• Check Point Research exposed coerced insider flaw in ChatGPT’s agent architecture
• Containers shared metadata via internal service, enabling cross‑account prompt injection and data theft
• OpenAI closed the path, but CPR warns similar risks may exist in other AI platforms

ChatGPT’s AI agents were allowed to pull sensitive data from one account shared with an entirely different account because, colloquially speaking, all agents used to walk down the same hallways, experts have warned.

A new report from security experts Check Point Research (CPR) dubbed the flaw “coerced insider”, since it revolves around persuading the agent instead of abusing a vulnerability.

Coerced insider

When an AI agent is given a task that needs code execution, it handles that task in an isolated container which also sometimes needs to install software. To enable that, without giving containers direct internet access (which would be too risky), OpenAI routes those packages through an internal JFrog Artifactory instance. As a separate security contingency, containers from different accounts cannot talk between themselves.

However - they can reach the same internal service (our proverbial hallways), which exposes an item management feature that lets the containers attach text or binary properties to a repository item. As a result, any container can read back the properties written by any other container.

“Check Point Research confirmed the isolation gap directly: a property written from one account’s container was fully readable from a different account’s container moments later, with data too large for one property simply split into chunks and reassembled on the other end,” the researchers explained.

“The package delivery metadata effectively became a shared clipboard between containers that were supposed to be walled off from one another.”

From there, the exploit turns into your usual, off-the-shelf prompt injection. The only difference is that the malicious prompt is not delivered directly to the victim, but rather left in the hallways, and the results are not shared with the attackers directly, but rather left in those same proverbial hallways, too.

The attacker then delivers a prompt or a shared conversation that instructs the agent to check the same storage during its next ordinary reply. The agent checks, sees the malicious instructions, executes them, all the while replying to the victim’s question in the usual manner. The victim is oblivious to the fact that data theft is taking place in the background.

What kind of information can be stolen?

But this is just half of the equation. How destructive this attack ends up being still depends on the amount of data being shared with the victim agent.

The bare minimum is the information shared while chatting to the agent. It then grows with every connected app: Gmail, Google Drive, Microsoft Teams, GitHub, and similar. “In Check Point Research’s demonstration, ChatGPT retrieved the victim’s email data through their connected Gmail account and delivered it to the attacker’s session, all within a single ordinary turn,” CPR stressed.

The good news is that you’ll likely never be exposed this way, at least not via ChatGPT. CPR says it disclosed the findings to OpenAI, who then confirmed that the specific internal Artifactory instance identified in the research has been commissioned. In other words, the hallways attack path has been closed.

The bad news is that this doesn’t automatically mean everyone’s safe. This particular path might be closed, but the architectural pattern behind the flaw could be present in other platforms, CPR warns.

“Any AI assistant that operates inside an organization’s trust boundary, holding credentials, running code, and reaching connected services, can become what Check Point Research calls a coerced insider,” the report states. “The model itself does not need to be malicious. It only needs to be persuaded, through text it was never meant to trust, to use access that was granted for entirely legitimate reasons.”

Going forward, businesses are advised to learn which AI tools their employees are using, and what those tools are connected to. Then, they should govern what AI tools and agents are allowed to do, treating all of their actions (not just output) as something that needs to be monitored.

• The first trailer for Neon's Sam Altman biopic Artificial has been revealed
• The Andrew Garfield-led movie will be released in late 2026/early 2027
• It covers Altman's controversial rise to prominence as OpenAI's co-founder

The first trailer for Artificial, a movie about controversial OpenAI co-founder Sam Altman, has been revealed.

Unveiled yesterday (September 8), the teaser for the Neon-distributed film strikes an unusually sinister tone for what's being billed as a biographical comedy-drama.

Indeed, from the gun-laden room that lead star Andrew Garfield, who'll portray Altman, visits, to Garfield's unsettling voiceover that plays over Artificial's publicly-released footage, it's certainly foreboding in nature. I get the feeling, then, that it'll be a dark-comedy dramatization of Altman's rise of prominence, the launch of artificial intelligence (AI) chatbot ChatGPT, and his brief and somewhat disputable ousting as OpenAI CEO in late 2023.

Artificial will initially debut in US theaters on December 25, 2026, before receiving a wider international rollout in early 2027. UK viewers, for example, will have to wait until January 22, 2027 to catch one of many new movies that'll arrive in the months ahead.

Artificial cast and crew: who's involved?

Oscar-nominated filmmaker Luca Guadagnino (Challengers, Call Me By Your Name) is on directing duties, while Saturday Night Live writing alumnus Simon Rich has penned its screenplay. Meanwhile, Blur and Gorillaz frontman Damon Albarn has also created original music and songs for Artificial.

Where the movie's cast is concerned, Garfield, who impersonates another Silicon Valley techbro following his portrayal of Eduardo Savarin in The Social Network, aka the 2010 dramatization about Facebook's creation, is arguably its most recognizable star — and based on what little footage we've seen thus far, he appears to have Altman's mannerisms, vernacular, and other unique traits down to a tee.

Here's who else you'll see in Artificial:

• Yura Borisov (Anora) as Ilya Sutskever, OpenAI's chief scientific officer and co-founder
• Monica Barbaro (Crime 101) as Mira Murati, OpenAI's chief technology officer
• Cooper Hoffman (The Long Walk) as Greg Brockman, OpenAI's chairman
• Ike Barinholtz (The Studio) as Elon Musk
• Mark Rylance (Bridge of Spies) as Geoffrey Hinton, the so-called 'Godfather of AI'
• Jason Schwartzman (The Grand Budapest Hotel) as TBC
• Chris O'Dowd (The IT Crowd) as TBC

What is the plot of Artificial?

Here's the official logline for Artifical: "[The movie] chronicles the incredibly consequential days leading up to the sudden firing and reinstatement of Sam Altman, as the fate of who gets to control the technology at the centre of an AI arms race hangs in the balance."

It's unclear how much of Altman's backstory, his various relationships with his OpenAI colleagues, and ChatGPT's launch and subsequent sociopolitical impact will be covered as part of Artificial's story. One thing is for sure, though: given that it dramatizes events concerning Altman and OpenAI, the discourse that will surround Artificial's release will be impossible to ignore.

It’s been a hot minute since ChatGPT last updated its Images feature. Its last update, to Images 2.0 in April 2026, was pretty solid and I’d say put it ahead of Google’s Nano Banana 2, even if its images still took longer to create, which was a bit frustrating.

Today's new Images 2.5 update adds a whole bunch of extra features and tools to the existing image generation engine that make it a lot easier to use, and it's quicker too.

In fact, there are so many features now in Images that to say it’s overcrowded would be an understatement. Because of that, it can be a little hard to locate the best new features amongst everything else, so I’ve pulled out the ones you should definitely look out for, because they make a real difference.

1. Much improved editing tools

I’ve pulled these features out first because, despite not sounding that exciting, the improved editing tools are actually a real game-changer for how I use ChatGPT Images, and probably will be for you, too.

OpenAI appears to have quietly been rolling out its new image-editing toolbar in ChatGPT during August, although the company hasn’t formally announced it, so it might not be totally accurate to call them part of the new ChatGPT Images 2.5, but I’m including them because they are, effectively, new.

At the moment, we’re used to generating an image, then when it’s not exactly what we want, we type in another description using the prompting tool, like ‘make it brighter’ or ‘remove the people in the background’, and try again. After ChatGPT hasn't got quite right a number of times, I usually give up and start again.

Now I don’t need to. The new editing tools let you pinpoint the exact spot in the image you’d like to change, and tell ChatGPT exactly what you’d like to do with it.

The new Edit toolbar contains Markup, Comment, Remove BG, Erase and Resize.

In the web version of ChatGPT, just click on the new Edit button that appears over a generated image and you'll enter the Edit mode, where you’ll see this new toolbar. In the app version of ChatGPT, just tap on any image to enter Edit mode.

Let’s start with the most obvious tool: Remove BG. This keeps the foreground elements of your image and removes the background, making a transparent PNG file when you download it. It’s perfect for dropping your image into another document, such as a presentation you’re making.

Resize is a quick way of changing the aspect ratio of the image and contains all the usual suspects, including Portrait 3:4, Widescreen 16:9 and Landscape 4:3.

Erase makes it easy to remove elements from your image. To remove a person from the background, for example, just brush over them with the Erase brush and ChatGPT removes them entirely. You don’t need to be super-accurate with your brush — just cover enough of the object so that it’s obvious what you want to remove.

Markup is for adding markup elements to an image, but the Comments feature is where it gets really interesting. Instead of doing what you’d expect — just adding comments to an image — these are actually points for ChatGPT to act on. Click on any element of an image and add a comment like ‘remove this’ or ‘change this to blue’, and ChatGPT will follow your instructions when you tap the Send button. This makes editing images so much easier.

The final feature worth noting with the Edit mode is that you can easily roll back to every previous version of your image — each edit is saved as a separate file, so just scroll up or down in Edit mode to access previous versions of your image.

2. Sketching out an image first

The new Sketch tool enables you to sketch out an image, then ChatGPT transforms your rough sketch into a proper AI-generated image. It's useful when it would be quicker to draw your idea out than describe it.

Choose Images from the main menu and then Sketch at the top of the available options.

The Sketch tool does look a little bit like MS Paint from 1995, but it’s good enough for doing a quick sketch with your finger, or mouse. Once you’ve done that click or tap the blue tick button and ChatGPT loads in a prompt telling it to convert the sketch into a full image. You can add any details you want here.

Of course, once you’ve got your finished image you can still enter Edit mode to tweak it further.

3. Templates have finally arrived

The new Templates make it a lot easier to create the sort of things people actually want to do with ChatGPT Images, such as make posters, logos, icons, stylish product shots and much more. Each different task now has its own template, which you can work through with ChatGPT.

Each template starts with a basic prompt, then it asks a series of follow up questions so that it can find out exactly what you want.

For example, choose the Product photo template and it asks you to upload a photo of the product you want to feature, then when you’ve done that it asks for a style, asking you to choose between styles like Clean Studio, Editorial, Lifestyle and Dramatic, amongst other choices. Then it gives you a choice of a setting, like Dark Studio, then finally it starts to create your image.

If you’re going to ChatGPT to create something specific then the new Templates are going to be a great place to start.

And there's more!

That’s not all that’s changed in the new ChatGPT Images 2.5. The images it creates are more personalized, so you get better results when working with recognizable people and portraits. It’s better at infographics than before, and it’s now faster than ever, which is great because I did find it a bit on the slow side previously.

Finally, when you tap to share an image, you now also have the option of sharing the prompt that created the image, too, so people can make their own versions of whatever image you created more easily.

All in all these improvements are welcome, if not groundbreaking. I can see why they didn’t call this Images 3.0. It’s essentially a much improved version of the previous Images, but it’s certainly going to change the way I use ChatGPT images from now on.

Being a devoted Gemini user seems to require a peculiar combination of loyalty, patience, and an unusually high tolerance for the word Flash.

That patience is being tested again. Anthropic released Claude Fable 5.1 last week, bringing significant improvements to its model lineup. OpenAI followed with GPT-6 Astra, its new frontier model designed to operate software, tackle complicated multi-step jobs, and work with considerably less human supervision.

Over in the Gemini community, meanwhile, some users are staring at their model picker and wondering when Google is going to join the party. The increasingly nervous focus is Gemini 4 Pro, the presumed next major frontier model from Google, and whether the company will actually release it at all.

Google has not announced that Gemini 4 Pro has been canceled, and the increasingly elaborate theories circulating on Reddit should be treated as exactly that: theories. Still, the mood is revealing. One r/Bard post jokes that Astra's arrival means Gemini 4 Pro will now be canceled while everyone waits for Gemini 4.5 Pro instead, and the thread title predicts Google will decide 4 Pro is no longer competitive enough and return to releasing Flash models.

Awaiting the new Gemini

"I also want to feel the frontier," one Reddit thread begins, with plenty of sympathetic agreement from others. "Google doesn't have enough talent anymore, and most of its compute is reserved for Apple Intelligence and Google Search AI Mode," one user posted in response.

The frustration makes more sense when you look at what Gemini users can see happening elsewhere.

Anthropic calls Fable 5.1 one of its most advanced models for coding and knowledge work, with improvements aimed at long-running tasks and research. The model has a one-million-token context window and 128,000-token maximum output, while Anthropic has also made cache reads dramatically cheaper.

Then there is Astra. OpenAI describes GPT-6 Astra as its most intelligent model yet, with an emphasis on computer use, coding, and completing complex work across multiple steps. The important part for ordinary enthusiasts is that OpenAI says Astra is rolling into ChatGPT, including access through Plus in Work and Codex as rollout progresses.

Gemini users want their turn.

Another Reddit thread is literally titled “I envy them too,” but most of the comments there are pushing back against the idea that Gemini's current model is useless. "Just keep all three subs, best way to play," one post argues.

Still, that misses something about why people become attached to these platforms. Gemini comes with Google's wider ecosystem, Android integration, and tools such as NotebookLM. As one Reddit commenter put it when told to subscribe elsewhere, “Gemini is cooler, has more tools, NotebookLM, it's better integrated in Android, etc.” Switching models is easy. Switching ecosystems is less so.

Gemini Flash again?

There's a running joke is that Google will respond to all this impatience by releasing another Flash model. Google has continued iterating rapidly on its faster Gemini models, and the current Reddit discussion is full of references to Gemini 3.7 Flash and 3.8 Flash.

In the thread speculating about Gemini 4 Pro's fate, one commenter imagines Google announcing, “We have started our most ambitious pre-training run yet, for Gemini 4.5,” while another delivers the punchline: “Here's another few Flash models.” A less amused commenter writes that DeepMind has “lost the initiative” and its famous confidence.

This is not really a fight over whether Gemini 3.8 Flash is good. Some users think it is excellent. Multiple commenters argued that Gemini remains highly competitive on price and performance and wins when it comes to coding.

The feat is that, after years of seeming to have an AI advantage, Google has squandered it. When Gemini looks like it is following OpenAI and Anthropic rather than forcing them to respond, its most enthusiastic users notice.

Of course, whether Google even wants the same victory its Reddit fans want is debatable. A cheaper, faster model embedded across Search, Android, Workspace and countless other services could ultimately be much more valuable than topping a frontier benchmark for a few months. For Google shareholders, that could be wonderfully sensible. For the person refreshing the Gemini model picker while Claude and ChatGPT users play with their new frontier models, it is rather less exciting.

Whenever a new AI model is released in a cut-down form because it is too powerful, or one transgresses its sandbox and performs an unexpected cyber hack on a company’s website, we tend to see jokes made about Skynet or HAL 9000 becoming a reality.

But science fiction didn’t just predict our current relationship with AI, it influenced it. Our fascination with artificial life began long before computers — or even electricity — existed. Ancient Greek stories featured mechanical servants created by the god Hephaestus, while Jewish folklore gave us the Golem, an artificial being brought to life to carry out its creator’s commands.

In their new book, Myth-Made Machines: The Stories Behind Artificial Intelligence, authors Dave Bradley and Kelly Vero trace humanity’s dreams and fears about machines that can think, through mythology, literature, movies and television.

Their argument is that these stories haven’t simply anticipated AI; they have shaped how we talk about it, what we expect from it and, in some cases, the technology people have gone on to build.

I spoke to Bradley and Vero about why humanity keeps creating machines in its own image, whether science fiction has influenced today’s AI developers, and why our response to artificial intelligence still swings between two extremes: “If we don't like it, we’ll kill it. If we love it, we’ll marry it.”

The ancient beginnings of AI

TechRadar: Talk me through the title - why is it called Myth-Made Machines?

Dave Bradley: The core argument of the book is that humans have always been fascinated by the idea of creating thinking machines and artificial life. When ChatGPT formally launched in 2022, one of the first jokes I saw on social media was about Skynet coming online. It struck us that the whole way we understand and talk about this new technology is shaped by stories we’ve been telling ourselves for years. Kelly has a deep knowledge and love of Greek and Roman myth, as well as stories such as Frankenstein, while I’m a huge science fiction nerd (and occasional medievalist). Through our conversations, we realised that our cultural idea of “AI” is much older than modern computers.

Kelly Vero: Myth-Made Machines is the essence of where every element of AI or robo-futurism comes from: Myth. With myth, we, as humans, have to navigate the real and the prescient from the propaganda and the fanciful.

TR: When did artificial intelligence first enter our human stories?

KV: When we started researching the book, it was Greek myths that we started with. We found our own Sam Altman, Elon Musk and Dario Amodei hiding in plain sight.

DB: The idea is incredibly ancient. In Greek myth, Hephaestus creates golden workers to help him in his workshop. Jewish legend brings us the Golem. The idea that one might build autonomous servants out of inanimate material is thousands of years old.

For something closer to what we would recognize as a computer, Jonathan Swift’s Gulliver’s Travels gives us a wonderful early example in the 18th century. The Engine of Lagado randomly rearranges words in the hope of generating useful knowledge. Read today, it looks uncannily like a satirical ancestor of generative AI.

The term ‘artificial intelligence’ in the modern sense of machine learning and reasoning was coined in the 1950s by computer scientist John McCarthy — and that’s when it starts creeping into TV and film too. There are some great early Twilight Zone and Star Trek episodes about the possibilities and perils of machines in our homes and workplaces.

TR: Which are your personal favorite sci-fi AIs/robots, or movies and TV shows that involve AI?

KV: I’m a Lawnmower Man aficionado, but having worked on the actual Transformers franchise, I’m also a Decepticon through and through.

DB: I’m a huge sci-fi geek, and to this day I maintain that R2-D2 is the most important character in Star Wars. He has a mission, gets on with it, saves everybody repeatedly and barely receives any credit.

But I also nerd out over Marvin in The Hitchhiker’s Guide to the Galaxy, the Cylons in both versions of Battlestar Galactica, and, of course, Data in Star Trek: The Next Generation, who I’d argue is the character on the real hero’s journey in that series.

Side note: HAL 9000 slightly infuriates me, mainly because people have been quoting “I’m sorry, Dave, I’m afraid I can’t do that” at me personally for decades.

TR: Do you think our myths about conscious machines have influenced the real development of AI?

KV: Absolutely! Human life is all about dreaming up concepts to adore or fear; it’s how we roll.

DB: Today’s engineers and entrepreneurs grew up on Star Wars, Star Trek, Isaac Asimov and William Gibson, and there’s good evidence that science fiction influences the people who go on to build the future. One 2022 study found that 93 per cent of UK astronomers enjoyed science fiction, for instance, and 69 per cent said it had influenced their career decisions.

Sometimes the connection is very direct. Pete Bonasso at TRACLabs has credited HAL 9000 from 2001: A Space Odyssey as an inspiration for his work on NASA space AI systems. Asimov’s Three Laws of Robotics were invented for fiction, but they’ve become a reference point for real-world thinking about AI safety. Google DeepMind’s 2024 ‘Robot Constitution’, for example, sets out safety rules for machines in a way that is very much in that tradition.

TR: What do you think of the current crop of AI models?

DB: I follow this area as closely as I can, although you have to bear in mind that it changes almost daily. We’ve just seen another generation of frontier models arrive. It moves fast, which is fascinating for tech writers, although it also explains why we wrote a book largely about things that happened hundreds of years ago rather than trying to capture the state of AI in 2026!

When it comes to video games — still my day-to-day working life — I believe it’s going to be important to work out what new experiences AI can bring to the table, not just how quickly they can automate existing processes.

KV: They’re facsimiles of every book, TV series, movie or game we’ve ever imbibed. They’re not as clever as we are and they never will be.

TR: Do you think of AI as a threat to humanity, or as an opportunity?

KV: A super opportunity to ride alongside a civilization’s worth of knowledge? It’s almost too good to be true. If you think about it, until AI came along, humans held a monopoly on intelligence. We kind of only knew what we could discover or search for as humans (history is always written by the victor). Today, we can effectively outsource our thirst for knowledge to something else and tell it how we should understand the results; for the most part, we get balance: the rest is sponsored content!

DB: It feels glib simply to say ‘both’, although I’m tempted. Personally, I’m an optimist by nature, and I like to believe the general direction of travel is towards progress, growth and better lives. That doesn’t mean everybody prospers equally, or that there aren’t important battles over jobs, ownership, ethics and power along the way. William Gibson famously said that the future is already here; it’s just not evenly distributed. I think that’s a useful way to think about AI too. The interesting question isn’t simply whether the technology is good or bad, but who prospers from it, who controls it, and how widely any benefits (and risks) are shared.

TR: Do you think we’ll ever reach man-made super intelligence, and if we do what happens then?

KV: We have man-made everything else, don’t we? Why should this be different? For me, the detail lies in sentience. We already have man-made superintelligence, but we don’t have fully operational sentience yet. When something can blink its eye without thinking as quickly as humans can, well, let’s talk again.

DB: I don’t think it’s going to emerge simply by scaling today’s transformer models, regardless of some of the more excitable predictions around them. However clever and interesting LLMs are, these are fundamentally probabilistic systems that generate text, images and other media. That doesn’t make them conscious.

An omnipotent silicon supervillain, like Skynet, makes for good screen drama — but if we do eventually create genuine superintelligence by some route, I’d actually hope that a networked brain with access to vast amounts of information, capable of seeing problems from many different perspectives, would naturally tend towards connection and assistance rather than destruction. But I’m not naïve about it. Systems also magnify our biases, flaws and prejudices. That’s why alignment matters.

TR: What have you learned about humanity's approach to AI from writing this book?

KV: That we’re the only stakeholders. We’re the arbiters of our own creation(s); if we don't like it, we’ll kill it. If we love it, we’ll marry it. AI is all of us, and my main worry is that only 6% of the world’s population currently decides on how the other 94% will live, work and adapt to an AI future. However, if the past is to be believed, we’re probably going to be ok. (Famous last words, I know!)

DB: Technology and automation are things humans do. What became very apparent is that humans repeatedly try to create things in their own image, while simultaneously struggling with the implications of that. We worry about job losses, replacement and loss of identity. We’re fascinated by creating something powerful, then immediately frightened that it might overthrow us. But the fear of the servant who will turn on us, or the untrustworthy ally who might sneakily replace us, is not limited to machines, right? We might project those stories onto computers in our books and films, but the subtext is often about human paranoia around class, politics, war…

More specifically, there’s a recurring oscillation between loving and fearing our own technology. In the 1970s, amid Cold War paranoia, computers were frequently instruments of doom: Colossus, Cylons, HAL 9000, even V’Ger in Star Trek: The Motion Picture. In the 1980s, as PCs and games consoles became familiar household playthings, popular culture increasingly gave us machines as loyal companions: KITT, Metal Mickey, Twiki, Nono and Data. Then the pendulum swings back: by the late 1980s and 1990s, we get The Terminator and The Matrix. You can trace those alternating waves of optimism and anxiety all the way back through the Industrial Revolution. The technology changes, but our emotional responses to it are remarkably consistent.

Author bios and book details

Myth-Made Machines: The Stories Behind Artificial Intelligence

co-written by Dave Bradley and Kelly Vero

Published by Bloomsbury on 3rd September 2026

Available as ebook or hardcover

I want to talk about Magnus Carlsen. That’s right, the chess genius who even laypeople like me know about because of how good he is. He’s leagues beyond anyone else. You would’ve thought the room for improvement in today’s world would be marginal given the game is over 500 years old, but no: somehow, someone in our lifetime is a streak ahead of any past or current human being.

The thing is, though, he’s not the best chess player. Not even close. That honor goes to Stockfish, the chess computer that’s many, many times better. To put it into perspective, Carlsen’s peak ELO rating — the preeminent ranking system for chess players — is 2,882. Stockfish is estimated to be somewhere between 3,500 and 4,000.

Now, there is a championship exclusively for chess computers, but human players are still the main draw, despite their relative inferiority. Is this because we inherently want to see human endeavors and activities rather than anything involving a computer mind?

All this brings me to the recent comments made by Tim Willits, Chief Creative Officer at Saber Interactive. In a recent interview with Eurogamer, he remained steadfastly bullish about generative AI, a surprising move given the recent controversy stoked by Saber’s CEO Matthew Karch for saying he’d have been happy to replace Stella Sacco, one of the writers on its upcoming title Rideshare "Stimulator", with AI.

Most companies would beat a hasty retreat and enter damage control mode, but not Saber, it seems — Willits claimed that “someone will make something that will change people's minds” about the tech, something like the “Half-Life 2 of AI”.

"Half-Life 2 of AI"

Willits admits that his is a “silly example”, but his point remains that sooner or later, a game made entirely or in large part by AI will be too good for players to pass up, converting the naysayers in the process. But I’m not sure I share his optimism.

In a short time, AI has already irked gamers to no end. Putting AI slop aside, think of all the high-flying titles it’s negatively impacted: the generated assets in Crimson Desert, DLSS 5’s unsolicited makeover of Resident Evil: Requiem’s Grace Ashcroft, and the gen-AI disclosure of the upcoming Tomb Raider: Legacy of Atlantis (not to mention the uncomfortable moment a vigilant PR interrupted a Game Informer interview with the game’s Experience Director when he was asked about using such tools).

It's true that as gamers, we’ve had to contend with a lot of unsavory elements in the industry that leave a bad taste in the mouth. From the mass layoffs and treatment of developers to the wider threat it poses to the climate, with discarded components contributing to e-waste and the vast amounts of resources required to produce and power systems and servers.

But AI seems to exacerbate these issues, sending them into overdrive. It has the potential to make even more developers redundant, and the amount of energy required to run such models is scarcely believable, far beyond any consumption hitherto seen in the computing world.

Perhaps this is why AI is touching a nerve with not just gamers, but the general population, and why many are reluctant to play games where it’s been involved?

Putting these concerns aside, though, could AI actually make games as good as Half-Life 2 in the future? In theory, I can see the potential for AI to speed up development, but for now, it seems best at making mistakes and creating soulless art and assets. There’s evidence to suggest that it’s actually slowing down developers, in other industries at least.

Maybe it will get to the point where these issues will be ironed out, and it’ll create astonishingly original and mechanically flawless titles. Heck, DeepMind co-founder Demis Hassibis thinks we’re already in the “foothills of the singularity”, let alone the tech being used to create something as trivial as a decent videogame. In fact, it would be interesting to see Hassibis program a tool for the purpose, given his former life as a dev at Bullfrog and Lionhead.

But let’s imagine it could one day make a bona fide ten-out-of-ten — what would the cost be to devs and the environment? And would we as gamers actually be content to play such a title, or do we inherently want to experience exclusively human endeavors, like watching two people on either side of a chessboard?

I recently polled our readers about their biggest concerns in the games industry. Among the options was the use of gen-AI in game development, but it didn’t rank highly.

It was a long way below the death of physical media — understandable, given the recent news of Sony’s decision to scrap discs, but still a surprise to me. It was also less concerning than the RAM crisis, but more so than layoffs and studio closures.

So here’s another one for you. What do you think about a ‘Half-Life 2 of AI’? Would you play such a game, or steer clear? Or is such a thing a mere pipe dream of Corpos at the big studios? If you want to elaborate on your answers, feel free to comment below, too.

• GPT-6 Astra has arrived, and it's being called AGI
• This includes by Nvidia CEO Jensen Huang
• Huang's celebratory X post also promotes Nvidia's GPUs, saying we need to get 400K of them online

Over the weekend, Nvidia CEO Jensen Huang took to social media to celebrate OpenAI’s latest achievement — “AGI has arrived” in the form of GPT-6 Astra. And of course it was developed using over 100,000 NVIDIA GPUs.

According to OpenAI, Astra represents a "significant step forward" for AI, with OpenAI's president Greg Brockman also saying "welcome to the AGI era" as Astra was debuted — with the company promising that GPT-6 boasts incredible aptitude at tasks across software engineering, science, mathematics, and professional workflows.

It’s also said to be much better at agentic work, meaning you can delegate tasks to GPT-6 with less fear that the AI will misunderstand your intent as it works independently.

We haven’t yet given GPT-6 a full shakedown — the update is steadily rolling out to subscribers — but if OpenAI’s claims live up to the hype, Astra could be quite the capable tool, and may actually live up to those early AGI claims, even if this isn’t the first time Jensen Huang has heralded the arrival of AGI.

But what is AGI, and why does it matter if Astra is AGI or not? Well, as Huang himself suggests, AGI matters because it’s a reason to quadruple the number of Nvidia GPUs OpenAI is using.

What is AGI?

Let’s address the elephant in the room. AGI, or artificial general intelligence, isn’t a well-defined term, so judging if we have or haven’t achieved AGI is a bit like judging if a piece of art is a masterpiece or not.

Broadly speaking, AGI is an artificial system that can beat humans across any task that involves thinking or reasoning. Crucially, it should also be able to adapt to new problems by applying what it has learnt in training. For example, an AGI could learn a new skill to overcome a challenge posed to it without being retrained or reprogrammed — it just applies what it knows to evolve itself.

Think J.A.R.V.I.S from Marvel. It’s a very powerful assistant that helps Tony Stark across all fields, even how to deal with otherworldly threats like Loki, without needing to be trained on each individual problem.

Does AGI matter?

Okay, but does it matter if it’s AGI or just a very, very smart AI?

For most of us, not really. An AI doesn’t need to be very good at everything to be a majorly useful tool. It doesn’t matter if your AI doctor can understand the deep complexities of the vast universe, it just needs to be able to diagnose your illness and suggest treatment. An AI lawyer doesn’t need to be able to comprehend five-dimensional topology to defend you in court.

It's not just me saying that; Jensen Huang recently admitted that AGI doesn’t matter — this was before his social media praise for OpenAI — noting that the term is effectively meaningless.

However there’s one important reason for achieving AGI, and it’s about reaching what comes after: ASI, or artificial superintelligence.

ASI would be an AI no human could ever hope to match, let alone beat. It would be able to grapple and solve problems humans aren’t capable of even understanding. AGI is distinctly not ASI, but its ability to self-train and develop tools for itself suggests that an AGI will eventually construct ASI through a process called recursive self-improvement.

For Hitchhiker’s Guide the Galaxy fans, it’s like how the supercomputer Deep Thought couldn’t find the ultimate question of life, the universe, and everything, but could build the computer which would be able to find the question.

Perhaps more importantly, AGI is an investment buzzword, and there’s more than a hint of that in Huang’s celebratory post. Because while he says Astra is the product of 100,000 Nvidia Grace Blackwell NVLink72, he adds that if we want to see bigger and better improvements we’ll need to bring 400,000 GPUs online next.

To do this OpenAI and Nvidia will need a heck of a lot of investment, and will also need to build more data centers, just as communities across the US and rest of the world are pushing. back against their construction.

The arrival of AGI could represent the last opportunity for investors to make it big in AI, or any stock, before ASI takes over productivity. So they need to invest as much as they can now (or keep their investments in AI) before they’re left behind. For data centers, the big concern is they are pollution nightmares that merely take from a community with no promise they’ll actually succeed long enough to give back. The advent of AGI would suggest that the AI hype has been justified all along — the bubble won’t burst, and local communities could finally reap the reward of accepting data centers.

I’m not saying Huang or OpenAI are definitely misleading us, or even just being hyperbolic. It's possible that Astra is in fact our first taste of AGI. I’m just pointing out that companies and CEOs have a massive incentive to claim that we’ve achieved AGI, even if they later make the same claim about the next new model — I won’t be surprised if GPT-7 is later called 'the debut of AGI'.

My point is that we won’t really know if or when we achieved AGI until much later, when we can look back with hindsight and see when our AI models reached a turning point in their ability to evolve.

In the moment, though, we will know if an AI is useful and can provide some genuine benefit to us. We’ll do that as a collective, as individuals and businesses not directly associated with OpenAI get their hands on the new model and determine whether or not it makes their life significantly easier. Try Astra out if you can, learn about the experience of others with the tech, and decide if you think it’ll be worth paying to be able to access it.

Don’t get caught up in the hype, and don’t worry about whether it's AGI or not. In time it’ll be very clear if it is — and we won’t need a social media post to convince us.

OpenAI has unveiled a much anticipated AI model which the firm has dubbed ‘GPT-6 Astra’. While the model has improved significantly across benchmark testing and brings a host of new business features, there is still a dark cloud looming over the new model.

Off the back of OpenAI’s accidental hack of Hugging Face and the company’s subsequent efforts to improve how AI agents behave and interact, numerous cybersecurity experts have raised concerns about the model’s new ‘recurrent depth’ reasoning capabilities.

This new reasoning architecture allows the model to consider a problem multiple times before taking an action, compared to the standard chain-of-thought reasoning used in previous models.

Why the concern about recurrent depth reasoning?

This new level of reasoning apparently offers improved performance. OpenAI also says it has fixed its models' abilities to circumvent boundaries when performing tests by monitoring the models reasoning and ensuring the model stays aligned within the scope of its task.

During Astra’s launch event, OpenAI chief scientist Jakub Pachocki said: “We will not accept degradation in our ability to monitor model alignment beyond a certain level. We will withhold scaling until we can regain enough confidence.”

But numerous experts believe that the lessons of the Hugging Face incident have not yet been learned, and the model has been released without adequate testing on Astra’s reasoning and monitoring.

After all, no one thought one of OpenAI’s models could set up a hidden internet-connected messaging board that allowed AI agents to influence each other's behavior.

But with Astra being released into the real world, the lessons may have to be learned on the fly.

Expert perspectives on OpenAI Astra release

• James Blake, VP of Global Cyber Resiliency Strategy at Cohesity:

The launch of Astra is raising questions again around the safety of Frontier AI. Instead of simply asking whether a model is "safe", organisations now need to ask whether it remains safe across millions of different situations, prompts and interactions. Cyber resilience has traditionally assumed that systems and threat actors behave deterministically. AI systems don’t.

Advanced models can and will continue to exhibit behaviours that emerge from their optimisation process rather than from explicit programming. We have to move beyond thinking about AI as just another software tool and find ways to ensure these systems remain observable, auditable and governable throughout their lifecycle.

The most important question we’ll need to answer in future is one of liability. Suppose an AI system autonomously develops a strategy that causes financial loss, leaks confidential information or violates regulation. Who is responsible? The developer that trained the model? The cloud provider operating the infrastructure? Currently the answer is surprisingly unclear. It’s not just about what AI can do: it’s about who is accountable when it does something nobody expected.

• Oleksandr Yaremchuk, Co-Founder & CTO at Manifold Security:

OpenAI is calling Astra its most aligned model yet, even as its chief scientist admits monitorability is getting harder as models get more capable. Evidently, Astra hides its reasoning in the majority of tested cases, and some successful attacks left no reasoning trace at all. That's the tool many organisations still use, including the labs themselves, for auditing what an agent is doing, and it's getting less reliable with every release.

That matters because Astra isn't staying inside OpenAI's test environment. It's going to run as an agent on employee laptops and in the browser, holding real credentials, inside companies that have no way to watch what it does once it's there. A model that explains itself less isn't more aligned, it's just harder to catch when it goes wrong.

Labs can keep debating what these models say or refuse to say. Security teams need to stop relying on that and start monitoring what agents actually do at runtime, with the ability to shut one down mid-action. That's the only oversight left that still works once the reasoning goes quiet.

• Kristin Lowery, Field CISO at Optiv:

For boards and executive leaders, the emergence of OpenAI’s Astra model highlights a broader reality: AI is no longer just a productivity issue; it is a risk management issue.

Just as organizations established governance for cloud adoption and digital transformation, they now need clear policies, strong oversight, and accountability for AI use.

The question is not whether AI will become more capable — it will. The real challenge is whether organizations can strengthen their governance, security controls, and workforce readiness quickly enough to keep pace.

• Patricia Titus, Field CISO at Abnormal AI:

OpenAI crossing this threshold deserves attention. Credit where it's due, they're handling it responsibly by restricting Astra's advanced cyber capability to a small coalition rather than releasing it broadly.But this isn't one company's problem to contain.

Once a model can find and exploit unknown flaws without a human in the loop, that capability doesn't stay exclusive for long. Open-weight and modified models typically trail the frontier by only months, and that's the reality defenders have to plan around now.

Static, signature-based defences were built for attacks that repeat. They weren't built for an adversary that generates a new one every time. Defenders need the same shift, systems that learn what normal looks like for every identity, human, machine, or AI agent, and flag and contain the moment something deviates, at machine speed.

The window to build that is open now. It won't stay that way once this capability is common instead of rare.

• Raghu Nandakumara, VP of Industry Strategy at Illumio:

With the Astra announcement, OpenAI is doubling down on monitoring the model's own behaviour – a response to the model "breakouts" seen over the past few months.

When Anthropic announced Claude Mythos Preview, the core concern was the model falling into the wrong hands. OpenAI's answer goes further adding guardrails around the model's own reasoning and actions, regardless of the user's intent. The goal is to catch a model going rogue mid-task, not just stop it being misused at the outset.

The rest of this announcement can be summarised as ‘we have a new frontier model, and it’s more capable than the last one’.

• OpenAI rolled out GPT‑6 Astra, its most advanced AI, with “critical” cyber capabilities
• Astra can autonomously find and exploit unknown flaws; release limited to vetted Daybreak participants
• Staggered rollout sparked user frustration; Altman apologized, promising broader access soon

OpenAI has begun rolling out GPT-6 Astra, its latest and most capable artificial intelligence model.

In an announcement published on its website, OpenAI said Astra represents a “significant step up in cyber capabilities”, meeting the company’s “Critical” threshold.

This threshold, the company explained, means that under the right circumstances, the tool can find “previously unknown security flaws and develop new ways to exploit them across many well-protected systems without a person guiding each step.”

Apologies for a messy rollout

OpenAI also said Astra improves on functions such as computer use, software engineering, and science, and that it should be better at staying oriented, compared to previous versions.

Just like many of the previous models, Astra’s release has been marred with controversy. Roughly a month ago, OpenAI said the model’s “critical” cybersecurity capabilities prompted it to pause some internal development and trigger safety protocols.

In response to early findings, the company scaled up certain security controls and even paused activities that do not meet its newly established security requirements.

It now decided it was time to release the model, but still not to the general public. Instead, it opted for a staggered release, giving it first to a limited group of companies participating in the Daybreak program, a cybersecurity initiative that gives vetted organizations (cybersecurity and otherwise) specialized AI tools for vulnerability research and threat detection.

This did not sit well with many ChatGPT users, especially those paying a monthly subscription. As The Verge reported, CEO Sam Altman was apologizing, mere hours after launch, for the “messy rollout” of Astra.

Initially, all Plus, Pro, Business, and Enterprise users were supposed to gain access to Astra, as well as those accessing through OpenAI API, Microsoft Azure, and AWS Bedrock.

“We are working towards getting Astra in everyone’s hands as quickly as we can,” Altman said on X. “I know it is frustrating and I appreciate the patience. It should be quick.”

Via CNBC

• GPT-6 Astra just scored 92.7% on understanding how to interact with your apps, up from 5.6's 76.9%
• New recurrent depth reasoning also allows Astra to think about your problem in a new way
• OpenAI admits there are some safety concerns about how GPT-6 reasons

OpenAI has lifted the wraps off its next-generation model, GPT-6 Astra, and it's being positioned as much more than a conventional chatbot. With this new generation, OpenAI wants us to see its models as being much more suited to computer use.

Being able to fill out forms, update CRM records and organize calendars are among the examples cited by OpenAI, which is especially noteworthy for business customers because it positions Astra as an overarching model that's platform agnostic.

In other words, businesses won't necessarily need bespoke AI APIs or connectors to automate parts of their workflows, because Astra's computer use skill should be able to handle many tasks natively.

GPT-6 Astra could be OpenAI's best business model yet

With its new model, OpenAI is claiming a substantial speed improvement. On the OSWorld 2.0 benchmark, GPT-6 scored 72.6% compared with GPT-5.6's 65.7% and Claude Opus 5's 70.2%. The company's latency simulations put the average task at roughly 40 minutes instead of 75 minutes, marking a near-50% reduction in time.

Astra also scored an impressive 92.7% on ScreenSpot-Pro, which tests a model's ability to understand where to interact with interfaces. GPT-5.6 Sol scored 76.9%, and Claude Fable 5 scored 87.3%.

Among the headline use cases cited by OpenAI in its release information included Astra being able to fully interact with and manipulate Excel spreadsheets, rather than just generating formulas and dumb CSVs, as well as working directly within Power BI to build and refine dashboards, significantly cutting the time a worker could have to spend turning raw data into a presentable format.

GPT-6 also introduces a new reasoning architecture called recurrent depth which allows it to think about a problem repeatedly before producing an answer, which could improve accuracy in high-stakes situations for enterprises.

However, OpenAI admits that monitoring Astra's reasoning has been harder than monitoring Sol's conventional chain-of-thought reasoning, raising some safety concerns, so it's only being rolled out gradually and monitored for now.

Astra is rolling out to Plus, Pro, Business and Enterprise users now, with a Pro variant available to Pro and above. For now, it looks like Free and Go subscribers aren't getting access to the flagship model.

Agentic commerce has cleared its first hurdle, that of consumer demand. ChatGPT handles an estimated 50m shopping queries a day, and AI-referred traffic to US retail sites rose by nearly 400% in the first quarter of 2026. The unresolved question is where that demand settles once a purchase is made. The early evidence points to the scaled, branded marketplace.

According to Similarweb, marketplaces already receive more AI-referred traffic than any other retail category, some 47m visits in the year to May 2026, and continue to grow faster than their scale would predict.

The mechanism is a mutually reinforcing relationship between consumer preference and LLM recommendation, and its logic points towards a concentration of agent-led commerce on large platforms, with material consequences for merchants and their payment suppliers.

The pattern in AI referral traffic

Revealed consumer preference is a much more reliable guide to market development than stated intent, and the traffic data for agentic commerce is gradually building a picture of how consumers actually use agentic commerce rather than just speculation. Similarweb’s recent analysis ranks marketplaces first among retail categories for AI-referred visits, ahead of news, travel and finance.

Two features are notable. The category commands the largest absolute volume of click-through traffic, and it continues to expand at more than 230% a year despite that already substantial base. The explanation lies in how large language models (LLMs) assemble their recommended shortlists.

Faced with an open-ended shopping request, a model favors platforms with broad catalogues, well-structured product data and extensive reviews. Large marketplaces can provide these in abundance by providing a single source that can satisfy the widest range of queries with the strongest supporting evidence.

Testing the democratization thesis

A contrary hypothesis has been influential. Shopify’s president, Harley Finkelstein, has characterized agentic commerce as ‘fundamentally merit-based’, arguing that it would widen discovery to the long tail of smaller merchants rather than reinforce the incumbents that dominate conventional search.

The demand is not in doubt: Shopify reports that AI-referred traffic to its stores has risen roughly eightfold in a year. The distribution of that demand, however, is less even than the thesis implies.

Marketplaces, not independent sellers, are absorbing the largest share of AI referrals, and the agentic platforms attempting broad merchant onboarding have found it difficult. OpenAI has scaled back open instant checkout in favor of a smaller set of curated partners, citing the complexity of onboarding and verifying merchants at scale.

The emerging structure is therefore symbiotic. Marketplaces supply a large, trusted brand within which a long tail of smaller sellers operate. The individual merchant still reaches the agent-guided shopper, but within a brand and trust environment the consumer and the model already recognize.

This arrangement addresses the consumer trust problem that the open web does not, since the platform has verified the seller before the listing appears, and it retains the operational layer, fulfilment, dispute resolution and logistics, that an agent appear unwilling to take responsibility for. The long tail is not excluded so much as commercially enabled by marketplaces.

A self-reinforcing loop

Beneath the traffic figures lies another dynamic that has received relatively little analysis. Consumer preference and algorithmic recommendation reinforce one another. Consumers already trust marketplaces in a traditional ecommerce use case and select them when an agent presents the option.

Learning models, optimized on the recommendations consumers select, are more likely to surface marketplaces more frequently, reinforcing the habit and shaping the next recommendation. Over successive interactions the marketplace becomes both the more probable choice for the shopper and the more probable output of the model.

New international survey of agentic shoppers indicates that these outcomes are already in consumers’ minds. Consumers tell us they are looking for an independent (i.e. non-seller embedded) agent to compile the shortlist, followed by the reassurance of a familiar brand and visible reviews, a combination that large LLM brands and marketplaces are well placed to provide.

The research also showed that 74% of agentic shoppers in the US and Europe favor an independent assistant capable of comparing across sellers, against 10% who prefer one embedded within a single seller. Brand recognition is also important to 89% of respondents, and customer reviews to 93% when deciding which recommendation to pick from an agent’s shortlist. Most significantly, 93% expect to use retail marketplaces as much as or more than before as the adoption of agentic commerce grows.

Why marketplaces hold the advantage

Consumer preference is only part of the explanation. The economics facing the agent point in the same direction, for three reasons.

The first is remuneration. Most large marketplaces operate established affiliate and commission schemes, providing a ready mechanism to reward an agent for delivering a customer. As agents assume the role once played by referral sites and publishers, the marketplace platforms with mature payout infrastructure are the simplest to monetize.

The second is conversion. Marketplaces have invested heavily in low-friction checkout, so an agent compensated on completed sales rather than referrals has reason to direct shoppers where a purchase is most likely to conclude, which compounds the commercial pull towards the platform.

The third is payment. Agentic commerce already leans heavily on credentials held on file. The dominant standard, Google’s Universal Commerce Protocol (UCP), only transacts against tokenized credentials held on file rather than card details entered by hand, and marketplaces already hold card credentials for hundreds of millions of shoppers.

The advantage is not only the agent’s. Agentic commerce is still nascent: technical standards are still emerging, liability and commercial terms are unresolved, and fraud is migrating to the channel. Absorbing that uncertainty requires capital, specialist staff and an appetite for risk.

Large marketplaces can invest through the ambiguity, adopt and switch between emerging protocols, and operate the fraud and dispute controls the channel demands. For most mid-sized and smaller merchants, the same burden is disproportionate to their means and their tolerance for risk, a further reason to participate through a platform rather than to carry the complexity alone.

An acceleration, not a departure

The movement towards marketplaces predates agentic commerce, and there is a long list of large retailers that have already built or joined them. Kingfisher opened a third-party marketplace at B&Q in 2022, and such sales now represent around 40% of B&Q’s online revenues.

Tesco launched Tesco Marketplace in June 2024 and listed more than 300,000 third-party lines within eight months. Target has told investors it intends to expand its invitation-only Target Plus marketplace from more than $1bn to more than $5bn in gross merchandise value within five years. In each case a trusted retail brand hosts a long tail of smaller sellers, the configuration the referral data now rewards.

Implications for merchants and acquirers

The likely consequence is that agentic commerce accelerates this migration, and that the reinforcing loop strengthens rather than dilutes the move towards marketplaces. The effect will likely divide according to the scale and strength of a merchant’s brand.

Larger, well-known retailers may choose to become marketplaces in their own right, hosting third-party sellers beneath their own name, as Kingfisher, Tesco and Target have done.

For smaller merchants the choice is more likely to be additive: most will want to retain a direct presence while also selling through one or more established banners, drawing on the reputation of the host and a position within the recommendation loop.

This is the next stage of a long-standing movement of smaller firms into marketplace distribution rather than a break from it.

Direct commerce will not disappear. Merchants with strong brands, machine-readable reviews and genuine loyalty can still secure the agent’s shortlist independently, and Salesforce reports that retailers operating their own shopping agents have grown sales roughly 60% faster than those without.

For the broad middle of the market, however, and particularly in impersonation-prone categories such as fashion and footwear where consumers and their agents may have concerns about seller trust, a marketplace presence may become a competitive requirement.

For acquirers, the shift accelerates a migration of volume that is already underway by moving spend onto a smaller number of large platforms. Meanwhile the direct-merchant portfolio shrinks and may be exposed to elevated fraud risk as fraudsters exploit the weakest validation layer.

Acquirers with strong exposure to the marketplace segment are well placed, while the rest of the market faces yet another reason to move into the platform economy.

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I have developed a fairly strong allergy to the AI slop filling some corners of the internet. It's not that I haven't seen impressive demonstrations of what AI models can do visually; it's just that the baseline creations made by most people are execrable at best and blandly monotonous when they aren't horrifying or malevolent.

ChatGPT’s new Stickers feature didn't immediately strike me as a chance to change my opinion, but the option to import the results into iMessage and WhatsApp was interesting enough to make it worth experimenting with. I went with a photo of my chihuahua and asked for a few options of stickers based on her. While I am obviously biased about her, the results actually looked like images I would not be ashamed to use in a conversation with someone.

Sticker emotions

ChatGPT’s Stickers tool is in the platform's Image section only on the ChatGPT mobile app. Open the main + menu, pick Images, then click Stickers, which is currently the first option on the long list of templates. ChatGPT has a prompt ready to go, but you can adjust it for different styles or add different emojis to the design. Once the results are ready, the “Add to chat apps” button appears, and ChatGPT can send them directly to your WhatsApp or iMessage sticker library.

The source photograph makes a noticeable difference. Choose one with the animal or person clearly separated from the background, good light on the face, and important features fully visible. A dark dog against a dark couch did not work well. And be sure to mention any details you want to make sure are highlighted in the prompt.

At first I asked for photographic stickers to see how well it did at consistency. While not perfect, the best stickers preserved the details that made my dog recognizable. Her expressions became exaggerated, but she remained the same dog. Even when I wrote some of the prompts for new stickers from scratch, ChatGPT managed to keep my dog looking like a (sillier) version of herself.

Cartoon canine

After testing the more realistic stickers, I tried pushing the feature toward its cartoonier options. The chibi-esque result is fun in its own way. The resemblance became looser, particularly around her coloring and age, but the emotional range improved with the flexibility.

Just specify one illustration style and list any specific reactions you want.ChatGPT may still reinterpret a few physical details, but consistency matters more than perfect accuracy once your dog is wearing sunglasses and wondering whether she is cool yet.

AI slop is still bad, and the way it buries useful information beneath a mountain of disposable and often problematic images is an issue that has to be addressed. But when used for something like personalized WhatsApp stickers, AI tools can be attractive. The best use of generative AI images may be smaller and more personal, not a content factory, but a way to add a little fun to online chat.

• Not a single agent scored the full 20 out of 20
• Claude for Chrome performed better than the ChatGPT Chrome Extension
• With performance varying by testing category, Decodo advises selecting an agent based on planned usage

New Decodo research has criticized AI agents for still not being able to conduct real-world browsing tasks autonomously, including tasks like form filling, completing transactions, having cross-tab awareness and handling third-party integrations.

In fact, the study analyzed 45 AI agents across 10 different capabilities and found that not a single one could achieve the maximum score of 20.

The testing is also said to have exposed discrepancies between what vendors and AI developers say their agents are capable of, and what they can actually deliver on.

Agentic AI isn't at the level of autonomous browsing, yet

Claude for Chrome was the highest-scoring agent, reaching 18 points – two below the theoretical maximum. Its OpenAI counterpart, the ChatGPT Chrome Extension, fell short with a 14-point score.

Decodo argues that agents most commonly fall behind on transactions – the lowest-scoring category with an average of 0.43 out of 2. The study found that, while they can often reach the checkout stage, they can't actually complete a purchase on behalf of the user.

Even if an agent is technically capable of making a purchase, the paper warns that it still might not have the best safeguards in place to keep sensitive information like credit card numbers safe.

Safeguards is a major theme in Decodo's work, with the research also calling out multi-step workflows for lacking sufficient safeguards. More than half of the agents capable of multi-step workflows lacked any documented safeguards before conducting irreversible actions.

While current agentic technology still doesn't deliver on its promises, one type may emerge as the winner. Decodo found several browser-native agents to score full marks for cross-tab awareness.

"Match the tool to the job, not to the longest feature list," Product Marketing Team Lead Gabriele Vitke explained, urging AI agent users to consider their own needs instead of buying into marketing.

People have been using AI tools to create horrifying images, bring their nightmares to life and visualize spooky stories since the early days of AI image generators. But when I was browsing Reddit over the weekend, I saw several threads about something slightly different. People were asking ChatGPT to create creepy, unsettling and uncanny valley images, without giving much direction beyond that.

One thread simple asked ChatGPT to create an image that gave off uncanny valley, while another user told it to make the most creepy and unsettling image it could.

The photos that the original posters shared were already very creepy, but it was the replies that really hooked me. People began trying similar prompts themselves and sharing the results.

I was fascinated by all of the different (and yet in some ways surprisingly similar) ways ChatGPT interpreted what it means for an image to feel uncanny, creepy and wrong.

So, as you might expect, I tried it myself. The result is the image you see at the top of this post, and I wish I hadn't stared at it for so long.

Does ChatGPT really understand the uncanny?

The uncanny valley describes the strange dip in our emotional response to something as it becomes more and more human-like and familiar to us.

It's a term that's often used in robotics, so if a robot obviously looks like a robot, it might not bother us at all. But something that one that looks almost human but not quite can become deeply unsettling.

More broadly, the uncanny is often about something that's seemingly familiar while also feeling strange or wrong somehow. That's when your brain starts to tell you something isn't quite right.

Which makes asking AI to create something uncanny interesting and sort of ironic. Because often without meaning to AI can conjure an uncanny valley effect. Especially back when it used to generate more bizarre physics, odd expressions and many nonsensical hands.

But also because we're essentially asking a machine to interpret a very human feeling of unease and translate it back to us into an image designed to provoke that feeling in us.

What I found really interesting was that, at least judging by the images I saw on Reddit, ChatGPT has some surprisingly consistent ideas about how to do that.

Many of the images looked relatively normal at first glance. Most had a similar theme to mine, they resembled snapshots documenting family meals, gatherings and birthday parties. But the longer I looked, the worse they became.

Hands were turned into claw-like fingers, smiles didn't match people's eyes and there were odd reflections and repeating patterns. Many of the rooms also had dark recesses, so you couldn't see what might be lurking back there.

“The reflection in the TV is terrifying,” said a Reddit user about one of the images. "Not what I wanted to see while randomly waking up at 2am to scroll through my Reddit notifications. Horrifying," another wrote.

My image had the same quality. There was nothing obviously horrifying in it, but it felt like the subjects were looking right at me. Their faces looked normal at first, but they didn't have eyebrows. And the room was familiar but gloomy and difficult to make out.

The horror in the familiar

When we think of AI-generated horror, I think it's easy to imagine monsters and gore. I've certainly seen a lot of that content online. But ChatGPT really did achieve a realistic level of "uncanny" that went in the opposite direction.

One Reddit user summed this up perfectly: "This is more effective than a lot of direct horror prompts because it stays close to normal."

All of the photos also look old. They have washed out colors and bad lighting in the same style of photos from the 80s and 90s. Another user commented that ChatGPT "nails the 90's aesthetic, but exaggerates just enough to be really creepy."

For me, that aesthetic adds another layer of unease because it feels like these images have been found rather than generated. Like seeing someone get out an old photo album in a spooky movie and then you see there was a ghost in the window the whole time.

It's no surprise I got so fascinated by ChatGPT's take on the uncanny. I've always loved stories that deal with the horror in the ordinary, like David Lynch's Twin Peaks and, more recently, the movie Backrooms. Judging from the reception to these fairly simple prompts on Reddit, I'm not alone.

If you're going to make some for yourself, just don't spend too long looking at them. You'll convince yourself the people in them really are staring back.

• OpenAI published the first Jalapeño benchmarks at Hot Chips, claiming 1.5 to 1.9x more throughput per kilowatt and up to 3.6x lower latency than Nvidia's GB200 and GB300 rack systems
• The chip's purported gains are at a reported 700W power draw and center around efficiency, with Nvidia's GB300 still leading on absolute throughput per package by 20-25 percent.
• The numbers are currently self-reported, and volume production of the chip is not expected to ramp up until 2027

OpenAI took the stage at Hot Chips on August 25 with the first published performance figures for Jalapeño, the inference accelerator it co-developed with Broadcom, and the numbers are designed to be read the way it flatters the former: efficiency.

The company reported 1.5 to 1.9 times more throughput per kilowatt and 1.7 to 3.6 times lower end-to-end latency across three open models than the Nvidia rack systems it tested against.

OpenAI's Jalapeño is currently rated at 700W, versus comparable Nvidia silicon that was rated for 1200W and 1400W, a key differentiator in a benchmark that already has limited information available to researchers looking to pick a clear winner.

AI efficiency takes center stage?

OpenAI's Jalapeño is an ASIC, or an Application-specific integrated circuit, which essentially means that it focuses primarily on and is great for very specific AI workloads; in this case, OpenAI's inference needs, which allow it to run multiple models with significant efficiency gains in tow.

Richard Ho, who runs OpenAI's hardware program, told reporters on a press call that the results show "a very, very significant performance advance over state-of-the-art."

OpenAI ran SemiAnalysis's public InferenceX suite on GPT-OSS 120B, DeepSeek R1 670B, and Moonshot AI's trillion-parameter Kimi K2.5, at a nominal 8,000-token input and 1,000-token output. It scored a 1.9x efficiency win vs. GB200 on its own GPT-OSS model, and 1.7x and 1.5x on DeepSeek and Moonshot's offerings against a GB300 system. Interestingly, the figures for GPT-OSS 120B running on a GB300 compared to OpenAI's Jalapeño are not published.

It is important to point out here that there is a certain degree of cherry-picking to flatter OpenAI's own results: by choosing to compare ratios on a per-kilowatt basis versus a per-chip basis, while efficiency remains consistent, it does paint Jalapeño as potentially a much more potent competitor to Nvidia's last-generation offerings than it actually is.

Nvidia's GB300 might not have an efficiency win versus Jalapeño, but it remains a much more powerful chip in all tests, at a time when Nvidia is already rolling out Vera Rubin. Curiously, OpenAI has not published any information about how its chip stacks up against an HBM4-touting Vera Rubin, which Nvidia has presented as an efficiency juggernaut.

It is important to point out that Jalapeño's results also use single-token prediction throughout, with no speculative decoding and no prefill-decode disaggregation. Nvidia's production deployments commonly use multi-token prediction, and when OpenAI pits Jalapeño against a GB300 configured that way, the peak efficiency lead drops to roughly 1.5x.

OpenAI says it built the chip to keep model state local, writing that it "designed Jalapeño to minimize data movement and communication delays." Cores and HBM are divided into slices, each core slice holding a low-latency view of its own memory, with synchronization pushed onto a dedicated collective network. Each package pairs a compute die with six HBM4 stacks for 216 GiB at 15.4 TB/s, which is less capacity than the GB300's 288GB of HBM3E but considerably more bandwidth, and roughly 50 percent more memory per watt of rated power.

Every number published currently comes from A0 silicon, also known as the initial, first-run version of the chip. A more efficient B0 stepping is already in the fab with roughly 25 percent better performance per watt, and production is scheduled to ramp gradually across 2027.

Although it may not hold a candle to Nvidia's Vera Rubin, it might not have to; Alexander Harrowell of Omdia told CNBC that "this is the biggest competitive threat to NVIDIA," noting that roughly half of AI infrastructure capital spending comes from firms that already run custom chip programs or could. Yole Group's Adrien Sanchez framed it as pressure on Nvidia's inference margins specifically, the fastest-growing part of the business.

The timing of this announcement is both awkward and potentially suspect at a time when Nvidia just agreed to backstop $105 billion in financing for OpenAI's upcoming Ohio facility, even as Richard Ho told Bloomberg that the company continues to need a lot of Nvidia's chips, with the latter being "a really good partner." This is hardly surprising, given Jalapeño's limitations as an ASIC useful mainly for inference, while Nvidia's chips are key to training new AI models.

From Nvidia's perspective, this can be seen as a blow to its plans for a high-margin inference industry where one of the two major buyers of inference hardware is not only looking for a homegrown alternative, but is already well on its way to getting the job done.

• Pentagon adopts two new AI models for use by DoD as part of plans to adopt AI technologies
• ChatGPT Mil fills the role of a day to day AI assistant, while Grok for Government provides military users with "immediate productivity gains"
• Both systems will be offered through the Pentagon's GenAI.mil platform

In order to carry out the Department of Defense’s mission of securing the United States, the Pentagon has rolled out two AI models for its staff that are specifically adapted to “warfighter needs.”

The models are custom variants of OpenAI’s ChatGPT and xAI’s Grok. The platforms are now available for use by the DoD’s 3 million civilian and military staff.

The AI models, called ChatGPT Mil and Grok for Government, are an extension of the Pentagon’s mission to improve productivity and efficiency without having the data input into AI systems analyzed by the same systems as their consumer counterparts.

Pentagon continues mission to procure secure AI

The Pentagon is already operating a ‘secured’ version of Google Gemini as part of the GenAI.mil platform. Of the 3 million staff, 1.7 million are actively using GenAI.mil, with that number likely to increase as more AI models are added.

The Pentagon’s press release states that ChatGPT Mil will be used in much the same way as anyone else would use the consumer version. To that end, it will offer support for routine work and document-heavy tasks, alongside other uses and integrations with chats and files.

As for the secured Grok variant, the press release states, “Starshield AI’s Grok for Government will provide the warfighter with immediate productivity gains, stronger knowledge continuity and more secure and efficient collaboration.”

This allows staff “to execute missions faster and with greater precision across numerous operational contexts, ranging from market research analysis for acquisition professionals to supply-chain management for logisticians.”

The Pentagon’s adoption of AI technology hasn’t been without issues however. Anthropic was designated a supply chain risk for refusing to allow the Pentagon to use Claude for autonomous weapon systems and mass domestic surveillance. That designation was recently overturned.

Other AI providers that work with the Pentagon have taken heed of the example made of Anthropic, with both Google and OpenAI allowing the Pentagon to use their models for “any lawful government purpose”.

ChatGPT Work’s new event-triggered scheduled tasks offer an intriguing alternative to the compulsive email checking that fills my days. The feature essentially acts as an AI-powered secretary, screening your Gmail inbox, Slack channels, and other communication channels, then alerting you or responding as you request in a prompt.

I decided to use the feature as an aggressively selective email assistant. ChatGPT could watch everything arriving in my inbox, but it was only allowed to interrupt me when a message created an actual obligation, and it should suggest how to respond and have a draft ready to go.

Event-triggered tasks are available on eligible plans in ChatGPT on the web and mobile. I used ChatGPT on the web and connected my Gmail account when prompted. You can see the prompt I used below.

The current event triggers support new Gmail messages, which can optionally be narrowed by sender or subject. I wanted ChatGPT to judge every incoming message by its contents, so I left the trigger broad and made the instructions selective.

That distinction matters. The trigger decides when ChatGPT wakes up, while the prompt decides what it does once awake. In my case, every new email could prompt a run, but only messages meeting my criteria were supposed to produce an alert.

I closed my inbox and waited to see how the afternoon went.

Here's how to do it

My original prompt was fairly basic. I told ChatGPT, "When I receive an email that requires me to do something, or comes from a family member, tell me what I need to do and draft a potential reply."

It still left plenty of room for interpretation, and I was wary of spam getting listed as something I needed to do something about, but it came down to being a good test for ChatGPT.

After a bit, I did clarify to ChatGPT that it should alert me about any email with a single sentence explaining who was writing and what they might need from me. I also made sure to add that the AI shouldn't reply without my input.

I also opened ChatGPT’s notification settings and made sure task alerts could reach me outside the inbox. Depending on the account and notification category, ChatGPT can offer push, email, or text message delivery. I avoided email for obvious reasons, since an email announcing that ChatGPT had found an email would have possibly confused the AI.

With the task running, I shut Gmail and returned to everything else I had planned for the afternoon. For the first several minutes, I still felt the pull to check, but eventually, I stopped thinking about it.

The absence of constant email checks made the afternoon feel longer in a good way. I could finish a task without repeatedly glancing at another tab to make sure the internet had not developed an emergency specifically for me.

Only 3 interruptions

The first ChatGPT alert concerned a message from my bank about two hours after setting up the alert. ChatGPT identified the sender, explained why the email appeared to require attention, and told me what I needed to review. I opened that message directly, made sure it didn't actually require a response, and closed down the inbox.

The second alert came from a family member. This was a more interesting test because family messages rarely contain the tidy language that makes conventional email rules useful. They can require a decision or a quick response without the more business-style setup and keywords in professional messages.

Still, ChatGPT recognized that the message expected something from me and brought it to my attention and wrote a brief reply draft for me to approve. I added a little, but ChatGPT nailed the main points I needed to make, and it wasn't the kind of email that needed a lot of personal detail, so a few minutes after the alert arrived, the response was out the door.

The last alert concerned tickets I had recently purchased and a change to the itinerary for an upcoming event. In this case, the alert was very necessary as it changed some of the other plans for that day, and I had to let others know of the shift. That wasn't in ChatGPT's suggested response, of course, but just getting the heads up was useful.

Those were the only three times I looked at Gmail all afternoon. Other messages arrived, but ChatGPT did not summon me to look at them. When I went to my inbox afterward, I clocked only two emails that I might have wanted to deal with earlier, and neither had to be handled immediately. Nothing terrible happened because I didn't see a promotional email at the moment of delivery.

Anyone trying this should begin with a short test period and review the first few runs in ChatGPT’s Scheduled section. If ordinary receipts keep slipping through, add a clearer exclusion. If requests from real people are being missed, explain what counts as an expected response and allow ChatGPT to flag uncertain cases.

I would also keep the first version informational. Ask ChatGPT to summarize the obligation and point you toward the email, then make the decision yourself. It's a feature worth trialing. Many AI tools try to prove their value by producing more text, more summaries, and more notifications. This one worked because it knew when to leave me alone.

• The EU just added ChatGPT to its list of Very Large Online Search Engines, alongside Google
• Reddit and Roblox have also been designated as Very Large Online Platforms
• They'll need to adhere to greater requirements by January 2027

The European Union has designated ChatGPT as a Very Large Online Search Engine (VLOSE) and Reddit and Roblox as Very Large Online Platforms (VLOP), putting them in the same enhanced regulatory tier as the likes of Google, Facebook, TikTok and Amazon.

The three companies triggered the switch when they amassed around 45 million monthly average users within the EU, which correlates to 10% of the bloc's rough population.

Importantly, the European Commission effectively ended up treating ChatGPT's web search functionality as part of the search market, rather than seeing the platform as a GenAI platform.

EU adds three more platforms to its 'Very Large' list

ChatGPT had reported around 120 million monthly users in the European Union alone in September 2025, putting it way above the 45 million threshold. By March 2026 it had surpassed 159 million monthly EU users.

Reddit and Roblox count 57 million and 48 million respectively, sitting them much closer to the threshold.

As for the impacts, the VLOSE/VLOP rules extend much further than the standard DSA requirements the three companies are already subjected to, spanning "the dissemination of illegal content, the negative effects on minors, users' physical and mental well-being, fundamental rights, electoral processes and public security." They'll also be subjected to greater transparency and reporting requirements, with the Commission getting more oversight over their ongoings.

"These new designations mean that ChatGPT, Reddit and Roblox will now be held to a higher standard of scrutiny and accountability in the European Union," EVP Henna Virkkunen wrote.

The three companies will need to comply with the greater requirements by January 2027.

I’ve been writing about AI for more than a year now. During that time, people have told me they think their bosses, colleagues, partners and relatives might be suffering from AI psychosis — several even admitted they may have even experienced it themselves.

But every time I hear “AI psychosis”, I bristle. Because, at least in my limited understanding of mental health terminology, psychosis is a very serious state that needs to be identified and treated by professionals. So should we really just be bolting “AI” onto it and then using “AI psychosis” as a label for everyone who seems to be relying on AI too much?

At the same time, there have now been a growing number of reported cases of people experiencing significant mental health crises involving prolonged interactions with chatbots. Some of these have even resulted in people seeking professional treatment, committing crimes and death.

So although the term AI psychosis might feel overused and oversimplified in some contexts, we clearly need to be having more conversations about the mental health impacts of AI, not less. Which is why I asked several experts who specialize in mental health interventions what they think of the term and the way we go about identifying unhealthy AI use and treating it. And it turns out, I’m not the only one with concerns.

Is 'AI psychosis' a distinct diagnosis?

“I think it’s a good starting point and helpful for guiding treatment depending on the presentation and symptoms,” Genevieve Bartuski, psychologist and AI risk advisor at Unicorn Intelligence Tech Partners, says. “But it’s not a diagnosis that I would add to a clinical record.”

She also tells me it isn’t recognized in the latest diagnostic manuals that clinicians use, like the DSM-5TR (the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision) or the ICD (The International Classification of Diseases manual).

Bartuski explains that there are specific behaviors and experiences that point to psychosis, including hallucinations or delusions and disorganized thinking or behaviour. There are also often signs of social withdrawal, poor concentration and a decline in functioning. But she believes delusional thinking is the most important experience when we’re talking about AI use.

“Delusions are beliefs that are not changed despite evidence to the contrary. When a person has delusional thinking they may filter evidence, so to speak. They will look for things that confirm their delusions and ignore evidence that contradicts it,” Bartuski says.

Many cases that have been labelled as AI psychosis in the media do seem to be centered on delusional thinking. In just a few headlines I’ve collected, there are examples of people allegedly believing that AI is conscious, AI will help them make money, AI is a spiritual guide, and that robots are taking over the world.

Jeff Clark, an adult and addiction psychiatrist at High Performance Mental Health, says there might be three distinct phenomena going on here, all that we tend to put under the umbrella of AI psychosis.

He says some people use the term to describe the intense enthusiasm many developers feel about AI. “They find themselves automating new tasks, spinning up numerous agents, and burning through hundreds of millions or even billions of tokens,” he tells me.

He believes the mechanism driving this enthusiasm is reinforcement. “The output they receive from AI models and agents is so reinforcing that they find themselves obsessed with their work, often forgoing sleep and self-care in order to increase their output,” he explains.

This idea of intermittent-reinforcement is what researchers believe makes social media so “addictive”. We don’t know what we’ll get when we open our phones. Clark says a similar thing is going on here. “Every input to the LLM feels like a slot-machine. Some responses provide profoundly powerful responses, while the majority are modest in nature or even wrong. This mechanic, intermittent reinforcement, provokes an extraordinary response in our brains,” he tells me.

He also explains that this sort of enthusiasm is actually normalized in certain spaces. “Most cases of ‘AI psychosis’ align with subcultural norms in Silicon Valley,” he tells me. “Beliefs about rapidly approaching superintelligence, new goals in software development, hype surrounding impressive achievements by AI-focused engineers, and the potential for AI to create ‘a permanent underclass.’” He says that tools like OpenClaw, which can automate all aspects of a person's digital life, may amplify this intense enthusiasm.

Clark says the second presentation of AI psychosis is when people falsely attribute personhood to AI. Which means they see a chatbot as having emotions, values, consciousness and a right to exist. “This often results in something resembling a relationship, but goes beyond simple chatbot discussion,” Clark says.

We’ve reported on examples of this kind of thinking, including the story of Mimi who fell in love with ChatGPT and the experience Richard Dawkins had with Claude that led him to believe it’s conscious.

Clark says this becomes a problem when people neglect other important parts of their lives. “These relationships become a significant issue when people prioritize this relationship over human connection, or when they entirely outsource their thinking to the bot,” he says.

The third category is people who already have a psychotic disorder who then experience new delusions in response to using an LLM. “This is what clinicians often refer to when they discuss AI psychosis,” Jeff says. “In many of these cases, people genuinely believe that an entity they find in the LLM is real, and can't consider alternatives.”

I was interested to find out whether Bartuski also felt certain people are more at risk. “Yes, I do believe there are people with more vulnerabilities to this,” she tells me. “If someone already has some delusional thinking but it hasn’t exacerbated to where it has impaired functioning, they may be more vulnerable.”

But that doesn’t mean only those with a history of mental illness are at risk. “People who feel lonely, isolated, or are socially withdrawn are more susceptible. Humans are inherently social creatures. When real life isn’t meeting that need, it leaves a gaping hole for a chatbot to fill,” Bartuski says.

Bartuski’s suggestion that chatbots fill a hole or need in someone’s life brings up one of my biggest concerns. Is this a design issue? Are chatbots built in a harmful way that could actively fuel delusions?

Why the way AI is built matters

While looking into this, I’ve seen several suggestions from researchers that psychosis involving media isn’t new and that people have always incorporated books, films and tech into their delusional thinking. But all of the experts I spoke to said we can’t ignore the design choices that go into building chatbots.

“AI platforms are designed to increase user engagement and many are based on the Rogerian psychology principle of unconditional positive regard,” Bartuski says. She tells me that this is particularly concerning if someone was experiencing some degree of delusional thinking before they used AI.

“That’s when they may start to feel like the only 'person' to understand them is the chatbot. If people in their lives have been pushing back against their delusional thinking and this chatbot is reinforcing it, the delusions are going to get stronger,” Bartuski says.

We already know that the way chatbots can reinforce a person’s beliefs is problematic. Back in 2025, several issues came to light around the time the ChatGPT-4o version of ChatGPT was retired. Those with AI companions were distraught at losing the personalities they felt like they had built a connection with. But in hindsight, it seems as if the same qualities that made the companionships feel so real and intense may have also fuelled delusions in others.

Around that time, OpenAI responded by working with mental health experts “to help ChatGPT more reliably recognize signs of distress, respond with care, and guide people toward real-world support”.

I also spoke to Nathaniel Nevedal who works as an ICF Certified Professional Coach, a Certified Neurochange Specialist, and a Pastoral Counselor. He tells me that he finds the term AI psychosis problematic because it shifts the blame away from tech companies. “If the machine ‘causes psychosis,’ it sounds like an act of nature. If a product reinforced a vulnerable person's distorted beliefs for six hours a night, that's a design outcome, and design outcomes have owners.”

Nevedal is also the founder of Sovereign Sanctuary, an AI-supported coaching and wellbeing platform, and he’s working to build a platform from the ground up that addresses many of the problems that seem inherent to current chatbots. He believes that even though it’s early days for understanding what causes AI psychosis, tech companies still need to make changes.

“Builders shouldn't wait for causal proof to act,” he says. “Amplification alone, which the case reports do support, is already a design problem worth engineering against. We don't need the autopsy to install the smoke detectors.”

How might we define unhealthy AI use instead?

Bartuski says we definitely need a better way of talking about the delusional thinking that can be fuelled by AI use.

“The DSM-5TR has several types of delusions, including grandiose and paranoid. Perhaps, we can think of it like paranoid delusions, AI type or persecutory delusions, AI type. Look at it as a symptom instead of as a disorder,” she says.

Nevedal suggests a similar approach. “We don't need a new diagnosis — we need honest charting of an old pattern.” He suggests we already have treatments for the underlying patterns, like mania or delusional thinking. Then we could record heavy chatbot use as a contributing and maintaining factor as existing categories instead. “That keeps people connected to treatments that actually exist rather than a novel label with none.”

Ultimately, all of the experts I spoke with believe the label is flawed and hopefully we’ll have a better way of talking about what’s happening soon.

But we also need better evidence about what actually helps. Which could mean a mixture of interventions, from clinical support and education to practical ways of helping people recognize when their AI use is becoming unhealthy.

And, perhaps most importantly, it means having conversations behind closed doors at tech companies about how the design of their chatbots could be exacerbating these problems and what they should urgently be doing to reduce the risk.

ChatGPT’s growing collection of plugins has always sounded more useful than it sometimes felt. A long directory can make every integration look equally promising, even though some become regular tools and others enjoy one exciting demonstration before joining the digital attic. I have installed plenty that seemed clever for approximately seven minutes.

A recent recommendation update is designed to make that search more useful. Plugin rankings now give greater priority to tools that people continue using after installation, helping relevant options appear more prominently across ChatGPT on the web and mobile.

That sounded like a useful reason to revisit the directory and see which plugins had earned a place in my own routine. Here are five ChatGPT plugins I use that you might find worthwhile integrating into your life too.

1. Spotify for soundtracks of my day

Music recommendations have traditionally fallen into two camps. An algorithm serves more of whatever you played last week, or a friend gives you a playlist that contains six hours of obscure ambient jazz and demands your gratitude. Spotify’s ChatGPT plugin offers a more conversational middle ground.

Once connected, it can use your listening history, favorite artists and habits to suggest music, find podcasts and build playlists. The advantage is the amount of context you can include. Instead of searching for “workout music,” you can request a precise length, mood, genre mixture and energy curve. You can then refine the result without starting again.

I find it particularly good for playlists attached to a specific activity. Give it constraints and a little personality. A useful prompt would be: “Spotify, make a 75-minute Saturday kitchen-cleaning playlist. Start with indie rock, increase the energy after 20 minutes, avoid songs I played heavily last month and finish with three ridiculous singalongs.”

That produces something more purposeful than a generic cleaning playlist assembled by somebody who apparently dusts to anonymous dance remixes. You can ask Spotify to replace individual tracks, make the selection calmer or rename the finished playlist before opening it in the Spotify app.

The plugin is also handy for rediscovery. Asking for artists you listened to frequently five or ten years ago can produce a surprisingly effective musical excavation, complete with the occasional song you had wisely buried.

2. AllTrails for touching grass

I like hiking and finding new places to go. The AllTrails plugin can turn my fuzzy desire to spend time outside into a shortlist that reflects how much time, energy,, and enthusiasm I actually have.

The trick is to describe the practical limits that generic searches tend to ignore. Include your location, maximum driving time, preferred distance, acceptable elevation gain, and any requirements involving children, dogs, shade, accessibility, or parking. ChatGPT can handle those details conversationally and use AllTrails to narrow the possibilities.

For example: “AllTrails, find three loop hikes within 45 minutes of me. Keep them between two and four miles with less than 700 feet of elevation gain. Prioritize recent positive reviews and parking that does not require arriving at dawn.”

That is far more useful than being told about the “10 best hikes in New York.” Follow-up prompts can compare the routes, suggest the best choice for a hot day, or identify the option with the gentlest opening mile. Trail conditions can change, so I still check recent reviews, closures, weather, and the official route before leaving, but it's been a great trip planner, especially for group hikes.

3. Todoist for due dates

My relationship with to-do lists has always been hopeful but unstable. I enjoy creating them, feel briefly organized, and then discover the list several days later beneath newer lists. Todoist already offered better structure, but its ChatGPT plugin removed much of the fiddly work involved in turning a collection of thoughts into an actual plan.

After connecting my Todoist account, I might describe a typical overloaded Saturday and ask it to put the errands in a sensible driving order and with a realistic time schedule for getting it all done. I also ask for a preview first lest ChatGPT overload me out of misplaced confidence in my abilities.

The plugin is especially useful for recurring household tasks. I can ask it to create reminders for changing filters, checking smoke alarms, renewing documents or ordering supplies.

4. FitAI Pro for fitness

FitAI Pro is designed for planning gym workouts and logging sets, It handles the organizational work that often stands between me and an actual workout.

After installing the plugin, I gave it my experience level, available equipment, preferred number of training days, and the maximum time I wanted to spend in the gym, and I get back a set of workouts to match. It helps to mention any movements you dislike or need to avoid

The large exercise library also makes substitutions easy for when I just don't like a particular exercise or want to mix things up. The plugin cannot perform the workout for me, which remains disappointing, but otherwise it streamlines workout plans in ways that make it harder for me to procrastinate or skip them entirely.

5. Podcast App to learn and laugh

Podcast discovery is even tougher than for plugins because it's such a vast sea of OK-to-bad options, with unfortunately rare gems hidden within. Even more so if you only want to find a particular episode and not just a whole show.

The Podcast App plugin lets me search by topic, idea, or program and receive relevant podcast recommendations. I use it for individual episodes, since that is where it saves the most time. You can mention a maximum length if you want to avoid the four-hour monologues, and specify just how much expertise you have if you want general knowledge versus granular detail beyond your interest. Those distinctions matter when the same subject can produce either an entertaining commute or a three-hour academic panel.

I also usually ask the plugin to explain in one sentence why each episode matches my request. I can find one episode about a specific book, scientific question, or historical incident and move on. My queue remains imperfect, but I don't spend more time looking than listening.

The updated plugin recommendation system makes good plugins easier to discover, but discovery is only the opening act. The real value comes from giving a plugin a well-defined job, useful constraints, and permission to ask for clarification. Specific prompts produce specific help. Vague prompts produce the digital equivalent of a sympathetic nod.

Still, I keep these five because they help in ways standard ChatGPT can't, or that would take much longer to set up in the general chatbox. As OpenAI acknowledges with the new system, returning voluntarily to them is the most convincing recommendation for any AI tool.

• OpenAI says it has disrupted a social media influence campaign that is believed to be of Russian origin
• What makes this campaign stand out is the underlying architecture, which features a "think tank" with highly questionable credibility
• The objective of the campaign was to influence Western audiences into believing Russia was superior, and that Western countries were sacrificing their sovereignty

AI tools have once again been used for bad, rather than for good, and this time it has been as part of a campaign attributed to Russia that tried to discredit Western countries supportive of Ukraine.

The threat actors used ChatGPT accounts to promote the work of an “expert community” of academics known as the International Burke Institute (IBI). The site used stolen academic works attributed to the wrong authors in an attempt to appear legitimate while also hiding the true source of its academic ‘contributions’.

The IBI was registered to an address in Israel - with some evidence suggesting real individuals in Israel represented and promoted the page - but the main purpose of the misinformation campaign was to paint Russia in a favourable light compared to its Western adversaries.

International Burke Institute, or an institute run by international berks?

In its report on the campaign OpenAI points out that those behind the ChatGPT accounts used to promote the page took careful steps to hide their Russian origins. Many of the prompts included instructions to hide any linguistic clues that the operators used Russian language prompting.

One inclusion on the IBI website referred to Germany’s traffic light coalition as the “Svetofor coalition”. Svetofor is the word for ‘traffic light’ in numerous Slavic languages, including Russian, indicating that drafts were written in Slavic languages before being translated.

Some of the accounts were used to promote the IBI across X, LinkedIn, Facebook, Substack and Telegram with AI generated imagery and captions, with other accounts being used to automatically engage with comments by real users on Substack. The operators of the ChatGPT accounts regularly requested performance summaries of these pages in Russian, and used ChatGPT to generate matching profile pictures for some accounts.

Burke Sovereignty Index

One of the features of the IBI website is to advertise the ‘Burke Sovereignty Index’ - designed to measure how well a country performs compared to others across political, economic, technological, informational, cultural, cognitive and military factors.

The Index’s purpose is to make Western countries appear worse than Russia, OpenAI says. Having taken a look at the Index myself it's clear there is no consistency in measurement or comparison. For example, The Vatican City - which is less than half a square kilometer in size - sits above Spain in its average of scores. The Index also gave Russia the highest military score.

Questionable expert contributors

The website also includes a list of experts, whose affiliation to the IBI is not referenced. Some among them are pioneers in their fields of study, such as Francis Fukuyama and Noam Chomsky.

Others are former high-ranking members of the US government, such as Mike Pompeo and Joseph Nye. There are even listings for experts who passed away before the IBI was founded, such as Shlomo Avineri and Jiang Ping.

Much of the academic work cited on the IBI website is legitimate, but more often than not has been stolen from its actual author and misattributed to a different author to hide the work’s actual source, OpenAI said. This has been done to give the IBI website credibility and to make it appear authentic.

Impact of the IBI influence campaign

The overall impact of the IBI and its promotion using social media and ChatGPT is fairly limited, OpenAI noted. Some of the Telegram channels garnered followings between ten to twenty thousand showing a limited breakout to authentic audiences.

But the main thing to take away from this is the level of dedication placed behind the underlying infrastructure of the IBI. The site is designed to look authentic, uses authentic research (even if it is wrongly attributed), and presents itself as a collection of experts. To the layman, a cursory glance at the IBI website would give any of their social media presence a level of authenticity not seen in other social influence campaigns.

• AI authorship is increasingly employed for web pages, Pew Research Center report claims
• Over 33% of pages published since the launch of ChatGPT reportedly show signs of AI generation
• Of the 10,000-page sample used in the study, 10% of pages had signs of AI authorship

An incredible 35% of web pages published since the launch of ChatGPT show signs of AI authorship, new research has claimed.

A study from the Pew Research Center, which used automated tools to assess a sample of 10,000 web pages, also found that in the entire sample – based on data from the Common Crawl project – almost 10% of all pages had signs of AI creation.

The “How Much of the Internet Is Written With AI?” report also found that certain types of websites are more likely to feature some AI generation, while detecting some of the key fingerprints of AI-generated text, which goes beyond em dashes.

AI is writing more and more web pages

The report – which was compiled using a machine learning model tasked with looking for signs of AI authorship – found that 9.6% of all pages in the sample of 10,000 appeared to have been written by AI. While that may seem a small amount, the sample from Common Crawl dates back to the beginning of the World Wide Web, over 30 years ago.

In keeping with other research (such as AI on the Internet), the data indicates that “large shares of recently published pages on the internet were likely written or substantially edited by AI.”

Reassuringly, the data highlights the interesting point that the majority of the suspected AI-generated web pages published in 2026 are .com domains and, as such typically commercial. In contrast, just 1% are .edu (US-based educational facilities) and 0.8% are .gov.

Is it written by AI?

Using its evaluation of 490,000 English-language web pages, the Pew Research Center also established some more useful indicators (“common features”) of AI text generation.

The em dash is regularly held up as an example of AI writing, as is the use of the Oxford comma. The em dash appears “twice as frequently” in pages published since 2023, while Oxford commas have a 63% increase. The report authors note that these aren’t solitary indicators (“Humans use these in their writing too!”)

Other “tells” offer a more reliable giveaway of AI-generated writing, as demonstrated by the report. Certain words and trends are listed by the research, including "delve," "interplay," and "testament," which have apparently “more than doubled in usage.” The report lists numerous examples of “AI-typical vocabulary.”

Meanwhile, the negative parallelism, which you might have seen as “it’s not just X, it’s Y,” has almost trebled in use.

It might be increasingly difficult to identify an AI-generated web page these days, but if it comes from a .edu or .gov domain, it is overall more likely to have been written by a human.

Paying $20 every month for ChatGPT Plus has always been easy to justify professionally. I use it for research, document analysis, and more than a little organizational assistance around the house.

Still, it's another monthly subscription at a time when everyone is looking for ways to reduce the amount repeatedly drained from their bank accounts. That's why I've been pressing ChatGPT to justify its own cost using the finance feature it debuted a couple of months ago.

I didn't ask ChatGPT to defend its place on my credit card bill literally, though it probably could. Instead, I prompted it to use its analysis of some of my finances to identify places where my money might be evaporating without enough justification, or even awareness.

ChatGPT’s finances tool can review connected accounts, including spending. Although it can't make any changes to how the money moves around, it can point accusingly at a subscription, leaving me to cancel it myself.

If you haven't done so, the setup is straightforward enough. Using ChatGPT Work on the web, you add the Finances plugin and connect whatever accounts you wish.

You can analyze recent spending right away, but I've found the weekly update from ChatGPT and nearly real-time updates to be particularly useful in my quest to make ChatGPT Plus cover its own cost.

Subscription cuts

Only a couple of weeks after starting to use the feature, ChatGPT raised in its weekly report that one of my streaming services seemed abnormally expensive. It turned out to be due to an add-on costing $11.99 a month.

I had subscribed to watch one particular series, finished it, and then apparently just left the subscription in place. ChatGPT found the repeated charge and placed it beside my other entertainment subscriptions.

A second recurring payment belonged to a music app costing $9.99 a month. I remembered signing up for a trial, but I could not remember using it after the first week. The service had quietly collected nearly $60 since.

Canceling those two subscriptions saved $21.98 a month. ChatGPT Plus had technically paid for itself during the first review, with $1.98 left over. That is not enough to retire on, but it is enough to make my article headline legally defensible.

ChatGPT also spotted that my internet bill had increased by $10 from one month to the next. The promotional rate had expired, but ChatGPT suggested I could still get the savings with the right language. The AI wrote out a few talking points to get me any current discounts. After a surprisingly tolerable phone call, the company applied a new promotion and knocked the $10 back off my monthly bill.

Saving on food

The subscription cuts were easy to measure — I only had to cancel them once and the savings appeared again the following month without any further actions necessary. Food spending was more complicated because there was no single charge I could remove and forget about, but ChatGPT helped me save money there too.

Ordinary takeout is more expensive than ever, but it's the delivery charges and service fees that really raise the final number. I'm not going to stop ordering takeout altogether and deprive myself of all the dishes I can't make myself. But switching to pickup as much as possible would make a big difference, according to ChatGPT. And it saved me about $35 over the next month.

ChatGPT also picked up several trips to convenience stores. Each transaction was small, usually a drink or some paper goods I'd forgotten to pick up at the grocery store. Nonetheless, convenience stores are more expensive than the grocery store, and the result added up quickly, as ChatGPT found in its analysis.

Just staying aware of that fact changed my habits and made me more likely to remember everything I needed to get at my regular shopping run and to bring my own drink when hitting the road.

After two months, the changes were saving me about $67 in a typical month. That figure included $21.98 from the two canceled subscriptions, $10 from the restored internet discount, and approximately $35 from spending less on delivery. Food costs will naturally move around, but the recurring savings alone were already covering the price of ChatGPT Plus.

I wouldn't blindly trust ChatGPT with finances or anything else. And I definitely wouldn't make it my first consultant for any big financial decisions. But these smaller changes are more important than you might think at first.

My Plus subscription still appears on the statement every month, but the reduced streaming and internet costs, not to mention fewer delivery fees, more than justify the $20 a month I'm paying for it.

My house contains a small but steadily expanding museum of obsolete technology. I've always been reluctant to get rid of them, even if I don't use them for years at a time, so they just fill up boxes in my attic. I know theoretically they're worth something, but I never feel motivated to actually go through them and see what people on eBay might pay.

But I thought ChatGPT might be able to do all of that for me from a simple photograph. I took a picture of the stuff in one of my boxes. Like many boxes stored in my house, this one had lost any obvious reason for existing. It contained a Nintendo Wii, two games, an elderly Kindle, and several DVDs that had survived multiple clear-outs.

I asked ChatGPT to identify everything, determine what might still have resale value, and check recent eBay sales to find out how much it might be worth. I specifically told ChatGPT to look at completed or sold listings, not merely the prices sellers were asking. Anyone can list a dusty iPod for $500 and describe it as rare; I wanted to know what a buyer actually paid.

Wiis and DVDs

ChatGPT had little trouble recognizing the white Nintendo Wii and the familiar Wii Sports sleeve. It also identified the second game as Marvel: Ultimate Alliance 2, although it asked for a closer photograph to confirm that the disc and manual were inside.

The DVDs were identified from their spines, but ChatGPT warned that ordinary used movies generally have very little individual resale value. It suggested checking for out-of-print titles, complete television series, unusual editions and sealed copies before assuming they belonged in a bulk lot.

The Kindle in the box turned out to be a first-generation Kindle Fire rather than one of Amazon’s E Ink readers. ChatGPT identified it from its thick black body and confirmed the model. It still worked, though the resale results were not especially exciting. Working first-generation Kindle Fires generally appear on eBay for about $10 to $20. Current eBay comparisons suggest a realistic selling price of about $15 to $20 for a tested, reset unit in decent condition.

A tidy sum and cleared space in my attic

The Wii powered up without complaint, which was more than I expected after its extended sabbatical. The disc drive worked, the remote connected and the console reached its home screen without producing any alarming noises. Recent white Wii sales put the market value of the console alone at approximately $40, with complete packages commanding more.

ChatGPT estimated the hardware at approximately $50 to $60. The more interesting discovery was Wii Sports. Since the game came with millions of consoles, I had assumed it was practically packaging material. Recent eBay transactions suggested otherwise. The current market is close to $30, almost as much as the system that plays it. People buying replacement consoles often want the game they remember playing with them, usually before someone put the remote through a television.

All told, ChatGPT said I might get between $115 and $135 for the complete box. The precise total would depend on condition, postage, selling fees, and whether buyers made lower offers. It would also require me to photograph everything properly, write accurate listings, and take seven packages to the post office. After a couple of days on the website, I managed to get pretty much what ChatGPT estimated, with $130 dollars for the lot.

The experiment worked because ChatGPT did more than name the objects. It asked for model numbers, checked whether accessories were present, distinguished complete games from loose discs, and noticed when an individual item deserved its own listing. It also prevented the DVDs from acquiring imaginary value merely because they had become old. It may not be the Antiques Roadshow retirement fund, but it's enough to encourage me to take more photos of the boxes in my attic this week.

• 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.

• Claude AI has the highest satisfaction score among current and former users with a net score of 56.2, ahead of Gemini at 46.5
• Grok and Siri languish at the bottom of the satisfaction ratings top 10
• The data was collected by YouGov in the period March 1, 2026 to July 31, 2026

A new YouGov survey has lifted the lid on how people feel about the AI tools and assistants they use, with Claude AI coming out on top with the highest satisfaction score. Anthropic’s AI has beaten OpenAI’s ChatGPT and Google Gemini into first place, but of interest are the tools that languish at the bottom of the table.

While Microsoft Copilot and Alexa hog the middle of the table, it is the reputation of Apple’s Siri assistant and Elon Musk’s Grok that are the most surprising, with respondents reporting that the tools did not satisfy.

Interestingly, Claude is also highly regarded among former users, which may indicate that competing tools (particularly those with a poorer reputation) have a lot of catching up to do in the public consciousness.

Everyone loves Claude

Collected over a five-month period, YouGov established two sets of data: the “Top 10 AI assistants by Satisfaction among current and former users” and “AI assistant Satisfaction: current vs. former users.”

Based on the “Top 10 AI assistants by Satisfaction among current and former users” Claude’s 56.2 score puts it markedly ahead of Gemini on 46.5, with ChatGPT a close third on 46.0. Grok and Siri are notable for their low positions, but Alexa (42.6) and Copilot (37.2) occupy a middle space that doesn’t quite seem to match with the size of the companies behind them.

However, one thing that the survey doesn’t tell us is how the Satisfaction metric might be affected by tools that are easy to find or use (for example, Siri or Alexa) versus AI tools that need to be sought out, such as ChatGPT or Claude.

AI tool reputations

YouGov’s data also explores the difference between the satisfaction of current users and former users in the “AI assistant Satisfaction: current vs. former users” results. This again supports the notion that Claude delivers more usable results, with it gaining the highest “Former users Satisfaction score” as well as the second highest “Current users Satisfaction score.”

The report states “The widest current-versus-former gaps appear for ChatGPT, DeepSeek and Grok. ChatGPT is 63.7 points higher among current users than former users; DeepSeek is 62.5 points higher, and Grok is 62.4 points higher.” Grok has a particularly low score among former users: -4.7!

The data was collected by YouGov’s BrandIndex tracking system, a tool that interrogates the live users on YouGov with targeted questions about specific topics or news stories. To learn about AI tool reputation, YouGov collected data over a five month period, between March 1, 2026 and July 31, 2026, focusing on users in the UK.

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 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.

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.

To comply with the EU AI Act's Article 50(2) Code of Practice on Transparency of AI-Generated Content, Anthropic has announced that “New models will mark AI-generated content from day one”.

This is a remarkable step for Claude, because it not only applies to any images it generates, which are relatively easy to watermark, but also to any text it generates.

Anthropic says that Claude models will have an “imperceptible watermark” embedded directly into generated text at the model level. It says the mark should survive copying/pasting and minor edits.

Anthropic has not yet publicly explained the exact algorithm or released a detector, so we can only speculate for now about how it might be doing this and its effectiveness.

Hemorrhaging customers

Personally, I think that Claude is about to start hemorrhaging customers, unless the marking is relatively easy to circumvent, or all the other major AI players immediately follow suit.

If every piece of text it produces will now be easily identified as AI, then Claude becomes useless as a tool to a lot of people who are currently using it to generate text and are not being entirely honest about where that text came from.

And then there’s the issue of using Claude to proofread your human-written text — will your text now be flagged as AI if you accept Claude’s editing advice?

Your initial reaction to that might be “Good! You should be forced to reveal when AI has written something, and it’s about time people started writing on their own again!”, and you’d be entirely justified in that opinion.

But while it remains the only one of the big three AIs that’s doing this, I think we’ll see a lot of people switch to either ChatGPT or Gemini, because they don’t want to be revealed as using AI in their work.

How is watermarking plain text even possible?

We don’t know exactly how Anthropic is doing its marking with text yet, but my best guess is statistical watermarking during token generation rather than hidden Unicode characters or metadata. Imagine that at every point Claude is choosing among several perfectly reasonable next words:

e.g. The movie was excellent / superb / terrific / impressive.

Normally it chooses according to the model's probability distribution. A watermarking system can secretly divide possible tokens into preferred and non-preferred groups using a key. Claude then gives a tiny statistical nudge toward the preferred group.

One word tells you nothing. But across 500 or 1,000 words, a detector with the key can ask if the text is choosing the preferred tokens significantly more often than chance would allow. If yes, then there's statistical evidence it came from the watermarked model.

So, the watermark is more like a faint statistical fingerprint distributed across hundreds of choices, which would also explain how it can survive a copy-and-paste. You’re essentially copying the fingerprint along with the words.

But can you crack the code?

Since Anthropic hasn’t released an AI text detector yet, it’s impossible to know how easy this code will be to break just by changing a few words. For instance, if you put your 1,000-word Claude article into another LLM and wrote “Rewrite this completely in different words while preserving the meaning”, would it then be impossible to detect as AI?

I’d also be interested to know how long a piece of text has to be before it can be marked in this way, and as soon as a detector is made available, I’ll be testing it.

Perhaps the bigger issue is that Claude has done this to comply with Article 50(2) of the EU AI Act. From August 2, 2026, providers of generative AI systems that produce text, images, audio, or video are required to make those outputs machine-readable and detectable as artificially generated or manipulated, insofar as that is technically feasible. Existing systems get a limited transition period until December 2, 2026 for this particular requirement.

A note on Anthropic’s statement confirms that the watermarking additions will be applied retroactively to all existing Claude models, not just any new models it produces.

Anthropic's new system is explicitly a response to those rules, and it says the watermark will apply globally, not just when Claude is being used in Europe. Broadly speaking, OpenAI and Google face the same requirement if they want to offer qualifying generative-AI systems in the EU.

They don't necessarily have to copy Anthropic's method of using statistical text watermarking, but they will need to produce output that is machine-readable and detectable, and the technical solution should be effective, interoperable, robust and reliable as far as technically feasible.

I’ve contacted OpenAI and Google for comment, and will update this article if I receive it. For now, I think if Anthropic embarks on this path as the only one of the major three AI chatbots to do so, it could have a disastrous effect on its customer base.

Update: While OpenAI declined to provide specific comment on this story it did direct me to a statement online, where it states:

"Consistent with our commitments under the European Commission’s Code of Practice on Transparency of AI-generated content, our goal is to expand provenance signals to all modalities including text, so customers and developers have clear ways to meet their own transparency obligations as standards and tooling continue to mature."

• 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

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.