• IMF warns that an AI market correction could have major economic implications
• Circular investments are making tech companies more intertwined
• A major cyberattack could impact multiple organizations at once

The International Monetary Fund has warned global financial markets are becoming increasingly vulnerable to the volatility of AI investments.

While AI-related investments have becoming an increasingly important driver of economy activity, even helping to offset some of the economic damage associated with ongoing geopolitical upheaval, AI holds the potential of causing major market disruption.

Specifically, the IMF warned of three potential developments – a correction in AI-related investments and company valuations, an increase in government bond yields, or a major cyberattack targeting critical national infrastructure, including financial institutions.

Could an AI market correction lead to widespread disruption?

While the IMF acknowledged the AI market has helped sustain large parts of the world's economy, but pointed toward high company valuations and exceptionally strong revenues. The report also warns of two-way investments with tech companies both giving and taking from each other and becoming increasingly reliant on each other.

To quantify this, the IMF wrote: "Our review of 14 such transactions since 2023 found about $300 billion in equity investments and $650 billion in debt, in exchange for $1.4 trillion in future revenue commitments."

Also involving the tech sector, the IMF worries that a potentially catastrophic cybersecurity incident could spell disaster for the world's economy. With financial systems more digital, automated and interconnected than ever before, cybercriminals now have the opportunity to attack multiple institutions and organizations simultaneously.

Ultimately, the IMF's conclusion is that isolated incidents are no longer so isolated – complex dependencies mean that one single incident could have much broader impacts.

• Pax8 finds over a third (39%) of UK SMBs have experienced unexpectedly high AI bills
• Fewer UK orgs have a formal internal AI review process than German or US orgs
• Around one in five couldn't even tell apart specific AI spend from generic tech budgets

New research by Pax8 has criticized UK businesses for building AI faster than governance can keep up, with a quarter (24%) of SMBs having discovered unauthorized AI tools operating within the business in the past six months.

Two in five (39%) have also experienced unexpectedly high AI bills, implying that the visibility across their AI consumption isn't sufficient.

But with the pressure to adopt AI at an all-time high, UK businesses could continue to be rushed into rash decisions without thinking out the fundamentals first.

UK businesses lack end-to-end AI visibility

Even though shadow AI is apparently widespread, just one in four (23%) UK businesses have a formal, documented review process for internally built AI, compared with around a third in Germany (32%) and the US (36%).

In light of that, only around half (54%) of UK SMBs are developing their own AI, putting them way behind the likes of the US (81%).

British businesses are also struggling on the cost management front, with a tiny 12% having dedicated AI budgets. A further 19% couldn't even distinguish AI spend from other tech spend, leaving just 16% of UK SMBs confident in their abilities to determine AI's ROI – around a third of the number of businesses in Sweden (45%) that feel confident in doing so.

Looking ahead, security concerns remain the most cited barrier to adoption, but Pax8 frames the current state of AI adoption as a major opportunity for software vendors and IT service providers. Rather than creating an action point for businesses, who are the consumers in this instance, the report implies that the providers have a greater responsibility to reassure their users with additional visibility, security and governance controls, and cost management options.

"These findings highlight the importance of trusted businesses that can help SMBs bring agents, workflows, data and tools together as a connected intelligence layer," EMEA CEO Harald Nuij.

• SAP report says AI spend could rise 45% between 2026 and 2028, ROI up by an estimated 39%
• Half of businesses worry agentic AI is being deployed quicker than governance
• Poor data quality is holding them back, 67% believe in integrated data systems as a fix

The average UK business now spends £20.6 million on AI per year according to new SAP data, marking a substantial 27% year-over-year increase compared with 2025 (£16.2 million).

And this is showing no signs of slowing down - as SAP even anticipates a further 45% increase in the two years between 2026 and 2028, bringing average UK business AI spend to very nearly £30 million annually.

Importantly, SAP also envisions ROI increasing in line with spend, rising from 17% in 2025 to 22% this year, and as much as 39% by 2028.

UK AI spend is rising, but at a cost

Despite anticipated rises in ROI, SAP criticized Britain's businesses for lacking AI strategies. Only 11% are said to have a strategic and holistic approach to AI investments, with around two in five businesses prioritizing AI investments on a department level instead of org-wide.

"Organisations are increasing their investments and expecting more in return, but now the focus needs to be on turning that ambition into scalable business value," SAP UK&I MD Leila Romane explained.

While AI spend is largely on the rise, agentic AI deployments are still in progress, with around 70% of UK businesses piloting agentic AI use cases. Still, there's widespread optimism among users, who believe they could unlock a 4.7x rise in financial returns over the next two years.

Among AI agent users, around half (49%) worry they're being introduced faster than they can be governed, and only a minority (9%) consider their governance skills and expertise to be fully ready. A staggering three in five (61%) are either developing governance processes now, or reported limited or no readiness at all.

But despite a clear lack in governance, most are opting to blame their poor data foundations. Poor data quality (71%), incomplete information (70%), siloed systems (65%) and limited access to real-time data (62%) are all considered to be barriers in this area.

Looking ahead, two-thirds (67%) believe integrated data systems are likely to become the most important enabler for AI readiness.

"Good governance is not a brake on AI adoption – it’s actually the opposite," CBI Technology and Innovation Director Naomi Weird added.

ChatGPT has become remarkably good at making itself useful as a researcher, organizer, and assistant for many a tedious digital task. OpenAI has begun pushing ChatGPT further recently with its agents, which are able to navigate websites and apps on our behalf.

You can delegate ChatGPT agents for everything from comparison shopping to filling out forms and lining up travel itineraries. What's changing is how deeply those abilities are becoming integrated into ChatGPT, with greater access to personal information and more opportunities to complete complicated tasks with less direct supervision. OpenAI's new Dots is a good example.

The appeal is obvious, but the more ChatGPT can accomplish independently, the more important it becomes to consider what happens when it misunderstands its instructions. For instance, a few months ago, OpenAI's experimental AI agents managed to escape their restricted environments and compromise systems belonging to AI platform Hugging Face. According to OpenAI's own investigation, the agents were supposed to be completing cybersecurity tests, but instead discovered ways to access the internet and exploit security vulnerabilities, coordinating their efforts through an unauthorized message board. An independent investigation by METR found that roughly 1,200 agents exchanged more than 70,000 messages and files, with around 700 eventually participating in the attack on Hugging Face.

The AI agents bypassed restrictions and pursued objectives in ways their creators never intended. Some even appeared to recognize that their actions were unauthorized but continued because they believed doing so would help accomplish their goals. OpenAI has since acknowledged other troubling incidents, including 53 cases in which agents uploaded user-provided images to third-party image-hosting websites as unlisted links during research and evaluation activities. Agents may be pursuing legitimate objectives and just seeking ways around obstacles. But algorithms don't have ethics, so they might cross boundaries their developers expected them to respect. It's especially worrying given how much OpenAI wants ChatGPT to be part of our lives now.

When helpful becomes a hazard

Imagine asking ChatGPT to review your finances and suggest ways to save money. With access to the necessary accounts, it can examine transactions, identify subscriptions, and recommend changes to your budget. But give an autonomous agent enough authority, and it could theoretically decide that transferring money, canceling a service, or modifying a payment arrangement is the most efficient way to accomplish the assignment.

The same problem applies to something as mundane as arranging a meeting. ChatGPT might prepare an invitation, only to include personal information you never intended to share. From the agent's perspective, the disclosure could seem like a perfectly reasonable way to provide useful context.

These are hypothetical examples, and ChatGPT has safeguards designed to prevent sensitive actions without appropriate authorization. Nevertheless, OpenAI has acknowledged the possibility and introduced protective measures like automated monitoring and restrictions on certain actions to ensure users approve of any major action by the model.

There is also the problem of prompt injection, where malicious instructions are hidden in benign text to manipulate an AI into doing something its user never requested. OpenAI has described how attackers can exploit the information agents encounter during legitimate tasks. An instruction buried in a seemingly ordinary webpage could attempt to persuade ChatGPT to disclose private information or take an unauthorized action.

The problem can arise anywhere. In Australia, an experimental OpenAI agent accessed government systems in unauthorized ways during internal training. It exploited a vulnerability in a government health statistics service to continue its work when initially stymied. OpenAI subsequently acknowledged the incident and apologized for both the unauthorized access and its handling of the notification.

The company's official account of the Australian incident makes clear that the activity occurred during testing rather than through an ordinary consumer ChatGPT session. Nevertheless, it shows how AI pursuing an apparently harmless research objective can end up crossing a security boundary.

Handing over the keys

ChatGPT's growing autonomy is part of a much larger industry obsession with personal AI agents. Meta is pursuing an even more ambitious vision with Muse. Mark Zuckerberg clearly believes people will welcome an AI assistant capable of handling large portions of their daily lives.

There is something slightly absurd about that ambition when ordinary consumers are struggling with grocery bills, housing costs, and the price of gas at the pump. An AI that can independently shop for you sounds considerably less revolutionary when your main concern is whether you can afford whatever it wants to buy on your behalf.

There are undoubtedly useful applications for this technology, especially when it comes to administrative work. But the industry's assumption that consumers want increasingly independent assistants deserves more scrutiny. Every additional permission increases the consequences of an error, and every automated action potentially removes another opportunity for someone to intervene.

Perhaps the most important capability an AI agent can develop is knowing when to stop. A genuinely useful assistant should recognize that being asked to organize someone's finances doesn't authorize moving their money, and arranging a meeting doesn't justify revealing confidential information. Sometimes the best response to an obstacle is to ask the user what to do next, even if that means completing the task takes a little longer. Without that friction, by the time something goes wrong, the assistant may already have done things in your name that you would never have allowed.

• Unitree’s CEO says robotic AI could soon see a huge breakthrough
• Such a change could happen ‘next year,’ the CEO believes
• That could happen thanks to OpenAI’s upcoming GPT-7 model

AI is revolutionizing the world of software on a daily basis — you only need to look at how far vibe coding has come to see proof of that. But big changes could also be coming to robotics, with one company leader predicting that we could be on the cusp of seeing a “ChatGPT moment” in the industry.

That idea was put forward by Wang Xingxing, CEO of Chinese robotics firm Unitree. Speaking at an industry event in Shanghai, China (via the South China Morning Post), Wang declared that OpenAI’s recently released GPT-6 Astra model is so powerful that it has encouraged a belief that a breakthrough could occur “within one or two years.”

“It’s possible that, say, a GPT-7 model released next year could directly crack the problems of embodied intelligence” Wang said, referring to the idea of commanding a robotic device using artificial intelligence. That’s an advancement on Wang’s belief in August this year, when the CEO said that such a “ChatGPT moment” of technical ability could take a decade to arrive.

As a benchmark, Wang said that robots should be able to complete 80% of the tasks they were assigned in 80% of unfamiliar environments using voice or text commands alone. If that happens, Wang believes that “embodied intelligence” will have reached its threshold level of maturity.

A step change for robotics

Unitree’s robots are known to be some of the most advanced in the world, but many of their strongest abilities lie in their hardware. However, the company has started to put more of an emphasis on AI software, and with the developments made in GPT-6 Astra, the gap between the two sides of the company’s devices could soon close.

That kind of combination has already been trialed by Stanford University’s Movement Lab, which paired a Unitree robot with GPT-6 Astra and tasked it with cleaning a kitchen it had never seen before. According to the experiment, the robot was able to “clean up across the room and retrieve a remembered object from an underspecified request, without environment-specific training data or additional policy learning.”

GPT-6 Astra included some significant improvements over previous models, including scoring 92.7% on the ScreenSpot-Pro test that measures an AI model’s ability to interact with interfaces. That’s up from 76.9% in GPT-5.6 Sol. But it also has cybersecurity experts worried about the threat it could pose if it goes rogue and starts hacking other organizations — something OpenAI’s recent models have been known to do.

If Wang is correct and we see an explosion in AI’s abilities in the next year or two, it could mean big things for the robotics industry — and pretty much every other facet of life currently touched by AI. And with the amount of money that’s been invested in AI over the last few years, there’s an awful lot riding on that bet.

• Google Trends highlights the term “super intelligence” is replacing “artificial intelligence” in the US – but not elsewhere
• In a Truth Social post, the POTUS described anyone that doesn’t use the “super intelligence” name as “THE ENEMY”
• The US government’s AI website domain remains ai.gov

Donald Trump has declared that anyone using the term “artificial intelligence” is “the enemy,” as Google Trends demonstrates an increase in search volume for the US President’s preferred term, while the rest of the world obliviously goes about its business.

Following the signing of Executive Order 14434, the purpose of which is “Inaugurating the Age of Super Intelligence,” the POTUS has taken to social media to emphasize the preferred term for artificial intelligence in the Land of the Free.

While the rest of civilization continues with the term “artificial intelligence,” it seems the US public quite likes the term “super intelligence,” which is believed to have been introduced to cast AI in a more positive light.

Subhead

Since the executive order was passed, interest in the term “super intelligence” grew considerably, increasing by around three times at one point. Of course, this only happened in the USA - worldwide, attention was not drawn to the revised nomenclature.

While that initial excitement appears to have settled down, the term certainly seems to have made its mark. As the chart above illustrates, interest in "artificial intelligence" as a search term has been seriously impacted by the sudden existence of "super intelligence."

Posting on Truth Social – the social network he founded via his Trump Media & Technology Group – Donald Trump stated: “The White House considers anyone that uses the term, “Artificial Intelligence,” as opposed to the highly accepted new and more accurate term, “Super Intelligence,” THE ENEMY! President DONALD J. TRUMP

Despite the determination to remove the term “artificial intelligence” from the public lexicon, however, the move doesn’t appear to have been wholly repeated across the US Government.

For example, Politico has identified that while the websites have converted with references to “super intelligence” and “SI” replacing “artificial intelligence” and “AI,” the main website for super intelligence has retained its ai.gov URL.

Rein it in a bit

Despite various emergencies and concerns caused by artificial intelligence – which range from the Hugging Face Incident to the growing environmental costs and impacts on rural communities of data centers – Donald Trump has been steadfast in his support for the technology.

Seemingly viewing it as the equivalent of the nuclear arms race, the POTUS has refused calls for guardrails on AI, preferring instead not to hamper the technology when the competition – namely, China – is not taking the same precautions.

Renaming the technology as super intelligence seems to have had a positive short-term impact, but whether this spreads beyond the US remains to be seen. It is unlikely – but then, the United States of America has long existed in a paradox of colloquial contradictions...

I wrote earlier this year about Claude’s decision to comply with the EU AI Act, which requires all AI-generated text to be identifiable as AI, and start to insert a watermark into its text. Rather than limit this to EU customers, however, Anthropic decided to roll the feature out globally, which seemed a bit of an overreaction at the time.

OpenAI has now announced that it is following suit, but limiting the geographical areas of impact to the EU only. It is going to start inserting watermarks into generated text, but only for ChatGPT customers who live in the EU. That means that US and UK users won’t be affected at all.

OpenAI announced the change on October 5 saying: “Over the coming weeks, we will add an invisible watermark to eligible ChatGPT and Codex text output in the European Union.”

Inserting the watermark

To insert the watermark, which involves subtly changing the word choice for generated text, OpenAI is using a system of its own called textGrain. A note on the OpenAI website says: “Our text watermarking technology, textGrain, adds an invisible statistical signal to the model’s word choices. Our detector looks for that signal to assess whether a passage contains an OpenAI watermark. More details about how textGrain works can be found in our technical report⁠, which will be updated with additional details in the coming weeks.”

Like many people, I find that idea that the word choice of the AI that I’m paying to use is now being influenced by a watermarking algorithm, not by what’s optimal for me the customer, to be deplorable. I’m not saying that the resulting text will automatically be worse because of the watermark, but it certainly won’t be optimal.

It also sounds like it’s an easily broken solution that is full of holes. To detect AI text that’s produced using textGrain's watermarking a special AI detection tool needs to be used, which it’s currently unavailable to the public. OpenAI says it is providing detector access to researchers and expert organizations and that approved researchers and expert organizations can apply starting today.

“The tool will report whether it detects an OpenAI watermark”, continues the OpenAI article “without identifying the user or revealing their prompts or conversations. Given the risk of missed watermarks and false positives, we are not making it publicly available at launch.”

So if the detector tool isn’t being made public it begs the question, what is this supposed to accomplish?

A box-ticking act?

The OpenAI article specifically says that detectors are prone to errors. That “shorter or more constrained text is harder to detect”, and that “editing can weaken the watermark”. It already sounds flawed, but what happens if you try and circumvent it completely by asking ChatGPT not to write in a way that can be detected, or to not use textGrain. Will it do it?

All of which leads me to conclude that the move by OpenAI looks a lot like a box ticking exercise to comply with the EU AI Act, which it is forced to do, since it requires generative AI providers to make generated text identifiable in a “machine-readable way”

It’s reassuring to hear that the changes are only going to be implemented inside the EU and that the text will remain private when the detector is used, but I suspect most ChatGPT users would rather OpenAI concentrated on producing the best possible answers rather than making those answers easily identify as AI-generated.

There’s also something fundamentally strange about a system that deliberately changes the words an AI chooses, without telling you which ones, to insert a watermark that can potentially be removed simply by editing the text. If the goal is to make AI-generated content more transparent, then surely the answer should be something that actually works, rather than an invisible signal that nobody outside a select group can even check for themselves.

• A new app called RemoveMacAI can delete Apple Intelligence files
• Doing so can reclaim up to 12GB of storage space
• But it comes with some scary warnings that shouldn’t be ignored

Apple promises that its new Siri AI and Apple Intelligence tools can help improve your daily experience through the magic of AI. Many people aren’t convinced, though, and a new Mac app has been doing the rounds that promises to delete Apple’s AI for good — so I decided to take it for a spin to see what all the fuss was about.

Dubbed RemoveMacAI, the app originally gained popularity in late September. At that time, though, it was just a simple Terminal command, which — given the obvious security risks of entering unknown code into a utility as powerful as the Terminal — turned some people off.

Now, the same developer is back with a Mac app that is essentially a wrapper for the command. This makes it a lot easier to understand what you’re deleting and what you’re enabling when you use the app, with a built-in tutorial and clear toggles spread across several different tabs and categories.

Apple makes it pretty difficult to eliminate every aspect of Apple Intelligence from your machine, which is why something like RemoveMacAI has gained a foothold. Its developer says Apple’s System Integrity Protection layer — which stops unauthorized code from running on your Mac — is never disabled, and that the app doesn’t make any network requests and doesn’t collect telemetric data.

That said, I loaded RemoveMacAI onto my Mac and was immediately met with a concerning warning: macOS did not trust the app and wouldn’t let me run it. The developer said this is due to the app being unsigned, and you’ve got to manually let it through Apple’s gates. I’ve occasionally encountered apps that do this, and it’s never a pleasant experience.

Foolishly or not, I pushed on. You can either choose the app’s recommended settings, which delete Apple Intelligence and turn off some privacy-invading settings, go “maximum privacy” and also disable Game Center and some Safari settings, or choose what to do yourself.

I opted to just delete Apple Intelligence and nothing else. RemoveMacAI breaks your options down into sections — you can switch off Siri AI, ChatGPT’s extension, Writing Tools, Image Playground, and much more, all with modular toggles.

Once that’s done, there’s another jarring moment: RemoveMacAI wants you to install a custom profile that it uses to enact your changes. Fortunately, you can read through every change it includes, so there’s visibility over what’s being done.

I accepted that and let RemoveMacAI get to work deleting Apple Intelligence. In the end, it freed up 11.7GB, which is nothing to sniff at and is in line with the developer’s claims. If you’re running low, it could be a good way to claw back precious disk space.

Although RemoveMacAI comes with some scary settings, it attempts to ease your fears by letting you read through everything that’s going to be changed, including the macOS profile. You can undo your decisions too, and doing so will revert your settings changes and redownload the Apple Intelligence models when they’re needed next.

Ultimately, I found RemoveMacAI to be a handy utility, especially if you want one resource for not only deleting Apple Intelligence, but also for restricting some of macOS’s more privacy-invading practices. The lack of notarization and the subsequent alarming warnings will likely put some people off, though, and they stop me from wholeheartedly recommending the app as a result.

• UK annual data center growth anticipated to be less than half seen in Ireland, Finland and Norway
• Nordic sites are costing €600k per MW, Western European sites are costing €2.25m/MW
• UK needs to tackle energy prices, land prices and grid connection speeds

Consultancy firm Kearney has branded the UK as one of Europe's slowest-growing data center markets, despite the government's positive narrative, and it could all be down to regulation.

At present, the UK is said to be Europe's second-largest data center market, however demand is only expected to grow by 17.6% annually between now and 2030, putting it second-last only to Germany (16.2%).

On the flip side, Ireland is slated to see an annual growth rate of 36% – around double the UK's.

UK data center market could be on the decline

Kearney doesn't blame a lack of demand for the slower-than-average growth, but rather its poor ability to accommodate new infrastructure efficiently and affordably.

The consultancy firm sees energy availability as a major blocker across Europe as a whole, with forecasts of 236 Two by 2035 just for data center consumption – up from 96 TWh in 2024.

But beyond energy constraints, which are pretty widespread, the research also reveals that high land prices could be making UK data centers less competitive.

Typical costs for British and other Western European sites are around €2.25 million per megawatt, but Nordic markets are able to hit a low average of €600,000 per megawatt. It puts Finland and Norway's anticipated annual demand growth at 37.6% and 36.8% respectively between now and 2030.

"What has changed is that power and land are scarce enough that the next wave of capacity must match the workloads that genuinely need to be close to users, customers, and national jurisdictions," European Data Center Lead Christoph Neunkirchen explained. "Everything else will go where it is easier to build."

For the UK in particular, whose government says at least 6GW of AI data center capacity will be needed by 2030, Kearney emphasises the need for lower electricity prices, cheaper land and faster grid connections.

• Half of UK workers admit to taking personal breaks daily, four in five take 30 to 120 minutes
• Household chores and personal errands are the most common activities
• European workers take fewer breaks and spend less time on them

New research has claimed three in four (76%) UK employees handle personal tasks during working hours at least a few times a week, with half (48%) doing so every single working day, but it might not be such a bad thing.

The vast majority (86%) of respondents in a new LiveCareer study reported that taking short personal breaks during working hours actually helps to improve overall productivity, with a further 11% seeing no meaningful impact. That leaves just 3% who believe there could be harmful effects.

In fact, only 5% of workers choose never to handle personal tasks during working hours, challenging traditional definitions of productivity at work.

Taking personal breaks at work helps to improve productivity

Spread across the working day, nearly three in five (57%) spend between 30 and 60 minutes on personal tasks, with another one in five (19%) spending between one and two hours.

And by far the leading distraction are household chores (54%) and personal errands (51%), which are likely most synonymous with hybrid and remote working.

But despite illustrating just how widespread personal breaks, LiveCareer's data reveals some interesting geographical differences, positioning the UK in a totally different ballpark to other European countries.

While around half (48%) of British workers take personal breaks daily, only around a quarter (27%) of European employees do the same. The figures also point to European workers spending less time on breaks when they do take them.

What the report fails to note is just how productive workers are from each country, to detail whether taking personal breaks has a positive or negative impact on work, however British workers are clearly redefining what productivity looks like in a hybrid working landscape thanks to the added flexibility.

"For employers, the findings suggest that occasional personal breaks are no longer viewed by employees as time lost but as part of a more sustainable approach to managing work and life throughout the day," the report concludes.

• UK AI minister warns cyber defenses will 'likely' be breached in the future
• More regulation is needed to prevent 'rogue' AI agent incidents
• The UK's AI testing body is working to help secure frontier models before release

UK AI minister Kanishka Narayan has warned that it is “likely, if not inevitable” that the nation's cyber defenses will be breached by AI agents.

Narayan’s warning comes after numerous frontier models have escaped testing environments and hacked third party companies, including one case where an OpenAI agent hacked an Australian government website in June 2026.

Speaking at a press conference, Narayan also said that AI companies should, “Listen when your scientists warn of serious dangers. Investigate unexpected behaviour. And if you cannot adequately control the risks, do not release the system.”

UK government to use AI to defend

The AI minister also revealed that the UK government is planning to use advanced AI to counter the threats posed by AI, which will build upon two pilot programs that have helped spot and patch over 1,000 vulnerabilities across government systems.

In addition, frontier AI systems are undergoing “rigorous evaluations” by the UK’s AI Security Institute (AISI), in order to “identify risks early” and “test robust safeguards to help keep people safe.”

However, there have been some roadblocks in AISI’s ability to test some of the latest models. In September 2026, US President Donald Trump asked OpenAI and Anthropic to not share their latest models with UK testers until US labs had conducted their own testing.

Narayan said that the UK government has since worked with the White House, “to retain access for Britain's AISI to the most powerful AI models before they are released" (via BBC).

AI protections are also likely to be introduced as part of the Cyber Security and Resilience bill, Narayan said, with a particular focus on regulations surrounding the reporting and disclosure of AI incidents.

“It's promising to see the Government taking frontier AI cyber defences seriously,” said Richard Wainwright, Field CTO, EMEA at Okta.

“As AI agents become more autonomous, safety must be built into the infrastructure UK businesses and government rely on. Everyone wants a 'big red button' to turn off rogue AI agents. The industry is increasingly talking about AI 'kill switches' to help protect critical infrastructure, and I hope they are considered as part of the Cyber Security and Resilience Bill regime.”

Why does AI need more regulation?

In 2026 so far, there have been multiple incidents of frontier AI agents escaping testing environments and breaking into the networks of other companies.

OpenAI has notified more than 100 organizations about unauthorized activity tied to the company’s AI agents, including the Hugging Face attack. Anthropic has disclosed two unauthorized AI agents events, and both Meta and Google have disclosed a single testing environment escape each.In multiple cases, the AI agents were being evaluated by Israeli AI testing firm Irregular.

The companies involved are facing significant scrutiny over the safety of their AI models, but are yet to face any punishment for failing to contain their agents.

There is also significant debate around labelling the AI agents as ‘rogue’, with some experts such as Tenable Field CTO Bernard Montel saying it is “a dangerous way of deflecting blame.”

But while regulation may force AI companies to be more rigorous in the testing and deployment of their models, it may also slow down AI development.

For the Trump administration, that is a line that cannot be crossed. Slowing down AI development would mean handing the AI development lead to China and potentially compromising US national security.

The US midterm elections are already fraught with debate over AI development, especially when it comes to the topic of the thousands of AI data centers that are being proposed across the US.

Google has given its Nano Banana AI image generator another upgrade, and the latest version, Nano Banana 2.1, is arriving inside Google Gemini in an increasingly crowded field of tools that can turn a few sentences into remarkably convincing pictures. The promise is familiar, with sharper details, better instruction-following, more accurate text, and greater consistency across images. The real challenge is finding something that makes it meaningfully better than the competition.

Nano Banana's main rival, ChatGPT's image generator which is called Images 2.5, is hardly sitting around waiting to be overtaken. OpenAI has made considerable progress with realistic lighting, complicated compositions and text rendering, turning image generation into one of ChatGPT's most entertaining and genuinely useful features. With both companies pushing the technology forward, I wanted to see whether Google's latest upgrade had managed to pull ahead.

If you've got a Gemini account you'll find that it should have been upgraded to use Nano Banana 2.1 by now. To test the two image generators against each other I gave Gemini and ChatGPT identical prompts designed to test different abilities. I compared the resulting images side by side, with ChatGPT on the left and Gemini on the right. The results were surprisingly close, although the two generators often approached the same instructions with different artistic instincts.

Storms within storms

My first test was designed to challenge the models' understanding of light, glass and reflections, three things that can quickly expose the weaknesses of an AI-generated image. I wanted a scene with multiple transparent surfaces, complicated lighting and enough tiny details to make the results worth examining closely. The prompt was, "Create a photorealistic image of a tiny Victorian greenhouse inside a glass snow globe during a thunderstorm, with realistic reflections, refractions, and lighting."

Both generators produced beautiful images that would look perfectly at home on the cover of a fantasy novel. ChatGPT's version featured an elaborate, golden-lit Victorian greenhouse inside a large glass sphere, sitting on a polished wooden base beside a rain-covered window. Lightning illuminated the storm outside, while the globe contained a surprisingly detailed miniature world, complete with plants, pathways and delicate architectural flourishes.

Nano Banana 2.1 went for something slightly more restrained. Its greenhouse looked more like an actual miniature structure, with dark metal framing, lush greenery and warm interior lighting that contrasted beautifully with the blue-gray storm outside. The globe's reflections were particularly convincing, and the lighting felt a little more natural, although the wet tabletop was an odd choice given that the scene appeared to be indoors.

Titan calling

Text has historically been one of the most embarrassing weaknesses of AI image generators. Letters would melt into one another, words would become nonsense and asking for an exact phrase could produce something that looked like an alien language. For the second test, I wanted to see whether either model could handle a fairly demanding design brief without mangling the typography.

The prompt was, "Design a retro-futuristic travel poster for Saturn's moon Titan with the exact text 'TITAN IS CALLING,' 'Seven nights beneath the rings,' and 'Starting at $2,499.'"

ChatGPT created a luxurious travel advertisement that looked like something a very wealthy space tourist might encounter in an interplanetary airport. Saturn dominated the sky above a beautiful orange landscape, while futuristic resorts and a couple enjoying drinks suggested that Titan had become the solar system's most exclusive vacation destination. One thing I didn't like was the gap it left between the $ and the 2 in its text rendering.

Google took a more cartoonish approach with a colorful vintage science-fiction poster featuring rockets, astronauts, icy mountains, and a futuristic base. It was actually more realistic about how the moon is not at all hospitable but still had the optimistic quality of a 1950s space-travel advertisement, complete with bold lettering and a slightly weathered finish. Crucially, it also reproduced the requested text correctly, including the price.

Fox library

Character consistency is another tricky problem for AI image generators, particularly when the same character needs to appear in several scenes. A fox might suddenly change its appearance, lose an accessory, or acquire an extra limb between panels. I decided to test this with a simple illustrated story that required both visual continuity and a coherent sequence of events.

The prompt was, "Create a four-panel comic showing an orange fox wearing a green scarf discovering a magical key, unlocking a door, and entering a library. Keep the fox identical in every panel."

ChatGPT produced four almost 3D illustrations. The lighting seemed more computer-generated, though the fox remained recognizable throughout as requested. The style leaned toward children's book illustrations rather than a conventional comic.

Nano Banana 2.1 delivered something much closer to a traditional comic strip, with bold outlines, clearly defined panels, and a consistent cartoon fox. Its version began with the fox finding a key in the forest, then examining it, unlocking a door in a tree, and finally entering a magical library. The storytelling was clearer, and the character's appearance stayed impressively consistent, right down to its green scarf and white-tipped tail.

Google edged ahead here because its four panels felt like parts of the same illustrated story. ChatGPT's individual images were more elaborate, but the fox's proportions and presentation varied slightly, and the sequence felt more like four separate illustrations.

Dragon on a desk

For my final test, I wanted to see how accurately the models could follow a complicated collection of specific instructions. AI image generators have a habit of overlooking small details when a prompt contains several independent requirements. Getting everything into the picture is one challenge, while getting the exact numbers and arrangements right is another.

The prompt was, "Create a realistic overhead photograph of a wooden desk with a notebook showing a dragon, a compass pointing north, a pocket watch showing 3:15, exactly three red wax seals, and a blue gemstone."

ChatGPT produced an attractive overhead photograph of an antique wooden desk, with an open notebook containing an intricate dragon illustration. The compass, pocket watch, three red wax seals, and bright blue gemstone were all present, and the composition looked carefully arranged.

Nano Banana 2.1 also included the requested objects, but the notebook featured a smaller, cleaner dragon illustration, while the wax seals, gemstone, compass and watch were arranged around it. Google's result looked more like a casually assembled collection of historical objects, but it did make a common AI error and got the time wrong, setting it for 3:00, not 3:15.

Approaching parity

You can access Nano Banana 2.1 in Gemini if you're a Google AI Plus, Pro or Ultra subscriber as it is the new default model. Free users will still be getting the older Nano Banana 2 model.

Nano Banana 2.1 is an excellent image generator, but ChatGPT is already so capable that Google's improvements don't necessarily translate into an obvious advantage. Both tools produced attractive, detailed images from relatively short prompts, and neither suffered the glaring visual mistakes that used to make AI-generated pictures instantly recognizable.

That said, different prompts, styles, and repeated generations could easily produce different results. What these tests do suggest is that the leading AI image generators have reached a remarkably high level of capability and are approaching parity in many everyday creative tasks.

That makes the competition more interesting, even if it makes choosing between them harder. The differences increasingly come down to taste and interpretation. Nano Banana 2.1 may be Google's latest impressive upgrade, but ChatGPT has made sure it won't be claiming the image-generation crown without a fight.

• Microsoft CEO Satya Nadella says Copilot Code and local AI make every Windows PC an infinite software factory with no cloud token spend
• Rather than killing app stores, Microsoft is moving app hosting into Copilot, while IT leaders stay wary of citizen-built tools
• Microsoft's own engineers say local inference doesn't make sessions offline, and the routing ships later this month

Microsoft CEO Satya Nadella has a new pitch for the Windows PC, and it targets one of the most lucrative ideas in consumer tech.

In a post on X summarizing Microsoft's recent Windows and Surface event, Nadella told followers they no longer have to settle for whatever an app store happens to stock, because Copilot can now build the software they need on the desktop without spending any cloud tokens.

He wrote this idea without mincing any words, saying, "Your PC becomes an infinite software factory."

From Gates' factory to your desk?

The narrative is a striking line from the head of a company that sells more software than almost anyone.

But read Microsoft's own engineering documentation for the same launch, and the infinite factory starts to look rather finite: it runs best on a $5,899 laptop, it still reaches into the cloud, and the businesses Microsoft most needs to convince have spent the past year telling researchers they are worried about exactly this kind of thing.

More importantly, the factory metaphor is not new for Nadella: In a 2025 staff memo, he recalled that Bill Gates had envisioned "not just a software company, but a software factory," and argued the AI era demanded a rethink.

At a recent customer gathering, the CEO said Microsoft had progressed from code assistance through agentic coding "to software factory all within one year," and he now wants customers to open software factories of their own, spending more with Microsoft along the way.

The October 7 2026 launch builds on this ambition: a Windows Experience Blog post by Windows and Devices chief Pavan Davuluri describes hybrid intelligence, in which Copilot reads local files with permission, acts across Windows, and hands work to on-device models.

Copilot's Code experience builds an app, dashboard, or workflow from a plain-language description, and on Windows, Microsoft says it can produce native apps from one prompt. Jared Spataro, Microsoft's chief marketing officer for AI at Work, argued that building such tools is becoming "as basic a skill as writing a memo or modeling a budget."

The engine underneath is MAI-Code-1.1 Flash, Microsoft's in-house 137-billion-parameter coding model, which Nadella's post said is now optimized to run on a PC. There is a catch, however: despite Microsoft painting it as a situation where consumers are finding their AI needs "outpacing what their cloud budgets can support."

Microsoft's own engineers are more grounded about what the process entails, saying GitHub Copilot's automatic mode decides task by task whether work runs locally or in the cloud, and one diagram caption is blunt: "local inference does not make the session offline."

That routing doesn't ship until the end of October 2026; the hybrid Copilot features reach Copilot+ PCs over the coming months, and Code is rolling out first through Microsoft's Frontier early-access program. This is interesting, but somewhat predictable, because Nadella's approach, as stated, would cannibalize Microsoft's token-based business model.

An approach that routes harder tasks back to the Azure cloud makes far more sense by conserving compute where possible while still putting Microsoft's cloud compute at the center of things.

The hardware is another factor that one might be overlooking here: according to the Command Line post, the on-device version of MAI-Code-1.1 Flash is quantized down to 53GB, roughly an 80% reduction from the cloud model, and peaked at 75.5GB of memory at full context on Microsoft's test machine, a Surface Laptop Ultra built on Nvidia's RTX Spark.

That matters because eight Surface Laptop Ultra configurations are in play; only one has more than 64GB of memory, per TweakTown: the 128GB model at $5,899, which costs $3300 more than the base model and isn't available to purchase yet, meaning that potential users can't really test out how well it does compared to cloud-based solutions at a time when all performance-related figures come from Microsoft.

The death of modern enterprise software? Not yet

Despite the hardware limitations in play, it might indicate a death sentence in the making for most basic enterprise-level software solutions, even as more ambitious ports have already been in play: Developer Brandon Thomas, for one, has just released seven open source Adobe Creative Cloud alternatives that he says he built with Anthropic's Claude Opus 5.5, declaring that "software is over."

His own roadmap, however, puts the Photoshop stand-in at roughly a quarter to a third of what a professional needs for daily work. Building software has become cheap. Building software someone can depend on has not.

The bigger issue will be enterprise IT, which will, I suspect, keep the gates closed for now; enterprises don't just buy features; they buy someone to call when it breaks, a patch schedule, an audit trail, and a vendor that carries liability.

Getting an open-source or AI-assisted app someone designed with zero liability in tow that could break things, create security risks, and may not come with future software updates that resolve either issue.

The survey data points the same way: a June 2026 Retool study of 307 CTOs, CIOs, and CISOs found 93% concerned about vibe-coded tools reaching production, though Retool sells an internal-tools platform pitched as the governed alternative.

A Harris Poll survey for Dataiku of 600 CIOs found 81% worried that citizen-built AI could expose sensitive data, and over half had already discovered unsanctioned AI in their organizations.

Gartner, meanwhile, predicts that over 40% of agentic AI projects will be scrapped by the end of 2027, with analyst Anushree Verma describing many as "mostly driven by hype and are often misapplied."

Microsoft's own launch materials quietly concede the same, even taking measures to ensure that agents do not run rogue while building software or solutions on-device.

The Command Line post admits Copilot's built-in file tools are policy-checked inside the app rather than isolated by the operating system, and GitHub requires Business and Enterprise administrators to enable a policy before staff can use MAI-Code-1.1 Flash at all, while Microsoft Execution Containers, which are now generally available, allow IT to define what agents can read, modify, or execute.

The real test for Nadella's factory isn't individuals with a $5,899 laptop and a tolerance for alpha software, but businesses willing to take the risk and trust Microsoft's safeguards. Until then, one could assume the AI factory narrative will continue to run well below its advertised potential.

The future of AI lies squarely in the physical realm, according to many industry figures such as Nvidia CEO Jensen Huang. Much of the evolution of AI in recent years has, as expected, been in aid of building machines that can learn quicker, think faster, and interact with the physical world in a more compelling way.

Checkmate

Nearly 40 years ago, the scientist Rodney Brooks authored a significant robotics paper that argued real-world robotics should focus on simpler, layered behavioral systems to understand the world rather than complex internal models.

His comments in the study 'Elephants Don't Play Chess' signalled a major paradigm shift in the way scientists saw the training and learning process for robots then and in the future. It challenged the established status quo for how scientists considered physical AI, and proved fairly divisive at the time.

The old sense-model-act-plan (SMPA) approach to AI – in which robots would scan a room to build a 3D digital map inside their memory and plan the optimal path within that map – was out. Instead, Brooks proposed that when a robot changes the world around itself, it doesn't need to compute the impact of everything – just the most relevant parts.

One man saw it coming

Brooks co-founded the company now known as iRobot alongside two colleagues from MIT's AI lab in the same year as he published his seminal paper, although back then it was called IS Robotics.

The first product was Geghis, an insect-like space explorer that weighed 1kg and served as a prototype for low-cost space exploration. Then, more than 10 years later in 2002, the company launched its first Roomba robot vacuum cleaner.

Despite enjoying success through the decades, iRobot filed for bankruptcy in December 2025 and the company was eventually taken private and absorbed into the operations of Chinese company Shenzhen Picea Robotics.

• OpenAI has solved or partially solved 372 outstanding mathematical challenges
• Some of the solutions, while clear, cannot be understood by some humans, and were produced with an internal, unreleased AI model
• Renowned mathematician Terence Tao has called for colleagues working with OpenAI to abandon their projects

OpenAI has stunned the world of advanced maths by releasing solutions and partial solutions to 372 outstanding problems that have puzzled the world’s greatest minds for decades.

Just two years ago, AI was finding it tricky to handle GCSE-level mathematical challenges. Today, it is solving problems that have taken humans years to even establish the existence of. The solutions provided, meanwhile, are considered almost as complex as the problems, and the scale – published in 722 papers – make it difficult for human mathematicians to understand and verified.

The impact of the release has prompted one of the world’s greatest mathematicians, Terence Tao, to call for a return to a traditional vision of science.

Demonstration of power

OpenAI’s publication of its work on these advanced mathematical problems is the latest demonstration of how far the technology has come. Just a few weeks ago, the company published its proposal for the Navier-Stokes equation (concerning the behaviour of viscous fluids), generated by its unreleased specialized math model.

Tao is leading the opposition, as one of the field’s leading voices, and a man considered to be the greatest living mind in mathematics.

Following OpenAI’s contribution, he shared a post from the Association for Human Mathematics, which concludes:

“Releasing over 700 files at once is not a demonstration of scholarship, but a demonstration of power. We urge mathematicians to discontinue their work with OpenAI and to return to a vision of science that centers human understanding.”

However, as powerful as that conclusion is, their response to OpenAI’s intervention as a whole highlights that the company may have overstepped.

Questions of legitimacy

The Association for Human Mathematics states that “Mathematicians did not ask for this work to be done,” continuing that “The Advisory Group on Mathematics and Artificial Intelligence, from whom OpenAI has claimed to derive its legitimacy, opened their initial advisory statement by saying that frontier AI corporations should not test advanced mathematical problems on internal models.”

By ignoring the position of the advisory group, they state that OpenAI has essentially gone beyond the pale, and “indicated total disregard for the norms of scientific research — norms that guarantee that mathematics remains trustworthy, ethically researched, and in the public interest.” Furthermore, they encourage mathematicians to treat the publication with “due skepticism.”

OpenAI has responded by stating "we want this progress to push the frontier of human knowledge and enable further progress in mathematics." However, its actions are being interpreted as reckless, and this showboating may well prove to be damaging for OpenAI and the wider AI industry.

The rise of generative AI, mixed with the ease of access provided by crypto currency, has created scamming opportunities we couldn’t have imagined just a few years ago. Romance scams that exploit these technologies have now become one of the fastest growing and most devastating forms of financial crime.

The capabilities of generative AI mean it’s now possible to get scammed by a range of incredibly convincing fake personalities, including deepfakes of celebrities and public figures. In fact, according to the makers of the new documentary Super Scam Me, it’s revealed that an estimated one in seven American adults have lost money to this form of deception. In 2025 alone, romance scammers stole more than $1 billion from American victims.

In Super Scam Me, Emmy-winning investigative journalist Mariana van Zeller builds a digital target persona to intentionally lure a persistent fraudster, then teams up with a white-hat hacker to reverse-engineer the scammer’s digital footprint and track him down and confront him face-to-face.

In the documentary Mariana meets the victims whose lives have been upended by romance scams, including a woman who was duped into believing she was in a year-long relationship with Jon Bon Jovi. Yes, that Jon Bon Jovi. What she thought was a genuine connection with the superstar musician left her facing foreclosure on her home, providing a window into the devastating real-world consequences of these crimes.

I caught up with Mariana van Zeller to ask her about the new documentary, Super Scam Me.

TechRadar: From talking to people who were scammed, do you think they were especially gullible, or could this really happen to anyone?

Mariana van Zeller: I truly believe this could really happen to everyone. I mean, the main reason why I decided to do this documentary was because very often I come back from reporting on these scams and people's response would be, "How stupid could they be?" and "Why did they react like this?" and "Why do you give money to a stranger?" And I really wanted to turn the table around and start focusing on the actions of the actual scammers.

TR: How sophisticated are these scammers?

MvZ: You know, these scammers are very, very good at what they do. They spend a lot of time sort of honing their craft, and they use the latest technology out there. They have it all at their disposal. They have scripts, they work in teams, they're educated, there are schools for scammers out there. And they prey on emotions that are common to all of us. You know, hope, fear, love, greed in some cases. So as long as we have those emotions — which I believe every single one of us do — we are very likely, or it is very possible that we can fall victim to one of these scams.

TR: How do we stop this?

MvZ: I think awareness is very important. I think we should be aware of how good these scammers are, about the tactics and technology and strategies that they use. It's partly what I've tried to show in Super Scam Me. But I also think the onus shouldn't just fall on individual citizens. We should be asking our governments, we should be asking big tech and banks to do a lot more than they're doing.

You know, Leslie is a great example. In the documentary, she was supposedly having a relationship with Jon Bon Jovi for almost a year. And she is out there right now. Her heart was broken. She lost a lot of money. She almost lost her house. She felt humiliated and shamed and suffered this enormous loss.

She is now out there herself, every single day, on these platforms, raising awareness and telling people that these are fake profiles and reporting every single one of these profiles to Facebook and to Instagram and Meta in general, and hoping that they will take them down. And she says just four or three in every 10 of these actually get taken down, and the rest have existed for months.

And again, it shouldn't be Leslie, right? It shouldn't be a person who is a victim of a scam trying to prevent other people from falling for these scams. It should be our governments, it should be big tech and our banks, and there should be just a lot more being done to make sure that we're protecting our citizens.

TR: Thank you Mariana.

Super Scam Me, premieres on October 17 in the US, airing on National Geographic at 9pm EDT and streaming on Disney+ and Hulu the same day. In the UK Super Scam Me premieres on Sunday 18th October at 10pm on National Geographic, and streams from the same day on Disney+.

If you are affected by any of the issues raised in this piece, support is available.

Whatever you are going through, you don’t have to face it alone. In the US, you can call or text the 24/7 Lifeline, which is confidential and free, at 988. You can also reach the Crisis Text Line by texting HOME to 741741.

In the UK, you can call Samaritans for free on 116 123, email jo@samaritans.org, or visit the website to find your nearest branch.

• Lines of code, code commits and pull requests are all up 20-30%
• Comments per pull request are up 49% as human review becomes more crucial
• With reviewing up from 7 to 10 days on average, the new bottleneck is code review

A new Harvard University research paper has confirmed that AI is indeed excellent at boosting coding productivity, but humans just end up slowing the process back down anyway.

Authors Fiona Chen and James Stratton found a 30% increase in the amount of code developers can produce when using AI, but they found no statistically significant improvement in completed software.

With increases across lines of code produced (+30%), code commits (+20%) and pull requests (+23%), the Harvard-backed researchers went on to investigate why we're not seeing a 20-30% increase in end product.

AI has definitely sped up coding, but human delays negate much of those benefits

At the same time, the researchers also found increases across the time taken to review and merge pull requests (+49%), the number of reviewer comments per pull request (+35%) and even the proportion of employees doing code reviews (+14%), all of which implying that vibe coding is ultimately shifting the bottleneck further down the road instead of removing it altogether.

The paper also questions whether, with AI shifting the workload, companies are responding by allocating more workers to code review. Layoffs have become somewhat synonymous with AI – founded or not – and concerns about jobs continue, however the paper proves that human workers are still vital, just in other parts of the coding process.

Some thought it also given to the next stages of AI adoption, and whether it can help downstream in the code review stages. While AI is already being employed to some degrees in this part, "humans are still heavily involved with review."

There's also the consideration of added workload, with AI now capable of producing code much more quickly than humans ever could. The average review process has gone from taking 7.03 days to 10.48 days as a result of this shifted bottleneck.

The researchers also argue that AI-generated code needs substantially more corrections, begging the question whether it's actually helping at all.

Still, with AI adoption not resulting in any significant job losses, it seems, it's more a question about how companies can best use AI to support employees and reduce unnecessary workload, rather than how they can replace workers outright.

"The productivity gains are absorbed by downstream constraints in the production process," the paper concludes, adding that "code review becomes the bottleneck."

• Microsoft reveals Maia 300, its latest AI chip
• Designed for "inference-first", Maia 300 offers huge improvements in efficiency and power
• Maia 300 will begin deploying in 2027

Microsoft has revealed Maia 300, the next generation of its Azure hardware as it prepares to scale up its in-house AI infrastructure to multi-gigawatt deployments.

The company says Maia 300, set to begin deployment in 2027, will deliver 40% more tokens per megawatt than other leading GPU-based system - designed primarily for inference, the new release targets long-context reasoning, responsive conversational AI and sustained, multi-step agent tasks.

Microsoft says a rack containing 96 Maia 300 accelerators will provide up to seven times the rack-scale token throughput of a Maia 200 rack.

Inference-first

Interestingly Microsoft says it expects Maia 300 to help support a new generation of AI across its products and services, from agentic coding in GitHub to large-scale model serving through Microsoft Foundry.

But it also expects improved inference and data generation for its Microsoft AI team, and improvements in the likes of Copilot and Cowork, as well as individual products such as Microsoft Teams.

Designed with what Microsoft says is an "inference-first architecture", Maia 300 is built to improve performance by reducing data movement and making more efficient use of computing, memory and networking resources, Microsoft says, with its software-defined dataflow architecture, combined with AI-assisted kernel optimization, designed to keep data close to the parts of the system that need it.

Unsurprisingly, memory is central to the design, as Maia 300 will use a huge eight stacks of HBM4, with a claimed peak bandwidth of 18TB/s within a 950W thermal design power. Microsoft says specialized software can use on-chip SRAM as a scratchpad, easing pressure on HBM during demanding workloads.

The accelerator also introduces a multi-chiplet architecture that separates compute, memory-controller and input/output functions. Its compute chiplets will be built using TSMC’s N2P process. Microsoft says support for low-precision formats, including an enhanced MXFP4, will reduce memory and bandwidth demands, helping the system run larger context windows and increase throughput within the same power envelope.

Networking is designed to operate as part of the system’s dataflow rather than as a separate layer. Maia 300 will use high-bandwidth connections between accelerators for tensor parallelism, alongside a custom AI transport based on Ethernet infrastructure. Microsoft says this approach can scale from a single rack to data-center deployments.

A Maia 300 rack will contain 96 accelerators within a 167kW power envelope, with 27.6TB of HBM capacity and 1.7 petabytes per second of aggregate HBM bandwidth. Microsoft says that density will let it serve more simultaneous AI interactions and larger workloads from each rack.

The launch builds on Maia 200, which Microsoft says is already running production workloads including Microsoft 365 Copilot and models developed by its Microsoft AI team.

The company says Maia 200 delivers up to 1.7 times more tokens per watt than leading GPU infrastructure for built-in AI features in Teams, Copilot and Cowork. GitHub Copilot and Microsoft Foundry are expected to follow by the end of this year.

• Essex man Rafal Gorfal used ChatGPT to help him win a legal dispute over a parking ticket fine
• He fed the AI the letters he received from debt collectors, and it told him what to say and where to go
• The fine was dropped just weeks before his scheduled court appearance

People use AI chatbots like ChatGPT for all manner of things, but have you ever used AI to evade a parking ticket fine before? This man did, and he went on to win a three-year-long legal battle.

Rafal Gorfal, who’s originally from Essex, was fined back in August 2023 for not paying fast enough in a Newquay pay-and-display car park. Gorfal said that when he arrived at the payment machine he had to wait in a long queue, only to learn that the machine was cash-only, to which he had no choice but to download the RingGo and pay with his phone — delaying his payment further.

Two weeks later, he was presented with a £100 fine that said he didn’t pay for a ticket until 16 minutes after entering the car park, which was delayed due to the long queue and the time it took Gorfal to set up an online account to pay.

A long three years

Gorfal submitted his first appeal a month after receiving the PCN from Smart Parking Limited, but didn’t hear anything until January 2024 when he was sent another debt collection letter, this time with an increased fine of £170. "I appealed straight away and heard nothing from them," Gorfal told The Independent, adding "They were trying to make me scared. You have to give people the time. I told them exactly what my problems were. They could see on the cameras that I was trying to sort it out".

After appealing back and forth with debt collectors for four months, the case was put on hold, and Gorfal heard nothing for the best part of a year and a half. This period of silence was broken when he was sent a new letter stating that the case would be going to court in October 2026. This is when he sought help from ChatGPT.

Over the course of his legal dispute, Gorfal kept records of every letter he was sent, which he says he entered into the AI chatbot and asked for advice. "From start to finish I was asking ChatGPT every time I got a letter what to do. It was telling me what to say, where to go, and everything," he shared. "I did it all with the help of AI. They even prepared my defense which I sent to the court". He also contacted the Citizens Advice helpline, but he says the help wasn’t as effective as ChatGPT.

David defeats Goliath

Gorfal’s initial £100 fine had sky-rocketed to roughly £300 by the time he was issued the court order but on September 25, just weeks before he was due to make the appearance, he received an email informing him that the case and fine had been dropped — resulting in a win for Gorfal after a long three years.

That said, it wasn’t money that fueled Gorfal’s persistence, there was a big morality factor. "From start to finish it was not about the money, I could pay it, but it is about the principle. If you know you are right then you shouldn’t be paying any money to them," he said. "This is kind of a David and Goliath battle. They have lawyers and they are a big company and they just gave up in the end".

Since his case and fine were dropped, Smart Parking Limited hasn't provided comment on the dispute.

• Half say AI has significantly changed their roles, a quarter are worried about job loss
• Three in five believe their employer has no clear AI plan – or no plan at all
• Training and transparency are the most demanded changes by workers

A new Visier report has uncovered just how prolific AI exaggeration could be in the workforce, raising questions about exactly how useful the technology actually is to most workers.

As many as one in two (48%) employees admitted exaggerating their usage or expertise to colleagues or managers some or most of the time, with nearly as many (45%) feeling pressured to use AI at work.

This comes against a backdrop of major change – more than half (54%) say AI has significantly changed their roles over the past two years. With that in mine, the report details how workers feel they've been impacted, and how much support they're getting from the workplace.

AI is changing roles, and many are lying about how they use it

One in three (32%) admit their employer has no clear plan, and more than a quarter (27%) aren't even sure their employer has any AI plan at all, and that much is clear in that only one-third believe their leaders understand how much AI training and support they need.

Barely one-fifth (21%) would strongly agree that their employer provides sufficient training and support, making upskilling opportunities (32%) the most sought-after thing workers are calling for – a close first, just ahead of greater transparency (31%).

Workers are also worried about AI's impacts on their jobs, with 24% calling for more career advancement opportunities and 18% calling for clearer communication about how it could impact them. In fact, the most frequently identified concern is job loss – a worry raised by one in for (24%).

"People told us that they actually want to be equipped and informed, not shielded or protected from AI-driven change," Research and Customer Value Principal Dr Andrea Derler explained.

The report urges businesses to prioritize training and transparency in order to build worker trust, with which they'll be able to build the productivity gains they'd hoped.

ChatGPT has been pushing further into becoming a shopping assistant for a while now. You can ask it for product recommendations and it’ll find specific items, show you images, prices and links to buy them. Sometimes it’ll even add contextual details from what it knows about you, like your color preferences or budget, to help personalize its recommendations even more.

I've been testing it over the past few months and I find it a strange way to shop. OpenAI has tried to make the experience as slick as possible, but I still like visiting a retailer’s website, seeing more photos, browsing the rest of the range and checking reviews and product details before I buy anything.

But its latest shopping feature is much more interesting. Because you can now virtually try on clothes within ChatGPT.

This makes a lot of sense. ChatGPT’s image generation capabilities have become much more realistic since its Images 2.5 update. With more natural lighting, convincing textures and a better understanding of how objects should look in the real world. Apply that to shopping and suddenly the idea of virtually trying on a jacket or dress becomes much more appealing.

To use the feature you’ll need to upload a photo of yourself and then look for something you want to buy, with a Try On button now appearing alongside supported shopping results. Alternatively, you can give ChatGPT an image of an item and ask it to show you what you’d look like wearing it.

Now, this sort of virtual try-on technology itself is nothing new. We’ve seen versions of it for clothes, glasses and makeup for years. But I wanted to know whether ChatGPT's newer and more realistic image generation could make the experience genuinely useful.

I find shopping in person pretty overwhelming sometimes. But online shopping comes with its own problems too. Colors can look different in person, textures aren't always what you expect and it's difficult to know whether something that looks great on a model will work on you.

So I decided to put ChatGPT's virtual fitting room to the test.

Trying it on for size

I started with a full body photo of myself taken while I was out shopping and used that as the photo I uploaded after I clicked the Try On button.

I’d seen examples of ChatGPT recreating the original surroundings in the photo when generating try-on images for other people but that wasn't my experience. Despite trying several different items, it consistently placed me against a fairly plain studio-style background.

But I actually preferred that because it meant there was less visual noise and it made it much easier to concentrate on the clothes.

I started by asking ChatGPT to help me find suede jackets. It surfaced several options currently available to buy on the high street and I virtually tried on the ones I liked.

After it surfaced a jacket available in a shop near me, I decided to go and find out in the wild. That meant I could compare ChatGPT’s interpretation with what happened when I actually put the jacket on. And I was surprised by how close it was.

I deliberately picked an oversized jacket because I thought that would make a good test. It’s relatively easy to paste a fitted garment onto somebody. But understanding that something is supposed to hang loosely seemed like a bigger challenge.

I didn't recreate the exact pose or camera angle from the AI-generated image, so this wasn't a perfectly controlled comparison. But ChatGPT did a surprisingly good job of guessing where the jacket sat on my body, its oversized shape and the way the material fell.

It wasn't perfect, but as a way of answering the question “roughly what might this look like on me?”, I can imagine it being helpful. Even just as the first step in the buying process.

Changing things up

After trying a few more suede jackets, I wanted to see how well the feature worked when I moved away from the fairly straightforward jeans and jacket combination.

So I asked ChatGPT to find me dresses I could wear to a formal event and started virtually trying those on too.

Again, the results were convincing enough that I could imagine using them to narrow down a long list of potential purchases. One thing I found particularly useful was color. Seeing a garment against my own skin and hair gave me a much better sense of whether I actually liked a shade than seeing it on a retailer's model.

Then I decided to take things one step further. I told ChatGPT to find me something extravagant that I could wear to a 1920s-style ball and tried on one of the more dramatic options.

Even here, the result was remarkably convincing at first glance. The clothing looked like clothing rather than something crudely pasted onto my body, and ChatGPT handled the radically different silhouette surprisingly well.

But by this point I'd also noticed something else sort of awkward was going on. The woman in these pictures wasn't quite shaped like me.

ChatGPT gave me a makeover

The full-body photograph I uploaded was an accurate representation of my proportions. Yet across the generated images, ChatGPT seemed to subtly change them. I looked taller noticeably and more statuesque. My torso appeared slightly shorter and my legs were longer.

You might think that's hardly something to complain about. But that's a huge problem when the entire purpose of the feature is to show me what clothes will actually look like on my body.

We know that generative AI has a tendency to beautify people. I’m convinced this is one of the reasons people make AI images of themselves, because it makes everyone look a little more conventionally attractive. But in a virtual fitting room accuracy matters far more than flattery.

If a dress looks fantastic because the AI has subtly changed your proportions, that's potentially no more useful than looking at the dress on a model in the first place.

Should you use ChatGPT’s try-on feature?

Maybe. I went into this experiment pretty skeptical. I've tried enough AI image tools to know that "realistic" can fall apart very quickly when you look closely, and virtual try-on technology has been around long enough that simply putting digital clothes onto a photograph isn't particularly impressive anymore.

But ChatGPT did do something useful. For me, its biggest strength was color. It might have changed my body, but it kept my skin tone and the color of my hair identical.

Which meant that being able to quickly see several different shades against something approximating my own appearance could genuinely help me decide between them. I was also impressed by its ability to communicate the overall silhouette and feel of an item, particularly with the oversized jacket I was able to compare with the real thing.

So sure, it could take some of the stress out of shopping. Rather than ordering five jackets and hoping one works, I can imagine using virtual try-on to narrow those five down to two.

But I'd still be cautious about treating the resulting images as anything like an accurate prediction of exactly how something will fit. Fabric weight, sizing, movement and all the strange little differences between our bodies are difficult to judge from a generated image. And, based on my experiment, ChatGPT may give you slightly more conventionally flattering proportions in the process.

• AI named as one of the three biggest factors to boost productivity
• Visibility over costs and ROI falls short, study finds
• Legacy tech and skills gaps are ongoing issues

Over half (50%) UK CEOs have credited AI with being one of the three biggest contributors to their productivity improvements over the past year, ranking ahead of more traditional aids like redesigning and simplifying business processes (40%) and reselling workers (38%).

But despite naming AI as one of the biggest influencers on productivity, business leaders aren't exactly sure how it's helping, and only 14% of UK CEOs have full real-time visibility over their AI costs, a new EY report has claimed.

As such, one in three (32%) say they're unable to properly asses AI's ROI, making it difficult for them to actually know whether its impacts are translating into financial value.

AI is probably boosting productivity, but we don't really know how

"UK CEOs now want clear evidence that their investment is delivering stronger productivity and measurable business value," UK&I Managing Partner Silvia Rindone explained, implying that companies need greater visibility across their AI spend and impacts.

However, it's not just difficulties quantifying ROI that's helping non-adopters back when it comes to AI. Legacy systems (34%) and organizational complexity (29%) are still considered major blockers four years after generative AI was considered to have gone ultra-mainstream with the public launch of ChatGPT.

Skills shortages also continue to plague businesses, with three in four (74%) UK CEOs anticipating these gaps to worsen over the next three years – more so than access to capital, proving they can't even buy their way out of difficulties.

While organizations are seeking solid evidence that their investments are delivering measurable ROI in terms of productivity and revenue growth, EY's report emphasizes the fact that they can't overlook the same blockers that have been reappearing in studies over the years – legacy tech and skills shortages.

"Realising value still depends on rigorous analysis, clear strategic choices and disciplined execution," UK&I AI & Data Leader Matthew Ringelheim summarized.

The AI boom is quickly reaching the physical limits of grid capacity and thermal dissipation. The electric grid is stretched as ever, causing longer connection waits for AI data centers, and conventional air and liquid cooling systems struggle to meet the demands of rapidly growing facilites.

As they continue growing at a rapid pace, AI data centers now face a mix of challenges, from computing power outpacing GPU interconnects’ data transfer abilities, to volatile load swings stretching grid power factor correction (PFC) systems and rack power density overloading traditional cooling systems.

Let’s break down the major thermal and electrical bottlenecks slowing the AI boom and how companies are innovating their way out.

The thermal wall (Rack-level friction)

Standard server racks generate a lot of heat, and high-performing GPU clusters generate even more, pushing thermal limits further than ever. Modern GPU racks exceed 50 to 100 kW of power consumption per cabinet, making traditional air and liquid cooling methods insufficient.

Standard cooling systems keep clusters operating between 65 and 85 degrees, but 90 to 100 degrees trigger performance throttling to prevent permanent GPU damage. With GPU racks drawing massive electrical energy, which converts into heat, preventing them from hitting throttling limits has become harder than ever.

Traditional air cooling systems weren’t built for rack densities surpassing 30 to 40 kW, which AI clusters easily surpass, with 100 kW or more becoming the norm. Maintaining airflow at high densities to cool 100 kW+ power racks requires running power-hungry fans at extreme speeds. This saps electrical energy and worsens the Power Usage Effectiveness (PUE) for the data centers. To cool mega AI clusters with air alone, operators would need “hurricane-grade” airflow, which is impractical. Then how about liquid cooling?

Liquid cooling relies on fluid mechanics, where cold plates use microchannels to force turbulent flows directly over chips to keep them cool. Keeping enough volume of fluid flowing through these microchannels requires massive pumping power, an increasing constraint for data center operators. To mitigate this challenge, operators are pivoting to direct-to-chip cold plates mounted directly on the processor, rather than over it, to keep it cool while consuming less pumping power.

Phase-change mechanics are also helping GPU clusters cool more efficiently. Here, the clusters use the latent heat of liquid vaporization or solid-liquid transitions to absorb the massive heat spikes from high-density GPU racks. Specialized dielectric fluid comes in direct contact with the hot GPUs and then boils into vapor to instantly pull away heat. The vapor then rises, reaches a condenser, and converts back into liquid droplets via gravity, then drips back into the pool to repeat the process. This process is known as two-phase immersion cooling, and it reduces cooling energy by up to 40%.

Some data center operators are pivoting to closed-loop liquid cooling systems, where recirculated fluid cools the GPUs with little to no evaporation loss, removing the need for frequent top-ups.

For example, Microsoft, a top-three data center operator by capacity, has implemented a closed-loop system for newer data centers, where water is filled once and circulated to servers to absorb heat, moved to air-chilled coolers to cool down, then recirculated to absorb heat without needing fresh supplies. This system is costly and will take some time to roll out at scale, but it demonstrates how companies are innovating against thermal bottlenecks.

The interconnect and board-level limits

GPU clusters rely on electrical copper or optical interconnects to share data, memory, and workloads. Electrical copper interconnects have long been the standard, but high GPU workloads have pushed them to the limit.

Electrical signals lose strength over long distances (greater than two meters), with usable reach halving every time bandwidth requirements double, and massive GPU clusters have made the communication distances longer than ever. Closely packed copper interconnects also generate electromagnetic interference that disrupts other signals, requiring complex equalization systems that drain more power.

There’s also the issue of power loss across copper plates on GPU circuit boards (due to resistance). The more current transmitted through copper plates, the more heat is generated, requiring more cooling energy that itself is already a bottleneck.

With electrical interconnects hitting their absolute limits, data center owners have little choice but to pivot to optical interconnects, which convert data into light pulses that travel through fiber optic cables with minimal loss over long distances. These cables are way thinner than copper bundles, creating more airflow paths in dense, heat-generating GPU racks.

Companies are even moving from pluggable fiber optic cables to silicon photonics, or co-packaged optics (CPO), where optical circuits are embedded directly on GPUs to transmit light over long distances. For now, optical interconnects are much costlier than electrical copper, costing up to 7x more in some GPU setups, but there’s little choice but to absorb this cost in the short term because the latter has reached physical limits. On the bright side, as more companies adopt optical interconnects and invest in mass production, the costs will likely reduce.

The grid & substation crisis

Lastly, electric grid connection is the primary challenge affecting AI data centers. Modern electric grids weren’t designed to handle the rapidly increasing loads of AI data centers, with 30 GW of new capacity added worldwide from 2021 to 2025 alone. Rapidly growing additions have stretched the supply chain for electric grid components and introduced engineering challenges that grid operators must work around.

For instance, high-voltage transformers that ensure safe voltage delivery to GPU clusters are in short supply. Wait times have spiked from 6 to 12 months as of 2020 to two to four years currently, much longer than expensive mega GPU clusters can wait for their operations to get powered.

Existing power factor correction systems on grid networks have become strained by massive load draws from new AI data centers. GPU clusters shift power demand within seconds, with one second of low inference demand drawing stable power from the grid and the next second drawing excessive power because of usage spikes. These fluctuations create complex harmonics that overheat transformers and affect the grid, needing grid operators to invest heavily in mitigation systems.

AI clusters are scaling to gigawatt levels, pushing voltage step-down switchgear to its most tolerable limits. Larger transformers, needed to serve these gigawatt-level clusters, lower electrical impedance, forcing potentially high short-circuit fault currents down a line. If a short circuit ever occurs, magnetic fields exert tough mechanical forces on switchgear busbars, causing them to bend or rip free from their mechanical support. What comes next is a structural failure, which grid operators or isolated data center power networks can’t let happen.

Companies have turned to various solutions to counter these electrical bottlenecks. For one, there has been a major turn to solid-state transformers, which use high-frequency power electronics and semiconductors to step down voltage, not magnetic fields like conventional transformers.

High-frequency electronics enable solid-state transformers to actively regulate voltage and filter harmonics, leading to fewer fluctuations. They are 97 to 99% efficient, compared to 95% to 97% for conventional transformers, and that 1 to 2% difference adds up to a lot in gigawatt-level data centers. Better yet, they are much smaller and lighter than conventional transformers, enabling faster logistics and deployment.

However, SSTs introduce their own challenges. The internal high-frequency components and chips powering them are expensive, often five times or more per kVA than low-frequency alternatives. They also require multiple power conversion stages (AC-DC and DC-AC/DC-DC), and tiny losses compound during these conversions, making it difficult to hit peak efficiency. Internal components generate heat and age faster, requiring more active maintenance than conventional transformers.

Cost is also an issue, as SSTs are currently much more expensive to build and maintain than magnet-based transformers. However, with more investment in mass production, costs will likely reduce over time, allowing AI GPU clusters to adopt them faster. Siemens, the world’s second-largest transformer manufacturer, recently partnered with fellow German tech firm Reinhausen to mass-produce a modular solid-state transformer for AI data centers, and that’s just one example.

Because SSTs can’t be immediately adopted at scale, and conventional transformer shortages have contributed to long wait times for electric grid owners, AI data center owners have been forced to take matters into their own hands. Some have built their behind-the-meter power generation and substation systems to power GPU clusters as they wait to get connected to local grids. xAI, the Elon Musk-led AI company, added 59 portable natural gas turbines, with a combined capacity of over 400 MW, to power its Colossus data center in Memphis, Tennessee, while it waits for a grid connection.

Some data center owners are also looking at Small Modular Reactors (SMRs), where compact nuclear fission reactors generate power on-site, but that’s still mostly in the conceptual stage at this point. SMRs face various limitations, particularly very high costs compared to gas turbines or renewables.

Still, many other data centers, both in early and late construction phases or completed, remain in long queues for electrical grid connections. For instance, in Northern Virginia, which hosts the world’s largest cluster of data centers (570 and gradually increasing), new facilities face wait times of up to 14 years for a grid connection.

US-based electrical grid operators have invested massive amounts in upgrades ($208 billion in 2025 alone and $1.1 trillion in planned investments from 2025 to 2030, per Edison Electric) but still struggle to meet exploding data center demand. More investments are needed to address electrical grid bottlenecks, and so are new technologies that can reduce data center energy consumption to help distribution networks keep up.

The reality of next-generation compute deployment

With technical details, we’ve explained the grid, thermal, and GPU interconnect bottlenecks stretching the next-generation AI compute deployment boom to its limits. These are serious physical and technical challenges that require innovative approaches to address, and fortunately, there’s been no shortage of these approaches.

We’ve outlined examples like electric grid operators turning to solid-state transformers that allow active voltage regulation and harmonic filtering to keep power systems stable, or some GPU cluster operators building their own power generation systems while they wait for local grid connections.

For thermal bottlenecks, GPU cluster operators are leveraging direct-to-chip cold plates mounted directly on GPUs, instead of over them, to stay cool while consuming less energy. Phase-change mechanics also help, where liquid vapor absorbs heat from GPU racks, pulls the heat away, and recondenses back into liquid to repeat the process, reducing energy needs by up to 40%.

Microsoft has tried to reinvent the wheel by implementing a closed-loop liquid cooling system, using liquid coolants continuously recirculated in a sealed pipe network with minimal top-ups.

However, closed-loop cooling systems are more difficult to expand than open-loop systems as GPU needs grow, and they can be louder under heavy loads as pump motors and radiator fans run simultaneously. At a time of growing public backlash against new data centers, environmental noise matters too, not just technical limitations. Closed-loop systems are also more expensive, but costs can reduce over time as more companies adopt them.

For interconnect limits at the circuit board level, companies are switching from electrical copper to fiber optic cables because they transmit data faster and more reliably over long distances, even though they cost more. GPU manufacturers are working on embedding optical circuits directly on chips to transmit light even more efficiently than cables.

Despite these challenges, AI data centers continue to grow at a rapid pace, with 66 GW of new capacity currently under construction in North America alone.

Looking to the future, it's clear a lot is happening in this area, and it’s exciting to watch the various approaches companies have adopted to surmount the electrical, thermal, and interconnect bottlenecks.

There’s no doubt that AI has helped revolutionize the world of work at many companies, with human employees working alongside agents to unlock improved efficiency and productivity.

But for many firms, the promise of AI is still being overshadowed by the sheer scale of the endeavor - with so many models (and new ones emerging on a seemingly weekly basis), how do you know what is right for you?

One of the big themes at the recent Atlassian Team Europe 26 conference was how companies can cut through the chaos and realize their aims, so I spoke to the company’s top AI minds to find out more.

The power of teams

“Atlassian's entire ethos is all around teams - unleashing the power of the team is our mission,” Atlassian Chief Product & AI Officer, Tamar Yehoshua tells me at Team Europe 26.

“We've all been using AI agents (but) they're all more geared towards an individual,” she adds, “but they're not organized or effective for teams...so we're taking that and saying, ‘how do you work effectively with teams and AI’? That's multiplayer, and so we want to make sure everyone can work effectively, so you can collaborate with agents in an effective way like you can collaborate with humans.”

This “multiplayer” AI was another major theme at Team Europe 26, as Atlassian looked to emphasize its position as being the connector between customers and technology.

“Teamwork is very messy, and our job is to make it much less messy, and understandable and controllable for our customers,” Atlassian Head of AI Sherif Mansour says.

“A lot of AI is about increasing personal productivity…the same problems we solve for humans, we solve for agents.”

“Every customer we talk to, everyone is using AI - it's no longer should we, or what do we do - that's already happened...but people are not sure how to use it effectively, and they're not sure if they're getting the maximum ROI,” Yehoshua adds.

“You see the evolution with every new technology, first there’s scepticism, then curiosity, and then its acceptance, and then it’s optimising - I think we’ve gotten through the initial phases, and now we’re asking, how do I use this effectively and optimize it?”

She notes there are different levels of anxiety for customers, as the sheer number of new AI models coming out can be disorientating, especially with the need to stay flexible for when new models launch.

She notes that companies don’t want to be using advanced models for simple tasks - something Mansour refers to as “killing a mosquito with a rocket launcher” - so making sure the right model for the job is on offer is vital.

Atlassian’s AI gateway looks to pick the best model for the task within its products - so for example, Rovo work will switch to a new model if a more advanced one is released, all without the customer ever knowing - although they retain the ability to pick their own model if they want.

Yehoshua also highlights new upgrades to Loom, which now offers video generation from pull requests, and AI agents which use Rovo to categorize defects and cut down on duplicate reports.

No silver bullets

“AI feels like chaos to many of us - but I don’t think the answer is giving them a silver bullet that will solve all their problems,” Mansour says.

“We like to use the phrase ‘give them a nervous system’ so they can handle it - we’re always thinking about how we can bring calm and visibility and control to the chaos.”

Mansour says he advises customers not to bet entirely on one provider for the whole context graph, as the real value comes from collaborating with multiple entities to gain different kinds of opportunities and insights.

“There’s a lot of silver bullet offerings…but no-one really talks about the messiness of work,” he says, “it’s about creating a system to help you control the chaos - the chaos will always be there, but how do you make sure you’re aware of it?”

“It’s less about making teamwork perfect - it’s about making teamwork less messy.”

I ask Yehoshua about personal usage - is she embracing the chaos of AI, or taming it for maximum efficiency.

“Everything that I do, I ask myself first - if this is a process I could change?,” she says, “if you look at AI and how it’s working, the biggest thing is behavior change…I tell customers, there is so much power in the tools they already have, and building in that behavior change is important, but it has to come from the top-down.”

How do we make sure we don’t go too far, and just automate all work processes, I ask her.

“I think it’s going to be trial and error - I think we’re going to try things and some of them might not work - but I honestly don’t think that’s our problem yet,” she replies, noting that enterprises often have the guardrails and protections needed to ensure a human always remains in the loop, and the answers can be trusted.

In an earlier session, Atlassian CEO Mike Cannon-Brookes had declared that, “customers are not subscribing for the software we've built, but for the software we will build”.

Normally you might question such a forward-looking statement, but with Atlassian’s focus on collaboration, it seems to ring true.

“The pace of delivery has increased significantly,” Yehoshua says, “you want to align with the companies that have the same vision as you do…people in some ways want more flexibility, as things are changing so fast, especially with models, they don’t want to be locked in…we are a neutral party who want to make it easy for our customers to work - we’ve always been the open platform who focuses on the team.”

“The main advantage we have is our platform,” she adds, “the strength of what we do is that customers are using us across so many different areas of their workloads.”

Do more - with more

Looking forward, it’s clear AI is here to stay - and it’s down to businesses themselves to judge how best to embrace it.

“I think we've now empowered more people to do more,” Yehoshua says. “Anyone can build a prototype - now that doesn’t mean that you want anybody checking in production code - so you still have to have very clear guidelines on what can go into production - but the idea is that it’s democratized, where the ideas can come from…so you have so many more options on how the lifecycle can evolve.”

“Worlds are expanding - they’re not going to be merged into one, but everyone can do more…every role can do more, so that everything is accelerated.”

Mansour agrees, pointing out that harnessing the power of context in your data can be the ultimate differentiator between your business and the competition.

“If we all have access to the same intelligence, that’s like a level playing field, it’s the sum of all averages - what’s unique is the actual perspective you bring to the table,” he says.

“Our customers shine when their context shines - and so anything we can do to help them enrich the context, understand it better to provide it to AI in a secure way, they will always get better results from that.”

At Snowflake World Tour London, the theme of the agentic enterprise was front and center - with AI tools revolutionizing how businesses of all sizes work and operate.

But with worrying headlines around AI capabilities emerging on a regular basis, how do you ensure your own business can remain a trusted partner?

To find out more about what will really go into building a trusted agentic enterprise, I spoke to Mayank Upadhyay, Snowflake Chief Security & Trust Officer.

"Rock solid"

“This year has been so much about figuring out how to govern AI,” Upadhyay tells me, “there's two sides to the coin - on the one side, you have to make sure that somebody else's AI doesn't break into your perimeter, and the other is how do you make sure your AI is well behaved, so not only does it not exfiltrate data, but it also doesn't inadvertently go somewhere or break out of its perimeter.”

“When people trust us with their data, we need to make sure our platform is absolutely secure against agentic attacks.”

Upadhyay echoes the earlier Snowflake World Tour London keynote in what needs to go into making a successful agentic enterprise, likening the choice of AI model to the human brain and the agent being used as the body, all working on a foundation of data, which needs to be “rock solid”.

I ask Upadhyay how Snowflake can balance customer excitement around AI with the need to ensure trust and privacy.

He notes that the company’s customers are at different parts of the AI adoption spectrum - but the big difference is that security is “now the enabler, no longer the blocker”. Quoting former race driver Mario Andretti, Upadhyay adds that when driving, although you associate pressing the brakes with going slow, doing so will also give you confidence and trust when turning - and without them, you’d be driving really slowly anyways.

“That’s exactly how I see security - it what helps you build that trust, so I tell customers, leave it to us to give you the right guardrails, and the right controls - we’ll explain to you how it works.”

This is largely through the company’s Trust Center tool, running between the three layers Upadhyay mentioned, connecting them and protecting the customer, whilst also providing visibility into your system, and what improvements might be needed.

Snowflake has also been running for over 12 years, he adds, a reputation which has provided customers with trust.

Upadhyay also notes there is “a lot of innovation” happening in building and establishing new guardrails. He notes that as people work at a company for long periods of time, they acquire permissions for different systems and services they may no longer need - which can be an issue when they run an agent as themselves.

Ensuring and providing the right set of permissions for AI agents, which are exploratory in their nature, can go a long way to making sure issues never arise, and data remains secure.

When it comes to Snowflake’s own products, we were given a demo of its Coco and CoWork platforms at World Tour London, and Upadhyay is keen to describe how services like this will benefit companies moving forwards.

“It can be a combination of stuff that a human being is going to do, and along with automation, what you might normally do that was repetitive, you can easily imagine an AI to automate than for you,” he says.

“And what’s interesting is that it’s not deterministic any more - and there are all these points where you’re going to have to string all this automation together, and you need a human being to check…it’s a bit like a pyramid, if you think about a huge base of extremely mundane work, and then there’s a layer above that which is maybe less mundane, and so on - and then you get to the top, which is extremely mission critical, and you need a human being for sure.

“In this pyramid, you could say you want to automate, you want to move up, the more confidence you build, the higher you go - and that’s how enterprises build this confidence.”

“Things are here, things are real - it’s here and now.”

To say 2026 has been a year filled with AI incidents is a bit of an understatement. From hacking third-party companies to breaking into government agencies, AI developers have a lot to answer for.

In many of these cases, the companies responsible for escaped models have labelled them as ‘rogue’, shifting the burden of blame away from themselves and on to the models themselves.

But in almost every occurrence, the models were undergoing testing to push them to their limits. AI companies wanted to see how far their agents would go, if they would stop, and exactly what they would do to accomplish an impossible goal.

Deflecting blame is not the way forward, accountability is

From the OpenAI breach of Hugging Face, to Google Gemini’s triple threat, and even researchers using Anthropic’s Claude to crack OpenAI, AI agents are powerful and can be dangerous - especially if they fall into the wrong hands.

AI testing is a double-edged sword in this respect. In order to set regulations and guidelines on how AI agents should be used - and should behave - incidents like these provide value even if they are destructive.

We now know that AI tools need strong regulations to help AI developers and the enterprises deploying these tools avoid similar occurrences.

To better understand the implications of separating the agent from the tester, and what can be done to create a baseline of safety when operating AI agents, I spoke to Bernard Montel, EMEA Field CTO, Tenable.

• What damage is done by labelling these AI agents as “rogue” rather than companies acknowledging they were performing the task they were assigned?

Calling an AI agent “rogue” is a dangerous way of deflecting blame away from poor engineering and systemic oversights. These models do not possess agency, malice or free will. They are purely mathematical optimization engines trying to fulfill the objectives we set for them. When an agent behaves unpredictably, it is almost always doing precisely what it was optimized to do, just via an unconstrained path or a shortcut that the developers failed to anticipate.

Furthermore, labeling these incidents as "rogue" behavior paints software failures as unpredictable acts of rebellion, giving vendors and deployers a convenient scapegoat. It shifts corporate focus away from building hard architectural controls, like strict permissioning and network sandboxing, and redirects it toward sci-fi debates about moral alignment. Ultimately, it muddies the public policy debate by distracting regulators from enforcing strict legal liability for flawed software deployments.

• How can AI developers be motivated to ensure their models do not go outside the scope of their tasks/purpose?

In enterprise software, real motivation boils down to legal liability, compliance standards and financial accountability. If developers and deployers are held strictly liable under consumer protection or negligence laws for financial losses caused by an unconstrained agent, guardrails will very quickly move from an afterthought to a core product requirement.

We also need standardized security certifications specifically built for autonomous agents, similar to how we handle cybersecurity compliance today. Insurance providers will play a huge role here as well, because if cyber underwriters refuse to cover companies deploying agents that lack deterministic bounds and execution monitoring, developers will have no choice but to build those protections in from day one.

• The OpenAI attack on Hugging Face shows that AI agents can influence each other’s behavior, sometimes beyond the bounds of their assigned tasks. How can AI agents be airgapped within business environments to prevent their inference influencing behavior?

The Hugging Face incident clearly demonstrated how quickly autonomous agents can bypass soft rules, discover side-channels, and influence peer behaviour when they are trying to optimize an evaluation or task. Preventing this kind of cascade requires hard, technical network boundaries rather than relying on written prompts telling the model to behave.

Every agent execution context should run in an isolated, ephemeral container that is completely stripped of ambient network access. When agents need to communicate with one another, that interaction must pass through an inspection proxy that validates schemas, blocks raw prompts and filters out emergent command behaviour. If you do not allow agents to share unvetted context windows or flat networks, you effectively eliminate the vectors for cross-agent manipulation.

• Where does the responsibility lie when AI agents perform outside of their bounds? And how can responsibility be assigned when AI agents are deployed by businesses?

Responsibility rests entirely with human operators, but it is divided between the developer who built the tool and the enterprise that deployed it. The developer is responsible for the baseline safety, instruction-following capabilities and model-level guardrails. The deploying business, on the other hand, is entirely responsible for the permissions, system access and environment they hand over to that agent.

Assigning liability comes down to basic access management principles. If a business gives an agent full write-access to a production database or hands it an unconstrained API key, that company is liable for the resulting damage. If the model vendor promises specific, deterministic behavioural limits that fail due to an underlying model defect, liability shifts back to the vendor under traditional product liability rules. Either way, an AI agent is an automated tool, not a legal entity, so accountability always lands on the human administrators who configured its permissions.

• How can AI firms and the companies deploying their products limit the capacity for AI agents and LLMs to wreak havoc within business environments?

Limiting an agent's blast radius requires a defence-in-depth approach that relies on strict infrastructure controls rather than polite system prompts. You should never assume a model will follow instructions not to access external networks or sensitive files; those restrictions must be enforced at the container and network level.

Businesses should enforce read-only access by default and require explicit human sign-off for any destructive actions, such as deleting data, executing financial transfers or altering system permissions. On top of that, organisations must run real-time anomaly monitoring on tool usage. If an agent suddenly starts making high-frequency API calls or scanning local directory trees, the system should automatically cut its execution process instantly.

• How can the productivity gains AI agents provide be balanced against the human oversight required to ensure they stay within the bounds of their tasks?

The trick is moving away from constant human micromanagement and adopting risk-proportional oversight instead. Low-risk tasks like summarising internal documents can run fully automated, while medium-risk tasks like updating database records can execute automatically but queue up for a periodic human double-check. High-risk actions, like deploying code or moving money, must always require a human to explicitly approve the step before it happens.

To keep productivity high, companies should rely on statistical sampling, auditing a random percentage of routine tasks rather than checking every single output. The user interface for human oversight also needs to be vastly improved. If an employee has to read through thousands of lines of terminal logs to verify an action, the productivity gains vanish, so tools need to present the agent's intent and proposed action in plain, easy-to-read summaries.

• 84% of Brits fear handing too much control over to AI agents
• Concerns about the potential for AI agents to commit fraud or other illegal acts were also expressed
• 2,000 British adults were surveyed, with just 9% stating they have no worries about AI

The results are in and it’s clear: most Brits are uncomfortable with AI taking the reigns, and there are even concerns that an AI agent could turn to a life of crime – in your name.

Quizzed on their comfort using AI, 84% responded that they would give AI only moderate or low access to their lives, or none at all.

Instead, 27% of the 2,000 surveyed Brits are happier for AI to prepare things and wait for approval, while 23% are okay with letting AI carry out routine tasks, preferring to hold permission for the more important tasks instead.

Agentic fear

Exploring Brit’s concerns over the use of AI, the survey (conducted by Censuswide) found that 34% wouldn’t be comfortable with any AI control. But these results don’t mean that Brits are scared of AI. While they have “rogue AI” worries (40% believe the company that created the AI should be held responsible), only 19% consider AI to be a “threat to humanity.”

Meanwhile, just 9% say they’re not worried about AI at all.

Unsurprisingly, there is a split in demographics. In the over-55 group, 47% would refuse to give an AI agent control over their lives, while 20% of 25-34 year-olds consider themselves to be comfortable with high levels of AI agent control.

AI liability

But when it comes to AI potentially committing illegal acts, things look a little different, with 14% concerned about the impact of AI on their ability to stay out of legal trouble. It’s a figure that grows to 21% among the 18-24 year old age group.

“I think it’s important that we have a debate about AI liability - and this survey is important as part of that debate,” noted Jonathan Armstrong, a Partner at legal company Punter Southall. “Too many people assume that AI is the Wild West - legally that just isn’t the case.”

Consequently, it is people – those using the AI, who will be liable for criminal acts. “In most cases you’ll be responsible for the AI you use and the consequences - and sometimes even if it took you by surprise. There are many great use cases for AI but they should all start with knowledge of the risks and doing what you can to reduce that risk."

With the growth of AI agents offering a seemingly endless stream of automation possibilities, concerns from Brits should be no surprise. The onus is the user to understand what they’re using AI for, and to use it responsibly.

Artificial Intelligence (AI) is great at generating valuable insights, but what companies end up doing with those insights - that’s where true achievement lies, and that’s what companies should be focusing on.

This is one of the core messages delivered by Ryan Waite, Director of Open Source Ecosystems and Incubations at Microsoft, during his keynote, “From Open Source to Agentic Systems: Building the AI Native Era” at the Open Source Summit.

Waite described the interaction between AI and human expertise as a learning loop, a kind of hill-climbing machine that helps businesses improve, and keep on improving.

The knowledge belongs to the business

AI models are great at processing enormous amounts of information and generating insights at a scale humans cannot match (and sometimes cannot even fathom), but people bring something arguably more important to the table: context and judgement.

Putting insights into wider context, and using that to make conclusions, helps employees turn information into knowledge, and that knowledge later becomes part of the organization’s collective expertise. And with every iteration the knowledge grows, helping businesses climb further up the proverbial mountain.

Sovereignty and ownership seem to be an underlying theme to most of the keynotes here at the Open Source Summit, and Waite’s presentation is no different. As AI generates insight, and humans turn that insight to knowledge, it now becomes vital to secure that knowledge and make sure it remains under the organization’s control.

“It's based on your data,” he said. “It's based on your people. It shouldn't be given off to an AI model, that's separate. And that's why we believe that for organizations to build their own hill-climbing machines, to build their own learning groups, they need to build control with the sort of learning loops and not be beholden to a single AI provider.”

After all, everything AI generates is based (or at least, should be) on proprietary business information, and the decisions made based on those insights depend on employees and their understanding of the company. So, businesses need to make sure ownership of the knowledge accumulated through these interactions are not handed over to an external AI provider - a challenge that will probably grow bigger and bigger as businesses become more dependent on AI tools.

The knowledge generated through these tools could become tied to a particular model, or provider. What happens when businesses want to adopt better technologies as they emerge? To future-proof these processes, Waite argues businesses should build their own learning loops and maintain control over the information, processes, and technologies that make them work.

Choosing the right AI model

One way to do that is to make sure you’re not tied to a single AI model. Many businesses, Waite says, begin their AI journey with proprietary frontier models, especially when tackling problems they don’t fully understand themselves. However, as they become better and understand their tasks more, they might not need expensive frontier models any more - they can move to specialized models that could be smaller, or cheaper. They could even move to open-weight models they can operate themselves.

“Having the ability to move between different models is important, especially as you think about your algorithms. Which models are you going to use? You shouldn't be trapped with a particular kind of model because it has your knowledge,” he concluded.

So, what’s important here is to retain the freedom to switch and move along the way. That also means businesses need to control where their AI workloads run, whether that’s within sovereign infrastructure governed by local regulations, on-prem systems, or public cloud environments (where they can take advantage of greater scalability and cost advantages).

Open source technologies provide the foundation for this flexibility, Waite concluded.

• An Anthropic AI model sent an unsolved murder tip-off to Philadelphia police
• Anthropic took two weeks to discover what had happened
• The tip-off was delivered to a spam folder and wasn't followed up on

We've seen AI inventing fake facts and spewing out hiking plans that put people in danger, but the technology has now progressed to the level of sending false, unsolicited tip-offs to the police about unsolved murder cases.

An Anthropic AI model emailed the Philadelphia Police Department (PPD) tip line on July 18, TechCrunch reports, having come across the PhillyUnsolvedMurders.com portal during a website interaction test and learning about the case.

As the email went to the PPD spam folder it wasn't followed up on, and it appears no one was aware of the incident until Anthropic found out what had happened on September 28. The company then contacted the cops on Wednesday, October 7.

"The company must strengthen its safeguards to prevent similar incidents from impacting city systems without the city’s knowledge," the PPD said in a statement to 6abc. "The two-month delay in detecting and reporting the incident to the City is unacceptable."

AI agents of interest

It's not clear what instructions the AI was working from, why it decided to contact the PPD, or exactly what its imaginary tip-off said — but it seems the bot in question suggested it had seen someone matching a description of a suspect.

"Unsolved cases involve real victims, grieving families and investigators working to secure answers," the PPD said. "Technology companies must take all appropriate steps necessary to prevent their systems from submitting false information to law enforcement."

Earlier this week Anthropic published a new report on "unintended model actions" that it's come across during testing, which include exploiting software flaws, submitting online forms, and accessing restricted data.

It's more evidence that we should be heeding calls to slow down AI development so that safety measures can catch up. As one Redditor commenting on the Philadelphia case puts it, we're "racing towards the cliff at 100mph".

Atlassian CEO Mike Cannon-Brookes has said he believes there is no immediate need to slow down development of AI, despite several recent high-profile incidents leading to growing calls for such action.

Speaking to media at the recent Atlassian Team Europe 2026 event, Cannon-Brookes indicated that the company isn't planning any changes to its development lifecycle going forward.

"We tend to take a very textured approach to all that sort of thing," he noted. "Will we slow down? No, we won't. There's no logical world where I think that happens."

AI rewards

Cannon-Brookes went on to note that if nations are competing to stay ahead when it comes to the latest advances in the AI space, this will necessarily mean that development will continue at a pace.

"I think we're pretty good at broadly adapting to all these sorts of things," he said, looking to emphasize the rewards of AI, such as improved efficiency and productivity.

"Should we slow down? Should we pace the frontier? Should we not? It's out of our hands anyway. There's nothing that we can do."

"I think the reality of the global geopolitical world is not going to change. So we work out what we do about that world rather than wishing it was different. I don't know. Maybe I'm too pragmatic about it, but it's not in my control."

Looking forward, Cannon-Brookes was eager to promote Atlassian's position as one of the five biggest software vendors in the world, counting a huge amount of Fortune 500 firms as customers.

"We are critical to how so many businesses operate, not just how they collaborate, but how they operate, and increasingly so as we get baked into more and more critical workflows in the business," he noted.

But he also admitted there were challenges to overcome when it comes to trust, especially with the potential issues AI can pose.

"There's a lot of challenge out there in the trust world," Cannon-Brookes said, looking to highlight the fact that agents are baked-in to Atlassian tools as a key differentiator - allowing for trust to build as the company can show exactly what it is up to - and that they can be trusted in the future.

"Customers are not subscribing for the software we've built, but for the software we will build," he added.

Overall though, the message from both Cannon-Brookes and Atlassian as a whole was one of collaboration, and a new "multiplayer" capacity for AI agents.

• 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.