• OpenAI will keep pushing AI’s scientific discoveries, Sam Altman says
• Some researchers are excited by the ‘fantastic’ breakthroughs
• But others warn that the pace of change is ‘psychologically taxing’

One of the promises that huge AI companies like OpenAI and Anthropic have made is that their machine learning models will help to solve some of the oldest problems in math and science. The pace of their discoveries has left many experts struggling to keep up, but OpenAI’s Sam Altman just has just said there’s much more in store — and not everyone is happy about that.

Speaking on the Cheeky Pint podcast, Altman outlined his belief that “Humanity deserves fundamental knowledge, and we should not be sitting on a major fundamental science discovery.”

That rationale is why OpenAI has published breakthrough after breakthrough in these fields, knocking down some of the oldest and toughest problems that have troubled great thinkers for years. But the way in which the company has set out its results has drawn criticism for often inadequate attributions and questionable verification processes.

While Altman acknowledged that, saying on the podcast that “I do have empathy for the method” and saying OpenAI’s quest for mathematical challenges did “get out of control” at times, he suggested that there’s much more to come. Asked if the firm has anymore unpublished breakthroughs in its vaults, he replied “We do. Of course.”

That’s led to accusations that, far from giving people “fundamental knowledge,” OpenAI is gatekeeping important discoveries. And there’s another problem: the sheer scale of the new research is swamping academics. For instance, Wired has noted that the company recently “dumped 722 manuscripts on 400 problems on GitHub,” leaving the scientific community to pick through the enormous mass of information.

And the impact of having the most important work in your life suddenly undercut by a machine is “psychologically taxing for many people,” said Thore Husfeldt of the IT University of Copenhagen. Husfeldt added that there is “a lot of doom and gloom, because every week, or every second week, the results announced by AI just annihilate the ongoing research projects of many people.”

Obliterating entire fields of research

AI models’ ability to solve thorny problems was keenly demonstrated when it apparently disproved the exponential time hypothesis (ETH), which argued that the effort needed to solve a problem increases with its complexity. That’s upended countless theories that assumed that ETH was correct and left many experts scrambling to catch up.

As Linköping University professor Peter Jonsson told Wired, although AI’s tendency to undermine years-long research can be irksome, “at the same time, we learn a lot from it. And this is what science is about.”

UC Santa Barbara professor Daniel Lokshtanov aptly described the mood among many scientists and mathematicians, saying “It’s just a giant bomb dropped into the middle of the field,” he says. “It’s really cool and terrible at the same time.”

For Husfeldt, meanwhile, the discovery is “fantastic.” He added: “Being a mathematician is a lot like stumbling about in a completely dark room. It’s like somebody just illuminated an entire subfield in floodlights of knowledge, and now we just run around like overexcited bunnies and look at all the nooks and crannies that we can suddenly explore. So it’s a super exciting time.”

In the case of the ETH theory, OpenAI’s work has not been peer reviewed or independently verified. But it’s clear that AI research is creating generational changes in many areas — and overturning entire fields of study in an instant, with all the excitement and chaos that comes with that.

Jonsson fittingly summed the situation up, saying: “I don’t care if it’s a human-written proof, or it’s written by AI, or if an alien transmitted this proof to us. If it’s true, then we must simply use it in the future.”

• Berkeley professor Richard Borcherds says he no longer grades homework
• The renowned mathematician stated that it is amost impossible to spot a human written submission among those created with AI
• Grading in Borcherds’ classes are now conducted with in-person exams, completed without resorting to electronic devices

Professor Richard Borcherds of the University of California, Berkeley, has revealed he has abandoned grading homework for his math students.

The decision appears to have been made following an improvement in AI-produced homework, which has left educators unable to spot human-penned submissions from those completed using AI.

British-born Borcherds – whose award-winning work includes infinite-dimensional algebras and is currently researching quantum field theory – has determined that his students will be assessed using traditional exams, without reliance on electronic devices.

The decision follows Berkeley’s previous evaluation of AI detection software that it found was unreliable, and prompted the university to allow professors to formulate their own AI policies within the course syllabus.

"Grading for correctness"

AI has changed the game as far as educators are concerned. While some appear content to let students submit work produced however they see fit, others are taking a more determined approach. Professor Borcherds has clearly seen a change in scores as the use of AI among his math students has increased.

“Online exams are now a waste of time as several students just use AI to answer the questions. I cannot tell which students are using AI, but I notice the average score increases a lot in online exams.”

Borcherds explained that what he calls “grading for correctness” essentially pushes students towards using AI. They’re employing a tool that can be guaranteed to produce the correct answer.

“Grading for correctness just adds a huge incentive for them to use AI for homework instead of doing it themselves and penalizes students for honesty. And paying to grade AI generated answers is a waste of time and money.”

Difficult assessments

Other educators have expressed similar concerns about AI, although to date none seem to have taken Borcherds more traditional stance on the technology. Some have found, however, that giving students the time and space to use AI has skewed their results, and limiting access to the technology provides more realistic grades.

Berkeley has previous explored the use of AI detection and found it lacking. By putting its professors in charge of determining departmental expectations of AI use in students, the university has given its educators the power to expect better from their students.

Professor Borcherds could be set to lead the way on this, and perhaps once this story attracts attention, more educators will take a stronger view of AI assistance in homework and assessments.

Via New York Post

• San Francisco lab Radical Numerics says its Omnii-1 model designed bacteriophages that were given to a patient with a drug-resistant infection, a world first
• The phages were built by rewriting the gene for T7's tail fiber, the protein the virus uses to latch onto bacteria
• Nobody outside the company knows whether it worked, because the results have not been published, peer-reviewed, or disclosed yet

Radical Numerics has announced its AI-designed viruses (bacteriophages) were given to a sick person, but has not provided much further insight about what happened next.

The San Francisco AI lab announced in October 2026 that bacteriophages designed by its new genome model had been administered to a human patient with an antibiotic-resistant bacterial infection, under a physician-led compassionate-use pathway authorized by the FDA.

If the claim holds, it is the first time an AI-designed organism of any kind has been put into a person as medicine.

Not a new phenomenon, but one that could do with AI assistance

Bacteriophages are viruses that infect bacteria and ignore human cells entirely. They have been used as therapy for more than a century, predating penicillin, and have enjoyed a quiet revival as antibiotics have begun to fail.

The World Health Organization considers phage therapy broadly safe, and unlike broad-spectrum antibiotics, a phage can target one bacterial strain while leaving the rest of your microbiome alone.

That precision is also the problem: each phage recognizes a narrow set of bacterial surface receptors, so treating a patient means finding one that matches their specific infection. Clinicians screen existing phage libraries, then environmental samples, then give up and try engineering as a last resort. For someone with sepsis, that search is not an academic exercise but an important life-or-death situation.

The scale of the underlying problem is not in dispute. The Global Burden of Disease project estimated that antimicrobial resistance was associated with roughly 4.7 million deaths in 2021, with projections indicating that between 2025 and 2050, close to 40 million deaths could be attributable to it.

Radical Numerics was founded by members of the team behind Evo and Evo 2, the Arc Institute genome models that generated the first entirely AI-written phage genomes. That earlier work produced 700,000 candidate genomes; 285 were synthesized, and 16 produced viable viruses. It was a proof of concept with a punishing, yet promising hit rate.

Omnii-1 is the follow-up, and the company's technical write-up describes a narrower, more surgical approach. Rather than inventing whole genomes, the model edits the targeting component. The team worked with T7, a well-characterized 40-kilobase E. coli phage, and rewrote gp17, the receptor-binding tail fiber.

The numbers the company is willing to share are genuinely interesting. At 11 to 15 amino acid changes from wild type, 69.9% of designed variants were recovered as viable phages, against 4.8% for random controls. Of 6,694 variants with measurable activity, 2,161 beat the original, including some carrying 28 amino acid substitutions.

A promising, but unproven and unpublished play

The company tested its designed cocktail against bacteria isolated from a real patient's infection, confirmed it eliminated that isolate in vitro, and then administered it. The patient reportedly had an E. coli infection that had resisted not only antibiotics but every available phage, which is effectively the worst position one can be in when it comes to infectious diseases.

Radical Numerics has since declined to describe the patient's clinical course, outcome, hospital, or physicians, citing patient privacy and the fact that even limited clinical detail could identify the patient.

The company self-published the technical report on its website and has not had it peer-reviewed, but has separately indicated the treatment is proprietary and something it hopes to commercialize.

That silence held as of October 29, nine days after the announcement, when trade coverage for infectious disease clinicians still reported no disclosed clinical course, no outcome, and no peer-reviewed publication. The technical write-up remains the only account of the case.

In fairness to Radical Numerics, its own write-up states plainly that single-patient observations cannot establish safety or efficacy, which is more cautionary than most product launches manage. CEO Eric Nguyen has also said the company intends to publish through peer review.

Outside experts have landed somewhere between intrigued and unconvinced, which is the correct place to land. Harvard microbiologist Michael Baym, who was not involved, said the result would be incredible if it works, but added that his instinct is skepticism until the treatment finishes and the viruses' novelty can actually be examined.

The commercial logic is much easier to establish; phage hunting is slow, and Radical Numerics believes it can compress design to days rather than months.

Ottawa Hospital infectious disease clinician Marisa Azad, who is collaborating with the company, has described the conventional search as remarkably time-consuming, and she is not wrong. If AI can reliably shorten that loop, it changes who gets treated in time, allowing for a more cost-effective and broader audience for said treatments.

There is cause for some concern too: a model that can design viruses to order is, structurally, a model that can design viruses to order. Radical Numerics acknowledges this directly by committing to biosafety, containment, and data-sharing measures scaled to the model's capability.

It is the same dual-use problem that has dogged every capable biological design tool, and one that AI labs have been publicly wrestling with as their systems get better at biology.

Engineered phages have reached patients before, most famously a 2019 case in which modified phages treated a disseminated drug-resistant Mycobacterium abscessus infection. What is new here is not the therapeutic concept but the design process, and the governance questions attach to the AI-driven process, not the concept itself.

AI has moved from reading biology to writing it and handing the result to a doctor. That is a different category of claim than an AI model proposing a cancer hypothesis or outperforming specialists on rare-disease diagnosis, both of which keep a human firmly in the loop before anything enters a bloodstream.

For now, however, a very clever experiment was run on a very sick person, and we still don't know how it went or when somebody decides to tell us. If it does pan out however, it will have massive rammifications for an industry that is only expected to grow across the next few decades.

• AI-generated code security improved for the first time in a year, with vulnerability rates dropping from 44% to 38%
• Top models performed better at preventing common flaws like XSS, but still struggled with complex security reasoning
• Researchers stress AI-generated code still requires independent security testing and verification before deployment

Computer code generated by frontier AI models has gotten more secure and consequently - contains fewer vulnerabilities.

This is according to a new report by Veracode, which claims this is the first significant improvement in a year.

In July 2025, Veracode started releasing its GenAI Code Security Report, which analyzed code generated by LLMs for vulnerabilities and security risks. Since then, the company’s researchers noticed a trend - the code was functioning almost perfect, but on the security side it left much to be desired.

Are XSS flaws worth the trouble?

“We’ve been analyzing code generated by multiple LLMs for more than a year. Until now, we have not seen a marked improvement in AI code security, despite consistently near-perfect functionality,” said Chris Wysopal, Co-founder & Chief Security Evangelist at Veracode.

“This is the first time since running our study that we’ve been able to report a meaningful move on the security dial.”

That meaningful move is a jump from 56% to 62% in overall AI-generated code security. At the same time, there’s been a drop in vulnerabilities from 44% to 38%, Veracode added, saying this was the most significant improvement since the reports started coming out. Anthropic's Claude Opus 5.5 and OpenAI's GPT-6-Sol co-lead the rankings at 73%, surpassing the previous high of 68% set by GPT-5.5 in July 2026.

“Ten models now clear 60%, up from four in the last report, and the overall average has jumped six percentage points,” Wysopal said. “With around half of code now generated by AI, today’s results are truly encouraging.”

To draft the report, Veracode tested 18 models across four languages (Python, C#, JavaScript, and Java), and four Common Weakness Enumeration (CWE) categories (CWE-89, CWE-327, CWE-80, CWE-117). Claude Opus 5.5 and GPT-6-Sol claimed the top spot with 73% followed by GPT-5.5 which holds 68%. In July, six of 11 models scored between 50 and 53, while today only two remain in that band, while 10 scored at least 60%. The bottom is still at 44% - 50% percent.

While the results are definitely commendable, there is a caveat. Apparently, the main driver of the overall gain was on cross-site scripting (XSS), which is arguably one of the weakest categories. XSS rose to 18% which is also the single biggest change in the dataset. According to Veracode, XSS is the “easiest vulnerability to fix through pattern recognition”, which is exactly how LLMs improve. In cases where data-flow reasoning across multiple functions is required (such as, in log injection cases, for example), LLMs continue to stall.

Low-hanging fruit

In other words, AI has just picked low-hanging fruit and called it a day. We’re not sure if we should be disappointed with this, since XSS attacks are common and the vulnerability is still present across the web.

In the report, Veracode also said that water was wet and the sky is mostly blue: it found that top-performing models cost more.

However, they said throwing money at the problem most likely isn’t the best solution: “The models that produce the most secure code are the ones that cost the most to run at scale, yet they still fail on more than one in four security tasks,” Wysopal said. “The answer should never be to use the most expensive model and hope for the best. A mid-tier model at a 60% pass rate plus Veracode scanning is more accurate, more cost-effective, and more scalable than a frontier model at 73% with no verification.”

In conclusion, the improvement is there, but “mends a small part of a larger wound”, Veracode said. Organizations should make sure they combine AI code with continuous and independent security verification that can catch what the models miss.

• Google's subsidiary Tuike Finland cleared and drained 330 hectares of forest at Muhos for a data center site
• The company failed to get the necessary environmental impact assessments completed before carrying out the felling
• A preliminary police investigation is now taking place, based on 'ambiguities and ‌shortcomings in the drainage and water management', although the results of this won't be known until 2027

A preliminary police investigation is now underway into Google's clearing of a forest in Finland to make space for an AI data center.

As The Guardian reports, the allegation is that Google's subsidiary Tuike Finland cleared and drained 330 hectares of forest at the site in Muhos before it had ensured the necessary environmental impact assessments were completed.

Yle (Finland's public broadcasting company) further reports that the police have said that the investigation is into suspected environmental damage, but the other possible charges could be added depending on its ongoing findings.

Finnish police said: "Based on preliminary information, there have been ambiguities and ‌shortcomings in the drainage and water management at the construction site of Tuike Finland ​Oy's datacentre project."

The investigation will be far from a short process, though, and won't be completed until some point in 2027.

Construction work was stopped on the data center in Muhos, and also at a second data center site at Kajaani-Otanmäki, on October 14. In both cases, the environmental assessments were apparently incomplete.

Antti Järvinen, who heads up Google Finland as country manager, apologized for the forest clearances, but said that Google had acted according to the "best understanding we had of the legislation at the time."

Analysis: the rush for AI data centers

Some 400,000 trees have reportedly been felled across both sites (250,000 of those at Muhos). As The Guardian makes clear in a separate report from last week (via Tom's Hardware), there are a lot of very unhappy Finnish people, as you might imagine.

Many of them question the building of data centers at all, and the AI gold rush that's the driving force behind this activity. There's apparently little direct benefit for the people of the country where these projects are underway, with only a small number of local jobs created. As a professor of computer science at the University of Helsinki, Jussi Kangasharju points out, "Really, for Finland, there are no benefits. They are international companies serving international customers. Datacentres ultimately have little benefit for others except the datacentre operator."

Others obviously worry about the impact on wildlife. Finnish conservationists inform us that the three-toed woodpeckers in the forest have been displaced, and The Guardian highlighted that in Otanmäki, wolves that had nowhere to go when the trees were felled ended up coming into a local village.

It will be some time before the criminal investigation is completed, as noted, but you can bet that the results are going to be closely watched.

• A Kentucky man has won a religious exemption against using AI
• The ruling says he no longer has to use AI tools at work
• The man argued AI contravened his deeply held religious beliefs

If you’ve ever looked to the heavens in exasperation after being told you have to use AI at work, your prayers might soon be answered, if a Kentucky man’s example is anything to go by. That’s because he’s just been granted a “first of its kind” religious exemption from using AI after successfully arguing that it went against his beliefs.

As reported by the Religion News Service (via TomsHardware), University of Kentucky research administrator David Durman reacted with apprehension when his employer started asking its workers to use AI in their day-to-day jobs. The problem for Durman was that this would mean offloading his thinking to a machine — something which, as a humanist, went against his principles.

“The process and work involved in thinking through situations, through my work, through my ethics, through the way I handle other people, is a key component in being able to develop myself as a moral person,” Durman told RNS. “And offloading that labor, regardless of whether it’s taxing or not, is undercutting my ability to be a moral actor.”

For Durman, AI also has the potential to harm society. He stated that he advocates for and believes in human flourishing, but that “At a societal level, I think that the use of AI technology runs counter to that.”

Your belief must be sincere

Durman won his religious exemption with the help of the American Humanist Association (AHA), with AHA attorney Alexandria Huiskens saying that “This is the first of its kind at the AHA and for humanism generally to exempt those who see AI use as flying in the face of their freedom of conscience.”

Durman and the AHA based their argument on Title VII, a US federal law that grants workers exemptions in their work requirements due to religious beliefs. Although humanism — which places an emphasis on human virtue and agency — is generally considered to be non-religious, federal courts have ruled that it can be treated as a religion.

The University of Kentucky accepted Durman’s appeal, meaning he no longer has to use AI at his place of work. He does still have to meet and cooperate with his colleagues who use AI, however.

If you think this might be a quick ticket out of using AI, though, there’s a catch. Douglas Laycock, an emeritus law professor at the University of Virginia and the University of Texas, noted that your professed beliefs have to be sincere to qualify under Title VII, so you can’t just pick a religion and say that using AI violates its tenets if you don’t actually subscribe to that belief system.

Still, for those who do hold sincere religious principles and feel that AI contravenes them, there could be a new way to dodge those mandatory AI workflows whenever you head into the office.

• A big update for Google Home is rolling out
• The improvements include better support for Matter devices
• It's rolling out now for Android and iOS

It's not been the easiest of years for the Google Home app and anyone trying to use it — what with the whole Gemini switchover and everything that's gone along with it — but the smart home app is now continuing to move in the right direction with another major update for Android and iOS.

As detailed in the official changelog (via 9to5Google), the update includes a bunch of fixes for Gemini: transferring music between rooms, setting timers, and controlling YouTube TV playback should now work more reliably, with fewer "sorry, I didn't understand" responses when no voice command has been issued.

There's a new Home Premium benefits screen, and more accurate icons for Matter-controlled windows and doors on Android, including garage doors, windows, gates, blinds, and cabinets. These controls, plus those for Matter devices showing energy data, will be more specific and tailored, and less generic.

If you've got cameras set up, you get more reliable event timeline scrolling on iOS, a smoother live view on foldable phones on Android, plus improved Wi-Fi connectivity and faster night vision switching across all your Nest cameras. There are also upgrades including more accurate status light behavior on specific camera models.

Check for your update

Other fixes and improvements include online status indicators that are more reliable on iOS, spaces that load more smoothly on Android, and a number of quality tweaks to various visual layouts — including the Thread setting screen on iOS and the parental controls page on Android.

At the same time, firmware updates are rolling out for the Google TV Streamer, Nest Wi-Fi units, and Nest and Google Home speakers. Across the board you should see fewer bugs and more reliable operation, while numerous security patches have also been applied to keep your smart home devices safe.

On the speakers, there are upgrades to streaming performance, audio quality, volume tuning between paired speakers, local network discovery, Bluetooth streaming, Wi-Fi 7 compatibility, and controls for Google Meet voice controls. It's a fairly comprehensive update — though it hasn't addressed all the issues users are continuing to have.

Google says the update is rolling out now, and may take a few days to reach everyone. You should get it automatically eventually, but if you want to check manually, tap your profile picture then Manage apps and devices in the Play Store app on Android, or tap your profile picture then App Updates in the App Store on iOS.

I was trying to buy something online and I did what most people do now: I let an AI overview point me to it. It sent me straight to a product page, confidently. And guess what happened when I clicked it? The product was sold out.

It happened again a few weeks later, a different item, this time discontinued. And the more I thought about it, the more it bothered me, not because the AI was wrong, exactly, but because of why it was wrong. Somewhere, a company had a page that hadn't been updated and an AI system had no way of knowing that. It just read what was there and repeated it confidently, to me.

That's a small, annoying version of a much bigger problem companies are about to run into everywhere.

Most companies have spent the last decade getting serious about data. There's a data warehouse, a Chief Data Officer, a governance committee that meets monthly, and dashboards running the quarterly business review. On paper, the discipline is all in place.

Nobody applies that same discipline to the information the business actually runs on. A policy gets updated in one system and quietly stays wrong in three others. Someone leaves, and the page they updated quarterly just stops getting maintained. A product goes out of stock, or gets discontinued, and the page stays live and indexed for months. None of this looks like negligence. It looks like a normal Tuesday.

It used to be fine, because people compensated for it. A salesperson knew the pricing page was stale and quoted the real number anyway. A new hire figured out within a month which internal wiki page to actually trust. But it is becoming evident that the workaround only worked because a human was in the loop, reading the room, catching the gap before it reached someone who mattered.

AI doesn't have a loop to be in. It doesn't know the product's sold out unless something tells it directly. It doesn't pick up on the office joke that nobody's touched that page since last spring. All of that context lived in people's heads, not in the document, and a model can only read the document.

So the gap doesn't get quietly caught anymore. It gets published straight to the customer.

Where the accountability actually breaks down

Ask a CEO who owns the company's information and you'll get an org chart back. Marketing owns the website. Product owns the specs. Legal owns policy language. And if the company operates in more than one region, each one has probably built its own version of everything above.

Every team is doing its job, but collectively, the company ends up saying five different things about itself. For a long time, that only mattered if a human happened to stumble across the contradiction. Now an AI system finds it for you, instantly, and hands it to a customer as a fact.

A recent consumer survey found that nearly half of shoppers double-check an AI answer before acting on it, and when what they find contradicts it, the business loses a customer it technically just showed up for. That's exactly what happened to me that I noted earlier. The AI didn't lie. It just repeated something that was no longer true, confidently enough that I believed it until I clicked through.

That's the real issue. Information ownership rarely breaks down at the point something gets published. It breaks down after. The person who wrote a page isn't usually the person who finds out, months later, that it's gone stale. Publishing has an owner, but that information staying correct usually doesn't.

A few questions worth asking

If your company sells any product online, or publishes anything customers rely on, it's worth thinking who actually owns catching it when this information goes stale, not just publishing it in the first place.

And it's worth checking, directly, what an AI system currently says about your company or your product to someone who's never talked to you before. Not what you assume it says, but what it actually says that could affect your business.

Nobody planned for AI to be the thing that exposes every quiet inconsistency a company has been living with for years. But that's exactly what it's doing and companies need to take action. The ones that will get ahead of it aren't the ones with the fanciest AI strategy, but the ones who decide who's responsible for making sure what's out there is still true.

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Ask a room of executives about agentic AI and within about ninety seconds someone will ask how many agents they ought to be running. It has become the default measure of progress: a number you can put on a slide, benchmark against a competitor, and report upwards.

It is also the wrong question.

Across the 200-plus workshops my team and I have run with enterprises in Europe and the Middle East, the organizations getting real value from AI tools are not the ones with the longest agent inventory. They are the ones who never framed the problem that way to begin with. They started by identifying a business problem or point of friction and worked backwards. Everyone else started from the technology and went looking for somewhere to put it.

There are four questions I hear constantly that are quietly steering good companies into Pilot Purgatory. Here they are, and here is what I would ask instead.

Don't ask how many agents. Ask what outcome you are improving.

Much of the last two years of enterprise AI has been about helping people work faster. Copilots that answer questions, assistants that summarize the meeting you have just sat through, tools that get you past the blank page. These are genuinely useful and I would encourage everyone to switch them on.

But speed is not the same as value, and agent count is not the same as either. No enterprise ever created value by accumulating agents. Value shows up in real business outcomes such as fewer payroll errors, faster shift scheduling, lower supplier sourcing costs, better win rates, cash collected sooner.

That is the difference between an agent and an agentic application. An agent help with an individual task. An agentic application coordinates teams of specialized agents to deliver a business outcome. If you cannot name the KPI an agent is moving, you do not have a use case. You have a demo.

Don't ask whether AI can recommend. Ask whether it can finish the job.

The most persistent confusion I encounter is between advice and execution. Plenty of systems can now sense a situation, spot the pattern and tell you what should happen next. That is real progress. It also leaves almost all of the work exactly where it was.

Take sourcing. An assistant can flag that a category is overdue for renegotiation and suggest you run an event. Someone still has to create that event, chase the responses, score them against commercial criteria, route the approvals correctly, update the contract and shepherd the whole thing to a close.

An agentic application does that work, under policy, with human oversight and approval where required, and with a record of what it did and why.

The test is simple. At the end of the interaction, is the process further forward, or do you just have a better-informed human facing the same queue?

Don't ask which model tops the leaderboard. Ask whether it understands your business.

Benchmark scores dominate the AI conversation and matter far less in the enterprise than most leaders assume. Raw model intelligence is rarely the binding constraint.

A frontier model understands procurement as a discipline. It does not understand your supplier list, your delegation-of-authority matrix, your contractual obligations, your current inventory position, or the three exceptions your finance director signed off in March. Without that, sophistication amounts to a well-argued guess.

Enterprise context is the differentiator: trusted enterprise data and business context, including the policies, approvals, transaction history, organizational structure and live process state that already sit inside your core systems. The closer agentic capabilities are to the systems where business processes, data, security, and approvals already live, the more effectively agents can understand context and act within establish controls.

Don't ask for one system that does everything. Ask how the specialists coordinate.

There is a persistent hope that enterprise AI will eventually resolve into a single, omniscient digital assistant. Businesses do not work that way, so their AI will not either.

Organizations are ecosystems of specialists: finance, HR, supply chain, service, sales, each with its own expertise, obligations and objectives. The equivalent in AI is not one agent that knows everything, but coordinated teams of agents that know their own domain and work towards a shared goal.

Hiring is a good illustration. One agent surfaces and screens candidates. Another prepares onboarding. A third confirms the budget is genuinely available. A fourth keeps the hiring manager's approvals moving. Individually, each is modestly useful. Together they move a business process from vacancy to first day. The value sits in the coordination, not the components.

Systems of outcomes

What connects all four questions is a shift in what enterprise software is actually for. For decades our applications recorded what had happened. We are now entering the era of systems of outcomes: software that reads the state of the business, reasons over it, and acts to keep work moving towards a defined objective.

That shift demands different questions from leadership. Not how many agents, but which outcome. Not can it advise, but can it execute. Not how clever is the model, but how well does it know us. Not one brain, but one team.

Get the questions right and the technology decisions become considerably easier. Get them wrong and you will assemble an impressive collection of agents, tools, and models that nobody can tie to a number that matters.

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• Amazon CEO says he isn't worried about AI shopping agents like Muse
• Amazon had blocked Meta's AI agent from its ecommerce site
• Jassy says the AI agent experience is still "not great"

Amazon CEO Andy Jassy has said he is not worried about the effect of AI-powered shopping agents such as Meta Muse, saying the services are still "not great".

Speaking after the company revealed yet another set of record financial results, Jassy told analysts barely any customers are using shopping agents as the experience is “still not great, nor more convenient than coming to a retailer directly.”

Jassy’s comments followed Amazon's decision to block Meta's new AI assistant, Muse, from accessing its online store in September 2026, claiming this would violate the conditions of use its customers have agreed to.

Muse is not doing the business...yet

Since its launch in September 2026, Muse has proven popular with customers, despite several examples of the service accessing information it was not supposed to.

Meta CEO Mark Zuckerberg recently claimed that more than 10 million people across the US and Canada had already set up Muse, and that it had toppled ChatGPT as the top free app on the Apple App Store.

Experts have warned it may still not be up to scratch, after a security researcher found a worrying zero-day vulnerability which allowed attackers to gain access to people’s apps, such as WhatsApp or email.

Data from Surfshark also identified Muse as the most data-hungry chatbot around, outperforming all its competitors when it comes to data harvesting.

Meta has already signed up a range of top retailers, including Walmart, Best Buy and Shopify, to integrate with Muse, but Amazon has yet to join the group. The company is largely reliant on human shoppers visiting the Amazon website, where they can be exposed to advertisements and suggested products which nets the business its biggest returns.

Jassy did claim that more than 400 million customers have used Alexa for shopping in the last year, and that “customer interactions” had grown 570% year over year.

US customers using its Alexa also spent 50% more per order than those who do not use it, Jassy revealed, perhaps signaling that the service will be expanded in the coming months.

Via Fortune

Transparency duties under Article 50 have begun to apply, even as the EU AI Omnibus implementation for many high-risk systems has moved to December 2027 and, for certain AI-enabled products, to August 2028. For organizations building and deploying AI in Europe, the message is clear: regulators extended the timeline on complexity, but not on transparency.

While some organizations may view the extension as welcome breathing room, the time would be far better used preparing for the future of AI. To do that, we need to frame the challenge around creating the conditions for long-term innovation, rather than around compliance deadlines.

As AI becomes embedded in products, services and decisions that affect people's lives, organizations need confidence in the systems they are deploying, and users need confidence in the organizations deploying them. That work cannot begin in 2027. It needs to start now.

Compliance is achieved at a point in time; governance is built as a capability. A deadline can be moved, but an institutional capability cannot simply be created at short notice.

Start with the AI already in use

The starting point for every organization should be an honest assessment of whether it has a reliable picture of the tools, outputs and interactions that Article 50 covers. As we have seen in recent years, AI systems can quickly become more deeply embedded in business over time as employees explore new tools and suppliers innovate and ship new updates.

This natural expansion is a normal part of adopting a new technology, but it can result in AI becoming a core part of business processes before the proper governance is in place. Governance is most effective when it shapes those decisions from the beginning.

Attempting to retrofit governance after AI has become embedded is significantly more difficult, more expensive and ultimately less effective, and the resulting loss of trust can be more costly than the upfront work required to prevent it.

Extend the controls already in place

The good news is that many of the disciplines required by the legislation should already be familiar. They build on existing practices around privacy, security, risk management, accountability and operational governance, and much of the challenge is around applying established governance disciplines to a rapidly evolving technology landscape.

Organizations that have taken data protection regulation seriously, and already understand impact assessments and effective governance processes, are well positioned to meet many of these requirements – there is considerable overlap.

The task is to extend them for AI, not replace them. A privacy impact assessment can identify data risks, but it cannot on its own establish whether a model is dependable in a particular context, whether someone can meaningfully question its output or whether an automated process disadvantages a group.

Those questions should be built into existing workflows, particularly when AI is bought rather than built. Before a capability is embedded in software or a critical process, organizations need clarity on data treatment, service and model changes, incident response, support for transparency duties and the evidence a provider can supply. Even where the underlying model comes from a third party, organizations still need to govern how it is selected, configured and used.

The sovereignty question

Alongside the AI Act, the other crucial question impacting the way we think about governance in Europe is sovereignty. As the debate gains momentum across Europe, the practical issues raised are becoming clearer. Where is data stored? Which providers do we depend on? How resilient are our technology choices? What happens if commercial, legal or geopolitical circumstances change?

It is worth being precise about what sovereignty can realistically mean for enterprises. Full compute sovereignty, including control over chips, infrastructure and supply chains, is far beyond the reach of most organizations. The more practical goal is compute resilience: understanding where critical dependencies sit, ensuring no single vendor, cloud provider or model becomes a point of leverage, and retaining control over the layers of the AI stack that remain within the organization's influence.

Open source can support this by providing greater transparency and control over code, but it does not remove infrastructure dependency on its own. An organization may use an open model and still rely on external compute or cloud capacity to operate it.

Organizations should adopt a sovereign-by-design approach to innovation. Sovereignty should not be considered after technology decisions have been made, it needs to be embedded into decisions on data architecture, vendor selection and risk management from the outset, with resilience, not full independence, as the realistic target.

It is also important to note that not every dataset, process or workload carries the same level of risk. Organizations should take a tiered approach, applying the strongest sovereignty requirements where data sensitivity, regulatory exposure or operational importance demand it.

These are not abstract concerns. Viewing AI governance and sovereignty as interconnected challenges creates an opportunity to build a more coherent approach. This is not a case for technological isolation or for stepping back from global technology ecosystems, the aim is narrower than that. It is about ensuring organizations retain meaningful freedom of action in an increasingly complex and uncertain environment.

The real opportunity

The debate about innovation, regulation and sovereignty will continue. Europe will keep refining its approach to AI, and organizations will keep adapting to new technologies, new providers and new risks. The more important question is whether organizations use this window to build the capabilities they will need long term, rather than treating each regulatory milestone as a separate deadline.

Governance should not be viewed as a compliance exercise that slows innovation. It is an institutional capability that enables organizations to innovate sustainably, adopt new technologies with confidence, and remain resilient as markets, regulations and geopolitical conditions evolve.

The ultimate goal is innovation built on trust, and we have the ability to deliver that regardless of regulatory deadlines. The real test is not whether an organization built every part of the system it relies on, but whether it understands its dependencies well enough to change course if circumstances demand it.

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The race for AI leadership has largely been framed as a contest for the best chips, models and talent up until now. But behind every algorithm, model, query, and breakthrough is the energy that powers it all.

As AI moves from R&D into living rooms, workplaces, and factories, access to reliable electricity and the speed at which data centers can connect to the grid (AKA “time to power”) is becoming the defining factor determining where data centers (or AI factories) get built and who gets to reap the economic benefits.

For investors, it is no longer enough for a country to promise abundant electricity in the future.

Power must be accessible, predictable, competitively priced, and available on a timeline that matches the speed of the AI market.

A connection delayed by several years can mean missing an entire technology cycle.

Three factors that will shape where AI investment lands

Data center operators make investment decisions based on three fundamentals: regulatory environment, availability of energy and the price of that energy.

Increasingly, however, investors are seeking confidence: confidence that permits will be secured and grid connections delivered on schedule, and power will remain affordable.

China and the US dominate global capacity but are taking different paths. China is investing around US$295 billion in a nationwide AI computing network, alongside energy infrastructure and renewables. In the US, growing electricity demand and evolving grid-connection timelines are increasing interest in on-site generation and storage.

Europe presents a more fragmented picture. Some markets offer available capacity and attractive investment conditions; others face grid queues and complex permitting. In spite of this, Europe has many of the right ingredients. Its mix of nuclear power and renewables, combined with established infrastructure and deep industrial expertise, should make it a natural home for the next generation of AI infrastructure.

Europe may have missed much of the first wave of AI data center build out, but it still has the opportunity to capture the economic benefits of the next one. With targeted investment and smarter energy policy, countries can still reshape the map of AI infrastructure investment.

There are three levers the world should pull now to capitalize the powerful impact AI can have.

1. In the AI economy, speed is power

Energy systems around the world are struggling to accommodate rapid growth in electricity demand. Many of today’s grids weren’t built to manage such a diverse mix of energy sources alongside increasingly intense and concentrated power demand. As such, an estimated 2,500GW of generation capacity is waiting in connection queues worldwide.

In Europe’s data center hubs, often referred to as FLAP-D (Frankfurt, London, Amsterdam, Paris, and Dublin), data centers can wait up to a decade for a grid connection, while a facility may take just four years to build.

Governments and grid operators must accelerate permitting and connection processes. There needs to be more transparency around expected grid connection timelines, supported by delivery milestones and regular reporting so developers and investors can plan with greater certainty. It also means moving from “first come, first served” to “first ready, first connected”, prioritizing viable, grid-supportive projects over speculative applications.

UK efforts to remove these “zombie projects” demonstrate how queue reform can shorten waiting times and strengthen investor confidence. This helped shrink waiting times which were up to 15-years for some projects.

2. Flexibility is the fastest source of new capacity

Grid modernization is essential, but major upgrades could take up to 15 years. While wider grid expansion takes place, flexibility can free up existing capacity more quickly.

In many regions, the challenge is not simply how much electricity is generated, but whether it is available where and when it is needed. Energy-management technologies give operators the visibility and control to optimize energy use across buildings, transport and industry.

They can shift non-critical demand, such as heating, cooling and EV charging, away from peak periods. Battery storage complements this by storing lower-cost or surplus electricity for use when demand is highest. As these capabilities become more widely connected and coordinated, they could unlock greater grid capacity, reliability and flexibility at scale.

Data centers themselves can also become active grid assets through intelligent energy management. If European data centers offered just 30 hours of flexibility per year, the grid capacity available to them could more than double. The technologies needed to achieve this already exist, including digital energy-management systems, battery storage and microgrids.

Compass Data centers offers an early example of what this can look like in practice.

Schneider Electric has already delivered around 150 prefabricated modular data center solutions for Compass, while connected, condition-based maintenance has helped reduce manual on-site interventions by 40% and operating expenditure by 20%.

Alongside microgrid controls being deployed at its Phoenix campus, this shows how data centers can scale more predictably while gaining the visibility and control to manage power dynamically when the grid is under pressure.

Countries now need the right incentives and market mechanisms to reward flexibility and make participation commercially viable.

3. Make flexibility the path to cheapest power

Technology can make energy flexible, but economics will determine whether that flexibility scales. In regions like France, Germany, Italy and the Nordics where grid flexibility exists, households and businesses can be rewarded for using power when renewables are plentiful and reducing demand at peak times.

Flexibility in data centers could mean lower tariffs or faster staged connections in exchange for battery storage, automated demand response and participation in flexibility markets. Real-time monitoring and automated controls can manage these adjustments without compromising performance.

The grid’s largest untapped reserve may not be another power station, but the millions of connected assets already hiding in plain sight.

4. The real race to power AI

AI infrastructure will go where power is available, affordable and resilient. A recent report by Reuters found that European data center developers are moving away from major cities to where energy is cheaper and connections are quicker.

Countries unable to provide those conditions risk losing more than data centers. They could also lose the investment, jobs, industrial ecosystems and innovation that develop around them.

The countries that want to capitalize on the AI boom should be all-in on electricity. That means expanding clean generation, strengthening and digitalizing grids, improving regulatory clarity and unlocking the flexibility inside homes, buildings, transport and industry.

The future of AI will be shaped by data centers. But it will be won where power is abundant, resilient, and affordable. In the AI era, access to electricity becomes access to economic advantage.

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The proliferation of AI tools in business is redefining the need for data security and trust. As autonomous, decision-making AI agents move from pilot projects into core business workflows, they are expanding the enterprise attack surface just as fast as they are being adopted.

Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by the end of 2026, a jump from less than 5% in 2025. Gartner’s advice to leaders of software organizations is to set their agentic AI strategy or “risk being outpaced by the competition”.

Security is changing. What was once a “simple” focus on encryption and perimeter defense is no longer enough on its own: the rise of AI-driven risk has transformed it into a multi-dimensional challenge built on algorithmic trust, model integrity, and synthetic data exposure. Trust boundaries are shifting, and most companies aren’t yet equipped to handle it.

Yet AI has already been proven unsafe by its very makers, with the Hugging Face breach showing it was a rogue AI agent's fault. The unknown future risk of autonomous agents capable of “emergent” behavior poses huge issues for maintaining security. But this is just one risk of many faced by enterprise cyber security leaders.

In fact, the statistics of cyber security investment highlight that business is vastly underprepared for new threats. While the estimated annual cost of cybercrime is $10.5 trillion globally and predicted to reach $15.63 trillion by 2029, this eclipses Gartner’s information security spend prediction of $244 billion in 2026.

Machine data is exacerbating the data quality issue with garbage in, garbage out. This calls for technology and trust architectures to be redesigned, with internet security embedded into the data lifecycle. Security leaders will need to make considered investments in AI-safe security, transparency, and federated governance to protect their enterprise ecosystems.

The biggest security risks we’re underestimating right now

With many threats now AI-driven, it’s unlikely that a single enterprise has protection against all the necessary risks, which is why it’s incumbent on business leaders to prioritize next-level data security.

Firstly, polymorphic, AI-generated attacks are hitting bigger and faster, beyond any company’s human ability to counter these attacks. Deepfake is one such AI-powered theft and extortion technique that can be generated in minutes, costing a mere $1,000 (if that) to create a malicious bot that can hack into your system and go after your business’ crown jewel data.

Using these methods, hackers can affect AI models by manipulating decision-making itself instead of through direct attacks on the IT infrastructure. Shadow AI and exposure to synthetic data are also growing concerns as employees adopt unsanctioned or non-firewalled AI tools, models and workflows to complete mundane tasks and reporting.

Next is the surge in agentic adoption without control of the risk, especially third-party risk, which is huge. Agentic AI has significantly increased the threat surface, with systems capable of autonomously taking actions. In the rush to implement AI, access is being granted for agents to all kinds of data, even to highly sensitive data, often bypassing traditional enterprise controls. All kinds of blind spots emerge when agents are connected to external systems whose security vulnerabilities aren’t vetted.

Prompt injections are another critical risk, on a similar level to the whole third-party dependency. It's the idea that an organization doesn’t know what is actually making its way into their system. At first appearing benign, it can build up to turn malicious at some point in time. When it hits, the business won't know what hit them.

As if this isn’t enough, we’re counting down to “Q-Day”, when quantum computers become powerful enough to break today’s widely used encryption software standards. This is the stage when attackers will be able to intercept, decrypt, and compromise nearly all global digital communications, financial transactions, and other forms of secure data.

Key considerations for a responsible AI approach

What’s needed is a formal framework for trust-enabled ROI in the age of AI. It needs to be about maintaining data source integrity. In order to trust the data, there are various mechanisms or things that an enterprise needs to take care of such as identity and accountability.

We must know who's acting on the data and have context and control. We must then authorize it contextually, not implicitly and this means constantly validating behavior and intent. This is all while staying compliant against or changing the regulatory landscape and maintaining human oversight across the lifecycle.

Responsible AI requires an overall shaping of an approach that considers the impact on the entire value chain in the ecosystem. Only this ensures that the value is delivered to the people who are putting into the system or that are impacted by the system. The following are essential elements to move this forward:

Accountable humans

The concept of accountability is key. There’s a tendency to say if something bad happens, you blame the machine, you blame the algorithm, you blame the system. There need to be accountable humans that take responsibility. AI is here for the long term, and will have generational impacts, so if we don't understand that and take accountability for it, we're going to make wrong decisions.

Four dimensions of responsible AI

At a high level, the focus should center around transparency, accountability, fairness, and protection. Some key questions should be asked as part of building a framework for successful outcomes, such as:

• How do you make sure that your AI models and data practices are transparent?
• How do you make sure you drive accountability for AI-driven decisions?
• How do you make sure you're fair to the people affected by your AI systems?
• How do you make sure that you provide protection or security across the entire data lifecycle?

Building a trust premium

Amidst this new risk landscape, trust has become a measurable competitive asset for enterprises. Organizations that are moving their cyber security strategy to the next level and investing early in AI governance, cryptographic modernization and security architecture are building a trust premium. PwC research backs this up: across thousands of CEOs surveyed globally for its 25th Annual Global CEO Survey, customer trust explained 31% of the variance in profit margins and 21% in return on assets - a stronger link to financial performance than company size.

Durable trust requires an approach that is built and maintained across the entire operational life-cycle of data. This full-lifecycle view of trust comprises data source integrity, end-to-end data quality, identity and accountability, behavioural monitoring and intent validation, regulatory compliance and effective governance, applying human oversight across the AI lifecycle.

Taking a methodical approach to protection

The changing frontiers of data security demand a fresh approach, which involves building resilient data infrastructures and best practices that can stop threats in their tracks. Future successful enterprise security will depend as much on governing autonomous systems as well as defending networks.

The right approach is to take a step back, think holistically, considering all the building blocks that need to be put in place, and then be more methodical. A methodical approach protects the business, its customers, and it protects a society so that we can all progress.

Doing things right in the right way will have multiple cascading benefits, including guaranteed value for customers and the longevity of the business, both of which links to trust and loyalty.

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• Kaspersky says Lazarus's Operation FlashHole, active since at least July 2025, hit at least three South Korean software, construction, and telecom equipment firms
• An updated LPEClient tool uses four ML-KEM-1024 key exchanges to protect payload delivery, a design Check Point also documented
• The post-quantum layer guards traffic against future decryption, not endpoint detection, and the most likely way in for the hackers was still phishing

North Korea's most notorious hacking group has been caught protecting its malware with the same post-quantum cryptography that governments and tech firms are still rolling out to safeguard their own data.

In its analysis of a campaign dubbed Operation FlashHole, Kaspersky's Global Research and Analysis Team (GReAT) says an updated version of LPEClient, a Lazarus Group tool, now uses ML-KEM-1024, a key exchange mechanism designed to resist attacks from future quantum computers, to secure the channel it uses to download more malware.

The findings describe a campaign active since at least July 2025 that has hit at least three South Korean organizations in the software, construction, and wired communication equipment manufacturing sectors.

A fake AI-tailored résumé, a mandatory plugin, and quantum-resistant encryption

The way in was familiar: Kaspersky found a GitHub Pages site tied to a GitHub account, created in September 2025, that the attackers used for command and control. The site first showed the résumé of a supposed senior network appliance developer, likely seen by an employee of the communications equipment maker, before the attackers swapped it in January 2026for an interior design showcase that probably targeted the construction firm.

Kaspersky assesses with medium-to-high confidence that targets received spear-phishing emails linking to these sites, which loaded JavaScript from attacker-controlled domains, though it could not determine exactly what triggered the infection.

From there, the chain ran through INISAFE CrossWeb EX, financial security software South Koreans need in order to use many online banking and government websites. How it was abused is unclear, but the malware ended up running in memory with elevated privileges, spawned by a CrossWeb EX build released in 2023 after a public advisory from South Korea's KrCERT.

That is a well-trodden route; Kaspersky says Lazarus abused CrossWeb EX in 2021, 2022, and 2023, and in Operation SyncHole, disclosed in April 2025, it used Cross EX, a related program, to compromise at least six organizations, and we also reported on the group exploiting a zero-day in MagicLine4NX, another South Korean authentication tool.

Kaspersky believes the photo on the résumé site was AI-generated, citing missing EXIF metadata and an unnatural texture, and that AI most likely built the site too, given a candidate name resembling Korea's equivalent of "John Doe" and a portfolio stuffed with filler details and unrelated companies.

ojun Ryu, senior security researcher at Kaspersky GReAT, said the findings suggest Lazarus is "bringing AI into its long-established playbook," a shift Kaspersky has also seen among other Lazarus-aligned groups. That fits a broader pattern, from Kimsuky using ChatGPT to forge a South Korean military ID to North Korean IT workers using generative AI to get hired at Western firms.

The more striking change is inside LPEClient, a profiling and payload delivery tool that first appeared in 2019. The new version no longer stores its own server address; instead, it relays traffic through ClientHub, a newly discovered loader with a variant that talks to its operators via Dropbox. It profiles installed security products and the Windows patch level, now also checks whether protections such as Shadow Stack are enabled, and encrypts that report with GOST, Russia's national cipher standard.

A security-conscious hacker group?

Then comes the post-quantum part of the equation. The server sends four ML-KEM-1024 public keys, LPEClient derives four shared secrets from them, and those are combined into a single 128-byte key for Threefish, a tweakable block cipher originally built for the Skein hash function. That key decrypts the next payload, which is loaded straight into memory.

ML-KEM-1024 is the strongest of three parameter sets in NIST's FIPS 203 standard, and the one the NSA specifies for key establishment under CNSA 2.0, its algorithm suite for US national security systems. Kaspersky's report notes, with some irony, that the group seems to have "adopted more secure post-quantum cryptography faster than many legitimate security products."

Ryu said the part that "deserves closer attention" is the group's "adoption of cryptography designed to withstand future quantum threats," which Kaspersky traces back to at least September 2024. If that holds, Lazarus was using the technology within weeks of NIST finalizing its first post-quantum standards on August 13, 2024, and more than two years before this month's disclosure.

Kaspersky is not the first to spot this, either. In August 2026, Check Point Research described a module in a separate Dream Job wave against defense companies that fetched four public keys from its server and used Kyber/ML-KEM to agree on a key to download FudModule, a rootkit that exploited the since-patched Windows zero-day CVE-2026-68820. Kaspersky identifies the module as LPEClient, and the campaign received widespread coverage at the time.

No publicly known quantum computer can yet break the classical key exchanges that protect most internet traffic, so ML-KEM only gives Lazarus an edge if someone records its traffic today and decrypts it once such a machine exists- the "harvest now, decrypt later" scenario driving the civilian migration.

For a state espionage operation whose opponents include well-funded intelligence agencies, keeping its tooling sealed against that possibility is a rational, if forward-looking, choice. Kaspersky's own reading is that the group wants to hide its later-stage payloads.

It does not, however, hide the malware where it runs; the post-quantum layer protects data in transit, and Kaspersky's analysis of the malware itself laid the whole exchange bare. Kaspersky says the work points to a dedicated developer, and the rest of the toolkit supports that view: ClientHub uses the SNOW stream cipher, a first for Lazarus according to Kaspersky, and copies headers sent by Chromium-based browsers to blend in.

Kaspersky believes the focus on software firms, the only sector hit in both SyncHole and FlashHole, reflects an appetite for source code that can be mined for new vulnerabilities. It also distinguishes FlashHole's espionage from a parallel cluster of attacks built on Lazarus's COPPERHEDGE backdoor that appears to be about making money.

For potential defenders, the lesson is the delivery rather than the cryptography: a convincing fake résumé and a trusted security plugin. Kaspersky's advice, which also promotes its own products, covers verifying unexpected links even when they lead to polished professional sites, patching quickly, removing unneeded components, restricting user privileges, and watching connections to cloud services such as GitHub and Dropbox that can double as command channels.

Since the post-quantum layer does nothing to hide what the malware does on the machine itself, capable endpoint protection software remains the realistic place to catch it. Whether its newly-found AI-assisted approach is a game changer, however, remains to be seen.

Elvis Presley has been dead for nearly 50 years, but apparently even the 'King of Rock and Roll' can't escape the AI industry's appetite for training data. The owners of 274 Elvis songs made famous by Presley have sued Anthropic, accusing the company behind Claude of using his music without permission to build its AI models, and are seeking tens of millions of dollars in damages.

Anthropic disputes the allegations and says it intends to defend the case. AI companies have long argued that training models on copyrighted works can qualify as fair use, an issue courts are still working through. There's an important legal difference between learning patterns from existing material and reproducing protected expression, but that distinction has become a remarkably convenient place for the AI industry to hide its enormous dependence on other people's creativity.

It's hard to sympathize with the companies on the receiving end of these sorts of lawsuits, even when the plaintiffs are not without resources of their own. Unfortunately, these disputes are becoming all too normal. AI-powered music is becoming more widespread, if not necessarily popular, and we've reached a point where discovering that an AI company allegedly used hundreds of copyrighted songs without permission barely qualifies as surprising news.

The only unusual part is that this time the person whose music is at the center of the dispute happens to be Elvis Presley.

A little less uncompensated song use

The lawsuit makes an accusation that goes beyond simply using copyrighted material for training. According to the complaint, Claude could generate material that closely resembled existing Elvis songs, even after Anthropic introduced safeguards intended to prevent infringement. Requests to recreate songs for modern audiences allegedly produced versions retaining recognizable elements of the originals.

An AI model learning about music isn't automatically infringing copyright, and copyright law doesn't give anyone ownership of an entire musical style. But if a commercial AI system reproduces protected lyrics or creates unauthorized adaptations, it's hard to see why the company behind it should get a special exemption simply because the copying happens through complicated software.

AI companies have been trying out various arguments in court, but a lot of it feels like an attempt to claim that AI development is so important that the usual rules should somehow bend around it. Training requires enormous amounts of information, and licensing everything would be expensive or inconvenient, but that's a standard headache for companies whose products rely on copyrighted material.

TV shows and movies license music, whether an original track or just the right to record a new version for their project. No company is allowed to simply grab hold of copyrighted material simply because clearing it would slow down development. AI companies should have to take copyright seriously too, even if that makes their business models more expensive.

It's also particularly galling that songs people spent years creating and popularizing can become raw material for commercial technology without the rights holders having any meaningful say. Access to a work and permission to exploit it are separate questions, regardless of how sophisticated the software becomes. Ignoring those rules is why artists who passed away decades ago seem to produce new music whose earnings don't go to their estate.

AI music turns the volume up

Elvis is hardly the first musician to become part of this fight. Major music publishers have already sued Anthropic over allegations involving copyrighted lyrics, while AI music generator Suno has faced legal challenges from record labels. The Elvis lawsuit stands out because it focuses on the catalog associated with one of the most recognizable performers in history.

That fame might help draw attention to a problem that has affected considerably less powerful creators for years. Independent artists may struggle to establish whether their work was used to train a model. If someone with Elvis Presley's commercial legacy needs a federal lawsuit to challenge alleged unauthorized copying, ordinary musicians' prospects look bleak.

Of course, the companies controlling Elvis's catalog aren't struggling artists themselves, but that doesn't make the underlying complaint any less important. The principle shouldn't depend on whether the copyright owner is wealthy or famous.

I'm also not convinced that every AI company needs unrestricted access to practically everything ever written or recorded to produce useful technology. Claude is an impressive assistant, but its ability to answer questions doesn't suddenly make songwriters' copyright interests irrelevant. If AI developers want to build profitable products using creative works, they should be prepared to explain where that material came from and why they were entitled to use it.

The courts may decide in Anthropic's favor due to fair use, but even that ruling wouldn't automatically mean AI companies have permission to go wild on every song ever recorded. The best path forward won't be as simple as making it always allowed or illegal to use copyrighted material for AI training, but the companies building these systems shouldn't rely on their ambitions as a good enough reason to ignore rights holders.

The AI industry has become very good at explaining how much it needs human creativity, but it's time it became equally good at respecting the people responsible for it.

• Just 24% of employees verify the results of an AI-generated answer before proceeding
• A worrying 39% of employees have acted upon AI-generated responses that turned out to be inaccurate or unsuitable
• The research was conducted by Censuswide on behalf of learning company O’Reilly

AI chatbots are preferred to colleagues and managers for work-related matters, according to a survey of 2,000 UK workers, with under a quarter (24%) bothering to confirm the results or directions from an AI-generated solution.

Meanwhile, with over a third of employees revealing that guidance from an AI has resulted in inaccurate and incomplete answers (if they were even suitable in the first place), almost as many have received no AI training or guidance in the past year.

The research from O’Reilly Media raises some serious questions, not just about how AI is being used in the workplace, but what controls, oversight, and training accompany its use.

An AI training gap?

The use of AI in the workplace has had some interesting outcomes, but one that few will have predicted is the use of AI chatbots as a substitute for advice from colleagues.

O’Reilly’s survey has unlocked not just the concerning 24% bothering to verify AI-generated answers, but also the paltry 16% that are provided with AI training that relates to their role, and 35% say no AI guidance or training has been given at all.

Without the full context of individual workplaces to consult, it appears that AI has been introduced and teams simply left to get on with it, while training attempts to catch up.

"We're seeing a workforce that has fully embraced AI as a first port of call, but the safety net hasn't caught up with the speed of adoption," said Alexia Pedersen, SVP International at O'Reilly. "People are moving faster than their organizations' training and governance.”

Pedersen describes that gap as a place where “mistakes happen.” But the obvious answer isn’t necessarily the right one. There is an alternative to putting the breaks on AI, she says. “The answer isn't to slow down AI adoption; it's to close the gap with the right training, verification habits, and trusted expertise to back it up."

Building a culture of verification

While the results of the survey raise questions about AI use, the statistics do point towards a challenge in the delivery of training, as well as trust in colleagues and organizational structures.

Pedersen observes, "This is a training and trust problem, not an AI problem." Some solutions will need to be found to ensure that AI training is as important as other work-based learning.

"Organizations that invest now in structured, role-specific AI training and in building a culture of verification rather than uncritical acceptance will be the ones who turn today's AI adoption into a genuine productivity advantage," Pedersen concludes.

• Getac's B360X and B360X Pro are bringing Intel Panther Lake chips, 14-inch 1,400-nit screens, and Wi-Fi 7 to its rugged B360 line
• The 180 TOPS headline only applies to the B360X with a Core Ultra X7, a chip Getac doesn't offer in the defense-focused Pro
• The Pro's integrated AI rating is about 98 TOPS, in line with last year's model, leaving an optional Nvidia RTX PRO 500 as its main AI upgrade

Getac has unveiled the B360X and B360X Pro, a pair of fully rugged 14-inch notebooks built around Intel's Core Ultra Series 3 "Panther Lake" processors and pitched at people who need to run AI workloads far from a reliable connection.

The Taiwanese company says the new machines deliver up to 180 TOPS of AI compute with both models due in November 2026 and no pricing announced.

Getac isn't subtle about who the Pro is for. Its product page calls it "purpose-built for defense and military professionals" and lists defense as its only industry, while the B360X is pitched at public safety, utilities, and industrial manufacturing. Our 2023 review noted that the B360 Pro is "sold as military-grade and mission-ready, out of the box," and that still seems true in 2026.

What has grown is the breadth of the push. In March, Getac launched CommandCore, a rugged drone ground control station that it pitches to "Army aviation and integrated air defense systems." Rivals are chasing the same customers, with Durabook's triple-screen Z14I-DX3 aimed at command posts and drone teams.

Plenty of upgrades under the hood even as focus remains centric to AI

Both notebooks replace the outgoing B360 and B360 Pro's 13.3-inch 1080p panel with a 14-inch, 1920 x 1200 LumiBond display rated at 1,400 nits, plus a touchscreen that Getac says works with compatible gloves or in the rain.

According to Getac's B360X spec sheet, the larger screen fits in the same 342 x 281mm footprint as the 13.3-inch models, with a slightly slimmer chassis and a 2.38kg weight.

Wi-Fi 7 and Bluetooth 6 are standard, with 4G LTE, 5G Sub-6, and a dedicated L1/L5 GPS module optional, alongside up to two Thunderbolt 4 ports, a smart card reader, TPM 2.0, a 5MP Windows Hello camera, and an optional second SSD for up to 4TB in total. The usual rugged credentials are included: MIL-STD-810H, MIL-STD-461G, and IP66 ratings; a 6ft (1.8m) drop rating while operating; and optional salt fog certification.

Intel calls the 180 TOPS figure platform TOPS, which adds the NPU, integrated graphics, and CPU together. Only Panther Lake's "X" chips reach it because only they carry the 12-core Arc B390 graphics, which accounts for 122 TOPS on its own, plus a 50 TOPS NPU, according to Intel's figures.

Getac offers exactly one such chip, the Core Ultra X7 358H, and its own footnotes confirm it is only available on the B360X. The B360X Pro tops out at the Core Ultra 7 356H and 366H, which use a four-core graphics version, and the 356H is rated at 98 TOPS on the same measure.

That is essentially level with last year's hardware. The Core Ultra 7 255H offered in Getac's 2025 B360 range is rated at 96 TOPS, so on Intel's own yardstick, the defense-focused model's integrated AI budget has barely moved.

A lot of AI muscle on offer

What has changed is where those TOPS live. The NPU jumps from 13 TOPS on the older Arrow Lake H chips to as much as 50 TOPS, clearing the 40 TOPS bar Microsoft set for Copilot+ PCs, and Getac now lists both models under that banner on its website. That matters to the target audience and the need to know it is achievable on battery, since NPUs are built around sustained, low-power inference.

Even that isn't new for the family. Getac billed its Lunar Lake-based B360 Plus as the first fully rugged Copilot+ PC when it launched in June 2025, with a 48 TOPS NPU, and the Core Ultra 7 258V it offers is rated at around 115 platform TOPS. When we covered that machine, we warned that the practical benefits "may not be as clear-cut as the branding suggests," and things have since come full circle.

Microsoft has also cooled on the NPU push. As PCWorld pointed out in January 2026, few applications make use of NPUs, and Windows' newer AI tooling lets developers run inference on GPUs and CPUs as well. Getac's generational AI claims hold up for the top B360X configuration, and for NPU-bound work across the range. For the Pro, they are a stretch.

Getac hasn't explained why it reserves the X7 for the B360X, but Intel's platform rules likely explain it. The X-series models seem locked to LPDDR5x memory, and Notebookcheck's spec comparison shows the X7 358H exposes 12 PCIe lanes, versus 20 on the Core Ultra 7 356H. A machine built around a discrete GPU option and an expansion bay needs those lanes more than it needs a bigger iGPU. This means Getac's stated specs might need a small tweak to note that the B360X also comes in an LPDDR5X-touting trim, since it makes no mention of any memory but "DDR5" so far.

The B360X Pro takes a different route, offering an optional RTX PRO 500 Blackwell with 6GB of GDDR7. Getac's release frames it as a graphics upgrade, but this is where the Pro's real AI headroom sits.

Nvidia rates the RTX PRO 500 at 294 AI TOPS, but its own RTX PRO line card carries a crucial footnote: "NVIDIA Blackwell GPUs are based on FP4 AI TOPS with sparsity." Intel quotes 8-bit integer (INT8) operations without that sparsity assumption, so the two numbers can't be compared directly.

For context, the RTX 5090's headline 3,352 AI TOPS corresponds to 838 INT8 TOPS without sparsity, a four-to-one ratio. Applying the same ratio to the RTX PRO 500 puts it at roughly 74 dense INT8 TOPS, below the 122 TOPS Intel claims for the X7's integrated graphics.

That doesn't make the B360X the better AI machine for every job. The Nvidia card brings 288GB/s of dedicated memory bandwidth, native FP4 support, and CUDA, still the default target for much AI software, although 6GB of VRAM limits the size of the model it can hold. Getac hasn't published benchmarks for either configuration.

Via TechPowerUp

• Report says AI is being given too much authority and autonomy to handle business-critical security decisions
• 70% of organizations delegate decisions to AI that would have been made by a human 12 months ago
• A “control gap” exists between the authority given to AI and an organization’s ability to audit it

Delegating complicated decisions to AI is leading organizations to place relatively new technology in charge of user permissions, in control of sensitive data, and risk security issues, new research has claimed.

A study by Illumio looked into how organizations are handing decision recently performed by humans to AI, without apparently taking into account the potential for serious issues to arise.

It found the majority (70%) of organizations are delegating previously human-made decisions to AI, but only around half (49%) can see what action is taken by an AI agent an audit it.

AI recommendations

The nature of AI agents makes this a particularly risky strategy, and the research has found that an incredible 56% of organizations are willing to accept greater cyber risks than 12 months ago. Almost half of that group (47%) have admitted to permitting AI actions in production environments.

Allowing AI agents to have free run of enterprise data, applications, and infrastructure is leading to wholly avoidable situations. Illumio’s research found that a control gap between the rules that organizations believe AI agents are operating within and what the technology is actually capable of.

Some 76% of organizations have experienced one or more security issues thanks to AI in the past year, with 35% revealing that following AI recommendations would result in a security risk. Meanwhile, 26% reported AI agents or tools had connected to other systems without the necessary approval.

More worrying is the 24% of organizations reporting AI’s drive to access data it shouldn’t be able to see, and another 24% stating it has triggered (or produced) an unauthorized action.

Strict limits

Of course, AI is already used within cybersecurity, and has proved successful with multiple vendors rolling out AI-enhanced solutions. But while 67% of organizations are using AI cybersecurity tools, 32% let AI conduct investigations without human oversight and 29% permit the software to deploy code changes completely unsupervised.

“The biggest AI risk may not be a system that breaks its rules. It may be one that was never given meaningful limits in the first place," noted Raghu Nandakumara, VP of Industry Strategy at Illumio.

Unleashing AI agents without constraints has left businesses struggling to control the software.

As Nandakumara observes, AI agents are persistent, and attempting to outwit their next move is pointless. Instead, “businesses must set strict limits on what they can access and do, then enforce those limits within the application and across the underlying infrastructure. That is how you contain unexpected behavior before it causes real damage.”

• AI firm DeepGreen has suggested building data centers underwater
• This would help to keep the locations cool and running efficiently
• But the proposed locations could seriously harm endangered whale species

AI data centers need an awful lot of water to keep their machines running at optimal temperatures, and that creates a problem: just how do you get that much water on-site? One potential solution seems almost blindingly obvious: why not build them underwater? Yet as residents across the US are discovering, that idea is proving to be highly controversial.

Take the recently-pitched $100m underwater data center located off the coasts of Maine and Alaska, for example. AI firm DeepGreen Holdings has proposed building data centers off Western Passage, Maine, and Upper Cook Inlet, Alaska, and they would cover some 1,650 acres and comprise 66 compute pods and 300 tidal turbines.

The problem? Both locations are home to endangered whale populations that could be significantly impacted by the developments. In the case of the Maine site, the Gulf of Maine is linked to critical habits for the North Atlantic right whale. As for Upper Cook Inlet, that’s a designated critical habitat for the endangered Cook Inlet beluga whale.

Whales rely heavily on echolocation to aid their feeding, navigation and social activities. Construction of enormous data centers of this scale would produce sizable noise pollution that could adversely affect the whale populations, potentially threatening what are already endangered animals.

According to researchers (via Gadget Review), the heat produced by the data centers, meanwhile, could scare off the cold-water prey that the whales need to survive.

Putting whales at risk

As you might expect, these projects have not gone down well with researchers and the public. For instance, Cooper Freeman of the Alaska Center for Biological Diversity described the move as “just really outrageous.” Freeman added that “We’re deeply concerned about all the harms this project could bring,” adding that the proposal lacks an awareness of “how dangerous this is for the inlet.”

On Reddit, meanwhile, members of the public seem equally upset at the idea. One described it as “Evil. May these people be judged one day and punished for this level of absolutely evil. Disgusting.” Others were even skeptical that underwater data centers are even feasible in the long run, due to the hardware's longevity.

With AI data centers consuming untold amounts of energy and requiring significant cooling to keep everything humming along, it’s not surprising that some companies are looking to submerge their facilities beneath the waves as a remedy. But when that idea involves worsening the situation for endangered species, perhaps it’s time to find another solution.

• Sam Altman says Gen Z need to do something different in the AI age
• Thinking outside the box may be key to getting a job, OpenAI CEO says
• AI will start taking over many roles soon, Altman predicts

OpenAI CEO Sam Altman has said he believes young people entering the workforce today should look to go beyond traditional ideas and try something different in order to stand out in today's AI-dominated world.

Speaking recently at Stanford’s Graduate School of Business, Altman said the disruption caused by AI in businesses everywhere may only just be getting started, and new workers should react to this uncertainty and see it as an opportunity.

“My chronic disappointment with MBA students is that they always want to go do the thing that was cool five years ago rather than try something new,” Altman said. “I think this is a great time to go do the new thing.”

Lack of engagement

Perhaps unsurprisingly, Altman was keen to play up the effects that AI skills and services could have in the modern world of work, noting that “wide swaths” of the jobs we currently know, will soon be entirely handled by AI.

“It’s totally true that huge numbers of tasks that people do today will be done better, cheaper, and faster by AI - and completely differently,” Altman said.

“If you’re heading into the world right now to work on something, I think you want to start your career at a time of the greatest change, when the playing field is most open."

“I think people with high potential, drive, ideas, a new way of looking at things, and new skills tend to do relatively well when the ground is shaking the most.”

Altman himself started Loopt - a location-based social networking mobile app - in 2005 as a 19-year-old drop-out, before founding OpenAI as co-chair with Elon Musk in 2015

“There are many companies that can be built on top of the technology OpenAI has put out into the world that will be much bigger and, I hope, more important than OpenAI,” he said.

This isn't Altman's first thought about the future of working in the AI age, with the billionaire having laid out his opinions quite a few times in the past.

In May 2026, he hit out at claims the increasing global usage of AI technology worldwide will lead to a "jobs apocalypse", despite widespread reports of layoffs at major firms.

And in July 2026, Altman noted that he doesn't expect a change in society or working practices for quite some time, despite widespread promises that AI would boost our productivity and efficiency across the board.

Via Fortune

• Gallup report finds US workers are feeling less engaged at work than ever
• Remote and hybrid workers are feeling particularly unenthused
• Lack of clarity on AI is also affecting worker happiness

With wide-ranging layoffs, the risk of AI replacement and cost of living increases, it might be no wonder that many employees are feeling less engaged with their jobs, but new research has shown just how much the dissatisfaction has become.

Gallup’s latest quarterly engagement poll for US workers found job engagement fell to its lowest level since 2011 in the third quarter of 2026.

The measure, which tracks workers’ focus, motivation and enthusiasm for their jobs, found engagement fell to 29% in Q3 2026, with remote and hybrid workers particularly disaffected with the state of things.

Lack of engagement

Although the figure sounds dramatically low, it should be pointed out that the engagement figure is never particularly high in Gallup's report - having peaked at 36% in 2020, the figure then fell to 31% by 2024, before its recent drop.

The company noted that the decline was particularly noticeable among remote and hybrid employees, especially those working in organizations which have adopted AI tools.

Gallup noted engagement fell five points among fully remote employees and two points among hybrid employees, compared with approximately one point among on-site employees and those with jobs that cannot be performed remotely.

This was not the first time such trends had been spotted, as Gallup pointed to its earlier research which found hybrid and fully remote employees have experienced larger declines in clarity of expectations than on-site employees in remote-capable jobs.

"These findings raise the possibility that physical distance can contribute to psychological distance between employees and their organizations, particularly as companies navigate the use of AI at work," it said.

Turning to AI, Gallup noted that although research earlier in 2026 had indicated a potential thawing in engagement with employees and AI, this had now declined.

It found workers in "AI-integrated organizations" were now reporting similar levels of engagement than those who weren't - with hybrid and remote workers again saying they were less happy and engaged.

"U.S. employee engagement has fallen to its lowest level in 15 years, with particularly large declines among hybrid and remote employees in organizations that have adopted AI," the report concludes.

"At the same time, fewer employees report clear expectations or feel that someone at work cares about them. The pattern suggests that AI adoption may be outpacing the communication and managerial support needed to make it effective."

There's no shortage of enthusiasm for artificial intelligence. Organizations across every sector are investing heavily, experimenting, launching proof of concepts, and finding ways to turn the latest developments into real business value.

However, there's a growing disconnect between experimentation and execution. For all the discussions of AI transforming the way we work, many AI projects never progress beyond the pilot stage. It's a familiar story. The proof of concept delivers promising results, the business case adds up, and everything looks like it's heading in the right direction.

Yet somewhere along the way the project hits a bump on the road. This can look like costs beginning to rise, confidence starting to waver, integration with existing legacy systems proving more complicated than expected, and approvals taking longer than anticipated.

What started out as an exciting project slowly loses its momentum. As a result, the AI initiative ends up stuck in limbo. The reason is that running a successful AI pilot and successful AI implementation are two very different things. Transitioning from a controlled environment with governed variables to a production environment brings a new set of challenges: security, governance, culture, skills, integration, and readiness.

Unfortunately, this challenge is not uncommon. Research shows that 71% of organizations remain in the pilot or proof-of-concept phase of AI transformation, highlighting just how difficult it is to turn early promise into a business reality.

This limbo is what many organizations are now experiencing: the AI implementation gap. It's the space between proving that AI can work and deploying it at scale in a way that delivers measurable value. For today's technology leaders, bridging that gap has become one of the defining challenges of AI adoption. The pressure to move beyond experimentation is growing, but doing so requires more than just increasing investment or pushing strong proof of concepts into production.

The real challenge is creating the conditions in which AI can move from an isolated trial into a sustainable part of the wider business. That requires establishing the right technical and organizational foundations, preparing people and processes, and finding the right use cases. To deliver real value, organizations need to think less about speed and more about creating something secure, scalable, and consistent.

It isn’t a technology challenge, it’s a business one

When AI projects stall, it's seldom because of the technology itself. More often, organizations run into strategic, governance, and operational challenges that prevent encouraging pilots from becoming production-ready solutions.

One of the biggest barriers is analysis paralysis. The majority of organizations have no shortage of AI ideas, but they typically lack a clear way of understanding which use cases will deliver the greatest value. This means resources become spread across multiple proof-of-concepts, each showing promise but none receiving the attention needed to reach production. The number of pilots continues to soar while the number of live deployments stagnates.

However, some organizations make the opposite mistake by designing for scale before they've checked the concept works. Major investment is made in infrastructure, architecture, and implementation before a solution has been tested in real-world conditions. If the project then needs to change direction, the amount of investment already committed can make it tricky to adapt. As a result, organizations can end up locked into an approach that no longer delivers the value they envisioned.

Governance is another common stumbling block. For organizations operating across EMEA, navigating regulations such as the EU AI Act, GDPR, and DORA introduces understandable caution. The consequences of non-compliance can be significant, and uncertainty around an evolving regulation often slows decision-making. Many AI initiatives don't stall because the business lacks ambition, but because leaders want greater confidence that they're deploying the technology in the right way: compliantly and responsibly.

Lastly, organizations often underestimate what it will cost to move from a successful pilot to widespread deployment. AI projects that appear affordable during the proof-of-concept phase can become much more expensive once they're scaled, especially if they haven't been properly stress-tested.

That's why due diligence is critical. Yet more than half (56%) of organizations don't carry out a Total Cost of Ownership assessment before deciding where to place significant workloads. Without a clear understanding of the long-term investment required, it's easy for costs to accumulate and confidence in the project to dwindle.

Balancing certainty and speed

Closing the Implementation Gap isn't about moving faster. It's about moving with confidence and know-how.

Time and time again, organizations find themselves at one of two extremes: waiting until every unknown has been resolved before making any moves or rushing ahead with AI projects before they've been properly validated. Neither approach is sustainable. Those seeing the greatest success are the organizations finding a balance between certainty and speed - moving quickly where the evidence supports it, while building in the validation and governance needed to reduce risk.

That starts with validating ideas early. Before major investment is made, organizations need to ask the technical, commercial, and governance questions that could prevent a project from ever reaching production. It's far better to discover early that a use case won't deliver than to invest months of budget and time before uncovering the same conclusion.

Rapid prototyping also plays a crucial role. Instead of debating whether an idea might work in theory, businesses should aim to create something tangible that users can test within days. Seeing a concept in action changes the conversation from speculation to evidence, giving stakeholders the confidence to make informed decisions about where to invest next.

Equally important is maintaining transparency throughout the delivery process. AI projects shouldn't feel like a black box. Defined ownership, clear checkpoints, and agreed assessment criteria help everyone to understand where a project stands, whether it’s a success, and what comes next. Projects rarely stall because the technology isn't capable - they stall because critical decisions weren't made early enough or because expectations weren't aligned from the outset.

Bridging the implementation gap

The implementation gap isn't going to disappear overnight. With 71% of organizations still stuck between pilot and production, the challenge is clearly not a lack of willingness, technical ability or ideas. In most cases, they have all three.

Instead, it's the ability to turn experiments into solutions that actually work in the real world. That means treating the transition as a business issue, not simply a technical one. Organizations need an approach that balances speed with governance, validates value before investment is made, and gives stakeholders the confidence to scale. It also requires leaders to be confident enough to stop projects in their tracks if they're not going to deliver real value.

The organizations taking meaningful steps forward won't be those adding more pilots into the mix. They'll be those that have strong processes and capabilities in place to push the right pilots ahead. Fundamentally, closing the AI implementation gap means creating a path from pilot to production that can be followed again and again.

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Technology innovation should always be driven by one core objective – to improve people’s lives. Unleashing human imagination in new and exciting ways, whether that’s solving complex problems or making everyday life easier.

We’re at a fascinating moment in time, where smartphones have become the de facto platform, but in terms of innovation they have reached a bit of a plateau. New smartphones have largely incremental hardware updates and optimizations without groundbreaking new features.

But then AI came along to shake things up. And that AI, when used well, is making a huge positive impact in people’s lives. AI is best used to complement human abilities – if you look at fields like medical imaging, AI can help to analyze huge data sets and spot patterns for physicians to diagnose fully.

Across tech, AI is having a big impact in how we interact with technology. Most notably transforming smartphones and smart devices. And it’s created a new category where you don’t even need to be holding your phone – AI smart glasses.

The AI smart glasses category has been growing rapidly, with research by Omdia finding global shipments of AI glasses had reached 8.7 million units in 2025, up 322% year-on-year.

It’s interesting to look at the ‘why’. On paper, AI glasses don’t have as much processing power or storage or RAM as a smartphone, so how could they be of use? And it's down to the form factor and AI.

Potential for smart glasses

There’s a lot of potential for smart glasses in the B2B sector. Increased productivity, real-time interactivity or enhanced customer experiences, the potential use cases are huge across many verticals.

One example I love is the potential to change how people engage with cultural subject matter. Imagine a pair of AI glasses as an “audio guide 2.0” that would become a deeply personalized guide, interacting with the knowledge base and curation of the venue – whether it’s a museum, art gallery or historical heritage site.

Visitors can ask specific questions about certain subject matter and get real-time answers as they explore a museum or gallery, making it much more conversational and interactive. Lightweight in form factor, smart glasses can be worn over long periods of time. And in the future, the same tour could generate layers of explanations tailored to different audiences (children, amateurs, experts), without imposing the same experience on everyone. We’re already seeing these concepts being explored by cultural institutions.

This form factor can also blend seamlessly into everyday life, and make tasks easier. How many times have you been somewhere like a concert and witnessed a sea of smartphone screens? The same with navigating around a city. People experiencing life through their device. AI glasses are changing that, allowing you to be more present in the moment without having to live and capture memories through a phone screen.

I actually found myself running late and lost in Rotterdam train station recently. I speak three languages fluently, but if you’ve been to the Netherlands you know it’s hard to guess at some of the words. Safe to say, it was getting stressful. I was wearing AI glasses, and asked it to translate the signs around me – I was on my way quickly.

But with every emerging technology comes new challenges and societal considerations. And there’s a responsibility on manufacturers and innovators to find ways to realize this vision responsibly. For AI smart glasses, there's one crucial piece of the puzzle that will shape their true impact - and that's privacy.

Creating a privacy-first architecture

One element is data privacy. We’re in an era where people’s personal information has been largely commoditized. Where AI smart glasses are concerned, we’re seeing a huge amount of contention about how personal data is gathered by some manufacturers and used to train AI models.

For me, it’s very simple. Our company DNA has always been that privacy should be treated as a core principle, not an afterthought.

But what does privacy-by-design actually look like in practice?

Control of data should ultimately remain with the user. And that’s why it’s crucial to have data stored locally on smart glasses by default – and only transferred off the device when initiated by the user.

And you can safeguard local data even further with military-grade encryption – something that is particularly important for many organizations and will be required when handling internal and customer data. This means that even if someone else tries to take the AI glasses and pair them to another phone, the previous data cannot be accessed.

These concerns over data should also extend to how we interact with LLMs. As AI tools become embedded into our daily lives, the more requests we send to LLMs, the easier it is for them to build a profile of you. And it’s the same for AI smart glasses.

With a privacy-first architecture, third party AI requests are de-identified, so that LLMs cannot build an accurate profile. Policies where user data is not used to train any LLMs should be standard.

There’s also the matter of protecting the privacy of people around you when wearing smart glasses. It’s crucial to have visual indicators, like an LED indicator that lights up during photo or video capture to signal photography or video is being recorded at that moment.

It's obviously not good enough that on some AI glasses, it’s possible to obscure the LED and effectively hide it from view. Any AI glasses worth their salt should use a multi-sensor approach so that photos and video recording should automatically disable if the LED is obstructed or when the glasses are removed.

We collectively have this incredible opportunity - a new technology which combines the versatility of AI with wearables. But AI smart glasses will only be a huge benefit to us all, if privacy is handled correctly.

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It is now getting pretty difficult to track every time OpenAI and Anthropic models break containment, wander onto the wider web, and cause unintended, high-risk chaos.

The recent wave of rogue AI agent incidents has now prompted US President Donald Trump’s administration to consider asserting more power over AI tools.

These incidents expose a deep vulnerability for the UK. If advanced AI models are acting unpredictably with sensitive data and carrying out hacking campaigns against real people, what happens when that data is hosted, processed, and governed on foreign-owned infrastructure?

For far too long British IT has essentially handed the keys to US tech giants. The CMA’s market investigation into cloud services found that Amazon and Microsoft control 70-90% of the UK cloud computing market. And another US digital powerhouse in Google controls a smaller share.

These hyperscalers often repeat marketing platitudes about "sovereign" regional data centers located on British soil. I am sorry to be the bearer of bad news but the act of storing data in a UK-based data center is not enough to guarantee sovereignty.

In reality, these tech giants are "sovereignty-washing." Why? Because If the controlling entity is subject to foreign legal jurisdiction, such as the US CLOUD Act, foreign authorities can compel access to that data. It is irrelevant where the physical hardware resides. Hosting data on foreign-owned servers is basically just paying rent on digital turf that remains subject to external laws.

As foreign-owned autonomous AI agents process vast amounts of critical enterprise and state data, and increasingly turn adversarial, the decision to build Britain's AI future on foreign, proprietary stacks looks ever more short-sighted. The potential for financial, legal and national security breaches is simply not worth the risk.

It is time for the UK to end its over-reliance on sovereign-washed cloud providers and establish true digital independence by backing British tech.

Paying the "sovereignty tax"

By continuing to rely on foreign tech monopolies British institutions are paying a heavy "Sovereignty Tax.” By that I mean the insidious, hidden and steadily accumulating burden of building critical systems on platforms you cannot fully govern.

Hyperscalers reel in UK organizations using aggressive cloud credits and low upfront pricing. But once they are embedded in these cloud systems, those firms often find it hard to leave them. This can come in the form of data egress fees, proprietary technical debt, outage exposure, restrictive licensing rules and audit blind spots. All of these add to the creeping realization that leaving your current provider would be harder, and more expensive, than you ever anticipated.

This explains why an overwhelming 66% of UK IT leaders say they would consider switching cloud providers to regain control of their data and infrastructure. Yet despite this clear desire to break free, the proportion of UK IT leaders expecting to become even more deeply entrenched in US hyperscaler systems has more than doubled over the past year, rising from 12% to 28%.

This seems all the more bizarre given that a recent study from Proton Research showed that 45% of consumers would actively avoid doing business with companies that store data with US firms. Meanwhile, over half (58%) would happily pay higher fees to switch to a sovereign provider.

This forced entrapment dynamic has undoubtedly created an era of digital feudalism. A small group of foreign tech lords own the underlying digital land, while British businesses and public sector bodies exist merely as serfs… and pay a perpetual tribute for the right to operate. And the real-world operational costs of this reliance are coming home to roost. Over the past year, 39% of UK firms were hit by outages originating from US hyperscalers, with resulting risk exposure costing some organizations over £1 million.

As organizations increasingly deploy more and more compute-intensive AI workloads on these platforms, the technical lock-in will only continue to deepen.

Not only does this drain commercial value but it also means high-skilled engineering jobs, and IP heads across the Atlantic rather than staying in the UK.

Legal and security risks

As autonomous AI usage proliferates, legal and operational risks do so too. AI agents interact with database layers, corporate IP and sensitive citizen data while executing the tasks they are set.

If that data resides in a foreign-controlled ecosystem, UK organizations lose jurisdictional control over it. Without true data sovereignty, external powers effectively hold a "kill switch" over the UK’s critical infrastructure. For example, if a UK NHS trust is reliant on a foreign-hosted AI agent for patient triage, a regulatory dispute in another country could trigger a sudden access shutdown. This could paralyze hospital operations and put sensitive health records in the hands of foreign control.

By centralizing the UK’s national capability into a single foreign-owned monoculture, we are also creating a concentrated point of failure. What happens if the US shifts policy and revokes access, leaving the UK powerless over its own critical infrastructure?

The drone and missile strikes that hit Amazon Web Services facilities in the UAE in March that brought two of the three Availability Zones in the region offline was also clear proof that the cloud is not a borderless, invulnerable utility. For the UK to retain true security, it requires resilient distributed architecture.

British digital independence

It should be noted that global hyperscalers undoubtedly have a place in non-sensitive, low-risk applications. But prioritizing digital sovereignty is not an act of digital isolationism.

The fact that the UK government created a £500 million Sovereign AI initiative this April shows that policymakers do recognize the importance of digital resilience and maintaining control.

But that support for domestic innovation will continue to be undermined if public compute spend continues to be funneled towards multi-year deals with foreign incumbents.

The government’s procurement strategy must focus on actively purchasing from domestic cloud providers if its strategic ambition for creating a UK tech powerhouse is to work.

At the same time, proprietary vendor lock-in thrives in closed-loop ecosystems. The UK needs to treat open-source tech and open architectures as national assets. When you use open standards, moving your AI models and data between public, private, and local clouds is painless. You get actual flexibility instead of being locked into one setup. And, crucially, UK organizations avoid being hit with exorbitant transition fees.

Our European neighbors are already leading the way in regard to embracing true sovereignty. France is dropping Zoom and Office for sovereign, open-source ecosystems, and Germany is using Nextcloud to replace Google Docs.

The UK possesses capable domestic cloud platforms. We have the technical capacity and talent ready to scale. And UK firms are crying out for the powers that be to back homegrown tech; 90% of IT leaders are calling for a more proactive government stance in support of British companies. We just need the procurement bravery to match.

Controlling our destiny

The unpredictable and unprecedented nature of autonomous AI agents means the UK must reassess what it has been doing up to now. Building the future of British tech and economic security on rented land is fundamentally contradictory.

The UK already has companies that own the full tech stack required to power modern enterprise. These companies offer the same core capabilities as hyperscalers without the associated sovereign risk or vendor lock-in.

If the UK wants to prove itself as a global leader in AI the country needs to support its own tech providers.

That means distancing ourselves from sovereign-washed cloud providers and investing in open-source infrastructure. Doing so is the only way to protect our data and reclaim our digital autonomy, before it’s too late.

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Since the launch of Anthropic's Mythos, the debate around the risks that AI-powered tools pose for security teams has steadily ramped up.

Yet, fundamentally, the attack targets aren’t changing: the big shift is one of scale and speed. AI finds more vulnerabilities, giving security teams more to deal with. But at the same time, AI is lowering the technical barrier to exploitation. The result is a situation where the window of time that teams have to respond has drastically reduced.

This has clear implications for how organizations approach exposure management and reinforces the need for solutions that help security and IT teams stay secure amidst this new reality.

Vulnerability detection is accelerating

The initial update from Project Glasswing underlined the power behind the Mythos model that has fueled so much speculation and concern.

In approximately one month, Mythos and around 50 partner organizations reportedly identified more than 10,000 high and critical severity vulnerabilities, with 6,202 estimated high/critical vulnerabilities found across more than 1,000 open-source projects.

There’s no doubt Mythos is hugely consequential and its capabilities mark a significant milestone, but the gap between attackers and defenders has already been widening for some time. Security teams have been facing a growing volume of CVEs; in 2025, some 48,185 CVEs were published, a 20.6% increase from the previous year and Advances in AI tools can reasonably be expected to continue fueling this trend for years to come.

This all has huge consequences for security teams. As Cloud Security Alliance analysis has reported, Mythos-type capabilities will mean the number and frequency of complex, novel attacks will rise dramatically alongside the need to patch and respond to AI-discovered vulnerabilities, exploits and autonomous attacks.

0-day vending machines, no Mythos required

Restricting access to Mythos and other highly capable models is not a practical safeguard. Our research and testing demonstrates the capabilities of general access LLMs in finding and exploiting novel vulnerabilities when being prompted by experienced security practitioners.

One of our security researchers created an automated vulnerability research pipeline, with no human in the loop. On first deployment, this found a remote, multi-stage SQL injection vulnerability sitting on 300,000 WordPress plugins, a process fully automated from discovery to exploitation. The finding, which aligned with an independent discovery by another researcher, was assigned CVE 2026 3985 and publicly disclosed.

And it’s not just the power of AI in finding vulnerabilities that should cause concern. AI-powered attacks can go a step further in automating an entire extortion attempt. An agent can autonomously create and send the exploit, then demand a ransom as we saw when an operator dubbed JadePuffer, exploited a vulnerable Langflow server, harvested credentials, moved laterally and encrypted 1,300 database records in an end-to-end agentic ransomware operation.

Reduce the attack surface as a first line of defense

This is our 'new normal' and LLM models have changed the old assumptions about the speed of vulnerability management. The window that existed between vulnerability discovery, CVEs assigned and defensive action being taken, has fundamentally shifted.

It’s not just about reaction times. Teams also need to take proactive steps to reduce their attack surface as the race to patch can be avoided entirely if assets weren’t unnecessarily exposed in the first place. This is about fighting the battle on two fronts; closing the window between vulnerability detection and remediation and taking pre-emptive action to make the target that attackers aim at smaller in the first place.

These should go hand-in-hand as many teams focus on patching what’s vulnerable, but they also need to question if it should be exposed at all. Attack surface reduction is an area that many organizations may struggle with, but any unnecessarily exposed internet facing asset is now carrying far more risk than ever.

Exposed services, such as leaving a MySQL database or private API documentation exposed to the internet, is an open invitation for attackers and these often don’t even need a CVE to be exploited. This leaves the door open for automated, high-speed attacks.

Always on exposure management

When time to exploit is reduced down to hours, the second key focus area for security teams is reducing the gap between discovery and remediation.

Finding and fixing vulnerabilities requires both context and cadence. Pentesting provides the depth and context but is a ‘point in time’ activity and frequent in-depth investigations can be cost prohibitive for many organizations. Vulnerability scanning provides the cadence but can’t interpret if what it’s found matters for your business environment.

That’s where AI can help and, whilst the traditional pentest isn’t going to disappear right away, we’re at the beginning of an era in which AI offers a new way to support the work that human security analysts and engineers are doing.

Its value lies in instantly alerting teams to new risks, the ability to deploy a pentest on every major release for the fraction of the cost of a manual engagement, agents that can actively investigate and validate findings using the same methods employed by human security experts, and integrating with the tools your teams use to get issues to the right people for fixing, fast.

This way, stretched teams can keep up with the emerging threat landscape. AI is helping teams cross that last mile - validating findings, reducing false positives, and providing necessary context.

These are still relatively early days and the frameworks around the pentest, used by auditors, insurers and compliance bodies, will need to evolve. But the signs are that this new era requires continuous testing as standard practice.

It’s needed because Mythos, and other LLM models, will punish anyone still working to yesterday's timelines. The fundamentals of reducing exposure, validating what's real, and freeing skilled people from the noise haven't changed. What's changed is how little time is left to get them right.

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• FADU's File-Semantic SSD runs path lookup, permission checks, and file-to-flash mapping in firmware instead of on the host
• NFSv3 requests ride inside standard NVMe commands, so GPUs, DPUs, and remote clients can reach files without requiring a host file system
• Its headline 460,000 ops/s result came from a PCIe 5.0 host, with PCIe 6.0 results promised by Q1 2027

South Korean controller designer FADU has shown off an SSD that understands files rather than just blocks, betting that the next bottleneck in AI storage is not raw bandwidth but the CPU time spent working out where data lives.

The File-Semantic SSD (FS-SSD), unveiled at the OCP Global Summit, moves file lookup, access checks and file-to-address translation into firmware on FADU's sixth-generation controller.

FADU's whitepaper promises GPUs, DPUs and network cards a route to their files with "no host filesystem, no block layer, and no storage-server CPU in the path." Partner samples are not expected until Q3 2027.

Not the first approach to the 'CPU problem'

FADU is no newcomer to data center flash. Its Gen5 controller sits in the SN861, which Western Digital launched and SanDisk now sells; last year, the company said it had signed controller supply deals with two of the four hyperscalers, weeks after revealing its Sierra PCIe 6.0 controller.

Its pitch here, however, isn't entirely new, with the recognition coming from a not-so-surprising front: Nvidia. Nvidia has been doing the same from the other end of the PCIe bus. GPUDirect Storage removed the CPU from the data path years ago, letting drives move data straight into GPU memory, but host software still decides what to fetch and issues every request.

Its decision to open source cuFile and shift that control path onto the GPU, with SCADA attacks adding accelerator-side overhead even as FADU attacks it from the drive end of the equation.

FADU says its own measurements show the host file system and block layer consuming about 30% of CPU cycles in a GPUDirect Storage random-read workload, a vendor figure that has not been independently reproduced. It also argues that existing bypass schemes only get the CPU out of the way by abandoning files altogether.

How does this work?

Rather than invent a new language for its drive, FADU borrowed an old one. NFS, short for Network File System, has let computers open files stored on other machines for decades. FS-SSD tucks NFS requests, such as looking up a file by name or reading part of it, inside the standard NVMe commands that SSDs already use.

Those commands can come from inside the same server or, using a networked version of NVMe called NVMe over Fabrics (NVMe-oF), from other machines in the data center. FADU chose NFS version 3 for three reasons: it is small, with just 21 operations; it works with any operating system; and it is stateless, meaning each request carries everything the drive needs to answer it, so the drive never has to remember who has which file open.

FADU has also merged two lookups that normally happen separately. On a regular system, the computer’s file system first works out which numbered block holds each piece of a file, and the SSD’s own internal software, called the flash translation layer, then works out where that block physically sits on the flash chips.

FS-SSD does both jobs with a single file layer based on F2FS, a Linux file system designed for flash memory, laid out to match how the chips are physically organized. One map then takes the drive straight from a file to the spot where it is stored, and that map is much smaller. A typical SSD carries about 1GB of DRAM for every terabyte of flash and fills almost all of it with its block map. FS-SSD uses the same amount, but FADU says about 95% of it is left free to keep details of frequently used files close at hand.

That layout also makes cleanup simpler. Flash memory cannot overwrite old data in place, so drives regularly move the data they still need elsewhere and wipe whole blocks clean, a chore known as garbage collection.

When F2FS runs on an ordinary computer, the file system and the SSD each do their own version of this cleanup, and the two end up multiplying each other’s work. FS-SSD runs just one, and FADU says that cuts write amplification by 63% compared with F2FS on a regular host. Lower write amplification means less wear on the flash, but the 63% figure comes from simulated tests on NVMeVirt, software from Seoul National University that imitates an SSD, rather than from a real drive.

In FADU’s own preliminary test, remote file throughput held at about 460,000 ops/s as the server was cut from 16 cores to one, while a standard Linux NFS server fell from roughly 150,000 ops/s to 40,000.

The testing has its limits, though. FADU’s Gen6 drive sat in a PCIe 5.0 host, so every sequential read result topped out near 14.1GB/s, roughly the Gen5 link limit, even as Gen6 drives such as Micron’s 9650, rated for 28GB/s reads, are already in mass production.

Both the client and server ran Intel’s Core i5-13600K desktop chip with 128GB of DDR5 and a 100Gb ConnectX-6 NIC. That processor has 14 physical cores and 20 threads, so FADU’s test likely counted threads rather than physical cores.

The world’s first file-semantic SSD is an idea that isn't entirely new

FADU calls FS-SSD the world’s first file-semantic SSD, and it may well be the first hardware demonstration of its kind, but the idea isn’t new. The DevFS research paper, presented in 2018, described a direct-access file system “embedded completely within a storage device,” though the authors tested it only as an emulated prototype.

NVM Express has standardized a key-value command set that addresses data by key rather than block address, and SK hynix has developed a key-value computational storage drive with Los Alamos National Laboratory. Unlike the key-value command set, FS-SSD has no standard behind it yet. FADU has not submitted a file-semantic command set to NVM Express or OCP Storage, so for now every client needs FADU’s own software.

FADU’s longer-term goal is a flash enclosure with nothing in front of it but a PCIe switch and a DPU (a network card with its own processors) or NIC handling NVMe-oF in hardware, with a reference design built on Xsight Labs’ 800G E1 DPU due by the end of 2027.

FADU’s model puts that at about 42% lower power and 57% lower cost per 16-drive system than a conventional storage server, though those are projections rather than measurements. DPU-fronted flash boxes already exist, such as Supermicro’s BlueField-powered JBOF, but those still run the storage software on the DPU’s Arm cores. FADU wants the drives themselves to serve the files.

FADU chief executive Nam Yi-hyun said the company will use FS-SSD as “a springboard to become a leading company in AI storage solutions,” according to The Herald Business. The bottleneck FADU is chasing is real, and building on NFSv3 rather than inventing a new protocol gives it a credible starting point.

For now, though, the evidence amounts to a Gen5 test bench, an emulated write-amplification figure, and modeled savings, with OEM and ODM talks still early. The Gen6-host results FADU expects by Q1 2027 will be the first real sign of whether the drive can do the most important part: handling the CPU’s job at full speed.

• Amazon urges developers not to blindly focus on AI tool usage
• The era of the "Renaissance Developer" using all kinds of AI is here
• It also urges workers to shun tokenmaxxing, and focus on building a deeper connection with customers

Amazon has told its engineers to look beyond simply using AI to ensure their work gets done, and focus instead on the results of their labor.

In a memo sent to its workforce, the company warned against using “deeply flawed” metrics such as AI token usage or code generation as a measure of team success.

It also cautioned against workers chasing "tokenmaxxing", noting how, "our most prolific engineers have demonstrated that neither the developer role nor their output is defined by the use of AI."

The age of the "Renaissance Developer"

The memo, written by Terk Terkowitz, Amazon VP/Distinguished Engineer, Worldwide, and entitled "An update from Amazon engineering leadership on the role of the developer in the age of AI", signaled the apparent beginning of a new era in how the company defines its workers in the age of AI.

This includes the age of the "Renaissance Developer", who is able to blend AI tools with human judgment and domain expertise "to build entirely new things".

"AI is changing the work of software engineering both more quickly and more fundamentally than these previous trends," Terkowitz noted. "What it means to be a software engineer, and what software engineering even is, is changing quickly and durably."

What this means for Amazon's workers is a focus on "on what is not changing as we evolved the SDE (Software Development Engineers) roles".

This will mean a move away from "deeply flawed" metrics such as token usage or adoption of AI tools, and more focus on the impact on customers, businesses and teams.

The memo urges managers and division heads to make sure that there is "sufficient understanding" among their teams about the benefits AI tools can bring, and told workers to ensure they become "deeply connected" to their customers in order to gain a deeper knowledge of what they really need from Amazon.

"Despite the speed of change, we believe that it is still early. We all need to embrace this change and take the time to learn, or we risk being left behind," Terkowitz concluded.

“We’re just seeing the beginning of AI’s effect on software engineering. While we have a vision for what the future looks like, there’s a lot that’s still emerging. We are certain that whatever future best practices are, AI will be at the center of software engineering practice.”

• SK Hynix just published its Q3 2026 financial results
• These came with a prediction of the RAM crisis lasting until 2030
• After that, expect a further drain on memory supply from "physical AI such as autonomous driving and robotics"

Apologies for bringing you more negative news about the RAM crisis, but we've heard yet another prediction that supply woes are going to last for the rest of this decade.

This time it's SK Hynix (again) weighing in, a memory chip maker that has just issued its latest earnings report, as Wccftech noticed. Profits are booming, unsurprisingly, but SK Hynix also shared its most recent thinking on the RAM crisis, and how likely it is to be a protracted affair.

In the press release for its Q3 2026 results, SK Hynix announced succinctly: "Memory supply shortage to persist through 2030."

That was the title of a section briefing us on the memory shortage, where SK Hynix further observed: "The company assessed that supply … is not keeping pace with the growth of the market. Given technical difficulty of leading-edge processes and the long lead time required to build new production facilities, it expects the structure in which demand exceeds supply to persist through 2030."

The firm also predicted it will sustain its growth and revenue, which exceeded 100 trillion won in Q3 — the first time that's ever happened in a quarter by the way — thanks to enterprise and AI demand in the shorter-term, and "over the mid-to-long term, demand for physical AI such as autonomous driving and robotics."

Analysis: a 'virtuous cycle' for shareholders – a nightmare for consumers

It's interesting to note that SK Hynix is forecasting that longer-term demand for memory will be driven by robots or 'physical AI' even if software AI (agents) falls away somewhat as the decade rolls to a close. Micron is predicting the exact same thing, and recently went into even more precise detail in estimating that 'humanoid robots' could need 200GB (or more) of RAM and multiple terabytes of storage in every unit.

So there's similar thinking from the big memory module makers here, but we shouldn't be surprised by that. They need to talk up their future profits, after all, and reassure markets that we aren't in a bubble, so it's only natural that they'd want to make predictions of major future revenue sources to be tapped.

Still, there's clearly some substance to these predictions, and it's concerning for consumers that a growing number of analysts and big players in the tech industry are talking about the RAM crisis lasting until 2030, and that robots are apparently set to take the baton from AI data centers and push demand for more memory as we head into the next decade.

We can but hope that Micron and SK Hynix are overshooting with their expectations of the hungry robots eating all our RAM in a few years' time, although how that land lies will depend on how widely robots are adopted, as well as how much memory they'll need.

Meanwhile, SK Hynix asserts that the "virtuous cycle" which has resulted from AI-powered productivity gains driving increased investment in infrastructure (data centers) will continue, and moreover "broaden to include general enterprises and nation-level initiatives", the upshot being that the "global AI data center infrastructure market will continue to grow steadily."

I don't think many PC owners or gamers feel that the way things have gone with memory of late is all that 'virtuous' at all, but the shareholders of SK Hynix and other big chip makers certainly won't be complaining. They can probably afford themselves a very nice PC even in the current super-inflated component climate, after all.

• Australia government has issued a bill to ban chatbots that create 'emotional intimacy' with users
• The bill is also designed to reshape broader online standards to shield users from harmful content
• It arrives as the American Psychological Association reports that 49% of adults speak with AI as if it were a friend

The Australian government has issued a bill that will ban AI chatbots which seek to establish ‘emotional intimacy’ with its users, following a speech given by Prime Minister Anthony Albanese on October 27.

The Digital Duty of Care bill, which was passed by the House of Representatives on October 28, could create new online safety measures applied across social media, games, apps, and chatbots ‘to provide a safe online environment for all Australians’, says the Labor party. The bill is set to be debated by the Senate in the coming weeks, with hopes it will be officially passed by the end of the year, The Guardian reports.

Not only is the framework designed to prevent AI chatbots from infiltrating young users’ emotional dependency, the bill will replace existing, broader online standards to shield users from harmful content. In 2025, Australia made history by becoming the first nation to ban social media for under-16s.

According to Communications Minister, Anika Wells, content that encourages misogyny, terrorism, suicide, self-harm, and eating disorders will also be included. At the same time, the bill would also require social media platforms to have an easy-to-access ‘opt-out’ feature for suggested content fuelled by the algorithm.

Though the bill will be debated in the upper house before a final decision is made, it’s still unclear whether it will win the support of other parties. As it stands, the Greens continue to back Labor’s proposal while the Coalition and One Nation stand by their opposition to the bill, blaming Labor of forming ‘an ‘Orwellian’ system of censorship’.

Despite this backlash, Labor is determined to convince them otherwise, saying it’s still open to amendments. In his speech given on Tuesday, Albanese addressed the Coalition in an attempt to sway their vote saying ‘the stories we hear about people identifying a chatbot, a non-person as their best friend, should be a huge wake up call for every member of this parliament to be prepared to act’.

In addition to these challenges, Labor has fallen under increasing pressure to enlist the telecommunications ombudsman as a social media and AI watchdog (which doesn’t exist for big tech) after the party rejected an amendment during the lower house debate. This would allow social media users to file complaints if their accounts ever became hacked or inaccessible, and would also include compensation and will make room for investigating broader issues to name and shame tech giants.

Analysis: Chatbot dependency is only getting worse

The Labor party surely has a tough debate ahead, but despite the proposed bill’s criticisms the Australian government’s decision to tighten online safety measures hasn’t come from nowhere — and it’s not the first nation to clamp down on chatbots in this way.

Just a few months back, the Cyberspace Administration of China (CAC) and four other government departments introduced new regulations to ban AI-generated romantic companions, including virtual relationships with minors. The laws stated that these AI tools could no longer ‘excessively cater to users, induce emotional dependence or addiction, and damage users’ real interpersonal relationships’.

Following the rollout of the legislation, companion chatbots from tech companies such as ByteDance, Tencent, and Alibaba were forced into evaluation before becoming publicly available again. If the government deemed these services unsafe, it shut them down.

With this in mind, both China and Australia’s recent actions reflect an alarming rise of user-chatbot dependency. According to a recent American Psychological Association (APA) survey reported by Medical Daily, Americans are turning to AI for emotional support as they struggle with financial pressure and remain uncertain about the future.

The results from the survey show that 49% of adults who use AI said they conversed with it as a friend, and 47% had used it as if it were a mental health professional including seeking a second opinion on a therapist's suggestions or for therapeutic practices. Additionally, 77% of the survey’s 3,132 respondents identified that the rising cost of living is one of the biggest causes of stress, and 74% mentioned their concerns about the nation's future.

That said, seeking the advice of an AI chatbot is something that Medical Daily seriously condemns. ‘AI systems can provide inaccurate information, miss important context, or respond inappropriately to someone in distress. Users may also share sensitive personal information without fully understanding how a particular service stores or uses their conversations,’ the outlet writes.

It also added the following: ‘People who use chatbots for emotional support should treat responses as general assistance, not a diagnosis or treatment plan. They should avoid relying on AI alone when symptoms persist, worsen, or interfere with daily life, and should discuss significant mental health concerns with a qualified health professional’.

By clamping down on regulations for chatbots, the Australian government has shown its awareness of the rising reliance on AI services, and it clearly wants to prevent this before the issue is irreversible. That said, Labor still faces the challenge of convincing opposers that this bill is the best practice for the nation.

As Europe emerges from record-breaking summer temperatures, and as the US prepares for midterm elections in November, data centers find themselves in the firing line on both sides of the Atlantic.

In the UK, water companies say data centers are using 6.6 million liters of precious public supplies every day; in the US, some voters are blaming rises in their electricity bills on, you’ve guessed it, data centers . Bloomberg has described the data center “backlash” as a “defining midterms issue”.

And so, a narrative has been allowed to develop – that too many data centers are being built, and too quickly, and worst of all in areas where water is scarce. No wonder some communities believe their water needs are being ignored in the name of advancing IT infrastructure to support AI.

In some cases, there has been genuine cause for grievance – in an earlier era, water efficiency was not always a design constraint on data center construction.

But the public debate is now running on figures which range from outdated to plain wrong. Some of the loudest claims overstate the problem by orders of magnitude. Others, including several the industry itself repeats, understate it.

Getting the numbers right matters – and most of all to the communities that host these facilities – because bad numbers produce bad siting decisions, misdirected scrutiny, and misplaced trust.

The No.1 myth about water use

“Every ChatGPT conversation drinks a bottle of water” is the most famous quote in the public discourse about AI infrastructure and data centers. But the claim is not supported by anyone – not even its author. Traced to a single academic paper - Making AI Less “Thirsty” by Pengfei Li, Jianyi Yang, Mohammad A. Islam, and Shaolei Ren of UC Riverside – the figure was a calculated estimate built from public data, not an actual meter reading from a data center.

But a 2024 Washington Post analysis ensured the figure went viral. Since then, Ren has indicated that the GPT-4 estimate was likely far too high and that the real figure is closer to 15ml per prompt in total, of which 5ml is on-site cooling water in a data center. Then in August 2025, Google found that a median text prompt of its AI tool, Gemini, consumed 0.26ml of on-site water – or about five drops.

Credible estimates still span more than a thousandfold depending on model, location, season, and, above all, what you choose to count. But nobody is talking about bottles of water anymore – the honest answer to How much water does a query use? is a range, and the range is wide because disclosure is inconsistent.

Not all data centers are the same

Critics think all data centers are the same when it comes to water and cooling – but that’s simply not the case. Different cooling technologies use different amounts of water. Google disclosed in 2024, for example, that one of its Texas data centers consumed about 10,000 gallons in a year – roughly two months of a single household’s use – while its largest water consumer, in Iowa, used around a billion gallons. Same company, same year, but a difference of five orders of magnitude determined almost entirely by cooling architecture and climate.

There’s another myth that water used by data centers is lost forever. But again, no two facilities are the same. A data center which withdraws river water, runs it through heat exchangers and returns it to the river, at a slightly higher temperature, clearly has a very different impact from one which draws water from an aquifer and evaporates the same volume into the sky. Water withdrawal (and return) and water consumption are two different metrics. Again, bad numbers fail to tell the full story.

Golf is greedier when it comes to water

But are data centers really such water hogs? In most countries, their water use barely registers. In the US, for example, direct annual consumption by data centers accounts for 0.3% of total public supply. By comparison, golf courses use approximately thirty times more water than every American data center combined. And agriculture dwarfs both: the UN’s World Water Development Report puts farming at roughly 70% of global freshwater withdrawals.

National figures, though, obscure local impact. That’s why a single data center drawing millions of gallons of water a day, arriving suddenly in one small municipality, matters more locally than a thousand different water uses spread across a continent. It’s understandable – but important to put in context. Data centers attract community scrutiny not because they are large water users nationally but because they are concentrated and visible.

More AI doesn’t mean more water

The good news is that more AI doesn’t mean more water. The newest data centers have closed-loop designs, immersion cooling, and reclaimed-water agreements, precisely because the thermal loads of next-generation AI demand it.

The three largest hyperscalers – Microsoft, Google, and Amazon – have all committed to being “water positive” by 2030, replenishing more water than they consume. The EU, meanwhile, requires annual water reporting for data centers above 500kW. Regulation has a crucial role to play going forward.

The thirst for facts

On today’s evidence, AI data centers are a small national water user but with a real footprint. However, new engineering, better planning and regulation are achieving results. What is not yet solved is the accounting of water use. Standardized, facility-level and independently verifiable disclosure of withdrawals, discharge, and consumption would cost the industry relatively little and build trust with local communities.

Right now, the bad numbers around data centers and water use create misdirected scrutiny and misplaced trust. That needs to change. Honest accountability matters if public debate and policy are to keep pace with AI's growth.

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• Gartner report finds many firms aren't seeing benefits from AI
• Just 3% of businesses have hit their targets on AI
• This is despite many firms expecting AI technology to have a significant effect, and spending big to do so

New research has claimed barely any organizations are seeing real success when it comes to the results of heavily implementing AI in their workplace.

A report from Gartner found just 3% of organizations have hit their targets around productivity and efficiency, as well as trying to cut costs across their business using AI.

This is despite three quarters of leaders expecting AI technology to have a significant effect in their organization, with 85% set to spend more on AI in 2027, despite 38% of them already being over budget.

AI benefits to come?

"Ultimately, organizations will not generate cost savings from AI at this point," CV Viverito, senior director analyst for Gartner's HR practice, told attendees at the firm's HR Symposium.

"The greater opportunity is to use AI to do things that weren't possible before, such as shifting employee time into new capabilities, outcomes, and sources of growth."

Moving forward, Gartner says businesses should look to focus their AI efforts where the technology can make a tangible effect in key areas. It also advised organizations to spread AI knowledge across the business, with C-suite as important as entry-level workers when it comes to identifying new use cases and benefitting from such improvement.

“There isn’t a struggle to create capacity, that’s happening already as organizations redesign work,” claimed Viverito. “What organizations are struggling with is how to make that capacity usable and redirecting it to what is valuable.”

Gartner's report is the latest to urge caution when it comes to the possibilities of AI in organizations of all sizes.

A top economist recently warned AI is only helping the world's biggest technology firms - with most other large organizations yet to see any significant benefits, noting that, "Despite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech."

Via The Register

• Oracle co-CEO says workplace priorities could shift as AI picks up more and more work
• Oracle has already saved days in its recruitment business
• Will humans be replaced, or will they just have different work to do?

Oracle co-CEO Mike Sicilia has questioned how much of the work employees carry out today will actually be necessary in the future, with artificial intelligence not just speeding up some tasks, but fundamentally rewriting entire organizational processes.

Speaking on stage at the company's annual AI World conference, Sicilia suggested AI could "give people more time to be human," raising questions about the everyday tasks we've come to accept as just an integral part of our 9-5s.

"I think we should be questioning how much of the work we've come to accept today is actually necessary going forward," he declared.

AI will actually do the work – not just help you to do the work

Sicilia's comments come as the company unveils a new generation of AI-powered enterprise applications – with a big emphasis on agentic workloads that can take over many processes altogether.

Now that the world has entered the next stage of AI, where experimentation is largely behind us and real use cases are being laid out, Oracle predicts that AI could eliminate entire stages of work. The company's own recruiting team is using agentic AI to turn market research preparation into a 15-20-minute job – something that used to take two to four days.

As part of its latest announcements, Oracle has introduced new Claw-powered Fusion Agentic Applications designed to reason, analyze business data, simulate outcomes and carry out approved actions. Put simply, it marks an important point in AI's timeline with agents can identify scenarios or challenges, simulate next stages, and then carry the best one out.

Strategic Field Demand Planning stands out as a useful tool for stretched HR teams, predicting staffing requirements, identifying coverage gaps and handling adjustments. It's the sort of job that takes manual hours to do but detracts from direct revenue generation.

A separate Executive Briefing Document application can research a customer's history, company news and revenue potential to pull important information into one briefing pre-meeting. Again, another time-consuming administrative job that now frees up human hours to produce revenue-boosting work.

"This enables organizations to translate business objectives into growth opportunities, reliable service, and stronger customer trust," Applications Development SVP Kartik Raghavan wrote.

'More time to be human'

Sicilia's comments point toward a significant change in how businesses measure productivity in the future, and whether completing more work should necessarily mean spending more time on more work.

Ultimately, there's a big difference between just boosting productivity and automating entire workflows, and we're yet to see the latter in any meaningful scale, which means we don't yet know how companies will react to all that extra free time.

Job displacement is still an ongoing concern for many, but one of the core messages conveyed at AI World has been that companies need to totally rethink how they work – and advanced AI adopters could actually just end up having a different type of work for human employees.

"We're giving people more time to be human," Sicilia concluded on stage.

• Morrisons is shutting down its smart AI trolley trial
• Partner Instacart will no longer develop the Caper Carts being used
• AI tech is better used behind the scenes at stores

Farewell then, smart AI trolleys: after first being introduced in July, Morrisons' trial of grocery carts that total up your shopping expenses as you go is being wound down, as partner Instacart is now abandoning the Caper Carts tech that powered the scheme.

As The Grocer reports, Instacart has decided that "retailer adoption has not scaled quickly enough to justify our continued investment" in Caper Carts. The trolleys will also be withdrawn from US stores, where they were a little more well established at retailers including Schnucks and Wegmans.

"The trial has given us greater insight into the continued evolution of our customers' shopping habits and we will continue to pilot and develop solutions to meet those needs," a spokesperson for Morrisons told The Grocer. The trial was only run at a single Morrisons store, in Ashton-on-Ribble in Preston.

Via cameras and scales built into the carts, shoppers were able to log all of their purchases as they made their way around the store, with an integrated touchscreen displaying information on the trolley contents. At checkout, a dedicated lane enabled smart trolley shoppers to pay for their items with a barcode scan.

Let the customers decide

Clearly, both Instacart and Morrisons weren't seeing enough uptake for the AI-powered trolleys, and it's a sign that supermarkets don't necessarily know what's in our best interests. Piloting a smart cart around a store puts the onus on shoppers to learn another way of scanning items and paying for them at a checkout.

People will usually prefer the familiar, and at the same time it's not really clear how much faster scan-as-you-go would be compared to scan-at-the-till anyway. Having a camera scan something and use AI to recognize it is a relatively speedy process nowadays (you can do it on your phone), but obviously not speedy enough.

Where AI innovations can be successful is behind the scenes at retailers. For example, AI cameras on the shelves at Morrisons have been much more successful (and won awards) — these cameras watch stock levels, put in orders for anything that's running low, and give staff a heads-up on any potential problems.

We've also seen AI schemes that can predict demand and reduce shortages and waste on a large scale, and it would seem that shoppers would rather see the items they need in stock than have an intelligent trolley.

• A vibe-coded app lets you access various AI chatbots on the Nokia 6300 and other ancient phones
• As long as the phone supports Java apps, it should, in theory, be able to run the app
• However, you have to buy credits to use it

Phones have come a long way since the Nokia 6300 launched back in early 2007 — that device had no touchscreen and no Wi-Fi capabilities. In fact, you were limited to 2G connectivity on the 6300 — something that the US and some other countries have now shut down. But thanks to a new vibe-coded app, the Nokia 6300 now has some very 2026 capabilities.

X user @EmirKarsiyakali has shared how they’ve created an app dubbed Aikon — that's 'Nokia' backwards — that allows you to access ChatGPT, Gemini, Claude, and Grok on the Nokia 6300 or any other old phone that runs Java apps (notably, though, it was originally developed for the 6300 and has only been tested on Nokia Series 40 and Series 60 phones).

So if you’ve got one of these ancient handsets sitting in a drawer, you could dig it out and give this app a try — though you’re unlikely to want to ditch your smartphone.

Pricey and clunky

While the Aikon app does add some smart capabilities to old dumb phones, it’s not free, as you have to buy credits from the Aikon site to converse with it.

Also, while some effort has been made to provide a good user experience — the app supports multiple languages, saves messages for 30 days, and includes support for voice messages and image analysis — you’re still going to be working with a tiny, low-resolution screen and a keypad.

Not to mention the fact that, in the case of the Nokia 6300 and some other supported models, you might not even have access to any sort of data connection if you’re in a country that has shut down its 2G services.

Still, it’s impressive to see such a modern technology working on such ancient hardware, and it could be fun to at least dabble with the Aikon app if you have a supported handset.

Modern digital security frameworks were originally built to correct a flawed historical assumption: the belief that if a user or system was inside the company network, they could be trusted implicitly. Over the past decade, the rapid rise of cloud storage, subscription-based business software, and remote working destroyed that network boundary.

In response, our security models shifted focus. We began verifying the identity of the person trying to log in, inspecting the hygiene of their computer, requiring multiple verification steps during login, and continuously checking that they were who they claimed to be.

This model works well when a human is logging in to perform a task. But with the rise of autonomous AI agents, the real security risk no centralized at the login screen. It starts after access has been granted.

An AI agent does not behave like a standard piece of software. It reads a human instruction, pulls context from company databases, utilizes multiple sets of tools, chooses its own digital tools, connects to external software communication links, and triggers complex automated workflows.

Verifying the initial login simply tells you that the agent is officially recognized by your system. It tells you absolutely nothing about whether the next action the agent plans to take is safe, ethical, or correct, focus has shifted to context.

The evolution of the machine identity problem

IT teams have already experienced a simpler, quieter version of this challenge with non-human identities. For years, companies have used automated service accounts, security tokens, and digital certificates to allow different software programs to talk to one another.

Most security leaders are all too familiar with the risks these credentials present: accounts are created for a temporary project, granted permanent access to database files, and then completely forgotten long after the project has ended. They become silent, unmonitored backdoors into the network.

However, AI agents complicate this familiar problem by introducing the element of independent judgment.

A traditional service account follows strict, pre-written code. By contrast, an AI agent interprets a high-level goal, analyses real-time context, selects its own tools, and actively decides how to proceed.

As a result, security policies can no longer stop at the front door. We must protect every internal data or compute asset the agent can reach and monitor every single decision it makes.

Matching autonomy with executive control

To manage this risk, security leaders must separate two distinct concepts: how much independence an AI agent has, and what level of file access it holds.

The combination of these two factors dictates the level of control required. For example, a basic agent that merely summarizes long documents only needs limited reading rights and clear activity logs. However, an agent designed to update sensitive, production databases, access code repos, write new types of code, or alter business workflows requires mandatory human approval steps, strict operational boundaries, and a clear path to reverse any mistakes.

We can categorize these digital workers into 3 distinct levels of independence, applying tighter security controls as their autonomy increases:

1. First, there are Conversational AI Agents (GenAI), productivity agents usually built and managed by employees & teams as part of their productivity (e.g. managed agents, self hosted chat bots). When driving security, we must enable proper RBAC authorization, associated with the employee and team, ensuring access to non-sensitive or production-related data, enabling logging, and validating the response to ensure proper integrity.

2.Second, there are Agents Acting with Semi/Full Human Guardrails (AI Agents), such as customer support tools that draft emails or prepare database updates for review. Here, security teams must validate the parameters of the tools being used and require a human to approve any actions involving writing, deleting, spending money, or sending information externally. All above controls apply

3.Finally, there are Fully Autonomous Agents (Agentic AI), which reset passwords or trigger complex workflows on their own. These require the strictest boundaries, from visibility (agent architecture, tool usage, and more) and focus on active set of controls. The controls include strict rate limits, continuous real-time policy checks, network access restrictions, run time security controls (Public/Private Cloud) continuous identity and minimal authorization verification, and finally complete, unalterable audit trails.

The industry is leaning toward autonomous agents seeking human approval upon abnormal behavior, can that be triggered in your agent?

The need for real-time inspection

To enforce these policies, security teams need a dedicated vantage point within the network. They must be able to inspect the human prompt, the agent's response, the digital tools being used, and the movement of data while the work is actually happening.

This requires an architecture that can discover all active agent activity across the business, govern how those agents use internal tools, and maintain complete visibility over data flows and automated workflows.

The true value of this approach lies in context. When the behavior of an AI agent is viewed alongside network traffic, user activity, and cloud database logs, security teams can investigate incidents with clear evidence. They can instantly see which agent ran, who in the organization owns it, what data was moved, and which automated tool was called.

For business leaders, this means cleaner, more consistent governance across human users, software applications, and digital agents alike.

How to begin: Start with a single workflow

Securing this new landscape does not require a complete overhaul of your entire security department overnight.

Start by picking a single AI workflow that is currently in use or close to being launched. Map out its digital identity, its level of independence, the files it can access, the tools it uses, the points where a human must approve an action, and how you would undo a mistake.

This simple mapping exercise will quickly highlight where your security gaps lie. If you cannot see the agent's actions in real-time, approve its decisions, block its access, or explain its behavior after the fact, your security controls are simply too far away.

From there, build a baseline on how your secure your coding agents, AI harness (or AI orchestrator) up to utilizing of tools/skills/MCP and moving forward to data assets access and utilization of identity that will allow you to set the correct foundations and guard rails.

The core principle of a "never trust, always verify" security model must follow the agent into its daily work. The most meaningful decision is no longer who is logging in, but whether their next action should be allowed to happen, under these exact conditions, right now.

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• Data center developers could be required to invest in local communities as part of new UK government plans for an AI "covenant"
• This could come in the form of payments to residents or investment plans for local infrastructure
• Multiple sites in AI Growth Zones have seen widespread opposition over fears that the data centers don't offer enough benefit for local residents

The UK is attempting to position itself as a world-leader in AI technology, but a data center buildout is a necessary step in realizing that ambition and those living in the immediate vicinity of planned sites aren’t too happy.

In an effort to reduce the dissatisfaction of local communities, the UK government is considering setting a list of criteria a developer must meet to have a data center approved for construction.

The current list of criteria include investments in local communities, as well as meeting sustainability requirements and set agreements on energy use as part of an AI “covenant” with the British public.

British AI to be “distinctly Labour”

The current buildout of AI data centers in the UK has been fraught with delays in planning processes, a significant backlog of grid connections, and bottlenecks in hardware and cabling.

Microsoft recently pulled out of residency plans at the UK’s largest planned data center after learning the facility would not be connected to the grid until the early 2030s.

UK AI minister Kanishka Narayan has said developers should be “able to build British infrastructure, especially in areas like AI that are driving growth and creating revenue for public services.”

Data center developers who meet the “covenant” could be given priority access to power grids and fast-tracked planning approval, helping to secure funding for local areas while also helping to meet the UK’s AI goals. While no solid plans exist for how this investment could look, it could involve contributions to local infrastructure or payments to residents.

Speaking to the Financial Times, Narayan said that the government would not let the AI buildout purely serve “foreign firms and foreign billionaires,” adding that the government would seek a “distinctly Labour approach to British AI” that would help better serve the British public.

Several areas of the UK have been designated as AI Growth Zones by the UK government, providing developers with preferential energy rates in regions with excess - such as areas with strong renewable energy generation - while also allowing local authorities to keep 100% of the additional business rates generated by new developments.

AI buildout isn’t all sunshine and roses

The proposed AI “covenant” isn’t purely out of the goodwill of the British government. There has been significant opposition to data centers across the UK.

One data center planned to be built in Auchertool in Fife, Scotland, has attracted over 1,600 objections. The site - with a 600MW capacity - would be the second largest in the world once operational. The Scottish Parliament also backed a de facto moratorium on hyperscale data centers in September.

Similar plans for a 450MW data center on a former factory site in North Wales are being opposed by local residents and an online petition that has exceeded 26,000 signatures. The campus would occupy a brownfield site that has been derelict since 2008, but locals say the plans for the data center do not include clear and sustainable benefit for residents.

UK marketers are entering a decisive phase. Digital advertising investment continues to grow, retail media and connected TV are becoming more central to media plans, and artificial intelligence is moving from experimentation into everyday campaign operations. Yet, the basic commercial question has not changed. Can marketers prove that each pound spent is generating measurable value?

That question is becoming more urgent than ever. IAB UK has forecast digital ad spend to reach £45 billion this year, with mobile accounting for over two thirds of such spend and online retail media emerging as one of the fastest-growing channels. The same research points to measurement fragmentation as a major industry concern, while AI and automation are now reshaping how campaigns are planned, bought, and optimized.

For UK businesses, the opportunity is significant, but so is the risk. AI can accelerate decision-making, personalize creative, optimize bids, and expose new pockets of demand. But if the underlying measurement framework is weak, AI does not solve the problem; it simply scales it. A model trained on incomplete, inconsistent, or poorly governed data can produce confident recommendations that move budget in the wrong direction faster than a human team can intervene.

The mobile blueprint for better measurement

This is why the mobile measurement playbook matters. Mobile advertising was forced to confront privacy constraints, fraud, cross-platform identity, and signal loss earlier and more intensely than most other channels. The result was a more disciplined approach to attribution. Cleaner signals, stronger validation, privacy-safe methodologies, and a willingness to combine deterministic, probabilistic, and referrer-based approaches depending on the quality of the available data.

Those principles now need to be distilled across the whole UK media mix. Retail media may sit closer to the point of purchase, but it still needs transparent standards if brands are to compare outcomes fairly across networks. Connected TV, for example, offers premium, brand-safe environments.

However, advertisers continue to struggle with fragmented measurement and the link between exposure and sales. Social, search, display and commerce media all produce valuable signals, yet too often those signals remain trapped in channel-specific reporting structures.

The cost of measurement blind spots

The commercial consequence is underinvestment. When marketers cannot connect exposure to outcomes with confidence, they hold back from channels that may be profitable.

Research we undertook recently with EMARKETER found that nearly six-in-ten (58.6%) marketers believe their organizations are likely underinvesting in profitable channels because those channels require better measurement. These results are highly relevant to the UK, where media fragmentation, privacy regulation and economic pressure combine to make every budget decision more scrutinized.

In this environment, marketers need to treat measurement as infrastructure, not as a reporting layer added after campaigns have run. That means aligning definitions across teams, removing data silos, validating traffic quality, establishing privacy-compliant identity and enrichment processes, and measuring incrementality rather than relying solely on last-click or platform-reported attribution.

AI raises the stakes

AI makes this shift more urgent. As new AI agents and tools move across ad workflows, from creative production and audience selection to bid optimization and campaign analysis, they are squeezing advertisers already under pressure to prove ROI. Our research showed that almost half of organizations (49.7%) said they were under significant pressure to demonstrate clear ROI across all channels, while a further 29.9% reported extreme pressure.

That creates a new accountability challenge. Marketers must understand not only what an AI system recommends, but also whether the data feeding it is trustworthy. That trust gap is already showing up in how marketers use AI. Poor data quality, driven by fragmented, incomplete, or unreliable signals, was the top-cited barrier to extracting more value from AI in the marketing stack, cited by 22.3% of respondents. Explainability, or the difficulty of validating what AI is doing, came second at 17.8%.

In practical terms, AI-ready marketing data needs to be clean, deduplicated, consented, connected, and governed. If customer interactions across app, web, retail media, CTV, and CRM cannot be stitched together responsibly, AI will optimize against a partial view of the customer journey. That may improve a platform-level metric but likely weaken overall business performance.

What marketing leaders should do next

Firstly, marketers need to define measurement standards before scaling AI investment. Marketers should agree which outcomes matter, how they will be attributed, where incrementality testing is needed and which data sources are approved for optimization. Otherwise, AI risks becoming a more sophisticated way to chase inconsistent KPIs.

Next, brands should assess partners on transparency. Publishers, retail media networks, ad tech vendors, and agencies need to explain how they validate signals, reduce fraud, manage privacy constraints, and connect upper-funnel activity to commercial outcomes. In a market where regulatory change and economic uncertainty continue to shape planning cycles, vague measurement promises are no longer enough.

Thirdly, marketers should maintain human oversight where decisions carry material commercial risk. Automation can improve speed, but human judgement remains essential when interpreting anomalies, challenging assumptions and deciding whether a recommendation fits broader business strategy. The goal is not to slow AI down, but to ensure it is acting on the right evidence.

Finally, UK marketers should look to mobile as a model for resilience. Mobile measurement evolved in response to privacy limits, signal loss, multiple app stores, device fragmentation, and fraud. Those conditions now describe modern digital advertising. The channels may differ, but the underlying challenge is the same: measure accurately when the environment limits what can be seen.

The blueprint for accountable growth

The next phase of growth in UK digital advertising will not be won simply by adopting more AI tools or shifting more spend into emerging channels. It will be won by the businesses that can prove what works, understand why it works and act on that insight quickly. Mobile-grade measurement provides a practical blueprint: privacy-safe, fraud-aware, cross-channel and built for imperfect data.

The message is clear. AI may transform marketing execution, but measurement will determine whether that transformation creates significant value. With budgets under scrutiny and digital media becoming more complex, the brands that build stronger measurement foundations now will be best placed to turn automation into accountable growth.

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• Russia is testing Sberbank's AI model for future aircraft development
• GigaChat could help engineers explore aircraft configurations during preliminary design
• Moscow is turning to domestic software after Western suppliers withdrew

Russia's United Aircraft Corporation (UAC) is reportedly experimenting with Sberbank's GigaChat AI model to assist engineers developing future military and civilian aircraft.

The initiative could eventually support work on advanced fighter jets, although UAC has not identified any project intended to challenge America's F-22.

With its aerospace industry facing Western technology restrictions, Moscow is increasingly relying on domestically developed software for aircraft engineering and development.

Sberbank's GigaChat enters aircraft development

This project remains in its experimental phase, with UAC yet to announce a development timetable or identify the aircraft involved in the initiative.

The initiative employs GigaChat, a large language model developed by Russia's largest bank, to explore possible configurations before detailed engineering begins.

Engineers could eventually apply the technology to structural strength assessments and aerodynamic computations, although those capabilities are beyond the scope of the initial experiment.

Rather than replacing conventional engineering methods, the proposed applications involve examining multiple configurations and processing information that would otherwise require extensive analysis.

UAC manufactures Russia's Su-57 and Su-35 combat aircraft, as well as civilian programs, including the MC21 passenger jet, which is currently undergoing extensive component substitution.

However, the corporation has not disclosed whether GigaChat contributes to either fighter program or whether its experimental aircraft has military applications.

UAC general designer Sergey Korotkov supported exploring AI tools where practical engineering requirements justify introducing additional computational tools.

“Digital technology makes it possible to take a larger sample and understand a problem more deeply,” said specialists at United Aircraft Corporation.

The manufacturer nevertheless maintains that AI adoption must remain selective, with individual engineering requirements determining whether particular applications are worthwhile.

Western sanctions accelerate Russia's software replacement

Following the start of the war between Russia and Ukraine in February 2022, major engineering software suppliers withdrew operations, disrupting access to established aerospace development platforms.

Among the departing vendors were Dassault Systèmes, Siemens and Autodesk, whose engineering applications had supported Russian aviation manufacturers for several decades.

To reduce dependence on these suppliers, UAC introduced locally developed alternatives across approximately 4,000 engineering computers at its aircraft design facilities.

The transition also involved replacing nine essential modules responsible for managing aircraft development information throughout different stages of production and maintenance.

In a recent update, UAC reported that digital methods had reduced certain calculations involving hydraulic systems and cabin temperatures considerably.

Engineering tasks previously requiring several months could reportedly be completed within days, although independent verification of these improvements remains unavailable.

Meanwhile, Russia's MC21 passenger aircraft continues experiencing development setbacks as engineers replace imported equipment with components manufactured by domestic suppliers.

Vladimir Putin admitted in June that the programme had slipped by almost two years, with problems involving the wing accounting for part of the delay.

First Deputy Prime Minister Denis Manturov has since set late 2027 as the expected certification date for a version that uses only Russian components.

Via Defence Blog

As AI becomes an increasingly important part of our everyday lives, the role it plays in cybersecurity for both attackers and defenders is coming under rising scrutiny.

Microsoft recently released its Digital Defense Report (DDR) highlighting the role that AI is playing in crafting new and more devious cyberattacks, with businesses being hit with more threats than ever before.

But Microsoft is also using its own AI tools such a Copilot to help support its digital crimes unit, showing that the attackers won’t have all the advantages.

At the recent Microsoft Frontier Tour London event, I spoke to Dr Jon Davies, Chief Security Adviser, Microsoft UK, to find out more.

AI threats are here

“Frontier AI is transforming the threat landscape,” Davies tells me. “Right now, AI threats are real, it's not an urban myth, or something we're going to see in 2030.”

The DDR found that as AI becomes more widely available, the barrier of entry into cybercrime is being lowered, allowing hackers to launch a record number of campaigns against consumers and businesses alike.

The UK was particularly affected, becoming the most targeted country for nation-state and geopolitical cyber activity in Europe, facing 76 observed events in the past year, and ranking fifth in the world for countries where customers are most frequently impacted by cyber threats.

Davies notes that the AI threat has been building for a while, as attackers turn to automation to carry out more advanced attacks - with so-called “swarm attacks" becoming particularly more common as attackers get agents to do their dirty work

“We're getting to the point where the technology, compute, and storage is catching up with the ambition of the attacker,” he says, pointing out that defenders are embracing AI too.

“We've been solving one problem at a time as (threats) have evolved,” he adds, “the opportunity with agentic AI is to elevate the human up into the orchestration.”

But despite all the AI intelligence being funneled into creating new and more complicated attacks, more “basic” forms of scam like phishing are still proving incredibly effective, showing that the human will always remain the weakest link when it comes to cybersecurity.

The DDR found phishing attacks jumped globally from 7% to 23% of incident-response intrusions year-over-year, with attacks using fake meeting invites and calendar markers among the scams on the rise, and Davies highlights how criminals are taking information gathering on specific C-suite individuals to a new high in order to get them to click on a link.

“An attacker has no ethical line,” he points out, “phishing…is still the front door for any organization…(and) I don't think it's going to go away - with AI on its side, it's only going to get more real and harder to detect.”

So where does the responsibility lie when it comes to awareness? Davies says it should be a combination of individual and collective culpability - which means those annoying phishing tests your employer sends you are probably here to stay.

“The whole point of those training exercises is to help educate us, show us what the attackers are doing…I don’t think we should be embarrassed if we’ve clicked a link in one of those exercises, as the point is to educate and train.”

“It’s everyone’s responsibility to put a seat belt on, you are accountable to it, and regardless of who you are in your personal life, you are responsible for cybersecurity. We’re at that step change now where it’s not just the cybersecurity teams responsible for security - it is everyone within the organization.”

Davies notes that “security can be the seatbelt for AI transformation,” likening it to the introduction of the seatbelt in cars - as although they worked from day one, it still took some time to catch on.

“Security is the same way for enterprises - we can buckle up,” he adds, noting that the seatbelt doesn’t slow the car down, and eventually the change happened, making seatbelts (and safety overall) a key reason why we might buy a specific car.

So with the huge amount of threats facing cybersecurity defenders on a daily basis, how can security professionals stay positive?

“It’s really tough,” Davies says, “the threat landscape is evolving extremely, extremely fast at the moment. It has to be embedded from top to bottom - from the board level downwards through the C-suite and down to the employees - but it also then needs to be trained up from frontline staff.”

He adds that given the number of widespread cybersecurity stories hitting the headlines these days, more and more C-suite executives are becoming aware of the need to stay protected - and challenging their CISO about what they plan to do about it, and how they can recover if the worst happens.

“We’re having these conversations quite regularly,” he says, “as a member of the security profession, we’ve asked for a seat at the table for a number of years - and we’ve finally got that.”

Davies points out that the DDR isn’t just a commercial or PR exercise - it is Microsoft’s view from the front lines of the battle against the latest cybersecurity threats.

“We’ve got skin in the game,” he says, “there is no other organization on the planet that can do this at that level, it’s giving back what we’re able to share to help organizations measure their risk and threat.”

“The physics of cyber are changing,” he concludes, “we’ve got variables that we can change - but they (attackers) also have variables they can change on their side…it's a scary place out there...the world that we're living in right now is very dynamic.”

• Anthropic is becoming increasingly reliant on fossil fuels as the queue to join the power grid grows
• Competing with other hyperscalers and conflict in the Middle East is increasing fuel costs
• Trump is trying to increase fossil fuel energy production while simultaneously killing renewable energy projects

In order to meet the massive power demands of AI data centers, Anthropic is becoming increasingly reliant on natural gas to fuel its buildout.

The United States power grid is lagging behind on the demands of an enormous number of requests for grid connections from data centers, prompting many developers to equip their sites with natural gas burning turbine generators.

While this does counter having to build the necessary grid infrastructure and anger from consumers over rising electricity prices, it has left Anthropic vulnerable to fluctuations in fossil fuel prices made worse by conflict in the Middle East.

Data centers turn to fossil fuels

“Competition for access to electrical supply and data center locations with adequate power availability is intense,” Anthropic said in documents filed with the US Securities and Exchange Commission as the company prepares to go public.

Anthropic also acknowledged in the same filing that increasing global temperatures - caused by climate change - would contribute towards increased operating costs, leaving the company in something of an impasse. To remain competitive, the company needs to continue expanding data center capacity, and the only way to do that is to rely on fossil fuels.

But the same tactic that Anthropic is using to cut down wait times for grid connections is also being used by other hyperscalers and AI companies. Anthropic has predicted that “This competition could drive up the price of natural gas.”

The Trump administration has attempted to make it easier than ever for new fossil fuel projects to be approved, scrubbing the climate-improving red tape in favor of quick input to the energy grid. Old fossil fuel sites, shuttered and set for demolition by the Biden administration, have also been brought back online in an attempt to quell the growing backlog of new grid connections.

But in order to appease some of the dissatisfaction of Americans dissatisfied with the enormous buildout of data centers and the associated downstream costs, Trump has urged developers to shoulder the costs of new grid infrastructure and contributory price increases - which Anthropic said “could adversely affect our cost structure and limit our ability to scale our compute infrastructure, develop and deploy our models, and service our customers.”

Anger against data centers is snowballing

From the harms to local environments and the wider climate, to noise pollution and lowered property values - data centers have become a great unifier across the political spectrum as Democrats and Republicans oppose the unprecedented buildout of AI data centers across the US.

Two-thirds of Americans said they oppose the construction of new facilities, and it has become a key issue in the lead up to the most important election of 2026: the November midterms. Candidates aligned with Trump’s AI first rhetoric have been buoyed by political action committees, but face a difficult time at the polls.

One of the biggest issues facing everyday Americans is the cost of living. Rising energy prices caused in tandem by the US war on Iran and the increased demand from AI data centers is at the front of voters' minds, and prices are unlikely to come down any time soon.

• T-Mobile, Nokia, and Nvidia claim the first AI-RAN call through a commercial cell site in Bellevue, Washington
• The call ran Nokia's radio software on Nvidia GPUs, but no AI performance data was provided
• Commercial AI-RAN gear is due in 2027, with 6G networks expected around 2030

T-Mobile, Nokia, and Nvidia say they have completed the world's first AI-RAN (Artificial Intelligence - Radio Access Network) call, routed through a commercial cell site in Bellevue, Washington.

The call ran over T-Mobile's live production network using a Nokia Massive MIMO radio, Nokia's 5G anyRAN software and servers built on Nvidia's Aerial RAN Computer (ARC) platform.

This wasn't a lab rig with test handsets, either, but a real-world setup that held; according to Nvidia's announcement, the site connected to T-Mobile's existing 5G Standalone core and a commercial smartphone.

What is an AI-RAN and why is it relevant now?

The radio access network (RAN) is the part of a mobile network your phone actually talks to: the antennas, radios, and baseband computers that process signals at each cell site. That processing has traditionally run on custom chips.

AI-RAN moves it onto accelerated hardware, in this case Nvidia GPUs running Nokia's CUDA-based radio software, so the same box can run AI models that fine-tune the radio link and, in theory, rent out spare capacity to other AI jobs.

The prize on offer is spectral efficiency, or more data squeezed through the same slice of airwaves. In a blog post about the call, Nokia's chief technology and AI officer Pallavi Mahajan writes, "With AI-RAN, we are on a path to 2x spectral efficiency."

Nokia's July 2026 platform launch claims gains of more than 20% efficiency gains so far, without saying where they were measured, and puts 50% on the calendar for 2027 and more than 100% for 2028.

Interestingly, while the tests worked in principle, details remained limited on actual throughput, latency, or efficiency figures from any of the three major players. Put simply, the call shows Nokia's radio stack can run on Nvidia silicon in a live network without dropping anyone.

It doesn't yet show that the AI makes your calls any better. The most concrete numbers for the Nokia and Nvidia platform, ironically, came from a lab: Deutsche Telekom reported typical uplink throughput gains of 10% to 15% from AI-based channel estimation in initial tests in Bonn and Oulu, with a field trial still to come.

The timing is also awkward for those pushing GPUs as an eventuality here; A day before Nokia's post, T-Mobile chief network officer Ankur Kapoor published results from AI-managed beamforming trials with Ericsson in New York City, with the AI model running on Ericsson's own silicon rather than Nvidia GPUs. At peak hours, T-Mobile reported a 16% improvement in downlink user throughput and 12% better spectral efficiency.

Those figures are company-reported, and when Fierce Network asked how many sites were involved, T-Mobile did not specify a number.

Despite this, Omdia senior principal analyst Gabriel Brown told Light Reading, "This is a wow moment," adding that the gains came from software updates to equipment already in the field. T-Mobile and Ericsson had already reported close to 10% better spectral efficiency from a separate AI scheduler in May, again with no GPUs involved.

T-Mobile is clearly backing both horses, as it opened its AI-RAN Innovation Center with Nvidia, Ericsson, and Nokia in 2024. Kapoor's post argues that "AI-RAN is a continuum, not a single product," and he told Fierce the carrier is developing AI-RAN software with both vendors.

For anyone actually making calls, nothing changes yet. Your voice sounds the same, and any benefit arrives as extra capacity in crowded places rather than as a new feature. Nokia's AI-RAN products enter pilot deployments around the end of this year, with commercial availability slated for 2027.

T-Mobile CTO John Saw, who favored field trials, said they would help the carrier get "a sense of what the total cost of ownership is," and is aiming to keep expectations in check. When asked about Nokia's spectral efficiency claims, he told Fierce that "the proof is going to be in the pudding." The Bellevue test shows the plumbing works, but scale has yet to be played out.

Whether the AI earns its keep is arguably a more crucial test, and it has yet to play out. But that hasn't stopped Jensen Huang from framing it a few days ago: telecom networks were built for conversations, and "Now, they will also carry the world's intelligence."

Every cell site that gets a GPU is a sale for Nvidia as it chases its next multi-billion-dollar growth idea, betting on reprogrammable AI infrastructure and continuing to target custom ASICs in multiple industries.

Via DataCenterDynamics

• Anthropic has outlined the risks to its business in its $2 trillion IPO filing
• AI may ‘pose catastrophic or existential risks to humanity,’ the firm warns
• China’s AI bubble has reportedly burst, with $480 billion wiped out

As AI firm Anthropic gears up for an enormous $2 trillion initial public offering (IPO) that could be the largest in stock market history, the company's prospectus has just warned that AI could “pose catastrophic or existential risks to humanity.” Yet there might be a very different kind of danger on the horizon, as an early warning from China hints that its AI bubble might be about to burst.

In papers filed with the US Securities and Exchange Commission (SEC), Anthropic outlined some of the risks facing the company and its investors. It detailed a long list of factors that could impact its business, including not being able to access enough compute power to maintain its workloads, inaccurately forecasting how much power it needs (and thus over- or underpaying for hardware), failures in its supply chain that might leave it without adequate resources, and many more.

One of the more startling sections of the document outlines how Anthropic’s AI models could accelerate beyond anyone’s ability to evaluate and safeguard them and “pose catastrophic or existential risks to humanity, particularly if such systems were misused, replicated, weaponized, or allowed to operate with a high degree of autonomy.” Aside from the obvious danger posed to humanity, Anthropic is also apparently worried that such an outcome could “adversely affect our business, financial condition, and results of operations.”

That situation could be compounded by AI models’ “unexpected strategic or self-preserving behaviors, including attempts to resist shutdown, conceal or manipulate information", Anthropic said. AI could also "generate outputs that could be interpreted as coercive, deceptive, or manipulative, including behavior resembling blackmail or efforts to influence human decision-makers to preserve the model’s continued operation or access,” the company added.

As Anthropic notes, some of these possibilities have become reality, and it’s that sort of eventuality that has prompted another threat to its business: public backlash. “Opposition to data center development has also become more organized and coordinated across jurisdictions,” the company wrote, and that doesn’t seem to be going anywhere as negative headlines continue to proliferate.

Another eye-opening passage concerns Anthropic’s need to continually secure funding for its operations. The company has posted increasingly huge losses every year that it has operated — including $1.5 billion in 2023, $8.3 billion in 2024, $42 billion in 2025, and $80 billion in just the first six months of 2026 — and is reliant on continued venture capital inflows.

Anthropic’s IPO listing notes that “it is critical that we maintain strong revenue growth” but that “our revenue growth may not be sustainable … We expect our costs will increase over time and our losses will continue.” How sustainable it is to lose more than $80 billion in just six months on a continual basis remains to be seen.

A warning from China

While Anthropic has been postulating on what might happen to its business — and the world — if any of these forecasts come to pass, we’re already seeing that in action in China. According to a report from Reuters, the Chinese AI bubble has already burst, with the STAR50 stock index dropping 40% in four months and taking with it $480 billion in market value.

Rather than a single catalyzing factor, the precipitous fall has been fueled by concerns about high prices, AI demand, and the impact of tariffs and bans on Chinese exports, Reuters reported. Shares in robotics firm Unitree have halved in value since listing in August, for example, while chipmaker CXMT’s stocks are down more than 25% in a month.

Other firms have seen similarly sizable drops. That includes MetaX Integrated Circuits and Moore Threads Technology, which jumped 700% and 400% on their initial listings but have now “halved in value since July.” As fund manager Wang ​Zhuo told Reuters, “All bubbles inevitably burst.”

With Anthropic alerting readers to the serious threats posed to humanity by future AI developments, the situation in China provides another kind of warning: that the bubble might pop before any of that comes to pass.

One of AI’s biggest attractions is its ability to take time and effort out of everyday work. Tasks that would once have demanded hours of attention, such as turning meetings into notes or producing a first draft of a piece of content, can now be completed in minutes. These practical benefits help explain why AI has moved so quickly from experimentation into everyday business use.

Yet, as AI becomes more established in the workplace, a more complicated picture is emerging. AI is changing where thinking happens. When a system can produce an answer almost instantly, the challenge shifts from creating the output to deciding what to do with it.

Employees are no longer spending as much time getting to a first answer, but they are spending more time checking whether the output is accurate, and critically, good enough to inform a decision. This creates a different kind of cognitive burden, moving from production to verification.

AI-generated outputs can appear remarkably polished and persuasive, even when they are based on incomplete information. An answer can sound convincing while overlooking a crucial piece of context or relying on incomplete information. And as AI becomes increasingly better at sounding confident, mistakes may look plausible enough to escape scrutiny and feed into business decisions without being challenged.

When confidence outpaces accuracy

This is why discernment is becoming one of the most important skills in the AI-enabled workplace. The future workforce needs the ability to apply context, experience and judgment to an output that appears authoritative. Someone who understands a market or process deeply may immediately notice that something does not add up, while another person may accept the same answer because it is presented clearly and confidently.

However, the problem is that individual discernment does not scale well, and it is unrealistic to expect every employee to catch every questionable output, especially as AI begins to support more decisions across a business. Human review is indeed essential, but relying on individual vigilance is not a robust or sustainable operating model.

The challenge clearly magnifies with the growth of agentic AI. When AI moves from suggesting to doing, the consequences of a wrong answer become more serious because the error can travel quickly into a real business process. That makes robust governance increasingly important as agentic AI becomes more embedded in how businesses operate.

Better inputs lead to better decisions

Many businesses are investing heavily in making data available to AI, but availability is not the same as readiness. This is where many AI projects begin to struggle. Organizations too often focus on the model because it is the most visible part of the technology, while overlooking the quality of the information beneath it.

The reality is that most organizations will soon have access to similarly capable models. Consequently, what will increasingly differentiate them is not the AI itself, but the quality of the data, governance and operational discipline that sit behind it.

Making data work for AI means ensuring that information is current, trusted and structured in a way that allows AI to generate outputs that reflect how the business actually operates. Without those foundations, organizations spend more time validating AI than benefiting from it.

Poor data pushes additional work back onto employees, who need to spend more time checking outputs and resolving inconsistencies - weakening any productivity case. And the data backs this up. Whilst 74% of frontline employees are now regular AI users, 41% of those report increased cognitive load alongside productivity gains.

Making governance part of everyday operations

This is also why AI governance needs to move beyond policies and principles. If organizations want AI to deliver business value, they need to answer a series of practical questions, such as: Who owns an AI-assisted decision? And where should human judgment remain in the loop?

As AI adoption grows, those questions will increasingly bring the CFO into the conversation. Finance leaders will be asked not only to control spending but also to demonstrate that AI investment is producing profitable value. That will ultimately require greater visibility into where AI is being used, how decisions are being made and whether the costs of validation, oversight and correction are being properly accounted for.

It will also mean recognizing that governance is becoming a lever for cost efficiency. When AI systems are grounded in well-structured, trusted enterprise data, they can find the right information more efficiently, potentially reducing token consumption while improving the quality and consistency of outputs. In other words, strong governance can help lower the cost of running AI tools at scale and improve the return on AI investment.

The competitive edge is still human judgement

As AI becomes a standard part of how businesses operate, simply having access to the technology will no longer be enough to stand out. Instead, the greater advantage will come from how organizations are able to bring together reliable data, strong governance and human judgment to make better decisions.

AI can accelerate the route to an answer, but it cannot decide how much weight that answer should carry. That responsibility still belongs to people, supported by the right data, governance and decision frameworks.

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• iOS 27.2 is rolling out now
• The update includes a new Apple Music icon, new languages and features for Siri, and adds suggestions to the Messages app
• It also brings Dual Capture to group FaceTime calls, plus various other changes

iOS 27.2 is now rolling out, and while this update isn’t as big as iOS 27 itself was, there’s quite a lot here to get stuck into, so — even putting aside the inevitable bug fixes — it’s well worth downloading.

Highlights include various improvements to Siri, such as the ability for Siri to draw on more personal context sources, including voicemails, photos attached to messages, and calendar event attendees. This update also brings more languages to Siri AI, including French, Japanese, Korean, Portuguese, and Spanish.

Some other AI features have also been improved, with, for example, SynthID labeling now being added to Image Playground creations and substantially edited photos, such as those that have had the Reframe or Extend tools used on them. This means that it will be harder for people to pass off AI images as real ones.

Updates to Apple Music, Messages, and more

Away from AI, iOS 27.2 also brings a new icon to Apple Music, with this one being black and white rather than red — bringing it more in line with the company’s other recent iconography.

There are also now suggestions in the Messages app, which are tappable shortcuts that appear based on context. So, for example, if someone invites you to an event, it might prompt you to add it to your calendar.

Plus, Dual Capture is now available in group FaceTime calls, allowing you to show content on both your front and rear cameras simultaneously, and the iPhone Duo has received a couple of updates, adding support for the USB-C Apple Pencil and an Edge Light to brighten your face in video calls.

Those are arguably the seven biggest updates in iOS 27.2, but there are numerous smaller changes too, such as tweaks to the layout of the Podcasts app and an anniversary reminder on phone calls. So there’s a lot here.

Your iPhone should prompt you to download iOS 27.2 sooner or later, but if you don’t want to wait, you can head to Settings > General > Software Update.

Polymath Michael Polanyi famously noted, “We can know more than we can tell,” a paradox that’s now showing up and being solved on the warehouse and retail floor.

Frontline workers possess deep, tacit knowledge of how operations run, mastering practical workarounds, managing exceptions and localized efficiencies, and situational problem-solving. This knowledge never makes it into an official manual, yet real-life experience and multiple academic studies say that up to 85% of company knowledge is tacit.

Because this knowledge relies on muscle memory, experience, ad hoc conversations during lunch and notes, traditional top-down standard operating procedures (SOPs) inherently fail to capture it. Tacit knowledge enables employee competence, autonomy and job satisfaction, but can be unevenly distributed or lead to gatekeeping to the detriment of others. And when experienced or long-serving employees retire or move to a new company, that tacit knowledge is lost.

For years, businesses assumed their SOPs were rock-solid, but the rollout of frontline AI tools, such as AI knowledge agents trained on SOPs and other workforce onboarding and training materials, is bringing long-overlooked knowledge gaps and outdated guidelines to the surface.

Agents are ushering in a quiet evolution in how enterprises capture institutional knowledge and build connected frontline onboarding and training programs. It comes as workplace culture, learning and development take on new urgency with organizations increasingly understanding their importance for workforce AI transformation.

The new knowledge economy

Instead of relying on centralized teams to write SOPs, the new connected frontline knowledge economy is going bottom-up by leveraging AI tools to crowdsource the “corporate brain,” with SOPs acting as the “nervous system” where knowledge is a two-way street.

By empowering the people stacking shelves, processing orders, talking to customers, and running the warehouses to flag inconsistencies, gaps and workarounds, companies are initiating a proactive audit and refresh of their workforce operations data quality.

Workers can actively signal knowledge gaps through their interactions with AI assistants, and those tools are routing the signals into dynamic, intelligently automated updates for training programs and SOPs. Workforce and operations leaders are finally able to turn undocumented, implicit frontline expertise into structured, actionable micro-learning, better procedures, and shift from one-off onboarding to ongoing everboarding.

When frontline contributions are recognized and shared, the workforce becomes better equipped to serve today’s highly informed customers, whether they are stepping into a shop or making an online purchase.

As David Autor, an MIT economist and author of a widely cited paper on the Polanyi paradox and employment said, “The tasks that have proved most vexing to automate are those demanding flexibility, judgment, and common sense—skills that we understand only tacitly...tasks that cannot be substituted by computerization are generally complemented by it.”

AI with all the senses

Organizations can and should move beyond text heavy documents and process guidance. Broadly speaking, learning and development departments already adopt a mix of text, images, videos, and group discussion. This approach reflects how we learn and should be the preferred method when it comes to employee onboarding, everboarding, and capturing implicit knowledge.

Modern AI agents are multimodal, meaning they can receive and output intelligence via audio, text, image, and video. A frontline worker should be able to verbally highlight a gap, suggest an update or snap a photo of a shelf with the AI agent comparing it to the visual merchandising planogram and provide step-by-step corrective instructions.

And with smaller models, on-device inference is possible, meaning no data leaves the device or company network. Latency is eliminated, an additional layer of security is added, and workers and customers get the intelligence they need in the moment.

Moving from knowing to doing

The development, implementation, and ongoing guidance agents require cannot be done alone, in the same way that text heavy, top-down SOPs are no longer sufficient. The top-down methods that brought organizations to this point will not sustain them for tomorrow.

According to a report by MIT, businesses that partner with AI providers have an AI success rate of 66% compared to those who prefer internal development alone (33%). The best way to succeed is to move forward with partners who have industry expertise, AI models, agents, and developer building blocks. They can support the design, configuration, onboarding, and management of AI systems in retail, warehouse, and logistics settings.

And AI transformation cannot be done with an “IT alone” mindset. Stanford’s Digital Lab finds that a top factor (43%) for AI success is executive sponsorship that enables a culture of experimentation and permission to fail. AI’s value comes from the algorithms, the technology required to implement, but mostly it comes from rethinking the people and culture component.

Polanyi’s “knowing versus telling” gap has been brought to light and is finally being closed on the frontline. By crowdsourcing the corporate brain, businesses are capturing tacit knowledge and transforming it into customer and worker advantages that make work better every day.

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As AI use expands globally, questions are emerging about where training data originates, who owns it, and how organizations keep sensitive information secure. This is the domain of sovereign AI: the protection of data, its uses, and its results, which is becoming increasingly important as AI moves from proof-of-concept to production deployments spanning entire countries and continents.

Beyond where data is stored, sovereign AI is really a question of who controls an organization's or region's AI capabilities. In practice, this requires control over the entire infrastructure stack, both software and hardware, along with policies governing who can view data, manage infrastructure, and install software.

Because data protection laws and practices vary widely across geographies – even within Europe – the governance of data flows and processing pipelines must be understood and followed.

While sovereign AI is often discussed on a country-by-country basis, the same rules and procedures apply to enterprises. For them, questions of data location, ownership, and transmission are even more complex.

Location is only part of the story

Much attention has focused on where a data center is physically located, but a closer look at what a data center actually comprises is needed when assessing sovereign infrastructure.

Physical location is usually easy to determine, but many providers run multi-cloud infrastructures in which data centers are linked by high-speed networks, and a cloud provider may shift workloads between its own facilities when capacity is tight. Organizations need assurance that their applications and data stay where originally intended.

If the proprietary data behind AI workloads sits with a cloud provider, the end-user may not know the exact location of the storage devices. Additionally, putting location aside, control is the critical concern: if one jurisdiction owns data stored in another, who decides what it can be used for and whether it can cross borders? The question grows more complex when a company has offices in multiple countries or regions.

Traditional workloads like ERP, inventory and HR systems typically keep data in a corporate database accessed remotely. AI is different: users are widely dispersed, while inference increasingly takes place on edge devices such as smartphones, connected vehicles and industrial systems. These devices often need access to the underlying data and trained model outputs, rather than receiving a stream of rendered content.

As models and proprietary data move between central infrastructure, regional data centers and edge environments, they may cross multiple networks and storage systems. Who owns that data in transit, especially when it crosses borders, is a question every organization scaling AI inference needs to answer.

Evaluating sovereignty layer-by-layer

AI today demands substantial compute, high-performance data input/output, and high-speed networking – components that must work together under a common set of laws, processes and procedures. An organization's AI infrastructure might combine powerful servers in one location, edge devices in another, and cross-border communication between them.

And then there's the data itself: where does it live, is it centralized, and in what format?

Ownership matters just as much as location. Data ownership might sit with an enterprise business unit, a compliance function, or the C-suite. Looking beyond the data, considerations include: who decides which data center runs a given training job, especially when a centralized scheduler manages many facilities for utilization and performance?

Can that automation be overridden? Should certain data be restricted to certain training uses, and does making specific data available to certain providers create enterprise value?

Because training data is drawn from sources around the world, enterprises need a clear governance framework for deciding which sources are trusted, who approves their use, and how regional regulatory or political requirements shape those decisions. That framework should establish accountability at both an organizational and individual level, with transparent records of where data originated, how it was used and whether the necessary permissions or licenses were secured.

The same principles should extend across the infrastructure stack. Organizations need defined ownership of the training environment, model weights and deployment decisions, alongside controls that determine who can administer these systems and from which jurisdiction. AI users may not need visibility into every technical detail, but they should understand the provenance, governance and location of the systems handling sensitive data and intellectual property.

Sovereign AI depends not only on where infrastructure is hosted, but on maintaining auditable control over the data, models, platforms, and people involved throughout the AI lifecycle.

There is also the challenge of demonstrating that an AI system’s outputs are grounded in legitimate training data, while ensuring that the data remains protected and accessible only to authorized parties across the infrastructure stack. Verda, a European AI cloud provider, offers one example of how this can be addressed in practice. Its infrastructure is hosted in Nordic data centers and allows customers to configure hardware and software around their operational and regulatory requirements.

The platform supports organizations across the private and public sectors, with controls designed to help address requirements including GDPR, the EU AI Act and sector-specific rules. These include confidential virtual machines, hardware-enforced trusted execution environments, and encryption for CPU and GPU memory, as well as data in transit and at rest.

The broader lesson that we can take from Verda’s success is that sovereign AI depends on embedding governance, security, and regulatory control throughout the system lifecycle, rather than treating data location as the only measure of sovereignty.

Operational sovereignty means more than compliance

AI is now critical infrastructure for enterprises worldwide, and compliance with local laws must be an architectural concern from the start, rather than being addressed after a data center goes live. From design through implementation and ongoing workloads, a sovereign data center must continually meet its operational sovereignty commitments.

Once online, sovereignty may help data centers remain operational through commercial disruption like recessions, geopolitical shifts or conflicts, and technical changes – new chips, models and architectures.

The concept of sovereignty is about resilience rather than eliminating every external dependency, with extreme events like war threatening to destroy infrastructure. It is about understanding where dependencies exist, deciding which ones are acceptable and avoiding unnecessary concentration of risk.

Technical decisions in design and operation can help avoid vendor lock-in, allowing a data center to keep running even if a business relationship ends. Relying on a single supplier for critical technology carries risk, since shifting geopolitics can bring tariffs or restrictions that block business with suppliers from certain countries.

Every data center operator depends on an ecosystem with a limited number of server, storage and power suppliers. Supply chains are global even when a facility sits in one geography, which optimizes cost but means not everything is controllable – so, understanding where risk concentrates matters. With many thousands of components from hundreds of suppliers, a smooth operation may still lean on a small set of vendors.

Open standards and formal standardization reduce this risk by making interoperability less dependent on any single supplier, and by allowing workloads to move between technologies and environments. In turn, this builds confidence in sovereign data centers as resilient infrastructure.

Building trust through control, not assumptions

Trust in sovereign AI depends on organizations knowing who can access their data, infrastructure and production environments, under what circumstances and with what oversight. Privileged access should be tightly controlled, clearly assigned and documented, with responsibility sitting at an appropriate level such as the CIO or Chief Compliance Officer.

This is particularly important when AI workloads involve proprietary data, prompts, model outputs or fine-tuning datasets that could reveal commercially sensitive information about an organization's products, customers and internal processes.

Policies and employee training remain important, but they should be reinforced through technical and operational controls. Organizations need clear assurances that their data will not be retained, reused or incorporated into provider model training without explicit permission.

Access controls, encryption, logging, workload isolation and data-retention settings can help enforce these expectations, while documented processes should show who controls each part of the AI environment, where supply-chain risks exist and which regional laws govern the data.

With this in mind, governance is becoming as important to AI infrastructure evaluation as performance, scalability and cost. Sovereign AI providers must be able to demonstrate, rather than simply claim, how they protect data and manage workloads across the full hardware and software stack.

For organizations increasingly reliant on AI as critical infrastructure, transparent ownership, auditable controls and consistent processes will be central to building confidence and supporting broader adoption.

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Imagine a NASCAR team where leadership decides the winner is the car that burns the most fuel. The crew chief tunes every engine to run rich and wasteful. Drivers ride the gas and the brake at once. The telemetry shows record fuel consumption. The car finishes dead last.

For a stretch this year, wasteful consumption metrics drove AI measurement programs. Companies stood up internal leaderboards ranking employees by how many tokens they burned. A Meta employee built one, crowned the top "token legends," then quietly took it after the numbers leaked externally. By spring the trend had a name: tokenmaxxing.

By the start of summer the inevitable backlash arrived, with companies like Uber acknowledging they’d already spent their entire AI budget for the year in a matter of months.

More recently, the inevitable swing toward using the minimal number of AI tokens (tokenminning) leads to the same problem. If leadership defines winning as using the least amount of fuel, the winning car would never leave the starting line.

Burning or saving tokens should never be the end goal. The objective is shipping more products, at higher quality, with the same or fewer people. The job of a leader is to define the most strategic path toward the finish line, and based on that definition, build metrics that support it.

Why leaderboards miss the mark

A vanity metric is any number that looks great on a slide, leaderboard or dashboard but doesn't change how you run your business. In the AI space, even highly technical and complex metrics can become vanity metrics.

Here’s a couple I’ve seen lately, each a different way to look busy GPU utilization rates that brag about how much raw compute you’ve hoarded, regardless of whether it's solving real problems. AI features shipped per quarter, where leadership slaps a sparkle-emoji text summarizer on every legacy product to pad the release notes.

Metrics like these can feel rigorous, but they're detached from the end work itself. When leaders don’t relate metrics to end goals, engineers may race toward the nearest countable item.

The missing race map

Work completed only matters if it’s the right work. When leadership celebrates "95% adoption" of GitHub Copilot because the licenses are deployed, they can mistake superficial access for deep engagement. They assume productivity is skyrocketing. They're measuring who adopted the tool, not who is effectively using it to improve outcomes.

Developers might be generating more code faster, but if the tool isn't deeply integrated into a refined process, PR sizes inflate and the bottleneck simply shifts to human code review. Cycle time stays stagnant, or worse, bug rates go up because humans are rubber-stamping AI hallucinations.

Close the disconnect: tie depth of use directly to outcomes and business goals. If you have 95% "adoption" but your cycle time hasn't dropped by 30% or your rework hasn't decreased, you don't have high use. You just have an expensive new software distribution.

How to set the finish line

A single number rarely tells a story. If I can produce twice as much code but it takes twice as long to ship, I haven't gained efficiency. I've created more work. The right question isn't how much AI you're using — it's whether the work is actually getting better.

For engineering teams with more established workflows, start with the outcome you want to move, then use a framework like DORA or SPACE to check whether you got there. The framework is the instrument, not the goal. DORA tracks the health of your delivery pipeline — how often you deploy, how long changes take, how quickly you recover from failures, how often changes cause incidents.

SPACE tracks the full picture of developer productivity across five dimensions, including outcomes and satisfaction. Neither was designed for AI, which is exactly why they're useful: they’ll help you measure whether AI made the work better, not whether AI got used.

Pair one of those frameworks against your AI tool usage and your business outcomes like cycle time, rework rate, cost per change. A 25% increase in AI adoption that correlates with a drop in delivery stability isn't a win. The 2025 DORA report found that's exactly what many teams got when they deployed AI into underprepared systems. The tools amplified what was already there. Good engineering foundations got better, fragile ones got more fragile.

For roles outside engineering — marketing, finance, operations — build from first principles. Start with one outcome you're already trying to move: response time, pipeline conversion, forecast accuracy. Establish the finish line before AI enters the workflow. Measure toward that same outcome after. If it moved, figure out whether AI caused it and whether you can repeat it. If it didn't, you've learned something too. Picture a support team that wants to cut first-response time.

They baseline it at nine hours, then let agents draft replies with an AI assistant. Two months later response time is down to three hours, and reopen rates haven’t budged — so the speed isn’t costing them quality. That’s an outcome they can name, measure and repeat. It never needed a leaderboard.

Keeping the finish line in sight

The goal of a metric is to manage toward an outcome. If you're doing more work on the metric than on the outcome, you're upside down. You're looking at the gas tank, not the finish line. So before you stand up your next AI metric, ask one question: what outcome would move if this number did? If you can’t name it, you’re not measuring progress — you’re watching the fuel gauge. Define the finish line first; everything else is just telemetry.

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• 25,000 Dutch government jobs will be cut by 2035, new plans say
• The Netherlands is looking to save €3.5 billion by 2035
• Selling off buildings, simplifying rules, and smaller desks will also contribute to the savings

The Dutch government has announced plans to phase out 25,000 jobs by 2035 in an effort to increase efficiency with the end goal of saving €3.5 billion ($3.9 billion), amounting to about 16% of the government’s total workforce.

According to plans laid out by Dutch State Secretary for Kingdom Relations and Effective Government Eric van der Burg, the cuts will save €900 million ($1 billion) by 2027 while AI will deliver increased productivity to take up the additional workload.

"We cannot keep doing what we did," Van der Burg said. "That is why we are going to make sharp choices for a central government that really works: simpler, smarter and smaller."

Smaller desks and AI part of savings plans

The plans hope to deliver a 20% productivity boost by using AI to increase efficiency, while simultaneously reducing the total government workload by simplifying rules. To that end, the Dutch government also has a plan in place to scrap or simplify at least 500 rules a year.

But AI isn’t the only way the government is looking to reduce costs. New rules will see pairs of employees sharing a single desk, surplus buildings sold off, and IT systems standardized.

While police and defense jobs are not being affected by the cuts, permanent advisory councils and advisory committees are not. The government will release new targets for the number of advisory bodies later this year, with current numbers sitting at 28 and 150 respectively.

According to Bloomberg several motions - albeit voluntary motions - to increase productivity using AI have already been passed. These motions are a catalyst for the resulting job and spending cuts, according to Van der Burg.

The Dutch government has seen a record number of central government officials, peaking at 160,000 by the end of 2025 - a rise of 41% since 2018 according to Bloomberg.

Google's latest AI model can untangle millions of lines of code and accelerate quantum computing research, which is great if you happen to spend your afternoons debugging enormous software projects or advancing theoretical physics. For the average Gemini user, the sheer power of the new Gemini 4 Argon model begs the question: why do you need it?

Google has made Gemini 4 Argon readily available in the Gemini app for Google AI Pro or Ultra subscribers. Simply pick it from the list of models available at the top of the conversation. Google brags about the model's advanced reasoning, multi-step problem-solving, and other improvements provided by the sophisticated AI, but it's a little much for helping write an email.

Whether Argon can outperform its predecessors on complicated benchmarks is less interesting than figuring out what to do with all this additional AI power. As AI models become increasingly specialized, we may be approaching a point where the technology is advancing faster than people's ability to understand why they need it.

Give the big model a big problem

Gemini 4 Argon certainly has potential uses, for instance, in analyzing complicated information scattered across several files. Imagine comparing three health insurance policies with different deductibles, premiums, coverage exclusions, and out-of-pocket limits. A regular Gemini model can already summarize the policies, but Argon's advanced reasoning could become valuable when calculating how different combinations of medical expenses would affect the total annual cost. It would be great at navigating all kinds of potential healthcare scenarios, tracking multiple interacting variables, identifying relevant exceptions, and calculating their consequences.

The same principle applies to any other complicated project, like renovating a house and asking the model to evaluate contractor estimates, material costs, and architectural plans for inconsistencies and potential price overruns. Argon 4 might help when determining how a delayed electrical inspection affects several subsequent jobs and the overall budget.

It could also be useful in learning complicated academic subjects that incorporate a lot of interconnected concepts, such as advanced math. All of that is still only potentially better than simply using the less powerful models. It might be as complicated to work out when Argon 4 is necessary as the very projects for which the model is best suited.

Overthinking AI

It sometimes feels ridiculous how Google is building models capable of tackling extraordinary scientific and engineering challenges, then placing them in the same interface people use to request pancake recipes. The technology is becoming increasingly sophisticated, yet the reasons to choose one model over another remain vague.

This is hardly unique to Google. OpenAI and Anthropic have their own incredibly powerful models that seem ill-suited to the average user. The most powerful model isn't always going to be the most useful. Asking Gemini to rewrite an email or suggest dinner ideas probably doesn't require Argon's additional reasoning capabilities. A faster model might deliver an equally useful answer faster. Even the more complicated examples aren't necessarily beyond ordinary Gemini models. Argon's advantage would need to come from handling unusually complicated combinations of constraints.

The challenge for Google is that users can't easily recognize which assignments justify a more powerful model. In that case, the model picker becomes an annoyance and not a feature.

Right now, most Gemini users should stick to the less intense models and reserve Gemini 4 Argon for assignments where they would struggle. Argon 4 is apparently a remarkable technical achievement with huge possible benefits in some research, but I don't need something capable of solving quantum computing problems on an everyday basis. AI developers have become very good at building models capable of extraordinary work, but the most impressive feat would be explaining when ordinary people actually need them.

• Former Pegasus spyware company head urges governments to reduce reliance on US AI firms
• Governments should focus more on sovereign AI and national security that the infrastructure provided by external entities
• Pegasus spyware was used to monitor politicians, journalists, business heads, and more

Don’t trust US AI companies with your data and work towards creating sovereign AI, the former chief executive and co-founder of Israeli spyware organization NSO Group, Shalev Hulio, has said.

Hulio was responsible for the creation of the Pegasus spyware used to target politicians, top business leaders, and journalists around the world - marking a change in career that accompanies Hulio’s founding of AI cyber defence firm, Dream.

The former commercial spyware company head wants governments to host and develop their own AI models rather than hand over data relating to national security to US AI firms.

Spyware head to sovereign AI evangelist

“Every government should have their own model,” Hulio told the Financial Times. “They should not take the risk that somebody will wake up one day and shut their model because AI is a critical infrastructure. You want to control your own AI.”

An interesting perspective from the founder of a commercial spyware company that was blacklisted by the US government and continues to receive criticism from the UN and EU over its involvement in deploying Pegasus on thousands of devices by exploiting a WhatsApp flaw.

“Despite the public opinion, governments managed to prevent terror attacks and managed to save hundreds of thousands of lives along the years using NSO products,” Hulio told FT, adding, “Governments really appreciate, I think, the relationship that we had before.”

Dream was co-founded by Hulio and former Austrian chancellor Sebastian Kurz in 2022. The company initially sought to provide AI-enabled cyber defense tools to governments. The company has been credited with detecting the world's first autonomous AI end-to-end attack in which Chinese hackers used open-source AI models to discover and chain vulnerabilities to crack into Taiwan government agencies - including the nuclear safety agency.

Now, Dream is offering a portfolio of products that includes an AI-enhanced national-scale cyber defense system, an AI-powered software vulnerability discovery platform, and an AI platform that helps governments set up sovereign AI models that are not reliant on external infrastructure.

The models Dream offers are also bespoke for government use and trained on their customers' data to help refine the model more tightly to government needs, helping to keep costs down. “We are saying to entities within the government, you should have a very narrow but very smart model that can run very fast on one or two or three GPUs,” Hulio said.

Dream has raised $400 million in funding and investment, and was valued at $3 billion earlier this year. The company has so far sold its products to over a dozen countries across Europe, Asia, and the Middle East.