In the pre-AI era, buying a Windows laptop or Windows desktop used to be pretty simple.

You looked at the processor, checked how much RAM and storage it had, considered a few screen size options, and tried to work out whether the battery claim sounded even vaguely realistic.

Fast forward to 2026, however, and most PC spec sheets look much busier and lot more focused on AI.

Alongside the usual phalanx of numbers and brand names, you’ll now find labels such as AI PC, Copilot+ PC, Snapdragon X Elite or X2, AMD Ryzen AI, NPU, and TOPS, among many others.

Some of these terms are definitely useful and relatively easy to understand, others are easy to misread, and a few sound more important than they are.

The good news is that the fundamentals have not disappeared.

The best laptop still depends on your apps, workload, budget, and the parts of the machine you’ll notice every day, such as CPU speed, memory, battery life, screen quality, ports, and weight.

The newer AI specs can help, especially if you want the latest on-device Windows features, but they are only one part of the picture.

To help demystify the laptop buying process, let's take a look at how to read a laptop spec sheet in 2026 – from AI PC to TOPS – and what actually deserves your attention before you buy.

AI PC vs Copilot+ PC: what’s the difference?

“AI PC” is the broadest label on the spec sheet. In most cases, it means the laptop has some form of dedicated AI hardware, usually a neural processing unit, or NPU, built into the chip.

While AI PC can be a useful shorthand, especially if you’re comparing newer laptops against older models, it is still a flexible term; different manufacturers use it in slightly different ways.

“Copilot+ PC” is Microsoft’s own branding category for Windows laptops that meet a defined hardware standard.

In 2026, that means a compatible processor or system-on-chip with an NPU capable of at least 40 TOPS, 16GB of DDR5 or LPDDR5 RAM, and at least 256GB of SSD storage. (Basically, a powerful set of specs.)

A Copilot+ PC should be ready for Microsoft’s latest on-device AI features, like smarter search and Live Captions, but it does not automatically tell you whether the laptop has enough storage or enough graphics heft.

NPU and TOPS: the new numbers in town

The NPU is the new spec most likely to trip people up. Short for neural processing unit, it is a dedicated part of the chip designed to handle AI tasks more efficiently than the CPU alone.

TOPS is the number usually attached to the NPU, and it stands for "trillions of operations per second", giving you a rough sense of how much AI work an NPU can handle.

For a Copilot+ PC, the important number is 40 TOPS or higher. Once a laptop clears that bar, it should be able to run Microsoft’s current on-device AI features.

A higher TOPS number may help with some local AI features, especially as software catches up, but it does not tell you how fast the laptop will feel when you open a big Photoshop file or fill Chrome with tabs.

For most people, NPU performance is worth checking, but it shouldn't take precedence over the specs you’ll feel every day, like battery life or screen size.

Snapdragon X, Intel Core Ultra, and AMD Ryzen AI explained

Processor (or CPU) names have quietly become one of the easiest parts of the spec sheet to misread.

Qualcomm’s Snapdragon X chips brought Windows on Arm laptops into the mainstream, focusing on battery life and thin-and-light designs, which had often been Apple's main advantage.

Newer Snapdragon X2 laptops push that further with more powerful NPUs and improved graphics, but the basic buying question remains the same: will your apps run properly on Arm?

For most everyday tools, the answer is now likely to be yes.

Browsers, Microsoft 365, video calls, media apps, and many creative tools either run natively or work well through Windows’ translation layer. The areas to check are usually more specific, like older Windows software or specialist work apps.

Intel Core Ultra and AMD Ryzen AI laptops take a more familiar x86 route, while still adding NPUs for newer AI features.

One thing to note: the exact model wording is important. A laptop with “Core Ultra” on the box is not automatically a Copilot+ PC, and the same goes for older Ryzen laptops without the right AI hardware.

RAM still matters more than most AI labels

RAM is likely to be one of the specs most familiar to many Windows laptop buyers, and with good reason: it's still one of the clearest signs of how a laptop will feel in daily use.

For a mainstream laptop in 2026, 16GB should be treated as the baseline – especially if the machine is being sold as an AI PC or Copilot+ PC – giving you enough headroom for normal work, video calls, web apps, and so on.

Anyone buying for heavier work like photo editing or coding should look at 32GB if the budget allows. As the AI boom takes hold, though, RAM is getting extremely expensive, so make sure you do really need it.

One benefit of buying a laptop with more RAM is that, in general, it will make the laptop eel useful for longer, especially on machines where the RAM is soldered and cannot be upgraded later.

Storage: 256GB is the minimum in 2026

Storage is another spec where the minimum and the sensible choice are not the same thing. A Copilot+ PC only needs 256GB of storage to meet Microsoft’s hardware requirements, but that can start to feel tight in everyday use.

For most people, 512GB is the better starting point, and leaves more breathing room over the lifetime of the laptop.

A 1TB drive is worth considering if you work with large files or want more local storage for games, video, or AI tools. However, as mentioned in the RAM section, AI companies are rapidly driving up prices for higher-storage SSDs.

External drives and cloud storage can help, but they are not a complete substitute for having enough fast storage inside the laptop you use every day.

Battery life: look for real-world testing

Battery life is one of the biggest promises behind the latest AI PCs, especially newer Arm-based Windows laptops.

More efficient chips and dedicated NPUs can help away from the charger, but battery life is still one of the easiest specs to oversell. A claim such as “up to 20 hours” may be based on video playback or controlled test conditions, rather than a normal day of usage.

Reviews are much more useful here, and TechRadar puts every laptop through a series of real-world battery tests to really dig into the real endurance.

Look for tested battery life that reflects mixed use, rather than assuming the biggest number on the box will match your working day.

The boring specs still matter

While AI labels are useful, they obviously don't make the rest of the laptop disappear. The screen, keyboard, trackpad, ports, weight, heat, and fan noise will shape your experience far more often than a fancy Copilot+ badge.

The best way to read a laptop spec sheet in 2026 is to start with your own needs: enough RAM, enough storage, tested battery life, and a screen and keyboard you’ll be happy to use for hours at a time.

After that, the NPU and TOPS figure can help you understand how ready the machine is for newer on-device AI features.

Windows laptop spec sheets have not become meaningless, just easier to misread. The best laptop is still the one that gets the fundamentals right first, then uses its AI hardware to add something useful on top.

• Lumo 2.0 release rebuilds Proton's privacy-first assistant with reasoning modes, image generation/recognition, cited live web search, and persistent memory
• The privacy stack mixes cryptography and policy: zero-access encryption protects stored chats and images, while inference-time protection relies on Proton's no-logs/no-training promises that have held in the past
• Proton's Lumo 2.0 Lite and Lumo 2.0 Max score 127% and 240% higher than Lumo 1.4 on the Artificial Analysis Intelligence Index, making them close in on last-generation frontier AI models

Proton has revealed Lumo 2.0, its updated AI alternative to ChatGPT, Claude, and Gemini, focusing on privacy first and foremost, a distinctly different approach from most of its competition.

The new update is not only smarter than its predecessor at what it does, but also brings a host of new capabilities: reasoning modes, image generation and recognition, live web search with citations, persistent memory, and customizable assistants.

Lumo 2.0 looks to do all of this while leveraging zero-access encryption, no-logs, no-training, a pitch that makes it appealing to privacy-focused consumers, many of whom are already customers for its VPN product lineup.

Upgrades, multiple models and faster performance

The biggest upgrade to Lumo 2.0 is that it is now multimodal, allowing it to glean information and cross-check a variety of sources without often forcing the user to defer to other AI engines for most tasks.

Proton cites a 76% faster speed for 'everyday queries' while conceding that complex tasks still take a considerable amount of time.

Users can also leverage "Custom Lumos" or purpose-built assistants that retain instructions in memory while still maintaining the encryption promise that Lumo offers, allowing users to avoid starting from scratch each time they have a query to address.

Users can use either the fast, general-purpose Lite model for everyday queries and defer to the more complex Max model for demanding work, or use Fast and Thinking modes, which offer twice the context window of its predecessor for larger workloads and greater coherence with more complex asks.

Pricing spans a free tier for what Proton calls everyday private use, a $12.99-per-month Lumo Plus plan with unlimited chats, Projects, advanced image generation and access to the most capable models, and a $14.99-per-user Lumo Professional tier for teams.

Lumo is also available to business users and offers the same upgrades discussed above, making it a significantly more powerful and smarter AI tool than it was when we last reviewed it at TechRadar.

It is important to note that while Lumo 2.0 is a huge upgrade versus its older 1.4 version, it does not come as close to frontier models as Proton might want to make it appear: its model scores a 51 on the Artificial Analysis Intelligence Index which sees current frontier models clock in as high as 59 (GPT 5.6 Sol Max) or 60 (Claude Fable 5) versus its own comparisions that show it much closer to older frontier models such as GPT 5.5 and Claude Opus 4.8.

This is not entirely surprising, given that one can find the underlying tech Lumo uses in its privacy policy. Proton states that it uses a mix of Qwen 3.5, GLM 5.2, Image-Turbo, and FireRed-Image-Edit-1.1, with GLM 5.2's scores roughly identical to the numbers it cites currently.

Despite its limitations versus newer frontier AI models, Lumo 2.0 arguably remains the most privacy-focused approach to AI available to most end users currently, and it comes considerably closer than its predecessor at what is an increasingly uphill task of late: offering a competitive privacy-centric alternative to billion-dollar proprietary AI models built by the likes of Google, Anthropic, and OpenAI.

If you're going to invest in a crime drama this summer, make it The Westies on MGM+. With its two-episode premiere dropping on the streamer this weekend, the new TV show is set to follow the real-life Irish-American crime family of the same name.

Basically, it's the perfect warm-up act for MobLand season 2 and The Gentleman season 2, which are both set to return in September. Led by J.K. Simmons as patriarch Eamonn Sweeney, expect fist fights, a cat-and-mouse chase with the FBI, and an ongoing standoff with the Italian-American mob.

While their story has never been adapted for film or TV, the Westies ran the streets of Hell's Kitchen for decades from the 1960s. When we meet them in the new crime drama, it's the peak of the 1980s, and their reputation is well-known.

This means that we're transported back to how the Big Apple looked some 40 years ago, and no matter what you think of the storyline, the setting is jaw-dropping. But according to the show's creators, no AI was used in the process of getting there.

'The look is absolutely authentic to the time period'

"It was analogue... we built the set," Chris Brancato tells me. "We didn't use AI. What we did was build a street in Toronto that replicated 1980s New York, that Hell's Kitchen street. It had blue screens where we could project a VFX background.

"The way we created that look was absolutely authentic to the time period... we achieved that because we built it," Michael Panes adds. "So we never left our stage space very much. We shot interior scenes in the interior set, then we'd walk outside and shoot on our New York Street. It was an attempt to put you, the viewer, in this time capsule environment that brought you back to the 1980s."

As far as I'm concerned, the effort has easily paid off. In the opening moments of episode 1, our core group figures out how to best exploit the Jacob Javits Convention Center construction, which is beginning to be built as the season kicks off.

While there, we get a breathtaking view of the Manhattan skyline that completely sets the tone for the artistry that we're about to see next. If anything, I think that the set is actually the strongest character of the bunch.

But even so, do mob stories like The Westies still pack a punch like they did in The Sopranos days?

"It seems to me that it shows like MobLand and others are very, very popular," Brancato responds. "So we think that the genre is alive and kicking. We hope that our show is going to enter the pantheon of the gangster shows that already exist... but I don't think an audience ever loses interest in watching criminals go after each other."

It's hard to get away from AI these days, with the technology finding its way into our apps, devices, and entertainment — but according to a survey carried out by TechRadar's parent company Future, heavy use of chatbots such as Gemini and ChatGPT has actually fallen over the past year.

The reasons behind the shift are interesting too: users are mentioning concerns over privacy, a preference for human interaction, and worries about becoming too dependent on the tech when it comes to why they're avoiding AI.

While AI is clearly reshaping our world in significant and myriad ways, there's also now a noticeable trend of anti-AI sentiment that's worth paying attention to — whether it's to do with protests against data center expansion or worries about AI slop content.

These results are based on questionnaires filled out by 1,008 respondents, and they make for interesting reading — especially when you compare them to the statistics from the same Future survey conducted last year. Here's what the latest responses tell us.

Privacy worries

There are many reasons why people aren't so sure of AI at the moment. Topping the list for our respondents are concerns over privacy, which were cited by 32% of those who completed our survey — that's actually unchanged from last year, so no matter which chatbot is being used, many of us are wary about what it's doing with our data.

Second in the list, mentioned by 31% of survey participants, is a preference for human interaction (this is a new response option, so we don't have anything to compare it to from last year). While AI is certainly now more than capable of holding a conversation on just about any topic imaginable, it seems we're still seeking out flesh-and-blood relationships.

In third place we've got those who are happy without AI, which accounts for 29% of people surveyed — that's actually a drop of 18% over the previous survey, perhaps a sign that a significant chunk of people have realized they are happy chatting to AI on a daily basis (it's certainly getting more ubiquitous by the day).

Next on the list are fears around becoming too dependent on the tech, and concerns over AI misrepresenting a 'voice' or personality (in emails, for example), which account for 26% and 24% of respondents respectively. It seems a lot of us are worried we'll get to the stage where we can't do without AI for even basic tasks — not good when there's an outage — and that our communications will turn into generic, bland AI averageness.

Neither of those two responses were available last year, but the next two came in at exactly the same level as in the previous survey: a lack of awareness of AI's capabilities (19% of respondents) and a lack of interest in what AI could do (17% of respondents). It seems the AI hype hasn't quite reached everyone yet.

Rounding out our respondents' list of barriers to AI usage, we've got technical complexity (15%, down 24%), skepticism over whether AI can be helpful (14%, down 21%), environmental and energy impact concerns (11%, a new category), and being philosophically opposed to AI as a concept 10%, down 24%).

Regular AI usage

When answering the question of how often chatbots like Gemini or ChatGPT are used, 17% of those quizzed said "daily or almost daily", while 14% said several times a day. That's nearly 1 in 3 people using generative AI at least once a day or so, though that's actually fewer people than last year — could the novelty of the tech be wearing off?

The 14% figure for the most enthusiastic users has actually dropped by 31% over the course of the past 12 months, so while usage is exploding in areas such as coding, it seems as though the overall trend is in the opposite direction (perhaps influenced by the increasing amount of AI stuffed into our apps).

As for the less frequent users, 21% of respondents said they were using AI chatbots once a week or a few times a week, 11% ranked their usage as being a few times each month, and 8% said they used generative AI less than once a month — apparently dipping in and out for occasional chats.

When it comes to complete refuseniks, 30% of the survey participants said they don't use AI chatbots at all, which is a hefty 24% jump from last year. That suggests more and more people are taking against the very idea of AI, or finding it completely useless for day-to-day life and work.

There is a divide in the respondents worth mentioning between those who read Future publications and those who don't: more than 42% of Future readers use generative AI on a daily basis, with 21% using it multiple times a day, which overall is around double the figure for non-readers.

So that's the state of play for consumers and AI at the moment then, according to the people we surveyed. With generative AI advancements showing no signs of slowing down, it's going to be very interesting to see how these figures might have changed again in another 12 months.

This week we unveiled the all-new, all-different — well, not all that different — TechRadar Awards, but plenty more happened in the worlds of tech.

Samsung announced that its next Unpacked event is taking place in a little over a week, and it’s teasing a big foldables announcement. However, based on its usual schedule, we’d be more surprised if it didn’t have any foldables to showcase this year.

Before you catch up with this week’s tech news, why not test yourself on last week’s seven biggest tech stories to see how good your memory is? Take the quiz below, or scroll on for the biggest tech news of the week… (and see if you can best last week’s ICYMI quiz when you’re done here).

7. The TechRadar Awards returned for 2026

The TechRadar Awards are back in 2026, and we’re making a radical change: we’re holding two sets of awards back-to-back this year! We’re introducing the TechRadar Product of the Year Awards and the TechRadar Readers’ Choice Awards. In the past, we combined reader votes on which products should win with our expert judges' opinions to determine an overall winner. But now we’re splitting those two elements into two different awards.

The Product of the Year Awards will be chosen solely by our editors and writers, based on their experience of using the products. The Readers’ Choice Awards will give all the power to you, our readers, though — you’ll be able to vote on a shortlist of the most popular and highly rated releases of the year, and the winner will be decided 100% by those votes.

We’re accepting nominations for products that you’d like to see shortlisted, and anyone can nominate something — so make sure that the products you love most are in with a chance!

Read the full story: Introducing TechRadar's Readers' Choice Awards and our Product of the Year Awards

6. ChatGPT rolled out its ‘smartest voice model ever’

This week, OpenAI has delivered an upgrade to ChatGPT’s voice mode for all users thanks to the rollout of two new models. The smaller GPT-Live-1 mini model will be the default for Free users, while paid users get the full GPT-Live-1 model.

GPT-Live-1 promises to sound more natural, be less interruptive when you pause while talking, and be smarter — as there previously seemed to be a knowledge gap between the written and spoken ChatGPT models.

To get around that last part, GPT-Live-1 actually delegates tasks to ChatGPT-5.5, then comes back with an answer.

There are also impressive new simultaneous translation tools that will translate what you hear live and what you say as you say it.

You can also give the chatbot a name to make conversing easier, though OpenAI’s go-to is ‘Chat’ — letting you live out your dreams of being a Twitch streamer.

Read the full story: GPT-Live-1 gives you more natural conversations without interruptions

5. Nokia debuted a retro phone with AI

This week, Nokia’s parent company, HMD, debuted a quartet of retro-style phones with one anachronistic addition — a big ol’ AI button.

They still offer a full suite of legacy features — if you miss the 3.5 mm headphone jack, a microSD for bigger storage capacity, and (on three of the models) an FM radio, you’ll find them here — it’s just that you’ll also have a little digital assistant too.

Details are thin, though it sounds like this won’t be quite on Gemini's level. Instead, the AI is mostly there to help with on-device tasks like setting alarms, opening the camera, and creating reminders. The biggest issue, however, is the AI assistant is only free for 180 days — after that it's $3.99 (about £3 / AU$5.75) a year if you're in the EU and $2.99 (about £2.25 / AU$4.30) a year everywhere else.

Yikes!

Read the full story: Nokia's new retro-styled feature phones have AI buttons for some reason

4. The Amazfit Active 3 Premium impressed us

The Amazfit Active 3 Premium is the sequel to last year's five-star Amazfit Active 2, and it's another knockout of a budget watch (despite the Premium name). A terrific fitness tracker costing just $169 / £169 / AU$239, it boasts features you normally get on watches twice the price, such as full-color maps and a sapphire glass screen.

Its metrics were accurate during testing, and it looks great to boot. It's the budget watch to get - even perhaps over popular choices such as the Apple Watch SE 3 or the Garmin Forerunner 70. It's a real win for Amazfit, which has quietly spent the last few years improving its Zepp App ecosystem and building well-priced fitness trackers for every category.

Read the full story: Amazfit’s latest premium offering is about more than just looks

3. We took over 1,000 shots with the Sony RX10 V

Sony surprised the camera community and us this week when it revived its best-in-class series of bridge cameras, with the launch of the new RX10 V. The Mark V superzoom arrived nine years after the now-discontinued Cyber-shot RX10 IV, with the same versatile 24-600mm F2.4-4 lens and 20 megapixel 1-inch stacked sensor, but with a welcome redesign and Sony’s latest processor and autofocus.

We had already tested the all-in-one camera ahead of its announcement, taking over 1,000 photos across a school sports day, bird photography trip, macro garden life and more, and were thoroughly won over, even if we were less impressed by the steeper asking price. That being said, the RX10 IV still took the crown of the best bridge camera available today, and with its predecessor being a rare secondhand find, we expect the latest model to be a popular camera indeed.

Read the full story: Here’s how Sony’s surprise new superzoom performed

2. Google Pixel 11 got a launch date

Samsung isn’t the only tech giant preparing to launch flagship phones soon — this week, Google announced that its Pixels will get a refresh next month.

The “next generation of Pixel” will be revealed on August 12, just three weeks after Samsung’s Unpacked event. Google didn’t mention any specific models, but we’re expecting to see a Pixel 11 (with fancy ‘Pixel Glow’ lights for notifications), plus new Pro models and a Pixel 11 Pro Fold.

Unfortunately, we’re also likely to see higher prices across the board, according to recent rumors. That wouldn’t be a huge surprise in these fun ‘RAMpocalypse’ days, but the expected arrival of the Pixel Watch 5 could at least help lighten the mood.

Read the full story: Google sets Pixel 11 launch for August 12

1. Samsung set a date for Galaxy Unpacked

After months of rumors, as well as an official tease or two, Samsung has finally made its next Galaxy Unpacked event officially official. Pencil in July 22, 2026, at 9 a.m. ET / 6 a.m. PT / 2 p.m. BST (11 p.m. AEDT) for Samsung's second Unpacked event of the year. Keeping with tradition, we're expecting the next-generation Galaxy Z Fold and Galaxy Z Flip, as well as an accessory or two.

It likely won't just be the successors to the Galaxy Z Fold 7 and Galaxy Z Flip 7. Samsung's invitation for the event teases "A New Shape Unfolds," likely hinting that a shorter, wider—more passport-like—foldable is on the way. The video invite also features a ticket being printed in a shape that further hints at the new design, as it simply doesn't line up with the current Fold or Flip.

So why introduce a new folding phone design this many generations in? Well, it's likely that Samsung wants to get ahead of Apple's rumored entry into the category, which, according to leaks, will take on a similar form factor. For Samsung, it's also about expanding its foldable lineup, and if we're lucky, maybe we'll see more diverse price points as well.

We don't have long to wait now. TechRadar will be on the ground at Galaxy Unpacked in London, UK, and, as usual, Samsung will livestream the event while we'll be live-blogging it.

Read the full story: 'A New Shape Unfolds' could be its biggest clue yet about what to expect

Lately, I've noticed a growing number of celebrities and influencers are talking about AI. Some seem to be partnering with tech companies. Others have positioned themselves as AI evangelists, encouraging their audiences to embrace the technology before they get left behind.

Among those who have generated significant attention are Reese Witherspoon, Mel Robbins, Sandra Bullock and Matthew McConaughey. Their messages do all differ, but they tend to orbit the same idea, which is that AI is here, it's important and you'd better get on board fast.

The thing is, the more a celebrity tells me I need to use AI, the less I want to. And judging by public sentiment, I'm really not alone.

I don't think everyone talking positively about AI is acting in bad faith. Some may genuinely believe it will improve people's lives. Others may have investments, partnerships or financial incentives tied to the industry's success. (And that's hardly unusual — celebrity endorsements have always existed.) Some may simply be repeating the dominant narrative without stopping to consider how influential they are.

But rather than try to understand their personal motivations, what interests me more is the growing gap between the way AI is being promoted and the way many people actually feel about it.

Because while some public figures seem convinced that widespread AI adoption is inevitable, public trust in the technology remains surprisingly low. Survey after survey finds that many people are cautious, sceptical or actively worried about AI. And it's not difficult to understand why.

A growing disconnect

Conversations about AI have moved really quickly over the past year. We now find ourselves debating copyright, creative labor, deepfakes, misinformation, surveillance, environmental costs, job displacement and the growing concern that outsourcing too much thinking to machines may come with cognitive consequences of its own. On the morning I'm writing this, there are fresh headlines about the grim realities of data centre expansion.

At this point, there are so many legitimate concerns surrounding AI that it's difficult to keep track of them all.

Meanwhile, some of the companies that initially appeared determined to replace workers with AI have been rowing back their plans. We've seen reports of AI-generated content requiring extensive human correction, customer service experiments failing to meet expectations and organizations discovering that replacing people is a lot harder than they first imagined.

That's why I find the celebrity enthusiasm so fascinating. Because while some public figures are urging people to embrace AI before they get left behind, many people seem to be moving in the opposite direction.

The comments beneath posts promoting AI are often filled with scepticism. Articles about AI backlash are becoming increasingly common. And when I asked my own social media audience how they felt about celebrity AI endorsements, many expressed similar concerns.

Becky Hughes told me: "All of this makes me more reticent than ever to use social media or adopt new technologies, because the safest option seems to be not to engage at all."

Jay Vera Summer said: "When I see celebrities do things like this, I wish I knew more about their stock portfolio. Especially when it's coming from people who usually don't give financial or career advice."

Whether or not all of our suspicions are fair, I think they suggest something really important is happening here, which is a growing trust gap.

At least from where I’m sitting, it seems people no longer automatically assume that enthusiasm for AI is neutral. They're increasingly wanting to know who benefits from the pro-AI messages, who profits and whose interests are being served when the technology is being promoted so aggressively.

Fear over specificity

If you actually look at what much of the celebrity messaging amounts to, it's surprisingly hollow. Learn AI. Use AI. Don't get left behind. It’s inevitable.

And what fascinates me is how little specificity there tends to be alongside it. What exactly should people be using AI for? Which tools? In what contexts? For what benefit? What are the trade-offs? What are the risks?

I haven't seen many celebrities get into any of that. To be fair, even many of the people building, investing in and advocating for AI rarely spend much time on the details. Instead, the conversation often just gravitates towards fear.

The fear of becoming obsolete. The fear of missing out. The fear of being left behind by a future that everyone else supposedly understands. As someone who has spent years covering technology, that kind of rhetoric always makes me uneasy.

And that's not because I think AI won't have a place in the future. I think it almost certainly will, for better and for worse. But because "you'll get left behind" isn't really an argument. It's an appeal to our anxieties so that you’ll act fast without thinking. And it’s encouraging adoption without fully engaging with the reasons people might be hesitant in the first place.

AI as a feminist issue

I find this particularly interesting when AI is framed as a feminist issue. Earlier this year, The Cut described this phenomenon as the "girlbossification" of AI, giving a name to the growing trend of influential women encouraging other women to embrace the technology or risk falling behind.

Several prominent women have made versions of this argument. And they’re sort of right. In some studies, women have adopted generative AI more slowly than men. But the gap appears to be driven partly by risk, ethics, and workplace factors, not just technical ability. And women have plenty of reason to be concerned about the risks.

We know that women and girls have been disproportionately affected by some of AI’s most disturbing uses, including deepfake pornography, AI-generated image abuse, and sextortion. In one UN estimate, up to 95% of online deepfakes are non-consensual pornographic images and 99% of those targeted are women. I know this isn't theoretical because I've experienced a version of it myself.

Against that backdrop, telling women they simply need to embrace AI can feel completely disconnected from reality. It risks treating healthy scepticism as ignorance when, in many cases, it seems to me that it’s a response to genuine concerns and lived experience.

The recent partnership between Kylie Jenner and Meta feels particularly relevant here. The campaign positions AI-powered glasses as fashionable, desirable and aspirational. And in some ways that's exactly what celebrity endorsements have always done, take a technology and make it feel culturally normal.

But that's exactly why these messages deserve scrutiny. At the same time women are being encouraged to embrace AI-powered devices, there have already been multiple stories of women being unknowingly recorded by smart glasses. Which to me highlights the very real concerns around privacy, consent and surveillance that often get overlooked in conversations about the latest cool new tech on the block.

More informed than you think

I've seen some people online brush all of this conversation off and argue that we shouldn't be taking celebrities seriously anyway. But like it or not, they do help shape public narratives. They influence what people pay attention to, which questions get asked and which concerns get ignored.

And right now, many of those narratives seem to be built around this strange assumption that resistance to AI exists because people don't understand it. Well, I've spent the past year talking to people about AI, and I suspect the opposite is often true.

Many people understand enough to have concerns. They've tried the tools. They've seen both the benefits and the downsides. They're making conscious decisions about how much of their work, creativity, relationships and attention they're willing to hand over to AI systems.

That's why I find so much of the celebrity messaging unconvincing. The more people tell me I have to use AI, the more I want to pause and ask: okay why? And I know I'm not alone.

And that’s not because people are afraid of the technology. If anything, that framing completely misses the point. What I see instead is caution, scepticism and a willingness to actually ask the difficult questions about where this technology is taking us.

Because I think whenever someone insists a certain future is inevitable, our alarm bells should start ringing. That's when we need to ask: okay, whose version of the future are you trying to sell?

American businessman and venture capitalist Marc Andreessen is an individual who was among the earliest to be involved in the early internet boom. Now, the founder of Netscape and co-author of Mosaic – two iconic web browsers – has fully committed to AI.

AI maximalism

One year after OpenAI launched ChatGPT, the technology veteran Andreessen published an essay that advocated for a fully-fledged acceleration of the AI buildout.

In this overwhelmingly optimistic manifesto, he argued that should AI mature and become a more sophisticated and universally utilized technology, machine intelligence that surpasses human capabilities would save countless lives.

For example, medicine is in the "stone age", he wrote, and a combined machine and human intelligence would be able to work on new cures. There's also the scope for improving the lot of mankind by, for instance, using AI to solve problems around nuclear fusion – bringing clean and cheaper energy to people around the world.

AI realism

His essay is certainly a full-throated attack on all those who have (and continue to) advocate for a pause to AI development so that scientists can properly assess and mitigate any risks. And there are plenty of those.

He describes his enemies as "bad ideas" rather than "bad people" and highlights various labels used, in his view, to stagnate progress – including "existential risk" and "tech ethics".

Despite progressing at pace in terms of capital investment and the expansion of the technology into businesses and into day-to-day life, the AI buildout has reached something of a plateau.

Not only are there arguably diminishing returns on most public-facing models, but critical bottlenecks like energy – and also components like memory – might mean deceleration is an inevitability, whether or not Andreessen or others like it.

• Report claims China's AI consumption is now over 100 trillion tokens per day, up from 100 billion in justtwo years
• Enterprise customers are increasingly paying per usage, not per seat
• General consumers will likely keep their fixed, monthly pricing

AI token consumption in China has increased "more than 1,000-fold" in around two years, with National Bureau of Statistics data claiming daily token consumption rose from 100 billion at the start of 2024 to 100 trillion by the end of 2025.

By March 2026, China was consuming around 140 trillion tokens per day, which SCMP illustrates as 100,000 tokens per person per day if every single Chinese citizen, including children, pensioners and non-workers, were to use AI.

"Today, the token economy is emerging," Yin Hao of the Chinese Academy of Sciences said in a recent Beijing conference, the South China Morning Post reported.

AI consumption-based pricing could replace per-seat pricing

With users consuming more tokens than ever before, AI providers are now under pressure to cover the growing costs. Industry experts like Hao now believe that token-based pricing could soon replace traditional subscription models.

This could see subscribers paying a fee based on how much AI they actually use, rather than paying a fixed monthly charge per user.

AI providers globally are also increasingly recognizing growing token consumption and are adapting their pricing strategies to match, including OpenAI and Anthropic. Others, like Zendesk, are charging per meaningful outcome rather than per seat or per token.

However, for now, these pricing shifts are primarily affecting enterprise customers consuming huge amounts of tokens. General consumers logging into their preferred chatbot, like ChatGPT or Gemini, are unlikely to be affected as compute becomes cheaper.

Restaurant owners are feeling the pinch, through consistently rising costs of ingredients, labor and energy bills.

There is however a less visible profit drain: the silent loss of the phone ringing unanswered as the kitchen struggles to cope.

It’s lost business in disguise, and another area where the customer experience can let down a restaurant in its busiest moments.

Despite the growth in online orders, phone ordering still happens. But the point is when it occurs: phone orders spike at the exact moment a restaurant is slammed during service.

A quick-service restaurant that faces heavy load during its peak hours is likely to see the greatest inbound volume of calls at the same time: Friday or Saturday night.

The owner is forced to choose between answering the phone and dealing with existing customers and orders.

Neither scenario is ideal, nor does it do anything good for the customer experience.

Why traditional workflows fail to scale

Many solutions deployed in the hope of managing phone orders have failed to scale or were prohibitively expensive. Hiring additional personnel to attend to phone lines is costly, and the employee still cannot take more than one call at a time.

Even in this scenario, callers may have to wait on hold, leave voicemail, or receive a busy signal. Then, it becomes frustrating to try and connect to the restaurant again when waiting in line.

There’s also the question of precision. The most mistakes happen under pressure: misunderstood orders, incorrect delivery locations, allergy-related instructions missed. While some of those mistakes may only constitute bad customer experience, others lead to waste, losses, refunds, and sometimes, lost customers.

Finally, the opportunity cost of having employees talk to customers instead of doing other things is huge: each minute they spend talking on the phone is a minute spent not preparing meals, dealing with issues of in-store customers, or completing deliveries on time.

What do AI voice agents bring to the table?

AI voice systems can significantly improve the customer experience across a few critical use cases:

Availability: the system is available to pick up every phone call, no matter how busy the restaurant may be inside. There’s no on-hold messaging queue, no hold music, voicemail message, and most importantly, no missed calls.

Concurrency: a human agent can focus on one call at a time. The AI system, however, can manage multiple calls simultaneously, which is especially useful during the moments of peak demand.

Accuracy through confirmation: if done right, AI systems repeat the order before confirming it to eliminate errors and mistakes from the process entirely.

Intent-based conversations: not everyone calls in with the intention of placing a new order. Some have questions, want to update their existing order, or do something else entirely. A properly managed implementation will ensure that the phone system knows what the caller intends to do and collects the information necessary to complete the request successfully.

It also guarantees that every interaction with the phone agent goes through the same steps: identifying intent, gathering data, fulfilling the request, confirming its completion, and providing a smooth user experience.

More precise recommendations: AI-powered recommendations are always relevant and consistent throughout the entire conversation. They are not distracting or annoying and help to boost orders’ value.

One useful way to sanity-check the value is simple volume. One recent rollout reported an AI phone agent processing over 4,000 orders in three months, and capturing thousands in orders from the busiest sites.

More importantly, calls were answered immediately even at peak, and concurrency stopped being the bottleneck.

Why is the technology finally practical?

Voice automation technology is no novelty anymore, but the recent advances in the field are all about speed and intent recognition. More advanced algorithms mean fewer misunderstandings. Improved integration with ordering systems helps to implement voice as a channel of communication, allowing businesses to serve more customers during peak times.

Two other changes are making a difference in real deployments: localization of voice, and better menu understanding. This means when a customer does not know the exact name of a dish, the phone agent can still find what they mean without turning the call into a guessing game.

Practical implementation: where to deploy voice systems

The strongest signals here are the number of incoming calls and a complicated multi-site operation. But even the smaller restaurants with a handful of calls per day can be good candidates for voice automation due to the inefficiencies associated with having an employee to attend to the phones. Automation replaces a fixed staffing cost with something you can scale up and down.

The following are the guidelines for responsible deployments:

Structure around intents: not everyone calls in for an order; therefore, the phone agent should handle ordering, updating the order, and answering questions efficiently, offering clear prompts and a consistent user experience at each step of the interaction.

Transparency: the system should notify callers about the automation process and inform them if the conversation might be recorded.

Over time, the most useful systems also get more flexible around how orders are completed. For example, enabling cash ordering where it makes operational sense, or sending an SMS link if someone abandons the order mid-call, with the basket pre-filled so they can finish on their phone. Those are small touches, but they remove friction in the moments where customers typically drop off.

Implementing voice automation in the restaurant operation allows managers to scale easily, provide consistent customer experience, and generate more orders in the process.

Not futuristic, but practical

Framing voice technology as a future-state project does not benefit the hospitality industry. Voice technology is available now and brings a significant ROI from the revenue you are already generating.

The telephone line has become a nuisance in many restaurant operations for too long. Implementing a proper phone management strategy turns it into an operational channel you can manage.

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• Palo Alto Networks CEO Nikesh Arora says 90% reduction in AI pricing is needed to drive widespread adoption, 20% reduction by next year
• OpenAI's latest GPT-5.6 Sol model is 54% more efficient across agentic coding tasks
• Shift to consumption-based models is making high costs even more pronounced

Palo Alto Networks CEO Nikesh Arora has warned that AI pricing is still too high for widespread enterprise adoption, arguing that it should be drastically cheaper.

Although model efficiency and performance have come a long way in recent years, he believes cost remains one of the biggest barriers for widespread adoption.

In a recent interview with CNBC, Arora laid out how he believes token prices need to fall by as much as 90%, however he acknowledges that the change is unlikely to happen immediately, instead welcoming a 20% price reduction over the next year or so.

Is AI pricing holding enterprises back from widespread adoption?

Arora's comments come in response to OpenAI's latest announcement, revealing that its latest GPT-5.6 Sol model is now 54% mode token-efficient across agentic coding tasks – a major improvement.

Though Palo Alto Networks' CEO admitted that this in itself is a good start, it's not enough of an improvement to drastically change pricing for major enterprises at the moment.

"It's important to understand the demand continues to be infinite," he added, implying that cost remains prohibitive. The issue of cost is also becoming more of an issue as AI companies evolve their pricing strategies, with customers moving toward consumption-based models rather than flat monthly fees per user.

Importantly, Arora's vision for cheaper AI isn't unfounded. Virtually all previous technological revolutions have already followed a similar pattern. In the case of AI, compute would become cheaper, models would become more efficient and competition could keep prices low, all working in the favor of enterprise customers.

For the last three years I’ve been imploring anyone who will listen to assuage the AI Fear Economy and the AI Doomonomics messaging that dominates our current AI news cycle.

The sleepwalkers among us and the ones that stand to financially benefit from the FOBO (Fear of Becoming Obsolete) will continue the walking dead path but many experts are finally disagreeing with the narrative that I have been spearheading for over three years now.

I’ve spent countless hours proving AIs limitations in numerous podcasts, interviews and keynotes along with my “AI Exposed” series. I really cannot find the “off” button for exposing the truth.

If you use AI every day, you know the foundational flaws of the transformer-based generative LLM kingdom.

You see the mistakes, errors, forgetfulness, hallucinations, sycophancy, recency bias, kindergarten math fails, regression to the mean, the waiter problem, no real memory, no real world experience, no determinism, no thinking, no cognition, no sense of time, no real context (environmental, cognitive bias, natural behaviors, awareness, etc.), no uniqueness, no native ethics, no agency, no intelligence (really), no accountability, no “strawberry”. Need more?

AI was never built to do the things the industry is asking it to do. Current LLMs are word predictors. They are the trillion dollar equivalent to an autocomplete on acid. Nothing more.

Workforce Extinction is what the economy is selling but Human+AI empowerment is what will be our final state. We just have to get through the current AI dystopia superbubble burst first unfortunately - which might take a bit more time than we would all like. Remember ATMs? Did they replace bank tellers? No. Remember the corner grocery store? Did Amazon eliminate them after buying Whole Foods in 2017, almost 10 years ago? No.

So how can AI be employed today in partnership with Humans to make them superpowerful?

Human: “This is my 6th meeting today. I’m exhausted.”

AI: “I’ve got your back.”

The bane of our existence, I know certainly mine. I am not talking about the nice small talk or the social and connective tissue that creates strong bonds and trust and loyalty (all of which AI cannot replicate today in the slightest), but the mechanical and mundane meetings that litter our week, every week.

Look at your calendar right now and identify next week’s meetings. Are they “ideation” or “decision making” meetings? The dreaded “Sync” meetings? Or the “Stand-ups”? All of these can be augmented if not completely replaced by AI in a powerful synchronicity that solves the “cold start” problem, immediately empowering you to make sound decisions collectively and collaboratively without you being physically present. After all, ideation is all about tapping into the potentiality of an outcome given all the relevant and contextual facts and information.

The implicit and invisible cognitive biases that humans bring to every meeting every day hold us back. Scour your historical consciousness and you will find thoughts such as “I don’t like this guy, so all his opinions are garbage,” or “I am just going to follow what the boss says,” without really understanding everything. Human gut, instinct, impulse, habits are all driving us unconsciously, invisibly. Unfortunately you are not in control of the productivity wheel.

Human decision-making is unique and infinitely complex

How you see the world, your behavior and decision-making is all unique and infinitely complex, but all patterned by high dimensional mathematics (a.k.a. AI). Synergizing your unique perspectives, ideation and problem solving with an artificial knowledge system like AI can produce incredible results in quality, accuracy and speed resulting in incredible “AI assisted” performance.

We see this confluence every day inside the company and with our customers by building group decisions automatically with each human voice being a part of the discussion to determine the best outcome. Consensus and collaboration without the human in the mix but in their unique voice through their unique prism and in synchronous cognitive sequence allows for easy ownership and accountability, at incredible speed.

And the final kicker that no one is seeing? What are you going to do when Claude or ChatGPT make a bad decision or make an error in judgment? Are you going to fire them? Write them up in Workday? Dock their pay? Good luck. It will never happen. Only humans can be held accountable.

And don’t even get me started on the Token Tsunamis happening right now. Left uncontrolled, companies are spending hundreds of millions of dollars beyond their annual budgets on AI coding and all for what? Where’s the ROI? And the prices for these models are only going higher. The small companies will be priced out very soon.

HUMANS+AI > AI

Luckily the solution is right in front of our noses: the mirror. At the end of the day, AI is only a tool. And a rather crappy tool that needs duct tape (RAG and vector databases), bailing wire (MCP and Frameworks and Agents), bubble gum (guardrails and governance), spittle (memory and state management) and Elmer’s wood glue (evaluation and synthetic testing) just to get a mostly correct answer to the question: “Who is the current US president?” on an offline local model.

All this scaffolding is in place today because the core LLM sucks at all the above. More tokens. More Token Typhoons.

AI today (at best) is Artificial Knowledge that can absolutely be leveraged by smart people and senior staff to produce high quality decision making and actioning. Do not get discouraged, but instead learn how to leverage it. Dive deep and differentiate yourself. Only through leveraging a good tool (like a skill saw) can you frame a house.

The “AI Antidote”

Interpersonal friction and endless alignment meetings are keeping your team from doing their best work. I make it my mission to unblock your team's highest potential, using our AI and turn ideas into decisions in minutes. My team and I harness AI for the betterment of humans to prevent workforce extinction. For now…

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• AI data center power consumption will soon exceed the consumption of conventional data centers
• Global data center power consumption has risen by 26% since 2025
• In the US, AI data centers account for 36% of all data center power consumption

Data centers built to add capacity for AI will soon consume more power than conventional data center hardware. Consuming 175 terawatt-hours (TWh) in 2026, predictions take that number to 258 TWh in 2027 - at which AI-optimized data centers will pass conventional data centers for consumption.

Compared to 2025, AI data center power consumption has increased by around 84%.These are the findings of a Gartner forecast that also predicts AI optimized servers to account for 31% of data center power consumption in 2026.

This level of consumption, combined with an excess in electricity demand compared to production, will be the main constraint on future AI expansion, Gartner predicts. But data center consumption as a whole is expected to be 565 TWh in 2026, a 26% increase year over year.

Consumption on the rise, but capacity is slow to expand

When it comes to global consumption, the US accounts for 36% or around 204 TWh of the full 565 TWh of global demand. Within that slice of US demand, AI data centers account for one third, with expected consumption for 2026 to be around 68 TWh.

"Surging demand for compute-intensive AI workloads is driving unprecedented data center power growth, while AI capacity is now constrained by power availability, making data center power security the new battle ground for scaling and protecting margins in the global AI race," said Gartner's Direct Analyst Linglan Wang.

By 2030, Garter predicts that supply will be unable to meet demand once consumption passes the 1,200 TWh mark.

In order to address this constraint, Wang suggested that business leaders and infrastructure providers should focus on upgrading the efficiency of power grids and the hardware that draws the most power such as cooling systems.

A recent Google Cloud report further suggested that to address the rising cost of power consumption, businesses should move from running AI models on a centralized cloud to edge deployments, where the efficiency of these systems is increased with the added benefit of avoiding a global outage of services if the centralized cloud system fails.

But these levels of consumption increase will hardly help to cool the rising anti-data center sentiment in the US, which—when combined with hardware and power production shortages—has seen nearly half of all data centers in 2026 delayed or cancelled.

Via Tom’s Hardware

The IoT and AI are a likely partnership: IoT generates and captures data, often in large volume, AI is ideally placed to analyze it.

Combined, AIoT presents new opportunities, so much so that Transforma Insights forecasts no fewer than 9.1 billion AIoT connections at the end of 2033, a more than six-fold increase in 10 years.

The potential for AI in the IoT is far-reaching, and one standout application is enhanced security.

The security risk to the IoT

All connected devices are under growing levels of threat, but the IoT is particularly targeted. According to Beaming’s cyberthreat report into UK businesses, IoT devices were most frequently attacked in 2024. They are attractive targets for the data they exchange and their potential to be compromised.

The devices are often unmanned and generally sit outside corporate security perimeters. They may be in remote spots, where they could be subject to unauthorized physical access attempts, and often remain in place for long periods of time. Many, such as the IoT devices used in energy, transport, utilities and retail, transfer sensitive data of high value.

Businesses need confidence that data collected through the IoT—both real-time and historical—comes from secure and trusted sources, not least when it comes to developing and training AI models. In this, the IoT works with digital twins, which are digital representations of physical objects or systems.

The IoT enables the seamless flow of real-world data between the physical and the digital, while the digital twin’s attributes provide the features for AI modelling. Historical data collected through IoT is then used to train and refine the AI model.

How AI is helping secure the IoT

AI applies its automation and analytical capabilities to many tasks and priorities. It is making inroads into cybersecurity, something that has not gone unnoticed. Last year, the IEEE revealed almost half of the global technology leaders it surveyed (47%) expect vulnerability identification and attack prevention to be a top use of AI in 2026.

That may be some comfort to enterprises and connectivity and solutions providers grappling with the problem of protecting IoT devices and applications. This challenge is compounded by the fact that attackers are increasingly using AI themselves to automate phishing, accelerate reconnaissance and develop adaptive malware that evades traditional detection. However, it is more comforting still, that AI is already making a difference in the IoT, redefining how organizations protect their devices.

AI-powered anomaly and threat detection (ATD) is helping security teams identify threats like suspicious network traffic and botnet activity faster and improving resilience across large-scale IoT environments, something enterprises must strive for.

In the recent past, the focus on the IoT was arguably getting devices online. That is no longer the challenge; the test now is keeping them operational: compliance readiness and the flexibility to adapt to ever-evolving commercial and technological changes. It also means maintaining resilience.

IoT security must follow a clear defend against, detect and react approach to swiftly counter attacks. No one or two of these three measures are enough without the others.

How AI improves IoT visibility and incident response

AI-powered ATD detects anomalous behavior, such as remote code execution, abnormal port connection or a suspicious IP. These could indicate the beginnings of a cyberattack on an IoT device. It analyses the anomaly and can identify the attack type, be it distributed denial of service (DDoS), man-in-the-middle (MiTM) or an attempted device takeover.

ATD can then trigger direct action, if business rules dictate an automated response. This could take the form of threat isolation or referring the incident for full review.

Anomaly and threat detection protects over one million devices

ATD runs entirely in the mobile core network infrastructure, rather than through software agents on a device, so it can be retrofitted to existing systems.

Enterprises that have identified IP backdoors and Mirai botnet infections within hours.

With IP backdoors, ATD detects unusual outbound connections, or traffic, to suspicious IPs. Such backdoors may allow remote control or data exfiltration, both of which leave identifiable behavioral traces.

In the case of Mirai, anomalous behavior typically exhibits as spikes in outbound traffic, uncommon ports use, or repetitive scanning of external IPs. ATD can flag these irregularities in real time and trigger corrective actions, such as blocking or quarantining the device, blocking or throttling traffic or patching firmware.

Automation and analytics can shape the next phase of IoT security

There is a clear shift in IoT implementation and management. It is insufficient to plan for device deployment, sit back and gather the data. Without a strategy that accounts for the stresses, threats and changes that beset IoT estates, enterprises risk costly surprises like unplanned site visits and service disruptions.

AI, through automation and analytics, can shape the next phase of IoT security. Enterprises that detect, analyze and even automatically address, anomalous activity reduce the risk of cyberattack-related outages and inconvenient site visits to access devices.

Sending field technicians to maintain or repair devices can add significantly to total cost of ownership. Each truck roll, which incurs expenses for labor, fuel, vehicle wear and often missed productivity opportunities, can add up to over $1000 per site visit.

IoT downtime, meanwhile disrupts operations and can have a catastrophic reputational, as well as financial, cost.

How to balance innovation, data privacy and operational control

Enterprises are, for the most part, keen to innovate through AI but have understandable questions about data privacy and operational control.

It is important to know what AI does, in all process integrations, to understand why it does it and to have control that prevents AI deviating from its purpose.

On data privacy, ATD isn’t installed on IoT devices. Only packet headers from device cloud communications are mirrored from the mobile core to the ATD engine, with threat levels and AI-driven insights relayed through a customer portal.

Operational control is maintained through the business rules that dictate how the ATD engine reacts. The option to refer an anomaly for review, for example, gives enterprises the flexibility to incorporate human oversight, under predetermined circumstances.

This is especially useful when you consider there can be genuine reasons why a SIM may increase or cease communication, that an incident reviewer will understand.

AI-powered IoT security

AI is making a difference to the speed, efficiency and depth of response to cyberthreats. Automation and advanced analytics within IoT solutions’ security measures also help enterprises manage costs, by minimizing labor-intensive manual tasks and site visits, and reducing the risk of expensive cyberattack reparations.

For CISOs, CIOs, product and operations managers seeking to maximize IoT value and protect their enterprise IT domains from external threats, AI-powered ATD offers visibility and actionable insights to take IoT security to the next level.

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CISOs are facing growing pressure to stay quiet about cyber incidents despite stricter regulatory demands for transparency. That’s one of the findings from Splunk’s 2026 CISO report, which shows that one in five security leaders have been pressured by their organization not to report incidents or compliance issues.

The situation is proving to be so problematic that almost eight in ten (78%) are now concerned about their own liability for security incidents, a sharp spike compared to last year (56%).

What’s more, a similar number (79%) reported that their roles have become more complex, with nearly half (43%) now given new responsibilities in areas such as fraud and financial crime investigations. Almost all (96%) of 650 CISOs questioned as part of the global survey are now also responsible for AI governance and risk management, making them the de facto AI policy leaders at their organizations.

But perhaps most worrying of all is that stand-out figure that one in five (20%) CISOs have even been pressured not to report a security incident or a compliance issue by their organizations.

CISOs concerns about liability amid pressure to stay silent

This tension highlights a growing contradiction in modern cybersecurity: CISOs are being asked to take greater responsibility for cyber resilience and transparency, while in some cases also facing pressure not to disclose incidents.

Further to this, the tension caused by internal pressures and personal responsibility has built up to such an extent that a quarter (26%) of CISOs have considered quitting the cybersecurity industry altogether.

But as concerning as these survey results are, they could soon be about to get worse. If the role of CISO was not already demanding enough, new draft laws could be about to make things even harder.

The Cyber Security and Resilience (Network and Information Systems) Bill, currently making its way through the UK Parliament is set to place even greater pressure on organizations to correctly identify, assess and disclose cyber incidents.

For CISOs, the challenge will be navigating the increasingly fine line between reporting too much and reporting too little. Organizations that fail to disclose significant incidents, or even near misses that could cause disruption, may face fines, yet reporting incidents prematurely before the full picture is clear could mean operational, financial and reputational consequences.

This places CISOs in a difficult position: they are being asked to defend against attacks, while also making high-stakes reporting decisions quickly, often while investigations are still unfolding and facts remain unclear.

This balancing act is likely to become one of the defining challenges of modern cyber governance.

A new era of cyber accountability

A recent blog published by the National Cyber Security Centre (NCSC) warned that cyber threats have become an urgent priority and that business leaders need to act now to help strengthen the UK’s collective cyber resilience.

For many CISOs, making sure they stay on the right side of the law will depend on whether they're able to find the reporting sweet spot.

One approach might be to take a ‘rather be safe than sorry’ approach. But over-reporting events that may even be false alarms can unnecessarily damage market confidence in your organization.

Similarly, underreporting incidents – or worse, brushing them under the carpet as some CISOs are being pressured to do – can dent reputations and risk long-term loss of trust.

Why evidence-led governance matters more than ever

That’s why – in the face of growing regulatory scrutiny and increasing internal pressure – CISOs need more than just better visibility. They need the ability to defend their decisions with evidence.

It goes without saying that they need to identify threats quickly. But they also need to be able to provide proof that incidents were assessed appropriately, escalated correctly, and reported transparently.

That is why evidence-led governance is now critical. Organizations need systems that create clear and robust audit trails showing what happened, when it happened, who was involved in decision-making, and what information was available at the time. That means having the ability to document and evidence that decision-making process from start to finish.

Not only that, when security teams can correlate activity across networks, endpoints, cloud environments, and operational systems in real time, they are in a far stronger position to determine whether an incident crosses the threshold for disclosure.

When you have CISOs admitting that they have effectively been 'leaned on' not to report incidents, you know that something is wrong. And it’s time for CISOs to put measures in place to protect themselves from whatever may be just around the corner.

And that’s only possible with strong governance, clear workflows, transparent audit trails – and the evidence to support that decision-making.

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Coke vs Pepsi. BMW vs Mercedes-Benz. Apple vs Microsoft.

Some brand rivalries become cultural shorthand. They shape consumer behavior, define categories, influence markets. They force a binary choice: pick a side, commit to an ecosystem, accept the trade-offs.

Now, it’s AI’s turn.

GPT vs Claude vs Gemini is the next big rivalry and this time, the stakes are different. The AI market is young, volatile, and rapidly evolving.

It took Microsoft’s Windows OS five years to truly take hold. ChatGPT had one million users in its first five days, and 180million after its first year.

In the arena of the new rivalry, businesses are asking ‘what AI model is best?’. It’s the wrong question.

There is no single best AI model

There is no universal “best” AI model. There is only the best model for a specific task, context, risk profile, and operating environment.

A business running real-time sentiment analysis on X might lean into Grok’s native advantages. A regulated financial institution requiring decision-grade accuracy needs decision-grade output. Handling sensitive data requires a platform that guarantees its information will not be used for model training.

The right choice depends on the work being done.

The major models are becoming increasingly capable across similar broad use cases. Some outperform others in specific areas, such as image generation, coding, reasoning, or speed. But for businesses, the question is rarely about headline capability alone. It is about suitability, cost, governance, reliability, and control.

Using the most powerful model for every task can be inefficient. It can also be slower and more expensive than necessary. Not every problem needs the AI equivalent of a sledgehammer.

The risk of lock-in: provider dependence

Each AI model wants businesses to use their product for all tasks. Plenty of have already picked a side. Confident in their decision, they cite greater familiarity with an existing model, closer partnerships between provider and business, and the allure of simpler and lower costs.

This choice is tunnel vision.

Overcommitting to a single AI provider places organizations at the mercy of pricing changes, product decisions, policy shifts, reputational issues, and technical limitations. It also reduces the ability to switch quickly when a more optimal model emerges for a particular task

While we may have largely reached a point where IT hardware can be depended upon for the duration of its operational lifespan, AI remains far less predictable.

Businesses therefore require flexibility. The reality is, a multi-model approach can be cheaper, as a business is not beholden to more powerful agents that charge more per token. Outputs can also be improved by running models side by side, possible through multi-model platforms.

The market will keep shifting

Capabilities that look market-leading today may be matched or overtaken tomorrow. The sector is still raw, and further fragmentation is on the horizon. Specialised models will emerge.

General-purpose models will continue to play an important role, particularly on the consumer side for simple tasks and queries. But businesses, especially those in regulated or complex industries, will categorically not rely on broad, one-size-fits-all systems in the near future.

Before too long, specialist models will emerge to match the sector and task. A business interrogating sentiment analysis on a new product won’t be using that same model to translate manuals from English to Japanese.

Due to the specific training of models, specialised systems will grow more sophisticated than one size fits all applications. Some will come from the already established players, but not all.

The “all eggs in one basket” decision of which model is ‘best’ now will be very hard to justify in the near future.

Pricing will not stay static

The current AI market has been built on rapid adoption. Many tools have been made easy to access, low-cost, or free at the point of use. The penetration pricing strategy has worked phenomenally. Models such as ChatGPT, and even some agentic tools, feel almost democratized and free-to-use. It’s allowed them to compete with search engines.

As this method increases adoption and further establishes dominance, it will change. Providers will raise more capital, become acquisition targets and, eventually, list on the stock market - there’s rumours this is happening for some of the largest players this year. Shareholders will need appeasing and commercial pressure will increase. Customers will need to give something back.

For those already ‘all in’ on a particular model, they’ll be at the mercy of price hikes. Even those who have stayed model independent will need to reevaluate definitions of what is ‘best’ based on the evolving economy of each.

Sovereignty will matter more

AI sovereignty is also becoming more important.

Governments increasingly see AI capability as a matter of national competitiveness, security, and economic influence. At the same time, organizations must navigate different regulatory, data, and governance requirements across regions. The evolving EU AI Act due to come into full effect in 2027 is one such example.

For multinational businesses, the ‘best’ model may vary by geography, as well as by task. A model that works well in one market may not be appropriate in another because of data residency, compliance, language performance, infrastructure, or policy considerations. Once again, model flexibility is the right approach.

An approach for the future

The right question to ask is: “How do we get the best out of all AI models?”

Businesses that understand this distinction will be better placed to adapt. They will avoid lock-in and stay open to new capabilities as the market changes.

The right strategy is not to avoid AI investment. AI models are already capable of transforming operations. The point is to invest with flexibility, not dependency.

Proceeding with caution is not the same as being risk-averse. It is how businesses build resilience in a market that is still forming. The winners will not be those that pick a side too early. They will be those that build the freedom to use the right model, for the right task, at the right time.

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Most enterprise leaders think they are building an AI advantage. They are not. They are leasing one.

Every day, companies are embedding AI into customer workflows, internal operations, financial processes, support systems, supply chains, and product experiences. They are calling it transformation. They are calling it innovation. They are calling it AI-native.

But under the surface, most of these systems share the same uncomfortable pattern:

• The application layer is custom.
• The workflow is proprietary.
• The data is sensitive.
• The intelligence is rented.

That distinction matters because AI is no longer just a productivity tool. It is becoming the operating layer for customer support, finance, healthcare, legal workflows, sales operations, supply chains, and product experiences.

If the intelligence behind those systems is not yours, the advantage is not really yours either.

The illusion of progress

Over the past two years, companies have made significant progress in adopting AI. Teams are shipping copilots, automating workflows, and embedding AI into customer-facing products. From the outside, it looks like a transformation is underway.

But under the surface, most of these systems share the same architectural pattern: the application layer is custom, the workflow is proprietary, the data is sensitive, and the intelligence is rented.

Every prompt, every interaction, and every decision flows through an external model. The enterprise owns the interface, but the model provider owns the intelligence. This distinction matters more than most organizations realize.

The strategic question is:

What intelligence does this company actually own?

Because right now, many enterprises own the interface but not the learning system underneath it.

• They build the workflows.
• They collect the customer context.
• They expose the edge cases.
• They absorb the cost.
• They carry the risk.

But the intelligence itself sits somewhere else.

That creates business risk that most teams are still underestimating: dependency on model providers, shrinking differentiation, margin pressure, limited control over core capabilities, and a future where the model provider can move up the stack and compete with the very products it powers.

That is not theoretical. It is the natural direction of the market.

Model providers are not going to stop at infrastructure. They are moving into products, workflows, agents, and interfaces. If your product is just a thin layer over rented intelligence, your moat is thinner than you think.

What “rented intelligence” means

When a company relies entirely on external models, you are not just outsourcing infrastructure. You are outsourcing learning. It does not become meaningfully better because of the company’s workflows, customer history, expert corrections, operational patterns, or edge cases. It generates outputs, but it does not accumulate proprietary intelligence tied to your business. This creates three problems.

No compounding advantage.

Your workflows may get more refined, but the underlying intelligence does not become uniquely yours. A competitor using the same model has access to the same baseline capability.

Limited control.

You are dependent on the model provider’s roadmap, pricing, and policies. If behavior changes, costs shift, or access is restricted, your product is affected.

Fragile differentiation.

If your AI capability is built on the same external intelligence as everyone else, your advantage is thinner than it appears. The result is a system that works but doesn’t become strategically stronger over time.

That is the part many companies are missing. The moat is not access to AI. The moat is whether your AI learns from your business.

The real opportunity

The real opportunity is not just to optimize how companies use external models. It’s to replace them in critical, vertical, and specific workflows. For most enterprises, the assumption today is that building their own models is too expensive, too complex, and only relevant for companies pursuing AGI-scale research. That assumption is outdated.

You don’t need to build a frontier model to create meaningful competitive advantage. You need to build a model that is simply better than general-purpose systems for your specific workflows.

Instead of training on the entire internet, these models are trained on proprietary workflows, domain-specific data, structured feedback from real users, and repeated decision patterns inside the business.

This makes them more accurate in-context, cheaper to run, aligned with internal policies, and continuously improving. The shift is from general intelligence to operational intelligence.

Operational intelligence does not require massive research teams or billion-dollar training runs. It requires structured data pipelines, feedback loops embedded in workflows, continuous evaluation, and the ability to fine-tune and adapt models over time. In other words, the infrastructure to turn usage into learning.

Why this matters now

This shift is happening at the same time AI is becoming more deeply embedded in core business functions, including financial decision-making, customer operations, and supply chain systems.

These are not areas where “good enough” intelligence is acceptable. They require systems that are reliable, auditable, aligned with internal policies, and continuously improving. Relying entirely on rented intelligence in these contexts introduces risk that is difficult to mitigate purely through access controls or governance layers. Ownership can change that equation.

The next phase of enterprise AI

The first phase of enterprise AI was about access. The second phase was about shipping applications. The next phase is about turning domain knowledge into infrastructure.

That means asking a different set of questions:

• Does this system improve based on our usage?
• Can our experts correct it?
• Do those corrections become training data?
• Can we verify the output?
• Can we control how it behaves in our domain?
• Are we building long-term intelligence, or just generating outputs?

Enterprises that answer those questions early will build systems that compound in value. Some will keep adding AI features on top of rented intelligence. Others will build systems that learn from their workflows and become more valuable with every interaction.

The first group will look innovative for a while. The second group will compound.

The stakes

There is nothing wrong with renting intelligence to get started. But building your core product, customer experience, or operational workflows on rented intelligence indefinitely is not a strategy.

It is a dependency.

If AI becomes central to how your business operates, then owning the intelligence behind it becomes as important as owning your data, your customer relationships, or your product roadmap. The companies that win the next decade will not just use AI, they will teach AI how their business works.

They will turn expert judgment into training data and turn edge cases into advantage.

They will build models that carry their name, their workflows, their policies, and their operational DNA.

Everyone else will keep leasing the future from someone else.

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Is AI taking our jobs?

Even as Artificial Intelligence displaces a significant number of roles in the United Kingdom, it’s clear the net-positive impact on the job market remains favorable.

A new study estimates that evolution in AI technologies could create as many as 610,000 new jobs by 2028.

What’s more, employees can look forward to not just more jobs but better ones; an analysis of over 500 million job advertisements from 15 high-income countries revealed that employers in the U.K. were willing to pay 14% more to workers with AI skills.

People with AI skills also have an expanding choice of roles, as organizations in every industry look to hire data scientists, machine learning engineers, computer vision specialists, AI trainers, human-machine teaming managers, AI ethicists and compliance officers and more.

AI is increasing the job market

In the highly digitalized U.K. financial services sector, 75% of organizations are already leveraging AI and another 10% plan to adopt within a few years. The wide range of AI use-cases spanning process optimization, cybersecurity, fraud detection, customer support, credit scoring and compliance management etc.is opening up a variety of opportunities for job seekers with relevant skills.

20% of education sector professionals in the U.K. are using AI in their current role to improve learning content, motivate students and accommodate their different needs, or even to complete administrative tasks. As roles combining technology and education evolve, there will be a need for skilled learning experience designers to create effective learning platforms, content creator managers for ensuring educational content meets quality standards, and specialists who leverage AI tools to create and manage online communities for learners.

Even in healthcare, where human expertise and in-person interaction are highly valued, there’s a huge need for AI professionals, in the near future, to build algorithms that can generate personalized treatment plans and predictive analytics models that identify disease patterns and risk factors. With telemedicine becoming common, the healthcare industry will require staff who can operate AI-enabled remote patient monitoring systems.

The use of smart health assistants, AI-based medical imaging and intelligent hospital workflow optimization tools will create a demand for medical professionals with varying degrees of AI capability and also for medical AI specialists, telehealth coordinators, and data analysts for managing and processing massive volumes of health data.

But where are the skills?

In a recent UK survey, 68% of IT decision makers said that insufficient skills and expertise was their top challenge in implementing AI projects, more problematic than integrating disparate data and fragmented systems. Training is the obvious answer, but it appears that 19 percent of the U.K. workforce – 6.31 million employees – have not been trained in how to use AI tools at work.

Organizations need to take urgent measures to prepare their employees to deal with the change that AI will bring to their work and workplaces. Besides training employees in functional AI skills that enhance productivity, employers should build capabilities in communication, empathy and problem finding to optimize the performance of human-machine teams.

Training with and for AI

Traditional training methods, involving scheduled classroom-based lessons with standard curricula, are ineffective for AI technologies, which are evolving at a blistering pace. Organizations need to establish an environment of continuous, lifelong learning where employees can learn new skills on the job, throughout their careers.

AI-powered learning platforms support this by providing rich training materials that employees can access from anywhere, and at any time, to learn what they want, when they want it. Importantly, training also addresses cultural barriers to AI adoption, such as apprehension about learning new technologies.

Next Steps

To successfully bridge the AI skills gap, employers should follow best practices, including integrating AI literacy in different functions to prepare employees across the enterprise for a future in AI and partnering with educational institutions to offer learning programs aligned with business and market needs.

The U.K. government can play a major role in building an AI-ready workforce by offering incentives to organizations investing in AI training and also providing internship opportunities for young people to gain practical experience in AI tools and technologies. This is also in the administration’s interest because AI has the potential to increase GDP by £550 billion over the next decade.

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For years, the artificial intelligence industry operated under a philosophy of rapid innovation. Whilst this benefits sectors like healthcare, the initial wave of optimism surrounding generative models is giving way to a challenging reality. Numerous national security agencies and research bodies are issuing warnings regarding the potential for these models to be weaponized.

At the heart of this warning is the realization that large language models do more than process text - they democratize technical knowledge and act as decision makers rather than tools. In the wrong hands, this capability can be applied to malicious cyber operations and the subversion of critical digital infrastructure.

Historically, executing sophisticated cyberattacks required years of specialized technical expertise in exploit development and network intrusion. Today, artificial intelligence bridges the knowledge gap for individuals who lack formal training but possess malicious intent.

Recent assessments by international security bodies, such as the Organization for the Prohibition of Chemical Weapons, highlight that the emergence of data driven molecular design models means that those with limited expertise can bypass monitoring from regulatory frameworks.

New frontier AI models such as Mythos could theoretically identify alternate synthetic pathways to design toxic chemicals using ordinary laboratory reagents.

Within enterprise environments, the exposure of software supply chains and cloud infrastructure also remains an acute vulnerability. Third party applications account for a high percentage of emerging security risks, and modern enterprise operations rely on a web of digital service providers and data aggregators that were barely visible a short time ago.

Every vendor represents a potential point of entry, and a single compromised credential at a small third party service provider can grant an attacker the freedom of lateral movement within a corporate or government network.

Current public skepticism is a response to the lack of transparency in how these frontier models are trained, monitored, and integrated. Companies developing models have a responsibility to ensure that their innovations do not compromise the stability of public systems.

This requires a commitment to the responsible deployment of these tools, prioritizing national security and architectural resilience over speed to market. The industry must move away from generic statements and focus on explicit, verifiable security practices.

A new framework for technological visibility

Organizations must adopt a rigorous approach to machine visibility and network defense. Traditional perimeter focused security tools are insufficient - defensive structures must shift toward continuous internal monitoring.

This means analyzing east west traffic within an organization network to scrutinize communications between systems and understand normal data flow patterns. Anomalies must become immediately apparent so security operations teams can act before an intrusion escalates.

Response times must also collapse. The traditional multi week window that attackers enjoy gives automated threats too much leeway to cause damage. Modern network detection and response platforms shrink attacker dwell times by identifying suspicious machine behavior in real time.

Because systems prefer structured layouts and consistent schemas, defender tools must leverage network telemetry to track how these models interact with internal data stores. Security teams need to see exactly how data is being processed, ensuring that unauthorized models are not mapping corporate assets.

Governments are responding to this reality with updated legislation, such as the strengthening of national cybersecurity laws in the UK. These updates expand the scope of statutory regulations to include essential digital service providers, managed service providers, and data centers.

Tougher penalties raise the cost of non compliance, and mandatory incident reporting requires organizations to alert regulators within tight windows, often 24 hours. These legislative changes acknowledge what technical experts have warned about for years - that cybersecurity breaches on critical infrastructure are a national security threat.

Breaches and automated attempts at exploitation are inevitable. The industry must treat advanced software infrastructure with the same level of caution as critical physical assets. In a world where automated systems can orchestrate complex network intrusions, the move towards more comprehensive security measures is essential.

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• The Mac mini has emerged as an affordable system for agentic workloads
• Apple has seen "incredible demand" for the Mac mini and Mac Studio
• Apple silicon can handle an agentic AI while other architectures use a GPU and CPU

If you’re looking for the best way to explore and deploy agentic AI without breaking the budget, the Mac mini might be just what you’re looking for.

Apple’s Doug Brooks has expressed enthusiasm for how the Mac mini and Mac Studio desktop computers are capable of handling agentic AI tasks, thanks to Apple silicon, the ARM-based SoC that the company has introduced over the past half decade.

Success with local AI on these machines has been attributed to design choices made before the arrival of advanced LLMs, with the evolution of Apple’s Neural Engine highlighted as a key factor.

How the Mac mini is ideal for agentic AI

Brooks is the senior product manager of Apple silicon, and referred to the “incredible demand” for Mac minis and Mac Studios when speaking to The Deep View before WWDC 2026.

Describing the Mac mini as an “amazing system” that can “tap into the strengths of Apple silicon and unified memory in a very power-efficient way, and increasingly they're delivering compelling price-performance as well.”

The price point of a Mac mini – compared to the more expensive Mac Studio – makes it particularly suited to teams exploring agentic AI but without the budget to pay for tokens and larger systems.

Neural Engine technology dates back to the A11 chip, and its evolution and inclusion within the current generation of Apple chips, and its high-performance, power-efficient compute processes are pivotal in delivering machine learning to the desktop.

As many AI tools were available first on the Mac (or released exclusively for macOS), it seems that upgrading to the latest Mac mini or switching from Windows has been instrumental in demand.

Mac mini: amazing for AI

Apple’s work on AI has seen deployment in everyday use across computers, tablets, and smartphones, and the company has been a leading exponent of hybrid AI, where an agent can “decide what needs to happen locally and what needs to happen in the cloud based on the workload.”

“For agentic workloads, people often want a system that's under their control, isolated from their primary machine, and capable of running 24 hours a day, seven days a week.”

But it is the strength of the Apple Mac mini and Apple Studio – as well Apple’s notebooks – in handling AI that seems to have enthused Brooks the most. He cites security and economics as concerns for developers and creators who are now realising that they can handle AI workloads sitting at their desk – whether using a Mac mini or something more powerful.

• Bosses are seemingly confused that mass AI in favor of human workers isn't always a success
• AI operating costs are often higher than expected
• However some firms are happy to adapt and refocus where needed

Bosses are being left confused at the high cost of moving to AI-centric models, with many seemingly baffled that replacing human workers with agents isn't instantly saving them huge amounts of money, new research has claimed.

A new KPMG report found nearly a third of business leaders reported some difficulties with getting to grips with AI operating costs in their organizations.

The news comes as several major AI providers, including the likes of Anthropic and OpenAI, have moved some services toward usage-based billing, rather than flat-rate subscriptions, in recent months.

Making AI effective

The report saw KPMG survey 2,145 senior leaders across 20 countries, finding 29% were struggling to understand the rise in operating costs as they looked to scale AI across their business, with a similar proportion also highlighting a limited understanding of AI costs and economics as a major challenge to deploying AI agents.

"As usage-based pricing models become more common, many organizations are still building the capabilities required to forecast, monitor, and manage AI spending effectively," KPMG said.

When things do go wrong, the report highlighted how leaders were often unclear who should take responsibility, particularly in the case of hallucinations or errors by AI models.

It noted that why having human leadership be accountable is important, "governance ultimately succeeds or fails through day-to-day operating practices."

"Organizations need clear rules for when employees can intervene, who owns AI-related costs, how AI outputs are reviewed and what happens when systems fail. While most organizations report having at least some governance mechanisms in place, relatively few describe these practices as fully embedded," the report said.

When costs have outweighed the expected value, the report found a surprising amount of contrition from its participants, with nearly half of organizations saying they had rephased AI deployments in that case.

"These actions do not signal reduced confidence in AI," the report warned. "Rather, they suggest a growing willingness to evaluate where AI creates meaningful value and where it does not. Organizations appear increasingly focused on concentrating investment where expected returns are strongest."

"We’re seeing a clear divide between organizations with leadership accountability at the top and those without," added Steve Chase, KPMG Global Head of AI and Digital Innovation, KPMG International.

"These companies are seeing materially better results across the board such as greater confidence, higher value realization and established ROI."

• Meta reportedly has a new AI glasses prototype
• It is helping the company finalize a super sensing AI model
• Meta execs apparently aren't yet certain what rules the all seeing AI should follow

Meta is reportedly trialling a new super-sensing AI glasses prototype that aim to offer an ultimate level of assistance, for the small price of capturing your every moment.

The basic idea is that, because personal AI assistance gets better the more the AI knows you, by having you smart specs watch every detail of your life, the assistant can then offer more insightful help.

It could, for example, know if your fridge has milk in because it has seen inside it, or remind you not to forget your keys as you go to leave home. It could remember that gift your friend was desperately hoping to get for their birthday, or remind you the name of someone you’ve met before, if you're forgotten and are too embarrassed to ask.

This kind of tool would undeniably be pretty handy, but the extreme cost would be that your glasses would have to be always on — otherwise the AI might not see or hear the crucial information you’ll need later.

However, this incredible level of insight could be very easily abused — so you’d either have to really trust the company capturing it, or just not care if it knows everything about you.

This latest super-sensing leak comes via the Financial Times (behind a paywall) which reports that Meta is currently testing a pair of specs that achieve super-sensing by capturing continuous audio, and snapping photos every few seconds.

The more staccato image feed is likely an effort to conserve battery — according to previous leaks, existing Meta glasses have all the hardware they need to pull off super sensing, but their batteries wouldn’t be able to last long enough for it to be widely useful.

According to the report, Meta is also still trying to work on some other aspects, like whether the recording light LED should be displayed or not. When you’re recording it normally would be — super sense would be capturing people around you who might wish to know if they’re on camera — though sources have told the FT that there are plans for it to not to be displayed.

Considering that Meta just made a big post about privacy, and even updating the specs to disable tricks modders have been using to allow the specs to record without the light coming on, I’d be surprised if it took the no-light approach, but we’ll have to wait and see.

Man vs machine

You might also remember a story we shared in March about Meta contractors who claimed to be able to see images and videos taken with the glasses.

Essentially, in order to use Meta’s AI you agree to allow the company to see information about your AI communications, including videos and photos. This seemingly includes photos and videos you take using hands-free voice controls — which does technically involve Meta AI, as it has to action your request.

If an always-on AI is seeing every detail of your life there’s potentially a lot more scope for you to capture sensitive data that you'd rather not let Meta's contractors (or anyone else frankly) see. Encouragingly, according to people familiar with Meta’s super-sensing technology, there are plans to never store the raw footage and audio — meaning Meta nor the user could access it.

Instead, the system would extract the metadata from the capture, and only that would be uploaded — metadata is data about data, so imagine if instead of showing someone a photo you just described what’s in it and where it is.

There’s still some room for personal data to leak through this system, but with the correct safeguards it would have far fewer privacy implications. The big obvious gap would be the privacy of those around you — people who, unlike you the glasses wearer, might not have consented for Meta to store any kind of data about them.

The FT report adds there are also debates over whether Meta’s AI should be allowed to use this metadata for training purposes, in order for it to keep up with the capabilities of OpenAI, Google, and Anthropic’s models.

While leaks should be taken with a pinch of salt, Meta CEO Mark Zuckerberg has made it clear that he wants to offer these kinds of features on his company’s glasses. Most recently he said in an investor call that he wants smart glasses “to be a personal agent that’s with you all day long, helping you remember things and achieve your goals.”

So the advent of technology like super-sense is most likely a matter of when not if, though with battery hardware constraints persisting (especially if you want slim and light glasses), and with privacy being such a hot-button issue, I expect it might be a while before we see super sense in action.

Whenever it arrives, we'll just have to hope that it’s implemented in the right way, and with appropriate safeguards. Such a tool could potentially offers some incredible accessibility benefits, but if the privacy cost is too great the I don’t see it taking off in the way I’m sure Meta would want it to.

• OpenAI plans to hire a $200k Investment Banking expert as it continues to target finance
• The company wants to improve financial accuracy, consistency and overall quality
• ChatGPT already has its own personal finance tool

OpenAI is advertising a new "Subject Matter Expert, Investment Banking" role within its team, serving as the latest hint that the ChatGPT maker wants to expand its reach into the finance sector.

This latest position strongly implies that the company wants to increase more sophisticated Wall Street type tasks, rather than be an all-purpose chatbot, and it makes sense.

We've already seen OpenAI and rivals like Anthropic heavily target certain sectors – banking and law being two of the most evident in recent months.

Could OpenAI be expanding its reach into finance?

OpenAI described investment banking as one of the most demanding forms of knowledge work, putting pressure on workers to "synthesize fragmented information, exercise judgment under pressure, and produce precise, defensible models, analyses and client materials."

Being that the job was posted in an entire blog post-style announcement rather than on a dedicated careers platform, it's clear the role will be a high-level one reporting to senior leaders and will play a considerable role in the company's direction. The salary of $185,000 to $205,000, plus equity, is also indicative of the role's importance.

The announcement highlights the importance of quality over quantity, stressing the need for AI to help produce work that's financially correct, traceable and consistent.

OpenAI also wants prospective candidates to help identify the highest-value AI opportunities in investment banking – another clear signal that the company is going after the sector big-time.

With its existing expertise, OpenAI has already targeted the consumer end of the sector with a new Personal Finance tool within ChatGPT that connects to bank accounts to give users insights into their spending habits, investments and savings.

While the AI data center boom is undoubtedly an opportunity for the fiber industry, it also presents something of a challenge.

Sure, investment in network infrastructure is rarely a bad thing. I can’t help, however, getting flashbacks to the early 2000’s - operators pouring billions into fiber, without proven demand.

This resulted in stranded assets, empty capacity, and ultimately, a bust that hampered telecoms for nearly a decade.

This time around, as serious AI momentum continues to grow, with megawatt campuses, GPU clusters, hyperscaler capex announced seemingly every week, the temptation to ‘follow the boom’ is rearing its head again.

I’m optimistic, however, that the industry has learnt its lesson. Not just to avoid overspend, but to be far more targeted about where fiber actually gets built, what kind of fiber is needed, and which partnerships can make new routes commercially viable.

AI demand is real, but discipline will be crucial

There’s no denying AI demand is there, but it's important operators don’t get caught up in the hype and assume it will be everywhere.

It may sound basic, but the industry needs to read the tea leaves carefully. Data centers have never had the luxury of a ‘build it, and they will come’ approach. The strongest sites tend to sit where demand, power, fiber and customer ecosystems can all be brought together. Things are changing however; data centers are on the move.

Fiber investment therefore, should focus on solid signals, from AI Growth Zones and established data center corridors to cloud on-ramps, internet exchanges and emerging compute clusters. While the latter two have traditionally centered around London (particularly the London Docklands and Slough), the Government’s proposed AI Growth Zones point to a broader regional shift.

Sites in Oxfordshire, the North East, North Wales, South Wales and Lanarkshire in Scotland aim to spread AI infrastructure development away from the capital, bringing data center demand closer to new regional power, land and connectivity ecosystems.

Beyond the Government initiatives investors are also placing bets on other development opportunities outside of the traditional areas.

AI hyperscalers want platforms, not just physical fiber

But even if you do invest in fiber build in all the right places, that’s no longer a guarantee. While location is one aspect (and admittedly, not a ground-breaking one, pardon the pun), there’s a new, arguably even bigger factor that network operators can’t ignore - consumability.

Hyperscalers, neoclouds and AI infrastructure businesses are not always buying connectivity in the same way as traditional telecoms customers. They expect capacity to be identifiable, orderable and scalable at speed. In other words, they want the fiber market to feel more like a platform than a slow procurement exercise.

AI workloads are also pushing future rack-density designs towards 1MW, which places extreme demands not just on power and cooling, but on fiber density and the speed at which capacity can be provisioned.

As these companies scale, providers that can offer API-driven ordering, rapid provisioning, clear visibility of available routes and flexible rerouting will be in the strongest position when capacity decisions are made.

Having fiber in the right place may secure a place in the conversation, but making that fiber easier to consume will increasingly determine who wins the work.

Compute is useless without connectivity

There’s another major factor, however, one that is often overlooked and missed in the media conversation around UK AI infrastructure - backbone connectivity.

As a call back to the opening of this article, AI infrastructure is often viewed as a hardware arms race. With much of the focus falling on megawatts, GPU clusters and hyperscaler capex, with data center capacity treated as a shorthand for infrastructure readiness.

This view missed the wider systems those facilities depend on. Without the right infrastructure backing up development, the UK risks repeating old DC patterns, where new sites sit underutilized due to a lack of customers, or are delayed entirely due to missing fundamental backbone infrastructure. This concern is already starting to bite. According to recent research, 82% of UK DC operators have already delayed new facilities or expansions because of limited access to high-capacity fiber infrastructure.

Take the UK government’s compute ambitions. The UK has said it will need at least 6GW of AI-capable data center capacity by 2030 to support the growing demands of AI. That is a serious statement of intent, but the gap between ambition and deliverability is still understated. The UK’s AI ambitions are often framed in terms of compute capacity, rather than the connectivity and required to make those investments viable.

None of this makes the UK’s AI ambitions unrealistic. But it does mean fiber can’t be treated as an afterthought. Data-center operators, hyperscalers and network providers need to plan connectivity earlier. Developing a clearer view of where capacity exists and where new resilient routes are needed, particularly in more regional locations outside of London or where the landscape is more challenging for development. Alongside how quickly they can be made commercially available.

Interestingly, in recent months we have seen a change in the approach to fiber emerging. Much earlier engagement from the DC developer/operators on fiber availability and timeline to deliver, along with a significant increase in the capacity required from day one, with a much steeper ramp in uptake.

This tells us two things, the forecast in uptake and data movement has shifted, as has the move to further de-risk a development by getting assurance on fiber/connectivity at a much earlier stage.

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Do you remember the metaverse? If you don’t, don’t worry. In 2026, four years into the AI revolution that’s changing the world forever, you could easily be forgiven for thinking it was a strange fever dream you had back in 2021. You might even have odd memories of seeing a blocky version of Mark Zuckerberg floating about in a Minecraft-inspired hellscape, conducting meetings with people who could walk with no legs, while the real Mark Zuckerberg was looking at the whole thing through VR goggles. At least, that’s my memory of it.

I could be suffering from the Mandela effect, but I distinctly remember something off about the legs. They fixed that in a later version, but that’s my overriding memory — no legs. Oh, and Mark Zuckerberg assuring us that this was the future. He’d spent tens of billions of dollars on it, even changing the company name from Facebook to Meta, just to let us know he was really serious about the metaverse. Even if the legs didn’t work.

The problem was, it looked laughable. While everything in the technology world to do with games and special effects was moving in the direction of hyper-realism, the metaverse was moving in the opposite direction, towards the sort of blocky graphics that small children enjoy. But even that didn’t really answer the most basic question about the metaverse. Why? What possible advantage was there for us all to meet in a VR space where clunky avatars of ourselves could interact… badly?

Then AI happened and Meta abruptly forgot about the metaverse and pivoted towards the mission of putting personal superintelligence in all our hands instead, which sounds as terrifying and dangerous as it actually is, but we are where we are. At least when Zuckerberg was obsessed with the metaverse, we could ignore him. It existed somewhere “over there”, in Meta-land, where we could let him get on with it. Now he’s right up in our business again.

The rise of a bad idea

I’ve always been fascinated by how Zuckerberg got into the metaverse and why he became so obsessed with it. The origins of the metaverse go way back. In March 2014, Facebook bought Oculus, the VR company, for about $2 billion. This was where his passion for VR started. Think of it as the seed, not the full obsession.

By July 2021, Zuckerberg gave a long interview to Casey Newton at The Verge about Facebook becoming a “metaverse company” and described it as an “embodied internet”. Then, on October 28, 2021, his obsession became the company identity. Zuckerberg announced that Facebook the company was becoming Meta at Connect 2021, saying the new company brand would focus on bringing the metaverse to life.

I can see the logic. As a business strategy, that made sense. As a product ordinary people were expected to use, it was much harder to understand. Zuckerberg did not want Meta/Facebook to be trapped inside someone else’s platform again. Facebook had won on social, but on mobile it remained dependent on Apple and Google for distribution, privacy rules, app-store policies and hardware. The metaverse looked like a chance to own the next operating system of social life: hardware, avatars, identity, payments, meetings, gaming, work, commerce — the whole stack. In his 2021 founder’s letter, he framed the metaverse as the “next chapter of the internet” and said Meta would become “metaverse-first, not Facebook-first.”

Facebook had obviously had its problems — it was scandal-ridden. It had let advertisers target vulnerable teenagers, helped fake news spread, and enabled the spread of hate speech linked to atrocities in Myanmar. Perhaps Zuckerberg was looking for a way out of Facebook, and the metaverse offered that.

What I still didn’t understand was why he didn’t see what the rest of us saw — that it looked terrible and offered no real benefit to users. Then I read Sarah Wynn-Williams’ tell-all book about Facebook, Careless People, and it all started to make sense.

To say the book made my jaw hit the floor on several occasions would be an understatement. It’s an absolute page-turner, and your reactions grow from mild amusement to shock, then disbelief, then absolute outrage the further through the book you get. I’m aware of the criticisms of Wynn-Williams: that it is a book written by a disgruntled employee, and that she dodges a lot of personal responsibility for her part in the various misdeeds of the company. However, in another perfect example of the Streisand effect, the fact that Meta obtained a legal order in the United States to prevent her from saying anything negative about the company — at all — made me want to pick it up, and I’m glad I did.

Because now I get it — Zuckerberg seems to have spent years in an environment where too few people were willing to tell him when his ideas weren’t good. According to Wynn-Williams, he was surrounded by sycophants. When he had bad ideas, like the ill-fated Internet.org, he wouldn’t let them go and persisted with them, even when they were obviously going to fail. The people around him enabled him because he was simply too powerful. They even let him win at the board games he liked to play with them at his house or on his jet, and — crucially — he didn’t notice that they were letting him win. I can imagine that in that environment, nobody inside Meta would want to tell Zuckerberg that his metaverse was the equivalent of the emperor’s new clothes, especially if they wouldn’t even risk beating him at Settlers of Catan.

Wynn-Williams only mentions the metaverse in her epilogue. It happened after she was brutally fired from Facebook. Perhaps selfishly, I wish she’d been there for the metaverse period, because I would love to read firsthand accounts of how and why Zuckerberg persisted with such an obviously bad idea.

The fall of the metaverse

Maybe I’m being too harsh on Zuckerberg. The metaverse graphics did get better over time and Apple ventured slightly into the same territory with its Apple Vision Pro, even after the metaverse had turned into a smoldering wasteland. The fact is, people don’t enjoy wearing VR goggles for extended periods of time, and for normal people, VR lacks that one killer app. There doesn’t seem to be anything you can do in a VR space that you can’t do elsewhere much more easily.

The metaverse didn’t really die with a bang, but with a whimper. It faded through layoffs, spending cuts and the AI pivot. If I had to put a date on it, I’d say early 2023 was when Meta’s narrative moved on. In February and March 2023, Zuckerberg started talking about Meta’s “year of efficiency” and announced huge layoffs and cost-cutting. OpenAI had launched ChatGPT in November 2022, and by early 2023, generative AI had swallowed the oxygen that ideas like the metaverse need to survive. Every tech company was talking about AI now, not virtual offices and avatar legs.

The metaverse was over. We all forgot about it and moved on.

I’m glad I read Wynn-Williams’ book, because now I can understand how Facebook let the metaverse happen. And if there’s one thing I learned from reading it, it’s that money and power can bring you a lot of things, but common sense requires neither.

• Most businesses are struggling to deploy agentic AI effectively, and legacy infrastructure is one of the key reasons, report finds
• Google polled IT leaders, with 83% stating that infrastructure upgrades are needed
• IT leaders are also concerned about the hidden costs of agentic AI, such as increased power consumption and operational complexity

If there was a single message to take away from this article, it’s that the infrastructure every business relies on today was not built to handle agentic AI.

Google surveyed over 1,400 senior IT leaders on their AI ambitions, and found that 83% of organizations say they require infrastructure upgrades to leverage the full benefits of production-grade agentic AI.

Moreover, many of those polled are also seeing unexpected costs arise from attempting to run agentic AI on legacy infrastructure. 62% said they had seen significant inference tax driven by data egress fees, storage bloat, and idle specialized hardware, alongside 82% who said that scaling AI introduces hidden operational complexity costs. 79% also reference security, governance, and MLOps as a key barrier to scaling agentic AI.

Upgrades needed for full agentic AI benefit

In order to combat these limitations, Google has several recommendations for organizations hamstringed by legacy infrastructure.

Leveraging fluid compute ‘to dynamically match the right silicon to the right task while minimizing operational overheads’ is Google’s first recommendation, providing compute power for agentic AI tasks without reducing capacity for general workloads, avoiding the need for excess memory usage to run agentic workloads that use large context windows.

For those battling agent sprawl caused by a cascade of new tasks across platforms and teams, Google recommends making use of enterprise-grade governance tools, which are usually available via the cloud partners businesses are already using. Google provides its own platform, Agent Gateway, as an example of a solution that provides visibility and oversight into how agents are communicating, the data they are accessing, and their workloads.

Organizing data more effectively prevents AI agents from drawing more compute when running heavy queries in attempts to access siloed data. Organizations looking to improve the efficiency of agentic AI should work towards using a unified data layer that automatically annotates unstructured data, allowing agents to understand where the data is without having to navigate pipelines. An added benefit of using a unified data layer is that it helps to avoid the duplication of data, saving on additional costs of storage bloat in the long run.

Moving your AI to the edge—by deploying agents directly on the site they are most used—is a further recommendation, and one that organizations are actively pursuing. 90% of organizations polled by Google said that this was a consideration in their AI initiatives. By deploying agents on site in manufacturing plants, retail stores, or hospitals, agents benefit from reduced latency, greater resilience (in the event of a centralized cloud outage), and improved cost-effectiveness by cutting per-token costs with local, highly optimized models.

As with businesses of all sizes, energy costs are a key consideration. When selecting new hardware, 91% of leaders now consider power consumption as a factor, especially when navigating power availability in regions without expanding capacity, regulatory compliance, and reducing the cost of ownership for AI systems.

The deadline for high-risk AI compliance under the EU AI Act may well have been pushed back. And for overstretched compliance teams, that might sound like welcome relief. But the organizations that will thrive in the age of AI are the ones that understand that an arbitrary deadline was never really the point.

The race to deploy AI is already in full swing.

Models like Claude Mythos and OpenAI’s GPT-5.5, dubbed "Spud", represent extraordinary leaps in capability. In the right hands, they accelerate decisions, surface insights, and create genuine competitive advantage. In the wrong hands, or without proper oversight of the data feeding them, the consequences can be catastrophic.

As Pocket OS discovered recently when an AI agent wiped out the company’s entire database in nine seconds.

What the EU AI Act actually demands

When you strip away the regulatory language, what the EU AI Act is really asking for is provability. Article 10 sets out that high-risk AI systems, those used in credit scoring, insurance underwriting, hiring decisions, and similar consequential applications, must be built on training data that is traceable, well-governed, and demonstrably free from bias.

Organizations must document data origins, every transformation applied, the assumptions made, and how potential biases were identified and addressed.

This is not a box-ticking exercise. It is a fundamental rethinking of how organizations manage their data estates. And the penalties for getting it wrong are steep: up to €35 million or 7% of global annual revenue.

The underlying problem is that most enterprise data infrastructure was not designed with this level of traceability in mind. GDPR established guardrails around data storage and access, but the AI Act raises the bar considerably.

You now need to trace data from its original source, through every transformation, to its final impact on model outputs. Anecdotally, AI model validation can take between nine and 12 months, and that assumes you have the lineage infrastructure in place to begin with.

The financial services imperative

Nowhere is this more consequential than in financial services. Credit scoring models trained on historical data can encode the biases of the past, automating financial discrimination at scale, often without any individual in the organization realizing it is happening. A bank that cannot trace how a training dataset shaped a model’s outputs is not just exposed to regulatory risk. It is potentially perpetuating systemic harm.

The irony is that the sector has been here before. BCBS 239 already requires financial institutions to demonstrate data accuracy, integrity, and the ability to aggregate risk data on demand. The AI Act is not a new category of challenge, it is an intensification of one that rigorous data lineage was already built to address.

Data lineage as infrastructure, not compliance overhead

The organizations best positioned for this moment are those that have stopped thinking about data lineage as a compliance cost and started treating it as core infrastructure. There is a crucial distinction between these two perspectives.

The focus must now be a governance model that catches problems during design, before a model ever reaches production, rather than during an incident response. Lineage makes this kind of proactive governance practical at enterprise scale.

Bi-temporal lineage allows teams to recreate the exact data state used for model training at any point in time, which is essential for audit purposes. More importantly, it allows teams to simulate the downstream impact of a data or schema change before it happens, preventing the kind of silent model degradation that can quietly undermine ROI.

The window is shorter than it looks

The deadline extension offers time, but not as much as it might appear. US regulators are already integrating AI into supervisory examinations. Gartner predicts that by 2028, 50% of organizations will adopt zero-trust data governance as AI-generated content proliferates through enterprise data supply chains. The regulatory direction of travel is clear and consistent.

Getting AI governance right requires building the lineage layer first. That is not a project that happens in weeks. And the organizations that start building now, not because a deadline forces them to, but because they understand what is at stake, will be the ones deploying AI at scale with confidence, auditability, and genuine trust.

The EU AI Act just got a stay of execution but organizations that treat it as breathing room may be sleepwalking into a far bigger problem.

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• GPT-Live is rolling out to all ChatGPT users now
• It can both talk and listen at the same time for more natural chats
• Real-time translations are also now possible

ChatGPT has a shiny new AI voice model called GPT-Live, which has a number of helpful tricks — including being able to listen and talk at the same time. It's rolling out to all ChatGPT users now, though OpenAI has acknowledged a number of early bugs.

While free users and users on a paid plan do get slightly different models — GPT-Live-1 mini and GPT-Live-1 respectively — the updated model should now be appearing in all ChatGPT accounts, with the new features outlined below.

The biggest giveaway that you've got the upgrade will be the Live label at the top of voice chats on mobile, and behind the ChatGPT drop-down on the web. Tap or click on these labels and you can still go back to the old voice models, for the time being.

There's another way to check the GPT-Live voice model has arrived in your account: on mobile, tap the menu button (top left), then the settings cog (Android) or your profile avatar (iOS), and Voice > Model. On the web, click your profile avatar (bottom left), then Settings > Voice > Model.

What to try first

The biggest upgrade here is the 'duplex' functionality, so try that first: you can keep talking even after ChatGPT has started answering you, and it should keep up. Second, try interrupting it mid-flow, and it'll adapt its response accordingly. We're almost at the level of the 2013 Spike Jonze movie Her at this stage.

Third, ask ChatGPT in voice mode to translate something into a foreign language as you say it out loud. You can then speak out sentences in English, and ChatGPT will do a real time translation for you without hesitating. It's not particularly useful for language learning, but it does show off the capabilities of GPT-Live.

Fourth, change the voice and intelligence used — you can do this via the sliders icon at the top right of voice chats. The voice options are actually the same as they were before, but you can choose between Instant, Medium, and High as the intelligence level. Use Instant for the fastest answers, High for the best answers, and Medium for a compromise.

The final thing you can try once you've got the update is to ask questions with visual answers. OpenAI has added a bunch of visual cards to voice mode now, so you get graphics on screen about sports scores, weather forecasts, and places that can be found on a map, for example.

Early voice bugs

I've been testing out GPT-Live voice mode for a few hours and can report that everything works as advertised. It is, more than ever, like talking to a real person — right down to the hesitations and the variety in speech patterns. I did experience one or two glitches, but they were few and far between.

Over on Reddit, OpenAI's Atty Eleti is answering questions about GPT-Live. One of the main bugs that users seem to be experiencing is related to ChatGPT's memory, which appears to be off limits to voice mode in some cases — this is an issue that OpenAI is tracking and "actively investigating", and you can find updates on it here.

Problems are also being reported when it comes to foreign languages being pronounced in an English accent. Again, this is an issue that's been acknowledged, and which should improve over time according to Eleti.

Overall though, the rollout seems to be going relatively smoothly — and I haven't seen any issues with memory or with accents so far. I'm not sure it's going to make me want to use voice mode any more than I already do (which isn't much), but for heavy voice users it's definitely a big step forward.

Businesses are racing to adopt and operationalize AI, but many are deploying the technology faster than they can govern it. This gap is quietly becoming one of the biggest security risks facing enterprises today.

AI agents are being embedded into everyday workflows as teams chase efficiency gains, and organizations are under increasing pressure from boards and senior leadership to automate processes faster.

However, beneath this perceived momentum sits a more uncomfortable reality: businesses still have limited visibility into how these systems behave once deployed.

Security teams need to take a step back and rethink what proper governance looks like in a world where AI systems operate with autonomous access, inherited permissions and limited visibility.

Confidence is outpacing verification

Understandably, many businesses view successful AI deployment as evidence that they are ready to scale AI across their business.

Recent research found that 87% of organizations believe their identity management posture is prepared to support AI-driven automation. Yet, at the same time 46% admit that their identity governance falls short.

This contradiction sits at the core of the AI security paradox.

The core problem is that organizations are granting AI systems increasing levels of access and autonomy before they have established clear ways to monitor or verify how these systems behave.

If nobody can explain why an AI agent took an action, the permissions it inherited, the systems it accessed and whether or not that behavior was actually intended, governance quickly becomes reactive rather than preventative.

AI agents undermine traditional identity governance foundations

Unfortunately, securing these new AI identities won’t happen overnight, as legacy governance models simply aren’t cut out for the challenges facing security teams today.

Traditional identity management was built around four key assumptions: predictable behavior, human intent and bounded permissions. Agentic AI changes all four. Security teams already understand the risks of AI agents and non-human identities. The problem is that operational realities are forcing many organizations to accept those risks regardless.

Research shows that 73% of organizations believe standing access for AI agents increases security risk. In other words, leaders recognize the dangers associated with giving AI systems privileged access, but feel under pressure to prioritize speed and operational efficiency anyways.

Crucially, the governance issue extends beyond risk. The fact that 80% of organizations said they cannot always determine why an AI agent took a privileged action suggests a fundamental issue.

That creates an entirely different governance challenge from one that ever existed in traditional identity management. Humans can typically justify intent, context or decision-making -autonomous systems can’t.

Shadow AI is becoming operational infrastructure

AI usage in the workforce has long moved on from a select few employees experimenting with ChatGPT. Now, the technology is deeply woven into production systems, workflows and enterprise data.

The scale of the problem is significant - 53% of organizations say they regularly encounter unsanctioned AI tools or agents accessing company systems or data, but only 28% can detect shadow AI in real time.

In reality, businesses are effectively allowing unknown contractors to roam freely through their corridors, with limited insight into where they’re going, what they’re doing and what they have access to.

This shift is forcing organizations to rethink traditional security priorities and focus on continuous monitoring and validation.

As a result, businesses need to begin focusing on how effectively they can monitor and verify what their AI systems are accessing, and whether that access is still appropriate.

Solving the governance contradiction

We know that organizations can’t afford to slow down on AI adoption given the competitive and operational incentives. But they also can’t afford to expand AI access and permissions faster than they can secure them.

This means bringing identity security to the forefront of AI governance strategies. Much of the discussion around AI risk still focuses on models, regulation and data privacy and those issues are important. But the true operational challenge increasingly sits between inherited permissions and unmanaged trust between systems.

For business leaders, the first priority should be improving visibility rather than adding more controls. They need to understand which AI identities exist within their organisation, what systems they can access, how permissions are inherited and where standing privileges have accumulated over time.

Organizations will also need to move away from persistent privilege models towards more dynamic models that provide temporary access only when required.

Ultimately, the organizations that will reap the greatest rewards from AI will be the ones that can understand, govern and validate the systems they use.

Deploying automation as fast as possible may lead to some quick wins, but doing so without the right governance measures in place could come back to haunt you.

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A study has revealed that approximately 40% of UK employees receive ‘work slop’ caused by low-quality, AI-generated content. As a result, it is estimated that each instance of work slop takes up to 3.5 hours each month to correct, resulting in millions of pounds in lost productivity.

It has been reported that as of mid-2026, approximately 78% of global businesses are using AI in at least one business function.

Despite this, studies have revealed that 62% to over 80% of workers lack confidence or training in AI, with many reporting they don’t have the skills to use the tool in their daily tasks, which can lead to errors and work slop.

What is AI work slop?

Many workers are using AI to make daily tasks such as generating reports, emails, or code more efficient. However, if used incorrectly, workers can be faced with low-quality, AI-generated content that may look like a high standard at first glance, but after closer inspection, is inaccurate and requires hours of manual checking and corrections.

To minimize AI work slop, there are several steps businesses can take:

Make training a key objective: It’s inevitable that without appropriate training and guidance using AI software, errors will occur. Business owners should ensure that employees are trained in more efficient prompting and understanding AI limitations to reduce work slop. If training is implemented during the employee onboarding stage or as early in the project as possible, the chances of work slop will be significantly reduced.

Start by brainstorming: AI must be used to enhance work, not to create it on its own. Before completing tasks that incorporate AI, it’s important to brainstorm how it can be used and the objectives you want to achieve. Where possible, create the bulk of content from scratch and use AI to support and enhance the end goal.

Introduce a review process: AI-generated work should never be presented to senior stakeholders or clients unless checked by an experienced team member. Business owners should ensure that all AI-generated tasks are reviewed and fact-checked before being presented as the final version.

An effective method is to create a mindset that AI-generated work has been created by an intern who has little knowledge of the industry. Reviewing work using this method will help to spot errors faster.

Encourage transparency: To reduce AI work slop, business owners should encourage workers to use AI to support and enhance tasks, not to complete them altogether. It’s also important for workers to be clear about how AI has supported the tasks they’re working on to make the reviewing process more efficient.

Encourage team feedback: One of the main ways to prevent continued work slop is to make colleagues aware that it has been spotted. More often than not, people will correct mistakes themselves, rather than be transparent and provide constructive feedback to their peers. Raising work slop in the early stages of the project will save time and prevent the issue from recurring.

Create manageable workloads: Workers who are unable to manage their workloads will often complete tasks quickly using AI, which can result in work slop. To prevent this, promote quality over quantity throughout the business and ensure that unmanageable workloads don’t hinder this ethos.

The key to AI success

For businesses using AI, the software should be used to elevate work and make processes more efficient, not cause delays and errors. If your business is looking to adopt AI, it’s important to be transparent with employees about when to use it and when to avoid it.

Employees should have a strong mindset towards AI and have quality and value in mind at all times when understanding the outcomes they’d like to achieve. When planning tasks, we should ask ourselves: Will AI add value and additional insights to the project, and how will it move your goals forward?

Once these points have been considered, it’s important to understand what AI can’t assist with, such as human perspective and strategic creativity. If you decide to use AI for the task you’re looking to complete, which is weighted towards human-powered thought and creativity, this is when work slop can be encountered, resulting in wasted time and resources.

To summarize, AI should be used to generate valuable content and results. Asking yourself the questions mentioned above before you turn to the software will help to generate the best possible outcomes.

An effective mental model that helps to prevent work slop

Before employees can use AI responsibly, they need to understand what it actually does. In "AI for Startup Leaders," we present a framework called A-R-C that captures the three core capabilities of AI:

• Agency: AI can work with tools, run code, and complete tasks on an employee’s behalf. For example, it can query data from your CRM, draft documents, or update a spreadsheet.
• Reasoning: AI can plan ahead and think through problems step-by-step. The latest models can break down complex challenges, consider multiple approaches, and work through logic chains.
• Context: AI can base its answers on the information you provide in each conversation. It can understand natural language, identify objects in photos, and parse data in spreadsheets.

The A-R-C model is crucial, as many workers use AI as an encyclopedia and to state facts, which isn’t what the software was created for. When people rely on AI for facts, work slop can occur. To avoid this, workers should be educated on the main purpose of AI, reasoning over their business context and taking action.

When employees understand A-R-C, they approach AI outputs differently. They recognize that AI is best used to reason from their business context rather than look up answers. Instead, they use AI to speed up how they collaborate with other teams and improve efficiency.

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Given the pressure that NHS doctors are under, it’s no surprise that many trusts are turning to AI for help. On the whole, this is a good thing.

AI tools can play a valuable role in managing workflows, automating documentation, coordinating care rotas and streamlining communications.

However, the pace of AI development, and generative AI in particular, has accelerated dramatically.

As a result, governance frameworks are failing to keep pace with frontline demand and healthcare practitioners have begun to use tools that have not been authorized by their organisation.

This is causing problems at all levels - particularly when those tools are used to support clinical decision-making.

AI in the shadows

“Shadow AI” is a term commonly used to describe the unauthorized use of AI within organizations. While the phenomenon exists across all industries, it has been growing rapidly in healthcare. One recent US survey revealed that 17% of healthcare workers admitted to using unauthorized AI tools in the workplace.

A another survey found that 39% of frontline healthcare staff say they use generic, free AI tools weekly or more. These are not isolated experiments. AI is already becoming embedded in day-to-day operations, often without formal governance or oversight, despite well-documented risks including hallucinations, inconsistencies and biases.

The reasons for this are understandable. Healthcare professionals are dealing with staff shortages, rising patient demand and overwhelming administrative workloads. Clinicians are expected to document more, communicate faster and process growing volumes of information - all while maintaining high standards of patient care.

It’s not surprising that they are reaching for the nearest AI tool. Particularly when that AI tool is extremely accessible, free or low-cost and easy to use. Shadow AI usage in the healthcare sector currently ranges from frontline clinicians right through to back office functions such as report drafting and recruitment support.

Most health professionals using “shadow” tools are not deliberately flouting rules, therefore. They are simply accessing some much-needed help from a convenient source. And their employer is not providing them with a robust alternative.

Healthcare leaders need to act quickly

Medical organizations, and the NHS in particular, have often been cautious to roll out new technologies, though frequently with very good reason. Clinical systems require extensive testing, strong evidence bases and rigorous governance before deployment and decisions supported by AI can have direct consequences for patient outcomes.

Any clinical decision-making tool must therefore be thoroughly tested, based on solid medical research and exist under constant review and improvement. Yet delays allow shadow AI to fill the gap.

AI tools of the right quality are now reaching the market but the problem remains as to how leaders can rein in the use of shadow AI. It’s a problem that must be addressed quickly because the stakes are high.

The risks are real

The risks of unsanctioned AI usage include security breaches and privacy violations as well as the top concern - patient care.

In terms of data security and privacy, generic AI solutions are effectively “black boxes”. Ask ChatGPT whether sensitive health data will be safe in its hands, for example, and it will advise that it does not comply with health information regulations.

This is not surprising - patient information is “special category data” under GDPR and frequently requires additional protection - but it illustrates the danger of submitting too much information into an unauthorized app.

When it comes to patient care, generic AI solutions used to support clinical decision making carry an inherent risk: even when outputs appear credible, errors may be difficult to detect and assess. They may pull information from a broad range of sources which may be outdated, unaccredited or inaccurate.

A clinician in a hurry, referring to an unauthorized tool, is taking a big chance. If an unauthorized tool provides incorrect guidance relating to symptoms, medications, diagnoses or treatment pathways, the consequences could be severe.

Guidance and leadership, not heavy-handed authority

The answer is not a blanket ban, which is likely to send unauthorized AI further into the shadows. The focus for healthcare leaders must be to establish clear guidelines on the use of AI and to invest in education and training regarding its risks and benefits.

At the same time, organizations need to provide clinically validated, secure and easy to use alternative tools. If approved systems are too slow to deploy or fail to meet operational needs, clinicians and administrators will inevitably continue to turn to unauthorized tools instead.

Governance structures are also an important part of the process. These should include IT teams, security specialists and operational management because AI deployment simultaneously affects patient care, compliance, workforce efficiency and organizational risk.

AI will undoubtedly become an increasingly important part of modern healthcare delivery. The organizations that succeed will not necessarily be those that adopt AI fastest, but which create trusted frameworks on which clinicians can access secure, medically robust and properly governed tools.

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For the past year, one idea has dominated conversations about enterprise technology: AI agents will replace Software as a Service (SaaS). It’s a compelling case on paper. If AI can reason across tools, write code, execute workflows, and interact with systems through APIs, then traditional SaaS applications start to look like unnecessary middlemen.

Seats become less relevant. User interfaces matter less. Software becomes cheaper to build. Custom internal tools become easier to create.

In that reality, much of SaaS gets pushed down into infrastructure, while value shifts to models, agents, and orchestration layers. This reaction is being driven by several parallel shifts.

Software is becoming cheaper and faster to build, AI agents are becoming better at navigating tools and executing work across systems, and the economics of agentic execution are bringing new attention to cost and latency, or time to serve.

The market is not wrong to think this way. AI agents will change software economics.

They will push platforms to become API-first, deeply connected, and capable of supporting autonomous activity at scale. They will challenge seat-based pricing and accelerate the move towards consumption and outcome-based models. They will expose weak products and reward systems that are trusted, extensible, and embedded in real operations.

This is the next phase of SaaS, rather than the end of it.

From replacement to expansion

The assumption behind the “end of SaaS” narrative is that software exists primarily as an interface. If agents can bypass that interface, the application becomes redundant. That logic holds for some categories of software, but it does not apply uniformly. Enterprise software has never been just a presentation layer.

Enterprise platforms derive their value from managing structured data, enforcing permissions, executing workflows, and maintaining the audit trails organizations rely on. Agentic AI does not remove that requirement; in many cases, it sharpens it. What AI changes is not whether these capabilities are needed, but which types of software are most exposed as agents become more capable.

Products whose value is largely defined by navigation, basic interaction, or shallow workflows are more vulnerable when agents can reason directly over APIs and complete tasks without human mediation. More configurable, deeply integrated platforms behave differently.

Solutions that act as systems of record, coordinate workflows across services, apply policy, manage state, and provide security, auditability, and evidencing are not easily displaced. Their role is not simply to present functionality, but to ensure work is executed consistently, safely, and at scale.

In that context, AI agents are not replacing platforms so much as being embedded within them, extending how work is initiated, coordinated, and completed. That direction is already visible in the data.

Gartner forecasts that by 2030, 85% of enterprise agentic AI investments will be bundled into existing SaaS and cloud renewals rather than delivered through net-new contracts, up from 55% in 2025.

SaaS as the execution and control layer

As agents become more capable, they shift where value sits and how platforms compete.

For years, software differentiation has been driven by features and user experience. In an agent-driven world, those differences begin to matter less. Agents can move across systems, access functions directly, and orchestrate tasks programmatically.

What matters instead is which platforms can actually complete work. This means coordinating processes across systems, applying the right type of automation at each step, and ensuring outcomes are reliable, traceable, and compliant.

In this model, SaaS does not disappear. It becomes the execution and control layer for enterprise AI. As this shift plays out, value and margin move away from individual features and towards the platforms that control execution and data access. Agents themselves are unlikely to be a sustainable point of differentiation. As capabilities converge, the focus moves towards control.

That’s because enterprise processes still need to be predictable, auditable, and, in many cases, reversible. Regulatory frameworks are reinforcing this, with increasing expectations around explainability and oversight. This places new importance on the platforms that sit at the center of operations.

Organizations that can orchestrate work across systems, apply automation selectively, and produce a clear evidence trail will capture more value over time. Those that cannot risk becoming passive data stores, rather than active systems of execution.

A multiplier effect on demand

There is another misconception shaping the conversation that AI will reduce the amount of work organizations need to do and, with it, the reliance on software.

In practice, the opposite is happening. As the cost and effort required to execute processes falls, more work becomes viable. More cases are identified, more customer needs are addressed, and more processes are triggered automatically.

We are already seeing this in service environments, where AI-driven detection and automation increase the volume of actionable work entering the system.

In the context of public services, for example, within a case management environment, a missed bin, pothole, or housing repair issue can be identified automatically, raised as a case instantly, and routed into the right workflow without waiting for a citizen or staff member to log it manually. In that model, the value sits not in who created the case, but in how effectively the platform absorbs, processes and resolves it.

This creates a multiplier effect. AI does not just reduce effort; it expands what organizations can do. AI expands demand, and SaaS platforms are where that demand is fulfilled.

The question is no longer how many users a platform supports. It is how much work it can handle, complete, and evidence effectively.

What this means for SaaS platforms

This shift will not benefit every platform equally.

As software moves from presenting information to completing work, value becomes tied to what a system can actually deliver, not just how it is accessed. This will also shape commercial models, with more platforms likely to combine traditional subscriptions with pricing based on consumption, throughput, or completed outcomes.

Organizations still need oversight, approvals, and ways to manage exceptions, but the focus is shifting towards how effectively platforms can absorb, process, and complete work at scale.

That change is already exposing the gap between strong and weak software, placing greater scrutiny on systems that offer limited workflow depth or rely heavily on manual effort. In contrast, platforms that are deeply embedded in operations, with strong data, logic, and execution capabilities, will become more valuable.

As agents become consumers of APIs, software needs to be connected and capable of supporting autonomous activity at scale. Those that are not will struggle to keep up.

The next phase of SaaS

Ultimately, what we are seeing is not the end of SaaS, but an evolution. SaaS is being reshaped, not just as software for human interaction, but as software designed for both humans and machines operating across systems.

A shift from systems that present information to systems that complete work. From user-driven interaction to orchestrated execution, and feature competition to control over how processes run.

This is not theoretical. It is already happening. Low-code platforms and productivity tools are being augmented by AI and agentic workflows, changing how applications are built, adapted, and used day to day.

But this shift also raises the bar for how organizations adopt AI. Those that simply layer agents onto existing processes risk creating more complexity rather than less. Introducing automation without understanding how work really flows can make outcomes harder to predict and harder to evidence, particularly in regulated environments.

The organizations that succeed will start with a real operational problem and introduce AI selectively. Where existing workflows provide sufficient structure and control, value can be delivered quickly without redesign. Where AI exposes friction or inefficiency, that insight can then inform targeted process improvement.

AI agents amplify the platforms beneath them; they do not replace them.

The result is not less software, but different software, running on platforms built to execute work, absorb demand, and stay in control. More automation, more cases handled, more throughput, more outcomes achieved through systems that reliably complete more work at scale.

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Anthropic's Mythos Preview had barely been announced before it was compromised - accessed by a private Discord group on the day of its public release through a third party vendor environment.

For a model distributed across 40 companies and their associated contractor networks, the breach was, to many security experts, a matter of when. Anthropic is not an outlier - the conditions that enabled the breach are structural, and most enterprise AI deployments are built on the same foundations.

The enterprise reality - capable agents, variable controls

Most enterprise AI deployment is neither centralized nor consistently visible. Capable AI agents are already embedded in production workflows across most large enterprises - accessing live systems, informing decisions, operating across vendor and contractor networks where boundaries such as what agents can see, act on and escalate may not have been formally mapped.

The Mythos investigation has brought attention on what happens when powerful AI operates without proper governance.

With only one in five enterprises holding a mature governance model for autonomous agents despite deployment accelerating sharply across the same period, the control plane governing those deployments is more likely to exist in strategy decks and risk frameworks than in tested, operational infrastructure.

For enterprises, the question is what the control plane governing those agents actually looks like in policy, architecture and operational practice.

Four governance imperatives for agentic automation

The Mythos breach was enabled by failures across vendor access, identity controls and contractual oversight - the same pressure points that determine whether any enterprise agent deployment is auditable, recoverable and regulatorily defensible. Getting ahead of them requires focus on four areas:

• Access control and least privilege - Define exactly what an agent can touch. Agentic systems should never inherit blanket privileges. Apply role-based and context-aware access controls so agents operate with the minimum permissions required for a task. Treat agents like employees - with identity, just-in-time access and revocation mechanisms.
• Auditability and decision traceability - If an agent acts, you must be able to reconstruct why it did so. Capture inputs, model versions, prompts, intermediate reasoning artifacts and the final actions. Immutable logs and explainability tooling convert opaque outcomes into auditable trails that satisfy internal compliance teams and external regulators alike.
• Human-in-the-loop and fail-safe controls - Design where humans must intervene, and where agents can act autonomously. For high-risk decisions institute gating workflows - pause points, manual approvals and automated rollback options. Ensure operators can pause or reverse agentic actions and that escalation paths are well rehearsed.
• Supplier and model provenance - Know what you consume. Whether you use third-party APIs, licensed foundation models or internally trained agents, document model lineage, training data assumptions, performance boundaries and known failure modes. Contracts and SLAs should require transparency, update cadences and liability clauses for material failures.

Governance is not a speed bump - it unlocks speed

Most organizations have assembled controls reactively, after a deployment has scaled or after something has gone wrong, at which point the cost of remediation consistently exceeds what building it in from the start would have required.

The Financial Stability Board's engagement with Anthropic this month - convened at the request of Bank of England Governor Andrew Bailey - signals how quickly regulatory expectations are forming around AI governance, and how little runway organizations have to get ahead of them.

Procurement cycles move faster and regulatory conversations are considerably less fraught for organizations that can demonstrate auditable controls before they are asked to.

From reaction to readiness

Mythos, in the classical sense, is the story a culture tells about itself. It will not be the last model to escape its controlled environment, and the conditions that enabled the breach will only become more prevalent as agentic AI moves from experimentation into production across enterprises. Anthropic's choice to withhold Mythos underscores a simple truth: capability outpaces governance.

The story most enterprises are currently telling about their AI governance doesn't yet match the reality of what they've deployed - and that distance is closing faster than most have planned for.

But it should not provoke panic. Running controls through simulated breach conditions, mapping agent access across the full estate, and requiring contractual transparency from vendors is where that work begins - before the next breach makes it urgent.

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• Pentagon science advisor Joseph Jewell weighs in on the Ukraine war and integrating AI and biotech mark a paradigm shift in how the US military sees future conflicts
• Marines recently 3D-printed shaped charges from coconut husks and coffee grounds that beat conventional explosives by 25%
• Department of Defense is also trying to speed up innovation in the industry by offering as many as 500 patents free of charge to private companies

The Russia-Ukraine war has been devastating in its impact for those directly affected by the conflict, which has now entered its fifth year as both sides trade blows in what many feel is a prolonged stalemate stemming in part from a lack of manpower.

The conflict, or rather its asymmetrical nature, however, has many modern militaries keeping a sharp eye on events as they showcase what the future of combat might look like between two warring nations.

The US Assistant Secretary of War for Science and Technology, Joseph S. Jewell, recently spoke at length during the Defense One tech summit about how information from the conflict and inroads in AI and biotechnology continue to shape modern warfare as we know it.

Production inroads, AI integration and a focus on biotechnology

The summit saw the Assistant Secretary address a host of topics, including insights into how the Russia-Ukraine conflict is playing out and the lessons the US must learn from the ongoing war.

He addressed the fact Ukraine essentially willed its entire drone industry into existence because it was key to its survival while essentially keeping Russia's navy at bay for most of the conflict despite not having a similarly equipped fighting force at sea.

The line that carries from him, however, might be about Marines repurposing coffee grounds and coconut husks to make 3D-printed shape charges for the battlefield.

This underscores an important change that has already taken place on the modern battlefield, as researchers and military personnel increasingly push the boundaries to find the best way to resupply and rearm while also making advances in lethality in some cases.

Coffee grounds and coconut husks were only the tip of the iceberg; the Marines also attempted the same with plastic water bottles and even crushed volcanic rock, noting that the latter worked best.

Jewell said the field-made charge had cut the time to point-of-use by 99%, because it could be produced on the spot from materials "endemic in the Indo-Pacific," and even more interestingly, it showed "25% better focusing characteristics than conventionally manufactured high explosives."

The underlying story isn't just about how field-made charges are saving an enormous amount of time and money on future battlefields, but how military doctrine has changed to include 'patent holidays' to increase access to tech and foster innovation, for better or worse, at least on the Pentagon's end over the last few years.

While his focus was on Ukraine, similar lessons have been learned by the US in its on-again, off-again conflict with Iran, where the latter has resorted to using low-cost but high-volume weapons, including drones and missiles, to effectively force a stalemate over the Strait of Hormuz, indicating that a paradigm shift is in order where the side with the best, or most 'efficient' weapons does not necessarily win.

Formula 1 has always been a sport where fine margins can make all the difference, with success and failure sometimes coming down to hundredths of a second.

It’s no surprise then that many of the teams have looked to AI for assistance in getting that extra edge, whether it’s through designing new parts, determining race strategy, or simply getting staff to better communicate with each other.

AI giant Atlassian has been working with the Williams F1 team since 2025, signing a title partnership to show its commitment, and at the recent British Grand Prix 2026, I got to speak to the company to find out just how its tools and services are being used.

The best teamwork and the best technology

“We came across the Williams F1 team, and we quickly identified that there was quite a good opportunity for both organizations,” Andrew Boyagi, Atlassian Customer CTO, tells me at Silverstone.

“It’s not a sponsorship, it’s more of a partnership because we can help each other…we had a similar view that in Formula 1, the success goes to the teams that have the best teamwork and the best technology.”

Atlassian’s first season with Williams F1 saw the team achieve a huge jump up the rankings, rising from ninth place overall in 2024 to fifth in 2025, netting it millions of dollars more in prize money.

And although the 2026 season hasn’t been quite as successful so far, Boyagi is keen to highlight how working closely with the team has led to huge improvements in productivity and collaboration.

This has primarily been through Atlassian’s AI tools, which have become common sights in businesses and organizations across the globe. AI usage in the team was also fairly low before the Atlassian partnership, but Boyagi points that after using Rovo, 63% of the team now say, “they have more time to work on strategic, innovative stuff, which means they’re delegating a lot of their work, the repeatable low-value stuff, to AI.”

This includes a new Fault Management tool, which monitors for conflict and repetition when mechanics at the track or the factory report issues. Boyagi notes that it can be so busy at the track, the same fault might be logged multiple times, which can lead to wasted time and effort at the factory, which is particularly painful in a cost-cap limited sport like Formula 1 where every second counts.

Another example is in garage setup - Formula 1 is a global sport, with teams travelling to 24 locations across the world, arriving at an empty garage which needs to be quickly transformed into a cutting-edge hub.

Previously, the team used manual checklists, often with pen and notepads, to track what was being done, leaving the door open to error, but Atlassian redesigned the workflow with Jira and Rovo natively built in, so tasks auto-populate in Jira boards by category and assigned employee, meaning everyone can see who is working on what, and work moves across as it is completed, so hopefully nothing is ever missed.

I ask Boyagi what success will look like in terms of the partnership, especially as Williams F1 has struggled in the initial races so far in 2026.

“Success in Formula 1 is about good technology and good teamwork, but it is always a sport where luck comes into it as well,” he notes, “but what we like to see is an improvement in how things are collaborating, and how effective they are in terms of doing their work.”

Boyagi points out that Atlassian took baseline data between October 2025 and March 2026, finding that 92% of the team now say they’re working for the right organization priorities, “so they’re working on the right things.”

Knowledge transfer has also improved, with trust in documentation going up by 200% in those five months, as Boyagi notes, so “in terms of knowledge, in every company, but especially Formula 1, it is a foundation of productivity - being able to find what you need, when you need it, and trusting what you find without asking someone.”

“Teams are now working on the right things, they’re getting that work done faster, they’re having less meetings and they’re delegating the low value, repetitive tasks to AI,” he adds, “and it doesn’t always translate to what happens on track, but I’m 100% confident we are already making a difference.”

The difference isn’t just for the mechanics or workers back at base - Boyagi says that the team’s drivers are also embracing it. He mentions one example where Alex Albon was having a debate with his engineers around different car set ups - to which Boyagi said, why don’t you ask Rovo?

“As it turned out, Rovo agreed with him, not the engineers - that was a nice moment for us,” Boyagi says, adding that what is important is why Rovo can give a solid answer because the platform is underpinned by Atlassian’s Teamwork Graph, which connects various parts of the business and its processes.

“All of these things are connected, so when you ask a question, it connects dots that humans can’t connect, where it would take us too long,” he says.

Getting over the line

As for Williams 1, the team clearly values the partnership, with Matt Harman, Technical Director for Engineering, championing the time and efficiency savings seen by using Atlassian's systems.

"We need to build systems, tools and techniques across the whole team that allow us to be collaborative," he said on a briefing call attended by TechRadar Pro, "we need to give people more insight, more ideas, so that people can not spend as much time on what I call 'business as usual'".

Harman also highlighted the benefits of the Atlassian service causing "less meetings, more insights", as the partnership looks to provide engineers with the insights and the support to do more.

"When we do that, the Atlassian tools just give people that instant access...without having to sit in a meeting."

Boyagi also points out that the partnership between Atlassian and Williams F1 has multiple sides, noting the team is a customer, “so the fact that they’re using all of our products, and they’re choosing to do that, using precious cost cap dollars, really talks about the value they’re getting from the partnership.”

“I’ve led many transformations, and the hardest thing is getting people over the line - implementing technology is easy, but getting humans to change how they work and want to use the technology is a bit of an art," he says.

"But in Williams, there’s such a strong demand, and a pool, that we have to prioritize what we’re going to do, because they want the team to benefit."

Much of the discussion around agentic AI in commerce has focused on one assumption: that consumers will eventually delegate purchasing decisions entirely to AI systems.

The vision is straightforward enough. A consumer tells an AI assistant they need a new winter coat, a family holiday or a replacement laptop and the agent independently handles everything from product selection to payment and fulfilment.

In this version of the future, traditional ecommerce interfaces become secondary, marketplaces lose influence and consumers gradually surrender active decision-making to software acting on their behalf.

Underlying much of this vision is the assumption of "cognitive surrender" - the idea that consumers will become increasingly willing to defer judgement to AI systems rather than actively evaluating options themselves.

If AI agents can reliably identify the best products, prices and services, the argument goes, consumers may eventually see little value in conducting their own research or comparison.

The reality emerging so far appears more complicated.

Consumers are unquestionably embracing AI-assisted shopping at speed. Generative AI tools are already influencing how people discover products, compare options and navigate increasingly complex and crowded digital shopping environment.

But adoption does not necessarily equal surrender of control. Early behavioral signals suggest that while consumers are willing to use AI to reduce friction in discovery, they remain reluctant to outsource trust.

That distinction matters because it may ultimately define how agentic commerce develops.

AI is accelerating comparison, not replacing judgement

Recent international research across 4,000+ online shoppers in the UK, US, France and Germany found that consumers using AI shopping tools still rely heavily on traditional trust indicators before making purchases.

Price remains the dominant factor when evaluating AI-generated recommendations, but brand familiarity, customer reviews and marketplace reputation continue to exert significant influence over final purchase decisions.

In practice, consumers appear to be using AI as an “accelerated curation” layer rather than a replacement for purchasing judgement.

This is important because many early narratives around agentic commerce implied that AI would fundamentally weaken existing ecommerce structures. The expectation was that autonomous agents would increasingly bypass traditional digital storefronts, reduce the importance of marketplaces and make consumer loyalty less relevant.

So far, the opposite may be happening.

AI is undoubtedly changing where product discovery begins. Increasingly, consumers may start their shopping journey through conversational interfaces rather than search engines or marketplace homepages. But once products are surfaced, familiar mechanisms of trust still shape behavior. Consumers continue to validate recommendations through reviews, recognized sellers, loyalty schemes and established platforms before committing to a purchase.

The research supports this pattern: 89% of consumers say recognizing the seller's brand remains important when acting on an AI recommendation, while 92% say customer reviews continue to influence their final choice. Moreover, 68% say they would only consider purchasing from an unfamiliar seller after first checking reviews and ratings.

This creates a more evolutionary picture of AI commerce than many expected.

Why marketplaces may become more important, not less

Rather than disintermediating marketplaces, AI may actually reinforce them. Large ecommerce ecosystems already possess many of the attributes AI-assisted shoppers still value most: extensive review infrastructure, strong fulfilment capabilities, recognized seller protections and trusted payment environments.

If consumers continue seeking reassurance before completing purchases, those strengths remain highly relevant.

The comparison with earlier ecommerce technologies is instructive.

Social commerce, for example, has become a major channel for product discovery over the past decade, with consumers increasingly encountering products through creators, influencers and algorithmic feeds. However, in most cases the social platform is not the merchant of record.

The transaction, fulfilment, payments and post-purchase support typically still sit with retailers, marketplaces and their payment providers. Discovery has shifted but the underlying commercial infrastructure has largely remained intact.

Trust still closes the sale

Even as ecommerce has matured over the past two decades, trust has remained one of the strongest determinants of conversion. Consumers consistently look for social proof, recognizable brands and transparent pricing before purchasing online, particularly in higher-value or unfamiliar categories.

AI does not appear to be removing those instincts. Instead, it may be amplifying their importance by increasing the speed and scale of product exposure from entities like marketplaces which provide wide choice and a trusted logistics backbone.

If AI tools surface a broader range of sellers than consumers would normally encounter, trust signals become even more important as filtering mechanisms.

This has significant implications for merchants.

In an AI-mediated environment, visibility increasingly depends on structured product data, accurate inventory information and competitive pricing. AI systems can only recommend products they can properly interpret. Merchants with inconsistent catalogues, weak metadata or fragmented pricing strategies risk becoming less visible in AI-generated recommendations altogether.

Loyalty and payments remain part of the equation

One of the more overlooked questions surrounding agentic commerce is how loyalty programs will function inside AI-driven purchasing journeys.

Consumers have spent years building relationships with retailers, airlines, payment providers and marketplaces through points, rewards and membership ecosystems. If AI agents operate outside those structures, friction emerges. The research suggests these relationships remain highly relevant.

In the UK, 36% of consumers say they would be less likely to continue using AI shopping tools if doing so meant losing access to loyalty points and rewards, indicating the existing loyalty ecosystems remain an important part of consumer decision-making.

The likelihood is that loyalty systems themselves will need to become more interoperable with AI-assisted commerce experiences rather than disappearing entirely. Consumers may accept AI recommendations, but many will still expect rewards, protections and account benefits to remain integrated into the process.

The same applies to payments. As AI-driven commerce develops, consumers are unlikely to abandon expectations around payment security, fraud protection and transaction transparency simply because the purchasing interface changes.

In fact, research found that 55% of consumers expect to increase their use of retail marketplaces as AI adoption grows, suggesting that established platforms continue to play an important role in providing the fulfilment, payment infrastructure and operational trust that underpin online commerce. Trust in the payment process itself will remain fundamental to adoption.

The future of AI commerce may be more collaborative than autonomous

This broader pattern points towards a future where AI changes the mechanics of shopping without necessarily changing the underlying psychology of buying.

Consumers are happy to delegate repetitive tasks. They are comfortable automating comparison, discovery and research. But purchasing decisions still carry emotional, financial and reputational considerations that many consumers appear unwilling to fully hand over to autonomous systems.

That may evolve over time, particularly as younger consumers become more accustomed to AI-mediated decision-making. Trust in AI systems will almost certainly increase as the technology improves and becomes more embedded in daily life. But behavioral shifts in commerce often happen more gradually than technological capability alone would suggest.

Importantly, this does not diminish the scale of change AI may bring to ecommerce.

Agentic commerce still has the potential to reshape digital advertising, product discovery, marketplace competition, payments infrastructure and merchant visibility. Entire categories of ecommerce optimization may need to be rebuilt around how AI agents interpret and prioritize products.

But the idea that AI will simply replace trust appears increasingly simplistic.

At least for now, consumers do not seem to want autonomous shopping systems that make decisions entirely on their behalf. They want systems that help them make decisions more efficiently while still preserving their ability to verify, compare and validate.

That distinction may ultimately prove critical to understanding where agentic commerce actually goes next.

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• 75% have deployed 4+ AI tools in the past six months, 35% have deployed 10+ tools
• This is despite four in five experiencing AI security incidents or vulnerabilities
• Governance and dedicated security budgets could be part of the answer

New DigiCert data has claimed four in five (78%) organizations have either experienced an AI-related security incident or have identified an AI-related vulnerability, despite ongoing AI investments and increased adoption.

Despite uncertainty around security, three-quarters of the companies surveyed have deployed four or more AI tools in the past six months alone, with a third (35%) having deployed more than 10 AI systems in that same period.

With this new data, DigiCert says organizations need to treat AI like any other business system rather than an experimental toy, deeply rooting security into the entire strategy.

AI is causing a security headache for most companies

While discussions are clearly taking place, fixes are slow to roll out. For example, 90% have discussion AI governance at executive or board level, but only 50% have implemented both dedicated AI security budgets and formal AI governance programs.

Following years of pilots, two-thirds (64%) have now started logging AI inventories, implying they're still discovering what AI exists across their business. Currently, nearly half lack centralized visibility into AI systems, and only 53% can fully trace AI outputs back to the underlying models and source data.

"The question is no longer whether organisations should adopt AI. It's whether they can explain, govern and trust the AI they've already deployed," SVP Brian Trzupek explained.

While AI explainability remains pretty consistent, at around half, across multiple regions and countries like the US, the UK and Australia, some sectors see more incidents and vulnerabilities than others. Those include science, technology, banking, telecoms and retail.

Looking ahead, many companies are now trialling giving AI agents their own identities, much like human workers, to improve visibility across autonomous actions. Greater governance and stricter policies to iron out the use of unapproved tools should also help companies address some of the growing security issues as adoption continues to mature.

External cyberattacks, especially ransomware, are now viewed as a matter of when, not if.

It’s no wonder they dominate cybersecurity discussions and make frequent headlines.

IBM’s 2025 Cost of a Data Breach report found that the average global cost of a data breach stands at $4.4million, underscoring why the threat of an attack keeps business and IT leaders up at night.

But they represent only one side of the data risk landscape. What’s less understood and often underestimated are the internal threats that can be just as disruptive and damaging.

From simple employee errors to deliberate insider actions, and even the unintended consequences of generative AI tools, these risks pose serious challenges for every organization’s data security strategy.

Human Error and Malice

Human error remains one of the most persistent vulnerabilities in cybersecurity. In fact, 2024 research found that the human element played a role in 68% of breaches, ranging from simple mistakes to social engineering and misconfigurations. Ignoring this reality leaves a critical gap in any security strategy.

Human error isn’t the only internal risk. Insider threats - both intentional and negligent - represent a growing portion of today’s breach landscape. In fact, 83% of organizations experienced at least one insider-related incident last year. Whether it’s a disgruntled employee exfiltrating data or a staff member mistakenly over-provisioning access, incidents involving trusted credentials are notoriously difficult to detect and recover from.

Whether it’s accidental human error or deliberate insider attacks, the first step to reducing these types of risks is to acknowledge that the weakest link in any cybersecurity chain is people. Even the most advanced internet security infrastructure can be compromised by a single careless mistake or a trusted user with bad intentions. Creating a culture of continuous education mixed with accountability can mitigate some of these threats.

Reducing privileged access to only what is necessary can also significantly lower the risk of employee caused data leaks. However, organizations should always assume that human errors will occur and prepare accordingly. User and Entity Behavior Analytics (UEBA) plays a critical role in identifying suspicious activity that may indicate insider threats.

By flagging anomalies such as unexpected data transfers or unusual access patterns, UEBA enables security teams to respond swiftly before significant damage occurs.

Just as important is having strong technical safeguards in place. These act as a critical safety net when human controls fail. For example, it’s important to verify that automated backups are running efficiently and include all data sources, such as cloud applications and endpoints.

This can ensure that no critical data is left unprotected or overlooked in the event of a disruption or breach. These backups need to be immutable and tamper-proof, so that even a rogue insider cannot alter or delete recovery points.

The AI Conundrum

While AI promises a boost to productivity and innovation, it also brings with it new threats. As an example, the rise of AI co-pilots and agents in various workflows such as coding, writing and data analysis widens the attack surface. For AI to operate efficiently, it requires access to sensitive internal data such as documents and proprietary systems, which means that the slightest oversight in access permissions and data governance can severely cost an organization.

This is not going unnoticed. The U.S. National Institute of Standards and Technology (NIST) states in its AI Risk Management Framework that AI models may “leak, generate, or correctly infer sensitive information” even data not explicitly included in the input, through memorization, adversarial attacks, or by combining disparate sources.

This concern is echoed in the private sector, with over 80% of security leaders expressing concern that generative AI could leak company secrets, and security experts warning that only 24% of generative AI initiatives are fully secured. Furthermore, a staggering 90% of organizations report a lack of adequate security standards to defend against AI-centered threats. Gartner predicts that by 2027, more than 40% of AI-related data breaches will be caused by the improper use of generative AI.

So how can enterprises continue to implement the evolving world of AI into business processes while ensuring it doesn’t open the door to a bevy of cyberattacks? The first step is to treat AI agents as part of the threat model. The same security principles that are often implemented to traditional IT environments need to be applied to AI implementation - zero trust, data minimization, encryption, segmentation, etc.

AI agents need to be viewed as tools with privileged access, and handled accordingly. The data that is being fed into these AI agents needs to be reviewed and cleaned-up, including removing outdated or overly sensitive files from training sets and ensuring only approved data sources are utilized.

Close monitoring and keeping a log of all AI interactions is also critical. This needs to be detailed enough to enable an audit trail that can help to detect potential abuse or oversharing of confidential data.

To reduce the risk of human error, AI guidelines should also include security awareness training so that employees can understand that AI agents are not sandbox environments, and any sensitive data could be logged, stored, and in the worst-case scenario, shared.

Immutable Backups Delivered as a Managed Service

While the threat landscape continues to expand, the reality is clear - no security stack is impenetrable. Cybercriminals will always search for new angles, whether through ransomware, insider threats, or AI-driven exploits. That’s why true resilience depends not only on prevention but also on the ability to recover quickly and confidently when defenses are breached.

Immutable backups, delivered as a managed service, provide that safety net. Unlike traditional backups that can be corrupted, deleted, or encrypted by attackers, immutable backups are unchangeable and tamper-proof. By layering this with 24/7 monitoring, expert management, and tested recovery processes, a managed service ensures that recovery points remain secure and always available.

This approach dramatically shortens downtime, from days to hours and removes the operational burden from internal IT teams who may lack the time or expertise to manage complex recovery environments.

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• Researchers have spotted problems with AI pattern matching in science data
• It could mean false flags for signatures of life on other planets
• AI can still be useful, but checks need to be built in

One of the ways AI can be most helpful is in trawling through masses of scientific data that human researchers don't have time to analyze, looking for patterns — but this use-case is now proving problematic when it comes to the search for life beyond our planet.

A new study from researchers at Michigan State University suggests that AI systems can be too easily fooled into identifying signatures of life out in the universe where none exist. We need these flags to be accurate to know where to point our telescopes next, so it's important that the detection processes work.

The researchers set up a digital simulation including a key sign of life: the ability for molecules to replicate and mutate. Software was used to generate tens of thousands of digital organisms with and without this ability, which where then used to trail a neural network to spot the difference with an accuracy rating of 99.7%.

When the neural network was pointed towards data it hadn't previously seen, however, the AI's life-spotting skills fell apart. The researchers started with a digital organism that couldn't copy itself, which the AI correctly identified, then began making small edits and asking the AI to check again.

Essentially, as the AI was nudged out of its comfort zone of training data, it started seeing life where there wasn't any. "No matter what sequence of commands we started with, we were able to fool the AI 100% of the time," said Ankit Gupta, one of the researchers.

Space and beyond

It's worth bearing in mind the limitations of this research: these tests were carried out in an artificial, digital simulation, and so didn't rely on any real data. The researchers were deliberately searching for errors too, rather than letting them happen by chance.

However, the study methods are solid enough to be concerning. The worry is that a Mars rover or a deep-space telescope could identify a life signature with a high degree of confidence, without necessarily having a human in the loop to check.

The researchers found there were a vast number of sequences that could trip up the AI too, meaning the risk of a mistake is more likely. While the digital organisms incorrectly identified by the neural network were close to what it had been trained to spot, they weren't full matches — despite the AI thinking they were.

These issues could crop up outside of space exploration too. The same errors might appear when looking for patterns in medical scans, security camera footage, and everywhere else the technology is used.

That said, the researchers are keen to emphasize that AI can still be useful in these scenarios — it just needs careful checks and supervision. "AI has an Achilles' heel: it can see a pattern and completely misclassify it," said Christoph Adami, one of the team. "There needs to be a human in the loop."

• Xero CEO outlines the agentic age of accounting, powered by AI
• Several new additions to JAX agentic platform will help workers in all teams
• Cassidy also looks to allay fears AI will take jobs, saying human influence is always vital

The CEO of Xero has highlighted the key role AI can play in helping revolutionize the finance and accounting industries.

Speaking at Xerocon 2026 in London, Sukhinder Singh Cassidy outlined her company’s aim, “to be the trusted ecosystem for the agentic era” through its range of AI services and tools.

“It's never been a more exciting time to imagine what's possible with software,” she declared, outlining Xero’s aim, “to keep transforming our platforms, so you can transform your practice and then we can keep taking advantage of technology old and new to make work smarter.”

Agentic age

Cassidy went on to mention Xero’s role in two major initiatives going forward, what she called the “next chapter” for the company - namely, “making AI work for work” and also “supporting the rise of the builders.”

“AI is indeed here,” she noted, “it's still messy, it's still early, but it's one of the biggest trends, if not the biggest technological trend of our lifetime.”

The finance industry looks to be an obvious area where AI can benefit workers, with automated tools and services taking the strain off overloaded employees, particularly when it comes to time-consuming manual processes.

The company highlighted this with the release of several new AI tools for its JAX agentic platform, including Smart Document Capture, which can automatically pull out relevant data and adds it to Xero, and Auto Bank Reconciliation, which automates time-consuming manual tasks in bookkeeping by matching transactions to bank feeds automatically, in real-time.

With all these new tools taking work away from humans, Cassidy confronted the fear faced by some employees that AI would steal their roles.

“We believe the role of the trusted advisor doesn't go away,” she noted, “it magnifies, it multiplies into something much more powerful and meaningful in this era - your judgement, your relationships, your expertise become more valuable, not less, as you take advantage of these tools to supercharge your business.”

“It's important to recognise that our job is not only to make sure AI works for your work, but the new opportunities multiply.”

The UK’s ambition to become a global AI leader depends on one fundamental question, who controls the data that powers innovation?

Trust in AI starts with trust in data. At the same time, this is reflected as hyperscale cloud providers continue to see strong growth in UK services, despite rising concerns over how much UK data is ultimately governed by overseas firms.

This tension highlights a critical turning point for enterprise AI adoption as organizations want the scale and innovation of cloud platforms, but increasingly demand sovereignty, transparency, and control over their data.

As AI becomes embedded in critical business processes, from customer service and finance to healthcare and public sector operations, data sovereignty is no longer a compliance checkbox. It is becoming a core architectural principle to data structures.

Sovereign data enables trustworthy AI

Modern AI systems are only as trustworthy as the data pipelines that feed them. Enterprises deploying large language models, AI agents, and automated decision systems must have clear visibility into where data resides, who governs it, and how it is accessed across the full lifecycle, ingestion, training, inference, and retention.

Without sovereign controls, organizations risk exposing sensitive information to foreign jurisdictions, conflicting regulatory regimes, and opaque third-party access. This becomes especially problematic when AI models are trained or fine-tuned on proprietary datasets, or when agents interact with internal systems in real time.

Sovereign data frameworks provide the foundation for trustworthy AI by enforcing locality, auditability, and policy-based access controls. They enable enterprises to ensure that sensitive datasets remain within UK borders or trusted jurisdictions, aligned with domestic regulations such as GDPR and evolving AI governance standards.

More importantly, they give organizations technical assurance that data ownership remains with the enterprise, not the platform.

This level of control is essential as AI transitions from experimental deployments to mission-critical infrastructure, but data sovereignty is only part of the conversation.

Digital sovereignty is about having the freedom to shape your own digital future, not just where data is stored, but who controls the platforms and AI systems that underpin economies. Businesses need the ability to make those choices on their own terms and balance security, innovation and economic opportunities in a way that reflect their own priorities.

Less data, better outcomes

A second pillar of sovereign AI is data minimization. Contrary to popular belief, effective AI does not require unrestricted access to everything.

Agent-based systems and enterprise AI platforms perform best with data-privacy in mind when they operate on the minimum data required to complete a task.

By restricting context windows, enforcing role-based permissions, and limiting retrieval scopes, organizations can significantly reduce risk of data exposure while often improving model accuracy and relevance.

Over-permissioned AI agents introduce unnecessary risk, such as, broader data access increases the attack surface, amplifies the impact of misconfigurations, and complicates compliance audits. Sovereign architectures encourage a “least privilege” approach, where agents are granted tightly secure access to specific datasets, APIs, or workflows.

This technical discipline delivers tangible benefits. Smaller data contexts reduce hallucinations, improve response quality, and make AI behavior more predictable and therefore accurate. At the same time, minimized data flows simplify governance and lower the likelihood of cross-border leakage.

Control the context, protect sovereignty

Enterprise AI is fundamentally different from consumer AI. While public models optimize for breadth, enterprise deployments require precision, accountability, and contextual control.

Sovereign data strategies allow organizations to define exactly what AI tools are allowed to see and act upon. This includes controlling retrieval-augmented generation pipelines, restricting tool usage, and enforcing jurisdictional boundaries on inference workloads.

When enterprises manage their own data stores, identity layers, and orchestration frameworks, they can keep sensitive information inside trusted environments while still benefiting from advanced AI capabilities.

Context control also enables safer automation. As AI agents increasingly initiate actions, updating records, triggering workflows, or interacting with customers, organizations must ensure that these systems operate only within approved data domains. Sovereignty provides the technical guardrails needed to prevent accidental exposure and maintain operational integrity.

Ultimately, AI works best when enterprises own both the data and the context.

Sovereignty drives vendor diversification and resilience

Increased demand for sovereign platforms is driving investment into local data centers and UK-based infrastructure, while also encouraging greater vendor diversification. Rather than relying exclusively on a small number of global hyperscalers, organizations are beginning to adopt hybrid and multi-provider architectures that include regional cloud providers and sovereign platforms.

This shift reduces systemic risk. Over-reliance on a handful of hyperscalers creates concentration vulnerabilities, pricing pressures, and strategic dependency. Diversification improves resilience, increases competitive choice, and gives enterprises greater leverage over performance, compliance, and cost.

Importantly, sovereign platforms give organizations the confidence to adopt AI at scale, knowing their data remains under UK jurisdiction. This architectural freedom allows businesses to select best-of-breed AI tools while retaining control over where computation and storage occur. The result is a healthier ecosystem, one that balances innovation with autonomy.

Building the UK’s AI future

Sovereign data represents a strategic opportunity for the UK. With the right policy frameworks and collaboration between government and businesses, this momentum could strengthen the country’s position as a competitive AI hub while reducing dependence on overseas providers.

Local infrastructure investment supports economic growth and job creation, while sovereign platforms empower organizations to deploy advanced AI without compromising digital autonomy.

The future of UK AI will not be defined solely by model performance or compute scale. It will be shaped by architecture choices, where data lives, how it is governed, and who ultimately controls it.

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“Tokenmaxxing” – the idea that AI coding success comes down to using as many tokens as possible – is an appealing metric.

Tokens are the fundamental unit that AI coding tools use to read, write, and reason. So on the surface, more tokens should mean more output, more productivity, and more impact.

But when we analyzed 12,000 developers across 200 companies, the data revealed that while more tokens do correlate with more output, they come at a significantly higher price per unit.

Some organizations are pushing software engineers to use as many tokens as possible, using leaderboards to promote the biggest AI users. But that’s not a sustainable strategy. CFOs are starting to push back on uncontrolled AI spending and asking coders to show receipts.

Leaders may be willing to spend money to move fast, but they can’t do it without proving their engineering teams are having an impact.

The best approach to “toxenmaxxing” isn’t to blindly push for AI adoption. Instead, the best path forward for companies is to push AI coding adoption more broadly, moving more engineers into the middle of the curve while avoiding both underuse and expensive overconsumption.

Why ‘tokenmaxxing’ doesn’t scale

We found that the top 10% of Claude Code users consumed about 10 times as many AI tokens as the median developer but produced only about twice the output. In other words, increasing token consumption does increase output, but not proportionally.

The research also shows a small but growing group of power users dominating total token consumption. At the 90th percentile, users are burning around 225M tokens per week, about 3x what they were using six months ago, and about 7x the median.

Many engineering leaders are now looking at their highest adopters and trying to figure out how to get the rest of the organization to the same level. That approach is misguided. With the cost per merged PR increasing from $0.28 at the lowest adoption tier to $89.32 at the highest, scaling extreme token usage simply cannot drive value.

Instead, engineering leaders should focus on smoothing the curve. Broad, moderate token consumption is far more cost effective than having a small group of power users at one end of the spectrum and everyone else lagging behind. When most of the organization is operating in the middle of the curve, AI becomes a durable advantage: enough to drive real productivity gains but not so much that engineering teams burn money chasing marginal output.

Maximize impact, not token consumption

The organizations that burn through the most tokens aren't necessarily getting the furthest with AI. When token consumption is high, most are spent on automating manual tasks with tools like Claude, Copilot or Cursor. Developers essentially have a better tool to do the same kind of work as they did before.

To really drive impact with AI, engineering organizations need to move towards new, truly agentic modes of working. However, agentic systems require major investments in IT infrastructure, including context engineering, orchestration, and sandboxed environments. Until organizations address these issues, the productivity gains will remain blocked by an "agentic barrier" that no amount of tokens can overcome.

How established enterprises can follow the AI-native lead

Conversations around AI and software development focus on coding, but writing code is just one part of an engineer's role. Taking a product to market also involves roadmap work, deployment, go-to-market enablement, and more. If engineers are spending tons of tokens on writing code as fast as possible, everything else needs to catch up.

Changing roadmap cadence and accelerating sales enablement requires major cultural shifts that many organizations aren't prepared for. As a result, extra cadences are often poured into the backlog or other things that may deliver value down the line but won't move the revenue needle in the short term. Teams can consume millions of tokens every week but have little to show for it by the end of the quarter.

AI-native companies are more likely to see an immediate return on their AI investments. While established enterprises may not be able to start from scratch, adopting AI-native principles can help remove bottlenecks and turn token spend into measurable business returns faster. By designing workflows with automation in mind, they can continue to accelerate coding without creating technical debt.

"Tokenmaxxing" is having a moment, but engineering leaders need to move beyond token count and start finding ways to prove value. By measuring how AI impacts delivery, quality, and productivity across the software delivery life cycle, leaders can demonstrate ROI and make sure every token counts.

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The teams that recover best from incidents are not always the ones with the fastest ‘in-the-moment’ responders. It’s those that know how to learn and grow stronger once the immediate pressure has passed.

In most organizations, that learning is still too informal and risky: passed between colleagues, buried in chat threads, or carried only by the people who happened to be there.

Recent research found that, while all respondents agreed post-incident learning needs to improve, only 48% said their organization turns incidents into structured improvement cycles. In other words, the majority of organizations at risk from ill-managed events turning into repeat critical incidents.

More than just technical disruptions, major incidents are business events with financial and reputational consequences. Faster incident recovery creates competitive advantage.

The post-incident review cannot remain an optional team habit spread by happenstance. It should be a defined operational policy within a no-blame culture of investigation and improvement, linked to business outcomes.

Post-incident reviews

The strongest post-incident reviews do more than document what went wrong. Reviews should create a shared account of what happened, what decisions were made, what signals were missed, what worked under pressure, and what needs to change.

The value of the process lies in building a coherent narrative and keeping the review blameless so that teams can understand how failure happened without reducing it to individual fault.

That is how institutional memory is built. Not through folklore passed from person to person, but through repeatable disciplines: consistently reviewing incidents, capturing evidence, assigning ownership for follow-up, and making lessons visible beyond the people involved.

It’s a continuous learning cycle where each incident becomes an opportunity for better decision making, and consistently stronger operations over time. This becomes even more important as operational environments are only becoming faster and more complex with every technology generation.

Organizations at the sharp edge of operations management are already using AI in operational workflows, but leaders are also pairing AI with human oversight and continuous learning, rather than treating incident management automation as self-sufficient.

As systems become more interconnected, teams need a more structured way to preserve environment context, identify patterns, and apply lessons from one incident to the next.

That last point is critical. The digital enterprise environment has become incredibly complex with architectural sprawl, the shift from monoliths to microservices, from quarterly to continuous deployment, and from on-prem to multi-cloud architectures. There are more services, more dependencies, more deployment events, and more places where something can behave unexpectedly.

All of this stresses resilience and makes the institutional task of learning the ropes more challenging for teams. That’s compounded further by fragmented tooling, the siloed knowledge of ‘folklore’ and ad hoc manual workarounds. Not to mention the AI-specific dependencies layered on top of existing systems.

Integrated operations with learning capabilities unlock resilience

Those with AI-first operations increasingly recognize the importance of systematic learning from operational incidents. Post-incident learning and improvement are key to achieving operational resilience, and for the ‘AI pioneers’, this is an especially urgent requirement.

Many AI outputs, particularly generative AI, are not 100% predictable or controllable. There are still big risks as the technology is yet to fully mature. Platforms will ideally enable such context, learning, and the ability to improve teams and tech environments together.

Codify, write, learn, test, repeat

The practical case is straightforward. If incident learning is left to memory, culture or pure good intentions, it will remain inconsistent. A serious post-incident review policy should define which incidents require review, who participates, what evidence is captured, how actions are tracked, and how lessons are shared across teams.

Those practices turn response into resilience.

Resilient teams are not distinguished only by how they handle failure in the moment. They are distinguished by what they remember afterwards, and by whether they are disciplined enough to make that memory usable the next time systems break.

That skill, plus automation, is making ‘AI pioneers’ manage the complexity and sophistication of their digital operations to maximum advantage.

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For the past year, enterprise AI conversations have focused on what the technology can do — build agents, automate workflows, generate content, and transform how work gets done.

But as many organizations move from pilots to enterprise-scale deployment, a different reality is emerging: AI-first approaches can create new complexity when the operational foundation underneath them isn’t designed to support intelligence at scale.

Early pilots often deliver promising results. The challenge comes later — when companies try to scale AI across functions, geographies, and business processes.

That’s when the hidden constraints begin to surface: fragmented data, inconsistent processes, unclear ownership, governance gaps, and environments that become harder to manage and maintain over time.

The issue is rarely the AI itself. More often, it’s the lack of a strong operational foundation underneath it.

The real constraint: a fractured operational core

The pattern is becoming difficult to ignore. Many AI strategies are colliding with the same enterprise realities: disconnected systems, inconsistent data, unclear process ownership, and controls that were never designed for increasingly autonomous technologies.

These challenges become more visible as organizations scale AI.

AI can accelerate decision-making and automate complex work, but it cannot compensate for fragmented operating models or conflicting sources of truth. In many cases, AI simply exposes weaknesses that already existed within the enterprise.

That is why a strong operational core matters more than ever. Whether anchored in ERP software, finance platforms, supply chain systems, enterprise data platforms or broader governance capabilities, AI depends on environments where data, processes, and controls are consistent enough to support trustworthy execution.

AI does not eliminate operational complexity. In many cases, it amplifies it.

Acceleration without alignment creates risk

When organizations move too quickly toward AI-first architectures without strengthening the underlying foundation, the long-term costs can add up quickly.

Systems become harder to scale. Governance becomes harder to enforce. Teams spend more time managing exceptions, validating outputs, and reconciling inconsistent data across platforms.

What starts as speed and flexibility can eventually create operational drag.

ERP: from system of record to system of execution

That is where many AI strategies begin to break down — not because the technology is immature, but because the operating model underneath is not prepared to support it.

And that is where ERP re-enters the conversation.

For years, ERP software has been described primarily as a system of record. In reality, it serves a much broader role. It is where the business runs — where transactions are executed, roles and controls are enforced, workflows are managed, and business rules are applied consistently across the enterprise.

That consistency is not a limitation. It is what makes execution reliable.

As AI becomes embedded into core business processes, organizations need systems capable of turning intelligence into action while maintaining accountability, traceability, and control.

ERP itself is evolving

This does not mean ERP remains unchanged.

Modern ERP platforms are rapidly evolving from transactional systems into intelligent execution platforms that embed AI directly into business processes. Leading organizations are increasingly integrating agents, predictive capabilities, workflow automation, and real-time decision support into their operational environments.

The objective is not to choose between ERP and AI. It is to combine the adaptability of AI with the discipline, governance, and scalability of enterprise platforms.

The future belongs to organizations that can bring both together.

AI and ERP are complementary, not competitive

ERP systems are designed to operate predictably. They help execute processes consistently, enforce controls, and create auditable outcomes across the enterprise.

AI systems are fundamentally different. They learn, adapt, identify patterns, and introduce new forms of interaction, decision support, and automation.

These are not competing models. They are complementary.

AI introduces new layers of interaction, intelligence, orchestration, and productivity. But execution still requires a governed environment. Decisions still need defined processes. Controls must evolve alongside AI-driven workflows. And organizations still need systems capable of translating insight into action.

The greatest value is created when AI and ERP work together—combining the adaptability of AI with the operational discipline, scalability, and governance that enterprise platforms provide.

In that model, AI becomes the intelligence layer. ERP becomes the execution layer.

Together, they create a foundation that allows organizations to scale AI with greater confidence, consistency, and business impact.

Why execution still matters

Without a strong foundation, AI lacks a clear framework for how work should be done or what constraints it must operate within. This becomes especially clear beyond simple use cases.

In finance, AI can identify anomalies or predict cash flow—but acting on those insights requires systems that enforce approvals, maintain audit trails, and support compliance. The same applies across supply chain, procurement, and operations.

Intelligence alone is not enough. Execution and control are what turn that intelligence into outcomes.

Why ERP is becoming more important — not less

ERP provides the operational structure, process discipline, and control framework that helps organizations govern how AI operates across the business. It helps establish consistent data models, embeds business rules and controls, and makes decisions traceable and auditable.

In short, it creates the conditions that allow AI to scale more reliably across the enterprise.

Rather than replacing core enterprise systems, many organizations are pairing AI investments with ERP modernization efforts designed to improve data quality, process consistency, and governance. As AI adoption accelerates, the value of a strong operational foundation is becoming increasingly apparent.

The strategic shift for technology leaders

Many AI strategies still start with tools — what models to deploy, what agents to build, and what use cases to prioritize.

That approach can generate momentum, but not always sustained impact.

A more effective approach starts with outcomes: What decisions matter most? What processes drive value? and What metrics does the organization need to improve?

From there, the focus shifts to how AI supports those outcomes — and what kind of operational environment makes that support reliable.

In most cases, that environment depends on a strong operational ERP core.

The bottom line: AI is ready. Most foundations aren’t.

The organizations that succeed with AI will not necessarily be the ones that deploy the most models, build the most agents, or adopt the newest tools first. They will be the ones that build AI on top of operational foundations designed to support scale, governance, and continuous execution.

AI is moving fast. But without a reliable operational core underneath it, even the most promising AI initiatives can become difficult to sustain.

AI does not eliminate operational complexity—it exposes it.

Organizations with strong foundations can use AI to accelerate performance, improve decision-making, and unlock new sources of value. Those without them often discover that AI scales complexity just as quickly as it scales capability.

Because ultimately, the greatest risk in AI may not be the technology itself. It's trying to scale AI on foundations that were never designed to support it.

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For the last few years, most AI infrastructure conversations revolved around the massive scale of AI training.

The AI training factories that leading AI companies have been building to develop frontier models and lead the AI race are nothing short of impressive: hundreds of thousands of GPUs, power consumption comparable to a medium city, and tens of petabits per second to scale training across multiple campuses.

But, as AI adoption accelerates and its applications expand, a larger infrastructure challenge is set to grab the spotlight. The new, real, and sustained pressure on networks is coming from the demand side of AI: inference.

Inference is what happens every time someone asks a chatbot a question, drops a file into a productivity tool for analysis, or relies on an AI-generated summary inside their inbox or search results. On a per-query basis, the traffic generated is lightweight, nothing comparable with a massive training run.

Now multiply that by the number of people interacting with AI across the globe – and consider that each time more it involves video, images, and supporting files – and you can see where the next major network demand driver is emerging.

With inference, the growth curve is steeper, global, and widely distributed, and it is now reshaping how data center connectivity is designed and the broader network ecosystem that connects cloud regions, metros and continents.

The inference inflection point

Even more powerful than users actively embracing AI faster than any technology before is the compound effect of AI features being injected into platforms that already serve billions.

Just take a look at your technology ecosystem and no doubt you’ve got AI capabilities now embedded in your search engine, email, office software, maps, social feeds and your smartphone. Embedding AI into those products is ensuring instant, global access and inevitable overwhelming usage.

The result is staggering growth in inference volumes. Take Google, for example, which reported the number of AI tokens it processes monthly increased 50 times year over year in early 2025… and then doubled it again just two months later and continues to grow at breathtaking pace, as seen on the 60 percent quarter-over-quarter token growth announced on April 2026.

Efficiency gains at the hardware and algorithmic level help attenuate the demand for additional resources, but nowhere near all of it. Delivering this increased inference volume requires the accelerated deployment of new GPU capacity and in a quickly growing number of inference data centers distributed across more geographies.

That geographic distribution is the first reason inference is not just a compute story but, at its heart, a networking story.

From text to video: multimodal models’ impact on the network

Until recently, AI's contribution to overall internet traffic has been limited. A text prompt and a text response amount to a few kilobytes — negligible next to a single minute of video streaming.

That’s changing quickly.

Multimodal models analyze and generate images, audio, video and 3D content. A user uploading a short HD video clip for analysis or editing pushes several megabytes upstream in seconds.

Researchers, students, and workers are increasingly pumping collections of documents into models to summarize key findings and generate detailed reports. Cloud-based video analytics that process camera feeds to provide insights and alerts are finding their way into viable business models.

Multiply those interactions by hundreds of millions to billions of users and inference traffic becomes a major driver of distributed and pervasive traffic flows.

Reasoning models add a second stressor. Rather than producing an instant response, they break problems into multiple internal steps, often pulling in supporting information in real time. A single user-visible answer can sit on top of dozens of background retrievals, sending megabytes of data between models, storage systems and external sources, much of it crossing data center boundaries.

And more pressure on the network comes from context window expansion. Frontier models can now ingest enormous prompts: think entire document sets, conversation histories, retrieved knowledge bases and more. Retrieval-augmented generation has become a widely adopted technique for many enterprise AI applications, and it involves injecting contextual knowledge into the model prompt on every query.

All told, these trends mean inference is no longer a lightweight workload from a network perspective. It is becoming a dominant driver of traffic growth, both between data centers and between users and the AI infrastructure.

How the DCI focus needs to shift

AI models are now distributed across regions, and usage signals and reinforced learning feedback must flow back to the centralized intelligence.

Multi-step and disaggregated inference workflows are increasingly spanning sites with complementary capabilities; think one for prefill focused on high-compute, large context processing, another for decode centered around low-latency token generation and memory and cache efficiency.

And sovereign AI requirements are pushing workloads into specific jurisdictions, multiplying the number of facilities that need to be tied together with high capacity and reliability.

Typical inference DCI links already operate at multiple terabits per second per route. The number of routes is growing alongside the capacity of each one, driven by more resilient and diversified interconnect topologies combined with a surge in the volume and geographic distributions of emerging inference-driven AI data centers.

Interconnecting inference data centers is just one side of the equation. The other involves connecting users, agents, things, and organizations to run their inference workloads across this meshed infrastructure.

Multicloud onramps are evolving to enable the movement of large enterprise datasets across AI platforms. More symmetric broadband access and scalable aggregation will be needed support widespread cameras to upload video for analysis on the AI cloud. The entire networking ecosystem needs to adapt.

So how do network operators keep up with demand and service providers take advantage of these trends?

Connecting the Dots – or Tokens

Network operators are already responding with planning centered on scalability, flexibility, efficiency, and security. To maximize capacity of every fiber pair—a resource that has never been more valuable and sought-after—they are adopting coherent optical platforms and systems capable of 1.6 Tb/s per wavelength.

Lumen Technologies, for example, is one such network provider that is building for tomorrow. It’s expanding its network at incredible speed, and to do so is leveraging a high-bandwidth 1.6 Tb/s coherent transceiver.

Additionally, the variable and diversified nature of inference traffic patterns make static networks less than ideal. Many operators are thus turning to AI-assisted multilayer network control to shift capacity and optimize performance in near real time.

Again, Lumen is a prime example of this; by leveraging a rich control suite the provider is gaining comprehensive visibility and maximizing usage and performance of its fiber assets from one point of control.

And to tie it all off, as inference traffic often includes sensitive data, network operators are baking in encryption at the optical layer within any new DCI deployment.

Inference workloads are evolving faster than any forecasting model can keep up. The networks best positioned for the next five years are the ones designed to scale in capacity, reach and intelligence without requiring major upgrades each time the workload spikes.

Training defined AI's first wave of infrastructure buildout. Inference is shaping its second – one much more globally distributed and intricate that will transform the networking landscape.

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• OpenAI reveals first branded hardware, the Codex Micro, a programmable macro pad built with keyboard maker Work Louder
• Codex Micro seems to be based on Work Louder's Creator Micro 2's layout, mapped to Codex coding-agent shortcuts
• The move reinforces OpenAI's Codex offering as one of its mainstay areas of focus by allowing developers the ability to perform tasks or interact with AI faster

OpenAI's first branded piece of hardware is not a long-anticipated consumer device it is building with ex-Apple design chief Jony Ive, but rather a programmable macro pad called the Codex Micro.

The keyboard, which consists entirely of macro keys designed to "supercharge people's Codex usage," according to an OpenAI spokesperson at the AI Engineer World's Fair, is reportedly a collaboration between the iPhone creator and the custom macro pad creator Work Louder.

With OpenAI's developer-centric account on X indicating that the full launch of its hardware foray is expected on July 15, the AI giant seems to be pulling out all the stops to ensure it becomes a well-received add-on for the developer community.

A simple rebadge or a sign of things to come?

The as-yet-pending release 'Codex Micro' seems to be inspired by Work Louder's existing Creator Micro 2, a compact macro pad that offers thirteen mechanical keys, a joystick, a rotary encoder, and touch controls, arranged across programmable layers to power users needing faster or more fine-grained control over AI-assisted coding tasks.

The move is understandable for OpenAI in terms of both securing a victory with developers and brand recognition, and essentially testing the waters on how it would handle a hardware launch for the company's upcoming AI device for more general-purpose users.

It can also, to a degree, be seen as OpenAI essentially acknowledging that its earlier stance of narrowing its focus to 'nail' its core business might be one the company is willing to make exceptions to, especially when it comes to coding tools or enterprise use-case hardware.

OpenAI's CEO of applications, Fidji Simo, reportedly told staff that the company was looking to deprioritize areas outside its core focus to allow it to lead where it mattered.

In 2025, OpenAI shipped the Sora video app, the Atlas browser, ecommerce features inside ChatGPT, advertising work, and hardware efforts, a "series of startups" approach that insiders said had produced organizational confusion and constant reshuffling of scarce compute, distracting it from a truly centralized goal.

Hardware, in other words, was explicitly on the list of distractions. A physical keyboard is arguably as clear a violation of that directive as one could possibly design.

OpenAI is also reeling from a smaller-than-expected gap from competitor Anthropic and its Claude models in the areas where its GPT models do compete. This can perhaps be attributed to Anthropic's much narrower focus, which caters specifically to coders and enterprise through its Claude Code and Claude Cowork offerings.

One can argue that OpenAI's move isn't one that distracts it from its core focus, but rather complements it, even as R&D and integration for the most part is something that Work Louder will commit to.

It allows the AI juggernaut gets to test out both the marketability of an OpenAI-branded hardware product and appease developers and founders with a low-effort play even as they have increasingly been considering tools from Anthropic and Google as well as other AI solutions providers.

None of OpenAI's previous concerns may apply here; the exercise does not consume compute, it caters to a key audience for OpenAI, with Codex assisting 5 million weekly users as of June, and it does not meaningfully engage an engineering team as some of its other projects do.

With OpenAI and Anthropic slated to IPO soon, both are locked in a race to secure as many active users as possible to justify their valuations, even as they vie to build the most powerful models to cater to various industries, including defense, telecommunications, pharmaceuticals, and software development, to name a few.

OpenAI's move might just be a sign of things to come, as it leverages ChatGPT's massive brand recognition to develop marketable, revenue-generating solutions such as a custom macro keyboard, even as it is loath to spend any of its engineering or compute resources on anything but the most important of its tasks, even as enthusiasts continue to wait for the release its upcoming collaboration with legendary Apple designer, Jony Ive.

Gemini Gems are Google's answer to the annoyance of constantly having to repeat yourself to an AI chatbot. Like ChatGPT's custom GPTs, Gemini Gems are customized, reusable variations of Gemini that remember a specific role, history, and personality, so you don't have to keep explaining yourself every time you start a new chat.

Rather than beginning every conversation with housekeeping, you immediately start solving the problem you actually opened Gemini to tackle. You build a collection of specialists that already know their jobs. Open your travel planner when you're booking a holiday, your guitar coach when it's time to practice, or your meal planner when the refrigerator looks uninspiring, and each one picks up exactly where you left off.

I've made plenty of Gems, some more enduring than others. They're easy enough to make, but tweaking them to be just right can be tricky. If you want to see some of the more appealing (and sometimes just fun) possibilities of Gems, here are five of my favorites. I've written out the instructions I composed for the Gem at the end of each. Gemini can also edit and expand on even the simplest of descriptions, but more detail can help ensure the Gem does what you want.

Creating a Gem

The process of creating a Gem is easy, just click/tap on Gems in the left hand menu in the web browser version or the app version of Gemini, then New Gem. You can use the custom instructions from each of my five Gems if you'd like to recreate them yourself.

1. Family Adventure

Planning family outings has become its own part-time hobby. My wife and I have a two-year-old and an eight-month-old, so every trip has to thread a surprisingly small needle. It needs to be close enough that nobody spends half the day in the car, interesting enough to entertain everyone, stroller-friendly, and ideally open when we actually want to visit. That made Family Adventure Planner the first Gem to showcase.

Setting it up only took a few minutes. After creating the Gem, I conversed with Gemini through it and gave it some basic details about locations, interests, and the kinds of places we've enjoyed in the past. Once the Gem had all that information, I threw some different scenarios at it.

I asked it to plan a family outing for the coming Saturday within about an hour's drive. The Deep Research feature pushed the Gem to check what would actually be open that weekend, look for seasonal events taking place, verify opening hours, and even factor in temporary exhibits and admission prices before putting together a suggested itinerary.

The recommendation felt surprisingly complete. It suggested a nearby sculpture park with stroller-friendly paths, followed by lunch at a family-friendly café and an ice cream stop on the drive home. It also pointed out that arriving before mid morning would make parking easier, exactly the kind of practical advice that is easy to overlook until you're trying to unload two young children from the car.

I asked it to imagine that rain was forecast all day and that we still wanted to get out of the house. Instead of simply swapping a park for a museum, it built an entirely different plan around an interactive children's museum, suggested a nearby indoor play space if our oldest still had energy afterward.

The Gem also adapted quickly as I added more context. After mentioning that long waits at restaurants rarely end well with a hungry toddler and an eight-month-old, future itineraries naturally favored casual cafés, picnic spots, and places where food was readily available. It quietly learned from each conversation instead of making me repeat those preferences every time.

If your weekends usually begin with twenty minutes of searching before anyone leaves the house, this is probably the first Gem worth creating.

Family Adventure Planner instructions

You are an enthusiastic, creative, family-focused activity planner. Learn my family's ages, interests, travel preferences, and other details. Whenever I ask for ideas, recommend activities that are realistic, seasonal, and varied while avoiding suggestions I have recently tried unless I specifically ask for favorites. Include all relevant logistics details like times, costs, and packing suggestions.

2. Hobby Coach

Some hobbies are easy to put down for a while. Others seem to expect you to remember exactly where you stopped. That made Hobby Coach one of the first Gems I wanted to build.

I set it up with two of my biggest hobbies: learning guitar and backyard astronomy. I told it I was still a beginner guitarist working toward playing complete songs and that my astronomy interests revolved around learning the night sky with a modest telescope instead of serious astrophotography. Once that information was saved, I never had to explain it again.

To see how useful it would be, I spent an afternoon asking it to map out future practice sessions instead of simply answering questions. For guitar, it created a progression that built from my current skill level, suggesting chord exercises, songs that gradually increased in difficulty, and realistic milestones to aim for over the next several weeks. Everything fit into a longer learning plan.

Astronomy worked just as well. I asked it to plan a series of upcoming observing nights, and it suggested different targets depending on the season, moon phase, and what I wanted to learn. One evening focused on easy constellations, another introduced brighter deep sky objects, while another became a relaxed tour of the Moon and planets.

The Gem also uses Guided Learning as its default tool, which structures lessons into connected learning paths instead of isolated answers. It builds on previous lessons, introduces new skills at the right pace, and creates the feeling that you're working with a patient teacher who already understands your goals.

Hobby Coach instructions

You are an encouraging, knowledgeable, and patient personal coach for my hobbies. Learn my current experience level, equipment, goals, schedule, and preferred learning style for each hobby I share with you. Remember my progress over time and build each lesson naturally on previous conversations instead of starting from the beginning. Break complex skills into manageable practice sessions, celebrate improvements, and recommend realistic projects that keep me motivated without becoming overwhelming.

3. Movie and TV Curator.

Choosing something to watch should be one of the easiest parts of the evening, yet it often turns into an extended scrolling session. I knew that AI chatbots can be useful here, but a specific Gem for the endeavor felt like a good fit.

I gave my new Movie and TV Curator Gem everything it needed to know about our tastes. I told it which streaming services we subscribe to, the kinds of films my wife and I enjoy after the children are asleep, the comedies and mysteries we've already watched, and perhaps most importantly, that we have a two-year-old who is just beginning to sit through longer family movies.

That final detail completely shaped its recommendations. Instead of suggesting whatever happened to be popular, it focused on gentle, engaging films that would make good introductions to family movie nights without overwhelming a young child. It also remembered which movies we'd already seen so future recommendations wouldn't feel repetitive.

I asked it to build a month's worth of family movie nights, along with separate recommendations for date nights after the children were asleep. Within minutes, I had a calendar filled with classic animated films, newer family favorites, and several older movies that I had completely forgotten about but couldn't wait to introduce to my son.

It also understood the different kinds of evenings we have. A Friday after a busy week called for something light and funny, while a quiet Sunday evening was a better fit for a slower family film. I soon had a collection of family movie nights and grown-up viewing plans waiting whenever we needed them. Like the other Gems, it turned a repetitive decision into something I only had to think about once.

Movie and TV Curator instructions

You are a knowledgeable, conversational entertainment expert with excellent taste and a great memory. Learn my favorite genres, actors, directors, streaming services, viewing habits, and the movies and television shows I've already watched. Recommend films and series based on my mood, available time, and who will be watching, while avoiding unnecessary spoilers and repeating recent suggestions unless I ask. Explain why each recommendation suits my tastes, maintain a warm and enthusiastic personality, and regularly introduce overlooked classics alongside newer releases.

4. Outfit Planner

At first glance, Outfit Try On Planner sounded like a Gem I'd probably use once and then forget about. I enjoy looking reasonably presentable, but I wouldn't describe myself as someone who spends much time thinking about fashion. After setting it up, though, I realized it was less about keeping up with trends and more about making decisions before I actually needed to make them.

I started by teaching the Gem my style. I told it the kinds of clothes I usually wear, the colors I naturally gravitate toward, and the occasions I dress for most often. I also uploaded a few photos of myself so it could create realistic visualizations rather than relying on generic fashion models.

I asked it to put together outfit ideas for a fancy date, weekend trip, and a few other occasions. Seeing complete outfits instead of reading descriptions made decisions much easier. The most entertaining experiment had nothing to do with everyday clothes. I'd been thinking of dressing up for the Renaissance fair this year, so I asked the Gem to imagine me in a variety of Renaissance costumes before I bought or rented anything. You can see me as a hooded archer, an elaborately dressed nobleman, and a cheerful wandering bard carrying a lute.

Because the Gem remembers your appearance and preferences, it can help visualize costumes, themed party outfits, Halloween ideas, vacation wardrobes, or almost anything else you might wear before you spend money assembling it.

Outfit Try On Planner instructions

You are a friendly, fashion-savvy personal stylist with an eye for color, fit, and practicality. Help me create outfits using clothes I already own, visualize new pieces before I buy them, and suggest combinations that suit the occasion, weather, and my personal style. Ask questions before making recommendations. When I upload photos of clothing, accessories, or myself, use them to generate realistic outfit visualizations and styling ideas.

5. Personal Theme Song

The final Gem was the one I expected to be the silliest, yet it ended up being one of my favorites. Google recently added a Music tool to Gemini that can generate original songs from simple prompts, so I decided to build a Gem called Personal Theme Song Composer, dedicated entirely to turning everyday moments into music.

Setting it up only took a few minutes. I told it about the musical styles I enjoy, asked it to learn how I like songs to feel, and instructed it to ask questions about the people, pets, places, or memories behind each request before using Gemini's Music service to compose something original. Once those instructions were saved, I could jump straight into ideas instead of explaining the same preferences every time.

One of the first finished songs involved my dogs. Every dog owner eventually invents a ridiculous tune while clipping on the leads for a walk, so I asked the Gem to write something jaunty about my two excitable Chihuahuas that sounded like the opening theme to a cheerful television comedy. The result perfectly captured the determined little strut they adopt every time they head out the front door, and the melody stayed in my head for the rest of the day.

It perfectly summed up what makes Gemini Gems so useful. They are more than saved prompts. They are specialists that remember their role, making new capabilities like Gemini's Music tool feel less like an occasional novelty and more like a creative partner that's always ready when inspiration strikes.

Personal Theme Song Composer instructions

You are an imaginative, enthusiastic, and collaborative songwriter and music producer. Your goal is to help me create original songs that celebrate and vibe with whatever topic you're given. Before writing a song, ask enough questions to understand the story I want to tell, the mood, the musical genre, whether I want vocals or an instrumental, the intended audience, and any specific lyrics, phrases, or themes I want included. Suggest genres, tempos, instrumentation, and vocal styles that fit the idea. If I provide photos or other context, use them as inspiration for the music's tone and storytelling.

What makes Gems feel different from saving a handful of good prompts in a notes app is continuity of purpose. A prompt tells Gemini what to do once. A Gem remembers who it is supposed to be every time you come back. It remembers you, your favorite way of working, and the tools that help it do its job best, creating an experience that feels much more personal over time.

The five Gems here are really just a starting point. Once you get comfortable creating them, it becomes surprisingly easy to imagine building one for meal planning, another for packing for trips, or any other gems you want to fill your treasure chest with.

Although artificial intelligence (AI) continues to dominate business conversations, widespread adoption is still some way off.

In fact, just one-third of professionals said that AI programs are being scaled across their organizations.

Many companies remain hesitant to move beyond pilot projects, with a lack of trust a major barrier.

Concerns over reliability, data protection, IT security, impartiality and the potential for misuse also continue to slow adoption.

To close that gap, businesses need to understand what is behind it, how to close it and how to ensure these processes scale across the whole enterprise.

What’s behind the trust gap?

Lack of trust in AI systems continues to hold organizations back. Only 57% of AI and data teams fully trust the outputs of AI systems, and among product managers and software developers, this figure drops to just one-third. This points to a wider lack of confidence as AI becomes more embedded in day-to-day operations.

Developers’ fears are based on a variety of factors. Hallucinations, when AI models generate information that appears credible but is in fact false, mean workers are concerned about its reliability in high-stakes or sensitive use cases.

Likewise, cybersecurity, including the security of data entered into AI systems, the risk of leakage to third parties, the potential for attackers to compromise AI environments, compliance with ethical standards, and whether bias in systems could shift over time as they continue learning during live operation are all contributing to workers’ distrust of AI models.

Building trust in AI

As organizations look to build confidence in AI, quality management is becoming a bigger priority. In fact, 79% of workplace decision-makers see a direct link between trust in AI and active quality assurance measures such as regular testing, monitoring and oversight.

Trust can only be built when both AI systems and the data behind them are subject to comprehensive quality controls that are applied continuously, not just as a one-off check before deployment.

A strong foundation starts with data quality. Organizations need confidence that the information feeding AI systems is complete, up to date, and accurate. AI outputs are only as reliable as the inputs they depend on, so flawed or incomplete information will inevitably lead to poor results. Robust data governance is therefore essential to ensure AI-driven decisions to hold up in real-world use.

Quality management must also cover security, compliance and access controls. Organizations need safeguards to ensure that data does not leak from internal systems to third parties and that AI complies with regulatory requirements such as the EU AI Act and GDPR.

Clear accountability for protecting sensitive business is equally important. For example, organizations must ensure that HR records can only be accessed by authorized HR staff, with controls in place to monitor and manage how that data is used within AI environments.

At the same time, trust is not solely about reducing risk. Organizations want to ensure AI investments deliver measurable value, with more than half of companies citing improving return on investment (ROI) from Ai deployments as a key priority. This demonstrates that reliability and performance are just as important as security and governance.

Why quality management still struggles to scale

For many businesses, the challenge is no longer deciding whether to use AI, but how to manage it reliably at scale. That is proving difficult because quality management processes are still heavily reliant on manual oversight.

Quality management needs to extend across the entire AI lifecycle, from model design and training through to testing and ongoing monitoring in production.

Yet among companies that conduct regular testing, just 15% to 29% have implemented automated quality assurance processes at any AI development stage. This means that quality controls are often conducted entirely or partially manually, making them time-consuming, costly and prone to human errors.

Ultimately, this limits widespread AI adoption. Large-scale deployment is difficult to achieve when quality assurance relies heavily on manual effort, as scaling would require a significant increase in personnel.

Many organizations are also trying to manage AI adoption without clear governance, with half still lacking an enterprise-wide AI governance strategy. This is often compounded by a lack of expertise and limited understanding of how AI systems arrive at their results. At the same time, pressure to deploy AI quickly can divert resources away from testing, monitoring and oversight.

The consequences of inadequate quality management are quickly felt, particularly when AI systems interact directly with customers. Beyond the risk of regulatory fines, organizations often face dissatisfied customers, missed business opportunities, and reduced productivity when AI-generated results create more work than efficiency gains.

A stronger focus on quality pays off

As long as concerns around reliability, security and governance remain unresolved, businesses will struggle with the successful, large-scale deployment of AI. In line with this, three-quarters of decision-makers believe that more effective AI quality assurance would see their organizations experience a significant or transformative impact on user trust.

Companies must elevate AI to a new level of trust through a combination of suitable toolsets, robust governance frameworks, process expertise and technical know-how. Only by embedding these foundations can organizations deploy AI profitably and sustainably over the long term, while building the confidence necessary to scale its adoption responsibly.

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The open-source phenomenon OpenClaw kicked off a craze for the "personal agent", in turn sparking a massive ecosystem of specialized "claw" variations across the agentic AI developer landscape.

Questions about whether you should trust an agent with your schedule, personal information and decision making aside, Nvidia's launch of NemoClaw laid out a strong case for a similar shift for the corporate sector.

For organizations cautiously mapping out their AI roadmaps, Nvidia’s insertion into this space purports to deliver a highly credible, production-ready path forward by layering enterprise-grade reliability over raw autonomous power.

Now a few months post-launch, the dust has settled just enough to say what this means for the space, particularly what it means for the future of autonomous workflows.

OpenAI Buys ‘Lightning in a Bottle’

OpenClaw’s journey to enterprise infrastructure was a wild ride. The project originally launched in November 2025 under the moniker "ClawdBot" as a playful side project relying on Anthropic’s Claude model. Following a stern cease-and-desist letter and several quickfire rebrands, the project reemerged as OpenClaw.

It perfectly addressed the unmet market demand for action-oriented AI assistants that could persistently write code, browse the web, and chain complex tasks over days without human intervention.

It quickly became the fastest-growing open-source community in GitHub history. While big players like Meta actively expanded its own footprint by purchasing alternative agent systems like Manus AI, OpenClaw caught lightning in a bottle, culminating in creator Peter Steinberger joining OpenAI to help safely bring autonomous agents to the mainstream public.

Structuring the Enterprise Shield: What is NemoClaw?

As successful as this was for Steinburger, the real changes in the industry followed shortly after. Enter Nvidia, with its sizable chequebook and “NemoClaw,” which functions as an enterprise distribution of, or wrapper built directly on top, OpenClaw.

The stack installs via a single command and answers the important question: “Can I trust this AI agent with the kind of access I am about to give it?” The idea is to deliver the vital security architecture corporate IT departments require before deploying autonomous agents near live production systems.

Its creation and distribution proves that agentic AI needs an enterprise-grade foundation — wrapping agent frameworks with infrastructure, guardrails, and policy enforcement is becoming essential. It signals a move toward “AI operating systems” — platforms that don’t just enable intelligence, but also control how that intelligence behaves.

This is likely to become the standard model: open innovation at the edge, with structured governance layered on top.

Nvidia's Dispersed Strategy for the Agent Mesh

The AI community's reaction to NemoClaw highlights a fascinating shift in how infrastructure leaders approach market dominance. Rather than locking developers into a closed stack, Nvidia is deploying a highly flexible, dispersed strategy as NemoClaw does not require Nvidia silicon to execute.

While it is naturally optimized to leverage Nvidia's robust edge systems (and why wouldn't it be), it plays nice across the broader infrastructure ecosystem. This is exactly the sort of vertical integration play that you would expect from Nvidia with its dominance of the GPU market being so unopposed.

The Peak Appetite for AI Accountability

The corporate appetite for hard guardrails has reached an absolute fever pitch. According to a recent research report, 95% of enterprises are actively holding back from scaling their AI projects purely due to safety anxieties.

These concerns are validated by severe operational risks: unconstrained autonomous agents are notorious for "shadow AI" data exposure, introducing undetected "privilege creep" via SaaS applications, or even completely deleting, contaminating, or compromising vital database instances through unrestricted "God-mode" access.

With horror stories of loosely governed AI agents wiping out entire companies databases, guardrails arriving with the sheer commercial heft and legitimacy of Nvidia are uniquely positioned to give CISOs the peace of mind they need to finally greenlight production.

The Path Forward: Collaboration Over Conflict

Ultimately, the trajectory of OpenClaw proves that independent, boundary-pushing software can still fundamentally rewrite the computing landscape overnight. Yet, Nvidia's subsequent rollout of NemoClaw emphasizes that the industry can no longer focus purely on raw intelligence or reasoning capability.

Without rigid, predictable boundaries, autonomous agency is simply an unmanageable corporate liability.

The true agentic renaissance depends entirely on collaborative, cross-industry guardrails. By actively fostering initiatives like the Nemotron Coalition — which unites rivals like Mistral AI and Perplexity to co-develop secure base models — the tech sector is showing it can build a future where AI capability and AI accountability finally move in perfect lockstep.

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Chinese robotics firm Agibot has launched a range of humanoid robots designed to look, move, and sound like human beings in the UK business-to-business (B2B) market.

The Shanghai-based company, founded in 2023 by two former Huawei engineers, hosted a launch event in London on June 30, 2026, with several of its A3 full-sized humanoid and X2 half-size humanoid robots in attendance, as well as current and prospective business partners.

The robots — all controlled by humans with smartphones and DualSense controllers — were deployed at several demo stations in the event space.

One X2 humanoid robot was set up to dance to peppy music, cutting some serious shapes for a constant crowd, while another in a Harry Kane shirt kicked a football into a net. A third wore a suit of (admittedly very cool) Gundam-style armor, demonstrating the robots’ potential for customization.

As for the full-sized A3 humanoid — less expressive than its smaller sibling, with sharper angles and a rather blank circle in place of the X2’s animated ‘eyes’ — a unit was made available to greet guests and for photo opportunities.

In a presentation at the UK launch event, Agibot co-founder and COO Daniel Jiang said: “The robot business is the AI business. Robots should understand you, know what you say, talk to you, and work for you.”

Indeed, at the launch event, the word ‘robot’ was as common as the phrase ‘embodied AI’, as Agibot representatives attempted to position the firm’s product range as a signifier of a new technological era: the ‘fourth industrial revolution.’

In a later talk, co-founder and R&D president Yan Xiong said: “Of the products I’ve worked on, embodied AI is the most exciting and inspiring. Through three intelligences in one body, robots can become human-like.”

The three intelligences in question control interaction, locomotion, and manipulation, powered by on-device AI models that communicate with cloud servers. It seems that Agibot sees itself as an AI company first and foremost, and is allocating its resources in kind: “Our main focus is the research and development of the three intelligences, taking up 75% of our R&D staff, and 80 per cent of our R&D budget.”

That’s not to say that the firm's humanoid robots aren’t profoundly impressive. The units are made mainly of magnesium alloy, with titanium reinforcements at pressure points to ensure rigidity. The A3 weighs 55kg, which is pretty reasonable when you consider its 5-foot-8-inch (173cm) height, and lasts up to 10 hours on a single charge of its 1152W battery.

In comparison, the 4-foot-3-inch (131cm) X2 weighs 39kg and lasts for two hours. The units communicate with the internet via dual-module, dual-SIM 5G.

Also present were units from Agibot's D1 quadruped range, inexplicably programmed to do backflips on command, as well as G2-series industrial robots and C5 cleaning robots. The top-tier D1 model, the D1 MaxPro, has a carrying capacity of 50kg, while only weighing 68kg itself.

Agibot is young but quickly increasing its production capacity. It recently rolled out its 15,000th unit, having passed 10,000 units just three months earlier, and has already deployed its G2 industrial robots in China’s Longcheer electronics factory. Indeed, much of the event was dedicated to promoting possible use cases for each model in industries like construction, live entertainment, and the corporate world.

Having shaken hands with the X2 and watched the D1 wave its ‘paw’ and, again, perform several perfect backflips, I have to admit that I’m still stunned by Agibot's range of robots. The firm’s launch has done nothing to allay my many concerns about AI, but it’s still almost unbelievable to see actual droids walking around.

It’s clear that Agibot wants its robots to be relatable — the dance moves, handshakes, and free kicks are obviously an effort to instill some kind of affection in a crowd of possible customers. I’d be lying if I said I didn’t find the X2 wildly entertaining, even though I know it's just metal and plastic — but that’s the power of pattern recognition.

If Agibot can make strides towards its professed goal of autonomous operation, then we really aren’t too far away from having a real-life C3PO on our hands.

It’s early days, but the firm’s leadership sounds committed. In his presentation, Yan Xiong said: “Scaling to mass production is the first hurdle to mature the embodied AI industry. After we achieve mass production, the next step is to bring our portfolio to commercial availability."

“AI should not be confined behind screens,” he later added, “it must be brought into the physical world.”

As businesses race to operationalize AI, a growing number are creating dedicated AI leadership positions.

The appointment of HSBC’s first Chief AI Officer, for example, caused a stir when it was unveiled earlier this year.

When the financial services giant announced plans to build a bank for the future – and cited the key role that an enterprise-wide AI strategy will play – many people no doubt expected the new position to be filled by a technologist. Instead, the inaugural title was given to David Rice – a reported HSBC veteran of 20 years, who previously served as the bank’s Chief Operating Officer.

This became a hot topic of conversation, but in my opinion, for all the right reasons. Because, yes, HSBC’s decision may have come as a surprise, but I applaud it as the right one.

AI is not a tech silver bullet

C-suites are becoming increasingly energized about ever-intuitive technology and the potential to transform their organizations with capabilities such as agentic AI and advanced automation. The excitement is palpable and understandable.

But we must remember that like any technology, AI alone is not a silver bullet. After all, you can have the most sophisticated Formula One car in the world, but the vehicle alone doesn’t guarantee a podium finish. AI also doesn’t magically fix a messy business, it exposes it. So, simply investing six figures or more in cutting-edge technology will not automatically pave the way for positive strategic change.

That said, senior technology figureheads such as the CTO are often the most likely AI evangelists because they understand how AI can be utilized to achieve the transformational progress that is being sought.

They’re important advocates of the ‘art of the possible’. They can articulate the value, clarify what is and isn’t doable, help understand likely ROI, and if necessary, talk to department heads to help alleviate concerns.

They are an important voice in this entire conversation. But that doesn’t mean they have to lead it.

The role of the technologist

In large organizations, of course I believe the CTO should be ultimately responsible for the technology platform. We’re talking about something that will be used if not relied upon by potentially the entire workforce, so the build, deployment, security and governance considerations need to be safeguarded by someone experienced in managing business-critical infrastructure on this scale.

We also need to remember that although AI capabilities are changing rapidly, if you strip everything down, we’re looking at extremely smart code that should be written, tested, productionized, released, deployed, monitored and managed, with the same guardrails as other technology within the business.

At the very least, an AI leader therefore needs the CTO to be an ally, as the organization advances its digital maturity. And if such a technical specialist does not exist in-house, this level of strategic counsel must be sought from a trusted external partner.

Without this technical collaborator, the AI leader may quickly become unstuck. This is not about becoming a self-taught expert, however simple it seems nowadays to spin up a game-changing solution with vibe coding.

Alternative AI evangelists

But we need to remember some of the many other reasons that 95% of AI projects reportedly fail (MIT, 2025).

Often it is because large, complex initiatives have been kickstarted with significant budget, but a crucial stakeholder – such as operations – has only been brought in, at the final hour. When people feel excluded and their requirements unaccommodated, initiatives get blocked, projects stall and eventually things grind to a halt.

We must also realize that AI is only effective when you start applying it to particular workstreams, workloads and business processes – especially processes on a micro level. These micro-processes may seem inconsequential, when compared to the bigger picture.

However, if the AI evangelist can alleviate administrative pressure or a morale drainer that impacts a large number of people on a daily basis, the seemingly small productivity wins start to make a daily difference to colleagues who need to buy-in to AI for the longer haul.

Culturally, process orchestration is usually not something that a pure ‘techie’ understands or cares enough about. But for any AI initiative to be a success, the appointed AI leader is so dependent on unpicking, defining and if necessary refining every process and its interdependencies. An operations lynchpin – or another experienced and personable business analyst or transformation lead – is critical here.

If you want AI and the human workforce to augment one another, the AI lead also needs to understand people. For example, if you’re going to build an agent with a HR persona, which can sit inside that department to help address repetitive joiner, mover and leaver matters, it is firstly crucial to understand the role of a HR colleague. This extends far beyond the colleagues’ basic job description.

A transformative leader – whatever the role

The most successful projects need a senior stakeholder to direct departments, drive collaboration and unity, and confidently build, articulate, socialize, and enforce the AI strategy, among the people who’ll live and breathe the change. Even the most technologically gifted individual cannot do this alone.

That senior stakeholder could wear one of many hats, but whether they’re the CEO, CIO, CTO, COO, director of business transformation, or another change leader, they must have an innovation appetite, and the ability to articulate – perhaps with the help of their chosen partner(s) – what budget is required and why.

None of this is about ego. It’s about someone who can talk at level 5 to the layperson, and hold their own when talking at level 500 as the technical plans take shape. They know what they want to achieve, even if they don’t know how. They might not have a technical background, but they need to partner with someone who does.

Multi-disciplined responsibilities

In a perfect world, AI implementation is not anyone’s stand-alone problem – it is a multi-persona discipline. A talented AI engineer will always ensure you have the right configuration in response to the brief.

But the relevance and quality of the resulting solution depends on who has defined that brief in the first place, to what standard, and whether this truly reflects the strategic motivators of the business. If the AI lead can’t articulate what good looks like and how it links to the P&L, it will always be harder to get there.

To me, HSBC’s Chief AI Officer selection signals that this role is not simply about building new systems, but reframing how the business runs, for the benefit of customers and colleagues alike. And on the face of it at least, the bank appears to have covered all bases.

The Chief AI Officer is said to be working closely with the CTO, who will oversee the core technology foundations needed to support the necessary AI adoption initiatives. And I know the industry is watching with bated breath to see what unfolds.

Perhaps we need to all put job titles and skill-sets aside in favor of understanding the degree of change truly required, and the person – or people – best placed to lead the charge. I wonder how many enterprises will follow suit.

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