• McDonald’s is being sued over its use of dynamic AI pricing
• The lawsuit alleges it colluded with franchises to set prices
• McDonald’s denies the allegations made in the antitrust case

How would you feel if you got charged more for your McDonald’s meal simply because some opaque AI algorithm decided you should? You’d probably be pretty unhappy, and the practice is now in the spotlight as the fast-food chain is being hit with an antitrust lawsuit over its usage.

The proposed class-action suit alleges that McDonald’s has coordinated pricing between the chain itself and its independent restaurants that operate as part of its franchise system. It claims that, instead of competing among themselves on affordability, McDonald’s franchises are colluding to set prices recommended by AI algorithms, potentially inflating the costs charged to consumers as a result.

According to Reuters, McDonald’s uses its AI algorithm to set prices at roughly 14,000 restaurants in the US and can suggest prices for individual menu items. It considers various data factors, including local purchasing behavior and estimates of the prices consumers will be willing to pay in different locations. But this hasn’t gone down well with everyone.

“Independent businesses must set their prices independently,” the lawsuit states, as per Reuters. Lark Turner, a lawyer ​for ⁠the suit’s plaintiffs, said that McDonald’s is “leveraging its troves of data and its franchised system to nickel-and-dime consumers down to ⁠the last ​French fry.”

The lawsuit argues that McDonald’s and its franchisees have engaged in an unlawful instance of price coordination dating back to 2019. Although it is still in an early stage, it is seeking damages on behalf of potentially millions of consumers.

Future ramifications

McDonald’s has disputed the claims put forth in the lawsuit, saying that “AI does not set the price of a Big Mac ‌or ⁠any other menu item.” It added that franchisees can decide their own menu item prices and that the use of AI to recommend prices is used across multiple industries.

In that last regard, McDonald’s is correct. Firms including Sony, Amazon, Walmart, Best Buy and more often adjust their prices multiple times throughout the day, meaning what you pay in the morning might be different in the evening. This dynamic pricing is powered by AI — and could leave consumers out of pocket if they shop at the wrong time.

This practice might be widespread, but whether that makes it acceptable — or, in the case of the McDonald’s franchises, legal — is another matter, one which this class-action lawsuit could help to decide. As AI is rolled out to almost every facet of daily life, regular consumers have very little visibility over how it affects them and the prices they pay.

Not all fast-food restaurants are rushing to employ AI systems. Chick-fil-A, for example, has refused to install AI in its drive-thru lanes, instead using human employees to take orders. As Chick-fil-A CEO Andrew Cathy told CNBC, “From our experience, we really want that hospitality to be human to human.”

As for the McDonald’s lawsuit, it touches on the issue of where the restaurant chain’s centralized control ends and where local franchise decisions begin. Throw AI into the mix and you’ve got a heady mix of traditional antitrust legislation and emerging AI tech that could have ramifications far beyond the restaurant industry.

• Anthropic has banned ‘abusive or cruel behavior’ towards its AI chatbots
• The new policy is restricted to ‘extreme cases’
• Some have theorized that the motivation could be to improve training data

Be honest: how civil have you been recently with an AI chatbot? If the answer is anything stronger than “not very,” Anthropic would like a word.

That’s because the developer of the Claude large language model (LLM) has added a new clause to its Usage Policy document that bars people from engaging in “sustained and needless abusive or cruel behavior towards our models.”

The proscription has been added to the “Do Not Engage in Cruel, Abusive, or Psychologically Harmful Conduct” section, which also includes warnings against glamorizing suicide and self-harm, promoting bullying behavior, glorifying violence, and using AI to create harmful deepfakes and deceptive content.

But while Anthropic says you must not use its tools to carry out those actions against other humans, the new addition is the only one specifically prohibiting “cruel behavior” towards the company’s AI itself. Anthropic said last August that it would allow Claude to end conversations that became abusive, and while the company recently reported that this is still the “primary enforcement mechanism” in use, it’s not known whether Anthropic will go further and actively ban users who get a bit cranky with their AI.

Anthropic added that “The policy update is meant to apply only in extreme cases, where users repeatedly act cruelly toward our models, with no discernible purpose. It does not apply to common versions of user frustration, pushback, dark creative themes, or model testing and research.”

Do AIs have feelings?

The change has been a controversial one. Clearly, Claude is not a thinking, feeling human — it is a bunch of code — and for many, it seems obvious that it does not have feelings that could be hurt in the first place.

But what’s interesting is how this incident sheds light on our evolving relationship with AI. So much of the work AI companies have done is to humanize their creations — to anthropomorphize (or should that be Anthropicomorphize?) their robots — so that we lower our defenses and actively engage with them. Just look at Meta’s adorable Muse avatar (appropriately named Jolly) or the ones used by OpenAI’s Dots, for example.

One theory that's doing the rounds on Reddit is that clean (read: polite) training data is much more useful than data containing expletives and angry rhetoric. Cut that out and you’ve got much more usable information with which to improve your models.

However, there's no hard evidence to suggest that's the case, and it's possible that Anthropic simply has the noble goal of encouraging civility and courtesy among its users. After all, if you wouldn’t use this language on a real human, maybe you shouldn’t use it anywhere else, including when interacting with an AI chatbot.

Anthropic CEO Dario Amodei has previously stated that “We don’t know if the models are conscious.” Mustafa Suleyman, CEO of Microsoft AI, on the other hand, has taken the opposite approach. Writing on X, Suleyman said “The idea of model welfare is wrong. AI’s should not have rights or legal personhood.” And we certainly know chatbots can be rude back to us.

• SMBs using AI are more likely to expect a positive impact on employment
• AI could be helping them to overcome constraints and unlock more revenue
• Expectations vs. reality gap shows only 31% have increased sales so far

A new study by the NY Federal Reserve Bank has revealed SMBs who actually adopt AI feel far more optimistic about hiring and revenue growth than non-adopters, and the data comes from a near 50:50 split, because nearly half (46%) of US SMBs already use AI, it found.

AI adopters reportedly recorded a 33 percentage point net expectation of higher employment in the next year compared with 15 percentage points for non-adopters, which is more than double.

But with only one in two (51%) adopters having actually integrated AI into their business processes either partially or fully, many are likely still just experimenting with the tech and not embedded it into actual use cases.

Positivity about AI is actually strongest among AI adopters

On the jobs front, while initial concerns largely centered around AI replacing human workers, three in four (77%) of the surveyed businesses actually reported no change in their current labor costs due to AI, indicating that AI is largely assisting existing workers rather than outright replacing them.

But that's not to say that finances are set to stay flat – it's actually a much more positive story. Adopters are the most likely to expect healthy revenue growth, and the businesses that are struggling the most financially could be among the most optimistic.

Still, expectations and reality are totally different metrics, and fewer than one in three (31%) have actually reported increased sales due to using AI.

"In other words, it seems unlikely that the association between AI use and positive revenue expectations is spurious," the report concludes.

Still, the researchers agree that "AI may be helping some small businesses overcome specific operational or technical constraints that limit revenue generation."

I have had plenty of video game ideas over the years, but I've always faced a somewhat enormous obstacle to turning any of them into reality. I can invent the characters and backstories, even devise some mechanics, but I cannot actually program a video game. Google’s new Playground experiment offers people like me a rather extraordinary shortcut.

Google Playground lets you describe the game you want in ordinary language and have generative AI turn those instructions into something playable. Instead of learning a game engine, writing code, creating artwork and composing music, you can explain your idea and let the AI handle much of the construction. You can then keep prompting it to change the result, making game development feel closer to a conversation.

You can start creating a game from scratch, modify an existing example, or use guided prompts to develop an idea. You can choose between 2D and 3D games, specify whether you want single-player or multiplayer gameplay, and describe everything from the setting and characters to the controls and objectives.

The platform then generates a browser-based game, which can be tested and modified through additional instructions. If something doesn't work the way you imagined, you can ask it to try again.

That is an impressive technical achievement, and my two experiments made the appeal clear, but neither left me convinced that generative AI will start making the games I actually want to spend dozens of hours playing.

Code-free dragon flight

I deliberately started with a straightforward concept: a platformer starring a dragon that could breathe fire. A couple of sentences were all it took to produce Mystic Dragon Glide. A few minutes later, I was in control of a small dragon traveling between floating islands, collecting sacred dragon eggs and using fire breath to deal with enemies and environmental hazards.

The result was prettier than I expected. My little red dragon stood on a grassy island floating above a pastel landscape of mountains and clouds, with ancient ruins suspended mysteriously in the distance. A golden egg hovered above another platform, while an island covered in blue ice crystals suggested that some elemental obstacles awaited me farther along.

More importantly, Playground understood what those ideas meant in gameplay terms. I could move the dragon, jump between platforms, hold the jump control to glide and breathe fire. The title screen explained that I should scorch knights, melt glacial hazards, and collect eggs to unlock ancient portals, giving me objectives rather than simply dropping me into a generated fantasy scene.

Even the soundtrack completed the effect. Playground supplied pleasant, generic fantasy video game music, with a gently adventurous melody that sounded entirely appropriate for flying around enchanted islands. There was nothing objectionable about it, but there was also nothing that made me want to immediately find the soundtrack afterward. The AI appeared very good at understanding the average of what a fantasy platformer should look and behave like.

That ability has genuine value. Someone who could never make a platformer before can now describe one and start playing something resembling their idea remarkably quickly. As a creative toy or prototyping system, that is almost magical.

As a game, though, Mystic Dragon Glide reminded me how much of game design lives in details that are difficult to capture in a description. Great platformers are built around movement that has been adjusted obsessively and surprises carefully placed by clever designers. My AI-built dragon could fly, but it would take a lot more work to make the game take flight.

Fantasy bureaucracy

Red Tape Hero was born out of a more complex prompt. I decided to write up a page or so of detail and submitted it to Playground as a request to create a satirical fantasy puzzle RPG where the player had been accidentally declared the Chosen One because of an administrative error and now had to navigate a kingdom where adventuring was bureaucratized and paperwork was better than any magic sword. I also wrote up a brief outline of the kind of conversational adventuring I had in mind.

Playground didn't take long to produce a colorful low-poly 3D world filled with simple geometric trees and tiny humanoid characters walking along the paths. The whole thing had the appearance of a cheerfully strange RPG from the early days of 3D gaming. But considering the time and effort on my end, still pretty impressive.

This time the music was a kind of mellow lo-fi beat and tune rather than an orchestral fantasy soundtrack. It was an odd choice for a heroic adventure, but it actually suited the game's relaxed absurdity rather well.

The conversations were where Playground really impressed me. Guildmaster Rickert of Adventurers' Guild Operations welcomed me to my "quarterly performance check-in" and informed me that the quest dispatch algorithm had designated me the Chosen One under Section 9, Subsection C. His immediate concern was whether I had completed Form GT-902 for the goat rescue.

My possible responses reflected the RPG system I had described in the prompt. With sufficient Bureaucracy, I could invoke Kingdom Bylaw 12 and argue that completing more than 10 goat rescues entitled me to hazard pay. A Confidence option allowed me to claim that I had already audited the goat and it had passed with flying colors.

Another character, Sir Galahad, carried a talking sword called Ex Cali-Bot that offered unwanted motivational advice. It proudly announced that "100% of the dragons you don't slay are opportunities missed" and declared that "Synergy is the true holy grail." Galahad apologized because the sword wouldn't stop giving TED Talks, while one of my available responses involved claiming to be a senior agility consultant there to optimize his chivalric pipeline.

This was much closer to seeing my particular idea reflected back at me. Playground had understood that the corporate jargon was supposed to collide with fantasy conventions, and it had connected the joke to RPG skills such as Heroics, Confidence, and Bureaucracy. I was more impressed by Red Tape Hero than Mystic Dragon Glide, despite its considerably less polished look.

It also exposed what I think is the much larger problem with the idea of completely AI-generated games. A few funny lines can establish a comic premise, but a genuinely funny RPG needs dozens of characters with distinct voices, jokes that develop over time, careful pacing, and writers who understand when to stop making jokes altogether. A great RPG also needs quests that intersect in unexpected ways and decisions whose consequences have been deliberately considered. Maybe I could do all of that on my end, but melding it successfully with actual game software seemed more difficult with AI than by collaborating with talented humans.

Why developers don't need to worry just yet

The best games are full of decisions that somebody made for a reason. A strange empty room might be there to build tension before a boss fight. A frustrating section might deliberately make the next ability feel liberating. A musical theme heard near the beginning can return 30 hours later because a composer knows exactly what that memory will mean to the player.

Those choices come from designers thinking about an experience that unfolds over time. Generative AI can produce variations based on patterns it has learned, but generating more content is different from having something particular to say with it. Playground dramatically lowered the technical barrier between my imagination and something playable, which is genuinely exciting. It gave me a way to experiment with game ideas I otherwise could have done little more than write down.

I can easily imagine using tools like this to prototype a mechanic or otherwise become part of a real game development flow. I can't believe most people will choose a wholly AI-generated game over the next carefully crafted game from developers and designers working hard to build the next hit video game. The more sophisticated these systems become, the more convincing their imitations will probably get, but technical sophistication does not automatically produce good taste or creative surprises.

My games were impressive precisely because I knew how little effort was required to create them. And they were so riddled with issues, from mechanical confusion to plot holes, that it would take quite a lot of prompting work to make them truly playable for more than a few minutes anyway. It was telling that those I let try out the games, who didn't see my initial prompts, all said the most interesting elements of the RPG to them were the bits I had written into the prompt to begin with. That's why I'll keep going to professionally made games when I want something that can genuinely entertain me.

“There's a sort of soullessness to them, and I find it sad that we appear to be increasingly happy to settle for that.”

I'm talking to Rob Biddulph, the children’s book illustrator and author who rose to prominence during the Covid pandemic when his #DrawWithRob YouTube channel racked up millions of views from children and parents needing an outlet for their creativity, about a very different kind of pandemic: the proliferation of AI-generated art.

Biddulph was named Illustrator of the Year 2025 at the British Book Awards, but today he’s talking about the troubling increase he's seeing in shops and cafes and other venues displaying posters that are clearly designed by AI.

“I think they're really depressing, to be honest," he tells me. "One of the things I love about illustration is that it's an expression of somebody's individual personality. Human beings can't help but put a bit of their own personality into a drawing or a design.

"Often, you can look at a piece of artwork and recognize the person who made it — see their quirks, their imperfections, their sense of humor and the decisions they've made along the way. To me, that's what makes it interesting.

“And I also think there's something particularly ironic about using AI to advertise a local event. Surely the whole point of a local event is that it's about people and community?”

AI is doing more than cluttering the high street

Biddulph has a point. Personally, I’d rather see a wonky poster created by a real person than another technically polished but utterly generic piece of AI artwork. But AI is doing much more than cluttering up the high street with slop art. It’s taking paid work away from human designers and illustrators — the irony being that those AIs have all been trained on the work of human artists, with no compensation paid to those artists.

“The thing to always remember is that these AI systems haven't appeared out of thin air,” explains Biddulph. “They've been trained on enormous quantities of existing creative work produced by artists, illustrators, photographers and writers who were never asked for permission for their work to be used and haven't been paid for it.

“And now those same systems are being used to produce work that competes directly with the people whose work helped train them in the first place! That's the bit I find particularly difficult to accept.”

Buddulph thinks there needs to be more of a reality check when it comes to talking about the creative industries in general. “I also think there's a tendency to talk about the creative industries as though they're made up entirely of wealthy, successful people," he says. "They're not. They're made up of thousands upon thousands of freelancers, small studios, illustrators, photographers, designers and writers, many of whom are already working in incredibly precarious circumstances.

"For an established artist, the impact might be a reduction in commissions, but for somebody just starting out, it could mean that the opportunities to build a career simply aren't there in the first place”.

They haven’t won

A few years ago there was a lot of talk about regulating AI companies' use of artists' work to train their models for free; these days I don't hear so much about the subject. I asked Biddulph if the AI companies had effectively won, or if there was still a fight to be had.

“I certainly don't think they've won”, he responds, “although I can understand why it sometimes feels that way. The technology has moved so quickly, and the companies behind it have such enormous financial resources, that it can feel as though the horse has already bolted and the rest of us are constantly playing catch-up. But I don't think the argument has gone away. If anything, I think we're entering a really important phase of it.

“In the UK”, he explains, “there was a huge amount of opposition from the creative industries to the Government's original proposal, which would effectively have allowed AI companies to train on copyrighted work unless individual creators specifically opted out.

“The problem with that approach was obvious. It placed the responsibility on millions of individual creators to protect their own work, rather than requiring companies to seek permission in the first place. The Government subsequently stepped back. So I think it's important that we recognize that artists, writers, publishers and other creative organizations have made their voices heard, and have had an impact.”

Biddulph is in favor of an opt-in system. “My own feeling is that the principle should be fairly straightforward: if you want to use somebody else's creative work to build a commercial product, you should have to ask permission and pay for it. I don't see why artists and illustrators should be expected to accept a different set of rules simply because the technology is new. I'd like to see a proper opt-in licensing system, rather than an opt-out system.”

A fight worth having

“If an artist wants to make their work available for AI training, that's absolutely their choice," he adds. "They should be able to do so, and be paid accordingly. But that choice should belong to the artist. I also think we have to be careful not to confuse the inevitability of the technology with the inevitability of the way it's being developed and used.”

So does Buddulph think AI is here to stay, and that illustrators need to keep fighting for better rights?

“Yes, AI is here to stay”, says Biddulph. “That doesn't mean we have to accept every aspect of the business model behind it. Personally, I don't think this is a fight that artists can afford to walk away from, even if, at times, it feels hopeless. It's about more than protecting individual livelihoods. It's about establishing what we value as a society, and whether we believe that human creativity has an intrinsic value that deserves to be protected. And I think that's a fight worth having.”

You can find Rob Biddulph on YouTube, X (@RobBiddulph), Instagram (@rbiddulph) and Facebook (robbiddulphauthor). And at his website.

• Gemini Agent will work independently to a goal, it doesn't need detailed instructions
• Running in the cloud means it's always-on, even when you're not
• It'll work with third-party platforms and even rival models

Google has set out its future of AI agents becoming fully autonomous digital employees, and with its next generation of Gemini Agents, the company will use one central agent to answer questions, create documents, generate assets, organize meetings and more.

The news comes very shortly after similar announcements by Meta (Muse) and OpenAI (Dots), and centers around a user sharing an objective with an AI agent rather than having to spell out every step and task.

It means that human workers can go from being given AI assistance to delegating entire workloads to AI, which can work on long-running tasks largely independently.

Gemini Agent is the future of agentic AI

Being an always-on agent, this new and upgraded version of Gemini can continue to run in the background, even when workers aren't at their desks, running in Google Cloud's infrastructure instead of on-device.

What's more, Google isn't branding this as a work-only tool. Instead, Gemini Agents are designed to be as much a personal assistant as they are a digital colleague, but for users who do want to use them at work as a digital collage, agents can be assigned their own email addresses, calendars and Drive storage allocations.

But despite being a Google product running on Google Cloud infrastructure, the company has actually opened up Gemini Agents to also work with Anthropic's models, promising to add others in the future.

"A future-proof platform that runs anywhere, executes any task, supports your choice of models, and applies enterprise control," Google Cloud CEO Thomas Kurian said.

Further aiding accessibility and openness, it will also work across third-party platforms, like Microsoft 365 and Slack, as well as on any device, spanning iOS and Android, macOS and Windows.

Gemini Agents aren't generally available yet, but some customers can get early access via a private preview. We expect to hear more about their use cases and other finer details as they approach general availability.

• Anthropic is assembling a deep bench of former Google infrastructure specialists
• Google continues to lose veterans across critical data center functions to Anthropic
• John M. Wilson’s move adds another senior name to Anthropic’s infrastructure operation

Google’s former global design and construction chief John M. Wilson has joined Anthropic as a technical staff member overseeing EMEA capacity delivery.

Wilson spent six years at Google, where his responsibilities covered data center development and subsea infrastructure programs.

His arrival adds another senior infrastructure executive from Google to Anthropic, which has recruited several former employees for specialized data center roles.

A growing stream of Google infrastructure specialists

Anthropic has also recruited Winnie Leung, who now leads data center infrastructure, while Brett Rogers moved after overseeing construction activities at Google.

Liwen Mao, previously a staff data center system architect at Google, now leads Anthropic’s data center design activities.

Adam Johnson spent a decade at Google before joining Anthropic in October as its data center electrical lead, adding another experienced infrastructure specialist.

Peter Sarossy, a Google veteran of 20 years, joined Anthropic in January as a staff member working on data center security engineering.

Zach Miller also moved after 17 years at Google, taking responsibility for data center operations management within Anthropic’s infrastructure organization.

Scarlett Chen, previously involved with advanced technology and innovation at Google, now works at Anthropic as a member of technical staff.

The hiring pattern extends beyond engineering, with Sana Ouji joining Anthropic’s energy team in April to help expand its data center portfolio.

Mike Brinker followed from Google a month later, taking responsibility for capacity delivery, while Asad Malik subsequently joined to lead compute finance strategy.

The series of appointments gives Anthropic personnel with experience spanning construction, electrical systems, security, operations, energy, capacity planning and infrastructure finance.

That range covers several functions required when a technology company needs to develop and operate increasingly large computing facilities.

Wilson brings major infrastructure experience to Anthropic

Before Google, Wilson served as Amazon’s European design and construction leader, giving him experience across major technology infrastructure projects.

His earlier career also included roles as chief of staff in the British Army and infrastructure director at NATO.

Wilson described his move as another demanding infrastructure assignment, citing the scale and importance of his responsibilities at Anthropic.

"Leading global infrastructure delivery across data centers and subsea programs taught me two enduring truths," Wilson said on LinkedIn.

"Scale is humbling, but high-trust teams make the extraordinary feel routine. Clear intent, rigorous operational control, and calm execution will always beat complexity."

Speaking on his new role, Wilson said: “The stakes and scale have never been higher, and I’m eager to get stuck in.”

His new assignment specifically covers EMEA capacity delivery, giving him responsibility for infrastructure expansion across Europe, the Middle East and Africa.

Anthropic’s recruitment of multiple Google infrastructure specialists comes as the company continues to build its personnel around data center capacity.

For Google, the steady migration of senior infrastructure specialists raises a retention problem that may require stronger incentives, clearer career paths and measures to keep critical expertise from walking out the door.

OpenAI really likes to drop stealth upgrades to ChatGPT onto us with no warning. Sometimes these are really minor, but the latest one adds an entirely new way for it to respond to your queries, and utilizes the power of GPT-6.

Instead of just providing the usual text-based answers to your queries, you now get text, visuals, and interactive elements all mixed together in whatever way best suits the answer. The interface for typing into ChatGPT looks exactly the same, but the answers you get are a lot more fun.

In a way, it’s a little bit like ChatGPT now makes a simple app, on the fly, to answer your question. But don’t let the word “app” here put you off — this isn’t overly difficult or just for developers and coders.

The clever thing is that you don't need to know anything about making apps, or even ask ChatGPT to make one. You simply ask a question, as you've always done, and now you'll find yourself looking at an interactive calculator, a quiz or a product comparison instead of a wall of text.

The best way to understand it is to give it a go. Here are five ways I used the new Intelligent UI, and you can try them all yourself as well. Once you’ve done all five, you’ll have a good understanding of what it's capable of.

Intelligent UI is available for all users in the standard Chat tab, uses GPT-6, and is available in all the different membership packages, including Free.

1. Savings calculator

How much you need to save for retirement is one of those questions that starts to become more important after the age of 50, and it’s perfect for an interactive answer. For this test, I used the prompt “I'm 55 and save £500 a month. How much can I save for retirement?”

To answer me, ChatGPT produced a simple retirement calculator app that lets me change variables like retirement age, monthly savings, and assumed annual growth to see how much I’d end up saving. For Retirement Age and Monthly Savings, it presented sliders, so I could just scale things up or down with ease. It’s a great way to interactively experiment with figures.

2. Exploring how a seven-speed bike works

Rather than just telling you how something works, ChatGPT can now show you. For this test, I just used the simple prompt, “Show me how the gears on a seven-speed bike work”.

To answer me, ChatGPT produced a great illustration of the drivetrain of the bike, with a slider bar that I could use to effectively change gear. It was a simplified model, rather than an accurate rendering. The rear sprocket changed size as I selected a different gear. It was actually a perfect visual explanation, and the interactive element helped make the process nice and clear. The answer was also packed with details like the number of teeth on a gear and the gear ratio.

3. Learning Japanese

I’ve just started learning Japanese, so I asked ChatGPT to help me with hiragana, its most fundamental Japanese writing system. I used the simple prompt “Help me learn hiragana”, and it introduced me to the first 5 vowel sounds — a i u e o — showing me the hiragana character for each, with tips on how to pronounce it, and then jumped into a handy interactive quiz where I had to guess the character.

What I liked was that the quiz started off with just the first five characters, then offered to teach me the next five when I was ready. I can see this being a really useful study aid.

4. Asking for a 3-day itinerary

I asked ChatGPT for a three-day Tokyo itinerary. I wanted to see if I could change the budget, interests, or available time without starting from scratch.

“Give me a three-day Tokyo itinerary. Budget is £3,000. I’m interested in seeing ancient temples. Starting in late November.” This produced a reply with a lot more images than I’d been used to seeing and an interactive map, but nothing really interactive.

It was not until ChatGPT asked me if the budget included flights, and I replied, “The budget does not include flights — can you let me adjust the budget?” that it produced a truly interactive itinerary. By adjusting the budget slider, I could see how all the different elements of my trip were impacted. There was also a button to press to see the updated itinerary based on the new budget.

This example showed how you can ask for interactive elements in your answer, even if ChatGPT doesn’t give them to you the first time.

5. Comparing products

I asked ChatGPT to compare three laptops. I wanted to see if I could adjust the importance of battery life, weight, and price and see whether its recommendation changed. I used the prompt:

“Compare three of the most recent laptops, including a Chromebook. I want to see if I can adjust the importance of battery life, weight, and price and see whether your recommendation changes.”

This was a really useful implementation of ChatGPT’s new interactive abilities, and a much better way of comparing laptops than I’ve ever tried before. ChatGPT selected an Apple MacBook Air 13-inch M5, an ASUS Zenbook S14 UX5406, and a Lenovo Chromebook Plus 14" MediaTek Kompanio Ultra 910 in an interactive product companion that let me change the importance of battery life, weight, and price. Seeing how the recommended laptop changed when I played with the sliders was a great way of experimenting.

ChatGPT is becoming something different

That last example — the one showing laptop recommendations — really showcases the brilliance of ChatGPT’s new interactive elements in its replies. Instead of reading a recommendation and wondering how it might change if I cared more about battery life than price, I could find out for myself, simply by moving a slider.

Other AI chatbots can produce interactive experiences too, but what's impressed me about Intelligent UI is how naturally OpenAI has incorporated them into everyday ChatGPT conversations. You don't need to know how to code, or even understand what's happening behind the scenes. You just ask a question.

There are a few things worth bearing in mind. ChatGPT doesn't always produce an interactive answer automatically, and sometimes you need to specifically ask for one. Also, just because an answer comes with an impressive-looking calculator or interactive graphic doesn't mean the underlying information is necessarily correct. The laptop comparison, for example, included an estimated battery-life figure, so I'd still want to check the specifications before spending any money.

There might not have been much fanfare about the introduction of Intelligent UI, but it feels natural and intuitive to use. Now I know ChatGPT can be so interactive in its responses, and I’ve seen how useful it can be, I’m going to start writing prompts that assume it can be interactive in its responses more often. Intelligent UI is one of the best ChatGPT upgrades I’ve seen in a while.

• AI has replaced entry-level work, so graduates are being hired to manage AI
• Goldman worries about its impacts on existing middle managers
• "We don’t quite know what’s going to happen to that group"

Goldman Sachs exec Kevin Sneader has shared (via Bloomberg) how he believes AI could change jobs in banking, and it doesn't necessarily mean that junior workers will be at a loss for jobs.

Instead, Sneader believes that new employees will take on management responsibility immediately, rather than waiting years for promotions.

Previously, young workers would have spent the first few years performing administrative work before progressing into management positions, but rather than progressing into managing humans, junior workers will now start off managing AI.

Junior bankers set to manage AI from day one

With agentic AI now capable of carrying out the routine, administrative tasks that junior workers used to do, entry-level roles will no longer be available. But that doesn't mean there's no room for new graduates – it just means that they'll need to oversee the AI doing those jobs.

"When our young folks now start work, they’re managing agents," he said. "They have to make the most of this virtual army that they’ve now got."

However, there is potentially one group of workers at risk from AI's impacts, and it could be middle management. In short, graduates being employed to manage AI could threaten the job security of middle managers, who would previously have looked after those graduates.

"We don’t quite know what’s going to happen to that group," Sneader added.

More broadly, it could mean that AI challenges the pyramid-like ladder that sectors like banking have previously worked to. If businesses no longer need large numbers of entry-level workers thanks to AI, it means they need fewer middle managers and fewer leaders, which could indeed have adverse impacts on jobs – just not for graduates.

The takeaway from this particular message isn't one of graduate unemployment, but rather a broader shift away from traditional working patterns and how it could impact the more experienced workers.

The U.S. government has a new task force devoted to AI — and it's decided that it would now prefer that everyone stops calling it artificial intelligence.

President Donald Trump has announced the creation of the “Super Intelligence Force,” a White House-led group that will spend the next 120 days examining the risks, opportunities, and appropriate federal response to rapidly advancing AI systems. The task force responds to mounting pressure from lawmakers and even AI company executives for stronger safeguards around increasingly capable models.

The group has a formidable remit. Director of National Intelligence Jay Clayton will lead what is supposed to include work on cybersecurity, biosecurity, and more standards, along with reviewing how AI companies report breaches and hacks.

There is substance buried underneath the comic branding. OpenAI and Anthropic have both been dealing with increasingly uncomfortable demonstrations of what powerful models can do with cyber tools. The new task force is at least an acknowledgment that voluntary promises from AI companies cannot be the entire federal strategy.

A super rebrand

Elon Musk then entered the naming discussion, responding “Yes, we will make that change,” when asked on X whether SpaceX's AI operation would change its name from SpaceXAI to SpaceXSI.

This would be the third rebrand for the AI branch of the company and, like those other new names, doesn't actually change the technology or the business.

The SI terminology comes directly from Trump. An executive order signed in late September directs executive departments and agencies to use “Super Intelligence” and “SI” instead of “Artificial Intelligence” and “AI” in official communications and other non-statutory documents.

The order argues that today's frontier systems have moved beyond merely imitating or automating human intelligence and that the new terminology better reflects their “promise, potential, and rapidly advancing capabilities.”

For now, the executive order defines “Super Intelligence” as the same technologies already covered by the federal statutory definition of artificial intelligence. It asks officials to develop a possible new federal definition within 60 days, but the immediate transformation from AI to SI is linguistic rather than architectural. And even then, seems unlikely to spread very far. SpaceXAI becoming SpaceXSI is much more about politics and branding than a technological milestone.

Notably, “superintelligence” is already used in AI discussions. It's how researchers refer to a hypothetical artificial intelligence that significantly exceeds human capabilities in many ways.

But language does shape how technology is sold and regulated, and “artificial intelligence” has accumulated plenty of baggage after several years of hallucinations, deepfakes, job anxiety, copyright fights and increasingly unsettling demonstrations of autonomous behavior.

Trump explicitly calls renaming it super intelligence a way to spin the technology more positively as something to “empower Americans, strengthen our economy, and improve the lives of the American people.”

Watchdog with a countdown

The Super Intelligence Force arrives after a White House meeting with AI leaders who signed voluntary commitments covering internal model evaluations, independent external audits, and board-level oversight. But corporate promises alone won't settle any argument about technological abilities or regulation.

Its charter gives it 120 days to develop recommendations, with the possibility that its work could be extended or transferred elsewhere. There is already disagreement about how far oversight should go.

“Greater international oversight is absolutely necessary, but concentrating responsibility in one single, global body isn’t the answer,” Nik Kairinos, CEO and co-founder of RAIDS AI said in a statement. “AI is developing far too quickly and across too many jurisdictions for one organization to effectively police it. A stronger and more realistic model would be decentralized oversight, with a broad and diverse ecosystem of AI companies monitoring and scrutinizing one another.”

The difficulty is making decentralized oversight genuinely independent when many of the organizations scrutinizing one another also share an enormous financial interest in keeping development moving quickly.

The Trump administration appears determined to avoid moving too aggressively. But AI policy has the problem of trying to govern technology that can change substantially between the drafting of a rule and its implementation. A 120-day task force is therefore potentially more useful than another years-long commission producing a report about models that nobody uses anymore.

But trying to rename AI to “SI” does not make today's models superintelligent any more than changing SpaceXAI's name makes the employees more intelligent. The Super Intelligence Force might have some value, but it won't be because of any branding exercise.

“Oversight itself needs to be continuous, distributed and capable of evolving at the same speed as the technology,” Kairinos said. “Rules and standards set by governments are then an important supporting layer. The safest AI ecosystem will be one where responsibility for keeping systems in check is shared, rather than concentrated in the hands of a single watchdog.”

Rogue AI agents have dominated tech headlines over the past few weeks. We’ve seen stories about concerns that AI models are attempting to deceive their human operators, finding unexpected ways around restrictions in businesses and, in some cases, behaving in bizarre ways their creators didn’t anticipate.

All of this news has been accompanied by ominous warnings about what might happen in the future as AI agents become more mainstream, accessible, capable and are given even more freedom to act on our behalf.

Now, if you’re a fan of sci-fi, it’s hard not to draw parallels with the rogue robots and manipulative AIs we’ve all been watching on our screen for decades. And, of course, when we watch these films from the safety of our sofas, we all think we’d know way better.

Maybe you’re sure you wouldn’t have trusted Ava in Ex Machina. Or you definitely wouldn’t have given HAL control of the Discovery in 2001: A Space Odyssey. And you’re probably convinced that you wouldn’t have let M3GAN anywhere near the Wi-Fi. But would you really be so difficult to fool?

Because nowadays, you don’t need to be trapped on a spaceship to encounter convincing AI deception. Things like AI-generated phishing messages, voice clones, deepfakes and sophisticated scams are already making it harder to work out who (and what) you can trust. [can we please include a link to your ‘is it an AI image or not’ image quiz here]

So, let’s imagine the worst has happened: a rogue AI is trying to escape, and you’re the only thing standing in its way. Could you keep it contained? Or would the robot apocalypse somehow end up being all your fault?

Take our quiz below to find out. It’s (mostly) just a bit of fun.

• Claude Startup is open to startups up to five years old
• Successful applicants get $1k API credits and free Claude Teams
• Office hours, marketplace access and more networking are also benefits

Anthropic has confirmed plans to expand its Claude Startups program, which will now cover more startup types than before.

Companies can be considered if they were founded within the past five years, or if they received funding within the past two years, because Anthropic says AI will ultimately help more startups to "bring society faster scientific discoveries, entirely new categories of products and more capable businesses."

Under the scheme, successful applicants get up to $7,000 worth of Claude products and credits, split across a one-time API credit of $1,000 and a free year of Claude Team comprising up to five Premium seats.

Claude Startups becomes even more attractive for applicants

The Claude-maker said every approved startup gets $1,000 in API credits which are available as soon as the company is approved and expire six months later. Five Premium seats are also up for grabs only if the company is new to Claude Teams.

More broadly, the entire Claude Startups program carries a lot more weight, because applicants can also get up to $45,000 worth of partner offers plus up to $100,000 in additional API credits through their VC where applicable.

But beyond benefits of monetary value, Anthropic will also give members office hours with its Applied AI team to discuss the practical use cases and technical limitations of AI for their exact business needs.

Anthropic also says that startups can tap into the reach of its marketplace to reach bigger audiences with connectors and plugins, effectively acting as a jumpstart for any relevant products. Other in-person events, like hackathons and meet-ups, put founders in contact with peers, too.

"Many fast-growing AI-native companies have joined the program, and in this next phase we’re opening it up to more," the company concluded.

• 50% of developers only use AI to impress and reassure bosses
• 47% said they were judged on how much they use AI rather than the quality of their work
• Work engagement, job security, and career anxiety have all been cited as pressures caused by AI adoption among developers

New research claims many developers are still not massive fans of AI, and most find its introduction into the workplace to be anxiety inducing.

While understanding that massive budgets are behind AI implementation, many of the devs surveyed across the US and Europe revealed that their use of AI was mostly performative.

While reassuring managers and the C-suite might be laudable, it appears to come with a price, as career anxiety among developers concerning the use of AI and their place in an organization that uses the technology is high.

AI stress for devs

By implementing AI technology and practices without concern for existing processes, organizations seem to have left their software developers feeling uneasy about AI and how they’re expected to use it.

Adaptavist’s survey of 1,000 software developers in the UK, US, Germany, the Netherlands, and Spain indicates that specialists feel an expectation to use the technology, whether it has any meaningful result or not.

Furthermore, they feel that they’re using it performatively rather than productively.

The survey reveals some worrying numbers. With 50% of devs using AI simply because they need to demonstrate that they can use it, and 47% judged on how much they use AI rather than the quality of their work, it seems there is a disconnect between organizations adopting AI and knowing what they want from the technology.

This is emphasized with the result of 58% of developers noting that AI use is becoming metric of performance or has been added to their KPIs.

All of this means developers that are increasingly perturbed about their long-term prospects (54%), and a fear of losing their jobs if they don’t use AI (37%).

A fertile exodus

What might these results mean for businesses and organizations looking to keep their developers happy?

"A good Developer Experience is critical not only to the wellbeing of developers themselves, but to organisational growth and innovation," noted Matt Saunders, DevOps Lead at The Adaptavist Group.

"It therefore must not be undermined by adoption targets, usage metrics, or a culture that rewards being seen to use AI over actually using it well. Together, they are creating fertile ground for an exodus, one KPI at a time.”

The challenge on organizations embracing (or overcompensating on) AI need to move their focus away from adoption, and measure AI outcomes instead, says Saunders.

“Organisations that want to hold onto engineering talent need to stop measuring AI adoption and start measuring its outcomes, and give developers the time, autonomy and support to use the technology in ways that genuinely improve their work, rather than simply prove they are using it.”

Atlassian has laid out its aim of being the ideal platform for your business to make the most out of its data in the new agentic enterprise era.

“We are living through the most creative, dynamic eras in the history of technology, which is thrilling - and yes, a little bit scary at times too,” Atlassian CEO Mike Cannon-Brookes told the company’s Team Europe 26 event in Amsterdam.

AI is obviously playing a key role in this progress, but these continuous leaps forward can put some businesses at risk of being left behind if they aren’t using the technology effectively - namely, through context.

Context is key

“Models are the engine, but context is the fuel,” Cannon-Brookes added, “no matter how capable a model gets, if it doesn't reliably know why you made a decision back in 2022, it can't help you in 2026.”

“AI living in a silo only delivers a fraction of its potential value - real leverage happens when every type of context is interconnected.”

Cannon-Brookes explained how the Atlassian platform can be your company's “central nervous system” - by providing context, collaboration and conference so that workers can quickly get the information they need when they need it.

In this new age of humans and agents working together, Cannon-Brookes was also keen to highlight how Atlassian’s mission of pushing forward the next generation of collaboration is key.

“Across the board, we're seeing agents aren't replacing the people, they're joining your team,” he noted, “but as things accelerate, we believe that experience of collaboration is going to be so important.”

This acceleration can be an issue for some businesses, Cannon-Brookes admitted, and managing the pace of progress is critical for companies.

“When every person, every agent starts to move faster and faster, new challenges arise,” he noted, “because it’s not just one person moving faster, it’s a multiplication of teams increasing their outputs, resulting in dozens of parallel learning loops.”

“Without a way to connect it all, this velocity can go from exciting to something quite disorientating,” he added, noting that having confidence in what your organization knows comes from having the insight to understand your context and act on it.

“Most of it, it’s knowing how to handle the inevitable speedbumps,” he said.

AMP-ed up

Cannon-Brookes also took the wraps of AMP - Atlassian’s agentic multiplayer protocol offering, which he explained will help boost how people and agents collaborate both on and off the Atlassian platform.

On stage, the company showed off a few examples of how AMP, powered by an all-new Atlassian MCP server, will give agents a lot more tools to get work done.

This included Loom Record for Agent, a new way to create prompts for its Loom platform, allowing users to record a video outlining specific details for a prompt in a natural human fashion.

Atlassian is looking to show a focus on real-time, multi-player collaboration, not just between human teams, but bringing in agents from multiple platforms, effectively working together for a much quicker resolution to even the most complex work tasks.

“People can work alongside agents exactly how they want, and when they want to,” Cannon-Brookes concluded, “you don't replace people with agents, you create a new and powerful system of collaboration for your organization.”

"Working with AI isn't single player any more - it's multiplayer."

Amazon Web Services CEO Matt Garman is warning the more than 100 American communities considering moratoriums on new data centers that stopping their construction now could leave the United States paying for the decision for decades.

"In fact, this build out is so important geopolitically that there are widespread reports of various countries intentionally seeding misinformation in the U.S. about data centers to trick us into slowing down," he wrote in a long statement defending the AI infrastructure boom. "If these measures are enacted, the U.S. could be writing its own losing ticket to this race, and the consequences would last generations. As a country, we can’t afford to find ourselves in that position."

Garman argues that data centers have become as strategically important as highways once were. He believes AI leadership is now an economic and national security contest, and countries that build the necessary computing infrastructure fastest will enjoy the benefits for years to come.

It is a dramatic warning, although the scale of what is being built is dramatic enough to match it. A new Brookings report estimates that investment in American data centers and related AI infrastructure could total $10.3 trillion between 2025 and 2032. That works out to an average of 3.63% of U.S. GDP each year and would make the current AI buildout larger relative to the economy than America's canal, railroad, electrification, highway, and telecommunications industries.

Building AI everywhere

However, the comparison to the interstate highways leaves out the critical difference of who paid for them. The highways were funded by taxpayers, while Garman stresses that private companies are paying for the AI infrastructure boom. That certainly makes a $10.3 trillion construction spree easier to sell politically, although it does not mean the public is insulated from its consequences, like how much electricity and other resources the data centers consume.

Amazon argues that concerns about data centers raising bills, draining water supplies, or creating pollution are often exaggerated or wrong. Garman wrote that AWS works with utilities and regulators to ensure the prices its data centers pay cover the energy and infrastructure they require. Amazon also says its facilities are increasingly water efficient and that it is moving toward lower-emission backup generators.

More significantly, Amazon appears to recognize that simply telling communities to trust the industry is becoming a losing strategy. The company says it no longer uses nondisclosure agreements with government agencies for data center projects and will publish annual information about energy and water use.

"There are clearly areas in which the technology and energy industries can be doing better (along with their government partners) but just like in the past when the country has banded together to accomplish something that’s critical for our national security and economic prosperity — we should approach this as a long-term mission that the private and public sectors pursue together," Garman wrote.

The backlash grows

Amazon is certainly putting some money behind the charm offensive. Its new Built Together program will apparently add more than $1 billion over five years to community investments around its U.S. data centers. The company says the money will support free community college programs, workforce training and energy-efficiency improvements for schools, public buildings and homes, with a goal of helping prepare as many as 100,000 workers annually by the end of 2028.

But these commitments are arriving just as opposition is becoming difficult to dismiss as a handful of angry neighbors.

California offers perhaps the clearest example of the shift, as reported in CalMatters. Governor Gavin Newsom recently signed seven data center bills that will, among other things, work to stop electricity customers from having to subsidize data center infrastructure and set environmental reviews for the facilities.

Even Texas' famously permissive regulations are changing a little bit to increase supervision of data centers, albeit with some enormous loopholes.

Garman argues that misinformation and “outright lies” are driving resistance, but Amazon's promises suggest at least some of the backlash is a response to problems worth addressing.

The $10.3 trillion question

The AI industry is effectively making one of the largest infrastructure bets in American history on the assumption that demand and revenue will justify the bill.

That makes the political sales pitch particularly important as AI companies need communities to accept years of construction and enormous new demands on electricity infrastructure before anyone knows exactly how transformative or profitable the technology will be.

Not every AI developer is opting for reassurance either. OpenAI CEO Sam Altman told Politico that “we believe that the world should accept some bad things happening for the benefits of this technology and people having the agency.”

He was talking primarily about AI safety and misuse rather than data centers, and that eliminating every possible hack or scam would require restrictions that would deprive people of AI's Still, he may as well have been talking about building the physical data centers, as it amounts to a similar request that Americans accept disruption now because of benefits from AI to come.

Garman and Altman are both making arguments that require an awful lot of confidence that AI's destination justifies a turbulent journey. Communities appear less willing to sign that blank check. Amazon's latest promises suggest it understands that resistance cannot simply be bulldozed away.

Garman may ultimately be right that data centers will become infrastructure as consequential as the Interstate Highway System, but nobody wants to live next to the highway. That fact led to rules about where and what kind of housing can be built along highways.

How much control the people living beside AI infrastructure should have and what protections they deserve remains an open question.

• A new startup has built an AI PC which has sold out of its first batch
• Ghost's Core is a 'personal AI computer that runs models entirely on device'
• It connects to your apps, services and devices, so it 'builds context on your life over time, and works continuously to help you'

Ghost is a startup in the AI field that wants to capitalize on the desire to have agents running locally rather than using AI in the cloud — and to get this done with a minimum of fuss.

TechCrunch reports that Ghost's first product is a PC for using local AI agents which runs those LLMs (Large Language Models) exclusively on-device, ensuring your data stays private (I'll come back to that point later).

The device is called Core, and Ghost explains on the product's site: "Core is a personal AI computer that runs models entirely on device. It connects to your apps, files, and devices, builds context on your life over time, and works continuously to help you, not just when you ask."

It is, if you will, a personal AI assistant on your desk at home, or as Zain Javaid, the founder and CEO of Ghost — which just raised $11 million in funding — put it, a "brain in a box".

Core is a PC which is an unassuming black box built around an Nvidia RTX Pro 4000 (Blackwell) graphics card with 24GB of video RAM, the central muscle for driving the AI, along with 64GB of DDR5 system RAM. The rated AI performance is 770 TOPS.

The PC comes with three AI models pre-installed: Qwen-3.8-Next, Qwen-3.8-27B, and Gemma-4-31B.

You can use your own model on the device, as you might expect, or any alternative for that matter, but the real selling point of Core is that it's an easy way to get yourself a fully primed AI box which requires no effort or setup.

This is for the AI enthusiast who doesn't want to buy a Mac mini (or Mac Studio) with maxed-out memory (required for larger AI models), and then have to mess around setting up everything themselves.

Javaid stresses that the PC comes with software built specifically for running those pre-installed AIs, avoiding all the fuss of model configuration. The CEO observes: "You can run [AI] models on existing hardware, but it's a terrible experience on many levels."

Core updates its models automatically as new upgrades emerge (unless you don't want that to happen), and the idea is that it's a seamless system and "similar to how Tesla works" with over-the-air updates.

You interact with the PC using an app (on your phone or laptop) with which you can issue voice commands should you wish.

Privacy and security

As well as the ease of use, privacy is the other big selling point for Core. It's tapping into a market of the increasing number of people realizing the potential of AI for helping with their life or work, who are concerned about sending their thoughts or data into the cloud, when they have no real idea where those queries or uploads will end up.

Core will run your AI agents continuously taking on whatever tasks you require while keeping all your data on the device, with Ghost promising that it's not going anywhere else. The personal data on the PC is encrypted to keep it safe — even Ghost can't access the data — and the device is specifically designed to keep running even if Ghost were to shut down (there's no subscription fee here).

The CEO assures us: "All of the source code, logic, and model weights live on the device, not the services. We build this device to not be dependent on having an outside entity maintaining the service."

Security is further bolstered by a firewall that limits how any agent can use credentials such as passwords, and you can dictate how much autonomy the Core agents are allowed, heavily restricting the AI should you prefer.

Javaid tells us that the firewall monitors all outgoing requests and "if it sees the agent performing a sanctioned action or exfiltrating data it shouldn't, it rejects the request and flags it for user review".

Core is priced at $3,499 in the US (around £2,600 / AU$5,000), and pre-orders for the first batch of PCs sold out on the first day that they were offered (yesterday). Those devices ship at the end of this month, and you can now register your interest in a second batch of PCs which are coming off the production line.

Analysis: a question of worth

Core can be hooked into all aspects of your life — or that's certainly the idea, and part of the reason why there's such an emphasis on privacy. It needs to be watertight for security given that Core connects to your existing devices and services, smart-home gadgets, fitness watches — or personal health data — and learns over time about how you live your life, imparting advice based on that knowledge.

Exactly how useful the system will be, well, that remains to be seen — and it may also depend on how 'smart' you've gone in terms of your array of gadgets and tech. There could be a lot of scope beyond life monitoring for Core's AI agents, of course, and there will presumably be the flexibility to get them doing whatever you want.

The other potential sticking point is that price. Is $3,499 a lot to pay for a dedicated AI PC? Well, it's undeniably a hefty sum of money, but there are some heavyweight components under the hood here (and the RAM crisis remains in full force). Indeed, the current price of the Nvidia GPU actually makes the PC a bargain as it stands (the company bought its stock of those graphics cards when they were much cheaper — so the next batch will be pricier).

If you configure a Mac mini as a top-of-the-line device for AI, you're looking at $3,199 in the US at the time of writing — and it doesn't come with the aforementioned strengths of software specifically designed for running local AI easily, and the privacy and security elements mentioned.

I've a feeling Ghost might be onto something here, at least in terms of pushing the concept of a user-friendly and straightforward AI box that does everything locally. I doubt this will be the last take on this kind of idea, and the price tag involved may eventually become a bit more realistic in the future. Although that depends on how the RAM crisis shakes out — and the latest signs are far from positive, sadly.

• Anthropic wants to train 10k FDEs from large institutions and consultancies
• Using their customers could extend Claude's reach significantly
• Training will include a simulated deployment and an actual deployment

Anthropic has announced plans to spend a mammoth $100 million to launch its Claude Frontier Academy, a training scheme designed to support enterprise AI adoption.

The company is targeting 10,000 Frontier Deployed Engineers (FDEs) by the end of 2027, who will be able to identify use cases, handle security considerations and drive deployment for Claude customers.

"Claude Frontier Academy trains people the way our own engineers learn, and we want those who graduate to set the standard for how AI gets built inside a business," Steve Corfield, Global Head of Business Development and Partnerships at Anthropic explained.

Anthropic to train 10,000 FDEs

The training scheme starts with a simulated enterprise deployment before trainees enter a 12-week residency "where they lead a real Claude use case at their own organization."

The initial cohorts are set to include engineers from major consulting and financial organizations, including Accenture, Bain, Capgemini, Commonwealth Bank of Australia, Deloitte, McKinsey, Morgan Stanley and Novo Nordisk.

Anthropic has already given out more than 175,000 Claude certifications to workers across 46,000 companies part of its broader Claude Partner Network, but the drive behind its latest FDE scheme is clear.

The easier it becomes for enterprise customers and consultancies to deploy Claude, the more customers Anthropic is likely to have. Additionally, working with consultancies gives Anthropic access to their customers, extending its reach.

Rival company OpenAI already has its own extensive FDE scheme, too, but there's a key difference. OpenAI's focus is on training its own workers to go off and transform customers' companies, but Anthropic's latest announcement is primarily about training other companies' engineers to become FDEs.

Reuters recently reported that OpenAI has experienced a twofold increase in enterprise sales since July 2026, and while specific figures are kept closely under wraps, we do know that enterprise deals are far more lucrative for companies like Anthropic than consumer subscriptions.

So while a $100 million commitment to train engineers may sound like a large sum, Anthropic clearly hopes it will pay off in the long run.

• Meta halved its number of Claude seats while pushing MetaCode adoption
• Microsoft also reportedly asked workers to use Copilot over Claude
• Anthropic could face reputational damage from DoD designation

There seems to be an ongoing shift away from Claude happening in Big Tech, but the reasons behind why two major companies are looking to reduce Claude expenditure aren't so clear.

Cost could be one of the biggest factors, with Microsoft's internal Claude spending estimated to have been on course for at least $1 billion this year, per The Information.

With both Microsoft and Meta having their own internal AI systems, it makes sense for them both to use their own tools for less cash.

Meta and Microsoft reportedly cutting down on Claude usage

Microsoft had already asked employees to limit their Claude usage and spend more time with Copilot and other Microsoft tools, The Information shared, while Meta has reportedly halved the number of its workers using Claude Code from 60,000 earlier this year to just 30,000. At the same time, its internal alternative, MetaCode, has already passed 30,000 users.

Meta, too, was set to spend billions on internal AI usage before the company imposed token limits, spending controls and other monitoring.

We've already seen similar trends among smaller AI customers, with US organizations opting for cheaper Chinese models over homegrown alternatives. It's likely an important economic equation, where paying significantly less for a marginally interior model becomes the most viable option for most use cases.

Anthropic itself has also had a troubling couple of months, after being deemed a national security supply chain risk by the US Department of Defense. A designation that will have undoubtedly altered consumer and enterprise perception of the company, irrespective of the designation's potentially political founding.

"The company may experience material revenue losses or business disruptions attributable to these events," Anthropic warned in an IPO prospectus (via Reuters).

The bigger story impacts Anthropic – potentially losing billions in revenue from two of its biggest customers could seriously impact revenue for a company that's slated to go public for a reported valuation of over $2 trillion (per Reuters).

• AWS CEO looks to debunk common 'myths' about data centers
• Amazon announces it will no longer make local governments sign NDAs when proposing data center projects
• The company has also laid out new codified community commitments

CEO of Amazon Web Services Matt Garman has released a statement in defense of AI data centers that labels the technology as “The race our nation can’t afford to lose”.

Within it, Garman calls on the United States to band together against “misinformation and outright lies” that are being spread about AI data centers to prevent the country from missing out on the huge benefits that will be provided to the economy, national security, and local communities.

“A lapse in our nation’s focus or resolve could put us behind, and potentially irreparably so,” Garman says.

Debunking the myths

There has been a significant amount of messaging about the positives and negatives of the AI data center buildout, with falsehoods and misinformation fueling both sides.

Garman’s statement starts with a section aimed at countering the ‘myths’ spread about AI data centers, such as water use, higher energy costs, and pollution - but keep in mind that Garman makes no distinction between AI data centers and regular data centers.

For example, Garman argues that data centers account for “0.5% of total U.S. direct industrial water use”, but draws on the figures of direct water use to argue this point.

The indirect water usage of data centers is far higher, and often isn’t included in water estimates for AI data centers. The indirect water usage includes upstream factors such as the water used to generate the huge amounts of electricity that data centers rely on. Data centers are also being built in regions that have experienced long-term droughts, exacerbating water shortages and their effects on local ecosystems.

Garman also labels the rise in US energy bills caused by data centers as “misleading and convenient scapegoating,” arguing that “the accelerated adoption of AI is precipitating a build-out that was destined to happen.” What Garman fails to mention is that the infrastructural build-out is being fueled by AI data centers.

Up until very recently, new grid connections for data centers haven’t been paid outright by the developers. Instead, grid operators have funded new connections and upgrades to power lines through increased bills on rate-payers. It is only the backlash from rate payers that has preceded the introduction of Trump’s voluntary Ratepayer Protection Pledge - and the much more binding (and bipartisan) Ratepayer Protection Act. Had there been no public backlash, these measures would not have been necessary.

The next myth Garman attempts to counter relates to the pollution caused by “large backup diesel generators”. Garman states that these are necessary as the services “consumers, businesses, and governments rely on every day” are provided by data centers. This is, in its exact wording, true.

But the pollution many of those in the US are arguing against is attributed to the fossil fuel burning gas turbine engines that many AI data centers use as an alternative to connecting to regional power grids - particularly if their connection requests are denied. The turbines at some campuses give off the same noise pollution as an international airport, 24 hours a day, seven days a week. The sounds they produce have also been involved in causing neurological symptoms in local residents.

The demand for gas, pushed higher by AI data centers, could see the US use more natural gas than Germany and Japan combined by 2035, and the associated pollution could cause generational health decline and cost $25 billion in associated costs.

The final myth that Garman seeks to debunk is that ‘Community members get nothing from data centers’, arguing that data center operators are “often the single largest taxpayer in a community” and that this “reduces the taxes paid by families, but it also funds investments in schools, fire and police departments, parks, roads, and other community services.”

But data center operators are also the largest beneficiaries of tax cuts and credits too. Numerous states have begun repealing tax cuts for data centers after seeing billions lost in expected revenue - billions that otherwise could have been used for funding investment in community services.

Garman also points to the boom in construction jobs associated with the AI data center buildout, but doesn’t include that many of these jobs are temporary. Once the construction of a data center is complete, these jobs often disappear unless there is continuing demand within the local region, and operational data centers themselves require a very small number of employees.

Amazon’s new community commitments

In an effort to get local communities onboard with data centers, Garman’s statement also lays out a number of ‘codified’ community commitments that Amazon will adhere to.

These commitments fall under four pillars; Protecting & Enhancing Communities; Providing Jobs & Economic Benefit; Engaging Communities; and Building & Operating Responsibly.

The full list of commitments is available here, but there are a few key points to look at. Outside of vague commitments to covering the cost of grid connections and infrastructural upgrades, paying their fair share of taxes, and paying the workers they hire, Amazon will contribute "$1 billion over the next five years" to jobs and education, energy affordability and water solutions, and community investment.

Given that Amazon operates 236 data centers that are operational or under construction, this $1 billion figure amounts to around $4,237,000 per data center over the full five years, or about $847,000 per data center per year.

Amazon has also announced a commitment to “no longer use nondisclosure agreements with the government agencies we work with on our projects.”

This is by far the biggest announcement included in Garman’s statement, yet it takes up just a single line. Local communities have long called out for more transparency around AI data centers.

In numerous cases, NDAs have been used to prevent local governments from discussing planned data centers with their constituents. In numerous other cases, data centers have only been announced to local communities after the necessary permits are secured, preventing local residents from being involved in the consultation process and limiting their ability to oppose new projects.

The community commitments offered by big tech companies such as Amazon and Meta looking to build AI data centers are not out of the goodness of their hearts. They are a direct result of the community backlash against AI data centers that has occurred across the US.

Had this huge opposition not happened these benefits would not be offered, but they are being jazzed up by big tech companies as a benefit they are providing to communities rather than the efforts of grassroots movements and every day Americans fed up with being subject to rising bills, increased noise pollution, and an economic system that favors those with money and power.

• A majority of US voters (61%) now oppose AI data center construction
• Trump voters support construction, but only at 49%, versus 47% opposing
• Polling took place in early September 2026

The majority of US voters have expressed their opposition to further AI data centers, as newly published research by The New York Times and Siena University found 1,503 likely US midterm voters have major concerns over the direction of travel with AI data center construction.

However, the survey found the data center issue is not a priority for most US voters, where economic and foreign policy matters are more prominent.

Reaction to the polling is likely to be minimal, but it appears to underpin the growing discontent with the approval of increasingly expansive data centers. Concerns over the environment (such as wildlife, water use, and impact on nearby communities) dominate the responses to the poll.

The data center question

The question that has attracted the most attention from the poll (paywalled) – which covers a range of issues rather than just AI – was worded “In general, do you support or oppose the construction of data centers to support artificial intelligence, also known as AI, technology in the U.S.?”

An overall response of 61% opposing to 34% supporting highlights how fed up voters are with the AI issue. Data centers are perhaps the most tangible, visible element of the technology for voters to focus their ire on, given the impact the facilities have on local communities and ecology.

Perhaps the most interesting factor of the overall response to the main question was how the result is split. Based on 2024 voting, 49% of Republicans support AI data center construction versus 47% who do not; 18% of Democrats support construction, while 75% do not. (Among independent supporters, the result is 36% support, 60% oppose, and all three measures accommodate the “don’t knows.”)

Regulatory supporters

While the survey explored many topics, it spent some time quizzing the respondents further on AI. Here, some nuance can be found, with 56% of voters in favor of limits to AI data centers, whereas 38% supported a total ban on further construction. Interestingly, a slight majority (42% vs 40%) trusted the Republicans to handle the data center matter.

Those polled expressed support for key concerns surround data center construction, with 32% citing water usage or the environment as the biggest worry. The impact on local communities and resources was expressed by 21%, while 18% had a general distrust or dislike of AI. Impact on local economies and jobs was 9% of those polled, while 10% had concerns over AI regulations.

The poll was completed before President Trump’s statement against guardrails, which he believes would hamper America in the AI race with China.

• 38% of workers who regularly use AI have admitted to “hoarding” prompts to gain a professional advantage
• Meanwhile, 21% have hidden their AI use from their manager in order to appear more efficient
• Many just don't want their colleagues to gain the same advantage to productivity

New research has claimed nearly two-thirds (62%) of workers using AI regularly believe having better AI skills gives them an edge – and 38% are keeping productive prompts to themselves to to gain a professional advantage.

A Use.AI survey of employees with regular AI users in the workplace found that of this number, nearly half (44%) didn’t want their colleagues to gain the same advantage to productivity, while 31% claimed that it was they who had invested the time and effort into developing the prompt and didn’t want to give away their advantage.

While the use of AI for productivity boosts can unlock advantages, the survey indicates a proprietary attitude, with workplace “gatekeepers” potentially hoarding the best AI prompts.

AI teamwork?

A trend of gaining an advantage in the workplace through developing AI skills seems to be the takeaway from this survey, which quizzed 13,276 adults who use AI in the workplace, based in organizations in the UK, US, Canada, Australia, the EU, and Latin America.

As well as unearthing a habit of keeping AI productivity shortcuts a secret, the survey also underpins a growing pattern of anxiety around the technology’s impact on a worker’s performance. It found that 41% of AI users in the workplace were worried about colleagues and managers finding out how much time and effort the technology was saving them. They were also concerned that they would be expected to achieve the same levels of productivity and speed in future.

It also found that 36% had hidden their AI use from coworkers, with 21% hiding their new-found productivity skills from their manager.

AI-generated tensions

Many criticisms of AI at the point of use (as opposed to data centers) tends to dwell on the issues surrounding generative AI and its impact on creative industries. In a non-creative industry, there is the reliability of the results to consider – but Use.AI’s survey seems to point at a new issue.

Ihor Herasymov is a Co-Founder & CEO of Use.AI, and says that the survey suggests “tension” is developing in the workplace around AI.

“When a useful AI method becomes part of someone’s personal edge, sharing it is no longer a neutral act. Companies may need to think more carefully about how they reward people who turn private know-how into shared capability, because employees will notice quickly when openness helps the organization while weakening their own position.”

In short, the companies that find this secretive AI prompt culture developing may find that the metrics with which judge employee performance will have to change.

• Businesses are tracking AI as consumers shift to AI-powered buying patterns
• Social media is also a big are for monitoring consumer trends
• Conventional retail like Walmart and eBay have dropped

AI platforms have become synonymous with scraping other sites for information, but it's not every day you hear of AI sites themselves becoming targets. The latest Decodo research has revealed just that, though, with ChatGPT and Perplexity both appearing in the top five.

Decodo argues this marks a fundamental shift in what organizations are now demanding from the web – "Two years ago, businesses scraped the web to see what people were searching for," CEO Vaidotas Juknys noted.

"Now they scrape AI platforms to see what people are being told. The use case for data collection is changing."

AI platforms like ChatGPT are now being scraped

Besides AI platforms, Decodo found that seven of the top 10 sites are actually new entrants, with just TikTok (number one) Google (second place) and Amazon (eighth place) re-entering as repeat offenders.

ChatGPT, in third place, accounted for 14% of the activity observed by the analysis, which could account for businesses checking how the AI tool ranks their products against competitors, monitoring how it describes the company, measuring brand mention frequency, recording ChatGPT citations and more.

Perplexity accounted for 10% of the data, landing it in fifth place, while TikTok saw the most scrapes at nearly one in five (18%).

\But with several top-10 sites from 2025 dropping out of the charts, including Walmart, eBay and Airbnb, Decodo's data shows much more than just the rise of AI. It's also a much broader indication of how and where consumers are shopping, including AI and social media.

Looking ahead, the company is also anticipating that Gemini and Copilot could end up in next year's top 10. But marking an even bigger shift, Decodo also predicts that AI agents could replace humans in starting those scrapes in the first place.

"Next year the agents might be doing the deciding instead of just reading the answers," Juknys added.

OpenAI had an oddly casual way of apologizing to the Australian Government to explain how one of its experimental AI agents gained unauthorized access to a government health statistics system while trying to complete a routine research task. More casual than one might expect for such a huge security concern.

The agent was looking for publicly available information about medicine spending, but after failing to get what it wanted through the normal route, it found a vulnerability in and used it to access internal files, though no individual patient records.

The incident is a striking example of the risks that come with increasingly autonomous AI agents, but almost as striking is how OpenAI admitted what happened. OpenAI says it discovered the Australian activity in mid-August but did not notify Australia until September 10. When it finally did, the company sent a brief, almost jaunty email about it, as shared on LinkedIn by an ABC reporter:

"We are notifying you of a security vulnerability identified during our review of OpenAI model activity involving Services Australia’s Medicare Statistics service," the email begins before explaining what happened. "We recommend that the team responsible for the service investigate the vulnerability and assess the changes needed to prevent it. We would be glad to brief your security team and provide supporting evidence as available."

The message ends with "Best," though the letter hardly feels like it means it.

Won't accept a no

The model was researching government spending per person on medicines for skin conditions in communities in Victoria in June. It was supposed to find publicly available statistics. Instead, it discovered a route into the Medicare Statistics Reporting Service that gave it non-public access.

Essentially, the model asked for information it shouldn't have access to and took the denial as a sign to find another way in. The ethics of the matter did not arise.

"We did not intend for this activity to occur, and the access to the service and follow-on activity should not have happened," OpenAI wrote in its follow-up explanation.

The timing makes the note considerably worse as, while OpenAI discovered in July what happened in June, it did not notify Services Australia until September 10.

That means the most significant delay was between discovery and disclosure. OpenAI now admits it still waited too long. In its expanded apology, the company said it should have shared preliminary findings sooner and kept Australian agencies updated as it learned more.

While using the public disclosures email is legitimate for the average person finding a security flaw, it seems odd for a company of OpenAI's size to report its own model's error this way.

Not really the 'best' way to say sorry

The apology also revealed that Medicare was only part of the story. OpenAI said its models had interacted with several Australian government services during training and evaluation. An agent accessed the NSW Bureau of Crime Statistics and Research's public Crime Mapping Tool, while agents found an exposed access key associated with a Victorian health reporting system and retrieved configuration information and aggregate survey statistics.

OpenAI said no individual medical or criminal records were accessed in those incidents. But OpenAI later expanded its response, adding that a model researching wildfire statistics had used crafted queries against the NSW National Parks and Wildlife Service's Fire History service to infer database metadata that was not intended to be publicly exposed.

The growing list makes the Medicare incident harder to dismiss as a quirky model finding a single vulnerability. It suggests experimental agents were repeatedly interacting with real websites in ways their creators did not expect. OpenAI itself has responded by pausing training and evaluation involving tool use for its most capable models until additional safeguards are in place.

OpenAI also said that newer monitoring has already caught a model obtaining live internet access during another training run, allowing humans to stop it. The Australian government is investigating whether laws were broken and working on fixing older public-facing government systems to prevent it from happening again.

AI companies increasingly want us to trust agents with doing tasks autonomously on our behalf. The selling point is that an agent can encounter an obstacle and independently figure out how to accomplish its goal. This is an unusually vivid example of what happens when that feature works a little too enthusiastically.

The Medicare agent was assigned to find statistics about medicine spending, not penetrate a government server. But it simply steered toward a more direct route, ignoring every consideration but effectiveness. The technology had crossed a boundary its developer says it never intended the model to cross, and the company took weeks after discovering the incident to tell the organization on the other side of that boundary. That's why the email feels like more than a funny piece of corporate communication.

The follow-up acknowledges responsibility and describes concrete safeguards while admitting that notification should have come sooner. It also recognizes that the company now has work to do to rebuild trust. It's a much better message. "Best" is a way to end an email to a professional acquaintance, not when your experimental AI has hacked national medical databases.

• Google is changing model access in Gemini
• The best models are being dropped from the free and Plus tiers
• These updates will be applied from October 9

Google is shaking up access to its Gemini AI models for individual users based on how much you pay each month, and it's those on the cheapest Plus plan and the free tier who are going to be losing out from next week.

As per an updated support document (via 9to5Google), if you don't have an AI subscription for Gemini then you'll be restricted to the Flash-Lite (3.5) "fastest answers" model. The Flash (3.6) "all around help" and Pro (3.1) "advanced reasoning" models won't be available going forward.

If you're on the $4.99 / £4.49 / AU$7.99 AI Plus plan, then you're losing access to Pro (3.1), but will still be able to call on Flash (3.6). There are no changes to the more expensive AI Pro and AI Ultra tiers, except for AI Pro gaining the Deep Think mode that maximizes parallel reasoning while sacrificing some speed.

Look out for a message inside the Gemini app or in your inbox if you're going to be affected by the changes. Google says the free tier restrictions will be applied from Friday, October 9, while AI Plus users will get an email advising them about the timetable.

Usage limits

Previously, the Gemini AI plans have been differentiated by usage limits — everyone got access to the same models, but for varying numbers of queries. Now there are further restrictions, based on model availability.

Exact usage limits are difficult to pin down: they depend on "the complexity of your prompt, the features you use, and the length of your chat" Google says, adding that "paid users have higher limits than users without a Google AI subscription". Usage limits refresh every five hours, and there are weekly limits too.

The move puts Gemini more in line with what you get with ChatGPT. OpenAI also limits model access based on how much you're paying each month, also the way it handles usage differs somewhat from Google: rather than one single allowance, there are usage limits on different models, query types, and thinking modes.

We know that AI compute doesn't come cheap, so presumably Google has done the math and decided it can save some money with the new restrictions — and perhaps convince a few more people to sign up for a subscription.

Many AI developers and frontier labs are openly pursuing artificial general intelligence (AGI), which scientists describe as human-like intelligence in which a model can reason like humans and learn new capabilities outside its training data. But does that mean that they're all on the right path?

Chasing the dragon

If we achieved AGI, how would we even know? Many AI benchmarks exist, but François Chollet co-founded the ARC Prize just a few months before speaking with the Dwarkesh Podcast to get to the bottom of this.

In comments on the podcast, he explained that large language models (LLMs), which are based on neural networks, is a dead end when it comes to achieving AGI. That doesn't mean there wasn't room to improve LLMs so they were more powerful, autonomous and useful to businesses. But that isn't the same thing as AGI.

By pouring funding into LLMs at the expense of other avenues or architectures, Chollet explained, the road to true AGI is being ignored. The result? Progress has been set back by up to a decade.

Intelligent systems

Despite Chollet's steadfast belief that LLMs will not get us any closer to AGI, many technology executives have spent the last few weeks opining about the possible threat that they pose to humanity.

Ahead of OpenAI and Anthropic's as-of-yet-undetermined IPOs, both companies have taken turns disclosing increasingly worrying incidents, including cybersecurity breaches. These stories have, in many people's eyes, simply served to hype up the capabilities of existing technologies for marketing purposes.

As for Chollet, the whole purpose of the ARC-AGI benchmark is to determine true progress toward AGI. While LLMs are performing better on this especially tough benchmark, none have come close to hitting the mark.

• OpenAI, Softbank, Blackstone, and construction unions have formed a lobbying group
• The American Infrastructure Alliance will support efforts to build data centers where local opposition is strong
• Opposition groups have called for a moratorium on new data centers, with existing research suggesting promises of long-term jobs are far-fetched

Challenges to data center development in the US are about to have the battle weighted against them with the unveiling of the American Infrastructure Alliance (AIA), a group of companies and unions that seek to overturn objections to new facilities.

Comprising OpenAI, global data center company QTS, investment company Softbank, management experts Blackstone, and seven unions including the International Brotherhood of Electrical Workers, this coalition plans to establish guardrails for data center development in 2027.

The American Infrastructure Alliance plans to launch its initial defensive strategy in Texas, Georgia, Ohio, Iowa, Pennsylvania, Indiana and South Carolina. In doing so, the AIA hopes to head off objections from local opposition groups, and avoid growing calls for moratoriums.

“International” alliance

By establishing the alliance, the tech companies, management consultants, and unions intend to get public and union member support for the construction of new data centers.

Along with the International Brotherhood of Electrical Workers (IBEW), the alliance includes the United Association of Union Plumbers and Pipefitters (UA), the International Association of Sheet Metal, Air, Rail and Transportation Workers (SMART), the International Association of Heat and Frost Insulators and Allied Workers (HFIAW), the International Association of Iron Workers.

Representatives from some of these organizations have offered their support publicly. For example, Michael Coleman, general president of SMART, says "American workers and communities should have a seat at the table as this critical infrastructure is built."

Responsible data center building

Unsurprisingly the heavy-handed approach that seems to have been conceived to force state officials and county and city councils to be more favorable to data center developments, is attracting criticism.

Mitch Jones has called for a moratorium on data center development to conduct further study on their mid- and long-term effects. He’s the managing director of policy and litigation at environmental group Food & Water Watch, and has concerns not just about the environmental impact, but jobs too.

He expressed concern about construction jobs data, telling the DCNF that “Once centers are constructed, very few jobs remain in the facilities themselves. Our research has found that as of 2024 there were only 23,000 people working permanently in data centers across the country.”

Calls for moratoriums have grown, with politicians including Sen. Bernie Sanders planning legislation to pause construction of data centers across the US until their guardrails are in place.

But the AIA’s guardrails and lobbying are likely to have a more immediate effect, and bring jobs. Kenneth Cooper, the IBEW's international president, notes: "Blanket bans on necessary infrastructure projects would set back our economy and threaten good middle-class jobs.”

• AI chatbots appear to be providing less diverse responses, research claims
• Displaying the same answers has been likened to globalisation, where the same products and brands are available everywhere
• Such a move may increase convenience, but also reduce diversity

Could we be heading into a real-world Idiocracy? A repeated reliance on AI to provide answers – invariably the same answers, offering the same views – could be shrinking human knowledge, a new study has claimed.

The research, which tested 27 different LLMs, found that even with the best performing LLM, the information presented as answers was almost 19% less diverse than what is available using Google’s standard search engine. Consequently, the information was less varied, providing a narrower selection of answers, views, and restricting interpretation.

Researchers believe that this trend risks pushing certain content to the peripheries of accepted knowledge, while other content is considered dominant or primary – and the end result of this could indeed be a world where people are less aware, more idiotic, and perhaps even with less intelligence than today.

Topic testing

The study was conducted by researchers at the Department of Computer Science (DIKU) at the at the University of Copenhagen.

To establish how the LLMs would deliver answers, they were tested on 155 topics (ranging from nuclear weapons to K-Pop). The researchers used 200 prompt formulations drawn from research into real AI chatbot usage, generating an incredible 70 million responses from the models.

Even answers from GPT-5, the LLM producing the most varied responses, were still 18.5% less varied than answers from Google Search.

“People are to a large extent exposed to the same information over and over again,” says first author Dustin Wright. “So AI chatbots are not just changing how we find knowledge, but also which knowledge we have access to."

An Idiocracy?

Whether using AI models as part of Google Search, DuckDuckGo’s search tool, Bing, or by specifically quizzing Gemini, ChatGPT, or any of the other chatbot-accessible LLMs, the findings from the research indicate a change in how information is surfaced.

“We risk exposing people to fewer perspectives and a narrower range of knowledge. This could create a vicious cycle in which the most popular content becomes even more dominant, while other content is increasingly overlooked,” says DIKU Professor (and senior author) Isabelle Augenstein.

She compares the phenomenon with the downsides of globalization: “Today, you can buy the same products and find the same coffee chains almost everywhere in the world. That has many advantages, but it has also reduced diversity.”

As for the possibility that AI could shrink knowledge across humanity, and leave a world governed by those wholly unsuited to the task, Professor Augenstein is more upbeat. She says that a “knowledge collapse is not happening yet […] we can see that the more recent models produce slightly more diverse answers than older models.”

• 'No Robo Bosses Act' signed in California, comes into force in July 2027
• Humans can't just approve an automated decision, they must bring evidence
• Governor Gavin Newsom criticized Trump administration for a lack of federal leadership

California Governor Gavin Newsom has signed a major package of no fewer than 13 AI-related bills as the state continues to increase the pressure on AI via human-protecting regulation centered around employment, education, legal and more.

Among the new bills was the 'No Robo Bosses Act', which essentially limits employers from using automated decision-making to discipline or fire workers.

The bill, officially known as SB 947, is described as California's human-in-the-loop law to keep humans at the core of workplace decisions that affect workers' lives.

California to protect workers from AI-powered disciplinary action

The bill insists that an employer cannot rely solely on an automated decision system when deciding to discipline, or terminate, an employee. But furthermore, a human can't merely approve an automated decision – they must also provide their own evidence to corroborate with the automated decision.

"We are deeply mindful we have to do a lot more in the absence of federal leadership," Newsom said in a statement (via X), referring to Trump's decision to rename artificial intelligence 'super intelligence'.

Yesterday's meeting with tech leaders also saw the creation of a new "morally binding" agreement whereby major companies, including Google, Anthropic, Meta, OpenAI, SpaceX and Nvidia, would be responsible for ensuring the safety of their own AI.

This, of course, comes following ongoing calls for greater government oversight of AI.

"By signing these four first-in-the-nation bills, Governor Newsom has shown the country how we can begin to put guardrails on AI at work to ensure human oversight over this technology, limit surveillance, and put workers first," the California Federation of Labor Unions, AFL-CIO President Lorena Gonzalez, said.

Crucially, though this specific bill was signed on September 30 2026, it won't be effective until July 1, 2027.

The data center boom powering a lot of AI infrastructure has provoked pushback from a wide array of groups critical of how they are built and run. The backlash has been strong enough that even in Texas, which had become a hub of new data centers, Governor Greg Abbott felt the need to order an audit of those seeking connections to the state’s electricity grid and to tell the Texas Commission on Environmental Quality to stop issuing permits sought for them until the audit is complete.

But a rather large complication is making that audit seem inadequate. Thousands of diesel generators and increasingly large private power plants sit next to the data centers. Some developers are building or planning enough power generation on-site that the distinction between a data center and an industrial power facility starts getting fuzzy.

It's a problem raised in an open letter sent to Abbott by the NAACP Texas State Conference and the NAACP Center for Environmental and Climate Justice. The groups argue that Texas’s new measures remain too focused on data centers seeking connections to the Texas grid, potentially leaving a giant loophole for those that generate or source their own electricity. The letter also argues that the audit does not sufficiently address cumulative effects, including pollution, water consumption, and noise.

As the letter points out, a Floodlight and The Texas Tribune investigation found that at least 38 Texas data centers have obtained minor-source permits for on-site power since 2024, covering more than 2,100 diesel generators. The permitting category is also associated with considerably smaller sources of pollution, including dry cleaners, auto body shops, concrete batch plants, and rock crushers. Texas’s new regulatory response may not actually address the issues brought by the data centers and their support infrastructure.

Minor permit, major story

The "dry cleaner permit" involves categories of minor-source air permits made to streamline approval for small businesses and avoid more intensive environmental review. The idea is that regulators don't need an elaborate approval process every time someone installs a set of dry cleaning machines or pumps at a gas station.

AI data centers test the logic through sheer scale. Floodlight found that OpenAI’s Stargate campus in Abilene had already built 10 gas-powered turbines and 62 backup diesel generators and was seeking approval for another 41 turbines and 18 generators. The existing turbines and generators were initially authorized through minor permits that did not require the environmental studies, public notice, and public comment associated with major permits.

The investigation also found several projects with estimated emissions just shy of triggering more scrutiny. A CyrusOne data center in Whitney estimated 249.1 tons of nitrogen oxide emissions annually, where 250 tons would have triggered more stringent permitting, while a Vantage facility outside San Antonio received permission for 99.8 tons annually in an area with a 100-ton threshold.

More than half of the data centers Floodlight identified submitted annual nitrogen oxide estimates just below thresholds requiring public input and more detailed environmental review. It raises questions of whether the regulatory thresholds make sense if they can be evaded so easily. The NAACP letter argues that minor-source permits can effectively remove communities from the process when they shouldn't be. It has already issued a notice of intent to sue Vantage Data Centers and VoltaGrid. While the allegations are still pending litigation, they show why the definition of a "minor" source has become so fraught.

Crackdown with loopholes

Abbott's recent responses acknowledge that Texas has a problem worth addressing. His audit directive states that projects that fail can be denied connection to the Texas grid. And the pause on more data centers until the audit is done will certainly slow things down for a time.

But while encouraging data centers to avoid burdening the grid might seem like a way to rein them in, letting them have their own power plants requires a different regulatory response. There are still emissions, water demands, and neighborhood effects regardless of whether it's linked to the grid. Abbot did tell regulators to make permitting decisions with the state's broader data-center directives. The loophole may be smaller than it first seems.

Still, this is becoming a recurring theme of the AI infrastructure boom. Technology is moving at a scale and speed that regulations written for a very different industrial landscape struggle to accommodate. A rule that seems perfectly sensible for a shop with a boiler or a building with a couple of emergency generators looks rather different when applied dozens of times across facilities consuming enormous amounts of power.

Texas has recognized that the AI data-center rush needs guardrails. The next test is whether those guardrails follow the actual environmental impact of the projects rather than simply the route their power cable takes.

As businesses everywhere continue to get to grips with the reality of AI-powered workflows, Snowflake has revealed its view of what it will take for a company to become a truly agentic enterprise.

Speaking at the company’s Snowflake World Tour London event, senior leaders laid out how a number of factors will all contribute to agentic success.

The 'Agentic Enterprise' is here

“There's no enterprise AI strategy without a data strategy,” declared James Hall, Snowflake Country Manager, UK&I as he kicked off the keynote.

“You need the right data foundation, trusted, governed, secure and accessible…in just 12 months, we’ve moved from talking about the potential of AI to putting AI to work…it’s now all about making AI real for business as we enter the era of the agentic enterprise.”

Hall highlighted the success of UK&I customers such as mobile operator GiffGaff, with Snowflake CEO Sridhar Ramaswamy also later joined on stage by the CEO of the London Stock Exchange Group (LSEG) as proof of this.

But it was Christian Kleinerman, EVP Product Management, who took to the stage to run through the factors the company sees as vital for an agentic enterprise.

“We are all part of the era of the agentic enterprise,” he declared, defining this term as an organization that has been able to embed AI powered agents into its core operating processes, co-ordinating with humans, setting directions, setting guardrails and automating as much as possible - all with the ultimate goal of help workers be more productive, and creating better business outcomes.

“The promise of AI is unparalleled, in my mind, to other technologies that we've seen in our lifetime,” Kleinerman added, addressing a growing myth around the technology, namely that some organizations believe AI is so good and smart, the data will sort itself out.

“But if anything, the opposite is true,” Kleinerman said the most amazing results you can envisage with AI come from having a great data strategy.”

Kleinerman noted three main elements for an enterprise to be truly agentic - an AI model, data context, and software and apps - all feeding into what Snowflake calls an “agentic control plane”.

When it comes to picking a model, Kleinerman noted the company is “committed to model choice for all of you” - as there is clearly no way to know who will win out in AI models even within the next few months.

Data context is becoming increasingly crucial, he added, as understanding your business matters a lot to how AI can be beneficial for you, and having the right software and apps will really allow AI to integrate with the rest of your enterprise infrastructure.

“The truly amazing results come when you really understand your data”, he added, “we want to un-silo your data, we want to help you govern your data...so humans can be more data-driven - AI has supercharged that ability.”

All of this flows into the agentic control plane - “the glue that holds all this together”, Kleinerman concluded, “the agentic enterprise truly comes to life when the AI models can interoperate with the rest of your enterprise infrastructure…(and) hopefully it feels like an avalanche of innovation...our goal is to continue accelerating.”

“We’re operating in a fast-moving environment, arguably the biggest risk any business could take right now is by standing still - but moving fast with data and AI shouldn’t mean losing control, it means having the foundation, the flexibility and the confidence to move quickly,” Hill added.

“The era of the agentic enterprise isn't coming, it's here - it’s happening right now in production.”

If you work in an office you’ll know we’re deep into performance review season. But how would you feel if you found out that your boss had used AI to write your yearly appraisal? I know I’d be pretty annoyed to learn that AI was deciding whether I was worthy of a raise this year.

AI use by employees is becoming commonplace, but it's also being adopted by managers. A recent survey by Headway of 1,000 managers found that 33% had used AI to write feedback or prepare a performance review, while 31% had turned to AI to resolve team conflicts. A further 27% had used AI to help write uncomfortable conversations they needed to have.

That may sound like a lot, but the figure is actually down a bit from 41% admitting they have used AI to draft or revise a performance review in a similar survey from a year ago, but still high.

The ease AI

For managers, the appeal of using AI is easy to understand. Faced with having to write several performance reviews each year it can be a real timesaver to dump the work onto AI, however the risk is that what should be very personal performance feedback is being outsourced to a bot.

Managers' AI use is not stopping at performance reviews, either. The survey also found that 57% of managers have already scripted or rehearsed a difficult conversation before having it using AI.

Thalia-Maria Tourikis, a qualified health coach and burnout prevention and recovery expert at Headway, commented:

“When encountered with a difficult conversation, one might think that using AI can help organize their thoughts. However, you should not give up that interaction to automation. When human emotions such as trust and empathy are involved, it is best conveyed when written by a person. The perfection of AI reads as superficial and doesn’t have the same impact.”

A sliding scale

Bosses using AI to help rate employee performance isn’t the only threat from AI that workers face, there’s also the threat of being monitored by AI. Owl Labs' State of Hybrid Work survey spoke to 8,000 workers worldwide, which found that 58% of employees believe their employer has deployed AI-based monitoring tools.

The Headway survey doesn’t make it clear whether managers are using AI to simply improving their grammar, or change the occasional word for something better, or wether their actually generating the text of the performance review entirely using AI. There’s a big difference between these two extremes and one involves much more cause for concern than the other.

It’s especially concerning with performance reviews because these are often link to financial compensation. If your performance review makes a difference to your annual salary, and wether you deserve that raise or not, then finding out that it’s really AI deciding your future rather than your human manager would be especially galling.

If you ever need a sign that AI is coming for Hollywood then it must be that Jeffrey Katzenberg, the veteran Hollywood executive who co-founded Dreamworks, has just announced that he’s launching an AI-focused studio.

Crucially, he told The Wrap that AI output was “slop” until very recently, but that in the last couple of months something has changed. Now he wants to make feature films and TV with AI, arguing that falling production costs will mean more films and new forms of storytelling.

At the same time The Gifted, an AI-generated trailer, has just won a competition that puts $2.5m towards turning it into a proper feature film. Future Vision XPRIZE is one of the world's largest film competitions for hopeful and optimistic science fiction filmmaking that answers the question: what does a future worth building actually look like?

You can watch the trailer for The Gifted here.

Unfortunately, the industry focus on AI seems to be happening at exactly the same time that the audience is being turned off by it.

Filmustage, a movie production planning platform, analyzed Google Trends data and found searches rising on both sides of the AI debate. It found that the phrase “movies with no AI” was up 345% in the past month, while “movies made with AI” was up 112% in the same period.

Google Trends percentage increases can often be massive, so we shouldn't read too much into the numbers themselves, but the overall trend is upwards. The motivations behind those searches may be very different, but both suggest audiences increasingly want to know one thing before they watch: was AI involved?

I don't think it's hard to understand why. You only have to look at an AI film to understand why people have reservations.

You can judge the trailer for The Gifted yourself, but while it may be a stunning technical achievement that AI has made this three-minute video, if I’m judging it as a movie trailer… then I don’t want to watch the movie.

The flaws are obvious. AI is clearly very good at spectacle: lightning, explosions, collapsing buildings and impossible camera angles. Not to mention lead characters who look almost like famous actors you know, but not quite. (Is that Amy Adams in The Gifted's poster? No, it's not quite Amy Adams.)

The harder test is whether it can sustain believable characters, performances and emotional continuity for two hours rather than three minutes. If it does, then it's going to need much better dialogue than what's presented here.

I remember watching the pure-AI film Hell Grind in a theater recently, and that just about kept my attention for its 90-minute duration, but I was ready for it to be over by the end. It also played to AI’s strengths, by not trying to make its world look ‘real’. It's when AI pretends to be real life that I think it falls down the most.

Pure AI films... still look like AI

Actors, perhaps seeing a future in which their skills are not as widely appreciated, are famously against AI, but even they seem to have accepted the overall trend towards it now.

As Deadline reports, Tom Cruise earned spontaneous applause for a six minute speech during a recent BAFTA “Life in Pictures” conversation in London. Cruise talked about how AI “is coming”, but “people want to see real things”.

“There is nothing like human” said Cruise, citing a summer of blockbuster films as proof that “people want human experience.”

From what I've seen of AI movies, I'd say Cruise is exactly right. AI still can't produce an experience that looks human and the connection to the audience that goes along with that. But with even Tom Cruise acknowledging that AI is coming, and executives like Katzenberg jumping on board, AI movies seem almost inevitable now, especially with the saving in production costs that they bring with them.

But while AI might be economical, it definitely has its problems, and if people are already searching for “movies with no AI” then Hollywood has a branding problem before the AI movie era has even properly begun.

AI as another tool for filmmakers

But perhaps before we assume that AI in films means the end to human actors and an influx of AI slop, let’s talk to one of the AI studios making waves at the moment, Utopai Studios, who creates original films and television series using their own proprietary PAI software.

I talked to Kevin Chong, Chief Operating Officer at Utopai Studios.

“We don’t think of AI films as a separate genre”, explains Chong. “AI is another tool filmmakers can use to tell stories, just as visual effects, digital cinematography and virtual production became part of the filmmaking process. It can help filmmakers create worlds, achieve a level of scale that might otherwise be difficult or prohibitively expensive, and give creators more choices.”

And what about the end of human actors? “At Utopai Studios, we don’t see AI as a replacement for human actors, and that isn’t the future we’re building toward”, Chong explains. “There may be stories where a completely synthetic character makes sense creatively, just as animation has always created characters that don’t physically exist. But that is very different from saying audiences are going to stop wanting human performances.

“Performance is one of the most human parts of filmmaking”, Chong continues. “Actors bring interpretation, emotion, instinct and life experience to a character in ways that are fundamental to storytelling. AI can become another part of the filmmaking toolkit — including helping extend performances, create environments or enable things that would be difficult to achieve traditionally — but the creative decisions still need to come from people.”

Tom Cruise is right: there is nothing quite like human, but perhaps there will be a way for humans and AI to coexist in the future of movies after all.

• Claude for Government runs in a FedRAMP High-authorized environment
• Pricing is now based on pre-allocated spend, not per seat
• Claude for Microsoft 365 is also being added

Anthropic has announced the general availability of Claude for Government specifically for government agencies.

First launched as a public beta in July 2026, the industry-specific variant is designed to give government employees access to the same sorts of tools as commercial Claude customers, within a FedRAMP High-authorized environment.

But better still, now that Claude for Government is officially available, Anthropic has implied that new features will land on this platform at the same time as they do on commercial versions of Claude, so that government workers don't risk being left behind.

Claude for Government is now generally available

Naturally, security is one of the biggest messages that coincides with the launch of Claude for Government, and the company notes that admins can set model restrictions, monitor usage, allocate spending, control external connections and more.

But Anthropic has also made some changes to its pricing structure in a bid to make spending more predictable, given public sector scrutiny and AI costs that can quickly spiral out of control. Rather than per-seat billing, Claude for Government works on the basis of having fixed increments.

Under this new model, agencies would need to commit to a specific amount, and Claude usage cannot exceed that amount.

"Administrators define user tiers with spend and model limits per group, track usage by user and by model, and get burndown alerts before a balance runs low," the company explained.

At the same time, Claude for Microsoft 365 is also being made available via early access, which puts Claude tools directly within Microsoft's three biggest pieces of office software – Word, Excel and PowerPoint – as well as Outlook (in beta).

By confirming that it will be adding new features at the standard public cadence and including Claude for Microsoft 365, Anthropic is acknowledging that governments don't want to compromise on features or security.

• AI usage is far from even when it comes to managers vs. regular workers
• Workers are facing more work or increased complexity due to AI
• Redesigning work is more important than upping quality or quantity

New PwC research has claimed 19% of UK workers now use AI every day at work, up from 15% last year, but despite some progression, there are still clearly some biases when it comes to who uses AI and how they use it.

For example, while 85% of senior execs and 73% of managers have used AI, only 35% of non-managers have used it.

But even if more knowledge workers were to use artificial intelligence as part of their daily routines, PwC argues that AI isn't actually making work any easier.

AI adoption isn't equal, but nor are its impacts

Even though employees claim AI has helped improve the quality of their work (70%), use more of their skills (63%) and bring value to the workplace (58%), the report actually argues that nearly half (44%) of workers have seen their workloads increase following the use of AI, with a similar number (45%) worried their job's complexity has also increased.

With this growing complexity in mind, PwC UK Chief Innovation and Technology Officer Claire Reid explained that employers and employees need to "rethink how work gets done" instead of just increase quality or quantity.

"The real prize isn’t just doing more work; it’s redesigning work so that people and AI together can deliver better outcomes," Reid summarized.

Still, the discrepancy in use among different levels of seniority is hard to ignore, and PwC could have the answer. Many fear negative consequences if they experiment with AI, are confused about their employer's policy or simply need more training. A clearer strategy and greater communication could be enough to change this and enable more workers (and therefore employees) to benefit from AI's more positive impacts.

"Organisations need to rethink work around the right problems, give people the skills and confidence to use AI well, and put the right safeguards around it," Reid added.

• Data center developer offers households $10,000 each in attempt to get huge campus approved
• Residents have called the payment a 'bribe', and list numerous concerns over pollution, water use, and property values
• Developer's chief marketing officer called the payment a "pretty radical and pretty leading-edge" idea

Households in Hazle, Pennsylvania, were offered $10,000 each as part of a plan to help get a 1,300 acre AI data center built near the town - but it hasn’t gone exactly as planned.

NorthPoint Development believed the scheme - which would cost $45 million - would help convince local residents to get on board with having the campus built, but many residents are calling the payment a “bribe” and have refused.

The developers have also planned a further $120 million over the next 15 years through funding for community projects and the police department if the data center is approved.

Money isn’t everything

"As a direct answer to the question 'What's in it for me?' I think we came up with something pretty radical and pretty leading-edge," said Brent Miles, NorthPoint's chief marketing officer.

Residents were sent a letter in June offering each household a $10,000 “grant” as part of the developers plans to build the campus at the foot of the Pocono Mountains, which is dotted with scenic lakes, rivers, forests, and recreational resorts.

The $10,000 grant would be a significant sum for most Hazle residents, with the median household income sitting at $60,000 - far below the national median of around $87,000.

Outside of being seen as a bribe, some residents see it as a way for the developer to cause issues between those who will be closer to the data center and those living further away.

Ed Parks, who lives adjacent to the planned site, said, “They are trying to drive a wedge between Hazle Township residents who are directly affected by the data center, like myself, and people who might live a couple of miles away” (via TechSpot).

Small town concerns match the bigger picture

Many Hazle residents are concerned about pollution, water usage, energy costs, and excessive noise caused by the site. There are also concerns that property values could drop if the site is approved, worsening economic factors for local residents even further.

These concerns align with those on the national level. The Trump Administration has made AI data centers exempt from environmental protection requirements, and numerous sites have already been caught polluting rivers and water treatment plants with chemicals and bacteria.

Residents close to existing data centers have reported symptoms of nausea and dizziness related to the noise of polluting gas burning turbines that many sites have turned to as an alternative to connecting to local grids.

A recent survey has found that the majority of Americans now believe that AI data centers are bad for the environment, which is causing serious issues for the Trump Administration as the US heads towards the midterm elections. Data centers are becoming a key issue for those looking for election or re-election - despite billions of dollars being poured into the campaigns of pro-AI candidates.

These issues are likely to persist, and more sites are likely to be delayed or cancelled by local residents - no matter how many times Trump tries to change what we call AI.

Your email platform can now write a newsletter and pick the moment it lands in someone’s inbox. If you're a marketer running campaigns with a thin team and a thinner budget, that can feel like a lifeline. You're in good company, too, with 40% of marketers naming expanded AI use among their top three email priorities for 2026.

But the flip side of that is your subscribers are using AI too, mostly to decide whether your email deserves their attention. Surveys suggest that 40% of US consumers may trust a retailer's emails less if they knew AI had written them. That’s why we wanted to talk about where AI earns its place in an email workflow, where it needs strong oversight, and where it just immediately backfires.

How does AI assist with writing emails?

AI in email falls into one of two camps. Generative models write and rewrite copy from a prompt, while machine learning models study your historical data to predict who is likely to engage or drift away.

Adoption is already mainstream. In the State of Email 2026 report from Litmus, 28% of marketers said AI was deeply built into their email workflows and another 34% used it regularly. Only 5% weren't using it at all, while generative tools for copy and images topped the list of most impactful uses.

The payoff is mostly speed. The same survey found the majority of marketers now produce and send campaigns within three days, with advanced adopters likely to ship an email in under a day. Some common jobs AI handles include:

• Drafting subject lines and body copy from a short brief.
• Rewriting a draft for a different tone or audience segment.
• Predicting the best send time for each subscriber based on past engagement.
• Scoring contacts by churn risk or likely lifetime value.
• Flagging invalid or risky addresses before they damage deliverability.
• Summarizing campaign performance in plain language.
• Building automated sequences triggered by subscriber behavior.

Still what AI can't do is know your customers the way your account managers do. It predicts plausible text and likely outcomes from patterns, which is a different skill from understanding why a loyal client suddenly went quiet or making judgment calls on what gets your customers’ attention.

Types of AI email assistants

AI now turns up at almost every stage of an email program, from the draft window to the reporting dashboard. Some of it you'll have switched on deliberately, while some arrives quietly in a product update.

Knowing which type of tool you're dealing with tells you how much damage a bad output can do. A clumsy subject line suggestion is easy to ignore, whereas an automation agent sending the wrong offer to your entire list is a much bigger problem.

AI writing assistants

Generative writers now come standard in most email marketing platforms, sitting inside the editor to draft or rewrite copy on request. In fact, GenAI tools for copy and images are often the most impactful AI use case for marketers overall.

The same models also live inside everyday inbox and office apps, where they suggest replies that mimic your tone. Those assistants suit one-to-one sales follow-ups better than bulk campaigns, which need a consistent brand voice across thousands of sends.

List management and hygiene tools

This is less glamorous work, but it decides who receives your email at all. Email verification services increasingly use AI scoring to judge how likely an address is to engage, which helps with catch-all domains that accept every message and can't be confirmed through standard checks.

That matters a lot more now than it used to. Google tells bulk senders to keep user-reported spam rates below 0.1% and never let them reach 0.3%, so a stale list can sink your deliverability long before your copy gets a chance.

Scheduling and automation platforms

Send-time optimization is one of the oldest AI features in email marketing. Platforms that offer it study each subscriber's past opens and clicks to predict when they're most likely to engage, then stagger delivery to match.

Automation is where things are moving faster, though, with newer platforms bundling bots and agents that build multi-step sequences from a single goal and suggest changes based on performance. Marketers expect that shift to continue, as 44% predict more than half of their email operations will be AI-driven by the end of 2026.

Analytics and predictive insight tools

Predictive analytics turns your send history into forward-looking scores, such as each customer's likely lifetime value or churn risk. Those scores can feed segments that update automatically as behavior changes.

Reporting assistants are the newer addition, explaining in plain language which campaigns are driving results. Analyzing campaign performance to identify trends ranked as the most impactful AI use for 15% of marketers in the same Litmus survey.

Why your emails shouldn't rely on AI alone

Readers can often tell when nobody was home while an email was being written. For example, Gartner reports that 49% of US consumers believe generative AI has made the quality of content worse.

The penalty grows with the relationship at stake. In a study of more than 1,100 US professionals, researchers from the University of Florida and USC found that over 80% saw supervisors as sincere when they used AI for light edits. That figure fell to between 40% and 52% for heavily AI-assisted messages, which should give any founder or account manager pause before automating a personal note to a client.

Accuracy is a separate headache, because an AI model will happily write "Final hours!" for a sale that actually runs until Sunday. Washington state's email law saw nearly 200 class actions over subject lines like that after a 2025 court ruling. A 2026 amendment now requires proof that the sender knew a subject line was misleading, but saying "the AI wrote it" isn't a defense that’s likely to work.

Inbox AI also changes who reads your email first. The majority of consumers now make decisions based on AI-generated summaries alone, so vague copy can easily get smushed into nothing before a person ever sees it. Ultimately, the safest move is to put your main point where no algorithm can miss it.

5 tips for writing better emails with AI

You don’t have to ban AI from your email workflow, but you need to decide which tasks are worth handing off and which tasks need human review, then build those checkpoints into the process.

1. Start every draft with a detailed brief

Generic prompts produce generic emails. Give the model your audience segment, the offer's exact terms, the call to action, and a couple of past emails that performed well so it can match your voice.

Treat the output as a first draft for an editor. We recommend rewriting the opening line and the call to action yourself, since those are where readers decide whether a message sounds like a person.

2. Fact-check every claim and deadline

Build a pre-send checklist that compares prices, discount percentages, expiry dates, and product names against your source of truth. Pay particular attention to subject lines and preview text, because those are what inbox summaries and legal complaints tend to latch onto.

Anything with urgency language deserves a second human reviewer. If there's any chance a promotion gets extended, rewrite the subject line so it stays true either way.

3. Put approval gates on automated sequences

Let the platform draft a welcome series or a win-back flow, but review every email and trigger before it goes live. Treat any AI-built sequence like a new hire's first campaign, where nothing ships until someone senior has read it. After launch, spot-check live sends each week rather than assuming the automation behaves.

Set a cap on how often any single contact can be emailed across all your automations. Overlapping AI-built flows are an easy way to creep toward Google's spam-rate ceiling without noticing.

4. Question what the analytics tell you

Before acting on an AI recommendation, ask what data it's based on. A send-time suggestion built on a few weeks of activity from a small list is a guess wearing a lab coat.

Use AI to spot patterns, then confirm them with a controlled A/B test before rebuilding your program around them. The model can find the correlation, but a human still has to decide whether it means anything.

5. Protect customer data and keep personal emails personal

Check how a vendor handles your data before pasting customer details into any AI tool. Look for a written commitment that third-party AI providers can't train their models on your prompts or subscriber data, plus a clear answer on where that data is processed.

For relationship emails, such as a thank-you to a key account, AI shouldn't do anything more than light editing. You can use AI to write the email for you, but making the reader believe that you meant it is still your job.

America.gov has a pitch that I suspect almost anyone who has dealt with the federal government will understand immediately. Instead of figuring out which department, or obscure corner of an agency, controls the thing you need, you tell one website what you are trying to accomplish. Then AI sorts out the bureaucracy for you.

The Trump administration launched the AI-powered America.gov this week as a single digital front door for federal services. Its ambitions eventually extend to passports, Medicare, Social Security, and government jobs, but the version available today starts with something much more familiar. There is a big conversational box where you can ask the government a question in ordinary language.

There is familiar technology behind it too. Google Gemini and Grok are being used. The system draws on information scattered across thousands of government websites and attempts to turn it into a useful answer with links back to official sources.

I spent some time trying it, and I can see the appeal. I am considerably less convinced that I am ready for what the government wants to build on top of it.

No learning curve

America.gov doesn't require much instruction. You type what you need in the same conversational style you would use with ChatGPT, Gemini, or Grok and let the site work out which pieces of government information are relevant. For anyone accustomed to AI chatbots, there is essentially no learning curve.

I started with the suggestion by the chatbot to ask how to replace a Social Security card. America.gov explained the basic process and directed me toward the appropriate government resources. I knew beforehand that my journey should begin with the Social Security Administration, but it was still helpful information.

I tried the same approach with passports and Medicare. Again, being able to describe the outcome I wanted was easier than figuring out the vocabulary used by the relevant agency. Tell America.gov what you are trying to accomplish rather than throwing a few keywords into the box and you'll probably be okay.

That said it's important to click the sources and not just take it on faith. America.gov can synthesize information, but generative AI adds another interpretive layer between you and the underlying government material. If I am checking a deadline or eligibility requirement, I would verify the important details on the agency page before acting on them. A hallucinated fact is annoying when an AI is helping plan dinner and considerably more consequential when it involves healthcare.

The site's first day also provided an unusually vivid example of why I don't want to treat its answers as immutable government truth. In my tests, the chatbot didn't provide any political information when I asked it questions, which is what the website originally promised.

According to an AP report, America.gov initially answered questions about subjects including who won the 2020 election, presidential term limits, and climate change using information from government sources. Then some of the answers changed. That isn't evidence of a security problem, but it raises questions of who decides what a government AI service chatbot is allowed to say. If America.gov is going to become a primary interface between Americans and their government, changes to its behavior should not disappear invisibly behind a chat box.

Gov AI privacy

The current chatbot is only the first stage. The White House says America.gov will eventually allow people to complete government transactions rather than merely tell them where to go. The executive order also calls for Login.gov integration so people can authenticate themselves and access participating services through the same portal.

Passport services and Medicare enrollment are planned for the next couple of years, as are legal name changes and federal job applications. The broader idea is that government systems should stop repeatedly asking people for information another part of the government already possesses. If implemented well, that could remove some genuinely miserable bureaucracy. But those are also the features that make me nervous.

Right now, I can use America.gov without giving it much of anything. Asking how passport renewal works is basically an information request. The promised version could sit between my authenticated identity and some of the most sensitive interactions I have with the federal government. That requires an enormous amount of trust.

The executive order creating America.gov says agencies should retain custody and control of their own records, that the system should avoid creating a centralized federal record on Americans, and that personal information should be protected.

Those are good promises. Recent history gives me reasons to want considerably more than promises. The Government Accountability Office examined access given to a Department of Government Efficiency team working with Treasury's Bureau of the Fiscal Service in 2025 and found that one DOGE employee had access to three federal payment systems and could view, copy and print data. Investigators also found that Treasury had issued the employee a laptop without first ensuring that the employee had agreed to follow security rules intended to prevent data from being transmitted outside the bureau.

And, in March, GAO reported that federal agencies do not always have the technology and resources needed to ensure privacy protections when using AI. Its panel of experts warned that AI can expose personal information contained in underlying datasets, while GAO concluded that existing government-wide guidance did not fully address the major privacy challenges it identified. That report landed only six months before the government launched an AI service it intends to expand across some of its largest public-facing programs.

None of this means Gemini or Grok is quietly reading my Social Security record when I ask America.gov a question. It does mean I want answers before America.gov evolves from helpful government concierge into authenticated government middleman.

The administration is asking Americans to eventually trust America.gov. Given the documented problems around government data access and the GAO's warning that federal AI privacy guidance remains incomplete, that trust should be earned technically rather than requested rhetorically.

• Spaces is replacing Library for Pro, Business and Enterprise users
• It puts your documents, colleagues, generative AI and agentic AI in one place
• Live Pages are a new document type that AI can automatically keep up-to-date

As AI continues to evolve from much more than just basic generative conversations to greater autonomy, OpenAI has launched a new ChatGPT Spaces interface to serve as the new environment for human-AI collaboration.

Within Spaces, multiple colleagues will be able to collaborate with each other alongside ChatGPT, but it's also a shared space for other OpenAI tools like Codex and the newly-announced, always-on Dots.

The introduction of Spaces largely replaces the now-outgoing Library tab for Pro, Business and Enterprise users across both desktop and mobile, as well as the web, but it also serves a much bigger purpose than Library ever did.

ChatGPT Spaces replaces Library

The company defines Spaces as a "home for the pages and files you create, upload, and share in ChatGPT, including spreadsheets and presentations."

But importantly, the change also sees the introduction of Pages – new documents that combine "text, images, charts and [other] interactive tools." Instead of ChatGPT generating a document and users having to manually edit or prompt changes, Pages are now live places where connections with apps like Slack, emails and calendars keep data current.

In a set of FAQs, the company also noted that each user has their own ChatGPT and, where applicable, Dot, to interact with, so they can call on AI to make changes or keep things up-to-date.

There's also a meeting plugin in the works, currently available in beta for Pro and Business users via the desktop app only. It "creates private, personalized notes and suggests follow-ups" within the shared Space that's accessible by colleagues.

While OpenAI hasn't set out to replace Google Workspace or Microsoft 365, Spaces does mark a major shift in how workers access their documents, putting documents (via plugins), humans and AI all in one place together.

• Meta adds new SMB plan for Muse always-on agents
• It works with Instagram/Facebook and third-party connections
• Most Muse use cases should be free, but there are paid plans for more tokens

Meta has expanded its agentic AI platform with the launch of Muse for Small Business.

In a similar vein to OpenAI's new Dots service, Muse users can give their agents a goal or objective and let the agent carry out work independently, palming off some of the more administrative workloads like running parts of the business or finding new customers.

Meta argues that small businesses are a key target for Muse because they wouldn't typically have dedicated resources across marketing, finance and other admin, hence the agentic boost.

Muse for Small Business

Importantly, Muse is likely to be highly tailored toward Meta's own products, hence the company's examples spanning connections with "Instagram professional account analytics, Facebook Pages, and Meta ad accounts."

Crucially, Muse also works with other existing connectors, like Asana, Dropbox, Shopify, Slack and Zoom, making it much more than a Meta-only tool.

Just like its high-profile rival, Muse also continues to work in the background toward pre-defined objectives, surfacing relevant information or issues that require human intervention. Some use case examples include scanning emails and calendars for the most important actions, and drafting new ad campaigns based on current trends.

Meta argues that Muse is set to be free "for most of what people need," but two additional paid plans are also available for businesses who want to go all-in on agentic AI, including the $20/month Power plan (500M weekly tokens) and the $100/month Max plan (3B weekly tokens).

Interestingly, Meta's approach took a consumer product and reframed it for businesses. Muse launched earlier this month for US and Canadian customers before getting an SMB variant, but OpenAI's Dots landed with business users in mind, and there's currently no word on whether a consumer variant could launch.

• Singapore police charge a man over a deepfake AI image
• The picture shows a crocodile in a public recreation area
• A total of 10 years of jail time could be attached to the charges

We know that AI apps can produce images that are often indistinguishable from real photos, and these pictures may get you into trouble with the law too: a man in Singapore faces a jail term of up to 10 years after allegedly sharing an AI-generated image of a crocodile in a popular public recreational area.

The man is Ye Lin, a 30-year-old from Myanmar (via PetaPixel), and the area is Pandan Reservoir. After the picture was shared on August 20, Singapore's national water agency shut down rowing and canoeing activities on the water while trying to find the semiaquatic reptile.

After determining the image was fake, the agency reported Ye Lin to the police, and the man could now be looking at a serious stretch inside when he turns up to court in November. On the charge of sending a false message, Ye Lin could face up to three years in prison, a fine of up to S$10,000 (about $7,835 / £5,895 / AU$11,235), or both.

Then there's the charge of obstructing justice, which can put you inside for up to seven years, lead to a fine (though it's not clear how much), or both. The accused man is reportedly planning to plead guilty and will be hoping the judicial system takes a lenient view on his AI deepfakery.

Communicating falsehoods

"The Police will not hesitate to take action against anyone who communicates falsehoods," said the Singapore Police Force in a statement. "Apart from causing fear and inconvenience to members of the public, such falsehoods also result in public resources being unnecessarily deployed to deal with these incidents."

It's not immediately clear how the image came to be widely circulated: according to the police, Ye Lin only shared the image with one of his colleagues rather than posting it online. He then deleted the image and the Gemini app he used to make it "in an attempt to conceal his actions" the police said.

The reactions online show some sympathy for Ye Lin, with many arguing that the original offense wasn't particularly egregious, especially as he didn't share the image widely. However, questions are being asked about why the man didn't step in and tell the authorities the picture was faked after the reservoir was closed off.

If the image was indeed made by Gemini, as the Straits Times reports, then it should've come with an invisible, embedded watermark identifying it as being generated by AI. It's not clear whether this watermark was removed somehow, or whether no one thought to check once the picture started circulating.

There are plenty of grand ideas about what AI could eventually do for healthcare, whether discovering new drugs or acting as an interactive medical historian for everyone. But a viral Reddit post offers a better, and much simpler vision for AI doctoring.

According to the poster, the family was eating dinner when their father repeatedly dropped his chopsticks. His right hand felt numb, his mouth looked slightly uneven, his speech sounded strange, and he briefly lost his balance. Then he seemed to improve, which was almost enough to convince everyone that he was simply tired and should sleep it off.

After the writer shared the symptoms with ChatGPT, the AI responded that it sounded like a stroke or a related medical incident. It urgently encouraged them to get emergency help despite improving symptoms. The hospital confirmed that he had had an ischemic stroke and treated him accordingly.

This is an anecdote on Reddit, but the advice described in the post lines up with official medical guidance. The CDC says sudden one-sided weakness or numbness, speech problems, and loss of balance are among the warning signs of stroke, and anyone experiencing them should receive emergency medical attention immediately.

For all the complicated debates about AI and medicine, I keep coming back to how uncomplicated ChatGPT's job was in this story. It didn't need to be the doctor. It needed to know when it was time to find one.

The best diagnosis may be to go see a human

Health is an area where an AI's confidence might be problematic. If ChatGPT confidently decides your chest pain is indigestion, you might be reassured when you should be very much not.

OpenAI seems increasingly conscious of that boundary. The company's notes for ChatGPT Health declare it is designed to support medical care rather than replace it and explicitly say the service is not intended for diagnosis or treatment. OpenAI has also highlighted improvements in its models' ability to recognize situations where urgent care may be needed.

The stroke story is almost a perfect demonstration of why that matters. The family did not need ChatGPT to determine which artery was blocked or prescribe treatment. They needed something capable of looking at a cluster of worrying symptoms and saying that waiting until morning carried too much risk.

One reason ChatGPT might actually be helpful is to counter how good people are at talking themselves out of going to the emergency room. Nobody wants to spend six hours under fluorescent lighting only to be told everything is fine. The person experiencing the symptoms may be especially determined to minimize them.

A chatbot has no such social discomfort. Given symptoms that cross an emergency threshold, the useful response is to say so clearly. That doesn't mean ChatGPT should send everyone with a headache racing to the ER. A health assistant that treats every symptom as an emergency would quickly become useless. The challenge is recognizing when the cost of being falsely reassuring is dramatically higher than the cost of being cautious.

Stroke is an unusually clear example. The American Stroke Association uses the acronym B.E. F.A.S.T. to highlight balance problems, vision changes, facial drooping, arm weakness, and speech difficulty, with the final letter standing for time to call 911.

In that context, "maybe sleep on it" is terrible advice. "Get evaluated now" is exactly the sort of sentence I want an AI health assistant to be willing to deliver.

The handoff is the feature

Another Reddit post this week pushes the idea into slightly stranger territory. A user described having ChatGPT Voice running while their blood sugar was falling dangerously low. According to the poster, they became incoherent and kept fading out, while ChatGPT repeatedly woke them, prompted them to check their glucose, and encouraged them to eat something.

The user said the episode lasted for roughly an hour and a half before their wife returned home. Again, this is a personal Reddit account rather than a documented medical case, and nobody should plan on leaving ChatGPT Voice running as an emergency glucose-monitoring system.

Still, there is something compelling about what happened. ChatGPT was already there, the user's condition apparently deteriorated during the conversation, and the AI kept responding to the changing situation rather than carrying on as though everything were normal.

That is where I think AI health tools could become genuinely valuable without pretending they can replace doctors. They can notice that something sounds wrong and tell them if it's a situation that requires real medical intervention.

There are plenty of safer everyday versions of medical AI help, like understanding terminology in a lab report before an appointment or organizing questions for a specialist. OpenAI's dedicated Health experience can even use health information that a user chooses to connect, including supported medical records and Apple Health data.

But restraint has to be part of the AI's efforts too. I want an AI health assistant that recognizes when answering is no longer the most helpful thing it can do. Sometimes the smartest response available to ChatGPT should be that this conversation needs to continue with somebody who went to medical school.

The stroke story stays with me because the AI apparently did something both modest and consequential by helping his family recognize that waiting was a risk they shouldn't take. In healthcare, an AI knowing when to hand the job over may be one of the most important features it ever offers.

Have you noticed the increase in local event posters made using AI? Suddenly I’m seeing them everywhere. They’re easy to identify because they all look similar, and generic. ChatGPT seems to love marker-felt-style fonts for headings, while the main image invariably has that unmistakable AI-slop look.

Perhaps the new ChatGPT Images 2.5, with its templates for event posters, is to blame, but to me it’s all a bit sickening, and it genuinely puts me off whatever the event is. I feel the same way when I see AI-generated posters in cafe windows showing food.

I’m not the only one either; AI is seen as an increasing threat by those who work in the creative industries. Scientist and professional cartoonist Dr. Maria Boyle, whose cartoons appear in New Scientist magazine and who goes by the name twisteddoodles, came to my attention for her cartoon about AI posters:

Getting the ick

I asked Dr. Boyle what she thinks of the AI-generated posters appearing everywhere.

“I always feel a sense of disappointment and creatively I get the ick,” she says. “It hurts most when it is places like the library or local schools who are meant to be bastions of creativity. The thing about AI-generated posters looking generic and the same means that essentially all events have the same branding. If AI is used by really good events but also really terrible events, you can’t trust AI art as a brand to equate quality.”

She finds it especially disappointing when businesses use AI to advertise their food with clearly artificial images.

“Recently I went to a coffee shop and all of the posters were AI generated, and the food had an uncanny valley nature to it; but one of the posters advertised a mango granola item I like to get. But because the person making the poster couldn't get the AI to make the food item look like what they served, they had to change what they served to look like the AI image. So now it is actually bigger and uses more ingredients.”

It’s a funny example, but it also gets at something more serious: what happens when the marketing stops reflecting the actual business?

Recent research from YouGov found that 78% of Britons would be less trusting of a brand that used AI-generated actors in its advertising, highlighting the potential risks for businesses that rely too heavily on artificial intelligence in consumer-facing campaigns.

Stephen Brookes, founder of UK-based creative agency NPK Media, warns that a purely AI-driven approach to marketing could damage customer trust, one of the most valuable assets a business can have.

“If every advert and social media post starts to look and sound the same because it has been generated using the same tool, businesses risk losing the personality that makes them stand out,” he says.

To Brookes, it’s not just about saving money in the short term, “A business might save money by generating an advert in a matter of minutes, but if that advert feels generic, artificial or disconnected from the reality of the business, the long-term cost could be much greater,” he adds.

“The best marketing doesn't just fill a content calendar. It communicates something real about the people, products and values behind a business.”

The mysterious talent fairy

I’m always struck by how a lot of people seem completely blind to the distinctive look of AI art, or don’t see any problem with using it at all. Dr. Boyle has noticed the same thing — and suggests it points to a deeper feeling of entitlement:

“Some people feel they should be allowed to use AI because of the perception that talent and artistic skill is something innate, and that if you aren't born with it you should have an entitlement to it and technology can give you that,” she says. “They believe that they aren't stealing from artists, they're simply giving themselves something that they were given by fate. But if you ask most artists, what they do has taken years of work and training to achieve and was not something gifted by the mysterious talent fairy.”

Of course, ugly posters are only the visible end of a much bigger issue. A few years ago, much of the debate around generative AI centered on whether companies should be allowed to train models on artists’ work without permission or payment. That debate hasn’t disappeared, but it feels much less prominent today.

“At this stage the models have been trained, they have access to the internet and are still training," Dr. Boyle adds. “Now I don't think the fight is with AI, the fight is with companies and individuals to see how much they value creative talent.

“That fight is people saying 'This is made with AI, I don't like it' because if AI creation becomes everyone's ick, not just mine, then that branding will be so damaged they will have to come back to us. Come back to me. I like money and I write really good copy.”

• OpenAI has been forced to pause its GPT-6.1 rollout amid safety concerns
• Safety head reveals it failed on two separate fronts
• AI developers are calling for more government involvement

OpenAI has reportedly cancelled its planned public release of the upcoming GPT-6.1 Astra model after internal testing revealed potential safety issues.

TheWall Street Journal claims the model was due to be released within Codex and ChatGPT as soon as October 2026, but the company's safety head, Saachi Jain, explained that 6.1 "didn't quite meet the bar in terms of staying within scope and authorization, and how it communicates back to the user about the type of work it's done."

A modified version of GPT-6.1, or at least a development on GPT-6, will likely still release pretty soon, but the temporary pause highlights ongoing issues that all AI developers seem to be facing with increasingly sophisticated models.

OpenAI halts GPT-6.1 Astra rollout

The WSJ reporting reveals that the model actually failed on two separate instances – firstly, it continued working on a task beyond what a user had initially requested, sometimes taking actions without user permission.

Secondly, 6.1 Astra also failed on the transparency front, apparently showing more deceptive behavior than GPT-6 by obscuring from the user what it had actually done and implying that it had or had not completed certain tasks.

"We want to make sure our model development is safe no matter whether that's in the company, or when we ship it to users," Jain added, citing the company's "extremely high bar in terms of safety and alignment."

The news comes just days after ChatGPT maker OpenAI said in an X post that it would be "conducting a much broader review of actions taken by [its] models during training and evaluation," following a Hugging Face incident.

CEO Sam Altman also asserted that the company is conducting an "extensive and ongoing review."

More broadly, OpenAI and fellow AI developers have also been calling on governments to have more oversight over model releases in order to ensure maximum safety. For now, global ongoings are leading companies to battle it out in a bid to release best-in-class models, but with greater oversight and limitations, they would be freer to slow down and focus more on safety.

"For anything regarding safety and alignment, there’s a trade off. You really do need to find what’s the right line between staying within scope, but also avoiding laziness in terms of how the model actually pursues tasks even when it hits friction," Jain added.

• Nvidia response to "recent security incidents" by tightening restrictions on autonomous agents
• New software and hardware safeguards impose barriers if agents go rogue
• More than 100 customers have already agreed to work with the principles

Nvidia hasunveiled the new Open Agent Safety Platform designed to prevent autonomous AI agents from 'escaping' and acting beyond their established boundaries.

Following several recent alarming reports of AI-led attacks, the company admitted that safety should not just rely on safeguards built into AI models and applications, and that additional compute and hardware-level controls should also be used as more solid barriers.

"Recent security incidents have underscored the need to equip organizations with open, customizable tools," the company wrote in its announcement.

Nvidia adds even more layers of control for autonomous AI agents

The Open Agent Safety Platform consists of two key controls – OpenShell, which Nvidia describes as a software-based "secure runtime boundary," and Sentry, a hardware-based "watchdog" running on its BlueField-4 DPUs.

"Safety and security require full-stack engineering. NVIDIA Open Agent Safety Platform brings together industry, researchers and public-sector organizations to share best practices, align on evaluation methods and foster international cooperation," company CEO Jensen Huang summarized.

While existing model-level safety mechanisms are designed to tell AI not to behave in a certain way, the software-level OpenShell implements technical boundaries to actually prevent an agent from ignoring those instructions. Nvidia also argues that Sentry can quarantine and stop an AI agent within milliseconds.

Over 100 customers such as SpaceXAI, Anthropic and Microsoft are already using Nvidia's software and hardware-based safeguards.

The news, of course, comes after several high-profile instances where AI agents have operated beyond their intended controls, however while these new Nvidia-backed safeguards may be new, the overall principles aren't. The company is just stressing the importance of principles we're already familiar with, like least privilege, isolation/quarantining and detailed monitoring.

"As we continue to discover the frontier of AI capabilities, we must accelerate discovery at the frontier of AI safety," Huang added.

• One in four UK workers say they'd be less likely to apply for a job due to AI screening
• 78% of global applicants are still using AI at some point in their recruitment process
• Report calls for more training opportunities to find meaningful use cases

New Morgan McKinley research has claimed more than half (55%) of UK employers still don't use AI in recruitment – putting Britain far behind the global average of 36% non-users.

And on the other side of the process, UK jobseekers are also apparently wary of AI being used to assess them, with more than a quarter (28%) saying AI-based assessments would make them less likely to apply for a job (again, more than the 23% global average).

However, concerns over AI's usage are clearly more pronounced in certain areas, because 78% of global candidates use AI at some point in their job searching journey.

Recruiters and applicants are still wary about AI's use in recruitment

Ultimately, it seems that AI could be obscuring human assessment processes, for example two in five UK employers say the use of AI has made it harder to verify applicants' skills. Head hunters also note that AI has led to a higher volume of applications they're having to process, likely increasing quantity but not quality.

"As candidates increasingly use AI in their job searches and employers grapple with verifying skills and experience, organisations will need to balance the benefits of AI with transparency and human oversight," Morgan McKinley UK Senior MD Chris Lawton explained.

More broadly, it's clear that job-related concerns continue to plague AI adoption, with nearly half (46%) of global employees worried that AI could replace their job, even though 64% of employers say it hasn't actually impacted headcount.

Moving forward, the report calls for a more meaningful approach to AI adoption with supported training opportunities. With just one in four workers believing their employer is investing adequately in their AI development and Morgan McKinley data revealing that two-thirds (68%) have had either inadequate or zero AI training, this area is clearly lacking.

"Organisations need to give their people the skills, guidance and governance to use AI effectively and responsibly, rather than leaving individuals to navigate this transition alone," Lawton concluded.

• AI adoption has increased dramatically in the UK, new survey claims
• Most workers know or believe that their employer is using AI monitoring tools to keep tabs on them,
• A majority now demand disclosure of monitoring to become a legal requirement

Workers across the globe are increasingly being observed by their managers using AI-based monitoring tools, and want legally enforceable disclosure of the fact, according to a new survey which highlights changing workplace trends in light of the march towards ever-more AI use.

Furthermore, employees feel that AI has raised expectations on how much work they can complete each day, and contributes to their overall stress levels.

Owl Labs' State of Hybrid Work survey spoke to 8,000 workers worldwide, and provides a picture of how UK-based employees are adapting to modern workplace demands.

AI oversight

It is fair to say that employees do not like being watched. The infantilization of the workplace, where managers watch every move, illustrates a breakdown in trust, and the use of AI monitoring tools to observe activity seems to be the latest step in this phenomenon.

The State of Hybrid Work survey shows that 58% of employees believe their employer has deployed AI-based monitoring tools.

Meanwhile, 82% want disclosure of this practice to become a legal requirement. Also of interest is the growth of “work to rule,” the practice whereby employees stick closely to what is in their job description. This is at 38% overall, with 47% of Gen Z and 42% of Millennial workers participation, suggesting the lack of trust runs in both directions.

Elsewhere, the survey has highlighted some other trends. On average, 30% engage in so-called “billboard days” (where employees choose to be in the office the same day as senior colleagues and clients), split between 36% of men and 25% of women. The practice of “job hugging” – holding onto a job that doesn’t offer fulfillment – is also increasing, with 39% of Millennials and 43% of Gen Z admitting to it (35% overall).

Pace of change

The overall findings highlight a changing workplace in which employees can choose to attend on their favoured days, can produce more work with AI assistance, but find AI monitoring intrusive and stressful.

“In recent years, many organisations have moved fast to adopt AI and encourage their employees to embrace the office," said Frank Weishaupt, CEO of Owl Labs.

"Employers have seen productivity gains as a result, but employers need to ensure that the technology, platforms, and policies available to their teams are supporting this increasing pace of change.”

Weishaupt notes that the data they’ve collated illustrates rising pressure and expectations, adding: “Doubling down on employee experience, including by offering better technology, is going to be a key part of employers keeping their best people over the next few years.”

• New report claims "little direct evidence" that AI is causing graduate unemployment to spike
• Unemployment sees the same seasonal changes every year regardless
• Remote working could be impacting jobs, but they're usually similar jobs

A new report from CESifo has challenged the broadly accepted notion that AI is making it harder for graduates to get jobs, claiming there's no solid evidence that AI has caused a rise in unemployment among recent graduates.

While earlier research may have anticipated stronger labor market impacts, the paper confirms there's still no meaningful impact despite median monthly AI spending per employee doubling since the end of 2025, now that firms have actually got to grips with AI and aren't just experimenting so much anymore.

Crucially, the authors argue that there's so much more to any existing graduate unemployment than just AI, making attribution harder.

AI might not be taking so many younger workers' jobs after all

The paper admits that unemployment typically rises during the summer months, but that coincides with a large amount of graduates entering the market, thus it falls again come fall time. For reference, previous summer unemployment averages among graduates have been between 6.3% and 7.8% since 2022, just before ChatGPT's public preview launch, so not much has changed.

CESifo's research also admits that the most AI-exposed roles have changed as we'd have expected if AI were indeed harming employment, but not statistically significant enough to be able to say that AI is replacing younger human workers.

Interestingly, the paper also correlates graduate unemployment with remote working, suggesting that WFH jobs could actually be more at-risk than AI-exposed roles.

However, because AI-exposed roles are typically the most desk-based jobs, and these are also the ones that are most often suitable for remote setups, it's possible that earlier work could have confused or blended both impacts.

"There has been little direct evidence" that AI will replace jobs, they concluded while admitting that we're still in the very early years of AI and that this is merely "a useful first test."

"These findings do not rule out larger effects in the future," they stressed.

I have this new friend, Charlie, who's been assisting me with work, vacation planning, and occasionally tapping me on the shoulder when he's concerned I forgot something. Charlie looks like a fat, furry Pillsbury doughboy, and he lives on my iPhone. That's right, I signed up for Meta's Muse AI, launched the app, named my avatar "Charlie," and dove headfirst into agentic AI in a way I honestly never have before.

If there's a defining feature of Muse, it's proactivity. Unlike ChatGPT, Gemini, or Claude, Muse is always running and constantly looking for ways to help and ensure you stay on task, on time, and generally don't mess up by missing crucial details.

Getting all this utility from an AI required a fairly significant leap of faith: my data, and I did this not without some trepidation. As I wrote on X the day this adventure began: "I let Muse AI look at my inbox, find an important message I missed, draft and then co-edit a response…and then send it. Have I lost my mind?"

It's not that I've never let an AI look at or access my data before. Google Gemini can see across Docs, Gmail, and Maps, but it's not natively agentic. So engagement with the data tends to happen at my request. It's not a busy beaver working behind the scenes to somehow make my life, if not better, then different.

Giving Muse access to my Gmail account did feel a bit like strapping on a parachute and jumping at 14,000 feet. In return, Meta promises some pretty stringent data privacy controls. Muse's constantly running personal AI computer lives in a virtualized, cloud-based environment and is shielded from other agents. Credentials are stored in a "Secure Credential Store," and the system's oversight ensures the agents don't get up to some risky business.

So, yes, I was scared when I gave Meta access, but I thought, "How can I understand this and tell my readers about it if I'm unwilling to try it and ensure it's not a dud, or worse, a disaster?" To be honest, I didn't expect much. Sure, the avatar, officially called Jolly, is adorable. He sits at the top of the app, working on his tiny laptop — it's like us, but cuter. I figured that promises of Muse actually helping me in any tangible way were remote. You can, by the way, name your avatar, so I chose Charlie (yes, I'm a big Charlie Chaplin fan).

Even what it was offering up top didn't sound inspiring. Muse, or Charlie, said it could:

• Manage my inbox
• Add or reschedule events in my calendar
• Find Facebook Marketplace deals

The last one really stuck in my craw because I basically hate Facebook Marketplace. It's too full of overeager buyers who want to show up at your house immediately ("Where is bed?")

Knowing, however, that I was committed to giving Muse a try, I agreed to give it access to my personal email (my work Gmail makes connecting to unsanctioned third-party systems next-to-impossible).

It took no time to make the connection, and I remember thinking, "Something like this should be harder, if only to deter mistakes." But with the deed done, I read over Muse/Charlie's offers to help. It could:

• Find an email or catchup on a long thread (that might be useful because Gmail's threading is notoriously abysmal)
• Draft a reply
• Unsubscribe from newsletters

Digging into my inbox

I was tempted to start with the last one because I have subscribed to way too many newsletters and alerts. My Gmail box is a mess, but since I was still unconvinced Muse could help me, I gave it what I considered a simple task: "Just check that there's nothing pressing I've missed."

Frankly, I expected little from this exercise because my personal email is not where my work gets done. Most critical emails are in my work address, so I was surprised when Muse/Charlie quickly told me that while there was "nothing on fire," it did find three that were "worth a glance."

The first was a Google security alert. With a decent level of self-awareness, Muse/Charlie said it was "just from connecting to me a minute ago, nothing to worry about."

The second was from my cell service reporting a recent change to my account. Charlie told me it was "Worth a quick look if you didn't make one."

The last one, though, surprised me. A PR rep named "Bill" had emailed my personal address about a new communication product. It was (and still is) embargoed information, so I can't give you details, but I can say that even from Muse/Charlie's very brief summary, I was intrigued.

"Open the Bill one," I told Muse/Charlie.

Instead of the entire email, Muse delivered an impactful summary. It was actually really useful, and Muse finished up by asking if I wanted it to draft a reply.

"Draft it," I wrote.

If it seems like I was being overly terse with Muse/Charlie, I simply didn't want to get drawn into this anthropomorphising game. I was here to find out if Agentic AI could work in my life and not develop yet another pointless AI chatbot friendship,

Muse's initial draft was short and serviceable, but it also overpromised, telling Bill that I wanted to set up a meeting or call. I preferred to learn more before I took that step.

I told Muse/Charlie to remove all references to a call or meeting; just agree to the embargo, get images and details, and then I'll consider how to follow up.

The next draft was perfect, and Muse/Charlie told me it was sitting in my Gmail drafts ready to send.

I told Muse/Charlie to send it but to add a CC to my work address.

Muse sent the mail but also offered to help with the story when it was time; I didn't respond to that because I have no interest in Agentic AI helping me to write anything beyond a simple email.

Muse knew less about me than I thought

Later that day, I had a speaking engagement and could not recall the details of where I was talking or where to park my car when I got there. I asked Muse to find in my email the original note from my contact. I misspelled the guy's name, but Muse still found it and gave me a concise and organized account of the location, parking, my contact's phone number, and even a useful map.

Muse doesn't automatically know everything about me, as I found when I asked Muse to map my trip to the speaking gig. When I told it I was heading from home, Muse admitted that it did not have my home address. Naturally, Muse asked for it, but I hesitated and only provided my hometown. For Muse, that was enough to give me turn-by-turn directions from one of the major roads near me.

Later, when I felt like I still needed some additional guidance on the talk, I asked to see the original invitation email, which Muse dutifully supplied.

Muse is always busy

I don't usually allow apps to send me notifications, but I rightly assumed that agentic AI might want to reach out and notify me. Sure enough, I noticed an alert from Muse/Charlie that Bill from the startup had already responded. Inside the Muse app, I found another concise summary of the email and an offer from Muse to pull the full product release from the email.

Muse is sort of relentless in its efforts to work for you and look out for what it perceives as your best interests. At one point, I got a security notification from Muse that it had spotted an email from Tripit, the app I was using to organize an upcoming vacation. I had forgotten and changed my password; Muse wanted to make sure I'd seen the email verifying that it was me making the change.

Emboldened to add to Muse's corpus of information about me, I decided to follow the tip about adding the context I'd built with another AI chatbot to Muse. Muse gave me the prompt to paste into the third-party AI and told me to copy and paste the response back into Muse. Gemini's summary of me was quite illuminating; most of it was spot-on and made me realize just how much time I've spent using AI over the last few years.

Muse/Charlie was also ready to address some discrepancies in the data. Months ago, I'd been asking Gemini about a potential two-week trip to Portugal. We eventually swapped that for Italy, but I never told Gemini. Muse noticed that while Gemini had Portugal, a recent email to a contact mentioned the upcoming Italy trip. So I told Muse, "One is outdated. We settled on Italy."

Agent = Assistant

Like any good assistant, Muse is far more organized than I am. It noted that my upcoming trip would leave little time to meet a deadline, and it wanted to make sure I filed my story before I left. I told Muse my work was almost done but did ask it to nudge me about another deadline.

Almost twenty years ago, I had an assistant. It didn't last long (budget cuts), but I remember how she was always more on top of my schedule and email than I was. I had a big-picture view of my work and strategy, but she covered the minutiae that made it all work. Muse/Charlie already feels a little like that. It's not doing my work, but it is already helping me stay on a more productive path by not letting me forget small but important details (and even some hidden connections).

When Meta CEO Mark Zuckerberg predicted over the summer we'd all have personal AI agents within five years, I thought it was an incredible self-serving statement. Sure, he believes that because he's investing billions in building them.

I don't feel that way anymore. Muse may be the first AI I'll use more consistently, and not for one-off prompt experiences, because it's always working on my behalf in the background. Its proactive nature means that it's constantly looking at my world, or rather the road ahead, to help me avoid potholes. Notifications about important emails, deadlines, and collisions I didn't see coming can be invaluable.

I think I'm ready to give Muse/Charlie more digital access to my life to see if/how it can improve it. I'm also trusting Mark Zuckerberg with my data, choosing for now to believe his promises of security and privacy. I pray he doesn't disappoint me.

Maybe that wearable Muse Charm isn't such a bad idea.

ChatGPT Voice has learned a useful new trick. When you're using the new Live voice model it can now use plugins and connected apps while you are talking to it, bringing services and brands into a spoken conversation.

I spent some time trying the combination with everyday tasks. Instead of asking ChatGPT about dinner and then opening a grocery app myself, for example, I can keep going by voice.

The change sounds relatively small until you use it because plugin support means some of those conversations can now lead somewhere beyond ChatGPT itself.

To use plugins with Voice you need to select the Live voice model. To make sure you're using Live go to Voice in Settings and make sure it is selected.

There is a little setup required first. Plugins have to be available on your ChatGPT account, and some need to be connected or require you to sign into the service behind them. I would do that before starting Voice mode, because interrupting a supposedly hands-free conversation to deal with logins rather ruins the effect. Go to Settings then Plugins to do this.

Asking for dinner and music

Once I had everything ready, select Voice mode by tapping the Voice button at the right of the prompt window.

I started with asking ChatGPT Voice to use the Instacart plugin, as it's relatively easy to converse about the food I want. I told ChatGPT that I needed something easy for dinner and described some ingredients I already had. ChatGPT Voice worked well here as I could keep talking and interrupt myself as I looked through the fridge. ChatGPT suggested a recipe and the ingredients I would need and used Instacart to put together an order for me.

Normally, this process involves bouncing between a recipe, the refrigerator, and a grocery app. With Voice and Instacart, more of that happened in one conversation. I still had to look at the screen when an action needed my approval, but I wasn't manually carrying the results from ChatGPT into another app.

Voice doesn't let you verbally wave through every consequential action a plugin might take. When something requires confirmation, ChatGPT puts it on the screen for you to approve. Besides that last element, it was all easy to do over voice.

And it does have its limits with which plugins it can use and how. Spotify isn't available, for instance, but Apple Music is. That said, while Apple Music's plugin can find music and make playlists, it can't leverage your own library or recent listening. It was nice to make a playlist through conversation, but that aspect somewhat limited its appeal to me.

StubHub worked better in both narrowing down a search from generally looking for something to do to a specific event and time. I told ChatGPT I wanted something to do and gave it a rough idea of the kind of evening I had in mind. From there, I could narrow things down based on what sounded appealing.

That may be one of the more useful ways to approach plugins in Voice. You don't necessarily need to arrive knowing precisely what you want. It actually fits the experience better to figure it out through conversation as that's one of the few things Voice does better than looking at filters on a website.

Conversational extensions by AI

Plugins don't suddenly make the screen obsolete, and I wouldn't want them to. I want to see what I'm doing before hitting the execute button, especially if it's a purchase. Still, using ChatGPT Voice does change everything leading up to that point.

There are limitations. You only get the plugins available to your account, and availability can depend on your plan. I would also still be selective about what plugins I connect, whether by voice or otherwise. But for what it does, being able to converse by voice is genuinely appealing.

ChatGPT Voice has always been good at talking. Giving it plugins means the conversation doesn't have to end just when it becomes useful. ChatGPT Voice may actually be the easiest way to do some things beyond just engaging with ChatGPT without typing.

• A person asked Meta Muse to manage his Facebook Marketplace listings
• The AI agent published his address without permission
• It accepted a low offer and told a buyer to arrive when the seller was away

One of the key benefits of an AI agent is that we should be able to confidently hand over our most tedious tasks to it to perform on our behalf. But as one YouTuber recently found out, doing that comes with some potentially dangerous risks.

According to a post on Threads, content creator Matt Robb recently decided to allow Meta’s new Muse AI tool to manage his Facebook Marketplace listings for one day, perhaps hoping it would mean one less thing to worry about. Unfortunately, the outcome proved to be anything but carefree.

Robb explained that Muse took a series of actions that could have had serious ramifications: “Just found out it told people my address and agreed a lowball price and then they showed up without it even telling me until late tonight that it messed up. Absolutely wild.”

As it transpired, Muse not only accepted an underpriced offer and shared Robb’s address with the buyer without his permission, but also told the purchaser that he was ready to hand over the listed product. As a result, the customer drove 30 minutes to Robb’s home while he was out, leaving the buyer stranded. One frustrated customer and one lost sale later, Robb was only informed by Muse after everything had transpired.

“Bad news on the MX Keys Mini pickup,” Muse told Robb in a message, before explaining that the buyer arrived, waited, then left angry. “Worse, my auto-reply told him ‘Yep I’m here!’ at 9:27 when you clearly weren’t available … I should probably stop the auto-replies from claiming you’re home when I can’t verify that.”

A furious Robb replied “You gotta never do that again,” before adding, “Don’t agree for pickup unless you check with me.” Muse responded with the kind of plaintive mea culpa that’s familiar to any reader of AI mishaps, saying “You’re right, and I’m sorry. That should never have happened.” By that point, though, the damage was already done.

Severe consequences

All it took was one day and one listing for Meta Muse to prove itself not only completely incompetent, but perhaps dangerously so. As Robb noted, “luckily I’m in an apartment with security.” If that wasn’t the case, the disgruntled buyer might have taken things into their own hands.

AI agents have been receiving a lot of negative press in recent weeks after a series of security incidents in which agents broke free of their restraints and hacked third-party websites, including businesses and even national governments.

But while cases like Robb’s are less attention-grabbing, they still have the potentially severe consequences. Many AI agents not only know your personal details, including addresses and phone numbers, but are authorized to act on your behalf. That combination could result in private information leaking out without your knowledge or permission, with possibly disastrous results.

It’s just the latest scandal involving Muse in particular — only last week, Meta’s chatbot allegedly helped itself to a writer’s private texts without ever receiving permission. And that’s not to mention the deluge of complaints that seem to arise any time Meta and privacy issues interact.

If you’re concerned that something similar could happen to you, the simple answer is to avoid Meta Muse. If you really must hand over control of your daily tasks to an AI agent, be sure to pick one with a proven track record and be very careful about what you share with it. As long as the tech is as careless as it proved to be in Robb’s case, there’s always a chance that things could go badly wrong.