Technology

• President Trump says data center developers should offer a 'dividend' to get local community approval for campuses
• Nebius CRO says the company is "100% in principle" considering some form of local investment
• Paying local residents to approve a local data resident didn't go so well in Hazle, Pennsylvania

President Trump has told data center companies that one way to get local residents to support new developments is to “make deals” with local residents.

Trump suggested offering communities a “dividend,” adding, “I have watched the process closely, and Data Centers that were highly unpopular in a certain area, have become extremely popular when a financial deal is struck.”

“MAKE DEALS, and get them built!!! GREAT FOR THE U.S.A.,” the President wrote on Truth Social. But as one town has already demonstrated, these payments are viewed as a bribe that threaten to turn communities against each other.

Dividend or bribe?

As the November 2026 midterm elections draw closer, data centers have become a make-or-break topic for some candidates. Data center developers and AI companies have been channeling millions of dollars into political action committees in an effort to get a more favorable view of data centers.

Multiple states are considering enacting moratoriums on AI data center development, which if approved could cost developers and AI companies billions in lost investment. Environmental concerns and issues relating to noise and light pollution are high on the list for residents protesting against local developments.

NorthPoint Development is currently seeking to build an AI data center in Hazle, Pennsylvania, and offered each household $10,000 in an attempt to convince residents to get on board with having the campus approved. But the residents did not react as planned, calling the payment a bribe and turning it down over fears it could split the community in two - those who live near the campus and declined and those who live further away and took the payment.

When questioned by Trump’s proposal, Marc Boroditsky, CRO of Nebius told CNBC that the company is “100% in principle” having discussions about potential investments in local communities.

“We see it today in terms of the tax revenue that are arriving. I could see something like a benefit of some sort that’s more direct economic opportunity for the community as well,” Boroditsky said. “A dividend is being paid in different forms — investing in education, job development, overall economic development. The concept that the president identified... it’s not crazy to consider something like that"

Via CNBC

OpenAI recently introduced GPT-6, a new model of ChatGPT. You've probably heard about a lot of its new features but one that I don't think has had nearly enough attention, especially considering how much fun you can have with it, is Intelligent UI.

It allows ChatGPT to generate interactive experiences directly within a conversation. OpenAI explains: “With Intelligent UI, responses can include graphics, tappable buttons, forms, charts, and interactive experiences you can use directly in your conversation.”

What's interesting is there isn’t a single format. Which means, depending on what you ask, ChatGPT might create an interactive diagram, a calculator, a mood board, a map or even a little gaming console.

There are some genuinely useful applications here, from making complicated subjects easier to understand to building simple tools on the spot. But I was particularly interested in something a little less practical — games.

ChatGPT can now create games you can actually play within the chat window. And while there are all sorts of possibilities for educational games, quizzes and entirely new creations, I wanted to see what would happen if I asked it to recreate some old favorites.

So I started with five classic games, including Snake, Pong and Tetris. I gave ChatGPT simple prompts to see how faithfully it could recreate them. I then asked it to reinvent each game with new aesthetics, unexpected gameplay mechanics and a few deliberately chaotic twists. Think Snake in a neon cyberpunk world and Tetris with rules that change every time you clear a line.

Here's what happened when I put ChatGPT's new game-making abilities to the test.

Snake gets a neon makeover

(Image credit: Future)

The prompt: "Create a playable version of the classic Snake game directly inside this conversation."

ChatGPT got the basics right straight away. The game was instantly recognizable, with responsive controls and even a slider to adjust the speed. It looked good and, importantly, it was actually fun to play for a couple of games.

I then asked it to give Snake a neon cyberpunk makeover, complete with moving obstacles, special power-ups and a speed increase every five points. Say hello to Neon Snake.

(Image credit: Future)

There was one small hiccup. Instead of creating the new game directly in the chat window, ChatGPT initially gave me the code. Which wasn't much use when the whole point was to play it there and then.

I tried another prompt asking it if it could make it directly playable in the conversation and that worked. The results of that experiment were fun. The neon aesthetic looked good, everything was color-coded and the new mechanics gave the classic (and, let’s be honest, sometimes boring) game an extra layer of challenge.

Tetris gets new rules

(Image credit: Future)

The prompt: "Create a playable game inspired by Tetris directly inside this conversation."

This one worked really well from the start. I’d just assumed it would be a bit buggy and glitchy because there’s so much going on compared to Snake, but the controls were intuitive and responsive, and the classic block-stacking mechanic was all there.

But what if Tetris changed the rules every time you cleared a line? That was the idea behind my follow-up prompt.

(Image credit: Future)

I asked ChatGPT to introduce unexpected twists, which could include rotating the board to adding unusual block shapes or special powers that could destroy part of the grid.

And it delivered. The changing rules introduced a fun element of unpredictability to a game that's usually all about getting into a rhythm. I also came away thinking about how much further you could take this. There are so many opportunities to experiment with different rules, challenges and power-ups.

Space Invaders becomes a First Contact situation

(Image credit: Future)

The prompt: "Build me a fully playable retro arcade game inspired by Space Invaders"

Again, ChatGPT recreated the classic arcade experience surprisingly well. It looked and played like the original, with straightforward controls and all the fun of shooting at waves of approaching aliens.

But I wanted to see whether it could do something a bit more imaginative than adding extra enemies or making everything move faster. So I asked it to introduce a choice: what if I could defend Earth by fighting the aliens or negotiating with them?

(Image credit: Future)

I wanted waves of pixel-art enemies, but also decisions between rounds that could unlock weapons, stop an invasion or make the next attack more dangerous.

ChatGPT called its reinvention First Contact, which I loved. Up until this point it was calling most of the games "chaos" or "chaotic" so this felt more inventive.

It was a fun way to introduce a new dimension to a familiar game. But did it work well? I don't know, I couldn't ever make it past the first level. This experiment really suffered with my poor alien-fighting abilities.

Haunted Pac-Man

(Image credit: Future)

The prompt: "Create a fully interactive retro maze game inspired by Pac-Man that I can play directly inside this ChatGPT conversation."

This was easily my favorite of the five, but I have always adored Pac-Man. ChatGPT's first attempt was a fun, responsive maze game with the familiar mechanics of moving around, collecting things and trying to avoid enemies. Bonus points for the fact it renamed it Maze Muncher.

For the reinvention, I decided to lean into Halloween. I asked it to turn the game into a haunted 1980s arcade, with me playing a little ghost hunter instead of Pac-Man.

(Image credit: Future)

I also wanted the maze itself to change. Every ten orbs collected would trigger a surprise, like shifting walls, new passages or an extra ghost. And I asked for temporary power-ups that would let me chase the ghosts for a change.

ChatGPT handled this well but didn't wait until I'd collected orbs. It was super fast-paced and there were new ghosts, secret passages and changing mechanics from the start. It was quick, easy to control and genuinely fun to play.

Pong reinvented

(Image credit: Future)

The prompt: Build me a fully playable version of Pong inside this conversation.

Pong might be the simplest game of the bunch, but ChatGPT made a really solid version. The controls were easy, the aesthetics were nice and it even offered easy, normal and hard difficulty settings.

I decided to ruin that simplicity and asked ChatGPT to create a more chaotic version where every point triggered a random modifier. The ball could split in two, the paddles could shrink, gravity could change or an obstacle could appear in the middle of the court.

The result definitely delivered on the chaos. The familiar back-and-forth gameplay was still there, but the changing rules made each rally a little less predictable. It was also a reminder of how much fun you can have with a simple idea. You don't need to reinvent every part of a game to make it entertaining.

(Image credit: Future)

I was genuinely impressed with how well all five games worked, especially considering how simple my original prompts were.

Yes, there were a few hiccups along the way, like ChatGPT deciding to give me code instead of a playable game a couple of times. But overall, the controls were responsive, the games looked good and the reinventions were more imaginative than I expected.

What I enjoyed most was how easy it was to experiment. You could take a game you already know, change the aesthetic, introduce an unexpected mechanic or come up with something completely ridiculous and see what ChatGPT made of it.

I can imagine this being a fun way to waste half an hour, especially if you loved these games growing up. Although, after all this experimenting, I do feel like I should remind you that the originals are still out there, and they're well worth revisiting too.

• Nvidia's RTX 6060 may not arrive until 2029 a leaker claims
• They note that when there are supply issues (the current RAM shortage), Nvidia tends to drag out the release of lower-end GPUs
• They also believe it's unlikely the RTX 6060 will match the PS6 for frame rates

Nvidia's budget GPU for its next-gen range may not be released for three years yet, if a new rumor about the RTX 6060 turns out to be correct.

Wccftech spotted a post from leaker Kepler on the NeoGAF forums, who chipped in with speculation on Nvidia's graphics cards in a thread about the cost of the PlayStation 6.

Kepler was responding to a comment observing that the PS6 is going to go up against the RTX 6060 when it launches in terms of comparisons to PC gaming, and the leaker said — add seasoning with this as ever — that the "6060 will launch much later (and based on recent trends I doubt it will match PS6)".

In a further post, the leaker added: "When there's a supply shortage Nvidia tends to really drag the release of lower end cards. There's a good chance RTX 6060 comes out in 2029."

This would mean we're looking at an early-to-mid 2028 launch for the RTX 6090, 6080 and 6070, which lines up with more recent rumors. Of course, that's not a foregone conclusion, and Kepler is clearly speculating here.

Recent reports from analysts indicate that the PS6 will likely launch in 2028, though some have argued that Sony's next-gen console could appear late in 2027 (alongside other devices, perhaps).

Analysis: late, as well as short on performance?

(Image credit: Future)

Of course, what Kepler contends here is what Nvidia is supposedly planning right now, and just as with the PS6, a lot of the direction that Team Green goes in with its next-gen GPUs will depend on the memory crisis. Graphics cards do, of course, use video RAM (VRAM), and the next generation of GeForce products will be expected to up the ante on that score. (Gamers are already unhappy with Nvidia's current-gen VRAM configurations).

The problem is that there's no shortage of predictions that RAM cost inflation is going to get even worse in 2027, after it has already spiked in a big way through the middle of this year. The theorizing then runs that 2028 is also going to be a difficult year, which lends weight to the argument that the RTX 6060 will indeed be late to the GPU party.

In short, I wouldn't rule out the possibility of a 2029 arrival for the RTX 6060 as suggested, which obviously isn't great news for those hoping to score a more wallet-friendly next-gen Nvidia GPU.

It's also interesting to note that Kepler believes the RTX 6060 won't be as powerful as the PS6 for gaming frame rates, but I'd take that claim with even more salt than the rumors around timing.

Gauging hardware capabilities at this stage is obviously a trickier task, but Kepler's assertion isn't an impossibility of course. Note that the current RTX 5060 matches the PS5 Pro's gaming performance (roughly, for rasterization as opposed to ray tracing), so if the RTX 6060 couldn't equal the PS6, that would be a clear disappointment for PC gamers on a budget (particularly if the VRAM loadout again turns out lean).

Customers are randomly awarded an in-game code for either a Domino’s skin, cape or cheesy cube pet. The pizza will come in one of six Minecraft-themed boxes.

• Two in three IT leaders say their infrastructure isn't ready to handle AI and analytics
• Complex data regulations are giving them a major headache
• AI PCs also form part of the broader infrastructure considerations

According to a new report by Dell, UK businesses are struggling more than their EMEA counterparts when it comes to AI, with three in five (61%) British firms lacking a clear roadmap covering AI, data and security, compared with 46% across EMEA.

But one thing they have in common with their Europe and Middle East counterparts is that legacy infrastructure is likely one of the biggest factors slowing effective AI deployment.

Nearly two-thirds (64%) of IT leaders say their infrastructure is not yet fully prepared to support AI and analytics workloads at scale, noting substantial gaps between ambitions and reality.

Data foundations and IT infrastructure are still holding back AI

Having to coordinate compliance and regulatory factors across their entire stacks is also making it difficult for enterprises to lay out solid foundations, the report claims. More than half (55%) say security and compliance concerns have already slower or delayed their AI strategies, with 64% of leaders worried about regulation compliance when using their data for AI.

And it's not just complex IT infrastructure that's holding back widespread deployment, because even end-user devices need to be considered. Nearly all (92%) respondents agreed that modern devices help them in terms of productivity, but four in five (79%) are being held back by outdated devices.

To that tune, 86% of EMEA leaders now agree that AI PCs will play an important role in their strategies going forward. As of the beginning of 2026, AI PCs accounted for around 44% of all PC shipments (per Omdia tracking), marking a healthy ongoing rise.

"The focus is shifting from exploring what's possible to connecting the infrastructure, data, security and workplace technology needed to turn AI into real business value at scale," UK&I MD and Dell SVP Steve Young explained.

While IT leaders clearly understand some of the factors holding back effective AI deployments, at least two clear challenges are identified in Dell's report – complex regulatory environments and an expensive PC market driven by component shortages.

• UK government cracks down on people who annoy others with phone music
• Fines of up to £100 for those people
• It's cheaper to just buy these headphones

People who play music, TikTok videos or out-loud video calls on public transport probably aren't self-aware enough to know how annoying it is, but apparently the UK government does, because it's finally cracking down on so-called "headphone dodgers".

From next Monday, transport authorities will be allowed to fine rule-breakers by up to £100 if they're causing a nuisance. Prime Minister Andy Burnham is fronting the campaign, after previous governments toyed with the idea.

It's a good idea, but the success is dependent on enforcement, as transport safety officers and not drivers are expected to hand out the fines. Given how minimally-staffed most public transport services are, some (me) are dubious that the rules will make a difference.

However if you've been a 'headphone dodger' and you want to turn a new leaf (good on you), it'd make sense to buy a pair of headphones that cost much less than the £100 penalty fare.

And naturally, we've tested a few that we can recommend…

Sennheiser CX 80U

(Image credit: Future)

Starting with something nice and cheap, we've got some wired earbuds, which you can easily plug into the USB-C port on a phone and keep bundled up in your pocket the rest of the time. No Bluetooth dropouts, no losing a bud as you board the bus, no apps, none of that.

The Sennheiser CX 80U sound great, block surrounding noise quite well, and offer a nice simple design. We gave them 4.5 stars out of 5 in our review, only knocking them for some missing features of pricier buds.

Best of all, though, they're really cheap. They launched for $39.95 / £34.99 in January this year, but you can find them even a little cheaper than that on Amazon.

Sony WH-CH530 (or even the Sony WH-CH730N)

(Image credit: Sony)

If you want great-value headphones, you can't go wrong with a pair from Sony, and its recently-released Sony WH-CH530N are really affordable at $69 / £50.

We haven't yet finished testing these on-ears, they're that new, but their predecessors the WH-CH520 got 4.5 stars in our review. We praised the balanced sound and long-lasting battery, and the newer pair will likely bring improvements.

As you can see in the picture, they come in a wide range of colors, and also have noise cancellation, which isn't something you can say about all options at this price point.

Prefer over-ears? Go for the just-reviewed, all-new Sony WH-CH730N: a solid four stars and they're just under that £99 (or equivalent, in your country of residence) fine line, if you'll pardon the pun…

JLab JBuds Mini ANC

(Image credit: Future)

Last, but not least, let's look at some standard wireless noise-cancelling earbuds, which we got to try earlier this year.

The JLab JBuds Mini ANC are tiny — you can even clip the case onto your keyring — so you've got no excuse for not having buds on the bus. At $49.99 / £49.99 they won't break the bank either.

We enjoyed the non-ANC JLab JBuds Mini too, when they came out three years ago, and they're ideal for people who want lightweight buds.

Have further thoughts? Drop us a comment — or better yet, fill out the poll below this text and then add your comment!

Businesses invest heavily in attracting, developing and retaining talent. At the same time, many continue to view workplace technology primarily through the lens of procurement cycles, standardization and cost. Yet the technology employees use every day shapes how they experience the business and how effectively they can contribute.

As AI becomes embedded in everyday work, that connection becomes even more important. The organizations that realize the greatest value from AI will not simply introduce new tools. They will ensure employees have the technology, support and confidence to use them effectively.

The employee experience starts with technology

When workplace technology works well, it is largely invisible. When it does not, the effects are felt in everyday moments: a video call that starts late because something will not connect, a laptop that struggles through a day of meetings, or a workspace setup that is inconsistent from one location to the next. Individually, these are minor frustrations. Collectively, they influence productivity, engagement and perceptions of the workplace over time.

Employees do not judge technology by specifications or refresh cycles. Increasingly, they judge employers by the quality of the digital experience they provide and whether they can work without friction. As a result, organizations are increasingly looking beyond individual devices and considering the broader technology environment.

The PC, display, peripherals and collaboration tools all contribute to the employee experience, shaping how effectively people can collaborate, stay productive and adapt to changing business needs.

From access to enablement

This conversation is becoming more significant as AI moves from experimentation into everyday workflows. As these capabilities become more commonplace, organizations are recognizing that successful AI adoption depends on more than simply providing access to new tools. It requires the surrounding systems, processes and technology environments that enable employees to use them effectively.

This challenge is reflected in Microsoft's 2026 Work Trend Index, which highlights a growing "Transformation Paradox": employees are increasingly ready to work with AI tools, while many organizations are still adapting their systems, processes and operating models to fully support them.

While AI is often discussed in terms of applications and platforms, employees experience it through the devices they use every day. For most employees, the PC remains the primary workspace: the place where meetings happen, content is created, decisions are made and work gets done. As AI is embedded in everyday business applications, the role of the PC is evolving.

It is no longer simply a gateway to information or a communication tool. Increasingly, it is becoming a platform through which employees interact with AI-powered capabilities as part of their daily work.

As these capabilities become embedded into everyday workflows, employees can spend less time summarizing meetings, searching for information or completing repetitive administrative tasks and more time on work that requires judgement, creativity and human interaction. The outcome is not simply productivity in the traditional sense. It is a better day-to-day experience of work.

The technology behind workforce readiness

Workforce readiness increasingly depends on whether employees have the tools and support required to make effective use of emerging capabilities. This is where device strategy becomes increasingly important. Ageing technology rarely creates a single catastrophic failure. More often, it limits potential through delays, workarounds and interruptions that make it harder for employees to take full advantage of new ways of working.

At the same time, AI is raising expectations about what employees should be able to accomplish. If organizations want people to embrace new ways of working, they need to consider whether the technology environment supporting them is fit for that purpose. That means looking beyond refresh schedules alone and considering the broader role workplace technology plays in enabling productivity and workforce readiness.

Why device strategy is becoming talent strategy

Organizations do not build successful workplaces through technology alone. Yet it is becoming increasingly difficult to separate workforce outcomes from the technology environment that supports them.

That is why the conversation around AI PCs is expanding beyond IT and procurement to include HR and business leaders. As organizations look for ways to improve employee experience, strengthen productivity and prepare people for AI-enabled work, device strategy is becoming part of a broader talent conversation.

In the past, technology was viewed as infrastructure that supported work. In the AI era, it is increasingly becoming infrastructure that supports talent. Organizations that recognize technology as part of their talent infrastructure will be better positioned to attract, empower and retain the people they need to succeed.

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Psychologist Abraham Maslow once warned: “It is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.” Agentic AI is becoming the enterprise version of that hammer.

Once people understand what agentic AI can do, the list of possible use cases grows quickly.

While that enthusiasm should be applauded, it can lead to strategies that are back to front “If AI is the answer, what’s the question?” By the time it comes to looking at results and whether the investment in AI has been fruitful, it’s hard to identify what’s been achieved and why achieving it was important in the first place.

Start with the value, not the agent

In the rush that followed ChatGPT, many organizations deployed AI tools to their workforces at large with no defined value or end goal. People started working differently almost immediately, but with no baseline captured, there was no way to know what had actually improved. Who knows how long it takes to write a coherent email and who knows what time is saved by having ChatGPT rephrase it for you!

This is what McKinsey calls the “gen AI paradox”. Its 2025 survey found just 39% of organizations reported any enterprise-level financial impact from their AI investment, despite mass adoption.

Organizations need to be precise about what they want to measure. There are three layers to this and most only capture the first.

• Adoption: are people using it?
• Behavior: are people doing their jobs differently?
• Outcome: revenue per salesperson, tickets closed per engineer, cycle time on a review gate.

Adoption and outcome together show a correlation; for example, people who use AI sell more. But it’s the, often missed, behavior layer in the middle that turns that correlation into causation, i.e. they sell more because they are now able to use AI to fill in more client reports, leaving more time to hit the phones.

Know your workflow

If organizations can’t see how work is really being done, two things happen. The baseline measures the wrong process, making any claimed improvement meaningless and an agent given the wrong steps cannot complete the job.

One procurement process at Atos looked like a four-step workflow on paper, but when we looked carefully and asked those doing it, it was closer to 10. People moved between Enterprise Resource Planning (ERP), Outlook and spreadsheets, copying information, waiting for replies and re-entering data manually.

A baseline on four steps would have measured the wrong process and an agent given four steps couldn’t have completed a job that takes ten.

Not every problem needs an agent

Understanding the workflow also shows whether agentic AI is the right technology at all. If tasks follow fixed rules “if this happens, do that” the answer may be Robotic Process Automation (RPA), workflow automation or a process change.

Agentic AI is better suited to workflows where the system needs to interpret context, coordinate across tools, make recommendations or act within defined boundaries. Even then, it needs a clear measure of success and a credible business case. The economics of deploying Agentic AI are still in infancy, but this will act as a natural filter for only the most valuable cases.

Prove the value before building

Value is where the tough work of a clear business case comes in. Proving the value of Agentifying a process before any code is written.

This requires access to good data sets. Evidence on cost and business value before introducing Agentic AI is needed before budget is committed. Where the benefits are measurable and enough open data exists, it’s possible to build something real in a week. Where the data still requires work, it is always worth refining the data you have before attempting to justify a business case.

Work managed upfront will save cost and time and give you evidence of value.

Build a portfolio, not a queue

Once there is a shortlist of use cases, organizations can prioritize against financial impact, feasibility, process impact, technology requirements, human impact and risk. Scoring across multiple factors rather than ranking on ROI alone creates a portfolio instead of a queue. A lower-value use case with fast impact might prove value early and build credibility for agents that follow. A high-value use case with a two-year runway may need to wait.

Budget for a new mortgage

Even once the right case has been selected, many organizations still struggle to see impact because they focus on the build, then under-resource the work needed to make the change land.

Gartner calls this a “transition mortgage”, where AI adoption demands more support than traditional technology, often requiring 25% more training effort and up to 200% additional change management.

In our own organization, we were guilty of building independent tools in different regions and teams to solve the same business problem. We have now established clearer oversight of our use cases. The issue was not the quality of the tools, but duplicated effort, cost and resources.

Not every workflow needs a hammer

C-suite leaders should start with fewer use cases where the value and workflows are clear, the baseline is measurable, the data is available and the role of the agent is well defined. Then they should budget for communications, adoption, measurement and scale.

The value from agentic AI is not measured by the number of agents an organization deploys. It is measured by whether the work changed and whether the organization can prove it. Agentic AI is a powerful hammer, but success lies in understanding which problems are nails and which are not.

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The most fascinating thing about Apple's new iPhone Duo – and others like it – may not be that it folds. It may be the innovation required to keep it cool. Because at a time when performance demands from edge AI continue to increase, we’ve now taken available smartphone real estate and – though not exactly halved it – constrained it more than ever. And that impacts heat. Which impacts performance.

What makes the Duo interesting isn't simply the engineering required to create a folding phone. It's that Apple is confronting the same challenge every edge device manufacturer now faces: AI capability is increasing faster than our ability to manage the heat it generates.

That’s why I believe we’re entering a new phase of device innovation, with thermal design increasingly shaping what’s possible. In the edge AI era, managing heat helps determine what a device can do, how long it can sustain performance, and how small or thin it can be.

The iPhone Duo is just the latest reminder that smartphone manufacturers have run into a stubborn engineering problem: How to keep mobile processors cool when there’s almost nowhere left to put a cooling system.

For its part, with the Duo, Apple has packed its A20 Pro and other heat-generating electronics into an exceptionally thin, foldable design. It’s also created a custom vapor chamber to help dissipate heat and sustain performance in the new form factor.

The latter is more than an engineering footnote. It’s a glimpse at a challenge the edge AI industry is starting to confront. Thermal design and industrial design are inseparable. And new solutions to thermal challenges must be considered.

Foldables Make the Challenge Obvious

Foldable phones make the thermal management challenge easy to perceive. Conventional smartphone design gives engineers a relatively predictable enclosure in which to arrange the processor, battery, cameras, and thermal management system. A foldable must accommodate a hinge and two display sections while remaining thin enough to fold comfortably and fit in a pocket.

At the same time, the processor and other electronics still generate heat – more, even, when we consider how people are starting to use mobile devices. Which means foldable designs necessarily concentrate significant heat-generating components into smaller and thinner portions of the device, precisely where space for thermal hardware is already constrained.

In general, vapor chambers remain one of the most effective ways to manage heat in a compact electronic device. They work by moving heat away from a concentrated source, such as a mobile processor, and spreading it across a larger area. But vapor chambers ultimately spread heat rather than remove it.

Sure, as processors become more powerful, manufacturers can increase the size and effectiveness of the vapor chamber. But eventually that thermal component itself becomes a design constraint, especially in foldable phones.

Agentic AI Makes the Challenge Urgent

In foldables, innovating around thermal management is itself an admirable design goal – if folding were the only technological advancement engineers had to tackle. But they’re pushing the envelope on physical design at the exact moment when smartphones are being asked to do much more. Namely, to process agentic AI workloads.

As agentic AI moves from the cloud to edge devices – AI-capable smartphones, wearables, glasses, and more – thermal management becomes the industry's most important challenge. Imagine a new foldable smartphone with its own specialized thermal management system, designed to accommodate an innovative new form factor. Now imagine asking that foldable to process vast amounts of data differently than before.

Unlike traditional mobile apps that perform discrete tasks, AI agents operate continuously. They sense their environment, preserve context, analyze streams of data, and act in real time.

In my view, the industry is still underestimating how significant this transition will be. Historically, a smartphone's thermal profile was defined by short bursts of activity: launching an app, streaming a video, capturing a photo. Agentic AI at the edge changes that model entirely. Devices are moving toward persistent intelligence, continuously sensing, processing, reasoning, and responding. With these sustained workloads, a device’s ability to manage heat becomes a defining constraint on its performance.

Passive thermal technologies remain indispensable. Vapor chambers, graphite, heat spreaders, and advanced materials will continue to play a critical role in future devices. The challenge is that Edge AI introduces thermal loads that increasingly benefit from more than heat spreading alone. As devices become thinner and workloads become more persistent, designers will need additional ways to not just to move heat away from sensitive components, but to move heat out of the system entirely.

Micro-Cooling Complements the Vapor Chamber

So here we are, with foldable smartphones capable of agentic AI processing. It’s a perfect storm of thermal design challenges. Engineers can continue enlarging or customizing the thermal systems they integrate into new and different form factors. Or they can find other ways to extract more thermal performance from available space.

The latter approach is where active, micro-cooling technology becomes a viable and important solution.

Unlike miniature rotary fans, which exist today and take up precious real estate of their own, MEMS-based piezoelectric actuators, fabricated in silicon, generate airflow inside extremely compact packages. Basically, it’s a fan on a chip, capable of precise active cooling that fits where conventional solutions cannot.

With micro cooling (µCooling), heat isn’t just spread out, it’s removed from the device entirely before it can sink into the case, delaying or eliminating thermal throttling. This hybrid approach to cooling in highly constrained spaces delivers better thermal performance in today’s AI-enabled smart devices.

It also creates greater design flexibility – a must as devices themselves become more physically flexible. If active µCooling can provide additional thermal performance, manufacturers may not need to maximize the physical dimensions of vapor chambers. A more appropriately sized, passive thermal system could be paired with localized active µCooling and thereby free up internal volume for other components and give designers more flexibility in how a device is assembled.

Put simply, with µCooling, thermal hardware wouldn’t have to consume as much of a device’s design envelope.

µCooling As Part of the Compute Architecture

The implications extend beyond folding smartphones. AI glasses, headphones, watches, robots, and other edge devices face the same fundamental problem, often with even tighter constraints on size, weight, and power.

The industry wants to put more intelligence into mobile devices while simultaneously making them smaller and more comfortable. That creates a physical contradiction. More compute generates more heat. Smaller devices provide less room to manage it.

µCooling offers a way to change the equation. Not by eliminating vapor chambers and other passive solutions, but by supplementing them with localized active cooling that provides additional thermal headroom while giving product designers more freedom.

As AI runs more continuously in the background, edge devices will spend more time computing and less time idle. Sustaining performance will matter as much as reaching peak capability. Foldable phones make this challenge acute, but it extends across the edge AI market as more intelligence and local processing place greater demands on compact hardware.

That intelligence has a thermal cost. Over the next decade, progress will depend as much on what physical devices can sustain as on what AI models can do. That’s why I believe thermal management is becoming a foundational technology of the edge AI era. The companies that solve this challenge will help define the next generation of intelligent devices.

Manufacturers already know this, which is why thermal management is so essential to product design. µCooling is the innovation behind the innovation – like smartphones that fold.

Check out our list list of the best product management software.

This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

The "enterprise brain" is a term featuring more and more prominently in the enterprise technology market and at the C-suite level. Technology vendors of all varieties are scrambling to connect internal documents, knowledge bases and structured data systems into a unified architecture.

The compelling goal is to provide AI agents with as deep organizational context as possible so they can reason, automate and assist with scarcely believable accuracy.

But as businesses pour resources into linking their databases, policy documents and CRM entries, a significant blind spot remains.

Efforts to build an enterprise brain often overlook the intelligence contained in the millions of conversations employees are already having with customers, colleagues and candidates across phone calls and messaging channels.

Documents tell you what your policies are and CRM records can tell you what happened, but business conversations tell you why an incident happened.

For any company that is serious about building a true enterprise brain, conversations cannot remain a missing layer of organizational intelligence.

Beyond the confines of structured data

Standard business tools are excellent at tracking outcomes but terrible at preserving nuances. Structured systems may record instances of an enterprise deal stalled, an account at risk of churning or a candidate withdrawing from an interview process. But it of course can’t put its finger on the actual trigger.

A deal doesn’t just stall, there will always be a key factor that changed the buyer's mind during a late-stage call. Similarly a candidate doesn’t just decline an offer, maybe a competitor introduced a work-from-home policy during a quick negotiation chat.

When organizations rely solely on static databases to train AI models or inform strategy, they are feeding their enterprise brain an incomplete diet. They receive the post-mortem without the symptoms, and AI needs the sometimes messy, real-time context found in human dialogue to understand the operational reality of a company.

Actionable context

Artificial intelligence has reached a level of maturity where analysing high-volume voice and chat interactions is no longer a nice to have or a bonus. But just recording every interaction and dumping raw transcripts into a data lake, which effectively hoards unstructured audio, solves absolutely nothing. To make conversational context truly useful, businesses must focus on a few core operational pillars.

First, conversational insights must automatically sync with existing enterprise systems rather than living solely within a VoIP dashboard, allowing real-time data from a support call to enrich the CRM and trigger automated alerts. Second, the extracted intelligence needs to be routed immediately to the teams equipped to act on it. For example, if sales calls routinely show that prospect friction stems from a specific pricing tier, that signal should flow directly to product strategy and marketing rather than remaining trapped in the revenue organization.

Finally, while AI should perform the heavy lifting of summarization, sentiment analysis and pattern recognition, human judgment must always remain the final filter for important decision-making. While this may seem obvious, it is amazing how overly reliant on AI many employees have quickly become, including using it for critical business judgement calls.

Breaking down knowledge silos

Knowledge silos continue to be huge challenges for many organizations. Sales teams learn what buyers want, support teams learn where the product fails, and recruitment learn how the market perceives employer branding. All is highly valuable information, but all too often these groups don’t share a unified intelligence pool.

When conversational context is ingested into the enterprise brain, these walls dissolve across every department. For instance, when a prospect mentions a niche feature gap during a sales demo, that insight can prompt product teams to prioritize the feature while sales updates its outreach messaging.

Similarly, if a customer casually mentions an upcoming corporate restructuring on a support call, account management can initiate proactive outreach to protect the account before contract renewal.

By connecting these conversational dots across departments, organizations shift from reactive firefighting to proactive alignment.

Value over volume

Now this is not to say that trying to capture, transcribe, and analyze every single internal interaction is the way forward. Because it goes without saying, this is borderline impossible. If attempted, it can lead to sky-high compute costs and even legitimate anxiety regarding employee monitoring.

To build a functional, trusted enterprise brain, organizations must prioritize value over volume. The focus should be on high-value workflows where conversation directly drives business outcomes, such as customer-facing revenue calls, critical support interactions or key talent acquisition touchpoints.

Focusing on these targeted areas minimizes data noise while delivering immediate ROI. Organizations must build trust by maintaining clear boundaries. Conversational analysis should focus on market trends, product feedback and operational blockers instead of micromanaging employee tone or enforcing rigid scripts.

The complete enterprise brain

A truly intelligent enterprise cannot run on paperwork and revenue tables alone. It requires the context and nuance that only human interactions provide.

As enterprise AI continues to evolve at rapid pace, having the largest static document libraries will count for nothing. The organizations that thrive will be the ones who successfully pair their structured business data with the rich, dynamic intelligence hidden inside their everyday conversations.

Checkout our list of the best business intelligence platforms.

This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

• US data center developers have requested 1,066GW of grid connections, but Wood Mackenzie expects utilities to commit to only about 28%
• Utilities are demanding collateral, longer contracts, and study fees; Exelon's high-probability load fell from a whopping 18GW to 11GW once it insisted on the former
• Because grid costs are shared, phantom demand risks landing on household bills, and nobody knows how much of it is real yet

US utilities and grid operators are now demanding higher rates, longer contracts, collateral, and steep engineering study fees from data center developers, in an attempt to weed out what the industry calls "phantom" data centers.

Reporting from The Wall Street Journal claims the reasoning is straightforward: Grid costs are shared, so if a utility builds substations and transmission lines for a project that never shows up, everyone else on the network ends up helping to pay for them.

The numbers back up the nerves in play: developers have requested 1,066GW of grid connections for US data centers, according to a Wood Mackenzie analysis first reported by Bloomberg in August 2026, a figure equal to roughly 83% of the country's entire utility-scale generating capacity at the end of 2025.

A queue full of ghosts as collateral becomes the norm?

Wood Mackenzie expects utilities to commit to serving only about 28% of it: roughly 12% that already has firm service commitments, plus a further 17% that looks likely. The WSJ also cites a separate Wood Mackenzie measure that puts just 23% of disclosed requests in its high-confidence category.

Much of the gap is due to duplication; developers who don't yet know which utility can deliver power fastest file the same project with several of them and keep every option open until one pans out.

Others are simply speculators: commentary in POWER Magazine argued that private equity funds, land brokers and shell companies are claiming queue positions without site control or a signed customer, betting that land with a grid connection attached will find a buyer.

The phenomenon isn't new. Utility Dive was writing about phantom data centers in 2025, and TR reported in July that 67% of electricity executives surveyed by Capgemini were seeing speculative applications for capacity. The change is that utilities are now pricing the problem, and it is causing many adverse effects.

Exelon's attempts to lock down potential consumers

Exelon, which runs utilities across the Mid-Atlantic and Midwest, offers the clearest look at what happens when developers are asked to put money down.

After it began requiring transmission security agreements (TSAs), contracts that bind customers to credit obligations, committed revenue contributions, and shortfall payments, its high-probability data center load fell nearly 40%, from 18GW at the end of 2025 to about 11GW in the second quarter. Its wider pipeline of projects under study dropped from about 43GW to 25GW.

Jeanne Jones, Exelon's CFO, told the WSJ that typical requests used to be around 50MW and now often run to 1,000MW or more. The question she kept fielding, internally and externally, was "How do you know what's real?"

Exelon, however, also told analysts its $41 billion capital plan through 2029 is unchanged, so a 40% cut in expected data center load has not, so far, shrunk the spending plan.

None of this would matter much if grid capacity were cheap, but it simply isn't. PJM, the largest US grid operator, saw its capacity auction clear at the price cap for the third time in a row this summer while missing its reliability target by 6.8GW.

In September, its independent market monitor told federal regulators that 4,017MW of forecast data center load, 42% of the growth PJM had assumed, had already been canceled or delayed.

Households are feeling the cost increase; reports in July indicated that Henrico County, Virginia, asked staff and schools to cut electricity use after a 24.9% rate increase. Virginia has also moved to stop utilities passing data center costs onto everyday users, and Texas bills are rising faster than inflation. Lindsey Short of the Ohio Manufacturers' Association warned against building the grid around "the most optimistic number on somebody's spreadsheet."

Underbuilding carries its own risks, though, especially when transformers can take years to arrive, as a recent deep dive on grid bottlenecks laid out. That is why utilities are locking in the customers they do accept. Average minimum contract lengths for large customers have risen to about 10.8 years from 4.6 before 2024, according to filings aggregated by Halcyon and cited by the Journal, and a separate research brief built on Halcyon's tracker found a similar jump.

What nobody can yet say is how much of the AI power rush is real, and that poses problems for planners, stakeholders, and households alike. Until it does, utilities are doing what any lender would: asking to see the money first; households just happen to be roadkill if things don't pan out.

• Trump administration officials asked CISA to hack a voting machine just months into his second term
• CISA was told not to disclose any vulnerabilities to the voting machine manufacturer
• CISA has been gutted by job cuts and a lack of funding, with many state officials hesitant to trust the agency with securing the midterm elections

Towards the start of President Trump’s second term, allies in the Department of Homeland Security tasked the Cybersecurity and Infrastructure Security Agency (CISA) with secretly acquiring a voting machine with instructions to hack it and not tell anyone except the White House about the vulnerabilities they found.

ProPublica, who spoke to two former administration officials with knowledge of the request, noted that the standard practices of CISA require the agency to let manufacturers know of vulnerabilities in their technology, but the political appointees explicitly asked CISA not to.

“My thought was they could keep it in their back pocket to invalidate any [election] results they don’t like,” one of the former officials told ProPublica. “This goes against everything CISA represents.”

CISA asked to hack voting machine

CISA has been a relentless target of the Trump Administration, with the agency’s jobs being cut by one third and the majority of its senior leadership resigning or being fired.

In total, CISA has cut 1,000 jobs since the start of Trump’s second term - largely down to Elon Musk’s Department of Government Efficiency - with additional cuts to funding in 2027 likely to result in a further 900 jobs being lost.

As a result of these job cuts, remaining CISA employees told the political appointees that the request to hack a voting machine couldn’t be fulfilled.

Since 2018, CISA has been responsible for helping to protect election infrastructure from cyber attacks and interference, but has had to cut back on much of its work in these areas due to a lack of funding and staff. Many state officials have been unable to seek election support ahead of the November midterm elections.

As a result of Trump’s cuts to CISA’s jobs and funding, many state officials fear the agency has become a shell occupied by Trump loyalists. “They’ve violated the entire premise of that information-sharing relationship,” a former high-ranking federal cybersecurity official told ProPublica. “There’s no fucking way they’re ever going to give any information to CISA. And it’s unclear if they ever will in the future.”

In response to the claims, White House spokeswoman Lauren Bis said, “President Trump has done more than any other President to ensure we have free and fair elections.”

“I don’t pay attention to what the president says. I pay attention to what his administration does,” said Arizona Secretary of State Adrian Fontes. “And what they’ve done is basically cut us off and left us on our own.”

Voting difficulties start early

Early voting for the midterms has already started in some states, but early polling sites at North Carolina Agricultural and Technical State University, the University of North Carolina Greensboro, and Western Carolina University have been shut down, with Guilford County board members saying the resources could be better allocated elsewhere.

Shutting the polling sites makes it significantly harder for the collective 40,000 students to vote, with university policy preventing some students from owning a car during their first year of study.

Shia Rozier, a senior at North Carolina A&T and cofounder of student organization Protect Ours NC told NPR, “In order to get to the polls, they now have to get a Lyft or get a Uber or get somebody to take them at a time where gas prices are extremely high, creating an additional barrier to the poll.”

In response, students organized a march to the nearest polling site over 45 minutes away.

• Amazon Prime subscriptions will increase to AU$12.99 per month or AU$99 per year, up from AU$9.99 and AU$79, respectively
• These prices are effective immediately for new subscribers, while existing members will be charged starting from November 25
• Inclusions and perks like Prime Video, Music Prime and sale events remain unchanged

Amazon Australia has revealed its monthly Prime membership fee has increased from AU$9.99 to AU$12.99, effective immediately for new subscribers — a significant 30% hike from the previous cost. The annual membership price also increases from AU$79 to AU$99, a 25% rise.

In emails sent to Prime members, Amazon said the new pricing reflects the growing range of products and benefits available through the service, although the inclusions remain the same, including free and expedited delivery, Prime Video, Prime Music, Prime Reading, video games with Amazon Luna, and member-only events such as Prime Day and Prime Big Deal Days.

Existing customers will continue to pay the usual AU$9.99 per month and the price increase will apply from their first membership renewal date on or after November 25, 2026.

In a statement, an Amazon spokesperson told TechRadar that this price change is specific to Australia and attributes it largely to the expanded delivery footprint, new Prime Video content and access to Alexa+.

"We are committed to growing the value of Prime for members, and in just the last six months we have brought Prime Free overnight delivery before 8am on hundreds of thousands of products to four cities, expanded Prime Free Same-Day Delivery available now in nine regions across Australia, added new TV series and movies to Prime Video plus live sport, given free access to Alexa+, provided exclusive deals through events like Prime Day, and more," the spokesperson said.

"With the continued expansion of Prime member benefits, and so we can continue growing the offering, we will change the price of Prime to $12.99 per month or $99 per year. Prime has never offered as much value for members as it does today."

This price hike comes just in time before Amazon’s own Black Friday sale in Australia, which we expect to start a few days before the actual Black Friday date of November 27, and will run until Cyber Monday on November 30.

While the US has seen multiple price increases recently, this is Amazon’s second round of hikes in Australia since launching in 2018. Initially just AU$6.99 per month or AU$59 per year at launch, Amazon later bumped that up to AU$9.99 per month (43%) or AU$79 per year (34%) in May 2023 as more inclusions were added. As mentioned, the price doesn’t reflect better inclusions, which is surely going to factor into household costs for many regular Amazon shoppers.

Earlier this month, Amazon opened its third Sydney fulfilment centre located in western suburb of Horsley Park which is “a 12-hour drive of 80% of Australia”, meaning even more shoppers will benefit from same-day delivery. Perhaps this goes some way in justifying a price increase, along with the fact that Amazon typically offers the best prices on thousands of items even outside of major sales. However, with an additional AU$2 monthly payment to get rid of ads on Prime Video, this news is sure to sting some customers.

While the Amazon Black Friday sale in Australia isn’t a Prime-exclusive event, the best prices will be for subscribers, so it does still pay to have a subscription. Thankfully, Amazon also makes it easy to cancel your subscription any time, although you won’t be eligible for the usual 30-day free trial if you’re a returning customer.

NPR reports that TikTok staff raised the alarm on youth bypassing age restrictions as well as being sexually exploited on the platform.

Early adopters are charging $5 per month for premium content, but pricing is up to them.

It wouldn’t be Halloween without some spooky games, too, and Xbox Game Pass has you covered.

Need a Wordle hint today? Get spoiler-free hints for NYT Wordle No. 1,950 on Wednesday, Oct. 21, then the answer, plus yesterday’s word.

Some fears remain the same, but new tech-related ones are emerging and climbing the ranks.

Apple's 512GB Mac Studio is coming in late October 2026. Here's what to know about its M5 Ultra chip, AI capabilities, pricing, and availability.

The post Apple’s 512GB Mac Studio Is Almost Here: What Buyers Should Know appeared first on TechRepublic.

• Lucasfilm Games vice president Douglas Reilly suggests more Star Wars games could be made following the success of Star Wars: Galactic Racer and Star Wars: Zero Company
• Reilly says that while "huge triple-A games like Battlefront are always going to be an essential part of the strategy," the "goal" is to find "the best partner"
• This goes for both triple-A and smaller independent studios

It sounds like Lucasfilm Games wants to release more Star Wars games following the success of Star Wars: Galactic Racer and Star Wars: Zero Company, but it's not necessarily looking for triple-A studio pitches.

That's according to Lucasfilm Games vice president Douglas Reilly, who told The Game Business that games developed by independent studios "very much is part of the strategy."

"Obviously, huge triple-A games like Battlefront are always going to be an essential part of the strategy," Reilly added. "But one of the great things about Star Wars is it has all kinds of other opportunities to tell stories in different genres.

Reilly said, "The goal has been to find the best partner who makes that experience come alive inside the galaxy far, far away," and it doesn't necessarily "have to always be triple-A."

"Sometimes it can be double-A, and it’s really about the experience that we’re delivering and the fantasy we’re fulfilling," he said, adding that Lucasfilm Games has "tried to be more flexible about who we’re willing to take pitches from."

Saber Interactive is quietly working on its Star Wars: Knights of the Old Republic remake, which it confirmed is still in development, while a new game from Arcnaut Studios and Mass Effect trilogy director Casey Hudson, Star Wars: Fate of the Old Republic, is now in the works.

In addition, a new report has now claimed that Destiny developer Bungie and DICE are "aggressively" pursuing a Star Wars: Battlefront 3 project, but official confirmation has yet to come in.

"Good ideas can come from a lot of different places," Reilly continued. "You don’t have to necessarily be a top five publisher to make a great Star Wars game. There are some hurdles.

"We have a high-quality bar, and that means typically we work with more experienced teams than ones with no track record. But we’re exploring all kinds of things, large and small, with partners that are going to surprise folks."

I reviewed both Star Wars: Galactic Racer and Star Wars: Zero Company, and they're among the best games of the year. Galactic Racer is the Star Wars racing game I've always wanted and an exhilarating roguelike racer with endless customization, while Zero Company acts as an excellent companion story to the Clone Wars era with immensely fun turn-based tactics gameplay.