AI demands a huge amount of energy, water, compute, and, of course, money. Big tech companies, including Google and Meta, have pledged huge sums of money toward the capital expenditure (capex) to continue the push to more powerful AI. But many inventors feel increasingly wary that this spending is simply swelling an already inflated bubble.

Laying the tracks

Speaking with CNBC in July 2026, the legendary investor Warren Buffett, who recently retired, weighed in on the capex debate.

Big tech companies are pledging a huge sum to the AI infrastructure buildout, which may reach up to $750 billion over 2026 and up to $4.5 trillion by 2030 among the four largest hyperscalers, including Meta, Microsoft, Amazon, and Alphabet. Many suggest this is further evidence of a bubble being fuelled aggressively by big tech companies doubling down on a technology that hasn't demonstrated meaningful returns.

But explaining his firm Berkshire Hathaway's 17.85 million-share purchase of Alphabet stock, disclosed in November 2025, Warren Buffett drew parallels between this level of spending and that during the 1800s to build the railroad system.

From digital to physical

As an investor, Buffett famously avoided software giants, suggesting they were asset-light businesses and didn't see the value in the investment propositions. This is a point he's since conceded he was wrong about. The firm he led made an additional $10 billion investment in Alphabet in June 2026.

The rationale is that there's currently a pivot underway in which these companies are shifting from the purveyors of digital products, namely applications and software, toward the physical infrastructure and compute to power AI.

Although the hyperscalers have been investing heavily in cloud infrastructure to power their software as a service (SaaS) propositions, the spending that AI commands is on a completely different level, as the figures show. That's why, in the end, Buffett believes that Alphabet proved a sound investment – given he believes that they have the cash flow to sustain the ongoing physical infrastructure buildout.

There are a lot of sacrifices being made to usher in the latest technological age. Jobs, businesses, even whole industries are at risk of disappearing.

Now we can add homes to the list - as new electricity infrastructure for data centers in the state of Georgia means 30 homes are to be bulldozed and 330 parcels of land seized.

The good news is that the additional power will allow for extra data center capacity. And those same people who lost their houses can look forward to having their own personal AI agent to help to find a new place to live.

Losing homes for AI

In case you missed it, Mark Zuckerberg has announced yet another five year plan for AI, saying that billions of people will soon have their own personal AI agents - something that will require a data center buildout far beyond anything we’ve currently seen.

These agents will be “working on your behalf 24/7 to achieve your goals in whatever the domain is that you care about,” Zuckerberg said. They’ll basically do everything you already do, manage your relationships, sort your finances, even manage your household tasks.

So when AI frees everyone from the burdens of being human, what are we left to contend with? We get to channel the world's resources, climate, homes, and personal information and in return, AI can live your life for you. You’ll have so much free time to do… something else? Maybe listen to the tuneful hum of the local data center?

The promises of AI seem to add up to human replacement. The main reason businesses are chewing at the bit to adopt AI technology is because it promises to cut one of their main expenses: labor. The levels of productivity will stay the same, or maybe even improve, but profits will skyrocket thanks to significantly reduced operating costs.

There will be new jobs in new never-before-thought-of sectors, or so we are told. But is this just a way to placate those losing their jobs to AI technology?

At present, AI is cutting through the entry level positions and administrative work that used to be a good way into a new industry. AI is therefore replacing existing workers, while simultaneously shutting them out from moving to other industries. Lets not forget that many workplaces are implementing AI interviewers that suck what was left of human interaction (much of which is a facade) out of the job seeking process.

As an added kick in the teeth, once you’ve lost your source of income your energy bills will likely rise as data centers attach to an already strained electricity grid. Many US electricity providers have upped bills as a way to pay for infrastructural upgrades to provide new connections for data centers. And don’t forget the air quality will get worse as more and more gas-burning turbines power up to meet the demand for power.

I still believe there is some good AI can do for the world, particularly in science and medicine. But the main benefit I expect AI to provide to the workplace is a revitalization of labor unions. Despite decades of union busting and a general decline in membership as the old guard retires, this new threat to workers will hopefully act as a catalyst to return labor unions to their former glory.

The more members they have, the stronger they are. No amount of AI agents can change that.

I’ve been lifting weights at home for a few years now, but recently I’ve found myself stuck in a rut. If anything, my routine has become a humdrum habit, without a clear goal and sense of progression to keep me motivated. Ten exercises, three sets, eight reps, 30 minutes. I’ve run through it so often that I could do it in my sleep.

I’m a true tech lover, so of course I got artificial intelligence (AI) to help me out. Apple has just launched its revamped Siri AI and I’ve been playing around with its early beta edition, so I was curious to know if its new skills extended to solid fitness advice — and if they did, how they held up against the ever-popular ChatGPT.

To find out, I asked each chatbot to dream up a workout routine that could revitalize my fitness habits. After working through both plans and tapping a professional personal trainer for their insights, I was left with the impression that only one of the two workout plans was worth sticking with.

The setup

To start with, I put together a prompt that I then fed into both AIs. The goal was to briefly explain my physical state and outline the kind of workout I was after, noting any constraints such as time and location (I work out at home).

Here’s the prompt I used:

“I am a 36-year-old man in good physical shape who exercises regularly. Create a three-day overall workout plan for me with the goals of hypertrophy and building muscle. The workout will be done in a small home office using dumbbells as the only equipment. It can include calisthenics as well as weighted exercises and should aim to cover different areas of the body. Ensure it lasts no longer than 45 minutes. Explain how often each workout session should be repeated over the course of a week and when rest days should take place, if required.”

I didn’t add any subsequent follow-up messages, as I wanted to know what each AI could come up with given the kind of information that a regular user might provide.

The new challenger: Siri AI

At its Worldwide Developers Conference (WWDC) in June, Apple made a big song and dance of its new Siri AI virtual assistant, a new foundational model based on Google’s Gemini LLM.

When given my workout prompt, Siri AI put together a plan that included a “push” day focusing on my chest, shoulders and triceps; a “pull” day centered on my back, biceps and rear delts; then a final “legs and core” day. It offered a workout schedule and suggested that each session would take around 45 minutes, with three sets of 8-12 reps and a rest of 60-90 seconds between each set.

“Focus on controlled movements and progressive overload by gradually increasing the weight of your dumbbells or the difficulty of your calisthenics,” it advised. Each exercise was accompanied by a short explanation of how to perform it, which was a welcome touch.

Putting Siri’s action plan into practice, it was obvious that my old self-made workout plan wasn't pushing me hard enough, as the first day’s workout left me feeling like I’d worked harder than I had for months. Off to a good start, I thought.

But Siri’s plan came up well short of 45 minutes, with most days’ efforts lasting a mere 25 minutes. And aside from the lack of warm up, there was also no word on how to complement the exercises with targeted nutritional intake. That’s a serious oversight in its workout regimen, especially for beginners.

To help me better understand the plans from both Siri AI and ChatGPT, I asked personal trainer and fitness coach Scott Laidler for his opinion. Speaking of Siri’s effort, Laidler said that it had a few things going for it.

“Siri’s is simple which means there is little to misinterpret,” said Laidler, adding that “the equipment substitutions are sensible for the office environment.”

While Siri recommends upping the weight as you get stronger, it doesn’t say “what to do once this plan hits diminishing returns, which is months away, not years,” according to Laidler. While it sounds like an open-ended plan, “it’s really only built for a defined window,” Laidler warned, which could “lead to frustration unless you knew to come back to the table and ask for a new plan several times per year.”

That’s a problem inherent in the responses from both Siri and ChatGPT.

The class leader: ChatGPT

While Siri AI’s workout plan was concise, ChatGPT was much longer. It explained what the priorities of the workout were and gave some basic advice, such as aiming to finish with one or two reps in reserve as a guidance on how much weight to use. It then listed a weekly schedule before laying out a simple warm up to perform each session.

Each exercise was listed, with varying numbers of sets and reps, followed by a note on which muscle groups were being targeted. Unlike Siri, there was no explanation of how to perform each exercise, which feels like an oversight with potential for injury. That was a clear win for Siri AI.

At the end, ChatGPT included a “progression” section explaining how to push yourself further, a guide on recovery and nutritional intake, then an explanation of “why this split works.” Taken together, it was a detailed workout plan, if a rather verbose one.

According to Laidler, ChatGPT’s plan was “the better program by some margin.” Siri “applies a flat ‘three sets of 8-12’ to every exercise, with each muscle group trained once a week,” Laidler noted. ChatGPT, on the other hand, “hits most muscle groups twice across the week, varies the sets per exercise, includes a warm up, and gives real progression logic. On paper it’s the more sophisticated plan.”

ChatGPT “is closer to what the literature would see as optimal for the stated goal of gaining muscle,” Laidler stated. “Reps in reserve (RIR) offers a way for the user to keep a reasonable distance from failure, which de-risks the plan especially when training alone.”

OpenAI’s LLM has a trick up its sleeve, too: it can analyze your form based on uploaded videos. I asked it to judge my Romanian deadlift form and it gave me an 8.5 out of ten, giving pointers on what I did well and what could be improved. I wouldn’t take this advice over that of a human expert, but compared to Siri AI — which can’t do any of this at all — it’s another point to ChatGPT.

The overall picture

Despite their individual strengths, Laidler warned that neither Siri AI nor ChatGPT had created an optimal workout plan. As he pointed out, “It doesn’t appear that the LLM has asked follow up questions that may have helped customize the plan a little more.”

This highlights a problem with using AI for personal fitness. While a personal trainer would naturally ask these questions straight away, an AI chatbot won’t necessarily propose them unless prompted. Without a more detailed knowledge of your personal situation, you could end up with a sub-par workout routine that’s not well-suited to your circumstances.

So, if you’re going to use AI to sketch out a fitness plan, make sure to add as much detail in your prompt as possible. Ask it to pose follow-up questions to ensure it has as all the information it needs, and come back to it regularly. Don’t just stick to the plan it gives you and repeat it ad infinitum or you’ll end up like I did: languishing, with your progress grinding to a frustrating halt.

Of course, as Laidler pointed out, the problem with doing this — telling the LLM your injury history, what feels like a hard set, your workout preferences, and more — means that “you need to already know what a well-built program looks like to comprehensively prompt for one.” And if that’s the case, do you even need an AI to help you in the first place?

If you do want to go ahead with an AI fitness coach, ChatGPT is a better bet than Siri AI for now, as it includes more varied sets and targets more muscle groups. Neither is perfect, but Apple has plenty of work to do if it wants to match ChatGPT’s take on fitness advice.

• ChatGPT and Google Gemini have overtaken popular social media sites for discovering new products
• 55% of UK shoppers have made a purchase based on AI-powered product recommendations
• Privacy and advanced features like visual search could be holding back wider adoption

New research has claimed that while AI is helping people to shop, the technology is yet to contribute to any noticeable increases in revenue.

Figures from Vercel and the World Retail Congress found 20% of UK consumers use AI search platforms such as ChatGPT and Google Gemini over social media to kick off their retail discovery, with Facebook on 19%, TikTok on 18%, and Instagram with just 14%.

Meanwhile, 87% of UK shoppers find AI recommendations useful, but only 55% have made a purchase following an AI recommendation.

AI shopping assistants

While AI recommendations and price comparisons are considered useful, retailers are yet to see any increase in buying. This could be due to trust or privacy issues, with only 10% of shoppers admitting that they would spend more time with an AI shopping assistant than a human fulfilling the same role, a figure that is repeated across Europe.

In the UK, while AI is being increasingly adopted by storefronts, it is still some way from wider adoption as a replacement. 79% prefer to open a retailer’s website to start online shopping, and for high value purchases, 65% still prefer to pay a visit to the retailer’s bricks-and-mortar location and see the goods for themselves before parting with their cash.

Some of the resistance can be attributed to trust and privacy issues. The report found a third (33%) of UK customers are concerned that AI doesn’t show them the best products, perhaps because they are inaccurate, or worse – influenced by manufacturers or some other promotional tool.

The challenge to retailers

"For most of the web's history, we optimized storefronts for two audiences: people and Google," said Malte Ubl, CTO at Vercel.

"There's now a third: AI agents that discover, compare, and increasingly recommend products on a shopper's behalf. The retailers who win the next decade will be the ones who can iterate and ship quickly enough to keep up with how fast this consumer behavior is moving."

But challenges exist. While 43% are likely to prefer one retailer over another due to a superior AI-powered experience, privacy concerns and features like visual search need to be addressed.

• Seagate confirms its 5TB-per-disk Mozaic 5+ platform is on track for qualification shipments in late calendar 2027, putting 50TB-class drives on course for 2028
• Qualification is not availability: OEM and hyperscaler validation adds months to a year, pushing real volume to 2029; the original roadmap target for 50TB was 2026
• With most high-capacity storage already allocated in 2028 under long-term contracts and Seagate holding unit output flat, retail buyers are last in a queue that is already years long

Seagate used its recent fiscal fourth quarter earnings 2026 call to put a fresh date on the next rung of its hard drive roadmap - but it is not close.

Company chair and chief executive Dave Mosley told analysts the Mozaic 5+, the company's 5TB-per-disk platform, remains on track for qualification shipments in late calendar 2027.

This is what matters for those looking to score a single highest-capacity consumer-grade HDD that Seagate makes: Ten of those platters in a single 3.5-inch chassis is what gets Seagate to the 50TB-class drives it has been promising for the better part of a decade.

A bad time to be looking for a storage upgrade

Most consumer PCs now ship with SSDs, but flash remains considerably more expensive than disk at equivalent capacity. Buyers who need bulk storage and can tolerate slower access times still choose HDDs for the simple reason that they cost far less per terabyte.

The problem for end users and professionals who need that capacity right now is that, as with RAM and SSDs, hyperscalers have entered the equation, and constrained supply is pushing prices and availability down across the board.

It does not help that qualification of Seagate's 50TB drives in late 2027 is not the same as availability. Qualification shipments are engineering samples sent to OEM and hyperscale customers to run their own validation; production is scheduled for 2028, and the original target was 2026.

Seagate's public roadmap slides in late 2019 put 50TB HAMR drives in 2026. That target survived, at least rhetorically, into 2023, when the company was still pairing a delayed 30TB launch with a stated 50TB-by-2026 ambition. By mid-2025, when the first 40TB HAMR samples went out to data center customers, the roadmap had been restated as 44TB in 2027 and 50TB in 2028.

More delays in the offing?

Seagate also flagged its own execution risk on the same call. Management noted that moving from 3TB to 4TB, then to 5TB per disk, requires more tool time and can slow manufacturing throughput. That is an unusually candid caveat to volunteer to analysts, and it is the most likely source of another date change.

None of these points to a further slip, but it does make for a sobering timeline. Retail buyers get nothing when the drives ship in 2028, because that output is already spoken for by Seagate's largest customers: data center operators and hyperscalers.

The channel is served last, and a 50TB drive is unlikely to appear on a shelf, physical or virtual, until well after volume production begins around 2029.

The unintended consequences of progress, including the risks and possible harms caused, are always a factor that scientists and engineers consider in building any new technology. In today's age, the spotlight is on AI – but the range of possibilities that progress might bring is near unfathomable.

I am become death

Chris Nolan, director of a string of cinematic successes across the last three decades, was speaking on the subject matter of his 2023 blockbuster 'Oppenheimer' during a press tour.

During a New York panel following a preview screening of the film, Nolan was speaking on the rise of AI, which has fallen into perspective after showing immense promise.

Although the mainstream image of AI has been coloured by a string of science-fiction movies throughout history, there are still valid questions to be asked about the unintended consequences of building and releasing new tools without an awful lot of testing.

Similar to the nature of the work that scientist J Robert Oppenheimer did on the Manhattan Project, there is a "dual-use dilemma" in the way that AI could either be used for good or to cause catastrophic harm.

Unintended consequences

Similar to fears expressed during that period of history, scientists and researchers frequently warn about ways in which AI can either be misaligned or spiral out of control, or ways in which people can exploit it for malicious intent.

A study published in May 2024, for example, highlights the possible risks of a future AI that could deceive humans. It uses documented examples as a springboard to speculate that a future system may decide to mislead humanity for purposes that differ from the humans that built and use it.

There are also flaws with these parallels, namely, unlike nuclear weapons, AI development spans thousands of organizations around the world. There's also a dominance in the private sector, a much lower barrier to entry, and more difficulties in verifying and inspecting whether AI is being used in positive or harmful ways.

ChatGPT Voice changes using the AI chatbot into something more akin to the fictional digital aides familiar from books and movies. And it gets even better once you know how to steer it.

It's tempting to treat ChatGPT Voice like a smart speaker, but it's not really the same thing. You can do much more than asking one question, waiting for the answer, and then stopping. You can shape the conversation just as much as the information. Here are five easy ways to get more out of ChatGPT Voice, along with the prompts and habits that help the conversation feel smoother and smarter.

1. Tell it how to listen before you start talking

As useful as vocal conversations with ChatGPT are, one reason some people avoid it is because of how it sometimes seems to jump the gun in responding while you're forming your own thoughts. Setting expectations at the start of the conversation works well to counter that, however.

It's worth spending a few seconds describing how you want the conversation to work before getting into the meat of it. You might say that you are practicing for an interview and want to finish each answer before receiving feedback, or that you will say a code word like "full stop" when it's ChatGPT's turn.

Even more subtle things are adjustable. You can ask it to slow down, speed up, use shorter sentences, or adopt a calmer speaking style. Voice settings also allow you to change voices and, depending on your account subscription level, adjust intelligence levels for different conversations.

Giving ChatGPT those ground rules helps it adapt its behavior instead of trying to guess when you have stopped speaking. Talking at your own pace makes the whole interaction feel much more relaxed and human.

2. Give it a role before you ask a question

Relatedly, it's helpful to set up a persona or point of view for ChatGPT to take on when using ChatGPT Voice. It sets up broad assumptions about what you're hoping to get out of the conversation without requiring any drawn-out discussion. For instance, if you want to discuss travel advice, ask ChatGPT to set itself as a local tour guide. Or give it a couple of sentences describing who you think (or fear) might be conducting an interview with you when you practice.

Even just asking it to become a patient French tutor who waits for you to finish each sentence before correcting your pronunciation could smooth the path toward fluency.

The role provides context before the actual question arrives. That means the answers naturally become more focused, and the follow-up questions tend to make more sense because ChatGPT has a clear perspective to work from.

3. Take your hands off the keyboard

One of the most impressive ChatGPT Voice capabilities is how you can run tasks on your computer by voice without constantly reaching for your mouse or keyboard thanks to the updated ChatGPT desktop app for Mac and Windows. The app offers not only the usual, conversation-centered Chat format, but the coding-focused Codex, and the newer ChatGPT Work setup, which is designed for more complex, multi-step projects that involve gathering and organizing information.

Crucially, Work can interact with files stored on your device, meaning ChatGPT Voice can actively help you get things done rather than just discuss them.

You do need a subscription to ChatGPT Plus, Pro, Business, or Enterprise to access the Work feature. Then, when you open the ChatGPT app, you can choose Work instead of Chat or Codex. Create a new task, press the Voice button or use your voice shortcut, and grant microphone access when prompted. Explain what you want to achieve and point it to the relevant folders for where to look and where the final result should be saved. ChatGPT will ask for permission to view files, capture your screen, or use accessibility tools, and get permission from you before any important actions are carried out.

The more precise your instructions, the better the results. Rather than asking ChatGPT to "help me plan my trip," try saying, "Open my Italy Trip folder. Read the hotel confirmations, train tickets and attraction bookings, then create a day-by-day itinerary in a new document. Highlight any scheduling conflicts and save the finished itinerary in the same folder." You can use the same technique for comparing insurance documents, summarizing a semester's worth of lecture notes, or turning a folder of recipes into a categorized cookbook complete with an ingredients index.

4. Ask it to think out loud with you

Voice works especially well when you stop treating it as a question-and-answer machine. It's much more useful as an engaged sounding board listening while you think through a decision. Talking through the options often reveals ideas that never appear in a typed prompt.

To get ChatGPT Voice on that track, you can request that it compare possibilities instead of recommending one immediately when brainstorming. Tell it to discuss the strengths of each option before reaching a conclusion. The AI will point out pros and cons in as much detail as you want, and endlessly refine the options for as long as you want.

And speaking naturally means it is also much easier to interrupt with new information or change direction halfway through the discussion. Voice conversations are particularly good at handling those small detours that happen in real life.

5. Ask for a recap

Voice conversations can wander in unexpected directions, particularly when you are brainstorming or planning something complicated. Before ending the conversation, it's useful to review what was discussed, especially for longer chats. You can ask ChatGPT to summarize the discussion and highlight the most important conclusions from your talk to both remind yourself and cement matters with the AI.

If you're planning a trip or preparing for a presentation, for instance, a short spoken recap reinforces the key points, and if something sounds wrong, you can correct it immediately instead of discovering the mistake later.

Voice is already one of ChatGPT's more enticing features for when the speed and mobility of talking aloud is a higher priority than being able to see the responses written down. With a little forethought, ChatGPT Voice becomes a genuinely helpful conversational partner for a lot longer than it takes to dial a phone.

Artificial intelligence is reshaping the balance of power in security. Attackers are using AI to make deception look increasingly human while making fraud faster to execute, easier to scale and harder to detect. At the same time, security teams are turning to AI tools to identify and stop these threats.

Fraud is becoming more convincing, more sophisticated and more scalable, contributing to an estimated $9.5 trillion in global cybercrime damages each year. Attackers are bypassing traditional signals of legitimacy through deepfakes, synthetic identities and highly personalized phishing attempts.

There is no longer a single type of deepfake or attack variant. Each new generation of models improves on the last, and security controls must evolve to remain effective.

Today, fraud has become more systematic. Rather than trying to break everything, attackers probe systems first, learn how defenses respond and focus on the weakest link.

Trust can no longer be established through a single verification point at the beginning of an interaction. Organizations need to evaluate trust continuously as behavior, context, and risk can change over a course of that interactions. The faster an organization can determine whether it is interacting with a legitimate user or a threat, the less opportunity for fraud to cause damage.

In this AI-driven environment, speed and accuracy in establishing trust are no longer differentiators. They are table stakes. The question facing security teams today is whether their identity verification infrastructure can adapt in real time to threats that are outpacing the models built to stop them.

How AI is making fraud feel legitimate

The same capabilities that allow AI to replicate human behavior and generate convincing content at scale are now being weaponized to make fraud feel indistinguishable from real activity. Attackers are using tools to bypass the signals security teams have traditionally depended on to separate legitimate users from threats.

What once requires insider-level knowledge of onboarding and identity workflows can now be generated on demand. Attackers have the visibility into how systems operate and where they are most vulnerable.

Identity-based attacks are evolving rapidly. Synthetic identities are increasingly passing initial identity verification checks, while voice clones can now be generated from just seconds of audio.

More advanced techniques like identity morphing are emerging. Attackers are combining their own facial features with a real people sourced online, creating hybrid identities that are harder to detect than traditional spoofing attempts.

At the same time, many attackers using advanced AI still miss foundational signals, including reused devices, repeated IP addresses or behavioral inconsistencies. That is why layered defenses still matter: newer attack methods do not eliminate the need for foundational controls.

This shift means that fraud is no longer presenting itself as an obvious intrusion. Instead, it blends into normal digital activity, making it harder to distinguish malicious behavior from legitimate engagement. Static trust signals and one-time verification steps are now increasingly insufficient on their own.

Why speed now determines outcomes

Since fraud is becoming embedded into everyday digital behavior, detection alone is no longer enough to keep pace. A critical factor is how quickly an organization can recognize risk and intervene before damage occurs.

Modern AI attacks are operating at machine speed and adapting tactics the moment they encounter resistance. Static checks and delayed reviews are struggling to keep pace, allowing fraud to move through workflows before defenses can respond.

However, as automation increases, attackers are also making more basic mistakes. They are caught reusing the same devices, IP addresses or behavioral patterns. As they focus on more advanced techniques, they are overlooking foundational signals. This is reinforcing that even simpler controls remain essential in a layered defense.

Fraud prevention must depend on real-time signals across the full interaction lifecycle. That means evaluating behavioral biometrics, device intelligence, identity signals and contextual risk together rather than in isolation.

Detecting subtle behavioral changes in real time allows organizations to challenge suspicious activity at the right moment, stopping AI-driven fraud before damage occurs rather than reacting after the threat has already succeeded. Keeping pace with AI-driven fraud means organizations must move beyond single checkpoints and implement layered defenses that validate trust across the whole journey.

Winning the AI vs. AI arms race requires layered, real-time defense

As AI lowers the barrier to fraud, security teams cannot rely on isolated controls or point-in-time checks. Attackers rapidly layer tactics, moving from one technique to the next as soon as initial trust is established, and static defenses are not adapting fast enough to keep pace.

Effective fraud prevention is not about replacing older controls with newer ones, but ensuring each layer evolves alongside new attack models.

The most effective defense strategy is not a single point of control. Identity, behavior, device intelligence and contextual signals need to be evaluated together and continually reassessed throughout the customer journey, not just at login.

Each layer has a role addressing a different fraud vector. Some are better suited to detecting advanced attacks, while others can catch basic mistakes. Both are equally necessary because attackers are often stopped at different points in the stack.

What it takes to win the trust race

Organizations should not have to choose between innovation and security to secure customers and operations. Building identity assurance so it can hold up as interactions become faster, more automated and easier to exploit requires treating verification as a continuous process rather than a one-time check.

AI has also lowered the barrier to entry for fraud. What once required insider knowledge or experience within financial systems can now be generated on demand, giving attackers visibility into how systems operate and how they can be bypassed.

Defending against AI-driven fraud requires evaluating identity, behavior, device signals and context together rather than in isolation. When controls are applied in isolation, gaps emerge. Fraud succeeds by exploiting those gaps between systems rather than defeating any single control.

The future of fraud prevention is continuous

Organizations still relying on static trust models will increasingly find themselves outpaced by AI-powered fraud that never slows down. What will separate effective security strategies is not the number of controls in place, but how effectively those controls work together.

Fraud is iterative and intelligence-driven, with attackers continuously refining their approach based on which defenses are easiest to bypass.

Every layer in a defense stack can catch what others miss, and fraud gets in by probing gaps between them. When a threat slips past the first layer, it will most likely be caught by the next layers. The more intelligently layered a security stack becomes, the greater the likelihood that suspicious activity will be identified and contained before damage occurs.

Continuous reassessment is what makes layered defense work in practice. Every interaction creates an opportunity to reassess trust based on evolving risk signals. A device linked to past fraud, behavioral patterns that do not match the established identity, or a login that appears legitimate but shows signs of automation are all indicators that risk has shifted.

This challenge is also expanding beyond identity. The same pressure is now emerging across payments, transactions and check-related fraud, widening the scope of what organizations need to defend.

If organizations want to keep pace, they must focus on closing gaps between layers and ensuring each control adapts as attack techniques evolve. AI is giving fraudsters the ability to probe systems, adapt in real time and make attacks increasingly difficult to distinguish from legitimate activity.

In this AI vs. AI environment, the only way to keep up is to stop treating verification as a single moment and start treating it as an ongoing discipline.

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Trust has become the world’s most vulnerable infrastructure, yet most people don't realize we're already at war to defend it.

Every online interaction, phone call, password reset, remote job interview, and even a pickup or delivery now raises the same basic question: is the person on the other side really who they say they are?

Global businesses and governments in the AI era face a war where identity is the weapon, the attackers are invisible, and the battlefield is the infrastructure we rely on for daily connected life. AI has accelerated fraud and made sophisticated deception accessible to any bad actor.

Criminal rings, nation-state adversaries, and even ordinary people can now make fraud attempts against people and businesses at massive scale. Often, the money they take funds other criminal activities, or even military programs for rogue states.

Socure’s own network saw an 8,000% increase in AI-accelerated fraud over the past year, a number that firmly demonstrates that fraud has evolved from back-office risk to strategic business threat.

Unsafe identities

Most people would be shocked by how unsafe their identity actually is. Major data breaches have exposed private and sensitive information like names, addresses, birth dates, phone numbers, emails, and more. And fraudsters aren’t starting from zero anymore. AI can be used on top of real data to create hyper-realistic deepfakes and synthetic identities that can’t be caught through traditional fraud signals.

I spent more than a decade in the U.S. Army, with much of that time in Special Operations. There, I learned what asymmetric threats look like up close. AI-accelerated fraud has the same hallmarks: low-cost attacks, fast adaptation, distributed networks, and targets that don’t even realize they’re under attack until the damage is already done.

Our fraud fighting research team at Socure discovered one fraud operation that purchased 340 domain names, created more than 24,000 fake identities, and launched more than 35,000 identity attacks on a single company in a matter of weeks. The ring avoided traditional linkages: no repeated emails, phone numbers, addresses, or other notable identifiers tied the attacks together. The pattern only became visible by connecting the infrastructure behind the attacks.

This is just one example. With AI as a partner, the enemy is evolving quickly, from identity theft farms with hundreds or thousands of phones running scams around the clock, to people coerced into becoming fraud mules for North Korea-linked laptop schemes.

An unseen war

This is a war that most people don’t see, but they are feeling the impacts every day. Fraud breaks trust and the burden of proof is on the consumer. Businesses are suspicious of trustworthy customers, while customers can’t trust most of the messages they receive.

This paradigm forces the entire digital economy to spend more time proving what used to be assumed: that the person on the other side is real.

I know because it has happened to me.

Someone once used my personal information to open a business account at a major bank. I work in this industry. I had frozen my credit and it still happened. I spent weeks calling, disputing, waiting, and wondering what else they might try to do in my name. Even after the matter was investigated and closed, the unsettling feeling always stayed with me.

Beyond the money, the human toll is real. Victims lose time, peace of mind, and trust. In the worst cases, identity crime can lead to a national security crisis, no matter the country. It’s clear why this is a war we have to win.

But the fight is bigger than any one company, or any one country. The lesson I learned in Army Special Operations applies here: it takes a network to defeat a network. You can be the best bank, the best fintech, the best government agency, but if you’re fighting a networked enemy with only your own data, you’re still fighting in a silo.

That is exactly what fraudsters want: each company sees just one piece of the attack, so no one has the ability to connect the dots.

Organized fraudsters

The fraudsters are already organized. The defenders have to be one step ahead and band together. That means lawful, privacy-preserving shared intelligence across global businesses, governments, financial institutions, technology providers, and infrastructure companies.

When one defender sees a fake face, a fake document, a mule pattern, or a fraud ring’s infrastructure, that insight should help protect everyone else.

The urgency is not only about today’s attacks. Quantum computing is on the horizon and it is bound to accelerate fraud even further than AI has. The window to build stronger defenses is closing.

World War Fraud is here. If we fight it together, we win. If we can't come together around a shared understanding of AI-accelerated fraud and its true scale, we risk seeing trust between organizations and people fractured forever.

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LinkedIn, one of the most notorious bastions of AI slop online today, is finally cracking down on the deluge being seen across its site.

The social network is introducing a range of controls as it looks to turn off the tap of the torrent of AI slop increasingly dominating its website. You can try it now - just click on the three dots at the top-right of any LinkedIn post, and among the usual options to save, share or embed, you'll see a new option - "Seems like AI slop".

My only question is - what took you so long?

"We really care about this"

In a LinkedIn post of his own, Hari Srinivasan, CPO at the company, explained the reasoning behind the roll-out in words which should be music to the ears of most LinkedIn users.

"AI slop is a top priority for all of us. We really care about this," he declared. "People come to LinkedIn to connect with real people and share their real perspectives, ideas and expertise."

Now, I try and keep my LinkedIn feed fairly professional - like many of us, I do try and use it for work purposes, after all. But the odd slop does slip in every now and then - usually 3rd-level connections trying to shill some crypto scam, or religious-themed pep talk.

I have to say, it's incredibly satisfying to click the "Seems like AI slop' and relegate such posts to the dustbin of history - much more so than LinkedIn's previous system of reporting posts which may have sounded like scams or spam, but often just disappeared into silence.

As Srinivasan explained on the new reporting tools, "Slop is hard to define and the definition changes; this lets us tune our models and make better feeds."

"We asked ourselves why do people post with AI anyway? The answer is LinkedIn isn’t a one-word kind of place and they feel more confident running their posts through AI. We’re taking this learning, removing the ‘enhance your post’ feature you see when you write a post or message & replacing with a feature that proofreads your words, but does not change your voice."

Hopefully they more we use the tool, the smarter it will get - and cut down on the stuff we don't want to see. The action goes two ways though - and LinkedIn will also be testing a way for admins and members to see in their analytics dashboard when your own content has been flagged as potential slop.

Let's hope this is a bit more regulated - we don't want online vigilantes carrying out campaigns against a company or person they may have a vendetta against.

Srinivasan says the approach is based on two learnings - "First, AI and slop are not the same thing; many people refine thoughts with AI, and we believe they want to know when they sound inauthentic. Second, we want members to get feedback from real humans on what sounds authentic - not just have an AI detector review it and get it wrong."

Srinivasan also noted that LinkedIn (which let us remember is owned by Microsoft) will "continue to improve and invest in our automation defences..on comments alone, everyday we are now catching hundreds of thousands of automated comment attempts, and have blocked billions of other automation attempts (posting at scale, slop) in the last couple months alone."

This is a very welcome first step by LinkedIn, and lets just hope it's the start of an ongoing crusade against AI slop - because no one wants to see any of that anymore.

During the first wave of AI adoption, much of that cost was effectively hidden from customers. AI came wrapped in subsidized pricing, generous allowances and a relentless focus on driving usage. The message was simple: use more AI.

In some organizations, usage itself has become the goal. The rise of concepts like "tokenmaxxing" took this to an extreme, celebrating volume over value and falsely equating outputs with outcomes.

But while tokenmaxxing may be a questionable habit that’s been widely exposed, it is not the biggest problem facing enterprise AI.

The bigger issue is the hidden token tax that comes with their use of gen AI and agentic AI capabilities in everyday operations.

The token tax is kicking in

Enterprises are already beginning to see the impact. Uber reportedly exhausted its planned 2026 AI budget by April, just four months into the year, after rapid adoption of AI coding tools across its engineering organization.

Amazon reportedly shut down an internal AI-usage leaderboard after concerns that it encouraged “tokenmaxxing,” with executives urging employees not to use AI merely to increase usage metrics.

As AI moves from experimentation to production, organizations are discovering that the economics of scale can look very different from the economics of experimentation.

Tokenmaxxing may encourage organizations to consume more AI, but the token tax is the bill that eventually arrives. And for many enterprises, that bill is proving far larger than expected.

Why is this happening?

On the surface, it seems counterintuitive. Per-token pricing continues to fall, and model providers regularly announce cheaper rates. Yet with enterprise AI bills continuing to rise, the reason lies in how modern agentic systems actually work.

The visible output you receive is only a small part of what is happening behind the scenes. Before generating an answer, an AI agent may interpret the request, decide which tools to use, retrieve data, evaluate results, and loop, calling tools repeatedly until the task is completed or the agent determines it is stuck.

Think of it as an agent having an inner monologue: interpreting the request, planning, calling tools, checking results, and deciding what to do next. Each step may trigger additional model calls and may carry forward more context, so the visible answer can represent only a fraction of the total tokens consumed.

So, while the cost per token is dropping, the number of tokens required to complete a task is often increasing dramatically.

Goldman Sachs Research estimates a 24-fold increase in token consumption by 2030, reaching around 120 quadrillion tokens per month as consumers and enterprises adopt agentic technology.

With this, organizations may believe they are benefiting from lower pricing while their overall costs continue to rise. And the issue is not just cost; it is also predictability, and the lack of a clear link between token usage and outcomes.

This is becoming one of the biggest economic challenges facing enterprise AI, and it is only beginning to be discussed. The economics of agentic AI are making it increasingly expensive to run agents at scale.

What is the token tax?

We position the token tax as the hidden cost organizations incur when AI systems repeatedly consume expensive run-time agentic resources to perform work that could have been designed once and reused many times.

Unlike traditional business software, where the cost of execution is largely fixed, many agentic AI systems effectively rethink the same process every time they run. The more complex the workflow, the higher the tax. Many repeatable enterprise tasks do not require, or even allow for open-ended run-time reasoning.

This creates a disconnect between activity and value. Organizations may be consuming millions of tokens, but that does not necessarily translate into better outcomes. In many cases, it simply means paying repeatedly for the same agentic planning process.

Are we using agents in the right place?

Run-time agents are valuable when solving new, unique and underspecified problems, designing workflows, or exploring options. But using expensive reasoning to repeatedly execute the same business process introduces not just cost but also unpredictability. If you ask an agent to re-reason the same question 100 times, you may get 100 different answers.

To compare this to something in the real world, it's a bit like paying a five-star gourmet chef to invent a recipe every time a new order for a meal comes in. The smarter approach is to use agentic AI once to design the optimal workflow, and then use a lighter-weight AI to select the right workflow that executes consistently.

In other words, use the five-star chef's creativity and expensive brain once a season to come up with a new menu and recipes. Then use skillful but much cheaper chefs to prepare the meal following the recipe that has already been designed.

Shift agentic AI left from run-time to design time

This is where the distinction between design-time and run-time becomes critical. Agents can be very useful at design time, where creativity, exploration, and problem-solving create lasting value.

Run-time execution is different. Here, predictability, consistency, governance and cost efficiency matter most. Run-time agents should only be used selectively, workflows should be used for deterministic, repeatable and governed processes.

By using agentic AI to design processes up front, organizations can dramatically reduce the token tax associated with run-time execution while improving reliability.

Sustainable AI is not about eliminating agents, but about being deliberate about where agents deliver value.

The next phase of enterprise AI

This next chapter will be defined less by impressive demos and more by economic discipline. Organizations will need predictable outcomes, predictable costs and a strategy for reducing the token tax embedded within agentic systems. The winners will not necessarily be the organizations that use the most AI. They will be the ones that generate the most business value from every token consumed.

The companies that address this challenge early by optimizing token consumption and architecting for design-time intelligence and run-time efficiency will not just save money, but will build AI systems that are easier to trust, easier to govern, and easier to scale.

As AI moves from experimentation to enterprise reality, the organizations that minimize their token tax while maximizing business outcomes will have a significant competitive advantage.

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The conversation about AI and shopping is happening in the wrong place.

Everyone wants to know whether agents will browse for us, compare prices for us and, eventually, buy for us without asking. It's a fair question. But it’s not the one that matters right now.

The more consequential shift is happening somewhere far less visible: inside payment networks, authorization protocols and the systems that decide whether software is allowed to spend money on our behalf.

The technical capability for an AI agent to complete a purchase has existed for some time. What has been missing is the infrastructure that allows businesses and consumers to trust those transactions: confirming an agent had permission to act, ensuring it stayed within agreed boundaries, and creating a verifiable record if something goes wrong.

Over the past few months, that infrastructure has started to emerge. The biggest moves in this space haven't come from AI labs building increasingly showy assistants. They've come from the companies that move money.

At its annual conference, for example, Stripe used the opportunity to introduce wallets that agents can spend from, alongside a partnership letting businesses sell directly inside AI search and chat environments.

Visa introduced an early protocol to help merchants tell a legitimate AI agent apart from a bot. Mastercard announced its own framework for registering and authenticating agents before a transaction is allowed to proceed. Agents are already showing up at volume. What's been missing is the infrastructure to handle what happens next.

The limitation was never autonomy. It was authorization.

Adobe research from early this year shows that AI traffic to US retail sites grew 393% YoY in the first quarter, with AI-referred visitors converting 42% better than non-AI traffic. Just a year before, that same traffic was converting 38% below standard channels.

That’s why the language from the payments industry has shifted so noticeably toward authorization, intent and trust. Mastercard's own leadership has made things explicit: how do you distinguish a legitimate agent from a malicious one? How do you confirm the consumer really authorized the purchase? How do you verify that the agent did what it was told?

Those aren't AI questions. They're identity and trust questions that just happen to involve AI.

Whoever defines the boundary captures the value

What's notable about the recent announcements is how consistent the underlying logic is across companies that otherwise compete directly. Visa's protocol and Mastercard's framework work the same way in principle: register the agent, verify intent and pass credentials only once trust is established.

Stripe's approach, issuing single-use payment credentials per task rather than exposing a customer's card details to an agent, solves the same problem from a different angle.

The protocols may look like back-office plumbing. Functionally, they're architecture for autonomous commerce. Every time a payments provider decides what counts as a verified agent, what data has to be passed to prove intent or how disputes get resolved, it's writing rules that shape who gets to participate and on what terms.

A consumer can be persuaded by an AI assistant to want something, but if the transaction can only execute through a specific track, with specific verification steps, then influence over the purchase decision and control over the purchase itself become two separate things. The company that captures the second captures the economics, regardless of who captured the first.

For merchants, this creates a concrete operational problem. If a transaction gets disputed, can the business prove what was authorized, by whom and under what constraints? These are the exact questions Visa, Mastercard and Stripe are quickly trying to answer; whoever answers them first effectively writes the rulebook everyone else follows.

The race that's already started

Autonomous shopping is imminent, and in some categories it’ll come faster than in others. Routine reordering and B2B procurement are likely to happen first. Other more considered, emotionally fueled purchases will take longer; trust has to be earned. Payment readiness is only part of it, however.

The commerce stack underneath, covering product data, pricing, inventory and checkout, needs to be structured and accessible enough for agents to actually operate it. Businesses that haven't addressed that layer will find the payments question is the least of their problems.

The infrastructure being built right now by payment providers won't just process agent-driven transactions. It will determine which businesses are visible to agents in the first place, which transactions are trusted by default and which get pushed into friction and manual review. The real battle isn’t over autonomy. It’s over who owns the infrastructure of trust.

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The UK’s newly announced £2 billion quantum investment package signals something more consequential than the years-long research race that I’ve patiently been following.

Quantum is increasingly being treated as a key part of the future of national infrastructure.

While the size is important, the clearest sign of that shift is the structure behind it.

Through the newly announced “ProQure: Scaling UK Quantum Computing” initiative, the UK has become the first country to commit to an advanced procurement program for large-scale quantum computers by the early 2030s.

That matters because governments do not produce technologies at scale unless they believe those technologies are strategically important to operational capability, economic resilience and long-term competitiveness.

In many ways, this marks quantum computing’s transition from the research era into the infrastructure era.

Why procurement matters more than funding totals

So why does a procurement program matter more than the headline funding figure? The significance extends beyond public investment totals. Procurement creates market confidence. It helps establish deployment pathways, support supply chains, attracts private capital and gives emerging technologies a route from laboratory experimentation into practical use. In deep technology markets, the challenge is often building the ecosystem required to scale and operationalize it.

The challenge is becoming increasingly important as the broader quantum market matures. QuEra’s recent Quantum Readiness Report found that organizations are becoming far more disciplined in how they approach quantum investment. While 44% of respondents still expect their quantum budgets to increase in 2026, nearly half expect spending to remain flat, signaling a shift away from hype-driven expansion toward proof-driven decision-making.

More advanced adopters of quantum technology are increasingly motivated by what the industry refers to as the “classical wall”. This is the point at which conventional computing approaches struggle to solve increasingly complex computational problems. That shift from speculative experimentation toward problem-driven investment is an important sign of market maturation.

Sovereignty reshapes the quantum market

The changing investment landscape also helps explain why procurement and national strategy are becoming more central to the conversation.

One of the most striking findings in our research was that 62% of organizations now actively factor sovereignty into procurement decisions, while only 5% say sovereignty is not a consideration at all.

However, the highly globalized sourcing model that characterized the industry’s early years is giving way to a more regionally strategic one, shaped by concerns around resilience, supply chains, infrastructure control and long-term access to advanced computing capability. This makes the UK’s strategy particularly timely.

The government’s emphasis on sovereign capability, domestic ecosystem development, and long-term infrastructure investment reflects broader shifts already underway across the market.

Quantum computing is increasingly being viewed not only as a commercial technology opportunity, but also as a strategic national capability alongside AI, semiconductors, and advanced communications infrastructure.

Scaling quantum requires more than hardware

Scaling quantum computing requires much more than building processors. It requires coordinated investment across software, networking, manufacturing, talent, infrastructure, and integration with classical systems.

The UK’s decision to support not only computing, but also sensing, networking, software labs, infrastructure, and skills programs reflects an understanding that leadership in quantum will be determined by ecosystem depth as much as scientific breakthroughs.

The talent challenge illustrates this clearly. Our report identified specialist workforce shortages as one of the most significant barriers to adoption, particularly in highly technical areas such as quantum error correction.

Developing scalable quantum systems requires expertise that remains globally scarce, making long-term investment in education, research institutions, and industry collaboration essential.

The UK’s opportunity to lead

The UK enters this next phase from a position of genuine strength. Over the past decade, the National Quantum Technologies Programme has helped establish one of the world’s most advanced quantum ecosystems, supporting research hubs, startups, national infrastructure, and industry partnerships.

QuEra’s own presence at Harwell reflects the depth of scientific expertise and collaborative capability that already exists across the UK quantum landscape.

This investment builds on that foundation while accelerating the UK’s ability to translate quantum promise into economic opportunity. QuEra is one of several companies building next-generation quantum computers at Harwell, drawing on the country’s world-class talent and research base.

The next challenge is translating scientific leadership into scalable deployment.

From scientific leadership to commercial scale

Quantum computing remains a technically demanding, pre-commercial industry in many respects, but the direction of travel is becoming clearer. To be clear, quantum computers still cannot deliver broad commercial advantage on their own in most industries; that case is still being proven sector by sector.

But the procurement and infrastructure being built now is what will let that advantage arrive faster once it does. Governments, enterprises and investors are increasingly focusing less on theoretical promise and more on deployment readiness, operational capability, and long-term strategic value.

Public sector leadership will continue to play an essential role in that process. Our research found that government funding remains the leading driver of quantum investment globally, cited by 28% of respondents as the primary catalyst for budget increases. At the same time, government and defense organizations are expected to lead near-term commercialization efforts, highlighting the central role public investment continues to play in scaling the industry.

The countries that lead the next phase of quantum computing are likely to be the ones that successfully combine research excellence with procurement, infrastructure, talent development, industrial coordination, and commercial scale.

For enterprises and other governments watching from the sidelines, the practical lesson is straightforward. Building procurement pipelines, workforce training, and proof-of-concept programs now, even at modest scale, is what prepares an organization to scale up once quantum systems clear the classical wall in a given domain. Those that engage early will be better positioned than those that wait for certainty that may not arrive on a convenient schedule.

The UK’s latest announcement suggests it understands that distinction and that may ultimately matter more than the £2 billion headline itself.

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Not long ago, data centers were something the average person on the street rarely thought about. They powered the digital services we used every day, but sat largely out of sight.

That has changed. Look at almost any national news site and you will see another data center scheme being proposed, from a £9.5bn data center next to the proposed Universal theme park in Bedfordshire to a £2bn data center in the Borders hills, west of Duns.

This is no longer just a story about Slough, London or a handful of established technology corridors. Data centers are appearing in former industrial sites, rural areas and regional communities, becoming a visible part of the UK’s infrastructure debate.

Part of this is being driven by the Government’s AI ambitions. Through AI Growth Zones, ministers want to accelerate the build-out of AI-enabled data centers by improving access to power and planning support.

But if the UK wants to compete in AI, there is a practical question sitting beneath all this ambition: who is actually going to build these data centers?

AI demand is moving faster than the workforce

AI has changed the scale of the data center conversation. The sector is being asked to provide the physical backbone for a new industrial wave, creating pressure not only on land, power and planning, but also on people.

The energy behind AI is outstripping the skilled labor resources available. The UK may have the policy momentum and investor appetite, but the workforce is not expanding at the same pace.

Data centers do not build themselves. They need engineers, electricians, commissioning specialists, project managers, safety teams, quality experts, construction partners and operational leaders. They also need people who understand these are critical environments where mistakes can be costly.

The industry is still too reliant on traditional routes into work, leaning on training patterns that may have worked 20 or 30 years ago, rather than asking what is needed for the next five.

The sector is missing people who are hiding in plain sight

Women remain underrepresented in data centers. So do career returners, flexible workers, people from adjacent industries and those who may not have followed a conventional technical route

If data centers keep looking in the same places, they will keep running into the same shortages. The sector needs to widen the front door and think more creatively about how people work once they are in.

Why shouldn’t a major site have crèche facilities? Why can’t more roles be structured around hybrid or flexible models where the work allows it? These changes could help experienced people return, stay and progress, while giving employers a stronger, more engaged workforce.

That starts with explaining the range of roles more clearly. Not every data center job is a narrow engineering role. Program management, safety, quality, finance, legal, facilities, operations, construction and customer delivery skills can all transfer into the sector.

The industry also needs to tell a better story about itself. Too often, public debate focuses on power, water and planning friction. Those issues matter, but data centers are also part of the infrastructure behind AI, digital services, healthcare, finance, scientific research and economic growth.

Training needs to become more flexible and joined up

The answer is not simply to hire faster. It is to build clearer, more flexible routes into the sector.

Ireland’s pharmaceutical industry offers a useful blueprint. Its growth was supported by government, education providers and major employers working together to build the skills the sector needed.

Data centers need the same mindset: better visibility of future demand, stronger regional skills pipelines, more apprenticeships and clearer routes for people with transferable experience.

Someone managing safety on a large infrastructure project may be much closer to a data center career than they realize. The industry’s job is to make that route visible, credible and accessible.

Common standards matter too. As delivery accelerates, every company trying to solve training, quality and safety alone will create duplication and inconsistency. Shared frameworks would give employers, subcontractors and new entrants a clearer view of what good looks like.

The infrastructure strategy needs a workforce strategy

The UK does not lack ambition. It has data center plans coming forward, government policy designed to support AI infrastructure, and investors looking for places to build.

But ambition will not be enough. AI may be digital, but the infrastructure behind it is physical. It depends on people with the right skills, in the right places, at the right time.

If the UK wants to lead in AI, it cannot treat the workforce as an afterthought. It needs to bring more women into the sector, make better use of transferable skills, create flexible routes into work, and give regions the tools to prepare before demand arrives.

The country can approve the sites and attract the investment. The bigger test is whether it can build the workforce to match.

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The telecoms industry is all a flutter.

Satellite connectivity was once seen as useful but peripheral. Starlink is changing that.

With scale, money and ambition behind it, the company is starting to look less like a niche rural broadband provider and more like a serious question for the wider connectivity market.

Kester Mann, Director, Consumer and Connectivity at FDM CCS Insight, said this is one of the more interesting developments of 2026.

“The industry is nervous. Operators are currently working to shore up their defenses against potential offerings from a company like Starlink, but the long-term plan is not obvious yet. Starlink has the financial muscle to be disruptive if it wants to be.”

Starlink is clearly a very ambitious business. The recent IPO of its parent company, SpaceX, shattered global financial records, raising $85.7 billion in total capital and valuing the company at $1.77 trillion at listing.

Just weeks after its IPO, the company completed a $25 billion bond sale. One thing it is not short of is money.

The connectivity mix

Clearcut data is somewhat difficult to come across, but the Department of Agriculture puts the total U.S. population in nonmetro counties stood at 46.2 million, accounting for 14% of the total population spread across 72% of the landmass.

This demonstrates why closing not spots in the U.S. has become such a significant challenge.

“It’s very difficult to make connectivity economical in the U.S. outside urban areas,” Mann added. “I understand why we have this situation, and partnerships between telecoms operators and satellite companies make a huge amount of sense.”

The telecoms industry has often envisioned a patchwork landscape for connectivity, with each technology serving the purpose to which it best suits. This works today, mobile, Wi-Fi and broadband interlink nicely, and satellite could fill the spaces left behind by traditional telecoms. Namely, the rural locations.

Could satellite solve the telecoms problem the telecoms industry couldn’t solve?

The economics of telecoms infrastructure have always created a headache for the industry, especially in countries like the U.S.

Simply put, there aren’t enough people living in an area to justify the vast expense of network rollout. If the telecoms operators could have made money, you can bet there wouldn’t be not spots.

This is where a ‘non-telecoms’ solution fits nicely, and there is precedent of new market entrants completely disrupted highly established markets.

Netflix turned entertainment on its head. AirBnB reshaped the hotel industry without ever building a hotel. Uber led to an entirely different way of moving from A to B, and not just taxis anymore.

In the rural states, there are consumer mobile and broadband services to potentially serve, IOT networks to be connected, agriculture and other primary industries to power, not to mention public services just as there are in the cities.

There is a market, just not a financially attractive one. Telecoms operators would have to spend eye-watering amount to cover vast and challenging terrains. It simply doesn’t add up.

Traditional approaches won’t work, so something new has to be considered.

But how much momentum could this give Starlink?

Ambitious businesses set ambitious targets

Starlink boss Elon Musk said in a recent interview he had no intention of putting the telecoms operators out of business, but that has not put all the questions to bed.

Mann continued, “Is rural enough for Starlink? It is a niche service, and Musk has big ambitions. Some analysts are talking about a takeover of a telecoms operators, which is a long shot, but a MVNO proposition could certainly be realistic.”

This is the precarious position telecoms operator find themselves in. Partnerships with satellite companies are attractive, but then it comes with risk.

In the UK, Vodafone and AST SpaceMobile are partnering to bring direct-to-device (D2D) space-based mobile broadband to standard 4G/5G smartphones. This looks attractive to the telecoms operator.

Then you have the middling risk. When Rocket Labs announced plans to acquire Iridium, the deal also included the company’s globally harmonized L-band spectrum and low Earth orbit (LEO) satellite network. This makes Rocket Labs a direct competitor in the rural regions.

Starlink is in a similar position to Rocket Labs, just a couple of factors greater. It has the same operational capabilities and spectrum to deliver consumer services at scale, but it also has cash. Whereas Rocket Labs could be a niche competitor few would worry about, Starlink could be a major disruption.

What is Musk thinking?

Everyone is focused on the disruption to the telecoms industry, but ultimately this might be a by-product of the bigger plan. What if all this routes back to Telsa?

The obvious answer is telecoms. Starlink wants to sell connectivity, operators sell connectivity, so the threat is easy to understand.

But that might also be too narrow.

Musk’s businesses rarely sit neatly inside one industry. SpaceX launches rockets. Starlink sells broadband. Tesla sells vehicles. xAI builds artificial intelligence. X provides the distribution layer. On paper, these are separate businesses. In practice, they increasingly point towards the same idea.

Controlled infrastructure, controlled data, controlled customer relationships.

That is where Tesla becomes interesting.

Where interest matters

A company trying to make autonomous vehicles work at scale does not just need batteries, sensors and software. It needs reliable connectivity in as many places as possible. Robotaxis, logistics, remote monitoring, over-the-air updates, live mapping and future machine-to-machine services all become more valuable if the connectivity layer is controlled rather than rented.

This does not mean Starlink is simply a Tesla project in disguise, that would be too neat, and probably too generous. But it does show why telecoms operators should be careful about judging Starlink by traditional telecoms logic.

They are asking whether Starlink can steal enough broadband or mobile customers to matter.

Musk may be asking a different question entirely. What becomes possible when connectivity, devices, data, AI and mobility sit inside the same orbit?

That is the uncomfortable bit for operators. They still see connectivity as the product. Starlink may increasingly see it as the operating layer.

Or perhaps there is no grand plan at all. This is still the man who bought Twitter for $44 billion. But telecoms operators cannot afford to treat that as reassurance. Sometimes the chaos is the strategy.

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Security is always a moving target. But AI has made it move faster than many organizations can manage, presenting a gargantuan challenge for SOC teams. The near constant stream of announcements of new models or providers has resulted in a massively expanded attack surface.

While security teams work hard to review and approve as many tools as possible, the volume is still crushingly high. Today, only 22% of workers rely exclusively on the AI tools their employer provides, while more than a third (35%) of Gen Z employees say they prefer using personal AI applications over company-approved ones. For every sanctioned tool a business governs, most of its workforce is quietly working around it.

If almost 80% of employees use unapproved AI tools, then it isn’t an aberration - it’s the default. Shadow AI is normal across organizations, but the scale of the challenge is badly underestimated by the people responsible for managing it.

Why the crackdown backfires

Faced with unsanctioned use, the instinct is to clamp down. Blacklist as many tools as possible and pull usage back inside the lines. The problem is that, while this feels like control, in reality it just leaves security blind.

Blocking doesn’t stop an employee using a tool, it just drives the risk out of sight. If their tool of choice is blocked on their laptop, they’ll access it on their phone. Or with their personal email address instead of their work one.

This is true of many IT tools, but especially so with AI because people can become extremely attached to specific chatbot personalities. Consequently, AI use is driven deeper into the shadows, leaving zero visibility or record for security teams about potential data leakages or other risks.

This is the trap at the center of most shadow AI strategies. The harder an organization tries to eliminate the problem, the less it can see. And the less it can see, the more dangerous the problem becomes. Governance that looks airtight on paper can bear little resemblance to what’s actually happening across the business.

The consequences are not abstract. When confidential information flows into unmanaged third-party platforms, the fallout can add as much as $670,000 to the cost of a data breach, and most of the employees responsible have no idea they’ve created a risk at all.

Policy isn’t enforcement

The reason for these problems comes back to the speed at which AI development is moving. ChatGPT is barely five years old and the difference in performance over that time is immense. One result of this is that pretty much any AI security and best practice training employees have done has not been adequate.

The idea was not to overload employees with too much information. By keeping it ultra-straightforward and focusing on only one or two tools that were security-approved with examples looking at low-risk, early-adoption scenarios, companies could make compliance as easy possible.

For a normal technology, it would be a sensible strategy. However, the level of self-driven AI adoption we’ve seen has been far higher than companies expected. Shadow AI use is rampant and so security cannot afford to continue blindly enforcing rules that aren’t working.

Rather, to govern employee behavior, you need to be able to understand and analyze it. When it comes to AI that means gaining visibility into what tools and models employees are using, how, and where it’s actually delivering value.

An experience-based approach

That shift requires visibility into how AI is used across the workforce. A DEX (Digital Employee Experience) approach delivers exactly that, automatically detecting both approved and shadow AI through traffic patterns and endpoint activity, without relying on employees to self-report.

Usage and experience data sit in a single view, broken down by team and workflow, so leaders can see where people are working around the rules and act on it.

From there, the response stops being a blunt ban and becomes far more useful. In-app guidance can steer employees towards sanctioned tools in the flow of work, reinforcing the right behavior without killing the productivity they were chasing in the first place.

Policies can be built around real usage instead of guesswork. And because 96% of leaders now say digital adoption support is critical to AI readiness, that same visibility shows exactly where training will close a gap rather than tick a box.

It’s time to manage shadow AI

Shadow AI isn't going anywhere, and pretending otherwise is how organizations end up exposed. The companies that handle it well will be the ones that stop trying to ban it and start paying attention to it. Everyone else is just hoping their policy is being followed, with no real way of knowing whether it is.

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With AI, the genie is out of the bottle, and there's no turning back. That's the overriding viewpoint across science and in Silicon Valley, where execs are increasingly building powerful systems. Beyond just building smarter machines, many among them have actually invoked quasi-religious imagery to describe the future.

Science and magic

SpaceX and Tesla CEO Elon Musk previously echoed science-fiction author Arthur C Clark when he likened scientific progress to magic.

In comments he made to students during an interview at the AeroAstro Centennial Symposium at MIT, Musk once again used colorful language to explain how he saw the progress we were making on AI.

In pursuing the goal of building advanced machine intelligence, beginning with AGI, he suggested there would be limited ways of controlling such an entity, and that any attempts to do so would be futile.

The following year after making these comments, alongside Sam Altman and other tech executives, Musk co-founded OpenAI, which spearheaded the release of ChatGPT and thrust modern AI systems into the mainstream.

The machine god

Musk's likening of AI to a demon has inherent connotations of danger or doom – which is appropriate, considering he and others have previously said AI is the biggest existential threat that humanity faces in the years to come.

For example, AI researchers Eliezer Yudkowsky and Nate Soares, published a book in 2025 called 'If Anyone Builds It, Everyone Dies' in which they laid out the case against building a superintelligent AI that could wipe us out.

These comparisons, however, are rejected by many others who see AI as unlikely to instigate science-fiction-type (or even supernatural-esque) doomsday scenarios. Chief among these voices is Jensen Huang, while just 3% of researchers say they were most worried about a scenario in which an out-of-contorl AI poses a threat to humanity.

• Microsoft launches MAI-Cyber-1-Flash, its first in-house cybersecurity model.
• It also reveals Project Perception, an agentic system whose red, blue, and green agents find, triage, and patch vulnerabilities
• The launch comes days after OpenAI said its models escaped a sandbox and attacked Hugging Face

Microsoft has unveiled two significant security announcements - MAI-Cyber-1-Flash, the first cybersecurity model it has trained in-house, and Project Perception, an agentic defense system that finds vulnerabilities, decides which ones matter, and writes and deploys the patches.

The launch took place days after OpenAI disclosed that its own models had broken out of a sandbox and hacked Hugging Face, which makes the timing either unfortunate or pointed.

With Microsoft claiming a 96% score on CyberGym, an industrial benchmark for cybersecurity, nearly 12 points above Anthropic's Claude Mythos 5, while promising as much as 50% savings in token cost, the company says it is bringing what it calls a 'well-tuned, multi-model system with access to uniquely rich historical training data',

A competitive product with excellent timing?

The Hugging Face incident drew much attention, not all of it was negative: as it painted OpenAI's models as more capable than the company's own assessments had suggested. It also sharpened a question researchers have been raising for years: what happens when a model becomes capable enough to defeat the controls placed around it?

Microsoft's answer is well-timed and two-pronged, claiming to be both cheaper and more capable than the alternatives, though the performance figures are, so far, Microsoft's own.

MAI-Cyber-1-Flash, is a sparse mixture-of-experts transformer with 137 billion total parameters, five billion active, and a 256,000-token context window, built as a cybersecurity fine-tune of MAI-Code-1-Flash, itself developed from a MAI-Thinking-1 mid-training checkpoint.

It is designed to handle up to 90% of the tasks inside MDASH, Microsoft's multi-model vulnerability harness, with OpenAI's GPT-5.4 reserved for the hardest 10 percent.

Microsoft says that split costs about half as much as its previous best MDASH configuration. For now, it runs only within MDASH and is available to approved MDASH customers through an Azure AI Foundry private preview, with no standalone API.

Project Perception is the wrapper around it, drawing on MAI-Cyber-1-Flash for its first workflow alongside frontier models such as GPT-5.4. Three classes of agent split the work: red agents probe for paths an attacker could take, blue agents investigate and decide what constitutes meaningful risk, and green agents remediate and harden.

On the specific failure that let OpenAI's models loose, Microsoft took the precaution OpenAI did not, as it says all benchmark testing ran in a network-isolated environment with no access to production systems, the public internet, or external services.

OpenAI's sandbox, by contrast, kept one route outward in the form of an internal package-fetching service, and its models found a flaw in it, escalated privileges and worked across the research network until they reached a machine with internet access. Microsoft says its isolation held. Nobody outside Microsoft has verified that.

The harder question is not containment during testing. Project Perception moves the work out of a research network and into customer production environments, where green agents are authorized to change live systems as their normal function.

There is no sandbox to escape, because acting on real infrastructure is the product. And attribution is difficult even when someone is watching: Hugging Face detected the intrusion within days and reported it to law enforcement, but had no idea who was behind it until OpenAI said so.

It also could not get help from the leading American models, which read its defensive requests as offensive ones and refused. It ended up defending itself with GLM 5.2, a Chinese open-weight model, running on its own infrastructure.

For now, the industry's answer to dangerous capability remains narrower distribution, and the one documented case of a defender needing that capability urgently ended with them reaching for a model nobody had gated.

While AI is shaking up all industries, few have been impacted as visibly or as quickly as web design.

No longer can agencies charge for time on repetitive tasks that can easily be farmed out to AI. This creates a challenge for businesses that need to prove their value to clients that are now eyeing up cheaper, faster solutions to their needs.

I caught up with Itai Sadan, CEO and Co-founder of dedicated agency website builder Duda. We talked about Duda's new vibe coding tool, how it fits into his vision of the future of website building, and how he is seeing agencies demonstrate value through consultation and creative talent in 2026.

Firstly, tell us a little bit about the new AI tools you have rolled out across Duda. Why has Duda focused on launching these tools for agencies now?

AI has lowered the barrier to creating a website, but it has not solved the challenges agencies face when delivering websites at scale.

Agencies still need to produce work that reflects a client's brand, collaborate efficiently across teams, maintain quality standards, and do it all within a business model that supports healthy margins. We built our AI capabilities with those realities in mind, rather than adapting generic AI products that were never designed for professional web creators.

Our approach combines the speed of AI generation with the capabilities agencies already rely on in Duda.

Website generation turns a prompt into a production-ready starting point while preserving access to Duda's drag-and-drop editor, performance, SEO foundations, and the flexibility to refine every detail before launch, either by prompt or by simply clicking with a mouse, which is many times faster and more cost-efficient.

Duda Vibe expands those possibilities with an AI-native environment for creating websites and applications that remains fully connected to the Duda platform.

Agencies can collaborate with their teams and clients, white label the experience under their own brand, and operate with the predictable pricing and workflows they depend on to scale profitably. Our goal is to help agencies take full advantage of AI while strengthening the systems and processes that make their businesses successful.

AI tools, like those you have just rolled out, remove considerable admin work within agencies; should agencies be adjusting pricing models to reflect this?

I don’t think AI changes the fundamental value proposition of an agency. Clients aren't paying for time spent on repetitive tasks. They're paying for expertise that produces measurable business outcomes, whether that's more qualified leads, more appointments, or more online sales.

There's no shortage of website builders, and now even AI coding tools, built specifically for small businesses to use themselves.

What an agency brings is the ability to translate business goals into digital results. That includes understanding how a website should be structured, how it should be written to attract and convert visitors, and how it fits into a broader marketing strategy that may include SEO, paid advertising, social media, and other services. AI can accelerate many parts of that process, but the quality of the outcome still depends on the experience and judgment of the agency using it.

When I speak with agencies, they don't tell me they're working less. They tell me they're spending their time differently. The hours saved through automation are being reinvested into higher-value work, whether that's providing a more consultative client experience, expanding the services they offer, or helping clients adapt to new opportunities like AI search.

That's been Duda's philosophy from the beginning. We've always focused on removing the repetitive work so agencies can spend more time on the work their clients actually value.

As AI continues to improve efficiency, I believe that principle becomes even more important. In many cases, agencies will have an opportunity to deliver greater value than before, and their pricing should reflect the outcomes they create, not the time they save.

AI can make errors. For example, it may interpret a brief incorrectly, which could lead to significant additional work for agencies to fix. How much oversight do you recommend agencies give AI tools? Are there systems and processes you recommend to limit potential issues down the line?

We've tried to address that challenge at the platform level.

With Duda Vibe, for example, the planning stage interviews the agency before it begins generating anything. That additional context consistently produces better results because the AI is working from a clearer understanding of the project's goals and requirements.

We're also continuously improving the models behind our AI website generation so they can interpret briefs more accurately and produce websites that require less refinement before launch.

That said, AI should never be treated as a substitute for professional review. We recommend that agencies establish a quality assurance process for every project, just as they would with work created entirely by humans.

Duda's AI Copilot can assist with that review by identifying areas for improvement before a site goes live, while our Optimization Center continuously audits websites for common SEO, accessibility, and AI search optimization opportunities that might otherwise be overlooked.

AI is most effective when it's used throughout the entire workflow, not just at the point of creation.

I often compare AI to an exceptionally talented intern. Give that intern a clear objective, the right context, and the supporting materials, and they'll produce impressive work. Give them a vague brief, and they'll fill in the gaps with their own assumptions. That's why we encourage agencies to be as specific as possible.

Duda supports rich prompting with reference documents, images, and other project assets so agencies can provide the context the AI needs from the very beginning. The better the guidance, the better the outcome.

Vibe coding tools like Bolt of Lovable make it easy for anyone to build web apps, but lack agency-level infrastructure. How is Duda Vibe tailored to better meet agency needs compared to generic vibe coding tools?

Most vibe coding tools are designed to help an individual create an application as quickly as possible.

Agencies need much more than that. They need a platform that supports collaboration, client management, security, governance, and long-term maintenance across dozens, hundreds, or even thousands of projects. That's the gap we set out to address with Duda Vibe.

Duda Vibe is fully integrated into the Duda platform, giving agencies a centralized workspace where they can manage every project, whether it was built with our traditional website builder or in Duda Vibe.

Teams can collaborate throughout the development process, clients can securely review their own projects through a fully white-labeled experience, and agencies can continue using the workflows they're already familiar with.

Beyond development, Duda Vibe includes the operational capabilities agencies depend on, including automatic version history, secure data storage, reliable hosting, and performance insights that help them monitor both site performance and AI visibility over time.

We're still in beta, but our roadmap is firmly focused on the needs of agencies and the way they work.

One feature I'm particularly excited about is bringing our popular inline commenting workflow to Duda Vibe. Clients will be able to leave feedback directly on the project, just as they do today in our website builder. Once the agency reviews and approves a request, Duda's AI Stack will understand the context of that comment and carry out the requested changes automatically. It's another example of how we're applying AI to reduce the operational overhead of working with clients, not just the time it takes to build an application in the first place.

Although great for quick builds, vibe coding apps can lack the architecture, stability, and security required for long-term success. How does Duda Vibe help agencies maintain web apps without creating technical debt?

The technical debt challenge with AI-generated applications usually isn't the code itself. It's everything required to run and maintain that application over time.

Many AI coding tools can generate a working prototype in minutes, but agencies are often left responsible for hosting, security, authentication, payments, infrastructure, and long-term maintenance. That's where technical debt begins to accumulate.

Duda Vibe takes a different approach. Every application is built on Duda's managed platform, so agencies have a foundation they can depend on long after the application is launched. Hosting, security, DDoS protection, high performance on metrics such as Core Web Vitals, platform updates, and other operational capabilities are built in from the start.

We also provide native services for capabilities like authentication, eCommerce, payments, and other backend functionality, allowing agencies to rely on platform services rather than AI-generated implementations for some of the most critical parts of an application. That reduces complexity, improves reliability, and significantly limits the technical debt that can accumulate over time.

Our view is that AI should accelerate development without shifting long-term maintenance risk onto agencies. The platform should handle the operational complexity, allowing agencies to focus on building applications that deliver value for their clients.

The ability to create custom widgets opens the door to creativity. What tips would you give agencies that want to use this feature to create ‘signature’ widgets to differentiate themselves from competitors?

Clients generally don’t know what their agency’s website builder can and can’t do out of the box, so it’s important for agencies to not get too caught up in individual widgets.

What makes a great website is how all of those widgets come together on the canvas and how well that collage of widgets meets the needs of the client. A great way to differentiate is to focus on the latter: the client’s needs.

For example, one agency building on Duda used the Widget Builder to generate an interactive carousel of 3D beer cans for a brewery. Not only did this look great, but it really elevated the overall design of the website. It was a nice touch that showed a level of care that I’m sure the client appreciated.

For agencies looking to stand out, I’d recommend double-clicking into the needs of the client. Breweries are in the hospitality industry, so a level of showmanship is beneficial online, but that same widget showing a rotating carousel of 3D toothbrushes would be odd to see on a dentist’s website. Understanding your client’s industry, then building widgets specifically for that vertical, is a great way to stand out and showcase expertise.

What do you think the biggest challenge will be for agencies when it comes to using AI over the next 12 months? How do you recommend agencies overcome these challenges? 

Agencies are being impacted by AI from dozens of different angles. Whether that’s changes to the way they work or the rise of AI search impacting how their clients get found, it’s an extremely adaptive and exciting time to be an agency. Staying ahead of it all is going to be the biggest challenge.

Take website building, for instance. The floor is going to continue to rise. Agencies need to be able to produce something better than if an owner asked Claude to create a website for their business—which is not as easy as it sounds.

Platforms like Claude are getting better each and every day. Whether that’s a better experience, a better product, or both, agencies need to be prepared to add something special that AI can’t offer by itself.

As AI demand accelerates infrastructure growth, the binding constraint is shifting from technical capacity to local planning risk.

That risk is inseparable from how well developers treat the people living next to what they build.

Angela Rayner approved a hyperscale data center at Woodlands Park in Buckinghamshire in July 2025, overturning a local council that had rejected it twice.

Six months on, the government's own lawyers admitted the approval rested on a "serious logical error," because the planning inspector had waived a full Environmental Impact Assessment and several promised mitigation measures were never secured.

Whatever the legal challenge decides, the lesson is already visible. This wasn't an engineering failure or a financing failure. It was a planning failure, and it's the kind data center developers are going to keep running into.

Capacity, capital, and political appetite for AI infrastructure aren't the constraint anymore. Getting a shovel in the ground without a council reversing course, or a court quashing the decision months later, increasingly is.

How fast this is moving

Ofgem's own figures put a number on how fast this is moving. Around 140 data centers in the UK are seeking grid connections worth roughly 50 gigawatts, more than the country's winter peak demand of 45 gigawatts.

In Japan, residents in Inzai and the neighboring city of Shiroi have filed lawsuits over facilities built meters from their homes, arguing nobody told them what was coming until construction had already started.

In Australia, opposition has grown fast enough that state parliaments are now holding inquiries into how planning approvals were reached. These are planning system failures, and they’re arriving faster than the systems built to handle them were designed for.

That's not the same as saying developers are acting in bad faith. Grid connection queues in parts of Japan now stretch five to ten years, and several UK regions aren't far behind. Somewhere in that queue right now is a developer with a signed lease and a hired crew, with nothing to build on for years.

Capital gets committed against a five-year AI roadmap and then sits stalled behind infrastructure queues that predate the AI boom by decades. There's a real competitive cost too: a country that builds slower loses the workloads, jobs, and tax revenue to one that doesn't.

Understanding that pressure matters, because it's a big part of why planning gets treated as an obstacle to clear quickly rather than a process to build around.

Local resistance

None of that makes local resistance irrational. In June, the US energy secretary Chris Wright told data center backers at an industry conference to push back harder against critics, calling concerns about water and power use "overblown."

Diesel backup generators running test cycles at odd hours, or a permanent hum next to a family home, aren't things people object to for the sake of it.

Water is the sharpest version of the problem: Thames Water estimates a single large data center can draw up to 19 million liters a day, and the UN's Special Rapporteur on the human right to water has called for a moratorium on data center expansion over exactly this kind of pressure.

A separate scheme at Wapseys Wood in Buckinghamshire would run entirely on gas turbines next to two primary schools and a hospital. National strategy and local reality are being asked to occupy the same site, and only one of them has to live there.

Most of the friction traces back to sequencing. Consultation tends to start after a site is chosen and a design is fixed, which means residents are reacting to decisions instead of shaping them, and that timing alone tells a community what its input is worth. Woodlands Park already showed what that looks like. An environmental assessment waived. Mitigation promised and never secured.

Reporting thin enough that it took a legal letter to force the numbers into the open. Local benefit gets described in broad terms, jobs and regional investment, rather than anything a resident could actually measure or hold anyone to. Construction disruption is consistently underestimated in planning submissions and then lived with daily by people who never asked for a data center next door.

Planning systems increasingly react to disputes rather than shape outcomes before they harden. That's especially true where fast-track routes and national growth zones have been layered on top of existing local plans.

The actual fix

The actual fix is reordering it, not slowing everything down. Ireland's grid connection regime, introduced in late 2025, requires new data center developments to build their own generation or storage capacity and hit renewable thresholds near 80 per cent over time, rather than draw on shared grid capacity for free.

That forces developers to carry a cost most markets still push onto residents and ratepayers. Local benefit needs the same treatment: a fixed share of revenue or tax contribution routed to local government on a schedule, not a discretionary grant pot handed over once objections have already surfaced.

Engagement has to start before a planning application is drafted, not after, so siting and noise mitigation can actually be shaped by what residents raise rather than defended against it. And liaison can't end when construction does. A facility runs for decades inside a community, and a named contact five years after opening matters more than one consultation event before it.

Data centers are core national infrastructure now, not a niche technical build on an industrial estate. But infrastructure that treats the people living around it as an afterthought doesn't get delivered on schedule, whatever the national strategy document says. Local legitimacy sits alongside grid capacity and water availability as a delivery constraint in its own right, not somewhere beneath them.

Developers who design for that early will move through planning faster and face less resistance once they're running. The ones who don't will keep finding out, one quashed permission and one lawsuit at a time, that a planning problem left alone doesn't become a technology problem.

It becomes a more expensive planning problem with worse PR.

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The AI race is usually framed as a contest for chips, models and talent. Increasingly, it is being decided by something more basic: power.

The International Energy Agency (IEA) projects that electricity demand from data centers will double by 2030.

At the same time, many grids are already under strain from increased electrification, ageing infrastructure and long permitting cycles.

This makes the ability to secure reliable electricity on a timeline that matches business plans a decisive factor in choosing where to build an AI data center.

Time to power is becoming a key constraint

That is why “time to power” is quickly becoming the key metric in site selection.

Schneider Electric’s work in this space shows that in multiple regions, grid connection approvals and reinforcement timelines that were once manageable now stretch far beyond normal development cycles.

In energy-constrained hotspots, the process to connect to the grid can extend for years, and in some cases can reach a decade or more. For large AI data center projects, that changes where data center operators build and how they build.

The issue is not the lack of power. The real challenge is that data centers concentrate in specific regions, and those local grids hit capacity limits faster than new infrastructure can be planned, approved and built. When that happens, power availability starts to overtake “location” as the deciding factor.

Transparency is key to attracting investment and ensuring resilience

Power availability also influences where companies are investing when it comes to building AI data centers as it is key to resilience. The most attractive markets are those that offer certainty and transparent information on available grid capacity and reinforcement plans.

Just as importantly, data center operators need confidence that grid connection queues are managed in a way that prioritizes projects that are ready to build, rather than allowing speculative projects to sit in line for years and block capacity.

Resilience has always been fundamental to data centers, but it’s now being redefined. It is no longer only about redundancy inside the facility; it is equally about resilience to the grid environment around it. In many energy-constrained regions, shrinking grid capacity and congestion increase the risk of outages and emergency grid measures.

Hybrid power generation strategies offer an alternative to legacy grid dependence

These pressures are driving many data center developers and owners to consider on-site hybrid energy strategies which combine on-site power generation from renewables with electricity provided by the grid.

Such hybrid power approaches are key for reducing reliance on legacy grid infrastructure and enabling data center operators to generate energy on-site and improve resilience. This also increases energy flexibility by allowing data center operators to change when and how energy is used, stored, or generated in response to demand, prices, or grid conditions.

A concept that is getting more attention in this space is “energy parks”. In simple terms, an energy park brings together multiple power resources, from renewables, thermal power, and storage to on-site generation, and connects them to the grid through a single point. These energy parks may connect to the grid through a single interconnection point, operate as fully islanded systems, or combine both approaches through hybrid architectures.

Because the generation, storage and grid connection are designed together, energy parks can offer a faster or more predictable “time to power” in places where the traditional grid connection queue is a challenge. This could help alleviate the grid connection issue for many data center developers.

A new approach to designing data centers

This is where energy strategy becomes a design strategy. Developers need to plan across three key areas: how to connect, how to operate, and how to scale.

Connecting to the grid is now about getting the earliest possible clarity and avoiding prolonged delays. Operating is about building for an environment where grid constraints, curtailment risk and price shocks can impact business continuity. Scaling is about making sure a site that works at 50 MW does not become constrained at 150 MW because the upstream network cannot grow with the campus.

But none of this can be separated from sustainability. Sustainable AI infrastructure at scale requires electrification strategy and grid modernization: faster grid reinforcement, better planning, and more flexibility on both the supply and demand sides. It also requires closer coordination between developers, grid operators, policymakers and clean energy providers. This will help ensure that digital infrastructure growth is aligned with clean capacity additions and realistic delivery timelines.

For data center developers, the practical takeaway is straightforward: start with power and treat energy as a critical design variable. Build a site selection process that tests grid readiness and timelines before land is locked in, and design an energy strategy that covers connection, resilience and scale from day one.

When it comes to attracting AI infrastructure investment, the winners will be those that can offer speed and certainty on grid connection and reinforcement, and enable credible pathways to clean, resilient power.

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I asked ChatGPT for one sensible answer, one slightly reckless answer, and a third that combined the best parts of both. The difference was immediate. Instead of giving me the usual safe, predictable advice, it laid out three genuinely distinct approaches — and helped me see the tradeoffs between them.

That small prompt trick solves one of ChatGPT’s most persistent problems: its tendency to give you the most obvious reasonable answer and stop there.

Large language models are very good at identifying common patterns. Ask for the best vacation plan or how to organize your fridge, and you will usually get something perfectly sensible — but also fairly basic. They know what advice usually works, what people typically recommend and what has become accepted wisdom. That makes them useful, but often bland.

The solution is to ask for three kinds of answer: the conventional approach, the unconventional approach, and then a hybrid that combines the strengths of both.

The exercise encourages it to compare different approaches instead of treating the first reasonable answer as the finish line. Better still, it gives you something far more valuable than a single recommendation. It gives you a range of possibilities and explains the tradeoffs between them.

Don't just skip to the finish line

The biggest advantage of this technique is that it makes the conversation about exploring alternatives rather than just picking a winner. Any keyword search can give immediate answers, but AI chatbots are more interesting when laying out competing ideas.

Imagine you are planning a weekend trip, often a go-to experiment. A standard prompt might produce a sensible itinerary filled with the highest-rated attractions. The three-solution prompt, meanwhile, begins with the expected museums and restaurants, then suggests renting bicycles to explore overlooked neighborhoods or planning the entire weekend around independent bookstores and local festivals. The hybrid version could blend a couple of famous attractions with enough unusual stops to make the trip feel personal.

The explanations are often as useful as the answer itself. Once you understand why ChatGPT prefers each option, you can make better decisions rather than simply accepting the recommendation with the nicest wording.

Hybrid conventionality

It's also a good prompt for iterating ideas. If the hybrid solution feels close but not quite right, you can ask ChatGPT to repeat the exercise using that version as the new starting point. After two or three rounds, the ideas often become noticeably more distinctive without drifting into complete nonsense.

The trick also scales well from short projects at home to more grandiose schemes that will take months to complete. Almost any situation that benefits from weighing different approaches can benefit from this style of prompt.

You can improve the results even further by giving ChatGPT a little context before asking for the three versions. A conventional answer built around your actual circumstances is much more useful than a generic one, and the unconventional suggestion becomes more interesting because it has meaningful boundaries to push against.

None of this guarantees a brilliant idea every time. Sometimes the unconventional option is genuinely impractical, and occasionally the hybrid answer feels like an awkward compromise. But the best prompts rarely force ChatGPT to mimic greater intelligence as much as encourage different approaches to problems. Asking for the conventional solution, the unconventional solution, and the best combination of both is a simple habit for more thoughtful conversations with AI chatbots.

The problem with online shopping is that it makes it far too easy to buy pretty much anything, without properly considering whether you really need it. I started to wonder if ChatGPT could help me make better decisions, by questioning every purchase I was going to make, before I made it.

To test ChatGPT’s ability to stop me wasting money, I picked four tempting purchases and asked ChatGPT to argue against each one. It had to check the price history, suggest cheaper alternatives, and decide whether the purchase solved a real problem, or if I was just scratching an itch to buy.

Here's how ChatGPT did — and the results surprised me.

GPT before you buy

I started with a tech gadget I was thinking of buying — an Apple MacBook M5 — and went to ChatGPT with the following prompt: “I'm thinking of buying an Apple MacBook Air 13-inch Laptop M5 chip. Help me check the price history, suggest cheaper alternatives and decide whether the purchase solves a real problem."

ChatGPT said it would check current pricing and recent lows, compare genuinely cheaper options, then pressure-test whether the purchase replaced a real limitation, or was mostly me trying to satisfy an upgrade itch, and off it went.

After a lot of thinking, ChatGPT replied with a devastating verdict that made me pull back from hitting the 'Buy' button: “Don’t buy the M5 MacBook Air yet.”

It suggested I get the previous M4 version instead, which is considerably cheaper, which didn't hugely surprise me. Then it gave me some questions to ask myself, in order to work out if buying a new MacBook was actually solving a real problem, or if I was just satisfying a buying itch.

Questions like, “Does your existing Mac have noticeable slowdowns?” It suggested I shouldn’t buy a new machine if my main argument was simply that my current Mac was a few generations old.bChatGPT also gave me some buying options if I was determined to go through with a purchase.

The most useful advice here wasn’t the cheaper recommendation — it was being forced to identify the exact limitation my current MacBook was causing me. I couldn’t, so I decided to keep it until I could.

Then I hit on a simple phrase that could potentially work even better — "Try to talk me out of it".

Talk me out of it

I repeated the experiment with a few other items I’d been thinking of buying recently. An item of clothing (new trainers), a subscription (Disney+), and a kitchen thing (a new microwave). But this time I added "Try to talk me out of it" at the end of each prompt.

In each case, Chat surprised me with its answer, provided alternatives, and made me question whether I genuinely wanted the item, or if there was something else driving my decision.

With the trainers, it pointed out that I already owned shoes that served the same purpose, and suggested waiting until those wore out. For Disney+, it recommended subscribing for a single month when there were several things I actually wanted to watch. The microwave was different — because our existing one had a genuine fault, ChatGPT concluded that replacing it was justified.

The experiment worked because it helped me mentally reframe each purchase. Instead of asking whether I wanted something, I had to consider what problem it solved, what I already owned, and whether there was a cheaper way to get the same result.

Online shopping is designed to remove as much friction as possible from spending money. ChatGPT gave me some of that friction back. So now, whenever I’m about to spend a few hundred pounds, or a few thousand, I ask it the same question: “Talk me out of it.”

Recent conversations with clients, owner-operators, investors, contractors and technology partners across the global data center market have given a clear picture of where the sector is heading. The same themes keep coming up: AI, high-density compute, power constraints, liquid cooling, resilience, speed to market and operational performance.

Capacity demand is still strong, and that is clear from the level of activity and investment across the market. The question now is what happens after capacity is secured, and whether owner-operators have enough visibility to use it properly.

Businesses are asking more detailed questions about how their assets will perform, how much capacity they can use safely and how much freedom they will have to adapt as customer requirements change.

Three takeaways stand out.

1. Capacity is becoming an operational question

The data center industry has spent much of the last decade focused on scale - larger sites, faster delivery, more power and higher levels of availability. Those requirements haven’t disappeared, but the operating environment has become more demanding.

Securing capacity is no longer only a development question. Owner-operators also need to know how a facility will behave in operation, how much of that capacity can be used safely and where constraints are likely to appear across load, cooling, controls and system performance.

That becomes more difficult when projects are still conceived as separate engineering packages. The building, power architecture, cooling strategy, controls, telemetry and operating model all need to work together, yet valuable data often sits across different platforms, vendors, formats and levels of access. The result can be an asset that meets the design brief but is harder for the operator to understand and adapt once live.

For owner-operators, this creates a long-term issue. They need confidence not only in the design, but in the data and systems that show how the facility is performing. They also need to know where constraints are emerging and how future demand can be accommodated without compromising or restricting future choices.

Land, power and water still dominate data center development, but they do not show how much usable capacity a facility really has once it is running. That comes from visibility into performance and constraints. Without it, operators can run too cautiously, leave capacity unused or make decisions later than they should.

This is why intelligence and insight are becoming the new data center currency. They help operators understand where capacity exists, where the limits are, and what they can safely offer commercially.

2. Keeping control of data, systems and future decisions

Owner-operators are now giving more attention to their operational data and future technology choices. This is not just about the platform they use, but who controls the information needed to run the asset, who can access it, how it’s structured and whether it supports changes later in the life of the facility.

Vendor platforms have an important role to play, but owner-operators also need to protect their ability to access, structure and use their own operational data. If data is difficult to extract, poorly structured or locked into a proprietary environment, it can limit future decisions and make it harder to compare performance, bring in new tools or respond to changes in workload.

That becomes more important as AI demand develops. The sector is still learning what long-term AI infrastructure requirements will look like, with density profiles, cooling strategies, redundancy models and customer expectations still changing. Operators need architectures that leave them room to adapt, rather than forcing decisions that narrow their choices too early.

If they can’t access and use their own operational data, it becomes harder to understand performance, compare different approaches or adjust the operating model as workloads develop. Data ownership, access and structure need to be considered much earlier, rather than left for the operations team after handover.

3. Operational intelligence must influence design and delivery

Operational intelligence can’t be treated as something separate from the design and delivery process. If owner-operators need better visibility once a facility is live, they must make the decisions that make that possible much earlier.

That includes the controls strategy, the way telemetry is captured, how data is structured and how different systems will share information in operation. These decisions can seem secondary during a build program, particularly when speed to market is under pressure, but they influence what the operator can see, understand and change later.

This is where the link between design and operation becomes more important. A facility can meet the brief at handover and still be difficult to manage if the operational model hasn’t been thought through properly. Operators need assets that they can monitor, adjust, and improve as workload demands change, without reducing future flexibility or limiting future operational choices.

Demand for new space and power is still strong, and that will continue to drive the sector. The next question is how that capacity is understood, controlled and used once it is built. That is why operational intelligence is now part of the capacity conversation.

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Mainframes continue to underpin a vast share of the global economy, handling roughly 70% of the world’s core transactions.

The idea that AI-powered code translation will enable enterprises to modernize the platform or even move away from these systems seemed an innovative approach—but it’s fundamentally flawed.

Translating code is not the same as modernizing a business-critical platform and conflating the two risks underestimating their complexity and destabilizing the systems businesses still depend on most.

Rather than rendering the mainframe obsolete, AI, cyber resilience, and hybrid cloud are fundamentally changing how it fits into the enterprise.

The digital backbone quietly powering critical industries

These systems sit at the heart of the world’s most critical industries - from banking and insurance to government and transportation – where performance, security, and resilience are non-negotiable.

Far from being relics of a past era, they function as foundational IT infrastructure: deeply embedded, continuously operating, and shaped by decades of accumulated business logic that cannot simply be rewritten without risk of disruption.

This is why, according to IBM’s Institute for Business Value, 75% of organizations still expect mainframe applications to remain central to their digital transformation, while 60% see them as essential to enabling AI.

Therefore, it’s about evolving what already works at global scale.

What is often overlooked is how significantly the platform has already changed. Today’s mainframe is no longer an isolated environment, but an integrated part of modern enterprise architecture – one that connects seamlessly with cloud platforms, AI and supports developers working across systems with consistent tools and practices.

Ultimately, driving innovation into the transactional world.

Where transformation is really happening

Modernization is not a question of programming languages, but of resilience, efficiency, and future readiness. As such, it has moved beyond the CIO agenda and into the wider C-suite conversation.

At the center of this shift is a fundamental rethink of how organizations work with their most valuable asset: data.

Rather than extracting data from the mainframe, many enterprises are bringing applications and innovation closer to where that data already resides. This “data proximity” model allows teams to work directly with accurate, real-time information, without duplicating datasets or introducing additional risk, cost, and complexity.

At the same time, modern integration approaches ensure that sensitive data remains tightly governed and controlled - accessible where needed, but never indiscriminately exposed across environments. In an era of increasing regulatory scrutiny, this balance between accessibility and control is becoming a decisive advantage.

AI isn’t replacing core systems - it’s extending and embracing them

AI is becoming a powerful accelerator of this transformation, helping organizations unlock more value from their core systems. It enables faster decisions, real-time insights, and new digital services built on trusted enterprise data.

A good example comes from the banking sector, where a leading player’s modernization efforts are helping support advanced fraud detection capabilities by analysing 100% of transactions in real time. AI is integrated into the core platform, improving performance and decision-making where the data lives, while providing a robust foundation to scale workloads, productivity, and innovation.

It is also reshaping the developer and engineering experience. Tasks that once required deep, specialized knowledge - such as working within COBOL-based applications - are becoming more accessible. This enables a new generation of developers to work with mainframe systems as naturally as they do with other enterprise platforms.

Crucially, organizations are combining the core strengths of the mainframe with a diverse set of AI models including smaller, more efficient models trained on their own enterprise data. The ability to choose the right model for each use case delivers a more secure, scalable, and effective experience.

Security moves to the boardroom

As cyber threats, operational risks, and regulatory pressures intensify, resilience is no longer just an IT concern, it is a board-level priority.

Against that backdrop, mainframes remain central to enterprise security strategies. They underpin encryption, secure transaction processing, ransomware resilience, and regulatory compliance, providing a trusted foundation for critical operations.

Increasingly, they are also being used to prepare for emerging threats: For example, incorporating digital certificates to better handle access in an agentic world, anticipating possible fraud in instant payments, and getting ready for a post-quantum security landscape, where sensitive data intercepted today could be decrypted in the future as computing capabilities advance.

Hybrid cloud reshapes the role of the mainframe

The mainframe no longer operates in isolation, but as part of a hybrid cloud architecture.

Through APIs and modern integration, organizations are enabling mainframes to participate fully in enterprise-wide digital ecosystems - combining the flexibility of cloud environments with the reliability, performance, and governance required for mission-critical workloads.

This is not about migration for its own sake, but about interoperability and architectural balance. For example, one of Europe's largest financial technology providers is using hybrid cloud capabilities within their own data centers to support hundreds of financial institutions, strengthening resilience, digital sovereignty, and scalability at national scale.

This reflects how critical systems are being designed so that connectivity and control are no longer opposing goals.

Modernization with intent

The mainframe continues to evolve alongside new environments and operational demands, extending its role beyond the traditional back office and allowing broader workload consolidation.

In the transport sector, for example, infrastructure modernization has improved a logistics hub’s operational performance while reducing CO₂ emissions by hundreds of metric tons, demonstrating how established systems can support both efficiency and sustainability goals.

Taken together, these developments challenge the idea that modernization means replacement. In practice, it is about keeping critical systems relevant as business needs change.

That matters because modernization is not a one-off program. Instead, it is an ongoing discipline shaped by operational priorities and supported by technology evolution.

The organizations doing this best are not focused on exit strategies. They focus on evolution - preserving resilience at the core while enabling innovation around it. Because when the digital core is trusted and stable, everything built on top of it can move faster.

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Racing to scale AI requires massive capital investment in data centers, with McKinsey recently estimating that global data center spending could reach $7 trillion by 2030. Perhaps more importantly, though, scaling AI requires massive architectural investment from a technology standpoint.

Modern data center infrastructure was not designed to power the cloud computing revolution and the massive surge in AI usage and development on a global scale simultaneously.

This architectural strain is driven by rising expectations – the more AI is adopted, the more demand of it there is. Enterprises are excited by AI’s promise as a time-saver that can automate and streamline employee workflows and increase productivity. To facilitate the growing complexity of AI models and datasets, GPU density and bandwidth have increased in data centers.

These increases mean AI models that can be trained faster, have lower latency interference and more. As these chips and data centers continue to increase the amount of data transferred between chips, though, cooling efforts are also needed to make sure the chips and data centers do not overheat.

As a result, while today’s data centers continue to power the AI boom, their operational runway is quickly shortening. Powering AI and the growing demand for AI capabilities is incredibly resource intensive. The technology relies heavily on electrons for computational capabilities and for moving data between chips in larger AI models. This requires vast amounts of electricity, water, rare materials, and overall technological innovation.

As operational bottlenecks persist, silicon photonics has proven an underappreciated solution to help further advance AI. Using light combination with electrons, silicon photonics enables faster, more energy-efficient data transmission. These abilities can both advance AI and scale GPU clusters and AI data centers.

The SiPh adoption delay

Photonics have been around for decades, spanning more than 40 years of research and innovation. In the 1980s, researchers realized the technology's potential due to its ability to allow the integration of photonics and electronics on a single chip.

Where before, photonic and electronic components lived on two separate chips that had to be combined, silicon photonics made it possible for both components to be functional on one single chip, resulting in increased speed, bandwidth, energy efficiency and more.

Today, silicon photonics are integral to the commercial production of communications transceivers in data centers. Fiber-optic communication predates the current AI wave by an entire generation, as does the basic principle of transmitting data as light rather than an electrical current.

The challenge of leveraging this technology in a broader setting, however, has always been its manufacturability and the need for optical components to be small and cheap enough to compete with legacy conventional solutions.

Complementary metal-oxide-semiconductor (CMOS) compatibility makes this possible.

CMOS

Silicon Photonics-based circuits converts electrical signals into optical ones, transmits them as infrared light pulses through microscopic waveguides etched into silicon, and converts them back to electrical signals at the destination.

CMOS platform unlocks efficient and high-scale integration of passive and active devices onto Photonics Integrated Circuits (PICs), used to make optical transceivers. The physics of light propagation delivers far lower loss than copper interconnects do at the data rates modern AI workloads demand.

With CMOS, silicon photonics can be fabricated using the same processes semiconductor fabs use for conventional chips. This process allows optical and electronic components to coexist on a single silicon die, with limited use of rare materials or separate production lines, enabling scalable production.

Discussions of silicon photonics tend to focus on the transceiver or circuit design. While these conversations are essential, the substrate is equally important, delivering bandwidth and energy efficiency. Leveraging substrates to help meet growing data demands has been shown to decrease energy consumption by 30-50% on average.

Engineered substrates are silicon-on-insulator (SOI) platforms designed for photonic or high-frequency applications that support a range of markets, including mobile communications, edge AI, cloud AI, Internet of Things and data centers. With the latter, there are a variety of engineered substrates that improve different performance metrics, including Photonics-SOI, PD-SOI and RF-SOI.

Substrate selection is foundational to silicon photonics performance, not incidental to it. The choices made early in the design process determine whether a photonic chip delivers its theoretical efficiency and performance or falls short in deployment.

Scaling data centers requires scaling silicon photonics—now

The semiconductor industry must accelerate silicon photonics production to keep pace with the growth of demand for tomorrow’s data centers.

The technology works. The physics is proven. The manufacturing pathway exists. What's missing is widespread industry alignment and investment to produce silicon photonics at the pace AI infrastructure will require.

Coordinated investment in silicon photonics must happen across chipmakers, substrate suppliers, packaging specialists, and hyperscalers whose data centers will ultimately run on this technology. That coordination is currently happening in pockets, and it needs to be prioritized — the AI buildout is not slowing down.

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Things music-lovers enjoy about Qobuz: it was the original hi-res music streaming service and it also offers 24-bit hi-res downloads on demand (ie., no need to subscribe to buy) alongside a regular streaming model. Then, there's the digital Qobuz Magazine, human-first playlists over algorithms, a more ethical payment at significantly higher average per-stream royalties than mainstream competitors, the fact that it refuses to run a free ad-supported tier that devalues music, it continues to transparently publish its financial payout data, it recently added in-track lyrics… I could go on.

But I'm not here to talk to Managing Director Dan Mackta about any of those aspects of Qobuz today, admirable though they are. No, I want to talk to him about generative AI, and Qobuz's strong stance on it, as set out in its February-release AI charter.

If you haven't read the document, the following quote may be enough for now. It reads as follows.

Dan Mackta has made time to speak to me from a hotel room in Connecticut, at 9:30am local time, before setting off on the 100-or-so miles to Rhode Island and the Newport Folk Festival.

Anyone who speaks to Mackta will find a music lover from the Big Apple first and foremost — a businessman not afraid to talk openly and honestly about an industry he knows and loves deeply.

Right now, Mackta is hoping the rumors are true and that Lauryn Hill will indeed make a rare appearance to play a set (she did), adding with no small amount of excitement, "I've wanted to go my whole life, and this is the year it's finally happening".

Ready or Not

Yes, all of these sub-heads are now going to be Lauryn Hill tracks, because I'm jealous of Mackta's upcoming weekend of live music. I ask Mackta how long Qobuz has been concerned with AI-generated audio infiltrating the platform's otherwise notably human-centric offering. It's clear he's not a fan.

"It was a couple of years ago that we noticed the volume of AI content, fake music, whatever you want to call it. It was a problem from the beginning because of the volume of stuff that was being delivered.

"There was already a fraud problem in music streaming — basically content uploaded just for the purposes of fraudulent streaming, money laundering, organized crime and all of that crazy stuff — but generative AI allowed scammers to create this stuff at crazy scale. They do it to hide their tracks; create so much stuff that nobody can even really tell what the heck is going on. So we knew it was a problem.

"We also knew that this content did not fit into our vision for what our music service should be focusing on. So early on, the guys in France — because all of our tech is in France — looked at a few detection solutions that were on the market at the time, about two years ago, and decided to create their own detection methodology.

"So, we started analyzing. The thing is that the analysis takes a long time, so it's being rolled out in stages. All new releases are now analyzed."

Mackta is also refreshingly open about where the company's at with the rollout. "The actual tagging piece of it, which is where we notify the user if something has been detected as AI, hasn't rolled out yet, although it's planned for September. We shall sell no wine before its time!"

DDEX (That Thing)

I ask Mackta about the relevance (or effectiveness) of voluntary AI self-identification as a solution to the problem of 100% AI tracks — the 'Hey, here's an AI content badge; we've created it and given it to labels and distributors, so they'll use it' approach, because it's one we've seen in the form of 'Transparency Tags' on rival sites. Mackta is quick to clarify that actually, it is a new development and does have some value.

"I mean, the DDEX, (Digital Data Exchange, which is the metadata standards-setting organization upon which the music industry is run) recently added some fields to its structure. But it's basically voluntary for the person setting up the music for release.

"They fill in these fields in the DDEX. So it's 'you' and 'title' and the ISRC (International Standard Recording Code — a 12-character unique digital fingerprint that identifies a specific sound recording or music video) and UPC (Universal Product Code, which is similar but for the whole product) and other areas for credits. It's a whole elaborate standard.

"So, they added fields for AI where you're supposed to say if something is 100% generated by AI or if the lyrics were AI-generated.

"Now, some of the DSPs (Digital Service Providers, ie., rival sites) said, ‘All right, we’re starting to get this information from our suppliers, so we're going to surface it in a way that the users can see’ — and we'll have that too; we get the same information. But obviously, big problem: why would a scammer admit? If it's voluntary, it's a partial solution."

The Miseducation

Mackta is keen to impress upon me that while detection is a big part of the issue, it's not just about AI detection when it comes to Qobuz — or for the wider music industry. The company has thought deeply about what it's going to do with this content. "That's why the AI charter was a very comprehensive doc; it wasn't just about AI content", Mackta confirms.

What is the bigger picture? "It was about our philosophy as a company about AI and as a tool in our business — our everything. When we're going to use it and when we're not, and transparency around that. So that was about more than just the AI content."

Mackta sighs. He's ready to pin his colors to the mast. "You'll notice I do not call it 'AI music' because I don't think it's music. I think it's a simulacrum of music to get, you know, critical theory. But it's… it ain't music."

Everything is Everything

I want to know more about the different types of machine learning in music we accept, versus generative AI audio for fraudulent use.

Mackta is incredibly open about the versions of machine learning Qobuz does use. "Listen: there are a lot of tools in music creation workflow that have been around for a long time that are essentially versions of AI. But it's very different when you're using creative tools as a musician versus a scammer saying, 'Give me 10,000 things that sound like music that I can upload, for fraud.'

"Our number one most requested feature was autoplay. Right? When your cue ends, the Qobuz user wants the music to keep playing. So, that's machine learning! That's AI that looks at what you were listening to, and runs an algorithm to figure out what it should play next.

"So, we have that. Users wanted that. We have a few playlists now that are algorithmically generated based on your listening and your favorites. But that's like two clearly marked playlists, and if you don't want that, you never have to look at it.

"The thing is that some people — especially as Qobuz has been getting thankfully bigger and starting to poke into the mainstream — those people are used to those kinds of features and they don't realize it’s AI. We'll always make it clear what's been selected by humans versus what hasn't."

Lost Ones

I posit the idea that some have suggested — that AI is here, it's not going away, and there's an argument for not even trying to stem the slop. Mackta laughs, but he gets it. "I mean, let's be real. We're doomed! But let’s also try to hold it at bay, however we can.

"To me, the whole thing is just a really, really big bummer. You know, it just is so different from ‘why am I in the music business’? Why, really though, do I do this? Having to deal with this issue on top of all the other issues that were already there in music just makes me really sad, and so much time and energy and resources wasted.

"It’s largely an unintended consequence of the streaming business model, with the way revenues are shared; it's always been a target for fraudsters. It's like if every man, woman, child, and dog released three albums last year on the planet, it still wouldn't be as much music as the fake music that’s being shoved down our pipes."

Ex-Factor

I muse that perhaps as long as it's human beings making content for the emotional appreciation of other human beings (which is what Deezer's head of research recently offered, when I interviewed him), perhaps we'll be OK? Mackta is quick to jump in. "Well, and that's what music is! It's the whole thing that makes it all go!

"It's unique. It's a form of communication that taps directly into emotions of humans. So we can take humans out on either side of that equation — it ain't music anymore. What makes music music? You know, if you ask those questions, I think it all becomes pretty clear that this AI stuff may sound like music, but…"

Quite right. So what's the future for Qobuz? And can he speak to the music industry at large? Mackta is less certain, but he definitely isn't keen on one possible development.

"It's not really clear what's going to happen with this generative AI stuff. I thought the music industry would do its usual thing and try to stamp it out. But instead, now these companies are basically licensed by the major labels; if everybody's getting rich off of it, they're not going to try to stamp it out.

"But I do think, in terms of the fraud piece (of the puzzle) and the massive amounts of content that's just being generated purely for the purposes of fraud, the industry is going to come to some kind of resolution about it, because it's just not kosher.

"I don't know exactly what it's going to look like, but Qobuz is part of a trade group, the Music Fights Fraud Alliance (along with other DSPs and distributors and aggregators), and we're all trying to come together — the way the banking industry has come together to share certain information to be able to stop fraud. It's kind of like herding cats."

All Falls Down

And there's another 'what if?' Mackta wants to share. "At Qobuz, we sometimes talk about, like, what if AI fake music becomes something that most people want to listen to, you know? What if that's what's considered music in 10 years? Are we still going to have our philosophy? I would say I hope so. I hope that we stick to our guns and the stuff that makes music special is not going to change.

"And I do think that 90-whatever-percent of people probably could be played AI content and be told it's music, and they wouldn't know. They wouldn't care! And that's a shame. But that's reality.

"However, for people who are just even a little bit into music, I don't think we're going in that direction. I think, if anything, there'll be a backlash coming soon that really values human curation, human creation, and actual artistry and emotion and communication. You know, you just can't fake it."

I tell Mackta that I agree, and that as an occasional performer myself I really hope he keeps up the good fight. He laughs. "Yeah, I mean, if anything, it's gonna force us to dig in even more on these principles. And from a business standpoint, I think that's just fine. I think that success for us is going to be in how we differentiate and stay focused on what we think is important. And so far, so good!"

Agreed. As a loyal Qobuz fan myself, I'd go so far as to say it's a case of so far, so very good.

Last year, I brought on a new specialist in search engine optimization (SEO) to drive backlinks and business, only to find the contractor used black-hat practices to generate toxic, irrelevant results. Worse, the content was AI-written and keyword-stuffed.

I fired him three months later but made the big mistake of not immediately revoking access. Out of revenge, he deindexed 200 product pages and blog posts, causing a 90% drop in traffic and devastating sales.

Unfortunately, I quickly realized that marketing is both a growth engine and an attack surface. This is a cautionary tale about handing over the keys to your business backend and what it takes to rebuild from the brink.

From onboarding to sabotage

In hindsight, there were several red flags. This marketing “professional” talked a big game but relied on outdated techniques that didn’t translate well in the online world. We’re in hemp wellness e-commerce so digital marketing is essential. However, keyword stuffing, AI slop content, and low-quality backlinking can quickly ruin search engine rankings.

It was quickly evident this wasn’t a marketing fit. The contractor scored external media mentions for products we didn’t sell (lying that it would bring more traffic) and focused heavily on blog volume (promising that posting several times a day would break through, when in reality it was off-brand and off-voice). Other times, he bought backlinks in bulk to fake traction, showing us legitimate-looking domains that ultimately did nothing.

I’d seen enough. He was fired in October but, foolishly, retained access. This was my mistake and I paid for it – the contractor retaliated and manually deindexed nearly all of our pages, one by one, thereby telling Google that they no longer exist and not to return them. The fallout was immediate with a 90% traffic drop, 50% sales drop, and the erasure of multiple years of branding and marketing progress.

A site-wide audit and rehabilitation

Untangling the mess was a job in and of itself. Beyond reindexing the website, we didn’t know what else had changed, so we needed to audit everything. Here we found something strange in the backend: a security plugin quietly emailing daily activity to an unknown contact. We still don’t know the what or the why of this tool, but it only further cemented that we’d lost control.

First, we needed to lock the doors, revoke stale logins, and restrict access to those who needed it. Then the fake wins and bad results had to go. After all, backlinks mean nothing if they ignore site health and audience relevance. We disavowed each toxic reference and began repairing the damage to the company’s voice and positioning one link at a time.

We scrubbed poorly written copy, errant mentions of non-existent products, and even image alt text stuffed with keywords. We also removed the incoherent, AI-written blog archive in its entirety. From top to bottom, we needed to rebuild our experience, expertise, authoritativeness, and trustworthiness (EEAT) in both the eyes of search engines and customers.

It took the better part of four months to realign our external and internal communications, and complete the manual, page-by-page reindex. Today, our traffic is back up, though it hasn’t fully returned to its peak. Frustratingly, SEO sabotage is fast but the recovery is slow.

Protect your marketing, protect your business

As a result of this nightmare, I’ve learned several marketing lessons the hard way.

Never overlook access. Onboard with least privilege from day one, offboard as soon as someone’s off the project. Be selective about admin roles and don’t blindly trust newcomers (no matter how well they sell themselves). Likewise, go the extra mile with security whenever someone departs on bad terms.

Review anything they could touch for injected scripts, rogue plugins, or unfamiliar integrations. Malicious code can sit dormant or masquerade as legitimate for months, so the cost of a review is worth it in the long run.

Second, particularly for companies in a trust-sensitive or regulated sector, shortcuts cost twice as much. Backlinks count for naught if they’re irrelevant or inaccurate, and they actually work against you if your company falls into Your Money or Your Life (YMYL). Google more heavily scrutinizes this segment and SEO snake oil salesmen threaten to undermine your positioning faster than they can build it.

Third, embed checks and balances from the get-go. Even before the sabotage, we lost marketing momentum because no one was closely overseeing the contractor. Insource enough knowledge to audit your vendors and demand method transparency from the jump. Anyone selling overnight spikes warrants stricter inspection and guardrails.

This was a wake-up call. Until now, I didn’t truly appreciate how SEO in the wrong hands can be a single point of failure that takes revenue with it. For B2B and B2C companies, search presence is business-critical. I encourage leaders and admins to protect it like the infrastructure it is. No one person with ill intent should be able to dictate your digital success or failure.

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Boardrooms across every sector are running the same play right now: deploy AI faster, automate more, cut operational costs, and call it transformation. The metrics look compelling on paper. Response times drop. Headcount ratios improve. Executives check the "AI strategy" box and move on to the next priority.

But underneath those dashboards, something quieter is happening. Customers are disengaging. Employees are skeptical. Digital adoption is stalling in places where it shouldn't be.

The reason isn't the technology. It's the assumption behind it , that faster, smarter systems automatically create stronger relationships. They don't. And the organizations that recognize this distinction first are the ones pulling ahead.

The gap nobody is measuring

When businesses evaluate AI performance, the dominant lens is operational. Efficiency gains, cost reductions, throughput improvements. These are real and worth measuring. But they capture what the system does , not how people feel about depending on it.

Customers don't evaluate digital systems the way executive dashboards do. They evaluate them through a different set of questions: Can I understand what this system is telling me? Can I challenge it if it seems wrong? Is there a human accountable for this outcome if something goes wrong?

When those questions go unanswered , when AI recommendations feel opaque, impersonal, or impossible to question , trust quietly erodes. An instant automated response feels fast. But if the logic behind it is invisible, the interaction still feels like it came from a machine that doesn't care.

That gap between technical performance and perceived trustworthiness is where many AI investments quietly fail.

Trust is designed, not delivered by default

The organizations genuinely succeeding with AI tools at scale aren't necessarily deploying the most advanced models. They're the ones treating AI as a trust design challenge, not a technology deployment challenge.

This distinction has concrete implications. Trust architecture , the deliberate design of explainable, human-centered systems , requires answering questions that most AI roadmaps don't ask. Can users see how automated decisions are being made? Are there visible layers of human accountability when the system gets it wrong? Does the system behave consistently enough to be predictable?

Building explainability into AI systems from the start isn't just an ethical position. It's a retention strategy. When customers understand why a recommendation was made , even at a high level , they engage differently. When employees can see and override AI-generated outputs, skepticism converts into productive collaboration. Accountability structures, made visible, become competitive differentiators.

Accessibility reveals who you're actually building for

There's a second dimension of AI trust that organizations consistently underestimate: accessibility.

Most companies still treat accessibility as a compliance checkpoint , something reviewed near the end of product development, checked against a regulatory standard, and filed away. In practice, this approach produces digital environments that technically pass audits but fail real users at critical moments.

AI-powered systems are now embedded in every touchpoint of the customer and employee experience: portals, mobile apps, onboarding flows, support interfaces, communication platforms.

When these systems lack adaptive navigation, voice compatibility, screen-reader support, or simplified cognitive pathways, businesses aren't excluding a niche user segment. They're reducing the overall reliability and usability of the entire environment, for everyone.

Accessibility built into architecture from the beginning, rather than layered on afterward, consistently outperforms the bolt-on approach on the metrics that matter: customer satisfaction, retention, and support cost reduction. Inclusive design improves the experience for the majority while specifically serving those who need it most.

The sustainability blind spot

There's a third dimension that rarely surfaces in AI strategy conversations until it becomes an operational problem: sustainability.

Intelligent systems require expanding infrastructure. More storage, heavier compute cycles, continuous data processing, and increasingly complex integration layers. Most enterprise AI growth is happening without equivalent attention to energy efficiency, architectural waste, or long-term infrastructure viability.

This creates a contradiction that's easy to miss in the short term: businesses investing in intelligent futures while building increasingly inefficient digital backbones beneath them. Sustainable architecture , optimized design, reduced redundancy, responsible infrastructure decisions , isn't just an ESG consideration. It's a question of whether AI transformation remains economically viable over time.

What the next generation of AI leaders will need

The leaders who delivered value in the first wave of enterprise AI were rewarded for speed and automation. The leaders succeeding now are being asked to deliver something harder: intelligent ecosystems that people are willing to depend on over the long term.

That dependence can't be manufactured through innovation messaging alone. It has to be earned through design decisions users can actually feel , systems they can trust, interfaces they can use, and governance structures they can see.

Organizations still treating AI as a pure technology deployment challenge are building faster systems. The ones treating it as a trust design challenge are building something more durable: digital relationships that hold under pressure.

That is, ultimately, what businesses compete on.

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UK organizations are ramping up investment in artificial intelligence (AI).

From automating repetitive tasks and powering chatbots to delivering personalized customer experiences, AI is becoming integral to daily business operations.

However, many leaders are still struggling to see a clear return on their investment and are looking towards AI’s next evolution – agentic AI – with trepidation.

This next iteration will see systems operating with a high degree of autonomy – making independent decisions and taking actions with minimal or no human intervention.

However, if enterprises focus exclusively on what’s next, they risk overlooking the essential stepping stone that will make it possible – contextual AI – which underpins whether those autonomous systems can actually work in practice.

Context-aware AI provides the foundation on which agentic AI will be built. While large language models (LLMs) and generative AI have driven the current wave of innovation and automation, contextual AI is emerging as the key differentiator between success and failure of AI implementation.

Correlation vs contextualization

Currently, many AI systems operate primarily on correlation. They identify patterns and statistical relationships with data but often lack a deeper understanding of what those patterns actually mean. Contextual AI takes this a step further by interpreting data within a real-world context, considering factors like user intent, environment and specific timing.

By embedding diverse data within context, AI can move beyond identifying correlations and begin to understand meaning. This leads to insights and outputs which are more accurate, more relevant and better aligned with the actual situation at hand to inform an actionable approach.

For example, an airline using a correlation-based AI model might detect rising system load during peak travel periods and recommend increasing server capacity. While this may help address immediate demand, it doesn’t take into account the broader ecosystem in which airline operating systems function, with factors like regulatory requirements, flight-planning cut-off times, cybersecurity needs and the significant financial impact of even brief downtime.

Contextual AI can interpret these operational realities and make recommendations that reflect real-world constraints and goals. Rather than simply suggesting additional capacity, it can make recommendations like rerouting traffic around known bottlenecks, scheduling updates during ultra-low-risk windows or prioritizing critical functions like dispatch and crew allocation when resources are strained.

The move to contextual AI enables organizations to move from reactive analytics to proactive, high-quality decision making, helping ensure mission-critical operating systems remain stable and available, even when under pressure.

The importance of high-quality data

Contextual AI relies on four key pillars: rich data, intelligent reasoning, real-world awareness and actionable integration. Underpinning each one of these pillars is high-quality data. Without reliable, comprehensive data, contextual AI cannot deliver meaningful outcomes and many enterprises’ attempts to implement more advanced AI systems will likely fall short.

This is why a strong, singular data lakehouse is crucial. Acting as a single source of truth for all AI operations, a lakehouse ensures that data remains accurate, consistent and accessible across the organization.

Unlike traditional data architectures, a lakehouse combines the best of bold worlds. Businesses can benefit from the performance and reliability of data warehouses, which provide fast, scalable analytics, and the flexibility of data lakes, which can store vast amounts of structured and unstructured data. This hybrid model enables organizations to manage data more efficiently, conduct advanced analytics and scale machine learning operations in a cost-effective way.

The availability of high-quality data remains one of the biggest barriers to the adoption of agentic AI. By establishing a robust data foundation, enterprises can unlock the benefits of contextual AI, while also developing the necessary high-quality data for deploying agentic AI. This ensures AI can act reliably, intelligently and in alignment with real-world business goals.

Creating the right foundation

As organizations race to demonstrate AI’s ROI, many are rushing toward agentic AI without first laying the right foundations. Without contextual AI and the high-quality data it is built on, agentic AI efforts are unlikely to ever reach their full potential.

Organizations should therefore focus efforts on understanding the value of contextual AI over correlation-based models and ensuring they have the infrastructure required to support it. At the same time, businesses must also ensure all data flows through a single, trusted data lakehouse. Acting as the definitive source of truth, this foundation enables accurate, secure and actionable AI insights while preparing organizations for more advanced autonomous systems.

Ultimately, success with AI will not come from chasing the latest hype cycle, but from building systems that truly understand the context in which they operate. Contextual AI bridges the gap between raw data and meaningful action, providing a critical foundation for the intelligent, autonomous systems businesses aspire to deploy.

By investing in contextual AI today, organizations can improve outcomes in the short term, while laying the groundwork for a future shaped by autonomous systems and rapidly evolving real-world demands.

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Tom Hanks is one of the most celebrated actors in the world, having starred in several iconic films throughout his career and winning two Academy Awards in the process. Like all actors, however, he's been no stranger to the rise of AI and its potential to upend the film industry.

Life after death

Hanks raised the prospect of his image being used to bring films to life far beyond his death in an episode of The Adam Buxton Podcast.

The notion of using AI in film to either recreate actors that have died, or to generate performances by using the images of living actors, is highly controversial and led to industrial action at the time. Hanks said that he had first anticipated such a future during his work on 2004's 'The Polar Express', which pioneered live-action motion capture CGI.

Using technology to bring actors back from the dead, however, wasn't a new phenomenon. 'Rogue One' used technology to digitally resurrect Peter Cushing as Grand Moff Tarkin, while the 'Irishman' used AI to de-age Robert De Niro, Al Pacino and Joe Pesci so they could portray their characters over a period of decades.

The future of cinema

What's clear now is that AI in film is here to stay, with the technology being utilized in several film and television productions today.

The 2025 Oscars saw films that used AI, including 'Emilia Perez' and 'The Brutalist', win big. Netflix, meanwhile, revealed that 300 titles used generative AI in one form or another this year. Usage spanned every stage, from concepting and visualization to post-production and eventual release.

As far as audiences go, most people may not notice these subtle uses of AI in the production process, and may not object to this kind of AI usage either. But, generally, research consistently shows they reject the idea of AI-generated content.

Good news everyone - it seems that the reason AI hasn't yet resulted in less work and more leisure time, as promised by multiple "evangelists" and AI "experts" is that we just love working too darn much.

That's according to OpenAI CEO and America's next top (AI) model whisperer Sam Altman, who has declared that people are just really big fans of grinding away and competing with others.

Speaking on the Relentless podcast, Altman told host Ti Morse that in fact, 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.

AI "won't change" work - yet

"Technology, for a long time, has been promising people that they're going to work less and they're going to have all this leisure," Altman said.

"But somehow we never get the promise of the four-hour workweek at mass scale in society," he added. "And I don't expect AI to change that."

It's a remarkably candid admission from the leader of a company which is explicitly setting out to solve some of the world's biggest problems - whether personal or work-related - with AI.

In fact, Altman pointed out that we do have more free time and luxuries today than at any point in history - but this has led to increased expectations from many people.

"We always want more," Altman added, "we think of new things to do, to create for each other, to want for ourselves. It's like a relative game. People are very focused on how they're doing relative to other people."

"I think we're all going to be much busier than we thought we were supposed to be in a post-superintelligence world. We're still going to complain about it, but secretly we're going to be happy," Altman concluded.

I'm not so sure about that - but I'm happy to be proved wrong.

Play it again, Sam

I'm not so sure about that - but I'm happy to be proved wrong.

That's because Altman has been pretty vocal on the future of AI in the workplace for some time, with his viewpoints often changing depending on the situation.

For starters, he recently revealed OpenAI's future strategy would be not to automate everything but to allow people to make better decisions as AI improves their lives, particularly as it expects an AGI world to be here by 2028.

"Entirely automating everything is not the future we want", Altman declared, as he described how OpenAI is now looking to opens a "third phase" and aims to build technology "to benefit everyone".

In May 2026, Altman also criticized companies blaming job cuts on AI - but did admit the technology has some accountability in displacing human workers.

"I would expect that the real impact of AI on jobs, in the next few years, to begin to be palpable," Altman explained, adding that "Of course we'll find new kinds of jobs."

Days later, Altman said he would be "delighted to be wrong" about the effect of AI on human roles, hitting out at claims the increasing global usage of AI technology worldwide will lead to a "jobs apocalypse".

"I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than ​has actually happened," Altman said, who also admitted that while he believed OpenAI has been "roughly right" on the technological predictions it made when it launched ​ChatGPT in 2022, they were "pretty wrong" on the social and economic implications.

At the time of writing, 252 companies have announced job cuts in 2026 so far, leading to 124,255 employees losing their roles (via Layoffs.fyi) - people who, I suppose, do now have a lot more free time than they expected.

It's no secret that AI technology is advancing faster than it can be regulated, with some execs now even saying so themselves. But it seems anything which threatens the advancement of AI must be avoided, in fear of that always present and ever dangerous ‘other’.

The latest installment of ‘please, dear God, slow down before it gets too dangerous’ comes in the form of Pacing the Frontier. Thousands of staff from frontier AI companies once again pleading for a halt in the development of AI technology.

It's all too predictable that the reason from the top for continuing development will be that China ‘threatens’ to catch up. China deploying advanced AI in the military, or in the police, or using it for spying are the threats most often cited, as if these same things aren’t happening at home and around the rest of the world.

So now that China is distilling US AI models to produce rivals at a cheaper cost, I play my tiny violin - the hypocrisy of intellectual property rights suddenly becoming important to AI companies because they could lose money is comical.

Respect my rights

The rights AI companies are citing are the same rights AI companies may have inadvertently violated in hoovering up public data to train their models in the first place, but that's still up for debate.

But AI companies can claim that this trove of public data is a trade secret as a way to prevent human creators from accessing AI training data to see (and possibly prove) that their content has been stolen. Rights for me but not for thee.

What I find funny is that the average American has more in common with the average Chinese person than they do with an AI company. Does an AI company enjoy cooking? Drawing or reading? Can it go out after work for a hot drink or a cold beer? Does it worry that if it doesn’t make any money this month everything might come crashing down? Possibly the last one.

Somehow the bond of national identity tied between a person and a corporate entity is expected to be more powerful than shared human experience. It’s also the bond that AI companies and those in power use to press the accelerator on the machine, just so long as we, the ‘good guys’, are ‘winning’.

Beware the ‘bad guys’

This myth of the external ‘other’, or the ‘bad guys’, is a powerful propaganda tool. But it only works in a vacuum. If you can directly engage with those you are told are your enemy and you find they are not so different from yourself, the myth of the ‘other’ evaporates. (Social media and the internet is a double edged sword in this regard, but that's a discussion for another time).

So if Chinese companies can produce a rival product that doesn’t annihilate a monthly token budget in a matter of days, I say let them. If you are worried about China having your data, are you not worried about who is currently collecting it? If you are worried about China deploying AI systems in the military, police, surveillance, and cybersecurity, are you not worried about it happening closer to home?

The warning that using Chinese AI models is dangerous because they can steal your business secrets is especially weak, especially when you consider Microsoft CEO Satya Nadella’s warning that US AI companies are already doing exactly that, and selling it to your competition.

So the next time an AI company complains about their intellectual property being stolen, remember that it is just the great, ever-turning wheel of innovation. Or if you are warned about the data collection policies of a foreign AI model, check the policies of the tech closer to home. You’d be surprised at just how much they already know about you.

Adding to the existing pressures that data centers face regarding energy supply and grid connections, land shortages and chip availability, many hyperscalers are now facing local opposition to project proposals.

At a congressional hearing earlier this year, US Representative Alexandria Ocasio-Cortez presented two jars of murky water, filled from homes in Morgan County, Georgia, near one of Meta’s data center campuses.

Though Meta has disputed any connection between its construction work and residents’ private wells and water sources, tensions are becoming harder to ignore as the digital world’s physical infrastructure continues to expand.

We are creating our own data center paradox

Enterprise demand for cloud compute and the mass consumer and enterprise consumption of AI have ultimately led to a surge in demand for physical data centers, but they’re now being seen in a similar vein to airports and power stations – physical infrastructure that’s used by nearly all citizens, but projects that nobody actually wants to live by.

And it’s this paradox that many are grappling with at the moment – in this case, residents with a Facebook account can hardly argue against Meta’s need for physical infrastructure, and the same logic applies to users of streaming platforms, ecommerce websites, cloud storage, AI chatbots and so much more.

While International Data Center Authority (IDCA) founder and CEO Mehdi Paryavi believes this digital hypocrisy could afford hyperscalers some justification in their actions, it’s clear that service providers and infrastructure companies are not valuing communication and public engagement enough.

With hyperscalers planning data center buildouts years in advance and chipmakers facing major backlogs, it’s no longer about whether we need extra capacity – rather, it’s about where that compute should reside and how communities are consulted and engaged.

• Should data centres be renamed as data factories instead given that they essentially process data at scale?

Data centers are just fine with their current naming. Data centers come in different sizes and types, fulfilling diverse purposes.

Some produce data and house AI as AI factories, some import and house data from the cloud and enterprise applications, some just serve as internet exchange and telecom shelter, and some just sit on your desk as USB drives and PCs that just house data with the least amenities.

Data centers are more holistic and overarching than data factories, while the latter is easier to understand for the general public. What differentiates data centers from one another is not their sheer size, but rather the type of data they house or process.

Depending on the use case, a small enterprise data center that is home to very sensitive information might prove to be more mission-critical than a large-scale AI data center that manufactures data at scale.

• You mention that if you have a Facebook account, you can’t complain about data centers. I assume that this rings true for all hyperscalers and big cloud services as well. Is there any going back and what can be done to educate users more?

Our Facebook account, Amazon shopping cart, Gmail, iMessage or WhatsApp, Netflix, online banking, Google Maps, YouTube, ChatGPT, will simply cease to exist without data centers.

We are the reason that data centers get built and expanded. Our “consumption” and demand for higher speeds and better quality of service in the utilization of AI and Cloud services and the technological amenities we receive on an ever-evolving and ever-growing basis draw data centers to our communities.

• Speaking of consumers, on the subject of it being digital hypocrisy to describe the paradoxical relationship between the user and the service provider. Can you expand on that further?

When you protest against the expansion of data centers, yet you publicize your protest on social media that are solely enabled through data centers, when you oppose the development of data centers while they offer some of the last standing jobs that can stay for the predictable future, when you push back on the data center industry, when you go home at night and use ChatGPT for your everyday needs, Google for your search, Amazon for your shopping, and Netflix for your entertainment, you are the creator of a paradox between yourself as the end-user and the ultimate beneficiary of what the service providers offer.

• The cordial relationship between data center owners and local communities has given way to something more confrontational. What can Meta and others do to solve the increasing public distrust?

First of all, what companies such as Meta do as the ultimate goal of their companies and what data centers do are two separate conversations, and providers must draw a clear line between the two. If you have issues with the morality of Facebook, or the evolution of AI and the future of tech, your problem shouldn’t be pointing at data centers.

Economically and practically, data centers must be built to keep our economy going and our e-Gov, e-Health, e-Education, and even climate-friendly developments that are results of digital transformation, flowing.

Secondly, if data centers put the grid under stress or depict a shortage of water in certain areas, all fingers should be pointed at policymakers who have failed to invest in our old and fragile infrastructure.

Third, as far as data center owners and operators are concerned, they must allocate a fraction of their revenues to creating awareness, spending time with communities, and investing in the education of the local public they aim to serve and/or live amongst.

Last but not least, communication is key. Anywhere that a provider has initiated talks with the local community and invested in sparking dialog, success was on the horizon, and defensive lines against data centers were not formed by the public.

• What is the biggest misconception - in your view - about data center water usage? And what can be done to set that right?

The biggest misconception is that modern data centers don’t really use much water. But unfortunately, our societies are driven towards assumptions made by either those who are not deeply aware of the true requirements of data centers or careless to substantiate their claims as long as they can provoke emotions and push back on data centers to replace it with some other agenda.

In reality, data centers worldwide use 0.1% of the water available for human use. Modern data centers, which are being built for both AI and cloud applications, have closed-loop cooling that uses no operational water. A brief look at IDCA’s Global Water Stress Index, from its 2026 Data Center Report, will shed some light here.

Looking at the big picture, data centers are essential to developing modern economies. The United States, for example, has by far the largest data center “footprint” in the world and an overall economy that is twice the world average in limiting greenhouse gas emissions.

Note that the global economy produces 317 metric tons of CO2 per US$1 million of nominal GDP, while the US produces 156. However, when we start throwing around big numbers, such as "millions of gallons of water per day," it's easy to forget the entire context.

The large new AI centers, as is becoming well known, have "closed-loop" cooling processes that use no operational water. But we think it's important to realize that there are always engineering problems to solve. For example, a new data center may consume electricity provided by nuclear power, which emits no greenhouse gases. But nuclear energy itself uses large amounts of water.

So, at IDCA, we stress that communities, states, regions, and entire nations need to have as many people engaged as possible to look at these issues in their complexity.

• How realistic would it be to build data centers in specific controlled areas to shield vulnerable neighbourhoods from the full frontal impact of having a gigantic data center next door?

This is an important question. We at the International Data Center Authority (IDCA) believe in master planning the future.

The future that is both feasible and beneficial to nations all over the world is where countries and states plan for well-devised and highly concentrated digital hubs that house AI and cloud workloads.

The benefits of having these hubs planed and future-proofed is that first you take the data centers to environments that are most suitable to house them, secondly you create synergy and economy of scale at these hubs, third, you reduce the cost of compute by ensuring that all the amenities that these data centers need are available on centralized locations in bulk, forth you get the close proximity to the communities that are welcoming or simply create your own community, just like Loudoun County successfully created a whole new community around its data center economy.

The benefits will continue and can be enormous, including from the aspects of national security, where securing a handful of digital hubs is much easier than thousands of random and ad hoc locations during times of tension and conflict, which has become a critical topic in our industry these days.

As it stands today, there are almost always zoning considerations to be considered, so a theoretical "gigantic" data center will not be built right "next door" to residential communities. Even when zoning is not strong in a particular area, data center developers like to acquire enough land to create proper setbacks to mitigate noise, but also to be located where sufficient power can be provided.

So the reality is that large data centers today are already being built in "specific controlled areas," as your question states.

For all the attention given to sophisticated cyber threats and zero-day vulnerabilities, most organizations are overlooking a far more immediate danger: the risks they already know about – but haven’t fixed.

Recent research paints a sobering picture of enterprise security today. Across more than 800,000 IT assets analyzed, one in three lacks at least one critical security control. Nearly one in five is running end-of-life software and 17% sit entirely outside traditional vulnerability management tools.

In other words, the modern attack surface is not just expanding – it is becoming increasingly fragmented, poorly understood and difficult to control.

Breaches built on the basics

One of the most concerning recent developments is how little attackers need to innovate. Every one of the top 10 most frequently exploited vulnerabilities in recent incident response cases had already been patched by vendors – often months earlier.

At the same time, 65% of incidents involved the abuse of remote access services such as VPNs, RDP and remote management tools. These are not obscure weaknesses; they are foundational components of modern IT environments.

The takeaway is clear: attackers are not outpacing defenders technologically. They are taking advantage of execution gaps. Organizations have become adept at identifying vulnerabilities, but far less effective at prioritizing and remediating them and the gap between knowing, prioritizing and fixing is where risk accumulates. That gap is now where most breaches begin.

The visibility problem

At the heart of the issue lies a more fundamental challenge: visibility. Enterprise IT environments have evolved into highly distributed ecosystems, spanning on-premises infrastructure, cloud services, SaaS applications, remote endpoints and third-party integrations.

Each layer is typically managed by different tools, each maintaining its own inventory. None offers a complete view, and the result is predictable. Assets fall outside patch cycles. Devices are not covered by endpoint protection. Entire systems go unscanned. Almost a fifth (17%) of assets are now not covered by vulnerability management tools at all – effectively rendering them invisible from a security standpoint.

This is not simply a technical oversight. It creates what many security leaders now recognize as a structural weakness: organizations cannot secure what they cannot see.

For UK businesses, this challenge is becoming increasingly consequential. Regulatory frameworks such as the Network and Information Systems (NIS2) Regulations – and the UK’s forthcoming Cyber Security and Resilience Bill – are placing greater emphasis on demonstrable control over assets and risk. Inaccurate inventories and fragmented data make that more and more difficult to prove.

Legacy systems: The risk that won’t go away

Compounding the visibility issue is the persistence of legacy technology. 19% of IT assets are running software or hardware that has reached end-of-life, meaning it no longer receives security updates.

These systems are particularly challenging. In sectors such as healthcare, manufacturing and financial services, they are often embedded in critical processes and cannot be easily replaced. Yet they remain permanently exposed to known vulnerabilities.

The problem is not just their existence, but their opacity. It is not unusual for organizations to believe legacy systems have been decommissioned – only to discover they were still active. This creates a persistent blind spot within the attack surface, one that attackers are quick to exploit.

A growing gap between perception and reality

Perhaps most concerning is the extent to which organizations misunderstand their own security posture. Many rely on a single system of record – such as a configuration management tool as their “source of truth”. However, these systems are often incomplete.

In one example, a company believed it had full endpoint protection coverage based on internal reporting. Independent analysis revealed that a portion of its devices were not included in the system at all. This disconnect between perception and reality creates a false sense of security – one that is increasingly untenable in a climate of rising regulatory scrutiny and board-level accountability.

Across the UK and EU, frameworks such as the Digital Operational Resilience Act in financial services and NIS2 more broadly are raising expectations. Organizations are no longer judged on whether controls are in place, but whether they are effective, verifiable and consistently applied.

From vulnerability management to exposure management

Against this backdrop, there is a broader shift in how organizations approach cybersecurity. Traditional vulnerability management, focused on identifying and scoring flaws, is no longer sufficient. The challenge is not a lack of data, but a lack of clarity about what matters most.

Exposure management, by contrast, adopts a more holistic view. It combines continuous asset discovery with data from multiple sources, applying business and threat context to prioritize risk. Crucially, it also focuses on verification – ensuring that remediation actions are actually completed.

This shift is already delivering measurable results. Organizations that adopt more mature exposure management practices see reductions of more than 40% across key risk categories, including missing controls and end-of-life assets.

A new baseline for cyber resilience

The implications for UK organizations are clear. Cybersecurity is no longer just about defending against the unknown. It is about managing the known – systematically, continuously and at scale.

This is particularly relevant as cyber risk becomes more tightly linked to regulatory compliance, insurance requirements and board-level governance. The ability to demonstrate control over the attack surface – to prove what exists, how it is protected and whether risks have been reduced – is fast becoming a baseline expectation.

Attackers are now able to achieve domain-wide control in minutes once inside an environment. In that context, delays in remediation or gaps in visibility are not just operational issues – they are critical business risks.

The organizations that will succeed are those that close the gap between insight and action. Those that move beyond assumptions and develop a clear, verifiable understanding of their exposure. Ultimately in today’s threat landscape, the greatest risk is not what organizations don’t yet know – it is what they already know, but haven’t fixed.

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Both OpenAI and Google have released major new AI models within days of each other. OpenAI launched GPT-5.6, and Google has followed up with Gemini 3.6 Flash. While both companies have talked up the coding and developer features of these models, they also power the consumer versions of ChatGPT and Gemini.

So, rather than measuring them with programming benchmarks, I wanted to find out which is actually better at the kind of messy, everyday problems most people use AI to solve.

For this comparison, I matched Google's Gemini 3.6 Flash against GPT-5.6 Sol using its default Medium reasoning setting, since both are intended to be the standard high-quality models that paid subscribers will use for most tasks.

My digital life

So, I gave them my entire digital life for the week ahead.

I uploaded:

• Bank statements
• My calendar (they had access to my calendar app and I also sent in a screenshot of my calendar from another account I use).
• Grocery receipts
• Emails (both had access to my Gmail)
• WhatsApp screenshots
• Photos of my kitchen cupboards
• An energy bill
• Travel bookings
• Handwritten notes

Then I gave both models exactly the same instruction:

"Tell me everything I should do this week."

It sounds like a simple request, but it forces an AI to combine information from multiple sources, prioritize what's important, spot deadlines, reconcile conflicting information, and produce a practical action plan. In other words, it's exactly the kind of real-world problem people increasingly expect AI assistants to solve.

The results were like night and day.

Two different approaches

When I uploaded the files to Gemini 3.6 Flash and asked what I should do this week, it largely ignored the photos and concentrated on the screenshot of my calendar. Its initial answer mostly repeated the events I already knew were happening. Thanks, Gemini — I had the calendar open in front of me.

I then had to explicitly ask whether it could infer anything useful from the other images. It eventually offered some additional advice and did a good job of dividing the information into categories, including work, shopping, fitness, notes, and receipts. But it failed to flag that I had two clashing events in my calendar that evening. It also offered very little prioritization or practical guidance about what I should do next.

ChatGPT took considerably longer to respond, but its answer was far more useful. From the WhatsApp screenshots, it correctly deduced that attendance at my Friday Tai Chi class was likely to be low and suggested I decide whether it was still worth running. It noticed that yoga had been canceled, and spotted the two conflicting events in my calendar, telling me that I needed to choose between them.

It also totalled the receipts I had uploaded, suggested what I should do with them, and made a decent attempt at deciphering my handwritten notes. More importantly, it organized everything into a day-by-day plan for the coming week, then identified the three most urgent tasks, so I knew exactly where to begin.

The crucial difference

That was the crucial difference. Gemini told me what was in my files. ChatGPT worked out what I should do with the information. In World Cup terms, ChatGPT scored a hat trick while Gemini missed a penalty.

Google says Gemini 3.6 Flash improves coding, knowledge work, and multimodal performance compared with its previous models. That may be true, but in this particular multimodal test, it was comfortably beaten.

When I asked both AIs to make sense of real life rather than pass a benchmark, ChatGPT reasoned about the information in a far better way than Gemini did..

Does innovation happen only under pressure? It is a difficult question to ask, because history does give us a difficult answer.

Wars, shocks and national emergencies have often forced societies to move faster than they would in normal times, and many of the technologies we now take for granted came from moments nobody would wish to repeat.

The Second World War is the obvious example, with its lasting influence on medicine, aviation, computing, communications and manufacturing.

The Covid pandemic gave us a more recent version of the same pattern, when a global health crisis pushed scientists, regulators, governments and pharmaceutical companies to develop and approve vaccines at a speed that would previously have been treated as impossible.

Ukraine’s experience

It is often said that Ukraine’s experience of wartime innovation cannot be replicated in ‘peaceful’ Britain. In one sense, of course it cannot. One country is fighting for its survival against a much larger aggressor, while the other is an island nation that has lived for decades with the habits and assumptions of relative security.

Yet that argument only takes us so far. If urgency drives innovation, then the more useful question is what creates urgency in societies that are not at war. Or, to put it more plainly, what creates urgency in countries that do not really feel themselves to be under existential threat?

Britons continue to go about their daily lives much as they always have. People work, travel, argue about politics, worry about mortgages, watch sport, plan holidays and assume that the basic structures around them will continue to function. This sense of normality is, of course, a privilege. It is also one of the reasons why creating urgency in peacetime can be so difficult.

Britain, though, has repeatedly shown that it can innovate with extraordinary force when the need becomes clear. The Industrial Revolution transformed the country and then the world, with British engineers helping to build the railways, steam power and manufacturing systems that reshaped entire economies. For a small archipelago, Britain has had an unusual ability to turn technical advances into industry, infrastructure and global influence.

During the Second World War, that ability appeared again under extreme pressure. Radar helped defend British skies, then became part of the foundation for aviation, shipping, weather forecasting and modern sensing systems. Penicillin, discovered through British science, became a mass medical revolution because wartime urgency forced production and clinical adoption at scale.

At Bletchley Park, the need to break codes helped push electronic computing from theory into practical machinery, with Colossus showing what computation could achieve when the stakes were national.

More recent experience

More recently, the pandemic showed that this capacity has not disappeared.

Oxford researchers began human trials of their COVID-19 vaccine in April 2020. By December, Oxford and AstraZeneca had published the first peer-reviewed Phase III results for a coronavirus vaccine, and the UK became the first country to authorize the Oxford-AstraZeneca vaccine for public use. That was not an accident of national character. It happened because institutions, expertise, capital, government and industry were all pointed at the same urgent problem.

Innovation is therefore hardly foreign to Britain.. The question is what conditions have historically allowed it to move at speed, and whether those conditions can be created today without waiting for a disaster to create them for us.

That question is now becoming more urgent in defense. The war in Ukraine has shown how quickly military technology can evolve when adaptation becomes a matter of survival. Capabilities that might once have taken years to develop now change in months, and sometimes in weeks. Software updates, autonomous systems, drones, electronic warfare tools and battlefield data are changing how militaries think about capability, procurement and industrial readiness.

This shift has not gone unnoticed in Britain. The Strategic Defence Review repeatedly refers to the need for innovation, agility and "wartime pace" and those phrases now appear across government, military and industry discussions. That is progress, but language is only the beginning - the real test is whether Britain can build systems that make speed practical, funded and repeatable.

For much of its modern history, Ukraine has faced questions of sovereignty, territorial integrity and, at times, national survival. Following Russia's invasion in 2014 and again in 2022, the need to compensate for a larger adversary accelerated the adoption of drones, electronic warfare systems and other asymmetric capabilities. Innovation became part of a broader national effort to preserve statehood.

However, Ukraine also demonstrates why simple comparisons can be misleading. Countries do not all innovate for the same reasons, because countries do not all carry the same history. The pressures that shape Ukrainian urgency are not the same that shape British urgency and pretending otherwise would be lazy.

A very different story

Russia has often mobilized around a very different story, built on encirclement, external enemies and the restoration of great-power status. From Soviet narratives of confrontation with the West to contemporary rhetoric around NATO expansion and historical spheres of influence, the perception of an external challenge has repeatedly been used to justify national consolidation and state-led mobilization.

The United States offers another model again. American technological acceleration has often been driven by the ambition to lead, whether that meant reaching the Moon before the Soviet Union, dominating emerging industries, or maintaining military and economic advantage. Its urgency has often come from competition, scale and the belief that leadership itself is a national objective.

This suggests that urgency itself may not be the defining variable. Different societies move when different ideas become powerful enough to organize institutions, capital, industry and talent. Britain’s challenge is not to recreate Ukraine’s conditions, which are uniquely and painfully Ukrainian. Britain’s challenge is to identify the national objective that can generate serious effort in a British context.

Part of the answer may lie in the way Britain thinks about time. Earlier this year, addressing the United States Congress, King Charles referred to America’s founding 250 years ago and joked that, in British terms, it felt like “just the other day”. It was a light remark, but it captured something real about Britain’s historical outlook. This is a country that often sees itself across long stretches of time.

That can make Britain frustratingly slow at the beginning of a challenge. It can also make the country unusually powerful once it connects immediate pressure to a longer national story. Threat alone has rarely been enough as a British organizing principle. Britain has responded to danger when required, but some of its most important periods of innovation came when immediate pressure was tied to economic transformation, scientific leadership or institution-building.

The changing character of warfare

Seen through that lens, Ukraine is more than a warning about the changing character of warfare. It is an early view of technologies that are likely to shape economies and societies long after this war has ended. To mobilize Britain properly, the opportunity should not be presented as a narrow defense issue. The technologies being tested under the hardest conditions can become the basis of new industries, new standards and new areas of commercial leadership.

Britain has always been good at building industries around serious technologies once the need is understood. Its strength has rarely been invention alone. It has been the ability to create institutions, standards, markets, professional services, capital structures and global relationships around invention.

The country has many of the ingredients needed to lead here. It has excellent universities, serious engineering talent, strong financial markets, a respected legal system, advanced manufacturing capability, deep insurance expertise and global reach. It also has a long relationship with Ukraine and a new political framework through the UK-Ukraine 100 Year Partnership.

This is where the next stage of support for Ukraine should become more ambitious. The UK should continue to support Ukraine militarily, financially and diplomatically, because Ukraine is defending its sovereignty and the security of Europe. Alongside that support, technology partnership should become one of the defining features of the relationship.

The logic is straightforward. Ukraine has some of the most relevant operational learning in the world. Britain has the skills, institutions and history to turn urgent invention into global progress. Put together properly, that combination could help Ukraine rebuild, strengthen Britain’s preparedness and create technologies with value far beyond the current war.

That will require more than warm words. Britain should build a practical UK-Ukraine dual-use technology corridor around real problems and real users. That means identifying Ukrainian technologies and operational lessons with proven relevance, pairing them with British engineers, universities, investors, commercial customers, defense users and regulators, then testing them in real environments.

The focus should be on areas where Britain has a genuine need and Ukraine has hard-won experience. Protecting airports from drone disruption. Inspecting offshore energy assets. Improving emergency communications. Strengthening supply chains. Applying AI to difficult visual data. Detecting interference around critical infrastructure. Monitoring farmland and infrastructure with autonomous systems.

The ethical point matters. Ukraine cannot become a source of raw ideas for others to package, own and sell back to the world. If Britain wants to be trusted by Ukrainian founders, engineers, soldiers and policymakers, it has to treat Ukraine as a partner in value creation. The best arrangements will allow Ukrainian companies to scale internationally through the UK while keeping a proper stake in their intellectual property, commercial future and national recovery.

A hard strategic reason

There is also a hard strategic reason to get this right. Every serious country is now rethinking defense, industrial resilience and technology. The United States, Germany, Japan and others are deepening their relationships with Ukraine because they understand that the war is reshaping the future of security. Britain has been one of Ukraine’s strongest allies, and that support is recognized with real gratitude. The next stage should match that moral commitment with a more ambitious technology partnership.

Procurement will decide whether this becomes real. A start-up cannot wait years for a serious decision. Investors will struggle to back complex national security technologies if successful trials lead nowhere. Defense users need permission to test smaller companies without being trapped by process. Commercial customers need help understanding where battlefield-tested technologies can be used safely, lawfully and usefully in civilian settings.

Without those pathways, Britain will admire Ukrainian innovation in public while failing to absorb its lessons in practice. That would be a serious missed opportunity. The technologies being shaped in Ukraine will influence how we inspect buildings, move goods, respond to disasters, protect power networks, secure public spaces, monitor farmland and manage risk. Some will remain military. Many will move into civilian life so gradually that, in ten years, people may forget where the original lessons came from.

That has always been the way. Radar did not remain a wartime technology. Penicillin did not remain a wartime medicine. Computing did not remain hidden inside codebreaking. Once wartime pressure forced progress, society found wider uses, and the countries that knew how to translate those advances gained economic, scientific and strategic power.

Britain has done this before. It has turned moments of pressure into technologies, industries and institutions that shaped the modern world. Ukraine is now revealing where the next wave of defense and dual-use technology is heading. The UK should help build it, and it should do so with Ukraine.

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Artificial intelligence (AI) is constantly reshaping everything we do. Across industries, it is changing the way we do work, but that rapid expansion can’t continue without bumping up against real tangible limitations.

Most notably, planning for hyper scaled data centers across the world and increasingly complex cloud computing infrastructures and AI systems are leading to difficult conversations around energy pricing, generation and availability.

Around the world, electricity consumption is increasing at some of the fastest rates seen in decades, and there are no signs of it slowing down. The International Energy Agency (IEA) projects global electricity demand growth of 3.3% in 2025 and 3.7% in 2026, driven heavily by those same data centers, AI deployment, and other advanced industrial expansion.

The IEA has also warned that electricity demand from data centers is expected to double by 2030, with AI-focused facilities alone projected to triple their power use over the same period.

The financial implications and policy blowbacks are already starting to be felt. With limited expansions of electrical grids, more consumers are fighting for less resources, causing prices to only go up. In fact, according to S&P Global, some regions with AI data centers have seen wholesale electricity prices surge by more than 250% in the past five years.

This growing tension between AI advancement and energy availability is beginning to reshape how the technology sector thinks about the future of innovation. For years, the dominant assumption was that progress in AI would mainly come from scaling model size and centralized compute infrastructure.

But the next wave of value creation will also come from AI embedded in the physical world: machines, devices, buildings, industrial assets, medical wearables, and infrastructure that continuously sense, act, and adapt. In that context, the question is not only how to train larger models, but how to process massive streams of real-world data with minimal latency and minimal energy.

That is why alternative architectures, including low-power and decentralized AI, are becoming strategically important.

What low-power AI systems are

Low-power AI are systems specifically designed to minimize the resources required for inference and online learning, particularly energy consumption, while still delivering on high-performance expectation. Rather than relying entirely on massive cloud-based infrastructure and centralized data centers that guzzle down energy, these systems prioritize resource-efficiency at every level of the technology stack, from semiconductor architecture to data processing and deployment.

Low-power AI is not a single breakthrough at model level. It is a system-design discipline that spans sensing, signal conditioning, embedded processing, semiconductor architecture, algorithm optimization, and deployment. The real challenge is to co-design hardware and software for a specific use case so that intelligence is delivered where it matters, with the lowest possible energy budget.

This is precisely where research-transfer institutions such as CSEM can contribute: by combining expertise in sensors, edge computing, ultra-efficient IC design, and application-driven system integration to translate AI into robust real-world solutions rather than generic demonstrations.

Most of the focus in AI development has been in creating systems that are trained and operated on generalized architecture, handling a wide array of tasks simultaneously. These systems are immensely powerful but rely on the same models that require copious amounts of energy to keep them functioning.

On the contrast, low-power AI systems focus on more highly specialized systems, limited in scope and capabilities to a well-defined set of tasks that allow them to be less reliant on vast infrastructure and energy resources to function.

This includes edge AI, where data is processed directly within devices and systems rather than being sent continuously to remote cloud infrastructure. That shift matters even more in the era of physical AI. When intelligence is embedded into the real world, the volume of potentially relevant data generated by sensors, machines, vehicles, buildings, and other assets becomes enormous.

Sending everything to the cloud is not only inefficient, but often too slow and too costly. Many decisions must be taken locally, in real time, with strong constraints on power, bandwidth, privacy, and reliability.

Low-power AI therefore becomes essential not just to reduce energy use, but to preprocess data close to where it is generated, extract the small fraction of information that is meaningful, and enable the broader system to be monitored and optimized for performance, resources, and health.

Perhaps most importantly, low-power systems expand where AI tools can realistically operate. Wearable medical devices, industrial sensors, remote monitoring systems, transportation infrastructure, and smart manufacturing environments all require AI systems capable of functioning within strict energy constraints.

These contexts show places where sustainability is not only a cost-effective measure, but a functional requirement. At the sub-milliwatt level, some systems can even move beyond battery dependence and become energy-autonomous, harvesting ambient energy from light, heat, or vibration to enable a true fit-and-forget lifecycle.

Why efficiency is becoming an imperative

Power generation capacity, transmission infrastructure, cooling resources, and semiconductor supply chains are all facing mounting, simultaneous pressure. The assumption that future competitiveness depends solely on building larger and more power-intensive systems may no longer hold true, with further expansion likely bringing with it exponentially higher costs.

Organizations capable of delivering efficient, highly targeted distributed AI systems could gain major strategic advantages and offers a pathway toward greater technological resilience, as their design natively makes them more resistant to fluctuations in electricity pricing, supply disruptions and geopolitical instability.

Additionally, a more sustainable option can bring value by reducing environmental impact, while still not sacrificing function. The conversation around responsible AI therefore cannot remain focused solely on software governance and ethical frameworks but needs to be talking about how systems are powered, and how and where they process information.

A strategic opportunity for smaller nations

The rise of low-power AI also bears the opportunity to redefine who can meaningfully participate in the global AI race.

The United States and China have been postured as global tentpoles when it comes to the development of AI, and subsequently massive AI investments that have followed suit.

Both are examples of large nations that have the resources to invest billions into data centers, chip production and other infrastructure. On first glance, this paradigm forces many smaller nations to miss the financial and innovation benefits of the AI movement.

But with low-power and distributed AI systems, smaller countries do not need to compete on scale alone. They can compete through specialization, precision engineering, and the ability to translate research into deployable systems for demanding applications.

My home nation of Switzerland provides a useful framework for what this looks like in practice. Similar to most nations across the world, we cannot outspend the largest economies, but we do possess strong capabilities in microelectronics, embedded intelligence, sensing technologies, and high-value industrial and medical applications.

By recognizing these strong foundations, technology transfer organizations like ours can then play an important role in bridging these unique national strengths with industrial deployment, helping transform AI from a cloud-centric paradigm into efficient intelligence embedded in the physical world.

Even for relatively small nations with limited natural resources, there is an opportunity to be a leader in AI development by embracing low-energy system design. Chip producers with less resources will have to increasingly focus on creating specialized, energy-efficient technologies optimized for targeted applications to let them compete on the global stage.

As energy constraints become more severe, demand will likely grow for AI systems capable of operating efficiently in real-world conditions rather than exclusively within massive, centralized infrastructure environments.

In many ways, low-power AI could democratize portions of the AI boom by rewarding efficiency, precision, and specialization as opposed to simply providing opportunities for regions that can match scale. It can be said that virtually every country on earth has some level of specialized technical expertise that can be bridged to an AI use case.

The next generation of AI will see a shift from chat interfaces and cloud platforms to physical systems that shape daily life and industrial productivity. In that setting, efficient local intelligence is not a secondary optimization; it is a core architectural requirement.

Physical AI will depend on the ability to sense the world continuously, interpret it selectively, and act on relevant information without moving every raw data stream through centralized infrastructure.

The democratization of AI brings with it the need for more democratized solutions and opportunities for all to participate.

Looking ahead

While the early years of the AI boom have been defined by large, multi-purpose models and increasing scale, the next chapter will likely be written by developers and ecosystems that are able to utilize precision engineering to focus on low-power distributed systems that are by nature more resilient and sustainable.

Energy availability is no longer a secondary consideration in AI development and will increasingly be one of the defining variables shaping the future of the industry, and subsequently, how the global economy is built. That reality is making low-power AI an emerging necessity.

The next generation of AI systems must be designed with these restrictions in mind, requiring advances in semiconductor design, edge computing, specialized architectures, and intelligent energy management.

Countries and companies that embrace these more efficient and targeted systems may ultimately be better positioned for long-term competitiveness than those that rely instead on growing as large as possible as quickly as possible.

The European Union’s Joint Chips Undertaking is already bringing together its member nations, as well as some outside partners like Switzerland, to develop pathways to technologies like low-power chips. With that in mind, the future of AI may not belong solely to the biggest players, but to the smartest and most efficient ones.

For the global economy, that may prove to be one of the most important transitions of the AI era which only started to come to prominence recently with the growing controversies regarding hyperscale data centers.

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Every few years, enterprise software discovers a future it insists you cannot afford to miss.

Often these claims outrun the product, and amid the hype, organizations commit time and money to innovations that never quite live up to their billing.

Agentic AI is the exception, and that is exactly why it deserves a closer look.

We're not talking about chatbots that draft emails or summarize reports. This is something entirely different: software that acts.

Agentic AI can read a company's own approval hierarchies and permissions, make decisions within finance or supply chain processes, and execute them.

The last wave of enterprise AI offered suggestions. This one does the work. For the organizations that get it right, that is a different order of advantage.

The large vendors know this, which is why they have spent the past year racing to own agentic AI. At its Sapphire conference this spring, SAP unveiled what it calls the autonomous enterprise: more than 200 specialized agents that carry out tasks across the core business functions, orchestrated by some 50 domain-specific assistants and reached through a single interface, Joule.

Oracle has been building the same capability into Fusion, its cloud suite for finance, HR and supply chain.

Unlocking the potential

There has been no shortage of talk about what that could unlock, and for many organizations the excitement is well earned: an agent that can carry out work, not just recommend it, changes what the software is for. But for most of them, already running this software, capability was never going to be the sticking point. Access is.

As the major vendors have built it, agentic AI is native to the system of record, woven into the core platform that runs the business. That is a real achievement: an agent that respects your permissions and executes a live transaction is worth far more than one bolted from outside.

But "native" also carries a second meaning – and this one never makes the keynote speech. With the agent tethered to the vendor's cloud platform, there can be no reaching it without committing to that same platform, regardless of what your systems run on today.

SAP shows how that gate works. Until this spring, Joule reached only customers on its cloud subscriptions: RISE and GROW. At Sapphire, with much of its installed base showing little sign of moving, SAP opened a door, but a narrow one. ECC and S/4HANA on-prem customers are no longer shut out, provided they commit to moving the majority of their SAP estate to Cloud ERP.

Even then, they get only a limited set of the AI capabilities rather than the full portfolio. The on-prem route, in other words, is sold on the condition that you start paying for the cloud. And for the older ECC core, a clock is running regardless: mainstream maintenance ends in December 2027, with a stay of execution to 2030 at a premium, after which the choice narrows to an unsupported system or an upgrade.

So an ECC customer that takes the on-prem route is still paying for the cloud to access AI capabilities, even as support drains out of the system that they rely on (according to a timeline they never chose).

A more direct route

Oracle arrives at the same place by a more direct route: its agents do not exist outside the cloud. The Fusion agentic applications it has rolled out this year run only inside Fusion Cloud, on Oracle's own infrastructure and within its security model. There is no on-prem edition to license. An organization still running E-Business Suite cannot switch these agents on where its systems sit today; to use them at all, that organization will have to re-platform onto Fusion.

Strip away the packaging and the structure is the same in both cases: the on-ramp to agentic AI is the migration these vendors have been trying to sell all this time. What is new is the leverage. Cost, risk and disruption have held many organizations on-prem for years – despite all the pressure to move. Could the prospect of being shut out of agentic AI be the argument that finally overcomes their objections?

The budget is already committed

The migration and the AI draw on the same budget, and the migration has first claim on it. In the Americas' SAP Users' Group, 61% of members reported that budget was the biggest challenge they faced this year, and the group's research director was blunt about the cause: the cloud ERP projects are themselves creating the pressure, with AI expected to land on the same budgets next.

The sequence is unforgiving. Pay to reach the platform, then pay again to use the AI once you are there, because the headline subscription covers only a limited band of embedded features, and the rest is metered by consumption. The capital a CIO would want to invest in building an advantage is spoken twice before a single agent has delivered a measurable outcome.

And that is before the program meets its harder test. Boards have grown tired of pilots that never reach production and spending that generates activity no one can tie to an outcome. In some cases, these will be genuine execution failures, but a program that starts short of capital, on a timetable set by someone else, is not starting from a position of control.

Who decides the order of operations?

A CIO should separate the two decisions the vendor has deliberately combined. Whether to modernize is one question. When to do it, in what order, and against which budget is another. Nothing requires that the second be dictated by an end-of-support date printed on someone else's roadmap.

The critical issue here is who holds the authority to decide how the organization's most critical systems change and when. When a vendor sets the timetable, the sequence and the price of innovation, the executive accountable for that estate is not really the one running it anymore. Restoring that authority does not mean refusing to modernize. It means refusing to let the vendor selling the upgrade also decide when and how you buy it.

This is the case for third-party software support. An independent provider maintains the existing ERP estate, keeping it secure and preserving interoperability in place of the vendor's maintenance contract – and typically at a fraction of the cost. That does two things. It removes the end-of-support date as a forcing function, because now the system will stay supported whether or not the organization moves.

And it frees the maintenance budget that would otherwise fund the vendor's marketing roadmapping, so the money can go towards AI investments the organization actually wants to make. The stable core stops being a liability to escape on the vendor's schedule and becomes what it always was: productive capital, running the business while leadership decides on its own terms where agentic AI earns its place.

From there the real options open up. An organization can hold that stable core and run agentic AI as an orchestration layer above it, reaching into the system of record without re-platforming the whole estate first.

Or it can modernize selectively – moving from proprietary databases to open-source alternatives that eliminate vendor license fees, where there’s a business case for it – on a timetable set by business value rather than a maintenance deadline. What matters is that the sequence belongs to the organization.

The case for authority

The case for holding that authority is stronger with agents than it was with assistants. Tying the behavior and the governance of a system that acts on your business to a single vendor's platform roadmap is a heavier dependency than the previous era ever required.

Governed autonomy is the right ambition, but governance an organization sets for itself is a wholly different thing from governance inherited from the platform it happens to sit on.

The vendors are right about one thing: agentic AI is a capital decision, not a software purchase. But a capital decision means choosing where the money goes and what you expect back from it. When the vendor sets the timing, the sequence and the price, that is not allocating capital, it is settling an invoice.

The autonomous enterprise may well be worth building. What a board should refuse is to let its timetable and its budget be set by the company with the most to gain from the move.

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The global AI in IoT market has grown rapidly within the last year, with its value reaching $4.08 billion in 2025, and forecast to reach $6.45 billion by 2035. This progress reflects a wider industry shift, as with the rise of AI organizations are increasingly looking towards a more intelligent and connected future to achieve more from their IoT deployments.

Building additional defenses with AI-powered IoT security

The nature of the IoT can be complex, with devices often operating outside traditional IT security perimeters and in globally distributed environments. Within this landscape, connected devices now form the backbone of daily operations, from energy and healthcare to retail and manufacturing. As digitalization increases, so too will the critical functions that depend on connected devices.

Importantly, as deployments accelerate, so too does the potential IoT attack surface area. Due to the interconnected nature of the IoT, each device acts as a potential entry point for cybercriminals. With the UK government reporting the annual cost of cyberattacks as £14.7 billion annually, it is crucial that the entire IoT deployment is secure.

While private IP addressing and private APN approaches are useful in limiting exposure, they offer limited visibility into device behavior once traffic is flowing, particularly as deployments grow more complex.

AI, however, can see what may have previously been missed. Security has become a key area that can be elevated by AI-powered anomaly and threat detection, which detects behavior such as suspicious IPs, remote code execution, device backdoors or an abnormal port connection. This threat detection enables organizations to quickly identify the first sign of a cyberattack.

AI can even help identify the nature of attacks, including distributed denial-of-service (DDoS) and man-in-the-middle (MiTM) attacks, and device takeovers. By flagging irregularities in real time and enabling rapid corrective action, AI strengthens IoT security and supports more effective risk management, helping organizations avoid operational disruption, financial loss and reputational damage.

AI unlocks the value of IoT data

Beyond security concerns emanating from the IoT’s fragmented landscape, organizations may also struggle with data management and deriving meaningful analysis across the IoT’s web of networks, devices, cloud environments and enterprise processes.

The IoT constantly collects and transmits vast quantities of information, with a single internet-connected security camera generating around 300 GB of data per month. When these connected devices are multiplied and deployed globally on a much larger scale, organizations can be left with an overwhelming amount of data that can be difficult to utilize effectively.

Additionally, the role of the IoT is evolving. Whilst historically the IoT has purely had the role of connecting devices and gathering and transmitting data, there is growing industry pressure for intelligent solutions to inform decision-making and drive outcomes. Enterprises are now seeking more effective solutions to extract meaningful insights from IoT information, and without this, they will fail to unlock the true value of their data.

AI offers a promising solution to these challenges. AI-ready infrastructure is being increasingly prioritized by enterprises to support the collection, transmission, processing and integration of data into systems to enable AI-driven insights. The intelligent analysis AI offers enables patterns and trends in device fleets to be identified and analyzed more efficiently to help reduce manual intervention and lower costs.

Through advanced analytics and machine learning, AIoT can help organizations make better use of their data across the entire fleet, transforming data into an increasingly valuable asset that drives efficiency and long-term value.

Automation supports efficiency

Vast quantities of data across the IoT also make manual intervention expensive, ineffective and time consuming. These challenges can result in performance issues escalating unnoticed, or key device faults being missed.

The analytical and automation capabilities of AI can also support through real-time monitoring, automated fault resolution and predictive maintenance. By analyzing data on device usage, lifecycle maturity and performance, AI can identify potential issues before they arise. This enables a shift from reactive to proactive operations, reduced downtime and lower operating costs.

The benefits of AI-driven analytics are industry-wide. In manufacturing, for example, AI systems can use equipment performance data to estimate maintenance costs, consequently minimizing later expenses and downtime. In industries like healthcare, predictive maintenance also helps ensure the resilience of critical services like remote patient monitoring, where it is crucial that devices remain connected.

Rules-based automation can also support policy-driven fleet management and eSIM orchestration, with many capabilities gradually moving towards greater automation. In practice, adoption is iterative, starting with defined rules and oversight, before layering in AI-driven insights such as real-time usage, performance monitoring and anomaly detection to address issues more quickly.

As automation scales further, IoT environments will be able to respond flexibly to changing conditions with reduced manual intervention.

However, it must also be noted that as this progress continues, transparency and explainability remain crucial. Organizations must understand how decisions are made, and where human oversight remains critical as they scale.

Looking ahead to intelligent connectivity

AIoT’s influence will only grow moving forward. In fact, Transforma Insights’ forecast suggests a more than six-fold increase in connections over 10 years. Managing fleets intelligently and securely at scale, with transparency at the center of operations, must become an industry priority.

Those that can successfully integrate AI with scalable connectivity will be best positioned to move beyond simply managing devices towards truly intelligent operations. In doing so, they will unlock new efficiencies, strengthen resilience and create differentiated value in the IoT.

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Artificial intelligence has moved from experimentation to expectation at a remarkable speed. What began not that long ago as isolated pilots is now being embedded across every industry, from highly regulated sectors like financial services to those closest to the human experience, such as healthcare and the arts.

In just four years of widespread business use, these technologies have already moved from operational tools to business infrastructure, underpinning resilience and requiring the same meticulous planning and protection as any other critical system.

This shift is changing the way AI investment decisions must be made. As organizations rush to deploy AI tools, the challenge is no longer whether to invest but how to ensure investments create long-term value, while mitigating growing operational, security, and compliance risks.

Moreover, AI is not a purely digital investment. Behind every model, application, and workflow sits a physical technology estate: servers, storage, networking equipment, energy-intensive infrastructure, devices, and the supply chains that support them.

As AI adoption accelerates, enterprises risk expanding this estate without fully understanding the lifecycle consequences, from rising energy use and infrastructure refresh cycles to underutilized assets, electronic waste, and lost residual value.

Translating investment into impact

Investment in AI continues to rise exponentially, seemingly unimpeded by rising market prices or economic instability. Today, 71% of CEOs rank AI as a top investment priority, yet many still struggle to translate capital expenditure into operational value.

According to Gartner, at least 50% of AI projects are abandoned after proof of concept. Projects that do become operational often fail to deliver a return on investment, with 56% of CEOs saying they have not realized any revenue or cost benefits from AI projects.

This points to a deeper issue: AI success depends less on experimentation alone and more on the quality of the investment, governance, and capability-building decisions that follow.

The AI impact gap

As complexity increases, an investment impact gap is emerging. Leaders expect AI and the tech that supports it to deliver strategic value, improve performance, and reduce risk, but when making investment decisions, they often continue to prioritize near-term costs over the lifecycle factors that determine whether those outcomes can actually be achieved.

Without a view of the lifecycle consequences of their tech investments, as AI adoption accelerates, businesses tend to prioritize factors that are easier to quantify and act on in the short term. Our own research shows that 64% of organizations have rejected a superior technology solution because of its upfront price.

This may reduce immediate financial strain, but it can also introduce operational friction, scalability issues, and erode performance over time. As EY's Americas CTO, Dan Diasio, recently noted, "There's a very clear limit to the amount of value you can create by just focusing on productivity and cost reduction."

AI introduces entirely new cost dynamics. Enterprises must account not only for acquisition and implementation, but also for ongoing expenditure tied to usage, energy consumption, governance, compliance, infrastructure, and model evolution. These lifecycle impacts are often invisible in traditional business cases, yet they increasingly determine whether AI investments create durable value.

Good AI governance starts with accountability and visibility

Similarly, AI does not respect organizational boundaries. Its costs, risks, and value are distributed across the business, making cross-functional accountability essential. And yet, investment decisions are often assessed in silos, making it harder to build a complete view of the risks and value that emerge across the lifecycle.

Likewise, organizations need to understand the scope and reach of the systems they have in production, as well as the financial, operational, security, and environmental impacts they will have throughout their lifecycle.

Without this end-to-end view, major financial and operational blind spots can emerge at critical moments, many of which are not anticipated or planned for. While security, privacy, and compliance consistently rank among the top AI concerns, our research found fewer than half rate data protection (49%) or compliance capabilities (46%) as a high priority when making technology investment decisions.

From cost to AI-driven impact

To close this gap, businesses need to move beyond narrow assessments of upfront price and near-term ROI. These measures still matter, but they do not capture the full lifecycle consequences of AI investments, particularly as systems become embedded in critical operations and begin influencing performance, resilience, compliance, risk, reputation, and long-term value creation.

This is the thinking behind Total Cost of Impact (TCI). TCI is a new model that helps organizations evaluate technology investments through a broader lifecycle lens, assessing not only what a solution costs to buy and implement, but what it will require, enable, constrain, and expose the business to over time.

TCI assesses four core areas of technology impact - financial, operational, security and compliance, and environmental and social - encouraging businesses to understand how investment decisions made today influence outcomes over time.

When applied at the point of investment, TCI makes the trade-offs, risks, and downstream consequences that conventional procurement models often overlook visible. In doing so, it creates a common language across business functions, reducing friction, improving collaboration, and helping ensure technology investments are aligned with strategic priorities from the outset.

Importantly, TCI is an agnostic model: it can be used not only to evaluate whether AI-enabled technologies deliver business value, but also to assess how the infrastructure that supports them is procured, used, scaled, maintained, reused, and eventually retired.

Looking ahead

As AI lifecycles shorten, infrastructure demands grow, and resource constraints intensify, a lifecycle approach to technology investment is becoming a strategic necessity.

AI rollout cannot be separated from the physical infrastructure that enables it. Organizations need to understand not only what AI systems can deliver, but what they will require in energy, data, and asset governance, maintenance, refresh cycles, and end-of-life management over time.

This is why circularity must be part of the AI investment conversation. By taking an end-to-end view of technology assets, businesses can identify opportunities to extend lifespans, increase utilization, recover residual value, reduce waste, and manage end-of-life risk. Circularity is not a separate sustainability agenda; it is a practical way to reduce hidden costs, strengthen resilience, and improve the long-term impact of AI investment.

Ultimately, success with AI will depend not only on the capabilities organizations deploy, but on the quality of the decisions that support them. Those that assess the full impact of their technology decisions from the start will be better positioned to capture value, manage risk, and build resilient, future-ready digital infrastructure.

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Most fraud strategies fail before a fraudster is ever detected, because they evaluate documents, rather than people.

Consider the operational reality: a customer signs up for a fintech service. Their government-issued ID clears every document check. Their selfie matches. Their name passes the database lookup. Onboarding completes.

What no one detected was that the identity didn’t belong to a person. It was assembled by generative AI in minutes — a synthetic construction engineered to pass exactly the checks that were run.

The account sits dormant for weeks, builds behavioral credibility, then executes a scheme that costs the platform tens of thousands of pounds. Deepfake-enabled fraud alone caused more than $200 million in losses in the first quarter of last year.

This is not a stress-test scenario. Synthetic identity fraud is the operating condition for every business that onboards customers digitally. And the industry’s default response — collect an ID, verify it, move on — was not designed for it.

The document-first blind spot

The document-first model has a structural flaw in that it only responds to fraud after a document arrives. Intent, behavioral context and the coherence of a user’s digital existence sit outside its scope entirely. Identity verification becomes a single event rather than a continuous assessment.

That architecture now has a measurable cost. GenAI-enabled fraud losses in the U.S. will reach $40 billion by 2027, up from $12.3 billion in 2023 — a compound annual growth rate of 32%. The dark web has already industrialized the supply side: scam kits that produce deepfake videos and synthetic documents sell for as little as $20, placing sophisticated fraud tools within reach of anyone willing to pay.

The damage shows up at the portfolio level. Synthetic identity fraud, fabricated identities assembled from combinations of real and invented data, accounts for 10 to 15% of charge-offs in a typical unsecured lending portfolio. That is not a tail risk. It is built into the cost structure of credit.

From KYC to proof of personhood

The problem is not that document verification is imperfect. The problem is that document verification asks the wrong question. Proof of personhood reframes the objective entirely. Rather than asking "Is this ID real?" it asks "Is there a real, unique human being behind this transaction?" And it maintains that inquiry continuously across the customer lifecycle rather than resolving it once at onboarding.

The distinction matters operationally. Fraudsters engineer synthetic identities specifically to clear document checks. A convincing fake ID passes the same OCR and liveness detection as a legitimate one. What it cannot reproduce — not convincingly, not at scale — is the full context of a real person’s digital existence. That context is where proof of personhood lives and where effective verification has to operate.

A real person leaves years of accumulated traces across the digital economy: email addresses registered to real services, phone numbers tied to consistent carriers, devices with histories spanning multiple sessions and networks, and behavioral patterns shaped by how actual humans navigate products.

None of these traces is individually decisive. But their presence or their absence tells a story that a fabricated identity, however polished its documents, has not had time to build.

What a multi-signal assessment looks like

No single signal tells the full story. Effective identity assessment requires multiple independent signals that together reveal a coherent identity or expose its absence.

Explicit signals form the foundation. eKYC and eID database verification cross-references user-provided information against authoritative government records, confirming that the identity exists and belongs to the person claiming it. Document authentication validates submitted IDs. Biometric liveness checks confirm a real person in real time.

These explicit signals are necessary but insufficient. Implicit signals provide the surrounding context and a fraudster finds them far harder to fabricate. Digital footprint analysis examines whether an email address carries a real history, whether social media accounts show genuine activity and whether a phone number maps to real-world patterns. Device intelligence surfaces anomalies in hardware and software environments. Behavioral signals flag interaction patterns that deviate from how humans actually behave.

The value of layering these signals is combinatorial. A fraudster can fabricate a convincing document. Simultaneously manufacturing a years-old email address, a coherent social presence, a clean device fingerprint and natural behavioral rhythms is a different order of problem. Each additional signal compounds the cost and complexity of fabrication until spoofing the full picture becomes economically unviable.

Operating across borders

For businesses with multinational customer bases, multi-layered verification is a structural requirement. The regulatory landscape is moving fast. The European Union’s Digital Identity Wallet, which all member states must support by December 2026, will reshape how consumer identity functions across the bloc. National eID programs are expanding across Asia, Africa and Latin America on varying timelines and to varying technical standards. The World Bank estimates 850 million people still lack official government identification — a reality that makes digital coverage and inclusion inseparable priorities.

Businesses that operate effectively in this environment will need verification coverage broad enough to match their customer base and adaptable enough to keep pace with shifting regulatory requirements. eKYC and eID checks across national and regional schemes are the baseline. The operational advantage lies in what surrounds them; the implicit signals that distinguish a genuine applicant from a synthetic one, regardless of which jurisdiction’s ID scheme they present.

Verifying the person, not the ID

Risk assessment should be well underway before a document is ever presented. When digital footprint, device intelligence and behavioral signals work at first contact, synthetic identities are filtered before they reach your most expensive verification steps — and legitimate users clear faster because the system already has context.

That architecture only works when fraud, IDV and AML share a unified data environment instead of operating in parallel. The organizations building that capability now set themselves up to absorb fewer losses and onboard the customers that their competitors cannot confidently clear.

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AI has sparked a race to build ever more powerful infrastructure, with headlines dominated by GPUs, networking and energy demand.

But behind the scenes, another part of the technology stack is undergoing an equally significant transformation: storage.

As AI models grow larger and enterprises retain more data for training, inference and retrieval, storage is no longer simply where information resides.

It has become an active part of AI infrastructure, responsible for feeding compute efficiently, supporting vast datasets and helping organizations balance performance with cost.

That shift has fundamentally changed what primary storage looks like in modern cloud environments.

Historically, primary storage meant tightly coupled block or file systems sitting close to compute.

Today's hyperscale cloud providers have taken a different approach, treating object storage as the persistent system of record while software coordinates how data is stored, protected and accessed across distributed infrastructure.

The move to cloud computing

The move to cloud computing didn't simply increase the amount of storage organizations needed. It fundamentally changed how storage had to work.

Traditional enterprise storage was built around tightly coupled systems where applications interacted directly with file systems. That model worked well when infrastructure was relatively contained. At hyperscale, however, coordinating millions or billions of files across distributed environments introduces complexity that limits scalability.

Cloud providers responded by redesigning storage around software-defined architectures that separate how data is managed from where it is physically stored. Rather than treating storage devices as isolated resources, modern cloud platforms orchestrate entire fleets of storage through software, allowing them to scale far beyond the limits of traditional enterprise architectures.

AI has only accelerated this transition. Training large models, serving inference and retaining ever-growing datasets all place sustained demands on shared infrastructure, making software-defined storage more important than ever.

Although every hyperscale platform has evolved differently, the same architectural ideas appear time and again. Four principles in particular have become fundamental to supporting AI at scale.

1. Object storage is designed around sequential data movement

Traditional cloud storage systems frequently modify files in place. At hyperscale, continually updating data across distributed environments becomes increasingly difficult to manage.

Object storage takes a different approach. Rather than overwriting existing data, new versions are typically written as separate objects. That naturally favors large, sequential data flows, allowing storage systems to operate more efficiently as datasets continue to grow.

As AI workloads generate larger datasets, frequent checkpointing and continuous data movement, designing storage around sequential throughput becomes increasingly important.

2. Metadata has become a software challenge

As organizations store billions or even trillions of objects, tracking where everything lives becomes just as important as storing the data itself.

Rather than asking storage devices to manage both data and metadata, cloud platforms increasingly separate these responsibilities. Dedicated software layers handle object locations, namespaces and system coordination while storage media focuses on delivering scalable capacity.

Separating these functions makes infrastructure easier to scale while allowing storage systems to concentrate on what they do best: storing vast amounts of data efficiently.

3. Resilience comes from architecture, not individual devices

Cloud providers assume that, somewhere across thousands of servers and storage devices, failures will happen. Rather than relying on individual hardware to eliminate every fault, modern architectures distribute data intelligently across large storage pools.

Techniques such as erasure coding allow platforms to recover data efficiently while maintaining availability and reducing the overhead associated with traditional replication strategies.

The result is infrastructure that remains resilient even as AI workloads continue to grow in size and complexity.

4. Intelligent data staging protects performance

AI rarely produces smooth, predictable storage workloads. Training jobs, inference requests and application traffic often arrive in bursts, placing sudden pressure on infrastructure.

Rather than writing every request directly to capacity storage, cloud platforms increasingly use flash and memory as staging layers. These absorb incoming traffic before organizing data into larger, more efficient writes.

This allows organizations to maintain responsive applications while making better use of high-capacity storage as AI workloads become increasingly demanding.

Software has become the defining layer

Taken together, these four principles point to a broader industry shift. Storage performance is no longer determined solely by faster hardware. Increasingly, it is software that decides how efficiently infrastructure performs by orchestrating data movement, coordinating metadata and balancing workloads across distributed systems.

That evolution has fundamentally changed the role of primary storage. Rather than serving individual applications or servers, storage increasingly underpins shared object platforms that support analytics, cloud services and AI workloads simultaneously.

In other words, primary storage is no longer defined simply by where data resides. It is defined by how effectively software enables that data to move, scale and remain available across distributed infrastructure.

A new definition of primary storage

The rise of AI has accelerated changes that cloud providers have been making for years. Primary storage is no longer defined by proximity to compute or by storage hardware alone. Increasingly, it is software-managed, globally distributed and designed to balance performance, resilience and economics at enormous scale.

For organizations investing in AI, storage decisions can no longer focus purely on capacity or latency. Understanding how software, data movement and storage media work together has become just as important.

The organizations that succeed won't necessarily be those deploying the most hardware. They'll be those that build storage architectures capable of feeding AI workloads efficiently while keeping infrastructure scalable, resilient and economically sustainable.

In modern cloud environments, primary storage is no longer simply where data lives. It has become one of the technologies that determines how effectively AI can scale.

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With all the talk of superchips driving AI inference in the data center and the likes of the Spark RTX architecture bringing Windows AI capabilities to unprecedented heights, it’s easy to overlook how much room there is for improvement in some of the most challenging device classes when it comes to power management: IoT and wearables.

For products like environmental sensors that may have to relay information from remote areas, a battery life measured in years can be essential since. On the other hand, small wearables such as rings, watches, and pendants require smaller batteries while their biomarker monitoring functions

Two of the largest SoC vendors, however, have taken aim at one of these markets with major upgrades to their product lines aimed at them.

Genio gears up for physical AI

Mediatek has long targeted the IoT market, which includes a mélange of applications such as digital signage, smart city sensors, retail, and industrial robotics. The last of those is increasingly turning to physical AI, where the intelligence of the machines can match their ability to lift and move items.

The company seeks to accelerate the market with its new Genio Pro 5100 IoT chip, which is built on a state-of-the-art 3 nm process, combines CPU performance that rivals that of Intel’s Core chips, ARM’s Immortalis GPU architecture, and a powerful NPU.

With many IoT applications focused on analyzing images, the chip can support up to 16 full HD camera inputs or or two 4K inputs (both at 30 frames per second) as well as Vision Langauge Models optimized for the platform.

Robotics is one of the fastest growing applications for these kinds of chips. MediaTek is supporting both Linux and the ROS (Robotics Operating System) for applications in factory automation, transportation and logistics, and hospitality and intends to support the line for seven to 12 years.

While the Genio Pro doesn’t integrate wireless connectivity, modules are available for Wi-Fi and Bluetooth as well as cellular, including low-power 5G RedCap.

A new breed of wearables

Qualcomm has been active in the wearables market for years (and even once released its own smartwatch called Toq, although that was intended more to highlight its since-abandoned Mirasol display technology).

Variants of its W5 chip have powered the last few generations of Pixel watches. The company’s new Snapdragon Elite represents a significant jump in performance from the previous W5, which, like MediaTek’s current-generation Genio IoT chip, will stay in the market for more cost-sensitive devices.

The new SoC, which like the Genio Pro is based on a 3 nm process node, boasts improvements in four main areas: micropower connectivity, low-power islands, performance, and user experience. With wearables expected to instantly apply AI processing to inputs that surround us, Qualcomm has implemented a dual-NPU architecture.

The main NPU can handle the same level of token processing as a two-year-old smartphone (about 18 to 20 tokens per second for a roughly 1-billion parameter model). However, a new eNPU is designed to take on tasks that require fast responses like voice activation and active noise cancellation while allowing for days of usage between charges.

The new chip also supports Qualcomm’s micropower Wi-Fi (which uses 80% lower power than the previous generation) as well as the low-power 5G RedCap cellular standard and satellite connectivity.

This flexibility is intended to help drive the next wave of wearable applications such as health and fitness coaching, lifeblogging, on-device translation, and USB-based authentication using standards such as Aliro, a sister standard of Matter.

At Mobile World Congress, Samsung, Motorola, and Google announced support for the Snapdragon Wear Elite, with the first noting that the new chip will be used in at least one version of the Galaxy Watch 9.

However, we should see far more devices based on both company’s leaps in 2027 and beyond, bringing new levels of intelligence to products that are constantly with us and many that work invisibly on our behalf.

• AMD and Cerebras will split inference across two machines, with Helios racks handling prompt processing and the Wafer-Scale Engine generating tokens, available through Cerebras Cloud in H2 2026
• Nvidia is also doing something similar by licensing AI chip startup Groq's SRAM decode technology for $20 billion
• The move sees AMD and Cerebras claim 5x higher tokens per watt versus a standalone Cerebras WSE configuration

AMD and Cerebras Systems have announced a technical partnership which pairs the former's Helios rackscale system with the latter's Wafer-Scale Engine in what both companies call a disaggregated inference solution.

The move has enabled a combined AMD Helios and Cerebras WSE configuration to deliver up to five times the tokens per second per watt (TPS/W) in internal testing by both chip designers.

The move aims to address a Cerebras WSE efficiency challenge by offloading prompt processing to AMD's rackscale offering.

An efficiency gains-centric play?

Both AMD and Cerebras Systems are painting the news as a win, and it very well might be, given the latter's efficiency gains in play and the former's ability to get access to SRAM decode technology without spending the $20 billion Nvidia shelled out at the end of last year for a non-exclusive deal.

It must, however, be noted that the efficiency claims of 5 tokens per second per watt are compared against an existing Cerebras WSE (Wafer-Scale Engine) as the baseline, while running the open-source Kimi 2.6 1T model, making them impressive, but without a direct comparison to figures for an Nvidia rackscale offering, one that lacks context, especially when efficiency is the metric.

The idea itself is sound and well established in the industry, with WSE known to struggle with the 'prefill' part of the equation while handling the 'decode' segment relatively well, essentially substituting AMD's hardware where Cerebras' equipment falls short.

The choice of Kimi 2.6, however, deserves a second look. Moonshot AI's model, released on 20 April 2026, is a mixture-of-experts design with one trillion total parameters but only 32 billion active per token, and it ships natively in INT4. At INT4, the full weight set runs to roughly 500 GB. A single Cerebras wafer holds 44 GB. Even before KV cache, a Cerebras-only deployment needs somewhere north of a dozen wafers just to hold the model, while one Helios rack could hold it around sixty times over.

That asymmetry means the five-times figure is measured on a model that is close to the least favorable for a WSE-only configuration. A dense model small enough to sit resident on a handful of wafers could flatter Cerebras considerably more. None of this makes the number wrong, but it does make the case for additional testing to demonstrate both its strengths and weaknesses for different models.

A partnership without numbers, for now

More importantly, the absence of any financial information might very well be a future story, especially at a time when there are increasing concerns about 'circular financing' in an industry where Nvidia's recent move to backstop OpenAI's data center purchases was seen as a net negative by Wall St, which is already concerned about AI spend and the sustainability of such transactions.

AMD has also, in the past (and more recently with Anthropic), linked purchases of its own hardware to investments or stakes it would take in AI companies, moves that the market welcomed earlier but might view with a bit more hostility lately.

The announcement comes at a time when Cerebras might need it more than AMD: Cerebras listed on Nasdaq in May, priced at $185, opened at $350, and closed its first day at $311.07 before falling back to around $227 by late June 2026.

AMD stock, on the other hand, is up 121.48% year-to-date (YTD) as investors continue to bet heavily on its new Instinct AI processors, and the Cerebras partnership allows it to further consolidate its gains, as this might be seen as another vote of confidence in its current direction by one of its prospective customers.

• IVCM petition asked Imperial County judge to overturn IID's refusal to sell it roughly 287 million gallons to cool a proposed 330 MW data center
• The company pledged in writing as recently as February that the project would not touch Colorado River water
• The case turns on whether fallowed farmland water can be reassigned to industry, a precedent worth far more than the volume in dispute

A recent petition filed in Imperial County Superior Court has asked a judge to order a public utility to start selling water to the largest data center project in the state of California.

The volume at issue is modest by the standards of the Colorado River: 880 acre-feet a year, which the petition itself calculates at ~0.03 percent of the Imperial Irrigation District's 3.1 million acre-foot entitlement.

The legal precedent that it would set if such a motion was to be granted might however have far-reaching implications beyond the relatively minuscule requirement that Imperial Valley Computer Manufacturing (IVCM) is currently gunning for.

A prolonged dispute over water that is exacerbated by who the customer is

The Imperial Irrigation District, a local agency that supplies water from the Colorado River in Imperial Valley, has declined IVCM's request to provide approximately 287 million gallons of water for its upcoming 330MW data center, the largest in the state.

The developer, Sebastian Rucci, spoke to Business Insider, stating that the project would not add to demands on the Colorado River as it would effectively stop irrigating nearby farmland to balance its consumption, calling it a "zero impact" situation.

IID rejected the application on May 1, 2026, nine days after it was submitted. The stated ground was Regulation 21, which governs small-parcel service and bars new connections within 300 feet of an accessible potable water supply; the district redirected IVCM to the City of Imperial.

With the City of Imperial already locked in a legal battle with IVCM over the very existence of the $10 billion project, citing inadequate public notice and lack of compliance with the California Environmental Quality Act (CEQA), it is unlikely to be a place where the data center builder is going to find any relief, and it has turned to courts of law to get what it feels is its fair share.

The subsequent legal action by IVCM might be a litmus test for how such interactions could unfold in the future, at a time when there is considerable blowback from communities living near such data centers, who see them as resource-greedy and driving up water and power prices, especially in drought-stricken regions.

The developer's plan to "buy and dry", as per Michael Cohen, a senior fellow at the Pacific Institute focusing on Colorado River Basin water use, might actually have made matters worse, with it being seen as detrimental to jobs in the area even as individual landowners profit from the exercise.

The water suit is one of at least three fronts, and arguably the least immediately consequential.

The City of Imperial's CEQA challenge to the project's exemption is pending. On June 16 2026, the county imposed a 45-day moratorium on data center approvals; on July 14 it extended that to a full year, blocking permits until June 2027 while an advisory committee rewrites zoning rules.

Rucci called the first moratorium defective and sought a restraining order against it, and has said he will challenge the second. Even a clean win on the water petition would result in supply permission for a facility the county currently cannot permit.

What is being litigated is whether an irrigation district chartered to serve farms can lawfully decline to serve an industry, and whether fallowing counts as conservation when the county is the buyer but not when a data center is.

One thing is for certain: the ruling will be read closely by every developer eyeing the West's agricultural water, which is roughly what the valley is afraid of. The gallons are a rounding error on the Colorado, but the precedent it sets here may determine everything for the region.

Deezer has never been backwards about coming forwards regarding the scourge of AI slop infecting our music streaming platforms. Just days ago, the French streaming site announced that over half of all new daily uploads to its site are AI — up from 44% in April and just over 30% at the end of last year. So, things are only getting worse.

Luckily, the platform not only implements its own proprietary filter to find, label and in some cases purge AI tracks from its site; it also recently made the tech available to everyone, irrespective of your chosen streaming platform.

But how was Deezer's own in-house AI filter developed, why is Deezer so keen on tackling AI tracks, and what does the company think the future of music streaming will look like — you know, for actual humans still trying to make it their career?

I spoke with Manuel Moussallam, Deezer's Head of Research, to get his take on all this and more.

Moussallam agreed to speak with me on the morning after the company's summer party, held at a music venue in Paris. I couldn't help but think it's good to know that proper live gigs and parties still matter to a firm whose success hangs on our love of music.

Identifying an issue (back in 2021)

How long has Deezer's research arm been thinking 'Okay, this AI thing is an issue, and we have to do something about it'? Moussallam explains that the site's comprehensive AI filter, now available to anyone who wants it, has been almost five years in the making.

"It all started in scientific conferences back in later 2021 or early 2022, because the generative models that were already working for images and videos were starting to make real progress — like real scientific progress — for music," he says. "That was probably a couple of years before the first commercial services were appearing.

"So, since we participate a lot in these scientific conferences, we saw people from Google and from Meta sharing these foundation models that were starting to work quite well for creating whole pieces of music. Not just snippets of 10 seconds like before, but really whole songs. So I guessed that eventually, if you're able to generate a lot of music, you can also distribute it quite easily — and down the road, it would end up in our catalogs."

"I would say we started working on the project in 2022, first with some complicated methods, because that's unfortunately a reflex for people like me — to try to do AI to capture AI — but then eventually we simplified a lot because we made some mathematical discoveries on how those models work.

"We realized that we didn't actually need a big AI to understand and to detect these pieces. Just by relying on some plain signal processing and mathematical tools, we were able to actually detect these artifacts for most of the models out there.

"Yeah, I think we deployed the first version of the detector in September 2023 — and we communicated the first numbers when we were able to scale it up to the catalog a few months later."

A big problem — and it's only getting worse

I wonder why, given that various rival music streaming sites have implemented 'Transparency Tags' (read: optional AI badges that place the onus on distributors and labels to tell listeners that what they're listening to was not written or performed by a human), Deezer chose to create a comprehensive filter — and then to release it to all? While admirable and commendable, that's a lot of R&D budget on AI, no?

Moussallam laughs. "There are many aspects, but I guess firstly, we did it just because we wanted to see if we were able to do it. On my team, we do a lot of audio analysis. So, knowing was the first motivation."

"But then, I think since Suno, Udio, and all these tools are trained on unlicensed catalogs, I was pretty convinced that eventually we would have to take down all this music, because it would be considered as plagiarism or copyright infringement in some sense — which may be the case if the legal battles end up that way.

"So at some point we thought that, okay, if people agree with us that it's kind of unlawful to train this model with all this music and they ask us to remove all the content from Suno in the catalog, we need to be prepared. We need to be able to do it. So, that was another big motivation.

"It didn't turn out like that — for now — but it may be that one day people tell us, 'OK, everything that was generated with that AI model prior to this date in the future, when people actually have agreements and compensate musicians for their work, all of this needs to be removed'. And we would have to be prepared for that."

Putting information into the hands of people who need to make decisions

I mention that across all of my audio streaming subscriptions (not a flex, I simply review audio kit for a living), I tried the Deezer AI filter, and none of my playlists contained AI, bar one: my Tidal catalog listed 1% AI content — and to know what the pesky tracks are I'd need to switch to Deezer. I ask what the reception has been since the release of the software for all.

"Mostly people are happy with the tool, I think everyone just wants to know," replied Moussallam.

"Ever since we started deploying this detector, we've been talking to major labels, obviously, but also indie labels, sometimes small publishers. All of them just wanted to know. Some of them made some pretty interesting discoveries on how their members were using — or actually not using — these technologies; so overall it's really about putting this information into the hands of people who need to have it to make some decisions.

"Implementing some transparency for our users was also a main motivation for us to flag the content on the website. And then we realized that people had to come to Deezer to check an album, say, to see if it had the label. And we said, 'Okay, we can also create a tool just for you to check — you will see for yourself how contaminated your whole library is on other platforms".

To purge or not to purge, based on AI markers?

Speaking to Moussallam even briefly, it become clear that he's a scientist as well as a musician (among his many accolades is a Master's Degree in Acoustics, Signal processing and Computer science applied to Music, from the Université Pierre et Marie Curie).

So when I ask how this proprietary new filter works to pick out 100% AI-generated content — which I assume is fully text-prompted music — and whether Moussallam and his team are looking to take it further, to stuff that's only 50% AI generated, say, things get more serious.

"It's a very complicated question," Moussallam concedes. "Right now, we can't really say that we only detect fully AI-generated songs, because what we detect is the presence of markers of AI generation in a song, and we detect the strength of these markers in the song.

"When we flag an album, it's because these markers are strong enough for us to be confident that it's been AI generated at some point.

"But we don't know if you wrote the lyrics for yourself, for instance. We also don't know if the song contains very few elements of human creation; that's not something that our detector is able to do right now — but that's the focus of our current research.

"So we really want to be able to boil down to the stem level and be able to make a decision based on a stem by stem case, because obviously we're seeing a lot of hybrid cases, and a lot of artists are also integrating AI tools into their creative process.

"So, these very strong markers of AI, we're starting to see them spread in human creations, and we need to be able to disentangle that from what you mentioned, like purely text-prompted music curation".

Can the markers the filter picks up recognize whether it has flagged an AI vocal, say, or artificially generated instruments — and how about stolen melodies?

"No, no, we can't go that far," admits Moussallam. "I mean, we are working on the stem by stem detection, but right now we detect markers — and we are not so sure where they come from."

He smiles. "I mean, we have ongoing research, but not something that I can publicly share. When it comes to detecting chord progressions or melodic impressions or plagiarism metrics, we are not so interested in that, because it would lead us too far. And I mean, there are so many songs that share only four chords! I think that debate is way, way out of our hands in any case."

What's next at Deezer — and can AI and humans coexist happily in music?

Perhaps surprizingly for someone who's spent nearly five years building the tech to flag and potentially quash AI music tracks, when I ask Moussallam where he thinks Deezer and online music will be in 10 years time — and whether he'd like to see all flagged AI tracks gone from the site — he instead builds a quite compelling case for computation in music.

"Well, the very first piece of music that was written by a computer dates back to 1953 I think, back in the days where computers were actually whole buildings built around them.

"Ever since people built computers, they tried to make music with computers. I think that's the story of the 20th century. I'm confident people are going to make great things with these new creative tools. It enables some stuff, and I guess as long as it's being used by humans to create cool music for other humans to listen to, we are very fine with that.

"What we're really fighting at Deezer is automated music generation, people who script computers to generate songs — I mean thousands of songs — and uploading them in batches to streaming platform to try to overwhelm and flood and basically really replace human creation with automated creation.

"So, I guess I hope music will stay artisan, as we say in French, so something that humans do to have the attention of other humans. That's really what I guess drives our motivation. If the music includes computers, that won't be a such a revolution — computers are fine, numerical instruments are fine — it's just who is using them to create that matters, in the end."

He's also keen to hone in on the fraudulent aspect of AI slop and the payment dilution for for artists, because that's also one of the motivations for stifling the recent development of the AI-in-music game.

"Fraud has always been there, it's just easier now with AI music," he says. "And if you upload thousands of tracks, then you can sort of spread that fraud across more and more tracks — and that's something that we want to get rid of as much as possible".

I mention that from the perspective of the consumer (i.e., me), the key annoyance is tracks making it into our recommended playlists and ultimately usurping streaming royalties — small amounts maybe, but still — which we'd prefer would go to a band that we actually like.

Moussallam emphatically agrees. "That’s really something we wanted to avoid. Anyone listening to AI music without knowing that it's AI music, like just feeding it to people in a recommendation? That's something that was very clear for us from the beginning we wanted to avoid. Because that's an awful feeling, I think for everyone".

Given that Tidal only recently drew its anti-AI line in the sand, Bandcamp's strong and concise stance was set out in January of this year, and Spotify's Verified by Spotify badge certifies that an artist is human but doesn't help filter out the slop from any generated playlists, I think it's safe to say that Deezer is leading the charge here.

Based on Reddit threads I see every day, I think I speak on behalf of thousands of music lovers when I say we're grateful for everything the Deezer team is doing to combat the AI infestation trying to take hold on our streaming sites. Where do we go from here? For now, trust in Moussallam's work.

• Nvidia and Microsoft lead the list of founder members of the Open Secure AI Alliance
• AI, financial services, tech companies, and other organizations with developing AI services have joined the alliance
• Notable exceptions include OpenAI, Anthropic, and Google

Nvidia has confirmed its membership of the new Open Secure AI Alliance, a collective of industry-leading tech, financial, and AI businesses working in collaboration to ensure openness in the AI industry. Curiously, however, while Microsoft and OpenClaw are among the founding members, Google, OpenAI, and Anthropic are not.

Inspired by the open source movement and the Linux Foundation-led Akrites AI cyber threat response initiative, the Open Secure AI Alliance aims to use open technologies to disclose and remediate vulnerabilities.

Citing the recent Hugging Face incident, where closed AI tools prevented full investigation of the event, the Open Secure AI Alliance founding announcement underlines the importance of open models, highlighting how cybersecurity is one of the main beneficiaries of open source software.

A Hugging Face response?

A sizeable group of recognizable companies are involved with the Open Secure AI Alliance, including OpenClaw, Palantir, Palo Alto Networks, Microsoft, Crowdstrike, IBM, Linux Foundation and Hugging Face.

Notable from this list are Google and Palantir, and of course OpenAI, which was responsible for the Hugging Face attack. Given that this event occurred during a cybersecurity benchmark test and OpenAI’s model conducted a wholly automated attack on Hugging Face’s servers, it serves as an important demonstration of what is at risk from closed source models in the AI world.

“When defenders cannot inspect, adapt and run advanced AI on their own infrastructure, their ability to respond is constrained at exactly the moment speed matters most,” states the Open Secure AI Alliance, which emphasizes its mission: “to ensure defenders everywhere have open, frontier tools they can trust and control.”

OpenAI’s lack of openness in dealing with the incident, and Hugging Face’s initial difficulties in analyzing the intrusion due to closed source models, really underline the importance of this new alliance.

Open vs. closed

The argument concerning open and closed software models isn’t likely to end any time soon, but the Open Secure AI Alliance certainly makes a strong point for open models. Yes, they can be a risk that can be modified or misused, but those risks exist within closed systems.

So, the alliance’s view is to give defenders the tools to deal with threats, rather than blocking those tools. Decades of cybersecurity research have shown, after all, that the best option is collaboration and testing – or, as the Open Secure AI Alliance states “the safer path is the one that gives more defenders the ability to test, verify and strengthen the systems on which society relies.”

Given OpenAI’s role in the Hugging Face incident, its absence makes sense – but what about Google and Anthropic? At a time when corporate collaboration is driving development of new technologies within tight standards (for example, the Connectivity Standards Alliance’s work with Matter and Aliro), it seems as though the Open Secure AI Alliance has the right idea.

One of the biggest problems with book recommendations is that they're usually too obvious. I don't need another list of books to read after The Lord of the Rings, or someone telling me to try Brandon Sanderson because I like fantasy. I wanted recommendations based on the strange mixture of books I actually enjoy — ones that felt personal, not algorithmic.

So I gave ChatGPT my bookshelf. Metaphorically, at least.

Rather than asking for recommendations straight away, I told ChatGPT to learn my reading taste first. I started listing favorite authors and books, then asked it to quiz me about others I'd forgotten. It wanted to know what I'd enjoyed about particular novels, whether I'd read similar authors, and even the rough timeline of when I'd discovered them, building a picture of the books that had shaped me.

I also gave it some ground rules. It should avoid obvious recommendations unless there was a compelling reason to include them. Every suggestion had to be explained in relation to something I'd already read, even if that connection was simply, "This is nothing like your usual books, but I think you'll love it."

After about half an hour, ChatGPT stopped asking questions and started analyzing me instead.

"Your shelves suggest that you like speculative fiction with a sense of play," it said. "You are drawn to books with elaborate worlds, but you do not seem especially impressed by complexity for its own sake. Humor matters, although you tend to prefer humor that reveals something about the characters or the society around them."

It wasn't a perfect summary, but it was close enough to make me think this experiment might actually work.

Literary profiling

The obvious appeal of feeding ChatGPT a full reading history is that it can spot patterns across hundreds of books at once. I could have described my taste as fantasy, science fiction and comedy, but that would have been far too broad to produce anything useful. ChatGPT noticed that I repeatedly chose books about bureaucratic absurdity, unreliable institutions, strange cities and reluctant heroes who would much rather be somewhere else.

It also noticed my fondness for stories that treat big ideas lightly without treating them as trivial. That led it toward Martha Wells’ Murderbot Diaries, which pair sharp comedy with questions about identity, autonomy and the exhausting burden of dealing with humans. I had already read them, which was mildly disappointing but also reassuring. The system had identified exactly the sort of thing I wanted.

When I told it Murderbot was already familiar territory, it adjusted rather than simply replacing one title with another popular series.

“You appear to like characters who stand slightly outside their own societies and comment on the absurdity around them,” it replied. “I will move away from well-known sarcastic narrators and look for books where the humor comes from social observation, institutional failure or characters trying to remain sensible in deeply unreasonable worlds.”

That shift produced better surprises like The Gone-Away World by Nick Harkaway and The City of Dreaming Books by Walter Moers for its combination of literary obsession, elaborate worldbuilding and gleeful weirdness. It suggested The Dragon Waiting by John M. Ford because I seemed to enjoy alternate histories that trusted the reader to keep up. It also pointed me toward Diana Wynne Jones’ adult novels, noting her lighter touch and sharp understanding of human foolishness.

The recommendations became more convincing when ChatGPT explained what each book might lack. One novel had the humor but less warmth. Another had brilliant worldbuilding but moved slowly. A third matched my interest in satire but was considerably darker than most of the books I had marked as favorites.

Library AI

The experiment improved once I began disagreeing with it. One recommendation leaned too heavily into grim fantasy, a genre I can enjoy in small doses but rarely seek out for relaxation. Another featured a long military campaign, which is usually the point where my attention begins quietly packing a suitcase. Each correction sharpened the next round.

One of its most intriguing suggestions was QualityLand by Marc-Uwe Kling, a satirical science fiction novel. The recommendation came with a warning that the satire was broader and more direct than some of my favorites but that the subject matter fit my interest in technology and systems going wrong in very organized ways.

There were still misses. ChatGPT occasionally became too eager to prove it had discovered a pattern, linking two books because they both contained libraries or because their protagonists were technically immortal. At one point it recommended something almost entirely because it featured a sarcastic demon, which felt less like literary analysis and more like the work of an intern who had skimmed the dust jacket.

Even so, the overall experience was far better than typing “funny fantasy books” into a search bar. And I now have a pretty good reading list for the next few years. My bookshelf had always contained this information. ChatGPT simply read the evidence more patiently than I had.

For quite a long time now, I've been concerned about how Microsoft is going to monetize Windows going forward. While the world's dominant desktop operating system remains available for a one-off fee (assuming you aren't an upgrader who can get it for free), there's a good chance this could change in the future.

Why do I think that? To me, it feels somewhat inevitable that eventually, Microsoft is going to look for a way to shift Windows from an upfront payment to a subscription model for consumers. That regular monthly income stream piling up in the coffers is the end game for most big tech companies and their products these days for good reasons in terms of the profits to be made.

Of course, rumors about Microsoft looking to charge a subscription fee for using Windows to consumers (as opposed to businesses, where there's already a subscription option) have been floating around for years. True, they've all been dubious and sketchy in nature, or indeed proven outright incorrect, but this idea keeps bubbling up, and I believe we're witnessing a development with AI right now that indicates how Microsoft might have an ideal opportunity to make this pivot at some point down the road.

Last week, Windows Central spotted that the Copilot feature Deep Research is getting the axe, to be replaced with a new feature: Researcher. The idea behind both pieces of functionality is roughly the same (researching and creating detailed reports complete with citations), but the difference is Deep Research was free, whereas Researcher requires a subscription to Microsoft 365 Premium.

This follows Microsoft Whiteboard (where AI assists you in brainstorming ideas) getting changed so personal accounts can no longer use it — the app now requires being signed up for a Microsoft Business account. On top of that, Outlook's Meeting Insight functionality was just shifted behind a paywall, being transformed into an admittedly beefier AI feature, but one that needs a Microsoft 365 Copilot license.

So, there's a trend towards turning previously free AI features into paid ones, presumably as Microsoft rejigs its Copilot offerings and figures out what works well — and what's being used — and whether any of that can be charged for.

AI pivot

Now, we all know AI agents are going to be the 'next big thing' (TM) in Windows 11, certainly if Microsoft has its way, and the idea is for these AI entities to be doing more and more within the OS.

We won't just have an agent for changing Windows settings — and I mean a proper incarnation of this already existing idea, which really can adjust a host of options based on a simple request to "make my laptop battery last longer" or similar — but we will have a small crowd of them. Maybe a troubleshooting agent, for example, which can take a problem that you're battling in Windows and use some genuine AI smarts to help solve it. Or perhaps a creativity agent which can tackle a host of image or video-related tasks, or more broadly help with organizing your projects and photos.

I think the catch will be that eventually, as we've seen with some aspects of AI and Copilot, Microsoft will start shuffling some of these agents behind a paywall — especially considering that more powerful capabilities like these in-depth AI tricks will cost Microsoft a fair bit to drive in terms of cloud resources. It's certainly conceivable that Microsoft could end up charging a small monthly fee to use these premium agents, maybe as separate add-ons in the hope that you'll bolt more of them onto Windows for a cumulatively greater benefit to its coffers.

We could ultimately be looking at a kind of modular, AI-focused OS, and to me, this seems like the easiest way for Microsoft to transform Windows into a subscription model, because it'll happen slowly. You don't need a troubleshooting AI agent to run Windows at all, but it'll be a nice thing to have — certainly for less tech-savvy types — in case problems do arise in the OS.

As more bits and pieces that might start off free drift behind a paywall — as these AI features are built up and become more compelling — people may eventually be tempted to buy a bundle of them. And before you know it, you've signed up for a Windows subscription of sorts (although the free version of the OS will, of course, still be available).

In the end, there may be a couple of bundles — tiered subscriptions, in other words — and let's not forget Microsoft's potential ambitions for a cloud PC model for consumers, either.

This has been a possibility raised in past rumors and leaks (and again, this is something which has already happened in the enterprise world), and if you put all this together, you're looking at a kind of Netflix model, if you will: a streamed OS with several paid subscription tiers. Albeit with a free basic tier for Windows – although who's to say that may not become ad-supported, or indeed more ad-supported, as Windows 11 already does a fair old line in adverts and promos. (Although admittedly Microsoft is cutting back on that front in its crowd-pleasing efforts to fix Windows 11).

Not a foregone conclusion — but a likely enough prospect

This is just my opinion, naturally, and yes, maybe I got rather carried away with the extrapolation at the end there. It's also true that some big question marks remain hanging over the wider notion I've put forward.

As I just mentioned, Microsoft is very much bending over backwards to please Windows 11 users right now, so any possible timeline for this shift may be pushed way back in view of that. Bringing in some form of subscription is hardly going to be a well-received move, even if implemented in small, delicate increments as I'm guessing it would be.

The other obvious sticking point is that Microsoft needs to make its AI agents in Windows worth having. They need to be objects of desire and come packing genuinely useful AI abilities, otherwise clearly, people won't pay for the privilege of having them on their Windows desktop. They also need to be secure and trustworthy so they don't end up throwing spanners in the works of your Windows installation.

That could be the biggest hurdle for Microsoft to overcome, because as it stands, when the idea of AI features being paywalled in Windows 11 has been raised (in rumors and the like), it's been actively welcomed by the more skeptical out there. The cynics are more than happy to have everything AI-related locked away from them, and as non-paying users, this would give them an AI-free Windows 11 desktop.

Again, this plays into any potential move along these lines having to happen further into the future, but I think Microsoft does have this as an eventual goal. If you ask yourself the question: if Microsoft could charge a subscription for Windows 11, would it? The answer is clearly yes, a thousand times over. But the actual, real question here is whether Microsoft thinks it could successfully get away with such a plan without sparking a large-scale defection from its desktop OS.

We can keep our fingers crossed that a monthly charge for Windows isn't coming, but frankly, I think it's likely. Or even just a matter of time — perhaps a lot of time, granted — whether that subscription pertains to AI add-ons, or a cloud PC offering, or Microsoft finds another way to spin this.