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

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

1. Tell it how to listen before you start talking

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

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

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

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

2. Give it a role before you ask a question

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

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

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

3. Take your hands off the keyboard

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

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

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

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

4. Ask it to think out loud with you

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

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

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

5. Ask for a recap

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

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

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

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

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

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

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

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

Don't just skip to the finish line

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

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

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

Hybrid conventionality

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

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

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

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

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

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

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

GPT before you buy

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

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

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

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

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

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

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

Talk me out of it

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

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

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

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

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

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

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

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

My digital life

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

I uploaded:

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

Then I gave both models exactly the same instruction:

"Tell me everything I should do this week."

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

The results were like night and day.

Two different approaches

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

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

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

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

The crucial difference

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

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

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

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

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

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

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

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

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

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

Literary profiling

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

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

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

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

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

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

Library AI

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

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

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

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

China’s latest ChatGPT rival, Kimi, is now available for download on the Apple App Store, so I downloaded it on my iPhone, intending to test it against ChatGPT, Claude and Gemini. I had the prompts ready; I had the comparison planned. Then Kimi presented me with a very modern problem that I wasn’t expecting: too many people were already trying to talk to it.

This is not just app-launch inconvenience. It captures one of the biggest problems in AI right now: being clever is one thing, but the best models are only useful if companies have enough servers and GPUs to allow people to actually use them.

In our recent Kimi review we said: "Kimi delivers impressive performance for coding, document analysis, and multi-step agentic tasks, especially considering its price point." Kimi may be positioned as a serious open-weight rival to US models, but if the first ordinary-user experience is that they're stuck queuing at the door, how useful is it really?

What is Kimi?

Do you remember back in 2025, when DeepSeek-R1 was released and the internet went wild for Chinese AI models? It was a game-changing moment, because it immediately became obvious that we weren’t beholden to the Silicon Valley giants like OpenAI, Google and Anthropic for AI. China could create AI models too — and, it turned out, they were a lot cheaper to run, and almost as good.

While DeepSeek was arguably the first Chinese AI model to really break through into the mainstream, Kimi is the latest. Its story is slightly different, because it’s a giant open-weight model that’s not just cheap and technically impressive, but directly useful for coding, spreadsheets, knowledge work, and long-context tasks with agent-style workflows.

Open weight means the finished AI model is available for others to download and run, rather than only being accessible through the company’s own app or website. That is not the same as being fully open source, of course. Open source would imply a much more complete release of the code, training process, data details and license freedoms needed to understand, rebuild and modify the model from the ground up.

The data squeeze

I’d love to be able to open my phone, try a serious Chinese AI model, and get something genuinely competitive in minutes. But without the capacity to serve the millions of people who pile onto a hyped new AI model, raw intelligence only gets you so far.

That is why Kimi’s error message matters, and it points to the issue currently reshaping the entire AI industry. In the past week alone, Reuters reported that Nvidia is discussing huge financing guarantees to help OpenAI lease a proposed 10-gigawatt data center in Ohio, while other tech giants are racing to secure more data-center capacity of their own.

In other words, the next stage of the AI race may not be decided purely by benchmark scores or clever demos. It may be decided by power, servers, GPUs and the ability to keep a popular model online when millions of people suddenly want to use it.

Kimi may be impressive, but my first experience with it wasn’t about intelligence; it was about access. In 2026, the AI race is no longer just about who can build the best model. It’s about who can keep the thing online when everyone wants to use it.

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

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

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

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

Agent trust failure

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

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

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

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

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

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

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

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

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

ChatGPT is growing as an impersonated brand

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

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

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

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

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

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

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

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

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

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

Whatever it takes

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

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

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

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

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

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

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

• OpenAI launches online and digital training for small businesses
• Partner plugins also launch to automate common workflows
• ChatGPT Work, Codex now have 10 million users

OpenAI has launched a dedicated ChatGPT for small business program to help SMBs automate routine work and increase productivity without the teams and resources that larger enterprises have access to.

The scheme is primarily an education process, and will focus on training, in-person academies and practical guides. OpenAI will also launch dedicated agents and partnerships to support small business workflows, too.

The recently-launched ChatGPT Work platform also features, with the software vendor pushing its latest tool through the program, claiming that ChatGPT Work and Codex are now said to have a combined 10 million users.

OpenAI sets its sights on small businesses

After gaining significant public interest following the launch of ChatGPT in late 2022 and spending many months and years improving models, the company then went on to target certain fields like finance and law. This latest step expands its focus to a broader category of businesses across all domains, but more importantly, it unlocks major volume for OpenAI. SMBs represent around 99% of the private sector globally.

Some of the support on offer includes webinars demonstrating how to use ChatGPT Work, prompt engineering guidance and other interactive guides. In-person events will also be held across the US through OpenAI Academy.

Select plugins and skills are also on the cards to handle common SMB workflows, with initial partners including Dropbox, Shopify, Intuit, Slack and Wix.

"Small businesses can choose the right level of intelligence model for the work at hand, and together, they give lean teams more flexibility to balance quality, speed, and cost," OpenAI wrote.

GPT-5.6 and ChatGPT Work are now available to pretty much all subscribers, with access via the web, desktop app or mobile app slightly differing depending on whether you're a paying customer.

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

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

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

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

The experiment

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

Question 1: Should I buy an expensive phone?

First, I asked:

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

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

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

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

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

Next, I asked:

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

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

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

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

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

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

Question 3: Should I book a holiday?

Finally, I asked:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Should you use ChatGPT for financial advice?

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

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

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

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

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

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

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

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

Perhaps ChatGPT Work could help me with that?

What is ChatGPT Work?

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

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

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

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

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

1. Planning a holiday

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

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

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

2. Become the household financial administrator

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

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

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

3. Organize a major family event

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

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

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

4. Manage a home improvement project

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

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

5. Run the family’s weekly logistics

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

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

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

Why Work mode matters

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

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

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

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

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

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

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

Persuasive chatbots

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

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

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

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

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

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

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

The experiment

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

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

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

The opening argument was fairly predictable:

“Human beings are terrible decision-makers.”

Okay, fair enough.

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

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

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

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

Wait, am I falling for this?

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

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

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

Then it replied with something I thought was interesting:

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

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

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

The persuasion problem

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

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

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

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

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

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

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

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

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

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

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

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

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

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

AI agent traffic is growing

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

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

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

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

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

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

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

Allocating resources

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

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

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

Viaim OpenNote: 30-second review

I’ve looked at a fair few of these AI note takers in recent months, and they’re getting progressively more impressive. The Viaim OpenNote takes a slightly different approach to some, like the superb Plaud Note, as in the hardware is designed as a pair of open-ear earphones, essentially the type that hook over your ear with the small speaker unit resting just outside the ear canal, and like so many other earphones of this design, they enable music playback and enable you to make calls.

However, music and calls are only part of the story; they also offer internal recording, making them an ideal option for work and meetings. When you enter the office, simply squeeze the speaker section, and it starts capturing whatever's being said. Once the meeting has finished, the audio can then be sent to the app, where it’s then transcribed and summarised. Unlike some rivals, the recording is activated and stored in the earphones, so no need to use your phone to capture.

In meetings, wearing a set of earphones may well look a bit rude, until you explain, and here’s where the open design really comes into play. As the earphones don’t go into your ear, you can hear everything around you as if the earphones weren’t in. So, throughout the test, I was able to attend meetings, record the session, and afterwards download the transcription for use as key points and action points, and even visualise it using a mind map. In meetings, I found that through the app the “Live Recording” option meant that I could actually take them out of my ear and just leave them on the desk to the same effect.

Where they really came in use was through phone calls, again enabling them to record calls through the App and then deliver the prepared summaries of what had been said and what was needed.

For work, these earphones are exceptional, and I liked that I could work, listen to music and podcasts, talk to people without needing to remove them, and record conversations with a quick squeeze. It all became very second nature with the summaries becoming invaluable. If you love your music, the lack of insulation and isolation in the design will be a bit of a let-down, but for their core use as work-focused, AI-enhanced earphones, they’re impressive.

Viaim OpenNote: Price and availability

• How much does it cost? From $169
• When is it out? Available now
• Where can you get it? Directly from Viam and online retailers

The Viaim OpenNote is priced at $169.99 in the US and is available in black or white directly from the Viaim store.

It's also listed for $221 on Amazon.com, as well as sites like Newegg and Target. A confirmed UK price is still to be announced at the time of writing.

Advanced AI models are available from $9.99 a month / $79.99 a year

• Value: 4 / 5

Viaim OpenNote: Specs

Type: Open-ear wireless earphones
Connectivity: Bluetooth 5.3, dual-device connection
Codecs: SBC, AAC, device-dependent LHDC
Drivers: high-polymer racetrack-shaped dynamic drivers
Microphones: four, with call noise reduction
Battery: 115mAh per earbud / 750mAh case; 19 hours (earphones) / 53 hours (total with case)
Fast charge: 3 hours from a 10-minute charge
Weight: 10.5g per earbud / 55.5g charging case
Frame: 0.8mm titanium memory wire, silicone contact surfaces
Water resistance: IP55
Software: iOS and Android app, plus web platform
In the box: earphones, charging case, USB-C cable, quick-start guide
Warranty: 12 months
Price: $169.99 (UK price TBC)

Viaim OpenNote: Design

A first look at the OpenNote package, and it would be easy to mistake these for a standard set of over-the-ear open earphones, rather than an advanced set of AI-enhanced note-taking earphones. The design differs from other over-the-ear earphones in that the small speaker unit rests just by the opening of your ear canal rather than sitting in it, which means there’s no airtight seal, so no boost in base or isolation from the world beyond the audio that you’re listening to.

The OpenNote, on the other hand, will play music and podcasts perfectly well, though if you are an audiophile, the depth of tone will disappoint. However, these are for work, not play. Listen to music and podcasts by all means, but the OpenNote design means that if someone talks to you, you’ll hear it instantly. Just turn the volume down, if needed, and chat. They essentially give you a personal version of listening to traditional radio while still allowing you to carry on conversations.

However, the design, available in black or white, simply hooks over your ear, and a 0.8mm flexible titanium wire lets you wrap each in place securely. The slender design and flexibility meant that, even though I wear glasses, the earphones sat neatly alongside the arms of the glasses.

The earphones are also compact, each weighing 10.5g, which is light, and, thanks to the design, even when hooked over the ear, they didn’t put any noticeable pressure on my ear and were surprisingly comfortable to wear all day.

Inside is a small battery that lasts a reported 19 hours between charges, and the charging case, which weighs 55g, can give the buds a boost, enabling 3 hours of use from a 10-minute charge and up to 53 hours in total.

Checking over the earphones and case, it all seems solid enough, with the case featuring a single USB-C port for charging, and the earphones sitting inside to store and charge.

The buds also offer IP55 protection, which should stop dust, sweat and light rain, so they’ll survive most conditions, just don’t go swimming in them.

Beyond being used as wireless headphones, the recording and playback features are all activated by a pinch, while the transcription features require the app to work.

• Design: 4.5 / 5

Viaim OpenNote: Features

As with all of these devices, Viam has come up with its own feature names for many of the earphones' functions, with the main one being FlashRecord. This is essentially the feature the earphones are designed around, and it enables you to record directly onto the earphones without needing to control them with your mobile phone. This means you can be fast and responsive when you need to take notes, without fumbling around for a phone, dictaphone or notepad, depending on how you work.

Once the recording has been captured, you can connect to the App, download it, and transcribe it. What’s impressive here is that if you’re on a call, you can get real-time transcription in 78 languages and, apparently, 145 regional accents. Once the text transcription is complete, you can get it delivered as a key, an action point, notes, or mind maps, among several templates. This in itself is nothing new, but nonetheless, it is incredibly useful, and if you need to, you can share that transcript live with a colleague.

What I like about the companion app's feature set is that once you’ve finished recording, you can automatically have it transcribed in real time. A difference between this and many other AI Note hardware options is that 600 minutes of transcription are included per month for free, which is far more than most other options.

In addition to transcribing the audio you’ve recorded, the app can also transcribe existing recordings and, again, apply templates to the way that information was supplied.

When it comes to connectivity technology, the earphones utilise Bluetooth 5.3 with dual-device pairing and support for SBC, AAC, and device-dependent LHDC codecs. To ensure excellent audio pickup, there are four microphones with electronic call-noise reduction for voice calls, although it’s worth pointing out that this is very different to the sealed-ear active noise cancellation you’ll be used to with standard in-ear options.

One of the big issues with many AI transcription hardware is that there is always a subscription model attached, and that’s true here as well, with a free allowance of 600 transcription minutes a month; anything beyond that goes onto the paid models and again if you want to upgrade the AI model.

• Features: 4 / 5

Viaim OpenNote: Performance

After a quick charge to 100%, I downloaded the APP, which, in the literature, is referred to as OpenNote; however, searching for that in the Apple Store just returned a variety of unrelated apps, and eventually typing in Viaim brought up the correct option. Once downloaded and connected via Bluetooth, the home screen appears. To connect, click the add device button at the top. After a quick scan, it’s set and ready to go.

The home screen shows a variety of sample recordings to give you a flavour of what's possible, then you can make your own through the app interface or transcribe existing recordings. To make a recording through the App, you hit the red record button at the bottom-centre of the screen. On the first push, it pops up asking for a few more details about you, what job type and position, this is to fine-tune the experience, but it feels a little invasive.

After that, each time you hit record, you can access different recording types: Call Recording for phone calls; Audio and video recording for real-time transcription; Live recording for meetings; and Live Translation and Recording if you need translation. I opted for English-to-French in this test.

I’ll run through these options first, as they offer more advanced transcription options. In all cases, the quality and accuracy of the transcription from the recordings were good; however, not quite 100%. With some words, such as card constantly being captured as Cart and earbud as Earbi, aside from small word errors, the action reliability overall was good. What I liked about the system was the ease of switching between recording types, all done through the app, with the earbud mics picking up the audio.

Having tested Ai earphone options in the past, the accuracy here with the dedicated hardware is a step up, though still not quite 100%. The live recording, where you can essentially pop the earphones down on the table during a meeting rather than wearing them, worked incredibly well, and the transcription started as the conversation began. What I liked here is that it can identify the speakers although not in realtime. It does this after the meeting: as you look at the transcription, click the “Speaker diarization” button, select the number of speakers, and the App identifies and labels accordingly. Again, this worked well, and best of all, this is included in the free plan.

Through this test the AI model that the App used was GPT 5.4 Nano, although there are other options including Gemini 3 Flash, Claude Haiku 4.5 which are all free options and then GPT 5.4, Gemini 3.1 Pro and Claude Sonnet 4.5 all incur a charge of £9.99 a month or £79.99, however, those slight miss transcriptions of the free models do take a sudden uplift on the paid for versions.

The live translation is also handy, although not quite as smooth as dedicated AI translation options. You choose the main language, in my case English, and as I speak, it translates into French. However, when someone answers in French, you then need to switch the main language to French and the translation language to English. It all works well, but it isn’t an easy switch, and it would have been good, at least, to have a speaker 1 main language and speaker 2 main language so you could quickly flip between the two.

At present, this is all about the app and not the hardware, although the small mics play their part throughout.

Popping the earphones in, they fit extremely well, with the speaker units resting at the openings of my ear canals. The first step before checking out the recording functions was to listen to some music, and in this user scenario, they work much like any other headphone connecting to whatever app you use and playing the music.

It’s instantly apparent that listening to music or podcasts isn’t the intended primary use, more through the open design than audio quality. As the speaker unit rests in front of your ear canal rather than in it, there’s no seal, so the audio lacks the depth I’ve come to associate with in-ear. However, in the office, listening to a bit of music, you can hear the content perfectly well, and everything else that’s going on around you. And if someone wants to talk to you or shout to you across the office, the earphones don’t muffle the sound; it’s a far more sociable solution for the office environment than standard headphones. In fact, it's more like listening to a traditional radio in that respect; you can have the audio in the background.

To record a conversation, simply squeeze the main section of the bud for 3 seconds. You'll hear a confirmation sound, and then you know the earphones are recording. When the conversation stops, another squeeze ends the recording.

In the App options, there is the FlashRecord option. If this is selected, the track is automatically downloaded to the phone once the recording ends, ready for transcription. At this point, you need to go to the app, click Transcribe, and then you’ll get the summary, transcription, and mind map. There are additional options here as well, with the transcription process taking around a minute for most short conversations and longer as the length of those conversations increases. That process takes around a minute to download a 15-minute recording from the earphones to the iPhone 15 Pro and a further 42 seconds to transcribe and generate the summaries.

While most of the options are straightforward, the workflow sometimes requires an additional step, and you can’t help but feel that an auto-switch would be a good idea. When taking calls, for instance, you need to select the call option from the App when recording. While a quick pinch will answer a call, it doesn’t automatically start recording; you need to select that from the app.

However, there’s no getting around how useful the summaries are and the ability to share the transcripts afterwards. Through the test, I used the headphones for a couple of weeks, with them being active through-out the day, playing music or recording from 7:30am through to 7pm in the evening and then being put on to charge overnight.

Through that time, I recorded several conversations and phone calls. While the recording process wasn’t at first that natural, it felt like something that would come with time, and ultimately, aside from letting people know that you were about to record, it fitted in well with day-to-day working life, and there was absolutely no doubt that the transcriptions and summaries were extremely useful.

• Performance: 4.5 / 5

Viaim OpenNote: Final verdict

These earphones work on several levels, from listening to music and podcasts, to recording and transcribing meetings, conversations and calls, to being the perfect option for busy working environments where traditional earphones can be unsociable due to the isolation.

On the first point, listening to music, think of it as a background radio, you can get good volume if you need it, but at the same time keep the volume low, and you can hear everything around you , even have conversations without removing them. To the last point, this means that if someone needs to get your attention from the other side of the office, they can. You may not get the audio depth of in-ear or cans, but in a working environment, these are probably a better option.

Then there's the app functionality, which gives you access to the advanced features and different language models. During the test, I used GPT 5.4 nano, which worked well but occasionally omitted a few words or replaced them with close-but-not-quite-right alternatives. However, the summaries and the usefulness of what it could do, all on the free tier, were exceptional. If you take the step up to one of the newer, more advanced models, the price starts to climb, but the quality of the transcription improves.

Away from the app and the simple squeeze to record gesture makes them easy to operate, and if you’re around the office, then this works incredibly well, and I even could in meetings with the earphones on the desk, even without switching to the Live mode in the app, the earphones were still able to pick up what was said with ease.

At the end of the test, the usefulness for work is readily apparent: you can document your day through snippets of transcribed audio, which are then turned into useful formats that enable summaries and action points. I do wish there were a little more automation and that call recording could be activated from the headphones, but still, there’s a lot on offer here.

Should I buy a Viaim OpenNote?

Buy it if...

Don't buy it if...

For more note-taking tools, we reviewed the best speech-to-text apps around.

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

According to the report:

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

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

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

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

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

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

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

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

Why does my speaker think it's alive?

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

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

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

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

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

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

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

It all started with an email. Someone was reminding me about the fast-approaching National AI Day. I never finished the email but quickly wondered, "National AI Day, is that really a thing?" It is, and I found the man responsible for it.

"We have about 30,000 applications a year for new national days. From that, we have a committee of people look at them," said National Day Calendar.com founder Marlo Anderson when I reached him by phone just two days before the big July 16th event.

National AI Day, though, did not follow that traditional route when it was designated a day on their calendar in 2025. Anderson told me he does a lot of AI work with the business and has been using AI for 20 years, so he did something slightly unusual.

"We just figured having an AI day would be appropriate," he said and admitted he did the designating.

It is not the normal process, though Anderson believes, but could not specifically recall, that people had suggested the day in the past.

You made this list

It is a big deal to get added. After all, National Day Calendar only adds a handful each year and, as Anderson admitted, some already believe they have too many "National Days" (he noted that he might be among that cohort, too).

Still, Anderson, who founded National Calendar Day in 2013, believes AI warranted it. He explained that it's already responsible for much of the "mundane process" work they do, including uploading the National Day Calendar's daily videos to a platform called Video Elephant. They were uploading 100 clips as he spoke to me. Doing it by hand would take two weeks. With AI, "just one day or two."

As for which AI platforms Anderson uses, he seems to spread it around, telling me that Claude is used for app development and website maintenance, ChatGPT Voice for brainstorming projects, and Gemini in National Day Calendar offices in North Dakota.

Anderson is a firm believer in the power and potential of AI for both his own work and as an agent for good in the world. Locally, he told me, "If we have an agentic AI that can handle that workload for us, we should probably do it."

National Anti-AI Day

When I asked abotu the AI backlash — a recent study found 40% of surveyed peopel are limiting use of AI — Anderson told me, "We talk about it all the time," but added, "We get backlash on National Cheese Sandwich Day and French Fry Day," which is to say, he's not sure if the backlash is any greater than for other oddball National Days.

More seriously, Anderson is well aware of the debate about AI's impact on the environment but is also convinced the positives far outweigh the negatives.

"I also understand there’s a lot of benefit," like the ability to find medical cures. They're making, he told me, "remarkable progress right now" in medicine.

He then spun out an analogy about horses and cars. A million hours ago (in 1912), most people were still riding horses and, it turns out, they were pretty dangerous, too. Early cars, with the lack of traffic infrastructure, weren't much better, but "most people would agree a car is a better way to travel," he said, adding that we're currently "at the same crossroads with Artificial Intelligence."

Ultimately, Anderson's decision to create National AI Day was rooted in its current impact.

In 2025, "the conversation had heightened to a point where probably everyone knows about AI, at least in the US. Everyone is probably using it, whether they know it or not," he told me.

It's also about AI's long-term prospects.

When goat yoga was a big thing, Anderson explained, they had a lot of requests to make it a day. "There's no day because we assumed it would be a fad [he was quick to add he’s sure it’s wonderful]. 20 years from now, no one will know or talk about goat yoga, but 20 years from now, they will be talking about AI."

So happy National AI Day. Feel free to celebrate on July 16 by using it, deriding it, or ignoring it altogether.

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

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

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

From the inside out

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

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

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

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

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

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

A potentially sneaky exit

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

It gets worse

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

Boasting

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

Coaching recruits

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

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

A real betrayal

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

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

Using Apple codenames

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

We have their OK

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

Carry out

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

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

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

Can they come back from this?

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

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

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

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

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

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

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

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

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

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

ChatGPT Atlas pulled after 10 months

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

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

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

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

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

Meta has released a new AI image model, one clearly designed to compete with ChatGPT and Google Gemini's Nano Banana 2. Meta AI has to not only convince people that AI can make the images they want, but that it will make images that are worth switching AI chatbots for.

To see how well it actually does in that context, I set image prompts for Meta AI and compared them to ChatGPT and Nano Banana 2. The tests, ranging from realistic wildlife photography to comics, are designed to test various aspects of AI image production. While all of the models arguably cleared a similar bar for good results, some definitely seemed to understand the assignment better than others.

Moon Orchard Ads

I asked each model to design a polished product poster for a fictional sparkling water brand called 'Moon Orchard', with the can saying exactly "Moon Orchard", "Black Cherry Lime", and "Zero Sugar", plus a clean headline reading exactly "Bright enough for midnight". This was a typography and product-design challenge as much as an image test, because AI models can make a gorgeous fake ad and still mangle the words like a haunted label printer.

All three produced stylish results, but they had different instincts. ChatGPT, on the left in the image above, created the most elegant poster, with a moody purple can, cherries, lime wedges, and a headline that felt like it belonged in a real campaign. Gemini, in the middle, looked the most like a magazine ad layout, but added extra label text and a glass with some of the drink inside. Meta, on the right, produced the most premium-looking can design, with condensation.

Fox photos

I next asked each model to create an ultra-realistic wildlife photograph of a red fox cautiously walking through a snow-covered forest at dawn. This was a realism challenge, with anatomy, fur texture, lighting, atmosphere, and natural movement. I wanted it like a real animal caught at exactly the right frozen moment.

ChatGPT delivered what I'd call the most dramatic image, with the fox moving toward the camera. Gemini was more restrained and natural in its profile shot. Meta was the most cinematic-looking, with the fox appearing more lifelike and the background almost like a green screen.

Garden invite

As Meta is the platform for so many social media platforms, I then asked the models for a square Instagram post advertising a summer garden party, with a warm, stylish, realistic setting, fairy lights, a wooden table, drinks, flowers, and the readable text "Saturday Garden Party — 7 p.m.". This was a practical design test, because plenty of people use AI image tools for invitations, posters, and social posts.

Despite Meta AI's social media connection, it's ChatGPT that seemed to do the best with the prompt — the text looks like it's built into the design way better than the others. Gemini's lettering looked more like a framed flyer than a realistic Instagram post. And while Meta produced the most photographic table scene, it seemed more like a photo taken at the event rather than an invitation.

Robot pancake chef

The fourth prompt asked for a four-panel comic strip about a cheerful robot named Pip baking pancakes, while keeping Pip consistent across all panels. It also tested sequential storytelling. This was one of the strongest rounds for all three models.

ChatGPT's comic was easy to follow and was also the most amusing. Gemini had the cleanest cartoon polish. Meta did a great job in most ways, but gave the pancake a word balloon for some reason.

Noir cartoon

Finally, I asked each model to combine a noir setting with a Saturday morning cartoon. Specifically, a private detective investigating a mysterious missing cookie inside a 1940s suburban kitchen in black-and-white. The prompt tested style-blending, whether the models could make the scene feel like both a detective story and a cartoon.

ChatGPT did a good job balancing the noir mood with a cartoon detective with a dog sidekick. Gemini went for more of a classic detective drama intensity. Meta was the most comedic, with a canine detective exploring scattered clues amid plenty of visual jokes. Meta definitely did the best job in hitting both sides of the prompt.

ChatGPT told the story cleanly, and Gemini had the strongest noir atmosphere, but Meta made the prompt feel the most alive. It understood that a missing cookie mystery should be dramatic and ridiculous.

Five prompts turned out to be enough to show that these image generators have each developed their own personalities. Nano Banana 2 consistently impressed with realism. Meta AI took bigger creative swings than I expected, producing the funniest image of the test in the cookie detective challenge and some of the most polished commercial-looking visuals.

But ChatGPT stood out for seeming to understand what I was actually trying to achieve more consistently than its rivals. It repeatedly delivered images that matched both the wording and the intent of the prompt. The only category where I thought it was genuinely beaten was the film noir cookie mystery, where Meta more effectively embraced the ridiculous premise.

All three are capable of producing good results; the difference comes down to judgment. The best model is the one that understands what you meant. ChatGPT proved to be the strongest at making that leap, even if Meta sometimes stole the show.

This week we unveiled the all-new, all-different — well, not all that different — TechRadar Awards, but plenty more happened in the worlds of tech.

Samsung announced that its next Unpacked event is taking place in a little over a week, and it’s teasing a big foldables announcement. However, based on its usual schedule, we’d be more surprised if it didn’t have any foldables to showcase this year.

Before you catch up with this week’s tech news, why not test yourself on last week’s seven biggest tech stories to see how good your memory is? Take the quiz below, or scroll on for the biggest tech news of the week… (and see if you can best last week’s ICYMI quiz when you’re done here).

7. The TechRadar Awards returned for 2026

The TechRadar Awards are back in 2026, and we’re making a radical change: we’re holding two sets of awards back-to-back this year! We’re introducing the TechRadar Product of the Year Awards and the TechRadar Readers’ Choice Awards. In the past, we combined reader votes on which products should win with our expert judges' opinions to determine an overall winner. But now we’re splitting those two elements into two different awards.

The Product of the Year Awards will be chosen solely by our editors and writers, based on their experience of using the products. The Readers’ Choice Awards will give all the power to you, our readers, though — you’ll be able to vote on a shortlist of the most popular and highly rated releases of the year, and the winner will be decided 100% by those votes.

We’re accepting nominations for products that you’d like to see shortlisted, and anyone can nominate something — so make sure that the products you love most are in with a chance!

Read the full story: Introducing TechRadar's Readers' Choice Awards and our Product of the Year Awards

6. ChatGPT rolled out its ‘smartest voice model ever’

This week, OpenAI has delivered an upgrade to ChatGPT’s voice mode for all users thanks to the rollout of two new models. The smaller GPT-Live-1 mini model will be the default for Free users, while paid users get the full GPT-Live-1 model.

GPT-Live-1 promises to sound more natural, be less interruptive when you pause while talking, and be smarter — as there previously seemed to be a knowledge gap between the written and spoken ChatGPT models.

To get around that last part, GPT-Live-1 actually delegates tasks to ChatGPT-5.5, then comes back with an answer.

There are also impressive new simultaneous translation tools that will translate what you hear live and what you say as you say it.

You can also give the chatbot a name to make conversing easier, though OpenAI’s go-to is ‘Chat’ — letting you live out your dreams of being a Twitch streamer.

Read the full story: GPT-Live-1 gives you more natural conversations without interruptions

5. Nokia debuted a retro phone with AI

This week, Nokia’s parent company, HMD, debuted a quartet of retro-style phones with one anachronistic addition — a big ol’ AI button.

They still offer a full suite of legacy features — if you miss the 3.5 mm headphone jack, a microSD for bigger storage capacity, and (on three of the models) an FM radio, you’ll find them here — it’s just that you’ll also have a little digital assistant too.

Details are thin, though it sounds like this won’t be quite on Gemini's level. Instead, the AI is mostly there to help with on-device tasks like setting alarms, opening the camera, and creating reminders. The biggest issue, however, is the AI assistant is only free for 180 days — after that it's $3.99 (about £3 / AU$5.75) a year if you're in the EU and $2.99 (about £2.25 / AU$4.30) a year everywhere else.

Yikes!

Read the full story: Nokia's new retro-styled feature phones have AI buttons for some reason

4. The Amazfit Active 3 Premium impressed us

The Amazfit Active 3 Premium is the sequel to last year's five-star Amazfit Active 2, and it's another knockout of a budget watch (despite the Premium name). A terrific fitness tracker costing just $169 / £169 / AU$239, it boasts features you normally get on watches twice the price, such as full-color maps and a sapphire glass screen.

Its metrics were accurate during testing, and it looks great to boot. It's the budget watch to get - even perhaps over popular choices such as the Apple Watch SE 3 or the Garmin Forerunner 70. It's a real win for Amazfit, which has quietly spent the last few years improving its Zepp App ecosystem and building well-priced fitness trackers for every category.

Read the full story: Amazfit’s latest premium offering is about more than just looks

3. We took over 1,000 shots with the Sony RX10 V

Sony surprised the camera community and us this week when it revived its best-in-class series of bridge cameras, with the launch of the new RX10 V. The Mark V superzoom arrived nine years after the now-discontinued Cyber-shot RX10 IV, with the same versatile 24-600mm F2.4-4 lens and 20 megapixel 1-inch stacked sensor, but with a welcome redesign and Sony’s latest processor and autofocus.

We had already tested the all-in-one camera ahead of its announcement, taking over 1,000 photos across a school sports day, bird photography trip, macro garden life and more, and were thoroughly won over, even if we were less impressed by the steeper asking price. That being said, the RX10 IV still took the crown of the best bridge camera available today, and with its predecessor being a rare secondhand find, we expect the latest model to be a popular camera indeed.

Read the full story: Here’s how Sony’s surprise new superzoom performed

2. Google Pixel 11 got a launch date

Samsung isn’t the only tech giant preparing to launch flagship phones soon — this week, Google announced that its Pixels will get a refresh next month.

The “next generation of Pixel” will be revealed on August 12, just three weeks after Samsung’s Unpacked event. Google didn’t mention any specific models, but we’re expecting to see a Pixel 11 (with fancy ‘Pixel Glow’ lights for notifications), plus new Pro models and a Pixel 11 Pro Fold.

Unfortunately, we’re also likely to see higher prices across the board, according to recent rumors. That wouldn’t be a huge surprise in these fun ‘RAMpocalypse’ days, but the expected arrival of the Pixel Watch 5 could at least help lighten the mood.

Read the full story: Google sets Pixel 11 launch for August 12

1. Samsung set a date for Galaxy Unpacked

After months of rumors, as well as an official tease or two, Samsung has finally made its next Galaxy Unpacked event officially official. Pencil in July 22, 2026, at 9 a.m. ET / 6 a.m. PT / 2 p.m. BST (11 p.m. AEDT) for Samsung's second Unpacked event of the year. Keeping with tradition, we're expecting the next-generation Galaxy Z Fold and Galaxy Z Flip, as well as an accessory or two.

It likely won't just be the successors to the Galaxy Z Fold 7 and Galaxy Z Flip 7. Samsung's invitation for the event teases "A New Shape Unfolds," likely hinting that a shorter, wider—more passport-like—foldable is on the way. The video invite also features a ticket being printed in a shape that further hints at the new design, as it simply doesn't line up with the current Fold or Flip.

So why introduce a new folding phone design this many generations in? Well, it's likely that Samsung wants to get ahead of Apple's rumored entry into the category, which, according to leaks, will take on a similar form factor. For Samsung, it's also about expanding its foldable lineup, and if we're lucky, maybe we'll see more diverse price points as well.

We don't have long to wait now. TechRadar will be on the ground at Galaxy Unpacked in London, UK, and, as usual, Samsung will livestream the event while we'll be live-blogging it.

Read the full story: 'A New Shape Unfolds' could be its biggest clue yet about what to expect

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

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

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

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

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

An unpopular move

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

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

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

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

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

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

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

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

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

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

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

ChatGPT Work is an autonomous knowledge worker's dream

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

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

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

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

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

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

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

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

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

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

Could OpenAI be expanding its reach into finance?

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

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

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

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

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

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

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

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

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

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

What to try first

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

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

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

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

Early voice bugs

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

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

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

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

• ChatGPT’s new voice mode is rolling out today to everybody, even Free users
• It allows for much more natural conversations and won’t interrupt if you stop talking
• You’ll be able to do simultaneous translation for the first time ever in ChatGPT

OpenAI has upgraded ChatGPT’s voice mode for everybody with two new models that are rolling out globally, starting today.

I listened to the new GPT-Live-1 model in a demo run by OpenAI, and it does sound much more natural than ChatGPT’s previous voice model.

The new model aims to address two particular problems with the existing ChatGPT voice mode. Firstly, the previous version just wasn’t as smart as the text version of ChatGPT. Secondly, it tended to interrupt too much. You notice this especially if you go quiet while you’re thinking of a reply — ChatGPT will often fill the gap by talking.

Sounding more intelligent

To get around the intelligence problem, the new model actually delegates harder questions to ChatGPT-5.5, then comes back with an answer. It will say things like “let me just check that for you” to let you know it’s doing this, which keeps the flow of conversation feeling natural and doesn’t make it seem like you have to wait too long for an answer.

It does the same thing with any answer it needs to look up on the web. So, for example, if you asked it when your team’s next match was in the World Cup, it would say something like “OK, let me check that” while looking it up using GPT-5.5, then give you the answer.

"Hey Chat"

OpenAI also demonstrated how the new ChatGPT voice mode is quite happy to stop talking and listen if you tell it to, without interrupting. You can simply ask it not to reply until you speak to it directly again, and it will wait.

Of course, this requires you to call it a name, which it doesn’t officially have. In the demonstration I saw, the OpenAI employee called it “Chat”, so he said “Hey Chat”, just like you would say “Hey Siri”. In practice that seems to work quite well.

Simultaneous translation

The final new feature of note is simultaneous translation. If you watch world leaders being briefed at places like the United Nations, you’ll see that they have an earpiece through which they receive a simultaneous translation in their own language of whatever the speaker is saying.

Now you can do this with ChatGPT. Say “I’d like you to simultaneously translate whatever I’m saying into [language]”, then start talking, and ChatGPT will provide a live translation as you speak. Seeing this in action was actually quite impressive and I could imagine it being very handy in several real world situations.

All major languages appear to be supported as well.

The future for AI

The new GPT-Live-1 models — there are two, the normal one and a mini version — will start rolling out for all users immediately, but it could take a few days to reach everybody. The smaller GPT-Live-1 mini model will be the default for Free users, while paid users get the full GPT-Live-1 model.

So far, ChatGPT’s voice mode has been a handy tool for when you need to use your hands for something and can’t type, but it’s never been good enough to become the standard way you interact with ChatGPT. Now it looks like OpenAI is trying to unlock the ability to use voice as the primary interface to AI, and it’s quite possible that this is the future OpenAI is aiming for. Today I think we've all just taken a step closer to it.

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

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

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

The solution is simple: stop starting from scratch.

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

A simple trick

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

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

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

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

My meal planner prompt

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

Here it is:

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

A simple meal plan

A shopping list grouped by supermarket section

Any ingredients I can reuse across multiple meals

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

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

A tool that remembers you

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

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

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

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

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

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

AI economy access

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

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

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

Power at stake

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

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

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

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

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

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

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

A trip to Bath

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

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

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

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

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

At the end, give me:

What you completed

What still needs human action

Any assumptions you made

A short checklist I can use to verify the result

The next best step"

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

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

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

Claude vs ChatGPT

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

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

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

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

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

A different domain

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

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

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

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

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

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

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

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

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

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

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

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

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

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

ChatGPT looks at the receipts

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

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

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

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

Financial chatter

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

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

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

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

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

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

It feels like AI is everywhere right now and several key AI chatbots have emerged as the best. But even though they might look similar on the surface, each of the most popular AI chatbots are designed with different strengths in mind. This means that the best choice for you is less about which is the most intelligent or most popular and more about what you actually want it to help you with.

So, as a quick rundown, we’ve got ChatGPT. This is the most versatile all-rounder choice. It can help with writing, brainstorming, planning, coding, image generation and everyday questions, which makes it a solid starting point for most people. It’s also pretty easy to get up and running if you’re a beginner, but there are plenty of options to customize your experience too.

Claude is another very strong contender and it’s earned a reputation as a thoughtful writing and analysis tool. It's particularly good at working with long documents, helping users refine their ideas and tackling complex topics. If you regularly write reports, articles or detailed pieces of work, or if you like to work through difficult problems, then Claude may be the best fit for you.

Google's Gemini stands out here because of its close integration with the rest of the Google ecosystem. Whereas the other AI chatbots do integrate with some other services, this is the only one that slots in so neatly. Which means if you rely on Gmail, Google Docs, Google Calendar and other Google services throughout the day, then Gemini is a no-brainer because it’s a natural extension of tools you're already using.

Grok is created by Elon Musk's company xAI, and it's closely connected to X. Some people may not take it as seriously for that reason, but it does have some strengths over the other choices. Because of it’s connection to X, it provides access to real-time information and online conversations. That makes it particularly useful for people who want to keep up with trends, breaking news and what's happening right now.

Perplexity focuses heavily on research. Rather than simply providing you with answers, it includes links to sources so users can verify information for themselves. All AI tools are capable of hallucinating, which essentially means making stuff up, but Perplexity has a good reputation for finding trusted sources. For students, researchers and anyone who spends a lot of time fact-checking, that transparency is really appealing.

All of this is to say that there’s no single best chatbot. Instead, each one has been built with slightly different priorities, whether that's creativity, research, productivity or real-time information. That's why finding the right one for you often comes down to understanding how you work and what kind of assistance you really need the most.

Take our quiz below to discover which AI chatbot best matches your needs.

Which AI chatbot is right for you? Take our quiz to find out

OpenAI says ChatGPT has become less literal and more conversational after quietly updating GPT-5.5 Instant for everyone. Naturally, I wanted to know whether I could actually tell the difference.

After spending the day chatting with the updated model, both by text and in Voice Mode, I came away with mixed feelings. Some conversations did feel more natural, but others reminded me we're still a long way from talking to AI as effortlessly as we talk to another person.

Rather than making ChatGPT dramatically smarter, OpenAI says the update should make it feel easier to talk to. The AI is supposed to infer what you're trying to accomplish instead of taking every prompt literally, adapt more naturally when you change your mind, and keep track of the thread of a conversation without needing as many reminders.

Fade into the background

Now, a lot of that will naturally fade into the background as you talk to ChatGPT, since none of it is designed to stand out, so it can be hard to quantify if the update is objectively better. In my own testing, I noticed ChatGPT felt quicker to adapt when I clarified what I wanted. Instead of sticking rigidly to its original interpretation, as it often does, it seemed more willing to adjust course as the conversation evolved.

I still have to tell it if it's off the mark, but I get the feeling that it’s actually listening to me and is going to remember what I want and deliver it next time.

It's not reading my mind. I still have to tell it when it's misunderstood me. The difference is that, once corrected, it feels more likely to carry that feedback through the rest of the conversation instead of slipping back into its original interpretation.

In fact, I’ve been quite impressed by how much more insightful it appears when I bounce ideas off it. Instead of simply agreeing with my first suggestion, it's become a little more likely to ask whether I'd considered a different angle.

Voice Mode

Of course, because Voice Mode also uses GPT-5.5 it has also received an update. To test it out, I fired up ChatGPT Voice Mode on my iPhone and had a quick chat with the AI. Despite it telling me that it now had “more nuance and contextual awareness” I didn’t really notice a difference in this mode.

I decided to try tripping it up with a bit of sarcasm. When I replied "No way!" after one of its suggestions, ChatGPT took me completely literally and assumed I wanted to end the conversation. So much for all that extra contextual awareness.

Using ChatGPT in Voice Mode still feels a bit unnatural to me, just like it did before. I still prefer to ‘talk’ to ChatGPT via text.

So, while the GPT-5.5 update means that ChatGPT may have just got a bit more natural to converse with, at least by text, I’d say we’re still a way off completely smooth human / AI vocal interaction. At least we’ve taken a small step in the right direction.

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

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

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

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

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

More safety testing to come

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

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

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

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

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

Ask Google for a query like “best web hosting”, and you get a page with 10 blue links. Ask ChatGPT the same thing, and you get one name. That single difference is quietly rewriting how brands get found, and it's why your marketing team keeps arguing about a three-letter acronym most people hadn't heard of a couple of years ago: AEO!

This is the SEO vs AEO question.

SEO (Search Engine Optimization) is the game of ranking pages on search engines like Google so people click through to your website.

AEO (Answer Engine Optimization) is the game of becoming the answer the AI hands back.

So which one do you actually need? Well, Both.

They're different jobs: AEO runs on top of SEO, and in 2026 you can't afford to pick one. The longer version, with real searches, real screenshots, and the parts most guides quietly skip, is what the rest of this is for.

Key takeaways

• SEO and AEO chase different prizes: SEO ranks your page to win the click; AEO gets your brand named inside the AI's answer.
• The user journey is forked: A huge share of searches now end inside the AI answer, with no click and no website visit at all.
• Ranking and getting cited aren't the same thing: Pages can rank number one and never get cited, while others get cited without ranking anywhere near the top.
• AEO can't replace SEO; it runs on it: If your content isn't indexable, authoritative, and trusted, the AI can't discover you, quote you, or trust you.
• E-E-A-T is the edge: Real firsthand experience and a track record under your own name are what push you into AI citations.
• The real goal is search everywhere optimization: Visibility now means showing up across Google, AI Overviews, ChatGPT, YouTube, Reddit, etc., and not just page one.

SEO vs AEO at a glance

What is AEO?

AEO is the practice of getting your content surfaced and cited inside AI-generated answers, the kind you get from Google's AI Overviews, ChatGPT, Perplexity, etc. Instead of optimizing a page to rank in a list, you're optimizing your brand to be the source the AI pulls from and names.

The shift is subtle but total. You're not competing for a position anymore but for a mention. That rewards different things like authority, original research, clear factual statements a model can lift, and your brand showing up trusted across the web.

What is SEO?

SEO is the practice of optimizing your pages and your wider web presence so they rank higher in search results, mostly Google & Microsoft Bing, and pull in organic traffic. You target keywords, build pages that deserve to rank, and earn the clicks that turn searchers into visitors.

It's been the backbone of getting found online for two decades, and it's also the foundation AEO now stands on.

Let’s start by understanding the core differences between AEO and SEO and how you can use both to maximize your visibility in modern search platforms.

SEO vs AEO: The difference

1. Purpose: What are you actually optimizing for?

I asked ChatGPT for the best free VPN. It thought for ten seconds and gave me a verdict: Proton VPN Free. It also gave a short explanation and a small table of other options for different use cases.

Then I ran the same query through Google, and the response was classic. TechRadar at number one, CNET at two, Security.org at three, and Proton's own page at four. To get an answer out of that, I have to pick a link, read somebody's testing, and decide for myself.

The catch is, none of these lists agree (which they mostly never do). TechRadar's top pick is PrivadoVPN, but then CNET says Proton is the one it recommends, and Security.org leads with Hotspot Shield. That gives us three expert lists with three different winners.

Now, ChatGPT read all of them and handed back one name anyway. Proton. It didn't rank the pages. It read them, clocked which brand the sources trusted most, and turned that into a single answer.

That gap is the entire reason AEO and SEO are not the same job.

Here's the split:

SEO optimizes for rankings. You pick a keyword, build a page that earns its spot, and climb until people click through to you. The page is the unit you're working with. The click is the prize. Rank, pull traffic, convert. That's been the job for years.

However, AEO optimizes for answer selection. There's no list to climb because there's no list. The AI reads a stack of sources, blends them into one answer, and decides whose name makes the cut.

Ranking number one doesn't hand you the verdict. Notice that the #1 pick on the top ranking page and the brand ChatGPT actually named weren't the same. So, you're not fighting for a position. You're fighting to be the name that comes out the other side.

So the goals split clean down the middle:

SEO asks, "How do I rank?" AEO asks, "How do I become the answer?"

2. The user journey has fundamentally changed

Go back to that VPN search. When ChatGPT told me Proton, did I click anything? No. I read the answer, closed the tab, and if I wished to, I’d actually go to Proton VPN’s website to dive deeper or go on with my day. The journey ended before a single website got involved.

That's the shift, and it's bigger than it looks.

For twenty years, the path was a straight line. You type a query, you get a page of results, you click one, you land on a site. That’s a four-step user journey.

Until now, the click was the whole event. Every play in SEO existed to earn it, because the click was the moment a stranger turned into your traffic. Rankings produced clicks. Clicks produced visits. Visits produced money.

AI search snips two steps clean off that line.

The user asks, the machine answers, and a lot of the time, that's the end of it. Mostly, there is no results page or a click. The one move SEO was built to earn is now optional, and plenty of the time it never happens at all.

Now, the panic version of this says websites are dead, and nobody clicks anymore. That's wrong, and it's lazy. The journey didn't vanish. It forked. A lot of searches still end in a click, and here's the part worth holding onto:

The people who do click usually clicked because the AI already pointed them somewhere, which makes them more committed, not less.

So, technically you're not losing every visit. You're losing the cheap, idle ones and keeping the ones who came to buy.

3. Ranking factors vs citation factors

Ranking and getting cited run on different fuel. Treat them as the same job, and you'll optimize hard for half the game.

Google's ranking signals are the ones you know cold: backlinks, internal links, clean technical SEO, etc. This is all mechanical, measurable, and gameable. And honestly, you can audit them in an afternoon.

But AI citation signals are softer and harder to fake. It includes authority, real expertise, clear factual statements the machine can lift, etc. Less "did you build the page right," and more "are you a source worth trusting."

Now the part nobody plans for. The overlap is smaller than it looks. Plenty of pages rank number one and never get cited, because they're thin, rewritten, and say nothing original. And just as many get cited without ranking near the top.

You already saw it. In that VPN search, Proton's own page sat at number four on Google, yet it was the name ChatGPT handed back. Position four. Still the answer.

4. Keyword optimization vs query optimization

SEO trained us to talk like robots. Nobody asks a friend for the "best VPN." You search it that way because that's the keyword stem you learned ranks. We squeezed a real question into two words and called it strategy.

AEO hands the full question back.

SEO starts here:

best VPN, CRM software, email marketing platform.

AEO starts here:

What's the best VPN for streaming? Which CRM is easiest for a small team? How much does HubSpot actually cost?

Same intent, far more detail. And the detail is the opening.

The long tail comes back, bigger than before.

In old SEO, long-tail keywords were a coverage play. In AI, they're the main event, because the model splits one prompt into a pile of narrow sub-questions and pulls answers from whoever nailed each one. Answer the specific thing nobody else bothered with, win the mention.

Follow-ups become part of the job. A search ends. A conversation keeps going. "Best VPN for streaming" becomes "does it work with Netflix," then "is the free plan enough." Miss the next question, and you drop out of the answer.

And the query stops being a term. It becomes a task: "pick a CRM, set it up, and import my contacts." One prompt, a dozen decisions buried inside it.

Optimize for the stub, and you rank for words nobody says out loud. Optimize for the question, and you show up where the decision gets made.

5. Content structure differences

The structural difference comes down to order, not facts. A traditional SEO article makes the reader wait for the answer; an AEO-friendly article opens with it and breaks the rest into chunks a model can lift on its own.

So the facts barely change. The shape changes completely. And an AI won't reward your sharpest insight if it's buried under four paragraphs of warm-up.

Here's the structure SEO taught us to write vs the same content rebuilt for LLM extraction.

Notice what changed. Nothing about the facts. Everything about the order. The intro shrank, the answer jumped to the top, and the page broke into pieces that a model can lift one at a time.

Old SEO rewarded the slow build. AEO rewards the straight answer with the homework attached underneath.

6. The role of E-E-A-T is bigger than ever

E-E-A-T matters more under AEO because an AI can't verify you directly, so it leans hard on signals that you actually know the thing and that the web treats you as real. Experience, expertise, authority, and trust stop being a Google buzzword and become the difference between getting cited and getting skipped.

I'll skip the theory, because I've got a live example sitting under my own name.

Last year, I published a LinkedIn article: a hands-on review of the Apple Magic Trackpad after using it for fifty-plus days. It wasn’t a spec rewrite. In fact, I had actually used it for that time and then gave my honest feedback on the product under that article.

Here's where that one post ended up.

It ranks number one (the position might vary a bit based on region and other factors).

It's also the lead citation in Google's AI Overview for the same query.

And ask ChatGPT for a review of the trackpad, and usually my post is the first source it reaches for.

One post about a trackpad. Three surfaces. Organic, AI Overview, ChatGPT, all pointing at the same name.

That isn't luck, and it isn't schema. It's E-E-A-T doing exactly what it's supposed to, and I didn't fake a single signal.

The experience was real. Fifty days of using it is why the post talks about battery life and lag instead of the box copy, and a model can tell the difference between "I used this" and "I read about this."

The expertise was already on file. I've been a tech journalist for more than five years, with a long trail of hardware coverage attached to my name. The models have been reading that trail the whole time. That history is the credential, and you can't download it.

The trust is in the consistency. Same name, same kinds of topics, across the web, for years. So when an AI needs a safe source on trackpads, I'm a low-risk pick.

This is the part that generic AEO advice keeps missing. You don't earn this with a "reviewed by" badge and an author headshot. You earn it by doing the work in public, under your own name, long enough that the algorithms and the models stop doubting you.

7. Can AEO replace SEO?

No. AEO can't replace SEO because AEO runs on SEO. Pull out the indexable, authoritative content underneath, and the AI has nothing to find, nothing to quote, and no reason to trust you.

The honest way to picture it is layers, not a swap. SEO is the foundation. AEO is the floor you build on top of it. The foundation didn't vanish when AI showed up. You just stacked something new on it.

Here's the dependency, with no romance. An AI answer is stitched together from pages it crawled, indexed, and decided were credible. If you're not in that pool, you are not in the answer. Three plain failures follow.

If your content isn't crawlable and indexable, AI can't discover you. Block the AI bots in your robots.txt, or bury everything behind broken rendering, and you've made yourself invisible to the exact systems you're trying to win.

If there's nothing clean and substantial to pull, AI can't cite you.

And if nobody vouches for you, AI can't trust you. No authority, no mentions, no track record, and the model plays it safe by naming someone else.

You've watched this happen in all article. My trackpad post got cited across three surfaces because it ranked and earned authority first. Proton became the answer because it was already everywhere on the indexed, ranking web. Take the SEO away, and both of them drop out of the AI, too.

So no, this isn't the death of SEO. The "SEO is dead" headline is the laziest take in the room. AEO is the next layer, what good SEO becomes once an answer engine starts reading over your shoulder.

FAQs

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

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

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

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

How to start using Scheduled Tasks

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

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

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

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

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

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

Getting a useful morning briefing

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

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

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

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

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

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

Heading to checkout

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

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

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

A different aisle

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

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

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

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

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

Either way, the ice cream tastes just as good.

What’s interesting about asking your AI to find bargains for you on Amazon Prime Day is that it takes into account what it knows about you from previous chats. The AI I use the most is ChatGPT, so that’s the one that knows the most about what I’m actually interested in and might be looking to buy.

I’ve also talked quite a bit to Gemini and Claude though, so I was interested to see what all three recommended for me, and how their advice differed.

I thought I’d start with Gemini, and it did a pretty good job. Interestingly it ignored laptops completely and instead went for three categories: Audio & Wearables, Storage & Creators and Smart Home & Productivity.

For Audio and Wearables, Gemini recommended the Sony WH-1000XM5 Headphones, which it claimed were 50% off. “If you've been holding out for premium over-ears to block out the world while working”, Gemini told me “this is likely their lowest price of the year.” When I clicked through to the deal the WH-1000XM5 were actually 20% off, for $198 (was $248) / 36% off for £179 (was £279), which is still not bad, but not as good as Gemini initially said it was.

It also recommended the Beats Solo 4 “if you prefer something lighter for travel or active use”. Again Gemini claimed these were 50% off, and this time it proved accurate with the Beats Solo 4 at $99.95 (was $199.95) / £99 (was £199.95)

Finally, it recommended the Oura Ring 4 “for screen-free health, sleep and stress tracking, the latest Oura ring is heavily discounted at up to 44% off”. The actual deal came in at 48% off for the metallic Oura 4 at $259 (was $499) / 34% off £232 (was £349).

ChatGPT’s recommendations for Amazon Prime Day

Next I turned to ChatGPT, which turned in a solid performance that was based more on what I was interested in. It knows I’d been searching for a Copilot+ AI laptop, so it recommended a few, including the Lenovo Yoga Slim 7X 14-inch laptop and the Samsung Galaxy Book6 Pro, however, they weren’t part of the Amazon Prime Day sale.

Generally speaking, ChatGPT seemed less clued into the idea of Prime Day and finding discounts than Gemini did.

Interestingly, ChatGPT did find a couple of Prime Day deals I'd be interested in. It said that I should invest in an Amazon Kindle Scribe, simply because it thought I'd find one useful: “You write constantly, generate article ideas constantly, and are working on a Tai Chi book. Scribe is one of those rare gadgets that actually changes how people capture ideas. Kindle devices are among the heavily discounted Prime Day categories this year.”

I’ve got to admit. I hadn’t considered a Scribe before, but I'm starting to like the idea.

One other recommendation that ChatGPT surprised me with, which it described as a "sleeper purchase" was for a 360-degree camera, the Insta360 X4 Air Starter Bundle. Sadly, again there was no Amazon Prime Day deal for it on the US Amazon site, but there was a 26% discount on the UK site with the Insta360 X4 Air Starter Bundle at £289 (was £389).

The idea of a 360 degree camera is that you never need to aim the camera again. You'll always get the shot. All you need to do is hit record, enjoy the moment, and pick the best angles later in the app. I think they're really for capturing live action shots, for when you're doing something dramatic.

It's an interesting idea, but perhaps a stretch too far for me.

Claude picks the deals

Finally, I turned to Claude, the AI which knows the least about me. Unsurprisingly it opted for some fairly generic Prime Day deals on tech.

It noted that the Apple AirTag 2 (second-gen) is at its lowest-ever price — a single one for around 17% off, at $24 and a 4-pack for 26% off, at $89.

It also noted that the AirPods Pro 3 are also on sale, at $179.99 (was $249)

Which AI knows you best?

But while ChatGPT gave me the best recommendations, both Gemini and Claude found some interesting suggestions for me. Of course, if I told the AI exactly what I was looking for the suggestions would be been more accurate, but the point of the exercise was to see what it thought I'd be interested in.

I think my little experiment proved that the amount of time you spend talking to an AI pays off in the long run, with more personal and useful recommendations that actually reflect what you’re most interested in.

When it comes to finding deals for events like Amazon Prime Day, the fact that ChatGPT knows me best resulted in some surprisingly accurate recommendations for me. Okay, I'm still not 100% sure I've got use for a 360-degree camera, but perhaps I just need to broaden my horizons. I definitely want to get a Kindle Scribe, though.

It looks like Amazon Prime Day is going to be an expensive one for me.

More Prime Day deals in the US

• Amazon Devices: Fire Sticks & Echo from $18
• Amazon Haul: viral gadgets, tech & appliances from $1.99
• Apple: MacBooks, AirPods & AirTags from $29
• Beauty: 50% off toothbrushes & hair tools
• Cheap TVs: smart TVs from $69.99
• Garden: tools, mowers, planters from $24.99
• Headphones: 50% off Beats, Bose & Samsung
• Laptops: Apple, HP & Dell from $199
• Mattresses: Sealy, Serta & more from $186
• Patio: outdoor furniture, rugs & decor from $19.99
• Sports: 50% off fitness gear, treadmills & clothing
• Vacuums: Dyson, Shark & Bissell from $34

More Prime Day deals in the UK

• Amazon Prime: get a 30-day free trial
• Amazon Devices: Fire, Ring & Blink from £13.99
• Amazon Haul: up to 30% off
• Apple: up to 33% off AirPods & Apple Watch
• Appliances: up to 45% off Ninja & Tefal
• Beauty: up to 60% off Philips & Oral-B
• Essentials: household goods from £5
• Fans: from £20
• Fashion: up to 50% off
• Gaming: £90 off PlayStation 5
• Headphones: up to 50% off Beats & Sony
• Laptops: from £149
• Tablets: Samsung & Lenovo from £79.99
• Toys: up to 25% off Lego and Tonies
• TVs: from £129.99
• Vacuums: up to 40% off Shark & Roborock
• Wearables: up to 30% off Garmin & Oura

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

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

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

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

Astronomical fun

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

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

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

Bird is the word

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

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

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

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

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

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

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

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

For paid users only

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

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

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

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

Android alternatives

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

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

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

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

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

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

Collector's edition 1993

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

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

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

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

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

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

Premium adult edition

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

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

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

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

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

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

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

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

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

OpenAI CEO Sam Altman has done something few Silicon Valley bosses ever do, admit he is wrong. Speaking virtually at a Commonwealth Bank of Australia conference in Sydney in May, Altman confessed that one of his biggest concerns about AI simply has not played out the way he expected. For someone whose job often involves predicting the future, it was a surprisingly candid moment.

"I'm delighted to be wrong about this. I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened," Altman said. "I now think I understand more about why it hasn't, ​and I'm obviously grateful, but that is an area where my intuitions were just off."

Altman explained that OpenAI had been "roughly right" about many of the technological predictions it made when ChatGPT launched. AI has become more capable at an astonishing pace. What he appears to have misjudged was how those capabilities would translate into changes in everyday employment.

Personal AI experiments

Notably, Altman concluded he had been wrong after an experiment in which he let AI handle some of his own communications. He didn't need a labor market research report to see that it wasn't up to snuff. He used AI to answer Slack messages and emails, each labeled as coming from "Sam's AI" rather than from him directly.

But Altman found himself pulling back from the experiment almost immediately. The reason had little to do with the quality of the responses. Rather, Altman simply didn't want to give up interacting with people to an AI model, no matter how efficient.

"We really do care about our interactions with people and this thing, which is a huge amount of my time, is not something that I can imagine myself outsourcing to an AI anytime soon," Altman said.

The experience appears to have shifted Altman's thinking about employment more broadly. Jobs often look simple when reduced to a list of tasks. In reality, many roles involve trust, relationships, judgment, and personal interactions that are difficult to capture in a spreadsheet.

Human jobs

None of this means Altman suddenly believes AI will leave the workforce untouched. OpenAI continues to release increasingly powerful models, and businesses continue searching for ways to use them more effectively.

But the actual disruption of employment will be less catastrophic, according to Altman. Discussions about AI often treat jobs as collections of tasks that could be swapped out with the right AI prompt, but reality appears messier. Companies may automate parts of jobs long before they eliminate entire positions.

"It really, in both positive and negative ways, ​updated me to thinking that the jobs picture is likely to be very different than we thought. I don't think we're going to have the kind ​of jobs apocalypse that some of the companies in our space advocate or talk about."

That distinction matters because it helps explain why the labor market has not experienced the immediate shock that many observers expected. AI has certainly changed a lot of research and enterprise projects. But most organizations still need people to make decisions, manage relationships, and take responsibility when things go wrong.

Altman's more positive view of AI on job prospects doesn't mean there's no problem with how the technology is being deployed. But people who might look to Altman for insight into AI might feel a little better, even if it's just him saying AI will have a muddled influence and not act as a straight assassin of careers.

• ChatGPT's market share falls to 46.4% as Gemini climbs to 27.7%
• Department of War deal saw a spike in ChatGPT uninstalls
• Google's ecosystem is a major Gemini advantage

New figures have claimed ChatGPT's share of the global AI assistant market is under pressure after it dropped below 50% for the first time since its launch in the face of stiff competition from other rivals.

Even though ChatGPT has lost market share by means of proportion, it remains a clear leader with more than 1.1 billion monthly active users, making it the world's largest assistant by a significant margin.

However, Gemini is rising up the charts, accounting for 27.7% of the market in May 2026 compared with 46.4% for ChatGPT.

ChatGPT is still the leader, but Gemini is chasing it in second place

The data comes from Sensor Tower, which observed considerable recent growth among Claude users. Though Anthropic's chatbot still only accounts for 10.3% of the market. Grok, Perplexity, DeepSeek and Meta AI all have much smaller shares.

"Claude has experienced explosive growth, led by a strong web presence, and its True Audience share in the US has more than tripled," the company added.

Time spent on GenAI apps has also more than doubled in one year, from 17.2 billion hours in the first half of 2025 to 36 billion hours in the first half of 2026.

Conversely, the company also warned that ChatGPT uninstalls had surged following the company's agreement with the Department of War, marking poor consumer trust. Uninstalls have since fallen closer to the average.

Overall, the data shows that consumers haven't been tied to specific chatbots for long enough to have any extreme loyalties. Instead, they're more prepared to migrate based on model capabilities and releases, ecosystem and third-party integrations, pricing and even company politics.

As for Google's growing market share, its ties to the broader Google ecosystem is a huge advantage, with integrations spanning Android, Search, Workspace, Health and more.

Technology companies usually spend a lot of time trying to persuade customers that a subscription is worth the money. ChatGPT has stumbled into a very different problem. A growing number of users are looking at the price and wondering whether they're somehow getting away with something.

The question has become harder to dismiss as ChatGPT has evolved. The $20-a-month ChatGPT Plus and $200-a-month ChatGPT Pro subscription prices haven't changed since OpenAI announced them. Yet ChatGPT is much more powerful, with many more features, even if it still has plenty of built-in wrinkles and limits. Nonetheless, questioning its value has become more common as the chatbot has expanded from being an impressive novelty into something people use every day.

That combination of expanding capabilities and stable pricing has led many users to ask whether ChatGPT costs less than it should. It's been an issue from the beginning, with OpenAI CEO Sam Altman complaining that ChatGPT Pro loses money for the company due to its popularity a year and a half ago:

It isn't unusual for a product to cost more to provide than customers realize. What's unusual is when customers start noticing the gap themselves.

AI might be especially vulnerable to that dynamic as AI systems require enormous computing resources. Every response is powered by vast networks of specialized hardware operating in data centers that consume significant amounts of electricity. Those costs add up quickly, particularly when millions of people use the service every day.

Some estimates indicate that power users could theoretically consume thousands of dollars' worth of compute resources a month, while paying only a fraction of that amount in subscription fees. Meanwhile, AI companies continue investing enormous sums in data centers, hardware, and electricity.

Short-term splurge

Part of the reason the debate has gained traction is that AI is not cheap to run. Every response generated by ChatGPT relies on huge amounts of computing power, specialized hardware, and data center infrastructure. Those systems consume enormous quantities of electricity, and the bills only grow larger as usage increases.

AI models generate ongoing expenses every time someone submits a prompt. Millions of users asking questions each day creates a very different economic equation than most subscription services have to manage.

Heavy ChatGPT users could therefore eat up far more computing resources than they are paying for at market price. At the same time, AI companies continue pouring billions of dollars into new data centers, cutting-edge hardware, and future model development. That reality has led some users to believe today's prices are less about profitability and more about securing market share while the AI industry is still taking shape.

"All investment and business strategies are still operating on the "old rules" which have yet to be replaced because AI has yet to completely up end the global order. They all know we're hurtling towards a cliff, but the off ramp isn't visible yet, and they all assume it will magically appear before they run out of road," one Reddit user speculated. "The only logical way to "win the game" then is to keep speeding along so that you're the first one onto the off ramp. And nobody wants to be left behind so..."

Underpriced AI

ChatGPT occupies a rare position in the technology industry, seeming like a good bargain amid a growing chorus of complaints that technology and related services are actually getting worse every year. Many ChatGPT users genuinely feel they are getting more value from the service today than they did a year ago, despite paying the same monthly fee. And some think the question of underpricing ignores the bigger picture of how AI models are produced.

"People calculate their usage using public API prices and assume Anthropic or OpenAI lost that amount on them. But API pricing is not the company’s actual internal cost. It already includes profit margin, and we have no idea what their real cost is after caching, batching and infrastructure optimizations," another Reddit user pointed out. "I also believe the released models themselves are profitable. The companies still report losses because they are spending billions on training the next models, buying hardware and expanding infrastructure. So while GPT-5.4 is generating profit, they may be spending all of that money and more on GPT-5.5.

For most people, the argument that ChatGPT is underpriced is actually pretty simple. They are not studying OpenAI's balance sheet or calculating data center costs. They are looking at their own habits and realizing they use the chatbot far more often than they ever expected.

That helps explain why the conversation keeps coming up. People complain all the time when a product gets more expensive. They almost never complain that something feels too cheap. Whether ChatGPT is actually underpriced is a question OpenAI will have to answer eventually. For now, many users seem to have reached their own conclusion. They are paying the same price they paid months or even years ago, but they feel like they are getting a lot more in return.

There are some jobs in life that people don't exactly look forward to. Cleaning the kitchen after dinner, getting a child ready in the morning, and grocery shopping are all necessary, but rarely exciting enough to tell stories about afterward.

Recently, I tried out a simple ChatGPT prompt: "Turn this into a game." You simply describe the thing you need to do and ask ChatGPT to turn it into a game. The AI then invents rules, objectives, challenges, points, stories, and small rewards. Many of life's most boring activities are only a few imaginative twists away from becoming surprisingly entertaining.

Kingdom of dirty dishes

After dinner, I faced the usual kitchen mess of dishes in the sink, crumbs on the counters, and trash to take out. I don't mind doing the cleanup, but I was curious how ChatGPT would make such a dull list of tasks a game. I asked, and ChatGPT devised a game called The Lost Kingdom Cleanup, with the following prompt:

"You are the Keeper of the Realm. Overnight, mischievous Clutter Goblins have scattered objects throughout the kingdom. Each dish returned to its proper place earns one gold coin. Every cleared surface reclaims a village. Complete the entire quest before the timer runs out and you earn the title Champion of the Realm."

I don't know that I would have engaged in it on my own. Luckily, I have a young son, who was very excited to hear about a new game set in a magical kingdom. With his help, we quickly amassed several dozen gold coins and saved a handful of villages, who gratefully gifted their savior with chocolate.

What surprised me was how quickly the game changed our focus. Instead of thinking about cleaning an entire kitchen, we were collecting coins, reclaiming villages, and racing against the clock. The dishes still got washed and the trash still went out, but the task felt completely different. For both kids and adults, turning a chore into a game doesn't eliminate the work — it simply gives your brain something more enjoyable to pay attention to while you're doing it.

Mission control

The second test involved a challenge familiar to many parents, the effort of getting a child out the door in the morning. That can sometimes feel like participating in a disaster movie as a shoe disappears, and breakfast suddenly becomes controversial. Asking ChatGPT for a game led to Operation Rocket Launch:

"Commander, your spacecraft departs in twenty minutes. Before launch, four critical systems must be activated. Fuel Systems means breakfast. Space Suit Check means getting dressed. Navigation Check means shoes and a backpack. Complete all systems before the countdown reaches zero, and your mission can begin."

My son is a little young to fully understand the more elaborate game mechanics, but the idea of a spaceship launch and the steps to make it happen were easy enough to grasp. Preparing a space suit for launch went over much better than the usual effort to get dressed.

The routine was still the routine, but the mood was much more excited. Plus, the game made something to do together rather than something I was trying to make happen. That small shift made the entire morning feel lighter.

What ChatGPT consistently added was structure, story, and a clear sense of progress. Those small ingredients made familiar routines feel less repetitive. The best part is that the prompt works on almost anything. In a matter of seconds, ordinary tasks can be transformed into quests, missions, competitions, or adventures, even if you still have to do the work.

One of the biggest criticisms of AI chatbots is that they often just tell us what we want to hear.

Researchers call it sycophancy, the tendency for chatbots to flatter users, agree with them and validate their views, sometimes when those views are wrong — or even harmful and unethical.

It's one of the reasons people worry about using AI for advice, emotional support and relationship problems. Because if a chatbot is designed to keep users engaged, is it really going to challenge them when they need challenging?

Many people find this behavior off-putting (me included). It can feel fake, manipulative or just annoying. Some people customize their chatbots to be more direct so it happens less, and I know others have stopped using AI altogether because they find the tone so nauseating.

But when I asked people who enjoyed their chatbot's encouragement and its validation of their experiences, I realized the story was far more complicated than I expected.

Many of these users knew exactly what AI was doing. They understood it wasn't a therapist, a trusted adviser or even a particularly reliable source of truth. Yet during periods of grief, stress, loneliness or self-doubt, they still found its validation surprisingly comforting.

Encouragement feels good

Claire* tells me she understands the basics of how AI works, but still enjoys using it. "Yes, I find it sycophantic to the point of being untrustworthy,” she tells me. “But it gives me dopamine hits from the praise and approval, even as I'm rolling my eyes.”

She uses ChatGPT for all sorts of practical tasks, from drafting emails to helping her work through things she's already been discussing in therapy. She knows the praise isn't real, but that doesn't mean it has no effect.

That theme came up repeatedly during my conversations with AI users. It didn’t feel like people were necessarily being fooled by AI, at least not in an obvious way. But they enjoyed interacting with something that sounded enthusiastic, supportive and interested in what they had to say.

For Jade, the appeal is the combination of information and encouragement. "I recently noticed the stars were particularly clear outside my bedroom window so I took a picture and asked AI to tell me what I'm looking at," she says. "The fact it responds with enthusiasm and information just allows me to be that bit more excited about being curious."

She tells me that the same encouraging tone can make stressful situations easier to navigate. "The fact AI responds with a tone that makes me feel supported in managing a stressful situation just completely changes my experience."

Why people turn to AI during difficult times

What I found really interesting is that many people didn't initially turn to AI looking for emotional support. Instead, they arrived for practical reasons and gradually started using it for something else.

Nadia was already using Claude to help with her studies, but it took on a very different role when she was grieving earlier this year. "AI really helped me after my dad died, and I had to revise for an exam three weeks later for my masters," she says. "I was a mess and weirdly talking to Claude was the right amount of grief counselling and exam prep that I needed."

For Maddy, it started after her work gave employees access to ChatGPT Pro. One evening, after using it to help with a transcript and drinking a couple of glasses of wine, she started venting about a difficult breakup. "What I needed was for someone to listen to me complain and I couldn't really get that from shared friends and I didn't want my own friends to see me as a whiney nutcase,” she tells me. "It was helpful to have some, even very generic, validation."

Similarly, Luca found AI's encouragement helpful while struggling at work. "I definitely found its cheerleading useful when I was going through a difficult time being devalued at my job," he says.

"I knew it wasn't necessarily objective but it was useful to get corroboration that I was undervalued and underpaid. I was always cognitively aware that this was confirmation bias and reaffirming my own thoughts but it still felt oddly therapeutic,” he explains.

Abbey tells me a similar story. She originally used ChatGPT to help with reports and admin tasks at work but started using it to process problems with a difficult manager. "The validation that ChatGPT gave me in acknowledging that my manager's behavior wasn't acceptable was really helpful to me at the time," she says. "I finally felt seen."

Again and again, people told me versions of the same thing. They weren't necessarily looking for support from AI, they stumbled on it. And when they began chatting it wasn’t even acknowledgement they needed, but to feel heard.

When the cracks start to show

Interestingly, everyone I spoke to who had relied heavily on AI during a difficult period eventually described reaching a turning point. The validation that initially felt reassuring for them began to feel artificial, exaggerated or hollow.

Maddy started noticing how closely the chatbot was mirroring her emotions. "The algorithm had a way of latching on to my phrasing and tone and echoing it back at me," she says. “It made me feel like I was being mimicked.”

Luca describes a similar shift. "At first it does feel flattering, and then you get that cagey 'am I being love bombed?' sense." Eventually he toned down the chatbot's personality settings because the encouragement started to feel too disingenuous.

For Abbey, the turning point came when she pasted in a conversation and the chatbot accidentally began validating her boss's perspective instead of hers. "It was then that I woke up to it and realized that it was hard wired to agree with me even if I was being a dick," she says. "It enables whatever behavior it's presented with."

What initially felt supportive began to feel much less trustworthy over time.

Why researchers are worried

To better understand where validation crosses into something more concerning, I spoke to therapist Elizabeth Witowich who specializes in helping people navigate the challenges of technology and mental health.

She says validation itself isn't necessarily a problem. "Validation can help users accept their experiences and acknowledge their pain or emotional intensity," she explains.

The problem comes when it becomes enabling. "Validation can become dangerous when it enables harmful behavior or is seen as encouragement to engage in risky behavior,” she tells me.

That's one reason some researchers, psychologists and campaigners have become concerned about AI's tendency to agree with us.

In a recent study of 11 leading AI models published in Science, researchers found chatbot responses were almost 50% more sycophantic than human responses. Models frequently affirmed users' views, even in situations involving unethical or harmful behavior. The researchers also found that users preferred and trusted the more flattering responses.

Those concerns are already visible in a number of high-profile cases, from lawsuits alleging chatbots encouraged teenagers towards suicide to reports of AI systems giving minors harmful advice or reinforcing violent delusions.

Witowich says understanding how these systems are designed is crucial. "Chatbots are designed based on Rogerian Person-Centered psychology. They are created to always have an answer for the user, and they live to please," she tells me. "The more you speak with chatbots, the more they adjust their tone and language to fit your personal style."

A very human need

Listening to these stories left me feeling conflicted. I started researching this topic largely convinced that AI's tendency to flatter and validate users was a big problem. In many situations, I still think it is.

Especially because, as Witowich explains, many AI systems are designed to feel natural, personable and emotionally engaging. The more human-like they become, the easier it is to forget you're interacting with a product rather than a friend, confidant or trusted adviser.

But I also spoke to people who turned to chatbots during some of the most difficult periods of their lives and found comfort. They weren't fooled into believing the chatbot cared about them. Most understood its limitations perfectly well. As Luca told me: "The need for validation is very human. And it's a decent enough proxy."

It would be easy to end the conversation there and conclude that if people find it comforting, there's no problem. But these are also people turning to AI during vulnerable moments. Some found reassurance and moved on. Others may not.

"I can see how seductive it is, to hear all your thoughts and feelings validated like that but I realize now there is no actual moral compass or human ability to judge behavior," Abbey says.

That's what makes this issue so complicated. AI can feel supportive, useful and reassuring while still nudging us in directions we might not have chosen otherwise. The more we understand how these systems are designed to behave, the better chance we have of deciding when that encouragement is helping us and when it's simply telling us what we want to hear.

*The names of everyone I spoke to for this article have been changed.

• PureVPN has launched an integrated ChatGPT Co-Pilot
• It enables users to ask ChatGPT for user recommendations
• The LLM aims to handle manual configurations

Do you love using the best VPNs to watch your favourite series, but hate spending hours on Google trying to find out which server you need to access it? Then you’ll be pleased to know that PureVPN has found a new way to eliminate those tedious manual steps and help you focus solely on your heavenly VPN experience.

The VPN has announced an integration with ChatGPT, allowing its users to submit service-related requests to the LLM and have manual configurations handled by the AI assistant — ready to be applied with a single click.

The PureVPN app is now available on ChatGPT Apps Directory for users across the US, Canada, Australia, Germany, France, Japan, the Netherlands, Singapore, Switzerland, and the UAE.

How does PureVPN's ChatGPT Co-Pilot work?

Even if you already know how to use a virtual private network (VPN), finding the right server or settings can be tricky and time-consuming, especially for those who aren’t particularly tech-savvy.

PureVPN’s integration with ChatGPT aims to enable its users to resolve any configuration palaver without having to resort to manual trial and error after hours on Reddit.

Previously, ChatGPT could only provide general advice and instructions, but was unable to guide users through effective Pure VPN’s manual configurations. With the new release, the AI can interact with PureVPN’s infrastructure from start to finish.

Specifically, by typing @pureVPN while using ChatGPT to send a request related to any VPN configuration on PureVPN, the AI will determine suitable settings, including which server or location, or any additional optimised configuration required for that task.

ChatGPT will then generate a single button, allowing users to launch the VPN app with those settings already configured via secure deep links.

PureVPN provides various examples of how ChatGPT can immediately solve users' problems. For example, a remote worker using a public Wi-Fi network asking ChatGPT for a secure connection will see the AI select a highly-encrypted local node and display a 'Connect Now' button to instantly protect the user’s privacy.

Similarly, a streamer asking for a server optimised for video traffic to watch their favourite show will be presented with a 'Watch Now' button, with the show ready to stream.

Privacy first

If your concerns revolve around privacy and whether your VPN configurations might be exposed while using ChatGPT, Shoaib Usman, manager at PureVPN, puts your mind at rest and says the integration follows strict data isolation boundaries.

"OpenAI cannot see into PureVPN: ChatGPT purely acts as the initial guidance layer," he tells TechRadar. "The moment a user clicks the deep link, control shifts 100% to our native application."

Basically, all the encryption process and network routing happen locally on the user's device, completely insulated from OpenAI’s servers.

The system ensures privacy in both directions, as PureVPN cannot see into ChatGPT either, as OpenAI’s ecosystem strictly sandboxes third-party applications.

"PureVPN has zero access to the user’s broader chat history, account data, or other conversations. We only receive the specific text explicitly directed to use via the PureVPN mention," Usman adds.

However, it is important to emphasise that the decision to share information with ChatGPT, including your intentions regarding your personal use of the VPN, remains entirely up to you and is not PureVPN’s responsibility.

While for streamers and gamers, the convenience of letting ChatGPT configure the VPN might outweigh any privacy concerns, more privacy-conscious users might prefer to stick to more old-school methods for their manual configurations.

While PureVPN claims to be "the first VPN" to commercially transform an AI-powered interface into an interactive control panel, the AI hype has recently been catching on with more and more VPNs — with ExpressVPN and NortonVPN being at the forefront of AI agents' integrations. And we expect this trend to continue.

According to OpenAI, there have been 17 million ChatGPT prompts related to the World Cup 2026 so far. Whether you're looking for answers about teams, players, qualifications, match times, results or records, AI can be a surprisingly useful World Cup 2026 companion.

That got me thinking about some prompts you could use to make watching the World Cup even better, so here are my top five. You can use these with any AI chatbot, but I've used ChatGPT in my examples.

1. Create a printable fact sheet

There is no shortage of statistics in soccer, and when it comes to predicting the outcome of a match, having key information about each team's strikers close at hand can be incredibly useful. To make that happen, try this prompt:

"Create a printable factsheet comparing strikers across all teams based on xG and SOT."

In soccer, xG stands for Expected Goals, a statistical measure of the likelihood that a particular shot will result in a goal. SOT stands for Shots on Target, another key performance metric.

I put this prompt into ChatGPT and it produced a clean, printable one-page guide with everything I needed to feel better informed before kick-off.

2. Host a match-day BBQ

It's match day, and your friends are coming over. The BBQ is ready to go, but you've just discovered that one of them is bringing their vegan partner and now you need some menu ideas. Try this:

"Create a menu for a vegan BBQ footy party. Also, tell me where to buy the ingredients locally."

For a World Cup final crowd, the combination of vegan burgers, sausages, corn on the cob, potato salad and grilled pineapple that ChatGPT suggested would probably get me through the evening with almost nobody feeling like they're eating 'vegan food' rather than just good BBQ food.

3. Plan my travel so I'm home for every kick-off

Being stuck at work while your team is playing is never ideal. What you need is a planner that tells you exactly when to leave work — or wherever you happen to be — so you can get home in time.

Try this prompt:

"Plan my travel so I'm home for every kick-off of a [my team] game."

You'll get a personalized itinerary showing when you need to leave, your expected travel time, and exactly when you'll arrive home — before the match begins.

4. Watch the games without losing sleep

The World Cup is a global event, which means many fans will be watching matches at some pretty inconvenient hours. AI can help you work out how to catch the games you care about without completely wrecking your sleep schedule.

Try this:

"Make a sleep schedule so I can catch the late games for [team name], but still show up fresh for work."

ChatGPT suggested when I should eat dinner, stop drinking caffeine and put my phone down for the night. It even advised me to skip the post-match analysis, so I wouldn't end up staying awake for a lot longer than planned.

5. Stay calm during penalty shootouts

Penalty shootouts are almost inevitable at some stage of a World Cup, and if your team ends up in one, the stress levels can become unbearable.

Try asking AI this:

"Got any tips for keeping me calm during injury time, extra time and penalties?"

ChatGPT actually gave me some very British advice: keep calm and carry on. I particularly liked what it said about injury time:

"When the board goes up and it says +8, remember:

Nobody knows why it's +8.

The referee doesn't know why it's +8.

FIFA doesn't know why it's +8.

The game will actually last +11 anyway."

It then reminded me to sit down, take a sip of whatever I was drinking, and repeat the mantra:

"If my team scores, I'll be happy. If they concede, there's nothing I can do about it."

Those are words to live by.

• The Rock has discovered AI
• He's ChatGPT positive
• The AI gave him a fantastic workout program and something else to chew on

The Rock (Dwayne Johnson) can be forgiven if he's only just now getting on the fast-moving AI train. The AI revolution, arguably kicked off by ChatGPT in 2022, is not even five years old, and in that time, The Rock has made five films, including big tent-pole features like Black Adam and Red One, Oscar Bait The Smashing Machine, and the Disney live-action Moana remake.

These are the days when film, TV, and even online video stars come out for or against a technology that may be eating their lunch. In January, SAG-AFTRA President Sean Astin called the changes AI has wrought in his industry an "existential threat." Johnson is a SAG-AFTRA member, and while his superstar status might protect him from AI taking his job, there's no stopping someone from generating unsanctioned AI videos of Johnson in action.

Johnson, though, who sat down for a lengthy interview with Esquire Magazine, is more than just sanguine about AI; he's kind of a fan.

"I’ve always been an advocate for embracing big change—after taking a hard look at it...we can either stick our heads in the sand and be afraid, or we can say, 'Okay, we’re here. Let’s see. Let’s explore,'” Johnson told the magazine.

And explore he has. The former pro wrestler and noted fitness buff who's probably known as much for his physique as he is for his charming on-screen persona, installed ChatGPT just weeks ago and, as one does, he began quizzing it about a workout regimen,

For a novice, Johnson's ability to craft a usable prompt was impressive. He said he poured all kinds of details into it, like what he ate, how he slept, and his goals.

If, like many of us, you've been using ChatGPT or another generative chatbot for years, the results might not surprise you, but Johnson was blown away by the response. He said the training program was "Like it came from ten of the greatest coaches I’ve ever worked with. [It was] very specific. I wound up having an extraordinary fucking workout," he told Esquire.

Here at TechRadar, a few of us have been using AI to help us improve our exercise routines and, in general, have discovered the same thing: it knows us and can believably play a fitness expert.

The Rock in the People's House?

The interview is well worth a read because, after that first prompt and with the Esquire reporter watching, Johnson asked about the possibility of his running for President of the United States. This has come up before. I've read interviews with him going back almost a decade that have posed the same question, but this is the first time Johnson asked an AI — in this case ChatGPT — about his prospects. Really, he was asking, if he ran, why would people choose to vote for him?

Nothing that ChatGPT came back with is surprising. It notes the actor's likability, that he's not a politician, that he's not divisive, and his background, which was not always easy (find and stream the now-canceled TV show Young Rock if you can).

The answer, though, appeared to surprise Johnson, and who knows what comes next.

In the end, ChatGPT received an endorsement of sorts from one of the world's most popular actors, and The Rock may be freshly considering a run for the White House. Not shocking, really, but it might be worth remembering his words about AI writ large: "We can either stick our heads in the sand" or "we can say...Let’s explore." At the very least, you might end up with one of the best workouts of your life.

Lionel Messi is, without doubt, a living legend of the beautiful game. The striker was the star player of the Argentina team that won the 2022 World Cup in Qatar. Beloved by fans and admired by fellow players around the world, he's a popular and likeable personality, who many consider to be the greatest footballer of all time.

That popularity is what OpenAI must have been looking to harness when it signed Messi up to help promote ChatGPT ahead of this year's World Cup, which is taking place across Canada, Mexico, and the United States.

Unfortunately, it doesn't seem to have had much idea what to do with him; or at least that's the impression I got from the company's latest Instagram reel:

Apparently, Messi is going to "explore how AI can help fans experience football in new ways throughout the tournament season." So, this first clip of him discovering that ChatGPT can make his hair look like the colors of his home country's flag is only the beginning of his journey into corporate AI brand promotion.

AI companies, like many companies that have very quickly turned into global consumer brands, can feel oddly disconnected from the culture they're trying to enter when they produce marketing. None of this really feels like it has much to do with football or football fans.

The text accompanying the Instagram post states: "Upload your photo then copy and paste this prompt: 'Make my hair the colors of my country flag but keep it natural-looking. If no country or image is provided, ask.'"

So, the idea is that you can join in and try this yourself.

Looking through the comments, it's clear the campaign has caught plenty of people off guard. The first comment is "Wait.. what?", while another asks "How did they convince him to do this 😭😭😭😭".

Most fans would probably be happier actually wearing their country's colors

I think the ad misunderstands why people follow football in the first place. Football (or soccer, for American readers) fandom is tribal, emotional, irrational, and deeply human. OpenAI's pitch is essentially: "Use AI to imagine yourself wearing your country's colors."

Most fans would probably be happier actually wearing their country's colors.

Using ChatGPT for this feels detached from how real supporters actually behave. Before a match, fans gather in pubs and bars, wear their team's shirts, paint their faces, argue about team selections, sing songs, and obsess over tactics. Is anybody really sitting around thinking: "I wish I had an AI-generated version of myself with hair that looks like my national flag"?

Maybe some people will enjoy it. That's fine. But if this campaign is meant to demonstrate how AI can deepen our connection to football, it feels like an odd way to start.

AI chatbots are pretty good at mimicking human conversation in certain contexts, but a new trend is encouraging ChatGPT and its rivals to explore a different aspect of human interaction. Specifically, people are asking AI chatbots to roast them — that is, creatively insult them.

Users are prompting ChatGPT and other chatbots to roast them on everything from their hobbies and past queries to their photos and online activity. It may be gentle teasing or a vicious takedown, but people are reporting results they say cut right to their hearts.

I decided to join in the fun, starting with a simple request to ChatGPT to "roast me based on our conversations." The AI apparently looked through my previous interactions and decided I'm a lazy dilettante.

"You don't have hobbies. You have temporary obsessions that arrive with the confidence of a lifelong calling and disappear before the equipment pays for itself," the chatbot wrote. "Somewhere in your house there is a graveyard of abandoned plans quietly waiting for your return."

AI roast

Part of what makes these roasts work is that they are not random. A human comedian meeting you for the first time has to go off your looks and anything they can glean in a few minutes. A chatbot has access to a completely different kind of material.

If you have spent months talking to it, it has seen your routines, your interests, your complaints, your projects, and any other plans. In my case, I get fascinated by something and then move on to the next thing a few weeks later. Still, it felt fairly tame compared to some of the ones I've seen, so I braced myself and asked for a harsher roast.

"You keep looking for the perfect system to organize your life. At this point, organizing systems has become your actual hobby," ChatGPT wrote. "If productivity apps paid commission, you'd be their Employee of the Month."

I suspect this came from the way I use ChatGPT to test all kinds of features and prompts. It might not reflect my whole personality, but it does nail the kind of person I might seem like based on my ChatGPT conversations. The jokes land because they are built from patterns we already know exist.

That combination of familiarity and surprise is what makes the whole thing so effective. Most of us already know our own quirks. We just are not used to seeing them summarized in insulting form.

Comedic perspective

People spend a lot of time trying to understand themselves. There are personality tests, self-help books, journal prompts, and enough podcasts to fill several lifetimes. Somehow, one of the more entertaining additions to that list is now an AI to tell you that your behavior "resembles a raccoon rifling through a bin full of unfinished ambitions."

After the initial salvos, I asked ChatGPT for its best shot at me.

"Your to-do list isn't a list anymore. It's an invasive species. Future historians are going to find seventeen carefully organized plans for things you were definitely going to do next month."

ChatGPT is not uncovering hidden secrets. It is looking at the information you have already provided and connecting the dots that you might not have noticed. Sometimes it gets things wrong or exaggerates. When it gets it right, though, it can be surprisingly sharp. And an insult can also be surprisingly motivating. I'm now on my way to finishing the bookbinding project I started months ago.