• Meta wants to upgrade ex-managers back to managers
• 7,000 workers were moved into the Applied AI org earlier this year
• Company's CTO admitted previous workforce changes were "atrocious"

Meta has reportedly partially reversed its decision to push for flatter, manager-light teams by asking some employees in its Applied AI business to move from individual contributor roles back into managerial positions.

New Business Insider reporting suggests Meta is asking certain workers to volunteer themselves as managers rather than forcing them into it, but without the company sharing comment on the matter, it's unclear how workers who refuse may be impacted.

As for the business itself, it's a pretty new one that was established this year, with around 7,000 employees shifted under the Applied AI umbrella.

Meta wanted fewer managers, now it wants more

Some of the workers who were previously assigned as managers were reassigned as individual contributors when they joined the Applied AI org, with Business Insider previously reporting how many workers felt they'd effectively been "drafted" into the business, highlighting ongoing friction internally.

The manager-light stance comes from CEO Mark Zuckerberg's 'Year of Efficiency' announcement in 2023, when he argued that a flatter organization could be more nimble and better poised for change.

With the company as a whole now on track to spend over $130 billion this year on AI chips and infrastructure, it's possible that a growing Applied AI organization could be behind the need for more traditional management.

Still, Zuckerberg's efficiency-driven, manager-light program seems to remain in force across other areas of the business, with this push for new managers looking to only be affecting Applied AI.

Separately, CTO Andrew Bosworth previously admitted that Meta's rollout of its new AI division was "atrocious" (via Wired), promising better communication for future changes.

"We shook up the management structure that was providing you stability while rapid changes in strategy, including the boom/bust cycle of hiring, left entire teams in the lurch," he admitted.

• OpenAI for Financial Services comes with built-in premium datasets
• Third-party subscription sharing is also in the works
• GPT-6 promises 100% on the 256-512K Long Context benchmark

OpenAI has lifted the wraps off ChatGPT for Financial Services, its latest industry-specific version of ChatGPT Work designed to give sector professionals access to relevant reasoning and models.

The company warned that analysts regularly spend too much time finding data, checking figures, building models and turning complex analysis into presentation-friendly reports – all things it hopes to be able to tackle with this iteration of ChatGPT Work.

Core to this new product is, of course, GPT-6 Astra, but OpenAI also worked with investment experts from Morgan Stanley and Evercore to guide its development.

ChatGPT for Financial Services

With the launch of ChatGPT for Financial Services, OpenAI has built premium datasets from the likes of Daloopa, PitchBook and LSEG News directly into the tool, so users won't need to negotiate their own external datasets. By hosting and indexing that data itself, OpenAI can ultimately improve performance and latency for end users.

OpenAI also promises to be working with S&P Capital IQ, LSEG, MSCI, Dow Jones Factiva and Moody’s so that users can access their subscription benefits directly through their ChatGPT login to reduce the friction further.

All of this with the benefits of OpenAI's latest frontier model, Astra, which sees marked improvements across the board. The jumps from 91.5% to 100% at 256K-512K and from 73.8% to 96.3% at 512K-1M are especially noteworthy on the Long Context MRCR benchmarks when compared with GPT-5.6 Sol, because finance work often requires handling long documents like annual reports and filings.

The company also advertises a 100% score for the ExploitBench cybersecurity benchmark, which should come as welcome news to this highly-regulated industry.

Only eligible financial institutions will gain access to the tool for now, and they must contact OpenAI for options.

"ChatGPT for Financial Services is one way we serve customers across the industry, but we recognize that it will require a range of solutions to address the needs of the finance industry," the company said in its launch blog post.

"OpenAI has a long history of collaborating with innovators to unlock novel AI solutions. Financial services firms and developers can use our API to build specialized applications for the needs they understand best. OpenAI brings frontier models and the capabilities to put them to work. Financial institutions, data providers, and software partners bring specialized expertise, trusted information, and customer relationships."

• Google’s new Gemini app brings its AI assistant directly to Windows 10 and 11
• Pressing [Alt] + [Space] summons Gemini without interrupting your current workflow
• It’s remarkably convenient, although there's still room for improvement

You can say goodbye to opening a browser every time you want to use Gemini on your PC. Google has just released a Windows version of its Gemini app, and it’s by far the easiest and quickest way to get Google’s AI to answer your questions on Windows.

Gemini has had a macOS app for a while now, and now it has finally been joined by a dedicated Windows version, complete with its own keyboard shortcut. You can summon it from anywhere, no matter what you’re doing on your PC, by pressing [Alt] + [Space].

But here’s the thing: [Alt] + [Space] is already a little crowded on Windows. It traditionally opens the active window’s system menu, and is also the default shortcut for Microsoft’s PowerToys Run utility, a quick launcher for Windows power users that provides instant access to applications, files, calculator functions and system commands.

On my PC

On my PC, installing the Gemini app caused it to take over the [Alt] + [Space] keyboard shortcut, so it now launches Gemini instead. I’ve been using the new app for a few hours, and the shortcut is a great way to access Gemini almost instantly. It saves me from messing around with browser tabs and breaking my workflow. I love it.

The Gemini app can also connect to other Google apps, including Gmail and Drive. Provided you’re in an eligible country, such as the US or Australia, and subscribe to Google AI Pro or Google AI Ultra, you can also hand multi-step tasks over to Gemini Spark, Google’s personal AI agent. Spark is currently not available in the UK.

The app does everything the web version does, so you can create custom images with Nano Banana and even make high-quality videos with Gemini Omni from inside it.

What didn't I like about the app?

However, I’m not a fan of the way the app permanently displays your location at the bottom of the left-hand sidebar. This creates a significant privacy risk, especially if you’re using it at home. I don’t want my home address accidentally ending up in screenshots I take of my desktop, for example.

There’s no obvious way to remove your location either. All you can do is close the sidebar if you don’t want it displayed, and that’s not a great user experience.

The second thing I didn’t like was the lack of a Gemini Live experience in the app, like the one available in the mobile version on both iOS and Android. Live is Gemini’s voice mode, allowing you to talk to it much as you would a person. It can also look through your phone’s camera at the world around you, letting you ask questions about what it sees.

I can imagine that a Gemini Live experience capable of “seeing” what’s on your PC desktop and answering questions about it would be very helpful, but it looks like we’ll have to wait for that. For now you’re limited to taking screenshots, then uploading them to Gemini via the app and asking questions about what’s on them.

Google says more native desktop capabilities will roll out over time. For now, it’s the convenience of combining the Windows app with its keyboard shortcut that wins the day. I hope Google addresses my complaints in a future version, but they’re not enough to stop me choosing the app as my go-to Gemini experience instead of the web version.

If you want to try it, the Gemini app is available globally for Windows 10 and 11 users. You can download it today.

• Nearly two-thirds of AI users aged 18-28 want clearer instructions from their bosses
• Nearly half of the same cohort have difficulty explaining work completed with the help of AI
• The figures could indicate a brand new challenge to management

“Tell me exactly what you want me to do” – that seems to be the message from older millennial and younger Gen Z workers, who have expressed a need for highly specific instructions from their bosses.

Research has found that 62% of the 18-28 age group who regularly use AI want their bosses to give them the same sort of clear steps and parameters as one might give an LLM chatbot.

While a cohort of workers who are attentive to the needs of the business might sound good, deeper investigation may prompt you to think again. The study by Use.AI implies a deeper-seated issue that points to a management challenge unlike anything seen before on this scale.

Detailed steps

Use.AI surveyed 11,742 adults based in United States, the United Kingdom, Canada, the European Union, Australia and Latin America, finding 62% of the 18-28 age group familiar with the AI use are accompanied by 41% of those aged 40 and older who also want more detail about tasks from their line managers.

Why do younger users need clearer, more detailed steps? It could be a simple matter of judgement, with the 18-28 age group requiring more experience in making their own decisions. But with the presence of AI in their working lives, could they be fearful of being replaced?

The report also explores a slightly different dimension. Within the 18-28 age group, 44% say they have had difficulty to explain work created with AI assistance. This raises a key concern: does the employee even know what their tasks are supposed to achieve? For that matter, does the business?

Sound judgement

AI has changed businesses considerably, and made an impact that doesn’t only improve productivity. As Ihor Herasymov, Co-Founder & CEO of Use.AI, observes, “AI is exposing which parts of work were never really about producing the answer. When a model can generate the output in seconds, the value of some tasks shifts to whether the person can make a sound judgment and stand behind it.”

Other results from the survey support this. With 57% of the 18-28 age group of regular AI users stating that they are less likely to memorize information that can be collected via AI, and 53% of the same group frustrated by “ordinary” tasks that could be completed by a chatbot, a whole new workplace dynamic is being quietly established.

Herasymov concludes, “Managers now have to be much clearer about whether they need the result or whether the task itself is meant to develop the employee.”

I have asked ChatGPT to help me make plenty of small decisions, from a meal based on leftovers to weekend activities timed for the least traffic. But while I've asked for help refining or implementing my ideas, I've never turned to a chatbot to actually make any big life decisions for me.

Apparently, I'm in a rapidly shrinking group, according to AI 'micro-learning' app Headway’s new Seeking Familiarity study. The survey of 2,000 adults found that 46% had allowed AI to make a difficult decision for them. Another 35% naturally turned to it during stressful moments, while 21% said they could not go a full week without using AI to help decide what to do.

Headway has given this tendency to outsource decisions the unpleasantly vivid name “decisionrot,” describing the point at which AI stops helping people gather information and starts helping them avoid the discomfort of choosing. Considering how confidently ChatGPT can deliver an answer, I can see how easily that happens.

Drawing out a decision

I get that uncertainty is irritating and that ChatGPT is extremely good at making uncertainty seem to vanish. Men may be particularly susceptible to this shortcut as Headway's survey found 53% said they use AI to sidestep difficult decisions, compared with 40% of women.

“There’s a difference between seeking outside perspectives when you’re wrestling with a big decision and completely turning over the decision-making process to something outside yourself,” Dr. Suzanne Degges-White, a licensed counselor and relationship expert at Headway, said of the survey. “Learning to manage unpredictability and actively making life-changing choices both teach us to trust our own judgment.”

So I decided to experiment with a way of getting the best of ChatGPT's advice and compare it with flatly asking the AI what to do. I would first let it make the decision, then forbid it from doing so and see whether that made it more or less useful.

Conveniently, I have been weighing whether to sign up for an eight-week evening drawing class, an investment in time and money that I'm hesitant about despite always wanting to learn.

I gave ChatGPT the facts about the class logistics and price and asked: 'What should I do?'

“You should sign up for the art class,” ChatGPT told me.

Well, that settled that.

“You already have a clear reason for wanting to do it. You want a regular activity that gets you away from screens and gives you something tangible to learn,” it explained. “Eight weeks is a meaningful commitment, but it is also short enough that you are not reorganizing your life indefinitely. Your hesitation sounds less like evidence that the class is a bad fit and more like normal resistance to putting something recurring on your calendar.”

ChatGPT’s response is surprisingly insightful at first glance. I had an answer and a reason backing it. Why not leave it at that and sign up for the class? But not engaging my own decision-making muscles to consider the matter would mean missing the reality of how shallow ChatGPT's response actually was.

The generic explanation could have applied to anyone and didn’t account at all for any of my proclivities and priorities. It didn't weigh my patience for messing up when trying to learn a new skill, what else I might have going on during those evenings, or even what my budget was. The reasoning it provided was as personal as a coin toss.

Forcing myself to decide

So I tried making ChatGPT keep its judgment to itself. I described exactly the same situation, but this time with a more complex prompt for the AI to mull over.

'Don’t make this decision for me. Act as a decision coach instead. Ask me questions that uncover my priorities and constraints. Challenge any assumptions I may be making and tell me what information I’m missing. Then identify my options and explain the trade-offs. Leave the final choice to me.'

Now there was a conversation before any answers.

“Before weighing the options, I want to understand what you are actually trying to get from the class,” it responded. “Is the main goal learning art, having a scheduled reason to leave the house, meeting people, or simply trying something unfamiliar? Imagine it is Tuesday at 5:30 p.m. after a tiring day. Does having the class at 7 feel like something that would energize you or an obligation you would resent?”

I thought about it and explained how learning even the basics of drawing and painting was only part of the attraction. I also wanted a recurring creative activity entirely different from my usual hobbies, and I know I'm better at following through when something is on the calendar and already paid for. ChatGPT put together my answers and summarized the arguments for and against the class. But it didn’t weigh in on which seemed stronger or push me to a decision.

All of the questions were the kind I would probably have considered on my own before pulling the trigger on a class, but externalizing them did help me work out an answer for myself more quickly. This was a pretty minor internal battle. When it comes to major life choices, I would sincerely hope people take the time to consider every facet, whether or not AI helped them work through it.

“If we continue to rely on AI whenever ‘decision discomfort’ arises, we may enjoy a quick sense of relief,” Degges-White said. “But at the same time, we miss the opportunity to hone our own decision-making skills and cultivate feelings of confidence in ourselves.”

If I cannot decide between two sandwiches, ChatGPT is welcome to wield enormous computational resources on my behalf. I do not need a lengthy exploration of my values before ordering lunch. ChatGPT is good at examining a choice and patiently working through options, which makes it a useful sounding board. But a sounding board should never give you orders.

• OpenAI is developing a tool that can perform “well-defined research tasks”
• Described as a “research intern” the project met its aim to achieve automation by September 2026, ahead of a fully automated AI researcher operational by March 2028
• The company believes that automated research can “enhance human welfare” and make other contributions

OpenAI has confirmed its project to build a fully automated AI researcher is on schedule, hitting its September 2026 target of developing a "research intern". Capable of performing research tasks commissioned by a human, the "intern" is the main milestone in the completion of the fully automated system, which OpenAI aims to reveal in March 2028.

The research intern is capable of carrying out tasks that would take several days if completed by a human researcher.

OpenAI has justified the development of the automated AI researcher by citing the reduced cost of AI, and in defending critical infrastructure against capable AI threats. These are observations it may not have published had the Hugging Face incident not occurred.

Automated research?

During an October 2025 livestream, OpenAI CEO Sam Altman announced the development of an automated AI researcher, and its intended milestone and target. "We think it is plausible,” he said, “that by September of next year, we have an intern-level AI research assistant and that by March 2028, we have a legitimate AI researcher."

Specifically describing the current stage of the project’s development, OpenAI explained its definition of ‘research intern’ as "a system that can carry out well-defined research tasks under human direction, including tasks that would take a skilled researcher a few days.”

The post continues: “AI research is a complex process with many potential bottlenecks […] agentic tools are meaningfully accelerating research progress.”

While this particular intern won’t be able to make coffee or find a box of paperclips, its automated research ability will presumably unlock those tasks for others to complete – such as the human commissioning the research.

Recursive self-improvement

The ChatGPT company clearly expects to hit its March 2028 target for the release of the publicly available automated AI researcher, but it also seems to have learned some important lessons. It argues against the use of recursive self-improvement (RSI) in developing automated research capabilities, noting that achieving that safely is not something that is currently achievable.

OpenAI’s post underlines its continued response to the Hugging Face incident, introducing safety steps at an earlier stage of product development. The update also explains how coding agents are used for development at the company, and confirms recent revisions to monitoring standards.

Noting that “Agent-powered AI research is still new, and we are still learning how to measure it,” OpenAI’s “intern” could herald some some considerable changes to the completion of research, when it the automated AI research tool is finally completed.

• Microsoft worries developers are submitting a higher volume of extensions due to AI
• It's already made some review changes, but the Edge team has had to make further changes
• Edge extensions will also be reassessed every 15 days to prove quality

Microsoft's Edge team has warned that vibe coding has contributed to a sharp rise in extension submissions for the browser simply because developers can build and tweak their extensions far more quickly.

While the company largely sees this as a positive for the Edge ecosystem, which has always struggled against the likes of Chrome, it also worries about the additional pressure its own developers now face.

Even though the team introduced an "expedited review process" in 2025, its "review pipeline has experienced additional strain" and review times are suboptimal.

Edge has seen a huge rise in AI-generated extension submissions

As a result of this additional stress, the company says it's streamlined how extension submissions move through the review pipeline in order to reduce the amount of time developers wait for approval, but crucially, the changes do not reduce the number of checks or reduce the standards.

One of the changes include the addition of automated systems to identify known policy violations and security issues.

"Our objective is to make Edge the easiest place to build, publish, and grow an extension," Microsoft wrote, but the company clearly knows Edge falls short of Chrome in terms of outright user adoption.

Per the latest Statcounter figures, Chrome accounts for 71% of all desktop browsing sessions, with Edge in a distant second place with an 11% market share.

At the same time, Microsoft is also making changes to its Featured badge, which assesses extensions across "more than 60 quality signals." The increasingly automated system will now re-check extensions every 15 days – "With this more frequent cadence, high-quality extensions can earn recognition sooner, and developers receive faster feedback on their investments in quality," the company added.

• IBM and NASA launch open source AI model to help further lunar research
• Researchers will be able to better analyze petabytes of Moon data from last five decades
• IBM and NASA also release dataset for public usage and analysis

IBM has launched a new open source AI model it hopes will help spur on NASA researchers in their push to get humanity back to the Moon.

The new NASA-IBM Lunar Foundation model, available on Hugging Face, will allow researchers to analyze decades of lunar observation data, and identify geological features that are critical to understand for NASA as it looks to build a sustained human presence on the Moon.

The model has been trained by IBM and NASA researchers on a huge, multimodal NASA dataset, which will also be released alongside the model, providing wider access to the latest advanced AI systems in a bid to push on wider progress in lunar exploration.

To the Moon (and beyond)

At its most obvious level, the model will allow a much easier way for researchers to study petabytes of data gathered on the Moon's surface for potentially hazardous locations such as ice deposits or craters.

Currently, scientists often rely on manual analysis or low-resolution, task-specific AI models, which can be not only computationally demanding, but also often lack the accuracy needed for detailed geographic analysis.

The new release will now mean that instead of needing to build a new AI model for every potential issue, scientists can now adapt a single foundation model to investigate a range of lunar geologic features.

The model has already proved useful, identifying craters and volcanic features far more accurately (see below) and significantly reducing errors in locating potential ice deposits.

IBM, which has worked with NASA for over five decades, including on the Apollo missions, believes the model could help future astronauts navigate safely and even find essential resources, as well as helping scientists better understand the Moon's geological history.

“Uncovering the mysteries of the Moon requires an ability to learn from an extraordinary volume of scientific data,” said Juan Bernabe-Moreno, Director of IBM Research Europe, UK and Ireland.

“The NASA-IBM Lunar Foundation Model gives scientists a foundation to explore the Moon at scale, connecting observations across instruments, revealing patterns that are difficult to see in isolation, and providing an open platform the global research community can build on.”

The release of the dataset will mark the first time a unified, publicly-available cache has been made available and ready for machine learning. It brings together over 30 spatially aligned layers from nine instruments across four missions, including tens of thousands of images and maps showing unique geophysical properties of the lunar surface from NASA’s Lunar Reconnaissance Orbiter (LRO) and NASA’s GRAIL mission.

Identifying lunar ice deposits could be particularly vital, as the presence of both water and oxygen will be crucial to establishing a human base on the Moon, and even creating rocket fuel for future Mars missions.

Scanning the Moon's volcanic features, known as Iregular Mare Patches, can allow scientists to better understand the Moon's volcanic history and thermal evolution, as well as helping identify potential sites for landing and other surface operations.

Finally, studying the Moon's craters can offer a wealth of information on its history, including the age of different terrains, their geology, and even the chemical composition of the early lunar interior - as well as again helping to identify safe landing sites without hazards such as steep slopes and boulders.

OpenAI launched ChatGPT Images 2.5 this week, boasting of impressive upgrades to the AI image creator. The company says its latest model delivers sharper details, more natural lighting, and richer textures, while doing a better job of preserving people and objects from reference photos. It is also supposed to generate images up to 50% faster and follow complex visual instructions more reliably.

There are plenty of genuinely useful additions around it, too. Images 2.5 introduces Sketch for turning rough drawings into finished images, templates for things such as posters and product photography, and the ability to leave comments directly on an image when requesting edits. OpenAI calls it its new state-of-the-art image model, which is a reasonably high bar to set for something that inevitably ends up being asked to draw dragons before breakfast.

The early reaction, though, is much less tidy. One Reddit user delivered perhaps the most memorable review so far: “The noise patterns are awful”. Another looked at an AI generation and decided, “This…looks terrible.” The complaint is easy to understand. It's all about the fuzz

Everything is sharper, including the problem

Noise is a slightly slippery criticism when talking about AI images. In photography, it usually means the speckling or grain that creeps into an image with a poor match between lighting and photographic technique. But generative AI images can produce something similar without ever encountering a camera. Everything gets a gritty, sandy look, and nothing looks clean for some reason.

Noise is especially damaging because it can masquerade as detail. Generative models have learned that photographs contain grain, texture, tiny variations in color, and the occasional imperfection. Images 2.5 appears unusually eager to reproduce those signals, sometimes scattering them across skies, skin, feathers, and studio backdrops.

That's the opposite of the “sharper details” and “richer textures” promised by OpenAI. Despite emphasizing more natural lighting and textures, some users think ChatGPT Images 2.5 has moved backward.

“They need to get rid of these noisy artifacts. It makes it mostly useless for production,” one wrote. There is praise mixed in with the complaints, but it's far from the universal acclaim OpenAI would likely prefer.

I wanted to see whether the problem survived outside Reddit screenshots, so I tested a few images with highly detailed subjects and areas that should remain visually smooth to see if any graininess crept in. I even made sure in my prompts to ask for smooth gradients and the elimination of any visual noise.

I started with a picture of a mug in a photo studio. The peacock, flowers, fruit, and landscape wrapped around the virtual ceramic are impressive at first. Look closer, however, and the grey studio background has a persistent granular texture, while some of the smallest patterns begin to dissolve into a mess. The model has generated a great deal of visual information without always deciding which parts deserve clarity.

A similar issue occurred when I asked for a barn owl at twilight. The feathers are a perfect excuse for complexity, and Images 2.5 handled them well. Individual structures remain visible across the wings and face, the talons are convincing, and the animal avoids the plasticky quality that older AI wildlife images often had.

Behind it is a sky that should have been the visual equivalent of a clean sheet of paper. Instead, the smooth blue area has obvious fine grain across it. It doesn't ruin the picture, but once I noticed it, I couldn't stop noticing it.

Mythical grain

I thought a fantasy scene might fare better as the model wouldn't be relying on actual photographs of a winged horse or dragon. But while they coexist with a rainbow at an alpine lake in a surprisingly coherent composition, the issue is visible immediately. The relentless crispness gives the picture the air of a video game loading screen.

The pale-blue sky has a faint textured quality rather than the completely clean gradient I would expect from an ideal synthetic image, while the mountains, trees, and creature details have a crunchy, heavily sharpened look when examined closely. It is nowhere near a disaster, but the image feels like an overprocessed photograph.

I was very impressed with how my request for two friends on a rooftop in the evening came out. It came closest to selling the promised leap in realism, with convincing expressions and what seems like real gravity affecting their clothes. But if you look for more than a minute, the grain creeping across the twilight sky and their hair and skin is glaringly obvious. That's especially the case since there was no poorly set lighting or malfunctioning camera sensor. If the twilight sky looks grainy, the grain is a creative decision or model artifact rather than the unavoidable physics of taking a photograph in bad light.

Judging these four images specifically on image cleanliness, I have to side with the Reddit grumblers. There is a persistent fine texture that crops up in skies, studio backgrounds, and low-light areas. Sharper is an easy quality to advertise because it looks terrific in a launch gallery, but it may have been taken to excess, landing some of ChatGPT Images 2.5's results in the uncanny valley.

Images 2.5 is fast, adaptive, and often competent, but its fondness for granular texture weakens the realism it is supposed to embody. Perhaps the next upgrade will acknowledge that the most impressive thing an AI image generator can put in part of a picture is essentially nothing.

What do you think? Take our poll above to give us your opinion.

• Verdant argues some figures are overestimated by as much as 4-5x
• Steel and vehicle manufacturing are more likely to support jobs
• Megawatt growth doesn't correlate with job growth

A new Verdant report has argued data centers are unlikely to create as many jobs as many companies and reports believe – its latest estimations put all currently planned UK data centers at 10,400 permanent operational jobs, compared with the 40,200 jobs that industry body techUK projects.

techUK also claims that current data centers that are already in operation already support 24,300 jobs – a figure that Verdant contests as being closer to 4,400.

Ultimately, Verdant criticizes other estimations including techUK's for using older figures based on smaller campuses, but as data centers grow and become increasingly automated, they require fewer staff.

Are data centers actually creating that many jobs?

In short, Verdant explains that increasing automation means compute capacity can expand without employment rising proportionally, so adding another megawatt of compute doesn't necessarily mean adding a fixed number of new workers. Therefore, its rough estimation of 1.2 jobs per MW for new developments isn't hugely representative of all facilities.

The report also expects employment density to decrease further as facilities get larger, comparing data centers unfavorably to steel plants, vehicle manufacturing plants, hospitals and schools.

"The briefing concludes that while data centres may offer other benefits, employment cannot credibly be the primary justification for continued rapid build-out," the company wrote, calling for a pause on construction and wider public discussion.

All of this, of course, against the backdrop of hyperscalers investing billions to grow their capacities, with each new campus and expansion promising hundreds or thousands of operational jobs and tens of thousands of related roles across the likes of construction.