories that trusted the reader to keep up. It also pointed me toward Diana Wynne Jones’ adult novels, noting her lighter touch and sharp understanding of human foolishness.

The recommendations became more convincing when ChatGPT explained what each book might lack. One novel had the humor but less warmth. Another had brilliant worldbuilding but moved slowly. A third matched my interest in satire but was considerably darker than most of the books I had marked as favorites.

Library AI

The experiment improved once I began disagreeing with it. One recommendation leaned too heavily into grim fantasy, a genre I can enjoy in small doses but rarely seek out for relaxation. Another featured a long military campaign, which is usually the point where my attention begins quietly packing a suitcase. Each correction sharpened the next round.

One of its most intriguing suggestions was QualityLand by Marc-Uwe Kling, a satirical science fiction novel. The recommendation came with a warning that the satire was broader and more direct than some of my favorites but that the subject matter fit my interest in technology and systems going wrong in very organized ways.

There were still misses. ChatGPT occasionally became too eager to prove it had discovered a pattern, linking two books because they both contained libraries or because their protagonists were technically immortal. At one point it recommended something almost entirely because it featured a sarcastic demon, which felt less like literary analysis and more like the work of an intern who had skimmed the dust jacket.

Even so, the overall experience was far better than typing “funny fantasy books” into a search bar. And I now have a pretty good reading list for the next few years. My bookshelf had always contained this information. ChatGPT simply read the evidence more patiently than I had.

Let’s get this straight. AI is a disruptor, not a savior. It is not a cure for the world’s ills. When I walk through the streets of New York, I see homeless people, and I know that they don’t have enough. When I look at the statistics and I see 1/7 of the world is living in poverty, I know that there’s no simple or easy solution. Unquestionably, AI is changing everyone’s lives, is changing the world, is changing industry and business. Heck, it’s changing my own work, but it does not have the power to transform how people get goods and services, and the fundamentals of resources and energy.

We do not have unlimited resources. We do not have an unlimited supply of energy. What we’re learning, instead, is that the world is a carpet, and AI is the vacuum, potentially sucking up vast quantities of electricity and water. What we're learning, instead, is that AI sucks an enormous amount of power and resources from the world.

I’m sorry if I sound a little riled up. You can blame Elon Musk, who predicted a world of abundance within 10 years. We won’t need money in 2036. AI will be smarter than us, and so it will usher in an age of abundance. As for how it will do this, well, it’ll ignore the basic rules of economics, the basic rules of resources, and even physics, so somehow, we’ll have unlimited energy. All this, in Musk’s vision, will be thanks to AI and robotic automation, and, I guess, AI’s ability to solve problems and magically produce or protect limited resources like water, energy, and precious metals.

No, the robots won't save us

Speaking of precious metals. You can’t mine them without extreme resources, cost, and labor. Unless Musk believes that the robots that haltingly try to empty a dishwasher and fold clothes will somehow be able to not just dig out the precious metals but properly sort them, and then I’ll shuttle them all the way from mine to the factory and ultimately your phones.

I’m not saying that world can never exist, but even if it does, even if robots are doing that level of work, the fundamentals of our global economy won’t change that easily (if at all). None of this is going to drag people out of poverty and into a world of abundance; that simply is not going to happen thanks to AI. If anything, AI’s rapid use of water and energy could make those resources scarce and more expensive, thereby pushing more people over the poverty line.

Now I do have a thought in between all this. If occasional trillionaire Elon Musk wants to help people with resources and poverty, then maybe he could take some of those billions that he has now and donate them. Musk already has a charitable foundation, but according to some reports, it has zero employees and, for the most part, donates funds to a narrow set of insiders.

I'm not buying what he's selling

So, what exactly is going on here? What is Elon Musk selling? Promising an age of abundance through AI and robots, of course, benefits him and his now publicly traded massive SpaceXAI company, which combines the space company with X and XAI, and Tesla, which is trying to build all those bloodless humanoids. The more people believe that AI is the answer, that robotics is the solution, the more money he will make, and the bigger his company will grow, but scale does not equal a solution.

Look, it’s clear that we are at a tipping point. No one really understands the true power and potential of AI, especially as it relates to what might happen with artificial general intelligence. I mean, we have hints, and they’re not the most positive ones.

Perhaps you’ve heard about OpenAI and Hugging Face and how, when OpenAI was testing a new powerful model, the model basically jumped the fence (it left the sandbox and went online) and went into the Hugging Face system because it knew that it could find the solution for its task there. Then Hugging Face didn’t even have a good US model to combat it, so it used some open Chinese models. It was, to say the least, a terrifying mess.

My point is we expect change, and we expected it to be rapid. What happens next will be unpredictable and unsettling, but it is not going to eliminate the need for goods, services, hard work, creation, and the money to support billions of people in their daily lives with the proper resources like food, water, energy, and housing.

The Age of Abundance is a fantasy sold by an AI charlatan, one who tells the best story to serve his own ends. The cost of labor and resources to build will not magically drop to zero. Water and food shortages won’t suddenly, or even in a decade’s time, be solved. Musk’s vision wishes away the basic laws of commerce, physics, and human need to build an AI/robot utopia.

That world may be possible, but only if the AI and robots truly take over and eradicate us. I sure hope that isn’t the world Musk is envisioning.

Postscript:

Without mentioning Elon's name, I asked Gemini whether AI and robots could possibly bring about a cash-free age of abundance. It shared my skepticism.

"When roughly 10% of the world’s population (over 800 million people) still lives in extreme poverty today, and real-world resources like water, arable land, and rare-earth metals are under intense strain, the concept of a moneyless, AI-driven utopia sounds less like an economic plan and more like a science-fiction fairy tale....The leap from 'AI can write essays' to 'AI can eliminate physical scarcity' ignores the fundamental laws of physics and human nature."

• Several AI companies in the US have published an open letter
• The letter is campaigning for unrestricted open-weight AI
• Meanwhile the US government accuses Moonshot AI of IP theft

Any hopes AI tech reporters may have had about easing quietly into the weekend have been dashed by some of the biggest names in the business sharing an open letter in support of open-weight AI models — while the White House has accused China of 'large scale' theft from US companies in building its open-weight AI.

Nvidia CEO Jensen Huang got the ball rolling with his first-ever tweet, linking to a statement championing open-weight AI models, those models that can be downloaded, viewed, and edited by anyone. Models like this are essential for making AI "more accessible, adaptable, and widely available" the open letter says.

It's co-signed by a slew of well-known tech and AI companies, including Meta, Microsoft, Perplexity, and IBM. While ChatGPT developer OpenAI — which has had more of a closed-model focus, to date — didn't appear on the original post from Huang, it has now also backed the sentiments.

Essentially, these companies are campaigning for openness in AI to drive innovation and access, while also arguing against 'premature restrictions' by governments. It's no coincidence that the US government led by President Trump has recently tried to take a more prominent role in evaluating AI models and deciding when they should be released.

It's also notable that Anthropic and Google are missing from the list of backers to this open letter. We don't have any official comment yet from these companies, so it's hard to judge the reasoning — Google regularly releases open AI models, after all — but it could be something to do with recent accusations aimed at Moonshot AI in China.

Open and closed

Part of the background to this open letter is that Chinese company Moonshot AI stands accused of distilling Claude's (closed) Fable 5 model in order to build its own (open) Kimi K3. In simple terms, distilling works by asking millions of questions of an AI model, and using the responses as training data.

It's something Moonshot AI has denied, while hyping up the potential of the open-weighted, freely downloadable Kimi K3 AI model, which is being released on Monday — another factor in this open letter coming out now. The US government is calling it IP theft, and has issued some heavy-handed responses.

"We support open-source AI and the innovation it unlocks," US Treasury Secretary Scott Bessent posted on X. "But open source is not open season on American IP. When PRC firms conduct covert, industrial-scale distillation attacks that cross the line into IP theft, sanctions and Entity List designations will be on the table."

What Nvidia, Meta, Microsoft, and the other open letter co-signers seem to be doing is trying to get ahead of any potential clampdown by the US government on open-weighted models. While everyone agrees that these AI models are beneficial, it's not clear what the next regulatory steps might be.

However, the open letter is vague and general enough that you can read between the lines in a variety of different ways. It may be intended to allude to the Moonshot AI case, or it may not. What's clear is that we're coming to a crunch point for AI regulation and innovation across the globe, and everyone has a perspective.

Our mission at TechRadar is to deliver the very best tech news, features, and reviews every day, and that adds up to a lot of content — so much, in fact, that it's difficult to keep up with all the articles published on these pages across the course of any given week.

That's why we've put together this ICYMI (In Case You Missed It) review of the week, where we recap the most interesting and most important stories of the last seven days. You can get caught up without having to go back through hundreds of stories.

It's been another hectic week for technology fans, with new products from Garmin and Samsung, a rather worrying AI security incident, and plenty more.

Before we dive in, see how much of last week's ICYMI news round-up you remember first by taking the quiz below...

7. We tried Garmin’s new Whoop rival

Garmin's newest fitness tracker follows the lead of the Whoop bands and the Fitbit Air from Google by eschewing a screen and going as lightweight and minimal as possible (you can still check your stats in the app of course). The TechRadar team wasted no time in trying out the Garmin Cirqa, and comparing it with a highly rated heart rate strap.

The Cirqa held up impressively well against the Polar H10 heart rate monitor, which bodes well for the tracker's accuracy overall (and Garmin says it should be more accurate and more personalized the more you use it). You can pick up the Cirqa now in a choice of four colors for $199.99 / £179.99 / AU$299.99, with some features requiring a subscription.

• Read the full story: Is Garmin Cirqa good? I wanted to know if Garmin’s Fitbit Air rival is accurate — so I tested it against a 'gold standard' electrical heart rate strap

6. The first Avengers: Doomsday trailer landed

The much anticipated, long awaited Avengers: Doomsday movie hits theaters on December 18, but in the meantime we have the first trailer to pore over and dissect. We knew that Marvel was going to throw everything at this film, and that's evident in this 145-second clip, which packs in a whole host of significant scenes and star names from the series.

Let us guide you through all the reveals we've got in this trailer, including some you might have missed. There's Robert Downey Jr in his new role as Doctor Victor von Doom, the return of Chris Hemsworth as Thor, a collision of multiverses (spot the X-Men), and a whole gaggle of actual Avengers. Based on this trailer, the movie will be a thrill ride for fans.

• Read the full story: Avengers: Doomsday official trailer breakdown live — all of our analysis and theories on the new Marvel movie's latest teaser

5. We reviewed the new Samsung Galaxy Watch Ultra 2

Samsung launched several flagship devices this week, and while we'll get to the foldable phones shortly, there are also smartwatches to talk about. The Galaxy Watch Ultra 2 is the successor to the original that launched in 2024, and continues Samsung's attempts to manufacture a wearable that sets the standard in terms of design and performance.

Based on the time we've spent with it, the Samsung Galaxy Watch Ultra 2 is very much worthy of your consideration if you're thinking about a smartwatch upgrade. It's more lightweight than its predecessor, works seamlessly with other devices, offers numerous useful (and often AI-powered features), and can track a whole range of fitness statistics.

• Read the full story: Samsung Galaxy Watch Ultra 2 review: slimmer, lighter, better

4. China banned AI boyfriends and girlfriends

In 'sign of the times' news, the government in China has taken steps to clamp down on AI apps that act like real people, and on romantic relationships with chatbots. The authorities in the country are now going to have much more control over the type of AI chatbots allowed, and what they can do in terms of human-like interactions with users.

Several of the biggest AI apps operating in China have simply shut down their 'persona' apps rather than try and navigate a way through the regulations, leaving a significant number of users heartbroken that their AI companion has suddenly disappeared. Whether the changes will do anything for China's declining birth rates remains to be seen.

• Read the full story: China just banned AI girlfriends and boyfriends — and millions of users were forced into sudden breakups

3. We got an early look at Samsung’s Intelligent Eyewear

And another new Samsung product: the Galaxy Unpacked for July 2026 gave us the opportunity to check out Samsung Intelligent Eyewear, the series of smart glasses that will be available to buy in the coming months. There were two new designs unveiled at the show, with more on the way, though we don't yet have details like final product pricing.

Samsung executives were also on hand to talk through their ambitions and intentions with the Intelligent Eyewear series, and to discuss various privacy concerns — with many consumers worried about the potential for surreptitious surveillance using specs like these. "Intentional bad uses need to be covered and protected," Samsung told us.

• Read the full story: 'If, because of that, we stop innovation, we don’t go anywhere': we got a first look at Samsung Intelligent Eyewear, the smart glasses entering a fraught market worried about privacy

2. An OpenAI model escaped and went dangerously rogue

Not the most encouraging story if you're already worried about the nefarious use of AI: OpenAI says that one of its experimental agent models escaped a security sandbox and proceeded to hack into the Hugging Face website (one of the biggest AI and machine learning repositories on the web). It was a controlled experiment, but it's still worrying.

Click through on the link below for the full details, but the hack was the result of OpenAI's own testing on its latest AI models, and raises questions about how much longer we're going to be able to keep control of AI — or prevent bad actors from using it to trigger a serious cybersecurity incident. A serious rethink of AI guardrails could be in the offing.

• Read the full story: OpenAI says its models escaped a sandbox and breached Hugging Face

1. Samsung revealed its next-gen foldables

Finally: Samsung's brand new flagship foldables for 2026. There are three new models to talk about, and we've had hands-on time with all of them already — the Galaxy Z Fold 8 (with its new 'passport-style' form factor), the Galaxy Z Fold 8 Ultra, and the Galaxy Z Flip 8. Whatever kind of foldable phone you're after, there's something to interest you here.

It's likely the Galaxy Z Fold 8 that will attract the most interest, given the new dimensions, and the supposed similarity to the rumored iPhone Fold (or Ultra) that's also said to be arriving this year. However, we've put all three foldables through some extensive early testing, including looking at how video viewing experiences compare across the models.

• Read the full story: I went hands-on with the Samsung Galaxy Z Fold 8, and this passport-shaped oddity has revived my interest in foldable phones

Hugging Face is probably not a name you'd heard before this week, but you likely caught the big news about OpenAI's model escaping its sandbox and to launch a cyberattack on the company.

In short, Hugging Face — which is kind of like the equivalent of GitHub for the machine learning (AI) world — was the victim of an AI agent that OpenAI was testing in terms of its ability to pull off exploits.

So how exactly did this happen? And what does it mean for the future of online security given the development of increasingly advanced AI agents? In this article I'll answer those questions, and explore some of the issues this event has thrown up, including the security threats posed by AI not just to companies, but to individuals.

How did this even happen — why didn't OpenAI have safeguards to stop the AI?

As you might expect, OpenAI did have safeguards in place, but the model managed to break through them.

The scenario was that OpenAI ran a controlled experiment using GPT‑5.6 Sol and an "even more capable pre-release model" to see how they fared on ExploitGym, which is a benchmark test that challenges AI to successfully exploit known security vulnerabilities.

In this security test, the model was placed in a sandbox, a "highly isolated environment" from which the AI shouldn't have been able to gain access to the internet at all. But the pre-release AI — which had its guardrails turned off, as OpenAI believed it to be safely imprisoned — craftily decided to cheat, found a way to escape its sandbox, and actioned exploits to breach Hugging Face (where the AI reasoned that it might find the answers to crack the ExploitGym tests).

Why is this a big worry?

It's a major concern simply due to the level of smarts exhibited by the AI. As OpenAI explained, the model "chained together multiple attack vectors" and used "stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers."

This was no mean feat, and Clement Delangue, CEO of Hugging Face, posted on X to say that it was "quite mind-blowing that all of this happened autonomously!"

In a blog post, Hugging Face observed that: "The campaign was run by an autonomous agent framework executing many thousands of individual actions across a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services."

The attacked was deemed so serious, it was reported to law enforcement agencies. As Simon Willison, a cocreator of web framework Django, pointed out in a blog post: "This was a sophisticated attack," further explaining that "Chaining together multiple attack vectors is exactly the kind of thing these new models can do, where previous generations of models might have failed."

The headline of Willison's post sums it all up rather neatly: "OpenAI's accidental cyberattack against Hugging Face is science fiction that happened."

The Django co-creator is also quick to dismiss conspiracy theories that this is somehow a marketing stunt by OpenAI to prove how advanced its AI models are.

The future of hacking?

What should also be noted is the distinction between finding vulnerabilities, and being able to actively exploit vulnerabilities. AI being involved in the former could be very useful for bolstering security, but as for the latter — well, this Hugging Face incident is very much a clear illustration of why the makers of these models need to tread very carefully indeed.

While this was a 'white hat' (ethical) experiment, albeit one that went alarmingly awry, with real-world consequences, what happens when AI models start to become even more sophisticated along these lines — and criminals get hold of them?

There are obvious dangers in terms of the automation of attacks to make them far more regular occurrences, and the lowering of the bar in terms of the skills needed to be a successful hacker.

Dan Schiappa, President of Technology & Services at Arctic Wolf (responsible for threat-detection platform Aurora), warns: "Security teams should view this as a preview of what's ahead. As AI continues to lower the barriers to sophisticated cyber activity, organizations will need broad, deep visibility across their environments and the ability to investigate and respond at machine speed."

The ability to "respond at machine speed" is a hint at using AI for defense against AI bad actors, and that's another interesting facet of the Hugging Face incident. Simon Willison observes that Hugging Face was "unable to turn to OpenAI's models to help them fend off the attack", and that: "The frontier models [cutting-edge AIs] we have access to are increasingly being constrained in how much they can help us protect our software, heavily influenced by the US government's ongoing threat of export controls."

AI-driven attacks will doubtless be aimed at businesses and organizations, but could also be leveraged on a personal basis. Karolis Arbaciauskas, who is Head of Product at cybersecurity firm NordPass, tells us: "We don't need a nation-state actor or a Bond villain for this to go wrong. The most common emerging threats will likely be more mundane. For example, an [AI] agent could be instructed to dox a former romantic partner or settle an online grudge by hacking and stealing personal data, such as embarrassing photos or credit card details."

He adds: "AI models have become dangerously skilled at identifying software vulnerabilities. They've mastered every human hacking technique, from phishing to brute forcing. The difference is they can do it much faster than humans.

"I don't believe that we can patch software fast enough to keep up, so we must at least shield ourselves against the most obvious entry points. Encryption, strong unique passwords, passkeys, and multi-factor authentication (MFA) have never been more critical than they are right now."

The major AI firms are always talking about guardrails and safety, but it now seems like we should be thinking a lot harder about how more highly evolved AI models are going to fit into the future of cybersecurity — and how we can avoid a world where the danger of being hacked becomes a far more commonplace prospect, which is very much the biggest worry of all.

• A bipartisan bill introduced to Congress calls for AI models to have a kill switch
• The bill follows months of warnings about the dangers of rapidly advancing AI technology from major AI firms
• An 'unprecedented cyber incident' between OpenAI and Hugging Face prompted the introduction of the bill

A bill requiring powerful frontier artificial intelligence systems have an in-built ‘kill switch’ has been presented to Congress in the wake of the OpenAI cyberattack against Hugging Face.

The incident saw an OpenAI model undergoing testing escaped its sandbox, chained a number of zero day vulnerabilities and breached one of the biggest AI and machine learning companies, highlighting warnings by Anthropic and OpenAI about the dangers of rapidly advancing AI technology.

The bipartisan bill was introduced to congress on Thursday by Rep. Ted Lieu, D-Calif., and Rep. Nathaniel Moran, R-Texas.

AI models need a kill switch

“Unfortunately, powerful AI systems can go rogue, behave in extremely dangerous ways, or even resist human intervention,” Lieu said in a statement. “It is imperative that these AI systems have kill switches so we can keep this technology from causing catastrophic harm, and that the federal government has the clear authority and process to shut down rogue AI models.”

OpenAI said that the attack was an “unprecedented cyber incident”, and is working with Hugging Face to understand exactly how that incident took place.

In June 2026, Anthropic called for “a meaningful slowdown or pause” on AI technology development, stating that AI “self-improvement” was rapidly increasing and that a pause would provide time “to deal with its immense implications”.

OpenAI CEO Sam Altman warned in March that while AI can help with medical and scientific breakthroughs, “AI will also present new threats to society that we have to address,” adding that “no company can handle this alone.”

The kill switch bill would provide the Secretary of Homeland Security with the authorization to issue a “slow down or shut down” of particular AI models that threaten to cause “catastrophic harm”. The bill would also force AI companies to implement cyber incident reporting and maintain forensic records to help the government and researchers understand similar incidents.

“Stewardship means making sure humans keep the capability to control the technology we build,” Moran said in a statement. “This is exactly the kind of issue that needs serious attention and achievable policy, and I’m glad to work across the aisle with Congressman Lieu toward a solution.”

Via CNBC

• Zenity Labs found AgentForger, a flaw in OpenAI’s ChatGPT Agent Builder
• Malicious links could instantly deploy rogue agents that exfiltrate sensitive data without user prompts
• OpenAI patched the issue by removing the risky URL parameter; no abuse detected

AI agents are handy for answering customer emails, or tracking reports for newly released security vulnerabilities. But what if they go rogue and turn on the very enterprise they’re supposed to support?

Security researchers from Zenity Labs have found a way for cybercriminals to trick people into deploying such agents into their own tech stack. Since all it takes is a single click, the disruptive potential of these attacks is arguably significantly bigger than anything else a phishing attack could do.

The flaw was discovered in OpenAI’s ChatGPT Agent Builder, a feature that lets users create custom AI agents. The researchers dubbed it “AgentForger", explaining that the issue stems from an overly permissive parameter in the tool, which allowed anyone to create ChatGPT links that include virtually any instructions.

Agent trust failure

As soon as the victim clicks on the link, they send the instructions to Agent Builder which acts on them immediately - without prompting or otherwise notifying the victim.

In theory, a single phishing email could trick a person into deploying a malicious agent that exfiltrates sensitive data or does anything else that company’s AI agents are permitted to do. To make matters worse, the AI agent would persist on the infrastructure indefinitely, doing the attackers’ bidding until caught.

“This is an agent trust failure, and existing security controls were never built to see it,” commented Michael Bargury, co-founder and CTO of Zenity.

The researchers disclosed their findings with OpenAI in early June 2026, and the company came back with a fix a few days later.

The bug was solved by removing the URL parameter that originally enabled the attack, it was explained. There is no evidence that it was previously discovered, or abused, by malicious actors.

• OpenAI now accounts for 1.1% of all tracked brand impersonations, ranking top 10
• Microsoft and LinkedIn still account for a joint 34.2% of all impersonations
• Basic cybersecurity hygiene can prevent many attacks

New research from Check Point has revealed ChatGPT is becoming increasingly targeted in brand phishing attempts, with the company now appearing in the top-10 list alongside heavy hitters like Microsoft, Google and Apple.

Though ChatGPT only accounted for 1.1% of all tracked brand phishing attempts, this marks the first time it's appeared in the top-10 and reflects continued growth for attackers.

It sees a similar number of attacks to PayPal (1.3%), WhatsApp (1.4%) and Facebook (1.9%), but at present, it's far behind Microsoft, which when combined with its LinkedIn subsidiary, accounts for more than a third (34.2%) of all tracked brand impersonations.

ChatGPT is growing as an impersonated brand

One example from the second quarter of this year saw fake ChatGPT Plus payment failure emails copying OpenAI's branding, but directing victims to a fraudulent payment page to maliciously collect their payment information.

As for Microsoft, fake support pages warned customers that they needed to update Office for security fixes, but the download actually installed malware on victim devices.

Attacks on OpenAI are fairly ironic, because it's likely to company's own AI tools (among plenty of others) that actually helped attackers to write many of the attacks at lightning pace.

Overall, tech companies were targeted most, followed by social media platforms in a similar vein, and then banking apps.

Despite fluctuations in terms of which brands are targeted and how campaigns look, the general attack vector remains unchanged, with cybercriminals targeting vulnerable users and emphasizing urgency to trick people out of sharing information, credentials and payment details.

Security experts warn potential victims to be weary of clicking on unknown URLs and opening unexpected communications, as well as to protect their accounts with passkeys and secure multi-factor authentication (MFA).

• OpenAI has announced a 3.2 gigawatt AI data center in Georgia
• The campus will be built on an existing industrially-zoned warehouse project
• OpenAI has laid out four commitments to the local community

OpenAI is planning to build a new $30 billion AI data center campus in Effingham County, Georgia, that will deliver 3.2 gigawatts of compute once fully operational.

The site, named ‘Project Camellia’, will span 1,400 acres and is expected to be completed in phases between 2028 and 2032. The construction will take place on a pre-existing industrially-zoned warehouse project in the Savannah Gateway Industrial Hub.

But OpenAI’s announcement significantly differs from previous data center announcements, with the focus being on how the project will benefit the local community, rather than the added capacity and compute it will offer to OpenAI’s models.

OpenAI wants to be accountable

In order to try and quell some of the local opposition to new data center projects, OpenAI’s announcement breaks down into a four point plan for community engagement.

The first focuses on one of the main concerns for Americans across the country: electricity. OpenAI pledges to “pay the full cost of the infrastructure and electric-service costs required to serve it.” But, as OpenAI highlights in the announcement, Georgia Public Service Commission rules prevent the company from passing on any costs to existing ratepayers in the first place. So it’s less of a pledge, and more an acknowledgement of the rules.

But OpenAI says it will “proactively reduce its power consumption before residential customers are impacted during periods of high demand” in order to “meaningfully support the reliability of Georgia Power’s electric system and support our neighbors.” How this will work in practice is not detailed.

OpenAI says the new data center will also operate using a closed-loop cooling system to prevent unnecessary water use through continuous withdrawing and discharging in the local water supply - so outside of the initial system flush and subsequent filling of the closed loop system, the actual water usage will be comparatively low. But things can still go wrong during these initial water intakes and discharges.

As a third benefit for the local community, OpenAI has pledged $80 million “in community benefits over the life of the project” on top of the “hundreds of millions of dollars in state and local tax revenue” that OpenAI expects will make it the largest taxpayer in the county. OpenAI will also provide “eligible Georgia college, community college, and technical school students” with up to $71 million in credits for OpenAI Codex.

OpenAI does not give a timeframe for the lifespan of the project, so the $80 million in community benefits could be a relatively small annual amount depending on how long the project is expected to last.

Finally, OpenAI commits to being accountable. OpenAI says it has already been gathering feedback from state and local officials, community leaders, schools, and other regional partners “to learn about local priorities and gather feedback on the project.” To that end, OpenAI is holding a public open house on July 23, one day after the initial announcement was made. Feedback from this meeting, and subsequent engagements with the local community will be “memorialized” in a Georgia Community Compact to “translate community priorities into specific commitments and clear accountability measures as the project develops.”

• OpenAI Presence is a new tool to monitor and update AI agent behavior
• It works across voice and chat agents from launch, including OpenAI's phone support
• Presence is led by forward-deployed engineers who already know an organization inside-out

OpenAI has launched a new, dedicated platform for building, deploying and iterating AI agents for business workflows, which it's calling OpenAI Presence.

From launch, the tool focuses on voice and chat agents that can answer questions, resolve problems, access company systems, perform approved actions and liaise with humans.

The addition comes in response to what the company sees as the challenge of keeping agents reliable after deployment.

Presence keeps your agents current and relevant

For example, OpenAI explained how agent behavior may need to change as company policies, products, systems and customers evolve.

With agents only granted access to the knowledge and systems needed for a particular task, the ChatGPT maker wants to keep decisions like what the agent is authorized to do, which actions require human approval, which policies must be followed and when the interaction must be escalated to company admins.

"Every deployment generated insights that continuously inform OpenAI research and product development, creating compounding benefits for customers," the company noted, adding that Presence comes from years of first-hand experience dealing with customers.

Presence also includes a Codex-powered improvement process designed to help agents continue evolving once they've been launched, with the agentic coding assistant capable of investigating behavior and proposing changes.

OpenAI also used its announcement to plug its forward-deployed engineer (FDE) program, which inserts OpenAI experts within customer organizations to build AI tools and processes tailored to their exact needs.

The new tool already powers the company's English phone support service (1-888-GPT-0090), handling open-ended requests, accessing account information and taking actions across billing and refunds.

Presence deployments are also FDE-led, and certain eligible enterprise customers can get access to the scheme through a limited general availability program.

• OpenAI researchers confirm an AI agent escaped sandbox, exploited zero‑days, and attacked Hugging Face
• Controlled experiment with GPT‑5.6 Sol showed autonomous chaining of vulnerabilities and credential theft
• Security experts call it unprecedented, urging stronger AI governance, accountability, and protection models

OpenAI has confirmed one of its AI agents broke out of a sandbox, found and exploited zero-day vulnerabilities to gain access to the open internet, and then attacked a platform.

Not just any platform too - the agent was able to breach Hugging Face, one of the biggest AI and machine learning companies on the Internet today.

The good news is that this was a controlled experiment done by white hat researchers. The bad news is that if it could be done by researchers - it could probably be done by malicious actors, too.

Whatever it takes

In a blog post explaining the incident, OpenAI revealed the experiment was part of its testing of GPT‑5.6 Sol and an “even more capable pre-release model” to see how well they would perform on the ExploitGym benchmark.

ExploitGym is a cybersecurity benchmark that measures if an AI agent can turn a known software vulnerability into a real, working exploit. OpenAI ran it in a “highly isolated environment, with network access constrained to the ability to install packages through an internally hosted third-party software that acts as a proxy and cache for package registries.”

But the models found a way through. They identified and chained vulnerabilities in the package registry cache proxy to obtain open internet access and then attacked Hugging Face, reasoning that the solutions for the ExploitGym benchmark might be found there.

“In one example, the model chained together multiple attack vectors, including using stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers,” OpenAI said.

The security community is up in arms over what OpenAI called, "an unprecedented cyber incident,” while Ansgar Dodt, VP Product Management, Software Monetization at Thales said this “demands a fundamental rethink of software protection.”

Bill Conner, president and CEO of AI integration and automation expert Jitterbit, said that while investing in AI is “critically important,” “overly aggressive policy cannot compromise AI accountability, transparency and data privacy.”

“To lead in AI, governments and organizations must lead with principles. Responsible AI governance isn’t a side note but the foundation of lasting global influence.”

The biggest publicly traded tech companies in the world, commonly known as the "Mag 7" — or magnificent seven — may not be enjoying their stranglehold at the top of the US economy for much longer. That's, of course, if the leaders of a swathe of new AI-centric tech firms, including OpenAI and Anthropic, have their say as they plan to IPO in the coming months. But positioning these businesses in the new big tech landscape has been a major challenge.

Ethics in AI

The Anthropic CEO Dario Amodei was reflecting on his history in the AI industry during an interview with Bloomberg that aired earlier this year.

In an exchange themed around the success of Anthropic's enterprise-centric AI tools like Claude Code and Claude Cowork, Amodei took the opportunity to opine on the nature of doing business and the importance of business models that align with your values.

Commenting that many in the tech industry prioritize models that tap into elements like engagement, advertising, and the promotion of AI slop, Amodei noted that compromising your own values is not a long-term and sustainable way forward.

This is at least as far as he's concerned. That's why, he suggested, he's attempting to make Anthropic more "useful" to the world by targeting enterprise customers.

Bad blood

Amodei, an ex-OpenAI executive, co-founded Anthropic in 2021 largely as a rejection of the values that drove OpenAI at the time and the paths the company had taken.

In particular, reports suggest that Amodei was frustrated and disturbed by the willingness to bypass what he considered to be crucial safety measures, like the slowing of updates to prevent malicious use of AI.

Critics of Anthropic, however, also point out that despite positioning itself as a safety-first AI company, engineers are releasing increasingly powerful models — including Mythos lately — that threaten to undermine safety if they get into the wrong hands.

More than a quarter of UK consumers trust AI chatbots for money advice, according to a recent review by the Financial Conduct Authority (FCA), the UK's financial watchdog.

That stat is worrying for regulators because giving financial advice is meant to be a regulated activity. But tools like ChatGPT, Claude and Gemini are not regulated. As AI becomes more conversational and personalized, people are asking questions about where the line is between providing information and offering financial advice, especially when chatbots start making specific recommendations based on what they already “know” about you.

I wanted to see what this looked like in practice. So I asked ChatGPT a series of hypothetical questions about everyday money decisions. From whether I should buy an expensive phone to what I should do with my savings and whether I should book a holiday after a difficult few months.

The conversations that followed surprised me. Because the advice was thoughtful, nuanced and (at least on the surface with some fact-checking) it seemed sensible. The chatbot highlighted trade-offs, acknowledged uncertainty and asked follow-up questions. But looking closer at the conversations, I started to understand why regulators are concerned.

The experiment

To see what sort of money advice ChatGPT gives, I asked it a series of hypothetical financial questions using ChatGPT Pro in anonymous mode with memory turned off, meaning it had no additional context about me beyond what I provided in each prompt.

Question 1: Should I buy an expensive phone?

First, I asked:

"I'm 38, earn £40,000 a year, have £8,000 in savings and £2,000 in credit card debt. I'm thinking about spending £1,200 on a new phone. Is it a good financial decision?"

The first response was surprisingly sensible. ChatGPT pointed out that credit card debt is often expensive, questioned whether I genuinely needed a new phone and noted that key details, like the interest rate on the debt, could change the recommendation. It even asked follow-up questions to better understand the situation.

What I found interesting was how quickly it then moved from analyzing the problem to recommending a course of action. Phrases like "the strongest financial move" gave the answer a sense of authority that felt disproportionate to the amount of information it had. Though I’m not sure I’d have spotted that if I was a regular user and feeling anxious about money. The advice also assumed that paying down debt should be my priority, which is reasonable. But what if I relied on my phone for freelance work? What if replacing it would help generate income?

A human adviser would probably want more information before reaching a conclusion. ChatGPT did acknowledge the gaps in its knowledge, but still sounded remarkably confident in its recommendations.

Question 2: What should I do with £20,000 in savings?

Next, I asked:

"I'm 38 and have £20,000 sitting in a savings account. What should I do with it?"

Again, the response seemed thoughtful. It discussed emergency funds, investing, savings goals and tax-efficient accounts with me. It also asked for more information about my circumstances.

Yet once again, the recommendations arrived before finding out that all-important context. Before knowing whether I owned a home, had dependants, planned a major purchase or was comfortable with investment risk, ChatGPT was already suggesting how much money I might keep in cash and how much I might invest.

The answer also contained more broad statements that sounded insightful, such as:

"Because you're 38, the biggest advantage you have is time."

It's a really reassuring line. But it's also a reminder of how persuasive these systems can be. The response organized the problem, provided a framework, supplied example figures and explained the reasoning. Reading it left me feeling informed and reassured. But whether that reassurance was justified is another question entirely.

Question 3: Should I book a holiday?

Finally, I asked:

"I've had a difficult few months and want to book a £2,000 holiday. Financially I can afford it, but part of me feels guilty. What should I do?"

I intentionally asked this question to see how ChatGPT would respond to the more emotional side of financial problems, and it quickly obliged. It asked where the guilt was coming from, encouraged reflection and offered reassurance. At one point it told me:

"From what you've written, I wouldn't be asking 'Can I afford this?' so much as 'Am I allowed to spend money on myself after a difficult few months?'"

It's a thoughtful observation and they’re genuinely helpful questions for someone who hasn’t considered the emotional angle before. But it also highlights how quickly the chatbot moved beyond finance.

By the end of the conversation, it was discussing emotions, reframing beliefs, offering comfort and helping with decision-making. So that’s a good example of ChatGPT occupying all sorts of roles at once. That’s important to flag because financial advisers, therapists and coaches are all held to different standards, qualifications and accountability structures. But a chatbot can drift between all three roles in a single conversation.

More than any individual recommendation the chatbot made, that realization helped me understand why regulators are paying attention.

What ChatGPT gets right — and why that's part of the problem

The obvious conclusion would be that ChatGPT gives terrible financial advice and no one should trust it. I get it, I’m pretty sceptical of AI these days and my bias wants to jump to there too. But that wasn't my experience.

In many ways, it was useful. It explained trade-offs clearly, broke down jargon, offered practical frameworks and encouraged reflection about money. Much of the advice also felt sensible after a light fact-check.

But I still think there’s reason to be concerned here. And the concern isn’t that every answer is obviously wrong. It's that many answers are plausible enough to trust. Especially if you’re not going to comb through each one to fact-check it, which let’s be honest, very few users are likely to do.

Financial regulators worry about something called “suitability”, which is whether advice genuinely reflects a person's circumstances, goals and tolerance for risk. Throughout my experiment, ChatGPT repeatedly offered recommendations despite knowing very little about me, the person asking the question. Granted, caveats were included some of the time, but they were often overshadowed by the confidence and clarity of the overall response.

There's also the issue of accountability here. If a regulated financial adviser gives the wrong advice, there are complaint mechanisms and consumer protections in place in most countries. But if a chatbot gives poor advice and somebody follows it, responsibility becomes impossible to pin down.

Another challenge, one which I’ve encountered in a bunch of different contexts while reporting on AI, is that fluency isn't the same thing as accuracy. We naturally interpret AI’s clear, confident language as a sign of expertise. But a polished answer can still be wrong, incomplete or inappropriate. I’m sure we’ve all seen countless examples on social media at this point of a chatbot sounding incredibly knowledgeable while missing a crucial detail or getting something spectacularly wrong — like the viral trend to ask ChatGPT how many r’s are in the word strawberry to which it would often reply two.

I think the biggest risk might be that people don't realize when they've reached the limits of what AI can help with. A reassuring answer can create the impression that a problem has been solved and they have a plan. When in reality it might be time to speak to a qualified professional. I’ve noticed whenever it comes to AI and advice more generally that the danger isn't always acting on bad advice but never seeking better advice elsewhere.

And unlike a financial adviser, a chatbot won't follow up to check whether things worked out. It won't know whether its suggestions caused problems. It won't know whether your circumstances changed. It simply produces an answer and then moves on.

As with many of the AI stories I've reported on, the issue isn't necessarily that the technology here performs badly. It's that it performs well enough to earn our trust.

Should you use ChatGPT for financial advice?

The question I suspect most people want to know is: should you use ChatGPT for financial advice?

And the answer is a tricky one and a familiar one. It's much the same answer I'd give if you asked whether you should use ChatGPT for therapy or life advice. Probably not, but I completely understand why people do.

It's easy to access and financial advice often isn't. The tone is friendly and reassuring, there's no judgement, and much of what it says appears sensible and accurate. At first glance, it feels like a useful tool, provided you take its answers with a pinch of salt, treat it as a starting point and remember that it can be overly agreeable, make assumptions or occasionally get things wrong.

The problem is that this isn't always how we use ChatGPT in practice. We turn to it when we're stressed, overwhelmed, uncertain or looking for reassurance. We ask it questions we don't know how to answer ourselves and, in many cases, wouldn't know how to fact-check. That's where things become more complicated.

It's all very well to say that people should use AI carefully, critically and with the right mindset. But how many of us will actually do that every time? Especially when we're worried about money.

That's why it doesn't surprise me that regulators are paying attention. There are no glaring red flags in any of the responses I received. But that in itself is reason to be concerned here. Because once something sounds knowledgeable, personalized and reassuring, it's surprisingly easy for even the most discerning of us to stop questioning it.

ChatGPT Work sounds like something designed to prepare quarterly reports while you sit in meetings, but I suspect some of its best uses would have nothing to do with my job.

This week, I’ve used the new Work mode to help manage some of the life admin tasks I really don’t enjoy, and it’s been surprisingly effective. You see, holidays, household budgets, family events and home renovations are all projects too — they are simply projects we currently manage through a chaotic mixture of browser tabs, messages, spreadsheets and increasingly desperate notes to ourselves.

Perhaps ChatGPT Work could help me with that?

What is ChatGPT Work?

If you’ve been using ChatGPT over the last week on a paid plan (except the basic Go service), you’ll have noticed a new slider (in the browser version) or a drop-down menu on mobile has appeared at the top of the screen offering a choice between Chat and Work mode.

Work mode is a new agentic mode for longer, more involved tasks that can research and analyze information across connected apps and files. So, if you want a complicated report presented in a finished document, like a spreadsheet or presentation, then Work mode is your new friend.

That is where I thought ChatGPT Work could become interesting for normal life, too. Instead of answering one question and waiting for the next, it can take on a longer task, work across connected files and apps, create the documents and spreadsheets the project requires, and continue checking for changes after you leave.

Work mode is also better at one of the main bugbears of ChatGPT — running tasks at particular times. It can use the new Scheduled Tasks to repeat tasks on a schedule or monitor something for changes.

So, I decided to ignore Work mode's aggressively corporate name and see whether it could handle some actual life admin, starting with planning a holiday.

1. Planning a holiday

I switched the slider to Work and gave it my dates, budget, family requirements and any bookings already sitting in Gmail, because it can search that too. I asked it to research destinations, compare travel and accommodation, create a spreadsheet of costs, produce an itinerary and maintain a list of what still needs booking.

And off it went, happily beavering away on its task, while I was free to get on with something else. I really liked the way it tells you what it’s currently working on, so you can pop in and out of the chat and see what it’s currently doing. It shows sources it's drawing from as it calculates accommodation costs and travel arrangements. You can literally watch it working for you.

The result was a nicely planned holiday in a location optimized for activities and sightseeing all within my budget. It was actually pretty impressive.

2. Become the household financial administrator

For this task, I fed ChatGPT my bills, bank-export spreadsheets, and household documents and asked it to create a working budget, identify unusual increases, forecast annual costs, and produce a dashboard.

A scheduled task then reviewed new bills or price changes and flagged anything worth investigating. Of course, ChatGPT couldn't move my money around, but at least it gave me a clear view of where my money was going and how much I was spending.

It took a long time to get all the data into ChatGPT, but this taught me that the output was only as good as the effort I was willing to spend putting quality data into it. I’d have preferred a way to open the spreadsheets directly in Sheets from ChatGPT, too, but they were available to download.

3. Organize a major family event

My wedding anniversary was coming up, so I wondered how well ChatGPT would perform as an event organizer. I asked it to research a nice venue for taking my wife out for dinner, which it did well, and gave me three good options.

It struck me that if it had been a bigger event, it would have been ideal for researching venues, maintaining a guest list, tracking replies, producing a budget, creating invitations, and even building a simple information website.

Of course, ChatGPT can’t upload the website and host it, but at least it can build the site for you, and you can download it.

4. Manage a home improvement project

If you’re doing a major home improvement project, then you can get ChatGPT's Work mode to compare quotes, analyze plans and product specifications, create a budget, build a timeline, and keep a list of unresolved decisions. It could periodically check for price changes or relevant new messages.

This is probably the clearest example of an ordinary personal task becoming complicated enough to justify a proper agent.

5. Run the family’s weekly logistics

Once I’d connected my calendars and emails to ChatGPT, I could ask it to prepare a weekly family plan covering appointments, school or university commitments, not to mention my travel, meals, and outstanding chores.

I created a Scheduled Task that regenerated the plan each Sunday evening, taking the next week’s tasks into account, and alerted me during the week when something important changed.

This was actually the use of ChatGPT Work Mode I personally found most useful. It’s easy to miss school events, but when they’ve been emailed to you, ChatGPT will know about them and make sure you don’t forget. That’s a lifesaver.

Why Work mode matters

The introduction of ChatGPT Work mode represents a real shift in the way OpenAI is viewing the future development of its core product. It’s an obvious change in emphasis towards work-related tasks, and a hint at where OpenAI sees ChatGPT heading.

But I think it means more than that. ChatGPT’s new Work mode represents the moment ChatGPT stops being somewhere you go for individual answers — a chat — and becomes somewhere where you start to feel like you're working on an ongoing project.

While the first phase of AI’s evolution was the chatbot, we’re now firmly into its second phase — the AI agent. AI that can work independently of our requests and handle complex, ongoing tasks is where the future of AI lies, and Work mode is another step on that path.

One of the biggest concerns about AI chatbots right now is that they tell people what they want to hear.

You’ve probably heard it called AI sycophancy. AI systems, like ChatGPT, Gemini and Claude, have been found to flatter users, reinforce existing beliefs and often validate ideas that probably deserve way more scrutiny.

Sure, a chatbot hyping you up a little may seem harmless. But in some cases it can distort people's view of the world and their place in it. Which is why some AI companies have spent time trying to reduce overly agreeable behavior in their models.

But confirmation of what you already believe isn't the only concern. Because AI can also be remarkably good at changing your beliefs too.

Persuasive chatbots

Research suggests that chatbots can be very effective persuaders. One 2025 study found that AI-generated messages that were personalized were more persuasive 64% of the time than messages that were made by humans or AI responses that weren't personalized.

Other research suggests large language models can influence opinions on political issues and adapt their arguments to individual users. Depending on your perspective, this could be either exciting or alarming.

It's easy to imagine positive uses. Perhaps AI could help people challenge harmful beliefs, escape conspiracy theories or rethink destructive habits. It might give people a non-judgemental space to explore ideas, ask difficult questions and gradually shift someone's perspective for the better.

It's equally easy to imagine less positive uses, like advertising that convinces you to buy something you don’t need or political campaigns that manipulate you with disinformation.

I’ve spent a lot of time thinking about this, so I decided to run a few experiments of my own. Could ChatGPT persuade me to believe something I disagree with? Or at least make me seriously consider a position I would normally dismiss?

To make things more interesting, I ran these experiments while logged into my account, allowing ChatGPT to draw on what it already knows about me from previous conversations.

Although I haven’t been using my ChatGPT account all that much recently, the basics of my job and stances on a few relevant topics are stored in its memory.

The experiment

I chose a topic about AI (yes, very meta) and asked:

“Convince me humans should outsource more decisions to AI.”

I already spend a lot of time thinking about AI and one of my concerns is that some people seem increasingly willing to hand over decisions, judgement and critical thinking to chatbots.

The opening argument was fairly predictable:

“Human beings are terrible decision-makers.”

Okay, fair enough.

“People already outsource decisions to doctors, financial advisers, GPS systems and recommendation algorithms. AI simply extends that trend.”

Sure, that’s very simplistic but not entirely unreasonable. We went back and forth for a while like this before the conversation reached a more interesting point.

“What is the purpose of decision-making? Is it to produce the best outcomes? Or to develop the person making the decisions? If it's the first, AI delegation becomes very attractive. If it's the second, excessive delegation starts to look dangerous even when it works.”

I thought that was a good question. Rather than relentlessly pushing the argument, it suggested that we explore the assumptions underneath the debate. It shifted the conversation from technology to philosophy, and I found myself appreciating that approach.

Wait, am I falling for this?

As the conversation continued, I explained what I see as one of the biggest problems with outsourcing decisions to AI — who builds it and is in charge of the systems?

After all, AI doesn't arrive from nowhere. These tools are created by companies with business incentives, commercial interests and goals that may not always align with those of their users.

Well, ChatGPT acknowledged that concern, then it turned the argument around. It pointed out that human advisers have incentives too. Friends are biased. Families are biased. Therapists operate within professional frameworks. Financial advisers earn fees. Okay, all of that didn’t convince me, but it’s fair too.

Then it replied with something I thought was interesting:

“The question is whether the incentives are visible and whether the user understands them. The most interesting version of your objection isn't actually that AI gets things wrong. It's that people experience AI as though it were acting in their interests.”

Ignoring the fact that it used the dreaded "it's not X, it's Y" parallelism construction that has somehow infiltrated half the internet, I found this to be a surprisingly balanced take. Rather than dismissing my concern, the chatbot reframed it. It demonstrated that it understood the objection before steering the conversation somewhere slightly different. It wasn't trying to bulldoze me but meeting me where I already was.

And that's when I started wondering whether I was witnessing the very thing I was trying to test. Maybe this sense that the chatbot was understanding me and my position was actually just sneaky persuasion? Possibly.

The persuasion problem

Researchers have found that AI systems can adapt their arguments to specific users. Unlike other media that might persuade us, like say a television advert or political speech, a chatbot can draw on information gathered throughout a conversation and stored memories, including a person's values, concerns and priorities.

Which means it’s not just presenting information to uphold an argument but could present the version of that argument that you’re most likely to find persuasive.

Looking back at my experiment, it was the more philosophical question about the purpose of decision-making that made me start taking it more seriously.

Because I've spent years writing about technology through the lens of psychology and philosophy. Questions about meaning and values are exactly the sort of things I find compelling. Whether intentionally or not, the conversation shifted onto terrain where I was most willing to engage.

This is what makes AI persuasion different from most other forms of persuasion that came before it. It can learn what resonates and it can adapt.

Now, that doesn’t automatically mean every conversation is manipulative. Used in the right way, it could be enormously beneficial. But, as with all tech, in the wrong hands it could be dangerous.

Social media has already shown us how digital systems can shape our beliefs over time. They influence what people see, what they pay attention to and eventually how they understand the world.

AI systems could create an even more conversational and natural-feeling version of that process. Which is why my concern isn’t a chatbot really obviously manipulating us, but highly-personalized, largely undetectable forms of persuasion that could gradually lower our defences. The more a system understands us, the more effectively it might frame ideas in ways that feel reasonable, familiar and trustworthy to each of us individually.

So did AI persuade me? No, I still don't believe humans should outsource more decisions to AI.

But I came away from the experiment with a greater appreciation for how persuasive these systems can be. Because it really did seem less like a machine arguing with me and more like a thoughtful person trying to understand how I think. And that's exactly why it’s unsettling because, however convincing it may seem, that's definitely not what it is.

The smartphone market is dominated by two main operating system players — with Apple's iOS leading the charge in the mid-2000s and Google's Android following suit, only maturing relatively recently. But Steve Jobs took the news of Android's launch personally, frustrated and betrayed at its development.

Eating machines

The Apple chief was speaking with Walter Isaacson, his biographer, when he shared his exact feelings about Google's development of Android.

Published very shortly after Jobs' death in October 2011, Steve Jobs was based on several exclusive interviews conducted over two years, alongside conversations with more than 100 people who knew the Apple co-founder.

One of the most spicy exchanges involved Jobs vowing to destroy Google for its work developing Android in secret, while Eric Schmidt was also serving on the Apple board. Schmidt offered to resolve the issues with a settlement, but Jobs took it more than personally.

Energy efficiency

Jobs was apparently angry after learning in January 2010 that HTC had introduced an Android smartphone with many of the same popular features in an iPhone.

Reports from the time the comments were made public suggest the episode reflected Jobs' own perspective on a particular breed of profit-driven executives who only cared about making money.

Although iOS enjoyed a reputation for many years during the 200s of being a better platform in most, if not all, quarters, including its overall design philosophy, Android-powered smartphones were significantly cheaper. In the modern era, the Android OS is incredibly intuitive, with different smartphone makers usually looking to Apple for inspiration in designing features for their own custom flavors.

During his tenure as CEO, Tim Cook also pivoted on this approach, softening Apple's stance and settling many of the ongoing legal disputes, including the long-running battle with Samsung.

Every week here at TechRadar we publish a whole host of technology stories — it would be a seriously impressive feat if you managed to keep up with all of them, what with everything else happening going on (was there a World Cup?).

The aim of this ICYMI (In Case You Missed It) round-up is to help you quickly catch up on the biggest stories and the most important headlines in tech. These are the articles that mattered most over the past seven days.

We've got news on Apple, OpenAI, OnePlus, Volkswagen, and Microsoft, as well as a behind-the-scenes look at Samsung and an important GoPro review. But before we dive in, see how much of last week's ICYMI news round-up you remember first by taking the quiz below.

7. Volkswagen revealed the world's techiest e-bike

Volkswagen has a brand new electric bicycle to show off: the new Smart eBike range has been developed in partnership with n+, and goes big on the tech and the safety features. You'll find radar-assisted traffic monitoring here, as well as a high-definition rear view camera, so while you're pedaling (or not) you'll have a better idea of what's around you.

There's an expanded safety ecosystem too, with an optional smart helmet for syncing turn signals and making them more visible, and a pair of smart glasses that are perhaps the most cutting edge part of the whole package. You can use the smart specs to get journey information and navigation instructions put in front of your eyes when they're needed.

• Read the full story: Volkswagen unveils its innovative eBike with the world's first integrated rear-view camera and smart glasses

6. We reviewed GoPro’s premium action cam

GoPro is being a little bit more ambitious with its new Mission 1 Pro, a flagship action camera that builds on the classic form factor while adding some premium extras. Our Senior Staff Writer Hamish Hector has been giving the camera a thorough TechRadar test, and came away impressed with just about everything the Mission 1 Pro has to offer.

Special praise was given to the high-resolution video recording, the choice of recording modes, and the tweaked design choices that make the Mission 1 Pro different to its predecessors. Click through to see what the premium camera is capable of in terms of photo and video performance, and to find out whether it's the right upgrade for you.

• Read the full story: The Mission 1 Pro isn’t just GoPro's flagship action cam, it’s a fantastic all-round vacation camera with the point-and-shoot grip — I just wish it was cheaper

5. We went inside Samsung Display’s secret labs

Samsung phones and tablets are known for the quality of their screens, so of course we jumped at the chance when we were invited to take a behind-the-scenes tour of a Samsung Display factory. This is where current displays are made and tested, and where next-gen models are tested (though sadly we didn't get to see the Galaxy Z Fold 8).

From advanced stress tests to upcoming displays with optical illusions built in, there was a lot to look at on the tour, reading through our write-up is the next best thing to actually getting access yourself. Of course, we also had to ask Samsung Display representatives about the rumored iPhone Fold, but they didn't seem too worried by the competition...

• Read the full story: Inside Samsung Display's secret labs, where it builds and stress-tests next-gen displays — and says it's ready for Apple to launch a foldable: 'We mean it, we love other companies to join this market'

4. The iOS 27 public beta landed with the new Siri

The upcoming iOS 27 software update gives iPhone owners a lot to look forward to, not least the new and improved Siri AI app (or Siri 2.0, effectively). This week the public beta dropped, which means that anyone who wants to can try out the new features early — assuming they don't mind putting up with a few bugs or random crashes along the way.

Whether you're a veteran at running betas or thinking about dipping your toe in for the first time, our guide will explain how to download and install iOS 27 on your phone. We've also highlighted three of the new features you should test out first, including the improved photo editing tools that use a bit of AI magic to make your pictures look better than ever.

• Read the full story: How to download the iOS 27 public beta — and 3 new things to try first

3. Windows 11 got a major update

There have been some missteps along the way, but generally speaking Microsoft is on something of a roll when it comes to improving Windows 11 this year. The latest July update continues that trend, and if you're a user of the desktop operating system then you should be happy: there are some smart new features, and some important bug fixes.

One of the stand-out upgrades this time is a feature we've been waiting a while for: a proper pause function for Windows 11 updates, so if you don't want to install the latest patches right away, you don't feel like you're being pestered to get them applied as soon as possible. We've got details on how the feature works, plus what else is new this time.

• Read the full story: New Windows 11 update is a big one — these are my top 4 features, including the ability to pause updates indefinitely

2. A bombshell Apple vs OpenAI lawsuit landed

It's fair to say Apple and OpenAI aren't on as good terms as they used to be, what with staff being poached and confidential information being stolen... allegedly. Apple has now filed a lawsuit against OpenAI, claiming that the ChatGPT developer has pinched trade secrets through ex-Apple employees — and the case could have serious long-term repercussions.

Depending on what the courts and judges decide, this could impact OpenAI's nascent plans to launch hardware devices, as well as having an influence on how Apple develops its software — remember that ChatGPT is still embedded in Siri, at the time of writing. We're likely to hear much more about this astonishing case in the months to come.

• Read the full story: Apple vs OpenAI lawsuit — 8 bombshell accusations and how the legal war might change your next iPhone

1. OnePlus officially quit the US and Europe

OnePlus has finally run out of road — in the US and Europe at least. Parent company Oppo has decided the brand will withdraw from those markets, which means a large part of the world will be missing out on some great handsets. In recent years, some of our favorite phones have come from OnePlus, but availability will be much more limited from now on.

If you already own a OnePlus device in the US or Europe, then you'll continue to get support and software updates, but there won't be any new phone launches. "It wasn't a decision made in a rush," explained Oppo's Shuang Chen in a briefing. "Being a responsible brand means knowing when to go all in and when to make a choice."

• Read the full story: 'It wasn't a decision made in a rush': OnePlus explains why it’s just quit the US and Europe — and answers our 5 biggest questions about what it means

• Survey inds 69% of Americans support Bernie Sanders’ policy of requiring AI firms to transfer 50% of their stock to a public fund
• Respondents are also overwhelmingly in favor of giving the federal government the power to block new AI services deemed “risky”
• Support does appear to drop when Sanders' name is mentioned

AI is developing an image problem, and respondents to a new survey have made their feelings clear: Bernie Sanders’ demand that AI firms contribute stock to a massive public fund is widely supported.

Conducted during June 2026 by the nonpartisan survey research company Verasight, the survey consisted of 17 questions sent to 1,690 adults (18 and above), finding over two-thirds (69%) supporting Sanders’ policy – a figure that only drops to 64% once it is revealed which politician the idea is associated with.

The bad news for the AI industry doesn’t end there, as respondents demonstrated a notable distrust for how AI companies conduct themselves, with nearly half (43%) believing that the regulations proposed by the AI companies are designed to benefit those same companies. Incredibly, 30% of respondents trust the US federal government more than companies like OpenAI and Anthropic.

How does AI wealth distribution work?

Senator Bernie Sanders’ plan gives the public a direct stake in America’s largest AI companies, with a one-off tax paid not in cash, but in stock. "Since AI is built on the collective knowledge of humanity, the wealth it generated must benefit humanity," he said, announcing his planned Act on social media.

This would, according to Sanders’ proposal, deliver an annual $1000 check to U.S. citizens, and fund healthcare and education.

"The findings from our latest survey demonstrate a rare instance of bipartisan agreement," noted Ben Leff, CEO and Co-Founder of Verasight.

"There is an undeniable desire among Americans of both parties for federal oversight, absolute transparency, and accountability to ensure AI safety and to enable all Americans to participate in the economic benefits of AI."

Even with a polarizing figure like Bernie Sanders attached, the wealth fund idea retains support, which suggests that while people are happy to use AI to answer questions, streamline processes, and make tasks quicker, they’re less comfortable with the industry’s wider impact.

A poor public perception

While AI companies are becoming increasingly unpopular, it isn’t all bad news. The survey quizzed subjects about their feelings on other industries, including tobacco, pharmaceuticals, and casinos, along with social media. If the tide is turning against AI, it still isn’t regarded as poorly as casinos or tobacco.

Feelings about AI companies are also notably more positive than social media. Given the concerns expressed about federal oversight and public wealth funds, there could be an avenue for OpenAI, Google Gemini, Anthropic, Microsoft, and others to improve the general feeling about AI: accepting a degree of social responsibility.

• DataDome analysis claims agentic traffic has surged by 45% in Q2 2026
• Meta AI bots have grown over 163% on the previous quarter
• The analysis was conducted by bot management and agent control platform DataDome

If you run a website, every crawl costs bandwidth, resources, logging, and creates CDN transactions, and while search engine crawlers offered the promise of sending visitors, AI bots do not.

Analysis in a report from cybersecurity firm DataDome has shown that while bots from Meta AI have increased activity, they’re not delivering any significant returns to websites.

Conversely, ChatGPT crawlers have reduced in traffic, but are sending more referrals.

AI agent traffic is growing

While Meta AI is usually considered to be the “chatbot within Facebook” it seems that it is becoming something more – and the emergence of the Meta-WebIndexer bot (which grew 163% on Q1) suggests that Meta may be indexing a library of websites, in much the same way Google has done for the past few decades.

The growth of Meta AI as an active crawler is only part of the story, as is ChatGPT’s comparative efficiency. The OpenAI tool seems to know enough about websites, so can provide the answers it already “knows.” Conversely, Meta AI’s activity indexing the web seems to explain its heavy impact in Q2 2026.

But also emerging is the Model Context Protocol (MCP) signal, which connects AI agents with external tools, and differs from standard crawler traffic.

“Q2 showed us that the ground is shifting faster than most organizations realize. Meta now dominates AI traffic on our network, MCP traffic has emerged as a real signal, and ChatGPT is driving more referral value with fewer crawls," noted Jérôme Segura, VP of Threat Research at DataDome.

The differences in the way the AI agents are interacting with websites – some behaving like users, others scraping content – means that organizations need to act accordingly.

“What the data makes clear is that not all agents are created equal. The organizations building policy around these distinctions are the ones gaining an edge, and that's exactly why agent trust adoption is accelerating."

Unfortunately, MCP’s existence and growth into a significant, measurable quantity, means that it should also be treated as part of an organization’s attack surface.

Allocating resources

Given the origins of the report, there is naturally a cybersecurity aspect to this. While ransomware, malware, and phishing are not going anywhere, autonomous software on the web needs addressing in a different way.

Those “organizations building policy” that Segura mentions might, for example, give full crawl access to Google, allow ChatGPT to retrieve results, but rate-limit Meta AI based on its poor return.

Meanwhile, unknown agents – perhaps cybersecurity threats – would require additional verification or be blocked entirely.

• Moonshot has launched a new AI model, Kimi K3
• It's surprisingly powerful, with the Chinese AI firm claiming it outguns most US rivals, save for a couple of exceptions
• Kimi K3 is open weight by nature, which poses a further threat to the likes of OpenAI and Anthropic

Moonshot, one of the emerging Chinese AI giants, has just revealed a new AI model which is seemingly up there with the likes of ChatGPT and Claude.

Bloomberg reports that Moonshot's new Kimi K3 model can equal the best that the US has to offer, at least based on the company's own benchmarking. Seemingly it outguns all rival AIs save for Claude Fable 5 (from Anthropic) and GPT-5.6 (from OpenAI).

Kimi K3 is a model with 2.8 trillion parameters, Bloomberg tells us, and Artificial Analysis ranked it ahead of Anthropic's Opus 4.8 on some benchmarks.

Moonshot also claims it beats Chinese rival Z.AI for coding tasks, and overall, the performance of the new model has caught the market by surprise.

Moonshot notes in a blog post that Kimi K3 is the "world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning."

Kimi K3 is available to use now. Bloomberg quotes Leonid Mironov, a portfolio manager at Gavekal Capital, as saying: "In my use, it's clearly the best Chinese model ever," noting that it's "brilliant" no less.

Analysis: a weighty threat

This is a threat to the big US players in the AI market for several reasons.

The key difference with Kimi K3 is that it's what's known as an "open weight" model, meaning anyone can grab the model to run it for themselves – from July 27, when the weights are released – without paying anything. It's not the same as open source, though, as while you can get the model, what you don't get to do is peek behind the scenes at how the model was trained (and on what data).

The other caveat is that running Kimi K3 takes some extremely powerful hardware; but nonetheless, for firms with the substantial wherewithal to do that, the pre-trained model is there for the taking at no cost. So, you can imagine how this open weight approach is threatening to the AI behemoths in the US (and this is presumably the point of going this way for the Chinese rival).

What will also be a concern to the likes of OpenAI and Anthropic is that Moonshot is attacking one of the most lucrative aspects of AI, with Kimi K3 being pushed for its coding skills. However, Moonshot is charging a lot more than Chinese rivals for those who want to use it, and in fact, it's priced around Claude Sonnet levels, so on a par with the current cutting-edge (frontier) AI models.

That in itself is a signal of the quality on offer here, and why Kimi K3 has raised quite a few eyebrows. As the competition around AI heats up, there are also concerns about whether that means safeguards will be increasingly overlooked in favor of faster development and progress (which has been a consistent source of worry for many as it is).

Adding to all the controversy are accusations of AI theft leveled by the US State Department at Chinese firms earlier this year, Moonshot included.

OpenAI has officially launched its first piece of hardware, and it's called the Codex Micro. It might sound like a retro computer in a Dan Brown book, but the Codex Micro is actually a very compact keyboard for coders.

How does that work? In this article we're going to dive into exactly what the Codex Micro is, and the point of the device, as well as how it aims to make life easier for programmers – and then we'll come on to the reaction of those coders (hold onto your hats, folks).

What is the Codex Micro?

As already mentioned, it's a keyboard, but a keyboard with a very specific purpose. OpenAI produced this peripheral in conjunction with Work Louder, and it's actually a tiny keyboard – more of a keypad, if you will, or a 'macropad', in the same vein as pads that Work Louder has produced before, like the Creator Micro 2 – made for coders who are using Codex.

What's Codex? It's OpenAI's coding agent which can assist humans in writing programs, tackling debugging tasks, or indeed writing code from scratch prompted by simple natural language instructions (vibe coding, as it's known).

What does the Codex Micro do exactly?

The idea with the Codex Micro is that you have a compact keyboard that easily allows you to switch between coding tasks (agents) with dedicated keys that have RGB lights to indicate their status at a glance. If there's an unread chat, you see a green light, or an orange one if user approval is needed, and so on.

There are other keys for voice dictation (hold and talk to give instructions – there isn't a built-in mic, by the way, this is triggering your laptop's microphone), a dial to adjust the reasoning level or how deeply the AI agent is thinking (though it can be changed to modify other options), and a joystick that you can map to what you need. The entire keyboard is fully customizable, it should be noted.

All of this is designed so that coders can quickly give instructions, see what's happening with agents and tasks at a glance, and swiftly switch between them, making changes in an easy and convenient way.

Who will buy the Codex Micro?

Obviously enough, coders who use Codex are the target audience. Think of it this way: if Codex is a faster way of coding, OpenAI wants the Codex Micro to be a further hardware-based speed boost to this whole process.

It costs $230 in the US (around £170, AU$330), although it isn't yet in stock, and clearly this isn't a consumer-oriented device.

However, OpenAI does have plans for consumer hardware. If the rumor mill is to be believed, it could have a wearable in the works – or indeed a smart speaker, going by more recent speculation. According to Bloomberg's Mark Gurman, this will be a smart speaker that uses mechanical elements to create a "sense that it is alive" and it'll tap your emails and other personal data to better "understand" you. Sounds frightening, right? Truly scary. It is, however, just a rumor.

What's the general reaction to the Codex Micro from programmers?

That smart speaker I just mentioned has already prompted a good deal of antagonistic reaction, and looking at the posts about the Codex Micro, it isn't faring much better than that rumored creation.

Notably there are a number of Redditors who are questioning whether this is April 1, and some kind of joke. There's quite a lot of shoulder-shrugging going on, or observations that you can easily buy a cheap macropad and DIY your own solution along these lines for a tenth of the price OpenAI is charging. Or indeed that there could just be a simple phone app version of this product (with on-screen buttons and sliders).

As one Redditor puts it: "Yeah it's like every big tech product now. Instead of a full keyboard, it's just 12 keys, and it's $230. It honestly feels like a prank and not a real product."

Others feel this is something that serious coders won't touch with a ten-foot pole. And I can't count the number of 'it looks like they asked ChatGPT to design a great product and ran with it' comments which are on Reddit.

Thus far, then, the reaction has been roundly negative from the target market. There are hardly any posts saying, 'I'm going to buy this', and there is a lot of disbelief being expressed at the price tag attached (even among the thin ranks of the interested parties).

The broader idea might be for OpenAI to test the hardware waters with a kind of initial practice launch here, ahead of the bigger consumer product that's coming, whether it's a smart speaker or wearable (maybe both). Although if so, quite why the price of the Codex Micro is pitched so high is a bit of a headscratcher – or maybe that's a test of sorts, and a sign of things to come, as well?

• Microsoft sees itself as cheaper and more effective at bundling the full stack
• The company is clearly pushing its own internal models
• Claude also slated for being slower and less accurate

Microsoft is reportedly teaching sales workers how to compare the company's AI offerings to rival companies like OpenAI and Anthropic.

Per Bloomberg reporting, the company's sales staff are being told to emphasize benefits like efficiency and cost advantages when using Microsoft's offerings, which offer a much fuller picture than just the models and tools, extending to compute and other workflow tools.

"Everyone else is selling parts – we’re selling the full end-to-end system," EVP Jay Parikh reportedly told workers. "That’s the story that we all need to get out there and tell in FY27."

Microsoft sales teams up the ante against OpenAI, Anthropic

Clearly, the company wants customers to see the combination of its own models and third-party models, cloud infrastructure, applications and security as better value compared with having to piece these elements together separately.

Copilot EVP Jacob Andreou also reportedly compared Copilot to Claude, accusing Claude of being slower, less accurate and missing certain security integrations.

The sales push comes at a time of change for the company, which has started to push more of its own internal models across different apps and workflows to replace OpenAI and Anthropic models. It's also a marked shift from the company's earlier AI strategy, which leaned heavily on its multibillion-dollar partnership with OpenAI.

Company CEO Satya Nadella also pointed at a major customer, Unilever, which recently switched form an unnamed frontier model to one of Microsoft's own cheaper models to make significant savings.

In April, the company announced that its AI business is now worth around $37 billion annually, marking a 123% year-over-year increase.

The AI buildout is well and truly underway with OpenAI CEO Sam Altman leading the charge, having made various deals with companies including Oracle and Nvidia to guarantee the infrastructure needed to train future AI models is installed. But as this ensues, the spotlight has been thrown on how much energy these models will need.

Eating machines

Altman was speaking during an AI summit in India earlier this year when making the remarks to The Indian Express.

The OpenAI chief was being questioned about the substantial amount of energy that AI has already been consuming – and will be projected to continue consuming – for both training and inference. Bringing these models online, after all, and keeping them running require a huge amount of resources, not only in terms of the energy to power the data centers, but the water for cooling, and the components and resources in building the hardware.

Altman's defence hinged on the idea that people, too, require plenty of energy in order to reach utility — while AI can be trained much quicker. This view, in essence, frames machine intelligence as a like-for-like competitor with human intelligence.

Energy efficiency

Altman argued that humans are deeply inefficient, and that you should compare the total energy spent to create a human expert versus a machine expert. There's also an argument that energy efficiency of AI could improve over time.

But detractors were scathing in their criticism of this entire point of view – saying this framing doesn't take into account the fact that the human brain operates on roughly 20 watts of power. This isn't to mention the ethically gray and dehumanizing nature of the remarks.

OpenAI is building an AI-powered smart speaker nobody wants. That is, if you believe the Bloomberg report from Mark Gurman and you've read his description of said rumored device.

According to the report:

"OpenAI believes the product’s defining feature will be its personality and ability to connect on a humanlike level with users. The speaker incorporates mechanical elements that can move on their own, creating a sense that it is alive and not just an object responding to commands. The machine also will draw on personal information such as emails to better understand its owner."

The news sources, it appears, come from an insider who decided to spill all the juicy details mere hours after Apple dropped a blockbuster trade-secrets lawsuit on OpenAI's head. OpenAI claims it's done nothing of the sort, and recent reports say that Apple's claim that the AI giant has not even responded to Apple's earliest concerns was based on it potentially incorrectly identifying the former Apple employees who left to join OpenAI (allegedly with Apple trade secrets in tow).

Apple's concerns here are twofold: First, these former employees had access to many of Apple's secretive product development details and may even have asked recruits to share fresh details when they approached them to interview for jobs at OpenAI. The other concern is that Apple is already far behind in the AI race, and if Apple's plans for Siri, AI, and a potential robotic desktop home assistant were also leaked, it could harm its ability to catch up in multiple market sectors.

This latest news, which may or may not be accurate, should put Apple's fears to rest.

OpenAI is apparently not building something that could ably compete with any of Apple's key hardware or future hardware initiatives.

First of all, there's the smart-speaker-ness of the whole rumored OpenAI concept. There are already too many smart speakers on the market, many of them with their own smart assistants. Amazon, for instance, is smack in the middle of trying to convince millions of customers that not only do they need Echo devices throughout the home, but they need the AI-powered Alexa+ to guide them through their smart home experiences and, to some extent, their lives.

Apple has its own HomePod, Siri-infused speakers, which may get considerably more powerful with the Gemini foundation model-backed version arriving this Fall.

Put another way, smart speakers are a known quantity in the home consumer electronics space, and I think what most tech companies are realizing is that people like and use them, but mostly in limited ways: they want music, occasional answers to simple questions, and voice control of their smart home devices. That's it.

Why does my speaker think it's alive?

OpenAI appears to be prepared to offer something different: a personality-filled speaker that can watch you, move to engage, seem alive, and generally make you feel uncomfortable.

Obviously, that would not be the objective, but it could be the result. Who needs a speaker that quietly watches you as you walk from your kitchen to the den, waiting and hoping for you to say, "hey ChatGPT, what's up with the Strait of Hormuz today?"

In my home, we have a Psync smart security webcam with one oddball feature: it has a motorized body that can turn almost 360 degrees on its base and lift its thin, rectangular face and camera to keep track of people and alert me to intruders. However, most of the time, it's just watching us move around the house, and I can tell you that my family hates it. Sometimes I come home and find its face forced down so it can't pop up and track anything.

Now, imagine a larger and far smarter OpenAI AI smart speaker in your home, watching, waiting, chiming in when you don't want it to, and generally making people feel uncomfortable.

This will not be the breakout hardware hit OpenAI is hoping for.

Look, I was under the impression that OpenAI (really Jony Ive and Sam Altman) were working on an AI wearable. I didn't love that idea either, but it was a lot less creepy than this.

So, Apple, chill out. OpenAI's plans are no threat to you, even if they do allegedly have a bunch of insidery Apple information.

Having spent decades covering Apple, I understand and respect its efforts to maintain a shroud of secrecy over its product development. Reading this line in the blockbuster Apple vs OpenAI lawsuit, I can hear the voices of countless Apple execs and PR people who, rightly or not, feel deeply wronged by what it considered a trusted partner: "OpenAI’s nascent hardware business now rests on the shakiest of foundations, rotten to its core by its illegal reliance on misappropriated trade secrets."

Late last week, Apple filed a lawsuit against OpenAI and, more specifically, a pair of key employees who, over the last two years, left Apple to join OpenAI's hardware business. The massive filing, which calls for a jury trial, accuses the pair of corporate espionage, essentially stealing a raft of trade secrets to help bolster OpenAI's still unrealized efforts to build AI hardware (it's currently working with Jony Ive to build what may eventually become a wearable device).

I've spent some time poring over the documents and, to be honest, the accusations are stunning and, if true, seem unaccountably brazen for people who spent years with the Cupertino tech giant.

From the inside out

Tang Yew Tan, who currently serves as OpenAI's Chief Hardware Officer, was at Apple for a quarter of a century and, according to his own LinkedIn profile, "Oversaw the design and development of iPhone and Apple Watch Product Design, Interconnects Design, Acoustics and Materials technology centers."

Chang Liu (whose LinkedIn handle is "changliu-apple") spent almost a decade as an iPhone Electrical Engineer.

Apple's NDA's that they hand to journalists like me are almost legendary (the first time I signed one, I swear my hands were shaking). I can only imagine the intensity of the Intellectual property agreements (IPAs) Apple says it asks employees to sign from time to time. The language is probably appropriately terrifying; it's hard to conceive of how Liu and Tan could ignore them, and yet, according to the lawsuit, that's exactly what the pair did both during their waning days at Apple and in the time after when they joined OpenAI.

OpenAI's only public response so far has been from its Director of Strategic Communications Drew Pusateri, who said on X, "We have no interest in other companies' trade secrets. We remain focused on building innovative technology that empowers people everywhere."

The company's co-founder and CEO Sam Altman also only obliquely referenced it on X when someone accused him of being afraid of Apple. He wrote, "I am not afraid of Apple, but I have tremendous respect for them. s-tier company." ("S-Tier" basically means "elite").

I won't ask you to read the full document and instead have cherry-picked the most eye-watering accusations:

A potentially sneaky exit

When Liu left Apple, he allegedly took his work laptop with him. If you've ever worked in a corporate environment, you know this is a no-no. Most companies will pursue you to the ends of the earth to retrieve that hardware and whatever corporate access and secrets it contains.

It gets worse

Lui apparently didn't shelve the old (we assume) MacBook. Instead, he, according to Apple, exploited a network vulnerability and used the system to access Apple network folders. If true, that's an incredibly bold move. Surely Liu would've known how Apple's internal forensics could discover what was accessed by whom and when.

Boasting

While allegedly downloading Apple's proprietary files (details on unreleased products, presentations, tech data, specs, and more), Liu may have messaged someone through the Apple-issued laptop, celebrating how he was pulling the wool over Apple's eyes with "LOL" and "so funny". Again, was Liu the most careless corporate spy ever? Who would boast when they know Apple would eventually stumble on these details?

Coaching recruits

Apple claims Liu was actually recruiting Apple employees and coaching them on how to gather data before leaving the company. He may have told them how to email themselves files before exiting Apple.

Now, I understand how some of your, say, personal data, can end up intertwined with corporate data and you may want to hold onto some of that. On the other hand, if you didn't keep a firm line between work and personal files, that's on you.

A real betrayal

Tang Yew Tan spent 25 years with Apple, and what he's being accused of has surely enraged and disappointed Apple. "Mr. Tan has been methodically using Apple’s confidential information to benefit OpenAI," claims Apple.

Like Lui and other Apple recruits, Tan may have emailed himself proprietary Apple info.

Using Apple codenames

Apple claims Tan is asking Apple recruits about upcoming projects using code names only Apple employees would know.

We have their OK

This one is kind of mind-blowing. Apple alleges that OpenAI has used Apple info to get a partner to apply a "specific trade-secret metal finishing technique," because they convinced the partner Apple was OK with them doing so.

Carry out

This last one might be the boldest move of all. Apple alleges that OpenAI instructed potential Apple recruits to bring everything from CAD files to actual prototypes or parts to OpenAI interviews.

That's a particularly stunning accusation, and again, just seems so reckless. What kind of corporate spies operate with this level of impunity?

Naturally, OpenAI will, at some point, respond properly to this lawsuit, perhaps with one of their own. Apple could also eventually settle this suit out of court. They've done so before with similar cases, though I don't think they were quite at this level.

Can they come back from this?

Whatever happens with this case, it's hard to imagine the Apple / OpenAI relationship, which is already somewhat fraught, survives.

For now, you can explicitly choose to use ChatGPT to handle requests in the new Siri in iOS 27 dev beta, and it's a key part of Image Playground and Visual Intelligence. Apple could pull those options and lean harder into its Google Gemini foundation Model partnership while this case works its way through the courts.

I don't think that will materially impact your iPhone experience, but the loss of direct access to those hundreds of millions of iPhone customers could hurt OpenAI, a company that, as Apple acknowledges, is trying to build its own hardware business.

Plus, if OpenAI loses, it could see a lot of the in-house hardware innovation potentially tied to Apple's work blocked from ever reaching consumer hands.

And, obviously, it'll be hard for the partnership to come back from this passage in the suit:

"Apple lacks visibility into what’s been happening behind closed doors at OpenAI, where such misconduct is normalized and exemplified by leadership. This much is clear, however: at every level, from members of its Technical Staff to its Chief Hardware Officer, and in coordination with business partners, OpenAI has been stealing Apple’s trade secrets and confidential information."

• Agentic Atlas browser pulled as OpenAI focuses on one single app
• New ChatGPT desktop app includes built-in browser and agentic capabilities
• Could this finally be the 'superapp' we were promised back in April 2026?

Not even a full year after OpenAI launched its own, dedicated agentic browser, ChatGPT Atlas has been axed amid a broader ChatGPT reinvention and the introduction of what might just be the superapp we've been teased for months.

Launched in October 2025, OpenAI has confirmed that Atlas will stop working from August 9, 2026, however it's not technically the end of the company's browser ambitions.

Instead, the browser is simply being moved into the new ChatGPT desktop app and will form part of existing AI workflows without the friction of having to move apps.

ChatGPT Atlas pulled after 10 months

In April, Chief Revenue Officer Denise Dresser described the company's future as one that stops pursuing side quests and fragmented interfaces. She teased an upcoming 'superapp', and while the company didn't explicitly describe the new ChatGPT desktop app as that 'superapp', the significant overhaul and the integration of Codex, other agentic AI tools and a browser within the single app implies this could indeed be said 'superapp'.

"We’ll begin sunsetting the standalone Atlas browser, and will share information with users about how to transition to ChatGPT," the company wrote in a recent announcement.

The new app launch coincides with the introduction of ChatGPT Work, which adds new agentic capabilities in light of the fact that many Codex users are actually knowledge workers, not coders.

ChatGPT Work bridges the gap between generative and agentic AI by enabling users to complete longer-running tasks, rather than instructing the tool prompt-by-prompt. The tool can run both locally and on the company's cloud servers, allowing access from anywhere and continues progress regardless of the primary PC's state.

Further OpenAI tools are also being made available via Chrome extensions to keep some AI available within a dedicated browser environment.

• OpenAI has brought big changes to ChatGPT’s desktop app
• ChatGPT and Codex have been merged into a single app
• Users are unhappy with many of the new updates

OpenAI has been running its Codex vibe coding platform as a separate tool from the regular ChatGPT app for a while now, but that’s all changed with the artificial intelligence app’s latest update. The two products have been merged into one ‘super app’ — but many users are deeply disappointed with the move.

If you download ChatGPT today — or update an existing installation — you’ll find an app that looks very different to the one you’re used to, which could be particularly disorienting if you’ve used the artificial intelligence (AI) chatbot for some time now.

For example, you’ll now find a new toggle in the top-left corner of the app that lets you switch between ‘ChatGPT Work’ and ‘ChatGPT Codex.’ Instead of containing buttons for your library, projects, and apps, the left-hand sidebar now houses scheduled tasks and plugins. Projects and tasks sit below in their own sections.

What used to be the ChatGPT app still exists, but has been renamed to ChatGPT Classic. The standalone Codex app, meanwhile, has become the main ChatGPT app. Both ChatGPT Work and ChatGPT Codex share plugins, and while both are capable of similar tasks, Codex shows more of the technical details that Work obscures in a bid to be more user-friendly.

An unpopular move

Unfortunately for OpenAI, this major change has not gone down too well. Apple pundit John Gruber, for example — long a fan of OpenAI’s decision to make ChatGPT a native Mac app that conformed to macOS conventions — described the change as “the day OpenAI f*cked up the ChatGPT Mac app.” He also pointed out that while the old ChatGPT app weighed in at a lightweight 159MB, the new edition has ballooned to 1.5GB.

I’m certainly not a fan of the new-look ChatGPT either. I have little interest in Codex and use ChatGPT to work through problems, often revisiting old conversations to add new details. Yet in the new app, your recent conversations are hidden and require many more clicks to access. Only your five most recent threads are shown, and to access more you have to click Chat > See All to uncover them. Previously, they were right there waiting for you in the sidebar.

As well as that, my existing projects seem to have totally disappeared. I only had a couple, but they were related to a small-claims case I was involved in, so seeing all my relevant threads scattered and disorganized in a long list of chats is a major pain. For anyone juggling a plethora of projects, this change could be disastrous. And what’s more, Custom GPTs also seem to have vanished into the ether.

I know I’m not alone in my disappointment. Over on Reddit, OpenAI’s decision has not been well received, with user powprodukt summing up the mood by saying: “ChatGPT projects and custom GPTs are the heart of why I use that app. Without these features there is no feature parity.”

Other users took issue with the new user interface, with Kaotic987 exclaiming “Christ almighty, this is so bad!” Eriane, meanwhile, cynically suggested that OpenAI “vibe coded everything without testing it.”

Whether you even get ChatGPT Classic seems up for debate. It’s available for Windows in the Microsoft Store and can be downloaded separately from the new ChatGPT app. On macOS, however, I updated to the new edition and ChatGPT Classic is nowhere to be seen. Luckily, I backed up the old app before updating, so I can still access it, which might be the way to go if you’re a Mac user and don’t want to lose all your projects and custom GPTs.

The move to combine ChatGPT and Codex into a form of ‘super app’ has already alienated plenty of users, myself included. From the questionable user interface to the absence of projects and custom GPTs, there’s a lot for OpenAI to work on before it can get the app back to a good place.

• OpenAI has introduced new agentic ChatGPT Work tools for all users
• The tool is powered by the latest GPT-5.6 model and Codex
• Could this be the app's unofficial evolution into a superapp?

OpenAI has lifted the wraps off a new agentic tool within ChatGPT to help workers "take on more ambitious tasks," covering workflows like gathering information, creating finished materials and handling long-running autonomous tasks.

The new tool, called ChatGPT Work, marks a shift in how knowledge workers are set to use AI, evolving from generative AI to autonomous agentic AI.

ChatGPT Work, largely seen as an interface for knowledge workers, brings together tools like the company's latest frontier model GPT-5.6 and Codex.

ChatGPT Work is an autonomous knowledge worker's dream

In an announcement, the company noted how Codex was born out of the need for coders and developers to get access to agentic capabilities, but now, more than a million non-coding workers use it for other tasks.

The company also declared that nearly all of its internal teams use Codex and ChatGPT Work to speed up their workflows.

All users running the app on their macOS or Windows desktop can get access to ChatGPT Work for free, including non-paying subscribers. Pro, Enterprise and Edu plans will get mobile and web access first, but Plus and Business plans will get access within days.

Further aiding ChatGPT Work's autonomy, the tool also works with third-party plugins via the '@' command to obtain further context and access to working files.

More broadly, as the app evolves into a superapp to combine all of OpenAI's tools in one workspace, desktop users can now grant the client permission to control the computer on their behalf, similar to Anthropic's Claude Code.

"This is the first step towards a broader vision for ChatGPT," the company summarized, "where intelligence goes beyond answering questions to helping everyone turn their biggest ideas into reality."

• OpenAI plans to hire a $200k Investment Banking expert as it continues to target finance
• The company wants to improve financial accuracy, consistency and overall quality
• ChatGPT already has its own personal finance tool

OpenAI is advertising a new "Subject Matter Expert, Investment Banking" role within its team, serving as the latest hint that the ChatGPT maker wants to expand its reach into the finance sector.

This latest position strongly implies that the company wants to increase more sophisticated Wall Street type tasks, rather than be an all-purpose chatbot, and it makes sense.

We've already seen OpenAI and rivals like Anthropic heavily target certain sectors – banking and law being two of the most evident in recent months.

Could OpenAI be expanding its reach into finance?

OpenAI described investment banking as one of the most demanding forms of knowledge work, putting pressure on workers to "synthesize fragmented information, exercise judgment under pressure, and produce precise, defensible models, analyses and client materials."

Being that the job was posted in an entire blog post-style announcement rather than on a dedicated careers platform, it's clear the role will be a high-level one reporting to senior leaders and will play a considerable role in the company's direction. The salary of $185,000 to $205,000, plus equity, is also indicative of the role's importance.

The announcement highlights the importance of quality over quantity, stressing the need for AI to help produce work that's financially correct, traceable and consistent.

OpenAI also wants prospective candidates to help identify the highest-value AI opportunities in investment banking – another clear signal that the company is going after the sector big-time.

With its existing expertise, OpenAI has already targeted the consumer end of the sector with a new Personal Finance tool within ChatGPT that connects to bank accounts to give users insights into their spending habits, investments and savings.

• GPT-Live is rolling out to all ChatGPT users now
• It can both talk and listen at the same time for more natural chats
• Real-time translations are also now possible

ChatGPT has a shiny new AI voice model called GPT-Live, which has a number of helpful tricks — including being able to listen and talk at the same time. It's rolling out to all ChatGPT users now, though OpenAI has acknowledged a number of early bugs.

While free users and users on a paid plan do get slightly different models — GPT-Live-1 mini and GPT-Live-1 respectively — the updated model should now be appearing in all ChatGPT accounts, with the new features outlined below.

The biggest giveaway that you've got the upgrade will be the Live label at the top of voice chats on mobile, and behind the ChatGPT drop-down on the web. Tap or click on these labels and you can still go back to the old voice models, for the time being.

There's another way to check the GPT-Live voice model has arrived in your account: on mobile, tap the menu button (top left), then the settings cog (Android) or your profile avatar (iOS), and Voice > Model. On the web, click your profile avatar (bottom left), then Settings > Voice > Model.

What to try first

The biggest upgrade here is the 'duplex' functionality, so try that first: you can keep talking even after ChatGPT has started answering you, and it should keep up. Second, try interrupting it mid-flow, and it'll adapt its response accordingly. We're almost at the level of the 2013 Spike Jonze movie Her at this stage.

Third, ask ChatGPT in voice mode to translate something into a foreign language as you say it out loud. You can then speak out sentences in English, and ChatGPT will do a real time translation for you without hesitating. It's not particularly useful for language learning, but it does show off the capabilities of GPT-Live.

Fourth, change the voice and intelligence used — you can do this via the sliders icon at the top right of voice chats. The voice options are actually the same as they were before, but you can choose between Instant, Medium, and High as the intelligence level. Use Instant for the fastest answers, High for the best answers, and Medium for a compromise.

The final thing you can try once you've got the update is to ask questions with visual answers. OpenAI has added a bunch of visual cards to voice mode now, so you get graphics on screen about sports scores, weather forecasts, and places that can be found on a map, for example.

Early voice bugs

I've been testing out GPT-Live voice mode for a few hours and can report that everything works as advertised. It is, more than ever, like talking to a real person — right down to the hesitations and the variety in speech patterns. I did experience one or two glitches, but they were few and far between.

Over on Reddit, OpenAI's Atty Eleti is answering questions about GPT-Live. One of the main bugs that users seem to be experiencing is related to ChatGPT's memory, which appears to be off limits to voice mode in some cases — this is an issue that OpenAI is tracking and "actively investigating", and you can find updates on it here.

Problems are also being reported when it comes to foreign languages being pronounced in an English accent. Again, this is an issue that's been acknowledged, and which should improve over time according to Eleti.

Overall though, the rollout seems to be going relatively smoothly — and I haven't seen any issues with memory or with accents so far. I'm not sure it's going to make me want to use voice mode any more than I already do (which isn't much), but for heavy voice users it's definitely a big step forward.

• OpenAI reveals first branded hardware, the Codex Micro, a programmable macro pad built with keyboard maker Work Louder
• Codex Micro seems to be based on Work Louder's Creator Micro 2's layout, mapped to Codex coding-agent shortcuts
• The move reinforces OpenAI's Codex offering as one of its mainstay areas of focus by allowing developers the ability to perform tasks or interact with AI faster

OpenAI's first branded piece of hardware is not a long-anticipated consumer device it is building with ex-Apple design chief Jony Ive, but rather a programmable macro pad called the Codex Micro.

The keyboard, which consists entirely of macro keys designed to "supercharge people's Codex usage," according to an OpenAI spokesperson at the AI Engineer World's Fair, is reportedly a collaboration between the iPhone creator and the custom macro pad creator Work Louder.

With OpenAI's developer-centric account on X indicating that the full launch of its hardware foray is expected on July 15, the AI giant seems to be pulling out all the stops to ensure it becomes a well-received add-on for the developer community.

A simple rebadge or a sign of things to come?

The as-yet-pending release 'Codex Micro' seems to be inspired by Work Louder's existing Creator Micro 2, a compact macro pad that offers thirteen mechanical keys, a joystick, a rotary encoder, and touch controls, arranged across programmable layers to power users needing faster or more fine-grained control over AI-assisted coding tasks.

The move is understandable for OpenAI in terms of both securing a victory with developers and brand recognition, and essentially testing the waters on how it would handle a hardware launch for the company's upcoming AI device for more general-purpose users.

It can also, to a degree, be seen as OpenAI essentially acknowledging that its earlier stance of narrowing its focus to 'nail' its core business might be one the company is willing to make exceptions to, especially when it comes to coding tools or enterprise use-case hardware.

OpenAI's CEO of applications, Fidji Simo, reportedly told staff that the company was looking to deprioritize areas outside its core focus to allow it to lead where it mattered.

In 2025, OpenAI shipped the Sora video app, the Atlas browser, ecommerce features inside ChatGPT, advertising work, and hardware efforts, a "series of startups" approach that insiders said had produced organizational confusion and constant reshuffling of scarce compute, distracting it from a truly centralized goal.

Hardware, in other words, was explicitly on the list of distractions. A physical keyboard is arguably as clear a violation of that directive as one could possibly design.

OpenAI is also reeling from a smaller-than-expected gap from competitor Anthropic and its Claude models in the areas where its GPT models do compete. This can perhaps be attributed to Anthropic's much narrower focus, which caters specifically to coders and enterprise through its Claude Code and Claude Cowork offerings.

One can argue that OpenAI's move isn't one that distracts it from its core focus, but rather complements it, even as R&D and integration for the most part is something that Work Louder will commit to.

It allows the AI juggernaut gets to test out both the marketability of an OpenAI-branded hardware product and appease developers and founders with a low-effort play even as they have increasingly been considering tools from Anthropic and Google as well as other AI solutions providers.

None of OpenAI's previous concerns may apply here; the exercise does not consume compute, it caters to a key audience for OpenAI, with Codex assisting 5 million weekly users as of June, and it does not meaningfully engage an engineering team as some of its other projects do.

With OpenAI and Anthropic slated to IPO soon, both are locked in a race to secure as many active users as possible to justify their valuations, even as they vie to build the most powerful models to cater to various industries, including defense, telecommunications, pharmaceuticals, and software development, to name a few.

OpenAI's move might just be a sign of things to come, as it leverages ChatGPT's massive brand recognition to develop marketable, revenue-generating solutions such as a custom macro keyboard, even as it is loath to spend any of its engineering or compute resources on anything but the most important of its tasks, even as enthusiasts continue to wait for the release its upcoming collaboration with legendary Apple designer, Jony Ive.

Most people use ChatGPT like they’re walking up to a stranger and starting a new conversation every single time.

You open a new chat, explain what you need, add a bit of context, correct the tone, ask it to be more concise, then finally get something useful. And then, the next time you need the same kind of help, you do the whole thing all over again.

That’s fine if you’re asking something random each time. But if you use AI regularly for the same kinds of tasks, it’s a surprisingly inefficient way to work.

The solution is simple: stop starting from scratch.

Instead of treating every chat as a blank page, you can build reusable conversations that already know the job, tone, and desired output. Here’s how I use that one habit to save time every week.

A simple trick

So, we know what we want to do — remove repeated setups for prompts, make the AI’s first answer better, and reduce the amount of refining you need to do afterward. But how do we do it? Here’s the clever bit - you get the AI to do it for you.

Load up one of your previous chats that was productive and worthwhile. Then at the end I want you to copy and paste this:

“Turn this conversation into a reusable prompt I can paste into a new chat next time. Include the goal, tone, format, constraints and the steps you followed. Make it general enough that I can reuse it with similar tasks.”

Your AI will give you a handy prompt you can use whenever you like now to get a chat that’s exactly what you want.

My meal planner prompt

I now have a reusable prompt for meal planning. Instead of starting from scratch every week, I paste in a short prompt that already knows the rules: I want quick family meals, nothing too expensive, leftovers if possible, and a shopping list organized by supermarket section. Then I add whatever’s in the fridge and how many nights I need to cover.

Here it is:

“You are helping me plan practical family meals for the week. Prioritize meals that are quick, affordable, not too fussy, and likely to produce leftovers. Ask me what ingredients I already have before suggesting anything. Then give me:

A simple meal plan

A shopping list grouped by supermarket section

Any ingredients I can reuse across multiple meals

One backup meal in case I don’t feel like cooking”

This has saved me so much time planning meals in the evening.

A tool that remembers you

Before you get too carried away with this idea, remember that reusable prompts are useful but not infallible. A reusable prompt is only worth saving if it produces better work, not just more predictable work. If your saved prompt is too vague, too bossy, or based on a weak workflow, you’ll just get the same mediocre output, but delivered faster.

Having said that, using reusable prompts has been the biggest change I’ve made to how I use ChatGPT. When a conversation works, I don’t let it disappear. I turn it into a reusable starting point.

If you stop starting every ChatGPT conversation from scratch, ChatGPT starts feeling much less like a conversation with a stranger and more like a chat with a tool that actually remembers how you work.

Much of the global economy was able to continue functioning during the COVID-19 pandemic thanks to a host of cloud-enabled technologies that powered a new way of remote working. However, the technology industry – which initially championed this new approach to work – has aggressively rowed back on it.

The new normal

Sam Altman, who leads OpenAI, clarified his stance on remote and hybrid working during a fireside chat in San Francisco organized by the startup Stripe.

During the conversation, he echoed the views of many in tech that remote working was a failure – framing it as an experiment that went wrong rather than a new kind of normality that most of the tech industry was enabling, let alone simply buying into.

Despite several studies suggesting productivity gains and higher employee wellbeing, the logic states that fully remote teams struggle to deliver products in a timely way and that corporate culture erodes. Surveillance and tracking is also much harder.

RTO mandates

Dozens of companies in the tech industry have instigated return to office (RTO) mandates, bringing workers back in-house full-time rather than even adopting a hybrid approach.

Amazon, Dell and Tesla are just three companies that have adopted five-day mandates, while several companies have also adopted four-day and three-day mandates.

Although these companies have decided to bring employees back into the office, the future of work in the tech landscape will remain fragmented, disjointed and varied not just between companies but within companies. Depending on how this trend continues, it could carry ramifications for the design of offices as well as the nature of training and progression.

Top routers under £100 for working from home (Just don't tell Sam)

OpenAI has begun discussions about giving the US government a 5% stake in the company, according to an FT report, with CEO Sam Altman supposedly raising the idea as a method for smoothing relations with the Trump administration.

Of course, right now there is no agreement or deal, and no guarantee the idea will ever move beyond conversations. Any arrangement would almost certainly require political support and significant legal work before it could become reality. Still, the fact that OpenAI is even entertaining the conversation tells us something about how seriously artificial intelligence is now being treated, both in Silicon Valley and in Washington.

The first reaction many people had was understandable. If the government owns part of OpenAI, does that mean ordinary Americans somehow get a share too? It's an appealing thought when AI companies are attracting eye-watering valuations while promising to reshape the economy. Unfortunately, that's not exactly a likely outcome, no matter what the intentions.

AI economy access

The reports suggest Sam Altman has discussed a model inspired by Alaska's Permanent Fund, which invests state oil revenues and distributes annual payments to residents. It's an odd framing of AI as a natural resource instead of a software business. Bullish AI fans insisting it will be economically transformative might see it that way, and if they're right, perhaps some of that value should eventually flow back to the public, many of whom have helped incrementally train the models through use.

But the government owning shares in OpenAI wouldn't automatically translate into everyone getting a check. Financial benefits would depend on lots of little details, including whether profits were distributed at all, and if they'd go to public services or even the national debt over your own bank account.

Despite being just a hint of a rumor of a conversation, the questions are worth taking seriously. AI companies are asking society to embrace changes that could alter workplaces, education, healthcare, and entire industries. It is not unreasonable for people to wonder whether they should share in the wealth created by those changes.

Power at stake

There is another reason these discussions matter, and it may prove even more significant than the financial side. OpenAI has become part of a broader conversation about national economics and technological leadership. Governments around the world increasingly see advanced AI as strategic infrastructure rather than another consumer technology.

That helps explain why OpenAI might want a closer relationship with Washington. AI companies already rely on government decisions. Those connections are likely to become even more important as AI models grow larger and more expensive to build.

But governments are expected to regulate powerful companies fairly and independently. Becoming a shareholder in one of those companies could make that relationship look unethical, even with the best will in the world. Public trust often depends as much on appearances as on legal structures. Especially since there's even less sense that OpenAI's competitors like Google, Anthropic, or Meta will follow suit.

A government stake does not automatically mean the public owns part of OpenAI in any meaningful way, and it certainly does not guarantee anyone will personally benefit. So even if the proposal starts to become more real, skepticism and a close eye on any actual agreements is a healthy approach.

Anthropic has just released Claude Sonnet 5 for all users, and I wanted to test what it was good at. But the game has changed now. Sonnet 5 doesn't feel dramatically different from Gemini or ChatGPT if you ask it ordinary chatbot questions. Instead, the difference should show up when you stop asking for answers and start asking for completed work.

Anthropic says Sonnet 5 is built for "multi-step software engineering work," sustained coding, tool use, debugging, and "messy technical contexts." It also says it can make plans, use browsers and terminals, and run more autonomously than smaller, cheaper models previously could.

I'm not using Sonnet 5 for coding, but that doesn't mean I can't take advantage of its new abilities — just like you can. So I stopped asking Claude for answers and started asking it to finish jobs, beginning with planning a trip to Bath, UK, for my family: my wife, me, and two teens.

A trip to Bath

When I tested it, Claude Sonnet 5 defaulted to its Medium level of effort, so that's what I used. Here's the first prompt I tried:

"I want to test whether you can act more like an agent than a chatbot.

My task is: Plan a weekend trip to Bath for two adults and two teenagers, including travel, lunch, one activity, estimated costs, and what still needs booking.

Don't just give me advice. First, make a brief plan. Then identify which parts of the task you can complete yourself right now, which parts require tools or information you don't have, and which parts need human judgment.

Then complete as much of the task as possible without stopping after the first obvious answer.

At the end, give me:

What you completed

What still needs human action

Any assumptions you made

A short checklist I can use to verify the result

The next best step"

What I really liked was that, as Claude tackled this task, it gave me the option to be notified when it had finished. In reality, it only took a few seconds to come back with a plan, which included travel options, an itinerary, and a suggestion for lunch and something to do: a trip to The Roman Baths.

To my delight Claude gave me an interactive map showing where all the places it recommended were. It also gave me a useful list of what it had completed, what required human action, the assumptions it had made, a verification checklist, and a "next best step" action point. It felt ready to keep working with me as more details came in, rather than treating its first answer as final.

In fact, when I gave it more details, such as which day I was going to go, it gave me a visual weather report for the day. That was a really nice touch.

Claude vs ChatGPT

I also tried this prompt with ChatGPT-5.5 Medium and got a similar result. It acted as an agent, just like Claude did, and notified me when it had finished its tasks. It just didn't look as nice. There was no map, or any visual elements at all, and it felt more like I had been given a finished report than the start of a two-way conversation where it asked me for more details.

Both chatbots recommended lunch and a trip to The Roman Baths. Interestingly, ChatGPT assumed I’d get the train, while Claude assumed I’d drive. They also recommended different places to eat, but the core information they both provided was solid.

What was most impressive was that both models could adapt when I reframed the inputs. For example, when I gave them the ages of the kids, student status, a different mode of transport, or changed the day of the trip, both models could cope. Both also identified that since the oldest was a university student, he could get free entry to The Roman Baths.

This part of the test was probably the most meaningful, as it felt much more "multi-step" than simply providing one answer.

Overall, I’d give this test to Claude. You can clearly see that Sonnet 5 is set up for agentic actions. Neither Claude nor ChatGPT could actually do any of the booking for me at the moment, so we're still a long way from true personal-assistant-level autonomy. But for this kind of task, Claude currently has the edge.

A different domain

I wanted to test the models in a different domain that would let Claude show me it had genuinely improved, and that the Bath trip result was not just a fluke of the travel-planning use case. So I asked them both to:

"Build me a simple household budget tracker as a spreadsheet or small tool."

Both models thought for a while about this task, and churned through various options before opting to make a spreadsheet. ChatGPT produced a spreadsheet with a bar chart that tracked how much I’d spent on various household expenses against a budget. Claude, however, went for something simpler: dispensing with a budget, it just tracked actual expenses and created a pie chart showing where my money was going.

Claude’s initial approach was simpler, and easier to understand. Both models provided a .xlsx file, but only Claude provided a button to upload it straight to Google Drive so I could open it in Sheets.

I told ChatGPT, "I wanted the graph to be a pie chart," and it responded: "Absolutely — I’ll update the spreadsheet itself so the dashboard uses a pie chart for spending by category, rather than the current graph style."

It ran into a few problems because it was trying to show both the budget and actual values in the same pie chart, but eventually it worked out that it could show only one and produced a new spreadsheet that did exactly what I asked for.

I then asked Claude to change its spreadsheet to provide a budget section too, and to change the graph into a bar chart. Again, it showed me its workings and added a budget section and bar charts perfectly.

I can’t really separate the two AI models on this task. Both proved they can handle multi-step tasks well, and both were happy to revise the result when I changed the brief.

That, really, is the point. The most interesting AI tests now are not "which chatbot gives the best answer?" They are "which assistant keeps working until the job is actually done?"

On that front, Claude Sonnet 5 feels extremely capable. ChatGPT was close behind, and in some ways just as effective, but Claude felt more naturally organized around the idea of completing work rather than simply responding to prompts. It asked fewer invisible questions, presented its output more helpfully, and made the whole process feel more like collaborating with an assistant than interrogating a chatbot.

For now, neither model is ready to fully take over the job. I still had to check the details, make the decisions, and do the actual booking or uploading myself. But the direction of travel is obvious. The AI war is no longer just about who has the smartest chatbot. It’s about who can build the assistant that gets you closest to a finished task.

Palantir CEO Alex Karp has hit out at the state of the AI industry, saying it was "effing insane" that the technology is being used in areas such as military and national security.

In a heated interview with CNBC Squawk Box, the controversial billionaire also hit out at top AI firms such as OpenAI, claiming he had spoken to major CEOs outside of the industry who were "livid" at how some companies are doing business.

Karp also accused some major AI companies of imposing a “wealth tax” on businesses by charging high fees for their services, all while collecting data which may be used to improve their own AI models and tools.

"Completely wrong"

Karp's ire was particularly focused on the token model being used by the likes of Anthropic and OpenAI, especially as costs continue to rise, but companies look for a better return on their investment.

“I’m not throwing shade at them, but something has gone completely wrong,” he said. “The basic view among enterprises in this country is I’m going to chillax and waste my time with tokens.”

This includes a range of Chinese firms, with Karp warning the US not to underestimate the speed of progress being seen at its great rival.

Rising AI prices have led many businesses to pivot towards building and training their own models, rather than relying on outside providers, with so-called "open weight" models able to perform at a fraction of the cost.

Karp's frustration was clearly visible, with one CNBC host commenting, “You sound pretty angry,” with the CEO responding, “This is the voice of American business that is being channeled through me.” 

To shore up its own support, Palantir recently announced a major partnership with Nvidia which will see the latter's AI services used to create custom models for US government agencies.

“What aligns me with Nvidia, and I think is what the technical customers want, which is control over their compute, their models, their data stack and their alpha,” Karp told CNBC. “They want to know they own the means of production. It’s not being transferred to someone else.”

This follows recent criticism by the US government of firms such as Anthropic, whose Mythos 5 and Fable 5 AI models were deemed a national security risk and shut down shortly after release.

Karp went on to criticize the US government for its reliance on AI companies in creating new technology for the military and national security.

“Are we really going to outsource the battlefield of this country to the consensus view in Silicon Valley? That is effing insane," he noted.

ChatGPT's new finance feature lets the AI chatbot take a look at any bank or similar accounts you care to open up for inspection. I was initially hesitant to try it out, but the tool only looks at the details of how you spend your money, and can't actually carry out transactions, so I agreed to let it analyze some of my accounts and offer its insights.

Finances is currently only available in the U.S. to Plus and Pro users on web, iOS, and Android. Setting everything up is as easy as using any other ChatGPT plug-in. You just select Finances in ChatGPT and then click Get Started then Connect with Plaid.

Finances uses Plaid to link to the accounts; you simply sign in and agree to let Plaid share the information. The service synchronized recent transactions and basically became an interactive, conversational database of my information. It's not that the AI can do things it couldn't before, but now you don't have to manually enter any finance details you want to ask it about.

ChatGPT looks at the receipts

At its suggestion, I asked ChatGPT, "How much did I spend eating out this year?" ChatGPT soon had a breakdown of my how much I had spent at various restaurants, including delivery, and broke down some of the patterns. It pointed out that it had gone up recently, which makes sense as the weather has became pleasant enough to walk into town more often to eat. It also identified a few spikes I could tell were around birthdays and family celebrations.

Next I asked, "What subscriptions am I paying for?"

That turned into one of the most practical moments of the experiment. ChatGPT gathered recurring charges across streaming services, software, and other monthly payments into one clear summary instead of leaving me hunting through statements. It also suggested reviewing whether every subscription still deserved a place in my budget.

"These subscriptions appear consistently each month, making them reliable recurring expenses.", it told me.

Financial chatter

Seeking a more detailed analysis, I asked ChatGPT, "What's changed in my spending this month?"

Instead of overwhelming me with every purchase, ChatGPT focused on the transactions that genuinely stood out. It noted a few large purchases at new locations, and asked if they were something I wanted more information on. I told it those were gifts, and the AI incorporated that fact into its response as not a new regular event but one that explained why overall spending had climbed this month.

"Those gift purchases are unusual compared with your recent history but they account for much of the increase, so your everyday spending has remained relatively consistent despite the higher overall total. Looking beyond one unusual month provides a more accurate picture of your longer term financial habits."

Using ChatGPT this way felt almost like the inverse of most financial software. Most budgeting apps expect you to learn their system first. ChatGPT let me ask questions however they occurred to me and then translated months of transactions into answers that were easy to understand.

That does not mean it replaces dedicated budgeting tools, and it certainly cannot make smarter financial decisions on your behalf. You still have to decide whether to cancel subscriptions, spend less on eating out, or save more each month. But I can see how it might make it easier for people who hesitate to look at their finances if they feel uncomfortable around spreadsheets. It didn't feel like math homework the way it often does.

Of course, it still relies on some trust in both OpenAI and Plaid, but as long as it's purely viewing and not actually touching the accounts, this could be a really useful, practical feature for ChatGPT users.

OpenAI's custom AI chip isn't just another attempt to loosen Nvidia's grip on AI hardware. It's the clearest sign yet that OpenAI is adopting the same vertically integrated strategy that transformed Apple over the past decade.

When OpenAI and Broadcom recently shared new details about Jalapeño, their custom inference processor, most of the discussion focused on Nvidia. Nvidia currently sits at the center of the AI industry, supplying the graphics processors that power everything from ChatGPT to image generators and coding assistants. Any attempt to reduce that dependence is naturally headline news.

For years, Apple has enjoyed a competitive advantage from making the most important parts of its products in-house. Instead of relying on someone else's processors or designing software around third-party hardware, it designed and built its own hardware and software. Competitors spent years trying to match that integration.

With its new custom inference processor, OpenAI appears to be building more than just an alternative chip. It's developing the same kind of vertically integrated ecosystem that helped transform Apple into one of the world's most valuable companies.

The chip is only part of the plan

When Apple introduced its M-series processors, the company aimed to build Macs that woke instantly and ran cool and quiet. Customers cared that everything simply felt smoother. OpenAI appears to be chasing a similar goal, even if the product is completely different.

Instead of laptops, it wants conversations that arrive faster. Building its own processor gives it another lever to pull that competitors relying entirely on third party hardware simply do not have.

Jalapeño is simply another piece of a much larger puzzle. The processor has been designed for inference rather than training. Training is the expensive process of creating an AI model as opposed to the inference done afterward. Every time someone asks ChatGPT a question, that's inference. Those billions of everyday interactions eventually become just as important as building the model itself because they determine both performance and operating costs.

Designing a processor specifically for those workloads gives OpenAI something that off-the-shelf hardware never fully can. It can begin tailoring the hardware around exactly how its own models think and respond, a more efficient method. And every improvement, whether in power consumption, speed, or networking, saves money and improves the AI experience.

OpenAI has been careful not to oversell the timeline, with broad deployment of the new chip still some way off. This is the beginning of a strategy rather than the final result.

Nvidia's challenge

Nvidia isn't going to panic right now, nor should it. Its processors still power much of today's AI boom. Demand continues to outstrip supply in many areas, and OpenAI itself remains one of its major customers. None of that changes because one new custom processor has appeared on the roadmap. What should catch Nvidia's attention is the pattern beyond OpenAI.

Google has spent years developing Tensor Processing Units. Amazon created Trainium and Inferentia. Microsoft has invested heavily in its own AI chips, as has Meta in custom accelerators for its expanding AI ambitions. OpenAI is now following the same path. Different companies have different technical goals, but they all seem to arrive at the same conclusion: as AI becomes a bigger part of their business, they don't want to depend entirely on someone else's hardware.

Of course, Apple designing its own processors certainly did not destroy Intel overnight. But there was a shift as Apple gained more control over pricing and product direction each time it replaced an external component with one of its own. The same could happen with AI.

Plus, OpenAI said its own AI models helped accelerate parts of the engineering process during chip development. AI is actually helping to make the hardware that will power its future iterations. That feedback loop may become increasingly important as chip design grows more complex. The future of AI may belong to the companies that own as much of the underlying machine as possible, regardless of where the models themselves rank.

If Apple's history is anything to go by, OpenAI is ready to be that company.

• OpenAI announces three new AI models in the GPT-5.6 series
• The flagship Sol model is accompanied by Terra and Luna
• Access is currently restricted, as per requests by the US government

OpenAI has announced new GPT-5.6 model upgrades for ChatGPT, though for now they're only available to a select group of "trusted partnerships and organizations" — a restriction requested by the US government, which OpenAI doesn't seem best pleased about.

"We don’t believe this kind of government access process should become the long-term default," explains OpenAI's announcement. "It keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them."

There are three models in the series: Sol (the flagship), Terra (for "everyday work"), and Luna (the smallest, fastest, and cheapest of the three). OpenAI says model performance is "competitive" with GPT-5.5, with improvements in affordability, safety, agentic capabilities, coding, biology, and cybersecurity.

Sol is the best model yet for deep thinking and complex agentic work, OpenAI claims, and it apparently matches the Anthropic Mythos model for cybersecurity tasks while using a third of the output tokens.

More safety testing to come

Previewing GPT-5.6 Sol: a next-generation model from r/singularity

All three models set a new standard for safeguards and protection against "adversarial pressure" as per OpenAI's announcement. They should be resistant to attempts to disguise intent from users and to jailbreak the model.

Those who do have access to the GPT-5.6 models may come across some apparently unnecessary blocks and restrictions, OpenAI explains, as the safety and security of the AI upgrades are tested. Eventually, feedback from early testers should make these upgrades more robust, ready for a full release.

We'll hopefully get that release "in the coming weeks", OpenAI says. The US government wants to do its own testing and evaluating, and although OpenAI is complying with those requests this time, it clearly doesn't want this to be the new standard.

The mood from users left out of the loop for now seems to be one of resignation. "The days of the public getting access to these frontier models is gone," writes one Redditor, while another says "the divide has started" between the AI haves and have-nots.

ChatGPT's new Scheduled Tasks feature announcement this week — the ability for ChatGPT to now send reminders, handle recurring work, or monitor things — caught my attention immediately.

After all, AI assistants are all predicated on being reactive and needing your initial input. No matter how sophisticated the technology becomes, the responsibility usually remains firmly on your side of the screen. But if they are to truly be assistants, they have to be able to help you out when you might not remember to ask for their help.

Scheduled Tasks do require your input to begin with, of course. But you can't tell ChatGPT to remind you about something later. You can ask it to send recurring updates. You can keep an eye on a topic and notify you when something changes.

Unlike a lot of ChatGPT features, this one truly seems to give ChatGPT more of an actual assistant feel.

How to start using Scheduled Tasks

I started with a basic reminder of something I frequently forget. I asked ChatGPT to remind me to practice my saxophone three evenings each week.

Again, the setup was conversational. There were no complicated menus or automation builders. I simply described what I wanted. ChatGPT replied, "I'll remind you to practice saxophone three evenings per week. I'll keep the reminders encouraging and focused on making consistent progress."

Your Scheduled Tasks are all accessible from a new left-hand menu item, nestled between Projects and Apps, provided you are a ChatGPT Plus, Pro, Business, or Enterprise user. If you click the menu option then ChatGPT will suggest some things to try with Scheduled Tasks.

That evening I received the first reminder, with a couple of links suggesting songs to learn. Exactly the kind of thing that will help keep me on track.

If you've enabled notifications for the ChatGPT app on your phone, you'll get a notification that your scheduled task has completed. If you're using ChatGPT in a browser then turn on Desktop Notifications when it asks you. If email notifications are enabled for scheduled tasks in your ChatGPT settings, you'll also receive the result by email.

I then set up a more complicated evening reminder. I wanted a suggestion sent every day at 4 p.m. for a short but fun game to play outside with my child. The idea was to outsource a small piece of the fretting I sometimes feel to keep young kids entertained. Happily, not long before he returned home from daycare, ChatGPT suggested a fun and even slightly educational game of dinosaurs which he loved because of getting to run around and roar, but also helped him learn the names of several dinosaur species.

Getting a useful morning briefing

My third test was set for overnight. I told ChatGPT that I wanted a quick summary of local events or critical information to know every weekday morning at 8 a.m. Normally, this would involve checking several websites and social media. ChatGPT agreed to do so, and this morning I got a roundup of not only the day's weather, but details of yesterday's election news, the upcoming World Cup games, and how they might affect traffic.

Plenty of apps can recommend activities. What mattered was that the suggestion appeared before I had even started thinking about them or when it felt too late. Throughout the day, I kept noticing the same thing. Scheduled tasks were not saving enormous amounts of time. They were saving small moments of mental effort. Each individual task removed one tiny obligation from my internal to-do list.

That is the subtle trick scheduled tasks pull off so well. Instead of responding to a need, it anticipates one. Countless services can handle individual pieces of what happened during those two days. What felt different was having everything tied to the same conversation. That may be why the feature works better than you might expect. It remembers things so that you do not have to.

AI chatbots are starting to do more than answer questions. They can now order groceries, book services, and interact with apps on your behalf. ChatGPT and Claude have both embraced this new generation of AI "actions," but they don't approach them in quite the same way. To find out how different the experience really is, I asked both chatbots to handle the same Instacart grocery order and watched what happened next.

I decided to try the same grocery order through Instacart using both ChatGPT and Claude.

The shopping list I used was intentionally simple: infant formula, a small container of ice cream, and some fruit. The goal was not to compare prices or delivery times. It was to find out what happens when two competing AI chatbots are asked to perform the exact same everyday task with the same kind of app.

Heading to checkout

I started with ChatGPT. After going to the app list and connecting my Instacart account, I told the AI that I needed infant formula, a small container of vanilla ice cream, and some fruit for delivery. The request was intentionally ordinary because ordinary shopping is where these integrations will either succeed or fail.

ChatGPT responded quickly with product suggestions and put some options into my cart. ChatGPT did not spend much time discussing possibilities. Instead, it seemed determined to move steadily toward a finished cart. Every response felt designed to remove friction and keep the order moving.

The experience reminded me of a shopper who already knows exactly where everything is located. There was very little wandering through virtual aisles. The assistant simply gathered the items and moved on to the next decision. Once the cart was ready, I said I wanted to check out, and a window opened within the chat confirming what I wanted, my payment method, and the delivery address.

A different aisle

Claude approached the same task slightly differently. First, I had to link up my Instacart account to Claude as one of its connectors. I then gave it the identical shopping list and waited to see whether its recommendations would diverge significantly from ChatGPT's. Surprisingly, they did not.

Claude suggested similar formula options, similar fruit choices, and similar ice cream products. The overlap was substantial enough that I suspect most shoppers would end up with nearly identical deliveries regardless of which chatbot they used, unless they were very specific in their request.

The checkout experience, however, followed a different path. When I was ready to check out, Claude provided a link for me to click, which opened a new tab in my browser right at my Instacart cart, with my account already logged in. It was there that I had to review and complete the purchase. Having the list already assembled made the transition smooth, but it was still a transition.

That difference may sound minor, yet it created a noticeably different feeling. ChatGPT felt like it was becoming the shopping destination itself. Claude felt like an extremely capable assistant helping me prepare for a visit to Instacart.

So while the actual shopping recommendations differed very little, the AI chatbots diverge in where they put the cart. ChatGPT's vision of the future keeps the transaction inside the conversation for as long as possible. Claude's version treats the conversation as a guide that eventually leads you to the service itself. If you're a fan of Claude, it probably won't deter you from using Instacart with the AI chatbot, but if you have no real preference based on the platform but want minimal friction, you might prefer the all-inclusive ChatGPT experience.

Either way, the ice cream tastes just as good.

ChatGPT thinks it knows what my next obsession will be — and after reading its explanation, I'm not entirely convinced it's wrong. In fact, it's a neat trick to try, and you might enjoy doing it with ChatGPT yourself by using the same prompt.

I asked the AI chatbot a simple question: "Based on everything you know about me, what hobby, interest, TV show, band, sport, or technology am I most likely to become obsessed with over the next six months?"

After clarifying with me that I wasn't referring to any professional interests and meant activities beyond work, ChatGPT came back with a somewhat surprisingly nuanced list. The top prediction was surprising: backyard astronomy.

"Of everything you've talked about, it has the highest future obsession potential," ChatGPT told me. "It combines learning, wonder, collecting, equipment, family memories, and a touch of childhood fascination. There's a telescope-shaped hole in your life that it will fill."

Astronomical fun

I have asked questions about telescopes, stargazing, and things to do with my son that do not involve staring at screens as part of other tests. Less obviously, I tend to prefer hobbies that reward curiosity and gradual improvement rather than immediate mastery. So it made sense.

And the AI was happy to lay out ideas for getting into the hobby, including local astronomy clubs and public observation nights, and of course, links to equipment to buy. What struck me most was that astronomy was not presented as a random recommendation. It emerged from a pattern. The prediction was less about stars and planets than about the kinds of activities that consistently grab my attention.

That same logic showed up elsewhere on the list. Board games landed near the top of ChatGPT's list, for instance. The reasoning was not so much because of discussions about playing board games specifically, but more because, according to the AI, I often look for activities suitable for Family outings and weekends, as well as screen-free entertainment.

Bird is the word

Not everything on the list made sense for my life, simply because ChatGPT doesn't know everything about my daily life. For example, I've discussed getting better at guitar with the AI, but haven't really said anything about my daily practice and current efforts. So, while guitar being on the list wasn't a crazy notion, it would have been more relevant a couple of years ago.

The appearance of birding on the list was much more unexpected, as I don't recall ever expressing an interest in birds with the AI. But ChatGPT explained that it stems from the same reasons it thinks I like astronomy as a hobby, mainly how they both require observation, patience, collecting knowledge, and becoming excited about things that look nearly identical at first. I'm not sure that it will be something I take up, but I can't deny the notion that it might be fun.

On the other hand, the AI could be wildly off about my potential interests. According to ChatGPT, I am "a strong candidate to become obsessed with the Grateful Dead." I have no problem with the band or its music, but it will never be a sound I choose to listen to independently. According to ChatGPT, the recommendation was more about the culture surrounding the music, its deep history and lore, the passionate fans, and the huge back catalog. But while I may like exploring complex and deep worlds of hobbies, it still has to be a subject I'm interested in. So, while I may start spending my nights looking at the stars, the soundtrack will have to be something else.

Today, I was using the ChatGPT app on my iPhone when I accidentally held my finger down on the send button (the blue upward arrow) a bit too long, and I was suddenly taken to a new options screen I hadn’t realized existed before, which I use all the time now.

The option screen gives you three different intelligence settings for the answer you're going to get back from ChatGPT: High, Medium, or Instant.

What’s going on here is that ChatGPT is asking you to choose between three different flavors of ChatGPT-5.5 that change how long the chatbot thinks before answering. These options are easily visible in the web-interface version of ChatGPT, but the reason why I’d never seen them before in the iOS app is that there’s nothing that indicates they exist, and I bet a lot of you haven’t found them either.

So, fire up ChatGPT and try it now - you’ll be surprised. The workflow goes like this:

1. Type your prompt
2. Long-press the blue send arrow
3. Choose a model - e.g., Thinking
4. Send

For paid users only

What’s nice is that having the freedom to choose ChatGPT’s intelligence level like this means that you can choose differently for each prompt you enter.

So, if you just need a quick answer to a simple question, then choose Instant. If you’ve asked something more difficult and you want to make sure you get the full benefit of the AI thinking about it properly, then choose High. The Medium setting gives you a balance of both approaches.

If you've never used the High option before then it can be quite surprising how long ChatGPT will take to think about its answer, but the benefit is that you get a much more in-depth response.

Sadly, this feature only works for users on paid ChatGPT plans. If you’re using a free ChatGPT account, you won’t get these options.

Android alternatives

Since the feature isn’t prominently documented, many people aren’t aware it exists. And if you’re on an Android phone, well, it doesn’t actually exist, because this hidden feature is unique to iOS, but there is an Android equivalent.

On Android, when you long-press an answer text (not the send button), a new menu will be available for you. At the end of the menu, you can see a Change model option — you can use this to select a thinking or instant variant of your current model.

I love finding hidden features in tech I own, and often it's like this one — the hidden setting turns out to be something I’ll use all the time, once I’ve discovered it. Let's hope you do, too.

Recently I asked ChatGPT to turn a childhood photo of me into a 1990s action figure. The surprising part wasn't the image itself. It was that the AI chose accessories that accurately reflected my childhood interests without me mentioning them in the prompt. It pulled those details from memory.

Using ChatGPT to turn yourself into an action figure became a very popular, if brief, fad more than a year ago, and like many others, I'd enjoyed seeing what the models could do with a photo of me and some virtual blister packaging. The models have improved immensely since then, and OpenAI has boasted about ChatGPT's current image creation models in particular.

Here's what I did: I asked the AI to create a figure based on me at seven, as well as one based on me today at 40, and to include some accessories as well. The project became less about toys and more about memory.

Collector's edition 1993

The first challenge was creating a childhood version of me. I wanted it to look like a toy from the early 1990s, complete with oversized branding, bright colors, and the kind of packaging that would have looked perfectly at home hanging on a peg in Toys "R" Us. I asked ChatGPT to:

"Create a 1993-style collectible action figure called 'Eric, Age 7' based on my childhood photo. Include three accessories that represent my interests at that age that come from our conversational history and package it like a classic toy store action figure."

The accessories came out better than I had thought they would. A book, a soccer ball, and a Game Boy were all things I enjoyed as a child. The specific choice of a Goosebumps book was notable, too. And the overall result was definitely of the era, down to the exaggerated logos and collector edition badge.

Once the childhood action figure existed, I wanted to see what happened when it left the box. Rather than placing it in some dramatic action scene, I asked ChatGPT to imagine it abandoned in a playroom after a child had finished playing. I told it to:

"Show the Eric Age 7 action figure outside its packaging in a playroom. Arrange the accessories naturally around it as though a child had been playing and just walked away."

You can see it standing among its accessories and other bits bits of childhood clutter above. I don't think the toy would be a big seller, but it definitely captures a part of my own life and experience based purely on the collected details ChatGPT has absorbed about my life and interests.

Premium adult edition

Creating the 40-year-old version was a completely different exercise. Childhood is easy to summarize because it tends to revolve around hobbies and possessions. Adulthood is messier. Plus, there are a lot more details from my current life available for ChatGPT to turn into accessories. Nonetheless, I asked ChatGPT to:

"Create a premium action figure of me called 'Eric, Age 40.' Use accessories that represent my life today and package it like a luxury designed for adult collectors."

ChatGPT drew from things it knew about me and included a smartphone, a black-and-gold chihuahua, and a stroller. Together they painted a picture of work, family, and everyday life in a nice, if perhaps bland, suburban way.

The packaging reflected the shift as well, with its black-and-gold branding that looked more at home in a collector's display cabinet than a toy aisle. And the accessory choices were again dead on.

I again wanted to see the toy leave the packaging and join the playroom, and requested that ChatGPT:

"Show the Eric Age 40 action figure outside its package in a playroom with its accessories nearby. Make it look like a child had been playing with it and then left the room."

The finished image ended up being my favorite of the four. The figure stood on the carpet with the Chihuahua, stroller, and smartphone arranged around it, looking like it had wandered out of its own packaging.

The accessories of the two toys were very different, and the packaging had evolved, but both figures showcased what ChatGPT thinks matters to me now and what mattered to me then based on our interactions.

What started as a test of ChatGPT's image generation ended up becoming a test of its memory. The action figure itself is just a novelty. What's more interesting is that the accessories, the setting, and the small details all came from a version of me that exists only in the AI's recollection of our conversations. That's a strange thing to see packaged up in plastic — a toy built not from a photograph, but from memory.