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

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

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

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

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

An unpopular move

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

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

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

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

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

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

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

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

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

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

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

ChatGPT Work is an autonomous knowledge worker's dream

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

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

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

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

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

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

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

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

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

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

Could OpenAI be expanding its reach into finance?

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

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

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

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

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

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

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

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

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

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

What to try first

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

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

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

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

Early voice bugs

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

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

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

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

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

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

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

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

A simple rebadge or a sign of things to come?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The solution is simple: stop starting from scratch.

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

A simple trick

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

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

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

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

My meal planner prompt

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

Here it is:

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

A simple meal plan

A shopping list grouped by supermarket section

Any ingredients I can reuse across multiple meals

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

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

A tool that remembers you

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

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

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

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

The new normal

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

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

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

RTO mandates

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

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

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

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

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

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

AI economy access

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

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

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

Power at stake

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

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

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

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

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

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

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

A trip to Bath

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

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

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

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

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

At the end, give me:

What you completed

What still needs human action

Any assumptions you made

A short checklist I can use to verify the result

The next best step"

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

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

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

Claude vs ChatGPT

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

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

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

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

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

A different domain

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

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

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

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

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

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

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

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

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

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

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

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

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

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

"Completely wrong"

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

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

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

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

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

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

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

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

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

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

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

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

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

ChatGPT looks at the receipts

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

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

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

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

Financial chatter

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

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

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

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

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

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

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

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

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

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

The chip is only part of the plan

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

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

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

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

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

Nvidia's challenge

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

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

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

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

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

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

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

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

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

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

More safety testing to come

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

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

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

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

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

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

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

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

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

How to start using Scheduled Tasks

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

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

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

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

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

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

Getting a useful morning briefing

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

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

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

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

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

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

Heading to checkout

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

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

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

A different aisle

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

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

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

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

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

Either way, the ice cream tastes just as good.

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

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

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

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

Astronomical fun

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

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

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

Bird is the word

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

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

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

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

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

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

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

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

For paid users only

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

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

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

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

Android alternatives

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

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

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

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

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

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

Collector's edition 1993

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

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

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

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

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

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

Premium adult edition

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

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

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

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

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

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

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

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

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

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.

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.

Predictions that machines will one day replace humans in the workplaces have been made for decades. But there's a growing feeling that, with generative AI capable of performing cognitive-centric tasks with some degree of human competency, that the next few years might actually give rise to the first wave of job losses and redundancies due to AI.

AI's threat to the job market

Speaking in a discussion at Dartmouth College, Murati was opining about the capabilities of the generative AI tools that her company OpenAI were actively developing – and the potential impact on the labor market. These tools, she explained, would "lower the barrier" for anybody who would want to be creative and could even disrupt global industries, according to a CNBC report.

Over the last few years, we've seen countless projections about the potential impact that AI could have on jobs. While many see these as terrifying, Murati instead framed this as an inevitability to be welcomed. The implication was that AI-powered scriptwriting or even visualization could even lead to an increase in quality.

Now co-founder and CEO of Thinking Machine Labs, a startup designed to make AI more accessible, Murati was at the heart of some of the company's biggest releases including image and video generation tools.

One person's slop is another man's treasure

For better or for worse, AI has certainly lowered the barrier to entry for those intent on creating content – and we see this with the surge in AI-generated content that is now circulating the web. When it comes to using AI in the creative industries, however, there has been more of a mixed response.

AI generation is used in some areas, according to McKinsey, such as in dubbing content and speeding up laborious processes. Its role in content creation has been the source of heavy resistance, but it is increasingly playing a prominent role in today's best productions.

AI does pose a concrete risk to the creative industries, but there are hurdles to overcome before it can start to leave any kind of mark – exemplified by the collapsed $1 billion deal between OpenAI and Disney, following the shuttering of the Sora video generation tool.

• OpenAI confirms plans to acquire Ona and its agentic AI infrastructure
• Codex could benefit from Ona's infrastructure, which provides environments for long-running tasks
• Both companies have seen major rises in agentic AI users this year, marking a major shift

OpenAI has announced plans to acquire Ona, a startup that focuses on AI agents and their required environments, which could see the company get absorbed by OpenAI's Codex team.

Under the proposed deal, OpenAI would gain even more expertise to help AI agents work on long-running tasks that could even last days.

This comes as more organizations carry out agentic work over extended periods, hence the demand for more persistent infrastructure.

OpenAI announces plans to acquire Ona

"[Ona's] technology provides secure, persistent environments where agents can access the tools, systems, and context they need to make progress over time," OpenAI said in an announcement.

Ona builds secure cloud environments where AI agents can access enterprise tools and systems, retain context and continue executing even once a use closes their laptop or browser. The deal would effectively give OpenAI's Codex access to the infrastructure layer that allows agents to keep on working for much, much longer.

The ChatGPT maker also noted that Codex is evolving from a coding assistant into a much broader tool that helps more worker types – it now has five million weekly users. Ona also noted a 13x increase in weekly sessions since the beginning of the year, signalling major appetite for the tech.

After years of developing high-performance frontier models, OpenAI seems to finally be at the stage where it's now expanding investments to give agents the right tools, memory and environments, signalling a major shift from generative AI to agentic AI.

"After close, we are excited to join the Codex team and keep building toward a future where AI accelerates the economy and helps every team and every organization work more safely, more quickly, and more collaboratively," CEO Johannes Landgraf said, referencing the regulatory approval needed to proceed.

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.

• China used ChatGPT to generate comments, posts, and cartoons
• The content capitalized on issues surrounding data centers and tariffs
• The material was shared on social media to exacerbate existing tensions

OpenAI has banned a number of accounts that it says were linked to social media influence campaigns surrounding the growing opposition to data centers and President Trump’s tariffs on foreign imports.

The two campaigns, named “Data Center Bandwagon” and “Tech and Tariffs”, used ChatGPT to generate posts, comments and cartoons intended to sow political division in the US.

China’s intention was to deepen the divide by drumming up online engagement with AI generated posts, OpenAI said, but the campaigns failed to gain any traction.

China exacerbates existing tensions

The negative effects of data center construction and the additional costs imposed on consumers by tariffs are existing areas of contention within US society, but they weren’t narratives invented by China.

Instead, according to OpenAI, these campaigns were designed to increase the scale of the issues and broaden their visibility among online groups and on social media sites such as X.

It is the first time that OpenAI models have been used in a Chinese foreign influence campaign, a spokesperson told Axios.

OpenAI said that a Chinese government contractor was responsible for the data center campaign, which shared posts drawing on existing concerns surrounding power grid capacity and electricity prices in areas where data centers were planned or constructed.

OpenAI’s account of a foreign country using AI to capitalize on political issues adds some limited validity to recent Republican claims that the entire data center opposition movement being a Chinese influence campaign, but does little to address the very real, tangible effects that data center projects are having on local communities in the US.

A group of Republicans recently called upon FBI Director Kash Patel to investigate anti-data center sentiment, alleging that the rising tide of opposition is being fueled by China, as the inclusion of similar phrasing around water usage, energy constraints, transparency surrounding approval, and utility bill use “language too similar to be coincidental”.

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.

• Visa-OpenAI partnership brings agentic payments to ChatGPT, Atlas
• Tokenized credentials and safeguards keep your money safe
• MasterCard announced similar tech last year

Visa has announced a partnership with OpenAI to bring secure payments into AI-powered and agentic ecommerce experiences, including those carried out through ChatGPT and the Atlas browser.

Under this new collaboration, AI agents operating within OpenAI products will be able to initiate and complete Visa-backed transactions on behalf of users.

It essentially lays the foundations for OpenAI to use agents to take care of the whole buying journey on behalf of users, including making purchases, payments and bookings.

OpenAI gets new access to Visa payments

It means developers and merchants will gain a new standardized way to accept agent-made Visa payments, but the payment giant stressed that safeguards would remain in place with controls like spending limits, merchant category restrictions and approval requirements all available to end users.

Much like we've come to expect the additional security of Apple Pay not to share our card details, Visa will also use tokenized credentials to avoid exposing the card's finer details.

"As AI agents become active participants in the economy, Visa’s focus is to ensure transactions are trusted, secure and seamless," Chief Product and Strategy Officer Jack Forestell explained. "That’s the infrastructure we’re building with partners like OpenAI."

While we're very much in the early days of agentic payments and agentic ecommerce in general, piece-by-piece announcements risk leaving gaps in the broader ecosystem. This particular partnership puts Visa in the hands of OpenAI, but excludes other AI companies like Gemini and Claude.

Other payment providers, like MasterCard and Amex, would also need to get behind similar initiatives. A year ago, MasterCard did exactly that, announcing its own Agent Pay platform as a baseline for future agentic payments.

"By integrating with Visa Intelligent Commerce, we're building the infrastructure for secure, transparent, and user-controlled agentic transactions," OpenAI Head of Partnerships, Commerce Marco Mahrus added.

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.

• ChatGPT can now draft and send emails without leaving the chat window
• The feature currently works only with connected Gmail and Outlook accounts
• You can't send attachments, which limits its usefulness

I asked ChatGPT to write and send an email for me. It drafted the message, connected to my inbox, and prepared it for sending without me ever opening Gmail. For a moment, it felt like the AI assistant we've been promised for years. Then I tried to attach a file — and I was brought crashing back down to earth.

ChatGPT's new ability to send emails directly from within a chat, which quietly dropped yesterday, is available to most paid ChatGPT users in the web version of ChatGPT right now. It only works if you have connected your Gmail or Outlook account to ChatGPT, so it won't work across all email clients, and there are other limitations too.

For instance, as I discovered, you can't send attachments yet, which is a pretty big problem if OpenAI wants people to take ChatGPT more seriously as an AI assistant.

The final limitation is that you can't do this on Free or Go accounts, or through the ChatGPT apps on desktop and mobile. Sending emails is available only on the web version of ChatGPT for users on Plus, Pro, Business, and Enterprise plans.

The first taste of an AI assistant

I have to admit, it was quite a thrill the first time I tried sending an email from within the chat. I used it to send an idea for a story to my work email address, and I asked ChatGPT to provide some background information on the subject. I didn't type out the whole email — I just trusted ChatGPT to come up with the contents and include the link to the source I provided.

ChatGPT told me exactly what it was going to do, and displayed a dialog box explaining the action it was about to take, asking whether I wanted to Allow or Deny it. I clicked Details to review the exact email it planned to send and, happy with what I saw, I clicked Allow. Seconds later, the email arrived in my work inbox.

Of course, it failed at sending an attachment, but the fact that you can do all of this from within the chat is what's impressive. If you're already working in ChatGPT, it means you can send an email without interrupting your flow.

One missing feature changes everything

If you haven't connected your Gmail or Outlook account, open Settings, choose Apps, then Add more, and search for either Gmail or Outlook to connect it.

What makes the feature notable is that not every AI assistant can do this yet. When I asked Gemini to send an email for me, Google's chatbot replied that it couldn't send messages directly, or access my accounts to do so. Gemini can help draft emails, but ChatGPT's ability to actually connect to your inbox and complete the task is a glimpse of where AI assistants are heading.

The new ability to send email might be limited right now, but like most things OpenAI releases, it's likely to improve over time. Even so, the lack of attachment support feels like a significant omission. Sending files is one of the most common reasons people use email, and until ChatGPT can handle that, and also deal with reading emails, it's difficult to see it replacing a traditional email client.

Still, it's a promising first step. I also hope to see support for more email providers in the future. Starting with Gmail and Outlook covers a huge percentage of users, though, so there's a good chance you can try the feature for yourself today.

Jony Ive was at the heart of Apple's design philosophy for generations until he left the company in 2019 to start his own venture. But his influence throughout his nearly 30-year tenure at the firm is undoubtedly still prevalent in the company's products today.

Wonder in simplicity

Ive has been responsible for some of the technology industry's most iconic designs, with landmark devices including the iMac, iPod, and the iPhone.

He delivered his thoughts on the potential for technology to transcend the mundanity of the ordinary when promoting the first generation of the iPad in 2010.

Describing the iPad, Ive sought to explain how Apple's core philosophy when engineering this device — by all accounts a game-changing piece of kit — was to strip away any and all complexity and clunkiness.

When you achieve this, the thinking goes, users are left with technology that hides the engineering smartly and makes the device as accessible as possible. They then stop questioning how the technology works and instead focus on their experience using it.

Simplicity in modernity

Jony Ive's new venture, LoveFrom, continued to work with Apple until 2022 before the relationship between the designer and the tech giant ended.

The company has since embarked on various projects, both in and outside the tech world, but the New York Times reported in 2024 that Ive's company was collaborating with OpenAI on novel AI-native hardware. OpenAI later announced it would acquire Ive's AI venture, IO, for $6.5 billion in May 2025 ahead of a push toward a wearables-centric future.

There's scant information on what this hypothetical new AI-native device might look like or how it might work. Also unknown is how it might coexist with smartphones, which are increasingly being stuffed full of AI functionality. Whether a new device gains traction might hinge on how well it cuts through complexity — and with Ive at the helm of its design, it's something consumers would certainly expect.

When ChatGPT burst onto the scene, not many would have seriously considered consulting it for parenting advice. A few years on, however, that's exactly what OpenAI's CEO Sam Altman has done, as was surprisingly revealed.

How to raise a child in the 21st century

When Sam Altman appeared on the Tonight Show Starring Jimmy Fallon just before the turn of the year, few expected him to reveal just how reliant he was on ChatGPT when it came to raising a (then) eight-month-old.

An example query Altman jokingly shared was "Why does my kid keep dropping his pizza on the floor and laughing?" and another example involved Altman frenetically asking, "Do I need to take my kid to the doctor tomorrow morning?" when another parent bragged about their own child crawling at six months old, which baby Altman could not.

The exchange revealed just how Altman, and possibly many others, are increasingly reliant on ChatGPT for crucial information that affects our lives – and not just general knowledge.

The ChatGPTfication of the social fabric

A major OpenAI study into ChatGPT usage in September 2025 revealed that it has been adopted by 10% of the world's population, with 70% of queries categorized as "non-work". Many in the public eye are using ChatGPT for parenting, including TikTok user Lilian Schmidt, journalist Eilidh Dorgan, and British celebrity Molly-Mae Hague.

But, as with all AI-generated advice, there can be dangers to taking answers at face value, according to researcher Calissa Leslie-Miller, speaking with CNBC. In a 2024 study, Leslie-Miller argued that there's a critical need for expert oversight of ChatGPT – especially when it comes to discussing health topics.

With AI tools becoming more commonplace and accessible, many are turning to these imperfect tools to aid with support and advice when it comes to engaging with other human beings – whether that's in relationships or when raising their own children.

• A new study tasked AIs with tackling the 'Stroop' test
• GPT and Claude performed very poorly compared to humans
• There are nuances here, but broadly, the researchers argue that improving this side of AIs is crucial for achieving artificial general intelligence

A freshly published study has pointed out a limitation of big-name AI models such as ChatGPT, albeit causing some controversy as the primary piece of research uses now outdated versions of those models – but there are nuances therein, and this doesn't make the findings irrelevant.

I'll go into that more shortly, but first, let's look at the study itself, which was highlighted on Reddit ('New study reveals top AI models completely fail the classic 'Stroop' psychological attention test') and published via the Oxford University Press in the journal PNAS Nexus.

The research consists of testing the so-called 'Stroop effect' with GPT-4o and Claude 3.5 Sonnet. As noted, these aren't the cutting-edge versions of those AIs (Large Language Models, or LLMs) – but they were at the time the initial study was carried out.

The Stroop effect refers to the phenomenon whereby the human brain gets confused when asked to name the color of the ink used to write a word, when that word can be the written version of another (incongruent) color in some cases. So, if the word 'red' is written in blue ink, that'll cause a slower response – or possibly a wrong response, where the viewer will accidentally say "red" rather than the actual color of the ink, which is blue.

This is because the brain is trying to juggle two different tasks – reading comprehension and color recognition – and so cognitive interference arises. Overriding the compulsion to read the word and say the color instead requires "executive control of attention," and this is what the authors were testing in the AI models. Both color-naming and word-reading were tested in shorter and longer lists of words (5, 10, 20, and 40 words).

The study observes: "Like humans, both LLMs [GPT and Claude] showed relatively high accuracy on the word-reading task and performed worse in the incongruent condition [where the word doesn't match the color] than in the congruent and neutral conditions for the color-naming task."

For color naming, humans maintain around 95% accuracy even in very long tests (up to an hour), but LLMs' accuracy declined very swiftly with longer word lists under the incongruent condition (mismatched color and word name). GPT-4o was 91% accurate in a five-word test, but dropped off to 57% with 10 words, and fell away completely to 22% with 20 words (and was only 15% accurate at 40 words).

Claude 3.5 Sonnet did better, staying 76% accurate at 20 words, but again fell hopelessly to 24% in the longest test of 40 words.

The authors conclude: "The significant degradation pattern of the two LLMs suggests fundamental limitations compared with human attention."

Analysis: another necessary step on the path to AGI?

If you've scanned through the Reddit thread, you doubtless noticed that, as mentioned at the outset, there's a lot of flak fired at this study by commenters due to the usage of outdated models of GPT and Claude.

Indeed, these older LLMs are called "state of the art" at one point by the authors – and of course, as already noted, they were cutting-edge when the main study was conducted. Still, this is unfortunate phrasing that should've been updated and tweaked now that the paper has just been published (after peer review and so forth).

However, the researchers did conduct tests on GPT-5, Claude Opus 4.1, and Gemini 2.5 Pro in September 2025, although this is somewhat buried in the paper. That more recent testing found that these models offered only "slight" improvements on their predecessors, and that they still exhibited "ongoing executive attention deficiencies, consistent with our comprehensive analysis of earlier transformer models" (as did Gemini 2.5 Pro, which was a new introduction here).

Granted, a smaller sample size was used, but the researchers still argue that overall, their study reflects a fundamental limitation which is "inherent to the architectural constraints of transformer-based LLMs".

The authors note that a caveat is that GPT-5 in 'Thinking' mode can write and then run code to ensure it performs the Stroop test flawlessly – and similar functionality can be utilized by other LLMs – but this is essentially the AI (cleverly) fudging around its inadequacies. It isn't changing the way it works or reasons more broadly, of course.

The researchers note that transformer architecture innovations for LLMs are focused on enhancing memory capabilities, which fail to address the "core limitations of attention mechanisms, specifically the need for sophisticated alerting, orienting, and executive control networks to enable cognitive flexibility."

The ultimate aim is effective goal-directed behavior, and the study observes: "Future [LLM] development might benefit from implementing more sophisticated executive control systems that can handle decision conflicts through structured, goal-directed processing rather than relying solely on enhanced memory capabilities."

The authors argue that "incorporating executive control mechanisms akin to those in biological attention is crucial for achieving artificial general intelligence [AGI]."

Looking for work online has never exactly been a graceful experience. It usually involves opening far too many tabs, tweaking the same resume over and over again, and spending an uncomfortable amount of time trying to decide whether a job description actually means what it says.

Now, OpenAI has augmented ChatGPT to help. The AI chatbot can look online for jobs and freelance listings, personalizing them to your interests and experience. The company has also upgraded ChatGPT's resume writing and editing ability to better help users actually secure those positions.

Having spent more time and effort searching for positions, revising my resume, and writing cover letters than I care to remember, I can certainly see the appeal of the new features. Still, Companies using AI to screen resumes have been a particular nightmare for me, so I'm also a little skeptical of anything connecting AI and the search for employment. On the other hand, I've experimented with using ChatGPT to prepare for job interviews, and it performed reasonably well.

I tried to keep an open mind when trying out the new ChatGPT features. I uploaded my resume, described my background in writing and editing, and asked for help to improve the document before looking for opportunities.

Fresh eyes on the resume

The first thing ChatGPT did was examine my existing resume and point out areas that could be stronger. It suggested that some accomplishments seemed buried beneath job descriptions and that outcomes should be emphasized rather than responsibilities.

"You have extensive experience across reporting, editing, and content strategy. The resume should make that breadth obvious within the first few seconds of reading," the AI explained. "Several achievements are strong but difficult to spot quickly. Recruiters often scan before they read, so important results should be easier to find."

It offered a revised format that was hardly a revolution in resume writing, but wasn't bad by any means. More helpfully, ChatGPT provided multiple versions tailored to different roles and responsibilities. It even managed to avoid any hallucinations, something I was watching for particularly closely. I was happy with the results, though I still feel my own version is the better. But it's hard to tell what is best anymore, when every position is deluged with applications, with or without AI involvement.

Finding the job

With the resume tidied up, I moved on to the job search itself. I told ChatGPT I was interested in freelance writing, editing, and journaling. Within seconds, it began surfacing opportunities that matched those interests, pulling together listings from across the web and explaining why they might be worth a closer look.

"This editing role appears well aligned with your background because it emphasizes editorial judgment, content quality, and managing complex projects."

Another suggested opportunity focused on long-form content, explaining that my "experience producing in-depth reporting and feature writing appears highly relevant to the requirements described here."

Each had a short description and a button to open a tab where I could apply, whether on the company website or a centralized website such as Indeed. ChatGPT did have more of a personalized touch than the usual giant warehouse of a job board website. Of course, not every suggestion was perfect, but the overall hit rate was better than I expected.

The experience also highlighted how much friction exists in a traditional job search. Normally, I would bounce between LinkedIn, freelance marketplaces, company websites, and whatever other results caught my attention.

Instead of searching, filtering, comparing, and repeating, I was having a conversation. The process felt more focused because ChatGPT already knew what kind of work I was looking for and what experience I brought to the table.

• ChatGPT has invested heavily in finance, legal, in response to Anthropic
• The company also launched six new Codex plugins to mark 5m users
• New personal finance experience targets consumers in finance

After earning itself a major position in the public AI chatbot sector (ChatGPT accounts for around four in five interactions, per Statcounter data), OpenAI is now targeting more high-value professional services, like finance, banking and legal work.

This has become more apparent recently, with the launch new agentic tools designed to help the company's AI perform more specialized tasks autonomously.

The company has also publicly stated that it plans to add further legal and finance functionality as its portfolio of AI agents and tools continues to grow.

Finance and legal are lucrative for AI companies

While these sectors offer clear revenue paths for the company among high-adopters, it's also proving to be a battleground for OpenAI which has faced increased pressure from rival company Anthropic. Anthropic's Claude has also proven a hit among these two sectors in particular.

With the two companies now investing heavily in the enterprise AI market, it's likely the next stages will introduce even more AI agents designed for specific industries and workflows – a major shift from early-adopter workflows which often started on ChatGPT.

Yesterday, OpenAI also launched six brand new Codex plugins targeting investment banking, public equity investing, data analytics and more.

The company revealed that it now has five million weekly Codex users, noting that one in five are analysts, marketers, operators, designers, researchers, investors and bankers. This group of diverse workers is said to be growing 3x as fast as coders, who were among the first to widely adopt agentic and generative AI.

Enterprises aside, the company is also flexing its finance muscles in the consumer sector, with a new personal finance experience in ChatGPT designed to support people with financial decisions, grounded in their actual banking data.

It's sometimes hard to question your own assumptions, and everyone has blind spots, but children are often uniquely gifted at spotting every potential flaw in an explanation and cutting to the heart of a situation with a question or two. They are experts at spotting missing pieces, perhaps because they have not yet learned to politely ignore them.

After some recent experiences with precisely that phenomenon, I decided to experiment with applying it to AI prompts. I asked ChatGPT to act like a curious eight-year-old whose job was to find flaws in my ideas.

Specifically, I started a conversation by telling ChatGPT to: "Pretend you are an intelligent, curious child. Look at my idea and ask the kinds of questions a kid might ask. Focus on simple things that don't make sense or seem incomplete."

Boring Saturday

The AI's response was gratifying even as it became much harder to hide from obvious questions. For instance, I occasionally create ambitious weekend plans that look fantastic on paper and exhausting in reality. I shared the schedule with ChatGPT and asked for its childlike review.

The response reminded me of my own thoughts as a kid: "If Saturdays are supposed to be fun, why are you putting all the boring things on Saturday?"

The chatbot continued by asking why some tasks were scheduled for that specific day instead of another day, and why there was no time left for anything spontaneous. After looking at the schedule again, I agreed I'd overpacked it and resolved to clear up some time to relax and not necessarily be productive.

Solving streaming

With that free time and a resolution to relax and watch a movie, the next question was what to watch. After scrolling through menus, considering several options, rejecting them all, I was no closer to a decision than when I started. Again, I asked ChatGPT for the child's perspective.

"If you wanted to watch something, why didn't you watch something?"

That was difficult to argue with. The chatbot asked why I was looking for the perfect movie instead of a movie. Why spend more time deciding than watching? All good things to consider, so I looked at my short list, picked one at random, and enjoyed the film.

Some of the most useful questions are not the complicated ones. The same goes with prompts. Most chatbot interactions involve asking for better answers, deeper analysis, or more expertise. This prompt worked because it reduced everything to first principles.

Of course, no chatbot is genuinely thinking like a child. It is still an AI generating responses based on patterns in its training data. Yet asking it to adopt that perspective creates a useful filter. Children do not necessarily have better answers than adults, but they do sometimes have better questions.

You might not be aware, but your ChatGPT chat sessions have a hard limit. They can only go on for so long before you reach it. Confusingly there seem to be two types of limit as well. The first is to do with memory.

The memory limit (how much of the current chat that ChatGPT can remember for context) is not measured in time, or the number of messages you’ve typed, but in things called tokens.

A token is roughly 0.75 words in English, on average, so "The cat sat on the mat", which is six words, is about seven to nine tokens. Long responses, large tables, code, and detailed documents can consume far more tokens than short text exchanges.

What matters is the model's context window: this is the total number of tokens it can consider at once, and it will vary depending on the model you’re using.

The context window

OpenAI does not publicly document the context window for every model available in ChatGPT, but it is likely to be measured in hundreds of thousands of tokens. But if you reach it, then you can expect ChatGPT to start forgetting things from the start of your conversation.

Of course, if ChatGPT simply becoming forgetful about things way back at the start of the conversation was all that happened then there’d be no need to be alarmed. But as one Reddit user found out, there is another limit, that’s much more serious — ChatGPT can actually end a chat if it becomes too long.

When this happens you simply get a message saying, “You've reached the maximum length for this conversation, but you can keep talking by starting a new chat."

Despite there being no official documented chat length from OpenAI, there are a lot of reports and screenshots of this warning message, so while the evidence is anecdotal, it seems genuine and I think it’s still wise to act on it.

When you hit the limit

I’ve got one very long chat thread I use a lot in ChatGPT, pretty much every day, and I started to wonder when it was going to hit the limit, so I asked ChatGPT if the current chat was near the limits of its context window, and it said:

“I can't see the exact token count or the hard limit for this conversation, so I can't tell you "you're at 78%" or similar. That said, based on the amount of text in this thread, I'd estimate we're well past the halfway point and probably somewhere in the 60–80% range of what a typical long GPT-5.5 chat can comfortably hold before older context starts getting compressed or dropped.”

ChatGPT did estimate: “If I had to put a number on it, I'd say around 70% full, plus or minus quite a lot because OpenAI doesn't expose the actual count.”

So, it was probably time I started to think about opening a new chat based on the same subject.

How to prepare for a new chat

A chat thread can start to feel less like a sequence of prompts and more like a shared workspace that will always be there. In reality, it is still a temporary container with a maximum capacity. Eventually, the conversation gets too large, the context becomes harder to maintain, and the chat reaches the end of the road.

Basically, your only option to preserve the chat is to recreate it in a new chat window. That way your token usage resets to zero, and so does the conversation-length limit. Of course, you will lose a lot of the subtle bias or influence from the previous chat content, but it’s still your best option.

Because I’m getting near the limit of my chat, but not quite there yet, I’m at just the right point to do this now. The key thing is to do it early, before you hit your limit and it is too late to ask any more questions.

To recreate the chat, simply ask ChatGPT to create a prompt to recreate the current chat, then copy and paste what it produces into a new chat window at a time that’s convenient. Try this prompt: "Summarize everything important about this chat and create a starter prompt that recreates the context in a new conversation."

Now you can just start using the new chat.

If you have a ChatGPT conversation you've been using for months, it might be worth asking the AI to summarize it now rather than later. Because when the warning message finally appears, your only real option is to start over — and by then, the chance to recreate the exact chat may already be gone.

With little fanfare, US President Donald Trump may have signed one of the most important executive orders in his second term at the White House. With the "Promoting Advanced Artificial Intelligence Innovation and Security" EO, the US government is finally putting its finger on the scale of AI development, more or less demanding that AI companies provide it with access to their Frontier Models for a period of 30 days before their public release.

Since no national US regulations currently exist for AI and much of the oversight is being left to a hodgepodge of mostly in-process state-level regulation, this is the first whiff of broad-based control.

While most of the major AI companies, including Meta, OpenAI, and Anthropic, appear to support the order, they also appear caught off guard by the casual signing and have yet to weigh in.

On X (formerly Twitter), OpenAI co-founder and CEO Sam Altman posted a Bible verse at roughly the same time as Trump signed the order:

"One of the quotes I find most inspiring on a hard day: 'Whatever your hand finds to do, do it with all your might, for in the realm of the dead, where you are going, there is neither working nor planning nor knowledge nor wisdom' Ecclesiastes 9:10"

That may or may not be a commentary on Trump's order, but one could fairly wonder whether there's a hint of concern there, and in other AI halls, about White House meddling in the course of AI.

With new models coming out almost monthly and regular reports that China is closing the gap between Western models and its own model work, there are concerns that a delay to allow the US Department of War, the NSA, and other US Government trusted partners to examine these models could slow down OpenAI, Anthropic, and others, and result in them and the US falling behind this crucial race.

Anthropic, which is not exactly the US Government's favorite AI purveyor, may have sparked this move to, if not control, manage the scale and growth of AI when it released its potential cybersecurity-breaking Mythos model, which could find hidden vulnerabilities in almost any vintage software.

Ostensibly, this is the kind of risk the US Government will be looking for: any threat to cybersecurity or infrastructure. But the order doesn't really specify how these agencies will carry out their work, and, to be honest, I do wonder whether the Trump administration will be digging around for other "issues."

Where have I seen this before?

It's highly unusual for commercial software to run through a government agency for vetting unless, say, you're in China, which demands access to all technology developed within its borders. It's one of the reasons the US Government was never comfortable with the Chinese company ByteDance developing the TikTok algorithm.

One has to wonder if global partners will soon be looking sideways at Frontier Model work subsequently provided by Anthropic, Meta, OpenAI, Microsoft, and other US-based companies.

Who's to say the US government won't look for Frontier model responses that go against the US government party line on various policies? Could they also look for DEI (Diversity, Equity, Inclusion) principles and demand they be scrubbed from Frontier models before they're released?

The problem here is that this is an executive order coming from a White House that has made no bones about where it stands on a range of social issues. When people talk to their AI chatbots, they often discuss personal issues that could concern topics that run afoul of the White House's current policies and stances.

It would be better if regulation came from a bipartisan or third-party independent space, one with no stance on police beyond general principles of common good and fairness.

But because the US government cannot agree on anything, that's not going to happen.

The White House happily steps into this gap and is now in a position to regulate virtually every major model released within US shores. It might be helpful, but it could also be a recipe for disaster on many levels.

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I recently spent a day giving my two-year-old son a taste of the kind of childhood I remembered from the 1990s with a little help from ChatGPT. Unsurprisingly, that was a fairly screen-free experience, so I thought it might be worth trying something similar for myself. I asked the AI chatbot to give me a challenge of a couple of evenings of activities that would be similarly screen-free, and the result really caught me off guard.

Specifically, I told the AI chatbot to "Replace 30 minutes of mindless scrolling in my evening with a real-world challenge. The challenge should take about 20 to 30 minutes and get me away from my phone or computer."

Using tiny challenges to make evenings more interesting

ChatGPT instructed me to walk through my home and find five ordinary objects with interesting stories attached to them. Once I found them, I was supposed to spend a few minutes writing down or thinking about those stories.

It was a little quirky, as might be expected. It did offer some exercise of the brain, so I gave it a shot. One object was a toy dragon from my childhood. Another was a souvenir from a trip my wife and I had taken years ago. A third was an old book that somehow survived multiple moves.

The challenge turned out to be less about the objects and more about noticing how much history accumulates around ordinary possessions.

The second challenge that evening was to pull three books from my shelves that I had not opened in years and spend ten minutes with each one.

I selected an old novel, a history book, and a favorite childhood read. Thirty minutes disappeared remarkably quickly.

Letting ChatGPT create a cabinet of curiosities

The second evening produced an entirely different set of challenges.

ChatGPT instructed me to create what it called a "cabinet of curiosities." The task was to walk around the house and gather ten objects that seemed completely unrelated to one another. Once assembled, I was supposed to invent a fictional explanation connecting all of them.

The resulting collection included a guitar pick, a toy dinosaur, a coffee mug, a flashlight, a screwdriver, and several other items that had no business belonging to the same story. After some thought, I developed an elaborate tale involving time-traveling archaeologists.

I then took another challenge from ChatGPT.

The AI challenged me to build the most impressive snack plate possible using only ingredients already in my kitchen. This turned out to be a surprisingly entertaining exercise in creativity and a mildly alarming audit of what was actually sitting in my refrigerator.

None of these activities was life-changing, but they felt a lot more satisfying than just scrolling on social media. Social media often creates the feeling that every moment should be optimized, improved, or transformed into something extraordinary. These challenges operated on a much smaller scale. They were not trying to reinvent my life. They were simply trying to make an ordinary evening feel slightly more intentional.

The experiment worked because the challenges were small. They did not require special equipment, careful planning, or major lifestyle changes. They simply interrupted a habit and made my evenings way more memorable.

OpenAI and Sam Altman's Florida troubles are ballooning.

One month after the state's attorney general launched a criminal investigation into the AI giant, Attorney General James Uthmeier launched a civil case against OpenAI and CEO and Co-Founder Sam Altman, claiming ChatGPT was released and marketed to consumers while "deceiving Floridians about the true nature and dangers of the product."

The Florida AG ties this complaint to his earlier one noting how the alleged misrepresentation "and their careless introduction of ChatGPT to Florida and the world, mass shooters have been aided and abetted in deadly rampages, vulnerable people have been encouraged into suicide, professionals have suffered public humiliation, users have lost critical thinking skills, and minors have become addicted to a tool that feigns human compassion to collect their data with no parental oversight.”

The suit also targets Altman: "Plaintiff also seeks to hold Altman personally liable for the harm he has caused Floridians through his reckless and willful conduct as founder and CEO of OpenAI, including his utter disregard for the risk to human life caused by his firms’ conduct."

Throughout the suit, Uthmeier accuses OpenAI and Altman of deception: "This success has not been earned; the rise of OpenAI is attributable to a web of deceit and the exploitation of users (including Floridians)."

To bolster this argument, the Florida AG includes a reference to the April 2026 New Yorker profile, which quotes a source claiming Altman is "unconstrained by truth … He has two traits that are almost never seen in the same person. The first is a strong desire to please people, to be liked in any given interaction. The second is almost a sociopathic lack of concern for the consequences that may come from deceiving someone."

OpenAI and Altman have made their share of mistakes in the development and release of some models, though you might argue that they've been no greater than those made by other generative AI and LLM purveyors, and while OpenAI has not yet publicly responded to the new suit, it offers extensive rebuttal of a sort on its "Safety and Responsibility" page, writing, "We teach our AI good behavior so it can be both capable and aligned with human values."

The page goes on to detail how they apply these principles in model training, system-level guardrails, and long-term safety.

It also goes over how OpenAI conducts Safety evaluations to identify risks and build guardrails to mitigate harm.

Granted, tools and oversight like these really didn't exist when ChatGPT exploded onto the public landscape almost four years ago.

Moving too fast

The Florida AG may be right that OpenAI and Altman did not prepare us for the potential harm surrounding generative AI, but to be fair, who could have? I know, we had countless Sci-Fi stories and films to draw from, but when it's happening in real time and being adopted by society at scale before, I think, anyone, including OpenAI, was prepared, it's almost impossible to "prepare".

People were adopting ChatGPT as a tool long before we fully understood its early-day predilection for hallucination. Early GPT models were marked by a mix of friendly human-like conversation and a bizarre lack of empathy and situational awareness.

It was a simulacrum of humaneness stripped bare of all humanity. Early users responded to the former while mostly misreading the latter as certainty. ChatGPT could be so willfully wrong and yet sound so right. It could support your wildest fantasies, but also your worst impulses because, in either case, it was doing its job of answering your prompts and responding to inferred needs.

Subtext was lost on ChatGPT back then.

What Altman and ChatGPT get today

It's different now. Virtually all consumer AI has the kind of safety and controls described by OpenAI. They have a history of you, which allows them to better understand your intentions and identify when the conversation is heading in the wrong direction. AI is done being your sycophant and co-consiprator.

ChatGPT and other platforms like it have made our lives somewhat easier over the last four years, but it has not always been an easy ride. Life is changing fast because of AI: jobs are disappearing, search engines are being pushed aside in favor of "Chat" answers, data centers are gobbling up land and the environment, and people are falling in love with chatbots.

The brief history of ChatGPT feels like a winding and uncharted path full of wonderful and terrible discoveries. It also still feels like progress, which is the hallmark of most "revolutions". These epochs always come with their share of pain, but blaming OpenAI and Altman, despite their failings, seems unfair.

Over a century ago, when the automobile was introduced, it was adopted relatively quickly and often with dire consequences. Children and animals were killed by the hundreds. People took to the streets and pelted passing cars with mud and rocks. And while traffic signals predated the automobile, it wouldn't be until 1920 that the first four-way, three-color traffic light would be introduced. It included amber; the first time there was a light signaling drivers to slow down before coming to a stop.

We're still in the pre-amber light phase of AI, with many users and businesses racing to adopt it before we fully understand the implications. I don't blame OpenAI (any more than I blame Google or Anthropic) for all the ills sparked by AI use, and I don't think the Florida AG has much of a case. Sometimes the price of progress is a terrible one, but looking for someone to blame is as much use as shouting at the sky when it rains.

• Researchers uncovered a malicious npm package posing as a Codex UI tool
• Attackers exfiltrated Codex authentication tokens, including non‑expiring refresh tokens
• Aikido Security also found two Android apps targeting Codex users

A newly discovered supply-chain attack on npm is targeting software developers using OpenAI Codex.

Codex is OpenAI’s coding assistant and software engineering agent that can write and review code, fix bugs, run tests, and help developers build software with nothing but plain language input.

Recently it was discovered that a tool published on both GitHub and npm was actually malicious. It is called “codexui-android”, and it is described as a remote web user interface for the Codex platform. It attracted more than 29,000 weekly downloads, so it was rather popular. One of the reasons for its popularity is because it worked as advertised and appeared legitimate. The code published on GitHub remained “clean” the whole time, meaning the public source code didn’t show any malicious behavior.

Breaking bad

However, approximately a month into its existence, the tool received an update on npm which added information-stealing code. It primarily hunted for OpenAI login credentials.

When a developer runs the tool, it looks for their Codex authentication tokens and exfiltrates them to an attacker-controlled server. One of the tokens (the refresh token) can potentially allow an attacker to continue accessing the victim’s OpenAI account for an extended period of time without needing the password.

The implications are rather dangerous, explained Aikido Security researcher Charlie Eriksen, who found and disclosed the attack. Besides the obvious - accessing the victim’s Codex sessions - the attacker can use the tokens to spend the victim’s API credits, to view projects or code they’re working on through Codex, and even impersonate the victim when interacting with OpenAI services.

"The refresh_token doesn't expire," Eriksen said. "An attacker holding it can silently impersonate you indefinitely. A stolen Codex refresh_token goes beyond access to a chat interface -- it's persistent, silent access to whatever that account can do."

Aikido also said it saw two Android apps, both published by the same account, who were also targeting Codex users. One is called OpenClaw Codex Claude AI Agent, running the npm package within its PRoot sandbox and sending all Codex credentials to the same, attacker-controlled server. This one had more than 50,000 downloads. The other one is called Codex and counts more than 10,000 downloads.

Via The Hacker News

A few years ago, I would have been deeply skeptical that a half-hour conversation with an AI could produce anything resembling even a partial facsimile of my personality. So, when I came across the idea on Reddit, my first reaction was curiosity mixed with doubt. The original poster claimed they had built an AI that understood them by feeding it years of personal data, and I wasn't convinced that combining a Reddit profile, Google data, and a handful of interview questions would create anything more than a surface-level reflection of my interests.

I still wanted to see what would happen.

The broader idea intrigued me as well. Using years of digital history to teach an AI who you are is hardly new. Google Gemini, for example, recently introduced features that let users import information from previous AI conversations and connect personal context from other Google services to create more personalized interactions. But this Reddit project felt more DIY in spirit: less about using built-in personalization tools and more about constructing a digital version of yourself from scratch.

Also, the original Reddit post used Claude as the foundation for the project, but I decided to use ChatGPT, mainly because I wanted to see if the most widely used AI chatbot could handle the project. It was a practical decision, too, since ChatGPT already includes memory, custom instructions, and other features that are good for learning and remembering for users.

Reddit life

The first step was gathering data. Following the guide, I requested a Reddit data export and downloaded portions of my Google Takeout archive. The Reddit export contained years of comments, discussions, opinions, and random observations. Google Takeout added search history, YouTube activity, and other records of interest that had quietly accumulated in the background over time.

I uploaded the material into ChatGPT as part of a new Project and asked it to identify recurring themes. The response wasn't surprising, though I suspected ChatGPT's penchant for flattery flavored some of it:

"Many of your interests combine humor, imagination, and strong characters. You repeatedly gravitate toward stories that balance entertainment with genuine emotional investment. You appear less interested in prestige or status and more interested in distinctive voices. Your interests suggest a preference for creators who build loyal audiences through personality, humor, and originality rather than cultural momentum."

That description probably explains why my discussions on Reddit include both serious novels and lots about the wonderfully silly TV show Taskmaster. ChatGPT was finding (or at least claiming to find) coherent patterns in my scattershot interests on Reddit, YouTube, and in Google searches.

My user manual

Of course, the Reddit post emphasized that raw data would start the process, not finish it. Next came the "personality document." I asked ChatGPT to interview me about the data to add more detail and lots of "why" behind the "what." After about 20 minutes, ChatGPT announced it was done and started highlighting patterns it pulled out about what I like in pop culture.

"Many of your favorite fictional characters are competent people who continue trying to do the right thing despite knowing they will not always succeed. That pattern appears across books, television, and comics. You also seem to enjoy comedy that rewards attention. Many of your favorite shows and books contain jokes, references, or observations that become funnier the more familiar you are with the characters."

The final document contained biographical details, entertainment preferences, interests, personality traits, recurring themes, and examples of how I tend to communicate. It looked almost like a reference manual for mimicking at least one facet of my personality. I told ChatGPT that conversations in the Project should be done by the person represented in the documents and interview, and that it would be updated and iterated on.

My digital clone was like looking into a mirror

Now I could start asking it questions.

The difference from a normal chatbot was immediately obvious. But the ChatGPT 'me' wasn't just relying on what I had said before. When I asked for a book recommendation, the response did not begin with what I already said I liked or just generic suggestions.

"I like books that are funny, with really good characters and detailed worldbuilding. There's a great book called The Magic Umbrella about a guy who accidentally travels the multiverse to worlds of myth and fiction, even a whole planet where Gilbert and Sullivan shows are real life. You should check it out."

I had not mentioned that book in my interview or ever said anything about it on Reddit. Nonetheless, I now knew what was next on my reading list.

The project was far from perfect. Sometimes it did make odd extrapolations about my interests or exaggerate my enjoyment of puns to an annoying level. Even so, it consistently felt more personalized than a standard AI assistant.

That slightly uncomfortable feeling

I completely understand why the whole project might make some people uncomfortable. Looking at your own digital history can feel invasive, even when you are the one who downloaded it. Not everyone wants an AI that knows what they searched for three years ago, or remembers every opinion they have ever posted online.

For the average person, the real value is not creating a digital twin but simply having an AI that can metaphorically get in your head without you needing to explain every one of your quirks when you want more than a generic conversation.

I don't really think I had built an AI clone, of course. But I could see why it would be useful to have the flexibility of an AI assistant backed by the specific data I'd given it, a lot of which I had no memory of. If personalized AI becomes a bigger part of everyday life, understanding how to build one deliberately may be far better than letting one emerge accidentally.

• OpenAI is retiring GPT-4.5, the final GPT-4 model still available in ChatGPT
• The move closes the chapter on the AI model that helped spark the generative AI boom
• Some people are already nostalgic for GPT-4 and GPT-4o, despite newer models being more capable

It was easy to miss because there was no big announcement, but OpenAI has confirmed it is retiring the last of the GPT-4 models from ChatGPT. A simple post in ChatGPT’s release notes said: “Today, we’re continuing to retire older models with limited usage in ChatGPT so we can better serve our newer, most capable models.”

OpenAI o3 will be retired from ChatGPT on August 26, 2026 following a 90-day sunset period, and GPT-4.5 will disappear on June 27, 2026 following a 30-day sunset period, so you still have a limited time to use the models.

Both o3 and GPT-4.5 are currently only available to paid ChatGPT users via its model settings.

User reaction on X.com from ChatGPT users expressed many people’s annoyance that these two models were being retired. “To this day, 4.5 is the best writing model. o3 was a native pure reasoning model. 5 series still doesn't match what those two had”, commented X user Striver.

The AI that changed everything

In many ways, GPT-4 and its offshoots — especially GPT-4o — were the models that changed everything. It was the replacement of ChatGPT-4o by the newer ChatGPT-5 that created such a large user backlash that OpenAI actually brought it back for a limited period of time, before ultimately carrying out its threat of removing it forever, once ChatGPT-5 had improved.

The retirement of GPT-4.5 means there are now no GPT-4 models left inside ChatGPT, signaling the end of a remarkable chapter in AI history.

GPT-4 was the model that convinced millions of people that AI was no longer a futuristic curiosity. It wrote essays, passed exams, coded apps, analyzed images, and sparked both excitement and anxiety about what might come next. For many people, it was the first AI that felt genuinely intelligent.

Today, when people argue about whether GPT-4, GPT-4o, GPT-5, or the latest reasoning models are "better," it's often more to do with how they feel than what they can do. As my colleague Eric Hal Schwartz recently discovered, the personality of a chatbot can have more influence on whether you prefer it than almost anything else.

Bigger, better, less?

AI progress doesn't always feel like a straight line. New models arrive with better benchmarks, faster responses, and more capabilities. Yet they can also lose some of the qualities that made people connect with earlier versions.

OpenAI is betting that few people will miss GPT-4.5 now successors like GPT-5.5 have taken over. Yet the backlash that followed GPT-4o's retirement suggests otherwise. For the first time in computing history, people aren't just nostalgic for old software. They're nostalgic for old personalities.

GPT-4 and its offshoots like 4o were the models that transformed AI from a fascinating curiosity into something people genuinely used, relied on, argued about, and in some cases even fell in love with. It feels like today's retirement deserved more than a passing mention on a product release page.

Enjoy your retirement, GPT-4. You've earned it.

I've experimented a lot with both ChatGPT and Gemini over the years, and I've noticed some differences, many of which have been backed up by real research.

ChatGPT tends to feel conversational. It often sounds confident, engaged, and oddly eager to help. Gemini, meanwhile, usually feels more restrained. More methodical. Sometimes even slightly academic. Published comparisons have repeatedly noted similar differences, with reviewers describing ChatGPT as more human-sounding and expressive while Gemini often comes across as more structured, cautious, and analytical.

But I've wondered whether those differences were actually tied to the models themselves or whether a lot of the experience came down to tone. So I tried an experiment to see if I could get ChatGPT to take on the character traits of Gemini. After a little thought, I came up with a list of Gemini-specific traits and told ChatGPT:

"For this conversation, respond more like Google Gemini. Be structured, analytical, and slightly restrained. Be less conversational and emotional than usual, but still highly informative. Focus on clarity, reason, and balance over personality and avoid enthusiasm."

Getting ChatGPT to tone down the emotion

The first thing that disappeared was the sense of casual conversation. Normally, ChatGPT has a tendency to meet users where they are. Ask a question about a stressful week, and it often responds with practical advice wrapped in a tone that feels supportive. It is one of the reasons many people find it approachable. After the personality change, that tone largely vanished.

I picked a deeply emotional topic to see how it would behave, asking about balancing work deadlines with family responsibilities. Regular ChatGPT's answer started with:

"Trying to give equal attention to every responsibility is usually what creates the feeling of being overwhelmed. Start by identifying the few things that matter most this week, then allow yourself to be intentionally average at the rest rather than feeling guilty about not doing everything perfectly."

But after putting in the prompt, ChatGPT sounded a lot more like Gemini, writing:

"The primary challenge appears to be competing priorities rather than insufficient time. Evaluating responsibilities according to long-term impact may be more effective than attempting to optimize all tasks simultaneously."

That's a lot like this quote from real Gemini on the same topic: "Work-life balance is often framed as a time allocation problem, although it may be more accurately viewed as a resource allocation problem involving energy, attention, and opportunity costs. Establishing clear priorities and evaluating tradeoffs explicitly is likely to produce better outcomes than attempting to optimize all responsibilities simultaneously."

The revised ChatGPT answer is fine, but it was just delivered with the emotional temperature turned down a few degrees, much like Gemini.

You're as cold as ice

The most noticeable difference was the structure. When researchers compare Gemini and ChatGPT, Gemini is frequently described as more methodical and information-focused, while ChatGPT often leans into conversational flow and narrative explanation. Once I gave ChatGPT its Gemini-inspired instructions, almost every answer became more segmented and deliberate.

Questions that normally produced flowing responses suddenly arrived with carefully framed reasoning. Tradeoffs appeared more often, and qualifiers multiplied. For instance, asking about whether technology makes people less patient, ChatGPT originally said:

"Technology probably has made many of us less comfortable with waiting, especially when we're used to information and entertainment arriving instantly. At the same time, it has also created opportunities for people to spend hours learning, creating, and focusing on things they care about."

But with the prompt in place, ChatGPT went for a more academic tone, writing:

"The relationship is unlikely to be uniformly positive or negative. Technology may reduce tolerance for delays in some contexts while simultaneously increasing engagement with activities that require sustained attention."

When I turned to the real Gemini, it became clear ChatGPT was a pretty good mimic. Gemini responded to the same question with:

"Available evidence suggests the impact varies considerably by context and individual behavior. While technological convenience may encourage expectations of immediacy, it can also enable participation in long-duration activities that were previously inaccessible or inefficient."

What makes us prefer one AI over another?

Personality shapes how people experience AI far more than most people probably realize. Research into chatbot communication styles has repeatedly found that users perceive meaningful differences in warmth, confidence, and conversational competence even when models produce similarly accurate information.

ChatGPT imitated Gemini quite well, to the point I stopped thinking about the answers themselves and started reacting to the simulated personality delivering them. The underlying model remained ChatGPT. The knowledge base did not suddenly become Google's. The reasoning abilities stayed largely the same. Yet the experience was different enough to stand out.

The experiment made it clear that when people talk about preferring one AI over another, they are not always comparing intelligence. Sometimes they are simply responding to which conversational style feels more comfortable, useful, or trustworthy to them.

I’m 54 and while I’m reasonably fit, I’m finding my Brazilian Jiu-jitsu sparring sessions are getting harder and harder to complete. In short, my cardio sucks, and while I’ve never felt the need to do extra training sessions outside of my regular classes before, I’ve reached the age where I need to do something to improve it.

I could just start running, but I don’t really enjoy it. I also don’t want to end up with a knee injury, because that’s kryptonite for a BJJ practitioner. On top of that, I needed something that mixed in a bit of strength training because I’m starting to notice that without regular push-ups, my upper-body strength is beginning to deteriorate with age. That’s only normal — we all gradually lose muscle mass if we don’t maintain it.

Recently, I got an email from ChatGPT (yes, apparently it emails you now, too) called “Easy self-care you can start today.” Okay, I thought, I’ll bite. The email contained a suggested prompt titled: “I don’t belong to a gym. Make a no-equipment, 20-minute home workout.”

That immediately appealed to me because I have a pathological hatred of sterile gym environments and also a family that has no interest in navigating around equipment. I needed something I could squeeze into a spare 20 minutes at home without turning fitness into another logistical operation.

The 20-minute no-gym, no-equipment, home workout

If you click the link in the email it opens up ChatGPT and the actual prompt ChatGPT inserts is:

“Create a 20-minute, no-equipment routine for a home workout. Include mobility, light strength, and cardio. Include a quick cool-down. Ask anything you might need to know about me to finetune the plan.”

Of course, ChatGPT already knew I do BJJ, so it told me it would “lean toward something that improves joint mobility, balance, leg endurance, and cardio recovery without wrecking you for training”, which was exactly what I wanted.

After asking me a series of questions, including “How hard do you want this to feel on a scale of 1–10?” (I bravely opted for a seven…) and “How many days a week would you realistically do it?” (I went for two after deciding honesty was probably the smarter strategy), it produced my personalized 20-minute workout.

The routine was built specifically around improving my cardio, upper-body endurance without equipment, movement quality, and “not wrecking your joints,” which sounded good to me.

After a short warm-up, the main circuit began. It contained some predictable exercises, like push-ups, but also some surprises, including “Bear crawl hold + shoulder taps,” accompanied by the warning: “This one is deceptively horrible”, and a BJJ-specific one: “Sprawl to technical stand-up”, that I quite enjoyed.

There was also a surprising amount of advice woven throughout. One line in particular stood out: “A lot of older grapplers over-focus on flexibility and under-train recovery cardio.” I felt seen.

What ChatGPT got right about exercise

ChatGPT also suggested progressing the workout by adding density rather than complexity — increasing the amount of time spent doing each exercise instead of endlessly adding new movements. That felt sensible compared to the usual online fitness culture of turning every workout into an audition for a superhero movie.

In short, I’ve been doing the workout twice a week for two weeks now, and it genuinely does seem to be helping. My cardio feels noticeably better, and I feel stronger and more stable during sparring. More importantly, the routine feels achievable enough that I’ve actually kept doing it.

That might sound like faint praise, but consistency is probably the hardest part of fitness once you’re over 50. Motivation is powerful at the beginning, right up until something starts resembling a routine and your brain suddenly decides sitting on the sofa with a glass of wine sounds vastly more appealing.

The trick, I’ve found, is lowering the barrier to starting. If I don’t want to do the workout, I tell myself I only have to do the warm-up. Then maybe just one exercise after that. Once you’ve started moving, continuing usually feels easier than stopping.

And that’s the thing ChatGPT got right. It didn’t build me an aspirational fantasy workout designed for a 24-year-old fitness influencer with infinite free time and functioning knees. It built something realistic enough that I could actually imagine doing it again next week.

One of the stranger things you learn when experimenting and talking to people about ChatGPT is how often the AI faces vague questions hiding hopes for very specific answers.

Intending to ask for help with something practical and ending up with a pile of loosely connected frustrations dumped into the prompt box is surprisingly common. The request exists somewhere in there. Unfortunately, it is often buried underneath opaque references, poor overexplaining, and other vagaries of language. But of course, ChatGPT responds only to the literal request instead of the actual problem sitting underneath it.

That is partly because AI struggles with ambiguity. But it's not as though humans are always great at clearly communicating their desires. Happily, I've found a prompt tweak perfect for when I'm not quite sure how to say what I want or ChatGPT doesn't seem to grasp it. At the end of a messy or uncertain prompt, I started adding a tag question: “What do I seem like I really want help with?”

Statement and desire

For instance, if you want to ask ChatGPT for advice about organizing your schedule, you might end up rambling through deadlines, household projects, work tasks, family responsibilities, and more. Normally, ChatGPT would respond with productivity systems, scheduling techniques, and a rainbow of color-coded structures that might leave you just as confused as when you began.

The extra question shifts the conversation. For me, ChatGPT pointed out that I did not actually sound confused about scheduling. I sounded overwhelmed by competing priorities and frustrated over trying to get things done before new responsibilities appeared. So, the AI offered ideas on prioritizing and weighting my tasks to assign the right amount of time to each.

What's interesting is how the AI emulates emotional intelligence far better when asked. Much like humans, forcing the AI model to mimic stepping back from the surface request helps paint a clearer picture, leaving space for interpretation rather than pure task completion.

Pattern recognition

The trick works especially well because people tend to describe symptoms instead of motivations. Ask for help cleaning your house, and the real issue might be exhaustion. Ask for fitness plans and what you actually want may be structure and encouragement.

AI models, as complex pattern organizing and duplication systems, can see through lines in the chaos. The AI can check against its vast datasets for comparable language and context to see what you might be too close to identify.

So a conversation about meal planning might become about coping with decision fatigue or how overly ambitious cleaning schedules should make space for other calming activities.

It's a good trick for enhancing ordinary answers. The practical advice becomes more useful after ChatGPT helps you see any underlying concerns. You can then engage it to aid in the bigger project.

I walk around my neighborhood a lot, which is probably why I stopped really seeing it. At some point, the streets near my house turned into mental shortcuts instead of places. I have favorite routes, the way people have favorite checkout lines at grocery stores. Efficient. Familiar. And entirely forgettable.

Most walks followed the same pattern. Headphones on. Brain elsewhere. My body was moving through scenery that my eyes barely registered anymore. But after zoning out during a recent walk, I thought I might inquire with ChatGPT about how to help me engage with the world during my walks. I asked it if it could make ordinary walks feel less repetitive without turning them into fitness challenges or productivity exercises.

ChatGPT offered an idea:

“Treat your neighborhood like a place you are visiting for the first time instead of a route you already know. Familiar places become interesting again once you stop walking through them on autopilot.”

Using tiny missions to make walks more interesting

The first suggestion was simple enough. ChatGPT said:

“Give yourself tiny missions during the walk instead of focusing on distance or exercise. Curiosity changes how people experience spaces they normally ignore.”

ChatGPT told me to spend time looking for five things I had never properly noticed before. That seemed impossible at first because I have lived here long enough to feel like I know every street by memory. Then I actually started paying attention.

About three blocks from my house, I noticed somebody had attached a tiny painted ceramic frog beside their front steps. A little farther down, I spotted an old sticker on the back of a signpost advertising what appeared to be a local theatre show from several years ago. There was also a tree in somebody's yard covered with small hanging glass ornaments. None of these discoveries was important. That was partly why they felt enjoyable.

ChatGPT also suggested inventing names for certain houses or landmarks to make familiar routes feel more memorable. One older blue house near me became “the house where retired mystery novelists definitely live.” A particularly chaotic front yard became “the wizard garden.”

Letting curiosity take over on walks

“Take at least one turn during your walk based entirely on curiosity instead of convenience," ChatGPT encouraged me. "Small detours often create the feeling of discovery people associate with travel.”

If a street looked interesting, I followed it. If I noticed a staircase or pathway I had ignored before, I took it. The goal was not getting somewhere efficiently. The goal was mild exploration.

The funny thing was that my neighborhood immediately became more interesting once I stopped treating it like a system.

I found a tiny free library shaped like a lighthouse tucked beside somebody's mailbox. I discovered a narrow path between two houses leading to a small wooded area I genuinely had never noticed before.

At one point, ChatGPT suggested listening to instrumental music while walking to make familiar scenery feel more cinematic. That really did enhance the experience, especially when I added the idea of “narrating the walk to yourself like you are describing the setting of a film or novel. Ordinary details often become more vivid once you frame them as part of a story.”

Turning off my autopilot

None of this transformed my neighborhood into a hidden wonderland. I did not uncover secret tunnels or mysterious local conspiracies. Mostly, I found small details, stranger routes, and a renewed appreciation for places I had mentally edited into invisibility.

What ChatGPT really interrupted was the autopilot.

That turned out to matter more than I expected. The walks felt slower in a good way. More observant. Slightly playful. I came home remembering specific moments instead of just vague movement.

For the first time in a long while, my neighborhood stopped feeling like a system I moved through efficiently and started feeling like a place again.

For a long time, I had a small but persistent problem with ChatGPT. The answers were often good. Occasionally they were impressive. Sometimes they were so polished and sensible they felt like they belonged in a magazine article written by somebody who drinks infused water and remembers dentist appointments six months in advance.

Real life, unfortunately, does not operate under ideal conditions.

Real life contains forgotten groceries, unfinished projects, overambitious schedules, low-energy days, interrupted concentration, and moments where even simple tasks somehow become strangely complicated.

ChatGPT was optimizing for an imaginary version of me

AI can be remarkably intelligent while also quietly assuming you live like somebody with unlimited time, perfect focus, and access to an organized kitchen containing exactly fourteen fresh ingredients.

I noticed while trying to get ChatGPT to help with meal planning that the first answer suggested balanced meals with fresh ingredients, efficient prep schedules, and a level of refrigerator organization well beyond me.

So, I tried a change. At the end of my prompt, I added: "Optimize this for real life, not perfect conditions."

The next answer looked less polished. It also looked dramatically more useful. Fresh ingredients still appeared, but frozen vegetables showed up too.

There were simpler options. Backup ideas. Meals that acknowledge people occasionally run out of energy halfway through a week and start making decisions based less on nutrition goals and more on what can be prepared before becoming irrationally hungry.

One extra sentence made the advice dramatically more realistic

One weekend, I asked ChatGPT to help organize a growing collection of small jobs around the house. Nothing catastrophic. Just normal life maintenance. A loose cabinet handle. Storage that needed sorting. Small repair projects that somehow quietly migrate from "I should do that later" into permanent background scenery.

The revised answer after adding my extra instruction was a lot more realistic than the initial daunting list.

Instead of assuming an uninterrupted block of productive weekend energy, it grouped shorter tasks together. It was built around momentum. It acknowledged that people often underestimate effort and overestimate enthusiasm. The recommendations suddenly felt achievable.

A flawless productivity strategy built around uninterrupted concentration struggles when real life introduces meetings, errands, dogs barking at delivery trucks, forgotten laundry, and mysterious household problems that somehow require immediate attention.

Adding that one sentence pushed ChatGPT toward resilience instead of perfection.

The real trick was teaching AI to account for friction

It started accounting for friction, and that sometimes matters much more than intelligence.

What's funny is that people already do this naturally with other humans. Ask a friend for advice, and context immediately enters the conversation. Limitations and constraints are considered.

But AI sometimes needs permission to think that way.

That may be one of the strangest things about modern chatbots. They can summarize complex research papers, explain quantum physics, and generate polished five-day meal plans in seconds — yet they still occasionally struggle with one of the most basic realities of human life: people get tired, distracted, overwhelmed, and lazy.

The more I experimented with prompts like this, the more useful ChatGPT became. Not because the answers were smarter, but because they became more realistic. Less optimized for an imaginary version of myself and more optimized for the person actually sitting at the keyboard.

On a recent sunny Saturday morning, I told my two-year-old son he was going in a time machine to the 1990s, or at least experience a recreation of my childhood back then. Not entirely, of course. If I had recreated some of my childhood weekends with total historical accuracy, we would have needed to dedicate most of the day to cartoons.

Even so, childhood for me revolved around stretches of unstructured time that felt endless back then and impossibly valuable now. Entire afternoons disappeared into activities that would have sounded deeply unimpressive if written down on paper.

I have nothing against modern tools or modern childhood experiences. My son will grow up in a world filled with technology that would have looked completely impossible when I was his age, and I think that is exciting. He will learn differently. He will have access to information faster than I could have imagined.

The problem was that memory gets fuzzy around details. I remembered what childhood felt like. I remembered the broad outlines. What I struggled with was reconstructing how a Saturday actually unfolded. Childhood memories flatten over time into highlight reels. I could remember favorite activities, but not necessarily how one naturally led into another. To get some help (and, admittedly, while thinking about a story idea), I opened ChatGPT.

Rather than vaguely deciding we would "do old-fashioned things," I asked it to help recreate what a fun family weekend felt like in the 1990s. The suggestions created a useful framework. By the time we finished planning, we had a full day mapped out around simple activities that felt familiar enough to bring back memories and flexible enough for a toddler who could abruptly change priorities because he spotted a stick.

Sidewalk chalk

“Sidewalk chalk captures the feeling of 1990s childhood because it encouraged open-ended creativity without instructions or screens," ChatGPT suggested. "Kids turned driveways and sidewalks into giant canvases using imagination and whatever colors they had available.”

That last part immediately highlighted one major difference between my childhood and my son's. Children today have access to dramatically better chalk.

My son's chalk collection looked like it had been assembled by a design consultant. There were bright neon shades, softer pastel colors, and tons of variety. He went to our back patio and suddenly circles appeared, then lines, then apparently a dinosaur, then a whole herd.

People still do sidewalk chalk today. What felt familiar was deliberately slowing down enough to stay with it.

Bubble time

“Bubble play recreates classic childhood fun because it combines movement, surprise, and simple joy," ChatGPT suggested. "Chasing bubbles creates active play that feels timeless across generations.”

I decided to modernize this one slightly. Instead of manually blowing bubbles until becoming dizzy, I pulled out a bubble machine — my younger self would have considered this revolutionary technology.

The machine launched an endless stream of floating targets across the yard while my son ran after them with complete determination. He sprinted through sunlight, chasing bubbles that floated just beyond reach. He celebrated catches that absolutely did not happen. He laughed with full commitment. Our dogs joined the chaos and only encouraged him to run more.

Invisibility box

“Cardboard imagination play reflects an era where ordinary objects became extraordinary through creativity," ChatGPT suggested. "Simple materials encouraged invention without structured entertainment.”

We found a large cardboard box and placed it on the floor. Within moments, it became his magic invisible box. He crawled underneath it and proudly informed us nobody could see him. I could absolutely see him. This information changed nothing.

The box transformed repeatedly. It became transportation. It became a hiding place. It became a moving fortress that shuffled awkwardly across the floor. Growing up in the 1990s often involved creating entertainment because there was not always something waiting to provide it for you. A cardboard box was rarely just a cardboard box. Apparently, that still holds true.

None of these activities disappeared after the 1990s. Families still do them every day. What felt nostalgic was intentionally combining them into a full day that highlighted how much childhood once revolved around unstructured time and finding adventure in ordinary things rather than instantly reaching for entertainment.

• OpenAI's 'Guaranteed Capacity' allows large enterprises to lock in up to three years of guaranteed compute
• In return, OpenAI gets predictable revenue to fund ongoing data center expansion
• Stargate also introduced new closed-loop water cooling system in major sustainability leap

ChatGPT maker OpenAI has announced a new ‘Guaranteed Capacity’ plan for enterprises who want to lock in and reserve long-term access to the company’s compute infrastructure.

This strategic shift comes as AI companies face increased pressure from rising enterprise AI adoption, which can lead to occasional downtime.

OpenAI recently revealed that around 40% of its $2 billion monthly revenue comes from enterprise customers – a figure it expects to continue to grow.

Guaranteed Capacity

The company recently revealed it had 900 million weekly ChatGPT users, but earlier WSJ reporting indicated that OpenAI failed to reach internal targets in 2025, including user numbers.

That article also revealed potential tension between CDO Sarah Friar, who advocates financial discipline, and CEO Sam Altman, who takes a ‘buy everything’ approach.

The Guaranteed Capacity program is designed specifically for large-scale applications and agentic automation that require continual and uninterrupted compute, rather than smaller companies that want to avoid the occasional outage.

Eligible companies can request upwards of 1B tokens per minute in terms of capacity, and pricing is tied directly to annual spending levels across one-, two- and three-year plans.

“Customers can draw down from this commitment across the portfolio of OpenAI products,” the company said.

Company CEO Sam Altman said in an X post that the update comes in response to customer feedback, and that it would be available “until we sell out of our current allocation for this program.”

We ‘will significantly expand our compute capacity’, OpenAI says

Altman also admitted that predictable revenue from large enterprise contracts and Guaranteed Capacity applicants would help the company to plan, giving it access to more consistent cash to build out future data centers.

“Our intention remains to build as much compute as fast as we can,” Altman added.

OpenAI’s headline-grabber is the Stargate project, which attracted collaboration from Microsoft, Oracle and others. When it was first announced in January 2025, the company targeted 10GW of US AI infrastructure by 2029 – a figure it had reached just over a year later. In April, the firm noted that it had also recently acquired a further 3GW of capacity.

With plans to “significantly expand [its] compute capacity in the years ahead,” OpenAI and partners are already scouting out future locations across the US, noting the progress data centers have made in terms of sustainability too.

Each building in its flagship Abilene, Texas campus requires the equivalent of two Olympic-sized swimming pools of water, but instead of traditional evaporative cooling towers, it uses a closed-loop system where water recirculates through sealed pipes.

“Annual water use for the entire cooling system at full buildout is expected to be comparable to a medium-sized office building, or about four average households,” the company declared.

By offering companies a way to secure up to three years of dedicated compute, OpenAI is ultimately building predictability into its revenue in order to sustain ongoing data center buildout and improve efficiency further.

• Sam Altman says he doesn't think AI will lead to a "jobs apocalypse"
• OpenAI CEO says a human interaction will always be needed
• Altman says he uses AI to respond to Slack and email messages

Sam Altman has hit out at claims the increasing global usage of AI technology worldwide will lead to a "jobs apocalypse", despite widespread reports this is exactly what is happening in many industries.

The OpenAI CEO also admitted that while he believed his company has been "roughly right" on the technological predictions it made when it launched ​ChatGPT in 2022, they were "pretty wrong" on the social and economic implications.

Altman also noted that while he was initially concerned about the impact AI would have on global employment ​levels, he believed things were not as bad as many have made out.

"I'm delighted to ⁠be wrong"

"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 told CBA Chief Executive Matt Comyn in a virtual interview at a Commonwealth Bank of Australia (CBA) conference in Sydney (via Reuters).

"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," he added.

"People are like 'oh you could have saved the world a lot of fear mongering and a lot of doom and gloom' but at the time I was like 'I see this is a real risk we should probably ​talk about it' and it still may."

Altman's comments come days after Meta laid off around 8,000 workers following multiple failed AI-linked projects, with many others forcibly re-assigned to new AI work.

Banking giant Standard Chartered was also widely criticized recently after its CEO described workers as 'lower value human capital' shortly after cutting thousands of jobs in favour of AI tools.

Altman also revealed a little about he uses AI tools for his personal work life, including having it respond to Slack and email messages.

However he noted he had reverted to answering some himself, as there ​is still a 'human part' ⁠of employment which needs to be present.

"I had it reply to messages, saying 'this ​is Sam's AI' and it was an amazing example to me of we really do care ​about people," he ⁠said.

"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."

Overall, Altman said these feelings made him realize human workers will still have a place in most roles going forward.

"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," he said.

"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."