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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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• Astell & Kern is launching an 'entry-level' DAP to supersede the SR35
• We'll have to believe it for now, because no actual price has been provided
• Highlights include EnviroTune ambient noise awareness and an Octa-DAC mode

Astell & Kern has straightened things out. We gave its wonky, quirky SR35 digital audio player a glowing five-star review, and now a successor is here in a more conventional form factor.

The A&K PD5 is the new 'best' in question, and very much an upcoming contender for our best high-res media player list. And we'll decide whether it is, as soon as we learn a few key details about it such as when it's actually coming out and how much it'll cost.

Pitched as an entry-level audio player, this hopefully won't cost a huge amount, but we'll have to wait to find out. It's been unveiled as part of an AV show in Hong Kong, and it'll be making its way around a few other similar shows in Shenzhen and Southern California before its wider release.

So we don't know much about it, but there are some key selling points which Astell & Kern have unveiled — and very promising they are too.

It's always listening

(Image credit: Astell&Kern)

The PD5's biggest spec is that it packs in eight DACs at once, and you can choose between 4-DAC or 8-DAC playback. The former lets you listen for longer (28 hours, to be precise), the latter allows you to enjoy higher-quality listening. A&K refers to 'class-defining performance'. Woof.

Another tool here is EnviroTune, which apparently is always listening to you... and, specifically, ambient noise around you. Then it can tweak the sound you're hearing to cater for different environments. It sounds like a proto-ANC, in other words.

For raw specs, the PD5 plays back in 32-bit/384kHz PCM and DSD256, with support for dual-band Wi-Fi, Bluetooth 5.3 and a vast range of codecs. The player runs on Android, though Astell & Kern didn't say which build of Android it'll run.

The player is housed in an aluminum shell, but A&K didn't provide dimensions, so it's impossible to tell how it compares size-wise to the SR35.

The real question here is still the price. This might be Astell & Kern's best entry-level audio player of all time, but its definition of 'entry-level' might vary wildly from yours. Evidently, we'll have to wait and see what the chips fall.

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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• You can now sync your Fitbit data from Google Health to Apple Health
• Previously, data could only flow in the other direction
• The change makes it easier to manage your fitness info in one place

A quiet update from Google has the potential to make a big difference for fans of all the best Fitbits who also use an iPhone. Instead of spreading your health data across multiple apps — or having to rely on Google Health alone — you can now use Apple’s own solution for wearables that are spread across different platforms.

The change in question involves a new feature found in the latest Google Health update on iOS. Specifically, data stored in Google Health can now be synced to the Apple Health app, including data such as “exercise records, sleep, vitals, steps, and more,” according to Google.

That’s important news for a couple of reasons. The first is that it finally opens up Google Health data to a wider audience. Previously, you could sync Apple Health data to Google Health, but you couldn’t send it back the other way, meaning anything saved in Google Health was siloed off and isolated. Now, it’s much more accessible to iOS users, especially those who prefer the Apple Health app.

The second factor is that it now means Apple fans — or those who would rather use Apple Health instead of Google Health — no longer need to compromise if they have any Fitbit wearables. From now on, those users can happily manage their Apple and Fitbit devices from one place inside Apple Health, instead of having to either use multiple apps or Google’s offering alone.

Apple Health is all you need

(Image credit: Google)

To sync your data from Google Health to Apple Health, open the Google Health app, then tap your profile icon and select Partner Apps. Choose Apple Health, and follow the on-screen prompts to send your data across to Apple’s platform.

As mentioned previously, one group of people who will benefit from this change are those who use two or more wearables that operate on different platforms. Before, if these users wanted to get all their data into Apple Health, they had to use a third-party app or workaround. Now, things are much simpler.

The change seems to have gone down well with Fitbit users online. Posting on Reddit, for example, one user commended Google, saying: “Good job from Google, I didn't think they would actually add that feature anytime soon.”

Another happily noted that “Google did it quite fast. Nice to see when they care about customers.” A third, meanwhile, lamented that cross-platform functionality like this “Should have been there a long time ago.”

If you’re an iOS user with a Fitbit device, you no longer need to stick to Google Health to keep track of all your health and fitness information. With this change, Apple Health is all you need.

Why food companies love labels like "organic" and "high protein," why our brains are misled by them — and how to avoid paying extra for marketing.

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Mini PCs under $600
Mini PCs over $600

A mini PC is a physically small yet very powerful productivity booster. It’s like compressing the performance of a high-end desktop into a compact device that fits in your palm. You can carry the mini PC around easily and simply connect it to a monitor.

Personally, a mini PC has been one of my best purchases, but choosing the right one was an uphill battle. Between weighing performance, compactness, energy efficiency, and price, selecting the best options from numerous choices felt daunting, but I ultimately narrowed it down after extensive research and testing.

I tested many devices to curate this list, including some of the best mini PCs you can get, so each recommendation is backed up by data. Whether your main consideration is the price range, performance, speed, compactness, or energy consumption, get ready to strike the right balance and make an excellent choice.

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This compact mini PC features an AMD Ryzen 5 7640HS processor, 32GB DDR5 RAM, a 1TB SSD, triple 4K display support, USB4, dual 2.5G Ethernet, Wi-Fi 6E, and Windows 11 Pro pre-installed.

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AMD Ryzen 7 8745HS | 16GB DDR5 | 1TB SSD

Powered by an AMD Ryzen 7 processor, this mini PC packs 16GB DDR5 memory, a 1TB NVMe SSD, USB4, WiFi 6E, 2.5G LAN, and supports up to 8K displays.

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The NucBox K13 is a superb mini PC equipped with an Intel Core Ultra 7 256V processor, 16GB of LPDDR5X memory, and a 512GB SSD. Interestingly, it boasts AI acceleration up to 115 TOPS, dual USB4 ports, 5G Ethernet, and support for triple 4K displays. In our review we called it a "thoughtfully engineered slice of modern computing." View Deal

Best mini PC deals over $600

AMD Ryzen 7 7735HS | 32GB LPDDR5 | 1TB SSD

The GMKtec K16 mini PC delivers Ryzen 7 7735HS performance with 32GB LPDDR5 RAM and a 1TB SSD. Featuring OCuLink eGPU support, triple-display output, dual 2.5GbE LAN, and USB4, it’s a versatile compact system for gaming and creative workloads.

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AMD Ryzen 9 8945HS | 32GB DDR5 | 1TB SSD

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Intel Core Ultra 9 285H | 32GB DDR5 | 1TB SSD

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The K8 Plus packs a high-performance Ryzen 7 8845HS processor, 32GB RAM, and a 2TB PCIe 4.0 SSD for gaming, video editing, and demanding workloads. With OCuLink eGPU support, dual 2.5GbE LAN, USB4, and 8K display output, it’s a powerful compact desktop alternative.

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Intel Core Ultra 9 (Series 2) ARL-HX | 32GB DDR5 | 1TB SSD | Nvidia GeForce RTX 5070 Mobile GPU

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AMD Ryzen AI 9 HX-370 | 32GB DDR5 | 1TB SSD

An absolute powerhouse AI mini PC workstation with the ultra-powerful HX 370 chip and OcuLink support. In our review, we loved the performance here, but it's a shame the RAM is soldered. View Deal

Intel Core i7-14700 | 16GB DDR5 | 512GB SSD

A true business mini PC well-suited to the office, HP's Elite Mini pairs a 14th Gen Intel Core i7-14700, with Intel UHD 770 graphics. 16GB DDR5 RAM and a 512GB PCIe SSD, both user-upgradeable via two SO-DIMM slots and dual M.2 slots. View Deal

He's a legendary superhero, a pop culture icon, the star of comic books, movies, TV shows, video games and more, but how well do you really know Spider-Man?

Ever since we've seen the web-slinger's first outing in comic book form, we've seen various takes on the character: different human alter egos, different actors taking the role on, different powers and villains.

There's a lot about the character to get your head around, and you should be sure to brush up on how to watch the Spider-Man movies in order and our ranking of the best Spider-Man movies if you need to do some research.

But how well-read and well-watched a fan are you? To find out, we've designed this 30-question quiz, that'll ask you all about Spider-Man and his (and, sometimes, her) media outings.

The questions will test your knowledge on the breadth of the media empire, more than specific details from Spider-Man's world. You don't need to know what Doc Oc was a doctor of, or when Uncle Ben's bed-time is!

We'll start with 10 easy questions, and then move into 10 medium ones, and finish with 10 difficult ones for the experts. So, let's find out just how much you know:

If you need to brush up on your Spider-Man, let's find you some schoolwork:

New data suggests Flock surveillance cameras have an abyssal recognition rate. Here’s what that’s a major problem for drivers.

You can enable, disable or revert Siri through a few quick steps in the Settings menu.

If you've been considering Photoshop, Premiere Pro, Illustrator, or any of Adobe's other professional creative applications, and haven't signed up before, you can save 50% on the Creative Cloud Pro plan for your first year.

This cuts the monthly cost from $69.99 to $34.99 (or from $91.99 to $45.99 in Canada) on an annual plan billed monthly. And the subscription comes with more than 20 industry-leading creative apps, alongside 4,000 monthly generative AI credits for premium features such as Text to Video.

Subscribers also gain unlimited access to standard AI tools like Generative Fill, Adobe Fonts, Adobe Stock assets, tutorials, collaboration tools through Frame.io, and integrated AI models from Adobe, Google, OpenAI, and others. The promotion for new subscribers ends August 10.

Today's top Adobe Creative Cloud Pro deal

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This deal is available to new subscribers signing up for the annual plan billed monthly, reducing the price from $69.99 to just $34.99 per month for the first year. Creative Cloud Pro includes more than 20 Adobe applications, including Photoshop, Illustrator, Premiere Pro, Acrobat Pro, and Firefly, alongside 4,000 monthly generative AI credits and unlimited access to standard AI features such as Generative Fill.View Deal

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Although there are many other creative tools available, Adobe continues to lead the way for professionals, and Creative Cloud Pro remains the easiest way to access its entire ecosystem.

I personally wouldn't use anything else and there's a good reason why Adobe's apps always come out top in our round ups of the best photo editors, best video editing software and best PDF editors.

The Pro subscription includes more than 20 applications, from industry staples like Photoshop, Illustrator, Premiere Pro, and Acrobat Pro to specialist tools such as After Effects, Audition, Lightroom, and InDesign, all designed to work seamlessly together.

Beyond the apps themselves, Creative Cloud Pro includes Adobe Firefly, 4,000 monthly generative AI credits for premium features such as Text to Video, unlimited access to standard AI tools like Generative Fill, plus Adobe Fonts, Adobe Stock assets, tutorials, and collaboration features through Frame.io.

At 50% off, this is one of Adobe's best ever offers for new subscribers in the US and Canada.

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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• UK’s Police National Legal Database (PNLD) breach leaks data of 100k+ criminal justice professionals
• Threat group ExfilSquad claimed responsibility, posting 1.9 GB of stolen records on the dark web and demanding ransom
• PNLD notified NCA and ICO, hired specialists, and confirmed passwords weren’t compromised but contact details exposed

The UK’s Police National Legal Database (PNLD) suffered a cyberattack recently, in which it allegedly lost sensitive data on more than 100,000 criminal justice professionals.

In a short press release, PNLD confirmed the breach, saying it happened over a weekend. The threat actors, which were not named in the announcement, were said to have taken names, organizations, and work email addresses belonging to police officers, staff, government partners, and customers.

The announcement also said the stolen information was already published on the dark web, adding that there is “no evidence to suggest that passwords or other security credentials have been compromised.” How the attackers worked their way in was not disclosed in the announcement.

ExfilSquad takes the blame

Following the breach, PNLD hired cyber-security specialists, and notified the National Crime Agency, which started their investigation into the incident.

“All affected organizations were contacted in the days following the incident and provided with further information and guidance,” the announcement reads. “The Information Commissioner’s Office (ICO) has also been notified.”

At the same time, threat actors calling themselves ExfilSquad claimed responsibility for the attack, BleepingComputer reported. The group alleges it stole 135,000 contact records, sharing samples to support their claims. They also said they demanded a ransom in exchange for keeping the data safe.

In the dark web post, ExfilSquad said it obtained 1.9 GB of data, which includes information belonging to 114,000 PNLD subscribers and 21,000 Ask the Police users.

‘Ask the Police’ is a public-facing website where users can find answers to hundreds of commonly asked policing or legal questions.

ExfilSquad is a relatively new threat actor that's not known for any major attacks so far. Prior to the PNLD incident, it claimed the attack against Analog Devices, a US semiconductor company.