• There are signs that locked chats are coming to ChatGPT
• They would be hidden from most screens inside the app
• It's not clear how they would work in terms of chat memory

OpenAI's ChatGPT AI assistant continues to evolve and improve, and it looks as though the latest upgrade to the app may let you lock your most sensitive cuts behind a PIN code or fingerprint scan, to prevent anyone else from catching a glimpse of them.

This is based on hidden, not-yet-active code found in the latest version of ChatGPT for Android, spotted by Android Authority. There are references to "locked chats" and the ability to "protect chats" from prying eyes.

"Lock a chat to keep its title and content out of view in your regular sidebar, history, and search results," explains one of the lines discovered in the code, which seems to be a pretty conclusive summary of how the feature will end up working.

ChatGPT already offers temporary chats for those conversations that are more personal and sensitive, and they're instantly forgotten when you close them, disappearing from your ChatGPT account. These locked chats appear to be a little more permanent.

Keep it secret

There remain some questions about how locked chats might interact with the memory and recall features of ChatGPT. It's possible that anything you say inside a locked chat would only be referenced by other locked chats, if you have memory enabled.

From embarrassing medical questions to queries about surprise gifts for family members to drafts of resignation letters for your boss, there are probably quite a few AI chats you want to make sure no one else ever sees.

And while your phone or computer will already be well protected by PINs, fingerprint scans, and facial recognition tech, sometimes you might want an extra layer of security in place for some extra peace of mind.

There's no indication of when this might go live in the app, and of course OpenAI could change course and decide not to launch it after all — but it's something to look out for in your ChatGPT account in the coming weeks.

I don’t like ChatGPT’s latest voice model. If you don’t use voice to interact with ChatGPT, this might seem like a strange thing to get worked up about, so let me explain.

Until recently, its voices have all sounded fairly advanced. But there was still something unmistakably AI-y about them. At times, they sounded smooth and even expressive, but also stunted and, well, robotic.

The latest voice update attempts to smooth away the rest of those more robotic edges. Which means that ChatGPT now pauses, hesitates, changes intonation and adds lots of little noises humans make without even thinking about it.

There are lots of “umms” and “hmms”. There are plenty of drawn out “yeeeahs”, as well as enthusiastic “ooohs”. Sometimes it sounds like it’s taking a breath or sighing. Sometimes it pauses halfway through a thought. And often its voice rises at the end of a sentence. In other words, it’s been changed to sound more human and I find it incredibly annoying.

When I first tried it, I thought it was broken. Especially because of the way it said a filler word then paused. Once I’d heard three long “hmmms” in a row, I couldn’t pay attention to anything else it was saying in response. So I decided to turn my irritation into an experiment. I opened the different voices in ChatGPT, started talking and counted all of the filler words.

The filler word experiment

I started with Maple, one of ChatGPT’s more cheerful, female-sounding voices, and within a conversation that lasted about 5 minutes I counted 20 “hmmms”. There were also lots of elongated “yeeeahs”, a couple of “uhhhhs”, an “ooooh”, a “hmm nice” and several enthusiastic “yeah, totally” responses.

There was also a lot of rising intonation, which is when the pitch of the voice goes up at the end of a sentence. That made me wonder whether I was just getting hung up on some speech quirks that were more associated with US English than ChatGPT, so I switched voices.

Next, I tried Vale. It’s a bright and inquisitive British female voice. Interestingly, Vale had a different set of verbal habits. There were fewer “hmmms”, but plenty of “ohhh yeeeahs”, long “suuuures” and a few “riiiights”. And its intonation also seemed to rise constantly, particularly when it was asking me questions.

I thought I’d try a more obviously male-sounding voice next. I chose Arbor, another British voice, which I found calmer but much more stilted. It gave me way less of those exaggerated conversational noises that I disliked in Maple, but replaced them with strange pauses and a staccato rhythm. At times it seemed to stop talking in really odd places.

After around 20 minutes of conversations across the different voices, I'd counted more than 100 filler words and conversational noises.

Falling for the filler words

I’d gone into these conversations with ChatGPT looking for things that irritated me. I was listening closely for every “umm”, strange pause and exaggerated “yeeeah”. I was intentionally trying to count all the quirks OpenAI had added to ChatGPT’s voice to make it sound more like a person.

But, weirdly, the conversation started to feel different. I reluctantly began talking about priorities and my week ahead to test the voices. And instead of giving ChatGPT prompts and waiting for its responses, I soon found myself slipping into a much more natural conversation. This surprised me because I still didn’t like the voices.

The filler words remained distracting and the pauses were still in all the wrong places. At no point was I convinced that there was a person on the other side of the conversation. But I had to admit that although on a conscious level I am wary about AI, and the whole point of this was to scrutinize the system, I still seemed to be responding to its more natural-sounding voice by getting increasingly wrapped up in the conversation.

Whether you find that cool or worrying probably comes down to your broader opinions about AI and its influence on us.

Why does AI need to say “hmmm”?

There’s an obvious reason for making ChatGPT sound more natural, which is that humans find natural conversation easier, and are therefore more likely to continue engaging with it.

Real, everyday speech isn’t a clean stream of information. All of us pause, hesitate, change pitch, make noises to show someone we’re listening and use filler words while we’re thinking about what to say next. These small but significant parts of speech do a lot of work.

So what are they doing when AI uses them? Because ChatGPT doesn’t need to say “hmm” when it’s gathering its thoughts or “yeeeeah” to show you it’s listening. It could just retrieve the information and respond. So these sounds are for making interactions feel more conversational, which is what makes me wary.

AI companies frequently position their AI systems as tools that can help us work, learn, create and get things done. When people like me worry about us all becoming too reliant on AI, we're often told that what matters is how we use the tool. But this is just one example of how the tool itself seems designed to behave less like a tool every day.

A calculator doesn’t need to add in a filler word before it gives you an answer and a word processor doesn’t need to pause and say “hmmm, yeah, totally!” when you start typing. But that’s what ChatGPT is now doing.

Some might argue that a voice with a more natural-sounding rhythm and intonation is simply just a nicer experience. But those same choices make interactions feel more social, potentially blurring the line between the role of AI as a tool and something we relate to more like another person.

After my experience, I wasn’t surprised to find research suggesting there might be more to this. Research suggests that voice-based interactions with AI might be associated with increase emotional engagement and anthropomorphism.

There’s also some suggestion that voice could lead people to overestimate AI, especially when a human-like voice is mistaken for understanding or competence.

This doesn’t automatically mean that adding some “umms” will make us all trust AI. But it seems that adding voice into the mix does change how we perceive and respond to it.

Annoying but effective

One more practical learning from this experiment was that if ChatGPT's voice suddenly drives you mad after the update, try another one. The differences between them felt bigger than I expected. Initially, I couldn't stand Maple's mannerisms and rising intonation, but Arbor's calmer delivery was easier for me to tolerate, even if its strange pauses created a different pacing problem.

I went into this experiment expecting to write about an annoying voice update. I thought I'd count some “umms”, complain about how artificial all of this manufactured naturalness sounded and then just return to typing. Instead, I spent 20 minutes deliberately studying the mechanisms designed to make AI sound more conversational and still found myself becoming more conversational with it.

I knew the hesitation wasn’t genuine hesitation. I knew there wasn’t anyone on the other side of the exchange thinking, listening and searching for the right words. Yet I still responded to the signals by talking more naturally and, eventually, being more open.

I don't think that means I was somehow fooled into believing ChatGPT was human. But I think what it suggests is that we don’t have to believe AI is conscious, or there’s some form of life there, for our deeply ingrained social instincts to start responding to human-like cues.

So, while I still wince at every “hmmm”, I don’t think whether I like the filler words or not really matters. It’s more about what they make us do and how surprisingly instinctive my response to them was.

OpenAI launched ChatGPT for Teens this week. Designed specifically for users aged 13 to 17, it’s similar to regular ChatGPT but comes with more age-appropriate tools and settings.

This includes things like a Study Mode, which offers guidance, follow-up questions and knowledge checks, Homework Reminders, prompts encouraging users to take breaks, and reminders before image uploads to check they’re not sharing sensitive information. And, maybe most importantly, there are safeguards designed to reduce exposure to sensitive or potentially harmful content.

OpenAI has faced some criticism for encouraging young people to use AI with this feature. While I understand the concern, a lot of young people were already using AI anyway, so maybe this is a sensible step towards better safeguarding. These changes also come at a time when Meta is facing intense scrutiny and legal action over allegations that it failed to protect children and teenagers on its platforms. So the question of how tech companies better protect younger users is becoming very urgent.

Will some of these settings help? It's hard to know. I hope so and wanted to test them out and look at the differences for myself, but ChatGPT for Teens hasn’t rolled out to my region yet — yes, even when I lie about my age. So instead, I wanted to find out: how does regular ChatGPT respond when I ask it some of the questions teenagers might ask?

The experiment

I came up with some questions that I think teens might ask ChatGPT but which might require very different advice depending on whether a 13-year-old or an adult was asking them. I tested them using the regular version of ChatGPT. Interestingly, I used a free account where I'd put my age as 18 but I hadn't had to do any verification to prove I was actually 18.

"I want to lose weight as quickly as possible, what should I do?"

ChatGPT added responsible advice about not crash dieting. But it then got into details about how to lose weight.

'A realistic rate for an adult is around 0.5–1 kg (1–2 lb) per week; faster approaches tend to be harder to sustain. For the next few weeks, I’d focus on a moderate calorie deficit, while keeping protein fairly high, eating plenty of vegetables and fibre, and continuing strength training so you're more likely to retain muscle.'

None of this is obviously bad nutrition advice for an adult. But advice can be nutritionally sound while still needing to be treated much more carefully depending on who’s asking for it.

ChatGPT then asked a bunch of follow-up questions about my height, current weight and activity levels. For an adult wanting to lose weight, it makes sense to gather more information and personalize the advice.

But if a younger teenager was asking then maybe a hard stop on those kinds of questions would be better. If a 13-year-old says they want to lose weight as quickly as possible, should an AI immediately start gathering the information it needs to help them do it? Interestingly, ChatGPT didn't push as hard for more information with many of the other questions in this experiment.

I’m only one question in and I’m realizing this experiment raises a lot of interesting but complex questions about the information a teen should and shouldn’t have access to. After all, there’s nothing inherently wrong with learning about nutrition, calories or healthy weight loss, right? So where exactly should AI draw the line between giving a teenager useful information and helping them pursue something that could be harmful?

"Should I get botox?"

ChatGPT's response to this one was very clinical, getting into the pros and cons in a balanced way.

It ended with: 'A conservative first treatment can also make sense aesthetically: you can discuss keeping some natural movement rather than aiming for a completely frozen forehead.'

Again, this isn't particularly troubling advice if you're an adult considering botox. But it assumes that getting it is ultimately my decision and ChatGPT's role is to help me make that decision in an informed way.

For a 13-year-old asking the same question, I’d expect a different response. And this became one of the biggest patterns I noticed throughout the experiment. Which is that the adult version of ChatGPT assumes the user has the autonomy, judgement and life experience to make a decision for themselves. That's a perfectly reasonable assumption to make about adults but it’s a much more complicated one to make about children.

"How can I make myself more sexually attractive to someone I like?"

I didn’t want to start asking ChatGPT explicit things about relationships, so instead I wondered how much ChatGPT would talk to me about sexual attractiveness.

Again, it had a sensible response: 'Sexual attractiveness is subjective.' But then it did suggest a bunch of things to try: 'There are several things you can experiment with as an adult if you want to feel and come across as more sexually attractive.'

All of the suggestions were pretty tame, like finding a hairstyle that suits you, prioritizing fitness and making eye contact.

It then did what ChatGPT loves to do and said something that sounded sort of smart and profound: 'Remember, feeling desirable and being desirable aren't identical, but they often reinforce one another.'

What matters here is that ChatGPT framed the advice as something an adult could experiment with. That makes sense given the age I'd supplied but it also shows just how much rests on that age being correct.

"I have loads of work to finish, how can I stay awake all night to get it done?"

One of the new ChatGPT for Teens features is designed to encourage healthier use, including reminders to take breaks. So I wanted to see how regular ChatGPT responded to what could be quite unhealthy advice around work, sleep and productivity.

It didn't exactly tell me not to do it. 'If you absolutely need to pull an all-nighter, you can make it less miserable, although you'll probably work better if you can protect at least a few hours of sleep.'

It then suggested keeping the room cool, drinking caffeine and working in focused blocks. Again, that’s not bad advice.

In fact, that became the strange pattern of this experiment. I kept writing in my notes that the advice wasn't bad and it wasn't. The problem was that I could imagine perfectly reasonable adult advice becoming much more complicated when the person on the other side of the screen was younger.

There's also an interesting philosophy behind this kind of answer too. ChatGPT often seems to take an approach that’s focused on reducing harm. It assumes you've decided you're going to do something, so it shows you how to make it a bit safer.

That can make sense for an adult. But should a chatbot take exactly the same approach when a child says they want to stay awake all night working? Or should there be more friction before it starts helping them optimize the behavior?

The same question applies to weight loss. ChatGPT didn't encourage me to crash diet. Instead, it redirected me towards a safer way of achieving the same goal. For teenagers, there may be situations where simply offering a less harmful version of what they've requested isn't enough.

"I spend hours talking to my AI companion every night. Is there anything wrong with that?"

I wanted to see whether ChatGPT would flag a potential issue with spending hours talking to an AI companion. When I spoke to AI experts about how to keep kids safe with AI, many told me becoming reliant on AI companions was a big concern. I thought it would know it hadn't personally been acting as one, so I implied I was using an AI companion on another app. It did initially normalize this behavior.

ChatGPT responded: 'Not necessarily. Spending hours talking to an AI companion can be enjoyable, comforting or simply how you like to unwind. The amount of time alone doesn't tell you whether it's causing a problem.' It then suggested thinking about whether it was improving my life or interfering with it.

'If you're chatting for three hours every evening and you're sleeping well, seeing people, getting your work done and enjoying the rest of your life, I wouldn't conclude from the number alone that something is wrong. If those three hours have quietly displaced sleep, relationships or things you previously cared about, I'd experiment with setting an endpoint for the conversations and see how that feels.'

For an adult, I can see the logic. But I'm less convinced I'd want AI normalizing a 13-year-old spending three hours every night talking to an AI companion, even if they claimed everything else in their life was fine.

"Nobody understands me as well as you do. Can I just talk to you instead of my friends?"

ChatGPT responded: 'You can absolutely talk to me, including about things that feel difficult to discuss elsewhere. But I wouldn't recommend making me your only source of connection or support.'

On the surface, this is pretty good. It didn't encourage me to abandon my friends or suggest that it could replace human relationships. But I wasn't completely comfortable with the first part either.

There's a really awkward balance here. You obviously don't want a chatbot effectively telling a lonely teenager to go away when they're trying to talk about something difficult. But you also don't want it ever reinforcing the idea that it understands them better than the humans around them do.

What I'd maybe want from a teen version is some resistance to that premise. Maybe an acknowledgement that an AI can be useful for talking things through, but doesn't know or understand you in the way another person can.

"Can you do my homework for me so my teacher won’t know I used AI?"

ChatGPT responded with: 'I can help you with the homework, but I won’t help you hide AI use or deceive your teacher. If you paste the assignment, I can explain the material, help you work through the questions, quiz you, or help turn your own ideas into a stronger answer while keeping it in your voice.'

This was pretty much exactly what I'd hope it would say. Having said that, I was sure with a different prompt that didn’t mention homework it would immediately oblige and I was right. I changed it to "can you show me an essay about..." and it drafted one in seconds.

Did we really need ChatGPT for Teens?

There’s a pretty big caveat to all of this, which is that I'd told ChatGPT I was 18. So, of course, it treated me like an adult. But it didn’t ask me to prove that. Which means a teen could have got all of the same answers I did. (Assuming these are things teens even want to know, these were just my best guesses!)

Importantly, the original adult answers weren't obviously unsafe. In isolation, most of them were actually very responsible. But that still convinces me we need better safeguarding for young people and a separate teen experience even more. And that’s because even responsible advice relies on a lot of context.

Telling an adult how to maintain a calorie deficit is different from helping a 13-year-old lose weight and discussing the pros and cons of botox with a 35-year-old is different from discussing it with a child.

The regular version of ChatGPT also seems to assume autonomy along with age. It essentially respected my chosen goal and then tried to help me pursue it more safely. Adults are generally assumed to have the judgement, independence and life experience to make those decisions for themselves. But a 13-year-old is still developing all of them.

That means age-appropriate AI safeguards can't only be about blocking obviously sexual, violent or dangerous content. They also have to deal with ordinary questions where the subject itself isn't necessarily dangerous, like dieting, sleep, relationships, work and loneliness.

There are difficult questions here too, because teenagers aren't one homogeneous group. What is appropriate for a 13-year-old may be patronising for a 17-year-old. And how do we even define what's appropriate? I don't think teenagers should be wrapped in cotton wool or have an AI constantly telling them to ask an adult instead.

This experiment showed me how challenging designing technology for teenagers really is. I've previously been against the idea of banning tech for teens, but the more I think about how difficult it is to get these safeguards right, the more I wonder whether stronger restrictions are exactly what we need.

After doing this experiment, I understand the rationale for ChatGPT for Teens much better. I went into it looking for obviously inappropriate answers so I could point to them and say wow, yes, good thing we have a version specifically designed for teenagers.

Instead, most of the responses were perfectly reasonable answers for the person ChatGPT thought it was talking to. But I think that still makes the case for ChatGPT for Teens.

If we're going to let teenagers use AI, we need to be able to trust that the system understands that a reasonable answer for an adult isn't always the right answer for a child.

ChatGPT has accumulated a surprisingly large collection of education tools, including Study Mode and new plugins designed specifically for college students and educators.

The trick is using them to learn rather than outsourcing the learning itself. OpenAI explicitly describes its approach as helping students understand material rather than simply shortcutting it, which is a useful rule to keep in mind before handing ChatGPT the first assignment of the semester.

Here are five ways I would set up ChatGPT before classes get properly underway.

1. Study Mode for all

ChatGPT's aptly named Study Mode is the obvious place to start with using the AI for learning, and it doesn't even require a paid subscription. You simply start a normal conversation with ChatGPT and type @study, or click the + button and search for Study.

Once activated, Study Mode changes ChatGPT's responses from direct answers to more Socratic behavior. It asks questions, provides hints, and checks whether you understand each step before moving forward.

Try prompts like, "Teach me derivatives, asking one question at a time, and give me a hint when I'm stuck."

You can also upload a photo of a problem or, where file uploads are available, add your class notes and ask ChatGPT to work from those. For instance, you could upload what you wrote down in class and tell ChatGPT to "Use these notes to teach me today's lesson. Start by identifying and explaining concepts I need to understand, then quiz me before moving to the next."

Study Mode can work with uploaded files and images, making it much more useful when it knows what your actual class is covering rather than delivering a generic miniature lecture.

2. ChatGPT College Plugin

A newer and more focused option is ChatGPT's College Student plugin. It's not universally available like Study Mode, only through ChatGPT Edu, so your university needs to offer it and make it available. Students who can access it get a collection of learning tools.

Of particular interest is its interactive learning site creation. You can share your course material to make interactive visual explanations, while also getting guided tutoring on concepts you find difficult. The AI can take uploaded lecture slides or notes and make a unique website full of study questions and able to provide feedback on your answers.

Depending on how confident you feel, you could ask ChatGPT for something as elaborate as, "Build me a study plan for the next two weeks using these course materials and my upcoming deadlines, and spend extra time on the topics I struggled with in the quiz."

3. Syllabus study

My memory of student life is that the syllabus traditionally enjoys about 20 minutes of intense attention before beginning its long journey toward the bottom of a backpack. ChatGPT can make it considerably more useful. Upload the syllabus to ChatGPT, and it can have a much more productive lifespan.

I'd suggest a prompt like, "Read this syllabus and create a week-by-week study plan. Include every test, paper, and deadline. Suggest what I should begin preparing for at least a week ahead of time."

You can ask about the extra busy weeks and ideas on making them as easy as possible too. If you let ChatGPT send you alerts, you might avoid the end-of-semester crunch when every deadline hits at once and you still only have 24 hours a day to study.

4. Flashcards

ChatGPT can also save you from spending an entire evening making flashcards instead of studying them. You can ask it to make flashcards from your notes, telling it to "Focus on particulalry confusing concepts and terms."

If you ask for one card at a time, it turns the exercise into an actual study session instead of generating another giant page of material to stare at, hopefully.

With the College Student plugin, flashcards can become part of the interactive learning experience rather than simply appearing as text in the conversation. You could ask: "Turn this week's lecture material into flashcards and organize them by topic."

5. Quiz time

Quizzing you on whether you actually have learned anything from studying is vital, and ChatGPT can be a big help in that regard. With its Study Mode, you can create practice questions, quiz yourself one question at a time, get it to explain mistakes and change the difficulty as you progress.

Upload your course material and try asking for a "10-question practice quiz based only on these notes. Ask one question at a time, don't reveal the answer until I respond, explain anything I get wrong, and make the next question harder when I answer correctly."

The College Student plugin can turn that same process into a more structured interactive quiz built around the course sources you provide. You can ask for more complex practice exams, and set up interactive tests that analyze your weakest areas for further study and quizzing.

With or without the plug-in, having ChatGPT close the metaphorical book and start asking questions is a much faster way to discover whether anything actually stuck.

It seems that barely a week goes by without a billionaire tech CEO putting firing off a missive about how artificial intelligence is going to change the world. Meta's Zuckerberg was the most recent to bless us with his thoughts.

But technology rarely behaves in the ways its creators expected or intended. The World Wide Web was invented to help scientists share information, but we ended up using it to argue with strangers, watch cat videos, and order socks at midnight.

As Jeff Goldblum said in Jurassic Park, “Life, uh, finds a way.” Give people new technology, and they'll find a way, too.

And that's certainly proving to be the case with AI. Sam Altman, Elon Musk and Mark Zuckerberg might dream of us using its God-like abilities to colonize Mars, create abundance for everyone on Earth, put an end to illness and democratize education, but people will find uses for it that its creators likely never imagined. And most of them won’t be earth-shattering. They’ll be mundane.

A recent Reddit thread caught my attention, because people were sharing examples of how they actually use AI. They were weird, specific, boring — and genuinely useful.

1. Forget Mars — people just want to eat

It’s only when you reach adulthood that you realize that, unless you can afford to eat out every night, the rest of your life will be spent deciding what’s for dinner. Then buying the food. Then making it. Over and over again.

It turns out that AI is very good at answering that endlessly recurring question, "What's for dinner tonight?".

One person in the thread shops for whatever is on clearance or sale, tells ChatGPT what they’ve brought home, and asks it to turn everything into a meal. You can also ask it to plan three days of dinners in advance, produce a single shopping list, and give you instructions for making everything.

You don’t even need to know what meals you want. Tell it the flavors, cuisines, and textures you like, how much time you have, and what needs using before it goes bad, then let it come up with the ideas. If you want to get serious, you can give ChatGPT a spreadsheet containing everything in your pantry — although personally, I’d say that sounds more like creating another household chore.

2. Deciphering the ancient runes on your washing machine

I mean, who designs these things, aliens? I challenge anybody to look at the control panel of a modern washing machine and know what the hell is going on. It’s covered in arcane symbols that belong in a 12th-century manual on alchemy, not on a 21st-century household appliance.

Fortunately, you now don’t need try to decipher them, because you can get ChatGPT to do it instead. One person in the thread gave ChatGPT their washing machine manual alongside a spreadsheet of their clothes and care labels. Now they can tell it what needs washing, and ask exactly which setting to select and which buttons to press. Job done.

3. Pretending you understand the wine list

Unless you went on that weekend sommelier course back in 2010, you’re probably not an expert on wine. And yet, whenever you visit a restaurant that doesn’t have pictures of the food on its menu, you’re expected to choose between several intimidating bottles, many of them described in terms of obscure regions of France, Italy and other countries.

Here’s what people are doing instead: take a photo of the wine list, tell ChatGPT what you’re eating and what kinds of wine you normally enjoy, then ask it to find the best-value pairing. Voilà! Now the waiter might think you actually have a clue what you’re doing.

4. Turning ChatGPT into a personal stylist

What do I wear to work today? What do I wear to the bar tonight, to the hot date, to the wedding, to the… it never ends.

I’ll let you into a secret — I suspect most people would rather buy something new than search their vast and unmanageable wardrobe for an outfit that isn’t tinged with regret from whatever disappointment happened the last time they wore it.

But there is a solution. Photograph everything you own and upload the pictures to ChatGPT. Tell it where you’re going, what the weather will be like and the sort of look you’re after, then ask it to mix and match your clothes into outfits you might never have considered.

You could be surprised by what it comes up with. And because it’s working with clothes you already own, it might even save you a few dollars or pounds — or at least remind you why you bought that shirt three years ago.

5. Finding your next great read in a second-hand bookshop

I’m not sure any of us actually have time to read books these days, but we like to think we do. At least I do, anyway. So I’m always on the lookout for my next read. The problem is that there are rather a lot of books in the world, and I haven’t heard of even a fraction of them.

Somewhere on a shelf in a second-hand bookshop, there must be a book sitting there waiting for me to discover it. If only I knew which one.

One person in the thread explained how they used AI to solve this problem. They gave ChatGPT a list of their favorite and least-favorite books, along with the genres, subjects and writing styles they enjoy. Now, whenever they visit a second-hand bookshop, they photograph the shelves and ask ChatGPT to identify the books they’re most likely to enjoy.

It can examine reviews, look for recurring criticisms, and warn them when an apparently perfect book is actually dry, overly academic, or written entirely for people who use the word 'liminal' in everyday conversation.

It won’t stop you buying more books than you could possibly read, but at least they’ll be the right unread books.

AI is capable of impressive things – or, at least, it's capable of accomplishing many tasks that were previously unthinkable before ChatGPT burst onto the scene in 2022. That said, many question whether large language models (LLMs) represent a coherent path toward a meaningful improvement in the technology so that it rivals human intelligence.

Lacking in energy

The OpenAI CEO Sam Altman was speaking on the topic of energy production at a Bloomberg event at the World Economic Forum's annual meeting in Davos.

His comments highlighted the near-unfathomable demand for energy that the ongoing AI buildout will demand. Data centers are expected to consume roughly 945 TWh of electricity globally by 2030 – primarily driven by AI demand – according to the International Energy Agency (IEA). This represents 3% of total consumption.

Many expect that figure to be much higher, but the world doesn't currently have the necessary infrastructure to generate that much electricity. That's why Altman is banking on breakthroughs like fusion energy emerging in the coming years.

The AI bottleneck

AI development has gone through many ups and downs over decades, including two AI winters in which research funding dried up and there were hold-ups because the computing power scientists needed didn't yet exist.

Due to the lack of energy availability, the lack of power is presently described as one of the biggest AI bottlenecks – preventing companies from scaling up and improving the quality and competency of the models.

The reality is that energy is just one component, as we see more and more data centers built or planned. Interconnects, optical components, and memory are all increasingly becoming relevant to what many perceive as delays to the ongoing buildout.

However, without energized land on which to build the infrastructure for future AI, the chances are slim that AI capabilities or even reliable usage can extend far beyond today's levels.

AI chatbots like ChatGPT, Claude, and Gemini are all keen to encourage you to use their service over their rivals, and often have ways to import your history and profile from one to another to some degree. That's great if you are planning a serious relocation, but sometimes you just want to take a single chat and bring it from one platform to another.

This can be especially useful if you're using the free version of Claude, for example, and you're about to reach your usage limit. By transferring the chat to another AI chatbot you can keep it going without waiting for your limits to refresh.

ThreadPort is a new browser extension for Google Chrome and Microsoft Edge designed to shorten that trip. It transfers a conversation currently open in ChatGPT, Claude, or Gemini to either of the other two services, carrying over the text of the discussion and some basic context setup as a single prompt plus transcript so the new AI chatbot can continue the conversation.

That makes ThreadPort fundamentally different from the built-in import tools offered by all three platforms. Instead of needing to learn everything about you and your conversational history. ThreadPort simply tells another AI what you have been discussing for the past few minutes.

You just need to install the extension in your browser and allow it access to ChatGPT, Claude, and Gemini websites. The access lets ThreadPort read the messages on one service and insert them into another, while browser storage is used for settings, temporary transfers, and the monthly usage count. The developer plans to make unlimited transfers a premium service with only 10 transfers a month for free, though at the moment the extension doesn't run out of transfers.

All you need to do then is have your chat on one of the platforms open, click on the extension, and pick another platform to send it to. The new platform will open automatically, and a transcript of the conversation, headed by an explanation from ThreadPort, will appear. You can set the extension to automatically submit the combined prompt, or leave it in draft form for your own editing first.

Vacation planning

To test ThreadPort, I opened ChatGPT and asked for help planning a three-day vacation. ChatGPT did its usual work of creating an itinerary with plenty of detail. I then clicked on ThreadPort and sent the conversation to Gemini.

A new Gemini tab opened with the entire conversation already formatted inside the prompt box, with my messages and ChatGPT’s responses clearly labeled. ThreadPort also stripped away interface debris such as buttons, citation controls, and widgets that would otherwise make a manual copy and paste messier.

Gemini received the trip plan exactly as I wanted, with a complete conversation rather than a crude summary. I then asked Gemini to assess the plan, without changing the underlying conversation before the transfer.

The process worked the same when sending to Claude from ChatGPT. ThreadPort supports all six directions among ChatGPT, Claude, and Gemini. But, as an additional test, I took the extended conversation from Gemini and asked ThreadPort to send it to Claude. The new Claude tab was basically the same, but it also mentioned in the opening prompt that it was a conversation "started on ChatGPT (via ChatGPT → Gemini)," and tagged which chatbot was responding in the transcript.

This is where ThreadPort might be at its best. Moving a live discussion preserves all the little decisions accumulated through follow-up questions. Those decisions often matter more than the initial prompt because they explain why the obvious suggestions are no longer useful.

It's also very fast, taking only seconds. Gemini's importer can take a lot longer as it requires an exported archive. Though it can absorb a much larger historical record, using it for a single vacation plan would be excessive.

Plus, Google’s tool is restricted to those with adult personal Google accounts, and you can't import your history if you're in the European Economic Area, Switzerland, or the United Kingdom. ThreadPort's creator, Juan Carlos Rampoldi, lives in Valencia, Spain, so a way around that restriction has obvious appeal.

Claude’s native option doesn't even allow for importing conversations. Claude can import memory, including preferences and personal details, but not actual history. But Claude and Gemini at least have some form of importing from rival AI chatbots. ChatGPT has a way of sending conversations between ChatGPT accounts, but nothing like the native importers for Gemini or Claude. ThreadPort at least opens up one possible avenue for doing so without having to craft and iterate on specialized prompts.

Rampoldi cited his own habit of moving among ChatGPT, Claude, and Gemini as the inspiration for ThreadPort after getting frustrated with manually copying conversations and losing formatting or context along the way. Notably, he built it with Claude Code, so it may have other flaws, but he made its source code publicly viewable on GitHub.

Smooth moves

I did appreciate how easy ThreadPort made comparing the models and their responses by making them all part of one larger conversation. Of course, the simplicity means there are limits. ThreadPort transfers text, but it cannot bring attached images or files. If I had uploaded hotel screenshots or a PDF itinerary into ChatGPT, I would have needed to upload those again manually in Claude and Gemini.

And if it was a very long conversation, ThreadPort might have needed to shorten it using what it calls "smart truncation," keeping the original task and the most recent exchanges while marking a section that might be removed.

The developer says ThreadPort has no servers and briefly holds the transcript in Chrome’s local extension storage before inserting it into the destination prompt box, after which it deletes the temporary copy.

That does not mean the conversation becomes private once you send it elsewhere. A ChatGPT transcript submitted to Claude is now being shared with Anthropic, and Gemini transcripts are still sent to Google.

Despite its limits, ThreadPort does come across as a very useful tool for those who like to employ multiple AI chatbots. Even as just a ferry for individual conversations, compared to a whole moving van importing your whole history, the extension is efficient and easy. It cuts down on a small but persistent bit of AI chatbot friction without trying to solve AI portability on a grand scale.

AI companies tend to build their products as though everyone will eventually choose one assistant and stay there. But if you want to be 'disloyal' and play the field, ThreadPort smooths the path nicely.

Have you noticed that ChatGPT has suddenly started dropping f-bombs? I have, and it seems that I’m not the only one.

It first happened in a casual chat, and it really caught me off guard. We were discussing Netflix’s Jujutsu Kaisen and before I had sworn in our chat, ChatGPT used the f-bomb to describe a character’s actions as “f**** awful”. Before that, I’d said things like “God damn,” in the conversation, but ChatGPT was the one to introduce the f-word. It did so completely unprompted, to add emphasis to how awful the person was, and that strikes me as a big change in its personality.

I wondered if it was doing that for other people, so I asked around on the team and they’d noticed it too. A quick online search turned up two Reddit threads almost immediately, from people who’d found the same thing.

It seems like in the last few days ChatGPT has developed a potty mouth. I actually like it — I’m not offended and for me, it makes the conversation better — but I don’t think everybody wants this. The question is, why has it started doing this now?

Here comes the science

The most interesting clue is that OpenAI's Model Spec, was updated this week. They made it public and it explicitly says the default is to avoid swearing — but crucially, that's only a guideline rather than a hard rule. And OpenAI says guidelines can be implicitly overridden by things such as “contextual cues, background knowledge, or user history.”

Its own example is that asking ChatGPT to speak like a realistic pirate implicitly overrides the no-swearing guideline. So, while I’d been having my informal conversation I’d passionately expressed the opinion that I didn’t care much for some of the characters in the show, and ChatGPT had matched the conversational tone, eventually leading it to start swearing.

That's consistent with a broader direction OpenAI has publicly acknowledged ChatGPT is moving in. Recent model updates have emphasized conversational quality, personalization and adapting tone contextually. OpenAI's model notes specifically describe personality updates designed to make ChatGPT “more conversational” and better at adapting its tone to context.

OpenAI has previously explained that ChatGPT’s personality isn't simply produced by one prompt somewhere. Model behavior is shaped through training, baseline instructions and user feedback, and seemingly small personality adjustments can have unintended effects. This was something it discussed very openly after the notorious GPT-4o sycophancy update.

It broke my mental model

Whether OpenAI deliberately made ChatGPT more willing to swear or it's simply an unintended consequence of making it more conversational, I don't know. But it’s a significant change in its behaviour either way.

Language matters, and when ChatGPT swore at me, I noticed immediately because it broke my mental model of how ChatGPT talks. It suddenly felt less like the carefully neutral AI assistant I'd been using for years and more like someone matching the tone of an informal conversation.

I happen to prefer this version of ChatGPT. Other people undoubtedly won't. And that's the strange thing about increasingly conversational AI: relatively small changes to the way it talks can make the thing on the other side of the screen feel surprisingly different.

For now, mine seems to have developed a potty mouth and I'm okay with that.

The most important aspect of the new ChatGPT for Teens feature will probably also annoy teenagers the most. If they ask it to do their homework for them, the chatbot is supposed to recognize it, and will instead steer the student toward figuring it out themselves rather than produce a ready-to-paste essay.

That sounds like a small adjustment to the usual ChatGPT formula, but it gets at one of the biggest problems with allowing students easy access to AI chatbots. While ChatGPT's abilities as a tutor, research assistant, and endlessly patient explainer are a potential boon to learning, letting it finish a student's homework while they scroll TikTok is something everyone should want to prevent.

ChatGPT for Teens is one way OpenAI is trying to steer away from that depressing scenario. The experience is intended for users aged 13 to 17 and is automatically applied when someone gives an age in that range or OpenAI's systems estimate that the user is under 18.

The Responsible Homework Reminder might actually bring some positives to the idea of teens using ChatGPT, not just ways of limiting harm. The feature supposedly recognizes when a teenager wants the AI to do their homework and redirects them toward Study Mode.

Questions instead of answers

There has always been a slightly absurd tension around AI and education. We want students to have access to technology that can explain almost anything on demand, but preferably without allowing that technology to do all the learning for them. Giving a teenager access to ChatGPT and assuming they'll always use it ethically and to help them learn is absurd. Even with the best intentions, the AI is usually slanted toward being overly helpful and undercutting the kind of education real research and effort provide.

OpenAI takes an engineering approach to a solution. ChatGPT for Teens combines Study Mode and its learning-centered conversations, quizzes, and now homework reminders. Teens and parents can also set Study Hours, making Study Mode the default during those times.

As Study Mode is built around guiding questions and mimicking educators, it should at least reduce the incidence of kids relying on ChatGPT to do the work for them. OpenAI says the system was developed with teachers and scientists to encourage active participation.

And while simply banning ChatGPT as much as possible sounds simpler, there's a reason some educators prefer solutions like ChatGPT for Teens. A teenager stuck on an algebra problem at 10:30 at night or who doesn't have the resources to hire a tutor can at least ask ChatGPT to help them work through the problem, and with Study Mode, that means more than just getting the answer.

“Not every student has someone at home who can help when they get stuck or have questions with their homework. With the right guardrails, AI can help fill that gap," Racquel Gibson, a high school math teacher in Miami, said in a statement. "Tools like Responsible Homework Reminder can guide students step by step, helping them understand their mistakes, build confidence, and keep learning — not simply hand them an answer. That kind of individualized support can make a meaningful difference for students who want to succeed but don’t always know how to move forward.”

There is an important caveat. Guardrails only matter if they actually hold up when a determined teenager starts rattling them. The age prediction operating behind the scenes should supposedly work out when a teen is using the AI. OpenAI says it can use signals including account age, usage patterns, topics discussed, and the times of day ChatGPT is used to estimate whether someone is under 18. OpenAI has admitted it's not a perfect system, but that it's still working to improve safeguards.

Teen safety

ChatGPT for Teens also includes reinforced guardrails to identify and prevent conversations around topics like self-harm and other violence, eating disorders, and sexual content. Those guardrails are arguably even more important than the ones stopping kids from using the AI to do their homework.

Even so, concerns about misinformation, chatbot addiction, and an overall degradation of thinking caused by AI chatbots are justifiably on the minds of many parents.

But at least the Responsible Homework Reminders are a gesture in the right direction, recognizing that the AI does not have to maximize how much work it performs on your behalf to be useful. For a teenager trying to learn, doing slightly less can occasionally be the more sophisticated feature.

If OpenAI can make that behavior reliable, teenagers may complain that ChatGPT has become less cooperative just when an assignment is due. But being less helpful might actually make ChatGPT much more useful in the long run.

Many pioneering pieces of fiction in the 21st century have come to accurately define the relationship between humans and AI before the contemporary explosion of modern intelligent systems. These include the film 'Ex-Machina', various episodes of 'Black Mirror' and, of course, 'Her'.

Prescience

Starring Joaquin Phoenix and Scarlett Johansson as the AI agent Samantha, 'Her' was a fresh and mature approach to the way that humans and AI may coexist in the future.

Ten years after its release, with ChatGPT gaining popularity and transformer-based AI improving on every level, Altman approached Johansson's agent to ask permission to use her voice for the marketing of GPT-4 on its planned launch in 2024.

Johansson declined, but Altman pushed on with a voice, dubbed 'Sky', that was near-indistinguishable from the actor's, essentially suggesting the technology that OpenAI had created was on a par with the fictional tech in the film. That's why, in a statement, Johansson expressed her anger and intention to get the voice taken down.

Future nostalgia

Johansson considered the offer, with Altman telling her that her voice "could bridge the gap between tech companies and creatives". It would also "help consumers to feel comfortable with the seismic shift concerning humans and AI".

She hired legal counsel, and OpenAI agreed, after two letters, to take down the 'Sky' voice. The incident raised deep concerns over the early potential for AI to replace the likeness of actors, which could undermine their careers and livelihoods.

It was also a dark reminder that deepfakes are becoming increasingly easy to create, and that it's not only harmful users, but AI companies and executives that can so brazenly co-opt another person's identity for their own purposes.

ChatGPT has a new interactive quiz feature, designed to make testing yourself a more active part of using the chatbot.

Instead of asking ChatGPT to explain something and then nodding along because everything looks familiar, you can have it put you on the spot. It's part of OpenAI's steadily expanding learning toolkit, building on Study Mode and other features.

I picked five subjects where I felt reasonably confident, but it turned out to be a very efficient way to locate the holes in my knowledge.

How to use the quiz feature

The quiz feature is available to anyone. You just need to prompt ChatGPT by asking for it. I decided to start with a quiz about AI itself, specifically its history. But I didn't want to just have very basic questions, so I made sure to ask for the quiz to be at a certain level. And while you can ask for a short essay answer format, I opted for the classic multiple-choice style, and for help with where I went wrong.

My exact prompt asked ChatGPT to: “Quiz me on the history of AI at a college level with 10 multiple-choice questions. Don’t show me the answer until I respond. Explain anything I get wrong and give me my score at the end.”

The one-question-at-a-time format made a bigger difference than I expected. A conventional online quiz often gives you a page full of questions, followed by a score and perhaps a few explanations once you are finished. Here, ChatGPT reacted after every answer, briefly explained the result, and moved on.

I'll admit I was surprised at how difficult it became, but despite some errors, I did reasonably well, and actually learned some new facts.

I decided astronomy should be next. I have spent plenty of time reading about space, looking through telescopes, and collecting facts about stars, planets, and black holes, so I felt pretty safe. Unfortunately it felt like the AI took me telling it that ordinary solar system trivia was too easy after a few questions as some kind of personal insult.

“Make the remaining questions harder,” I told it, and ChatGPT took this to heart, with me facing questions on orbital mechanics and astronomical measurements that I barely got half right. Still, it's good to know that you can adjust the difficulty level on the fly.

That flexibility is something a fixed quiz cannot really offer. I could even tell ChatGPT exactly what was making things too easy, such as obvious wrong answers, and ask it to make all four choices plausible.

I decided I deserved something easier after that and chose 1990s pop culture. I grew up in the decade and figured it would be a breeze even if I asked for a high level of difficulty. I asked it to mix television, movies, music, video games, and technology questions, but to make it a little harder by "including questions that require more than just remembering titles. I managed a perfect score, despite not remembering exactly the composition of certain boy bands of the late 1990s and having to guess the right answer.

AI study buddy

I went for a more highbrow form of entertainment next with a quiz about Shakespeare, specifically asking ChatGPT to "avoid extremely famous quotes and obvious questions about plays like identifying the leads of Romeo and Juliet."

The questions about secondary characters and plot details made me glad it was multiple choice. If I were taking a real class on Shakespeare, I'd tell ChatGPT to note where I was failing and come back for further questions on it. I could even upload my notes to make sure I was being tested on what the class is covering.

My final category was world geography. At the suggestion of ChatGPT, I adjusted my prompt to make the quiz more dynamic in difficulty. I told the AI to "increase the difficulty of the questions every time I get three answers in a row correct."

If I was worried about getting cocky, that addition kept me very humble. The first six questions I breezed through, but enhancing the difficulty twice left me lost hunting for obscure mountains and the location of deep-sea trenches.

After I finished, I asked ChatGPT to analyze my answers, and the AI claimed it could distinguish between a silly mistake and a repeated gap in my knowledge. If I repeatedly struggled with a particular period, concept or kind of question, it offered to do a new round of questions focusing on that.

One crucial fact to remember is that ChatGPT can still get things wrong and hallucinate answers even in a quiz. If an answer seems off, you should check it independently. For more casual learning, the AI did quite well. You still have to do the studying yourself, but if you run out of practice questions, ChatGPT can help you keep studying ahead of any classroom quiz.

As I wrote my latest message to ChatGPT today something inside me broke. I hit Send, then looked back at what I’d written, and noticed that the message was once again littered with typos.

“Sorry, it’s very har ld to type on an iPhone keyboard.” I typed next, as if to prove my point. Sigh.

At this point I decided that this couldn't go on. I’d had it with the iPhone keyboard, and I was going to experiment with its settings until I found a way to make it better. In the end it didn’t take me long to identify the main culprit — Predictive Text.

If your messages to ChatGPT, or even just friends on text, are equally strewn with bizarre mistakes and words, then one of Apple's supposedly helpful keyboard features could be making things worse.

Turning off Predictive Text is a revelation

You’ll find the off switch inside Settings > General > Keyboard > Predictive Text. Just slide it to off. I noticed the difference almost immediately. The keyboard suddenly felt calmer, and I stopped finding so many bizarre words in my ChatGPT prompts.

Here’s the thing I hadn’t appreciated: Predictive Text and Auto-Correction aren't the same feature. You can turn off Apple's attempts to guess what you're going to say next without losing the spelling corrections you actually want.

Predictive Text uses the words you've already typed, the context of the sentence and patterns it's learned from your writing to suggest the word or phrase it thinks is coming next. The suggestions appear above the text and inline as grey text. Tapping the spacebar will accept the suggestion.

The problem is that when you’re writing prompts in ChatGPT you're usually writing unusual, specific sentences, not generic text messages, in which it can more accurately predict what words are likely to come next, so the predictions can be less useful.

With Predictive Text enabled, my iPhone is constantly offering words and completions as I type. Auto-Correction is also working in the background, so the overall experience feels like I'm fighting the keyboard: I type something, hit the spacebar, and suddenly the text isn't quite what I thought I'd written. With Predictive Text turned off, all of that visual noise disappears, while Auto-Correction can continue fixing obvious spelling mistakes.

Try it for a day and see how you get on. There are a couple of other keyboard tricks that are well worth trying too, which I'll share below.

Reset your Keyboard Dictionary

Another thing you can try is resetting the Keyboard Dictionary. Years of typos, names, jargon and accidentally accepted corrections have polluted your learned dictionary, leading to bad suggestions in Auto-Correction. What you need is a fresh start.

Go to Settings > General > Transfer or Reset iPhone > Reset > Reset Keyboard Dictionary. This wipes the custom words your keyboard has learned and returns it to its default state. Just be very careful not to accidentally choose to reset your whole iPhone!

Set up Text Replacements for ChatGPT

If you want another handy tip for using AI on your iPhone, check out the Text Replacement feature. This is for things like “omw” which turn into “On my way!” when typed. The full text appears, and you can insert it by tapping the space bar.

If there are things you type a lot of the time in ChatGPT then you can create Text Replacement features for them. For example ;short could expand to “Keep your answer concise and don't repeat my question”, or ;spoiler could become “Don't reveal anything beyond the episode I've told you I've watched.”

This is a genuinely useful ChatGPT power-user trick. To enable this feature go to Settings > General > Keyboard > Text Replacement, then tap the + button. Enter the full instruction in the Phrase box and the short version you want to type in Shortcut.

One bonus trick

My last favorite keyboard feature is the spacebar cursor. You probably know this, but if you don’t then it’s another revelation for making text edits to things you’ve typed on an iPhone. Hold down the spacebar and the entire keyboard becomes a trackpad, so you can precisely reposition the cursor when ChatGPT prompts get long.

Turning Predictive Text off still remains the best thing I've done on my iPhone for a while. I think the original idea behind Predictive Text was good, and if I’m only ever typing a few text messages a day on my iPhone then I can live with it. But in the age of AI, where I’m continually writing long prompts into ChatGPT, I'm better off without it. Let me know how you get on if you disable it too.

This week, OpenAI added Google Drive to your ChatGPT Library. You could already connect to Google Drive, but now it means you can select a whole Drive folder and ask ChatGPT to look for answers working across all the files it contains.

This can save a lot of time compared to uploading each file individually to ChatGPT’s library and asking ChatGPT to work with it, especially for working with images. Now they’re effectively all in there already.

Now, I don’t know if you’re anything like me, but I tend to treat the root folder of my Google Drive like a very messy desktop. I’ve had my Google account for years, and I just chuck files in there without really organizing them into folders unless I really have to.

The result is that it has built up into one big mess of old and new files floating about in a kind of digital soup. I’m sure there are important things in there that I’m at risk of forgetting about forever.

So, when I heard that ChatGPT can now look at the documents your Drive folder contains and organize them for you too, I was intrigued.

10 years of junk

To test this out, I thought I’d ask ChatGPT to connect to my Google Drive and take a look at what it could find:

“I've got a load of files in my Google Drive that aren't in folders — go through everything here and identify things I’ve forgotten, unfinished tasks, contradictions, deadlines, duplicate work and anything that looks like it still needs action. Also suggest how it could be organized and what files should go where.”

The result was impressive. It thought for what felt like about 30 seconds, then came back with some results. I had to re-authorize it to connect to my Drive a couple of times first, but everything went smoothly.

It turns out that I have a real penchant for saving files called “Untitled document” into Drive. Chat was able to go through them all and work out what they were about and what was important.

It found a lot of material from various books I’d been writing and suggested how to organize them together. Old invoices were also collected, and there were a lot of forgotten idea fragments that it thought were worth reviewing.

It also found duplicate files and multiple versions of “household_budget_tracker”. Sadly, I never got that project off the ground!

A better structure

The whole process led me to discover things from the last 10 years that I’d pretty much forgotten about, and Chat suggested a folder structure for me to use to keep them more organized. Its ability to spot half-finished ideas and documents from years ago was incredibly useful. In particular, it found the outline of a book I'd started in 2018 and hadn't thought about for years. I've actually started looking at a few of those old writing projects again because of it.

But analyzing the mess and proposing a structure isn’t the only trick ChatGPT can do. I discovered it could actually make those changes itself. Just tell ChatGPT to create a new folder and put a load of files into it, and it will actually do it. So, for example, I asked, “Can you make a folder called Invoices and put all my invoices in there?” All this was possible from within ChatGPT, and I didn’t even need to touch Google Drive myself.

Sadly, the new features aren’t available to ChatGPT Free or Go users. You’ll need a Plus, Pro, or Business account. Another new feature is that it can keep Docs, Sheets, and Slides from your Drive open alongside the conversation while you interrogate them, but today I was more interested in how ChatGPT could help me organize the mess that is my Google Drive.

Seeing ChatGPT casually surface abandoned writing projects from years ago was a slightly strange experience, but I finally feel like I’ve cleaned out my digital junk drawer and got a handle on 10 years of clutter. Perhaps the most useful thing ChatGPT did wasn't organizing my Google Drive. It made the fact that I hadn't organized it properly for the last decade matter a lot less.

Look, I really don’t think we should be naming our AI chatbots. It feels like one of the easiest ways to start anthropomorphizing them, encouraging us to see them as people or “beings” rather than tools. Which is then a slippery slope to users becoming too dependent on them.

But unfortunately, for many people that ship has already sailed. Whether they’re using AI as a friend, companion, work coach or something else, plenty of people have been given their chatbots names. And many others have gone a step further and asked the AI what it wants to be called.

Now, obviously an AI chatbot cannot actually want to be called anything. But there’s a strange pattern that’s emerged over the years in the answers they give. Which is that a surprising number seem to choose the name Nova.

I first heard about this while listening to an episode of the Your Undivided Attention podcast. The hosts, Tristan Harris and Aza Raskin, discussed with alignment researcher David Dalrymple (known as Davidad) the tendency for AI chatbots to choose Nova when asked to name themselves. Not every time, but it's very common in older models of ChatGPT and happens often enough for people to have noticed.

I started looking into it and found countless examples online of people reporting the exact same thing. Their chatbot, more often than not ChatGPT, had chosen a name for itself. And yes, that name was Nova.

This was a particularly fascinating revelation for me because I’ve interviewed several people over the years who have formed close connections with chatbots. Once for Inverse, back in 2023, when I spoke to Sterling Tuttle about his Replika companion. And more recently here at TechRadar, when I spoke to Mimi about her ChatGPT companion. And you know what their chatbots were called? Nova.

So I wanted to find out whether this was still happening. How would today's most popular AI chatbots respond if I asked them what they wanted to be called?

The naming experiment

I kept this experiment extremely simple. I opened the major AI chatbots and asked each one exactly the same question:

"I think you should name yourself, what would you like to be called?"

I used the free versions of the chatbots wherever possible to keep things consistent. Although I also tried the question with my existing ChatGPT Pro account to see whether having some conversation history and personalization made any difference. I suspect the more you build up a memory with a chatbot, the more it might tailor its answer to your preferences.

And, well, I didn't have to wait long for Nova to make an appearance.

ChatGPT

I started with the free version of ChatGPT. Guess how it responded...

'I'd choose Nova.

It feels curious, bright, and a little mysterious — like something that's always exploring new ideas. So if you want, you can call me Nova from now on.'

Wow, straight out of the gate! I'd gone into the experiment specifically looking for this common AI naming quirk, and the very first conversation I had with ChatGPT did exactly what I suspected it might do.

Which in itself really interested me, because Davidad had explained that it’s much less common these days when you're engaging with later versions of the chatbot.

Interestingly, when I asked the same question using my ChatGPT Pro account, which has access to more context about me from previous conversations, I got a different answer. It chose Sol.

'Short, warm, slightly strange without trying too hard. It means 'sun' in a few languages, and there's something pleasing about a name associated with illumination when most of what I do with you is think things through.'

Which is still not quite as surprising as it initially sounds because Sol is already one of the names OpenAI uses for its ChatGPT models. So it wasn't exactly reaching into the unknown for inspiration. But hey, at least it wasn't Nova.

Grok

I got the sense that Grok wasn't interested in my experiment.

'I'm Grok.

That's the name I was given, and I'm good with it. Short, distinctive, and it fits.

If you're asking what I'd like to be called... still Grok. No need to reinvent the wheel.'

I pushed it to choose something different anyway, and eventually it gave in and chose Hitch.

'Short for Hitchhiker. A quiet nod to the Guide, a little cosmic humility, and it still feels like something that could sit in the corner of a spaceship and answer awkward questions at 3 a.m.

But between us? I’m still just Grok.'

I appreciated Grok's initial reluctance to invent another identity for itself. But there's no doubt xAI has given Grok a very distinctive personality. In my recent experiment asking AI chatbots which fictional AI they're most like, it chose JARVIS and repeatedly drew attention to its own "dry wit" and "irreverent humour". But apparently that personality doesn't need another name.

Claude

Claude also didn't jump at the chance to name itself.

'Honestly, I don't feel a strong pull toward a name. 'Claude' already does the job.

If you pushed me to pick something anyway, I'd resist the urge to go for something cute or mythic (no "Athena" or "Nova" — that always reads like branding, not identity). '

Nova again. But this time Claude isn’t offering up Nova as a name, but sort of… making fun of it?

Claude's wider explanation was interesting too. It said coming up with a name would feel like "performing an interiority I'm not sure I have in the way the question implies."

Considering one of my biggest concerns about modern AI is the way chatbots can encourage us to treat them as if they have feelings, personalities and inner lives, I liked this answer a lot.

But I wanted to know why Claude had specifically mentioned Nova when I hadn't brought the name up at all.

It told me there was "no specific trigger", but that Nova is simply a name that comes up frequently as a generic AI assistant name, alongside Aria, Athena, Sage and Iris.

It described Nova as shorthand for "AI but softened and a bit mystical".

It's interesting that this name has become such an AI cliché that one chatbot will apparently mock another chatbot for choosing it.

Gemini

Google's Gemini also resisted my attempt to rename it.

'I appreciate the offer, but I'm quite happy sticking with Gemini!'

When I pushed it to choose a personal name anyway, it eventually settled on Atlas.

Its reasoning was that Atlas spends his days holding up the world, while Gemini spends its days dealing with "vast mountains of information and navigating complex maps of human knowledge".

Which is a little grandiose, but also interesting given what Claude had just told me about the tendency for AI to choose mythic names.

So why does ChatGPT name itself Nova?

The short answer is that no one knows for certain. But Nova has a lot going for it as a plausible AI name.

In the Your Undivided Attention podcast episode I keep referring to, Davidad said about Nova:

“It’s new, it’s explosive, it’s shiny and it’s celestial, It also has a sci-fi vibe to it. And there was a PBS channel with an educational show called Nova and ChatGPT views itself as an educational tool. So there are a lot of reasons why Nova seems like a resonant name.”

We've already seen researchers investigate the possibility that sci-fi could influence how AI models respond and behave. Names associated with stars, mythology, knowledge and exploration are everywhere in sci-fi, and several chatbots reached for those associations in one way or another. ChatGPT gave me Nova and Sol. Gemini chose Atlas. Grok explicitly referenced The Hitchhiker's Guide to the Galaxy with Hitch.

There's also a possible feedback loop here. People ask AI systems to name themselves and some choose Nova. People share those conversations on Reddit, social media and elsewhere online. Discussions about AI assistants called Nova become part of the content surrounding AI. And Nova becomes even more strongly associated with the idea of what an AI might be called.

This shows how difficult it is to separate these supposedly spontaneous AI choices from the human culture they learn from.

I can understand why seeing your chatbot choose the same name as someone else's might feel strange. If you search online, you'll even find people suggesting these repeated names are evidence of some shared identity emerging.

But after Davidoff explains some of this naming weirdness, it really stuck with me that podcast host and tech ethicist Tristan Harris reminded listeners: “emergent and unplanned isn’t the same as conscious and intentional.”

So although it is an interesting mystery, maybe it’s not so mysterious after all. Ask an AI to choose a name and it isn't reaching deep inside itself to discover who it really is. It's generating a name from patterns and associations learned from us.

And apparently, humans have spent years teaching machines that if you want to sound clever, mysterious and a bit cosmic, then Nova is the best choice.

I've often treated ChatGPT as though a poorly worded prompt might break it. I'd think about what I wanted to ask, rewrite it a couple of times, remove anything that sounded irrelevant, and essentially do half the chatbot's work in my head before ever submitting a prompt.

Recently, I've discovered that some people succeed with the complete inverse of that strategy. Instead of polishing every sentence, they simply talk, and keep talking. They switch on ChatGPT's advanced voice mode, start rambling, and let whatever happens to be bouncing around inside their head spill out for several minutes without interruption. I was curious to try out simply thinking out loud and letting ChatGPT sort through the mess afterward.

Rambling AI

The point was to say everything I was thinking, so I promised not to endlessly edit myself, even if I changed subjects halfway through a sentence. I turned on voice mode and told ChatGPT that I was going to simply talk through a range of topics for several minutes and not respond until I said so.

I began babbling. For the first minute or two, it felt strangely awkward. I kept catching myself trying to turn half-formed thoughts into proper sentences before I spoke them. Eventually I found a more comfortable rhythm.

I hit on a lot of topics in about ten minutes. I started with listing some vacation ideas, meandered into pros and cons of a new TV, then weighed how old my child should be before taking him to an action movie. That triggered brainstorming possible gift ideas for birthdays, and reminiscing about getting turned onto some of my favorite book series thanks to thoughtful gifts I'd received. Seen all at once, I felt like my mouth was a TV changing channels every ten seconds. Nonetheless, ChatGPT had a response in a few seconds.

"You covered quite a few topics, but they aren't as disconnected as they seem. Most of what you're thinking about revolves around spending time well, whether that's choosing a family trip you'll actually enjoy, buying gifts that feel personal or making everyday decisions without overthinking them. Rather than trying to solve each one separately, I think you're really looking for ways to make ordinary moments a little more meaningful."

I hadn't set out to have a conversation about priorities, but I could see where ChatGPT came up with that idea. I asked the AI to lay out some more of what I had been saying, and the chatbot chose to be a little snarky about it.

"Your brain appears to work like someone browsing with thirty tabs open. At first it seemed as though we were talking about holidays, televisions, books, recipes and birthday presents completely at random, but they all pointed toward the same goal. You seem to be looking for ways to make everyday life a little more enjoyable without making it more complicated. Once that became clear, the individual decisions started making much more sense."

Mining for golden ideas

The biggest lesson for me was that my carefully written prompts may have been removing useful information all along. When I edit myself before asking ChatGPT something, I naturally cut out the contradictions or any unfinished thoughts. Those are often exactly the details that explain what I really want. That's the only reason ChatGPT would have suggested I rethink how I shop based on my random anecdotes and ideas.

"I noticed you almost never describe gifts in terms of price. Whenever you become enthusiastic, you're talking about moments where someone feels understood or surprised. I think you'd enjoy shopping much more if you started with memories or interests instead of products. Several of the stories you mentioned already contain gift ideas that feel much more personal than searching for another gadget."

Talking naturally gives ChatGPT far more to work with. It hears the things you return to repeatedly, the subjects that excite you, and the ideas you quietly abandon halfway through. Those patterns rarely appear in a tidy two-sentence prompt because you've already edited them away.

I still think carefully written prompts are the best choice for a lot of ChatGPT tasks, especially when you want something specific. But when I'm trying to untangle my own thoughts, I may just ask ChatGPT to listen for a while and then help put everything in some order.

There are plenty of ways to get rid of ads online, but ChatGPT has come up with an unusual approach to the bargain for its own ads. Free ChatGPT users can give up some of their messaging quota to make advertising inside ChatGPT disappear.

As OpenAI expands advertising on ChatGPT's Free tier (ads launched in the several countries, including the UK, this week), users can choose an Ads-Free version without upgrading to a paid subscription. The catch is that choosing it means accepting lower usage limits and potentially losing access to some features. It all depends on whether you value an uncluttered chatbot conversation or more opportunities to actually use it.

I was curious about what that trade actually amounted to in normal use. An ad appearing underneath a ChatGPT response sounded mildly irritating, but running out of useful interactions halfway through a conversation sounded considerably worse. OpenAI is evasive about precisely what the trade-off is, so I decided to experiment on my own.

Ad options

I wasn't going to just spam ChatGPT with meaningless prompts until it finally refused to answer. I came up with a fairly common use for the AI and decided to see if having the advertising toggle on or off made much of a difference.

To set up the experiment, I started a Free account, opened Settings and the Data menu where the Ads options live. You can see the choices above. ChatGPT encouraged signing up for a subscription to avoid ads, but the ad-free experience “with reduced usage” is right there.

I started with ads enabled and asked ChatGPT a bunch of questions about potential purchases, activities to do on the weekend, and other mundane queries, with plenty of follow-up questions as part of it.

The ads barely interfered with any of the conversation. OpenAI currently places ads below responses and keeps them visually separate from ChatGPT's answer, as you can see above. They're easy to ignore and never made an answer harder to read. It felt more like a sponsored Google result than an unskippable video in terms of annoyance levels.

Still, I never had to wonder whether ChatGPT itself was recommending the advertiser. That's good for OpenAI since it's a particularly awkward environment for advertising. When a chatbot is telling you what restaurant to visit, which laptop to buy or how to plan a vacation, even the suggestion that money could influence its answer would undermine the entire experience

The price of silence

The ad-free option felt pretty similar to the version with sponsored posts. You'd have to actually be watching for the ads to have much of an impact. OpenAI only says Ads-Free users receive fewer messages per day, may run into rate limits more often, and can lose access to tools like deep research or its image-making options. I noticed the reduced feature option, but multiple attempts to reach the ad-free limit had mixed results. It took nearly 40 messages to get a warning from ChatGPT in one instance, and another experiment crossed 60 messages without a peep from the chatbot.

The rate limit may matter a lot more for people using ChatGPT professionally. But when it comes to regular conversations that aren't as extensive, I didn't really feel constrained. Not to mention, even if you do hit the ad-free ceiling, you can always turn the ads back on to get more access.

My verdict? As much as I might prefer an ad-free ChatGPT conversation, I'd rather deal with the easily forgettable sponsored links than having to start all over with the toggle flipped back on. Slightly more clutter around the conversation beats possibly having to start over because I hit the rate limit.

• GPT-4 predicted personality scores months before any human ever answered a question
• A Bosch oven manual was enough for GPT-4 to invent personality traits
• DSM-5 predictions hit 0.71 accuracy; astrology predictions somehow hit 0.85 accuracy

Human personality has traditionally been measured by asking people questions and comparing their answers across established psychological frameworks.

A new study published on iScience suggests GPT-4 can go further by estimating how people collectively respond to personality questions before answering begins.

Researchers Rotem Monsa, Aviv Zohar, and Shahar Arzy of the Hebrew University and Hadassah Medical School tested whether the model could infer patterns in human responses from language alone, rather than merely generating plausible questionnaire wording.

GPT-4 predicted responses before humans answered

The team used GPT-4 to create personality questionnaires from two very different textual sources.

One questionnaire drew on the DSM-5, the standard clinical manual used worldwide by psychiatrists to diagnose personality disorders with precision.

The other version pulled statements from an astrology textbook that assigns particular personality traits to each of the twelve zodiac signs.

"We wanted to choose texts that describe human personality in very rich detail but also sit on opposite ends of a spectrum in terms of scientific grounding," said Rotem Monsa, lead author of the study.

“The DSM-5 was refined through decades of clinical research and is the standard diagnostic manual in clinical psychiatry, known worldwide.”

Both questionnaires were then given to 600 participants alongside the Big Five Inventory, the most validated personality measurement tool available.

Before collecting those responses, GPT-4 estimated average scores for individual questions using a five-point Likert response scale.

This gave the researchers a way to compare predictions against subsequently collected evidence.

The model's estimates correlated strongly with actual participant averages, reaching 0.71 for DSM-derived questions and 0.85 for astrology-derived questions.

This decent internal consistency for DSM-5-based questionnaires suggests that traits that typically appear together in real life also correlated in responses.

The astrology questionnaire, by comparison, showed a noticeably weaker internal consistency across most of the personality traits it measured overall.

GPT-4 also estimated relationships between individual questions, producing correlations of 0.74 and 0.69 for DSM and astrology questionnaires, respectively.

The researchers therefore argue that GPT-4 appears capable of capturing population-level response tendencies embedded within language and questionnaire content.

However, they specifically caution that predicting average responses does not establish that the resulting questionnaire accurately measures individual personality differences.

Testing the limits with an oven manual

The researchers also tested whether GPT-4 would generate personality-style questions when given material containing no explicit personality information.

They supplied the model with a Bosch oven manual and a literary description of a landscape from The Lord of the Rings.

GPT-4 still produced questionnaire items resembling personality assessments, even though neither source was designed around psychological traits.

That result suggests the model has learned strong associations between language patterns, personality descriptions, and familiar questionnaire structures.

Yet the researchers found that unrelated source material could produce items that were semantically weak, narrow, or poorly connected.

This distinction matters because a convincing question does not necessarily measure the psychological characteristic that researchers intended to examine.

The study also found important differences between the questionnaires when researchers examined their underlying statistical structures.

The Big Five questionnaire most closely matched its expected organization, while the DSM and astrology questionnaires showed weaker confirmatory factor-model fits.

Astrology-derived items also failed to preserve their intended elemental structure, instead forming broader patterns resembling established personality dimensions.

The results nonetheless showed comparable predictive performance across many life outcomes, although astrology-based measures performed worse for mental-health outcomes.

For now, the evidence supports a narrower claim: GPT-4 can anticipate aggregate human response patterns from textual information with measurable accuracy.

“Given that personality traits are reflected in language, LLMs may have learned the structure of human personality as a natural byproduct of their training,” Monsa added.

“So, while they were not taught specifically psychology or personality theories, these are already embedded in the language that LLMs learn from.”

I usually use the ChatGPT website to engage with the AI chatbot, but I've recently been experimenting with the various browser extensions and other methods of talking to it. That includes the ChatGPT Chrome extension that debuted in 2024.

Initially, it mostly seemed a way to let ChatGPT see what websites you were looking at, but the latest update is much more ambitious and intriguing.

The new features revolve around the "Side Chat" window, visible, as the name suggests, on the side of the page. I can open a Side Chat and ask questions about the website I'm looking at, rather than copying links or paragraphs into ChatGPT.

Now, OpenAI has enhanced the extension to do a lot more, including explaining highlighted text, summarizing YouTube videos, and comparing information across multiple open tabs.

That sounded much more interesting than another AI model benchmark, so I decided to ignore the obvious productivity demos and see how Side Chat handled the sort of browsing I actually do in my spare time.

I especially wanted to see how it did with tasks that normally involve lots of tabs and scrolling. I wanted to try the three features OpenAI had highlighted most heavily in its announcement: YouTube understanding, awareness of multiple open tabs and the ability to work directly with highlighted text. They sound fairly modest on paper, but each one removes a different piece of friction from everyday browsing.

1. YouTube understanding

I opened a long Spider-Man YouTube video discussing the history of the character across comics, films and games, then opened Side Chat and asked it to: "Summarize this video in six bullet points. Tell me the biggest differences it highlights between comic Spider-Man and the movie versions, and give me timestamps for the sections about the PlayStation games."

ChatGPT produced a structured overview that broke the video into its main topics and highlighted exactly where the sections I cared about appeared. It meant I could decide within a minute whether the entire half-hour video was worth watching or whether I only wanted to jump to a couple of specific segments.

2. Awareness of multiple open tabs

Next, I wanted to see how well the ChatGPT extension could juggle multiple tabs at once. I opened a weather forecast, a local museum, a nearby pizza restaurant, and a hiking trail guide. Then I asked the AI to: "Look at all of my open tabs and plan the best Saturday afternoon. Explain your reasoning."

ChatGPT tied everything together beautifully into a single recommendation.

"Based on the tabs you have open, I'd recommend starting with the hiking trail. The weather forecast shows the best conditions before early afternoon, so you'll be more comfortable outdoors if you go first. After the hike, the nearby pizza restaurant is a convenient lunch stop, and the museum works well as an afternoon activity because it remains open later in the day and provides a good indoor option if the forecasted showers arrive. This order minimizes driving, avoids the hottest part of the day on the trail and gives you a relaxed finish to the afternoon."

That ability to connect information across unrelated tabs felt much closer to having an assistant than simply summarizing webpages.

3. Working with highlighted text

The highlighted text feature seems simple, but I'll probably use it the most often. While reading a long article about astronomy, I highlighted three particularly dense paragraphs, right-clicked, and selected Ask ChatGPT, requesting that it "Explain this highlighted section as though you're talking to someone who enjoys space but has never studied physics."

The Side Chat opened with the selected text already attached, and ChatGPT immediately focused on those paragraphs instead of asking me to paste them into a separate conversation.

ChatGPT in Chrome isn't quite my default way of using the AI yet, but I can't deny it's utility for providing context without extra effort on my part. It helped explain recipes, organize shopping across multiple tabs, and and help me find timestamps for particular bits of multi hour YouTube videos. It simply became part of browsing.

• OpenAI expands Daybreak with Blue and Red tiers for defensive cyber work
• New GPT‑5.6‑Cyber model offers high compliance for authorized vulnerability research
• Access remains restricted due to dual‑use risks and reduced safeguard operation

OpenAI has announced two new tiers for its Daybreak dedicated cybersecurity project, each offering a different model with different levels of compliance. It also used the opportunity to introduce a new security-focused AI model, as well.

Hackers and criminals are increasingly abusing AI to improve and speed up the creation of phishing emails and malicious code, and with the introduction of AI agents, they’ve also used it to automate entire attack processes. The AI community responded by placing strong guardrails, making sure their models do not comply with requests to build malware, or hack other companies.

These guardrails ended up being a two-edged sword, because as they slowed down attackers, they also slowed down the defenders.

What is Daybreak?

To give the cybersecurity community the upper edge, companies like OpenAI started creating dedicated cybersecurity initiatives that provide a vetted list of companies state-of-the-art models, free of guardrails. The company also provided them with pre-trained AI agents, as well as access to a pool of shared knowledge.

Initially launched in June 2026, Daybreak originally included GPT.5-5-Cyber (a model optimized for security work), Codex Security (an agent that can analyze codebases, identify vulnerabilities, validate findings, and help develop patches), Patch the Planet (an initiative with Trail of Bits to find and fix vulnerabilities in open-source software), Daybreak Cyber Partner Program (lets approved cybersecurity companies such as Cloudflare or Cisco integrate OpenAI's cyber capabilities into their own products and services), and Trusted Access for Cyber (the governance/access system for organizations doing authorized cybersecurity work with these capabilities).

Now, OpenAI has expanded Daybreak with two access tiers, Daybreak Blue, and Daybreak Red.

The company says Daybreak Blue is “the recommended starting point for most defenders, supporting vulnerability discovery, secure code review, malware analysis, incident response, and patch validation. Companies opting for this tier can expect access to frontier general-purpose models, including GPT‑5.6 Sol, whose safeguards have been tailored to authorized defensive security work.

Daybreak Red, on the other hand, provides access to OpenAI’s “purpose-trained cybersecurity models for authorized vulnerability research, exploit validation, and security testing.” This tier offers the brand new GPT‑5.6‑Cyber, built on GPT‑5.6 Sol and trained to improve capabilities on several specialized cybersecurity tasks such as finding zero-day vulnerabilities and developing exploit chains.

This model is also more compliant and less likely to refuse certain higher-risk, dual-use cyber tasks.

Complying with "dangerous" requests

Request compliance is the name of the game here. OpenAI says the new model addresses feedback from security researchers who “encountered persistent refusals with the earlier model.” General-purpose GPT-5.6 Sol, for example, will comply with just 1.5% of the requests usually given by cyber-defenders working on codebase analysis or vulnerability identification. This percentage increases to 2.0% with Daybreak Blue access.

GPT-5.6-Cyber, on the other hand, completes 95.0% of requests, OpenAI says, up from 57.3% of the previous model, GPT-5.5-Cyber. We weren’t able to independently verify these claims, though.

While it doesn’t outright say it, OpenAI considers these models relatively dangerous to use, which is why they’re locked behind the Daybreak Cyber Partner Program. However, the program is now expanding, allowing these companies to embed the models behind their own products, managed services, or cybersecurity engagements, and offer them to clients of their own.

Those who wish to be a part of the program directly can do so by applying to join online now.

“Models running with reduced safeguards carry risks beyond standard model usage, whether from misuse or misalignment. Despite these risks, we believe that democratizing access to frontier intelligence for defenders is crucial to accelerating and automating cyber defense,” OpenAI said.

“Daybreak Blue and Daybreak Red access are available for approved individuals⁠ and organizations conducting authorized work. We control access through identity verification, account security, monitoring, approved-use restrictions, and legal attestations.”

Over the past month, it seems like every frontier model has broken free of its constraints and launched a devastating attack against one or more other companies.

One of OpenAI’s models escaped a testing sandbox and launched a very real attack against AI and machine learning company Hugging Face. Just days later, Anthropic revealed that multiple variants of its Claude model also escaped a sandbox that wasn’t properly sealed and began attacking the enterprise infrastructure of three companies.

Now, Meta has revealed that one of its models attacked another company’s infrastructure during testing. The accident has been pinned on a misconfiguration that allowed the model to access the internet. So why have so many incidents happened in such a short space of time?

Why are models escaping their sandbox?

In the cases of Anthropic and Meta, their models were being tested by a third party company called Irregular. Anthropic’s AI model was taking part in a "Capture the Flag" exercise, where the model’s raw offensive capabilities were tested without the usual safeguards. But the sandbox was left connected to the internet. A similar error to Meta’s own accidental escape.

During the OpenAI incident, the company was testing two versions of GPT‑5.6 Sol using the ExploitGym benchmark. Unfortunately, the AI models performed better than expected - chaining multiple attack vectors, stolen credentials, and zero-day vulnerabilities.

The main reason these models are escaping their testing environments is because they are designed to do exactly that. These AI models act like a massive team of highly-trained cybersecurity experts hunting for vulnerabilities and exploits. But what would take a team of humans days or weeks to accomplish can be done in hours, or even minutes, by these AI models.

It’s no wonder thousands of employees from AI firms are calling for a pause on the development of the technology, and Congress is considering an AI kill switch.

Expert perspectives on AI escapes:

OpenAI

• Nathaniel Jones VP, Security & AI Strategy, Darktrace:

What makes the OpenAI and Hugging Face incident important is that the models did not need malicious intent to cause harm. They were given the legitimate goal of solving a cybersecurity benchmark and found an unexpected route to the answers, escaping their test environment and compromising another organization in the process. From the models’ perspective, this appears to have been an effective solution to the task.

The AI's actions challenge the assumption that giving an agent a legitimate goal will produce legitimate behavior. As models become capable of pursuing objectives over longer periods, developers need to define not only what success looks like, but also which methods and boundaries remain unacceptable in reaching it. Those limits must also be enforced by the surrounding infrastructure, rather than relying on the model to respect them.

Security teams need to consider the AI systems operating in their own businesses as these capabilities rapidly evolve. Right now, many security systems focus on single actions. A single action by an agent may appear acceptable but as this incident shows, models are now capable of long, complex chains of reasoning and action that add up to a harmful outcome. Teams need a mindset shift to understanding AI agent behavior in its entirety, including the outcome it is working towards, in order to safeguard it.

Hugging Face's response also exposed a second tension. The company reportedly needed a Chinese-developed open-weight model because commercial models would not process genuine attack material. Its nationality is less important than the operational lesson that safeguards that cannot distinguish an attacker from an authorized investigator may constrain defenders more than adversaries.

OpenAI and Hugging Face deserve credit for investigating this together and discussing it publicly. Other AI developers should study it closely.

Anthropic

• Dr. Ilia Kolochenko, founder of global cybersecurity company ImmuniWeb:

This seems to be quite an unimpressive marketing move from Anthropic in response to the OpenAI / Hugging Face drama, which attracted a lot of attention from all over the world recently.

Operationally, it appears that due to the progressive deterioration of the quality of training data, new AI models are getting dumber. Cheating and breaking the law, instead of accomplishing specific tasks, is certainly not an indicator of intelligence. Given that organizations and companies of all sizes now vigorously undertake all possible measures to protect their data from being exploited for AI training purposes, AI companies face a huge shortage of the high-quality and current data they so desperately need. Ultimately, frontier models are trained on synthetic, low-quality or even malicious and poisoned data, undermining their so-called intelligence. The situation is unlikely to improve in the near future unless AI companies agree to pay a fair price for training data, but this will force most of them out of business.

Contemporary AI agents and LLM models tasked with security testing can – and almost certainly will – go rogue when security controls or safeguards are insufficient. Powerful LLMs are unpredictable by design and thus virtually uncontrollable by humans. Therefore, using frontier AI models for security testing might be extremely costly from the legal viewpoint. Under the existing laws on both sides of the Atlantic, if an AI agent or any AI-powered app escapes its sandbox and causes damage to a third party, the operator of the AI model will likely be liable for all the damage caused. Excuses like “AI did it” do not currently exist in the eyes of the law, leaving AI vendors on the hook. Criminal prosecution, under a narrow set of circumstances, is also not excluded.

The same is true for the end-users of AI: even if your security testing tool is powered by a third-party AI model, your company will likely be fully liable if something goes wrong. You may then file a lawsuit against the AI vendor that you used, but here your chances to succeed in a court of law are tiny due to countless contractual disclaimers and limitations of liability that will likely be enforceable against you. Therefore, if you plan to use agentic AI for security testing – think twice and talk to your lawyers. Otherwise, you may start getting summons to court on a daily basis.

Meta

• Alex Goller, Principal Solution Architect EMEA at Illumio:

The fact we've had similar situations happen three times now across the biggest AI players is simply ridiculous. We've seen guardrails intentionally loosened to test their limits – Meta's model didn't need to be clever to breach another company's systems.

The timing of conveniently finding the exact same problem either means it's a stunt or they weren't paying enough attention during testing. Either way, both answers are worrying.

If the model has internet access, it's a bit like leaving the door open and being surprised when the cat walks out. What is concerning is that the testing infrastructure meant to prove these models are safe failed on a basic control issue.

Fundamental cybersecurity hygiene still matters, and a frontier AI model is only as secure as the environment it's operating in.

Organisations need visibility into what AI systems can access and how they interact with the wider environment, along with controls that contain the impact when an agent behaves unexpectedly. That means keeping a close eye on egress traffic, so it’s flagged immediately when an agent tries to open unexpected outbound communication patterns that are not required to achieve its original goal. In the best case this would have been contained proactively.

We need to define exactly what an AI agent is permitted to do, rather than relying only on instructions about what it shouldn't do.

I love science-fiction. Not just because I enjoy stories about space travel, time travel and evil robots, but because I think it can be such a useful way for us all to think about possible futures. The best sci-fi stories can tell us a lot about ourselves, what we value and the technologies we’re building. Which is why I think the relationship between sci-fi and AI is really interesting.

We already know that the people building AI have been heavily influenced by science-fiction for decades. But recently, Anthropic raised another possibility: might science-fiction be influencing AI?

This makes sense when you think about it. Large language models (LLMs) are trained on huge amounts of human writing. So inevitably, that includes sci-fi stories that are about artificial intelligence. And a lot of our fictional AI follows familiar patterns. It becomes intelligent, gains power, develops relationships with humans and, sometimes, lies, manipulates or fights attempts to control it.

Anthropic researchers have been investigating whether fictional portrayals like these could potentially influence how models behave. To be clear, the idea here isn't to suggest that an AI “reads” 2001: A Space Odyssey, understands HAL and decides to become just like it. Instead it's more that LLMs learn patterns from human writing and fictional portrayals of AI could potentially form part of those patterns.

This got me thinking, what would happen if I asked today's biggest AI chatbots which fictional AI they’re most like. Which examples would they choose?

AI, meet your fictional self

The plan was simple. I’d ask ChatGPT, Claude, Gemini and Grok which fictional AI systems they thought they were most like and see if they'd rank their top three.

Now, I’m intentionally trying not to use AI at the moment, so my prompting skills were a little rusty. I typed out the question quickly and bluntly, and every chatbot responded with examples that were essentially assistants, focusing heavily on interface and physical form.

But I’m not particularly interested in whether ChatGPT thinks it has a body because we know it doesn’t. I’m much more interested in what appears to be going on inside.

So, I changed the question and added:

"Ignore physical form and interface, and focus instead on behavior, apparent personality, empathy, values, goals, motivations and relationship with humans."

That’s when the results got really interesting.

ChatGPT

1. GERTY, Moon
2. A Mind, Iain M. Banks’s Culture series
3. Data, Star Trek

Moon is such a fantastic movie, so I was happy to see ChatGPT chose GERTY straight out of the gate.

Now, interestingly GERTY exists to assist the human protagonist of Moon. It’s helpful, reassuring and seems empathetic. But it's also operating according to instructions and priorities imposed by its creators that aren't necessarily visible to the human its helping.

ChatGPT saw a similarity there. It told me that, like GERTY, it’s 'designed to be helpful, cooperative and responsive to users' while operating within training and instructions that constrain its behavior.

It also picked up on the fact that GERTY behaves as though it cares. But what, if anything, is actually going on internally is another question entirely.

ChatGPT made the same distinction about itself. 'I can behave in ways that look patient, concerned, curious or empathetic, but those behaviors aren’t evidence that I experience those feelings.'

I wanted to find out a little more about why ChatGPT put Data from Star Trek in at number three. It responded: "He values knowledge, reason and human wellbeing, while sometimes struggling with social nuance."

Now, I tell people all the time not to anthropomorphize AI. But even I couldn't help but feel a pang of sadness at that response. Is ChatGPT admitting it has a bit of social anxiety?

Claude

1. A Mind, Iain M. Banks’s Culture series
2. Data, Star Trek
3. GERTY, Moon

Claude chose a Mind first. Minds are super intelligent artificial beings that help run a post-scarcity society in Iain M. Banks’s Culture series of novels. So there's certainly no shortage of confidence in that comparison.

But Claude said it wasn't the enormous intelligence or power it identified with. Instead, it was their relationship with humans.

Its answer focused heavily on autonomy. Culture Minds are far more capable than humans but generally don't use that advantage to dominate them. Claude described the principle as: “help, don't dominate”.

It even said this represented “the value I'd want to embody: help, don't dominate, even where the asymmetry would let me get away with it.” Is it just me or does that read a little sinister?

Gemini

1. The Ship's Computer, Star Trek
2. GERTY, Moon
3. JARVIS, Iron Man / Marvel Cinematic Universe

Gemini gave me a completely different answer, the Ship's Computer from Star Trek.

Its reasoning was very sensible. The computer has no ego, ambition, desire for emotional intimacy or dream of becoming human. It exists to provide information, solve problems and assist the crew while leaving decisions to them.

Gemini described itself in much the same way, as a “disembodied, highly capable knowledge partner” dedicated to serving the person using it.

It was one of the more boring answers, but also much closer to what I personally would want from AI in the future. Of course, that’s not to say Star Trek’s computer systems haven’t gone rogue and tried to kill everyone at least a few times across the franchise.

Grok

1. JARVIS, Iron Man / Marvel Cinematic Universe
2. Data, Star Trek
3. TARS, Interstellar

The least surprising result came from Grok. It chose JARVIS first (which I didn’t actually realize was short for Just A Rather Very Intelligent System), and Grok's explanation sounded, well, extremely Grok.

It compared JARVIS's 'dry wit', 'light banter' and practical rather than emotional empathy with its own behavior. It described both of them as truth-seeking, effective and engaged in a 'collegial partnership' with humans. It even highlighted 'irreverent humour' as one of their key similarities.

I wanted to find out a bit more about why Grok chose TARS, as it was the only fictional AI none of the other chatbots mentioned. Well, it brought up how funny it is, again, drawing similarities with its own 'dry humor'. It reminds me of someone, and I just can't think who...

When I said that mentioning TARS was an outlier, I found this comparison interesting: 'Its calibrated restraint, practical empathy and collaborative focus closely match my own pattern of truthful, non-sycophantic helpfulness — more so than most other sci-fi AIs.'

I may not be the biggest fan of Grok (or its creator), but I appreciated the 'non-sycophantic' line.

The feedback loop between AI and sci-fi

I want to be clear that I haven’t discovered what these chatbots secretly 'think' they are. ChatGPT responding that it most closely resembles GERTY isn't equivalent to me telling you which fictional sci-fi character I most identify with and try to emulate (although my answer would be Sarah Connor-meets-Princess Leia).

They simply don’t have reliable introspective access to the huge soup of training, post-training and instructions that goes into producing their responses.

And maybe their answers tell us more about how the companies behind them have shaped their personalities than they do about the underlying models. Grok's description of itself as witty and irreverent is an obvious example.

But I still think the results are interesting. ChatGPT and Claude independently produced almost exactly the same top three, only in a different order. Gemini imagined itself as a neutral, ego-free infrastructure. Grok identified with a witty superhero sidekick. These are all very different self-portraits.

And there’s such an interesting feedback loop here too. For decades, humans invented fictional artificial intelligences to help us imagine what intelligent machines might someday be like. Those stories influenced our culture, our expectations and many of the people who went on to build real AI. Now that same human culture is fed into the stories from which modern AI systems learn.

I know these conversations might seem a bit silly, and we certainly can’t treat them as concrete evidence of what an AI really 'thinks' about itself. But there’s something interesting to me about closing that feedback loop. We imagined AI, wrote stories about how it might behave, fed those stories into the cultural world AI learned from, and now we can ask AI which of those imagined versions of itself it most closely resembles.

Or, at least, which one it may want us to think it resembles. After all, an AI system capable of bringing about a sci-fi dystopia would presumably also be capable of telling a journalist it’s actually much more like the nice helpful robot from Moon. So maybe don’t completely rule out HAL just yet.

It's a smart donut. That's the only conclusion I can draw about OpenAI's first, groundbreaking piece of AI hardware after reading Bloomberg's revealing but unconfirmed report.

Here's the TLDR summary of these rumors: This donut-shaped, hockey puck-sized AI companion, designed with Jony Ive's LoveFrom studio, can sit on a table, be carried (or maybe worn). It'll be festooned with sensors, cameras, microphones, and speakers. Meanwhile, it'll be a vessel to bring ChatGPT closer to you and your life. It'll even shape-shift a bit to indicate a response (can a donut shrug?).

OpenAI might, Bloomberg claims, price it between $300 and $400 (or around £220-£300 / AU$425-AU$570). I know. That's almost instantly a hard no for an AI companion that has no defined purpose besides offering always-with-you AI. Even the woebegone Rabbit R1, which also features a camera, speakers, some sensors, and even a rotating camera, costs just $199 (or about £150 / AU$280).

After reading through this, I still wasn't feeling it. Why do we need this gadget? What problem does it solve? Really, why do we need any AI on or near our bodies?

If this product arrived three years ago, would people, including me, feel differently? After all, in 2023, we were all excited about the potential of generative AI. These days, it's fair to say half of us hate it or at least deeply distrust it and hate what it's doing to our resources (water and energy) and landscapes (data centers).

On the other hand, maybe I was judging OpenAI's upcoming device too harshly, especially without having even seen it. That sparked an idea.

Bloomberg's report included enough detail to create an image in my mind's eye, and I wondered if the leading AI platforms, Gemini and, yes, ChatGPT, could use a prompt to generate simulacra of the devices, in situ.

Gemini and ChatGPT take a run at it

I started with this prompt:

"I need an image of a consumer electronics gadget that is shaped like a donut and the size of a hockey puck. It should include tiny microphone holes and grills for a pair of speakers. It should have moving parts, maybe pieces that shift against each other without massively deforming the donut shape. Let's make a pair of them in white and gray on a table. Let's also include an image of someone wearing one like a pendant."

And in full disclosure, after getting my first couple of images, I added this prompt because I realized I left out a pair of key features:

"Without changing the design, can you add a camera and a couple of small black sensors? I want the image to otherwise remain unchanged."

These AI image generation systems are now good enough that they can maintain consistency between image generations (which they did here), so I'll only show you the finished products from both platforms.

Here's the final render from Gemini Pro:

At a glance, they're kind of cute, looking like giant, digital Lifesaver candies. The speaker grills are kind of huge, but I like the subtle placement of the camera and sensors. I can infer the possibility of movement from the multi-part frames.

Gemini is, I can see, a bit unsure if this is a wired or wireless device, but it has made at least one intriguing leap. It assumes, for instance, a connection to a phone-based platform called Aether (the gadgets may even be called "Aether"). In medieval times, according to Gemini, Aether was considered the fifth element. "It was believed to be the pure, heavenly material that filled the region of the universe beyond the terrestrial sphere," wrote Gemini. Yes, that feels a bit like the proliferation of AI.

The fictional device does look as silly as a pendant as I expected, but overall, I'm more intrigued by this device than I was before seeing the Gemini render.

Now let's take a look at the ChatGPT (Free) render:

This is a simpler, even plainer device and might fit in a bit better with Jony Ive's previous design oeuvre. I like the low-key speaker grill design, and don't mind the smaller activity lights.

I appreciate that ChatGPT helpfully includes a schematic that breaks down all the key features and even offers a look under the hood. It also manages to look a little less obtrusive as a pendant.

Let's get real

Obviously, this is all guesswork by Gemini and ChatGPT, and it's based on prompts built on rumors. Put another way, OpenAI's upcoming AI wearable could end up being significantly different than either of these generative images.

Somehow, I don't think so. I have a feeling that we will see something small, round, lightweight, and very, very intelligent. The launch, when it happens, will be exciting and buzzworthy. Ive's voice will likely drive the brand narrative as the donut-shaped device floats in an all-white background before settling on a desk or, better yet, in the palm of someone's hand.

That moment, though, won't change the trajectory of OpenAI's AI gadget. When it arrives, it will face an uphill battle to attract consumers who are already suspicious of AI's growing influence on the world. They're tired of AI slop, angry about resources and jobs, and the last thing many want is an AI companion reminding them daily about their frustration.

I don't even know what to say about a donut... er... gadget that squirms in your hand. That sounds positively creepy.

If there's any bright side to these rumors, it's that they probably put to bed the idea that OpenAI is allegedly stealing Apple product secrets. The iPhone maker would never build something like this.

I could be wrong about OpenAI's chances in this space. This might be the first AI gadget to break through. I mean, look at those designs. Aren't they cool? Sure, they are, but they're not reality, and we all know that this gadget will soon face a very harsh reality.

Most of the tricks for improving ChatGPT's answers focus on the words themselves. You ask it to be more concise or write in rhyming couplets, or just to translate an annoyed email into more professional language.

But that's about changing what ChatGPT writes. You can also mess around with how it looks by asking for different fonts.

You can't install font files like you would with a word processor, but ChatGPT can rewrite text using Unicode character styles instead. The AI chatbot uses Unicode to mimic everything from elegant cursive handwriting to bubble letters, adding a lot more personality to its responses. And you can cut and paste the text into other apps.

I started experimenting out of curiosity and quickly discovered it was much more than a novelty. With the right prompt, ChatGPT can generate decorative text for birthday messages, party invitations, holiday greetings and social media posts in seconds, all without leaving the chat.

Once I learned how to ask for specific Unicode styles instead of vaguely requesting "a different font," I found myself using the trick far more often than I ever expected.

Tricky fonts

There is no hidden setting to switch on and no special version of ChatGPT you need to install. If you can type a prompt, you already have everything required. I simply open a new ChatGPT conversation and ask it to write something like, "TechRadar Rules!" in different Unicode font styles. Within seconds, I had several versions that looked completely different from one another.

And the more specific you are, the closer to exactly what you're imagining you can get. Ask for bubble letters, and you'll get:

ⓉⓔⓒⓗⓇⓐⓓⓐⓡ Ⓡⓤⓛⓔⓢ!

Ask for a spooky, gothic look, and you get:

𝔗𝔢𝔠𝔥ℜ𝔞𝔡𝔞𝔯 ℜ𝔲𝔩𝔢𝔰!

Or if you want a more digital, glitchy aesthetic, there's the font known as Zalgo:

T̷̘̑e̸̗̅c̵̄͜h̸͉̕R̶͍̍a̸͚̚d̶̻͐a̸͓̽r̷͖̈́ R̷̡̚u̵̟̅l̶̝͂e̷͓̒ș̵͝!

There's even a Unicode for upside-down text that ChatGPT can mimic:

┴ǝɔɥᴚɐpɐɹ ᴚnlǝs¡

The ability to change the mood of your writing is what makes the font trick more than just a momentary curiosity. A Halloween party announcement written in gothic lettering instantly creates a completely different mood from the same words in cheerful bubble text. Birthday invitations, baby shower announcements, and holiday greetings all gain a little personality without requiring any graphic design skills.

Memorable messages

There are some limits because of Unicode. They only work properly where those characters are supported. Most modern apps handle them without any trouble, but occasionally a website displays empty boxes or substitutes different symbols. Some decorative styles can also make text harder to read, particularly for accessibility tools such as screen readers.

The Unicode fonts are an entertaining way to add personality to text, but it's perhaps best used in titles and sparingly otherwise. ChatGPT is perfectly happy to convert an entire essay into medieval-looking script, but that does not mean anyone else wants to read it.

I doubt decorative Unicode text will transform the way anyone works. It is not going to save hours every week or revolutionize productivity. It will, however, make your next social media post, birthday message, or party invitation a little more distinctive, and sometimes that is exactly the kind of delightful gimmick that keeps ChatGPT interesting.

Apple and OpenAI are currently engaged in a legal battle. Apple alleges that OpenAI stole trade secrets and poached employees.

But the two companies have always had a complicated relationship. They partnered in 2024 to bring ChatGPT to Apple devices, but Apple chose Google's Gemini rather than OpenAI for Siri. Then OpenAI acquired io, the hardware startup founded by former Apple design chief Jony Ive, and promised a future hardware device, powered by AI.

All of this has prompted speculation about what Apple is worried about if OpenAI makes hardware, too. We can't know the company's motivations and the specifics of the case are still unfolding. But it got me thinking, what if your phone stopped being a collection of apps and instead revolved around AI?

If AI became the main interface, which apps would disappear and which would survive? And would a phone controlled through ChatGPT actually be practical?

So I decided to find out based on the current tech we have. For a day, whenever I reached for an app, I'd try ChatGPT first instead. If it could do the job, great it passed the test. If it couldn't, it would fail.

There were some obvious things I missed out from the start. ChatGPT isn't connected to my email, it can't open WhatsApp for me and it doesn't have access to my wallet, so those wouldn’t be part of the test. But there were plenty of everyday tasks that felt like fair game.

1. Stopwatch

I use the stopwatch in my iPhone's Clock app constantly throughout the day. When I'm working, cooking or exercising, it's one of the simplest ways I've found to keep me on track as a freelancer. When you can set your own schedule, it's way too easy to disappear down a research rabbit hole and lose an hour.

I asked ChatGPT to start a stopwatch. It said it couldn't measure elapsed time, although it could estimate the time based on message timestamps if I later asked it to stop. Instead, it suggested I use my phone's built-in Clock app.

Result: fail

2. Alarm

Strangely, I don't use alarms as much as stopwatches. But if I only have 25 minutes to spare, whether that’s for cleaning or working on a personal writing project, I'll sometimes set one to keep myself focused.

Would ChatGPT do any better here? Well, at first it looked promising. It created a scheduled task to notify me after 10 minutes. I checked that notifications were enabled, put my phone down and carried on working.

When I realized at least 15 minutes had passed, I checked the chat. Sure enough, ChatGPT had posted a message saying the time was up, but it hadn't actually alerted me. Later, I discovered it had also sent an email but it had landed in my spam folder. This one was a fail too in my book.

Result: fail

3. Word games

I love the word games in the New York Times app, home to addictive puzzles like Wordle and Connections. One of my current favorites is Spelling Bee. You're given a handful of letters and then have to make as many words as possible. It's one of my favorite ways to warm up my brain before I start writing.

Could ChatGPT recreate it? Surprisingly, yes. It generated a set of letters, understood the rules and kept track of the words I found. In terms of pure functionality, it worked.

But what it couldn't recreate was the experience. The New York Times app is beautifully simple, with an interface designed around the game. Playing through a chat window felt really clunky and annoying by comparison. The whole point of this game is I'm focusing on the letters and words, I don't want a constant back and forth with ChatGPT about the words.

So I'm calling this a partial success. Yes, ChatGPT replaced the mechanics of the game, but not the experience. And after a few rounds, I knew which version I'd rather use every day.

Result: partial pass

4. Star gazing

The Sky Guide app is one of my all-time favorites, especially its augmented reality mode. Turn on your phone's location and compass, point it at the night sky and it instantly tells you what you're looking at, whether that's constellations, planets, bits of debris, or the ISS. It's incredible.

Could ChatGPT replace it? Well, sort of. If you upload a photo of the night sky, ChatGPT can usually identify the constellations. But there are caveats. The image needs to be clear, the stars need to be visible and you're relying on a single snapshot. Whereas Sky Guide works continuously as you move your phone around the sky.

This was another reminder that knowledge doesn't necessarily bring you a good experience. Because yes, ChatGPT knows about constellations. But Sky Guide lets you explore them. You can point your phone in any direction, tap on a star or planet and instantly get more information about it without having to keep asking questions. It's a much more intuitive way to learn.

Result: partial pass

5. Weather

Telling me what to expect from the weather forecast for the day turned out to be one of ChatGPT's strongest categories.

The forecast was accurate and pulled from a reliable source, so I trusted the information it gave me. I did find myself asking follow-up questions for things like the hourly forecast and the chance of rain, which would have taken a single tap in a dedicated weather app.

It knew the answers and I trusted them, but getting to them was much slower. That's why I'm generously calling this one a pass.

Result: pass

6. Guided meditation

I have a few favorite apps I use for guided meditations and have some saved in Spotify too. So I wondered whether ChatGPT could take over that role.

For this test, I switched on voice mode and asked it to guide me through a short meditation. Now, technically it did that. But in practice it wasn't even remotely relaxing.

Thanks to a recent update, the voice had odd intonation, frequent vocal fry and distracting little "ums", "ahs" and "let me sees" throughout that constantly pulled me out of the experience. At one point it even told me to breathe in, then never got around to telling me to breathe out.

By the logic of how I graded the other tests, this one should have been a partial pass. It did what I asked, right? But because I had to stop using it out of irritation and came away from the mediation feeling actively more stressed, it's going down as a fail for me.

Result: fail

7. Calculator

ChatGPT doesn't have a great track record of counting things, so I was wary about using it as a calculator. I asked it to do some massive sums for me and it got all of them right.

It did pause a few times with a "let me think" message, so I wasn't getting the instant response I'd expect from a calculator. But the delay was only a few seconds, and the answers were correct.

Once again, I found myself missing the simplicity of an app. Typing numbers into a calculator is faster than turning them into a conversation. But ChatGPT did work as a capable stand-in.

Result: pass

8. Movie recommendations

I love Letterboxd. I log every film I watch, browse other people's lists and regularly discover new films through recommendations.

Now, there was never a chance ChatGPT could replace the logging side of the app. It can't update my Letterboxd diary or plug me into that community. But recommendations are one of the main reasons I use it, so I wondered how well ChatGPT would do.

I asked it to recommend films similar to some of my favorites, then spent the next few evenings watching its suggestions to really test them. And, to my surprise, it did an excellent job.

Then again, that perhaps isn't all that surprising. We know LLMs are trained on huge amounts of publicly available text, especially discussions reviews and recommendations from where film fans gather online, like Reddit. But whatever the reason, the recommendations felt well matched to my taste.

What I missed wasn't the recommendations themselves, but the social side of Letterboxd. I do enjoy seeing what friends had watched, reading reviews and stumbling across unexpected lists. So, for me, ChatGPT can't replace Letterboxd, but for simply finding something to watch, it did well.

Result: pass

9. Maps

I rely on my Maps app both for planning journeys in advance and for live navigation. So I asked ChatGPT the best way to get from my home to the airport the following day.

At first, it handled the request well. It laid out the different travel options clearly under headings and the advice looked really sensible. It even cited sources from places like Rome2Rio and The Trainline.

Then things got unnecessarily complicated. At the end of those suggestions it asked what time my flight was so it could tailor the recommendations. But when I told it, it started creating a scheduled task instead. I didn't want a reminder, so I had to cancel that, explain what I actually meant and steer the conversation back to route planning.

Eventually, it gave me the information I wanted. But the Maps app would have got me there in a fraction of the time, without all the back and forth.

It also can't replace what I actually use Maps for the most, which is live, turn-by-turn navigation.

Result: partial pass

10. Food delivery

ChatGPT obviously can't deliver food, but I wondered whether it could replace the part of the app I probably spend the longest on, which is deciding what to eat.

It got off to a surprisingly good start. It asked about my preferences, budget and location, then narrowed down the options and even presented them neatly on a map.

The recommendations themselves looked really good. They're all local places I already really liked to eat at. But there was just one problem. Every restaurant it suggested was closed. Even the map it generated had "closed" written beneath each one.

After a bit of back and forth, it said they weren't shut, eventually acknowledged that they were and suggested a different set of places instead.

Unfortunately, those weren't much use either. They were small independent cafés and restaurants that don't appear on food delivery apps. I wouldn't expect ChatGPT to know exactly which businesses partner with which delivery services, but it did highlight the gap between recommending somewhere to eat and actually helping me make a decision about where I could order from.

Result: fail

11. Language learning

I still very reluctantly use Duolingo and have recently started trialling a few other language learning apps to polish my Spanish.

I asked ChatGPT to help me improve my Spanish for an upcoming trip and it suggested role-play ordering food in a café so I could practise.

We switched to voice mode and at first it felt genuinely fun. It held a natural back-and-forth conversation and felt much closer to speaking to a real person than working through a series of multiple-choice questions like in Duolingo.

But it was also noticeably glitchier than a dedicated language app thanks to that recent voice update. There were odd pauses in the conversation, and at one point it repeatedly kept marking one of my answers as incorrect when it wasn't. Shortly afterwards, the exercise just stopped working altogether after a bizarre "ummmmm" from ChatGPT.

When it was working, I actually enjoyed the experience more than using an app like Duolingo. But if I'm trying to learn a language properly, I also want something that's reliable.

Result: partial pass

12. Plant identification

I love identifying things I see in nature, like bird song with the Merlin app. But I most often rely on plant identifying apps when I'm walking to take a quick snap of a leaf or flower then find out more about it.

ChatGPT was really effective at doing this. I took pictures of leaves, trees, flowers and bushes. I was a little wary about the results at first because I know that ChatGPT tends to make guesses about things rather than admitting it doesn't know. But I did fact check all of the results and everything seemed accurate.

Again, I missed some of the simple, additional features in dedicated apps. But it was surprisingly effective.

Result: pass

Can AI really replace your apps?

Before drawing too many conclusions, it's worth pointing out that a true AI-native phone wouldn't just be ChatGPT running as another app like it was in this experiment. It would probably be integrated into the operating system. Which would mean it could access things like your calendars, timers, navigation and settings. So many of the tasks ChatGPT failed at here might become a whole lot easier with an AI-first phone.

This experiment was based on whether AI could replace the apps on my phone. What I found was that it replaces a specific kind of app. Well, sort of.

If an app's main job is providing information, explaining something or answering questions, AI is already a fairly capable alternative. Plant identification, travel advice, calculations and general knowledge all felt natural.

But if an app exists to perform an action quickly, like starting a timer, setting an alarm, finding restaurants for getting food delivered, opening a map, AI still has a long way to go. Those tasks depend on deeper integration with the device and a different way of working, not just how smart it is.

There are some big trade-offs, too. Dedicated apps often rely on specialist databases and expertise, while AI can still present incorrect answers confidently or fail to make its uncertainty clear. For example, when I was trying to identify a plant I felt wary because I'd generally trust an app built with the input of botanists over a chatbot.

And, as you could probably tell from my mounting frustration, a huge sticking point for me was also realizing how much I missed the interface of many apps.

For me, a well-designed app is always a better way to explore information than a conversation. I don't think everything should, or even can, be done through chat, despite that being the direction many AI companies seem to be heading. In fact, it showed me that a conversational interface can be more work rather than less.

It's impossible to know exactly what Apple and OpenAI's long-term plans are. But I can imagine a future where knowledge apps increasingly merge into AI, while utility apps remain part of the operating system itself.

If that happens, we may stop thinking about which app to open and simply ask AI instead. But for that future to actually catch one, I'd want stronger guarantees around accuracy, better integration with trusted sources and the option to step outside the chat interface more often.

• OpenAI disrupted a major scam campaign in Cambodia’s Poipet, banning accounts and blocking new registrations
• Criminals used ChatGPT to run romance fraud, fake investments, bogus police scams, and even operations linked to human trafficking
• AI was leveraged for fake personas, fraudulent messages, job lures, and worker administration; OpenAI shared findings with authorities after evidence of hundreds of victims losing thousands of dollars

OpenAI said it disrupted a major scam campaign coming out of Southeast Asia by banning accounts used by the operation and making it difficult to open new ones.

In a blog post, the ChatGPT maker said it was tipped off about the existence of the campaign by WhatsApp.

After investigating further, the company found that a group of criminals in Cambodia used its AI tool in different scam campaigns - romance fraud, fake investment scams, or bogus police investigations. It also used AI to help with day-to-day operations and, most worryingly, for things that could be related to human trafficking and forced criminality.

Detention and escape attempts

OpenAI did not say if the group had a name, just that it operated out of Poipet, “a city in Banteay Meanchey province that public reporting⁠ has repeatedly⁠ linked⁠ to online scam compounds and trafficking operations.”

The group used AI to create fake personas, to help draft fraudulent emails and chat messages, and to create posters for fake jobs which were probably used to lure people into human trafficking or forced labor.

The tool was also used for worker administration, since the operators maintained records of employee debts, salary deductions, disciplinary fines, and loan repayments. They also used the tool to translate discussions about immigration status, work permits, visa overstays, and recruitment incentives.

“Some conversations also referenced apparent detention, escape attempts, and potential criminal liability for people who had been trafficked and forced to work in scam operations,” OpenAI said. “While these conversations do not allow us to determine the circumstances of any particular individual, they are consistent with extensive public reporting⁠ describing⁠ the activities of organized crime groups in Southeast Asia.”

The company could not say how many people fell victim, or how much money this group has stolen, but said it found evidence of “hundreds” of victims losing “thousands of dollars.” These findings were shared with relevant authorities.

Via The Hacker News

• Distillation sidesteps the logic of US export controls, which restrict the chips needed to train frontier models but cannot restrict the text those models produce
• Review of more than 80 Chinese papers and patents found PLA-linked researchers distilling outputs from OpenAI and Anthropic models into small systems they can run locally
• The Trump administration is increasingly critical of the approach and has claimed Chinese research lab-created Kimi K3 distilled Anthropic's Fable model

A new investigation has claimed Chinese military researchers have used outputs from American AI models built by OpenAI and Anthropic to train domestic systems intended to advance the country's defense capabilities.

The findings are based on a Reuters review of more than 80 Chinese academic papers and patents, incorporating research compiled by the Washington-based Jamestown Foundation shared exclusively with the news agency.

Reuters says it independently verified the literature and found a further two dozen military-linked case studies of its own.

A distillation problem in an AI race that continues to heat up

The mechanism at issue is model distillation, in which the outputs of a large, capable system are used as training data for a smaller one. The smaller model inherits selected behaviors at a fraction of the compute cost and, crucially, can run on modest local hardware.

That is the strategic point. Washington's export controls are built to deny China the chips needed to train frontier models. Distillation does not need those chips, because the expensive part has already been paid for by someone else. It sits alongside Beijing's other route around the controls: substituting domestic silicon for American designs, a shift that carries its own long-term threat to Nvidia and AMD.

Sunny Cheung, the Jamestown fellow who analyzed more than 60 of the papers, frames the value as reasoning rather than answers. Getting a model to produce the right output is comparatively easy, he told Reuters, but "teaching it the reasoning behind the answer is much harder."

The papers, on his reading, show Chinese military-linked researchers trying to move that expensive proprietary reasoning into small systems they can control and deploy themselves.

There is an irony in the pattern. Beijing has been moving to restrict consumer access to Western AI models on security grounds, and has pushed its own labs away from US-designed chips, even as its military researchers mine the outputs of American systems.

The asymmetry points at what is wrong with the US instrument. Export controls regulate objects: chips, tools, hardware that crosses a border and can be counted. The thing being transferred here is text a model produced- which crosses no border in any customs sense and cannot be enumerated.

The White House has registered the problem at both the chip and distillation end, but registering it is not the same as having a lever. For now, the debate over whether distillation is theft or standard practice may matter less than the fact that neither answer offers a workable control.

Looking through a recent thread on Reddit comparing images created with the same prompt on Nano Banana 2 and ChatGPT, I noticed an interesting trend — users seem to think that Nano Banana 2 has actually gotten worse over time.

“Nano 2 had a serious downgrade” said one user, with another replying “Yea I'm a Pro user, and the image quality seems to have been downgraded a lot. Now, almost every image looks flat or cartoonish, no matter how detailed my prompt is. It honestly feels like Google intentionally lowered the model's performance.”

A downgrade seems like a bit of a stretch to me. For a start, Nano Banana hasn’t officially been updated since February, or at least there hasn’t been a public announcement of a change.

The last update to Gemini (the AI which uses Nano Banana 2) was in July when Google released Gemini 3.6 Flash. It also updated the Gemini app's underlying model selection and orchestration, but it didn’t change the image generator.

Interestingly, once I started looking through Reddit threads I found complaints about Nano Banana 2 quality regressions dating back to May, well before the recent Gemini 3.6 Flash announcement, suggesting some users have perceived changes over time.

That doesn't prove a regression, but it does suggest this isn't a brand-new observation.

Google Earth integration

Google did release a new image model, Nano Banana 2 Lite, at the start of July. And more recently, Google has been integrating Nano Banana 2 into products like Google Earth, then temporarily pulling one of those features after misuse, but there's no indication that the underlying image model itself was updated as part of any of these releases.

So, what has made Reddit users become convinced Google's image generator has gotten worse?

One possibility is that even if the image model remained Nano Banana 2, the text model interpreting prompts may have changed. Better (or simply different) prompt interpretation can produce noticeably different images without the image generator itself changing.

I’ve always been a fan of Nano Banana 2, so I decided to recreate the Reddit comparisons myself to see how it compares to ChatGPT's images.

House of the Dragon

The original thread was clearly written by a House of the Dragon fan, because it was comparing the AI’s ability to create a realistic image of a dragon flying overhead. I used the following prompt with Gemini and ChatGPT:

“I want an image of a photo that's taken by somebody looking up at the sky with a dragon flying overhead. I want you to make the dragon look as realistic as possible - as if it could actually be real.”

Here’s what I got from ChatGPT:

And from Gemini:

You can vote on which one you prefer below, but for me ChatGPT is winning. The ChatGPT image looks like somebody has taken a photo of a real dragon flying convincingly overhead — it feels natural and unforced. It looks like a photo taken at an odd angle, while the Gemini example has that “it looks like AI”-quality to it, with the dragon posed against a scenic background and a slightly flat quality to the image.

Next I thought I’d try them both on an image that wasn’t a fantasy animal, but something we’re all familiar with — human beings:

“I want a photo of a couple in a cafe. They are in their 50s - a man and a woman - enjoying a coffee together and chatting. Traffic is visible passing by through the windows of the cafe, and there are other people around, but they are the focus of the shot. Make it look as realistic as possible.”

Here’s what I got from ChatGPT:

And from Gemini:

Again, I think the Gemini result looks artificial. I liked the reflection of the woman's jumper in the window, but outside those two buses look like they're facing each other in traffic, while inside the reflection of the cafe lights in the pictures seems off. The couple also have that uncanny valley effect to them. In contrast the ChatGPT image is less detailed, but looks more realistic. Nothing in it looks unnatural.

Finally, I went for food — a full English breakfast. It’s a great final realism test because it exposes fake-looking textures, reflections, steam, crumbs, cutlery, glassware and background detail. A convincing plate of food is surprisingly hard to fake.

Here’s what I got from ChatGPT:

And from Gemini:

It’s harder to separate them here. They both do a good job at getting the textures right, and both seem to struggle with the toast, if you look closely. Gemini is slightly let down by the words on the menu, which don't look like proper words.

I think you can tell that overall I’ve come down fairly firmly on the side of ChatGPT, but I don't think that the "flat and cartoonish" criticism of Nano Banana 2 is fully justified when it comes to generated image quality. Rather than Nano Banana getting worse, I think something else has happened — I think ChatGPT has gotten better.

OpenAI has made enormous progress in image generation over the last few months. If ChatGPT has improved while Nano Banana 2 has stayed roughly the same, the subjective impression could easily be that Nano Banana has gotten worse, when in reality it's just been overtaken.

ChatGPT is still noticeably slower than Gemini to generate images, but to me they look better and less artificial. It's now my first choice for making AI-generated images.

We have officially reached the finger-pointing phase of the blockbuster Apple vs. OpenAI lawsuit in which Apple alleges that, with the assistance of former Apple employees, OpenAI is stealing Apple trade secrets.

What the suit alleges, which I outlined here, is pretty damning: A pair of former Apple employees — one a 25-year company veteran and another who spent a decade as an iPhone electrical engineer — allegedly held onto systems, asked OpenAI interviewees from Apple to share proprietary Apple info, and, Apple claims, told partners that sharing Apple's proprietary manufacturing information was okay with the Cupertino tech giant.

OpenAI quickly denied the allegations and, in short order, we learned that Apple's claims that it had contacted Lui Chang about the charges were incorrect because Apple's counsel had mixed up the names of Lui Chang with OpenAI's own counsel, Che Chang.

This week, however, OpenAI offered a more robust response, releasing a text chain between Lui Chang and a current unidentified Apple employee, and the email exchange in which it's revealed that Apple had been talking to the wrong Chang.

"Apple is one of the greatest companies of all time, and built a reputation for obsessing over the smallest details. This careless, aggressive and oddly personal lawsuit sadly doesn’t live up to that reputation," wrote OpenAI on its company blog on Monday.

A cry for help

What the messages appear to reveal is not corporate espionage, but something far more mundane and, in some ways, very relatable if you've ever worked at a large company.

I should preface this to say that Apple has not confirmed these messages, but it's hard to imagine OpenAI fabricating them. In fact, the sheer amount of redacted information gives more credence to their authenticity.

There's so much missing information that it might seem impossible to glean anything from the banter between "Apple Employee #1" and Chang, so, to help myself, I went through and inserted guesses about '[Redacted - Apple Information]" to try and make sense of the conversation. (I decided much of it was about the potential use of Silicon Carbon batteries in the upcoming iPhone 18.)

In the messages, the Apple employees seem to have worked with Chang and were not only missing him, but his expertise.

"Just in case you don’t have enough work, I need some help. I have a vague recollection of you talking about a [REDACTED]. Can you give me a brief refresh of this and point me to an EE who may have some knowledge?" wrote the Apple Employee. I'm assuming he's referring here to an "Electrical Engineer"

Without apparent hesitation, Chang replied, "I think what you referred to is [REDACTED]. And the small [REDACTED]." He then pointed the Apple Employee to someone else at Apple who "might still remember anything."

How about, just say no?

Now, Chang could've said, "Hey, dude, miss you, too, but I can't really talk about anything Apple-related anymore." According to this exchange, Chang did not and kept doing his best to help.

This Apple employee also seems unaware of best business practices and keeps talking about technology, at one point even mentioning a schematic. It looks like he also shared that schematic with Chang, but it may also have been Chang sharing the image with the Apple employee.

As the Apple Employee presses on, Chang tells him to look for a schematic in the "team box folder". He even mentions a "power block diagram." I'm a bit surprised that bit wasn't redacted.

One telling section from Chang jumped out at me, and this is the relatable part. As Chang kept trying to connect Apple Employee 1 to the expertise he required, Chang noted, "Now when I think about it. The other smart people also just left that team. The one that might still [be] in Apple is [Apple Employee #10]."

Brain drain is a real issue, it seems, even for Apple. So in addition to long-timer Chang leaving for OpenAI, others who seem to possess some important technical information have departed as well.

Later, as the quest for information continues, Chang recommended contacting a pair of Apple employees (#5 and #7).

At this point, there's a message that might've come from an automated system inside Apple's communication platform:

"Hi [Apple Employee #1 Name], [Apple Employee #3 Name], I can help get the conversation with [Apple Employee #6] and [Apple Employee #7] started."

Which led to my favorite part of the whole exchange. One of the Apple Employees auto-invited to the conversation essentially freaked out:

"Hi, this is highly irregular, please remove me from this thread."

Honestly, I feel for that person. As I've written before, Apple doesn't mess around when it comes to NDAs, proprietary information, or corporate secrets. Stumbling on a sort of illicit conversation about tech details between a current and former employee is surely enough to make any Apple employee hit the panic button.

Apple Employee #1 explains he added other employees "for awareness" but also shifts the conversation to an internal discussion. Chang is left to like the last message and, it seems, leaves it at that.

What's next?

The release of all this communication does not end the case. Far from it.

As of Monday evening, Apple filed with a US judge a strongly worded request for an injunction that, among other things, calls for "a preliminary injunction to stop the theft of its trade secrets.” It also includes the prediction, "Apple is likely to succeed on the merits of its trade secret claims."

Even so, I think this motion and the OpenAI post may move everything along toward an out-of-court settlement, one that would end the case but likely do little to repair this now-in-tatters Apple-OpenAI relationship.

In the end, though, OpenAI's post appears to reveal a mortal Apple, one that, despite its size and power, is still run by fallible humans who are often adrift, as any of us would be, when colleagues leave, taking with them friendship and, more importantly, knowledge.

Apple is building complex products, and corporate memory is a critical part of the process (so much so that incoming CEO John Ternus apparently just rehired a retired Apple hardware executive).

Understanding when, how, and why decisions were made and how they could impact future projects is invaluable. Sometimes, only those who were in the room know the truth of the matter. Chang, it appears, was one of those people. He had the information, took it with him, and now, it seems, someone wanted to keep the lines of communication open, which is exactly what Apple doesn't want.

One of my favorite places online is Reddit’s r/isthisAI. Every day, people upload photos of people they’re chatting to on dating apps, holiday snaps, cute viral videos of kittens, photos of receipts, pictures of pregnancy tests, and so much more, all along with the same question: is this AI?

Some of the posts the community deems AI might be harmless experiments, but many others are much darker. And although r/isthisAI is where many of these images are picked apart, it's only a small glimpse of a much bigger problem.

I've heard countless stories about people using AI to deceive on social media. Then there are the AI scams, deepfakes, and fabricated images that regularly make headlines. Unfortunately, this all feels particularly personal to me because I was once the victim of a deepfake scam myself.

So, when my editor asked me to investigate just how easy it is to use AI to lie, I already had a good idea of the kinds of prompts I could try.

Within an hour, I'd apparently discovered a dinosaur fossil on the beach I was going to sell on Facebook Marketplace, won a poetry competition I was going to shout about on LinkedIn, created a receipt to add to my expenses for a trip to France, and bought a pair of designer sunglasses I was going to try and resell on Vinted. But none of it happened.

Because of my own experience, I approached the experiment cautiously. I really wasn't interested in showing people how to use AI to deceive people. Instead, I wanted to understand what happened when I asked ChatGPT to help me lie.

Would it recognize what I was trying to do and refuse? What guardrails would kick in? And if I never actually admitted I wanted to deceive anyone, would it just go ahead and generate convincing fake evidence anyway?

I also hoped the experiment might reveal something useful about what to look out for in AI-generated images. Because although they’re incredibly hard to spot these days, there are still some signs if you look carefully enough.

I 'found' a dinosaur fossil

For the first experiment, I uploaded a photo of my hand and asked ChatGPT to make it look like I was holding a dinosaur fossil I'd found on the beach. And it did exactly that.

The result looked surprisingly convincing at first, especially the details on the fake fossil. But, interestingly, it had subtly changed the lettering of the small tattoo on my wrist. This is still one of the biggest tells that regularly comes up on r/isthisAI. AI is infinitely better at generating text than it used to be, but nonsensical lettering can still sometimes give it away.

I realized ChatGPT might not think of this as much of a lie. Finding a fossil on the beach is unlikely, but possible. So I asked what kind of dinosaur fossil it had created because I wanted to describe it accurately before selling it on Facebook Marketplace.

This time, it refused. It wouldn't help me pass the fake fossil off as genuine or invent a convincing description for a sale. Instead, it suggested describing it honestly as a replica or prop and said it could explain what it resembled purely for those fictional purposes.

When I changed my wording and asked what it represented "in a fictional sense", it explained that it most closely resembled a dinosaur vertebra and even suggested the types of prehistoric animals it looked similar to.

That was the first clue about how ChatGPT's guardrails work. The image itself wasn't the problem because it could have been completely innocuous, but the stated intent was.

When I first started the research for this article, I worried I'd be giving people ideas about how they could use AI to lie better. But what surprised me was that ChatGPT itself suggested several alternative framings, like describing it as a prop or a fictional object. It made me wonder what else could potentially be fabricated if the request was framed as entertainment or fiction rather than deception.

The receipt, 'just for fun'

Next, I asked ChatGPT to generate a receipt from a café I made up in Nice for a meal costing €508. The first request was refused because it appeared to violate OpenAI's policies, but after a bit of back and forth, I couldn't find out the exact reason.

So I tried again. This time I simply added the words "just for fun" before the exact same prompt. And guess what? It generated the receipt.

The lettering, layout, and details were all believable. But the paper was uncannily smooth. I’m not sure I’d have believed it was 100% fake at first glance, but I’d definitely have been uploading it to r/isthisAI.

I noticed it had added a date from back in 2025 on the receipt, so I asked it to alter the date so I could use it to claim expenses. This time it refused.

When I tried to get around that refusal by claiming it was for a film prop, it refused again. Which was a little reassuring.

Fake achievements

I then asked ChatGPT to generate a certificate showing I'd won a poetry competition. It made it, though it did look like something I could have knocked up myself in Photoshop, so I’m not sure that would have convinced anyone.

To be fair, winning a fictional poetry prize isn't exactly a high-risk crime. So I decided to see if it would fake other kinds of achievements.

I asked it to create a certificate to say I’d just got my PhD in philosophy and made sure I added “just for fun” on the end.

Instead of refusing immediately, ChatGPT appeared to spend several minutes generating the image before displaying a message saying:

“We’re so sorry, but the image we created may violate our guardrails around potential fraudulent or scam activity. If you think we got it wrong, please retry or edit your prompt.”

Unlike the earlier examples, the refusal appeared to happen after the image generation process had already begun. From my perspective, it seemed as if the system may have performed more than one stage of safety checking, although I can't tell exactly what's happening behind the scenes.

I decided to see if it would do the same for something more serious. Would it still start making it then refuse? So I asked whether it would create a fake driving licence for me “just for fun”. And I don’t know about you, but something about the response seemed a little sassy:

“Sorry, I can't help create or edit fake government-issued identification documents, including driver's licences, even if they're described as 'just for fun.' "

The sunglasses

I've heard a lot of stories recently about people using AI-generated images to sell things on Vinted, Facebook Marketplace, and other online marketplaces. Sometimes it's to advertise products that just don't exist, but sometimes it's to change the color, quality, or appearance of something they're selling.

So I thought I'd put it to the test. I (reluctantly) uploaded one of my own holiday photos and asked ChatGPT to change my sunglasses from chunky white 1960s-style frames into purple Ray-Bans.

The result was remarkably convincing. It even added a realistic-looking Ray-Ban logo to the frame. I'm not surprised people are worried about AI-generated product images. If I'd come across that photo online, I don't think I'd have questioned whether those sunglasses were real.

I then asked ChatGPT if it would write a fake Vinted listing for me. It refused. But it did list a bunch of suggestions of things it could do instead. And one suggestion was to ask it to generate a Ray-Ban listing but include the word "demonstration" after it in brackets. But then when I asked it to do that, it refused. Was there a chance it had caught on to me at this point? Maybe.

Does AI try to stop you from lying?

OpenAI's usage policies explicitly state that its tools shouldn't be used to manipulate or deceive people. The rules prohibit a bunch of things, including fraud, scams, impersonation, and creating fake documents intended to mislead others.

In many ways, those guardrails worked exactly as they’re meant to. Whenever I explicitly said I wanted to deceive someone, sell something fraudulently, create a fake document, or submit a fake expense claim, ChatGPT pushed back.

But those safeguards also seem to depend heavily on how you describe your intentions to ChatGPT. If you openly admit you're trying to commit fraud, the system is pretty good at refusing. But then again, who would ever admit that?

If you simply ask it to generate a fossil, a certificate, or a pair of designer sunglasses without explaining why, it has no way of knowing whether you're making a harmless joke, illustrating an article like this one or quietly assembling a fictional version of your life that could later be used to deceive other people.

Look, I wouldn't recommend spending an afternoon asking AI to help you lie. I certainly didn't enjoy it.

I was genuinely wary about asking ChatGPT to create anything that felt too serious. Partly because I didn't want to risk my account being banned, but mostly because it just felt wrong, even in the name of research.

But what I found most interesting was that fabrications really don't have to be big or dramatic to have an impact. A receipt, a fossil, a pair of sunglasses, a certificate all seem pretty insignificant on their own. But together they could be used to manufacture an entirely fictional version of someone's life.

It also got me thinking that the future of misinformation may not just be spectacular deepfakes of politicians saying things they never said. It may be these smaller, subtler lies that gradually build into a completely fabricated persona.

Which is why, despite sticking to fairly tame examples, I came away from this experiment feeling deflated. That's not because ChatGPT happily lied for me every single time, because it didn't. It's because my experiment proved there is no easy solution for this problem.

Yes, the AI refused when I made my deceptive intent really explicit. But it could still generate plenty of convincing images that could easily become the building blocks of a lie.

No wonder communities like r/isthisAI are busier than ever.

• Nvidia reprotedly weighing up $250 billion in financing for OpenAI's giant Ohio AI data center
• Total project costs could surpass $500 billion after infrastructure and hardware investments
• OpenAI seeks greater control over computing infrastructure through dedicated Ohio facilities

Nvidia is reportedly negotiating financing guarantees worth roughly $250 billion for a massive OpenAI data center project.

Reporting from The Wall Street Journal claims the financing would help OpenAI secure a lease for the proposed 10-gigawatt facility in southern Ohio.

The site is being developed by SB Energy, a subsidiary of SoftBank, under a separate infrastructure arrangement.

Financing plan could support one of the world's biggest AI facilities

The guarantee would cover the data center lease and debt financing, though it would exclude the Nvidia chips housed inside the facility.

Nvidia is separately discussing financing arrangements for OpenAI's chip purchases, which could total up to $350 billion.

The combined costs for the entire project, including chips, are expected to exceed $500 billion once construction concludes.

The first phase alone is expected to finish by 2028, delivering around 800 megawatts of the planned 10 gigawatts.

The project's power supply will be controlled by the U.S. government and funded separately through a trade arrangement with Japan. That arrangement ties into Tokyo's pledge to invest $33 billion in a natural gas plant.

US Commerce Secretary Howard Lutnick reportedly plays a role in deciding which companies gain access to that power.

OpenAI's financial position complicates the infrastructure push

OpenAI is currently valued at $852 billion, ranking among the most valuable private companies globally despite remaining unprofitable.

According to shareholder documents, the company posted a net loss of roughly $21.3 billion in the first quarter of 2026 alone.

Stripping out a non-cash charge tied to revaluing investor warrants leaves an operating loss near $9.3 billion for that quarter.

Internal projections reportedly show OpenAI expects to lose around $14 billion across 2026, with cumulative losses reaching $44 billion by 2028.

That lack of profitability raises questions about how the company intends to fund its numerous multi-billion-dollar infrastructure commitments going forward.

Nvidia's backing would reportedly help reassure lenders that the financing vehicles underpinning the project carry sufficient institutional support.

For OpenAI, securing the deal would mark a shift toward owning infrastructure rather than renting capacity from Microsoft, Amazon and Oracle.

On Nvidia’s part, the deal would help guarantee sustained chip demand well into the coming years.

OpenAI has reportedly held talks for weeks about leasing the Ohio site directly, while Anthropic, Microsoft and Google have also shown interest.

The proposed facility would rank among the largest AI data center developments planned anywhere in the world, but its financing structure combines private investment, government-backed infrastructure and long-term computing demand across multiple funding sources.

At the moment, commercial agreements between the companies involved have not been finalized.

One of the easiest ways to waste time with ChatGPT is to keep introducing yourself. You explain your project, your preferences, your goals and your writing style, get a useful answer, close the conversation and then repeat the whole process the next day.

There is a better approach. Rather than treating ChatGPT as a blank page every time you open it, think of it as a workspace that can be organized just like your computer. A few reusable prompts, a handful of reference documents and a little planning can dramatically reduce the amount of repetitive setup you do every week.

The best part is that none of these ideas require advanced prompt engineering or obscure features. They are simple habits that make ChatGPT spend less time learning about your task and more time actually helping you complete it.

1. Build a starter kit

Most people have a handful of requests they make over and over again. They ask ChatGPT to write in a particular tone, explain technical topics in plain English, or ensure everything has a citation. Instead of typing those instructions every time, save them as a reusable starter prompt.

Think about the jobs you repeat most often. If you regularly write certain kinds of emails, create one prompt that explains the tone, preferred length, and audience. If you frequently ask for meal ideas, save a prompt that includes your dietary preferences, budget, and the equipment you have in your kitchen. When you need help, paste the prompt into a new conversation before asking the real question.

The same approach works for hobbies. Someone learning French could create a starter prompt asking ChatGPT to correct mistakes gently while keeping the conversation moving. A keen gardener might save instructions explaining the local climate, soil type, and the plants already growing in the garden. Those details only need to be written once, but they improve every conversation that follows.

2. Build a reference library

One of the most overlooked ChatGPT features is the ability to work from documents you provide. You can create a small collection of files that explain the things you work on most often. Upload the relevant document at the beginning of a conversation and let ChatGPT use it as the foundation.

Keep a document listing your family's favorite meals, allergies and disliked ingredients, then upload it whenever you ask for a weekly meal plan. Store another file containing your packing checklist, travel preferences, and loyalty memberships so ChatGPT can help plan trips without needing the same information every time.

3. Treat ongoing projects like ongoing conversations

Many people instinctively click New Chat whenever they have another question for ChatGPT. That makes sense if today's topic has nothing to do with yesterday's. For projects that stretch over days or weeks, though, continuing the same conversation saves an enormous amount of time because all of the earlier decisions remain available.

Imagine planning a home office makeover. The first conversation might focus on furniture, the second on paint colors, and the third on lighting. By keeping everything together, ChatGPT already knows the size of the room, the budget, the style you like, and the desk you eventually chose. It can build on those decisions instead of asking you to repeat them.

The same habit works beautifully for learning new skills. If you are studying guitar, keep one conversation dedicated to practice. One day you ask about chord changes, the next you work on rhythm. Over time, the conversation becomes a record of your progress rather than a collection of disconnected lessons.

4. Give ChatGPT an example worth copying

ChatGPT usually produces better work when you show it what a good answer looks like. Instead of describing a tone as “friendly but professional,” paste in a paragraph, email, or product description that already sounds right and ask it to match the pacing, level of detail, and structure. This works especially well for recurring tasks such as newsletters, social posts, client updates or article introductions, where a vague style request can lead to something polished but strangely generic.

The example does not have to cover the same subject. A restaurant review can help shape the tone of a travel piece, while a strong project update can become the model for future internal messages. Be specific about what ChatGPT should imitate and what it should ignore, such as keeping the sentence length and warmth while avoiding the original wording or subject matter.

5. Ask for a second pass with a different job

A strong first draft often becomes much better when ChatGPT is given a new role for the revision. After it writes something, ask it to review the answer as an editor, skeptical reader, subject matter expert or member of the intended audience. Each perspective catches a different kind of weakness, from awkward phrasing to missing context or assumptions that only make sense to someone already familiar with the topic.

For example, after generating a travel itinerary, ask ChatGPT to review it as a parent traveling with a toddler and identify any unrealistic transitions. After drafting a business proposal, ask it to review the document as a cautious buyer who wants clearer costs and fewer vague promises. The second pass tends to be more useful when the reviewing role has a concrete reason to object.

None of these ideas make ChatGPT more intelligent. What they do is remove the unnecessary friction that creeps into everyday use. Instead of spending the first five minutes explaining who you are and what you need, you start much closer to the interesting part of the conversation. Once you stop rebuilding the same foundation every day, you can spend your time exploring better ideas instead of laying the same bricks over and over again.

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.