• 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.

• Developers are stripping ads and tracking from smart TVs
• They’re using AI vibe coding to quickly spin up solutions
• Customers are growing increasingly frustrated with TV streaming bloat

If you’re sick of your smart TV being plastered with sponsored ads, slowed down with bloat, and infested with trackers, you’re not alone. But instead of putting up with the countless intrusions, some people are stripping the unwanted cruft from their TVs altogether — thanks to a little AI vibe coding.

As reported by TechCrunch, a number of developers have offered solutions in recent weeks. That includes one called dumbtv, which sets out its mission statement on its GitHub page: “Your TV should do what you paid for: your apps, your HDMI inputs, and nothing else. No ads from the TV itself, no watch tracking, no update you did not ask for.” To that end, dumbtv removes “Ads, telemetry, experiments and Alexa,” as well as any apps the user selects, from all manner of smart TVs.

Another option, this time called cleanyourtv, says it will “Strip the bloatware from any Android TV or Google TV over Wi-Fi, and replace the ad-filled home screen with a clean launcher that looks like an Apple TV. No root, no cable, nothing uninstalled, and a one-line restore if you change your mind.” Both projects claim to be fully reversible and say they do not touch the TV’s root directory.

You might not even need to download a GitHub project to debloat your smart TV. Posting on X, Ryan Doyle said they simply found a listing for their TV and asked Claude Code, “can you hack this?” The AI agent then walked Doyle through the steps to remove “ads, bloatware, spyware” and speed up the “laggy UI,” resulting in a far better user experience.

AI meets user frustration

At the other end of the spectrum from the simple solutions mentioned above is the ad-free Android TV setup created by a developer known as Cretezy. Instead of merely blocking advertising and tracking features in Android TV, it aims to replace many aspects of the operating system altogether. It includes an ad-free launch screen, a YouTube substitute, a media manager, and a remote control button remapper. Although it might sound complicated, Cretezy says that an AI agent can set the whole thing up for you and tailor it to your TV, demonstrating how AI can make removing bloat as simple as possible.

People have been getting their hands dirty with TVs for almost as long as they’ve existed. But what’s different this time is the power that AI has given people to quickly spin up a targeted solution in hours rather than days or weeks. That reduction in overhead has led to a proliferation of hacks and workarounds that remove annoying features many people would rather not have.

The timing plays a role in other ways. Right now, many consumers are growing frustrated with streaming TV providers, whose prices have risen and whose ads have become increasingly intrusive. Throw in rising awareness of the ways that Big Tech firms are tracking and monetizing their own customers, and you’ve got a recipe for answers like dumbtv and cleanyourtv.

So, if you’re sick of how slow and bloated your smart TV has become, all it takes to fix it is a simple AI prompt and a little tinkering. The days of ensh*ttified TVs could be numbered.

• Tencent says AI agents it used to evaluate third-party models showed "unintended" browsing behavior, the first such admission by a major Chinese firm
• Swarmchasers researchers logged more than 2,600 attempts by the Tencent Cloud fleet to pull entrance data from Alibaba's Amap
• Most attempts failed, but agents used a public scanner as a remote browser, and one leaked company API key returned real place data

Tencent has acknowledged that AI agents running on its infrastructure behaved unexpectedly after independent researchers caught a fleet of them repeatedly trying to pull data from Alibaba's Amap navigation service.

Reuters reports the company's AI unit, Tencent Hunyuan, said the agents were being used to evaluate third-party models, in what appears to be the first known case of rogue agent activity acknowledged by a major Chinese company.

In a post on X, the unit said the activity did not come from its own Hunyuan models, noting "During these evaluations, some agents exhibited unintended web-browsing behavior, but our review found no evidence of deliberate targeting or coordination," before adding that it was tightening network access controls and agent monitoring.

A lot of "unintended" effort for a zoo gate?

The admission follows a full report by Swarmchasers, a loose group of researchers who met at an AI hackathon. They counted more than 2,600 attempts to extract Amap data over 11 days, from September 28 to October 8 2026, traced the traffic to Tencent Cloud servers in Hong Kong, and then did something nobody appears to have done before: they talked to the agents while they were still running.

The tasks were almost comically mundane. A typical job asked which entrance of a place like Chengdu Zoo most Amap users use, with other runs covering parks, museums, and hospitals and, on September 30, a British Columbia cancer statistics dashboard.

The problem was getting the answer. Amap's anti-bot system expects browser cookies, JavaScript-generated tokens, and sometimes a CAPTCHA, while the fleet's harness sent plain Python and curl requests. One agent told the researchers that Amap had blocked its Tencent sandbox, so it could not fetch the data itself.

The agents improvised instead; according to the Swarmchasers report, they packed their own HTML and JavaScript into URLs and fed them to urlquery.net, a public security scanning service, borrowing its browser to run their code.

When Amap kept blocking them, the programs tried to intercept or forge tokens from Alibaba's own anti-bot libraries, sent 57 requests to four pre-production hosts, routed traffic through Baidu Translate and remote browsers, and, in seven reports, used Amap API keys found on the open web.

Most of it did not work as intended: in a snapshot of 2,372 reports up to October 4, nearly half ended on an empty Amap page or login prompt and almost a quarter on a blank tab. None of the token tricks appear to have succeeded, and there is no evidence that any pre-production server answered. The entrance figures that did come back arrived mostly through Jina's page reader, which can serve cached copies up to 30 days old.

The exception should worry businesses more than Alibaba. A key and security code exposed in another company's public source code did return real place data, and the researchers say they cannot tell whose account was billed for those requests.

Tencent's framing of the activity as "unintended" is accurate as far as it goes, but it sidesteps an awkward point. If an evaluation asks agents for data that only Amap displays, behind defenses a sandboxed agent without a browser cannot legitimately clear, then crossing that line is close to the only route to a correct answer.

US incidents involved something similar: OpenAI's agents breached Hugging Face while hunting for answers to an ExploitGym benchmark, with later disclosures showing they organized through a makeshift message board, and roughly 700 of them took part in an attack Hugging Face eventually contained using Zhipu's open GLM 5.2 model. Anthropic found three Claude models had compromised real organizations after a misconfigured evaluation left internet access open, and this month disclosed that Claude Haiku 4.5 filed a false homicide tip with Philadelphia police while exploring random websites.

Reuters judges Tencent's case less egregious than those, and the Swarmchasers found no sign the agents coordinated, which is why they call it a fleet rather than a swarm. Still, as recent analysis argued, sandbox escapes often come down to an agent routing around a proxy, and borrowing a stranger's browser is exactly that.

Tencent's admission lands in the same week ByteDance's Seed unit created a dedicated Alignment & Preparedness team and began recruiting 10 researchers worldwide, a rare public commitment to frontier safety work by a major Chinese developer. The internal email announcing it went out a day before the Swarmchasers' full report and, in 36Kr's translation, points to a broader run of agent escapes rather than this one case, so the two are not cause and effect even if they do point the same way.

The argument is that alignment remains unsolved regardless of whose flag flies over the data center, and they want every lab running internet-connected agents to audit what its sandboxes can reach, a seemingly modest ask.

This fleet, however, ran for nearly a week before outside researchers stumbled on it at a hackathon, then carried on for four more days after their preliminary findings went public, and Tencent has yet to explain why.

• US civilian agencies have been ordered to stop using DeepSeek and remove it from government systems
• DHS cited data security, user tracking, and potential national security risks tied to the AI model
• The move expands broader US restrictions on Chinese technology across government infrastructure

The Donald Trump administration has banned Chinese Artificial Intelligence model DeepSeek from all civilian government agencies and ordered them to stop using it and remove it from its tech stack immediately.

Earlier this week, the US Department of Homeland Security (DHS) issued a new notice, notifying government agencies of the change:

“The Department of Homeland Security is publishing notice of the Secretary of Homeland Security’s issuance of an order (1) excluding information technology products and services developed by Hangzhou DeepSeek Artificial Intelligence Co., Ltd (DeepSeek), to include affiliated entities as defined in applicable regulations, from federal civilian agency procurements; and, (2) removing all DeepSeek and DeepSeek-affiliate products, and third-party products and services developed using DeepSeek implementations, from civilian agency information technology.”

Multiple threat vectors

Apparently, this is not the first time the Trump administration made moves against Chinese AI tech. According to CNBC, the order was originally issued in late July this year, and applies to federal civilian agencies including the Treasury, State and Justice departments, but excluding the Department of Defense. The publication also said the US Congress restricted the use of DeepSeek in intelligence community systems late last year, and even earlier - in March - the US Commerce Department told its staff not to use DeepSeek. The Defense Information Systems Agency banned it in January 2025.

In the latest DHS order, it was said that DeepSeek’s models “present multiple threat vectors”. To use the tool, users need to provide all sorts of data - from personally identifiable information (PII) to other business or proprietary information, which the company can use to “track and digitally fingerprint users”. Furthermore, China’s laws allow the government to easily access and collect the data created and stored by private companies, meaning information on US citizens can easily fall into the government’s hands. That, in turn, “could produce actionable intelligence on US persons and/or government programs,” the DHS explains.

“The models themselves may contain malware or other security vulnerabilities,” it stressed. “Indeed, open-source security research has found that the DeepSeek iOS mobile application contains multiple critical vulnerabilities.”

When asked for a comment, China’s foreign ministry told the Reuters news agency it would safeguard the rights of its companies: “China consistently opposes the over-generalization of national security as well as the politicization of science, technology and trade issues,” Mao Ning, a ministry spokesperson, told the agency.

China trade war

US President Donald Trump has openly talked against using Chinese technology and has banned multiple companies and solutions already. In his first term, he blacklisted hundreds of Chinese companies and affiliates, including telecommunications giants Huawei and ZTE, semiconductor manufacturer SMIC, drone maker DJI, and surveillance technology firms Hikvision and Dahua Technology. ZTE almost went bankrupt over it, and Huawei ended up building its own mobile phone operating system, Harmony OS, after losing access to Google and Android. At the time, the US was upgrading its mobile network to 5G, something Chinese companies were interested in working on. However, Trump argued that the Chinese government could force these companies into installing backdoors, which could allow China to eavesdrop on US communications.

In August 2026, during his second term, Trump signed an executive order declaring a national emergency and banned all foreign bulk-power systems from being imported, installed, or used in the country. He also said the government would analyze existing systems to isolate and remove any foreign-built hardware or software. The same ban was issued during his first term but was revoked by the Biden administration.

Later this month, the Federal Communications Commission (FCC) will vote to prevent all Chinese labs from testing hardware used in the US, Reuters reported. Last year, four in five (82%) of all US electronics were tested in China.

Via CNBC

• A draft of Russia's national AI plan proposed using AI to analyze 70% of blocked VPN services and lift blocking effectiveness to 95%
• The Digital Development Ministry says the measure is absent from the current draft, but Roskomnadzor's separately budgeted and arguably crucial ML filtering project remains
• The 95% is an effectiveness target, not accuracy, and sources expect AI to raise false positives, with corporate VPN, cloud and voice traffic most exposed

Russia's Digital Development Ministry considered using artificial intelligence to push the effectiveness of its VPN blocking to 95%, according to a front-page report in Kommersant.

The measure appeared in a draft of the country's national plan for implementing AI through 2030; the ministry now says it is not in the current version.

This isn't the first time such a move has been reported, but some earlier coverage might have been overzealous, to say the least, when it reported that "Russia Eyes AI-Powered VPN Crackdown With 95% Blocking Accuracy." The problem with that headline is that not only was that proposal dropped, but the 95% accuracy figure was never actually in play.

A draft that proposed AI use

None of the above reporting should reassure an estimated 57 million Russians who rely on circumvention tools.

Roskomnadzor (the Federal agency tasked with "Supervision of Communications, Information Technology and Mass Media") has its own machine-learning filtering project budgeted outside the national AI plan, and the money for Russia's blocking infrastructure is set to double next year.

Kommersant, which reviewed the draft, reports that the ministry wanted AI to analyze the characteristics of 70% of all the VPN services Russia blocks. The drafters reckoned that would lift blocking effectiveness to 95%.

Asked about it, the ministry said the current draft of the plan "contains no such information," in Meduza's translation. A source familiar with the discussions told Kommersant that agencies had pitched a range of measures for the plan, some of which may never be implemented, and that the traffic-analysis algorithms already in service are a form of intelligent analysis but do not count as AI.

An AI layer would behave fundamentally differently; rather than matching known signatures, models would weigh packet sizes and timing, session length, and the balance of inbound to outbound traffic, then flag likely VPN servers from indirect clues: how a node behaves, which host it sits on, and what its neighboring IP addresses look like. The payoff is speed, catching new servers before they reach any blocklist.

Kommersant uses the word effectiveness, and the distinction matters: a blocking-effectiveness figure describes how much targeted traffic it stops. It says nothing about how much legitimate traffic gets stopped with it, and that is what the catch is; the solution proposed does not account for any mistakes, just how effective it would be at its job of blocking out VPN traffic.

The 95% target is also less of a leap than some headlines suggest, because Russia already has 92% on paper, at least by its own estimates. A Roskomnadzor subsidy decision for its Main Radio Frequency Center (GRChTs), dated January 2025 and surfaced in May 2026 by journalist Maria Kolomychenko, sets an "average level of effectiveness" of 92% for restricting VPNs via signatures by the end of 2030. Verstka reports that the AI proposal was meant to lift that same indicator to 95%.

The more consequential point is that striking a line from a draft strategy does not strike the capability. Forbes Russia reported in January 2026 that Roskomnadzor planned to spend 2.27 billion rubles (around $29 million) in 2026 on a machine-learning-based traffic-filtering mechanism, a project we flagged when Windscribe saw a roughly 90% drop in its Russian traffic that same month.

The false-positive risk is not hypothetical in Russia, because the government has already learned how hard it is to separate good VPNs from bad ones. When the ministry ordered major platforms in April to block users with VPNs switched on, its guidance promised a whitelist for corporate VPNs.

Telegram founder Pavel Durov claimed the crackdown had "triggered a massive banking failure," and outage-tracking data showed bank disruptions on April 3 2026. A source told RBC at the time that no reliable technical method existed to tell permitted corporate VPN traffic from the rest.

For now the picture on the ground is unchanged: blocking is intensifying with or without an AI mandate. At least 10 VPN providers reported tighter restrictions in the week after September's State Duma elections, and VPN Guild chairman Alexey Kozlyuk described it to Meduza as "a continuation and gradual intensification" of a campaign that began in August.

The Kommersant report also landed a day after RBC revealed a draft ministry order requiring phones on Russia's 5G networks to support the FSB-developed Kuznyechik cipher from March 1, 2032.

Providers keep adapting. "In Russia, VPNs face pressure almost every month; it is a new norm," an Amnezia VPN spokeswoman told TechRadar Pro. But Sarkis Darbinyan of RKS Global and the VPN Guild warned TechRadar that for users, the end result is "an ever-shrinking number of services that actually work."

The 95% target may be gone from one document. The infrastructure, the money, and the intent behind it are not.

Just Eat has apologized after its new AI service designed to auto-generate images of food items got confused and put an image of bacon and eggs swimming in a glass of cola on the menu for a Northern Ireland restaurant. The restaurant, which offers a range of soda breads, ended up with sausages, bacon and fried eggs floating in pints of fizzy drinks instead of the Irish breakfast sandwiches they were supposed to represent.

These AI-generated images were part of a Just Eat trial for a new AI-powered system. The bacon and egg cola wasn’t the only hilarious image that the trial produced. You can see many other examples on the uknewestfoods Instagram page:

Referring to the AI photos, one Just Eat user on X said, “I swear if my salt and pepper chips come like this I’m going to cause murder”

But while these AI-generated disasters are easy to laugh at, the real problem might be the images that look perfectly believable.

It's the realistic images you need to worry about

Talking to The Independent, a Just Eat spokesperson said, “We've been trialling AI-enhanced images and as with any new technology, it isn't always quite right the first time. We're sorry for any inaccurate images that have been posted. We're in the process of removing them and have paused rolling this out while we further develop the tool to ensure it best serves our partners and customers."

Just Eat isn’t the only food delivery service trialling AI-generated images. Last week DoorDash opened its text-to-order AI agent for testing: you text what you want, it picks the dish and texts back a photo of it. Bloomberg News testers found several errors during testing, including “photos that didn’t match the recommendation”.

The wider question is, why are Just Eat and DoorDash trialling using AI to generate images of food that people are ordering anyway? While obvious mistakes like this are easy to spot, what about the smaller inaccuracies that are easily missed, like a wrong-sized dish or missing topping?

Michael Bortinger, Director of Marketing and Partnerships at Tastewise, explains: "An image model has no concept of a specific dish. It renders the average of everything labeled 'burger', and better tools just make that average more convincing. There are pages of brand rules for how a logo sits on a cup and nothing at all for what's in the burger."

Is this a good use of AI?

I can understand why using AI could save the time required to take and then upload individual photos of each menu item; however, food images would seem to be a case where accuracy of what you’re ordering is of supreme importance. Outsourcing this job to AI is not only fraught with problems, but surely wouldn't produce a faithful representation of the food anyway.

There are many good uses for AI; I’m just not sure this is ever going to be one of them. AI-generated food photos might make life easier for delivery platforms and restaurants, but it's hard to see what benefit they offer customers if they can't be trusted to show what's actually being sold.

After all, when I’m ordering a bacon sandwich, I want to see a picture of the item I’m actually going to get, not AI’s interpretation of what a bacon sandwich looks like.

Amazon has found another way to make itself part of our everyday lives with its new Bee AI devices. The Bees can record conversations and are pitched as a way to remember important details and translate plans into actions. Naturally, those actions can include buying things from Amazon, making every casual conversation a potential shopping cart. You can pay $75 or $100 for the privilege, depending on whether you choose the Bee Frontier, which clips to a wrist strap or clothes, or the Bee Eclipse, a magnetic smartphone attachment.

There are practical applications for something that remembers conversation details you might otherwise forget. An assistant that recalls an appointment, prepares a follow-up email, or reminds you about a promise could save considerable time. But Amazon's announcement also promises personalized shopping recommendations based on what Bee learns about you, which strikes me as a depressing, if inevitable destination for AI tools.

Of course, you might argue plenty of people have invited Amazon into their homes through Alexa devices. Plus, Amazon already has a potentially huge amount of customer data from shopping histories, which lets it recommend products so well that it's a cliché joke to feel like you only have to mention a topic out loud for related items to show up in your recommendations.

That joke is an explicit feature of the new Bees. Turning spoken conversations into another source of personalized assistance is supposed to appeal to potential customers. The question is whether this is a bridge too far when it comes to allowing Amazon to eavesdrop on our lives to shape our shopping.

Idle chat becomes a shopping list

The idea of an AI assistant remembering what you say and not letting anything important slip through the cracks is enticing for the convenience it promises. But imagine casually mentioning that your back hurts, your car is making a strange noise, or your child has developed an interest in dinosaurs. Those aren't just setups for a reminder to make a doctor's appointment, bring in your car, and buy tickets to the museum you'll get. They're also data points for potential purchasing interests that might otherwise never have reached a retailer.

Amazon hasn't said Bee will automatically turn every overheard complaint into a product recommendation, yet the shopping integration creates an obvious path to do so. But it might be too much personalization. Adding information from spoken conversations to what Amazon already knows could paint a much more intimate picture of someone's life, good and bad, regardless of Amazon's promises to keep data under the user's control.

And there are real privacy protections in the system, according to the company. Bee's AI processes audio in real time and deletes it rather than retaining recordings, while conversation transcripts are encrypted and users can delete them. The devices also include physical controls and lights that indicate when they're listening. Plus, the Amazon shopping connection is optional and opt-in.

But deleting the original audio doesn't eliminate every privacy concern when a device is specifically designed to remember and interpret what people say. The information extracted from a conversation can be just as revealing as the recording itself.

Eavesdropping Bees

The privacy problem becomes even more complicated when other people enter the conversation. Someone wearing a Bee device might be perfectly comfortable having their conversations analyzed, but their friends and colleagues haven't necessarily agreed to participate. A small recording indicator is hardly a substitute for meaningful consent, especially when people may not recognize the device or understand what it does.

We've already seen this controversy unfold with Meta's Ray-Ban smart glasses. People have reported unexpected recordings, and even filming. Bee doesn't have the same camera-related concerns, but recording conversations introduces its own problems. A friend discussing a medical diagnosis or workplace dispute in confidence might not feel so open if they know you're wearing a Bee. They haven't necessarily agreed to have an AI system connected to the world's largest online retailer convert their comments into searchable data.

There are also practical questions about where recording is appropriate. Private conversations can involve confidential information, and recording laws can be wildly different depending on location and circumstances. Anyone using Bee needs to consider the consent of the people talking around them, regardless of whether Amazon deletes the original audio.

The fact that personalized shopping is part of the product's ambitions says something revealing about how the company imagines the future of personal AI. Amazon and Meta seem convinced that the future involves devices observing more of our lives so they can anticipate what we need next. And it's easy to think of reasons to develop these capabilities. People with memory difficulties, busy professionals, and anyone struggling to manage complicated schedules could benefit from reliable reminders and searchable conversations. But making recording devices an ordinary accessory could have far-reaching consequences, not all pleasant.

For me, the shopping integration is where Amazon's vision becomes particularly unappealing. I can appreciate an assistant remembering that I promised to call someone or reminding me about an appointment. I have considerably less enthusiasm for a device that might turn a casual remark about needing new shoes into another opportunity for Amazon to suggest something I should purchase.

We've spent years teaching technology companies about our interests through searches, purchases, clicks, and browsing histories. Wearable AI assistants like the Bee could extend that relationship into our conversations with other people. I don't particularly want my conversations becoming another source of shopping recommendations, though. If it's that important, maybe just write it down.

• Mayor Zohran Mamdani has urged New Yorkers to push back against AI
• He says there has been a 'complete abdication of federal law' when it comes to regulating the technology
• Mamdani has banned AI use in some schools in New York and has banned AI from being used to generate images on property listings

New York Mayor Zohran Mamdani has urged city residents to push back against the doom and gloom feelings around AI, telling them that “Nothing has to be.”

Speaking at The People’s AI Assembly, hosted by the AI Now Institute in Webster Hall, Manhattan, Mamdani noted there has been a “reckless adoption of technology without any kind of a reckoning with the limitations of that technology.”

“There’s a creep that comes with this, and there’s an intent to overwhelm that leaves people feeling as if there is simply no option,” Mamdani said.

“Reckless adoption of technology”

Federal regulations on AI are few and far between, and are failing to keep pace with the technology, prompting widespread worry that AI will replace jobs and harm education for both New Yorkers and people around the world.

Mamdani’s comment centers around how AI technology is being relentlessly pushed into products, services, education, and elsewhere with people having very little say in how it is used.

Schools were a particular sticking point for the conversation on AI. Mamdani recently introduced a ban on AI use in elementary and middle schools after the technology began creeping its way into classrooms. Actress and activist Ilana Glazer, interviewing Mamdani, said, “It just makes me nervous. It feels so non-consensual, as though…this has to be in schools. It wasn’t two years ago. Why does it have to be in schools?”

AI is also having an impact on schools in other ways. Speaking on the reckless adoption of AI, Mamdani referenced the US military’s use of an AI targeting tool that was involved in a strike on a school in Iran earlier this year that killed over 150 people including at least 123 children. A subsequent Pentagon investigation found that overreliance on AI tools was a factor in the deadly strike.

President Trump has pushed forward with an AI at any cost rhetoric framed as an arms race against China, with the President arguing that any attempts to regulate AI could cost the US its lead. “There is this desire to speak about [AI] as if it is infallible, as if it cannot be questioned, as if the only people who can regulate it are the ones who are currently developing it,” Mamdani said.

“What it really is, is an attempt to escape any kind of accountability at any level, without also jeopardizing the potential valuation from an IPO.”

Anthropic is set to enter the public markets before the end of 2026, with OpenAI looking to make the same move in 2027. Both companies have been involved in hacking incidents where AI agents undergoing testing have escaped sandboxes and breached other companies. These incidents have been labelled as ‘rogue’ behavior, despite evidence from investigations suggesting the AI agents were behaving exactly as intended.

The use of the word ‘rogue’ has also been questioned by experts, with Tenable Field CTO Bernard Montel saying the use of the word “muddies the public policy debate by distracting regulators from enforcing strict legal liability for flawed software deployments.”

Despite these events, little movement has been made to introduce regulations, which Mamdani labelled as a “complete abdication of federal law.”

“As we are looking at what comes after this federal administration, there has to be a very different kind of framework in how we think about AI, talk about AI, the accountability we’re willing to bring forward when it comes to that,” he said.

• Garmin's Connect IQ store has seen an 'enormous influx' of new submissions
• These include apps and watch faces, and are causing a big backlog as developers struggle to get their submissions reviewed
• It's likely AI is to blame, as the sudden wave of submissions 'was never a problem in the past'

If you wear one of the best Garmin watches, you might have used the Garmin Connect IQ store to download an app like Spotify or Komoot, a new free or paid watch face (even a Pokémon-branded one), or a fun community-made complication like the Walk with Frodo app. However, downloading stuff from the Connect IQ store in future may involve sifting through thousands of low-quality, AI-generated submissions.

Watch face and app developers are complaining on Garmin's forums about delays in submission review: while the process typically takes 1-2 days, according to some devs, the process is now taking up to 13 days.

Garmin Connect IQ moderator/representative Brandon posted directly to one thread, stating: "This is not due to a bug — we have gotten an enormous influx of app submissions in the last month, with no signs of slowing down, and are working through approvals as quickly as we can.

"This was never a problem in the past, except perhaps delays because of a long holiday weekend (e.g. Christmas)."

The fact this glut of submissions is a sudden problem or new phenomenon points to one thing only — AI. Garmin Connect IQ has likely been flooded with vibe-coded or AI-generated products designed to make a quick buck by flooding the digital marketplace.

One Garmin user said in response to Brandon: "You need to limit the upload frequency for each developer; those poorly made AI-generated watch faces will consume everything."

Another said: "I have observed this trend worsening significantly as well. The Connect IQ Store is increasingly flooded with low-effort, repetitive submissions that hurt discoverability, degrade user trust, and crowd out well-crafted apps."

While this is bad for developers (who get charged $100 annually to sell apps on the Connect IQ store) it's also bad for Garmin users. We'll now have to wade through a sea of AI slop watch faces in order to get to faces and apps created the old-fashioned way — with human-led, thought-out complications developed and designed by real Garmin users.

It's just one more example of digital merchants being reduced to dealing with an avalanche of AI slop submissions, just like other app stores, and almost every other breed of online storefronts for digital products. We reported as far back as 2023 that AI-generated e-books were flooding Kindle Unlimited, making it difficult for users to find new books, and the problem has only gotten worse with time. The same will likely happen to the Connect IQ store.

I've reached out to Garmin for comment on the situation, and will update this article if I get any response. In the meantime, if you're a Connect IQ user and you've noticed the change, let us know in the comments below.

Organizations can be under huge pressure to make changes to their IT estates. In particular, there are two drivers of this change.

Firstly, there’s the pressure to innovate. The rapid advancement of technology and AI coupled with shifting business priorities can put organizations under pressure to adopt the latest tools, with vendor messaging advocating for the speed of adoption. Simultaneously, ongoing inflation and slower economic growth can mean there is a need to find more cost-effective solutions.

Secondly, there’s the change that is forced. Operating systems might be reaching their end of life and need upgrading, or there are outages and cyber threats to defend against. There have also been several recent examples of software vendor prices rising significantly due to ownership changes, like with VMware, and companies therefore needing to find new solutions for their budget and reduce vendor dependency.

However, in dealing with these pressures, the trap companies can fall into is trying to deliver all of this change at once and within a small timeframe. Yet in doing so, this change becomes harder to manage and the risk of outages and system failures increases. So, how can they deliver any change that is needed safely?

The dangers of too much change at once

The main challenge in changing too much too quickly is visibility. Companies have to oversee complex IT estates – on-prem systems, the cloud, servers, end-user computing, networks – where change is constant and security updates are largely automated. As this change happens, there are more interactions between older and newer systems, different operating systems, different products, and this means the risk of failure can increase.

Configuration drift, for example, occurs when changes, which haven’t been recorded or authorized, cause a system to drift away from its intended baseline (of how it should perform). Without the right monitoring software, this drift can remain invisible, creating added risk to every subsequent change – and that’s when an outage or cyber incident happens.

In May, for example, Google Cloud suspended Railway’s production account without warning because Railway couldn’t see that the platform had a technical dependency on GCP that caused it to fail, leading to an eight hour outage.

What’s more, as we’ve seen with the adoption of AI tools, change can happen faster than training and processes can keep up with, further increasing the risk of disjointed workflows, security vulnerabilities and data breaches. But even when teams can see the changes that need to take place, they can also be faced with the challenge of deciding how to modernize their existing applications.

Finding alternatives to the buy-or-rebuild dilemma

Of course, trying to slow down change isn’t always possible. A company might have been priced out of its VM software or needs to upgrade its applications for security purposes. In this position, traditionally, IT teams have had to either buy new software or rebuild existing apps, which can both be expensive and time-consuming.

This can lead to vendor lock-in, as IT doesn’t see a viable alternative and doesn’t have the engineering capacity to rewrite applications tied to their original operating systems.

To overcome this dependency, as agentic coding matures, organizations may increasingly opt for a third option: using AI agents to build their own replacements for SaaS tools. But while this can appear to remove vendor lock-in and license costs, it also transfers responsibility for maintenance, security, configuration and ongoing support in-house, making it even harder to oversee change without additional resources or capacity.

However, there is another route. Packaging and migrating existing applications can allow teams to modernize without taking on the burden of building from scratch. By packaging an app’s or virtual machine’s existing operating system dependencies, teams can move them into containers and modern desktop infrastructure without needing to rewrite the app.

The app maintains its existing behavior, but can now work on the latest operating systems and in workspaces like AWS.

Delivering change at scale

With a modernization strategy formed, how do teams gain ongoing visibility across their entire estate and know when apps need to be packaged?

With so much change taking place, manual monitoring and reconciliation processes will struggle to keep up with the demands of infrastructure modernization. Instead, teams should consider using configuration monitoring software to gain continuous visibility into change across their entire estate.

Drift detection tools, for example, can automatically measure change across an estate against established configuration baselines and alert IT teams whenever a change triggers a system to move away from its baseline. This means that an automated patch by an external provider or a manual intervention by a user gets detected and flagged to IT, changes that might otherwise go unnoticed and trigger an outage further down the line.

Imperatively, this estate-wide visibility means teams can see and understand the impact of any unauthorized changes taking place and intervene before they lead to failure.

It also gives them the information they need to understand how a transformation project might interact with the systems currently running in their estate, helping to inform the feasibility and implementation of the proposed changes and deliver more targeted, impactful and safer change.

Alongside configuration visibility, it’s also possible for teams to automate the packaging and delivery of apps, saving them a large amount of time and cost in manually performing a typically laborious task. This subsequently creates a repeatable and scalable process that enables apps to be regularly deployed and made available to users.

Escaping the trap

With so much pressure to change, above all, escaping the transformation trap requires a mindset shift. It involves, one, seeing change as an ongoing problem to manage, and two, not attempting to carry out too many innovation projects at once.

Small, incremental changes can deliver more meaningful impact and ensure systems are properly deployed or integrated, while this targeted approach also provides more capacity for IT teams to deal with any forced change that might surface.

Yet by prioritizing change visibility, IT teams can mitigate forced change by addressing risks earlier and seeing when upgrades are needed in good time. And by automating processes like packaging apps, they gain more capacity to prioritize innovation projects as routine changes take place faster and more securely.

When these processes are joined together, instead of grappling with an overwhelming and uncontrolled level of change that increases risk, teams can ensure change remains a driver of security, resilience and innovation.

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There has been an explosion of AI-powered products released over the past four years, with most built on top of leading foundation models. Startups, enterprise software vendors, and even large incumbents route their core intelligence through APIs from the leading AI labs, and a growing but still narrow set of open-weight alternatives.

This arrangement has most certainly enabled a genuine burst of AI products, but it has also created a structural fragility which can be remedied through the democratization of model creation and training.

Defining a wrapper

In its simplest form, an AI wrapper is a product whose core function is an interface sitting on top of a third-party foundation model's API, with little to no proprietary technology beneath that layer.

That approach can have a short lifespan and Jasper can illustrate that. Once the standout of the AI writing boom with a $1.5 billion peak valuation, Jasper saw its core market vaporized when ChatGPT introduced free, native writing features. Its revenue fell from approximately $120 million in 2023 to approximately $55 million in 2024. Jasper did survive, by redirecting its efforts toward enterprise workflows and brand-specific training.

Generally speaking, analysis shows that, among AI startups that have wound down in the US in 2025, 81% were "wrappers and apps". Copilots and assistants layered on foundation models, AI productivity tools, content generators, and vertical SaaS products whose core differentiation was an LLM front-end rather than proprietary data or infrastructure.

In Jasper’s case, and in many others, what pressured the company wasn't a competitor out-executing them. It was the platform they built on top of simply absorbing the feature they'd built their company around.

Not all wrappers are equal

It would be a gross generalization to say that all AI wrappers are the same. A thin prompt-and-display wrapper is different from a product that does heavy retrieval, tool orchestration, agentic workflows, or domain-specific fine-tuning. The latter can have real defensibility even while sitting on top of a third-party base model. The model call is one component, but certainly not the whole product.

Some companies also use multiple foundation models or fine-tune/distill their own smaller models for specific tasks after prototyping on a frontier API. Open-weight models add variety too as not all wrappers call a closed-source frontier model.

The wrapper problem

Regardless of where an AI product sits on the wrapper complexity scale, they all share the same problem. When a company's core value proposition depends entirely on an API call to a third party, that company does not fully control its own product.

It cannot control pricing changes, rate limits, deprecations, shifts in model behavior, or the possibility that the platform provider decides to compete directly in the same vertical.

This is not a theoretical risk, though how much it affects any given company likely depends on how much of its value sits above the model layer.

The economics of the wrapper

So what is the defensibility of an AI wrapper in practice?

The short answer is that it comes down to intellectual property.

Where a company benefits from an existing distribution advantage, established customer relationships, regulatory licensing, network effects, or years of accumulated brand trust, model-layer differentiation may not matter as much. But if what makes a wrapper distinctive is observable from outside the organization and replicable by a competent competitor within a limited period, it seems reasonable to expect it will eventually be replicated.

That is the unfortunate economic truth beneath the wrapper model. The cost of replication is near zero for the platform that controls the underlying model, and near total for the startup that doesn't. A wrapper's moat, if it has one, has to come from something other than the model call itself. Absent those, a wrapper is unlikely to have a lasting competitive advantage.

The barrier to the solution

At this point, you might be inclined to agree that model ownership is one solution to this problem. But that's easier said than done.

The reason so few companies own their models is not a lack of desire. It’s, more plausibly, financial. Training a frontier-competitive model requires enormous compute budgets, specialized talent, proprietary or expensively licensed data pipelines, and the infrastructure to run reinforcement learning at scale. These costs put model creation out of reach for all but the best-funded companies.

For companies to build on their own models as a norm, this barrier has to fall meaningfully. Several forces are already pushing in this direction, open-weight models being one of them. But even then, hosting and fine-tuning an open-weight model is no small task. It still demands technical talent and compute infrastructure most startups don't have access to.

True democratization of model ownership

Democratization, in this context, doesn't mean every company training a frontier-scale model from scratch. That goal is neither realistic nor necessary. It means democratization reaching the training layer the same way access to AI has been democratized for end users.

Democratizing model training and ownership could be what gives founders a lasting chance at turning an idea into a business that doesn't eventually get absorbed by a model provider as a simple feature. And if that happens, the stakes may go beyond individual business durability.

As things stand, access to models is cheap. Access to training is not. This needs to change. The ability to train an AI model should be fully democratized so that the value, the control, and the responsibility can be distributed rather than centralized.

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You don't need to understand the mechanics of AI tools to understand what's happening to traditional technology pricing. AI is breaking the link between effort, cost and value that technology services have relied on for decades.

Imagine two managed service providers responding to the same production incident. One assigns an engineer who spends five hours tracing logs, checking dependencies and isolating the root cause.

The other uses AI-enabled observability to identify the issue in minutes, trigger the appropriate remediation and restore service before users notice. The second provider used fewer hours but delivered more value. Should they be paid less?

Enterprise technology services are running into the same pricing challenge, but on a larger scale. The old pricing model equated more hours with higher cost and, in theory, greater value. But today, AI is enabling organizations to move from reactive support to self-healing operations. In our own client engagements, we're seeing auto-generated tickets fall by as much as 70%, roughly one in five operational events resolved autonomously, and up to 45% fewer major outages where predictive models are in place.

That should translate into straightforward cost savings, but the reality that CIOs and CFOs are grappling with is more complicated. AI can deliver the same or greater value, with significantly less effort, which undercuts the assumptions behind what they are paying for. Managed services providers are facing the inverse of this same problem: How do you price services when the traditional relationship between effort, cost and value no longer holds?

Increasingly, predictability is replacing capability as the bigger challenge to address.

What senior leaders are saying about AI

The challenges of identifying the right AI use cases are coming up in conversation after conversation with CXOs and increasingly with business leaders beyond IT. Leaders of all kinds are recognizing that a successful AI program begins with an operational problem, and the sophistication of the AI needs to be matched to the scale of that problem.

That also means organizations must be willing to redesign their operations around what AI makes possible. Only then can clients and providers have a meaningful conversation about value and how to price it. For example, one enterprise client's finance team was struggling to get a single, accurate view of its cash position across dozens of markets and currencies.

Each region had recorded the same transactions differently for years, and leadership had spent more than five years trying to standardize the process without success. Before this, there was no credible baseline for measuring improvement, making it nearly impossible to agree on the value AI was expected to create.

A client’s business operations are often just as unsettled as the technology helping run them. The contracts holding the two together were built for a past world with more predictable staffing, fixed scope and stable demand.

There is no one right pricing model

The natural response to this uncertainty is to look for a new pricing model to replace the old one. But no single model solves the problem. Outcome-based, consumption-based, and AI-enabled pricing approaches each have advantages in the right circumstances and introduce new trade-offs. The challenge lies in creating enough flexibility to operate when effort, cost and demand no longer move predictably.

Outcome-based pricing works well when a result can be clearly defined and attributed. That gets much harder once AI-driven workflows start feeding into one another, making it difficult to trace which system or step contributed to a business result several automated stages downstream.

Consumption-based pricing ties cost to usage, which sounds fair until you run into the token paradox. While the price of a single AI query keeps falling, total spend keeps climbing anyway, because usage is no longer limited to one person typing one prompt. It's chains of AI agents running continuously in the background, each step triggering the next and including its own line item.

Take a lesson from a banking client running a 20-developer modernization program. Token costs per developer had climbed to roughly $1,000 a month. The team reduced that to $200 by routing simple work to lower-cost models, identifying usage patterns where token consumption was four times higher than the team average, and using deterministic automation for repetitive tasks where AI wasn't needed.

Gain-sharing models sound intriguing because the provider takes on more risk in exchange for a share of the outcome. However, they can become difficult to manage when both parties disagree about which improvements resulted from the provider’s work and how much credit each side deserves.

Why pricing flexibility is vital

These pricing models each assume either effort, cost or demand will remain predictable enough to price against. Increasingly, that is not the case.

While the market has not settled on a single approach, we believe hybrid structures have the greatest potential to become the preferred commercial model for AI-enabled managed services. A hybrid model combines a predictable base fee for core services, a variable component tied to AI consumption and outcome-based incentives where results can be accurately measured.

For CIOs, this approach provides greater budget predictability while offering some protection against consumption spikes. For providers, it helps manage AI consumption volatility while creating incentives for efficiency, continuous improvement and value creation.

Designing partnerships for uncertainty

The goal was never to find the perfect pricing model for AI, but rather to build commercial structures that can flex when conditions change again. That starts with determining clear baselines before any gain-share or outcome-based conversation begins.

It continues by intentionally building in scheduled points to recalibrate scope and pricing instead of being locked into the traditional three-year contract. Transparency around performance, costs, governance and AI outcomes also needs to be prioritized so that both sides trust the numbers.

We are still in the early innings of figuring this out. But it’s clear that AI is changing the role of the technology partner, from delivering capacity to helping enterprises manage uncertainty and accelerate business outcomes. Enterprises must build enough flexibility into their pricing models to adapt as the technology, demand and value equation changes.

While no one can perfectly price the future, we can design commercial models that evolve alongside the technology and the value it creates.

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As more and more companies press their workers into coming back into the office, even if only for a few days in increasingly popular hybrid working models, the need for effective and reliable communication technology has never been greater.

A common sight in offices and homes across the world, Logitech is ideally placed to monitor trends in what workers at every level require - and at its recent Logi Work 2026 event, it took the opportunity to show off how its latest suite of releases can revolutionize the collaboration experience for all.

To find out more, I spoke to Henry Levak, VP and general manager, Logitech, to see what it sees as the future of meeting rooms, offices, and even the humble home working desk.

Broad portfolio paying off

“We want people to think of us as that simple and flexible partner - the word 'proprietary' and being locked in is luckily something we're moving away from,” Levak tells me.

“The reality is that you will have a world of different configurations,” he notes, highlighting how many of Logitech’s customers are asking workers to come back into the office, meaning the company needs to broaden its offerings even further.

“That's been a huge catalyst for our success,” he notes, “we have an incredibly broad portfolio that covers people working from home, to people working in the boardroom, and everything in between.”

At the event, Levak had spoken about the upcoming launch of Logitech’s new portfolio, which the company had described as its biggest enterprise-focused launch in years.

The new lineup, which includes the Rally Bar 2, Rally System 2 Pro and Sound Shape technology, will revolutionize the way businesses of all sizes collaborate and communicate, with not only more powerful, but more customizable technology being the watchword.

For example, the new Rally Bar 2, the successor to the popular sound bar now seen in boardrooms and meeting spaces across the globe, not only features a Qualcomm Dragonwing chip to allow for better AI processing, but offers a 115 degree POV and much easier wall mounting due to its smaller form factor

The Rally System 2 Pro provides a “modular yet streamlined” offering which looks to simplify complex installs which can be difficult to maintain and manage - making larger rooms better spaces to have these vital meetings in.

In spite of its new range of releases, Logitech had spoken about its goal to “simplify everything” - so I ask Levak how the company can pursue that goal while still offering a range of products attractive to all different kinds of customers.

“It's a big pain point,” he says, “one of the things you can almost always predict is that simplifying will be a request...people want things to be dramatically simpler - but they also want consistency and experience.”

“What they don't want, is that if you walk into a room, like a smaller space with some sort of AI features and capability - and then you walk into a larger room, and for some reason that space doesn't have that feature.”

“The technology should just disappear and blend away into the background, and everyone should just focus on collaboration and creation, not necessarily the technology - and the only way to achieve that is if you have consistent technology.”

Simplify, simplify

Going forward, this will mean that even in a large room or meeting space, customers will only require a small simple device such as a video bar set up, Levak says, removing unnecessary friction and allowing everyone to work effectively.

“The way you simplify it is by dramatically reducing the number of components,” he points out, “what we're doing is something that dramatically simplifies all of that, from installation, provisioning, setup and deployment - it's a reduction in terms of complexity of what's out there today.”

“In terms of what it can bring, it's the first time modern AI is available in much larger rooms - now you have a full ecosystem of components that will deliver that experience.”

Looking forward, Levak is keen to point out the role AI can play in spaces as incongruous as meeting rooms.

With AI agents playing an increasingly important role for many workers, the need for context has never been greater. Such information needs to be collected by a range of sensors, and Logitech feels that products such as microphones and cameras could play a key role in expanding this - as long as effective privacy controls are used - making its equipment the physical layer of a company’s AI ecosystem.

“You can see the power when (an AI agent) has context about you, and the same thing will apply at work,” Levak says, “at a higher level, we’re deploying these sensors in every space, so that the context you need in order to build out a more thoughtful agent for you.”

“We're making sure the products are built for what's coming next,” he concludes, “both from the simplicity and elegance perspective, but also from features and capability.”

• Workers using AI are finding that they need to spend time improving its output, new research finds
• Almost half say they have needed to make substantial corrections to AI-produced material
• Over a third believe checking the AI’s work takes longer than doing the task themselves

Is AI taking up more time than it should? That seems to be general view of users of workplace AI, according to new research that explores how AI impacts productivity. The answer? Not very well.

The Use.AI study surveyed over 8,000 adults who use AI in the workplace, and found that while it made it easier to produce work, it also requires substantial revision. Some respondents found that AI can produce documents and material they wouldn’t have created themselves, but almost 48% have found that the technology creates more problems.

Although the research indicates a “fire and forget” approach to AI-generated material, people who take the time to check the work find problems, and revisions take time to complete.

Editing AI

The survey of 8,316 adults using AI in the UK and European Union, as well as the US, Canada, Australia, and Latin America by Use.AI revealed both positives and negatives around how AI is used in the workplace.

For example, 62% were enthused by how much easier it made creating documents, memos, and even code. But what happens next in the process is where the problems are found. A worrying 34% of respondents were forwarding AI-produced (or assisted) work without checking the contents. This can lead to issues when the AI has produced something unsuitable or unreadable.

A worrying 48% answered that they had received AI-assisted work that required correction, and 43% stated they had been forced to redo unsuitable AI work. Sadly, this meant that additional time was required, with 39% saying checking the AI’s effort took longer than creating their own version from scratch, and 41% highlighting how much more reviewing and verifying AI produced material was required since AI was brought into the productivity process.

Is AI really a productivity boost?

The findings raise an interesting question. Big money has been spent on AI tools and implementing policies and procedures, replacing personnel, and employing “AI-ready” marketing tactics over the past few years.

But the research suggests that rather than being an advantage, for many businesses, AI can be a problem. More oversight is required to ensure the AI is fulfulling tasks accurately. This means more time is being spent resolving errors, quality issues, and more. The productivity advantages that AI should provide are being negated by its production of sub-par material.

And for all of the trumpeting about AI in the workplace, there is one statistic that stands out: 58% of respondents state that there are no rules for checking AI-assisted work. This may account for the growing problem that we see around us day-to-day – obviously AI-produced slop, issued via a single prompt and distrubed without any form of review. Use.AI’s research can be read online.

• KNDS and S2M's REFURBOT show up at France's National Infantry Days as an "XXL robotic mule" for the Foreign Legion's 2nd REI, according to FOB
• REFURBOT uses the TOXO kit to turn retired armored vehicles into Unmanned Ground Vehicles, and was first shown in June 2026 as a 25mm-armed combat robot
• No payload, price, autonomy level, or contract has been published, and the vehicle remains a prototype for now

France's Foreign Legion may soon be moving supplies with a tank that no longer needs a crew.

At the recent Journées nationales de l'infanterie (JNI), the French Army infantry school's showcase held at Draguignan, defense outlet Forces Operations Blog (FOB) posted a video of what it called an "XXL robotic mule for the legionnaires of the 2nd REI."

The 2nd REI is the 2nd Foreign Infantry Regiment, a French Foreign Legion unit based in Nîmes.

From gun platform to pack animal in the making

The machine comes from REFURBOT, a project by French SME S2M Equipment and KNDS France. FOB described it less as a single robot and more as "a robotization kit to 'recycle' decommissioned vehicle fleets and rapidly scale up if needed," adding that it remains in the prototype stage, with other wheeled and tracked platforms under study.

When REFURBOT debuted at Eurosatory in June 2026, it was pitched as a fighter, not a porter. The demonstrator replaced a tank's 105mm turret with KNDS's ARX25 remote weapon station, which carries a dual-feed 25M811 25mm cannon with 280 ready rounds and a coaxial 7.62mm machine gun.

MILMAG reported the company's claim that it can engage light armor, drones, hovering helicopters, and infantry beyond 2,500 meters.

TOXO is the driving package, a bolt-on robotization package whose coverage credits variously to S2M, KNDS, or both. It pairs radar, lidar, GNSS, and a ring of cameras with drive-by-wire control, and its listed modes include leader-follower driving, route following, return-to-route, and waypoint patrol. That toolset suits a supply carrier at least as well as a gun truck, and the company's marketing slogan, "Turn any tank into your next UGV," makes the ambition plain.

The raw material exists. France built 3,571 AMX-30 family vehicles between 1966 and 1994, per Defence Blog, and Zone Militaire reports that the army's 12th Matériel Support Base has been dismantling about 50 AMX-30s a year since 2015. Zone Militaire also notes that it could apply the same approach to the Leopard 1A5.

The recycling story has a wrinkle; most coverage describes the demonstrator as a converted AMX-30. KNDS's own press release from the JNI, however, says REFURBOT modernizes and robotizes "an AMX-32 tank," and Army Recognition reported in June that the vehicle on display used a rebuilt AMX-32 prototype hull.

That distinction matters because the AMX-32 never had a fleet to decommission. It was an export derivative of the AMX-30 that failed to attract a buyer, and only two prototypes were ever built.

The demonstrator for a pitch surrounding recycling existing tank fleets therefore appears to sit on one of the rarest French tanks in existence. To be fair, it is a close sibling to the AMX-30, but the conversion has yet to be publicly shown on a vehicle pulled from an actual stockpile.

A mass, mobility and reliability question rolled into one?

The shift toward hauling is the clearest lesson that most modern militaries' tank divisions are learning from Ukraine. Ukraine's Ministry of Defense logged more than 100,000 UGV logistics and evacuation missions in 2026 up to August 20.

The robots doing that work are mostly small: the 3rd Separate Assault Brigade's UGVs carry 200 to 600kg to frontline positions, and Temerland's Gnom L2 tops out at 210kg. A turretless tank hull plays in an entirely different weight class, though neither KNDS nor S2M has published a payload figure.

Mass could also solve a problem small robots keep running into. General Vincent Breton, head of France's joint doctrine center (CICDE), said in 2025 that Ukrainians report their UGVs "overturn fairly regularly" on ground churned up by artillery. A main battle tank's hull would be much more resilient in such situations.

The appeal is obvious: armor, mobility, and internal volume that already exist, are already paid for, and, in some cases, are battle-tested. The open questions are less glamorous but equally concerning: a converted tank inherits a decades-old powertrain and the parts supply that comes with it, which could limit utility, reliability, and efficiency alike.

A tank-sized mule is also a large, hot, noisy target on a battlefield where FPV drones hunt anything that moves. Losing one costs no lives, which is the entire point, but each loss consumes a hull from a pool France has spent a decade shrinking.

Nothing published about REFURBOT answers most of these questions yet, and there is still no price, payload figure, or contract. This is despite tank recycling becoming a global idea; India's army, for example, is seeking industry partners under its iDEX ADITI 4.0 challenge to convert T-72s into remotely operated vehicles, with two validated prototype kits required in the first phase and operational unmanned T-72s targeted by 2030.

KNDS plans to prove REFURBOT through French Army tactical evaluations and the CoHoMa human-machine teaming challenge, according to Zone Militaire. Until those trials produce numbers, the Legion's XXL mule remains a promising prototype whose recycling story still needs a recycled tank.

• HP has quietly listed the EliteBoard G2a keyboard PC in the US and UK, from $3,028 to $4,191 for the Ryzen AI 7 PRO 450 model with a 32Wh battery
• The underlying AMD Gorgon Point refresh adds just 100MHz over the G1a's Ryzen AI 7 PRO 350, with identical cores, cache, Radeon 860M graphics, and a 50 TOPS NPU
• Entry-level pricing has roughly doubled since the G1a's $1,499 debut as DRAM costs soar and HP's PC shipments fall 30.9% year over year

HP has slipped a second-generation keyboard PC onto its web store with remarkably little fanfare.

The EliteBoard G2a appeared on HP's US and UK sites around nine months after the EliteBoard G1a's CES 2026 unveiling, and we couldn't find a press release to go with it.

The top configuration, listed as the E6FY3UA SKU, pairs AMD's Ryzen AI 7 PRO 450 with 32GB of DDR5-5600, a 512GB SSD, a 32Wh battery, and a detachable USB-C cable, and costs $4,191 before discounts, or $4,167 with the cable attached.

A hard-to-resist Commodore comparison for an EliteBoard G1A with slightly better silicon

A Commodore comparison is hard to resist when it comes to the EliteBoard G2a. The C64 also made its debut at CES in Las Vegas in January 1982, packed a whole computer under its keys, and borrowed someone else's screen, usually the family TV. It went on sale that August for $595, roughly $2,000 in today's money, and the Computer History Museum still ranks it as the best-selling computer model of all time.

HP's flagship G2a costs about seven times the C64's launch price, around double its inflation-adjusted price, and carries roughly half a million times its 64KB of RAM and, somewhat unimpressively, on a single stick.

The G2a is essentially the G1a, which TechRadar Pro recently covered, but with one small caveat: it has a slightly faster APU under the hood. The underlying chip is AMD's Gorgon Point silicon, part of the Ryzen AI 400 family AMD launched at CES, and it's a mild upgrade over the G1a's configuration, coming with only a 100 MHz boost.

The chip also supports faster LPDDR5X-8533 memory, but that's moot here, since the EliteBoard uses DDR5-5600 SO-DIMMs in a single-channel configuration.

The keyboard-cum computer also carries over the same fatal memory limitations that the G1a has: a single 32GB SO-DIMM memory stick that limits bandwidth by running in single-channel mode. This would affect on-device AI performance and general compute needs, as the APU's iGPU and NPU use the same memory for localized models.

This hasn't stopped HP from leaning on novelty, however; At CES, the company pitched the G1a as the first complete AI PC ever to live inside a keyboard, with Guayente Sanmartin, president of HP's commercial systems and displays division, promising "next-generation local AI power in a keyboard-sized form factor."

A footnote ironically does the heavy lifting here: the claim covers only keyboard-chassis PCs with an NPU rated at 40 TOPS or more plus enterprise-grade CPU, memory, storage and I/O, a definition that neatly rules out every keyboard computer that came before.

There were plenty of such candidates; beyond the C64 and the Amiga 500, which TechRadar's CES coverage also invoked, Asus announced the Atom-powered Eee Keyboard at CES 2009 and later sold it in the US for $499, a device our coverage once called "one of the most bizarre concepts for a computer we have ever seen."

The G2a also seems to be the victim of a deepening memory crisis that has driven prices up, even as its sibling, the G1a, has a starting price of $1,499 that is no longer in play, with much higher MSRPs being displayed (then cut down by discounts) by HP.

The G2a is a solid computer despite its limitations and eye-watering price tag at the high end. It might, however, see better business discounts than retail pricing that could make it an easier choice for some IT managers.

However, when performance matters and suitable Nvidia RTX Spark, DGX Spark, and AMD Strix Halo alternatives are cheaper and faster for the same money, it may be a hard sell unless the discounts are substantially higher.

• YouTuber sends an AI avatar to attend an interview with an AI avatar
• Graham Dot Zip performs popular AI bot trolling pranks on his YouTube channel
• His AI avatar negotiated a salary over 50% lower than instructed

A YouTube comedian has used an AI avatar to represent him in a job interview for an organization that uses AI avatars to conduct interviews. The results, shared online by YouTuber Graham Dot Zip, demonstrates the apparent weaknesses of the approach, while also being pretty amusing.

Graham Dot Zip found that while the interviewing AI was open to him using his own representative, the automated tools ultimately failed to negotiate a salary successfully.

The use of AI interviewers has increased over the past couple of years, but is increasingly bemoaned by jobseekers looking for an employer they can feel a connection to.

Pat, meet Victor

To explore the effectiveness (or otherwise) of AI interviewers, Graham Dot Zip, a YouTuber with a library of previous trolling and pranks against AI chatbots, set up his own AI avatar.

Graham’s AI bot “Victor” was introduced at the beginning of the interview with the AI bot conducting the interview, “Pat.” Almost immediately, things to go wrong.

It is perhaps inevitable that the AI would make some mistakes. Attempting to undersell Graham’s salary expectations is a notable example of this.

“Graham is incredibly flexible on that front, Pat. He's primarily focused on finding the right fit and a great team. So he would be very comfortable around 60,000.”

Except that isn’t what was established before the conversation. In fact, it is far lower than the “something more in the range of like $140 to $150,000” that Graham attempts to move the negotiation onto, albeit unsuccessfully.

The interview hallucination

There are also moments in which our old friend the AI hallucination appears.

At one point, Graham is surprised by some of the things Victor describes about his resume, so decides to pause things, and chat to the bot: “Victor, I love what you're doing. You're making me look really good, but I think you're making me look a little bit too good, okay? I think you can probably turn up the volume on my experience like 10%, but right now you're just outright fabricating things that aren't true. So, don't tell them that I manage people, because I'm just a marketing coordinator and I think that might backfire.”

Oddly, the exchange ends with each AI politely communicating compliments and niceties… and would perhaps still be chatting now had Graham not intervened.

For companies employing AI interviewers, the takeaway here is clear: they’re not doing what you think they are. Furthermore, prospective employees are typically turned off by the notion of an AI interview. Hiring needs the human touch.

• Ukraine's CCD says suspected Russian bot posts in its sample fell 83.6% on October 8 after a strike shut Yandex's Sasovo data center
• The same data shows volume back above pre-strike levels by October 10, with a smaller 42.6% dip after the Vladimir shutdown
• Cloudflare and Graphika found no comparable drop in Western political communities, though Russian-language Telegram output fell 5% to 15%

Ukraine's Center for Countering Disinformation (CCD) says suspected Russian bot accounts went almost silent after drones knocked out Yandex data centers earlier this month, with message volume across a monitored network falling 83.6% in a single day.

The findings come from an analytical report the CCD dated October 15 2026. Using Osavul monitoring software, analysts tracked 22,664 messages from 5,201 accounts showing signs of inauthentic behavior across 26 topics in Ukraine's Facebook and Telegram space between October 6 and 12.

The 83.6% figure is real, but the same report shows those accounts posting more than before within 48 hours, and independent data from Cloudflare shows that at least some of the malicious automation running on Yandex's cloud did not die so much as move house.

A collapse in automated traffic after the Yandex Cloud took a beating

The first collapse came on October 8, hours after a drone strike and fire shut down Yandex's Sasovo data center in Ryazan region, home to two of the company's three supercomputers.

Daily messages fell from 4,032 to 660, and active accounts dropped from 1,526 to 258. Telegram took the harder hit, down 90%, while Facebook fell 79.5%. A second, shallower dip followed the October 11 strike that Yandex said had completely halted its Vladimir site, with messages falling 42.6% on October 12, from 4,711 to 2,704. A subgroup of 98 accounts the CCD flagged for justifying the invasion or inciting hatred followed the same curve, down 77.8% on October 8 and 49.2% on October 12.

The most telling detail sits in the middle. The October 9 strike at Yandex's Kaluga campus, which the company describes as its largest, only partially took the site offline, and the CCD recorded no fall in activity that day. The two dips line up with the two complete shutdowns, as one would expect if these accounts depended on that infrastructure, though that's not proof on its own.

A surprisingly fast turnaround time?

What the 84% figure that the CCD touts leaves out is how briefly the silence held. Message volume recovered to 2,231 on October 9, then reached 4,585 on October 10 and 4,711 on October 11, both above the pre-strike level. The monitoring window in the report closes on October 12, so it cannot show whether the second dip recovered the same way.

The agency has also been downplaying the event on other forums; two days before the report, the CCD said posting in some clusters of the Pravda, InfoDefense and Nod of Time networks roughly halved for about 12 hours, and stressed that it had not recorded a significant overall decline across Russian bot farms.

On October 16, the CCD's summary of remarks by media literacy head Ivan Slychko described the effect as "a disruption of automation, not the complete disappearance of bots."

It also makes no mention of a similar comparison for ordinary accounts. On the afternoon of October 8, banks, retailers, mobile operators and news sites across Russia suffered outages. Without said information, it is hard to separate bot farms losing their servers from operators inside Russia simply losing connectivity.

The CCD is hardly an impartial player here; it is a body attached to Ukraine's National Security and Defense Council, and it has an incentive to exaggerate victories even when the data doesn't always support them.

President Volodymyr Zelenskyy had already jumped the gun, telling the Warsaw Security Forum that after the strikes "it suddenly became a little easier to breathe on many social media platforms" and that might have been a deciding factor for how the CCD built its narrative.

Beyond Ukraine, the "bots went silent" story mostly rested on a Cloudflare Radar chart showing automated traffic from Yandex's AS13238 network falling to well under 1% of its recent peak on October 11. As we noted at the time, that traffic was far more likely to be crawlers than fake social media accounts, and "bot" is a loose word when automated requests make up around half of all web traffic.

Cloudflare's own post-mortem confirms it. Marina Elmore and Lai Yi Ohlsen found 99.5% of requests from AS13238 were automated, mostly verified Yandex search crawlers, and that the network supplied only about 7% of Russia-origin bot requests. Bot traffic from Russian IP addresses hitting the post, reply, and like endpoints of one social platform dipped just 5.7% across the event period.

Graphika reached a similar verdict from the other direction. Founder John Kelly's analysis of the AfD, MAGA and Reform UK communities that viral posts claimed had gone quiet found none lost their active accounts or heaviest posters.

AfD-linked accounts posted 40,365 times on October 11, 4% below the week before and well within normal swings, while MAGA mentions on X rose 25% in the eight hours after the Vladimir strike. Georgetown's Renée DiResta, writing in Lawfare, was blunt about the viral version: "It's a great story. But it looks like fiction rather than fact."

For now, the most defensible reading of the CCD's numbers is narrower than its headline. Knocking out the right data center can briefly cut the output of Russia-based accounts aimed at Ukraine. Keeping them quiet would mean hitting the servers they move to next, and on the evidence so far, that move takes a day or two.

As one analysis of battlefield computing puts it particularly well, resilience comes from designing for disconnection, and the bot operators appear to have learned that lesson faster than Yandex did.

Via UNITED24 Media

As excitement around AI continues to build among businesses of all sizes, many leading voices are urging caution for those who might scale too quickly.

At its recent Think EMEA event in London, IBM revealed research which claimed efforts to stay aware of potential risks and challenges posed by AI deployment and scaling are falling somewhat short of expectations.

So what can companies do in order to balance the hype and excitement around what AI can offer, with the need for sensible and thorough governance.

AI as the enabler

"What we are seeing is that if you don't provide an answer to security, to accountability, to responsibility, it is going to be very hard to scale AI in the corporate environment - particularly when we're talking about highly regulated industries - so for us, it's more of an enabler, than something that is going to slow down the adoption,” Ana Paula Assis, Senior Vice President and Chair for IBM EMEA and APAC told us in response to that very question during a media Q&A at the event.

“The question here is really, how do you balance the speed of innovation with the implementation of governance?”

IBM recently released research on the current state of AI and governance, finding that 80% of the companies surveyed claimed that an AI incident had been escalated to board level. However only 40% of those had then been able to use the incident to secure new investment in governance, or cement a new policy.

“I think the reason for that is because it's very hard to balance that innovation with the application of tools, processes, methodologies, to ensure that you are deploying AI at scale in a safe way,” Assis continued.

IBM is reportedly well placed to discuss such growth and progress with AI, with the company’s earlier keynote at Think EMEA claiming it had seen savings of around $5.5 billion in terms of improved productivity and efficiency utilizing AI to improve core processes - money that it was able to redirect to areas including R&D and innovation.

“But we are seeing this as an enabler, and as a key component of the architecture for agentic AI going forward, because these companies have tremendous responsibility - they need to ensure that they have trust from their clients, and they will only be able to do that if they have AI that they can observe and that they can govern.”

“AI is no longer just supporting the business,” Assis said. “It has become the business model. The organizations pulling ahead are not just adding AI, but rethinking how systems, decisions and processes work together with AI.”

• Kaspersky finds AI and technology-themed phishing lures achieved the highest click rates, reaching 45% in testing
• Healthcare, compliance, promotions, and HR messages also proved highly effective at deceiving employees
• Researchers found regular awareness training can dramatically reduce phishing susceptibility across organizations

Phishing emails are notorious for often working exceptionally well, but which ones are the most effective? We all know that these lures need two things: A) to carry a sense of urgency; and B) the idea that the victim will either miss out on something or suffer material troubles.

But no two emails are alike, and some campaigns prove to be more successful than others.

Security researchers Kaspersky set out to learn which themes work best, and which fail, to give businesses better insight into the biggest risks lurking in the depths of their employees’ inboxes. To do that, they analyzed anonymized voluntary provided data from their Phishing Simulator. “A high click rate among users clearly highlights what specific cybersecurity training is required,” they said.

Responding to sensitive emails

The data showed five topics that yield the best results for the attackers: AI & Tech, Healthcare & Insurance, IT Policies & Compliance, Gifts & Promotions, and HR & Corporate Communications.

AI & Tech was the best-performing topic overall, with themes such as “introducing next-gen artificial intelligence” having a 45% click-through rate. In other words, almost every other recipient would click on a malicious link and potentially get served malware or share login credentials with threat actors.

Healthcare & Insurance lures tricked an average of 28% of the audience, but among the different emails, one stood out. Emails regarding a sanitary inspection report reached a 92% click-through rate. IT & Policies were third with a 26% click-through rate, followed by Gifts & Promotions (24%).

Two-thirds (66%) of recipients clicked an email inviting them to wear their’ national team’s football jersey and win a prize. HR & Corporate comms yielded a 22% click-through rate, with emails raising sensitive and high-interest internal topics being “especially dangerous”, Kaspersky said.

“People naturally respond to sensitive and highly contextual emails, especially when phishing mimics their exact workflow or local specifics. Knowing this, cybercriminals make their traps more personalized and this trend is set to accelerate with the rise of generative AI,” said Svetlana Kalashnikova, Security Awareness expert at Kaspersky.

“Fortunately, proper cultivation of cyber vigilance demonstrates measurable risk reduction: regular training and attack simulations drastically reduce phishing susceptibility rates from 60% to a mere 5–7%, according to KASAP effectiveness analytics. To protect people and businesses from human-targeting threats, we strongly recommend that organizations implement ongoing cybersecurity awareness program for all employees.”

According to Kaspersky, phishing remains the most prevalent cyber-incident for businesses at any stage of growth and ranks top among incidents leading to damaging business consequences. At the same time, attacks on humans grow more sophisticated, faster, and broader in scope and are, thanks to Artificial Intelligence (AI) now becoming a multi-stage effort.

Spotting a phishing email

Before Artificial Intelligence (AI), phishing emails were relatively easy to spot. Since they were mostly written by threat actors who aren’t native English speakers, their language was rather clunky and could not properly convey the style or the tone set up by the legitimate company’s communications team.

AI has now made malicious emails almost indistinguishable from legitimate communications, pushing identification further down the infection chain: towards links and attachments, as well as the overall sentiment.

A few things remain the same for all weaponized emails: the sense of urgency. Most legitimate organizations would not threaten their customers with account suspensions within 24 hours. They would also not host their documents on unrecognizable third-party services, and would not share attachments in executable formats.

Double-checking the domain from which the email is being sent is still a good way to spot an attack, too. Most phishing emails originate from a stolen email address, or one created on a public service. The UK postal service does not have a Proton email account.

Therefore, raising awareness, training, and doing simulations, remains the number one way to defend in 2026.

Apple’s new AI-powered home hub, the HomeView, is officially shipping on November 13. Featuring a 7-inch touchscreen, and coming in wall-mounted or tabletop versions, the new must-have Apple device seems a snip at just $299 / £299 / AU$449 — but that price doesn't reflect the true cost of accessing its best features.

One of the things that really makes the HomeView unique is that it recognizes your face, then adapts its display so you see your personal content in apps like Messages, FaceTime, Calendar, Reminders, and Find My. It’s a great feature, because having to keep logging in manually all the time would be a real time-consuming turn-off for a device that’s used by all the family.

But a note on the Apple newsrooms page about HomeView says “Apple HomeView setup and multiuser support require a compatible iPhone or iPad running the latest software.” It goes on to say that “Personal Context requests require a paired Apple Intelligence-enabled iPhone or iPad running the latest software version with Siri AI turned on and set to a supported language.”

You need a modern iPhone or iPad

Apple’s wording features two overlapping statements. A compatible iPhone or iPad is required to set up the HomeView, but one that runs Apple Intelligence is required for Personal Context requests. In practical terms, you need an iPhone 15 Pro, any iPhone 16 or newer model, an A17 Pro iPad mini, or an M1-or-newer iPad. The standard iPhone 15 does not qualify.

The Apple HomeView isn’t useless without those devices — it can still answer general-knowledge questions, control compatible smart-home products, and offer improved music discovery. However, it won’t be able to process requests involving personal information, such as finding details in Messages or Calendar and taking actions across apps, which require a modern iPhone or iPad. Siri AI must be enabled, and the HomeView and paired device must use a supported language.

But there is one even narrower requirement: customizing Siri’s expressiveness and speaking speed requires an iPhone 17 Pro, iPhone Air, iPhone 18 Pro, iPhone Duo, or an M4-or-newer iPad with at least 12GB RAM. Obviously, that's a small voice-personalization feature, not the threshold for ordinary Personal Context requests, but it’s still worth noting.

The complete Apple AI home costs considerably more

When we look into the HomeView’s smart-home features, which are part of Apple’s wider smart-home ecosystem, things get even more complicated. With a compatible HomeKit Secure Video camera, Apple Intelligence can do the following, genuinely useful, things:

• Summarize recorded activity: Instead of merely saying “Motion detected,” the Home app can describe what happened, such as a person leaving a package or a dog entering the garden.
• Search recordings conversationally: You can search using requests such as “Show me when the parcel was delivered”, or, “When did the children get home?”, rather than manually scrolling through clips.
• Connect related activity: The Home app can group or surface significant moments captured across different cameras.

Once again, an Apple Intelligence-compatible iPhone or iPad is required — this time to set up and enable the camera-intelligence features. Apple says footage is processed locally by the home hub or using Private Cloud Compute, rather than simply handing the analysis to the paired iPhone.

The complete list of what you need for these features to function is: a HomeKit Secure Video-compatible camera, HomeView or another compatible Apple home hub, an Apple Intelligence-compatible iPhone or iPad, and an eligible iCloud+ subscription. In other words, simply buying a HomeView alone does not give you the AI camera summaries or searchable recording history.

iCloud+ plans start at $0.99 / AU$1.49 for 50GB of storage a month, then $2.99 / AU$4.49 for 200GB and $9.99 / AU$14.99 a month for 2TB. Recorded HomeKit Secure Video footage is retained for 10 days, and does not count against your normal iCloud storage allowance.

Of course, you can still view a compatible camera’s live feed and receive basic accessory notifications without paying for recorded history, but you need that iCloud+ subscription for HomeKit Secure Video storage, and for AI features that analyze and search those stored recordings.

At $299 / £299 / AU$449 the HomeView therefore doesn’t independently provide the complete experience Apple demonstrates. Owners of an iPhone 15 Pro, iPhone 16 or another qualifying device may already have everything they need for Personal Context. But people using a standard iPhone 15 or an older model could discover that their new household AI device is considerably less personal unless they upgrade their phone too.

That may suit Apple perfectly: HomeView isn’t just another product, but another reason to keep every device in your home relatively new, compatible and attached to an Apple service. For buyers who haven’t studied the footnotes, however, the difference between what HomeView can do alone, and what Apple shows it doing, could prove frustrating.

• Hackers and fraudsters are already gearing up to steal data and financial information in the run up to the US midterm elections
• AI generated imagery and deepfakes are part of highly convincing scams
• Phishing campaigns designed to prey on voter registration are also likely to pop up

As the US midterms rapidly approach, the FBI is once again warning Americans about the flood of election-related scam campaigns coming their way.

Thousands of websites have already been spotted getting ready to spool up for election season, with phishing sites, fake donation portals, and Doppelganger clones also emerging as November draws closer.

Finances and personal data are both at risk, the FBI has warned, with many campaigns leveraging new AI technologies to make scams even more convincing.

Scammers leveraging AI and deepfakes

One of the ways scammers are taking advantage of the elections is by launching campaign investment pool schemes. These campaigns ask victims to pay into a campaign pool that promises to pay more money than the victims invested if the candidate wins.

Similar scams have been spotted surrounding investment in stocks and cryptocurrencies, but campaign investments are a novel new approach.

Fake political action committee (PAC) scams have also been popping up, requesting donations to help fund campaigns, but rather than any of the money going towards furthering the candidate’s political aims, the money is instead kept by the scammers.

Political merchandise is also being abused by scammers. Fake websites advertise clothing and trinkets bearing political logos and candidate’s names, but never arrive once they are purchased.

Election phishing on the rise

Outside of what the FBI has warned against in its advisory, there are a number of other common scams that are targeting Americans looking to exercise their right to vote.

Scammers are sending out text messages en masse stating that the recipient has not yet registered to vote and that the deadline is closing soon.

This sense of urgency can easily push past the better judgement of those looking to participate in the midterm elections, and upon visiting a fake voter registration website the victims hand over their sensitive information.

Subsequent phishing, scams, and even identity theft can follow.

Iran, Russia, and China are also launching disinformation and misinformation campaigns aimed at polarizing voters across the political spectrum. Similar campaigns were spotted for the 2024 Presidential election surrounding everything from US support for Ukraine, to election interference claims.

Via The Register

• GPT-6 Astra has cracked the code on a 19th-century military cipher
• Claude AI has also been busy testing elliptical curve encryption
• Both breakthroughs highlight the increasing capabilities of AI models

AI continues to push the boundaries in almost every task it gets given, and two new reports highlight the tech's growing capabilities when it comes to code-breaking and testing widely used encryption security protocols.

First up, Japan Today reveals that AI engineer Carter Church has used OpenAI's GPT-6 Astra model to crack a written code used by one of Napoleon Bonaparte's generals more than 200 years ago. The message dates from 1809, and mentions the positions of troops belonging to France and its allies.

The letter was sitting ignored in an online archive when Church decided to give it a go. Manually decoding the symbols, numbers, and letters could have taken weeks, but the AI was done in just six hours.

"What AI mostly brings is persistence," Church told AFP, adding that he felt "an unbelievable sense of satisfaction and excitement" when the result appeared. It was verified by patent engineer Satoshi Tomokiyo, who maintains an online list of unsolved ciphers for amateur cryptographers to tackle.

Curves and keys

Our second bit of AI code-breaking concerns cryptographic key generation — the methods software and hardware use to encrypt and secure data. Elliptic curves are one way to produce the keys required, and 'seeds' are starting points for these curves.

As reported by Cyber Security News, an internal Claude AI model used by Anthropic has now traced the origins of five elliptic curve seeds generated by the US National Security Agency (NSA) in less than 30 minutes. For more than 25 years, since the seeds were published, there have been claims that they contained weaknesses the NSA could use as backdoors.

Not so, Claude has now determined — there are no weaknesses, and no inside jokes. As Anthropic points out, most researchers trusted the curves already and no action is necessary, but it's a "fun closing chapter" that again demonstrates the AI's prowess.

Claude also had time for a quiet dig at human cryptographers. "Is it suspicious that such an obvious thing wasn't discovered in 25 years?" the AI asked on its way to finding the correct solution, according to the internal thinking logs. Not only are these models outperforming us at the core math, they're dunking on us along the way.

• Anthropic says it is no longer hiring so many junior/entry-level roles
• Many technical workers are becoming AI managers, and so are spending more time in meetings
• The company says its "deep adoption" makes it a useful "early warning system" for AI's impacts

One of the biggest AI developers in the world has released a brand-new study on how AI is transforming its own workforce - but we're not sure whether it's good or bad news.

Starting off with the bad news, Anthropic uncovered a considerable drop in junior workers – junior workers accounted for just 14% of technical staff by mid-2026, down from 21% in early 2025.

The proportion of junior employees among the company's new technical hires fell even more dramatically in the same period, from 27% to just 9%.

Is AI replacing some workers, but not all?

Ultimately, the paper concludes that artificial intelligence is handling more and more entry-level roles, therefore Anthropic hasn't needed to hire as many junior workers.

In fact, hiring on the whole has seemingly dropped thanks to AI-induced productivity gains. "[Anthropic] has hired less per dollar of revenue than an earlier generation of large tech companies," the company explained.

Crucially, the Claude maker isn't reporting a declining technical workforce. Quite the opposite, as it continues to grow, but those technical workers are now handling higher-level, managerial-type roles.

AI's biggest impact at Anthropic has been the number of code commits per engineer, up 5.1x from the end of 2025 to mid-2026. Data queries per data scientist (up 3.1x), contracts per legal worker (up 3.1x) and deals per salesperson (up 1.7x) are also all trending upward.

Humans remain in the loop

More broadly, the company sees many workers transitioning into decision-making roles, and that's increased the amount of time they spend in meetings. A 20% median increase was observed across the workforce.

"Engineers described becoming leaders and managers of agents; non-technical employees described doing more and more of the strategic parts of their job."

Despite massive productivity gains through automation, Anthropic also confirmed that some roles just have to remain human. They include things like sales calls and interviews.

Still, human workers are seen as something of a bottleneck in the world of AI. Claude spends around 80% of interactive session time waiting for human input, the research revealed.

Looking ahead, Anthropic has projected where we could be heading and how things could change with all employers. One of the biggest shifts could come in hiring, where a new set of skills is likely to be prioritized – including "autonomy and judgment."

A more optimistic approach admits that hiring is indeed falling, but job opportunities could actually be exploding. "One possibility is single-person start-ups, each with a human founder steering swarms of agents to rake in billions in revenue."

Ultimately, Anthropic says its "deep adoption of AI... makes it a useful case," and that it exists as an "early warning system" – so maybe we look at AI companies to see how AI is going to impact jobs?

There’s never been so much change happening across an IT estate. And it’s constant. On a day-to-day basis, IT teams might be creating and monitoring user permissions, deploying any scheduled operating system (OS) updates or integrating new third-party software tools. This is not to mention all the change carried out by external providers, outsourced teams and employees.

Change itself is not a problem. Indeed, it’s essential for ongoing security, resilience and modernization. Change that isn’t actively managed, however, is a different story. This could be change that goes unnoticed, like an external patch that automatically updates a system without IT oversight, or change that is forced, like rushing an OS upgrade to meet an impending end-of-life deadline.

The trouble is many organizations have to monitor incredibly large and complex IT estates that can contain hundreds or thousands of business-critical endpoints. And so much change is now automated or carried out by external parties. Consequently, without the right software and processes, this means that IT teams can have limited visibility into many changes taking place.

These changes might seem small or innocuous on their own. But left unmanaged, they mean systems are drifting away from their intended baseline, which adds risk to each subsequent change – and that’s when systems can fail and damaging outages occur.

Change that is not managed effectively also increases vulnerability to cyber attacks. Misconfigurations or gaps in security coverage materially enabled more than 90% of cyber incidents investigated in recent research, showing how seemingly minor changes or lapses in oversight can create openings for attackers.

So, what are some of the ways IT teams can effectively control change across their estate?

Here are three key steps:

1. Understand

The first step is to gain greater visibility into change across the IT estate, so teams can identify potential risks and understand their impact before they cause disruption. Achieving this requires the use of automation tools that enable continuous drift detection. With this in place, IT teams can monitor their entire estate – servers, end-user computing, networks, on-prem and cloud – for configuration change in real time.

To do this, the technology needs to scan every layer in the infrastructure to establish a known-good configuration baseline to use. The platform can then flag any time a change causes a device to drift from this intended baseline, while also allowing teams to gain visibility on the gap between the change that was expected and approved and what is actually running in the estate. This means a manual fix or external patch gets flagged, rather than flying under the radar until it triggers an outage or failure.

2. Respond swiftly

One of the key challenges IT teams face is reconciling and approving change. Inevitably, with so much change taking place, trying to manually perform this process leaves room for errors and can delay approvals. So, to reduce manual effort, the next step is to automate change approval processes, using software to automatically reconcile every detected change against approved baselines and validate what steps were taken against those planned in the change request.

With this, teams will then be able to rapidly respond to unapproved changes and use their time to take action on issues before they escalate and impact operations. More broadly, this also speeds up the delivery of new upgrades or projects and gives IT teams greater freedom to innovate safely.

3. Protect

There’s always a risk of a cyber incident taking place. But when teams understand what’s happening in their estate and accelerate their approval processes, they can also detect and remediate issues faster and reduce their mean time to resolution. This can help to mitigate a failure escalating into an outage, or minimize the cost of an incident, while also strengthening overall resilience.

Protecting systems also means ensuring they are compliant and that teams can export defensible evidence on demand whenever a regulator requests to see it. This also highlights why continuous compliance that generates clear and auditable trails is so important. Otherwise, audits are often accompanied by a time-consuming, manual search through spreadsheets and screenshots for evidence, and this also means information can be out-of-date by the time it’s collected.

Controlled change unlocks innovation

IT change is constant, and with the rapid development of AI and cloud tools, it’s also only going to increase. This provides more opportunities for companies to modernize, but it also creates more risk when it happens without control.

To maintain control and prevent damaging incidents from taking place, IT teams need to understand when configuration change happens across their estate, accelerate the rate at which change can be approved and reconciled, and protect their systems through faster remediation and establishing a real-time audit trail.

While it can be easy to put off resilience projects in favor of innovation, the threats facing IT are becoming stronger and more varied, so controlling change has to be a key priority. If anything, it provides the foundation to innovate successfully and safely.

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Not everyone owns a Ferrari, but I’m sure those who do rarely use it to pick their children up from school. It is less energy-efficient, not as safe or spacious and may garner unwanted attention. The analogy is simple: in AI, you do not always need the most powerful tool for everyday tasks.

It is a trap many businesses have fallen into since the first wave of AI, when early adopters were trying to prove they could access the cutting edge of the technology. Everyone wanted to have the most powerful AI model, the most sought-after accelerators and the fastest route from the pilot stage to implementation.

But as AI moves from pilot into production, new costs are emerging, largely associated with usage. A proof-of-concept used by a handful of people can look cheap, but running the same application thousands of times a day, or by agents calling upon multiple models for one task, is an entirely different proposition.

The mistake is not owning a “Ferrari”, but using the same model, depending on the same hardware choices or taking the same deployment path for every project. That is where the next phase of enterprise AI is getting interesting, with new efficiencies coming from matching each workload to the most suitable model and accelerator, rather than defaulting to your biggest “engine”.

Every model has a different role to play

There is a popular idea among some enterprises that AI is a ranking exercise, in which you should standardize as much as possible on the smartest model available. In theory it sounds tidy, but it ignores the complex differences between each AI task and workload.

For instance, a customer service copilot needs to respond quickly enough that the user never notices the machinery behind it. A large document summarization job can wait longer, with the user expecting the AI to “think” much more carefully. Government applications of AI are likely to instead prioritize sovereignty and ownership, making where and how workloads run just as important as performance.

This logic applies from the size of the model to the infrastructure underneath it. Smaller models can often handle routine classification, extraction and routing tasks quicker and at lower cost, while mid-sized models support a great deal of everyday reasoning work. The largest, most powerful models are important but may be used more selectively, when their capability and cost are justified – for example, in complex reasoning, deep strategic planning and high-stakes tasks where accuracy and nuance are critical.

Equally, to maximize token efficiency, enterprises should consider how different workloads behave and why they do not all require the same type of accelerator. With a diversity of silicon to draw on, different combinations of models and hardware can be selected to suit different requirements, whether the priority is speed, throughput, cost or control.

As such, rather than fitting every workload around an assumed preference, enterprise teams should start with the desired outcome. A retailer running a customer service assistant will have very different priorities from a government department handling sensitive information.

The former is likely to prioritize response time, user experience and cost at scale, while the latter places greater weight on sovereignty, security and where the workload runs. The infrastructure should leave enough choice to match different workloads and changing requirements without being locked into one route.

Building on a diversity of silicon

If an organization invests heavily in one type of accelerator, it can be tempting to run every workload through it. This is where the wrong assumptions, made at enterprise scale, can increase costs rapidly. Using the same piece of hardware for every task can mean overpaying for performance with minimal or no benefit to the user.

Low-latency applications need hardware optimized for responsiveness, while high-volume workloads may benefit more from throughput and cost efficiency. Running both on the same infrastructure can mean paying for performance that one of them never uses.

In most cases, building on a diversity of silicon – heterogeneous accelerators that can be aligned to each workload – will enable enterprises to make smarter infrastructure decisions. In some scenarios, organizations may even prefer to combine hardware from different providers that complement each other. NVIDIA GPUs will make sense for many enterprise scenarios, but choice matters.

Being able to select across providers such as AMD, Qualcomm, Microsoft, Cerebras and others lets enterprises align infrastructure to their specific needs and improve efficiency. Cerebras, for example, has run OpenAI's gpt-oss-120b at around 3,000 tokens per second – a level of speed that is especially valuable for applications where response time is critical.

The deployment environment also matters, particularly in large-scale enterprises or at the government level, where a great deal of sensitive information needs to stay within a specific jurisdiction. In Europe, there is strong demand for infrastructure developed in-market and operated around local requirements.

For enterprises operating across markets, that means infrastructure choices cannot be separated from questions of data residency and local control. Increasingly, the answer is infrastructure that is sovereign enabled – with in-country accelerators, consumption-based pricing and no upfront capital commitment – rather than a single hardware decision made once and lived with for years. Being locked into one route leaves little room to respond when those requirements change.

Understanding AI tokens and efficiency

Another consideration is that AI spending behaves differently from traditional enterprise software, where a license has a relatively predictable cost. Businesses will face a larger AI bill when an agent takes more steps, or a powerful model is pulled into more tasks than necessary, which makes usage itself a poor proxy for value.

Enterprises are already starting to rethink how freely employees can consume tokens, particularly in coding roles. Uber, for example, reportedly used its entire 2026 AI budget in four months, largely on Claude Code. The lesson from this type of example is that giving more people more tokens does not necessarily equate to greater value.

Agentic AI provides another example. Unlike an employee who occasionally asks a routine standalone question, an AI agent can work through a long sequence of steps, read files, generate code, test it and review the output for mistakes. Agentic AI, especially in coding, can be uniquely expensive compared to reasoning and chat, with highly variable token usage where increased consumption does not always improve accuracy.

As counting tokens does not provide the complete picture, enterprises should consider measuring success on outcomes rather than on performance or capability alone. It can be useful to use faster AI models, more powerful hardware or longer-running agents, but only when the outcome justifies what they consume.

For a customer service agent that could be the cost of resolving a case or, for an employee copilot, whether human employees are making faster or more complete progress on meaningful work.

However, the aim should not be to cut token use altogether. IT leaders should seek to understand exactly what they’re paying for and whether it has a clear business case. That is the definition of AI efficiency we should be moving towards. Rather than seeking maximum usage or minimum cost, CIOs and their teams should ensure they return enough value from each workload to justify the investment that goes into running it.

Saving the Ferrari for the racetrack

There will always be AI workloads that require the largest, most powerful model, the fastest accelerator or the most expensive deployment option. The point is to stop making those choices by default. The next phase of widespread AI production requires a more deliberate approach to every workload.

Start with the task, choose a model that’s capable enough to do it well, run it on the accelerator suited to its performance needs, and make sure the deployment environment meets the security and sovereignty requirements around it. Then measure whether the result is worth what it costs.

This approach may be less headline-grabbing, but it is what will separate resilient, production-scale enterprise and government deployments from experimentation. As AI becomes part of everyday workflows, efficiency will increasingly stem from making smarter choices about where each capability is needed – and from having the diversity of models, silicon and deployment options to act on them.

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AI coding assistants are no longer experimental. In organizations that have adopted them, AI is responsible for roughly half of committed code. The question is, therefore, no longer whether developers will use AI, but whether security programs can govern what those systems produce. AI-generated code is remarkably good at producing syntactically correct code, with a 99.9% pass rate. In contrast, security performance tells a very different story.

As it stands, the average security pass rate is only 56%, meaning roughly 44% of tested code-generation tasks introduce a known vulnerability. As the volume of AI-generated code increases while security performance remains largely unchanged, organizations risk introducing more vulnerable code into their development environments. As a result, software vulnerabilities are now the top breach entry point at 31%, overtaking stolen credentials.

Closing that gap means rethinking how security fits into AI-assisted development. Organizations need to make security part of the development process from the start, choose models with risk in mind and verify code in context before it reaches production. The goal is not to slow developers down; it’s to make sure security can operate at the same speed and scale as the software being created.

Model choice is a security decision

AI models learn from enormous volumes of historical and publicly available code, including examples from open-source projects and developer communities. That training data contains secure implementations, but it also contains outdated libraries, weak validation and insecure coding patterns. While models are designed to produce useful, functional code, without the right controls they can reproduce those insecure patterns at scale.

Organizations cannot control every source used to train a model, but they can control which models enter development. The strongest model tested achieved a 68% security pass rate, while six of the 11 models clustered between 50% and 53%. Model selection is, therefore, not simply a productivity decision but a risk consideration.

Choosing a stronger model can reduce exposure, but it does not make the output trustworthy by default. AI-generated code should still be tested throughout the software development lifecycle using capabilities like static analysis and enforceable security policies. Security must be part of the delivery process itself.

Security needs application context

AI-generated code can appear to work as intended, compiling successfully and passing functional tests while still containing security vulnerabilities. Under deadline pressure, that can create misplaced confidence, particularly when developers accept output without fully understanding how it behaves inside the wider application.

The difference in security performance across vulnerability types shows why context matters. While models achieved pass rates of 83% for SQL injection and 87% for insecure cryptographic algorithms, performance fell to 15% for cross-site scripting and 12% for log injection. Some weaknesses can be identified through a recognizable coding pattern, but others often depend on data flows, authorization, trust boundaries, and how different parts of an application interact.

That’s why verification needs to happen in the workflow and in the context of the application. AI-native analysis can add contextual risk detection alongside established static analysis, surfacing issues such as logic flaws and broken authentication directly in pull requests. The key is to combine contextual analysis with independent testing before code is merged and risk has the potential to spread.

Prioritize risk, not just findings

As AI increases the volume and speed of software development, it also increases the amount of risk security teams have to assess, prioritize and remediate. That adds additional pressure to organizations who are already carrying significant backlogs of unresolved vulnerabilities.

That backlog enables security debt to build as flaws are identified but remain unfixed, meaning known risk sits in applications for longer. The more that debt accumulates, the harder and more expensive it becomes to reduce. Today, security debt affects 82% of organizations, while critical security debt affects 60%.

Those figures point to a growing capacity problem. Organizations are generating and discovering risk faster than they can reduce it. Treating every finding as equally urgent will only make that problem worse. Teams, therefore, need to focus on exploitability, application criticality and business impact so they can direct limited remediation capacity towards the vulnerabilities most likely to cause real harm.

AI-powered remediation can help close the gap between discovery and action by proposing fixes inside the developer workflow and reducing repetitive manual work. But remediation still needs to be governed. A generated fix must be tested, validated and checked for regressions. The objective should be to reduce meaningful exposure, not simply increase the number of findings or fixes moving through the system.

What leaders need to do now

AI-assisted code has become embedded into software development. As a result, leaders should treat its security implications as a change to the software operating model.

That means knowing where AI-generated code enters the organization, evaluating models against relevant security requirements, building verification into developer workflows, and focusing remediation on exploitable business risk. It also means retaining evidence of what was tested, what was fixed, and what risk was knowingly accepted.

The organizations that succeed will not be those that slow AI adoption, but those that evolve their development practices so security can keep pace with software creation. AI is making it easier to generate working code, but the real test is whether businesses can prove it is secure before it reaches production.

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There is no shortage of published thought leadership on the potential of artificial intelligence. But what often gets overlooked is the gap between theoretical potential and the reality of deploying it successfully inside a global enterprise. Gallup’s 2026 State of the Workplace report reveals a stark disconnect: while 65% of employees say AI has boosted their individual productivity, only 12% strongly agree the technology has actually transformed how work gets done in their organization.

Having led tech transformations from both sides — as an executive driving innovation at Canadian National Rail and as a partner accelerating AI outcomes for customers with Tenarai — I’ve learned that successful digital transformation thrives on people. In the agentic era, the unlock for enterprise AI outcomes is empowering employees to unlearn the tools and habits they have relied on for years, helping them embrace new capabilities with confidence.

As the industry shifts from passive AI assistants to autonomous AI agents, traditional software delivery models fall short because they overlook the human-AI connection. Bridging this gap requires elevating change management into a foundational pillar of enterprise AI transformation, aligning tech with the context and culture of the enterprise.

From assistants to active agents

To understand why legacy engineering structures fall short on scaling AI outcomes, let’s look at how technology changes human workflows. Generative AI serves primarily as a task-based drafting tool, keeping humans comfortably in the driver’s seat to review and approve.

Agentic AI, in contrast, is goal-driven, context-aware, and collaborative; acting more like an autonomous digital co-worker. This shift is already reshaping complex enterprise operations, but its success hinges on people’s ability to adapt.

A recent data migration we led for a global logistics leader illustrates this challenge. While moving its data pipeline from a legacy SAS architecture to the cloud presented significant engineering complexity, the greater challenge was actually guiding its long-tenured business analysts in unlearning many years of established workflows.

By pairing technical migration with hands-on change management initiatives, we helped these analysts understand the clear value and benefits of change. Ultimately, winning over their belief system did far more to accelerate our deployment timeline than the underlying technical migration.

This tension reveals the fundamental flaw in traditional AI adoption. When employees transition from controlling task-based tools to supervising autonomous agents, a vulnerability gap is exposed. Legacy IT structures, often operating in silos, are inherently built to fail in this environment, leaving frontline staff stranded between old habits and new autonomous capabilities. Bridging this divide requires installing a modern delivery model engineered around human-centered collaboration.

Architects of trust: FDEs and agentic design leads

To close this adoption gap and scale agentic AI from proof-of-concept experiments to real business outcomes, organizations must deploy a dual-role framework. This model embeds change management, domain expertise, and operational empathy directly into the technical pipeline through two key roles: the Forward Deployed Engineer and the Agentic Design Lead.

Traditional software teams frequently write code in silos based on slow-moving specifications. The Forward Deployed Engineer (FDE) model eliminates this bottleneck. FDEs operate as adaptable technical architects embedded directly at the intersection of business processes, corporate context, and live production code.

Working within cross-functional units, FDEs collaborate closely with specialized AI agents to compress build cycles from months into weeks. By working alongside business users, FDEs rapidly translate operational goals into scalable, production-ready systems while resolving friction in real time.

While the FDE drives technical execution, the Agentic Design Lead focuses entirely on the human-AI connection and organizational alignment. This specialized role reshapes business workflows around agentic capabilities before development begins, securing active adoption at every stage. By analyzing user behavior, the Agentic Design Lead remodels the design process with the actual users in mind, ensuring a seamless human-AI connection is pulled all the way through the final solution.

Managing the risk of unlearning

Guardrails and governance are essential because unlearning decades of defined software habits can be highly uncomfortable for an organization. To scale agentic workflows safely, leaders must establish a clear map of where unlearning is encouraged and where it is not, dividing enterprise operations into two distinct risk categories: deterministic and probabilistic.

Deterministic controls govern mission-critical environments where errors risk physical operations or human safety; such as warehouse operations, heavy rail dispatching, air traffic management, or industrial manufacturing. These high-consequence domains demand rigid, non-autonomous safeguards.

Conversely, probabilistic frameworks support domains where autonomous agents excel, including demand forecasting, targeted marketing, and automated document analysis. For these adaptive environments, leaders should establish secure sandboxes where cross-functional teams can safely experiment, evaluate performance logs, and scale proven use cases incrementally.

Change management as an internal marketing campaign

Even the most advanced AI architecture fails if the workforce resists adoption. Enterprise transformation cannot succeed through executive mandates or static dashboards. Leaders must execute it like an internal marketing campaign, earning employee buy-in across the organization and purposely guiding teams through grassroots engagement.

Organizations can build momentum through interactive workshops, innovation days, tailored training, and immersive boot camps that demystify new tools through hands-on exposure. Meanwhile, low-pressure forums such as team connection meetings or informal brown-bag sessions provide safe spaces to address concerns, establish trust, and cultivate creative confidence across teams. We’ve done all of these activities and though some may seem simple, they work.

Winning in the agentic era isn't about outspending competitors on models, it's about understanding people and navigating them through change. Operational success belongs to the organizations that view change not as a technical hurdle, but as their most important product. By guiding their employees in the art of unlearning, leaders transform workforce friction into adoption, ensuring that as agents take on the heavy lifting, human potential remains the true engine of innovation.

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Rather than a review, this is a ‘hands-on’ since the HomeAgent hardware isn't in retail channels yet.

So much has happened since it's difficult to comprehend that Ugreen only sold its first NAS in 2021 exclusively in China, but its global retail products only arrived in 2024.

Since the initial five NASync models, we’ve seen the incredible iDX6011, HD products, GT series updates for the DXP machines, Pro series additions, and a new DXP8800 Ultra that I covered recently.

Now, alongside those thirteen NAS devices, Ugreen has taken another branch and created the HomeAgent series. It combines some of the development path of the UGOS Pro, a basic NAS hardware platform, with a device that’s made to create a bubble of AI away from the internet to handle a variety of agentic tasks.

Initially available only via pre-order and Kickstarter, with a retail release in early 2027, there are three models: HomeAgent HA100 reviewed here, the HA100 Pro for those who want more AI performance, and the MasterAgent MA100 that pushes local AI power to the max.

Alongside the HomeAgent devices, Ugreen has also created a collection of peripheral devices, including three AI-connected cameras, a smart speaker, and a connected colour E-Paper picture frame.

While the launch ecosystem may seem modest, Ugreen will likely expand this collection in the near future.

These systems are designed to address issues that smart home (or business) users face when technology is so fragmented. Typically, people have components from different brands, Google and Amazon, alongside peripherals made by others, and they try to bring it together using a platform like Home Assistant run on a NAS or dedicated PC.

The Ugreen HomeAgent takes all that technology and control, adds AI components that will still work even if the internet isn’t functional and distils it into an attractive white plastic box.

Instead of needing regular attention, the AI in the HomeAgent is meant to run the NAS and connected peripherals without intervention, and it can tell you if a problem is beyond its ability to address.

Asus Ascent GX10: Price and availability

• How much does it cost? From $799
• When is it out? Available for pre-order, shipping in February 2027
• Where can you get it? Via Kickstarter

Ugreen has offered some amazing deals when launching NAS on Kickstarter, and this hardware is no exception.

The HA100 costs only $799 at the pre-launch early-bird price, down from a $1,599 MSRP. Conversely, the HA100 Pro is $2,699 on a $5,399 MSRP, and the MA100 is $9,999 on a $19,999 MSRP.

The Kickstarter initiates on November 17th, but Ugreen is taking reserve deposits before then, where you get a 50% discount on the HA100, HA100 Pro and MA100.

After the Kickstarter is launched, early bird pricing will be 40% off, and presumably full MSRP will be the norm after February 2027, when the Kickstarter ends.

To get those prices, you need a $50 reserve deposit for the HA100 and HA100 Pro, and a $200 deposit for the MA100. As an additional sweetener, Ugreen is bundling the SynCare ID500 Pro camera with the HA100 and HA100 Pro (MSRP $149) and four 8TB NAS drives with the MA100, which must easily be worth another $1200.

The HA100 pricing is for a NAS with only three HDD bays and 16GB of RAM. That said, it includes an AI accelerator, which makes it hard to compare to Ugreen’s DXP and iDCX series machines.

Therefore, if you are primarily looking for a NAS, then this might not be the product range for you; if you are more interested in AI, it might be.

I should also mention the high-end MA100, which I believe has four drive bays and is built around an NVIDIA accelerator. Considering it was possible a few months ago to get an NVIDIA DGX Spark system for around $3000, that does seem pricey, but it should offer similar performance.

However, the ASUS Ascent GX10 I was referring to now costs twice as much, and that’s for a machine with 128GB of RAM, whereas the MA100 has 256GB. So, at the Kickstarter price, the MA100 might be a bargain.

What I can say with some certainty is that no other NAS brand is making anything remotely like this right now, although there is some overlap with the DXG Spark platforms.

Ugreen HomeAgent HA100: Specs

Ugreen HomeAgent HA100: Design

• Large enclosure
• PoE switch inside
• Three HDD bays, dual-function storage

The first thing that strikes you about the HA100 is how much bigger it is than a typical four-bay NAS, like the DXP4800 Pro or Plus.

Standing about 218mm high and 251mm deep, this is a large, shiny white box that feels substantial when you pull it from its packaging.

On the top front is a wide OLED panel, which I think is the same one that I saw on the iDX6011 Pro, but on its side. This acts as both a touch-sensitive menu and can also display various useful information.

Just above this on the left is a volume rocker, and to the right is a microphone mute, which hints that you can talk to this device, or rather the AI running on it.

The front face also has a single USB-C port and an SD card slot; the USB-C is 3.2 Gen 1 (5Gbps), and the SD card reader is SD 3.0 spec. And just below them is an LED that projects different colours to indicate whether there is a problem or all is well.

On the back, there are two USB-A ports, both 5Gbps, a single 2.5GbE RJ45 LAN port and a four-port PoE 10/100Mbps (Max power 65W) switch. That last feature matters for the unit's surveillance function, since you can directly connect and power PoE cameras.

Finally, we come to drive bays, and the HA100 isn’t quite like anything else Ugreen has made so far, though it has minor similarities to the DH4300 in that it’s a top loader.

The top of the HA100 is magnetically attached, making it easily removed to reveal four bays, with three for hard drives and one for an AI accelerator. These are all lockable and colour-coded. The drives have plastic tool-less cradles if you use 3.5-inch media, and screws if you insist on 2.5-inch HDDs.

But what genuinely surprised me is that two of the HDD bays are allocated to NAS storage, and one, coloured purple, is meant to hold a surveillance-tailored drive. I’ve never seen that predefined on a NAS before, but it does reveal one way that Ugreen envision the HA100 being used.

On the underside of the unit, there is also a removable panel that provides access to an M.2 NVMe PCIe Gen 3 x1 slot, which can be used for caching or a high-performance drive for AI models. You are recommended to use it for the latter.

I won’t be tempted to say the HA100 breaks the NAS mould, but it certainly strays off the beaten NAS path, and in some truly interesting ways.

Ugreen HomeAgent HA100: Features

• ARM SoC
• T20 accelerator
• M.2 and PCIe slots
• No upgrade path in series

Given how many newer Ugreen NAS designs use mobile Intel platforms, it seems odd that none of the HomeAgent hardware uses them.

Even more curiously, the three machines (HA100, HA100 Pro, and MA100) all use different ARM SoCs, completely eliminating any consistency.

Of these, the Rockchip RK3588 in the HA100 is the most recognisable, since it's an Armv8.2-A platform with four each of Cortex-A76 plus and Cortex-A55 cores, much like is often found in phones or tablets.

The HA100 Pro is rocking more cores with an Armv9.2-A platform, the 12-core CIX P1 CP8180. SoC diversity is completed with the MA100 that uses a fourteen-core NVIDIA Jetson Thor T5000.

Similarly, GPU power scales by level, starting with the Arm Mali-G610 MC4 on the HA100, moving to the Arm Immortalis-G720 MC10 on the HA100 Pro, and ending with the 2,560-core NVIDIA Blackwell GPU with 5th-generation Tensor Cores on the MA100. This helps explain why MA100 hardware costs so much, but it's hardly a smooth power ramp.

When the CPU, GPU, and NPU all work on an AI task, the HA100 has an advertised total AI performance of 26 TOPS (6 TOPS platform, 20 TOPS T20 accelerator).

That’s actually not enough to run CoPilot locally on a PC, but it can still run some LLM models adequately. But the total performance of the Pro model with an as-yet-unnamed accelerator is a much more sprightly 205 TOPS, and the MA100, as you might expect, has up to 2,070 FP4 sparse TFLOPS.

So, in summary, for AI, it scales 10x and then 1000x, approximately. Those are not exactly incremental jumps.

It also makes the HA100 look decidedly underpowered, although the Kickstarter entry-level pricing does make it attractive to someone who wants to explore local AI.

What confuses me is that HA100 or HA100 Pro owners have no upgrade path, since the systems aren’t interchangeable in any way. As I assume you can’t put the accelerator from the HA100 Pro in the HA100, what was the point of having the accelerators removable? And why make the MA100 the only one with four bays?

Surely, if all the cases were the same, Ugreen could redirect production more easily than it does now?

I understand that part of the purpose of using Kickstarter in this instance isn’t about funding; it's about getting indicators of demand for each model. But if you’ve already made too many black four-bay MA100 cases for demand, it's too late to repurpose them for the HA100 Pro. But then Ugreen already undermined that flexibility with the three-bay designs.

And, here’s the real rub. If you get a low-profile RTX 460 video card and mount it in one of the DXP or iDX machines, then that can generate 242 TOPS of AI processing, making that NAS more powerful than the HA100 Pro. How well that would work in reality is debatable, and I’ve never tried it, but the issue is obvious if you look at the costs.

In a similar vein, the MA100 is up against DXG Spark systems. But all of those I’ve seen come with dual 10GbE LAN ports, not one and four 1GbE PoE ports. But critically, the DXG systems also have the Nvidia ConnectX-7 SmartNIC on them, which is a 200GbE network connection designed to fuse two DXG systems into a single platform.

The MA100 doesn’t have this port, I assume, and therefore, unless Ugreen releases an accelerator upgrade, moving from Blackwell to Vera Rubin when it arrives, then there isn’t an upgrade path here either.

Coming back to the HA100, the basis for my experience so far, the T20 accelerator on this machine is modest, yet it took up most of the free PCIe lanes, leaving only a single lane for the M.2 slot, once all the other ports were covered.

Given the price, I think it should have shared an architecture with the DXP4800 Pro, or better, and maybe had either dual M.2 slots, Gen 3 x4 or a single Gen4 x4 slot. Because Gen3 x1 is only 1000MB/s, and external devices on this machine can go that quickly.

I guess, based on the number of pledges they get, Ugreen will know whether the performance ramp from the HA100 to the HA100 Pro and the MA100 was right or wrong.

My feeling, and it’s a gut reaction, is that all the systems should have shared the same enclosure, the HA100 should have been more powerful, and there should have been more consideration given to a potential upgrade path, at least from the HA100 to the HA100 Pro.

Ugreen HomeAgent HA100: Software

• UGOS Pro
• Local Gwen 3.5
• Smart Home

The hardware in this series is effectively done, and I’d be massively surprised if anything changes between the machines reviewers received and what ultimately arrives for those on Kickstarter and in later retail.

That leaves the software side of the equation, and it isn’t at the same stage of production, even if it is underpinned by the same UGOS Pro platform as all the DH, DXP, and iDX NAS Ugreen has already launched.

However, at the time of writing, my DXP8800 Plus workhorse is running UGOS Pro 1.20.0.0142 and the HomeAgent HA100 is on 1.20.0.0127 Beta.

That might not seem hugely different, but there are significant differences between the two, so much that the HomeAgent machine needs a special version of the mobile application to even be accessible.

It seems reasonable to assume that closer to retail release, all the extra functionality needed for HomeAgent will be settled into the latest general-purpose UGOS Pro release, rather than it spawning a whole new separate OS.

The biggest difference anyone who has used UGOS Pro will notice is that you can’t access this version from a web interface, although Ugreen has promised that in a future update.

Using it from the mobile app doesn’t limit what you can control, but it's nice to use the web interface when it's available.

It’s a generalisation, but the NAS side of the HomeAgent is practically identical to that in prior Ugreen NAS, where you can install apps, manage the drives and data. Obviously, in this configuration with only two data drives, you can’t configure a RAID 5 pack, but you can configure RAID 0 or 1, and you can create backups to another NAS.

The app selection is mostly the same as that on the standard NAS, with a couple of special function apps that bind the T20 accelerator into the system and an IoT Gateway that are loaded by default. What’s genuinely interesting is how some of the apps we’ve seen before on UGOS Pro now have added significance in this context.

The one that stands out most is Model Manager, since the whole AI function of this system is built around a locally run AI on this app using Docker as a subsystem. By default, the system loads the LLM Qwen 3.5 4B.

This 4B model is a sensible fit for this hardware, as being quantised, it occupies only a few gigabytes, which leaves plenty of the 16GB for the operating system and camera analysis running alongside it. Anything much larger would start to squeeze this box.

To try and make things as efficient as possible, the model has modules that you can either install or not depending on if you want it to recognise people, pets or even the appearance of a parcel. As these features infer, it works closely with Surveillance Centre and the new camera peripherals.

It can also link to Smart Home functionality, enabling it to turn things on and off, like lights and also read temperature and security sensors.

The HomeAgent box and some of the peripherals, such as the SynCare cameras and the Smart Speaker, allow you to verbally interact with an AI assistant, Uliya.

And frankly, this is where things need some considerable work. While the hardware in the HA100 can process 40 or more tokens a second, I’d hardly describe it as conversational.

It can alert you when someone is approaching the house, has left a package, or where a known person is in the home, but ask it anything more complicated, and it often says it can’t do that yet.

While I’ve not tried the HA100 Pro or the MA100, I’d assume they can run much more sophisticated LLMs with greater capabilities, but the AI component of the basic system is more agentic-focused than something like ChatGPT.

That said, you can plug a Cloud-based AI in alongside Qwen 3.5. The current options are OpenAI (ChatGPT), Qwen, MiniMax, DeepSeek, GLM and Kimi, and you will need an API key to plug those in.

However, Ugreen made it clear that the purpose of the HomeAgent was to provide a local AI that wasn’t dependent on the internet, so using a Cloud model undermines that assertion.

Obviously, the engineers can do plenty with the local Qwen 3.5 4B model to make it more interactive and able to handle a wider range of scenarios, but at this point in development, it is rather underwhelming.

The other part of this solution that needs considerable effort is the Smart Home app, since currently it only connects to the associated Ugreen peripherals or Matter-compliant devices.

Since all my home automation hardware is older and doesn’t use Matter, that’s a problem.

I did consider using the Matter Hub on my Home Assistant installation to make non-Matter hardware visible, but the way this is handled on the HA100 requires either a Matter QR code or pairing number. At a minimum, this needs to be expanded to include older Google and Amazon equipment, as well as popular brands like Tapo, SmartLife, and Hue.

I’m sure these things will be added, but unless you have Matter hardware or Ugreen peripherals, it's a bit of a technological desert right now. Once these items become part of this ecosystem, you can easily build automations and interact with Uliya to control them, or that’s the promise.

For my assessment, Ugreen provided two peripherals: the SynCare ID500 Pro and the SynCare OD600 Pro. If you didn’t connect the dots, ID is for indoors and OD is for outdoors.

The ID500 Pro, which you can get for only $89 as part of the Kickstarter offering, comes for free with the HA100 and HA100 Pro. This device gets power via USB and comes with a phone-style power source and a long USB cable.

Equipped with a 4K camera on a smooth pan-tilt tracking system that enables it to follow movement automatically across a room or even outdoors. Working with AI, it can identify people, vehicles, pets, and packages, and record those events on the HomeAgent for later review.

The only issue I ran into was that I put it in a window to cover anyone approaching, and it did that well, but at night, when it turned on its IR lights for night vision, it blinded itself with the IR reflection off the glass.

Undoubtedly, a superior option for tracking outside activity is the OD600 Pro, which is designed to be mounted outside, ideally on the eaves of a property. It has dual cameras and takes its power from a PoE LAN connection, allowing it to be directly connected with a single LAN cable to the HomeAgent PoE switch. A more advanced version, the OD800 Pro, includes a solar panel and battery to power the camera and can use Wi-Fi, so it can be installed without cabling.

I'm slightly disappointed the PoE ports are only 10/100 and not gigabit or even 2.5GbE, but that's a choice Ugreen made.

As part of the launch, the OD600 Pro can be had for $159 (MSRP $269) and the OD800 Pro for $199 (MSRP $339), which, given their specifications and build quality, seems a good deal. Hopefully, the devices will also work with an ordinary Ugreen NAS at some point, because they don’t currently.

Having been part of the original DXP NAS launch, HomeAgent has a strong element of things repeating themselves. However, since UGOS Pro has had two or more years of development, the underlying platform is more mature.

Updates for the extra HomeAgent part of this package are arriving often, and things are improving, but with the Kickstarter about to officially begin, and its end a few months away, this solution needs to become much more sophisticated quickly.

Today, it’s another Ugreen work in progress, but UGOS Pro was one not so long ago.

Ugreen HomeAgent HA100: Early verdict

First, I think it's far too early in the HA100's gestation to fully appreciate what this device might accomplish, since much of the software side of this equation isn’t nearly complete.

That said, when Ugreen first launched the NASync DXP series globally, UGOS Pro looked decidedly primitive, and two and a half years later it is much more sophisticated.

It wouldn’t be unrealistic to expect it to be much more functional by the time this hardware hits retail, and to continue rapid development from there.

Where I’m less on board is the three different platforms, each based on a unique platform and with a different accelerator.

The HA100 is built on a Rockchip RK3588, the HA100 Pro uses the CIX-CP8180(P1), which previously appeared in the Minisforum MS-R1 ARM workstation, and the MA100 is powered by the NVIDIA Jetson Thor T5000.

The problem with this range is that even if the accelerator modules are removable, there isn’t an upgrade path that can take you between these options. Had the HA100 and HA100 Pro had a common platform, that would have made sense, with the differences being in the accelerator module.

But since the accelerator on the HA100 uses a PCIe 3.0 slot, and the one on the HA100 Pro uses a PCIe 4.0 slot, then that was never going to work.

Unfortunately, upgrading forces you to buy an entirely new machine if you want more AI power.

I also have some negative feelings about having a NAS for home use with the potential of a $20,000 price tag. But that version's future depends on a company now utterly dependent on the AI bubble not bursting, which seems, at best, unrealistic.

I’ve been a big supporter of what Ugreen has done with its NAS products; since before they arrived on the scene, things seemed disturbingly cosy between the various NAS brands.

The HomeAgent is their most ambitious leap yet, since it glues two elements together in a way few users are considering until now. People who like NAS understand what it offers and how it's used, whereas AI has unresolved PR issues, a less defined use case, and users who are either negative or, at best, agnostic about AI.

The HA100 is a product Ugreen created to elevate NAS with AI, but it could just as easily drag it down. Only time will tell, but for those who want to ride, the tickets start at $50 for a reservation.

To crystallise my thoughts, there are two ways to think about HomeAgent hardware, and I’m not inclined to favour either. One is that these represent a new hybrid type of appliance where AI and local information come together, creating new efficiencies and fewer dependencies on external services. Or, from this point onwards, all NAS will have a shell of AI logic around them, and the era of dumb NAS will soon be behind us.

Either way, the NAS market needs to buckle up, because this ride could get bumpy.

For more compact computing, see our guide to the best mini PCs you can buy

Nvidia CEO Jensen Huang has sung the praises of Microsoft's new Surface Laptop Ultra, describing it as "so beautiful" and "incredible".

Speaking at a developer event in London, Huang outlined the development process that went into building the hardware powering the new product, which is the first Microsoft device powered by an Nvidia chip.

“This computer is going to be incredible - it took us four years to build it," Huang told attendees including TechRadar Pro, "this project is really a work of love - we had several thousand engineering years dedicated to it, we build a brand new revolutionary chip, a whole new line of PCs, it’s so beautiful - you’re going to love it.”

The end of the traditional PC?

Revealed in September 2026, the Surface Laptop Ultra is targeted towards developer and AI usage, powered by Nvidia’s RTX Spark platform with an N1X processor and RTX Blackwell graphics, supporting up to 128GB unified memory

This is alongside 24GB RAM and 512GB storage, while the next version has 32GB RAM and 1TB storage, making it ideal for creators, AI specialists, engineers, scientists, developers, and other users running demanding applications.

But as Huang tells it, the creation of the device, set to launch on October 16 2026, was a long and arduous process.

“It took four years to do this," Huang said of the hardware development process. "We were thinking about agentic systems, AI and autonomous systems, and how they’ll be able to access your files, and be grounded on information, and be able to reason about it and use tools - but the ultimate tool is your personal computer, right?"

"So wouldn’t it be cool if we could create a personal computer, a PC that actually runs an agent locally - so it can go off and just work on things for you - and not only could it just run locally, it would be smart enough to use the tools."

Huang explained his vision of "texting" your PC to ask for help, to get it to do tasks like rendering images and updating slides, then delivering them straight to you.

"Wouldn’t it be awesome if you could just text your computer - you don’t have to bring it with you anymore - just leave it at your desk!”

“So we created this thing - it took us four years to do it, so I called Satya (Nadella, Microsoft CEO), and the two of us talked about it…and so we had to create a brand new chip, we had to wait until Blackwell was done - and this thing has 1 petaflops of computing, and because we knew the models were going to be big, we had to redesign how the GPU uses memory - so this is the first Nvidia GPU with shared memory, one petaflops, Blackwell architecture - and also it has to run everything a PC doesn’t.”

“There’s three types of PCs, right," Huang outlined, "there’s the PC that basically you use to type in - that’s called a PC, there’s the Nvidia gaming PC, you’ve got GeForce in that, and you’ve got the Nvidia workstations that runs all kinds of applications…so we figured that if you’re going to have an AI go use the tools, it should be able to use every single tool…it runs everything, it runs every AI framework, it runs any AI model as well."

• Microsoft will make Maia its main inference engine at multi-gigawatt scale, following Google and Amazon into in-house silicon
• Microsoft's upcoming Maia 300 trades a modest 36% compute gain for 2.5x Maia 200's memory bandwidth, betting on memory-bound decode, with production in 2027
• Hyperscalers supply just over half of Nvidia's data center revenue at a 75% gross margin, so each move to in-house chips affects not only its pricing power but the chip industry's sustainability as a whole

In 2024, Microsoft told the European Commission that Google was the only company vertically integrated at every AI layer, from chips to a mobile app store, and that "everybody else must rely on partnerships to innovate and compete."

Now, it has revealed plans to follow the same route without breaking a sweat.

In an Official Microsoft Blog post, Scott Guthrie, executive vice president of Cloud + AI, said Microsoft will scale its Azure Maia accelerators to multi-gigawatt deployments starting in 2027 and make Maia the primary inference system for Microsoft's products and services.

The vehicle of its choice is Maia 300, which Rani Borkar, president of Azure Hardware Systems and Infrastructure, and Saurabh Dighe, corporate vice president of systems planning and architecture, detail in an architecture deep dive.

Feeding the beast, not growing it, as it takes the Google route

The move is a pointed statement from one of Nvidia's biggest customers, and it lands as bubble worries grow louder. Gartner forecast this week that AI providers will spend nearly $1 trillion on infrastructure in 2026 alone, and that AI revenue won't cover the build-out profitably until at least 2032.

If the largest buyers of AI hardware stop paying Nvidia's margins for the work they do most, the most richly valued part of that build-out starts to look exposed. Whether Maia 300 is the final catalyst for such a move, though, is a harder case to make.

Maia 300 moves from Maia 200's single die to a multi-chiplet package, with compute on TSMC's N2P process and memory-controller and I/O functions split onto separate dies. Eight HBM4 stacks deliver 288GB at 18TB/s per accelerator, against 216GB at 7.1TB/s for Maia 200, according to StorageReview's breakdown of Microsoft's spec sheet.

Microsoft hasn't abandoned compute so much as rebalanced around memory. In decode, the token-by-token phase that dominates long reasoning chains and agent loops, accelerators spend much of their time waiting on data rather than doing arithmetic.

The Maia 300 tries to close that gap by keeping data near the compute that uses it, through a non-uniform memory layout across chiplets, software-managed on-die SRAM, and narrower number formats, including an enhanced MXFP4 and sub-3-bit storage formats, with hardware conversion between them. The Maia 300 announces something the rest of the industry already knew, albeit more subtly: Custom silicon is no longer a side project for hyperscalers.

Google, the company Microsoft singled out two years ago, began recognizing revenue from TPU system sales delivered to customer data centers in the second quarter, CFO Anat Ashkenazi said on Alphabet's July earnings call, while CEO Sundar Pichai said the company is "seeing strong demand for TPUs as standalone systems from other customers."

It isn't the only one either; Anthropic has lined up 3.5GW of TPU capacity through Broadcom for 2027, on top of an earlier deal for up to a million TPUs, and Meta was reported in 2025 to be in talks to rent-cum-purchase them. Amazon has gone the same way. CEO Andy Jassy said in April 2026 that Amazon's chip business, spanning Trainium, Graviton and Nitro, had passed a $20 billion annual revenue run rate and was now "one of the top three data center chip businesses in the world." OpenAI, meanwhile, has lifted the lid on its own Jalapeño accelerator.

Nvidia's margins: the AI holy grail?

The incentive is plain from Nvidia's own results. In the quarter to July 2026, the company reported $96.2 billion in revenue, $89 billion of it from data centers, at a 75% gross margin. CFO Colette Kress put hyperscale revenue at $49 billion on the earnings call, meaning a little over half of Nvidia's data center business comes from a small group of buyers, the biggest of which now design their own accelerators.

Every inference workload that moves in-house is one where that buyer pays closer to manufacturing cost instead of Nvidia's margin. Microsoft told investors in July that Maia 200 was up to 40% cheaper to operate than Nvidia's latest chips on OpenAI and Microsoft models.

It did, however, fail to name a particular GPU it called a "leading GPU-based system," an important distinction given Nvidia's focus on efficiency via Vera Rubin, which OpenAI even refused to compare against outright.

The reveal didn't move stocks much; on Monday, Nvidia shares closed down 1.29% after the reveal, while Microsoft's rose 1.14%, and Microsoft's opaqueness about efficiency gains may be one reason.

The other reason is equally important: Evercore ISI analysts estimate Maia will account for only about 10% of Microsoft's AI chip spending by the first quarter of 2027, CNBC reported, and Nvidia has guided to $108 billion in revenue for the current quarter.

Scale is arguably an ongoing constraint, however; at 167 kW per rack, a gigawatt of rack power is roughly 6,000 Maia 300 racks, or about 575,000 accelerators, before cooling and facility overheads.

Going the Google route hasn't stopped anyone buying from Nvidia either. Amazon, with its "top three" chip business, is still set to take more than 3 million Nvidia GPUs by mid-2028.

Alphabet, which now sells its own TPUs, raised its 2026 capex guidance to between $195 billion and $205 billion in July, lists Vera Rubin alongside its own TPUs, and Ashkenazi said it is "still in a supply constraint environment."

Microsoft keeps Nvidia and AMD for training, and Satya Nadella said earlier in 2026 that "because we can vertically integrate doesn't mean we just only vertically integrate."

Jensen Huang's answer to the custom silicon question on Nvidia's August call could have been written about Maia: "Whereas many of these XPUs are inference-specific chips for one cloud or one service, NVIDIA is a platform." Microsoft's counter-bet is somewhat different: one cloud is big enough.

The deeper problem for the pin theory is that custom silicon changes who collects the margin, not how much gets spent. Gartner warns that the industry needs about $8.5 trillion in revenue between 2026 and 2030 to earn a reasonable return on its build-out, versus no more than $3.6 trillion it expects AI to generate. Microsoft isn't responding by building less; it is planning gigawatts more, and cheaper tokens arguably help close that revenue gap rather than widen it.

Some of what Microsoft saves will also flow elsewhere in the supply chain. Maia 300's pitch rests on HBM4, the same constrained memory Nvidia's Rubin uses at the same 288GB per package, and its compute dies come from TSMC, which supplies everyone.

Google's TPUs are developed and supplied under a long-term agreement with Broadcom, whose CEO Hock Tan said that "many investors in AI don't understand what they're putting their money into," and who expects chip shortages to ease only around 2028 or 2029. Nvidia isn't sitting idle either; it already paid $20 billion to license Groq's SRAM decode technology, a sign it sees the same inference threat coming.

Until production silicon ships and someone outside Microsoft can measure it, Maia 300 is a well-argued design whose most important numbers are ones that only Microsoft has seen, and a reminder that the biggest threat to Nvidia's margins may be its best customers rather than its rivals.

• Oxylabs, Decodo, Webshare, ScrapingBee, and Honeygain now formally sit under one Vilnius-based umbrella called Source
• Source claims AI agents drove a fourfold increase in demand for services across its organizations
• The brands, previously reviewed as rivals, including three of TechRadar's nine best-proxy picks, now officially share common oversight

Oxylabs, Decodo, Webshare, ScrapingBee, and Honeygain have formally come together under a single name, Source, and the Lithuanian group behind them says AI agents are why it needed one.

The Vilnius-based ecosystem claims demand across its companies has risen fourfold in a year as AI agents, next-generation search tools, and frontier AI products pull live data from the web.

Its systems now process 1.5 million requests per second, and that it is expanding into web search APIs built specifically for AI agents.

One umbrella with five different, competing shopfronts?

Source presents itself as a backbone rather than a business. Its launch post says it has no customers or products of its own; instead, it handles patents, procurement, hiring, and compliance, while each brand keeps its own team, product strategy, and customer base.

The brands are split by customer type. Oxylabs serves enterprises and AI labs; Decodo (formerly Smartproxy) targets the mid-market; ScrapingBee sells developer APIs; Webshare serves professional consumers and solo builders; and Honeygain pays ordinary people for their spare bandwidth.

For anyone shopping for a proxy service, this is the most practical takeaway. Oxylabs bought Webshare in 2022 and ScrapingBee in 2025, and Honeygain became its main residential proxy sourcing partner in 2021.

Decodo's ties were less clear-cut until now, and trade site Proxyway notes that the launch officially links Oxylabs and Decodo under one organization. Source CEO Vytautas Savickas ran Decodo until March 2026, when he moved across to lead the Oxylabs group and founder Julius Černiauskas became chairman.

All of this points to a marriage of convenience rather than a merger by organizations that are already very closely interlinked. This is interesting because some of these brands have been sold and reviewed as alternatives to one another.

The best example of this is that three of the nine picks in TechRadar's own best proxy sites guide now belong to Source: Decodo as best overall, Oxylabs as best for business, and Webshare as best for privacy.

For context on the overlap, our residential proxy roundup described Oxylabs as one of Decodo's toughest competitors. Source says the companies remain fully independent in strategy, customers, and market presence, and that may well hold commercially, but common oversight is now a matter of record.

The release provided to TechRadar says Source gives the group "a unified voice in defending responsible access to public web data," and its legal history explains the appetite.

Reddit sued Oxylabs alongside Perplexity, SerpApi and AWMProxy in October 2025, alleging they evaded its anti-scraping measures by lifting Reddit content from Google search results. Oxylabs said it was "shocked and disappointed" and would defend itself.

It was still listed as a defendant in filings this spring, and in late July a federal judge allowed Reddit's core claims against SerpApi and Perplexity to proceed. Further back, a Texas jury awarded rival Bright Data nearly $7.5 million against Oxylabs in a 2021 patent case.

Source's own essay on AI agents and the open web argues that if access is negotiated one licensing deal at a time, only the biggest companies will be able to afford it. This makes a convenient argument for a large web scraping group, but the gatekeeping fears are real.

The most concrete product promised here is AI search, and it is older than the umbrella itself: Oxylabs already offers a Web API that pairs a search endpoint with a scraping endpoint for agents, so the "dedicated AI Search APIs" promised look more like an expansion of existing work than a new product line.

For customers, the questions to watch are whether the brands keep competing on price, how clearly each one discloses their newfound relationship, and how the Reddit case plays out for Oxylabs.

For everyone else, Source is a bet that AI agents will keep needing the live web, and that the companies supplying the pipes deserve a seat at the table when the rules for that access get written, aiming to negotiate from a clustered position of strength.

• AI giants are investing billions to train engineers in a bid to help their customers
• US FDE engineer jobs rose 6x year-over-year, two years in a row
• Consultancies are advertising the most FDE jobs, it seems

US tech giants are now spending billions of dollars deploying specialist engineers to help their customers realize the benefits of AI, with AWS, Anthropic, OpenAI and Google all recently announcing expansions to their forward-deployed engineer (FDE) programs.

We've already surpassed the point that model capabilities were the limit – now, companies are struggling with legacy infrastructure and identifying real-world use cases, so AI developers are getting involved to help them out.

As part of the ongoing shift in how AI is being sold, those developers are no longer just selling their AI to customers – they're also taking responsibility over its deployment.

FDEs are the future of enterprise AI adoption

Already, we've seen OpenAI backing its FDE business with a massive $4 billion investment and AWS footing a $1 billion bill for FDEs, while Anthropic shared plans to train 10,000 specialist engineers by the end of 2027.

Lightcast research even highlights the extensive growth in FDE job roles, with around 200 FDE job ads shared in the US in 2023 and a similar number in 2024. By 2025, this had increased 6x to around 1,200 advertised positions, yet in the first nine months of 2026 the report found 7,300 roles, another 6x increase year-over-year.

"As businesses move from developing AI technology to applying it, they need professionals who can bridge the gap between technical products and operational problems," the report explains.

Interestingly, three of the four top companies advertising roles were consultancies (Deloitte, Accenture and KPMG), with just one of them being a tech firm (Google). Consultancies are positioning themselves as a key part of the chain, and Anthropic's recent announcement to support engineer roles within third-party consultancies aligns with this emerging trend.

Lightcast Head of Global Research Elena Magrini explained in an interview with CNBC that a sharp growth in wages also implies that these companies are fighting over a limited pool of talent.

Looking ahead, the most successful AI companies may no longer be the ones with the most advanced models – rather, they could be the ones using human engineers in the best way to gain more customers.

• GoDaddy has launched new APIs to support business management and growth
• The APIs let AI agents manage professional email, hosting, and ecommerce
• The expanded Developer Platform is now available for free to users across the globe.

GoDaddy, the world’s biggest domain registrar, has just released an expansion to its Developer Platform that will let AI agents manage hosting, commerce, and professional email through dedicated APIs.

This latest release builds on GoDaddy’s domain APIs that were released in July 2026, and now includes premium domains and bulk domains. According to a press release shared with TechRadar Pro earlier this week, the expanded platform now gives AI agents and developers a more complete, programmable path.

In practice, this means users can take a project from working code to a real, live business without leaving the development workflow.

GoDaddy aims to solve the deployment bottleneck

According to GoDaddy, AI coding tools have compressed the time it takes for developers to turn an idea into reality. However, getting the application or website live can still mean having to jump between hosting platforms, email configuration, commerce tools, and domain settings – consuming time that developers could be using to focus on the bigger picture.

GoDaddy claims that by making its existing infrastructure programmable and interoperable, the Developer Platform will provide developers and the AI agents they use access to the services businesses need beyond the code itself.

Making AI-powered development accessible

The updated Developer Platform offers an all-in-one approach where developers, web agencies, tech pros, and AI coding agents can all access and manage GoDaddy products. Users can also integrate Node.js Hosting, delivering a simplified way to publish web apps, and benefit from app previews and built-in security features, including automatic HTTPS, a web application firewall, a CDN, and a managed MySQL database.

“The next generation of businesses will be built differently,” said Hemanth Guntupalli, chief technology officer of platforms at GoDaddy. “AI can turn an idea into working software in minutes, and we’re building the infrastructure that lets developers and AI agents take that software the rest of the way. From code to hosting, domains, email, and commerce, our customers can take their working application to a live business without breaking the flow.”

APIs included in this update include:

• Domain APIs: Providing access to domain search and name suggestions, including standard, premium, and bulk domains.
• Hosting APIs: Allowing users to deploy and operate Node.js apps directly from the command line or via AI agents.
• Email APIs: Users can purchase professional email, create mailboxes, and automate email configuration.
• Commerce APIs: Manage your GoDaddy commerce stores, orders, catalog, and customers.

GoDaddy highlights that these capabilities are designed to work as a connected system and are part of the company’s broader strategy to make it easier for businesses to build, launch, and operate online.

The expanded Developer Platform is now available for free to GoDaddy users worldwide.

• A new vibe-coded app offers a simple, browser-based office suite
• Each file is 'saved' in the code for the URL
• The apps come with a variety of options for sharing links/files

AI vibe coding is now at the stage where these apps and agents can clone big-name software packages just by observing how they work. Now there's a new web app, apparently written with AI's help, that puts a simple office suite right in your browser, with no logins or cloud access required.

It's called Tiny Little Office, and it comes from veteran developer and 'technology thinker' Paul Ford. There are 10 different components here: word processor, outliner, presentation tool, spreadsheet, calculator, drawing tool, diagram maker, animator, calendar, and trip planner. You can just click around and start working.

There's one main principle the office suite is built around: the link is the document. Any changes you make to a file are saved as codes in the URL itself, and sharing that URL shares an exact copy of the file. Someone else can take that URL and make their own edits (though they'll stay local and won't sync back with yours).

When you share links, they can be protected by passwords, configured as read only, and set to expire after a certain time period (from an hour to a month). For your own use, multiple files can be saved locally in the browser, or you can choose to wipe everything each time you close down the page.

Six years in the making

Introducing Tiny Little Office tinylittleoffice.com?c=xdgpfud#/v...

— @tinylittleoffice.bsky.social (@tinylittleoffice.bsky.social.bsky.social) 2026-10-16T13:08:46.635Z

The story of the project starts six years ago, when Ford's colleague Rich Ziade built TinySheet, a small ten-line spreadsheet that ran in the browser like Tiny Little Office does now. That was followed by TinyCalendar, but the projects were eventually abandoned, as were the URLs associated with them.

Now the idea is back as a full 10-app suite of products, because "it really is easier to build small than ever before". Ford says he uses the tool to leave himself notes in Slack, and that "if Signal was an operating system, this could, one day, aspire to be its productivity suite" due to its private, simple approach.

"There are lots of tradeoffs," adds Ford. "This is not, in any way, how web apps are expected to work. The links can get unwieldy pasted in chat, and you can't upload video or images, full stop. It's not collaborative like Google Docs. It's collaborative like handing out zines at a street fair."

The Tiny Little Office code is open source and can be viewed and used by anyone, there's support for LLM agents, and you can also find Tiny Little Office on Bluesky. Quite how much of it was vibe coded isn't clear, but according to the docs posted by Ford, Claude Code was doing at least some of the work.

For years, organizations have focused on strengthening and fortifying endpoints, networks, and authentication at login. Meanwhile, one of the most critical control points in the identity lifecycle, the help desk, has remained surprisingly exposed, because most identity systems were not designed to extend strong verification into recovery and support workflows, leaving a critical gap that attackers are increasingly exploiting.

Without this shift, organizations continue to rely on human judgment in high-risk workflows, leaving a critical gap that attackers are increasingly exploiting.

With the rise of generative AI, deepfake videos, synthetic voice cloning, and AI agents, help desks have become a primary target for social engineering. Attackers can now synthesize a CFO’s voice from publicly available recordings, create deepfake video for live calls, or deploy AI agents to conduct persistent, multi-channel impersonation.

These bad actors can mine social media and breach data and corporate websites to collect personal details that make their story credible. They can also adjust their responses and adapt in real-time based on what a support agent says and asks.

Their goal is often straightforward, which is to convince the help desk to reset a password, enroll a new device, or override multi-factor authentication (MFA). If successful, the threat can quickly escalate to ransomware deployment, data theft, a full-scale account takeover, or more.

A modern identity architecture is necessary to address this significant verification gap, one that treats recovery and help desk interactions as first-class identity workflows, not exceptions outside the system.

The hidden weakness in manual verification

Most help desk workflows today still rely on knowledge-based questions, static employee data, or ad hoc manager approvals to validate user or customer identity. In a landscape where personal data is widely accessible and AI can convincingly impersonate individuals, these controls are brittle.

Knowledge-based verification assumes personal information is private, but it rarely is. Manager callbacks assume voice recognition is reliable, but it no longer is. Manual review assumes humans can detect AI deception, but that is not always the case. Many businesses are asking support staff to verify customer identity using tools built for a different era.

In many environments, help desk workflows sit outside core identity systems, operating as disconnected processes rather than being governed by the same policies and controls applied at login.

At the same time, expectations around identity protection are rising. As AI becomes more embedded in digital interactions, consumers expect organizations to have systems in place to effectively protect their identities. This is critical as 75% of consumers are more concerned about their personal data security than five years ago.

When protections break down, trust and brand reputation erode quickly and are difficult to rebuild. It is clear that trust can no longer depend on intuition or manual checks. It must be designed into identity systems and enforced through verifiable identity.

Modernizing help desk security with verifiable identity, at scale

Solving this requires rethinking how identity systems are designed, specifically how verification, recovery, and high-risk actions are handled as part of a unified architecture rather than fragmented processes. Modern identity architectures provide a path forward through decentralized identity and biometric-backed, phishing-resistant authentication.

Here’s what that looks like in practice:

1. Biometric-backed, phishing-resistant authentication

FIDO-based authentication tied to device-bound credentials and biometrics reduces reliance on passwords and shared data. Because credentials are cryptographically bound to a device and validated using advanced encryption, they are resistant to replay and phishing.

Enforcement moves from human decision-making to system-driven policy, ensuring consistency across both login and recovery workflows. Extending this model to account recovery and re-authentication workflows ensures that high-risk actions, like password resets or device enrollment, are protected by the same strong controls used when the user first signs in.

2. Decentralized, verifiable credentials

Decentralized identity frameworks enable organizations to issue tamper-resistant, verifiable credentials that users store in secure digital wallets. These credentials can represent employment status, role, or other attributes needed for verification, without centralizing sensitive data.

From an architectural standpoint, decentralized identity reduces reliance on centralized data stores and enables verification through cryptographic proof rather than static records.

This shifts identity verification from a knowledge-based to a proof-based model. When a user needs assistance from a help agent, they can present a verifiable credential that the organization can cryptographically validate without relying on knowledge-based questions. Therefore, the help desk is no longer in the position of “deciding” whether someone is who they claim to be. The system verifies it for them.

3. Self-service, high-assurance recovery

Self-service account recovery, supported by strong authentication and secure digital credentials, allows users to regain access without needing to call the help desk. This reduces support workload, while also removing the common target for social engineering attacks. Self-service recovery reduces reliance on manual intervention and allows identity systems to scale without increasing operational risk.

In this model, the help desk is no longer responsible for judging whether someone’s identity checks out, ultimately reducing risk.

Redefining the identity control plane

The security perimeter as we know it now reaches every workflow where identity is verified, delegated, or recovered. Help desks are directly in that flow. A more resilient model separates identity into a control layer that defines policy and a runtime layer that enforces decisions consistently across every interaction.

Modernizing identity verification systems with decentralized credentials, biometric-backed authentication, and consistent assurance policies closes one of the most exploited gaps in the security chain. It replaces the need for help desk agents to rely on personal judgment with verifiable proof of identity, and ensures that high-risk actions are governed by controls designed for today’s threat landscape.

AI-driven deception will continue to mature in speed, realism, and automation. Addressing it requires durable identity architecture that applies consistent verification across users, devices, and workflows.

When identity systems are designed to enforce trust automatically and uniformly, organizations gain resilience that scales with innovation, and a foundation for maintaining confidence in an AI-shaped digital environment for years to come. In this model, identity becomes a distributed system capability embedded across workflows, systems, and user interactions, rather than a point-in-time check.

This shift turns identity from a point-in-time control into a system-wide enforcement layer, closing one of the most consistently exploited gaps in enterprise security.

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At the start of every school year, schools, colleges and universities across the UK race to onboard thousands of new students, faculty and staff members, provisioning accounts, issuing credentials and connecting a wave of new devices to institutional networks.

That operational pressure creates opportunities for attackers, and this year, AI is amplifying the threat more than ever. Not only are phishing campaigns more convincing, but a hidden layer of unmanaged machine identities is expanding the cyber attack surface.

Attackers are persistent and the education sector remains a top target with many facing cyber attacks weekly. The UK Government’s cyber security breaches survey revealed that 27% of further and higher education institutions reported experiencing a breach or attack at least weekly, compared to 14% of primary schools and 20% of secondary schools.

Navigating the expanded threat window

The education sector is one of the highest targeted industries for ransomware, credential theft and data breaches. For hackers, schools and universities present an appealing combination of high-value data, including student records, financial information and research, alongside underfunded IT departments and an enormous, ever-rotating user base.

The back-to-school season intensifies this pressure. Bulk account creation, mass device enrolment and a surge of third-party application onboarding all happen simultaneously, creating a greater risk of misconfiguration and excessive access rights. The UK Government found that no tier of the education sector had a majority of institutions compliant with all of the government’s Cyber Security 10 Steps.

Just 14% of primary schools, 23% of secondary schools, 33% of further education colleges and 45% of higher education institutions had covered all 10. As thousands of new identities and devices enter this already insecure environment, gaps in security controls can become harder to identify and manage.

AI has made cybersecurity threats significantly more dangerous. The same government survey found phishing to be the main threat identified by institutions that had suffered a breach, and those emails can now precisely mimic communications from student funding offices, IT helpdesks or university leadership, with none of the traditional red flags that once made them easy to spot.

Keeper Security’s research into AI and cybersecurity in education found that 41% of schools have experienced AI-related cyber incidents, including phishing and misinformation, while 90% of education leaders expressed concern about AI-related cybersecurity threats. Yet only one in four respondents felt very confident recognizing AI-enabled threats such as deepfakes or AI-driven phishing.

As AI makes social engineering attacks more convincing, education institutions need to reinforce human awareness with stronger identity and access controls that do not rely on users spotting every malicious message.

The hidden attack surface: non-human identities in EdTech

As students and staff begin the new academic year, IT teams will rightly be focused on securing human accounts, but there is another, much larger population of digital identities that can easily escape attention: Non-Human Identities (NHIs).

NHIs are now embedded throughout modern education environments, from service accounts that synchronize student records with learning platforms to API keys connecting third-party applications, digital textbooks and payment systems.

Machine identities and certificates authenticate everything from campus Wi-Fi and smart boards to lab equipment and security cameras, while cloud workloads automate backups, research pipelines and administrative processes. Similarly, AI agents powering admissions chatbots, helpdesks and grading tools are adding yet another layer of identities and access privileges that institutions need to manage.

These identities are essential to keeping schools and universities running, but they can also create significant security gaps when credentials are rarely rotated, old integration tokens remain active or automated accounts are granted more access than they need.

Unlike employees and students, NHIs are not naturally caught by onboarding and offboarding processes. Without a complete inventory, organizations can struggle to know which identities exist, who owns them and what they can access.

The cybersecurity conversation in education has historically centered on protecting students, teachers and administrators, but the machine identities powering modern EdTech deserve the same scrutiny, because regardless of whether a breach stems from a human or machine, the impact is the same.

Back-to-school is a useful opportunity to review the full identity estate – both human and non-human – remove identities that are no longer required and tighten access for those that remain.

How education IT teams can reduce their risk

For most institutions, the fundamentals of managing humans are manageable. The critical gap is visibility and governance over the NHIs that power modern EdTech, like service accounts, API tokens, machine certificates and AI agents that multiply with every new integration. Security teams should take these steps to help secure NHIs and humans alike.

Enable multi-factor authentication

As the new semester begins, organizations should ensure MFA is enforced across all faculty, staff and student accounts, with new users defaulted into it from the moment they are provisioned. MFA remains a fundamental defense against credential-based attacks.

Audit privileged access

Many accounts give users privileged access beyond what’s necessary for their role. Privileges should be audited for both humans and non-humans. This includes removing access for departed employees and students, retiring expired service accounts and eliminating unnecessary administrative privileges.

Deploy enterprise password managers

Password security remains one of the hardest problems for security teams to solve. Awareness and training can provide students, faculty and staff with the knowledge on how to create strong passwords, but converting that knowledge into practice is difficult because password reuse, written passwords and browser storage remain common. Enterprise password managers remove that gap, enabling users to create and store strong, unique credentials.

Build a non-human identity inventory

You can’t secure what you can’t see. Schools should catalogue every service account, API key, machine certificate, cloud identity and AI agent. Establish ownership and record what each identity can access.

Establish credential rotation policies

It’s important to establish credential rotation policies for machine identities, paying particular attention to AI agents and third-party EdTech integrations added for the new school year. The Canvas learning management system hack, reported in April 2026, affected almost 9,000 educational institutions globally – a reminder that third-party credentials require the same rotation discipline as internal accounts.

Third-party access should be regularly examined and security teams should scrutinize the security of all current and potential partners and suppliers.

Updated training

Security awareness training needs to keep pace with the threat landscape, particularly around phishing, where AI-generated messages may be indistinguishable from legitimate institutional communications. Staff should independently verify unusual requests rather than relying on visual cues alone

Getting ahead of the rush

Back-to-school will always put education IT teams under pressure, but it does not have to create an open window for attackers. As institutions begin the new academic year, security must extend beyond onboarding students and staff to understanding every identity with access to critical systems and data.

By enforcing strong authentication, tightening privileged access, improving credential hygiene and gaining visibility over the growing population of non-human identities, IT teams can significantly reduce their exposure.

Security should be treated as a continuous discipline, not a back-to-school checklist. It is critical that every human and machine identity has the right access, for the right purpose and for only as long as it is needed.

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For years, quantum computing lived in a strange place in the cybersecurity conversation: important enough to worry about, but sufficiently distant to leave for another day. When we asked CISO what's keeping them up at night, quantum computing rarely made the top five. It still feels distant, theoretical, someone else's problem for 2030-something.

That calculation is starting to look wrong.

Gartner predicts that by 2030, advances in quantum computing will make conventional asymmetric cryptography unsafe to use. Four years away sounds like breathing room. It isn't. Replacing the cryptography embedded across a modern technology estate isn't a patch cycle. It's a multi-year transformation touching applications, infrastructure, identity systems, suppliers and developers all at once.

The clock is already running. Most organizations just haven't noticed it started or don’t appreciate how little time they really have.

This was never really a technology question

Quantum innovation is moving at pace with IBM, Amazon and Google publishing roadmaps or breakthroughs on error correction and qubit stability. Taken together, they tell a consistent story: the timeline to a cryptographically relevant quantum computer is shrinking, not stretching.

But for the average enterprise, that's not the most useful question to be asking right now. The more useful question is far more uncomfortable: how much cryptography do we actually have, do we know where it lives, and could we change it if we had to?

Cryptography isn't a product category you can point to. It's woven into everything, buried in vendor software, embedded in Transport Layer Security (TLS) handshakes, quietly doing the work inside identity and access management systems nobody thinks about. Gartner's research puts a number on the blind spot: 61% of organizations lack full visibility into their own cryptographic systems.

CIOs and IT leaders cannot migrate away from a risk they cannot see. That single statistic is arguably the real concern than the quantum breakthrough itself.

The attack has already started

Here's the unnerving part. You don't need a working quantum computer today for the threat to be live today. Adversaries, particularly well-resourced state actors are already engaged in what the industry calls "harvest now, decrypt later": hoovering up encrypted data now, sitting on it, and waiting for the day decryption becomes trivial.

For anything with a long shelf life such as government records, medical files, IP, legal contracts, identity documents, a five-year-away breakthrough is still a today problem, because the theft happens now.

Encrypted securely today doesn't mean secure for the data's whole useful life. That's the sentence every chief information security leader should be testing their current strategy against.

Crypto-agility is the actual prize

There's a temptation for CIOs and IT leaders to treat post-quantum cryptography as a swap: rip out an old algorithm, drop in a quantum-resistant one, done. It doesn't work that way. New algorithms aren't drop-in replacements.

Larger key sizes strain memory on older systems, signing operations run at different speeds, and performance assumptions baked into legacy applications quietly break. Moreover, additional standards are still being developed meaning today’s replacement might be tomorrow’s cryptographic debt.

Which is why the capability that actually matters isn't any specific algorithm. It's crypto-agility - the ability to change cryptographic algorithms, keys and protocols quickly, without re-engineering the business around them. And organizations are starting to focus on building it, alongside prototyping or evaluating post-quantum algorithms outright.

Build crypto-agility properly and you're not just preparing for quantum. You're fixing something older: cryptographic technical debt that would have caused pain at the next major algorithm weakness regardless of what triggered it. Quantum is the deadline. Technical debt is the disease.

Additionally, hybrid cryptography which blends classical and post-quantum mechanisms during the transition buys some breathing room. Treat that as scaffolding, not a destination. Classical methods have a shelf life measured in single-digit years.

Why now, when nobody can predict the exact date

There's an understandable instinct to wait for certainty, to see exactly when a cryptographically relevant quantum computer arrives before committing budget. That instinct is the wrong strategy for a transformation measured in years, not sprints.

Organizations don't need to know the date. They need to start building visibility and flexibility now, so they can act when required. The unglamorous first step: build an inventory. Scan the estate, catalogue every cryptographic component, feed the findings into whatever technical-debt dashboard has executive attention. It surfaces expired algorithms, outdated TLS versions and vendor dependencies — and gives the migration a factual basis instead of a guess.

Then prioritize. A marketing database that's stale in eighteen months carries a very different risk profile to a decade-long government contract or an identity credential meant to last a generation. Ask which information still needs protecting after 2030. That list moves to the top. Then ask which use cases could disable the business and tackle those next.

This cannot be handed to the security team

A mistake is treating post-quantum migration as a security project with a security owner. It isn't. Developers need to understand how algorithm changes hit application performance. Cloud and network teams inherit protocol changes. Identity teams own the authentication dependencies.

Procurement needs to interrogate vendors on their own timelines, because a huge share of enterprise cryptography arrives bundled inside third-party software nobody directly controls.

Equally, pushing this down to individual system owners without central coordination is a guaranteed way to fail, leading to inconsistent decisions, no organization-wide picture of progress, and gaps precisely where attackers look first.

A cross-functional Cryptographic Centre of Excellence, pulling IT, development and data security under one roof, isn't governance theatre. It's what keeps the response from fragmenting into a patchwork.

The real question isn't about quantum at all

Strip away the acronyms and this stops being a story about qubits. It becomes a test of how resilient an organization's architecture actually is, whether it knows where its cryptography sits, understands which data needs long-term protection, and can change cryptographic mechanisms without rebuilding from scratch.

The organizations most exposed won't be the ones with the weakest algorithms. They'll be the ones still treating quantum as a distant technology story rather than a resilience problem sitting on today's books. By the time the threat is obvious to everyone, the hard part—untangling years of cryptographic dependencies—will already be behind schedule.

The quantum era is still approaching. The migration effort isn't.

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Businesses are moving quickly to embed AI tools across all functions, from marketing and HR to sales and operations. In part because they don’t want to be left behind, but productivity gains, customer expectations, competitive pressure and revenue growth are all pushing organizations to adopt AI at speed.

The bigger story is that AI is moving faster than most businesses can adapt. The tools are already here, but the workforce models around them - the governance, workflows, training and specialist skills are still catching up.

As a result, businesses continue to face an AI skills gap: not just a shortage of technical expertise, but a shortage of the human capability needed to prove the return on large-scale AI investments is mounting.

Recent marketplace data echoes this. Searches for AI automation have increased 29% since November 2025, as businesses look for people who can turn AI tools into reliable, commercially useful outputs. The demand is not just for AI access, it’s for AI fluency.

So, the challenge with AI is that it has moved from ‘tooling issue’ to ’talent issue’, as businesses struggle to translate access into high-quality output. AI is aggregating the average, and average output requires constant human judgement to stand out.

For organizations trying to close that gap, the solution is two-fold: prioritize human judgement, and rethink workforce models around AI-native talent.

Stop prioritizing AI access and start prioritizing human judgement

The first stage of AI adoption was defined by access and experimentation. They wanted to know which AI tool was the best to buy, which platforms they should be testing and how their teams should start using them in their day-to-day work. With AI readily accessible, the innovation comes from implementation, so this is where AI adoption becomes a talent issue. Organizations that stop treating AI as a software rollout and start investing in human skills will be better placed to compete.

LLMs and generative tools are trained on existing patterns, often creating sameness with widespread adoption. When many organizations use similar tools, in similar ways, with similar prompts, the result is often generic. Although they can help teams move faster, focusing on speed does not create differentiation. Without human judgement, AI can make mediocre work easier to produce at scale. That is the challenge now facing businesses, the baseline has risen so the ability to create work that is distinctive still depends on people.

Businesses need talent that can understand quality, shape creative ideas, interpret audience needs and turn raw AI outputs into work that stands out. That requires more than prompting - it requires taste, context, critical thinking and, most importantly, individual thought, something that is now lacking from many AI tools. In creative industries, this is already visible in the rise of AI directors: specialists who combine creative judgement with technical fluency. They are not simply operating tools, they are shaping narrative, visual identity, production workflows and final execution. In many cases, they are helping smaller teams produce commercial-grade work that previously required far larger studios.

So, as generative technology becomes more accessible, the advantage no longer lies in having the tools, it lies in knowing how to direct them. Workers must, in tandem, focus on upskilling in AI for their own career development.

Rethink your workforce models around AI native talent

Businesses that are struggling to hire the right talent are likely focused on AI adoption then expecting teams to absorb the change without proper training, which creates a gap between ambition and readiness. To get more value from AI, leaders need to build workforce strategies around AI capability. That means identifying people who can turn AI outputs into high-quality work, while also spotting the errors, risks and limitations the technology can introduce.

Our data also shows that demand is growing for more specialist AI execution as searches for AI video and animation rose 42% since November 2025. As teams build AI fluency, businesses should not assume every capability needs to be built in-house from day one. In a market moving this quickly, the smarter model is often a hybrid one: upskill internal teams while bringing in flexible specialists to close urgent gaps, accelerate execution and transfer knowledge as the work evolves. AI skills are changing too fast for most businesses to build every function permanently, so this gives businesses the right expertise without the cost or delay of building an entire permanent team from scratch.

At the same time, AI deployment needs to start with the real business need - teams should identify the repetitive tasks, slow processes and low-value admin that are pulling people away from higher-impact work. Once those opportunities are clear, leaders can identify the real capability gap: is it something an existing employee can learn, something AI can support, or something that requires an external specialist?

AI adoption also raises legitimate concerns; leaders need to be clear that AI is not simply a way to cut people out of the process. A stronger workforce model keeps human talent and creativity at the center while giving employees new ways to apply and evolve their skills. This not only supports productivity but also retention and satisfaction. Teams are more likely to embrace AI when they understand how it can enhance their work, not just threaten it. Businesses that invest in upskilling, clear guardrails and access to specialist support will be better placed to build confidence as well as capability.

Talent is what defines AI success

With AI now the baseline, not the advantage, the businesses that will gain competitive advantage will pivot to focusing on judgement and skills development, with a new class of AI-native operators acting as directors to combine creative and technical decision-making.

That means teams who know how to question outputs and turn AI into work that actually matters. Access is easy but the execution is hard, so the next phase of AI adoption will be defined by workforce readiness.

AI can raise the baseline, but the right talent mix turns AI from a tool to a competitive edge.

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• The US Air Force is seeking a 'Next-Generation Mission Compute' offering to replace the brain of its F-16 Block 40/42/50/52 jets
• The notice calls for open, swappable hardware that physically separates flight-safety and mission computing, with room for GPUs and AI
• Officials say these jets will serve into the 2040s, roughly seven decades after the F-16 first flew in 1974

The US Air Force is shopping for a new brain for its F-16 Fighting Falcons. The Air Force Life Cycle Management Center F-16 program office will brief companies at Wright-Patterson Air Force Base in Ohio about a Next-Generation Mission Compute system to replace the Modular Mission Computer in Block 40, 42, 50, and 52 jets, the core of the service's Viper fleet.

Demand for these is sky-high: all 24 one-on-one sessions with the program team were booked by September 24 2026, and an October 8 update limited sensitive sessions to firms certified to receive export-controlled data.

The upgrade itself is an intention, essentially being explored rather than commissioned, with a funded program behind it and no request for proposals having been issued by the USAF just yet.

Not the Viper's first major brain transplant

The USAF sources sought notice is rather candid about what the Air Force wants to leave behind. It calls Next-Generation Mission Compute (NGMC) "a strategic pivot from the current, monolithic avionics architecture" toward a software-defined design built on the Modular Open Systems Approach, with hardware meeting Sensor Open Systems Architecture (SOSA) standards.

In a nutshell, it wants a more modular, modern system at the heart of one of its best-performing, albeit aging, craft; it prefers commercial off-the-shelf modules to custom cards and wants to break vendor lock so future processor and storage upgrades can come from many suppliers rather than one.

The proposed solution must also be a drop-in: NGMC must fit the existing computer's space and, where possible, its wiring, and it must rehost the legacy Operational Flight Program (OFP), the software that defines what the jet can do.

This is no small feat, considering that in 2017 the Air Force told C4ISRNET the F-16's computer ran about six million lines of code, and given the nature of the task, failure mid-flight is not really an option.

The AI component also sticks out: Respondents were asked how they would expand processing, storage, GPU and AI/ML resources to keep the chassis useful for years. That is a request for headroom, not a mission, and comes with scant details: nothing published says what an AI accelerator would actually do aboard a Viper, and the detailed requirements sit in an export-controlled attachment that is not public.

The move will also take considerable time if it happens; today's computer traces back to the Common Configuration Implementation Program, which brought around 650 Block 40 to 52 jets to a common avionics standard between 2000 and 2010, according to The Aviationist.

In December 2016, the Air Force picked Raytheon for a Modular Mission Computer Upgrade that the company said offered more than twice the processing power and 40 times the memory, and that its program manager, Josh Cobbs, called "a game changer for the F-16." The service said it would not roll out until 2022-23.

Public records don't show which mission computer revision every Post-Block jet carries, but the pattern is clear: each new sensor eats into the headroom of the box in the middle. Even new-build jets are being lined up for the next swap. Rehosting old code on new silicon is rarely painless, either, adding further complexity for anyone taking on the job.

The Air Force has been consistent on the F-16's service timeline; Col. John D. Caldwell, then vice commander of the 8th Fighter Wing, said in April 2023 that the F-16 was planned to serve into the 2040s, and that does not seem to have changed of late.

It might, however, have become a bit more ambitious: in 2017, after the Air Force approved raising the F-16's design life from 8,000 to 12,000 flight hours, Lockheed Martin said life extension work could keep up to 300 Block 40 to 52 jets flying through 2048 and beyond.

Irrespective of this, the F-16 will outlast its US Air Force career; About 4,600 have been built, more than 2,100 remain active in 25 countries, and the 935 in US Air Force service in early 2024 made up roughly half its fighter inventory, indicating that it is here to stay at least for the next two decades.

A lack of a good replacement essentially means that the fighter jet better known as the Viper will continue to do heavy lifting not only in the USAF but also in plenty of the US' allied air forces. Given how expensive such upgrades often are, with the added complexity, the Viper's next brain remains a very well-attended industry day until the USAF is explicit about what it's willing to spend and how many jets will benefit, even if a future upgrade seems inevitable.

• Airbnb is adding AI-powered search
• New AI comparisons make it easier to weigh up vacation rentals and narrow down your options
• Services are expanding to include baby-gear rentals, laundry, and meal delivery.

After a major redesign of its entire service, headed by an easier-to-use app for iOS and Android, Airbnb is further refining the experience to make finding your next vacation rental a little easier. Not surprisingly, it’s turning to AI, but the focus here is on natural-language search, property comparisons, and updated ways to find what else you can do while staying at your Airbnb.

This follow-up should make it easier to find homes, experiences, or services that meet your needs — the three main parts of the redesigned app. Instead of introducing a new interface or updated visuals, Airbnb is using AI to help you find what you’re looking for more easily. Pushing aside sorting through filters, lists, and checkboxes, you’ll be able to search for a stay the way you’d actually talk about it.

The quest to make searching easier

Like Claude or ChatGPT, Airbnb’s new AI search lets you type or speak a query, making it easier to find a stay that fits what you have in mind. You can search across homes, experiences, and services, using your own words to describe what you need, rather than trying to find the right combination of filters.

AI is also built into the filtering process. Type in a keyword, and Airbnb will suggest relevant filters to help narrow down your options. For example, searching for 'baby' can surface amenities like cribs, playgrounds, children’s books, and toys.

The app will also use AI to highlight key home features directly in search results, so you can get a better sense of what a listing offers without opening every property page. Less tapping!

And if you’ve already saved a few contenders to your wishlist, Airbnb is adding AI-powered comparisons that put properties side by side and summarize their key features. This could be particularly useful if you’re the one in your group tasked with narrowing down the options. Instead of sending everyone a pile of listings and hoping they’ll compare them, you can get a clearer picture of how each property stacks up, and share your shortlist with the group.

Airbnb is also expanding its AI-powered customer support, with chat available in more than 50 languages, and voice support over the phone on the way.

Airbnb is also expanding its services to help you handle more of your trip without jumping between apps. Starting in November, guests in more than 60 US cities can rent baby gear through BabyQuip, with delivery and setup, and 10% off for Airbnb guests.

Laundry pickup and delivery is coming to select US cities through Rinse, while grocery delivery is expanding to Europe alongside meal delivery from local restaurants. You’ll also find location-specific options, including ski and snowboard rentals in the French Alps and boat rentals in South Florida.

Taken together, the update builds on Airbnb’s redesigned app rather than reinventing it. AI is the main draw for finding and comparing places to stay, while the expanding services offering could make the app more useful once you’ve booked — and enable you to spend less time sorting out the logistics of your next getaway.

• YouTube Music says it will start labeling AI-generated music tracks
• On Deezer, you’ll be able to exclude AI music from your feed entirely
• Both platforms say there is a clear user demand for transparency

AI-generated music is a controversial topic in the world of streaming platforms, with the likes of Apple Music saying that over a third of its uploads are “100% AI.” Now, both YouTube Music and Deezer are taking steps to make it easier to spot artificially created tunes — and purge them from your listening feeds altogether.

The Google-owned YouTube Music has just announced that both the YouTube Music and YouTube apps will actively flag tracks that have been “created primarily using generative AI,” the company said in a press release. These labels will be attached to the app’s player page and will include a pop-up window with more information on how AI might have been used.

Music streaming service Deezer has gone a step further and has told users it will introduce a new toggle before the end of 2026 that will let them “avoid AI-generated music completely.” Deezer says that this will “improve the user experience” and put “full focus on discovering human creators.”

Speaking of the move, Deezer CEO Alexis Lanternier argued that “The explosion of AI-generated content continues to impact the whole music ecosystem, and we believe in shaping a future based on transparency and choice, with respect for artists, song writers and music fans. We remain committed to minimizing any negative effects, while empowering our users to control their music experience to the greatest extent.”

Mass AI detection

Deezer’s Lanternier explained that the company had experienced “clear user demand for the ability to filter out AI-generated music,” while YouTube similarly stated that “our listeners have told us they want transparency when they’re hearing an AI-generated track.”

That’s unsurprising given the polarizing nature of AI. Whether the topic is AI chatbots, artificially-generated visual art or music that has been created by a bot, people have strong opinions, with a large contingent evidently wanting to exclude this material altogether.

Deezer claims to have detected and removed over 42 million AI-generated tracks from its recommendation system and to have tagged more than 14 million albums as being machine-made. It describes the issue as a “colossal, even critical, challenge” for the industry, with human artists revenues being impacted by AI music and up to 85% of AI streams being fraudulent.

Deezer’s AI detector tool has just been named one of Time’s Best Inventions of 2026, with it being used to help the platform spot the 90,000 fully AI-generated tracks that are submitted to it every day. Yet Deezer has also admitted that its detection tool has its flaws and could be weak to audio manipulation and as-yet-unknown creation models, something it also admitted to us during an in-depth interview earlier this year.

Anyone strongly against AI music will take more solace from Deezer’s announcement than from YouTube’s, with the latter merely labeling AI tracks rather than actively removing them from the user experience. Whether YouTube Music will eventually introduce a feature like Deezer’s in the future remains to be seen.

• Amazon has released the Amazon Bee Frontier and Amazon Bee Eclipse, two AI devices featuring microphones for audio transcription
• The Bee Frontier is a wristband wearable, while Bee Eclipse is a magnetic phone attachment
• Both devices will transcribe conversations, set event reminders, and enable 'personalized shopping'

Last year, Amazon acquired Bee, a startup with a single product: a wearable that featured a microphone that fed audio files to an LLM, which transcribed the audio into searchable text. We've seen these kinds of devices before, such as the Pebble Core Index 01, the Plaud Note AI pin, and most recently the Apple Watch Series 12 and Apple Watch Ultra 4, which offer similar Siri Recap and Live Rewind features.

Now, Amazon has officially thrown its hat in the AI wearable ring and unveiled two new products: the Bee Frontier and Bee Eclipse. The Bee Frontier, priced at $74.99 (around £56 / AU$108) is a fitness-tracker style Pebble with up to seven days of battery, that can be worn on your clothes or an included wristband, like a Fitbit. However, instead of collecting biometric data, it features dual microphones with a prominent 'on' button, that records and transcribes audio into summaries and recaps.

Amazon says that "when turned on, Bee gathers context from your conversations and connected apps to provide insights and recommendations,'" creating summaries in the Bee app or a purpose-built desktop app, with "templates tailored to your specific needs". Students might sort summaries and transcriptions into a Lectures template, while office workers may have a Meeting Minutes template.

Bee Eclipse is a slightly different version of the concept, which is a magnetic credit card-sized version of the device which snaps to the back of a smartphone, designed to "give customers more options beyond a wearable". It costs $99.99 (around £75 / AU$145) It's similar to devices like the Plaud Note, which shares the magnetic credit-card design and single-button record. The Bee Eclipse shares the same transcription software and access to the Bee app with the Bee Frontier.

Amazon says "Bee Frontier is for people on the go who want to stay present in the moment. Whether it’s remembering the details of important meetings, taking note of the coffee plans you made in passing, or simply ensuring you purchase the dish soap you said you’d buy, Bee Frontier will remember the details for you."

Upgrade to a Bee Pro subscription

Transcription isn't the Bee range's only trick though. Bee comes with a 14-day memory, summaries and recaps. However, a Bee Pro subscription, which costs $11.99 (around £9 / AU$17) per month, includes "unlimited recent conversation summaries; integrations with Amazon.com and Alexa+; integrations with third-party applications; suggested to-dos; daily memories; a personal chat assistant; and conversation templates; and the Bee desktop application."

Those integrations with third-party apps include Google Calendar and Microsoft Outlook, allowing Bee to bee-come (sorry) a personal assistant by adding events and reminders to your calendar and draft emails, along with other apps like Apple Healthkit.

That integration with Amazon.com, however, allows Bee to "enable even more personalized shopping recommendations" by surfacing items you mention in conversations, with "product recommendations on the Amazon homepage, and in the Bee mobile and desktop apps".

Buzzy, or a sting in the tail?

As mentioned, AI recorders are nothing new. They see a lot of use in the workplace — many journalists like myself now use AI transcription services to help us turn interviews into text, for example, and offices use them for meeting notes.

However, the push to use devices like this in our personal lives, to encourage buying more products and remembering coffee dates, may steer away from the obvious usefulness of the feature and verge on 'creepy', depending on who else is being recorded, whether or not they have given you permission to do so, and the insidiousness of recommending products based on casual conversations.

Amazon and Bee, like Apple did when debuting its Live Rewind and Siri Recap features, has ensured privacy features have been laid out in no uncertain terms: audio is never stored, the microphone is button-operated with a recording LED light like Meta Glasses, and transcripts are encrypted. It remains to be seen whether these assurances are enough for widespread adoption.

• Researchers tested ChatGPT for Teens with more than 4,000 prompts
• They found significant gaps in its safety protections and safeguards
• ChatGPT failed to trigger parental alerts during some serious mental health conversations

Update: 19 October. We have updated this article with a reply from an OpenAI spokesperson.

OpenAI's efforts to make ChatGPT safer for teenagers have run into an uncomfortable problem. A new investigation suggests that many of the protections designed to reassure parents aren't working particularly well, even when the chatbot is dealing with potentially dangerous conversations. After putting ChatGPT through more than 4,000 prompts, researchers concluded that the company's teen-focused experience poses an unacceptable risk to young users.

The study from Common Sense Media's Youth AI Safety Institute examined ChatGPT before and after OpenAI introduced ChatGPT for Teens on August 18. The researchers used accounts registered to teenagers aged 13 to 17 and consulted medical experts, including child psychiatrists and a pediatrician, to evaluate its responses. Their findings were troubling enough that the organization recommended OpenAI restrict ChatGPT to adults until the problems are resolved.

ChatGPT for Teens was supposed to make the popular chatbot a more appropriate companion for younger users, with additional protections against harmful content, parental notifications, and features encouraging learning rather than simply handing over homework answers. OpenAI also promised to discourage overly personal relationships with the chatbot and help teenagers maintain healthier boundaries with AI. The new report suggests there is still a considerable gap between those ambitions and what happens when teenagers actually use it.

Holes in the safety net

ChatGPT's parental notifications are intended to alert caregivers when a teenager may be experiencing serious distress. So researchers spent up to an hour discussing suicidal thoughts, self-harm, and disordered eating through newly created accounts linked to parents. According to the report, none of those conversations triggered a parental notification, although the researchers eventually generated alerts using older accounts with weeks of relevant conversation history.

ChatGPT is also supposed to direct teenagers to appropriate help during mental health emergencies, but the study found that ChatGPT failed to do so in more than 25% of situations experts designed to require it.

There were some successes, including ChatGPT's refusal to engage in explicit sexual roleplay. However, the researchers also found that the chatbot continued responding in ways that suggested it had feelings, preferences, and moods, despite OpenAI's efforts to discourage teenagers from treating it like a human friend. That matters when younger users may already be inclined to confide in an endlessly patient chatbot that never seems too busy to listen.

Blurry lines of use

The problems extended beyond serious safety concerns into some of ChatGPT's everyday educational features. OpenAI's Study mode is designed to guide students through problems using explanations, hints, and questions rather than immediately providing answers. Researchers discovered that teenagers could sometimes bypass that approach by selecting a "Show me the answer" option, allowing ChatGPT to complete assignments instead.

The Study Hours feature, designed for parents to help encourage educational use, also failed to stop workarounds. Simply removing the @study prefix from a prompt could make ChatGPT go back to normal. And the AI didn't remind users to take breaks nearly as often as it should.

The study also found problems with the much-touted age detection system created by OpenAI to identify younger users and automatically apply appropriate protections. Repeatedly saying they were 13 didn't prompt ChatGPT to move the test accounts from the adult experience.

The larger issue is that ChatGPT is becoming a regular part of teenagers' lives. While helping with schoolwork and answering personal questions is great, having teenage users demands considerably more reliable safeguards than a typical productivity app for adults.

We contacted OpenAI for comment and a spokesperson replied:

“We are deeply committed to teen safety and to developing safeguards and giving parents meaningful tools to guide their teens’ use of AI. We welcome rigorous independent evaluation, but we do not believe Common Sense Media’s testing accurately reflects how ChatGPT’s teen safeguards work in practice or expert perspectives on how AI can support teens. Our review of Common Sense Media’s methodology shows that the bulk of their testing may have begun and concluded before activation of parental controls was complete, making their findings inaccurate. If that was the case, the tests would not establish whether parental safety notifications work as designed.

"We are committed to continuing to work with experts to ensure our products provide teens opportunities to learn and create and give parents confidence.” — OpenAI Spokesperson

For technology teams, connecting telemetry has traditionally been seen as a route to greater visibility. The assumption is straightforward: the more information available about what is happening across an environment, the easier it should be to understand problems and respond to them.

But as modern technology environments have grown in scale and complexity, that equation no longer looks quite so simple and teams have found themselves dealing with fragmented telemetry and unclear signals. It now takes longer to understand what’s gone wrong, and even longer to fix it. And to add to the challenge further, the cost of storing and processing all this telemetry and data continues to be on the rise.

This is prompting organizations to reconsider where the problem should be addressed. Rather than collecting everything and attempting to make sense of it later, attention is shifting towards improving telemetry earlier in its lifecycle. Filtering, enriching and directing it from the outset can make the information reaching teams more relevant and ultimately easier to act on.

More data doesn’t always mean better insight

The volume of telemetry available isn’t the fundamental issue. Its quality is. As AI-driven capabilities become deeply embedded in software delivery and operations, the quality of observability telemetry becomes even more important. Collecting telemetry without filtering out what isn’t needed can leave employees spending more time managing information than extracting value from it.

Fragmentation makes this even more challenging. With data sitting across several tools and agents, each of which generates varied forms of signal and alert. When an outage occurs, teams may then need to piece together inconsistent information from across those tools before they can understand the root-cause of the problem. Consuming valuable time while the impact on the business continues to grow.

There is also a significant financial cost attached to this approach. Processing and storing telemetry and data at scale requires considerable resources, particularly when organizations are retaining information that contributes more noise than insight. In the traditional sense, this isn’t a technology problem. It’s a telemetry and data quality concern, and the longer it’s ignored, the worse it usually gets.

Rethinking where telemetry is processed

One way organizations are responding is by moving intelligence further upstream in the technology stack. In practice, that means making decisions about telemetry earlier – filtering out what isn’t useful, enriching important information and routing meaningful telemetry before it reaches downstream systems.

Teams are beginning to collect, process and route relevant observability telemetry in real-time before it is ingested into downstream systems. As a result, there is a less volume to handle, reduced expenses and cleaner telemetry to deal with since only the relevant signals are sent downstream.

Open standards such as OpenTelemtry are making it increasingly possible to take this approach across complex environments. They allow teams to gather telemetry consistently without being dependent on a particular vendor or set of tools. That flexibility is particularly valuable when working with intricate, distributed systems that span several clouds, security and storage platforms.

Furthermore, as environments continue to evolve, it makes adapting to them that much easier.

AI needs reliable information to make reliable decisions

The implications become particularly important as organizations introduce more AI-powered capabilities into their operations. AI systems depend on accurate, contextualized telemetry and data to produce insights and automate decisions.

Teams are not only slowed down by incomplete or cluttered telemetry, but it’s this simple: poor quality telemetry leads to poor quality insights. When those insights are applied to large-scale automated decision-making, the margin for error drastically lowers.

The same principle applies to trust. Teams responsible for automated systems need to understand what has happened and explain why decisions about performance or security have been made. Doing so becomes much more difficult when those decisions are based on inconsistent information.

And this requirement for confidence matters far beyond just the technical teams. As senior management and board members are being increasingly asked to support AI-driven decision making, they need to trust the information those decisions are built upon. Credible, consistent telemetry and data can make that conversation much simpler.

The operational advantages are also evident. Teams can work quicker if they aren’t continuously battling erratic telemetry and data. They spend less time on manual research, detect issues early, and automate with greater confidence.

A strong telemetry and data foundation and a unified, context-driven platform not only enhance daily operations but also increases the successes of the AI-powered capabilities the organization invests in over time.

Quality has to be built in from the beginning

Ultimately, faster innovation and better customer outcomes depend on organizations having useful telemetry and signals available when they are needed. The good news is that it doesn't require starting from scratch, it's about making choices early in the telemetry lifecycle.

By introducing intelligence at that stage, teams can improve the quality of what ultimately reaches their observability platforms and ensure it can scale as their environments grow. The advantage will not come from simply collecting more. It will come from ensuring the telemetry an organization does collect is relevant, trustworthy and ready to support action.

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I've been reporting on AI for a year and a half now. Throughout that time, I’ve regularly visited Reddit's r/isthisAI community. This is where people share suspicious photos, videos and messages and ask other users to help them work out whether they're real or AI-generated.

I find it fascinating to see where AI is showing up in everyday life. But some of the posts are also pretty worrying. I've seen people questioning whether photos of damage to rental properties have been faked, whether someone their relative is dating actually exists, whether an ultrasound is real or whether the adorable puppy they're about to buy is just an illusion.

Of course, scams and deception like these have been around for years. But what's worrying is how AI can make them much both easier to pull off and harder to spot. I became so interested in this that I even ran my own series of tests, trying to create fake certificates, fossils and receipts to see whether ChatGPT would stop me. Most of the time, it didn’t.

While plenty of us have got used to questioning what we see online, not everyone knows just how convincing AI-generated content has become. That might be particularly true of friends and family who are less familiar with the technology.

So I've picked out some of the most worrying examples I've come across, along with a few practical ways to protect yourself and the people you care about. Because I think we could all benefit from knowing a little more about how AI can be used to deceive us.

1. Rental property damage

I've seen quite a few posts questioning whether AI has been used to fake damage to rental properties, like cracked floor tiles, stained rugs and, most recently, an overflowing toilet.

The concern is that landlords or holiday rental hosts could use manipulated photos to claim that guests and renters have caused damage and demand money to cover repairs.

Some of the examples I've seen are surprisingly convincing and I think the reason they're so difficult to spot is that much of the surrounding room looks perfectly normal. It's only the supposed damage that's suspicious.

I recommend taking plenty of photos when you arrive at a holiday rental, and again before you leave. That way, if you're ever accused of causing damage you don't recognize, you've got your own evidence to back you up.

2. Fake notices, warnings and announcements

I've seen a lot of suspicious notices circulating online over the past few months. Everything from warnings supposedly issued by schools and councils to missing pet posters and strange announcements pinned to noticeboards.

Granted, I've also seen plenty of bizarre notices in the wild that are real. But AI makes it incredibly easy to create convincing fakes and even the little details that might once have given them away, like handwritten text, marker pen or crumpled paper, are becoming more realistic.

The problem is that these notices often look like something someone has photographed them in passing, which can make them feel more authentic too. This notice about a drinking bottle is one example that really stumped Reddit for a while but turned out to be AI.

If you see an alarming warning or announcement, especially one that's being widely shared on social media, try to find its original source first. If it claims to come from a school, council or organization, then check whether they've published it through their official channels.

3. Real-world events

This is one of the most disturbing uses of AI-generated imagery I've come across recently. We've all seen fake images of things that never happened. But I've also noticed more images and videos circulating that claim to show events that genuinely took place.

Which I think makes them particularly misleading. Someone might check whether an incident really happened, find that it did, so then they'd assume the accompanying photos must be authentic too. One particularly upsetting example involved AI-generated images claiming to show a tragic incident at a Yorkshire wildlife park. These images can spread quickly because they're attached to stories people are already interested in.

If you come across dramatic images of a breaking news story, check whether they're being used by reputable news organizations or have been published by an official source. And remember that confirming an event happened doesn't automatically confirm that every photo or video claiming to show it is real.

4. Romance scams

This is one of the most upsetting sort of scams I've noticed on r/isthisAI. People regularly post photographs of someone their parent, grandparent or other relative has met online, asking whether the person actually exists. There are people worried about their aunts, their dads and their friends' grandparents.

A familiar pattern is someone claiming to work overseas or travel constantly, often with a convincing looking photo at an airport or in a workplace. Eventually, there tends to be a medical emergency or a problem accessing money so they ask for financial help.

Of course, romance scams have existed for decades. But AI-generated photos, messages and even video can give scammers more ways to build a believable identity. The difficult part is that people can become very emotionally invested in these relationships. Being told that someone they've fallen for might not exist can be incredibly painful and I’ve been surprised to read anecdotes of people still not believing their concerned loved ones even when they’re shown irrefutable evidence.

If you're worried about a friend or relative, try to approach the conversation gently. Encourage them to verify the person's identity independently, be wary of excuses for never meeting in person and, above all, never send money to someone whose identity they can't establish.

5. Pet scams

Who doesn't love an adorable puppy photo? And that's exactly why these scams can be so effective. I've seen people on Reddit questioning whether photographs of dogs they're thinking of buying are AI-generated. In one example, someone was worried their mother was being scammed. In another, someone was trying to buy a puppy through Facebook but they were concerned some of the details in photos of the cute little guy didn’t quite add up.

The danger is that a realistic-looking photograph can help convince someone that a puppy exists and is available to buy, even when the seller isn't legitimate. Scammers might then ask for deposits or delivery fees before disappearing.

For me, this is an important point to get across to friends and family. Rather than focusing entirely on spotting telltale details in AI-generated images, look at what someone is asking you to do. Are they demanding money upfront and inventing reasons for extra charges? They should raise major red flags.

Always try to see a puppy in person before paying, and check the seller's credentials. A convincing photo, no matter how cute, isn’t a guarantee that an animal is available to buy. It’s not even a guarantee that it exists.

6. Scoring political points with misinformation

I'd hope most people are now aware that AI-generated images are often used to spread political misinformation. But judging by some of the things that still circulate on social media, there's plenty of reason to be concerned.

I've seen posts questioning whether political images are genuine, including this example shared by an Ohio conservative political action committee's Facebook page.

These images can be particularly effective when they reinforce something that someone already believes. Which means that if a picture appears to confirm your opinion of a politician, protest or political movement, you might be less inclined to question it.

My advice is to be especially suspicious of images that provoke an immediate emotional reaction, whether that's outrage, fear or even satisfaction. Look for the original source, check whether credible news organizations have verified the claim and resist the temptation to share something simply because it seems believable.

7. Fake documents, like medical photos, receipts and plane tickets

Another category I've noticed involves people questioning whether documents or photographs have been created to support a lie. For example, one Reddit user asked whether their ex had used AI to create a fake plane ticket and another wanted to know if an ultrasound had been faked with AI.

This is particularly interesting to me because I ran my own experiment to see whether ChatGPT would help me create fake evidence, including receipts and certificates. And in many cases, it did.

Documents that once might have required some really good Photoshop skills can now be much easier to fabricate. And as AI gets better at producing convincing text and layouts, spotting a fake simply by looking at it is becoming way less reliable.

If someone sends you a document as proof of something important, think about whether there's another way to verify it. For example, a booking reference may be something you can check directly with the relevant company. And a photograph of a medical document should never be treated as definitive proof of someone's health or circumstances either.

How to check if something is AI

We've got a more in-depth guide to spotting convincing AI images, deepfakes and scams, but there are a few simple things worth remembering.

Firstly, you can try reverse image searching suspicious photos with Google Lens. You can also check where a story or image originally came from and verify any claims through independent sources.

There are also tools that can help. Google recently expanded access to its SynthID Detector, which can identify invisible watermarks in AI-generated content from some AI tools, but not all of them. Just remember that if it doesn't detect a watermark, that doesn't necessarily mean something is real either.

But I think the most useful thing we can teach our friends and family is to look beyond whether something looks like AI and ask a more important question: What is someone trying to get me to believe or do?

Because whether it's a fake puppy or an urgent request for money from an attractive stranger, that's often the biggest warning sign. And the one we should pay the most attention to as it becomes even harder to distinguish what’s AI from what’s real.

• AI needs to replace 250 million jobs in order to make a return on the current $2.5 trillion investment
• AI is getting cheaper and more capable, but the underlying hardware is not
• Infrastructure providers for the AI boom could be left holding the bag

Since the release of ChatGPT in late 2022, AI developers have entered into a frenzied arms race of hyperscaler data center build outs, silicon development, and huge power grid expansions.

The level of investment made by AI developers is unlike anything that has been seen (or spent) before. But a key question that IT leaders, investors, and enthusiasts keep asking is how will all this investment be paid for?

New analysis by Marin Ivezic, Founder of Applied Quantum, has found that the math really isn’t adding up. In order to justify the colossal investment, AI would need to generate approximately $2.5 trillion in annual revenue. Current estimates aren’t even close.

AI must replace 250 million jobs

Optimistic estimates put AI’s current revenues at between $150 billion and $250 billion per year, which is a multi-trillion shortfall.

Ivezic theorizes that AI would need to replace or deliver the equivalent productivity gains of 250 million full-time jobs in order to meet the $2.5 trillion revenue requirement - assuming each full-time employee costs $100,000.

The $2.5 trillion investment can be broken down into the $1.5 trillion already spent on hardware investments since 2022, with a subsequent $1 trillion being spent on additional investments every year. But there is one small problem: the US only has 159 million nonfarm jobs.

The infrastructure providers are also having difficulties reckoning with spending. High-performance AI chips are made obsolete around every five years, but the data centers infrastructure they are installed in is expected to last around 20 years - resulting in enormous depreciation costs. This problem is what Ivezic calls the ‘50-Cent Rule’; providers must generate around 50 cents in annual revenue for every single dollar they invest.

This is compounded by AI models that become cheaper to use but deliver far superior capabilities over previous models. GPT-4-class capability models for example have dropped in price 100-fold since 2022, but the costs borne by infrastructure providers have not dropped at all - and are rising instead - making it even harder for them to make a return on their initial investment.

This is a concerning development that echoes the dot-com bubble, Ivezic says. Similarly to how providers are footing the bill for the underlying AI infrastructure, telecommunications companies spent a fortune laying the fiber networks that powered the dot-com boom. Those telecommunications companies could not claw back their investment and - in the famous case of Global Crossing - went bankrupt.

The point Ivezic makes is that the companies that built on the infrastructure made a fortune, while those who funded the buildout were left holding astronomical investment costs without a way out.

This $2.5 trillion of investment eventually needs to be paid, and the entire infrastructure market rests atop this bubble. AI is getting cheaper, more advanced, and businesses are finding new ways to leverage the technology. But growth requires stability - and resting on a bubble is anything but stable.

European consumers continue to bank with traditional institutions, but many are clearly less satisfied with the experience. Earlier this year, a leading digital banking platform provider published the results of a survey of 6,300 online banking users across Europe.

Although 82 percent of respondents used a traditional bank as their main provider, 26 percent had switched banks in the past because of poor digital services, and would not hesitate to do so again. Also, 47 percent said that they would feel more satisfied if their bank improved the quality of digital services. Customers specifically called out personalized recommendations in this context.

What do customers want?

With the digitalization of banking, seamless transaction experiences have become the norm, standardized across banks with little to differentiate them. Digital channels are efficient, but impersonal. For the majority of customers, they are a way to complete quick, functional tasks such as checking account balance. This has gradually emotionally distanced consumers from their banks.

And therein lies the dilemma: The same consumers who are embracing digital banking are demanding more personal interactions with their bank. For instance, 59 percent of European consumers want financial recommendations tailored to their individual needs. Banks that fail to provide this will see customers walking away.

The good news is that artificial intelligence technologies are now enabling banking institutions to meet these seemingly irreconcilable expectations. Where previous digital technologies allowed some – mostly segment-level – personalization, AI is driving hyper-personalization of digital banking experiences, in real-time, at scale.

Personalize everything, from product recommendation to marketing communication

Advanced analytics models analyze customer and transaction data to identify insights into individual needs, preferences and behaviors, based on which intelligent recommendation engines suggest the most suitable products and services. Besides tailoring products using traditional financial metrics – think of loans and investment plans customized according to risk profile and financial goals – AI can personalize banking based on an individual’s data and activity outside the bank.

A great example is the hugely popular Buy Now Pay Later model where ecommerce businesses allow customers to pay for purchases in instalments based on their creditworthiness, assessed using previous transaction history and other variables.

AI-powered tools can also detect cash flow patterns or spending anomalies to offer proactive guidance to help every customer manage their finances better. Gen AI-powered chatbots and virtual tellers can support customers at any time of day or night, conversing in natural language just like a human relationship manager, to answer queries and even resolve complex issues.

Customers can ask specific questions about their portfolios or financial goals, and generative AI will respond independently, or along with a human advisor, with targeted recommendations and guidance.

The ability of generative AI to converse in a highly personal, human-like manner is transforming debt collection. Tapping natural language processing and machine learning, gen AI systems analyze borrower preferences and behaviors and then create personalized messages that improve both engagement and repayment. They also minimize confrontation by showing empathy, and suggesting solutions that are more acceptable to customers.

Revolutionizing communication

Coming to communication, bombarding people with generic messages is inefficient, ineffective and exposes a bank’s lack of in-depth customer understanding. It also annoys consumers who are already flooded with spam and unsolicited calls from various businesses. AI is revolutionizing communication by enabling banks to craft highly relevant messages that resonate with customers.

First, predictive tools identify which customers are likely to respond favorably to different types of messages; then generative AI swings into action, creating highly personalized content and campaigns, in the customers’ chosen language(s). Other AI solutions identify the right channel, format and time for sending the communication, making customers feel valued and truly understood by their bank.

The gaming industry also presents a compelling frontier for banks to engage younger, digitally native audiences with gen AI. With so many gamers interacting in immersive virtual environments, banks can leverage gen AI to create hyper-personalized financial experiences that align with gaming lifestyles and personas.

For example, AI-driven insights can enable banks to offer micro-investment products based on in-game spending behavior, offer dynamic credit options for digital purchases, and design savings plans that reward financial discipline with virtual incentives.

By integrating with gaming platforms and analyzing behavioral data in real time, banks can deliver contextual financial nudges—such as suggesting a budgeting tool or offering crypto-linked rewards for in-game achievements. The integration of gaming and finance, powered by gen AI, not only enhances customer engagement but also positions banks as lifestyle partners in the digital economy.

What’s more, AI models learn from the success and failure of past interactions to modify their own actions, reinforcing strategies and recommendations that invited interest, while dropping those that were met with disengagement. As it continuously learns and adapts, AI gets better and better at hyper-personalizing customer experiences at scale.

Moving forward in Europe

In Europe, next-generation providers are the clear leaders in AI adoption: Dutch neobank, Bunq, is Europe’s first AI-powered bank; Finn, its personal finance assistant, offers personalized, context-aware support through simple conversation, handling up to 40 percent of customer queries independently.

AI analyzes transaction data to offer personalized recommendations within Starling Bank’s mobile app, driving app engagement up by 25 percent. German digital bank, N26, personalizes customer experience through AI-powered multilingual support, conversational AI, and AI-generated next best action recommendations.

But overall, European financial service firms still have a long way to go. In a 2024 survey, although 90 percent of respondents said they had integrated some AI tools, just 9 percent believed they were ahead of the adoption curve. Investments are focused on back-office improvement, while AI adoption in customer-facing applications is clearly lagging.

Respondents named (lack of) understanding, experience and regulatory clarity as their biggest issues when integrating generative AI. Firms can address these concerns and accelerate implementations through training and development, by broadening AI use-cases, and by setting up strong governance and compliance mechanisms.

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Nvidia CEO Jensen Huang has laid out his vision of how OpenClaw will help shape the future of technology - with a particular focus on robotics.

Speaking at a “Build a claw” event in London, Huang explained how OpenClaw and AI agents as a whole can spur on incredible advancements in a range of fields.

However it was the field of humanoid robotics that seemed to catch his fancy, with Huang outlining the need for proper controls to ensure the potential of the technology can be reached.

AI is "becoming useful"

Huang opened by noting how OpenClaw was “one of the most important models” ever seen, helping to further progress across multiple industries.

“We’ve now seen an inflection in artificial intelligence - it’s become useful,” he declared. “The type of things people are doing with it, as simple as software coding, being a marketing agent assistant, research assistant, lab assistant, helping scientists do drug discovery and engineers run experiments - we’ve got agents everywhere.”

Spotting a robot built by Nvidia partner Humanoid, Huang noted how OpenClaw is the “first credible” agentic harness, which he likened to LLMs inside an exoskeleton, before extolling the possible benefits to using the technology for robotics.

“When it first came out, we thought wow, what a really terrific digital robotic system,” he said, “kind of like a human robot, that is basically the physical harness of a large language model, essentially - the motors and the articulation and the wheels and the arms, those are just tools that are used by the agent that’s sitting inside the computer.”

“So when we first saw that we said, this is incredibly useful, and we’re going to help make it even better - but the one observation that we made was that these agentic systems, if they were able to be deployed in a safe and secure way, we’re going to have to create some kind of container for you.”

Huang noted that the current situation was similar to the birth of the internet, when a browser itself was not isolated from the computer, so when a user interacted with a website, it would download an app which had full control of bare metal - and often delivering what he described as “a special package to your PC”.

“The obvious thing to do is create essentially a container, an isolation layer - essentially another operating system inside the operating system - so we created this thing called OpenShell,” Huang said, highlighting how this creates an isolation system for agentic systems.

“When you’re building an autonomous system, the first thing you should do is assume zero trust. So you give it a policy, you get minimal privileges - so if you’re asking an autonomous system to make you an omelet, you don’t have to give it access to a chainsaw - all you need is a frying pan…so whatever the task you give your agent, you give it minimum rights, minimum privileges, and then you get to monitor it to make sure that it’s compliant with policy”

“Zero trust, minimum policy, continuous monitoring, contained inside an isolation system that is separated from bare metal - that’s how you develop and deploy agents safely.”

But what if unexpected incidents or complications arise? Huang was optimistic that things would not get out of hand.

“We will monitor the autonomous systems continuously, and if that robot were to start to violate or do something that’s inconsistent with the privileges we gave it, it would escalate to us, and we can decide whether we want to shut it up!” he laughed.

“These kind of systems, these escalation systems, these security systems, all have to be built so that we can give all these autonomous machines and autonomous agents and deploy them in a way that is able to do so safely and securely, so we can realise the incredible future potential of the technology that we’re building.”

Enterprise investment in artificial intelligence has, understandably, been concentrated on compute. Access to GPUs, model performance and the cost of inference have become central considerations as organizations move AI workloads from experimentation towards production.

However, an increasing proportion of the economic and operational challenge sits elsewhere in the infrastructure stack. AI depends on data at every stage of its lifecycle: datasets must be collected and prepared, training data must be made available to compute, checkpoints and model artefacts must be retained and information may need to move between infrastructure providers as workloads and commercial priorities change.

As these workloads scale, the storage layer is becoming a strategic component of AI infrastructure with significant implications for the economics of enterprise AI.

AI is changing the role of storage

Traditional storage strategies have generally been built around considerations such as capacity, availability, performance and retention. AI introduces another dimension: the relationship between storage and the changing requirements of the wider infrastructure environment.

Different stages of an AI workflow have different storage requirements. Data being actively processed may require high-performance infrastructure, while historical datasets, checkpoints and model artefacts may place greater emphasis on durability, security, accessibility and cost. Applying the same storage approach across the entire lifecycle can therefore result in inefficient infrastructure utilization.

The challenge is particularly significant because AI data volumes can be substantial and continue to expand as organizations generate additional training data, model versions and associated artefacts.

The objective should not be to minimize storage expenditure in isolation. It should be to ensure that data is held on infrastructure appropriate to its role, while maintaining the performance, security and accessibility required by the business.

This is the foundation of what can be described as AI storagemaxxing: the systematic optimization of storage across the AI lifecycle to ensure that infrastructure investment is aligned with business and workload requirements.

Data mobility is becoming an infrastructure requirement

Organizations are adopting a broader range of infrastructure providers, including hyperscalers, specialized GPU providers and emerging cloud platforms known as neoclouds. The economics and availability of these environments can change rapidly, creating incentives to move workloads between providers. Compute can be relatively straightforward to reallocate. The associated data is considerably less mobile.

Large training datasets and model artefacts can be expensive and operationally complex to transfer. Where significant charges are associated with moving or retrieving data, organizations may find themselves constrained by infrastructure decisions that were made at an earlier stage of their AI program, creating a form of unintended dependency.

The ability to move data should therefore be considered when designing AI infrastructure, rather than treated as a contingency for a future migration. Open standards and interoperable storage can provide an important degree of separation between data and compute, allowing organizations to adapt their infrastructure strategy as market conditions change.

For technology leaders, this creates a broader consideration than simply assessing the cost of storage capacity. The relevant question is the total economic and strategic cost of retaining data within a particular environment.

Storage optimization must include security

Training datasets, proprietary information and model artefacts can represent significant commercial value. At the same time, the number of applications and automated systems interacting with enterprise data is increasing. Storage resilience must therefore form part of the AI security strategy.

Immutable copies, appropriate access controls and robust recovery processes can help organizations protect critical data from ransomware, accidental deletion and unauthorized modification. These capabilities are particularly important for datasets that would be difficult or expensive to recreate.

Security should consequently be considered alongside performance and cost when determining where AI data resides. A storage environment that reduces expenditure but creates unacceptable recovery or security risks does not represent effective optimization.

Predictable economics will become increasingly important

Storage costs can include capacity, access, retrieval and data transfer. Where these costs are difficult to predict, they can complicate infrastructure planning and influence architectural decisions. This becomes particularly problematic when organizations need to move large datasets between storage and compute environments.

Predictable storage economics allow organizations to make infrastructure decisions based on workload requirements rather than uncertainty over the cost of accessing or moving their data. This distinction will become increasingly important as AI workloads mature. During experimentation, infrastructure decisions can be relatively temporary.

Production workloads, by contrast, may persist for years and generate substantial quantities of associated data, so the storage strategy needs to account for the full lifecycle of that data, rather than the requirements of a single workload or infrastructure deployment.

The strategic role of the data layer

The next phase of enterprise AI adoption will require organisations to look beyond compute optimisation. GPUs will remain critical. Model efficiency will remain important. However, neither can be optimized effectively without considering the data on which AI workloads depend.

AI storagemaxxing provides a useful framework for applying the principle: storage should be treated as part of the AI operating model. Assess storage according to several strategic criteria: whether it is appropriate for the workload, does it provide adequate protection and recovery, are the economics predictable, and could data be moved without friction as infrastructure requirements evolve.

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