The world is full of weird and wonderful inventions that never really took off, with the Segway perhaps one of the most overhyped technologies out there. Invented by Dean Kamen, it was once considered the future of how people would move around towns and cities – and it had plenty of huge supporters.

Codename Ginger

Speculation ran wild when these leaked comments by Jon Doerr hit the mainstream media at the start of the 21st century.

This statement was initially disclosed when a copy of a book proposal by journalist Steve Kemper was leaked to the media and published on the online tech magazine Inside.com. They were centered around an exotic new invention described only as 'Codename Ginger' – but the trouble was that nobody knew what it was.

What Kamen was working on behind closed doors was, in fact, a two-wheeled, self-balancing personal vehicle that used smart sensors and motors to keep the rider upright. This was the Segway – and, well, we all know how that story ended.

Wheels of time

Some predicted at the time that cities would be redesigned around the Segway – but the product sold less than 150,000 units before production was shuttered. It's now widely considered the most overhyped invention in history, with high costs and huge regulatory hurdles proving insurmountable obstacles for mass adoption.

The last Segway was actually manufactured as recently as 2020, with the company now transitioning to other forms of transportation, like e-scooters and UTVs. There are still uses in security and in tourism, but it's largely a redundant technology.

As for Kamen, his most recent venture, DEKA, is focusing its efforts on machines in the biotech space. Specifically, he has recently invented a large-scale regenerative medicine manufacturing and organ preservation system.

Letterboxd might just be my favorite app — I love tracking the media I consume and making lists, and Letterboxd is basically a perfect take on that for movies. And while I don’t love Goodreads quite as much, I find that it serves a similar purpose for books. But I’ve struggled to find an app like this for shows.

That’s not to say no such apps exist — of course they do. But for one reason or another, I’ve always bounced off them. Until, perhaps, now.

It’s early days, but so far, Bingers — a new app from the co-founder of TV Time — seems like it might mostly fit the bill, though it doesn’t quite address all of my issues with other TV show tracking apps.

Beautiful and customizable

One of the things I love about Letterboxd is just how much of a joy it is to interact with, and part of that comes down to it being aesthetically pleasing, with the same being true of Bingers.

Here you get big posters and backdrops for each show, along with the ability to swap them out for other options, and the interface beyond that feels polished and — for the most part — well laid out.

Adding shows to your watchlist can be done with a single tap, and marking episodes or whole seasons as watched is similarly speedy, so there’s minimal friction.

Switching over from another app is potentially easy too, as you can import your data from TV Time, TV Time Liberator, or Refract.

It’s not perfect; for example, while you can search through the filmography of cast and crew members, there’s no way that I can see to sort or filter those lists. But the core parts of the app at least are largely well laid out. And Bingers has only just launched, so it’s sure to improve over time.

Impressively full-featured

There’s also a lot here. You can add shows to your watch list and then get alerts when new episodes or seasons are out, so you never miss them. You can also rate and review shows and episodes, and even attach an emoji for how they made you feel.

There’s even an option to select your favorite character in an episode, and then see what percentage of people picked each option. I’d love to see more stats, which is something Letterboxd still has over Bingers, but that — along with a total watched time and number of episodes watched — is at least gesturing towards an interest in this sort of data.

Plus, you can mark shows as favorites, create lists, and follow other users — with that last point giving this app the community feel that things like Letterboxd and Goodreads also have.

You can also explore trending shows and genres if you’re looking for something new to watch, and you can log rewatches, so you can see how many times you’ve viewed something.

There are movies here too if you want all of your visual media in one place, though for now, at least, I’ll mostly be sticking with Letterboxd for those.

Keeps me coming back

Still, while I’ve only been using Bingers for a few days so far, that’s longer than I managed with most apps like this. I think all of the above contributes to that — the beautiful design, the lack of friction in logging, and the reminders and alerts for new episodes, plus a handy timeline view that shows what you haven’t yet watched, and what episodes are landing in the coming days.

I do wish there were a Bingers website, as sometimes I’d rather interact with these services from my computer, and it could still do with a few more features within the app — more stats, an overall average rating for each show, and the inclusion of trailers, for example.

But for a new release, Bingers is off to a very promising start, and as long as the app keeps evolving — and continues to find an audience — this could become the Letterboxd for TV.

The WikiLeaks founder Julian Assange carved a reputation as a firebrand and intense critic of the modern tech companies after rising to fame following the WikiLeaks disclosures. Although he's been absent from the public eye in recent years, he frequently commented on the emerging trends in the technology landscape.

Big Brother is watching you

Paraphrasing a famous line from George Orwell's 'Nineteen Eighty-Four', Assange used Google's failed smart glasses as a rhetorical device to critique their philosophy in an op-ed for The New York Times.

In his column, he was critiquing a new book titled 'The New Digital Age' by Google's then-executive chairman Eric Schmidt and businessman Jared Cohen, who was then director of Google Ideas.

This book outlined Google's vision for how the world was changing and laid out the company's role in reshaping it. But, to Assange, it was a bland and uninspired account of how this company was implementing George Orwell's prophecy without even really understanding how.

Drawing from the original line, Assange's disdain for the company is as clear as day in the way that he compared people wearing Google Glass to "a boot stamping on a human face".

Don't be evil

The modern form of this Orwellian vision is centered around consumer technology, Assange believed, and gadgets like the Google Glass were akin to tools that would serve to sap users' independence and privacy.

Although Google Glass itself was a failure, the point is more about the fears that this device represented. Now, more than a decade on, developments have more or less vindicated his dystopian perspectives – with a growing perception that a mass surveillance state is being precipitated by the surrender of privacy.

Some may argue this is a feature inherent to the use of consumer devices as well as software and services, and present in modern trends such as doomscrolling.

It appears that the RAM crisis is getting worse than ever as we head further into 2026. I've been closely watching and reporting on memory price hikes — which were soon followed by other PC component cost increases — since this crisis first began, and the worrying thing is, I can't recall a gloomier stream of negative news than I've witnessed over the past few weeks.

That includes Samsung recently declaring that the RAM crisis is going to become more severe in 2027, and rumors that even mighty Apple is floundering in its attempt to secure new RAM supplies from China (as other notebook makers consider this alternative route to the three main memory chip giants: Micron, Samsung and SK Hynix).

Previous to that, we saw laptop maker Framework break news of a truly eye-opening cost increase for mobile RAM, just as one gauge of DDR5 pricing saw it jump in price to a new all-time high (after plateauing earlier this year). On top of that, the boss of SK Hynix voiced the opinion that 2027 will be the worst year in the RAM industry's history, and that the crisis is likely to be drawn out to the next decade.

This isn't just about RAM, of course, but other components, including CPU price rises, and devices themselves — PCs and laptops (and phones). But most notably over the past month it's been graphics cards in the crisis limelight. We've witnessed the resurrection of old GPUs to try to bolster stock levels of more affordable cards, and more recently we've been subjected to what seems like an endless parade of negativity about imminent price hikes.

Apparently, Gigabyte is set to increase the price tags of its graphics card to the tune of 20% to 40% in Japan, and MSI is going to jack up prices of Nvidia GPUs in China by 20% or more. Asus is supposedly preparing a similar 20% hike (as Wccftech reported).

Another recent gloom nugget is the assertion that Nvidia RTX 5000 models will get hikes of 30% in South Korea this month, and all of this pain is mainly rooted in the increased price of video RAM, of course. (Those cost increases are the reason the RTX 5000 Super refreshes have been delayed, according to the grapevine, as those rumored cards are packed with VRAM).

While a good deal of this is individual pieces of regional activity with GPU pricing, it's obvious that these hikes are happening as part of a concerted shift, one that will surely be reflected globally — it's not like Asian markets are in a bubble of their own.

The overarching theme is a worsening of price hike misery, and that the pricing storm is likely to intensify this year, and probably in 2027, too. While previously the RAM crisis has been more of a light-and-shade affair, now it feels like the depression has been turned up a notch. Whereas before, there was certainly more darkness than light, we've had notable spots of relief where an exec in the memory industry stepped forward and theorized that maybe things aren't as bad as we think. But lately those embers of optimism appear to have burned out.

To me, it feels like there's a shift underway towards a full acknowledgement that we really are going to experience a lot more pricing pain in the foreseeable future. If it's so bad that even Apple is purportedly scrambling to secure RAM supplies, and is having trouble doing so, I think it's time to turn up the worry meter by a notch or two.

What should you do?

Here's my advice on the current landscape with PC components and hardware (season it appropriately). Obviously, worrying isn't going to help you, but awareness of what might be sensible upgrades or purchases to make now will, based on the likelihood of hikes (we could call it the 'hike-lihood' – or maybe not). Nothing is certain about the future of components, of course, but it does feel like there's a clear area in which the potential price hikes are a bigger threat: GPUs.

As I discussed above, there's a lot of recent evidence that graphics card pricing is going up. Yes, it's a collection of rumors in the main, but there's a lot of it all pointing in a very similar direction — and a video RAM toll was always going to be exacted in the end. We've seen it already with higher-end graphics cards, and now I believe we're going to see it in the mid-range, and even budget models. Indeed, top-end GPUs are likely to get even more expensive as well.

If you're thinking about a GPU upgrade for this year or next, given all this, I think now really is the time to buy — especially with a lower-tier model, as you can still get more affordable graphics cards at their MSRP. Ditto for mid-rangers, although it's tougher to recommend higher-end Nvidia boards when they are already so expensive.

As noted, though, that could get worse. And granted, you may still want to wait for Black Friday — it's not that far off, and there might be some GPU deals then. However, I wouldn't bank on anything hugely compelling (or discounts that aren't offset by the price rises which are predicted to take hold in the next few months).

So, if there's one PC component that I think you should buy now, it's a graphics card. I think the signs are pretty clear on that (and see here for my recommendations on different models to consider). By extension, higher-end gaming laptops could also be a wise move for purchasing sooner rather than later. That's for the same reason, really — they have beefy (mobile) GPUs (with plenty of VRAM in some cases).

I don't think it's a bad move to buy any laptop, by the way, gaming or not (away from the higher-end), come your earliest window of opportunity. I think we'll see further price rises here, too, and RAM is going to cause more upward pricing pressures for notebooks — just look at Framework's recent revelation as mentioned.

What about RAM and SSDs themselves? While system memory kits and storage have already seen huge price rises, it now looks like there's worse to come (somehow).

On the other hand, there's a ceiling as to how expensive this stuff can get before buyers withdraw from what they see as an increasingly unrealistic and out-of-touch market. This is a much more difficult call to make, but if you can find something that looks relatively reasonably priced, I don't think you'll regret the purchase in the next couple of years. But that said, I can't recommend a RAM upgrade in particular at current pricing levels, unless it's unavoidable, frankly.

Above all, though, consider a GPU upgrade if you're in the market for a new card, or you think you'll need one in the next couple of years (yes, I think it's wise to be looking quite far down the road here).

The other thing to bear in mind is that there's a danger there will be a rush for GPUs, a flurry of buying that puts further strain on supply and therefore prices. As VideoCardz reports, one Japanese retailer has warned of Nvidia RTX 5000 graphics card sales increasing "sharply", even just with the news of rumored price increases — let alone the confirmation. Prices are already rising and restocks of the RTX 5070 Ti and RTX 5080 are already being labelled as "unstable", hinting that inventory could start to dry up quickly.

That's just one report, of course, and it pertains to the high-end of the market, so it's not something to start panicking about yet. But it does make some sense that if pricing begins to creep up, more PC owners may start to act on GPU upgrades. I wouldn't blame them, frankly.

The Garmin Cirqa is making waves in the fitness community. I put the new screenless tracker through its paces over 10 days of intensive testing, and awarded it 4.5 stars in my Garmin Cirqa review. As part of the testing process, I wore it for all of my workouts over the past couple of weeks — and, unusually for a device I've been testing for review purposes, I'm still wearing it now.

I normally alternate between an Apple Watch Ultra 3 or a Garmin Fenix 8, but I've found myself loathe to ditch the Cirqa just yet. This isn't only because it's good: I'm gravitating towards the Cirqa because it interfaces with a system I already use, and because it's helping me get over the dreaded "Strava-itis".

Last month, our writer Becca Caddy wrote about optimization culture — how wearables are causing us to engage in unhealthy mindsets and disordered behaviors. I can certainly attest to that: I'm guilty of glancing at my watch during a run, wanting to push my pace even on zone 2 sessions and easy days, because I know the end results are going to end up on my Strava account for all to see.

Even uncoupling my watch from automatically uploading my run to Strava is only half the battle. I'm constantly glancing at the watch to check my pace and time, engaging in subconscious judgement, comparing my own performance to that of my mates, colleagues and acquaintances. I didn't realize quite how much my self-judgement was sapping my enjoyment of exercise.

The Cirqa, and to some extent the Google Fitbit Air I tested earlier in the year, goes some way to changing all that.

While the Cirqa offers up some fitness and wellness metrics (certainly plenty to understand the effect of exercise on your body and health), what it doesn't give you is workout specifics. You won't find split pace per kilometer, for example, which runners use to measure average speed across a distance run, or heart rate zones to display a workout's intensity. With other models, I get those statistics piped into my ears at regular intervals, and my watches offer them at a glance.

Having access to such statistics removed during the workout can be detrimental to performance or for those times you're training for a specific event, but it's oddly freeing for day-to-day fitness training. I ran a 10km route during the testing period (at least, I assume it was 10km based on previous runs) and the Cirqa recorded plenty of heart rate-based statistics and applied them to recovery metrics such as my Training Readiness Score, along with how it improved stats such as my VO2 Max and Fitness Age.

However, it informed me of these statistics after the run, and didn't deliver any specific run performance information at all beyond time and heart rate information. Without my watch to glance at during the workout, I wasn't adhering to a target pace: I was running on vibes and perceived effort.

I concentrated on how my body felt, and responded to that in the moment. I paid attention to my form, my music, the trees and the road. I didn't feel guilty about stopping to stretch part way through, nor did I pause the Cirqa for that particularly pause, as doing so would invalidate the whole "complete, holistic workout overview" thing it's got going on. For the first time in quite some time, I had no idea at all about how fast I had run — but I knew that it was a good workout as a result of every other metric and the fact that I felt awesome.

Although it isn't a running-specific device, the Cirqa is exactly what I needed to reinvigorate my relationship with the road. When the time returns to take my training seriously again, taking speed and times on board again, I'll switch back to my best Garmin watch, with all my fitness stats ready to be incorporated into Garmin's workout plans.

Do you run without a fitness tracker, or use a screenless model, to better run on vibes, or are you a metrics-fiend with a dedicated running watch? Let me know in the poll below.

Campaigners have long feared the influence of big money and influential corporations in politics – with this situation arguably worse than ever. In modern times, lobbying by technology companies has even given way to technology executives playing a role in devising government policy.

The path to progress

At the end of his two-term presidency, Dwight D Eisenhower used his final address to the American people to warn about the dangers that he foresaw lying ahead.

In particular, the military veteran warned about the rise of a new elite in society dominated by figures in science and technology, whose power would come to overwhelm democratically elected officials. He also suggested that the path of progress would be wrought with a lack of morals and ethics, with projects pursued in the name of progress regardless of the consequences to society at large.

A major component of this would be that the pursuit of major government contracts would dominate the scientific world, whereby the pursuit of money would dampen any genuine curiosity and lead to fewer meaningful discoveries.

The dawn of Silicon Valley

The former US president's warnings have largely come to fruition, first with the rise of Silicon Valley elites during the original internet age and now, subsequently, these forces are arguably entrenching their power in the AI era.

Technology companies have spent billions of dollars collectively on lobbying the government, with a small handful of companies including Microsoft, Meta, X and Snap channeling more than $260 million between 2020 and 2024.

The Trump administration has taken this influence one step further by giving vested interests a seat at the table and in the heart of government.

It's a smart donut. That's the only conclusion I can draw about OpenAI's first, groundbreaking piece of AI hardware after reading Bloomberg's revealing but unconfirmed report.

Here's the TLDR summary of these rumors: This donut-shaped, hockey puck-sized AI companion, designed with Jony Ive's LoveFrom studio, can sit on a table, be carried (or maybe worn). It'll be festooned with sensors, cameras, microphones, and speakers. Meanwhile, it'll be a vessel to bring ChatGPT closer to you and your life. It'll even shape-shift a bit to indicate a response (can a donut shrug?).

OpenAI might, Bloomberg claims, price it between $300 and $400 (or around £220-£300 / AU$425-AU$570). I know. That's almost instantly a hard no for an AI companion that has no defined purpose besides offering always-with-you AI. Even the woebegone Rabbit R1, which also features a camera, speakers, some sensors, and even a rotating camera, costs just $199 (or about £150 / AU$280).

After reading through this, I still wasn't feeling it. Why do we need this gadget? What problem does it solve? Really, why do we need any AI on or near our bodies?

If this product arrived three years ago, would people, including me, feel differently? After all, in 2023, we were all excited about the potential of generative AI. These days, it's fair to say half of us hate it or at least deeply distrust it and hate what it's doing to our resources (water and energy) and landscapes (data centers).

On the other hand, maybe I was judging OpenAI's upcoming device too harshly, especially without having even seen it. That sparked an idea.

Bloomberg's report included enough detail to create an image in my mind's eye, and I wondered if the leading AI platforms, Gemini and, yes, ChatGPT, could use a prompt to generate simulacra of the devices, in situ.

Gemini and ChatGPT take a run at it

I started with this prompt:

"I need an image of a consumer electronics gadget that is shaped like a donut and the size of a hockey puck. It should include tiny microphone holes and grills for a pair of speakers. It should have moving parts, maybe pieces that shift against each other without massively deforming the donut shape. Let's make a pair of them in white and gray on a table. Let's also include an image of someone wearing one like a pendant."

And in full disclosure, after getting my first couple of images, I added this prompt because I realized I left out a pair of key features:

"Without changing the design, can you add a camera and a couple of small black sensors? I want the image to otherwise remain unchanged."

These AI image generation systems are now good enough that they can maintain consistency between image generations (which they did here), so I'll only show you the finished products from both platforms.

Here's the final render from Gemini Pro:

At a glance, they're kind of cute, looking like giant, digital Lifesaver candies. The speaker grills are kind of huge, but I like the subtle placement of the camera and sensors. I can infer the possibility of movement from the multi-part frames.

Gemini is, I can see, a bit unsure if this is a wired or wireless device, but it has made at least one intriguing leap. It assumes, for instance, a connection to a phone-based platform called Aether (the gadgets may even be called "Aether"). In medieval times, according to Gemini, Aether was considered the fifth element. "It was believed to be the pure, heavenly material that filled the region of the universe beyond the terrestrial sphere," wrote Gemini. Yes, that feels a bit like the proliferation of AI.

The fictional device does look as silly as a pendant as I expected, but overall, I'm more intrigued by this device than I was before seeing the Gemini render.

Now let's take a look at the ChatGPT (Free) render:

This is a simpler, even plainer device and might fit in a bit better with Jony Ive's previous design oeuvre. I like the low-key speaker grill design, and don't mind the smaller activity lights.

I appreciate that ChatGPT helpfully includes a schematic that breaks down all the key features and even offers a look under the hood. It also manages to look a little less obtrusive as a pendant.

Let's get real

Obviously, this is all guesswork by Gemini and ChatGPT, and it's based on prompts built on rumors. Put another way, OpenAI's upcoming AI wearable could end up being significantly different than either of these generative images.

Somehow, I don't think so. I have a feeling that we will see something small, round, lightweight, and very, very intelligent. The launch, when it happens, will be exciting and buzzworthy. Ive's voice will likely drive the brand narrative as the donut-shaped device floats in an all-white background before settling on a desk or, better yet, in the palm of someone's hand.

That moment, though, won't change the trajectory of OpenAI's AI gadget. When it arrives, it will face an uphill battle to attract consumers who are already suspicious of AI's growing influence on the world. They're tired of AI slop, angry about resources and jobs, and the last thing many want is an AI companion reminding them daily about their frustration.

I don't even know what to say about a donut... er... gadget that squirms in your hand. That sounds positively creepy.

If there's any bright side to these rumors, it's that they probably put to bed the idea that OpenAI is allegedly stealing Apple product secrets. The iPhone maker would never build something like this.

I could be wrong about OpenAI's chances in this space. This might be the first AI gadget to break through. I mean, look at those designs. Aren't they cool? Sure, they are, but they're not reality, and we all know that this gadget will soon face a very harsh reality.

Up until now, enterprise AI has been largely dominated by a familiar conversation: which models should we use, where do we deploy copilots and agents, and how quickly can we move from experimentation to measurable business value?

These have all been pertinent questions, but as teams reach a new stage on their AI journey, they're no longer the pressing ones.

The first wave of AI was one of discovery, with organizations asking a simple question: ‘Can AI help us at all?’ That was followed by a period of rapid experimentation, as enterprises launched proof-of-concepts and pilots across every conceivable business function to understand where AI could deliver value.

Now, the conversation has changed again. Enterprise leaders aren't struggling to identify AI use cases - most organizations already have dozens - the real challenge is moving from experimentation to scaling. It's no longer about asking ‘Where can we use AI?’ but ‘How do we make it work consistently across the business?’

And this shift in focus has exposed a problem many organizations originally underestimated: data.

AI exposes weakness in the foundations

In the earlier stages of developing AI, many enterprises focused on accessing proprietary datasets for one primary purpose: model customization. Although this challenge still exists, there’s now a bigger issue at play that involves data quality, accessibility and governance across the organization.

AI is only as effective as the information and processes it operates on. The tricky part here is that AI is a master of exposing weaknesses that have existed inside organizations for years. And that should serve as a wake up call for enterprise leaders investing in AI right now. Because when data is fragmented, processes are inconsistent and operational maturity is lacking. In this scenario, AI won’t fix the problem – it’ll simply amplify it.

Viewed this way, enterprises should best understand AI as a force multiplier, not a correction mechanism for the legacy inefficiencies. However, the opposite is equally true. When organizations treat data as a first-class product, AI has the potential to multiply the quality coming out of it.

And this is the key difference between implementing AI and being genuinely prepared for it. You might have a strong model, but if you’re working with fragmented and unstructured data, your efforts will only continue to produce inconsistent results. I’ve seen it first-hand.

This isn’t just an anecdotal point. Gartner predicts that through 2026, 60% of AI projects will be abandoned because they aren't supported by AI-ready data, while 63% of data management leaders say they either lack - or aren't sure they have - the data management practices AI requires.

Those findings reinforce what we’re seeing many organizations discover first-hand: AI success is increasingly determined by the quality of the underlying data rather than the sophistication of the model.

The data pilot trap

Many organizations saw encouraging results during their early AI pilots, and that isn’t surprising. Typically, pilots are built on curated samples of data that are absolutely real, but to some extent pre-selected and considered synthetic.

On that kind of data, it's natural to see success because the information has been carefully selected to demonstrate the technology's potential.

The real test begins when organizations start scaling AI and release it from the boundaries of those pilots into the real enterprise environment.

Suddenly, models have access to many different types of data that all have to work together. They're exposed to years of duplicated records, conflicting business definitions, incomplete customer data, inconsistent documentation and disconnected systems. Information that appeared reliable in a controlled pilot becomes far less dependable in production.

That's the point where leadership teams often get their biggest surprise. They realize the true state of their data is much worse than they expected. What worked well during the proof-of-concept stage simply doesn't work in the real production environment – not because the AI has become less capable, but because it has finally encountered the reality of enterprise wide data.

Unfortunately, that experience is becoming increasingly common. What’s abundantly clear is that the defining challenge now is not proving that AI works, but ensuring the data behind it is ready for enterprise deployment.

Signs your data isn’t ready for AI

How do you know if your data’s ready for AI? In many cases, the answer only becomes obvious after projects fail to deliver the expected results. But long before that happens, there are usually warning signs.

Your data governance has gaps: Can you identify where your data lives, who owns it and whether it can be trusted? If every analysis depends on manual reconciliation, AI will simply scale those inconsistencies. Years of organic growth often leave enterprises with siloed data, conflicting definitions and inconsistent governance. Which means before AI can deliver reliable results, organizations need clear ownership, consistent standards and dependable data pipelines.

AI initiatives are happening in isolation: When different teams are experimenting with AI independently, it's often a sign that the underlying data isn't connected. And it’s happening more often than you might think – McKinsey research found that fewer than 30% of organizations have their AI agenda directly sponsored by the CEO. Valuable information remains trapped in departmental silos or legacy systems, making it difficult to build a complete picture of the business.

Treated like this, it’ll always remain more function-level experimentation rather than coordinated enterprise transformation. AI performs best when it can draw on integrated, trusted data rather than fragmented datasets created for individual functions.

Your data isn't connected to business outcomes: AI creates value by improving business decisions and processes, not by analysing data for its own sake. If it's unclear how your data supports the outcomes you're trying to achieve, AI initiatives are unlikely to produce meaningful results. Incomplete, outdated or poorly maintained data will also undermine confidence in AI outputs, making it harder to move beyond isolated pilots.

You're spending more time choosing models than improving data: Selecting a foundation model is important, but it's rarely what determines success. The bigger challenges are preparing enterprise data, identifying high-value use cases, embedding AI into existing workflows and driving adoption across the business. Continually chasing the latest release will not deliver tangible results, investing in the data foundations that will make a model effective, will.

Potential will only be realized with strong foundations

Arguably, the biggest shift facing enterprise leaders is one of mindset: moving the focus from advanced models to strong foundations. McKinsey's research supports this, finding that organizational readiness accounts for 48% of the difference between companies that successfully capture value from AI and those that don't—making it a stronger predictor of success.

For enterprises, the ultimate goal with AI should not simply be to automate existing tasks, but how to rethink how the business operates. That means redesigning processes around AI's capabilities, rather than layering AI onto inefficient ways of working.

For that to happen, data quality, governance and clear ownership can no longer be treated as minor, back-office concerns, they need to be recognized as strategic priorities.

The opportunity for enterprise AI is enormous, but without trusted data to build on, its potential will remain just that, potential.

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For many, the release of ChatGPT in 2022 was transformative.

Difficult emails were drafted in seconds, complex documents that would have taken hundreds of man-hours to scan were checked in minutes.

But for some, whose livelihood depended on providing just the type of service that ChatGPT offered - providing copy, for example - 2022 was a difficult year.

We were in the latter group. Our company, Jacquard, was founded in 2015 - seven years before ChatGPT hit email inboxes and LinkedIn feeds around the world.

Jacquard’s basic offering was a platform that used natural language generation to write better email subject lines. Around 2015, we tried taking the technology to investors. They were skeptical. Intrigued, perhaps, but skeptical.

At that time, we were one of very few companies in the language generation space, and we felt it. It wasn’t all bad though - and we eventually got used to being one of the few players in the game.

Then ChatGPT launched.

Our offering, AI-powered language generation, was suddenly in the hands of millions. For free. The investor skepticism we'd spent years navigating disappeared overnight, and in its place: a gold rush.

Hundreds of startups emerged, many founded by people who'd never worked in marketing or natural language processing.

On paper, this could have been the moment it all went wrong for us. Instead, if anything, it only propelled us further forward.

Surviving AI

It wasn’t an easy few years, but looking back, I can now attribute Jacquard’s survival to four key factors: AI fatigue; proprietary data; user base evolution; and an outcome-focused approach.

First, AI fatigue. When ChatGPT launched, everyone rushed to build, and then market, a GPT-wrapper. If it was a gold rush - to take the metaphor a little further - then too much gold flooded the market, and the value depreciated fast. Fatigue was everywhere, and it set in quickly.

Stakeholders grew tired of sitting through pitches that promised something revolutionary but delivered mediocrity.

Instead of a one-size-fits-all solution, what we all saw was generic copy, off-brand tone, and outputs that, at best, needed significant human reworking before they were anywhere close to usable.

85% of brands now use ChatGPT, in some capacity, to produce their marketing content. You can find the exact same turn of phrase in the marketing materials from a multinational conglomerate, as in your local café.

Our offering - the ability to maintain a genuinely distinct brand voice across complex segmentations at scale - became more valuable precisely because the market had made the problem worse.

Which leads to proprietary data. We have 60 billion data points built up over a decade of real enterprise campaigns. Far from being just another wrapper, we had ten years of learnings that no newly founded startup could acquire overnight. We trained a prediction engine on that data - and could back up our promises to deliver increases in engagement.

Of course, not every business starts from this position. But proprietary data, whatever form it takes for your industry, is worth finding. It’s likely more valuable than you think.

An evolving userbase

Our user base evolved alongside all of this. The client conversation shifted. Early on - pre-GPT - every sales meeting began with an education: what is natural language generation? Why trust an algorithm with brand copy? How does machine learning actually improve campaign performance? Post-GPT, those questions disappeared entirely.

They were replaced by a harder one: what makes your AI different from everything else? And so the bar moved. Clients came to us already exhausted by generic tools, already aware that AI-generated copy had a sameness problem. We no longer had to explain what AI was - now, all we had to do was prove that ours was worth the switch.

Last, and perhaps most important, our platform was never built solely around AI. AI was simply the most effective route to our solution. The businesses that folded had built their entire proposition on the novelty of AI. Once it wasn't, they had nothing.

We had been working on the same fundamental problem since 2015, and the arrival of large language models, ultimately, didn't change what that problem was. It just changed the tools available to solve it.

What we know now, having been through the full cycle - from obscurity to gold rush to consolidation - is that the brands who came out ahead weren't the ones who adopted AI fastest.

They were the ones who were clearest about what they were trying to say, and had the infrastructure to say it consistently. That is a harder problem than it looks. It is also the one we have spent ten years solving.

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Recently in Geneva, the world's largest forum on AI for Good, gathered around a simple question: how can technology best serve society? While the sentiment goes back to pre-industrial times, the conversation has firmly moved to algorithms. What about the machines that will come next?

Quantum technology has crossed a line. It is no longer a distant promise, but a growing portfolio of real applications. Quantum and quantum-inspired solutions are being applied to the simulations that underpin nuclear power, to protein design in drug discovery, and traffic forecasting in congested cities.

Quantum sensors are being tested for medical imaging, navigation where satellite signals fail, and monitoring carbon storage sites deep underground. These are no longer mere laboratory curiosities: they are increasingly emerging as working programs, with industrial partners and delivery dates.

For those of us in the industry, these developments are both timely and long anticipated. But they also force us to confront a less comfortable reality: the same machines that will one day simulate new molecules will also break the public-key cryptography that secures much of the modern internet.

By some estimates, up to 80% of the world's digital infrastructure is exposed. The world's digital economy - not to mention every AI model celebrated in Geneva, runs on encrypted data. Every health record or financial transaction owes its confidentiality to mathematical assumptions that a sufficiently powerful quantum computer will overturn.

Store now, decrypt later

The threat timeline is worse than most realize. Rather than waiting for when the machine arrives, the clock started the moment adversaries began harvesting encrypted traffic: the so-called “store now, decrypt later” approach.

Data that remains valuable for years, from medical records to state secrets, may already be beyond rescue. Nobody knows exactly when “Q-Day” comes. But those who harvest our data today have time on their side.

This is why quantum-safe security cannot be a footnote to quantum computing. It is the other half of the field, and it needs to mature on the same schedule.

Quantum trip-wire

Two defenses exist, and they are not interchangeable. Post-quantum cryptography replaces vulnerable algorithms with mathematics believed to resist quantum attacks; it is software, it scales, and it is the workhorse of the migration now beginning worldwide.

Quantum key distribution takes a different route: it distributes encryption keys using quantum states of light that cannot be intercepted without being disturbed. In this way, these schemes deploy a “quantum trip-wire", an eavesdropper reveals herself by the simple act of listening-in

Far from being in competition, both algorithmic and physics-based solutions have roles to play in tomorrow's networks. Serious infrastructure will layer both.

Neither of these solutions are theoretical. New post-quantum standards have been finalized, with contributions from research teams around the world. And quantum-secured networks are already running in the field: since 2022, Abu Dhabi has hosted entanglement-based metropolitan networks, like the ADGM Quantum Testbed announced in August 2025.

These are initiatives that bring quantum security to where data actually lives. Long-range terrestrial links, satellite connections and “last-mile” access networks are the next steps, extending that protection between cities and, eventually, across continents. If there's one lesson the field has learned from operating these systems, it's this: migration takes years, so the time to start is before the threat matures, not after.

Depth of capability

There is a broader lesson in how this capability is being built. A growing number of nations have chosen to be builders of quantum technology rather than buyers, developing the full portfolio of sensing, communications and computing solutions. From processor fabrication to control software, to the hardware enabling quantum networks: the word “sovereignty” is often heard as a synonym for walls.

But our experience suggests otherwise. It is precisely the teams that build every layer themselves that can contribute most readily to the global commons, from working with international standards organizations like ITU and ETSI on quantum-safe networking, to releasing open-source middleware that researchers worldwide now use to program quantum hardware, wherever it was made.

It is this depth of capability that turns a nation from a spectator of the quantum era into an active participant.

That should also be the model for the decade ahead. No single country will own quantum technology, and no single vendor should be the sole authority on quantum-era trust. What the field needs now is what the AI community showcased in Geneva: open tools that lower the barrier to entry, interoperable standards so quantum-safe systems can talk to each other, and honest engagement with the risks alongside the promise.

For years, the defining question about quantum computing has been when the machines will arrive. Increasingly, I think that's the wrong question. The more important question is whether the digital world they inherit will still deserve their trust. The answer depends on what we choose to secure, build, and share today.

Quantum for good does not start with a breakthrough. It starts with trust.

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For years, cybersecurity was built on a simple assumption: systems follow defined rules.

Applications do what they are programmed to do, while people log in, are given permissions and access the resources they need. Artificial intelligence (AI) is changing that.

With nearly 50% of cybersecurity solutions buyers expecting AI to be embedded across the cyber stack within three years, organizations are no longer focused solely on protecting applications and access rights.

They also need to secure intelligent systems that can make decisions, interact with users and act autonomously.

AI can draw on models, prompts, context and external tools to understand a goal, make decisions and determine how best to achieve it.

As organizations give these agentic systems greater autonomy across enterprise workflows, the consequences of failure extend beyond generating a wrong answer.

A mistake can now disrupt business processes, influence decisions and trigger unintended actions across connected systems.

An expanded attack surface

Greater autonomy creates new points of vulnerability whenever AI is given access to data, systems and external tools.

Cyber threats, such as attackers manipulating the information AI receives or impersonating trusted users, can alter how it responds or the actions it takes. This could lead an AI agent to retrieve inaccurate information or approve unauthorized actions.

Traditional cybersecurity controls were designed for humans and applications, but autonomous agents don’t fit neatly into either category. Security therefore needs to extend further across the entire AI lifecycle: before go-live, during operations, and at every point where AI learns, decides, and acts.

Organizations need a unified security architecture that provides consistent visibility and controls across both AI and traditional systems, which makes it easier to identify threats, enforce policies and respond quickly when incidents occur.

Trusting AI safely

However, a unified architecture is only part of the solution. Businesses also need to ensure the AI agents themselves can be trusted. Like any trusted user or system, AI agents should have a verifiable identity, tightly controlled access to data and systems, and auditable records of the actions they take.

Without these safeguards, organizations risk creating AI systems that can bypass security and compliance controls because of design flaws rather than malicious intent. Security also cannot stop once an AI system is deployed. Unlike traditional software, AI systems learn from new data, operate in changing contexts and can behave differently over time.

Organizations therefore need continuous monitoring to ensure agents stay within defined boundaries and policies continue to be enforced. That includes clear ownership, escalation paths and kill switches that can safely contain or stop an agent behaving unexpectedly. Some organizations are also beginning to use "guardian agents" that monitor other AI agents and flag unusual behavior.

The level of oversight should reflect the level of risk. AI agents carrying out low-impact tasks can remain largely autonomous, while higher-risk activities - such as updating customer data, approving financial transactions or interacting with production systems - should have stronger guardrails.

Applying controls in proportion to risk allows organizations to capture the benefits of AI while maintaining security, compliance and trust.

Context as a security boundary

Securing AI also means securing the information it relies on. Context is what gives AI agents their power. This includes internal documents, customer information, business rules and previous interactions, helping agents understand a task and decide what to do next.

That also makes context a new security boundary. If the information an AI relies on is inaccurate or has been deliberately manipulated, the decisions it makes can be wrong, even if the underlying model is working exactly as intended. This is known as context poisoning.

Protecting against this means controlling what AI can see as well as what it can do. Agents should only have access to the data and systems they need for a specific task. For example, an AI assistant answering employee questions should not have the same level of access as one authorized to approve payments.

Guardrails must go beyond filtering outputs and extent to protecting the integrity of the information AI uses, ensuring it is accurate, up to date and appropriate for the task at hand.

Governance at scale

Technical controls are most effective when they are supported by effective governance. This requires a joined-up approach that brings together AI and traditional systems, with consistent controls across the business.

Organizations should also define where human intervention is required and who is responsible for the decisions models make. Oversight should focus on the activities that carry the greatest operational, financial or regulatory risk, supported by investment in the skills needed to govern AI effectively and maintain trust in autonomous systems.

The path forward

Ultimately, securing AI is about more than protecting systems from attack. Organizations need the right technical solutions, clear ownership and continuous oversight throughout the AI lifecycle. Trust depends on these elements working together.

The organizations that succeed with AI will not necessarily be those that move fastest, but those that scale it securely and responsibly. The question for leaders is no longer whether to trust AI, but whether they are building systems that deserve to be trusted.

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Recruitment has long relied on signals that serve as proxies for candidate skillsets: degrees, previous job titles, years of experience, and employer names. Those indicators don’t always show whether someone actually has the skills needed for the role they’re applying for. As AI reshapes work, businesses need clearer evidence of what people can do.

This imperative has led to a drastic shift in hiring norms In the UK, 99 per cent of employers are using skills-based hiring in some capacity, according to research. At the same time, 58 per cent expect more than a third of core job skills to change by 2030. Recruiters now need to hire for current requirements while also thinking about how roles are likely to evolve.

For hiring teams, five changes stand out.

1. Hiring is becoming more skills-first

AI is accelerating the move away from degree-first evaluation. When tools, business needs, and ways of working change quickly, employers can’t just rely on static indicators of academic achievement. They need to know whether candidates have practical, current and job-relevant skills.

Degrees still retain significant signaling value. A degree offers an essential foundation, particularly for critical thinking, communication and domain knowledge. But employers increasingly want additional proof that a candidate can apply those strengths in workplace settings.

For recruiters, job descriptions and selection criteria need to become more skill-oriented. Instead of asking for broad experience in a field, businesses should define the specific capabilities needed for the role: data analysis, AI literacy, cloud computing, cybersecurity awareness, project management, or the ability to interpret AI-generated outputs. A clearer skills profile can also help organizations identify strong candidates who have not followed traditional pathways.

2. AI credentials are changing how experience is weighted

Experience still carries weight, but AI is changing how that experience is judged. In fast-moving areas such as generative AI, data, and cloud, a candidate with recent, verified learning may be better prepared than someone with greater experience but outdated skills.

That’s why 42 per cent of UK employers say they would choose a less experienced candidate with a GenAI credential over a more experienced candidate without one. It shows how quickly the value of verified AI capability is rising.

This has practical implications for recruiters. Years of experience shouldn’t be treated as a signifier of readiness. In some roles, recent evidence of applied learning may be more useful. The challenge is to distinguish between candidates who’ve completed primarily theoretical training and those who can show they’re ready to apply what they’ve learned.

3. Verification is becoming more important

AI has made it easier for candidates to produce polished CVs, cover letters and portfolios. It has also made it harder for employers to know which evidence to trust.

This is likely to increase the value of credentials that verify skills to employers. In the UK, 95 per cent of employers say micro-credentials help identify candidates with real-world, applied expertise in areas such as AI, data, and cloud. That matters because recruiters need evidence that can stand up to scrutiny.

The most useful credentials are those that assess applied skills, rather than only content completion. Employers will increasingly look for evidence that a candidate has built something, solved a practical problem or completed a project relevant to workplace needs.

Recruitment teams should reflect this in their processes. CV screening should be supported by practical assessments, structured interviews, and work-sample tasks. This blended approach will make decisions more accurate.

4. Recruiters need to assess human judgement alongside AI skills

AI literacy is becoming a common requirement, but it is insufficient to simply possess technical knowledge, particularly for those deploying AI in non-technical roles. Businesses need people who can work effectively with AI, including knowing when to question it.

As AI becomes embedded in everyday work, candidates will need to show that they can evaluate outputs, check sources, spot weak reasoning, and apply context. A candidate who accepts AI-generated content uncritically will introduce risk, irrespective of technical proficiency.

This adds novel new stages to the assessment process. Recruiters may need to ask candidates to critique an AI-generated response, improve a flawed analysis, or explain what further evidence they’d need before making a decision. These exercises can reveal whether someone has the judgement and domain expertise required to use AI tools responsibly.

For many roles, the differentiator won’t be whether a candidate can prompt a system: it’ll be whether they can turn AI output into sound business action.

5. Skills-first hiring must complement skills-first training

AI is compressing the shelf-life of skills. If, as expected, over a third of core skills will change by 2030 for many UK employers, hiring alone will not be enough to keep pace. In fact, 74 per cent of tech leaders acknowledge they cannot depend on new hires alone to fill AI skills gaps. That means skills-first hiring needs to become part of a wider talent development framework.

The same approach that helps recruiters identify the capabilities needed for a role can also help businesses map the skills they already have, spot gaps across the workforce, and create clearer routes for employees to build the capabilities the organization will need next.

Some capabilities will still need to be brought in from outside the organization, particularly in fast-moving areas such as AI, data and cloud. But many skills will also need to be developed internally as roles evolve. The strongest businesses will combine skills-based hiring with ongoing upskilling, giving employees clear routes to adapt as roles change.

The new recruitment advantage

AI is changing what employers need to know about candidates. Businesses that can identify real capability, verify applied skills, and recognize potential beyond traditional signals will be better placed to hire well. The value of this approach is already visible in workplace outcomes. 92 per cent of employers say entry-level hires with micro-credentials perform better in their first year on the job.

For candidates, the message is just as clear. In an AI-enabled labor market, employability will depend less on what someone once learned and more on what they can prove they can do now, and the evidence they’re still learning.

For employers, recruitment needs to become part of a broader skills strategy. AI may be changing the work, but the hiring challenge remains human: finding people with the skills, judgement and adaptability to help businesses compete and innovate.

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Suno, the most popular AI music creation tool, spent years making it incredibly easy to generate music. Now it's introducing download limits, watermarking and fingerprinting to stop people flooding streaming services with AI slop.

That suggests something important to me: even the companies building generative AI are starting to realize unlimited AI creation comes with unintended consequences.

Suno CEO, Mikey Shulman, shared a blog post about the company's principles for building the future of music responsibly. He says “AI should help people create something new, not imitate someone else’s work. This philosophy has guided how we’ve built our models and platform from the start.”

Great music is made by people

In a section titled “Our Principles”, Shulman says that “great music is made by people”.

The principles themselves aren't new. What's new is that Suno is now dedicating significant engineering effort to limiting abuse rather than simply enabling creation.

In his blog post Shulman writes “We will soon introduce a new downloads policy designed to limit the ability to mass distribute songs on streaming platforms, while preserving the professional, creative, and personal ways people use Suno. These changes won’t affect the vast majority of our users, but they will make large-scale abuse much harder.”

Schulman continues: “In the coming weeks, we will also be adopting new audio watermarking and fingerprinting technology so we can partner even more closely with distribution platforms on combatting fraud and misuse.”

That evolution fits the broader trend we’ve been covering from the music streaming services like Spotify, Deezer, Tidal, and Qobuz, who have started to fight back against AI-generated music flooding their platforms.

Managing the consequences

Streaming giant Tidal has published a comprehensive AI policy with the strapline "Promoting Fairness and Economic Empowerment in the Era of AI-Generated Music". Tidal will identify it, tag it and crucially, not pay any streaming royalties for it.

Spotfiy has introduced measures to prevent fraudulent streams and AI impersonation . Deezer announced that over half of all new daily uploads to its site are AI — up from 44% in April and just over 30% at the end of last year. It launched a free site to scan your playlists for AI in June. Qobuz has announced that it is taking a human-first approach to its recommendations and “developing detection and monitoring systems to identify AI-generated content and fraudulent streaming patterns.”

This announcement from Suno feels more significant than another AI company publishing a set of principles. It marks a shift in priorities. For the first few years of generative AI, success was measured by how much content these systems could produce. Now it looks like success is being measured by how effectively companies can prevent that content from overwhelming everything else.

The AI music industry is moving from maximizing generation to managing the consequences of generation, and it's about time.

Artificial Intelligence (AI) is rapidly evolving. Across industries, many organizations are increasingly deploying AI into systems that must run continuously, securely, and at scale.

As AI adoption accelerates, one thing is becoming clear: infrastructure planning cannot wait.

AI workloads are becoming more interconnected, distributed, and operationally integrated across cloud, data center, and edge environments. Infrastructure planning now requires organizations to align compute, networking, software, memory, and operational requirements across increasingly complex environments.

As a result, many enterprises are beginning infrastructure planning sooner rather than later.

The cost of waiting

As AI becomes more integrated into everyday business operations through continuous inference and agentic AI systems, infrastructure demands are evolving significantly.

Modern AI deployments increasingly require:

• Continuous inference running around the clock
• Multi-agent systems coordinating across applications and databases
• Real-time orchestration across cloud, data center, and edge environments
• Strong governance, security, and operational efficiency

These workloads require more than raw compute performance. They require balanced infrastructure where compute, networking, software, memory, and operational workflows work cohesively at scale.

Because of this, enterprises are beginning AI infrastructure planning earlier, recognizing that planning, testing, and Proof of Concepts (PoCs) for complex systems like this take time.

At the same time, the cost of delaying AI infrastructure planning is becoming more apparent. Delays can slow deployment readiness and postpone AI-driven benefits such as productivity gains and operational automation. As AI demand continues to rise, organizations are prioritizing earlier planning to secure the compute capacity needed to support long-term AI growth.

As AI infrastructure becomes more complex, infrastructure planning needs to begin earlier than traditional IT upgrade cycles. Evaluating workloads, validating deployment models, and ensuring scalability across environments takes time and time is of the essence if we want to be ahead of our competitors.

AI is now a systems challenge

The conversation around AI infrastructure often begins with Graphics Processing Units (GPUs). But as deployments scale, AI performance depends not on individual components, but on how the entire system operates together.

Modern AI infrastructure relies on Central Processing Units (CPUs) for orchestration and data movement, GPUs for large-scale parallel compute, high-speed networking for low-latency communication across systems, and open software platforms for portability and scalability.

As AI systems become more distributed and inference-driven, orchestration and system balance become critical. CPUs play a pivotal role in managing workload coordination, memory access, and GPU utilization, ensuring infrastructure operates efficiently under sustained demand.

This shift reflects a broader industry reality: AI is no longer just a GPU problem. It is a full-stack infrastructure challenge that organization must tackle early on.

Planning for distributed AI

AI is also scaling in multiple directions at once.

Some workloads are expanding into large, centralized clusters, while others are moving closer to where data is generated – including edge deployments such as in factories or hospitals, and AI-enabled endpoints like the PCs.

For organizations, this creates unique infrastructure considerations around hybrid cloud, on-premises deployments, edge AI, compliance, and latency-sensitive applications.

This diversity underscores the importance of infrastructure strategies designed for modularity, portability, and adaptability that necessitates upfront planning.

Openness and flexibility matter more than ever

As AI innovation accelerates, organizations are prioritizing infrastructure flexibility to support rapidly evolving models, frameworks, and deployment environments.

Open ecosystems can reduce integration complexity while supporting broader compatibility across software frameworks, cloud environments, and deployment architectures. They also provide greater flexibility to evolve infrastructure strategies over time while helping avoid the migration costs that can come with highly closed or single-vendor environments.

For many organizations, openness is no longer just a developer preference. It is becoming an important consideration for balancing performance, operational efficiency, cost optimization, and long-term infrastructure investment.

This is another reason infrastructure planning must happen early. Building AI environments that remain scalable, portable, and adaptable over time require long-term thinking around openness and interoperability from the beginning.

Infrastructure readiness will define the next phase of AI

The next phase of AI growth will reward organizations that take a proactive approach to infrastructure planning.

Organizations that delay infrastructure planning may find it more challenging to deploy AI tools down the road, not only due to not having ample time to plan and test, but not securing the compute resources needed early on.

The cost of waiting is becoming ever clearer.

Ultimately, the companies that succeed in the next phase of AI will not necessarily be those with the largest clusters, but those that plan early and build balanced, scalable, and open infrastructure designed to support continuous innovation in an increasingly AI-driven economy.

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Every four years, football's best players are tested on the world's biggest stage.

Less visible is the test taking place behind the scenes.

As billions tune in, broadcasters and streaming platforms face their own high-stakes challenge: delivering seamless live experiences at a scale few events can match.

FIFA estimates that around 5 billion people engaged with the 2022 World Cup, with the final alone reaching nearly 1.5 billion viewers worldwide.

In 2026, that audience has been presented with an even bigger tournament: 48 teams playing 104 matches across Canada, Mexico and the United States.

More than 54 million viewers across the three host countries watched their national teams' opening matches, while the United States' game against Paraguay drew a combined 27.5 million across FOX and Telemundo, the most-watched FIFA World Cup match ever broadcast in the country.

By the end of the group stage, 4.64 million spectators had filled 99.7% of available seats. That scale is a real-time stress test for every part of the live-video ecosystem.

From passive viewing to active participation

Beyond sheer audience size, the difference lies in how fans consume it. They no longer simply watch. They move between platforms, share highlights, expect instant access to key moments and want experiences tailored to their own interests.

Streaming is no longer just a distribution channel; it's a product in its own right. Brazil’s group-stage match against Haiti reached 51.3 million viewers across Globo's wider media ecosystem, while CazéTV set a worldwide YouTube record for the most-watched football match streamed on the platform.

World Cup content generated 11 billion video views across social media platforms during the group stage alone, and official broadcasters published more than 44,000 pieces of content on TikTok.

A modern match is simultaneously a live program, a source of social clips, a statistics feed and a second-screen experience.

Rethinking the production workflow

That shift starts with the production workflow. The same match is now produced simultaneously for stadium scoreboards, connected TVs, mobile apps and global streaming platforms, with capture, ingestion, encoding and delivery all part of a single content pipeline.

Sixteen optical tracking cameras installed in each stadium can produce more than 150 million data points per match, helping officials review incidents and giving media partners new ways to produce highlights.

The real challenge is bringing together live delivery, audience data, advertising, captions, multi-language audio, and interactive experiences alongside tracking, commentary, graphics, and officiating data.

What fans expect: reliability, personalization, speed

For viewers, success comes down to three things: reliability at scale, personalization and speed. Fans will tolerate a lot, but they won't forgive a stream that buffers during a decisive goal or runs so far behind live play that social media spoils the moment.

Personalization must happen without undermining performance, delivering different recommendations, languages, statistics, camera feeds and advertising while maintaining the resilience of a mass broadcast.

AI is reshaping live sports production

Artificial intelligence is central to delivering those expectations. Rather than replacing production teams, AI is enabling rights holders to produce and distribute content at a scale that would previously have required far larger operations. Automated highlight clipping is one of the clearest examples: AI can identify key moments, package them and distribute them within minutes.

For rights holders, the difference between publishing a goal two minutes after it's scored rather than twenty is the difference between leading the conversation and chasing it. Match summaries, commentary, captions and translations can also be generated automatically, and platforms can match each fan with the content they are most likely to watch next.

A glimpse of the future: personalized sports at scale

The broader ambition is visible at the top of the game. The PGA TOUR now turns each week's action into roughly 7,000 AI-generated highlight clips across dozens of markets, so a fan can follow one player or catch up on key moments without waiting for the main broadcast. The 2026 World Cup has produced a similarly vast library of stories. A record 215 goals were scored during the group stage, an average of three per match.

Tournament debutants Cabo Verde went undefeated, with Kevin Pina scoring the country's first World Cup goal, while Japan's 4-0 victory over Tunisia was both the 1,000th match in World Cup history and the biggest ever by an Asian team. These are exactly the kinds of stories that automated tagging, rapid clipping and intelligent recommendations can bring to the right audience.

Immersive viewing and accessibility

The next generation of live sports streaming will be defined by richer viewing experiences. Multi-view streaming allows fans to follow simultaneous matches, while alternative camera angles and player-specific feeds provide greater control over how the action is consumed.

Real-time data integration can bring live statistics directly into the viewing experience without interrupting the match. AI is also making captioning, translation, and audio description production-ready at scale, allowing broadcasters to localize live coverage without a proportional increase in costs.

However, human oversight remains essential for names, sporting terminology, and cultural context.

The technology behind global scale

Supporting all of this requires resilient IT infrastructure and scalable delivery platforms capable of broadcast-quality reliability and low latency, even as millions connect simultaneously. It also requires organizations to move beyond fragmented technology stacks.

A unified architecture makes content easier to reuse: one live signal can support a full broadcast, mobile highlights, social clips, advertising inventory, archive content and personalized recommendations.

Furthermore, protecting that content is just as important as delivering it. Live sport is uniquely vulnerable to piracy because its commercial value exists almost entirely during the match. Digital Rights Management remains the foundation, but forensic watermarking is becoming increasingly important, embedding invisible identifiers so pirated feeds can be traced and removed while the event is still live.

What's next: The future of live sport at scale

The World Cup ultimately highlights that live video has become a complex, data-driven product where success is no longer defined solely by picture quality or reach, but by how effectively AI, unified content workflows. and scalable technology work together under pressure.

The challenge for the industry is not understanding what works at World Cup scale, but applying those lessons consistently across every live event.

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When was the last time you met your newest hire in person?

For many organizations, particularly those operating remotely, the answer is increasingly never. With one in five companies worldwide adopting a fully remote model, hiring virtually has become increasingly popular, enabling businesses to access global talent pools and scale faster than ever before.

But in removing geography as a constraint, it has also stripped away one of the most fundamental layers of trust: the ability to verify, face-to-face, who you are actually employing.

This shift is giving rise to a new and largely under-recognized threat, the “deepfake employee”, where threat actors use synthetic identities, voice cloning, and real-time deepfake video to pass interviews and secure legitimate employment.

Accelerated by advancements in AI, it is now possible to create convincing digital personas at scale, lowering the barrier to entry for fraud and enabling highly organized operations to target corporate hiring pipelines.

In practice, these attacks can be surprisingly difficult to detect. A candidate may appear on a video interview with a natural-looking face and voice, answer questions fluently, and provide what seem to be legitimate credentials. Behind the scenes, however, AI tools can subtly alter facial expressions, sync lip movements to a cloned voice, or even feed real-time responses.

To the hiring manager, there is little reason to suspect anything is wrong. The deception often only becomes apparent much later, if at all, when activity inside the organization begins to raise concerns.

This risk is already playing out in the real world. In a recent experiment, a cybersecurity expert used AI to create deepfake personas – one a white man similar to himself and another of an Asian woman, and successfully secured two separate tech roles, beating hundreds of other candidates.

Using AI-generated credentials, real-time voice modulation and live deepfake video, both synthetic identities progressed through interview stages undetected, with employers unaware they were interacting with an entirely fabricated candidate.

Cloudflare’s latest threat research highlights the scale and sophistication of this activity. Organized “remote worker” fraud operations are using fabricated identities, deepfake-assisted interviews and remote access “laptop farms” to infiltrate payrolls. In some cases, multiple individuals operate behind a single employee identity, maintaining persistent access while appearing as one consistent, legitimate user.

Once hired, these actors are no longer external attackers. They become insider threats with valid credentials, company-issued devices and trusted access to systems. As Cloudflare notes, by the time these individuals are identified, they are already operating inside the perimeter, often blending in with normal business activity.

What needs to change

With nearly 60% of organizations having experienced deepfake-driven incidents, and 48% reporting damage from AI-generated impersonation or misinformation, it is clear that identity infrastructure has become a primary attack surface. Attackers are shifting away from “breaking in” to “logging in” using legitimate credentials obtained through deception.

At the heat of this issue is the flawed assumption that identity can be verified once and then trusted indefinitely.

In physical environments, identity is rarely in doubt. You can see who walks through the door, recognize familiar faces and detect inconsistencies in behavior. In virtual environments, however, organizations rely almost entirely on screen names, login credentials and video, none of which reliably confirm who is actually behind the screen.

Accounts can be shared, credentials can be compromised, and even live video can be manipulated.

This creates a critical vulnerability at the point of hire. A candidate may present documentation, pass background checks and complete onboarding, but in a world of synthetic identities, that initial verification is no longer enough.

Building continuous identity assurance

To address this challenge, organizations need to move beyond static identity checks and towards continuous identity verification. This means verifying not only who someone is who they say they are and that they are a real human at the point of hire, but ensuring that the same individual remains present and authentic throughout their interactions with the organization.

The strongest form of defense lies in continuous biometric verification of the user’s identity. Rather than relying on a single factor, such as facial recognition or voice authentication alone, fused biometrics combines multiple identity signals, such as facial characteristics, voice patterns and behavioral cues, into a single, layered verification process, verifying directly that a real, live human is there.

It is no longer enough to confirm a person’s identity at a single moment in time. Organizations need confidence that the same individual is consistently present across every critical interaction, from interviews and onboarding, through to system access and sensitive transactions. Without this continuity, identities can be shared, replaced or hijacked without detection.

Fused biometric verification can create layered validation that is significantly harder to replicate or manipulate, enabling it to detect and block attempts to impersonate users through synthetic voices, deepfakes, or recorded audio and video.

While a single modality might be fooled by a sophisticated synthetic input, combining biometric modalities; facial recognition, voice recognition, and speech pattern recognition, it becomes significantly harder for fraudsters to mimic an identity.

Advanced biometric verification technologies are trained on large datasets of both genuine and synthetic voice samples, enabling them to recognize subtle acoustic differences between natural and deepfake voices. This helps organizations detect voice-cloning attempts, even when the audio sounds convincing to human listeners.

Crucially, fused biometric verification can operate passively in the background, consistently verifying that the right person is accessing systems, enabling smoother, lower-friction experiences and reduces the need for frustrating repeated verification attempts.

As organizations continue to embrace remote work and digital-first operations, cybercriminals will increasingly find new vulnerabilities to exploit. The question is no longer just how to keep threats out, but also how to ensure that those already inside are truly who they claim to be.

In a world where identities can be fabricated, cloned and manipulated with ease, trust cannot remain static. It must be continuously proven.

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As AI develops into the latter half of the 2020s, scientists have repeatedly warned of the dangers that may arise from its widespread deployment. In particular, there is an overriding view that advancing AI is a tool that can be used for good or for bad depending on who is wielding the tool at any given time.

Breaking out

The British computer scientist Geoffrey Hinton was giving an interview with the New York Times upon leaving his role at Google, primarily so he could speak freely and publicly about the dangers of the technology that he helped create.

In this landmark interview, the 'Godfather of AI' was clear that he couldn't continue working for Google while harboring the deep-rooted concerns that he held over the use of AI in various domains. The scientist is, for example, opposed to using AI on the battlefield in "robot soldiers", the newspaper reported.

At the time, he continued his relationship with Google because he saw the company as being a "proper steward" for AI as it was being developed. His concerns materialized when Microsoft incorporated Bing with a chatbot, forcing Google to act fast so it could incorporate AI into Google Search.

Frankenstein's monster

Hinton, who was jointly awarded the Nobel Prize in Physics for his work developing neural networks in the 1980s, has since remarked in an interview with CBS News that he didn't think we would make such progress in the 40 years since.

He has also floated the notion that there's a non-zero chance that AI could take over, comparing AI with a cute tiger cub that could, one day, potentially kill you when it's grown up. That came alongside warnings that various organizations and individuals, like cyber criminals or authoritarian regimes, could weaponize AI for their own agendas.

A reckoning is underway in enterprise AI. Chief executives who spent two years bolting frontier models onto their businesses are now asking harder questions.

What did we actually get for the money? Where does our data go when it flows through someone else's model? And when the model we standardized on last year isn’t the best one this year, how much of our business have we quietly handed over to a vendor we don't control?

That last question is the one that should keep leaders up at night, because for most companies, the honest answer is: far more than they think.

The conversation about AI has mostly been about which model is best.

That’s the wrong question.

In a field where leadership changes hands every few quarters, “best model” is a snapshot, not a strategy.

The question that actually matters is architectural: when the state of the art moves — and it always does — can you move with it, or do you have to rebuild your business every time?

Models are temporary. Plan accordingly

Here's what a decade of running AI at production scale teaches you that no benchmark chart will: models are disposable, and they're getting more disposable by the year. The transcription engine that led the market when we started is a footnote now.

The computer vision system that dominated three years ago has been lapped over and over. We've swapped best-in-class engines in and out of live customer workflows hundreds of times — across speech recognition, translation, object detection, face redaction, and now large language models — and each cycle turns over faster than the one before it.

An company that’s hard-wired to one provider inherits that provider's roadmap, pricing, and priorities as its own. When the provider raises prices, you pay. When it deprecates the version you built on, you rebuild.

When a smaller open-source model, fine-tuned on your own data, would actually do the job better and cheaper, you can't reach for it because your workflows only speak one dialect. That isn't a partnership. It's a dependency — and depending on a moving target is about the most expensive position you can be in.

That's the reasoning behind building systems where model lock-in isn't an option from day one: an orchestration layer that connects and manages hundreds of commercial, open-source, and proprietary models across different cognitive tasks, routes each job to whatever engine is best for it, and swaps models out as the state of the art shifts — without anyone having to rebuild a workflow.

The model becomes a component, not a foundation. The real foundation is the orchestration layer and the data underneath it, and those stay owned by the enterprise, not the vendor.

The discipline that makes model plurality real: evals

Model plurality sounds good in a keynote and collapses in practice without one thing: the ability to prove, on your own data, which model is actually better for your job. “Best” isn’t a leaderboard position.

A model that tops a public benchmark can badly underperform on your accents, your camera angles, your legal thresholds, your definition of “good enough.” Public benchmarks measure general capability.

They tell you almost nothing about how a model will perform inside your specific workflow. So the real currency of the next era isn't the model — it's the evaluation.

The companies pulling ahead are the ones who can put any engine up against any other on their own content, with their own quality bar, and make swap decisions based on evidence instead of vendor marketing.

That means scoring engines against each other continuously, on real customer data, so “which model” stays a measured decision you can remake any time the field shifts — not a one-time choice you're stuck with.

That evaluation muscle is itself a strategic asset. It's what turns a pile of interchangeable models into a compounding advantage — and it's precisely the capability an enterprise forfeits the moment it standardizes on a single black box.

Ownership over your own audio and video

There's a reason this matters most in audio and video. Text is basically commoditized at this point. The proprietary, defensible, hard-to-replicate data in the enterprise is the recorded record of what a company actually said, did, made, and witnessed — decades of broadcast, footage, calls, and captured events. It's multimodal, it's rights-encumbered, and it can't be replaced, which also happens to make it exactly what this generation of AI is hungriest for.

The appetite for training and tuning data has outrun what the open web can supply. What's actually needed now is what enterprises already have sitting in their archives: vast, rights-cleared, real-world, multimodal data, plus expert human judgment about what “good” looks like.

Which makes the default posture of the last two years perverse — companies have paid premium prices to push their proprietary audio and video through third-party models, with limited visibility into what's retained, learned, or one day competed against them. If your data is the scarce input everyone's after, the last thing you want to do is hand it over as a byproduct of your software bill.

The alternative is building the enterprise business the other way around: turning an organization's raw archives into AI-ready, enriched assets it actually owns, with rights and governance metadata baked in at the point of creation rather than tacked on afterward — because when the data in question is a witness's voice, an athlete's likeness, or a rights-encumbered broadcast, provenance and consent aren't optional extras.

From there, rightsholders can put that data to work themselves — licensing it to model developers and cloud providers on their own terms, with consent, provenance, and compensation built into the deal.

The world is moving toward fine-tuning and open source

Watch where the sophisticated buyers are going and the pattern is unmistakable The old reflex — route everything to whichever frontier model is biggest — is giving way to something more deliberate: a portfolio approach where smaller, open-source, and fine-tuned models handle most of the day-to-day workloads, and the giant models get reserved for the problems that genuinely need them.

The reasons are practical — cost, latency, data control, and the ability to specialize a model on proprietary data until it beats a general-purpose giant at your specific task.

This is where the two ideas come together. Fine-tuning and open source only work in your favor if you actually own the data to tune on and have the architecture to deploy into. A company locked to one vendor can't fine-tune an open model on its own footage and slot it into production as the plumbing simply won't allow it.

An enterprise with an orchestration layer and governed, AI-ready data can do exactly that, and can keep doing it as better base models emerge. Owning your data and having freedom in your architecture are what actually make this new era of specialized, fine-tuned, open models available to you at all. Without them, you're watching a shift from the sidelines that was supposed to be your advantage.

Sovereignty is a posture, not a product

It’s okay to be wary of how fast “sovereign AI” is becoming a marketing category, because sovereignty delivered through a new single-vendor dependency is just lock-in with better branding. Real sovereignty is an architectural posture with three commitments.

First, model plurality, proven by evaluation: every model — commercial, open-source, or fine-tuned — tested on your data and replaceable at will.

Second, governance that travels with the data: provenance, auditability, consent, and policy enforced at the data layer.

Third, actual ownership: your audio and video, enriched and controlled as an asset you deploy on purpose, not one that leaks out incidentally. None of this is an argument against the frontier labs — they build extraordinary technology, and plenty of companies use it every day, through architecture that keeps the leverage on their side of the table.

The enterprise doesn't have to choose between using the best models in the world and controlling its own future. The whole point is to do both: orchestrate every model worth using, prove which one wins on your own data, and own the audio, video, and judgment that make any of them worth running.

The model is temporary. Your data and your judgment are not. Build for that, and you spend the next decade compounding an advantage your competitors rented and lost.

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The enterprise race to scale AI operations is in full swing – both from a deployment and security perspective. While conventional wisdom suggests that the teams who deploy the models first get the edge, it’s the wrong approach for enterprises.

For nefarious actors, speed is the name of the game. Malicious actors leveraging AI to probe for vulnerabilities don’t need to share their decision trail to audit committees or regulators – making speed alone the key advantage for attackers.

Enterprise security teams, on the other hand, operate under entirely different parameters – which also happen to be where the opportunity lies.

Security teams don’t just need the capability to identify anomalies, screen transactions, or make access decisions – they also need to be prepared to explain what happened, when, and why to key stakeholders.

Every action and every outcome needs to be clarified and justified to a board, an auditor, or a customer.

Scaling AI at the enterprise level is not relegated to who moves fastest, but rather who can embed the necessary accountability frameworks, emergency brakes, and audit trails.

The Hidden Data Problem

In truth, speed is not the primary challenge for most enterprise teams. The bigger, more difficult challenge is found in data and governance. Policy models and detection frameworks are only as impactful as the information inputs. However, most organizations are devoid of structured, well-governed data – particularly as it pertains to AI activity. Across the majority of organizations, data is scattered.

Activity logs, access records, transaction histories all live in disparate systems with inconsistent formats and no single source of truth. No matter how much of this data is fed into an AI model, clarity will never be achieved. Instead, teams generate a false sense of confidence built upon a shaky foundation.

Meaningful scale begins with auditability and accountability as infrastructure, not an afterthought. As AI systems become increasingly embedded across organizations, the requirement heightens for more consistent records of actions that occurred and policy enforcement where actions are checked against defined rules prior to execution.

Equally as important, organizations need to have the internal muscle memory to explain automated decisions to stakeholders outside the engineering team, because they've been doing versions of this for years in compliance and risk functions.

When this approach is foundational for enterprises, AI systems become genuinely useful. AI models built upon strong data and governance standards can meaningfully screen actions against known risks before settlement, flag patterns that humans may have missed, and maintain updated records that can be shared across key stakeholder groups. Devoid of this foundation, fragmented data and ungoverned systems compound – resulting in faster mistakes.

Enthusiasm around AI systems centers on the deployment side of the equation, without asking whether the underlying infrastructure can support what's being layered on top of it. This approach is suboptimal and leaves an open door for breaches – akin to installing an alarm system in a building without putting locks on the doors.

Embedding infrastructural policy enforcement, audit trails, and human-reviewable records does bring an additional layer prior to deployment. And in a domain where nefarious actors operate free of this operational layer, it's fair to ask whether enterprise security teams are creating a permanent speed disadvantage for themselves.

However, that mindset misses what the added operational layer actually delivers: the difference between an AI system that fails safely and one that fails silently.

Enterprise security teams who operate slower but can consistently and proactively identify and reverse bad decisions are in a fundamentally different position than enterprises who operate quickly and discover a failure three weeks post-incident. The tradeoff is real, but it’s the wrong tradeoff to optimize away.

Trust Beyond The Engineering Teams

A common misconception is that security decisions are made for and by security teams. That’s not the case. Security decisions made by automated systems need to satisfy stakeholders far outside of this workstream – including regulators, insurers, customers, even boards.

Those stakeholders aren’t moved by sophistication. They care whether the organization can clearly and consistently demonstrate what the system did and why. Organizations without that operational layer find that AI adoption increases their potential risk exposure, because they're now making faster decisions with insufficient guardrails.

As AI systems continue to increasingly exhibit autonomous behaviors, the stakes continue to change. An AI system that can initiate payments, approve transactions, or move funds independently doesn't just need a policy to follow – it needs brakes to press in the event of a bad decision.

Without this layer embedded, enterprise security teams not only carry the accountability problem – they also have no chance of catching a mistake before it becomes permanent.

The Readiness Gap

Readiness requires the proper sequencing. Before scaling AI operations, it’s critical to make an honest assessment of three things: whether underlying data across systems are structured, whether there are firm policy checks in place, and whether verifiable records of automated decisions can be produced on-demand.

Starting here positions AI to become a force multiplier for security teams. These three elements make all the difference between catching what humans miss and doing it fast enough to matter – or simply adding velocity to a faulty process.

The “AI race” will not be won by having the newest models. Really, it’s about devoting effort to the unglamorous work – building the data infrastructure and governance systems that make AI models trustworthy.

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Investment in AI continues to accelerate at pace, with global spending projected to reach $2.59 trillion in 2026, a 47% year-on-year increase.

Yet only 28% of AI projects are currently generating a measurable ROI, with many failing to deliver expected business outcomes.

As organizations race to capitalize on AI opportunities, many continue to increase investments without fully understanding how AI will operate within their existing technology environments, particularly their data storage infrastructure.

Without careful planning in this area, businesses risk higher costs, growing technical debt and increased compliance complexity, all of which can undermine the value AI is set to deliver.

No data, no AI

The IT industry often talks about AI in terms of performance, computing power and processing speed. But at its heart, AI is a data system.

In recent years, high-performance processing GPUs and NPUs have been getting much of the attention and investment. While compute remains essential, AI data is constantly evolving, expanding and requiring ongoing management throughout its lifecycle.

As organizations move from AI experimentation to deployment at scale, the importance of a robust data infrastructure becomes increasingly clear.

Businesses need the ability to capture, store, access and manage growing volumes of data efficiently at every stage of the AI journey.

To maximize AI ROI, organizations should view storage and data infrastructure as strategic enablers rather than supporting technologies. Long-term AI success depends on ensuring storage infrastructure is aligned with wider technology and business objectives from the start.

AI requires a different approach to storage

Historically, data storage needs were relatively predictable. A ‘set-and-forget’ approach was often sufficient. The AI era demands a fundamentally different mindset for several reasons.

For one, the scale of data associated with AI exceeds anything many organizations have previously encountered. According to IDC, annual global data creation is expected to more than triple over the next five years, reaching 718 zettabytes by 2030, representing a CAGR of 26.9%.

AI workloads also continuously generate additional data through logs, metadata, synthetic outputs, model updates and training datasets. Just as importantly, AI performance is heavily influenced by the quality of the underlying data infrastructure. The ability to access large, well-managed datasets efficiently has a direct impact on business outcomes.

When discussing return on AI investments with the C-suite, technical leaders should connect infrastructure decisions to financial performance. GPUs are typically among the most significant items within AI budgets, and any time spent waiting on storage input/output represents underutilized investment.

Storage architectures that are not designed for AI workloads can constrain performance and reduce overall returns from compute investments.

One example for an AI-optimized architecture approach is tiered storage. Not all data delivers the same value at every stage of the AI lifecycle, nor does it require the same level of performance. Frequently accessed datasets used for active model training and inference may benefit from high-performance storage, while historical data, archived outputs and compliance-related records can be moved to lower-cost capacity tiers.

By aligning storage performance and cost with the value and usage profile of different datasets, organizations can optimize infrastructure spending while maintaining access to the data needed to support AI innovation, governance and future model development.

Organizations that invest in cost-effective scalable, future-ready, AI workload optimized storage and data infrastructure are better positioned to unlock value from AI initiatives, while building a foundation that can support future growth.

AI data, compliance and regulatory risk

Legal and regulatory considerations should also form part of AI planning from the start.

As AI laws are established, organizations must continue to comply with existing data-related obligations, including regulations like UK GDPR and the Data (Use and Access) Act 2025.

Any organization with EU customers should also consider EU requirements around data governance, transparency and record-keeping. Retention periods for training datasets and model records can often be longer than anticipated, making long-term storage planning a decisive factor.

Addressing these needs early helps organizations avoid costly remediation efforts later and supports more effective governance as AI deployments scale.

Talking a proactive approach to protecting AI ROI

As AI adoption grows and data volumes continue to expand, demand for storage capacity is increasing rapidly. This is creating new supply chain and procurement challenges across the industry.

As a result, organizations can no longer assume that capacity will be readily available whenever it is needed, particularly for large-scale AI projects.

In practice, this means considering forecasting storage requirements alongside GPU and infrastructure investments, exploring longer-term capacity planning arrangements, and incorporating storage needs into AI business cases from the beginning.

A proactive storage strategy is becoming essential for businesses looking to support future AI workloads with confidence, minimize operational risks, and maximize long-term returns from their AI investments.

The last word

AI ROI depends not just on the quality of its algorithms and applications, but also on how effectively organizations manage, store and govern their large-scale data estates.

Even a small difference in cost per terabyte can become significant when applied across petabyte- and exabyte-scale environments.

Organizations that integrate forward storage planning into their AI strategy, treat data infrastructure as a strategic asset and prepare early for future capacity requirements will be ideally positioned to realize the full value of their AI investments.

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For years, the UK connectivity debate has focused on fiber availability. Coverage maps, rollout targets, premises passed and investment updates have dominated the conversation.

That focus made sense. The country needed better infrastructure, and there was real pressure on operators, government and the wider market to show progress. Better networks had to be built, and in many places, they have been.

Ofcom’s Spring Connected Nations update shows how far that conversation has moved on. Full fiber broadband is now available to 82% of UK homes, while gigabit-capable broadband reaches 89%. Coverage is not solved everywhere, and regional gaps still matter, but the center of gravity is shifting.

For many mid-market and enterprise organizations, the question is no longer only: can we get fiber? Increasingly, it is: when something goes wrong, who owns the problem?

The market has not always been able to answer that clearly. As connectivity becomes more tightly linked to business-critical operations, that is becoming an expensive weakness.

The problem with modern connectivity supply chains

The infrastructure build-out of the last decade has been a success in many ways. Fiber has reached new places. Wholesale models have grown. Alternative networks have created more choice. Access supply has become more competitive.

But that same progress has also created a more complex operating model. A typical enterprise connectivity arrangement can involve several parties: an access network operator, a wholesale aggregator, a reseller and a managed services provider. Each party may have a role to play, but each also has its own systems, escalation routes, commercial incentives and operational boundaries.

When everything is working, that complexity is easy to ignore. When there is a fault, it quickly becomes the problem.

For a multi-site organization running business-critical applications, a manufacturer managing connected logistics, or a professional services firm with regulated data dependencies, the cost of delay can be significant.

A business’s dependency on connectivity has changed in both character and volume. Organizations are moving quickly to support AI workloads, distributed teams, hybrid cloud environments and more automated operations.

Ofcom’s Connected Nations UK Report 2025 shows that between September 2024 and August 2025, providers reported 616 resilience incidents across fixed and mobile networks. Outages above the reporting thresholds affected 12.7 million customers and caused around 192 million customer hours of lost service.

Ofcom also noted that third-party failures, including street works causing cable breaks and failed backhaul circuits from wholesale providers, made up around 14% of reported incidents.

For enterprise buyers, that is the accountability problem in plain sight. The failure may sit in one part of the chain, but the operational impact is felt by the customer trying to keep sites, systems and services running.

Why mid-market organizations feel the gap the most

There is a particular pressure point here for mid-market organizations. They are running connectivity that supports business-critical operations, distributed sites and cloud-dependent workloads. But they are typically buying through channels designed for volume, not accountability.

They carry the operational dependency of an enterprise customer without the contract size that historically bought priority escalation, named account management or direct access to the people who own the network.

That gap does not appear in the SLA document. It appears on a Sunday night when something breaks.

A few years ago, a slow SLA response might have caused a few headaches. Today, it can stop production, delay customer service, interrupt access to data or leave a site unable to operate properly. Businesses are built on connectivity.

AI inference at the edge, real-time data processing and cloud-connected operations are not designed for fragmented fault resolution. They do not fit neatly with unclear escalation paths or supplier chains where responsibility moves from one party to another before anyone fixes the issue.

Yet many buying conversations have not caught up. Bandwidth, speed and price still dominate procurement. Headline SLAs are scrutinized, but service assurance, ownership of the underlying infrastructure and escalation clarity are often treated as secondary details.

That mismatch will become more costly as the operating environment becomes more demanding.

The infrastructure question buyers should be asking

Most organizations know the standard questions to ask when they buy connectivity. Is there coverage? What is the cost? What is the headline SLA? What capacity is available? Who else is using the provider? All are important questions, but they do not fully describe the service experience.

The questions that really matter are more practical. When a fault is raised, how many organizations are involved in resolving it? Does the provider own the physical layer the service runs on, or is operational control spread across several parties? What does the escalation route look like at 11pm on a Sunday? Is there one organization accountable for the outcome, or a contractual matrix that has to be navigated before anyone can act?

The market has spent years explaining what infrastructure investment looks like. The next conversation needs to be about what operational accountability looks like.

That does not mean every provider needs to own every asset in every location. The UK connectivity market will always involve partnerships, wholesale relationships and specialist delivery models. But buyers should be much more interested in where responsibility sits when service degrades.

A shorter, clearer operational chain matters. It gives providers better visibility of the network, fewer hand-offs during fault resolution and a stronger ability to act rather than simply escalate. It also gives customers something they increasingly need: confidence that someone is accountable for the service, not just the contract.

Ownership is becoming the real differentiator

The next phase of business connectivity will not be won on availability alone. It will be won by providers that can combine reach with operational control, service assurance and clear ownership.

For buyers, that means looking beyond headline speeds and asking harder questions about accountability. Who sees the fault first? Who has the authority to fix it? Who is responsible for the customer outcome?

Because when connectivity is simply a utility, ambiguity might be tolerated. When it is the foundation for AI, cloud, data and day-to-day operations, ambiguity becomes a business risk.

The fiber rollout has changed what is possible. The next battleground is making sure someone owns the problem when possibility turns into pressure.

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Open-source malware has changed shape.

What once focused on noisy cryptomining has moved toward something far more valuable: access.

Our recent data shows attackers are increasingly targeting credentials and secrets embedded in software dependencies, with UK organizations firmly in scope.

This shift marks a move away from opportunistic abuse toward deliberate supply-chain compromise. Instead of draining compute cycles, attackers are positioning themselves inside build pipelines and developer workflows.

The goal is persistence, not disruption.

For organizations that rely heavily on open source software, this fundamentally changes both the threat model and the potential impact.

This is what “shift left” actually means in 2026: controlling what enters the build, not just detecting what runs in production.

Why credential theft has overtaken cryptomining

More than half of malicious open-source packages now focus on stealing credentials and secrets, overtaking cryptomining as the dominant threat type. The reason is straightforward. Credentials offer lasting value. They provide persistent access, broader reach across environments, and a lower risk of detection than resource abuse. A stolen token or API key can unlock entire systems, not just a single machine.

Cryptomining, by contrast, is easy to spot and quick to shut down. It consumes resources and triggers alerts. Credential theft blends in and can be executed in seconds. It exploits the trust placed on developer workflows to operate in a safe environment.

For attackers looking to maximize return while minimizing exposure, this approach maximizes returns whilst doing away with the risk of being discovered.

The implication is clear: protecting runtime infrastructure is no longer enough. The security boundary now starts at dependency intake and at the developer environment.

Multi-stage malware becomes the norm

Modern open-source malware is rarely single-purpose. Our analysis shows dropper and loader behavior increasing by nearly 2,900 percent year over year in Q1 2025, signaling a shift toward engineered, multi-stage attacks.

Around 77 percent of malicious packages distributed through open source ecosystems now combine multiple threat types. Droppers appear in nearly all observed cases, while secret exfiltration features in close to two-thirds.

These packages are designed to evolve after installation, pulling in additional payloads or changing behavior over time. This reflects industrialized campaigns rather than opportunistic experimentation. Attackers are investing in resilience, stealth, and scale.

For defenders, this means signature-based thinking is outdated. If malware is modular and adaptive, controls must focus on provenance, behavior, and prevention before execution. Again, this is what “shift left” actually means: securing the build graph itself, not just the workloads it produces.

Supply chains under direct pressure

The widespread use of open source, particularly within the JavaScript ecosystem, creates systemic exposure. Modern applications routinely depend on hundreds of direct and transitive npm packages. That density of reuse creates efficiency, but also amplifies upstream risk.

Recent activity linked to the Lazarus group illustrates the threat. More than 200 malicious packages were identified, almost all concentrated in npm. When a single ecosystem underpins financial services platforms, government services, and critical national infrastructure, concentration risk becomes a strategic issue.

A compromised dependency does not stay isolated. It propagates through shared frameworks, internal libraries, and CI pipelines. In sectors built on speed and reuse, upstream compromise quickly becomes downstream impact. This is why dependency governance is no longer just a developer hygiene issue; it is a board-level supply-chain concern.

Automation turns one package into thousands of compromises

Today’s malware increasingly targets CI/CD pipelines and developer workflows optimized for automation. When a compromised dependency enters a build, it can quietly extract API keys, certificates, and access tokens without triggering runtime alerts. Automation does the rest.

What starts as a single poisoned package can spread across hundreds or thousands of builds. The very systems designed to accelerate delivery now accelerate compromise.

The practical takeaway is uncomfortable but necessary: if build systems are automated, security controls must be automated at the same level. Manual review cannot scale against automated distribution.

AI coding assistants and the hallucination problem

AI-assisted development introduces an additional layer of risk. Studies and testing have shown that large language models can, in a meaningful percentage of cases, suggest packages or functions that do not exist. Developers under time pressure may attempt to install or rely on these hallucinated dependencies, unknowingly expanding the attack surface.

Hallucinated package names, fabricated examples, and unsafe dependency suggestions can quietly undermine supply-chain integrity. Attackers are already exploiting naming conventions and trust models to seed packages that appear legitimate to both humans and machines.

Each hallucination creates rework, friction, and lost productivity. Much of this waste could be reduced if AI systems were grounded in authoritative, real-time package intelligence rather than pattern prediction alone.

Our recent research reinforces this point. The company found that smaller AI models augmented with live package intelligence significantly outperformed larger standalone models when handling dependency upgrades and package selection tasks. The findings suggest that real-time ecosystem context matters more than model size alone when developers are making security-sensitive decisions. It also helps smaller models are 70x cheaper compared to frontier models.

This has direct implications for software supply-chain defense. If AI coding assistants recommend dependencies without verifying package provenance, maintenance status, or ecosystem trust signals, they risk accelerating the spread of malicious or hallucinated packages into production environments.

In practice, secure AI-assisted development will depend less on increasingly large models and more on whether those models are connected to authoritative, continuously updated software intelligence.

Here too, the lesson is upstream control. Guardrails must sit at the point of dependency selection, not after the code ships.

Why defenders are falling behind

Many UK security controls remain focused on detecting threats after code is deployed. Attackers have moved upstream. They target the build process, the dependency graph, and the trust relationships developers rely on.

This mismatch leaves organizations well prepared for runtime incidents but exposed during development. As long as defenders assume malware announces itself loudly, supply-chain compromise will continue to slip through unnoticed.

“Shift left” is often treated as a slogan. In practice, it means enforcing policy before installation, validating provenance before execution, and blocking malicious packages before they enter the graph.

Stealing the keys, not the cycles

Open-source malware has evolved from stealing compute to stealing access. Credentials unlock ecosystems, not just machines. For UK organisations, this makes supply-chain security a strategic concern rather than a technical afterthought.

Preventing malicious code from entering the build is now more effective than responding after deployment. The quiet shift from coins to credentials has already happened. The question is whether defenses will adapt quickly enough to match it.

If it isn’t automated, it won’t scale.

What organizations should prioritize now

To respond effectively, UK organisations should focus on a small number of structural controls:

● Gate dependency intake with automated policy enforcement before packages enter CI/CD.

● Continuously monitor for secret exposure within build environments and revoke compromised credentials rapidly.

● Enforce provenance and integrity verification for open-source components, including transitive dependencies.

● Ground AI coding tools in authoritative package intelligence to prevent hallucinated or malicious dependency suggestions.

None of these measures eliminate risk. But together, they realign defenses with where attackers are actually operating: upstream, automated, and inside the supply chain.

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On 1st October, Microsoft will begin disabling its Exchange Web Services (EWS) API, ahead of a full shutdown in April 2027. For many organizations, EWS has long been part of the invisible plumbing behind everyday workplace tools. It allows applications to connect to Exchange mailboxes, making it possible to access and manage data from emails, calendars, contacts and folders.

But EWS is almost 20 years old. Microsoft says it no longer aligns with modern requirements for security, scale and reliability, and its involvement in 2024’s Midnight Blizzard attack has added urgency to its retirement. The wider risk is also clear: APIs, particularly older and less visible ones, have become attractive targets for cybercriminals. Recent research found that 99% of organizations encountered API security issues in the past year.

Seen in that context, Microsoft’s decision to move customers towards more modern APIs such as Microsoft Graph makes sense. But API modernization is rarely as simple as swapping out one connection for another, and many organizations may soon face a rude awakening.

You can’t migrate what you don’t know exists

The main challenge organizations will face when migrating from EWS to Microsoft Graph is visibility. Despite its limitations, EWS still sits behind many everyday workplace processes, particularly in larger and older organizations.

Booking meetings, syncing calendars or allowing a CRM to log email activity automatically may all depend on the API. After almost two decades of use, EWS has become deeply embedded in day-to-day operations.

That creates a problem. Many organizations may no longer have a clear view of where EWS is being used, by whom or for what purpose. Some integrations will have been built years ago by developers who have since left the business. Others may sit inside legacy workflows that IT teams rarely touch.

And third-party tools that are outside of the IT team’s control commonly connect to EWS. As a result, some of these hidden dependencies are almost certain to fall through the gaps during migration, because organizations can only replace the EWS integrations they know exist.

For some organizations, this lack of visibility may make migration feel daunting, especially when EWS is tied so closely to everyday operations. It may be tempting to fall back on the old “if it isn’t broken, don’t fix it” adage, particularly if a poorly managed migration could disrupt critical tools and workflows.

But as Microsoft phases out EWS and ends support, doing nothing is not a viable option. Vital applications could lose access to Exchange, while the security risks of leaving legacy APIs embedded in the business will only become more pronounced.

Other organizations may try to avoid a difficult migration by intercepting EWS calls and translating them into Microsoft Graph, effectively rerouting traffic rather than replacing the underlying integration. At first glance, this may look like a clever workaround. In reality, it is only a temporary fix.

The same security, visibility and integration challenges remain, while every additional dependency built around this approach adds another layer of complexity. At scale, that could quickly become unmanageable.

Moving beyond avoidance

Instead of burying their heads in the sand or looking for clever, but ultimately ineffective, ways to avoid migrating from EWS, organizations should treat its retirement as a reminder of the need for robust software lifecycle management.

Many teams evaluate an API thoroughly during implementation, then rarely review it again. But APIs evolve, security issues emerge and providers introduce updates that require action. Organizations that build resilience and lifecycle management best practices into their API estates will be better placed to respond quickly to forced migrations, minimize disruption and avoid being caught out by sudden changes.

In practice, that means treating APIs as part of the digital supply chain and applying the same level of scrutiny used for third-party suppliers when evaluating a potential integration.

Critical APIs should be continuously monitored so organizations know where they are in use, what they support and how they are performing. IT teams also need to stay informed about planned provider changes, however small they may seem, and assess how these could affect their wider API management program.

From there, organizations need a fully prepared API migration plan. Some changes may demand immediate action, such as when a third party issues a critical vulnerability disclosure. Others, such as Microsoft’s EWS retirement, may come with longer lead times but be broader in scope and scale.

In either case, organizations need a holistic strategy covering API discovery, dependency mapping, ownership and management, testing, and ongoing monitoring. Without that foundation, they risk migrating only the integrations they can see, while leaving hidden dependencies to fail later.

Preparing for the future today

Microsoft disabling EWS is not the first event of its kind, and it will not be the last. As technology estates evolve, more legacy APIs will be retired in favor of newer versions that integrate more effectively and securely with modern solutions. The retirement of EWS should therefore be treated as more than a one-off Microsoft deadline. It is a warning about what happens when critical integrations are allowed to become invisible.

That makes resilience essential. With robust software lifecycle management, organizations can move away from panicked, reactive migrations and take greater control over their API estates. Those that prepare now will be better placed to manage the EWS migration, and whatever comes next.

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IT security has traditionally been about keeping threat actors out and preventing breaches. However, while the volume of software vulnerabilities has gone up and up over the past few years, the biggest risk that many companies face is not about a hacker getting in. Instead, it is how long they can stay inside and how far they can move within that network.

The reason why threat actors can move laterally is how difficult it is to manage identities effectively. Identities and credentials are used all the time to allow access, but they also serve as ways to support provisioning and software deployment processes. Simple software tokens that can make life easier for staff are targeted for the access and permissions that they provide.

Companies are waking up to this threat. Omdia research estimates that spending on identity management will increase at 75 percent of organizations during 2026, compared to 57 percent in 2025. But what issues have to be resolved, and what is the fastest way to get to those resolutions?

Understanding attack paths and identity

Threat actors want to make money. They do this by stealing company data or encrypting files for ransom. They achieve their goals through getting initial access in one place - an endpoint that is missing an update, or through a phishing email and malware download, for instance. After this, they want to move laterally through the network to something valuable.

For defenders, this lateral movement, or attack path, represents an opportunity to deny access or block attacks. Attackers use the same methods that legitimate staff have to access assets: their identities. For many attackers, there is no need to use zero day attacks when they can simply log in and get what they want, or move closer to their goal.

Understanding attack paths can be hard. For defenders looking at the business crown jewels like Intellectual Property or mission-critical applications, attack paths are often visualized as direct routes from an initial access point to those valuable assets. While this is simple to understand, attackers don’t work that way.

They don’t know your internal network topology or the most direct route to what is valuable. Instead, they are sniffing out what they can access.

This is made more complicated because each account will have its own level of access, and different accounts will have more or less permissions. Each asset will have multiple accounts that can access it. The complexity scales up rapidly as networks grow larger.

For organizations with around 1,000 employees, we estimate that they have more than 5 million attack paths. Companies also have to reckon with the number of identities they have in place - an organization with 10,000 identities would have around 22 million potential attack paths to manage.

Attack path problem compounded by AI and NHIs

Adopting Artificial Intelligence (AI) and Non-Human Identities will increase this further, from the current ratio from around five identities per employee towards 20 or 40 identities per human employee. This imminent surge of identities will then grow at an expected 20 percent year on year.

That is a lot of numbers. To put this into perspective, looking at how you understand maps and graphs can help. For example, London has 330 stations in its public transport network covering the underground and rail networks, as well as buses and riverboats.

There are millions of different routes that travelers can take to get from any place, from the fastest and obvious journeys through to more circuitous and obscure routes that don’t make sense except in context. Travelers can also shift from one kind of transport network to another where they need to. Understanding all those networks, timetables and options is very hard for someone to hold in their brain all at one.

Using a map can show up the best routes and directions to take, including those paths that are a combination of different networks. From a defense perspective, understanding the most high profile locations in that network and where security is most important can help prevent attacks. Looking at attack paths and permissions, you can see the most important locations within the network based on the level of access that they have.

In the London transport network, this would be the equivalent of security at Piccadilly Circus or King’s Cross St Pancras, as these stations are the busiest. Securing those specific nexus points can prevent an attacker taking more potential routes to something valuable, reducing the risk that attackers can get to mission-critical systems or data. Once you have locked down those central hubs, you can then look for the next major point and secure this one as well.

Getting to a secure future

This process around locking down identities and access permissions removes whole swathes of potential attack paths, rather than looking at each attack path one after another.

There are too many individual paths to manage, so corralling issues and securing those environments as a whole reduces the number of ways that threat actors can move across a network. This forces attackers to make their presence more obvious, leading to them being removed from the network.

Attacks on IT are getting faster. Trying to fix all the gaps that exist is now impossible, and defenders cannot achieve perfect security. We need to live with these imperfections, and manage security as effectively as possible in advance of any issue coming up.

While security teams might be used to defending the direct paths between systems, threat actors look for the other routes that can get them to where they want to go. Instead, we have to manage our networks based on preventing both the most risks and the most impactful risks.

By understanding attack paths and identity risks, we can look at what the highest priority issues are and then secure them. Using identity attack paths, we can understand where the risks exist and what work needs to be done.

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Prince Harry and Meghan Markle have taken direct aim at Grok after Minnesota became the first state to enforce a ban on AI "nudification" technology, using an unusually blunt official statement to criticize xAI's attempt to stop the law. Days before the legislation took effect, Elon Musk's AI company filed an emergency legal request seeking to block it, arguing that the measure violates the First Amendment. A federal judge refused to grant the request, allowing the law to go into effect while the broader case continues.

The Sussexes made it clear which side they believe this fight should be on. "Technology should not enable predators to target children," their statement begins. "Yet, ahead of Minnesota's first-in-the-nation law banning AI 'nudification' apps taking effect tomorrow, one of the world's largest technology companies sued to stop it. Why?" It is a remarkably direct criticism, one that shifts the conversation away from legal arguments and back toward the people these tools can harm.

Whether Minnesota's law ultimately survives every constitutional challenge is a question for the courts, but Harry and Meghan have identified something larger than one lawsuit. AI companies are racing to make image generation more powerful while governments scramble to prevent those same tools from being weaponized against real people. When a company fights to preserve technology that can create convincing fake nude images of recognizable adults and children, it is easy to see why the couple argues that the industry's priorities deserve much closer scrutiny.

Sussex vs. Musk

AI has become extraordinarily good at manipulating images, but that includes convincing fake intimate photographs with only a few prompts. And it has spread far faster than the laws designed to deal with it.

Minnesota's legislation attempts to tackle the problem at its source by preventing apps and websites from offering AI nudification tools in the first place. Rather than waiting until fake images have already spread across social media or messaging apps, lawmakers are trying to make the technology itself less readily available. Whether every provision survives constitutional scrutiny remains to be seen, but the intention is difficult to misunderstand.

Harry and Meghan clearly believe that technology companies have had plenty of opportunities to address the issue voluntarily and have failed to do so. Their statement praises Minnesota's bipartisan action as "an example of leadership fit for the digital age," adding that lawmakers recognized "this technology, if not stopped, would protect predators and hurt innocent people, especially women and girls."

The Sussexes are slicing through the tangled debate over algorithms against constitutional doctrine. Those issues matter, but it can miss the forest for the trees if people forget that these synthetic nudes begin with an identifiable person whose image has been manipulated without permission.

AI models as neutral tools whose morality depends entirely on the user. But laws often are stricter when any tool is used to hurt children for a reason. And the claim that Grok's moderation system is enough has proven untrue. But the feature does not stop being Grok's responsibility simply because someone else typed the prompt.

Safety should not be an optional feature

The Sussexes refuse to treat this as an abstract policy dispute.

"Big Tech companies are raising billions claiming AI will bring society forward, yet they retaliate against basic safety measures to keep children safe," they wrote. "Can AI make our world better while it enables the worst in humans? Should our children pay the price while we wait to find out?"

Those are uncomfortable questions for AI companies racing to release increasingly capable products. Every major developer wants to ship the next breakthrough before its competitors do. Safety work, moderation systems and abuse prevention rarely generate the same excitement as flashy new features demonstrated on stage.

xAI is hardly alone in facing this challenge. Every major AI company has struggled with image generation, impersonation and deepfakes. The difference here is the explicit pushback against a state trying to make them take some responsibility for how their technology is misused.

The legal arguments will continue for months, and there's no way to tell yet what the final version of the law will look like. Courts have to balance free speech and public safety, and that's not simple. But the fact that AI has made creating nonconsensual intimate imagery dramatically easier remains, and Harry and Meghan are right to zero in on that human cost over legal theory.

The debate matters because children's safety matters. Whether the companies building these tools can be made to accept meaningful responsibility when those capabilities are turned against children may be decided in courts, but Harry and Meghan are correct that it shouldn't take lawyers for them to do the right thing here.

The thought of infusing AI into weapons immediately evokes deep fears, not only based on the idea that autonomous weapons may make life-and-death decisions on our behalf, but also on the prospect of errors creeping in. But for Palmer Luckey, founder of Anduril Industries, there's no alternative but to add intelligence into the weapons systems of tomorrow.

Dumb vs smart weapons

Luckey was speaking in a Q&A portion of a TED talk when he made the comparison between the weapons of today and the AI-powered weapons of tomorrow.

The talk was themed around his notion that adding intelligence to weapons systems could prevent future catastrophes, based on the notion of deterrence preventing different superpowers from waging war.

On the subject of the ethics of incorporating AI into weapons, Luckey pointed out that 'dumb weapons' may indiscriminately kill without context or an understanding of a particular target.

He used a land mine as an example of a weapon that may kill innocents or combatants – possibilities that wouldn't be considered ethically superior to his vision for smart weapons.

Intelligence-driven warfare

The rise of AI weapons has been quick but consequential, with new technologies being deployed on battlefields across the world. The war between Russia and Ukraine, in particular, has shown the potential for automated systems to dominate conflicts.

But the use of AI doesn't end at weaponry. It also involves the use of machine learning, for example, to classify different moving elements from several sources of imagery. It's the holistic picture, Luckey argues, from which he draws his conclusions rather than isolated implementations.

When Anthropic's CFO revealed that over 90 per cent of the company's code is now written by its own AI, it landed as a milestone.

Tasks that once consumed hours now take 30 minutes. The productivity gains are significant.

But Anthropic is an AI-native company with some of the world's best engineering talent.

For most enterprises, the question isn't whether AI can generate code at that speed. It's whether they can govern what it generates.

Shadow AI: The new shadow IT

"Vibe coding", the term for generating code via AI, has moved into the mainstream. By some estimates, almost half of all new global code is now AI-generated. Developer productivity is up. So is debt that nobody fully understands.

Part of what's driving this is necessity. AI is helping close the engineering talent gap. Teams that lack experienced developers are using it to build at the pace the business demands. The problem is that the same shortage that makes AI indispensable also means there aren't enough senior engineers to review AI-generated code.

Research across Fortune 50 enterprises found that AI-assisted developers introduce security vulnerabilities at ten times the rate of their peers. Forty-five per cent of AI-generated code contains OWASP Top 10 vulnerabilities. Independent analyses indicate that technical debt increases by 30–41 per cent following the adoption of AI tools.

Traditional technical debt is at least visible. Engineers who cut corners know they did it. Vibe coding debt is different: developers often don't realize they have incurred it, because the code looks correct – right up until it doesn't.

We used to worry about Shadow IT. The new threat is Shadow AI: code generated at pace without architectural review, security auditing or institutional understanding of what's been built.

Unlike Shadow IT, which was typically contained within a department, Shadow AI compounds across organizational boundaries. In enterprises where systems are still deeply siloed, complex problems span multiple departments and platforms; no single team has a complete picture of what's been generated or what it depends on.

Enterprises have spent decades paying for yesterday's shortcuts. AI risks creating the next generation of legacy systems, only much faster.

Repeating the COBOL mistake

The market is flooded with tools promising to read millions of lines of COBOL or Java and convert the functionality into a modern language. This is technically impressive, but strategically flawed.

Legacy systems are full of inefficiencies, redundancies and “swivel chair” workarounds baked in years ago to compensate for other systems' limitations. Translating code line-by-line replicates that bad logic in a newer language, now running on cloud infrastructure with a modern interface on top of old code and dated processes.

To get modernization right, organizations should avoid treating it as a technical exercise. Instead, they should step back and ask whether a process is still valid, not just how to replicate it.

True modernization is about reinventing how work gets done, designed around the employee and customer experience rather than the constraints of systems built decades ago. It is a state of consistent change, and it must be driven by the North Star of clear business objectives.

The agility layer

Probabilistic AI needs to operate within deterministic boundaries to be safe and useful at enterprise scale. AI that can generate anything is not the same as AI that generates the right thing reliably with full traceability, in a context where every decision might be scrutinized by a regulator.

This is why high-stakes organizations are looking for an agility layer: a governed process platform that imposes structure, ensures auditability and keeps AI outputs within maintainable boundaries. The US Army followed this approach for security with agility to operate with certainty at speed.

Ordering ammunition in disconnected field conditions is mission-critical, with zero tolerance for ungoverned outputs. Similarly, pharmaceutical giant Merck's clinical supply chain, where regulatory scrutiny is intense and errors have patient safety consequences, required a platform that could accelerate delivery without sacrificing auditability.

These are examples of organizations trying to address the hardest part of modernization: discovery. They are using AI to extract specifications from even the most poorly documented legacy applications, converting them into visual plans covering UI, data models and process flows.

They're generating software components rather than custom code to reuse in other applications, accelerating development time and reducing technical debt. AI agents then build against those specs under human supervision, with developers assigning tasks and iterating throughout – at roughly 25 per cent of the time traditional approaches require.

The future is bespoke, not general-purpose

There is a temptation to conclude that general-purpose AI will soon render enterprise platforms obsolete. This is premature and in regulated environments, dangerous. General-purpose models are extraordinary at generating plausible outputs. They are not equipped to ensure those outputs are auditable, compliant or maintainable by teams that did not generate them.

The vibe coding wave is real, and so is the debt it is creating. The organizations that navigate the next decade successfully and avoid falling into the same old legacy traps will not be the ones that generated code the fastest in 2025 and 2026.

They'll be the ones to build governance and process boundaries into how AI was used from the start – so what was built quickly can still be understood, maintained and trusted years later.

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Not too long ago, one of the world's most data-driven companies admitted that its AI spending was becoming “harder to justify”. Uber's President and COO, Andrew Macdonald, told the Rapid Response podcast that the company had blown through its entire 2026 AI budget in roughly four months, with around 5,000 engineers leaning on Anthropic's Claude Code.

Uber isn’t alone. Forrester research found that enterprises are deferring around 25% of planned AI spend to 2027, as CFO scrutiny over ROI intensifies. And McKinsey's State of AI report summarized that while 62% of companies are experimenting with AI agents, only 23% have scaled them in even a single business function.

While these may look like the statistics of a technology that isn't working, they’re actually the statistics of a technology being used in the wrong way.

If we rewind back to 1987, Nobel laureate economist, Robert Solow, observed something that many at the time really resonated with: “You can see the computer age everywhere but in the productivity statistics.” Computers were everywhere across the innovative businesses that had invested heavily in them, but the productivity numbers didn’t move.

The returns only materialized years later, once organizations stopped bolting computers onto old processes and started fundamentally redesigning how they worked.

Many executives, alongside Uber’s COO, are at exactly that inflection point with AI - people are using it, running out of budgets to maintain usage, and at the same time, not really seeing the productivity boost they were hoping for.

This is the Solow Paradox repeating its course, which begs the question: will enterprises learn from previous mistakes?

The flatline behind the hype

There’s a critical distinction that most enterprise leaders are still failing to make: AI activity is not the same as AI maturity. You can run 40 pilots, adopt six platforms and report impressive usage statistics, and still be no closer to measurable business value.

Uber found this out in painful, public fashion, and has since openly questioned whether the rising cost of AI token usage is translating into proportional productivity gains. What makes Uber's situation instructive is not just the financial exposure, but how the organization approached adoption. Internal leaderboards were introduced to rank teams by AI tool usage, with the incentive being to use more tools.

The outcome was more usage, but the business impact slowly became harder to justify.

This is what happens when you gamify adoption without redesigning the workflows underneath it. You optimize the tool usage metric, not the business outcome. Uber has now joined several other top organizations, including Microsoft, Meta and Amazon, in capping AI usage to tackle the issue.

What’s interesting here is that when AI token usage is unconstrained, activity becomes the proxy for progress, but when it’s capped, organizations are forced to confront a harder question: what is each token actually producing?

In that sense, token spend behaves like an economic mirror. It scales immediately with adoption, while productivity only improves when workflows are redesigned. The gap between the two is where most AI ROI disappears.

The real culprit: Individual task optimization

When AI tools are deployed at the individual level, they tend to optimize the task, not the workflow.

A developer writes code faster, a marketer drafts copy in a fraction of the time, or a data analyst generates a summary report in minutes rather than hours.

All of these examples are real gains, but if the code still sits in a review queue for four days, if the draft still passes through three rounds of manual approval, or if the report still requires someone to manually transfer it into a decision-making dashboard - the time saved will pool at the next bottleneck.

Individual productivity gains that don’t translate into workflow redesign don’t compound. They stagnate, and this is the core of the maturity gap. AI maturity isn't about how many tools you've adopted, or how many pilots you've launched. It's about whether you've moved consistently from opportunity to outcome.

That shift requires a fundamentally different way of working.

Now, the term ‘production-ready AI’ gets used loosely. It’s worth being precise about what it actually means in practice, because most enterprise AI deployments fall short of the bar.

Production-ready AI has four characteristics. First, the output feeds directly into a downstream decision or action without manual transfer. It’s embedded in the workflow, not adjacent to it.

Second, the system has clearly defined failure modes, so the organization knows exactly what happens when the AI gets something wrong, and who’s accountable for remedying it. Third, there’s a named owner responsible for performance, adoption and iteration. Finally, and most critically, the surrounding process has been redesigned - not merely augmented.

The framework that closes the gap

So, how can businesses make this shift in practice? The answer is maintaining disciplined execution.

One way to build that discipline is a structured cadence we call the 3-3-3 framework: three days to prioritize, three weeks to prove value, and three months to launch a first release. The logic is deceptively simple and deliberately structured.

In the prioritization phase, the question is not “what can AI do?”, it’s “which specific opportunity, tied to a specific business outcome, has the right combination of value, feasibility, data readiness, and organizational sponsorship to pursue right now?”

That focus alone eliminates a significant proportion of AI initiatives that consume resources without clear purpose. It’s the antidote to the open-ended experimentation that left Uber burning through its budget before April was out.

In the proof phase, the focus is validation - not in technical terms, but in commercial ones. Can this solution create measurable value for users and for the business? A proof of concept that demonstrates technical possibility without demonstrating business value isn’t a proof of concept, it’s a prototype without a destination.

In the launch phase, the solution moves into a real environment. It integrates with existing systems, gets adopted by the people it was built for and gets measured against the outcome it was designed to improve. Not against token usage and not against adoption rates.

This rhythm isn’t a rigid formula, however. The shape of the work always depends on the business problem, the data environment, the technical complexity and the organization's appetite for change. What the framework provides is momentum and the discipline to keep that momentum anchored in value.

Shifting from adoption metrics to outcome metrics

The most consequential change enterprise leaders can make right now is a measurement decision.

Enterprises that are serious about closing the gap between AI activity and business impact need to make three shifts. The first is from tool deployment to operating model redesign. Rolling out AI tools is table stakes but building a repeatable operating model - a structured path from idea to proof to scale - is the competitive differentiator.

The second shift is from adoption metrics to outcome metrics. Usage rates, logins and token volumes tell you whether people are using the tools, but they don’t tell you whether the tools are working. Define success in business terms from the outset: cost reduction, time-to-decision, revenue impact, customer satisfaction, and build your measurement framework around those outcomes.

The third shift is from centralized experimentation to distributed accountability. The AI factory model - where a central operating model connects business priorities with delivery, adoption and value measurement - works precisely because it distributes accountability across the organization. Every initiative starts with a clear owner, a clear problem and a clear definition of what success looks like.

Winning the AI day

The productivity paradox Solow identified in 1987 eventually resolved itself. Not because computers got better - though they did - but because organizations learned to reorganize work around the technology, rather than fitting the technology around old ways of working.

The same resolution is available to enterprises deploying AI today. But it requires leaders to make a deliberate choice - to stop measuring success in terms of how many tools have been adopted and start measuring it in terms of how many outcomes have been delivered.

The organizations that win the AI era will not necessarily be those with the largest number of pilots, but rather the ones that build the maturity to turn the right ideas into value and then scale that value with confidence.

That kind of maturity doesn’t happen by accident. It requires structure, discipline and the willingness to ask harder questions about what AI is actually delivering, and what it’s not.

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Thanks to rapid developments around AI, cybersecurity pros seem due for a reminder that the threat landscape has fundamentally changed. This time it's OpenClaw ringing the alarm bells, but in truth we see the same cycle repeating itself time and time again, just with new technology.

Within days of OpenClaw’s open-source, researchers discovered exposed management interfaces and malicious "skills" packages designed to trick users into installing compromised functionality.

While the legitimate security community did what it always does, innovate rapidly, attackers moved just as fast.

That reality should force organizations that haven’t already done so to rethink a dangerous assumption: the belief that if something malicious appears inside our environment, internet security products will detect it quickly enough to stop serious damage.

That assumption is unrealistic in the world of AI-driven automation and open-source agent ecosystems.

The better question is: ‘Why should unknown software be allowed to run in the first place?’

Uncontrolled AI adoption is the core issue

OpenClaw highlights the much larger issue of Shadow AI running across organizations. Employees are downloading local AI agents, experimenting with open-source models, installing community-developed skills and connecting all of these tools directly to corporate resources.

Unlike traditional SaaS applications, many of these agentic platforms execute directly on endpoints. They request filesystem access, interact with browsers, connect to cloud services and automate business workflows.

Every new skill, extension or plugin expands the attack surface. While AI usage is an important progression of technology, the problem that’s emerging is that organizations frequently have little visibility or control over which AI tools are entering their environment, let alone what they're allowed to do once they arrive.

Detection can't keep up with automated attacks

The industry continues investing enormous resources into detecting threats faster and there is absolutely value in that; but OpenClaw illustrates why detection alone cannot be the primary strategy.

By the time a detection platform identifies suspicious behavior, an AI agent may already have accessed sensitive files, authenticated them to cloud services, downloaded additional components or exposed confidential information. Instead of asking how quickly we can detect something, organizations need to first ask whether it should have been able to execute at all.

Control AI without disrupting the business

To effectively mitigate AI-driven cybersecurity threats, the industry must embrace a Zero Trust approach that moves from an allow-by-default to a deny-by-default posture.

In the context of Shadow AI, application allowlisting ensures only approved agents run inside your environment, while application containment further limits what those trusted agents are allowed to do.

This way, if an agent is compromised, any unnecessary access to files, memory, scripting engines, networking functions or other applications is blocked entirely.

Together, these controls enforce the boundaries your business already intended to have.

One of the biggest misconceptions surrounding Zero Trust is that it requires organizations to lock everything down overnight. This isn’t true.

When done correctly, application control allows organizations to understand what's already running, establish normal operating behavior and gradually enforce policies without disrupting users.

Define what good looks like

Cybersecurity has traditionally focused on identifying bad behavior, yet attackers are constantly inventing new forms of it. A more sustainable model is to define what good looks like for your organization.

If a script or application like OpenClaw is not explicitly approved inside your environment, it shouldn’t be allowed to execute.

The bottom line is that if an AI agent doesn't require access to sensitive directories, cloud resources, PowerShell or credential stores, those interactions shouldn't be possible. This deny-by-default approach dramatically reduces the opportunities available to both attackers and compromised applications.

Rather than chasing an endless stream of new threats, you're enforcing known business requirements.

The leadership lesson

Open-source innovation has enormous value and will continue driving technological progress, but every major cyber incident teaches a lesson.

The Open Claw incident shows that organizations can no longer afford environments where any new tool is free to operate with minimal oversight. Leadership teams need to stop measuring success by how quickly they respond to breaches and start measuring how effectively they've reduced the opportunity for one to occur in the first place.

Open-source AI ecosystems will continue evolving at remarkable speed, new capabilities will continue to emerge. In tandem, attackers will continue to innovate their own methods. Fortunately, your security strategy doesn't need to change every time a new threat emerges.

The principles behind hardening your environment remain the same: know what belongs in your environment, and allow only what you've explicitly approved. Restrict what trusted applications can do, and treat every request for access as untrusted until proven otherwise.

Organizations that embrace that philosophy won't just be better prepared for OpenClaw, they’ll be ready for whatever comes next.

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Quantum risk has moved from theoretical to operational in 2026, and this shift is now impossible for enterprises to ignore. Google has committed to completing its post-quantum migration by 2029, the NCSC 2035, and the G7 says 2034. NIST has finalized its first full suite of post‑quantum cryptographic standards, triggering mandatory migration planning across regulated industries.

The timelines might not be perfectly aligned, but the message they send is: the risk is real, and the time to act is now. Breakthroughs in quantum hardware and AI‑accelerated quantum optimization are bringing “Q‑Day”ever closer. This is elevating quantum‑safe visibility, cryptographic discovery, and encrypted‑traffic intelligence to a now‑priority for every enterprise.

This is not the first time the industry has faced a widely anticipated but imperfectly understood threat. The parallels between quantum computing and Y2K are difficult to ignore. What began as stories of a supermarket system rejecting food as 80 years out of date, or a 104-year-old being invited to school because a computer registered her as four, soon evolved into fear as the scale of the problem became clear.

By 1995 the New York Stock Exchange had spent over $30 million remediating its systems. Y2k evolved into a defining moment for risk management and the key lesson was that organizations must understand and respond to exposure quickly and decisively.

Successfully addressing the threat quantum computing poses to encryption demands the same mindset, although this time applied across a far more complex and interconnected digital landscape.

The invisible threat already underway

A key distinction between Y2K and quantum computing is that the latter is not anchored to a single moment in time. The phrase “harvest now, decrypt later” describes the practice of adversaries collecting encrypted data today with the expectation that quantum capabilities will allow them to decrypt it in the future.

The implications of this tactic are significant, with 87 percent of organizations expressing concern about such scenarios as quantum computing advances.

Financial records, personal data, and intellectual property retain their value long past creation and the information encrypted today may still be sensitive well into the future. Decisions made now about cryptographic resilience will directly shape an organization's future security and reputation.

Why PQC is fundamentally more complex than Y2K

At its core, the Y2K challenge was a remediation problem with a relatively well-defined scope. Migrating to post-quantum cryptography is fundamentally different.

Cryptographic controls are deeply embedded across modern digital infrastructure, underpinning applications, APIs, cloud services, IoT devices, operational technology, and a growing web of third-party integrations. In many cases, they operate invisibly and are poorly documented. Organizations are not simply upgrading known systems, but first discovering what encryption levels have been used and where.

Addressing this means building a comprehensive inventory of cryptographic assets. All weak cipher suites, expired certificates, and non-compliant encryption methods must be identified.

Once found, organizations should standardize on stronger protocols such as Transport Layer Security (TLS) 1.3. This faster, more streamlined and longer protocol is ultimately more secure than older versions like TLS 1.1 and TLS 1.2, which will be broken by quantum computers in a matter of hours, minutes, or even seconds.

You cannot secure what you cannot see

In addressing both Y2K and today’s cybersecurity challenges, one principle consistently determines success: visibility. As organizations plan for a post-quantum future, 91 percent report that visibility into encrypted traffic is critical for PQC readiness.

Network-derived telemetry provides a scalable way to gain this. By analyzing traffic flows and metadata, organizations can build a comprehensive picture of cryptographic usage across both managed and unmanaged assets. This outside-in perspective complements internal inventories and helps uncover dependencies that might otherwise remain hidden.

With improved visibility comes the ability to assess risk more accurately, prioritize remediation, and ensure that the adoption of quantum-resistant approaches does not introduce unintended vulnerabilities.

Avoiding a repeat of history

The response to Y2K ultimately succeeded because organizations acknowledged the threat and took action. It was a forcing function that led to massive technology infrastructure upgrades and tech stack modernization.

Like Y2K, today’s quantum challenge isn’t just the potential event itself, it’s the scale of the remediation effort required across systems, applications, and embedded technologies, which makes early action critical.

The transition to post-quantum cryptography will not be achieved overnight. It will require a coordinated effort across security, infrastructure, development, and compliance functions, alongside close collaboration with vendors and strategic partners.

More importantly, it requires a shift in how the challenge is framed. Organizations that take proactive steps now by establishing a comprehensive inventory of cryptographic assets, improving visibility across their environments, and developing structured transition plans will be far better positioned to navigate the shift.

They will retain control over their timelines and reduce the likelihood of disruptive, last-minute change.

Those that delay may find themselves in a position that feels uncomfortably familiar. A known problem with a shrinking window in which to respond.

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We’ve been sold a comforting idea about artificial intelligence: that it’s making us dramatically more productive. Faster outputs, smarter tools, less effort. A quiet revolution in how we work.

But step back for a moment and ask yourself a simple question.

Do you actually feel more productive? Not faster. Not busier. Productive.

Because for most professionals I speak to, the answer is no.

Work feels quicker, yes. But also more fragmented, more reactive, and oddly more exhausting. The promise of efficiency is there on paper, but the true experience tells a different story.

That disconnect is worth paying attention to.

A typical working day

Look at how most of us spend a typical working day. We move between email, calendar, tasks, notes, messaging platforms, documents. Each tool holds a piece of the puzzle, none of them are the full picture. So, we become the system that stitches it together.

We check an email, then jump to our calendar to understand the context. We open a task list, then search our notes to remember why that task exists. We respond to a message, then dig through previous threads to find what was agreed.

This is not the work itself. It’s the management of work.

Now add AI into the mix.

We have tools that can summarize emails, draft responses, transcribe meetings, generate notes, and even suggest tasks. Each of these capabilities is impressive in isolation. They save minutes here, seconds there.

But they don’t remove the fundamental problem. In many cases, they amplify it.

Instead of switching between tools, we now switch between tools and their respective AI layers. An assistant in your inbox. Another in your document editor. Another in your meeting tool. Each one helpful, but none aware of the others.

So, we’re still managing everything ourselves. We’re just doing it faster.

This is where the narrative around AI productivity starts to unravel.

Defining success

We’ve defined success as speed. How quickly can a tool help you write, summarize, respond, or organize? And to be fair, AI has delivered on that front.

But speed without context is a blunt instrument.

If you’re responding faster but to the wrong priorities, you’re not more productive. If you’re generating more output but not moving meaningful work forward, you’re simply accelerating noise.

The real friction in modern work isn’t the execution of tasks. It’s the constant need to decide what matters, to reconstruct context, to align fragmented information across multiple systems.

AI, as it stands today, rarely addresses that layer.

At warpSpeed, we’ve approached this from a slightly different angle.

We didn’t start by asking how to make tasks faster. We started by asking why work feels so disjointed in the first place.

The answer was fairly obvious: everything is scattered. Email lives in one place, calendar in another, tasks somewhere else, notes somewhere else again. Every decision requires jumping between them.

So, we focused on bringing those elements together into a single, connected environment. a system where context flows naturally between them, facilitated by AI.

Not as a feature bolted onto individual tools, but as something that can see across them. Something that understands not just a single email or a single note, but the relationship between your communications, your commitments, and your priorities.

The difference, while subtle, is meaningful. This is how I like to think a successful assistant would function.

For example, when someone asks, “What should I focus on today?”, the answer isn’t generated in isolation. It draws on overdue tasks, unread emails that require responses, upcoming meetings, and previous commitments. It reflects the reality of that person’s day.

Small changes

Similarly, we’ve seen how small changes in interaction design can shift behavior. One example is email. By rethinking how users move through their inbox, we’ve seen people process large volumes of emails in a fraction of the time they previously spent. Not because they’re working harder, but because the system reduces friction and surfaces what matters.

These are not dramatic, headline-grabbing transformations. They’re incremental improvements grounded in real workflows. And importantly, they’re imperfect. We’re still learning, still refining, still discovering where the real value lies.

But they point to something broader.

If AI is to genuinely deliver on its promise of productivity, we need to rethink what we’re asking it to do.

Right now, most tools are designed to assist with tasks. Write this. Summarize that. Suggest a response. Create a list.

What’s missing is a deeper understanding of context and personalization.

Who is this for? Why does it matter? What else is happening around it? What should take priority?

Without that layer, AI remains reactive. It responds to prompts, but it doesn’t help you navigate your day.

Moving forward

To move forward, the industry needs to shift in three ways.

First, from isolated tools to connected systems. The value of AI increases exponentially when it can operate across your entire workflow, not just within a single tool.

Second, from generic intelligence to personal context. The most useful AI will be shaped by how you work, what you care about, and how you make decisions.

Third, from output to outcome. It’s not enough to generate content or complete tasks. The goal should be to move work forward in a meaningful way.

None of this is easy. It requires rethinking product design, data architecture, and user experience at a fundamental level. It also requires a degree of restraint. Not every problem needs another feature. Sometimes it needs fewer moving parts.

Productivity at scale

The irony is that the more powerful AI becomes, the more important simplicity becomes. Productivity at scale depends on removing the need to think through complexity, making intuition more valuable than ever.

Because ultimately, productivity isn’t about doing more things. It’s about doing the right things with less friction.

AI isn’t broken.

But the way we’re using it might be.

If we continue to layer intelligence on top of fragmented systems, we’ll keep getting the same result: faster work, but not better work.

The real opportunity lies in something quieter, but far more impactful. Using AI to remove the need to manage work in the first place.

Not to help you keep up.

But to help you stay focused on what actually matters.

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Mention high-frequency semiconductors, and the conversation often begins with gallium arsenide (GaAs). For decades, GaAs has been the material of choice for high-performance radio frequency (RF) devices that enable a wide range of applications, including satellite communications, radar, and mobile networks.

It is a mature, well-understood technology, and large, high-quality wafers can be grown economically, enabling consistent device performance and scalable manufacturing.

Yet a transition is underway. Increasingly, the industry is turning away from GaAs and towards gallium nitride (GaN), a material that offers clear performance advantages, particularly at higher frequencies, higher power levels, and more demanding operating conditions.

The promise of GaN is well established. The challenge lies in making it reliable, scalable, and commercially competitive.

And, as with many emerging semiconductor technologies, meeting that challenge is as much about measurement as it is about materials.

A material with advantages, and constraints

GaN enables the development of devices that can operate at higher voltages, higher temperatures, and greater power densities than their GaAs counterparts. This makes it particularly attractive for next-generation RF systems, including 5G and 6G communications, defense applications, and space technologies – where extreme environments are commonplace.

In principle, GaN can offer a straightforward upgrade path. It is sometimes thought of as a functional replacement for GaAs to deliver improved performance. Yet, in practice, the situation is more complicated, and a whole-system approach is required to redesign a module. Furthermore, the manufacturing approach for GaN is fundamentally different.

The hidden cost of heteroepitaxy

Unlike GaAs, which can be grown as large, high-quality wafers using established methods, GaN presents a fundamental manufacturing challenge because producing large, defect-free GaN substrates remains difficult and expensive.

As a result, most GaN devices are produced using a process called heteroepitaxy, whereby a thin layer of GaN is grown on top of a different substrate, typically silicon or silicon carbide. This approach allows manufacturers to leverage existing wafer technologies. But it comes at a cost.

When GaN is grown on a dissimilar substrate, differences in lattice structure and thermal expansion introduce defects into the material. These defects can affect everything from electrical performance to long-term reliability.

This results in a difficult trade-off: GaN offers superior theoretical performance, but achieving that performance consistently across wafers and devices is far more challenging than with GaAs. The advantage of using GaN therefore increasingly depends on material quality, and on the ability to control and understand it.

That relies on measurement.

Measurement as a competitive tool

For companies developing GaN technologies, metrology plays three distinct and essential roles.

The first is in process development. Growing GaN through heteroepitaxy involves carefully balancing multiple parameters, including temperature, deposition rates, and substrate preparation. Small changes can improve or degrade material quality. Without reliable measurement, it is difficult to know whether a process adjustment has made the material better or worse. Metrology provides the feedback needed to refine growth techniques and reduce defect densities.

The second role is in demonstrating material quality. In a market where performance depends heavily on the underlying material, manufacturers must be able to show that their GaN is superior to that of competitors. This requires measurement methods that are not only accurate, but also comparable across organizations. Customers need confidence that a claim about material quality means the same thing, regardless of where it is measured.

The third role is in device performance validation. Ultimately, customers care about how a device behaves in real applications. For RF components, this includes metrics such as power output, efficiency, frequency response, and thermal stability. Linking these device-level characteristics back to material quality is essential. It allows manufacturers to demonstrate that improvements in material growth translate into tangible performance gains.

Across all three areas, measurement is not simply a supporting activity. It is a central part of how competitive advantage is created and communicated.

From materials to systems

The challenges associated with GaN are part of a broader shift in the semiconductor industry.

As devices become more specialized and operate under more demanding conditions, performance is increasingly determined by subtle interactions between materials, structures, and processes.

This is particularly true in RF systems, where small imperfections can have outsized effects on signal integrity and efficiency.

It also connects to a wider trend seen in other areas of semiconductor technology, including photonics. There, heterogeneous integration is bringing together different materials and device types within a single system, creating similar measurement challenges.

In both cases, success depends on the ability to understand and control complexity at multiple scales.

A strategic inflection point

For the RF semiconductor industry, the transition from GaAs to GaN represents more than a simple material substitution.

It marks a shift towards technologies where performance is less constrained by established manufacturing processes, and more dependent on how well new materials can be engineered and characterized. This creates an opportunity.

Countries with strong capabilities in materials science, process development, and measurement can play a defining role in shaping how GaN technologies evolve and are applied successfully in the semiconductor industry.

The ability to measure material quality, correlate it with device performance, and establish trusted benchmarks will influence how quickly GaN is adopted across global markets. This means the future of high-frequency RF semiconductors will not be determined by materials alone.

GaN may offer superior intrinsic properties, but those advantages must be realized in practice. That requires consistent, high-quality material growth, reliable device fabrication, and credible performance validation – all of which depend on effective measurement and standards. This is why metrology will become increasingly central to RF semiconductor innovation, not as a downstream check, but as an integral part of development, manufacturing, and market adoption.

In high-frequency semiconductors, as in photonics, the ability to measure well is no longer just a technical requirement. It is becoming a defining feature of competitiveness.

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Prompt engineering was briefly the face of the AI jobs boom.

In 2025, technology recruitment firm SPG Resourcing reported that UK job listings for AI prompt engineers had grown by 180% over the previous year.

It was an eye-catching figure that captured the mood of the first commercial wave of generative AI. Businesses were trying to understand how to talk to LLMs and turn early experiments into something useful.

That requirement has not disappeared.

With job site postings for specialist AI roles in the UK rising by 61% from last year according to PWC, it’s clear that good prompts still matter. But most of that new demand is for people who can apply AI inside a business, not just talk to a model.

Many organizations have moved past the first demo. They are now trying to build AI agents, retrieval-augmented generation (RAG) systems, and AI-enabled workflows that operate inside the business.

That creates a different skills gap.

This is the shift behind what I refer to as ‘context engineering’. Although that term is not universally used, the capability is becoming essential.

Companies that want useful AI agents and retrieval-augmented generation systems need people who can design the environment around the model, not just the prompt sent to it.

From better prompts to better context

Prompt engineering is about the instruction, while context engineering is about the world around that instruction.

A support agent does not only need a well-written prompt, it needs the right customer record, policy, product history, and permission boundary. Similarly, a developer agent needs the relevant code, tests, dependencies, and deployment constraints.

In both cases, output quality depends on context. Without it, the model is guessing from incomplete evidence. With too much of it, the system becomes noisy and difficult to govern. The job is to make context useful, current, and controlled.

That’s what makes context engineering distinct from prompt engineering.

Agents raise the stakes

The rise of AI agents makes this more urgent. A chatbot with poor context may give a weak answer, but that same poor context may result in another agent making a serious error.

Once an AI system can call tools, query business systems, maintain state, and act across several steps, context becomes an essential part of the production architecture. It decides what the agent can see, what it can do, and how much confidence the business can place in the outcome.

I’m reminded of a joke about boundary testing. A developer walks into a bar and orders a beer, then he orders five beers, then he orders 999,999,999,999 beers, then he orders -1 beer. The bartender blinks, but everything is okay. A user walks into the bar, asks where the bathroom is and the whole bar explodes.

The same principle applies to AI projects. The first prototype may work against a narrow set of examples, then become fragile when it meets real data. Customer information sits in one system, operational data in another, and important knowledge in documents and files. The agent is expected to reason across all of it, but the context layer has not been designed for that job.

A financial services team, for example, may need to connect CRM data, an existing data platform, and internal documents before an agent can answer accurately. The hard work is not only moving the data. It is shaping it so the agent can retrieve the right evidence and stay inside the right permission boundary.

The job title is still catching up

This creates an awkward hiring moment. The need for context engineering is becoming clearer, but the job title is still unsettled.

Some organizations may seek to specifically hire ‘context engineers’, but many will not. The capability is more likely to appear inside roles such as AI engineer, agent engineer, AI platform engineer, applied AI engineer, or data engineer. In other businesses, it will be a team responsibility shared across data, platform, security, and software engineering.

Leaders therefore need to hire for the work, not the label. A candidate does not need to have ‘context engineer’ on their CV to be useful. The better signal is whether they understand how data moves through systems, how permissions are enforced, and how a prototype becomes something reliable enough for production.

This also means the talent pool is wider than many companies assume. Machine learning expertise is valuable, but context engineering draws heavily on existing engineering disciplines. Data engineers understand pipelines and retrieval.

Platform engineers understand operational resilience. Security teams understand access control and auditability. Software engineers understand how to turn messy requirements into maintainable systems.

The best candidates may look like full-stack AI engineers. They do not need to be specialists in every model, database, or framework, but do need enough range to connect the model layer with the business systems around it.

The Kubernetes lesson and what leaders should do now

The shift has a parallel with the move to cloud-native architecture and Kubernetes. Many companies treated Kubernetes as something to install, then discovered that the harder work was changing how teams built and ran software.

AI creates a similar risk. Companies can buy tools and hire a handful of specialists, but still fail to change the engineering habits around them. Context engineering requires teams to think differently about everything from documentation, and data ownership, to access, testing, and accountability.

It also changes the culture of software development. Engineers are already using AI to write, review, and iterate code. That can improve productivity, but it does not remove responsibility. In areas where performance, reliability, or security matter, human judgement becomes even more important.

CTOs and CIOs should not wait for context engineering to become a mature hiring category. They should start identifying the capability now.

The first step is to examine where AI projects are failing. Is the model genuinely weak, or is the system retrieving poor context? Are permissions clear? Can the team explain why the agent produced a particular answer?

The second step is to build cross-functional teams. AI cannot sit apart from data, platform, security, and product. In many cases, the best approach will be to upskill existing engineers who already understand the organization's systems.

The final step is cultural. Engineers need to become fluent in AI-assisted development while staying accountable for the systems they ship. Leaders need to make room for experimentation, but they also need clear standards for review, evaluation, and governance.

The model is not enough

AI hiring is changing because AI itself is moving into production. Models will continue to improve, and businesses will have many ways to access them. The harder advantage will come from knowing how to connect those models to the right business context.

Companies that understand this will build agents and RAG systems that are more useful, safer, and easier to govern. Companies that ignore it will keep blaming the model when the real weakness is the environment it has to work in.

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There has never been a construction race like the one now under way in the data center industry.

AI's demand for compute is driving the largest and fastest infrastructure buildout the sector has ever seen, with hyperscalers and developers racing to bring capacity online faster than power grids, planning departments or supply chains can comfortably keep up.

In that scramble, enormous attention goes to the things visible on a spreadsheet: megawatts, cooling, chips, network and, rightly, cybersecurity.

The dimension that gets quietly deprioritized is the physical protection of the buildings themselves. It is the easiest thing to defer under deadline pressure, and the hardest to retrofit once the concrete is poured.

A regulatory change in the United States is about to make that blind spot worse, and it is worth understanding even if you never operate a US federal facility, because of what it signals.

On 30 September, the Federal Data Center Enhancement Act is due to expire, with no replacement waiting. It set minimum standards for federal data centers, including, unusually, protection against physical intrusion, and it was the operational mandate that forced data-center-specific assessment.

Broader frameworks such as FISMA and the NIST control catalogue still apply, but they provide the principle; the Enhancement Act provided the practice. Principles without a mechanism to enforce them tend to be interpreted generously.

And when the government's own floor is allowed to disappear, the benchmark private operators quietly measure themselves against tends to go with it.

Security baselines

This is not a hypothetical worry about whether the requirement comes back. The Act's predecessor lapsed in 2022 and only survived by being folded into the following year's defense bill. A rule that needs a legislative vehicle to return is one that can quietly fail to, and security baselines rarely erode through a single dramatic decision. They erode through the absence of one: a mandate that simply never gets renewed because nothing forces the issue.

It helps to be concrete about what is at stake, because physical security is not an abstraction. It is the contractor with unescorted access to a hall of servers; the unmonitored loading bay; the maintenance door propped open for convenience; the departed employee whose credential still opens the cage. These are the routes by which data is stolen, infrastructure is sabotaged, and a facility the size of a warehouse is taken offline.

The real exposure is not in the data centers we already have. Established operators keep that spending in place through existing contracts and their own risk appetite. It is in the new builds, the AI-era expansions specced and procured at extraordinary speed, most of them private, built by developers no mandate ever bound. Remove the assessment framework and physical security becomes something that can be scoped down in procurement to hit a budget or a timeline, with no compliance flag and no one formally alerted. The gap opens in the facilities we are racing to build.

There is a contradiction at the center of this. Governments increasingly classify data centers as critical national infrastructure, the UK now does, and rightly so. Reducing their security baseline at the same moment runs in two directions at once. You cannot call something critical and simultaneously make its protection optional.

A sensible fix

The fix is not simply more regulation, though a sensible renewal would help. It is to stop treating physical security as a compliance obligation that rises and falls with the statute book, and start treating it as core design.

The consistent lesson from securing large-scale critical facilities is that physical security fails when it is a collection of disconnected tools, a camera here, an access reader there, bolted on at the end of a project.

It works when it is designed from the start as one integrated system, where access control, video, identity and alarms inform each other and an anomaly anywhere triggers a coordinated response.

Treating the physical and the digital as separate problems is part of how the gap forms in the first place. In a modern data center they are the same problem: a propped door, a cloned badge or a rogue contractor is a cyber incident waiting to happen, and a facility that cannot correlate a door event with an access log or a camera feed will always be reacting after the fact rather than stopping an intrusion in progress.

For operators, the practical implication is simple: the physical security of an AI data center should be specified at the same moment as its power and cooling, not bolted on once the shell is up. Retrofitting protection into a live, fully-loaded facility is far harder, and far costlier, than designing it in.

The AI buildout is a genuine engineering achievement, and the energy and compute challenges are real. But the industry is optimizing hard for the risks it can measure and deferring the one it finds inconvenient. A statute lapsing in Washington should not be what decides whether the buildings holding the world's most critical compute are properly protected.

For IT infrastructure we have all agreed is critical, getting its physical protection right should be a given, not something we quietly leave to whoever is under the most deadline pressure.

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With AI advancing in capability and availability, many scientists and experts have begun highlighting the risks of deploying this emerging technology. There's an overriding desire to deploy AI ethically, or to instill models with human-based values, but it remains to be seen how to standardize such an approach or even whether to regulate the industry. That's where organizations like the Stanford Institute for Human-Centered AI (HAI) come in.

Computer says no

When interviewed for the journal Issues in Science and Technology, the so-called 'Godmother of AI' Fei-Fei Li tried to explain how scientists and engineers can build AI that truly adheres to our values.

Her thesis around the responsible use of AI centers not on anthropomorphizing AI or considering it an intelligence capable of navigating the world and arriving at its own independent conclusions. Rather, it will reflect the values that we instill into it, whether that's through training or continued use of these tools.

She added in this interview that it's also about recognizing that AI, as any tool, can both empower and harm humans.

Values-driven AI

The organization that the American computer scientist leads, HAI, is an interdisciplinary research center that focuses on how best to guide AI development so that it benefits humanity and doesn't incur or lead to harm.

In recent years, there's also been an increased emphasis on whether to regulate AI nationally (or even globally) and what shape that might take. Doing so, however, is far easier said than done, with progress occurring at different paces, in different guises, and inconsistently across oceans.

There are, for example, different values that different nations or groups of people may wish to instill into the AI models they build that clash with the values of others.

We have officially reached the finger-pointing phase of the blockbuster Apple vs. OpenAI lawsuit in which Apple alleges that, with the assistance of former Apple employees, OpenAI is stealing Apple trade secrets.

What the suit alleges, which I outlined here, is pretty damning: A pair of former Apple employees — one a 25-year company veteran and another who spent a decade as an iPhone electrical engineer — allegedly held onto systems, asked OpenAI interviewees from Apple to share proprietary Apple info, and, Apple claims, told partners that sharing Apple's proprietary manufacturing information was okay with the Cupertino tech giant.

OpenAI quickly denied the allegations and, in short order, we learned that Apple's claims that it had contacted Lui Chang about the charges were incorrect because Apple's counsel had mixed up the names of Lui Chang with OpenAI's own counsel, Che Chang.

This week, however, OpenAI offered a more robust response, releasing a text chain between Lui Chang and a current unidentified Apple employee, and the email exchange in which it's revealed that Apple had been talking to the wrong Chang.

"Apple is one of the greatest companies of all time, and built a reputation for obsessing over the smallest details. This careless, aggressive and oddly personal lawsuit sadly doesn’t live up to that reputation," wrote OpenAI on its company blog on Monday.

A cry for help

What the messages appear to reveal is not corporate espionage, but something far more mundane and, in some ways, very relatable if you've ever worked at a large company.

I should preface this to say that Apple has not confirmed these messages, but it's hard to imagine OpenAI fabricating them. In fact, the sheer amount of redacted information gives more credence to their authenticity.

There's so much missing information that it might seem impossible to glean anything from the banter between "Apple Employee #1" and Chang, so, to help myself, I went through and inserted guesses about '[Redacted - Apple Information]" to try and make sense of the conversation. (I decided much of it was about the potential use of Silicon Carbon batteries in the upcoming iPhone 18.)

In the messages, the Apple employees seem to have worked with Chang and were not only missing him, but his expertise.

"Just in case you don’t have enough work, I need some help. I have a vague recollection of you talking about a [REDACTED]. Can you give me a brief refresh of this and point me to an EE who may have some knowledge?" wrote the Apple Employee. I'm assuming he's referring here to an "Electrical Engineer"

Without apparent hesitation, Chang replied, "I think what you referred to is [REDACTED]. And the small [REDACTED]." He then pointed the Apple Employee to someone else at Apple who "might still remember anything."

How about, just say no?

Now, Chang could've said, "Hey, dude, miss you, too, but I can't really talk about anything Apple-related anymore." According to this exchange, Chang did not and kept doing his best to help.

This Apple employee also seems unaware of best business practices and keeps talking about technology, at one point even mentioning a schematic. It looks like he also shared that schematic with Chang, but it may also have been Chang sharing the image with the Apple employee.

As the Apple Employee presses on, Chang tells him to look for a schematic in the "team box folder". He even mentions a "power block diagram." I'm a bit surprised that bit wasn't redacted.

One telling section from Chang jumped out at me, and this is the relatable part. As Chang kept trying to connect Apple Employee 1 to the expertise he required, Chang noted, "Now when I think about it. The other smart people also just left that team. The one that might still [be] in Apple is [Apple Employee #10]."

Brain drain is a real issue, it seems, even for Apple. So in addition to long-timer Chang leaving for OpenAI, others who seem to possess some important technical information have departed as well.

Later, as the quest for information continues, Chang recommended contacting a pair of Apple employees (#5 and #7).

At this point, there's a message that might've come from an automated system inside Apple's communication platform:

"Hi [Apple Employee #1 Name], [Apple Employee #3 Name], I can help get the conversation with [Apple Employee #6] and [Apple Employee #7] started."

Which led to my favorite part of the whole exchange. One of the Apple Employees auto-invited to the conversation essentially freaked out:

"Hi, this is highly irregular, please remove me from this thread."

Honestly, I feel for that person. As I've written before, Apple doesn't mess around when it comes to NDAs, proprietary information, or corporate secrets. Stumbling on a sort of illicit conversation about tech details between a current and former employee is surely enough to make any Apple employee hit the panic button.

Apple Employee #1 explains he added other employees "for awareness" but also shifts the conversation to an internal discussion. Chang is left to like the last message and, it seems, leaves it at that.

What's next?

The release of all this communication does not end the case. Far from it.

As of Monday evening, Apple filed with a US judge a strongly worded request for an injunction that, among other things, calls for "a preliminary injunction to stop the theft of its trade secrets.” It also includes the prediction, "Apple is likely to succeed on the merits of its trade secret claims."

Even so, I think this motion and the OpenAI post may move everything along toward an out-of-court settlement, one that would end the case but likely do little to repair this now-in-tatters Apple-OpenAI relationship.

In the end, though, OpenAI's post appears to reveal a mortal Apple, one that, despite its size and power, is still run by fallible humans who are often adrift, as any of us would be, when colleagues leave, taking with them friendship and, more importantly, knowledge.

Apple is building complex products, and corporate memory is a critical part of the process (so much so that incoming CEO John Ternus apparently just rehired a retired Apple hardware executive).

Understanding when, how, and why decisions were made and how they could impact future projects is invaluable. Sometimes, only those who were in the room know the truth of the matter. Chang, it appears, was one of those people. He had the information, took it with him, and now, it seems, someone wanted to keep the lines of communication open, which is exactly what Apple doesn't want.

For many enterprises, virtualization has become so foundational that its strategic importance is easy to overlook.

Hypervisors, management tools, and the surrounding infrastructure stack quietly support the systems that run finance, operations, customer engagement, supply chain and other business-critical processes.

When that layer changes, the impact is rarely confined to IT.

It can affect cost, resilience, agility and the organization's ability to set its own technology roadmap.

That is why the latest shift in the VMware ecosystem deserves close attention from technology leaders.

With vSphere 7 reaching the end of general support and newer platform models emphasizing bundled, subscription-based private cloud architectures, many organizations are being pushed into a compressed decision cycle.

The question is no longer simply which version to upgrade to. It is whether a vendor-driven platform shift aligns with the business, operating and financial outcomes the organization is trying to achieve.

That distinction matters. A version upgrade is a technical project, but a virtualization strategy is a business decision.

And business decisions should be guided by requirements, value and timing, not simply by changes in a vendor’s roadmap.

Don’t confuse urgency with strategy

Support deadlines have a way of focusing attention. They also have a way of narrowing choices. When a critical platform approaches the end of vendor support, the instinct is often to move quickly toward the recommended next step. In some cases, that may be the right decision. In others, it can lead to organizations into unnecessary cost, disruption and loss of flexibility before they have fully evaluated the alternatives.

The risk for IT leaders is treating the vendor product roadmap as if it were automatically the enterprise roadmap. The two may align, but that alignment should be proven, not presumed. A vendor’s timeline is not, in itself, a business case for broad infrastructure change.

A vendor’s priorities are shaped by product strategy, recurring revenue, portfolio simplification and platform consolidation. An enterprise’s priorities are shaped by uptime, security, cost control, application performance, operational continuity and business agility. Those priorities can overlap, but they are not identical and treating them as interchangeable can result in decisions that solve for the vendor’s direction more than the customer’s needs.

This is where CIOs and infrastructure leaders need to step back and ask a more fundamental question: what business outcome are we trying to achieve, and does this change materially advance it?

If the answer is improved resilience, better automation, simplified management, stronger security or a more cloud-like operating model, then modernization may be justified. But, if the answer is simply maintaining continuity or preserving support status, organizations should examine the full range of viable paths before committing to a major platform shift. The right response is not always the most disruptive one.

The cost question is bigger than licensing

Pricing changes often capture the most attention, but the true cost of an infrastructure change extends well beyond the license or subscription line item. In many cases, the larger costs are organizational: the time, risk and disruption that accompany a broad platform transition.

Virtualization platforms sit at the center of complex environments. A major change can trigger downstream work across storage, networking, backup, monitoring, disaster recovery, security tooling, compliance processes and application dependencies. Even when the technical migration appears manageable, the operational validation can be extensive, and the burden on teams can be significant.

Organizations should consider the full cost of change, including staff time, retraining, integration testing, potential downtime, consulting support and hardware implications. Just as important is the opportunity cost. Every major platform transition consumes budget, leadership attention and skilled talent that could otherwise be directed toward security improvements, automation, AI initiatives, customer-facing innovation or other strategic priorities. A more bundled platform may simplify some parts of the stack, but it can also introduce new commercial and architectural constraints.

This is particularly important for enterprises running stable, heavily integrated environments. Many virtualization estates have been tuned over the years to support specific workloads, service levels and operational requirements. Stability is not a weakness to be corrected by default; in many cases it is a business asset. Replacing or restructuring that environment should be justified by measurable business value, not just by pressure to conform to a new delivery model.

The three choices: migrate, modernize or maximize

Most organizations facing a virtualization decision have three broad options, and the right answer may differ by workload, business priority and time horizon.

The first is to migrate. This could mean moving to the vendor’s latest platform, shifting workloads to a hyperscale cloud provider, adopting an alternative hypervisor or pursuing a hybrid architecture. Migration may be the right choice when the current environment no longer supports business needs, when hardware refresh cycles align with broader transformation goals, or when the organization has a clear cloud operating model, budget and execution plan for the next state. But migration should be a deliberate strategic move, not a reflexive response to a platform event.

The second is to modernize in place. This approach keeps the core environment intact while improving the capabilities around it. That may include stronger automation, better observability, improved security controls, more resilient backup and recovery, tighter cost management or more intelligent workload placement.

For many enterprises, modernization does not require a wholesale migration. It requires identifying the gaps that matter most and addressing them with targeted investments. Modernization and migration are not the same thing, and organizations should be careful not to treat them as if they are.

The third is to maximize the existing environment. This option is often overlooked because it sounds less transformational, but it can be the most rational business decision when a platform is stable, secure, performant and well understood. Extending the value of an environment that continues to meet requirements is not inertia; it is intentional lifecycle management. If the platform remains fit for purpose, the better decision may be to maintain it effectively while redirecting budget and talent toward higher-value initiatives.

The right answer may include elements of all three. Some workloads may be ready for cloud migration. Some may benefit from in-place modernization. Others may be best left alone because they are stable, cost-effective and do not justify major reinvestment. The goal should not be uniformity for its own sake, but alignment between each workload and the business value it is expected to deliver.

Start with business requirements

Before committing to any virtualization path, CIOs should bring the conversation back to business requirements. What systems does the platform support? What uptime is required? Which workloads are growing, and which are stable or declining?

What regulatory or compliance obligations must be met? Where does the business need more agility, and where is predictability more important than change? Those are the questions that should shape the roadmap.

From there, organizations can conduct a practical assessment:

1. Which systems truly need to change now for security, compliance, performance or support reasons?

2. Which workloads can remain in place with the right resilience, security and operational controls?

3. Which applications are real candidates for cloud migration, and which are not?

4. Where would this decision increase lock-in or weaken negotiating leverage?

5. Which investments will deliver measurable business value over the next three to five years?

6. What strategic initiatives will be delayed if budgets and talent are redirected to this transition?

This kind of analysis helps technology leaders avoid making decisions based on urgency, assumption or vendor pressure. It also gives CFOs, boards and operating leaders a clearer view of the trade-offs, including where change is necessary, where it is optional and where it may create more disruption than value.

Control the roadmap before the roadmap controls you

The vSphere 9 era is part of a broader shift in enterprise technology: vendors are consolidating platforms, simplifying portfolios and steering customers toward subscription-based operating models.

That approach can offer advantages, including more integrated tooling, simplified procurement and a more standardized operating model. But it can also come with trade-offs, including higher costs, reduced flexibility and greater dependency on a single vendor’s pace and direction of innovation.

Virtualization remains too important to be managed as a reactive upgrade cycle. It underpins mission-critical operations and increasingly shapes hybrid cloud strategy, resilience planning and long-term infrastructure economics. Enterprises should treat it with the same strategic discipline they would apply to any other decision that affects business continuity, cost structure and future flexibility.

As infrastructure models evolve, the message for CIOs is simple: don’t let a support deadline become your strategy. Use it as the moment to define one based on business requirements, financial reality and the level of change the organization needs.

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In a monoculture, a single species or type dominates a system, to the exclusion of all others.

The term comes from agriculture, when a field is planted entirely with one crop. It may be efficient and easy to manage, but a single disease, pest, or environmental change can wipe out the entire yield.

Different varieties have different vulnerabilities, so a field of diverse crops can contain the damage naturally. A monoculture has no such defense.

That same monoculture condition is now occurring in software development.

Many organizations using AI tools for coding rely on a single AI model to both generate code and review it. On paper, this is efficient. But in reality, it creates a closed loop where the reviewer can't catch things that the generator is blind to.

Security flaws go undetected in reviews and suboptimal architectural decisions go unchallenged because the reviewing system shares the same assumptions as the system that produced the code.

When issues arise, engineers look at recent commits, failed deployments, and configuration changes. The assumption that a single AI platform can objectively review its own output is rarely questioned.

The solution to an agricultural monoculture is to introduce different crop varieties with different vulnerabilities, so that no single threat can devastate the entire field. The same is true in AI coding monocultures. Organizations need to introduce AI tools that are genuinely independent of the systems they're reviewing.

Separation of duties

So, why can't the same AI just review its own work?

Asking a coding assistant to review itself is like asking a human to proofread their own writing. The human may catch typos and grammatical errors, but they often won't catch logical inconsistencies or arguments that don't hold up.

This is because the human writer often subconsciously reads what they meant to write rather than what is actually on the page. A model reviewing its own output does the same thing; it evaluates the output against the same patterns and objectives that produced it in the first place.

Similarly, an AI model trained on the same data and optimized for the same signals as the model that wrote the code will carry the same blind spots. It will catch what it's capable of catching, and miss the things that the generating model missed.

Finance figured out this separation-of-duties principle long ago. If the same person who initiates a transaction can also approve it, that opens the door to fraudulent or erroneous transactions. The approval step only works if the approver is independent of the person who initiated the transaction.

Likewise, organizations must introduce a dedicated review tool that operates independently of the system that generated the code. Choosing a different vendor won't automatically solve the problem if the underlying models share the same training data or architectural assumptions.

Genuine independence requires different training data and different signals, and the tool must specifically be built for scrutiny, not completion.

Defense-in-depth

A dedicated review tool is a strong starting point, but separation of duties alone isn't enough. The same principle that argues against a single AI doing everything also applies within the review function itself.

Code review is not a single task. Finding bugs, enforcing standards, assessing risk, and understanding how a change fits into the broader system are separate forms of reasoning, even when they appear in the same pull request.

Asking one agent to handle all of them at once forces tradeoffs between depth, speed, and coverage. In other words, some things will get less attention than they require.

Just as a mature security architecture layers independent controls so that what one misses another catches, a mature review architecture assigns distinct responsibilities to systems optimized for each one.

In practice that might mean one agent focused specifically on security vulnerabilities, another enforcing architectural standards and coding conventions, a third assessing the blast radius of a change, and a human reviewer making the final judgment call on anything flagged as high risk.

Each layer asks different questions and operates on different signals. The goal is ensuring that no single blind spot, including those shared across an AI monoculture, can let a problem through unchallenged.

The shifting role of platform engineering

As AI generates a larger share of production code, organizations will need to designate clear ownership over which AI does what, and ensure the review function doesn't get quietly collapsed into the generation function in the name of efficiency. That responsibility will increasingly fall on platform engineering teams.

The role has traditionally been about making developers more productive by simplifying tooling, maintaining infrastructure, and reducing friction. That work hasn't disappeared, but as developers move from writing code themselves to directing agents that write it for them, platform engineers will need to govern the systems that generate code, not just maintain them.

That means owning the standards those agents follow and ensuring visibility into what’s actually happening across the codebase. Most importantly, it means treating code generation and code review as distinct functions that require distinct capabilities, and resisting the organizational pressure to consolidate them into a single platform because it's simpler to manage.

Organizational memory

As AI handles more of the writing and reviewing, accumulated institutional knowledge gets lost. Why was that architectural decision made two years ago? Which parts of the codebase affect each other in ways that aren't obvious? What broke before and why? AI coding tools don't carry any of that context. They look at the task in front of them, complete it, and move on.

This kind of institutional knowledge used to live in the heads of the engineers who wrote and reviewed the code. As AI takes over more of that work, it needs to be captured in places such as documented standards and recorded review decisions or it simply vanishes. When organizations build that kind of memory into their development process, their systems get smarter about their codebase over time.

That means a review function that doesn't just flag problems in isolation, but learns the organization's standards, remembers past decisions, and understands how different parts of the codebase connect and depend on each other.

Getting ahead of monoculture risk

The AI monoculture risk is what happens when no one asks whether the system reviewing the code is genuinely independent of the system that wrote it.

Microsoft CEO Satya Nadella recently argued that the real opportunity in AI is not in picking the best model, but in building a learning loop where human knowledge and AI capability compound together, and that a company should be able to swap out a generalist model without losing the expertise built into its own systems.

That's only possible if the governance infrastructure is built at the same pace as the generation capability.

The organizations that do that early will be in a much better position than those that wait until something breaks.

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As telemetry becomes central to how modern platforms are built, trained, and automated, its perceived future utility has driven a dramatic increase in collection and retention. A recent study shows telemetry volumes tripling in many enterprises over the past year, with agentic AI expected to drive nearly 10x more growth expected within the next two years.

Clearly, telemetry is starting to look less like infrastructure and more like capital. The mistake is treating it as a pure asset and overlooking the other side of the ledger. Exploding log volumes and rising costs are the most tangible inconveniences, and often the first triggers for concern, but they are merely symptoms.

The bigger problem is that telemetry gradually becomes a system of knowledge that exceeds an organization’s ability to understand, govern, or explain it and, therefore, to reliably bound its cost, risk, and downstream use.

When AI systems both produce telemetry and consume it

Traditional telemetry was largely retrospective. It described what happened. In AI, it starts feeding back into the system. A session log captured to troubleshoot a crash today may become training data tomorrow, evolve into a model feature later, and eventually drive automated decisions without human supervision.

In essence, the same data serves multiple functions throughout its lifecycle, many of which have little to do with why it was collected in the first place.

This sets a loop in motion. The system produces telemetry, the AI consumes it, which produces new signals and predictions, and those create an appetite for still more telemetry. As a result, the value organizations place on data keeps growing, often ahead of any clear understanding of how it will be used.

Once telemetry becomes a form of organizational memory over time, you are past just discussing observability. You are now dealing with governance, cost, and control.

Telemetry retention is fundamentally biased by asymmetry

It’s easy to justify the seemingly “small cost” of storing another terabyte when weighed against the hypothetical cost of discarding it, which can seem enormous, because someone can always argue that the data you threw away might have been a golden ticket.

The prospect of regretting its deletion feels potentially irreversible. Could it have solved a problem? Could it have trained a model or shown you an opportunity you missed? Caving to uncertainty, most organizations just keep everything.

The flaw in this reasoning is a myopic focus on the asset that ignores its liabilities. Every retained dataset carries ongoing storage and governance costs, security and compliance obligations, and discovery risk if you ever land in litigation. Each new dataset can also be combined with existing ones, amplifying its informational value in ways that were never foreseen.

Is there a guarantee that organizations will never regret a deletion? No, but there can be a clear rationale. You knowingly forgo some option value because the expected benefits of keeping the data do not outweigh the costs and risks of holding it. That is a decision you can stand behind later, even if it turns out the data might have been useful.

Asking whether something could be useful someday is not helpful, because almost anything clears that bar. It’s better to ask what specific capability you are keeping it for. If you have a clear answer, retain the data and govern it accordingly.

For CTOs, the blind spot is treating telemetry growth as a scaling problem

As important as ingestion pipelines, storage, query speed, and tooling are, what often catches people off guard is that telemetry turns into a body of knowledge that no single person in the company actually understands. While most teams can tell you where their data lives, far fewer can explain what it reveals once you start putting it together.

The math here is quite unforgiving because exposure does not grow one stream at a time. Every new source can be matched against every source you already have, so the number of possible combinations spirals into something no longer tractable.

Consider clicks, session length, support tickets, device IDs, login records, or approximate location – on their own, none of it is sensitive, and nobody thinks much of collecting any of it. But put them together, and you can reconstruct someone’s daily routine, flag changes in their financial behavior, or predict upcoming life events.

The sensitivity is not attributed to any single stream but is born out of correlation across streams. In reality, most organizations have never fully explored what those correlations could actually enable.

For a CTO, the thing to worry about is not the size of the data, but whether you can still explain what your organization knows and where that knowledge came from. Give every stream an owner, a stated purpose, and a date it expires. If a stream cannot answer what it improves, then it has no business being retained indefinitely. A rule like that will do more to reduce risk than any amount of clever storage engineering.

For CFOs, the blind spot is filing telemetry under infrastructure cost

What makes telemetry different from other infrastructure is that it compounds. More telemetry produces more analysis, which creates new use cases, which extend retention and increase demand for more storage, processing, tooling, and oversight. Before long, what started as a small storage expense has become an ongoing commitment.

The next year’s cloud bill may seem like the thing you should brace for, but at least you can put a number on it. What’s more concerning is that telemetry can become an ever-growing liability with unpredictable cost, risk, and duration. Unglamorous as it may be, the fix is down to the same thing – focus on the bounded business outcomes it produces.

Can you take any category of telemetry you hold and say plainly why it exists, what value it earns, how long it should live, and what happens to it when it stops being useful?

What would a bounded telemetry architecture look like in an AI-driven world?

It starts by overturning the most basic assumption that you have to collect and keep everything until it turns out to be absolutely impractical. Instead, assume most things should not be collected and that nothing stays forever unless there is a reason for it to.

The effect is a change in default behavior. When every stream is set up with a clear purpose, owner, retention policy, and expected outcome, data starts following a lifecycle instead of piling up. Raw events might exist briefly at full detail, collapse into aggregates after that, and, once they are no longer useful for decisions, eventually disappear.

Thinking in lifecycle terms reframes data as something that interacts across systems rather than existing as isolated stores. That’s why the boundary you draw is not around a single database, but around what the combined system is allowed to infer and act on. What you collect should be based on the decisions you want to improve.

The complication in an AI setting is that these systems do need substantial telemetry to work, so it is not as simple as collecting less. The challenge is to collect enough while still keeping control over what the system learns from and what it ultimately produces.

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The use of AI across finance functions is soaring as it quickly becomes a key tool for getting the job done.

As such, businesses are investing heavily and spending continues to rise, meaning adoption has more than doubled since 2024.

But governance isn’t keeping pace, as almost half (49%) of UK finance leaders admit their organization has gaps in its AI governance strategy.

That’s a concern for two reasons. One, because governance is a compliance exercise, and two, because it underpins how confidently businesses can adopt AI at scale.

Without clear guardrails, employees will naturally start making their own decisions about which tools to use and how to use them – creating ripe conditions for Shadow AI to emerge and thrive.

The widening adoption-governance gap

I speak from experience when I say that AI is and will continue to be transformative for the finance function.

And for an industry that is largely accepting of the tried and tested status quo, it’s genuinely encouraging to see how positively leaders in the finance space view AI. Our research showed that 8 in 10 (83%) believe it will play an important role in helping them achieve their business goals.

What’s less encouraging, and somewhat worrying, is that almost a quarter (23%) say they have little to no AI governance measures in place. And that disconnect really matters.

Too often, governance is viewed as something to tackle only once adoption of new technology is well underway. But it has to be built alongside adoption. There’s often a fear, not always unfounded, that governance can slow innovation. But that’s not always a bad thing, because the point of governance is to make sure innovation happens safely and in a way that business can measure and trust.

Without it, AI adoption can quickly become fragmented, increasing a business’ exposure to compliance and security risks that will only intensify as AI becomes more deeply embedded.

When processes create friction, people will find another way

While governance gaps are directly linked to organizational risk, they also shape employee behavior. If approved tools are difficult to access, limited, or policies aren’t clear, people will look for another way to get the job done. Employees as a whole want to embrace the productivity benefits of AI and won’t let a lack of formal guidance stop them.

And that’s exactly what research tells us.

More than a quarter (27%) of UK employees admit to purchasing AI tools for work without approval in the last year. More broadly, 67% say they regularly bend rules or find loopholes to access company money, while 27% report missing business opportunities because of delays accessing spending.

These findings aren’t suggestive of employees deliberately trying to undermine company policy. More often, it’s a sign that existing processes aren’t keeping pace with the way people want to work.

That’s where shadow IT starts to emerge.

Shadow AI is a symptom of a wider governance problem

It’s easy to think of Shadow AI as the problem itself. But in reality, it’s usually a symptom of something bigger.

The concern is that this unchecked use of AI can lead to data leakage, compliance failures, poor record-keeping and inconsistent decision-making. So, it’s an important problem to nip it in the bud before it spirals out of control.

When employees feel they need to work around approved processes to stay productive, businesses quickly lose visibility over which AI tools are being used and how company data is being handled. Finance teams can quickly lose track of where money is being spent.

That creates a practical challenge for finance leaders, while businesses can find themselves managing duplicate tools, fragmented AI adoption, unmanaged spend and inconsistent governance. Exposure to scrutiny and compliance risks can also increase exponentially.

The longer those issues go unaddressed, the harder they become to unwind.

Good governance enables AI

The goal is never to slow AI adoption or place unnecessary barriers in front of employees. When governance is implemented well, it makes the approved route the easiest and best way to support employees in new, more productive ways of working.

It means making sure employees have access to tools that help them work effectively, putting clear policies in place for how to use them, and making it clear what’s expected.

When governance supports productivity and innovation, helping employees to address areas of friction in their roles instead of adding to it, they’re far less likely to look elsewhere for solutions.

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When we wrote about the “AI speed trap” in the software development process for TechRadar Pro a year ago, the central concern was whether faster delivery would come at the expense of quality. A year on, what we’re seeing are actually deeper issues around trust, governance and organizational alignment.

As AI-generated code becomes more routine and ecosystems become more complex, the challenge is no longer simply keeping pace with delivery. It is deciding who owns quality, how much risk is acceptable, and what confidence looks like in an AI-enabled environment.

AI has become firmly embedded across the software development lifecycle, accelerating delivery at an unprecedented rate. But the real story is no longer just speed. Organizations are now being forced to make more explicit decisions about where quality ends and acceptable risk begins, and to reconcile trade-offs around testing, trust and accountability.

AI is accelerating software delivery, but quality strategies are falling behind

Nobody would dispute that AI has transformed software quality over the last year. AI-assisted coding, automated testing and increasingly autonomous workflows are no longer experimental; they are becoming standard practice.

Nearly seven in ten organizations have now implemented AI tools across at least some of their software delivery workflows, and almost half have fully embedded it into their development environments.

The impact on productivity is undeniable. Development teams can generate code faster than ever, automate repetitive tasks and accelerate release cycles that once took weeks into days, or even hours. But the ability to create software has advanced far more quickly than organizations' ability to validate it.

As a result, 60% of teams are knowingly releasing untested code. Not accidentally, as our 2025 research showed, but because they are under pressure to ship faster, and because the sheer volume of code is outpacing what many teams can realistically test. In other words, these are conscious business decisions. Organizations aren’t even trying to test everything; they’re deciding what they can afford not to test.

As software delivery accelerates, quality has become less about eliminating every defect and more about determining which risks are acceptable. But that requires stronger governance, clearer accountability and a shared understanding of what constitutes an acceptable release.

The definition of quality is changing

In an AI-driven world, organizations will never be able to test every line of AI-generated code. While AI has removed many of the bottlenecks associated with writing code, it hasn't created more time to validate it. Traditional quality models were designed for an era where development speed largely dictated testing speed. That assumption no longer holds.

The answer, however, isn't to lower the bar for software quality. It's to rethink what quality actually means. Rather than attempting to preserve old approaches by asking teams to test everything, organizations need to rethink what software quality actually means.

Increasingly, success will depend on understanding where quality matters most. Which applications are business-critical? Which customer journeys carry the greatest commercial or regulatory risk? Which changes genuinely require exhaustive testing, and which can be validated through risk-based approaches?

In other words, software quality is becoming intelligence-led rather than activity-led. The focus is shifting from test volume to confidence in release decisions, based on how well organizations can identify and manage the risks that matter most.

Why governance, visibility and continuous validation are becoming the new foundations of software quality

As organizations move towards risk-based quality models, a critical gap is emerging: how those risks are actually governed in real time.

In traditional software delivery, governance was often applied at fixed stages. In AI-accelerated environments, that model breaks down. Code is generated continuously, pipelines move faster than human review cycles, and release frequency has increased beyond what stage-gated governance was designed to support.

This requires three structural shifts.

First, visibility becomes foundational. Without real-time insight into what AI is generating, what is being tested, and where coverage gaps are emerging, organizations are effectively making risk decisions without a complete view of the system.

Visibility is the precondition for trust, allowing leaders to understand not just whether software works, but whether it is being adequately validated as it evolves.

Second, governance must move closer to code creation. Instead of acting as a final checkpoint before release, governance increasingly needs to be embedded earlier in the lifecycle, where risk is first introduced. This means bringing quality signals, compliance requirements and risk thresholds into the development process itself, rather than evaluating them retrospectively.

Third, continuous validation replaces discrete testing phases. As AI increases both the speed and volume of code production, organizations are shifting from episodic testing cycles to always-on validation. Rather than asking whether software has been tested, the more relevant question becomes whether it is being continuously validated against the organisation’s risk appetite as it changes.

In AI-driven environments, confidence in software quality depends less on point-in-time approvals and more on the ability to observe, measure and validate quality as it is being created.

Organizations are making deliberate decisions about risk

Software quality has always involved balancing competing priorities, but AI is making those trade-offs more explicit, with “good enough” increasingly defined at the intersection of engineering constraints and business pressure.

The challenge is that organizations don’t always agree on what that looks like. Nearly half report only partial alignment between executives and software teams on what high-quality software actually looks like, suggesting that release readiness is increasingly open to interpretation rather than governed by consistent standards.

This also explains a key finding in our data: that while more than nine in ten C-level executives express confidence in their testing strategies, almost one-third of quality assurance (QA) and DevOps leaders remain uncertain that those strategies adequately address the most critical software risks.

That gap reflects fundamentally different perspectives. Executives naturally focus on strategic outcomes and business velocity, while engineering teams experience first-hand the operational realities of validating increasingly complex software systems.

Closing that gap will become increasingly important as AI continues to accelerate software delivery. Trust cannot exist if different parts of the organization operate under different definitions of quality or acceptable risk.

Software quality has become a business decision

For years, software quality was viewed as a technical responsibility - a discipline concerned with finding defects before customers did. That is no longer sufficient.

When software underpins customer experiences, regulatory compliance and critical business operations, decisions about quality inevitably become business decisions. Shipping software with known gaps can no longer be merely a technical compromise; it represents an organizational risk assessment.

The consequences extend well beyond development teams. Poor software quality can increase technical debt, introduce security vulnerabilities, create compliance challenges and erode customer trust. Many organizations are already attributing hundreds of thousands (if not millions) of pounds to the downstream impact of software failures and rework.

At the same time, governance has struggled to mature at the same pace as AI adoption. While many trust agentic AI to make release decisions, significantly fewer believe they are ready to do so at scale. That disparity between adoption and oversight is likely to become one of the defining challenges of the next phase of AI-enabled software delivery.

Ultimately, software quality is no longer something that can be delegated solely to engineering teams. It requires leadership alignment, shared accountability and governance that keeps pace with increasingly autonomous development.

Conclusion

AI has accelerated software delivery to the point where existing approaches to testing and governance are struggling to keep up. The organizations most likely to succeed are those that make deliberate, well-governed decisions about risk and build trust into the software delivery lifecycle rather than treating it as a final checkpoint.

Software quality can no longer be just an engineering discipline. In the AI era, it has become a leadership responsibility.

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AI spending is forecast to reach $40.74 billion by 2030.

While AI adoption is accelerating on both sides of the Atlantic, UK and US retailers are taking differing approaches. US organizations are using AI to unlock new revenue opportunities and reshape the customer experience. UK retailers have largely focused on what AI can do internally.

There are understandable reasons for the differing approaches. But UK retailers risk falling behind if they don't broaden their strategy to focus on embedding AI across the full customer journey.

The Transatlantic AI divide: Operational efficiency vs revenue growth

For UK retailers, early AI adoption has been cautious. The focus has been on internal operational efficiencies, such as creating marketing content, handling surface-level customer service and answering post-purchase queries.

US retailers, on the other hand, are embedding AI directly into the shopping journey itself. A third of US adults now use AI agents when shopping online, such as Amazon’s Rufus, to discover or research products. In-chat checkout experiences are becoming increasingly popular. US retailers are positioning AI more like a digital sales assistant than a back-office tool.

While both approaches have their merits, there is a risk UK retailers get left behind. The gap won’t happen overnight. It will emerge quietly, starting with share of attention and then showing up in revenue.

Global retailers using AI for discovery, conversion, and lifetime value will start owning key decision moments. UK brands focused on operational efficiency may find themselves absent from the journeys that matter most.

Compliance vs experimentation

UK retailers didn’t arrive at caution arbitrarily. Regulation and the consumer expectations shaped it. GDPR means shoppers expect clear consent, transparency on data use, and control before engaging with AI-driven experiences.

That’s why many UK retailers started with safer operational use cases, including automating support, enhancing content and streamlining fulfilment, before fully reinventing the ecommerce journey. Explainability and trust came before experimentation.

In the US, fewer regulatory guardrails made it easier for retailers to test AI tools publicly, iterate quickly and showcase value directly to consumers. The UK’s position isn’t a weakness. It’s a different starting point.

By ensuring customer-facing AI functions are both compliant and secure, UK retailers can both unlock new revenue and strengthen consumer confidence, rather than risking the need to roll back features due to compliance concerns.

Turning AI into a commercial growth engine

The faster US adoption has been less about technology and more about how the internal conversation is framed. The boardroom conversation isn’t “How do we save cost?” — it’s “How do we acquire smarter, convert faster and grow lifetime value?”.

US retailers treat AI as a growth lever across the full customer journey, from personalizing discovery and optimizing pricing, to guiding promotions, streamlining checkout and automating lifecycle communications. AI is framed as a revenue driver, not an operational nicety.

UK retailers have strong operational foundations. The shift is about pointing that discipline toward growth, opt-in recommendations and assisted checkouts. Controlled experiments that build insight and customer confidence at the same time.

For these experiments to work, the product data underneath them has to be ready: rich specifications, reviews, imagery and supporting content that gives AI something real to reference during conversational discovery and search.

Predictive personalization can prioritize high-value customers and surface relevant offers. Controlled agentic shopping experiences build familiarity and insight over time. Dynamic pricing and AI-driven lifecycle communications improve both conversion and retention. None of this requires abandoning the trust UK retailers have built.

The risk of falling behind may be gradual, but it is real

UK retail has the foundations. Now they need to be built upon.

Customer acquisition costs will rise if competitors are winning the discovery moment first. Conversions will happen earlier – and elsewhere – if the journey isn’t being shaped. Lifetime value will lag as AI-driven retention compounds faster for the brands that moved sooner.

Cumulative gaps are harder to close than visible ones. The retailers that combine strong governance and trusted data practices with genuine customer-facing AI innovation are the ones that will compete well in what comes next.

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Democratization may be the AI industry's favorite word, and one that, in fairness, it has earned the right to use.

At the beginning of the decade, building anything with machine learning meant a large research team and a significant budget, and that put it out of reach for many.

Today, however, the barrier to using advanced AI has fallen almost entirely, and what used to be a quiet technology in the background has been thrust to the forefront of everyday work and everyday conversation.

But access is just the surface layer. Underneath it sits another layer which, arguably, matters more than access to AI itself, and that layer is training.

Unlike access, model training has not been democratized at all. The industry has made it easy to consume a model and almost impossible to make one.

This needs to change.

A gatekeeper to innovation

If we look closely at what "democratization" has really delivered thus far, we see that what has opened up is only consumption. Anybody can now interact directly with an AI model, even without a technical background, and can build an app in an afternoon with minimal effort. Open-weight releases mean anyone can even run a model on their own hardware, free of an API. These are genuine advances that have helped define the AI-powered world we now live in.

What has not open up is the ability to train a model. Currently, producing a frontier model still requires capital measured in billions, scarce specialist talent that only a handful of labs can attract and retain, vast datasets, and retraining cycles that have to be repeated every six to 12 months just to stay current.

It is a cycle that is stifling progress, and a barrier that no startup, cash strapped healthcare organization, or mid-sized manufacturer can clear. It functions as a gatekeeper to innovation, and it is one that well-funded organizations have every commercial incentive to keep locked, because the scarcity of training capability is precisely what protects their market position.

There is a dichotomy. The cost of using a model has fallen toward zero. The cost of making one has climbed toward the limits of what private capital can sustain.

Can open source help?

Some might argue that the answer is to turn to open-weight models. Open weights, the argument goes, solve the ownership problem. You can download the model, run it, fine-tune it, and deploy it without asking anyone's permission. This is not democratized training though. It may be closer, but owning the weights is not the same as owning the model itself.

An open-weight model remains the frozen output of a training process run by the organization that produced it. What users receive is the result of that process. It is true that they can somewhat adjust the model. But doing that effectively demands expertise, compute, and clean data. And as the world moves on, the model ages and retraining is needed.

So despite their advantages on the surface, open weights do not offer users the ability to produce and continuously improve a model. That ability is still held by the labs that produced it.

The change that could revolutionize innovation

In my view, if democratization reaches the training layer and allows users the genuine ability to create, train, and own a model, several important structural changes follow that could meaningfully accelerate innovation across industries.

Ownership survives the vendor. If a model is trained by its user on the data they own, the weights are theirs without licensing restrictions, it will not evaporate if the company that gave them the tools to build it disappears. The relationship stops being a subscription. That single change alone removes the dependency the current market is built upon.

Models must learn continuously. If training is something that can be done continuously rather than a cyclical and centralized event, the model does not have to freeze at deployment. It can keep learning from new data in production without expensive retraining cycles and without a team of specialists being needed to run it. The workarounds become unnecessary because the underlying limitation is gone.

When control of what a model learns and how occurs at user level, the chain of responsibility is legible. That is the kind of traceability regulators are expecting and that black-box frontier models simply cannot provide.

These are the distributed benefits that make "democratization" the right word at last. If the ability to train a model is widely held, then the value, control, and responsibility are widely held too. General Learning Intelligence treats learning as part of the architecture rather than a cost only the largest labs can bear.

Access to models is already cheap and getting cheaper. However, access to training is not and that is where the next phase of competition will be decided.

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Artificial intelligence (AI) is reshaping the scale and complexity of data center infrastructure.

Traditional data center facilities were designed around relatively steady CPU workloads and predictable growth in power demand, allowing developers to secure energy supply alongside growing demand, cooling systems based on known and mature technology and infrastructure capacity with a reasonable degree of certainty.

AI workloads, however, demand far more power with greater energy density.

Electricity consumption from data centers has grown at 12% per year over the last five years and expected demand growth, particularly in AI training data centers, is set to drive a substantial increase in power demand.

Meeting this demand, while maintaining efficiency, is pushing developers toward gigawatt-scale data centers and with the timeframe for delivering these facilities rapidly compressing, the ability to deliver new infrastructure efficiently is increasingly critical.

In addition, as the scale of these developments grows, so does the complexity of delivering them. Grid interconnections can delay timelines by years, equipment supply chains are stretched, and projects must meet stringent reliability targets while navigating regulatory, environmental and community requirements that vary by region and country.

For owners and developers, the challenge is no longer simply constructing another data center building. The next generation of AI data centers requires a fully integrated approach across power, cooling, transmission, water, digital systems and long-term operations. Success depends on designing these facilities as resilient, flexible and energy-optimized industrial campuses.

Balancing site trade-offs to unlock faster delivery

Site selection is one of the clearest expressions of this dynamic where teams are typically assessing a series of imperfect options, each with its own advantages and constraints. For example, one site may offer lower cost land but lack the existing infrastructure required to support large scale development, while another may provide access to grid power but at a significantly higher cost or with timelines that delay delivery.

In practice, few locations offer everything required, and selecting a site becomes an exercise in understanding what should be prioritized, what can be mitigated, and what must be accepted.

Factors like water availability, land constraints, fiber connectivity, permitting timelines and social license to operate are all deeply important to success. Developers must consider how to optimize within these constraints. Where grid power is unavailable or delayed, for example, off grid or hybrid energy solutions may be introduced.

While these approaches can accelerate delivery, they also bring different capital requirements, financing structures, and operational considerations that must be carefully weighed.

Combining power solutions can accelerate bringing capacity online more efficiently

As AI workloads drive unprecedented levels of demand, power strategies also require a reassessment against expected scale timelines. Grid supply does offer lower long-term energy costs, stability and resilience advantages eventually but hinges on capacity constraints, and extended interconnection timelines.

In contrast, behind the meter generation, such as gas turbines or reciprocating engines, can be deployed more quickly and provide greater operational control. This, however, comes with higher upfront capital requirements, higher operational costs, fuel dependencies and more complex permitting considerations.

As speed-to-market is a key competitive driver, many large-scale developments are willing to pay a premium for off-grid or hybrid architectures, including battery storage and integration of renewables where accessible.

These systems are coordinated through microgrid controls, allowing operators to manage load variability, maintain resilience through islanding, and optimize overall system performance. The final configuration is shaped by how factors such as time to market, grid availability, resilience, and overall cost evolve.

Rethinking cooling can support high-density AI and optimize when energy is used

With this increase in power demands comes a corresponding increase in heat generation. The physics and economics of air cooling are struggling to keep pace with the thermal loads generated by AI workloads, forcing a shift toward alternative solutions.

One solution is liquid cooling, which is gaining traction as a more effective way to manage higher heat loads. Transferring heat more efficiently, it enables facilities to operate at the densities required by AI infrastructure. However, it does also introduce new dependencies, particularly around liquid cooling solutions and the infrastructure required to support it.

At the same time, taking a broader view of cooling opens up new opportunities. Cooling systems can be integrated with wider power infrastructure, excess heat can be connected to industrial processes that can utilize it and waste heat from data centers can be repurposed for applications such as district heating, which is already quite common in the Nordics.

Approaching cooling in this way allows developers to design systems that make better use of energy and create additional value through heat reuse and integration with surrounding infrastructure.

Additionally, thermal energy storage gives AI data centers the ability to shift cooling demand away from peak periods by producing chilled water when electricity is cheaper or more available and using it later when loads are highest.

This creates valuable demand response capability, allowing the facility to reduce its grid draw during periods of system stress, lower demand charges and support utility programs without impacting data center operations. In combination with batteries and advanced controls, thermal storage can help stabilize both the data center and the surrounding grid.

Early efforts on permitting can identify the fastest development route and avoid delays

Permitting and regulatory considerations sit alongside these technical decisions, shaping what is possible and how quickly projects can move forward. Requirements vary by region, country and project type, but in all cases, they influence how projects must be designed from the outset.

For example, grid connected developments may be constrained by connection approvals and capacity limits, while sites incorporating on-site generation may require air quality or emissions permits that influence technology choices. Land use restrictions, environmental approvals and community considerations can further shape site layout, development timelines and even overall project viability.

Addressing these requirements early, and in parallel with technical and commercial decision making, is therefore as important as those other factors. When permitting is treated as part of the initial planning process, it allows projects to be structured in a way that is both deliverable and aligned with regulatory expectations from the beginning.

This, in turn, reinforces the need for a coordinated approach across the full range of stakeholders involved. Energy providers, technology companies, developers, regulators and local communities each play a role in shaping outcomes, and the interaction between them becomes a critical factor in how effectively projects can progress.

Having the right expertise in place to connect these elements enables developers to navigate this complexity more effectively, ensuring that decisions made early on are aligned across disciplines. This early alignment helps create a more integrated delivery pathway, reducing friction between project phases and supporting smoother progression from planning through to construction and execution.

Turning AI demand into operational capacity at the speed and scale the market requires

The importance of this becomes clearer when looking at how these challenges play out in practice. In Texas, for example, early engineering work on a gigawatt scale AI training data center helped define the infrastructure strategy for one of the largest behind the meter energy systems supporting AI workloads.

The project includes 5 GW of gas generation capacity, up to 1.25 GW of solar PV, utility scale battery storage and a microgrid supporting 20 buildings totaling 10 million square feet, each designed for around 250 MW of power demand.

Projects of this scale reflect the sheer pace and ambition of AI demand, but ultimately, success comes down to how effectively that demand is translated into deliverable infrastructure. That means making early decisions that can withstand real world constraints, from power availability and permitting through to long term operational performance.

Bringing these elements together into a coherent strategy, and aligning the stakeholders needed to deliver it, is what will enable projects to move at the speed and scale the market now requires.

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The rise of generative AI has undoubtedly lowered the barrier to entry for creating film, with individuals as well as massive production companies leveraging the technology in different ways. But many, including Academy Award-winning filmmaker James Cameron, have insisted that generative AI is no substitute for human actors.

Lights, camera, action

Speaking to CBS ahead of the official release of 'Avatar: Fire and Ash', Cameron described his horror at encountering AI acting.

Having enjoyed a long and successful career in Hollywood, the filmmaker has dedicated the latter period of his career to the Avatar franchise.

This is primarily a means to explore innovation and the introduction of technology in cinema, with Avatar a torchbearer for 3D and its sequel extending it with new camera and performance-capture innovations.

One area that he's categorically unenthused about, however, is AI — and the use of generative AI as a substitute for human creative input, in particular.

Bad robot

After praising the motion-capture innovations in his film during the interview, he then opined on the "other end of the spectrum", expressing his disdain for the way that generative AI threatens to supplant creative input.

He described the notion of generating actors and performances simply using a text prompt as "horrifying" and said it's the opposite of what he was hoping to achieve with the technology being developed for the 'Avatar' film series.

Cameron is also the director of Stability AI, a company that delivers professional-grade generative AI tools for content creators. However, Cameron has insisted that generative AI can never create something completely original, based on the underlying transformer-based architecture that powers these systems.

Somewhere, Myspace Tom is looking over his shoulder, frowning. Everyone's first friend on the once-leading and iconic social and blogging platform, Tom Anderson's smiling face is the sole reminder of what Myspace once was. While so many loved its messy, GIF-filled countenance in 2004, it's unlikely he's cheering its potential comeback.

MySpace didn't survive the rise of Facebook (then "The Facebook"), let alone Tumblr, Twitter, and Instagram. Teens left in droves when NewsCorp bought it for $850M in 2005, and adults, who never understood it in the first place, stayed away by the millions. By 2011, it was sold for a measly $35M to a consortium that included Justin Timberlake. The shift to a more purely music-focused platform did little to distinguish or revive it. The domain survives, but according to Similarweb, it has just over 1.7M monthly uniques. That might sound like a lot, but it is a far cry from the 76M monthly visitors it enjoyed 20 years ago.

Now, Chris and Tim Vanderhook, who bought the platform with Timberlake in 2011, confirmed they plan to "relaunch Myspace."

Myspace version 3?

That, though, my friends, is not the surprising part. Companies relaunch valuable IP all the time in the quixotic belief that they can squeeze just a little more value or revenue out of it. It rarely goes their way.

No, what's truly astounding is that the Vanderhooks did not quickly follow that statement with, "It'll be built on an AI foundation."

I mean, seriously, what is the point of relaunching Myspace if not to house some new LLM, presumably one that connects in some fashion to music. Maybe a big partnership with — gag — Suno.

Myspace has, for the last 15 years, been all about music. The first menu item on the current site is "Featured," which is all about trending musicians, and the second menu item is "Music." "Videos", which is all clips of either "Getting Nailed" or "The Pedicab interviews," comes next, and "People," once the foundation of Myspace, comes last.

If you happen to click on "People," Myspace encourages you to sign up with your Facebook account, which is the deepest cut of all when you consider how Facebook ate Myspace's lunch.

Profile types leave little room for the normies that once populated the platform: Your profile options include "Artists," "Writer". "DJ/producer," "Comedian," "Filmmaker," "Models," and similar extra types. It's like an Influencer reality show casting call. It's unclear how or if you can join as just a person.

If AI were a part of this promised next phase of MySpace, I assume it might help these influencer industry archetypes connect with their audiences and produce exactly the kind of content each wants and would consume at scale.

No way you'll recognize the next Myspace

I find it hard to imagine that the old-school Myspace, in all its chaotic luster, is somehow rising from the ashes. No online audience is still interested in that, right? Though, I guess a sort of anti-social media could work.

No algorithms, no filters, no cool interface, maybe even no AI, just point-to-point encrypted messages, space to say your piece, an occasional meme-worthy GIF, and that's all. It could be ever more bare-bones than the old Tumblr. I see no reason for that to exist, but at least it would stand out from the current selection of overwhelming communication and social media platforms.

Even so, in our current space, no technology is more energizing for investment and emergence than AI. So I fully expect the Vanderhooks, who are "just waiting for the right time to do it," to lean fully into the space and produce a Myspace-awkward blend of earnestness and AI.

They might even get Tom Anderson's permission to use his likeness in AI figures that show him looking over his shoulder, smiling and offering custom greetings on your Myspace page. Tom could be the platform's AI chatbot. It'll be creepy as heck and the polar opposite of what new Myspace should be, but I also see no other realistic path forward for this thing.

As enterprises rush to deploy AI, many are discovering an uncomfortable reality: their greatest obstacle isn't the technology itself, it is the decades-old systems underpinning their operations.

While AI promises productivity gains, automation, and smarter decision-making, legacy platforms were never designed to support the data accessibility, interoperability, and real-time intelligence modern AI requires.

In fact, legacy infrastructure continues to be one of the most significant barriers to successful AI adoption today.

The real challenge facing organizations isn't whether to adopt AI, but whether their existing technology foundations can support it at scale.

Enterprises that fail to modernize their underlying systems may find themselves trapped in a cycle of rising costs, fragmented workflows, and underwhelming AI outcomes, making it harder to compete and capture meaningful return on investment.

Despite major initiatives and large amounts of capital invested into digital transformation over the past decades, the journey still isn’t over. According to research by Synergy Labs, 62% of organizations in the U.S. still rely on outdated software in 2026, with maintenance alone consuming up to 80% of IT budgets.

Shockingly, McKinsey reports that as much as 70% of the business software used by Fortune 500 companies was developed more than 20 years ago. Bottom line: enterprises are collectively losing $370 million annually on technical debt and addressing it is increasingly becoming a business imperative rather than just another IT project.

With AI quickly becoming a competitive advantage that organizations can’t ignore, the question is whether their existing technology foundations can support it at scale.

The perils of ‘bolted-on’ AI

To avoid the painstaking process of full modernization, many organizations are relying on third-party AI overlays and disconnected point solutions that deliver incremental improvements but add complexity, cost, and technical debt in the process. In many cases, these patchwork integrations are being marketed as transformative AI capabilities, fueling a growing wave of "AI washing" that risks disappointing and frustrating employees, customers, and investors alike.

Organizations should carefully vet AI tools that aren't fully re-architected. While these solutions may demo well, bolted-on AI typically sits outside the core data architecture and acts like a separate tool grafted onto an existing workflow, forcing users to context-switch between the core system and the AI interface. The promise of AI is streamlining work, but when it's a separate plugin or module, it tends to create friction instead of removing it because teams must leave their workflow to interact with the AI layer and bring results back manually.

As a result, data quality and context suffer. Legacy systems weren't designed with AI in mind, so bolted-on layers often work with incomplete or poorly structured data exports rather than full, live datasets. That means recommendations based on a filtered or skewed view of reality could pose a significant liability in regulated industries like financial services and healthcare. Native AI, by contrast, can draw from the full system architecture, including real-time data, digitized records, user behavior, and historical data.

Additional disadvantages

Three additional disadvantages of non-native AI include:

More maintenance burden: When the core platform updates, the AI layer may break or lag behind, creating reliability issues that erode user trust over time. Keeping the connection stable requires ongoing engineering effort and coordination between two separate vendors, independent of any new capabilities either is building.

Security and compliance gaps: Legacy systems were built long before modern cyber threats existed. Bolting AI onto that IT infrastructure exposes old vulnerabilities to new attack vectors, complicates audit trails, and strains security teams already stretched thin managing systems they can barely document, let alone modernize.

Limited depth of capability: Typically, bolted-on AI can only do what the API or integration layer exposes; however, it can't reason across the full system or trigger actions deep within the platform. Native AI, trained on the full proprietary dataset, can observe longitudinal patterns and act natively. This represents a fundamentally different level of intelligence as opposed to bolted-on AI which has a narrow view, limiting its ability to personalize, predict, or automate in any meaningful way.

The barriers and strategies to achieving true transformation

There’s a lot of talk about vendor lock-in being the main hurdle to true modernization, but that’s not the whole story. Vendor lock in is a real constraint, but it tends to be more of an accelerant of the other problems than a root cause. Proprietary data formats, closed APIs, and long-term contracts make the switching cost higher but organizations often find that even when they can leave a vendor, the internal complexity of doing so is the harder problem.

In reality, technical debt is usually the real barrier. Legacy systems accumulate decades of undocumented customizations, workarounds, and interdependencies that no one fully understands anymore, and the complexity of data migration is usually underestimated. Moving decades of structured and unstructured data to a new system, while maintaining integrity and continuity, is enormously difficult and expensive.

Starting with data modernization may be the best approach for companies still dependent on legacy infrastructure because fragmented, siloed, and inaccessible data is a very common pain point. Organizations migrating to a data lakehouse or building API access to existing data without impacting core systems will unlock AI capabilities. This approach also drives internal momentum by enabling teams to score early wins and carve the path for larger modernization projects.

Re-architecture is necessary to drive real AI ROI

Bolted-on AI can demo well, but it rarely delivers the operational or productivity gains organizations expect. In many cases, legacy systems and technical debt are the silent culprits. When AI is layered on top of dated infrastructure, fragmented data pipelines, and decades of accumulated workarounds, it inherits every constraint those systems carry.

Until the underlying technical debt is acknowledged and addressed, organizations will keep running into the same challenges: slow integrations, inconsistent data quality, and systems that weren't designed to support the feedback loops modern AI requires.

The organizations accelerating modernization projects to adapt to today’s AI-driven business ecosystem will reap the rewards: operational and productivity gains that will provide the foundation for success for decades to come.

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As AI copilots, autonomous agents, and conversational companions continue their march into the mainstream, the teams tasked with evaluating them are no longer asking: Did the model produce the correct answer?

Increasingly, they are asking whether the system was engaging enough and created enough value for users to return tomorrow, next week or next month.

It is a shift that fundamentally changes what evaluation means.

In the age of AI, "good" is a moving target. What delights one user may frustrate another, and what offers value to one business could be deemed irrelevant by the next.

That’s why success can no longer be measured solely through generic, external benchmarks, telemetry dashboards, or "LLM-as-a-judge" scores.

Real-time signals

Models grow stronger today not by adhering to an external standard, but based on traces and real-time signals from inside the organization. Evaluation, in fact, is becoming a core part of how organizations build and protect their competitive advantage.

Companies are increasingly creating private evaluation systems in-house that measure progress against outcomes that matter to their business, using real workflows, institutional knowledge, and accumulated judgment as the standard.

The new goal is not simply to assess model performance, but to create a learning loop where human expertise continuously improves AI systems and AI systems amplify human expertise in return.

This feedback cycle turns organizational knowledge into a compounding asset. Every interaction generates new training signals, strengthens institutional memory and improves future performance.

In this new world, private evaluation is the mechanism through which firms build, retain and compound their unique intellectual capital.

Tools no longer fit for purpose

In agentic systems, the quality of the experience emerges over long sequences of interactions rather than individual outputs.

Many evaluation benchmarks still rely heavily on turn-level analysis, measuring isolated prompt-response pairs against predefined criteria. Those can identify a model's capabilities in theories – hallucinations, toxicity or syntax errors – but they can’t reliably determine whether a forgotten preference, a broken memory chain or a subtle degradation in user experience caused a user to disengage days or weeks later.

With the growing adoption of consumer AI, preference learning increasingly operates across the entire user journey instead of within isolated prompts. A dashboard can score an individual response, but it cannot fully understand why a user returned three days later, abandoned a workflow midway through a session or learned to fully trust one interaction versus another.

Those signals often live inside the product itself.

AI redefining evaluation: first-party takes the stage

This is why evaluation is moving from a support function on the sidelines to a core product capability.

Development teams are increasingly moving away from external dashboards and generalized scoring systems and building proprietary feedback loops directly into their products. These systems combine behavioral analytics, user retention data, preference learning, reinforcement signals and post-training pipelines tailored to their own applications.

Their reasoning is simple: staying close to the user is the only way to understand what "good" actually means.

The AI companies poised to win are those building closed-loop systems that connect user behavior, offline analysis, reward-model recalibration and online validation. The most advanced AI products already use live behavioral feedback to refine responses, personalize interactions and improve retention.

Static offline benchmarks are giving way to live preference learning. Isolated, single-turn tests are being replaced by trajectory-level behavioral analysis. Evaluation is moving from the support layer to the core operating system of AI products.

The fate of traditional eval vendors

So where does this leave well-known platforms such as LangSmith, Arize, and Weights & Biases as AI providers absorb more of the stack?

Interestingly, the biggest threat these companies face may not come from Anthropic or OpenAI, but from their own customers.

These vendors are not necessarily being displaced from above. They are increasingly being bypassed and disregarded from below as AI companies realize that evaluation is inseparable from the product itself.

Generic third-party platforms can determine whether an answer resembles benchmark data. External observability vendors can process telemetry and surface analytics. But they are not truly connected to the context that increasingly defines product quality.

Every serious AI company is now discovering the same thing: evaluation is the product. And companies cannot outsource their product.

Defining success is the new critical moat

This shift is accelerating because the rest of the AI stack is becoming increasingly commoditized.

Access to high-performing foundation models is rapidly expanding. Startups and enterprises alike can access powerful APIs with relatively low barriers to entry. Prompt engineering is unlikely to remain a durable differentiator. Basic orchestration layers are becoming standardized. Even generic "LLM-as-a-judge" scoring systems are increasingly available as built-in platform features.

As those layers become commodities, a company's internal understanding of user success becomes a more important differentiator.

The signals that matter for a coding assistant differ from those that matter for a healthcare agent, a tutoring system or an AI companion. Even within the same category, companies may optimize for entirely different outcomes, including engagement, trust, efficiency, emotional resonance or long-term retention.

In a world where models, infrastructure and tooling are increasingly rented, defining success is something that can and must be owned. In fact, it may become the most important piece of intellectual property a company owns.

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Perhaps the issue is not that AI is creating new sprawl, but that the drive to adopt is exposing existing, disconnected processes as a deeper, more urgent problem.

The explosive growth of AI adoption across enterprises is impossible to ignore, but so is the complexity that's building alongside it. A recent study from Harvard Business Review suggests that instead of making our workloads easier, AI may actually be contributing to mental fatigue, caused by excessive interaction with AI tools beyond a person’s cognitive capacity.

It raises an important question, how can organizations manage the next wave of automation, without creating even more complexity?

The real battle aren’t your tools, it’s everything around them

There is a growing narrative that AI is SaaS's logical replacement but there’s more to it than a simple swap out. For the technology to be truly transformational, it’s about the implementation.

Through the ease of adoption, AI tools will cause more pain if simply layered on top of current systems rather than rethinking them. This isn’t a specific AI problem, it's a deployment one and organizations need to confront it by asking, “where is the real friction in the work process, and how can it be eliminated with AI?” Most people today don't realize how important that differentiation is.

The productivity gap that AI alone won’t fix

To be able to address this challenge, businesses need to properly understand the issue at hand. This starts with looking closely at where time and money is actually going, but reading through this data can be an uncomfortable task.

UK businesses are losing billions of pounds every year to “shadow work” - manual tasks outside of people’s core jobs that are exacerbated by disconnected systems. UK employees spend hours a week on this kind of non-core work, and this is only set to increase, leading to a very real problem. Every employee, team and seniority level is affected by this structural drain on output.

Whilst most businesses don’t or only partially automate tasks like planning trips and submitting expenses, these recurrent weekly friction points build up to have a quantifiable effect on the employees time for their real work which drives impact. The drive to adopt the technology is there and for leaders, automating this work should be a high priority but only when clear plans are in place for how to do so.

The fundamental problem is that shadow work emerges because systems are unable to communicate with one another, and just layering AI tools on top won't fix the problem. Often, the biggest roadblock to solving the issue in the first place is poorly integrated legacy systems.

This misalignment is draining employees productivity and satisfactions and harming business growth.

2026 - the year of stack rationalization

A true turning point may start to happen when businesses start cutting the amount of tools they have rather than adding more. For years, the cost of complexity has been gradually escalating. It's getting harder to ignore. The argument for simplicity is not just significant but essential when workers are managing shadow work, using multiple tools which are often not properly integrated.

Previously, increasing the efficiency of individual systems was the goal of enterprise automation. Payroll ran itself and IT provisioning took place in a matter of seconds. However, the coordination, approvals and administrative work that spans operations, finance and human resources were mostly ignored.

Adding more tools won't unlock the next wave of productivity. It will only come by integrating intelligence into the operational core, where people and systems come together as one.

When simplifying your tech stack is done well, it doesn't mean doing less. It involves being deliberate, putting simplicity ahead of growth and placing people before software sprawl. The CIO’s with the most self-control to base their success on what they remove will be the ones leading the next generation.

The productivity gap won't close itself

The issue for many organizations isn't the lack of technology, it’s that systems and tools are misaligned. The automation is there, but in ways that have enhanced systems without freeing up those who use them. AI won't automatically address the shadow work issue, and it won't go away on its own.

A change in perspective is now required, to move away from just pursuing the next goal and towards creating workflows that really benefit the individuals performing the task.

When complexity is outpacing productivity, the real measure of progress won’t be calculated on the volume of AI deployed within the business, but instead on how much friction an organization has managed to remove.

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Security leaders have made significant progress in improving threat visibility across businesses.

Most organizations can now identify vulnerabilities across their applications, dependencies, and development pipelines with far more consistency than previously.

Yet, a fundamental imbalance remains — vulnerabilities are being discovered faster than they can be remediated.

This imbalance is growing. As it stands, the majority (82%) of organizations currently carry security debt, categorized as accumulated vulnerabilities that have remained unresolved for more than a year.

At the same time, the share of vulnerabilities that are both severe and likely to be exploited continues to increase.

As a result, vulnerabilities are persisting in production environments long enough to be discovered and weaponized.

Despite this growing risk, many CISOs still need to convince the C-suite that reducing security debt is a business-wide issue that justifies sustained investment, rather than a challenge limited only to security teams.

Treating security debt like financial debt

Business leaders must reframe their thinking to view security debt with the same level of scrutiny as they would financial debt. Like financial debt, security debt accumulates over time, compounding when left unmanaged to create spiraling costs for the business. Those costs appear in delayed releases, emergency remediation efforts, audit findings, incident response and, ultimately, greater organizational risk.

As with financial debt, security debt requires active management rather than periodic damage control. Organizations need to clearly understand how much security debt they are carrying, distinguish between the vulnerabilities that matter most, and make deliberate decisions about where to invest their remediation efforts. Without that discipline, the backlog continues to grow while the organization's overall risk increases.

Boards already monitor financial performance, operational resilience, and service reliability because each affects the organization's ability to operate. Security debt belongs in the same category. It is a measurable indicator of organizational exposure and should be managed with the same level of oversight and accountability as any other business risk.

Capacity is the key problem, not visibility

Most organizations already know where many of their security vulnerabilities exist, however are constrained when it comes to remediation capacity. When vulnerabilities are identified faster than engineering teams can resolve them, security debt continues to grow regardless of how sophisticated the detection tools they use are.

To secure buy-in from the wider C-suite, CISOs must demonstrate this capacity gap in business terms. This includes highlighting the volume of vulnerabilities being discovered versus fixed, how long high-risk issues remain unresolved and where critical systems remain exposed.

Framing remediation as an operational constraint makes it easier for executives to understand the wider business benefits of addressing it – from improving engineering capacity to reducing costs and maintaining service availability.

Success in reducing security debt should be measured by reducing exposure, not just counting the vulnerabilities that have been found or closed. Metrics such as the number of exploitable vulnerabilities in critical systems, their average age, and overall security debt provide a clearer picture of organizational risk.

Formal risk acceptance for unresolved high-risk issues, combined with dedicated engineering time, automation, and AI-assisted remediation, can significantly improve remediation throughput without slowing development.

Prioritize the vulnerabilities that create the greatest business risk

Crucially, not every vulnerability presents the same level of risk. While severity scores such as the Common Vulnerability Scoring System (CVSS) can be useful, they do not account for exploitability, enterprise context or whether an affected application is business critical.

A more effective approach combines severity with exploitability alongside organizational context to identify the small percentage of vulnerabilities that are most likely to impact the business. Every organization has ‘crown-jewel’ applications – whether customer-facing platforms, revenue-generating services or applications handling sensitive data – and these should be prioritized for remediation.

To put this into perspective, 11% of vulnerabilities are deemed both highly severe and exploitable. Focusing resources on this subset enables organizations to reduce risk far more effectively than treating every vulnerability equally, while giving security leaders a clearer way to explain remediation priorities in business terms rather than technical jargon.

Reframing the conversation around security debt

The ripple effect of security debt extends beyond the security function. It influences resilience, regulatory compliance, and an organization's ability to operate software with confidence.

CISOs have an opportunity to reshape the conversation by framing security debt as an enterprise risk rather than a technical backlog. When leadership can understand the relationship between remediation capacity and business risk, decisions around investment and prioritization become far clearer.

Security debt will never be eradicated completely. What matters is how effectively it is measured, governed, and reduced over time. The businesses that invest in remediation capacity and focus on the vulnerabilities that matter most will be better positioned to control risk at scale.

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