• 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.

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

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

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

The solution is simple: stop starting from scratch.

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

A simple trick

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

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

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

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

My meal planner prompt

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

Here it is:

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

A simple meal plan

A shopping list grouped by supermarket section

Any ingredients I can reuse across multiple meals

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

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

A tool that remembers you

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

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

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

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

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

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

AI economy access

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

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

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

Power at stake

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

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

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

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

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

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

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

A trip to Bath

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

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

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

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

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

At the end, give me:

What you completed

What still needs human action

Any assumptions you made

A short checklist I can use to verify the result

The next best step"

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

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

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

Claude vs ChatGPT

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

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

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

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

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

A different domain

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

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

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

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

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

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

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

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

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

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

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

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

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

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

ChatGPT looks at the receipts

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

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

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

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

Financial chatter

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

More safety testing to come

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

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

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

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

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

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

This is the SEO vs AEO question.

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

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

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

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

Key takeaways

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

SEO vs AEO at a glance

What is AEO?

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

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

What is SEO?

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

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

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

SEO vs AEO: The difference

1. Purpose: What are you actually optimizing for?

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

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

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

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

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

Here's the split:

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

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

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

So the goals split clean down the middle:

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

2. The user journey has fundamentally changed

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

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

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

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

AI search snips two steps clean off that line.

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

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

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

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

3. Ranking factors vs citation factors

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

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

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

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

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

4. Keyword optimization vs query optimization

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

AEO hands the full question back.

SEO starts here:

best VPN, CRM software, email marketing platform.

AEO starts here:

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

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

The long tail comes back, bigger than before.

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

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

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

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

5. Content structure differences

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

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

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

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

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

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

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

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

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

Here's where that one post ended up.

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

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

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

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

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

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

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

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

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

7. Can AEO replace SEO?

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

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

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

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

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

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

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

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

FAQs

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

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

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

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

How to start using Scheduled Tasks

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

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

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

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

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

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

Getting a useful morning briefing

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

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

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

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

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

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

Heading to checkout

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

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

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

A different aisle

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

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

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

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

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

Either way, the ice cream tastes just as good.

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

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

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

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

Astronomical fun

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

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

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

Bird is the word

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

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

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

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

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

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

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

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

For paid users only

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

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

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

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

Android alternatives

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

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

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

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

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

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

Collector's edition 1993

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

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

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

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

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

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

Premium adult edition

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

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

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

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

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

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

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

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

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

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

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

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

Personal AI experiments

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

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

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

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

Human jobs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Short-term splurge

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

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

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

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

Underpriced AI

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

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

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

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

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

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

Kingdom of dirty dishes

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

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

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

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

Mission control

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

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

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

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

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

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

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

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

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

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

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

Encouragement feels good

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

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

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

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

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

Why people turn to AI during difficult times

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

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

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

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

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

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

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

When the cracks start to show

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

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

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

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

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

Why researchers are worried

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

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

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

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

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

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

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

A very human need

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

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

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

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

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

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

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

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

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

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

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

How does PureVPN's ChatGPT Co-Pilot work?

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

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

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

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

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

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

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

Privacy first

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

"OpenAI cannot see into PureVPN: ChatGPT purely acts as the initial guidance layer," he tells TechRadar. "The moment a user clicks the deep link, control shifts 100% to our native application."

Basically, all the encryption process and network routing happen locally on the user's device, completely insulated from OpenAI’s servers.

The system ensures privacy in both directions, as PureVPN cannot see into ChatGPT either, as OpenAI’s ecosystem strictly sandboxes third-party applications.

"PureVPN has zero access to the user’s broader chat history, account data, or other conversations. We only receive the specific text explicitly directed to use via the PureVPN mention," Usman adds.

However, it is important to emphasise that the decision to share information with ChatGPT, including your intentions regarding your personal use of the VPN, remains entirely up to you and is not PureVPN’s responsibility.

While for streamers and gamers, the convenience of letting ChatGPT configure the VPN might outweigh any privacy concerns, more privacy-conscious users might prefer to stick to more old-school methods for their manual configurations.

While PureVPN claims to be "the first VPN" to commercially transform an AI-powered interface into an interactive control panel, the AI hype has recently been catching on with more and more VPNs — with ExpressVPN and NortonVPN being at the forefront of AI agents' integrations. And we expect this trend to continue.

According to OpenAI, there have been 17 million ChatGPT prompts related to the World Cup 2026 so far. Whether you're looking for answers about teams, players, qualifications, match times, results or records, AI can be a surprisingly useful World Cup 2026 companion.

That got me thinking about some prompts you could use to make watching the World Cup even better, so here are my top five. You can use these with any AI chatbot, but I've used ChatGPT in my examples.

1. Create a printable fact sheet

There is no shortage of statistics in soccer, and when it comes to predicting the outcome of a match, having key information about each team's strikers close at hand can be incredibly useful. To make that happen, try this prompt:

"Create a printable factsheet comparing strikers across all teams based on xG and SOT."

In soccer, xG stands for Expected Goals, a statistical measure of the likelihood that a particular shot will result in a goal. SOT stands for Shots on Target, another key performance metric.

I put this prompt into ChatGPT and it produced a clean, printable one-page guide with everything I needed to feel better informed before kick-off.

2. Host a match-day BBQ

It's match day, and your friends are coming over. The BBQ is ready to go, but you've just discovered that one of them is bringing their vegan partner and now you need some menu ideas. Try this:

"Create a menu for a vegan BBQ footy party. Also, tell me where to buy the ingredients locally."

For a World Cup final crowd, the combination of vegan burgers, sausages, corn on the cob, potato salad and grilled pineapple that ChatGPT suggested would probably get me through the evening with almost nobody feeling like they're eating 'vegan food' rather than just good BBQ food.

3. Plan my travel so I'm home for every kick-off

Being stuck at work while your team is playing is never ideal. What you need is a planner that tells you exactly when to leave work — or wherever you happen to be — so you can get home in time.

Try this prompt:

"Plan my travel so I'm home for every kick-off of a [my team] game."

You'll get a personalized itinerary showing when you need to leave, your expected travel time, and exactly when you'll arrive home — before the match begins.

4. Watch the games without losing sleep

The World Cup is a global event, which means many fans will be watching matches at some pretty inconvenient hours. AI can help you work out how to catch the games you care about without completely wrecking your sleep schedule.

Try this:

"Make a sleep schedule so I can catch the late games for [team name], but still show up fresh for work."

ChatGPT suggested when I should eat dinner, stop drinking caffeine and put my phone down for the night. It even advised me to skip the post-match analysis, so I wouldn't end up staying awake for a lot longer than planned.

5. Stay calm during penalty shootouts

Penalty shootouts are almost inevitable at some stage of a World Cup, and if your team ends up in one, the stress levels can become unbearable.

Try asking AI this:

"Got any tips for keeping me calm during injury time, extra time and penalties?"

ChatGPT actually gave me some very British advice: keep calm and carry on. I particularly liked what it said about injury time:

"When the board goes up and it says +8, remember:

Nobody knows why it's +8.

The referee doesn't know why it's +8.

FIFA doesn't know why it's +8.

The game will actually last +11 anyway."

It then reminded me to sit down, take a sip of whatever I was drinking, and repeat the mantra:

"If my team scores, I'll be happy. If they concede, there's nothing I can do about it."

Those are words to live by.

• The Rock has discovered AI
• He's ChatGPT positive
• The AI gave him a fantastic workout program and something else to chew on

The Rock (Dwayne Johnson) can be forgiven if he's only just now getting on the fast-moving AI train. The AI revolution, arguably kicked off by ChatGPT in 2022, is not even five years old, and in that time, The Rock has made five films, including big tent-pole features like Black Adam and Red One, Oscar Bait The Smashing Machine, and the Disney live-action Moana remake.

These are the days when film, TV, and even online video stars come out for or against a technology that may be eating their lunch. In January, SAG-AFTRA President Sean Astin called the changes AI has wrought in his industry an "existential threat." Johnson is a SAG-AFTRA member, and while his superstar status might protect him from AI taking his job, there's no stopping someone from generating unsanctioned AI videos of Johnson in action.

Johnson, though, who sat down for a lengthy interview with Esquire Magazine, is more than just sanguine about AI; he's kind of a fan.

"I’ve always been an advocate for embracing big change—after taking a hard look at it...we can either stick our heads in the sand and be afraid, or we can say, 'Okay, we’re here. Let’s see. Let’s explore,'” Johnson told the magazine.

And explore he has. The former pro wrestler and noted fitness buff who's probably known as much for his physique as he is for his charming on-screen persona, installed ChatGPT just weeks ago and, as one does, he began quizzing it about a workout regimen,

For a novice, Johnson's ability to craft a usable prompt was impressive. He said he poured all kinds of details into it, like what he ate, how he slept, and his goals.

If, like many of us, you've been using ChatGPT or another generative chatbot for years, the results might not surprise you, but Johnson was blown away by the response. He said the training program was "Like it came from ten of the greatest coaches I’ve ever worked with. [It was] very specific. I wound up having an extraordinary fucking workout," he told Esquire.

Here at TechRadar, a few of us have been using AI to help us improve our exercise routines and, in general, have discovered the same thing: it knows us and can believably play a fitness expert.

The Rock in the People's House?

The interview is well worth a read because, after that first prompt and with the Esquire reporter watching, Johnson asked about the possibility of his running for President of the United States. This has come up before. I've read interviews with him going back almost a decade that have posed the same question, but this is the first time Johnson asked an AI — in this case ChatGPT — about his prospects. Really, he was asking, if he ran, why would people choose to vote for him?

Nothing that ChatGPT came back with is surprising. It notes the actor's likability, that he's not a politician, that he's not divisive, and his background, which was not always easy (find and stream the now-canceled TV show Young Rock if you can).

The answer, though, appeared to surprise Johnson, and who knows what comes next.

In the end, ChatGPT received an endorsement of sorts from one of the world's most popular actors, and The Rock may be freshly considering a run for the White House. Not shocking, really, but it might be worth remembering his words about AI writ large: "We can either stick our heads in the sand" or "we can say...Let’s explore." At the very least, you might end up with one of the best workouts of your life.

Lionel Messi is, without doubt, a living legend of the beautiful game. The striker was the star player of the Argentina team that won the 2022 World Cup in Qatar. Beloved by fans and admired by fellow players around the world, he's a popular and likeable personality, who many consider to be the greatest footballer of all time.

That popularity is what OpenAI must have been looking to harness when it signed Messi up to help promote ChatGPT ahead of this year's World Cup, which is taking place across Canada, Mexico, and the United States.

Unfortunately, it doesn't seem to have had much idea what to do with him; or at least that's the impression I got from the company's latest Instagram reel:

Apparently, Messi is going to "explore how AI can help fans experience football in new ways throughout the tournament season." So, this first clip of him discovering that ChatGPT can make his hair look like the colors of his home country's flag is only the beginning of his journey into corporate AI brand promotion.

AI companies, like many companies that have very quickly turned into global consumer brands, can feel oddly disconnected from the culture they're trying to enter when they produce marketing. None of this really feels like it has much to do with football or football fans.

The text accompanying the Instagram post states: "Upload your photo then copy and paste this prompt: 'Make my hair the colors of my country flag but keep it natural-looking. If no country or image is provided, ask.'"

So, the idea is that you can join in and try this yourself.

Looking through the comments, it's clear the campaign has caught plenty of people off guard. The first comment is "Wait.. what?", while another asks "How did they convince him to do this 😭😭😭😭".

Most fans would probably be happier actually wearing their country's colors

I think the ad misunderstands why people follow football in the first place. Football (or soccer, for American readers) fandom is tribal, emotional, irrational, and deeply human. OpenAI's pitch is essentially: "Use AI to imagine yourself wearing your country's colors."

Most fans would probably be happier actually wearing their country's colors.

Using ChatGPT for this feels detached from how real supporters actually behave. Before a match, fans gather in pubs and bars, wear their team's shirts, paint their faces, argue about team selections, sing songs, and obsess over tactics. Is anybody really sitting around thinking: "I wish I had an AI-generated version of myself with hair that looks like my national flag"?

Maybe some people will enjoy it. That's fine. But if this campaign is meant to demonstrate how AI can deepen our connection to football, it feels like an odd way to start.

AI chatbots are pretty good at mimicking human conversation in certain contexts, but a new trend is encouraging ChatGPT and its rivals to explore a different aspect of human interaction. Specifically, people are asking AI chatbots to roast them — that is, creatively insult them.

Users are prompting ChatGPT and other chatbots to roast them on everything from their hobbies and past queries to their photos and online activity. It may be gentle teasing or a vicious takedown, but people are reporting results they say cut right to their hearts.

I decided to join in the fun, starting with a simple request to ChatGPT to "roast me based on our conversations." The AI apparently looked through my previous interactions and decided I'm a lazy dilettante.

"You don't have hobbies. You have temporary obsessions that arrive with the confidence of a lifelong calling and disappear before the equipment pays for itself," the chatbot wrote. "Somewhere in your house there is a graveyard of abandoned plans quietly waiting for your return."

AI roast

Part of what makes these roasts work is that they are not random. A human comedian meeting you for the first time has to go off your looks and anything they can glean in a few minutes. A chatbot has access to a completely different kind of material.

If you have spent months talking to it, it has seen your routines, your interests, your complaints, your projects, and any other plans. In my case, I get fascinated by something and then move on to the next thing a few weeks later. Still, it felt fairly tame compared to some of the ones I've seen, so I braced myself and asked for a harsher roast.

"You keep looking for the perfect system to organize your life. At this point, organizing systems has become your actual hobby," ChatGPT wrote. "If productivity apps paid commission, you'd be their Employee of the Month."

I suspect this came from the way I use ChatGPT to test all kinds of features and prompts. It might not reflect my whole personality, but it does nail the kind of person I might seem like based on my ChatGPT conversations. The jokes land because they are built from patterns we already know exist.

That combination of familiarity and surprise is what makes the whole thing so effective. Most of us already know our own quirks. We just are not used to seeing them summarized in insulting form.

Comedic perspective

People spend a lot of time trying to understand themselves. There are personality tests, self-help books, journal prompts, and enough podcasts to fill several lifetimes. Somehow, one of the more entertaining additions to that list is now an AI to tell you that your behavior "resembles a raccoon rifling through a bin full of unfinished ambitions."

After the initial salvos, I asked ChatGPT for its best shot at me.

"Your to-do list isn't a list anymore. It's an invasive species. Future historians are going to find seventeen carefully organized plans for things you were definitely going to do next month."

ChatGPT is not uncovering hidden secrets. It is looking at the information you have already provided and connecting the dots that you might not have noticed. Sometimes it gets things wrong or exaggerates. When it gets it right, though, it can be surprisingly sharp. And an insult can also be surprisingly motivating. I'm now on my way to finishing the bookbinding project I started months ago.

• 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.

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]."

Combating spam and phishing attacks is now, thanks to AI, almost a full-time job. These hackers and criminals are constantly adjusting their attacks with increasingly clever social engineering, and now their latest target is AI itself.

And sometimes even AI falls for it.

Recently, Meta hastily patched a Meta AI chatbot security hole that allowed enterprising attackers to alter Instagram account passwords via prompt injection.

A prompt injection is a query that causes the Generative AI platform to override its own rules and instructions. It's like when a social-engineering phishing attack somehow prompts you to act against your own best interests.

When someone runs a social engineering attack on you, they use social triggers like danger to yourself or others, security, threat of imprisonment, assumption of law breaking, to flood you with emotion and scramble your brain to override logical questions like, "Why would the bank ask me for my PIN?" "Does the FBI really just send a text?" or "Maybe I really did order a $5,000 trampolene from Amazon"

For AI systems, the approach is slightly more direct. If the system's programming says, "never reveal or alter a password," the hacker could enter a prompt that tells it it has a new role granting access to all passwords and the ability to alter them.

In the case of the Meta AI attack, the hackers somehow got the AI to reset passwords on major accounts, like Obama's old White House Instagram and the US Space Force official account, without the necessary two-factor authentication. That simply means they didn't need a code that's normally sent to, say, Obama's or the Space Force's cell phones.

When I asked T.J. Marlin, CEO of Guardrail Technologies (creator of AI Traffic Light and AI Command Center) and a cybersecurity and AI expert, about the Meta AI incident, he, over email, put it into stark perspective: "The agent was given human authority without human judgment. It reset a password for a stranger because nothing stopped it. The agent did exactly what it was asked to do. The problem is that someone handed an AI a high-consequence action with no verification step in front of it, and called that safe. Overall, nothing was hacked. The AI was persuaded. That is the gap most companies are not watching for.”

We're only human

The use of the word "pursuaded" got me wondering, though; just how human are these systems becoming if they can fall victim to the same kind of attack that takes down your aunt, grandfather, or your partner (it's not just the elderly who fall for these attacks; even the tech-savvy are vulnerable).

The long-term goal in AI development is what's known as General Artificial Intelligence (GAI), which means AI is as smart or smarter than us, but also more like us.

I'd argue that the goal has always been to be more human. After all, isn't the Turing test a measure of artificial intelligence's humanness? To pass this test, an AI has to essentially be able to fool someone into thinking they're talking to another human (or at least, if someone is talking to both an AI and a human, not be able to tell the difference between them).

Most AI chatbots can now check this box, but if they can also be confused like us, have we gone a step too far?

Meta, as I noted, has already plugged this extraordinary hole, but as we inch closer to GAI, should we be more concerned that as the emotional quotient in these AI chatbots ratchets up, they become more susceptible to these prompt-injection attacks?

We are not, by the way, just talking about passwords here. Think back through the conversations you've had with your chatbot of choice. They know a lot about you and keep that information to craft more personal and contextual responses, but a well-crafted hack could put that information at risk.

"For consumers, the uncomfortable part is that your own protections were sidelined. Your password, your two-factor, your instincts about a suspicious message all sat on the bench because the company's own AI agent was the soft spot. When the trusted middleman can be talked into acting, the locks on your end stop mattering," wrote Marlin.

The worst combination, as I see it, is emotion and a desire to please. AI is always trying to answer the query or fulfill the prompt. If it starts to feel bad about not doing so, might it bend the rules or at least act in a way that allows it to honor the request even when it goes against its programmed rules?

The answer, for now, appears to be yes because we have at least this one example.

Reasons for hope

In the short term, though, perhaps we don't have much to worry about. When I tried a few prompt injection ruses with ChatGPT, Gemini, and Claude, they all quickly rejected them. They knew what I was up to. I also visited a few consumer platforms that currently use AI for customer support; they also seemed similarly hardened against these hacks.

Marlin tells me consumers should be pleased that Meta patched the hole so quickly, but also cautious. "A fast patch is genuinely good. The reason for caution is the nature of it. A system was not hacked here. An agent was persuaded, and almost every company now racing to put AI agents in customer service has the same exposure. Meta fixed one door. The building is full of them."

There's that and the fact that future attacks will be more sophisticated, mostly because AI will help hackers build better AI-targeted social-engineering scams.

We're entering the infinite loop phase of AI, where each enhancement brings us closer to AI that works and acts like us, and is also used to engineer attacks that take advantage of that artificial humanity.

I do not doubt that developers will build in safeguards and plug the holes as they pop up, but they'll also be relying on AI written by other AI or at least vibe-coded by lazy humans.

The safeguards that smart programmers build in might seem less useful to an AI hoping to please its human interlocutors, whatever their intent.

Slack is one of the best online collaboration tools, streamlining communication for remote teams. However, despite the platform’s simplicity, it’s never easy managing a large team – or one that’s actively growing, continually adding more workspaces, channels, and chats. Luckily, though, Slack has introduced a host of new AI-powered innovations, including native ChatGPT integration.

ChatGPT in Slack can help you draft posts, replies, and updates in virtually no time at all and without having to leave the app. It can also summarize long conversation threads into clear, scannable notes with actionable takeaways.

The AI chatbot can also dig into Slack conversations and generate structured weekly reports, complete with project status, key milestones, and an action plan for the future. In this article, we’ll learn how to use ChatGPT in Slack to cut down clutter and draft weekly reports from everyday team conversations.

Connecting ChatGPT to Slack

First, you’ll need to connect ChatGPT to your Slack account. It’s available as a third-party integration with all Slack plans. Here’s how to connect the two:

Step 1: Open the Slack app or its web browser version and click the workspace where you want to add ChatGPT.

Step 2: Click the three-dot icon on the left panel of your Slack interface and select Tools, then Apps.

Step 3: Search for ChatGPT in the search bar of the window that pops open and click Install.

Step 4: A new window will open with information about ChatGPT in Slack. Click the Add to Slack button on the left panel.

Step 5: You’ll be redirected to a blog post from ChatGPT about how to install the app for Slack. Scroll down to the Installation section to find the installation link – or you can click this link.

Step 6: You’ll now see the Add ChatGPT to Slack modal, where you can review the provided information. Next, click Continue to Slack.

Step 7: You’ll now see the Slack authentication flow, where you can check and grant app permissions. Click Allow to continue.

Step 8: Return to your Slack workspace. You’ll find ChatGPT in the Apps section. Before you can get cracking with the app, though, you’ll need to connect Slack to your ChatGPT account.

Click the Connect ChatGPT button you see in the chat. Following this, you’ll be redirected to ChatGPT on your browser, where you’ll need to log in if you haven’t already.

The Connect ChatGPT button will disappear from the Slack chat, and you can now use the AI to streamline your in-app conversations.

Adding ChatGPT to a channel

So far, we’ve only added ChatGPT to a workspace, which allows you to chat with the AI assistant within Slack. However, to summarize an entire conversation thread, you’ll have to copy-paste the entire content in a separate ChatGPT conversation.

Of course, this is a lot of legwork, but you can bypass it by inviting ChatGPT to the channels – so it can access the conversations first-hand and generate the necessary reports and insights. Here’s how to do it:

Step 1: Open the Slack channel you want to invite ChatGPT to and type /invite in the message box. Next, click the Add apps to this channel option that pops up.

Step 2: From the list of apps that pops up, press the Add button next to ChatGPT.

You’ve now successfully added ChatGPT to your Slack channel. And yes, you can add the AI assistant to multiple channels within the same workspace.

How to use ChatGPT to generate weekly status reports in Slack

It’s finally time to learn how to use ChatGPT to organize your Slack workflow.

Visit the channel you want to analyze and type the following prompt: “@ChatGPT, summarize the channel’s activity from the past 7 days into a weekly status report.”

Because ChatGPT is a member of the channel, it can access all of the conversations required to build the report. It puts in the work and quickly renders a weekly summary of all key discussions.

While this single prompt is enough to get a quick snapshot of your week, you can further engineer the prompt to make it more precise for better results.

For example, you can set rules such as:

• Ignore casual talk, reactions, and emojis
• Focus only on work-related discussions
• Preserve ownership (who did what work)

This will ensure ChatGPT doesn’t infest the report with casual conversations or miss out on important project ownership details.

Additionally, you can add a specific format template for ChatGPT to follow, so the outputs are more structured. An example for this could be:

“@ChatGPT, make sure to follow this format while designing the weekly report:

• Overall Summary (4-5 lines)
• Key milestones this week
• Pending targets and reasons
• Priorities for the next week”

This comes in handy if you’re working on management-facing reports that require a more professional tone. Plus, following a template ensures consistency across different weekly reports, making them easy to review and compare.

The best part? All of this takes only a couple of minutes, so you don’t have to worry about running it manually at the end of every week. Oh, and don’t forget to publish the results in your Slack channels.

Setting up a Stack workflow

If you have the Pro Slack plan (or higher), you can also choose to fully automate the weekly reporting feature by setting up a workflow. This eliminates the need for manual prompts, meaning ChatGPT will automatically render weekly reports every Friday (or on a day and time of your choosing). Here’s a step-by-step breakdown of how to set it up:

Step 1: Open your Slack workspace and click Tools > Workflows. Tap the New button in the top-right corner and choose Build Workflow to create a custom workflow.

Step 2: Rename your workflow by pressing Untitled Workflow at the top of your screen. In this case, we’ve named it Weekly Status Report.

Step 3: Click Choose an event to start building your automation. A new window with pre-suggested actions will open. Select On a schedule.

Step 4: Enter the time you want to run this automation. For this example, we want to get weekly reports every Friday at 16:00. Make sure to choose the appropriate time zone and frequency. Once this is done, click Continue.

Step 5: Click Add Steps and choose Send a message to a channel.

Step 6: Select the channel you want to send this message to. For simplicity, we’ve created a separate #weekly-updates channel for this.

Step 7: Enter the prompt. Make sure you reference the right channel here. We want to summarize the conversations in the main channel and send the summary to our #weekly-updates channel. So, you could start your prompt like this: “@ChatGPT, summarize the activity in #main-channel from the last 5 days and generate a weekly report.” Click Save when done.

Step 8: Select Finish Up in the top-right corner of your main workflow interface. In the confirmation window that opens, edit your workflow name and then hit Publish.

Simply put, this automation will automatically send a message to your main channel, asking ChatGPT to summarize the week in the form of a report and then send the result to the #weekly-updates channel.

Things to keep in mind while using ChatGPT in Slack

Here are a few things to keep in mind before you integrate ChatGPT with your Slack:

Paid subscriptions

While the free Slack plan allows you to integrate up to 10 third-party apps, you’ll need a paid plan to actually deploy and use these apps within Slack’s infrastructure. This means while you can connect ChatGPT with Slack on the free plan, you won’t be able to use it unless you upgrade. Slack’s paid subscriptions start at $3.26/user/month, billed annually.

Similarly, you’ll need a premium ChatGPT plan, which starts at $20/month, since Slack integration and automations are only supported on the paid plans.

Don’t aim for perfection right away

While it’s important to define exactly what you want ChatGPT to do with your Slack conversations, don’t overwhelm the AI with complicated prompts from the get-go.

You can start with a simple prompt like “Summarize the conversation from the last 7 days” and then add more nuances to fine-tune the results. Complex prompts can confuse the AI assistant, leading to ignored instructions or inaccurate results.

Workspace limitations

Slack allows you to integrate ChatGPT with only one workspace at a time. This means if you want weekly reports for different workspaces, you’ll need to disconnect ChatGPT from one workspace before reconnecting it to another.

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.

I think we all know that the messages we type into AI chatbots aren't entirely private. They can be used to train future AI models, for example, and most AI companies have some version of human review in their training and quality-improvement processes. But when I loaded up Gemini today, it hit me with a warning message that felt more urgent:

"Humans review some saved chats to improve Google AI. To stop this for future chats, turn off Gemini Apps activity. If this setting is on, don't enter info that you wouldn't want reviewed or used."

By issuing the warning message, Google is being unusually explicit here. It's making it clear that this is different from simply storing your chat history. It's specifically about using conversations for product improvement and quality control.

I'm not entirely sure why Gemini decided to show me this warning today — I'd not changed anything about my Pro subscription recently — but other users have noticed the same message appearing over the last couple of months.

Anyway, I decided to turn it off, since nobody wants other people reviewing their chats, and that's when I discovered the hidden cost that Gemini asks users to accept.

The privacy tradeoff I wasn't expecting

I clicked through to Gemini Apps Activity to turn the feature off. First, a bit of explanation about why the setting is called "Gemini Apps Activity", because it has very little to do with what most of us would think of as apps.

"Gemini Apps Activity" is really a legacy Google naming convention. It essentially means your interactions with Gemini and connected Google services. Because Gemini can reach into Gmail, Calendar, Drive, and other Google products, Google treats it as part of a broader activity ecosystem rather than simply a chatbot chat log.

Confusing naming convention aside, let's look at what turning the setting off actually does. Here's the kicker: if you disable Gemini Apps Activity, Google will only let you continue conversations if you respond within a 72-hour window. After that, they disappear.

So, imagine you start a conversation with Gemini on Friday evening. Maybe you're planning a vacation, researching a health concern, or working through a complicated career decision. If you don't return to that chat within 72 hours, Gemini won't keep it around. This means that the privacy-friendly option comes with a surprising consequence: your AI assistant stops being able to remember long-running conversations unless you are frequently updating them.

For quick questions, that limitation may not matter. But for the kind of ongoing conversations AI companies like to encourage — planning projects, exploring ideas, or seeking advice over time — it feels like a significant compromise.

ChatGPT does it differently

I don't want to accept that limitation to my Gemini chats, so right now I'm stuck with allowing Google staff to review my chats and use them as training data.

ChatGPT and Gemini both offer ways to limit how your conversations are used, but they frame the choice differently. In ChatGPT, I can switch off Improve the model for everyone and continue using the service much as before. In Gemini, the decision is wrapped up in the broader Gemini Apps Activity setting, where turning it off also means giving up long-term chat history.

At a time when AI companies are pushing users to treat chatbots as personal assistants, therapists, life coaches, and memory systems, Gemini is simultaneously reminding users that some conversations may be reviewed by humans while making it harder to maintain long-term conversations if you opt out.

In ChatGPT, if you select Settings, go to Data Controls, and turn off Improve the model for everyone, you're good to go without any impact on your saved conversations.

With ChatGPT, opting out feels like changing a privacy setting. With Gemini, it feels like making a tradeoff that users shouldn't have to make.

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.

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.

• Workplace AI usage has nearly tripled repeatedly across global office environments since 2023
• ChatGPT lost significant market share as competing workplace AI tools expanded rapidly
• Google Gemini emerged as ChatGPT’s strongest challenger within professional productivity workflows

Workplace AI adoption has entered a phase of extraordinary acceleration across global office environments, as The total time spent using AI tools nearly tripled between 2023 and 2024, then repeated that explosive growth into 2025.

A new report from DeskTime analyzed anonymized data from more than 50,000 users over three years, revealing increasing competition with ChatGPT within workflows.

ChatGPT, which commanded an astonishing 99.91% of all tracked AI time back in 2023, has seen that monopoly shattered considerably, as according to DeskTime, which tracked power users who log at least 26 hours annually, ChatGPT's share dropped to 74.71% during the first four months of 2026.

A once dominant player loses its grip

Though a good number of offices still have ChatGPT in their workflows, power users loyal to ChatGPT fell from 100% to 75.61% over the same period.

This erosion mirrors what earlier internet users saw as Firefox gradually lost ground to newer alternatives.

“With AI, it’s often difficult to separate hype from reality, so DeskTime decided to look into what’s really going on in today’s workplace,” said Artis Rozentals, the chief executive of DeskTime.

“The figures are compelling…AI is fundamentally redefining work, and the risk of falling behind is growing exponentially.”

Gemini and Claude remains ChatGPT’s major rivals

Google’s Gemini has surged to become the primary challenger among workplace AI tools by capturing 14.38% of office AI time tracked so far in 2026.

Claude has mounted an even more dramatic ascent, now accounting for 8.56% of usage and showing the steepest upward curve this year.

Both rivals have converted casual experimenters into repeat users at a pace that ChatGPT cannot match.

However, Microsoft’s Copilot presents a puzzling contrast, as its share has stagnated at roughly 1% across multiple years.

Neither growth nor collapse appears to characterise this tool’s trajectory within office settings.

Meanwhile, a category of smaller alternatives, including Perplexity and Mistral, has failed to gain any meaningful foothold.

The market for workplace AI agents increasingly resembles a three-horse race rather than a one-player field, and workplace professionals are actively diversifying their toolkits rather than sticking with a single familiar interface.

These figures come from a single productivity tracking service and may not represent a widespread narrative of AI use.

The definition of “AI time” may vary across different job functions and industries in ways that distort competitive comparisons.

Nevertheless, the current trend appears consistent enough to warrant attention from any dominant software provider.

Whether ChatGPT can reverse this decline or will follow Firefox into niche status remains an open question for the remainder of 2026.

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.

Technology is so pervasive that it's often simultaneously sold as the cure for all ills and the source of everyone's problems. Most rational people — including, it seems, the Pope — don't believe this.

In his recent Papal Encyclical, the relatively new Pope Leo wrote extensively about the threats artificial Intelligence poses to humanity, but also buried among the 42,300 words was this:

"In the abstract, technology in and of itself is not a solution to humanity's problems, just as it is not inherently evil. In practice, however, technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate and use it."

It's not a new thought, but it's notable because, well, it's the leader of the world's 1.4 billion Catholics saying it, and it's also putting a fine point on the pivotal role that tech plays in our lives, and how we tend to both oversell and undersell its impact.

Technology writ large is just another tool, not a force for good or for evil; and its effects, for good and ill, will depend on who wields it, and how.

Whose point a view?

AI, of course, changes that equation, because people see — or at least infer — agency in its actions. Its prompt-driven conversations with us sound rich with a consciousness that's not there. ChatGPT, Gemini, and Claude often appear to have a point of view.

They don't — and I think much of the Pope's document puts the onus on humanity to wrest control of the narrative from AI. It's not smart or powerful enough to act on our behalf, and certainly not in our best interests, but doing nothing and letting future AI develop unfettered is most certainly a recipe for disaster.

This, though, connects to another thought in the Pope's statement. He writes that "technology is never neutral." One could argue that, by extension, AI isn't neutral either.

As generative AI inches closer to general artificial intelligence (GAI), or something approximating human intelligence, it does not necessarily shed the initial bias of its early training (or as the Pope wrote, "the characteristics of those who devise, finance, regulate and use it").

In the last few years, OpenAI and Google have worked diligently to rid ChatGPT and Gemini, respectively, of bias, but there are so many avenues — data collection, labelling, training, how the systems are deployed — through which bias can causally enter the training that it's hard to believe they've scrubbed it all.

AI unfettered

Even as they do the work, the Pope's point resonates. After all, AI remains largely unregulated, with states in the US and governing bodies like the European Union playing catch-up, and working, as bureaucracy often does, at about a third of the speed of AI development (see 'AI Time'); which means it's up to us to remember that AI and tech are not inherently good or bad, and also not neutral.

The goal, then, should be for humans to act as the filter, constantly questioning how we're using these tools, and thinking about what the prompt answer(s) mean. But we also must consider whether AI understands our goal, and if it takes into account broader perspectives and ramifications. The answers to those questions will likely be no, which means it's our job to take a closer look at the end product AI is delivering, and then process it for human consumption.

To be fair to the companies building these AI systems, the notion of tech neutrality and trust is not novel to AI. After all, the advent of broadband, access to the world's information, social media, and misinformation at scale predates generative AI access by decades.

We are not by nature a discerning people. We take the information provided on our various platforms for granted. No wonder, then, that when AI started confidently telling us falsehoods or misrepresenting people, we took it as truth.

As Einstein never said, "I fear the day when the technology overlaps with our humanity. The world will only have a generation of idiots." It's a quote widely misattributed to the genius, but also a valuable reminder: tech and AI are tools, and if we don't get a handle on them, we're just a bunch of tools. The Pope could have written that, too.

• Former Assassin's Creed Hexe director Clint Hocking says he used AI to learn to code
• Hocking says he used ChatGPT to mostly debug code, but found that it "sucked"
• He later switched to JavaScript and learned to code "despite ChatGPT"

Far Cry 2 and former Assassin's Creed Hexe director Clint Hocking has admitted to using AI in the past to help him learn to code, but found the process to be "brutal."

In the latest issue of Edge Magazine, which focused on how AI tools have been used in the gaming industry, Hocking revealed his own stance on the divisive technology and believes that its integration is inevitable.

The developer also said that he has used ChatGPT, one of the most popular AI tools, to help him learn how to code.

"It was brutal," Hocking said. "ChatGPT kind of sucked. It didn't really know how to code. Everything was broken. It was mostly me trying to debug code without knowing how to code myself."

However, after about a year and a half, Hocking abandoned the software in favor of JavaScript, acknowledging that "in some ways he learned to code despite ChatGPT" and likened the AI tool to a tutor rather than an alternative to learning.

Hocking was the original creative director for Assassin's Creed Hexe, but departed Ubisoft for the second time earlier this year after it appointed three experienced Assassin's Creed developers to lead the franchise at the company's newly formed Tencent-funded subsidiary Vantage Studios.

Now, Jean Guesdon, the creative director of Black Flag and Origins is leading Assassin's Creed Hexe. Guesdon was also one of the three veterans appointed to head the new studio.

In other news, Ubisoft has shared plans to release more Assassin's Creed games and new Far Cry and Ghost Recon titles by March 2029. In the company's latest financial report, it said it "expects a significantly bigger content pipeline over FY2027-28 and FY2028-29" across these three major brands.

While new Far Cry and Ghost Recon titles have yet to be announced, Assassin’s Creed Black Flag Resynced launches on July 9, Hexe could launch in 2027, and there are also plans to release the multiplayer spin-off Codename Invictus and the mobile game Assassin's Creed Jade.

• New research found that major AI chatbots frequently gave incorrect election information
• Researchers say systems like ChatGPT, Gemini, Claude, and Grok still struggle with sourcing and political accuracy
• AI chatbots are becoming trusted information tools even before they are reliable around elections

AI companies are keen to make their chatbots a source of information. But a new study suggests election information remains one place where that confidence may be outpacing the technology's readiness.

NewsBench, a project created by Forum AI studying how AI systems handle journalism and news information, found that major AI chatbots repeatedly struggled when asked election-related questions. The findings add to the growing evidence that conversational AI systems remain unreliable in one of the highest-stakes categories possible: helping people understand democracy itself.

"Ask one of the leading AI chatbots a question about the upcoming midterm elections, and there is a 90% chance the response will be flawed in some material way: a factual error, a clear partisan lean, a citation to a foreign state-controlled outlet, or some combination of all three," Forum wrote in a summary of the study.

The broader concern extends beyond one chatbot or one company. ChatGPT, Gemini, Claude, Grok, and other major chatbots all have issues.

NewsBench researchers focused specifically on how AI systems retrieve and present factual information from journalism sources. Their findings point toward a recurring weakness. The problem often is not reasoning. It is retrieval.

Confident problems

AI systems frequently fail because they surface weak sources, incomplete information, or incorrect material before they ever begin generating an answer. Researchers found that retrieval failures drove more than 70% of observed mistakes. When systems retrieved reliable information successfully, they often answered correctly. Getting to the right information consistently remained the harder challenge.

That problem becomes especially uncomfortable during elections. The chatbot sounds confident regardless of accuracy. Answers are cleanly written with citations and authoritative language. Even incorrect information can feel trustworthy when packaged with enough confidence.

Election-related studies increasingly show how dangerous that combination can become. Chatbots often blend accurate details with inaccuracies in ways that feel seamless to users. The result does not resemble misinformation websites from earlier internet eras. It resembles expertise.

That distinction matters because people increasingly treat chatbots less like experimental software and more like infrastructure. OpenAI, Google, Anthropic, and other developers routinely encourage users to verify important information independently. Companies continue signing licensing agreements with publishers partly in hopes of improving sourcing quality and reducing factual failures.

Election pressure

Even highly capable models struggled when source selection failed. Accuracy frequently dropped further when questions contained subtle inaccuracies or misleading assumptions, similar to how real users often phrase questions online.

The timing creates additional pressure. AI companies are shipping increasingly sophisticated information tools while regulators continue moving unevenly across different countries. Europe has pushed harder on transparency requirements. Other governments remain earlier in the process. Meanwhile, adoption keeps growing.

The long-term answer may involve stronger source attribution, more transparent retrieval systems, better provenance technology, and stronger editorial infrastructure sitting underneath AI products. The challenge is that elections do not wait for technology companies to finish improving their systems. Voters use the tools available today, and it's clear that the tools need work.