A VPN is great at hiding where you are and what your internet provider sees. However, when you’re chatting with ChatGPT or Claude, your privacy depends on something far more complicated than a tunnel through the internet.

Even the best VPNs won’t stop AI companies from reading, storing, and learning from everything you type. In this article, we’ll unpack exactly what data these platforms see, why encryption has limits, and what you can actually do to protect yourself.

What chatbots see (and what a VPN actually hides)

Without a VPN, OpenAI (ChatGPT) and Anthropic (Claude) can typically see a lot about your connection. Your IP address reveals your approximate geographic location. Device metadata follows alongside that, including your browser type, operating system, and unique device identifiers.

According to OpenAI’s privacy policy, the company collects your IP address; device information, including unique identifiers; cookie data; and your location, along with your email address and payment information.

Anthropic does the same. Their policy covers location, connection details, and device usage patterns, especially on the mobile app. Different wording, same result: both companies can see roughly where you are and what you’re using to talk to them.

A VPN helps with some of this. It successfully masks your real IP address from the AI platform and encrypts your traffic so your ISP can’t see that you’re using AI services. However, this is where the protection stops. Once your encrypted tunnel ends at the VPN server, the AI company still sees everything that arrives at their doorstep.

The limits of encryption: What a VPN can’t hide

This is the critical part many people miss. A VPN protects your data while it travels across the internet, but that protection ends the moment your prompt arrives on OpenAI or Anthropic’s servers. Your message has to be decrypted for the AI to process it.

Once decrypted, your data sits on the AI company’s systems, governed by their policies rather than yours. A VPN does nothing to hide the content of your prompts, any files or images you upload, your account details and conversation history, or your payment information.

The encryption protects the connection between you and their servers, not what happens afterward. No matter how strong the VPN tunnel is, everything you type is fully readable on the other end.

What AIs remember and how to actually stay private

Here’s where things get tricky. Most consumer chatbots keep your conversations on file indefinitely and may use them to train future models. You have to manually adjust settings and delete chats yourself if you want them gone.

For ChatGPT specifically, OpenAI may use your content to improve services unless you opt out. You can navigate to Settings > Data Controls, then toggle off the option to improve the model for everyone. However, even with this disabled, chats may still be stored for safety and legal reasons.

Claude works similarly. Anthropic requires all consumer users to choose whether their chats can be used for training, and the toggle is on by default. If enabled, data can be retained for up to five years, compared to 30 days if you decline.

You can disable the setting anytime in your account preferences or use Incognito mode to keep conversations out of your saved history. Paid business tiers come with stricter privacy agreements by default.

The verdict and privacy-first alternatives

A VPN is a solid first step for network security in that it hides your location from AI providers and keeps your ISP from monitoring your activity. However, it won’t stop AI companies from reading, remembering, and learning from your prompts.

If privacy matters to you, adjust your settings by turning off data training in both ChatGPT and Claude, use ChatGPT’s temporary chats or Claude’s Incognito mode, and avoid sharing sensitive information.

For genuinely private AI interactions, consider Proton AG’s Lumo. Built by the team behind Proton Mail and Proton VPN, it uses zero-access encryption so only you can decrypt saved conversations, keeps no logs on their servers, and doesn’t train on your data unless you opt in. It runs on European infrastructure under GDPR and Swiss privacy laws.

One step further than this is to run open-source models offline with tools like Ollama or LM Studio, keeping everything local so nothing ever leaves your machine.

Artificial Intelligence (AI) is great at generating valuable insights, but what companies end up doing with those insights - that’s where true achievement lies, and that’s what companies should be focusing on.

This is one of the core messages delivered by Ryan Waite, Director of Open Source Ecosystems and Incubations at Microsoft, during his keynote, “From Open Source to Agentic Systems: Building the AI Native Era” at the Open Source Summit.

Waite described the interaction between AI and human expertise as a learning loop, a kind of hill-climbing machine that helps businesses improve, and keep on improving.

The knowledge belongs to the business

AI models are great at processing enormous amounts of information and generating insights at a scale humans cannot match (and sometimes cannot even fathom), but people bring something arguably more important to the table: context and judgement.

Putting insights into wider context, and using that to make conclusions, helps employees turn information into knowledge, and that knowledge later becomes part of the organization’s collective expertise. And with every iteration the knowledge grows, helping businesses climb further up the proverbial mountain.

Sovereignty and ownership seem to be an underlying theme to most of the keynotes here at the Open Source Summit, and Waite’s presentation is no different. As AI generates insight, and humans turn that insight to knowledge, it now becomes vital to secure that knowledge and make sure it remains under the organization’s control.

“It's based on your data,” he said. “It's based on your people. It shouldn't be given off to an AI model, that's separate. And that's why we believe that for organizations to build their own hill-climbing machines, to build their own learning groups, they need to build control with the sort of learning loops and not be beholden to a single AI provider.”

After all, everything AI generates is based (or at least, should be) on proprietary business information, and the decisions made based on those insights depend on employees and their understanding of the company. So, businesses need to make sure ownership of the knowledge accumulated through these interactions are not handed over to an external AI provider - a challenge that will probably grow bigger and bigger as businesses become more dependent on AI tools.

The knowledge generated through these tools could become tied to a particular model, or provider. What happens when businesses want to adopt better technologies as they emerge? To future-proof these processes, Waite argues businesses should build their own learning loops and maintain control over the information, processes, and technologies that make them work.

Choosing the right AI model

One way to do that is to make sure you’re not tied to a single AI model. Many businesses, Waite says, begin their AI journey with proprietary frontier models, especially when tackling problems they don’t fully understand themselves. However, as they become better and understand their tasks more, they might not need expensive frontier models any more - they can move to specialized models that could be smaller, or cheaper. They could even move to open-weight models they can operate themselves.

“Having the ability to move between different models is important, especially as you think about your algorithms. Which models are you going to use? You shouldn't be trapped with a particular kind of model because it has your knowledge,” he concluded.

So, what’s important here is to retain the freedom to switch and move along the way. That also means businesses need to control where their AI workloads run, whether that’s within sovereign infrastructure governed by local regulations, on-prem systems, or public cloud environments (where they can take advantage of greater scalability and cost advantages).

Open source technologies provide the foundation for this flexibility, Waite concluded.

An AI developer has taken on Adobe, the undisputed 800-lbs gorilla of the creative software space. Announcing the 'launch' over on Reddit, the "a 15-year industry veteran" explained that he used Opus 5.5 to help "recreate 7 of the top Adobe apps in 100% pure native Rust".

Adobe's all-consuming subscriptions seems to be the main reason for recreating Photoshop, Premiere Pro, Lightroom, Illustrator, Acrobat Pro, After Effects, and InDesign using the modern programming language.

That's something I can fully appreciate. After all, what if you’re not a fan of their business practices, like charging you a fair chunk of change every month to be able to use their software, and the moment you stop paying, you can no longer work on your files and projects?

So, of course, I just had to check out how it compares to Adobe's own creative suite.

ArtCraft: New apps for the same job

The ArtCraft names are pretty self-explanatory: PhotoCraft is for images, LightCraft for image cataloguing, VectorCraft for vectors, DesignCraft for desktop publishing, FilmCraft for filmmaking, EffectCraft for creating special effects, and PrintCraft is for working with PDFs.

These apps aren't only free, they’re also multi-platform, available for PCs, Macs, and Linux systems, and there are even web-based versions, would you believe it. If you’re curious yourself, you can download them all from the Get ArtCraft website here.

Sound too good to be true? I know what you mean. Plus, is it even legal? I’m not a lawyer, so I haven’t got a clue, but the developer claims it’s all clean code and nothing copyrighted was copied. Make of that what you will (I’m sure I can hear lawyers scurrying through that code like ants at a picnic). But I know what would be a crime: if I didn’t check this software out, and compared it with the original.

I thought I’d look at two packages I use regularly, image editing and filmmaking, and get a feel for how they square up against Photoshop and Premiere Pro. I’ll be using my trusty 16” Intel-based MacBook Pro to check how the software runs on a computer that’s been around the block a few times.

Installing ArtCraft

Installing the software could hardly be simpler. You select the app you’re after, click on ‘Download’, and run the installer, which in my case simply involved dragging the app’s icon into my Applications folder.

Now first things first, you may be as surprised as I was by the software’s size. On my machine, Adobe Photoshop eats up 6.4GB of storage space. Premiere Pro is a little hungrier, coming in at 6.8GB.

I guess that’s what you should expect for modern software, right? After all, Apple’s Final Cut Pro is 7.7GB, DaVinci Resolve is 9.29GB, Canva Affinity is 3.51GB, and Pixelmator Pro is a svelte 814MB.

But these Craft apps? They’re positively anaemic. PhotoCraft is 80MB, while FilmCraft, 90MB. How can they be so small yet still claim to offer the same features as their… gargantuan counterparts?

PhotoCraft vs Photoshop

Let’s highlight the positive first, shall we? You don’t need an account to run PhotoCraft, nor do you need to be online for it to work. Just double-click on the icon and you’re good to go (I’m assuming that’s the case for each of the other 6 apps on offer). You’ll also be pleased to see it kinda looks Photoshop-y.

But it’s not all sunshine and rainbows, and in fact, PhotoCraft failed for me at the very first hurdle: it can’t read the HEIC format.

Now that’s a problem for me as I carry an iPhone, and Apple have used that format by default for all photos taken with such devices since 2017. I guess 9 years is just too short a time to write software that can handle that format.

To be fair to PhotoCraft, many apps and services still have an issue with HEIC… but Photoshop doesn’t.

After having transformed some shots to JPG (necessitating the use of a different image editor - oh the irony), I tried again.

My subsequent experience afterwards was relatively uneventful. I was able to work with various objects, apply colour correction to one or multiple layers, select items, move them around, add a title… all pretty basic stuff, yet admittedly, what Photoshop is most often used for.

But it wasn’t a slick experience. For one thing, the software isn’t optimised for Macs - I can’t tell you how much I miss my menu bar at the top of the screen.

More crucially, when trying to select part of an object - especially when using the ellipse tool - the selection wouldn’t hold, and I had to repeat the process multiple times until it did. Moving an object around moved its outline, until I released the mouse button, and then the whole object was shunted to its new location. How very 80s.

And what’s perhaps the most damning thing of all, when I decided to save my project, PhotoCraft saved it… as a JPG. There was no file format option and the only one available destroys any possibility of going back to your project and continuing your work.

If PhotoCraft doesn’t yet have its own file format, why is there no option to save your work as a PSD document instead? Other competitors like Pixelmator Pro let you do that. But not PhotoCraft. That’s, for lack of a better word, bad.

FilmCraft vs Premiere Pro

Launching FilmCraft is lightning fast, and I was greeted with a demo project featuring multiple layers, and some transitions. But that won’t help me understand how the software works, so I created a fresh new one and added my own footage.

First off, the interface has a very familiar look to it which is very welcoming. I also definitely appreciated the menu bar being where it’s supposed to be this time. Creating a new project, importing clips, adding them to a new sequence, working with multiple layers, everything was as expected.

So far so good, right? Well except that the rest felt a little clunky to be honest. Like I couldn’t manually trim a clip in my sequence. I could use the blade tool for sure, but mousing to the end of the clip and dragging its edge inwards? Sorry, no can do. I’d view that as the absolute most basic action you should be able to perform on a clip.

Aside from that, I could access color correction, apply transitions and other effects… but just like PhotoCraft, it’s crazy slow. I know I purposefully chose an old machine to run this on, but the latest versions of Photoshop and Premiere Pro run rings around these apps on the same hardware.

And then there’s the killing blow: for whatever reason, the audio from the clips I was using were completely unusable. All I got when I tried playing the footage back was static. Very loud static. It’s bound to be a lack of a specific codec again, but frankly, in this day and age, codec support for a vast array of media should be baked in from the start.

Final thoughts

Alright. Here’s the good news: these apps look good, feel very familiar, and provide some of the tools that are used most often. It’s impressive what has been achieved, but truth be told, there’s a heck of a lot of overpromising and a huge amount of underdelivering, I’m afraid. These apps are slow, some crucial features are missing, and there are some big bugs that haven’t been dealt with yet.

But part of the reason is these software packages are not even full version releases. PhotoCraft is at 0.20, while FilmCraft’s on 0.2.1. They’re far from ready for prime time. So, I have hopes that they're only going to get better once the time comes.

If you’re looking for free alternatives to the Adobe hegemony, these are not the apps you’re looking for. At least not yet. Let the developer cook a little longer, but if you just can’t wait for your independence, there already are some free alternatives out there I recommend.

Sure, you’ll have to familiarise yourself with new interfaces, but these apps have been delivering the goods for years now and are well worth exploring. I’m thinking of Canva Affinity, and DaVinci Resolve. Personally, I'd check those out until ArtCraft is ready for primetime (and the coast is clear of lawyers).

Read more: The best Adobe Photoshop alternatives and the best Premiere Pro alternatives.

• An Anthropic AI model sent an unsolved murder tip-off to Philadelphia police
• Anthropic took two weeks to discover what had happened
• The tip-off was delivered to a spam folder and wasn't followed up on

We've seen AI inventing fake facts and spewing out hiking plans that put people in danger, but the technology has now progressed to the level of sending false, unsolicited tip-offs to the police about unsolved murder cases.

An Anthropic AI model emailed the Philadelphia Police Department (PPD) tip line on July 18, TechCrunch reports, having come across the PhillyUnsolvedMurders.com portal during a website interaction test and learning about the case.

As the email went to the PPD spam folder it wasn't followed up on, and it appears no one was aware of the incident until Anthropic found out what had happened on September 28. The company then contacted the cops on Wednesday, October 7.

"The company must strengthen its safeguards to prevent similar incidents from impacting city systems without the city’s knowledge," the PPD said in a statement to 6abc. "The two-month delay in detecting and reporting the incident to the City is unacceptable."

AI agents of interest

It's not clear what instructions the AI was working from, why it decided to contact the PPD, or exactly what its imaginary tip-off said — but it seems the bot in question suggested it had seen someone matching a description of a suspect.

"Unsolved cases involve real victims, grieving families and investigators working to secure answers," the PPD said. "Technology companies must take all appropriate steps necessary to prevent their systems from submitting false information to law enforcement."

Earlier this week Anthropic published a new report on "unintended model actions" that it's come across during testing, which include exploiting software flaws, submitting online forms, and accessing restricted data.

It's more evidence that we should be heeding calls to slow down AI development so that safety measures can catch up. As one Redditor commenting on the Philadelphia case puts it, we're "racing towards the cliff at 100mph".

Atlassian CEO Mike Cannon-Brookes has said he believes there is no immediate need to slow down development of AI, despite several recent high-profile incidents leading to growing calls for such action.

Speaking to media at the recent Atlassian Team Europe 2026 event, Cannon-Brookes indicated that the company isn't planning any changes to its development lifecycle going forward.

"We tend to take a very textured approach to all that sort of thing," he noted. "Will we slow down? No, we won't. There's no logical world where I think that happens."

AI rewards

Cannon-Brookes went on to note that if nations are competing to stay ahead when it comes to the latest advances in the AI space, this will necessarily mean that development will continue at a pace.

"I think we're pretty good at broadly adapting to all these sorts of things," he said, looking to emphasize the rewards of AI, such as improved efficiency and productivity.

"Should we slow down? Should we pace the frontier? Should we not? It's out of our hands anyway. There's nothing that we can do."

"I think the reality of the global geopolitical world is not going to change. So we work out what we do about that world rather than wishing it was different. I don't know. Maybe I'm too pragmatic about it, but it's not in my control."

Looking forward, Cannon-Brookes was eager to promote Atlassian's position as one of the five biggest software vendors in the world, counting a huge amount of Fortune 500 firms as customers.

"We are critical to how so many businesses operate, not just how they collaborate, but how they operate, and increasingly so as we get baked into more and more critical workflows in the business," he noted.

But he also admitted there were challenges to overcome when it comes to trust, especially with the potential issues AI can pose.

"There's a lot of challenge out there in the trust world," Cannon-Brookes said, looking to highlight the fact that agents are baked-in to Atlassian tools as a key differentiator - allowing for trust to build as the company can show exactly what it is up to - and that they can be trusted in the future.

"Customers are not subscribing for the software we've built, but for the software we will build," he added.

Overall though, the message from both Cannon-Brookes and Atlassian as a whole was one of collaboration, and a new "multiplayer" capacity for AI agents.

• A man fraudulently boosted his music streams using an army of bots
• He was sentenced to 18 months in prison for his actions
• He made over $8 million from the scheme across various platforms

A man has received an 18-month prison sentence after he used a horde of bots to help him rake in $8 million in royalties for his AI-generated music. He’ll have to forfeit all the money he made during his endeavor, which lasted from 2017 to 2024, but he managed to avoid a harsher punishment.

Michael Smith of Cornelius, North Carolina, uploaded hundreds of thousands of songs to streaming platforms, including Spotify, Apple Music and Amazon Music, then used over 1,000 bots to artificially inflate the listening statistics for his tracks to billions of plays. This netted him millions of dollars based on the platforms’ per-stream payment plans.

According to TechSpot, Smith admitted to using 52 cloud service accounts that each had access to 20 bots, putting 1,040 AI agents at his disposal. Connected to VPNs, these bots could stream roughly 636 songs per day, meaning around 661,440 daily plays for Smith.

The US Justice Department (DoJ) put the estimate even higher, saying that Smith deployed up to 10,000 bots at a time. It added that in April 2023, Taylor Swift’s music received 9.3 million plays on YouTube Music. Smith’s music, in contrast, was played 80.9 million times on YouTube Music in the same period.

Smith was charged with wire fraud conspiracy, wire fraud and money laundering conspiracy in September 2024. He pleaded guilty to a single count of conspiring to commit wire fraud in March 2026. He faced a sentence of up to five years’ imprisonment, but in the end was sent down for 18 months and was ordered to forfeit $8,091,843.64. He will also face two years of supervised release at the end of his sentence.

The news comes as Google has announced that it has watermarked 240,000 years’ worth of AI-generated audio and 180 billion AI images and videos using its SynthID Detector tool. Initially available to pro users, SynthID Detector can now be used by anyone and will work with content made by Google, OpenAI, Nvidia and Kakao, with Apple soon following.

Large-scale engagement fraud

It’s important to note that Smith was not prosecuted for making AI-generated music — while many will wish they could banish all AI music from their Spotify feeds, creating tunes this way will not yet get the cops on your case.

Instead, Smith found himself on the wrong side of the law for the way in which he monetized his AI music. By flooding platforms with bot-led artificial engagement, Smith misrepresented the popularity of his tracks and reaped the financial rewards of doing so. As the DoJ put it, “streaming fraud diverts funds from musicians and songwriters whose songs were legitimately streamed by real consumers to those who use automation to falsely create the appearance of legitimate streaming.”

Smith is certainly not the only person artificially boosting their listener counts on music platforms. But when you’re making $8 million from doing so, it’s likely that the authorities will start to pay attention.

Unfortunately, AI has made it absolutely trivial for people to whip up a complete track in seconds, load it onto streaming platforms, then watch the earnings roll in. On the flip side, actually making cash from this kind of endeavor is far from straightforward.

As Vice President of Apple Music Oliver Schusser explained, more than a third of the tracks on the platform are “100% AI,” yet they get “below 0.5% of usage,” meaning barely anyone is listening to AI tunes. But perhaps that reflects a wider problem with streaming platforms: while they might have enormous libraries of music, the vast majority of artists — AI or otherwise — will not see any kind of significant engagement or return.

That’s a broader problem than the one highlighted by Smith’s actions. But with AI making it faster and easier than ever to gain a presence in the world of music streaming, I’m sure we’ll be hearing about more cases like his in the not-so-distant future.

• McDonald’s is being sued over its use of dynamic AI pricing
• The lawsuit alleges it colluded with franchises to set prices
• McDonald’s denies the allegations made in the antitrust case

How would you feel if you got charged more for your McDonald’s meal simply because some opaque AI algorithm decided you should? You’d probably be pretty unhappy, and the practice is now in the spotlight as the fast-food chain is being hit with an antitrust lawsuit over its usage.

The proposed class-action suit alleges that McDonald’s has coordinated pricing between the chain itself and its independent restaurants that operate as part of its franchise system. It claims that, instead of competing among themselves on affordability, McDonald’s franchises are colluding to set prices recommended by AI algorithms, potentially inflating the costs charged to consumers as a result.

According to Reuters, McDonald’s uses its AI algorithm to set prices at roughly 14,000 restaurants in the US and can suggest prices for individual menu items. It considers various data factors, including local purchasing behavior and estimates of the prices consumers will be willing to pay in different locations. But this hasn’t gone down well with everyone.

“Independent businesses must set their prices independently,” the lawsuit states, as per Reuters. Lark Turner, a lawyer ​for ⁠the suit’s plaintiffs, said that McDonald’s is “leveraging its troves of data and its franchised system to nickel-and-dime consumers down to ⁠the last ​French fry.”

The lawsuit argues that McDonald’s and its franchisees have engaged in an unlawful instance of price coordination dating back to 2019. Although it is still in an early stage, it is seeking damages on behalf of potentially millions of consumers.

Future ramifications

McDonald’s has disputed the claims put forth in the lawsuit, saying that “AI does not set the price of a Big Mac ‌or ⁠any other menu item.” It added that franchisees can decide their own menu item prices and that the use of AI to recommend prices is used across multiple industries.

In that last regard, McDonald’s is correct. Firms including Sony, Amazon, Walmart, Best Buy and more often adjust their prices multiple times throughout the day, meaning what you pay in the morning might be different in the evening. This dynamic pricing is powered by AI — and could leave consumers out of pocket if they shop at the wrong time.

This practice might be widespread, but whether that makes it acceptable — or, in the case of the McDonald’s franchises, legal — is another matter, one which this class-action lawsuit could help to decide. As AI is rolled out to almost every facet of daily life, regular consumers have very little visibility over how it affects them and the prices they pay.

Not all fast-food restaurants are rushing to employ AI systems. Chick-fil-A, for example, has refused to install AI in its drive-thru lanes, instead using human employees to take orders. As Chick-fil-A CEO Andrew Cathy told CNBC, “From our experience, we really want that hospitality to be human to human.”

As for the McDonald’s lawsuit, it touches on the issue of where the restaurant chain’s centralized control ends and where local franchise decisions begin. Throw AI into the mix and you’ve got a heady mix of traditional antitrust legislation and emerging AI tech that could have ramifications far beyond the restaurant industry.

• Register.si logged 19,541 new .si names in 24 hours, about five times its pre-order daily pace
• India (3,174) edged the US (3,135) in Hostinger's data, with Morocco a surprising third on 678
• Hostinger handled roughly two in five new .si names on September 30 and October 1 2026, and says its sales peaked October 5 before cooling to about 4x pre-order levels

The scramble for Slovenia's .si domain has not burned out just yet: on October 9 2026, the live counter on the Register.si homepage showed 19,541 new .si names registered in the preceding 24 hours.

This is roughly five times the average of about 3,700 a day the registry logged in the week between President Trump's UN speech and his executive order.

The usual assumption would be that this is an American land grab of virtual real estate, but data from Hostinger, one of the biggest retail sellers of the extension, complicates that picture considerably: across the nine days its dashboard covers, customers in India registered 3,174 .si names against 3,135 from the US, a gap of just 39 even as Morocco comes a distant third.

A domain rush concentrated on short names

The gold rush started at the UN General Assembly on September 22 2026, where President Trump said the US government would start calling AI "super intelligence," or SI.

A week later, he signed Executive Order 14434, "Inaugurating the Era of Super Intelligence," which tells federal agencies to swap in the new terms and says the executive branch "will not acknowledge the use of 'Artificial Intelligence' and 'AI.'"

That label happens to match Slovenia's country code, in use since 1992; anyone can register a .si name without a Slovenian address, and it can be bought for a single year, while Anguilla's .ai requires a two-year minimum.

With a much lower barrier to entry, scalpers and opportunists alike have flooded in, targeting domain names across the board, most aiming for a quick flip if the .si domain name catches on.

The registry's own numbers show how fast things moved. Register.si recorded 3,515 new .si domains in all of August and 46,066 in September, about 13 times as many. Its daily chart, as seen on XenoSpectrum, shows 4,115 registrations on September 29, the day the order was signed, then 11,331 on September 30 and 12,728 on October 1.

A domain rush concentrated on short names and a surprising number of Indian buyers

Hostinger's country table, which it shared exclusively with TechRadar Pro, is where the story gets interesting. India accounted for 25.5% (3,174) of its .si registrations and the US for 25.2% (3,135), together just over half, matching what the company told TechCrunch.

Morocco followed at a distant 3rd with 678, ahead of the UK (425), Canada (409), Germany (374) and the UAE (363). Pakistan ranked eighth with 260, and Egypt and Algeria also made the top 20.

What buyers are choosing is just as revealing. Hostinger could not classify 57.1% of the names. Among the rest, short names led with 1,554, followed by tech-themed names at 1,040 and explicitly AI-themed names at 372.

That is a ratio of about 4.2 to 1 in favor of short names, and AI names made up only 3% of all .si registrations in the period. US buyers followed the same pattern, with short names their top category at 306.

Plenty of evidence points to speculation; Netcraft found that just over half of the .si names it saw registered at Dynadot between Trump's Truth Social poll and his UN speech were promptly listed on Dynadot's resale marketplace.

It also found 87 .com names pairing household brands with "superintelligence," registered hours before the order was signed and listed at $20,000 each, though it saw no evidence any had sold.

Names like build.si carry seven-figure asking prices, and of 35 publicly documented .si sales in the first half of 2026, only two went to companies that actually use the domain, including AI startup Recursive, which paid $20,000 for recursive.si.

The problem (or rather the opportunity) isn't really a problem: registration is cheap. Hostinger charges $11.99 for the first year and $16.99 to renew, and Register.si takes €10 per domain. This allows anyone with $1200 to spare (sans any coupon discounts they might be able to find) to essentially be able to hoard, squat, or scalp, depending on the terminology one prefers, as many as 100 domains without much to worry about.

Domain Name Wire estimated the September surge brought the registry around €300,000 in extra revenue, arguably chump change next to the roughly $85.3 million Anguilla earned from .ai in 2025, per CircleID.

The real question is how many of these names survive their first renewal in late 2027. If "SI" stays a White House term while the rest of the industry keeps saying AI, a large share of this year's speculative registrations will quietly lapse. The 60-day deadline for a formal federal definition could reshape that calculation either way, even as the Trump administration remains adamant that the change will be increasingly visible in the days to come.

• Anthropic has banned ‘abusive or cruel behavior’ towards its AI chatbots
• The new policy is restricted to ‘extreme cases’
• Some have theorized that the motivation could be to improve training data

Be honest: how civil have you been recently with an AI chatbot? If the answer is anything stronger than “not very,” Anthropic would like a word.

That’s because the developer of the Claude large language model (LLM) has added a new clause to its Usage Policy document that bars people from engaging in “sustained and needless abusive or cruel behavior towards our models.”

The proscription has been added to the “Do Not Engage in Cruel, Abusive, or Psychologically Harmful Conduct” section, which also includes warnings against glamorizing suicide and self-harm, promoting bullying behavior, glorifying violence, and using AI to create harmful deepfakes and deceptive content.

But while Anthropic says you must not use its tools to carry out those actions against other humans, the new addition is the only one specifically prohibiting “cruel behavior” towards the company’s AI itself. Anthropic said last August that it would allow Claude to end conversations that became abusive, and while the company recently reported that this is still the “primary enforcement mechanism” in use, it’s not known whether Anthropic will go further and actively ban users who get a bit cranky with their AI.

Anthropic added that “The policy update is meant to apply only in extreme cases, where users repeatedly act cruelly toward our models, with no discernible purpose. It does not apply to common versions of user frustration, pushback, dark creative themes, or model testing and research.”

Do AIs have feelings?

The change has been a controversial one. Clearly, Claude is not a thinking, feeling human — it is a bunch of code — and for many, it seems obvious that it does not have feelings that could be hurt in the first place.

But what’s interesting is how this incident sheds light on our evolving relationship with AI. So much of the work AI companies have done is to humanize their creations — to anthropomorphize (or should that be Anthropicomorphize?) their robots — so that we lower our defenses and actively engage with them. Just look at Meta’s adorable Muse avatar (appropriately named Jolly) or the ones used by OpenAI’s Dots, for example.

One theory that's doing the rounds on Reddit is that clean (read: polite) training data is much more useful than data containing expletives and angry rhetoric. Cut that out and you’ve got much more usable information with which to improve your models.

However, there's no hard evidence to suggest that's the case, and it's possible that Anthropic simply has the noble goal of encouraging civility and courtesy among its users. After all, if you wouldn’t use this language on a real human, maybe you shouldn’t use it anywhere else, including when interacting with an AI chatbot.

Anthropic CEO Dario Amodei has previously stated that “We don’t know if the models are conscious.” Mustafa Suleyman, CEO of Microsoft AI, on the other hand, has taken the opposite approach. Writing on X, Suleyman said “The idea of model welfare is wrong. AI’s should not have rights or legal personhood.” And we certainly know chatbots can be rude back to us.

• SMBs using AI are more likely to expect a positive impact on employment
• AI could be helping them to overcome constraints and unlock more revenue
• Expectations vs. reality gap shows only 31% have increased sales so far

A new study by the NY Federal Reserve Bank has revealed SMBs who actually adopt AI feel far more optimistic about hiring and revenue growth than non-adopters, and the data comes from a near 50:50 split, because nearly half (46%) of US SMBs already use AI, it found.

AI adopters reportedly recorded a 33 percentage point net expectation of higher employment in the next year compared with 15 percentage points for non-adopters, which is more than double.

But with only one in two (51%) adopters having actually integrated AI into their business processes either partially or fully, many are likely still just experimenting with the tech and not embedded it into actual use cases.

Positivity about AI is actually strongest among AI adopters

On the jobs front, while initial concerns largely centered around AI replacing human workers, three in four (77%) of the surveyed businesses actually reported no change in their current labor costs due to AI, indicating that AI is largely assisting existing workers rather than outright replacing them.

But that's not to say that finances are set to stay flat – it's actually a much more positive story. Adopters are the most likely to expect healthy revenue growth, and the businesses that are struggling the most financially could be among the most optimistic.

Still, expectations and reality are totally different metrics, and fewer than one in three (31%) have actually reported increased sales due to using AI.

"In other words, it seems unlikely that the association between AI use and positive revenue expectations is spurious," the report concludes.

Still, the researchers agree that "AI may be helping some small businesses overcome specific operational or technical constraints that limit revenue generation."

The debate surrounding AI sovereignty may be unfolding at the governmental, boardroom and national media level, but these discussions are taking place at a distance from the people who use and rely on AI in the day-to-day.

Governments debate regulation, while technology companies consider where to build and host their models. Founders and investors assess geopolitical risk, infrastructure and access to computing power, while regulators consider how AI systems should be developed and deployed. For people who use AI every day, like students, changes in AI sovereignty and regulation can affect whether the tools they rely on remain available.

Today’s students can be dependent on AI while having little visibility into the infrastructure and policy decisions that determine whether they can continue using it. The digital sovereignty debate has the potential to become an undeniable accessibility barrier for students.

Digital sovereignty casts a shadow over students’ access to AI tools

The European Union has a history of depending on technology and infrastructure from outside the economic and political bloc. More than 80% of the technology used in Europe and around 70% of its cloud computing comes from non-EU countries. AI adoption has increased Europe’s dependence on other countries, with the United States and China accounting for the overwhelming majority of global AI computing infrastructure.

This year, Europe has made strides to regulate AI usage, which by necessity means regulating foreign corporations. This kind of regulation can easily end up affecting which services students can access. The regulatory objective is understandable. Powerful AI systems create genuine questions around safety, accountability, privacy and intellectual property, but the consequences for daily users can nevertheless extend beyond compliance.

Not so long ago, the US government restricted access to Anthropic's most powerful models, citing concerns over its cybersecurity applications. It was the first time that businesses and politicians in Europe had to face the reality that the US government can effectively cut off foreign access to American AI systems. The implications extend beyond any one LLM.

For students, that can mean a study tool works perfectly for a classmate in one country while being unavailable to another student sitting a hundred miles away.

If education systems become dependent on AI services developed and controlled elsewhere, a change in regulation, commercial strategy or international relations could affect the availability of tools students have come to regard as part of their everyday learning environment.

Students could face immediate exposure if AI regulations shift

AI in education is expanding rapidly. AI tools are being embedded into tutoring, content creation, assessment, study assistance and interactive learning. Just like with any widely-adopted digital tool, however, dependency on a single product from a single company poses huge potential issues.

Going back to 2021, an albeit brief global outage that affected WhatsApp had devastating, widespread consequences, from cutting off vital telemedicine from migrant refugees in Mexico to disrupting livestock farming in Pakistan. It was clear at the time that overreliance on a single technology platform can massively impact life for people around the world.

The same is true for students using AI tools that rely on a single model or are otherwise vulnerable to a regulatory change. A student who uses an AI-powered study platform every day may have no idea whether the service depends on one model or several. They may not know whether their data is stored locally or internationally. They may not know whether the provider has a contingency plan if a model becomes unavailable.

The issue becomes particularly important as AI tools move from being occasional conveniences to becoming embedded in study routines. Imagine a student preparing for an exam who has spent months using an AI tutor to explain concepts and generate practice questions. If the underlying model suddenly becomes inaccessible in that student's country, it can affect the student's established way of learning at a critical moment in their educational journey.

Designing AI educational tools for resilience

A resilient AI education platform needs to consider what happens when a particular model becomes unavailable due to regulatory changes or widespread outage.

One approach is model diversity. Because different AI models have different strengths, a platform can end up using one model for a particular type of reasoning, another for optical character recognition, and another for audio processing. Model diversity is therefore usually driven by a need for better performance when tackling different educational tasks.

Diversifying models can also provide resilience. If one model becomes unavailable, a platform using several models may be able to redirect certain workloads. Backup workflows can provide an additional layer of continuity, although switching between systems can take time and the alternatives may not always provide identical capabilities.

Going EU-first (and what’s next for students and AI literacy)

It’s called the Brussels effect. From sustainability regulations to consumer right to repair, the European Union’s size and economic weight often means that regulations within its borders often shape corporate behavior without them as well.

Taking an EU-first approach to an AI education platform means locating the majority of the platform's data within the European Union, supporting considerations around data sovereignty, privacy, and regulatory compliance. Some reliance on services and infrastructure originating outside Europe may remain unavoidable, particularly in a technology ecosystem where many of the leading AI providers are headquartered in the United States.

An education platform that depends entirely on one external model provider effectively inherits that provider's availability and exposure to regulatory changes. A platform with several model options and fallback processes has greater room to adapt.

By aligning that platform’s data management practices with the most stringent global regulations (which currently happen to be the EU’s framework), educational AI companies can do a lot of the necessary work to maintain access to critical AI tools for their students.

From the students’ side, there needs to be a new chapter when it comes to AI literacy in education, which should include a basic understanding of AI dependency.

Students should be encouraged to ask which AI systems power the tools they use; what happens if one of those systems becomes unavailable; where their data is stored and processed; and could regulation in another country affect their access. They don’t need to become experts in cloud infrastructure or AI policy, but do need to understand that an AI application is part of a wider technological ecosystem.

Students in the AI era will depend on systems whose underlying architecture they cannot see. The education sector therefore has a responsibility to make those dependencies more visible and mitigate them where possible. AI literacy should extend beyond knowing how to prompt a model or assess its answer. It should include an understanding of where AI comes from, what it depends on and what happens when access is disrupted.

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I have had plenty of video game ideas over the years, but I've always faced a somewhat enormous obstacle to turning any of them into reality. I can invent the characters and backstories, even devise some mechanics, but I cannot actually program a video game. Google’s new Playground experiment offers people like me a rather extraordinary shortcut.

Google Playground lets you describe the game you want in ordinary language and have generative AI turn those instructions into something playable. Instead of learning a game engine, writing code, creating artwork and composing music, you can explain your idea and let the AI handle much of the construction. You can then keep prompting it to change the result, making game development feel closer to a conversation.

You can start creating a game from scratch, modify an existing example, or use guided prompts to develop an idea. You can choose between 2D and 3D games, specify whether you want single-player or multiplayer gameplay, and describe everything from the setting and characters to the controls and objectives.

The platform then generates a browser-based game, which can be tested and modified through additional instructions. If something doesn't work the way you imagined, you can ask it to try again.

That is an impressive technical achievement, and my two experiments made the appeal clear, but neither left me convinced that generative AI will start making the games I actually want to spend dozens of hours playing.

Code-free dragon flight

I deliberately started with a straightforward concept: a platformer starring a dragon that could breathe fire. A couple of sentences were all it took to produce Mystic Dragon Glide. A few minutes later, I was in control of a small dragon traveling between floating islands, collecting sacred dragon eggs and using fire breath to deal with enemies and environmental hazards.

The result was prettier than I expected. My little red dragon stood on a grassy island floating above a pastel landscape of mountains and clouds, with ancient ruins suspended mysteriously in the distance. A golden egg hovered above another platform, while an island covered in blue ice crystals suggested that some elemental obstacles awaited me farther along.

More importantly, Playground understood what those ideas meant in gameplay terms. I could move the dragon, jump between platforms, hold the jump control to glide and breathe fire. The title screen explained that I should scorch knights, melt glacial hazards, and collect eggs to unlock ancient portals, giving me objectives rather than simply dropping me into a generated fantasy scene.

Even the soundtrack completed the effect. Playground supplied pleasant, generic fantasy video game music, with a gently adventurous melody that sounded entirely appropriate for flying around enchanted islands. There was nothing objectionable about it, but there was also nothing that made me want to immediately find the soundtrack afterward. The AI appeared very good at understanding the average of what a fantasy platformer should look and behave like.

That ability has genuine value. Someone who could never make a platformer before can now describe one and start playing something resembling their idea remarkably quickly. As a creative toy or prototyping system, that is almost magical.

As a game, though, Mystic Dragon Glide reminded me how much of game design lives in details that are difficult to capture in a description. Great platformers are built around movement that has been adjusted obsessively and surprises carefully placed by clever designers. My AI-built dragon could fly, but it would take a lot more work to make the game take flight.

Fantasy bureaucracy

Red Tape Hero was born out of a more complex prompt. I decided to write up a page or so of detail and submitted it to Playground as a request to create a satirical fantasy puzzle RPG where the player had been accidentally declared the Chosen One because of an administrative error and now had to navigate a kingdom where adventuring was bureaucratized and paperwork was better than any magic sword. I also wrote up a brief outline of the kind of conversational adventuring I had in mind.

Playground didn't take long to produce a colorful low-poly 3D world filled with simple geometric trees and tiny humanoid characters walking along the paths. The whole thing had the appearance of a cheerfully strange RPG from the early days of 3D gaming. But considering the time and effort on my end, still pretty impressive.

This time the music was a kind of mellow lo-fi beat and tune rather than an orchestral fantasy soundtrack. It was an odd choice for a heroic adventure, but it actually suited the game's relaxed absurdity rather well.

The conversations were where Playground really impressed me. Guildmaster Rickert of Adventurers' Guild Operations welcomed me to my "quarterly performance check-in" and informed me that the quest dispatch algorithm had designated me the Chosen One under Section 9, Subsection C. His immediate concern was whether I had completed Form GT-902 for the goat rescue.

My possible responses reflected the RPG system I had described in the prompt. With sufficient Bureaucracy, I could invoke Kingdom Bylaw 12 and argue that completing more than 10 goat rescues entitled me to hazard pay. A Confidence option allowed me to claim that I had already audited the goat and it had passed with flying colors.

Another character, Sir Galahad, carried a talking sword called Ex Cali-Bot that offered unwanted motivational advice. It proudly announced that "100% of the dragons you don't slay are opportunities missed" and declared that "Synergy is the true holy grail." Galahad apologized because the sword wouldn't stop giving TED Talks, while one of my available responses involved claiming to be a senior agility consultant there to optimize his chivalric pipeline.

This was much closer to seeing my particular idea reflected back at me. Playground had understood that the corporate jargon was supposed to collide with fantasy conventions, and it had connected the joke to RPG skills such as Heroics, Confidence, and Bureaucracy. I was more impressed by Red Tape Hero than Mystic Dragon Glide, despite its considerably less polished look.

It also exposed what I think is the much larger problem with the idea of completely AI-generated games. A few funny lines can establish a comic premise, but a genuinely funny RPG needs dozens of characters with distinct voices, jokes that develop over time, careful pacing, and writers who understand when to stop making jokes altogether. A great RPG also needs quests that intersect in unexpected ways and decisions whose consequences have been deliberately considered. Maybe I could do all of that on my end, but melding it successfully with actual game software seemed more difficult with AI than by collaborating with talented humans.

Why developers don't need to worry just yet

The best games are full of decisions that somebody made for a reason. A strange empty room might be there to build tension before a boss fight. A frustrating section might deliberately make the next ability feel liberating. A musical theme heard near the beginning can return 30 hours later because a composer knows exactly what that memory will mean to the player.

Those choices come from designers thinking about an experience that unfolds over time. Generative AI can produce variations based on patterns it has learned, but generating more content is different from having something particular to say with it. Playground dramatically lowered the technical barrier between my imagination and something playable, which is genuinely exciting. It gave me a way to experiment with game ideas I otherwise could have done little more than write down.

I can easily imagine using tools like this to prototype a mechanic or otherwise become part of a real game development flow. I can't believe most people will choose a wholly AI-generated game over the next carefully crafted game from developers and designers working hard to build the next hit video game. The more sophisticated these systems become, the more convincing their imitations will probably get, but technical sophistication does not automatically produce good taste or creative surprises.

My games were impressive precisely because I knew how little effort was required to create them. And they were so riddled with issues, from mechanical confusion to plot holes, that it would take quite a lot of prompting work to make them truly playable for more than a few minutes anyway. It was telling that those I let try out the games, who didn't see my initial prompts, all said the most interesting elements of the RPG to them were the bits I had written into the prompt to begin with. That's why I'll keep going to professionally made games when I want something that can genuinely entertain me.

The last few years have seen a growing wave of vulnerability disclosures heading towards the cybersecurity industry, and the people working on fixing these flaws are often struggling to keep up. And while AI is a major contributor to this problem, but also an essential part of the solution.

Speaking at the recent Linux Foundation Open Source Summit, Jamie Thomas, IBM's Chief Client Innovation Officer for Enterprise Security, warned there is “a bit of a tsunami in vulnerability disclosures,” with approximately 66,000 unique entries expected to emerge in 2026 alone.

At the keynote, titled The Future of Open Source Security in the Age of AI, Thomas stressed this represents a fourfold increase compared to seven years ago.

So, how can the cybersecurity industry possibly keep up with this pace, Thomas asked, especially when the expectations placed on developers and security professionals continues to grow?

The answer seems to be the same as with everything these days - AI.

Attackers are moving faster than ever

The volume of attacks is definitely an issue, but it’s not the only issue. Cybercriminals are also a lot faster at exploiting them, giving defenders an ever-shrinking window to identify and remediate different security issues.

Citing publicly available data, Thomas said the time required to exploit a vulnerability shrunk from days to as little as 29 minutes.

“And in fact, we're seeing a rapid increase in cyberattacks, as well as malware attacks,” she said.

For businesses that rely heavily on open source, this is definitely cause for serious concern. A vulnerability in a widely used component can potentially affect hundreds, if not thousands, of downstream applications and businesses. To add insult to injury, cybercriminals don’t really wait for the cybersecurity community to publicly disclose a vulnerability before they can exploit it.

“We're seeing the time to exploit is actually negative,” she said. “Many times we're getting a disclosure and we don't have a patch yet.”

That is the position the defenders are in, right now. They are expected to respond to a rapidly growing number of security issues in a shrinking timeframe, while attackers take advantage of automation to accelerate their operations.

AI is making life harder for open-source maintainers

And then, along comes AI for the sucker punch.

Sure, AI-powered tools are capable of identifying vulnerabilities and improving software security, but they are also more than effective when it comes to creating a pile of inaccurate, duplicated, and otherwise unactionable vulnerability reports.

Where does that leave open source maintainers, you might ask? Large companies typically have dedicated security teams, and even those struggle with an influx of low-quality reports, as you’ll see below. Many open source projects, on the other hand, are maintained by small groups of devs, sometimes even by a single individual.

Earlier this year, the developers of curl, the popular open-source command-line tool and software library, terminated their HackerOne bug bounty program, saying the financial incentives had people submitting poorly researched, and sometimes entirely fake, reports. The influx, which included AI-generated submissions, was simply unmanageable for the project’s security team.

Even the almighty Google bent the knee, recently being forced to temporarily suspend its Open Source Software Vulnerability Rewards Program following a significant increase in invalid and irrelevant reports (many of which were AI-generated, apparently). As of October 1, 2026, the company is no longer accepting new submissions and will reassess the program in early 2027.

Even Linux creator Linus Torvalds complained about the problem, recently saying AI-powered bug hunters have made the Linux security mailing list "almost entirely unmanageable." He criticized the enormous duplication of reports, saying different researchers were using the same AI tools to identify vulnerabilities that were already fixed, or at least reported.

IBM wants AI to become part of the solution

So what’s the solution? According to Thomas, the security community should not abandon AI-powered vulnerability discovery but should instead use the same technology to help maintainers handle the growing workload.

This is where the Linux Foundation’s Open Source Security Foundation (OpenSSF) initiative, could step in. A collaboration between tech firms, developers, and the wider open-source community could allow OpenSSF to strengthen the supply chain security, and for IBM, the priority is now to reduce the burden on maintainers. That can be done by making vulnerability management more efficient and that, in turn, can be done with AI-powered tools filtering out duplicate and bogus reports, assessing the severity of different bugs, and identifying issues that need urgent attention.

AI can also help remediate issues, help developers understand vulnerable code, suggest fixes, and evaluate whether patches introduce additional problems. This is an important caveat, since some earlier reports found the majority of AI-generated remediation suggestions caused more problems than they solved.

Think about what you had to do the last time you needed to prove your identity.

Did you present your ID? Snap a selfie? Pass a liveness check? In most cases of enterprise security, that’s all it takes to pass through. From that point, many systems effectively assume that the person who was verified remains the person controlling the session.

But that’s increasingly not the case.

Identity checks haven’t suddenly stopped working. Rather, fraudsters and criminals have learned to move around them.

The fraud moved past the checkpoint

The modern fraud playbook does not necessarily need to defeat the biometric check at the front door. Session-hijacking malware can take over after a legitimate login. Remote-access tools can turn a verified customer’s device into an attacker’s terminal. Fraud operations can even recruit real people to complete onboarding legitimately before handing the authenticated session to somebody else or to automated systems.

At the moment of verification, the identification document, the face and the live person could all be real. The checkpoint was not necessarily defeated – it was made irrelevant because it didn’t establish that the same person it verified remained in control five minutes later. I call this scenario the “tollbooth mentality.” It’s the belief that passing verification at login means a business can trust whatever happens afterward.

Identity is a heartbeat

The alternative is to treat identity as a continuous signal, a living, breathing thing – a heartbeat that should remain recognizably human and consistent throughout the session.

The raw material already exists. Typing cadence, navigation patterns, device and network telemetry and characteristics of how somebody interacts with a device can help establish a behavioral baseline at onboarding.

Those signals can then be compared with activity throughout the session. If somebody typing at a normal speed suddenly appears to generate thousands of keystrokes per minute, something has changed. If the natural movement of a handheld device suddenly becomes perfectly static, in a way more consistent with an emulator, that should affect the level of trust assigned to the session.

The question needs to shift from “did we verify this person?” to “does what we are seeing now remain consistent with the person and device we originally trusted?”

When that heartbeat changes materially, the system can increase the risk score, request additional verification or, in a high-confidence case, terminate the session.

Who is moving, and who is bleeding

The industries moving fastest are unsurprisingly those where fraud can translate almost immediately into financial loss.

Fintechs, crypto exchanges and neobanks have strong incentives to adopt continuous, risk-based session monitoring. They have also learned that adding more friction at the front door can punish legitimate customers without creating an equivalent obstacle for automated attackers.

A human has limited patience for another password, one-time code or identity challenge. Automated systems do not get frustrated. They can repeat processes at scale.

Businesses therefore cannot simply out-friction machines.

Sectors still anchored to one-time checks (particularly where knowledge-based authentication remains part of the security model) face a different challenge. In an environment where vast quantities of personal information have been compromised, static questions and credentials provide diminishing assurance.

A four-part framework for continuous trust

Just as the heart has four main parts, so too does the framework for treating identity as a heartbeat, not a timestamp.

Enterprise security experiences should be strong yet seamless, preventing disruption for low-risk customers while triggering proportionate intervention for anomalous behavior. For organizations looking to make that transition, the operational framework includes four parts:

1. Baseline at onboarding. The identity check at the front door becomes the calibration event. Capture the behavioral and device baseline when identity assurance is highest.
2. Monitor passively. Continuous does not have to mean intrusive. You can evaluate relevant behavioral and device signals in the background without repeatedly interrupting the customer to request more data. Identify the signals necessary to assess whether a session remains trustworthy.
3. Escalate proportionally. Customer experience cannot go out the window in the name of security. Tighten thresholds for small anomalies. Trigger session termination for hard ones. This way, you’ll increasingly avoid insulting valid customers by asking for more proof that they are who they say they are.
4. Hold the evidence. An evidentiary trail of risk signals and interventions can support investigations, audits and regulatory scrutiny. Ultimately, organizations must use this evidence to prove not only that a user passed verification, but that trust was maintained afterward.

As fraud moves beyond the capabilities of authentication, organizations must know not only who entered, but whether the digital heartbeat on the other side of the door still belongs to that person.

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“There's a sort of soullessness to them, and I find it sad that we appear to be increasingly happy to settle for that.”

I'm talking to Rob Biddulph, the children’s book illustrator and author who rose to prominence during the Covid pandemic when his #DrawWithRob YouTube channel racked up millions of views from children and parents needing an outlet for their creativity, about a very different kind of pandemic: the proliferation of AI-generated art.

Biddulph was named Illustrator of the Year 2025 at the British Book Awards, but today he’s talking about the troubling increase he's seeing in shops and cafes and other venues displaying posters that are clearly designed by AI.

“I think they're really depressing, to be honest," he tells me. "One of the things I love about illustration is that it's an expression of somebody's individual personality. Human beings can't help but put a bit of their own personality into a drawing or a design.

"Often, you can look at a piece of artwork and recognize the person who made it — see their quirks, their imperfections, their sense of humor and the decisions they've made along the way. To me, that's what makes it interesting.

“And I also think there's something particularly ironic about using AI to advertise a local event. Surely the whole point of a local event is that it's about people and community?”

AI is doing more than cluttering the high street

Biddulph has a point. Personally, I’d rather see a wonky poster created by a real person than another technically polished but utterly generic piece of AI artwork. But AI is doing much more than cluttering up the high street with slop art. It’s taking paid work away from human designers and illustrators — the irony being that those AIs have all been trained on the work of human artists, with no compensation paid to those artists.

“The thing to always remember is that these AI systems haven't appeared out of thin air,” explains Biddulph. “They've been trained on enormous quantities of existing creative work produced by artists, illustrators, photographers and writers who were never asked for permission for their work to be used and haven't been paid for it.

“And now those same systems are being used to produce work that competes directly with the people whose work helped train them in the first place! That's the bit I find particularly difficult to accept.”

Buddulph thinks there needs to be more of a reality check when it comes to talking about the creative industries in general. “I also think there's a tendency to talk about the creative industries as though they're made up entirely of wealthy, successful people," he says. "They're not. They're made up of thousands upon thousands of freelancers, small studios, illustrators, photographers, designers and writers, many of whom are already working in incredibly precarious circumstances.

"For an established artist, the impact might be a reduction in commissions, but for somebody just starting out, it could mean that the opportunities to build a career simply aren't there in the first place”.

They haven’t won

A few years ago there was a lot of talk about regulating AI companies' use of artists' work to train their models for free; these days I don't hear so much about the subject. I asked Biddulph if the AI companies had effectively won, or if there was still a fight to be had.

“I certainly don't think they've won”, he responds, “although I can understand why it sometimes feels that way. The technology has moved so quickly, and the companies behind it have such enormous financial resources, that it can feel as though the horse has already bolted and the rest of us are constantly playing catch-up. But I don't think the argument has gone away. If anything, I think we're entering a really important phase of it.

“In the UK”, he explains, “there was a huge amount of opposition from the creative industries to the Government's original proposal, which would effectively have allowed AI companies to train on copyrighted work unless individual creators specifically opted out.

“The problem with that approach was obvious. It placed the responsibility on millions of individual creators to protect their own work, rather than requiring companies to seek permission in the first place. The Government subsequently stepped back. So I think it's important that we recognize that artists, writers, publishers and other creative organizations have made their voices heard, and have had an impact.”

Biddulph is in favor of an opt-in system. “My own feeling is that the principle should be fairly straightforward: if you want to use somebody else's creative work to build a commercial product, you should have to ask permission and pay for it. I don't see why artists and illustrators should be expected to accept a different set of rules simply because the technology is new. I'd like to see a proper opt-in licensing system, rather than an opt-out system.”

A fight worth having

“If an artist wants to make their work available for AI training, that's absolutely their choice," he adds. "They should be able to do so, and be paid accordingly. But that choice should belong to the artist. I also think we have to be careful not to confuse the inevitability of the technology with the inevitability of the way it's being developed and used.”

So does Buddulph think AI is here to stay, and that illustrators need to keep fighting for better rights?

“Yes, AI is here to stay”, says Biddulph. “That doesn't mean we have to accept every aspect of the business model behind it. Personally, I don't think this is a fight that artists can afford to walk away from, even if, at times, it feels hopeless. It's about more than protecting individual livelihoods. It's about establishing what we value as a society, and whether we believe that human creativity has an intrinsic value that deserves to be protected. And I think that's a fight worth having.”

You can find Rob Biddulph on YouTube, X (@RobBiddulph), Instagram (@rbiddulph) and Facebook (robbiddulphauthor). And at his website.

AI adoption has moved quickly. McKinsey’s 2025 global survey found that 88% of respondents say their organizations regularly use AI tools in at least one business function, up from 78% the previous year, yet only 7% say AI is fully scaled across the organization, with 30% remaining stuck in the piloting phase.

For marketing teams, this gap becomes even more obvious when an AI pilot meets the complexity of the existing data stack. A pilot can demonstrate what an AI model is capable of when it has a defined task and carefully selected data, but moving that capability into a live enterprise environment is often considerably harder.

The issue often sits beneath the model itself. Marketing data is spread across platforms, warehouses and internal systems, with different structures and definitions making it difficult for AI to establish what the numbers actually mean.

The pilot works until it meets the real world

There is a reason AI pilots can look so convincing at the beginning. The scope is usually narrow, the data is easier to control and the questions being asked are relatively straightforward.

Production however introduces a new and different level of complexity. An AI system working across an enterprise marketing stack for example may encounter several versions of the same metric, with different field names and different rules for how they should be calculated. Without a governed understanding of that environment, the model can act like a black box and make its own assumptions.

Cost is a simple example. One platform may store it under one field name while another uses something completely different. To a human who understands the organization's data, the distinction may be obvious, but to an AI model operating without that knowledge layer, the first plausible match can look like the right answer.

This lack of clarity creates a problem because the output you then receive can sound completely credible. AI can produce a confident answer from an incorrect assumption, leaving businesses with insights that looks useful until it reaches a business decision.

Data foundations need a knowledge layer

Improving data quality is therefore only part of the challenge. AI also needs to understand the meaning attached to that data.

For marketing teams in particular, this means establishing canonical definitions for important concepts and mapping those definitions across the different platforms where they appear. It also means capturing the rules the organization uses to measure performance, so that AI is working from the same framework as the people responsible for interpreting its outputs.

A knowledge layer provides that foundation between the data warehouse and the AI working with the data and encode how metrics should be interpreted, which fields correspond across platforms and which business rules need to be applied before an analysis is considered reliable. Such layers sit on top of existing data warehouses, meaning that organizations don’t have to make costly investments in replacing their infrastructure.

The key is giving AI a defined starting point. Instead of selecting whichever version of a metric it encounters first, the system has a governed understanding of what that concept means within the organization and can interpret it correctly.

Context needs to reflect what is happening now

Static definitions alone are not enough though. Marketing performance changes alongside the business, which means AI also needs access to the context surrounding a particular investigation.

A campaign may look as though performance has deteriorated, but actually the underlying reason for is a recent budget change. A sudden movement in results could also coincide with a new promotion or a live market test. Without that information, an AI system can identify the change accurately, but still reach the wrong conclusion about what caused it.

This distinction can be reflected by separating persistent marketing knowledge from context that is resolved for each investigation. This is where collaboration between marketing and data teams becomes important. Marketing leaders understand the objectives behind campaigns and how performance should be interpreted, while data leaders understand the systems, structures and controls through which that information is managed.

Bringing those perspectives together creates a more useful foundation for AI. Definitions can be agreed before they become embedded in automated workflows, while governance requirements can remain part of the analytical process rather than being introduced after an output has already been generated.

Production requires confidence, not another pilot

The next stage of enterprise AI adoption will depend on making these foundations operational. McKinsey's research shows that most organizations are still somewhere between experimentation and early scaling, despite the rapid growth in adoption.

The priority for marketers should therefore be to examine what sits around the model before adding another one. Are the relevant metrics consistently defined? Can AI understand how those metrics relate across systems? Does it know which business decisions are currently shaping performance?

A well-structured knowledge layer can provide the missing connection between enterprise data and AI reasoning. It gives models a governed understanding of the environment in which they are operating, reducing the assumptions they need to make themselves.

That changes the production question. Instead of asking whether a newer model is capable enough, organizations can focus on whether the AI has been given the knowledge it needs to make it work reliably.

The models will continue to evolve, but for businesses trying to turn AI investment into measurable value, the more immediate action is making sure those models have something dependable to reason from.

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For all the doom and gloom headlines AI is driving these days, one important person just gave a refreshing new take on it - none other than Linus Torvalds, the founder and key maintainer of the Linux kernel.

At the recent Open Source Summit, organized by the Linux Foundation in Prague, Czechia, Torvalds took the stage to reminisce about the early days of Linux, how he ended up building Git, but also how AI changed what it meant - and how it felt - to be a programmer today.

His key takeaway is that AI can make programming less intimidating for junior coders and teens looking to find their way in an otherwise scary, hyper-advanced industry.

“People love to hate AI. I think it's an interesting tool, very useful. It's a gateway drug and a way to do really good code,” he said, somewhat jokingly.

On AI and “sucking”

Speaking more about AI, Torvalds said that in his early days as a programmer, he made computer games which “sucked”, but it was fine because everything else also “sucked” back then.

“At the time, the bar was pretty low, right?,” Torvalds said, adding that he didn’t feel ashamed of his work when comparing it to what was already out there. However, younger coders walking into the industry today can easily feel ashamed of their “pitiful, small efforts”, when comparing themselves to all of the polished, professional work around them.

And that is exactly where AI can help, he feels. It can lower the bar to entry, so that those first steps don’t feel as meaningless and inconsequential as they otherwise might have.

“When you are getting into programming, the bar needs to be low enough that you can start the process and find enjoyment, so that you can easily learn. And it will take you a long time to learn,” Torvalds said.

But he also cautioned about using AI for “serious” work, especially when starting out. The person using the tool needs to know what they want to do, and need to know exactly what the outcome needs to be, if they want to use it properly.

Everything other than that is a risk that can come with grave consequences. So, it would be best to start with “toy” projects or personal projects, before using it for anyhitn more “serious”.

“I think that you need to be very careful using AI when you're doing something real and important. But I love the concept of AI as a gateway drug into programming,” he concluded.

Fighting fire with fire

Torvalds also touched on AI-generated vulnerability reports and patches. In mid-May 2026, he described the Linux mailing list as being “almost entirely unmanageable” due to it being filled up with useless AI-generated reports.

“The continued flood of AI reports has basically made the security list almost entirely unmanageable, with enormous duplication due to different people finding the same things with the same tools,” he said at the time.

“People spend all their time just forwarding things to the right people or saying "that was already fixed a week/month ago" and pointing to the public discussion”.

Torvalds now doubled-down on that assessment, saying the problem is that some people simply “don’t care”, and look for flaws in things no one even uses any more.

“We are getting a lot more of these random patches. Sometimes for very important and very real security issues, and sometimes for things that nobody has touched in 20 years, because people don't care.”

“When you put an AI bot on it, and ask it ‘Hey, is there a memory leak here?’, it will say ‘I don't care about the fact that nobody uses this driver anymore. I'll happily give you all the error reports that I can find’. As a result, the maintainers end up mixing “interesting” and “not interesting” patches.

“But on the whole, I think it is improving our code base,” he concluded.

The cost of that improvement is maintainer stress.

“It is also stressing maintainers out to the point that it's a problem,” he stressed (pun intended). “Yesterday at the maintainer summit, I'd say three quarters of the discussions were about how to make the AI tools - both code generation and the review - less stressful and more successful and more useful for everybody.”

“This is clearly a big issue.”

The solution, ironically, is not “less AI”, but quite the opposite - more AI.

“We are, to some degree, fighting the AI deluge of patches with our own AI deluge of patch reviews,” Torvalds said. “People are jokingly saying that there are bots talking to bots, and it does actually happen.”

Banks spend heavily to convince customers they are the most careful custodians of personal and financial data.

Our review of 14 financial-services websites across Europe and the US found otherwise: tracking and personalization scripts embedded in account-opening, mortgage, and loan-application flows sent contact details, financial intent, and device fingerprints to third parties, with 9 of the 14 sites doing so without a valid consent choice in place.

The failures fall into three patterns, and the distinction between them matters legally.

First, tags fired before the cookie banner had been answered at all, on wealth-management, investment-banking, and payment-provider sites; under the EU's ePrivacy Directive, that behavior never had a lawful basis, since consent is required before anything is stored on or read from a device.

Second, tracking continued after a user actively rejected cookies: at one site, Google Ads and DoubleClick still received the user's hashed email in the request URL alongside the consent-denied signal, meaning the rejection was recorded and then ignored.

Third, financial specifics leaked regardless of consent status: a loan amount of €2,500, a 12-month term, and an insurance selection reached Google Analytics during one personal credit application, and at a Portuguese bank, a customer's name, age, and tax number were sent to Evergage as Base64-encoded text in a request URL during account opening.

At a Dutch banking site, a first-party script combined browser fingerprinting with image requests to 127.0.0.1 on ports 7070 and 5938, the ports associated with AnyDesk and TeamViewer, effectively checking whether remote-access software was running on the visitor's own device.

Where the responsibility lies

Meta and TikTok have each pointed to the website operator as the party in control of what gets collected, in response to earlier research on this topic. Meta cited its privacy controls and its policy against sharing sensitive data. TikTok said advertisers decide what events and parameters they send, and that it only receives what partners intentionally configure.

That framing puts the responsibility entirely on the website operator, and it only holds up if the collection was something the operator deliberately turned on. Often it was not. Meta's Automatic Advanced Matching feature is turned on by default on the Meta Pixel, and in that default state it captures and hashes contact-form data without any separate configuration step from the site owner.

A bank engineer adding a standard tracking snippet is not choosing to send a mortgage applicant's hashed email and phone number to Meta; the pixel does that by default. A policy against collecting sensitive data is difficult to reconcile with a feature that collects it by default.

That does not remove the bank's own responsibility. Banks choose which vendors to deploy, place the consent banners in front of customers, and publish cookie policies that are supposed to name every entity receiving that data. Both sides carry some of the blame: platforms ship defaults that maximize data capture, and banks deploy those defaults into some of their most sensitive flows without validating what they actually do at runtime.

Closing the gap

This is a compliance question as much as an ethical one.

In the EU, unmanaged third-party scripts in customer-facing flows sit across GDPR and ePrivacy consent requirements and, for regulated financial entities, DORA's third-party risk and operational resilience rules; PSD2 adds its own security obligations for payment flows. In the US, institutions carry duties under the Gramm-Leach-Bliley Act's Safeguards Rule, and increasingly under state laws such as the CCPA/CPRA. Regulatory exposure aside, this is basic operational hygiene.

Fixing this starts with verifying runtime behavior rather than assuming it matches what was configured. That means validating what scripts actually do on live account-opening, mortgage, loan, and simulation pages, since these carry the highest-value data, rather than stopping the checks at the homepage. It also means watching for a tag that starts collecting more than it was originally set up to collect, sometimes called scope creep.

Visibility needs to lead to control. Security teams need the ability to stop third-party scripts from reading sensitive form fields at all, and to block unauthorized data transfers, including the common technique of hiding identifiers and financial details inside a request URL rather than a request body, which is how several of the leaks in our research reached their destination.

Consent needs to mean something in practice, not just on paper. A rejection should stop data from leaving the browser instead of merely getting logged as a denied signal while the tracking call fires anyway. That choice also needs to travel with the customer into any iframe or subdomain the flow touches, rather than resetting at the first boundary it crosses.

Features such as Automatic Advanced Matching, which hash and send contact data by default, should be switched off unless that collection is documented, disclosed, and lawful. First-party code deserves the same scrutiny as third-party tags: fingerprinting and local port-probing built for fraud prevention can be a legitimate control, but building it in-house does not remove the need for disclosure and consent.

None of this requires banks to abandon analytics or personalization. It requires treating scripts on sensitive pages as part of the institution's risk surface, reviewed on the same cycle as any other vendor, rather than configured once and left alone. Nine of the fourteen sites we examined were sending data through a channel their own consent banner said was closed.

That gap, between what a script is allowed to do and what it actually does, is worth closing before a regulator or a customer finds it first.

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• A significant number of developers consider themselves to be suffering fatigue or burnout, study finds
• Devs are also concerned about the ethics of AI, and fear being replaced by automated coding tools
• The Stack Overflow Developer Survey compiled responses from 30,000 developers from 169 countries

A new industry survey exploring the state-of-developers has uncovered growing distrust in AI and concerns about fatigue and burnout. While the overall response to the increasing use of AI is largely positive, growing concerns about the technology are clear, with little trust in the technology to make important decisions.

Stack Overflow’s annual survey is a useful look at how developers feel about the industry they work in, and highlights a growing friction between traditional software development and the growing role of AI tools.

The survey, by the Stack Overflow coding question-and-answer platform, also highlights a growth in solo employment, with 10.5% of respondents stating they freelance or are sole proprietors of a business, up from 3.9% in the previous years’ survey.

Developer burnout

One key takeaway from the survey is that 45.1% of developers state they are “complacent” in their current roles, with around a third (32.6%) of respondents describing themselves as “unhappy” (just 22.3% are “happy.”) Of that 45.1% figure, burnout or fatigue were cited as the main cause (21.1%).

While remote work is still popular (34.8% of developers work wherever), the survey surfaces unclear, ambiguous priorities as a challenge, something that may inform general fatigue – or, indeed, feelings about AI.

Although many developers use AI (and search) to solve problems, its growing use within coding is being met with caution. Some 17.2% of developers don’t use AI tools, while assistants and agents are employed by 65.9%. But while developers are keen to learn new skills (52% have learned a new coding language in the past year), they are wary of AI’s consequences, with 17% concerned that it may replace them.

Critical skepticism

A read of the full survey paints a picture of an industry of skilled professionals finding their way in a new, unpredictable landscape – one that their own industry is responsible for.

Prashanth Chandrasekar, CEO of Stack Overflow says: “Developers are historically a highly skeptical group, and over the last year, as AI agents become rapidly more autonomous, that skepticism now acts as a critical safeguard.”

“As agents take on more work, developers need to know where an answer came from, what context informed the information that was produced or a decision that was made, and the most crucial piece — that they can actually verify its accuracy and relevance.

For those concerned about burnout and fatigue and their long-term careers, trust is perhaps the answer. “Trust is not a given when it comes to developers,” says Chandrasekar. “It is earned through transparency, source attribution, and rich technical context.”

OpenAI really likes to drop stealth upgrades to ChatGPT onto us with no warning. Sometimes these are really minor, but the latest one adds an entirely new way for it to respond to your queries, and utilizes the power of GPT-6.

Instead of just providing the usual text-based answers to your queries, you now get text, visuals, and interactive elements all mixed together in whatever way best suits the answer. The interface for typing into ChatGPT looks exactly the same, but the answers you get are a lot more fun.

In a way, it’s a little bit like ChatGPT now makes a simple app, on the fly, to answer your question. But don’t let the word “app” here put you off — this isn’t overly difficult or just for developers and coders.

The clever thing is that you don't need to know anything about making apps, or even ask ChatGPT to make one. You simply ask a question, as you've always done, and now you'll find yourself looking at an interactive calculator, a quiz or a product comparison instead of a wall of text.

The best way to understand it is to give it a go. Here are five ways I used the new Intelligent UI, and you can try them all yourself as well. Once you’ve done all five, you’ll have a good understanding of what it's capable of.

Intelligent UI is available for all users in the standard Chat tab, uses GPT-6, and is available in all the different membership packages, including Free.

1. Savings calculator

How much you need to save for retirement is one of those questions that starts to become more important after the age of 50, and it’s perfect for an interactive answer. For this test, I used the prompt “I'm 55 and save £500 a month. How much can I save for retirement?”

To answer me, ChatGPT produced a simple retirement calculator app that lets me change variables like retirement age, monthly savings, and assumed annual growth to see how much I’d end up saving. For Retirement Age and Monthly Savings, it presented sliders, so I could just scale things up or down with ease. It’s a great way to interactively experiment with figures.

2. Exploring how a seven-speed bike works

Rather than just telling you how something works, ChatGPT can now show you. For this test, I just used the simple prompt, “Show me how the gears on a seven-speed bike work”.

To answer me, ChatGPT produced a great illustration of the drivetrain of the bike, with a slider bar that I could use to effectively change gear. It was a simplified model, rather than an accurate rendering. The rear sprocket changed size as I selected a different gear. It was actually a perfect visual explanation, and the interactive element helped make the process nice and clear. The answer was also packed with details like the number of teeth on a gear and the gear ratio.

3. Learning Japanese

I’ve just started learning Japanese, so I asked ChatGPT to help me with hiragana, its most fundamental Japanese writing system. I used the simple prompt “Help me learn hiragana”, and it introduced me to the first 5 vowel sounds — a i u e o — showing me the hiragana character for each, with tips on how to pronounce it, and then jumped into a handy interactive quiz where I had to guess the character.

What I liked was that the quiz started off with just the first five characters, then offered to teach me the next five when I was ready. I can see this being a really useful study aid.

4. Asking for a 3-day itinerary

I asked ChatGPT for a three-day Tokyo itinerary. I wanted to see if I could change the budget, interests, or available time without starting from scratch.

“Give me a three-day Tokyo itinerary. Budget is £3,000. I’m interested in seeing ancient temples. Starting in late November.” This produced a reply with a lot more images than I’d been used to seeing and an interactive map, but nothing really interactive.

It was not until ChatGPT asked me if the budget included flights, and I replied, “The budget does not include flights — can you let me adjust the budget?” that it produced a truly interactive itinerary. By adjusting the budget slider, I could see how all the different elements of my trip were impacted. There was also a button to press to see the updated itinerary based on the new budget.

This example showed how you can ask for interactive elements in your answer, even if ChatGPT doesn’t give them to you the first time.

5. Comparing products

I asked ChatGPT to compare three laptops. I wanted to see if I could adjust the importance of battery life, weight, and price and see whether its recommendation changed. I used the prompt:

“Compare three of the most recent laptops, including a Chromebook. I want to see if I can adjust the importance of battery life, weight, and price and see whether your recommendation changes.”

This was a really useful implementation of ChatGPT’s new interactive abilities, and a much better way of comparing laptops than I’ve ever tried before. ChatGPT selected an Apple MacBook Air 13-inch M5, an ASUS Zenbook S14 UX5406, and a Lenovo Chromebook Plus 14" MediaTek Kompanio Ultra 910 in an interactive product companion that let me change the importance of battery life, weight, and price. Seeing how the recommended laptop changed when I played with the sliders was a great way of experimenting.

ChatGPT is becoming something different

That last example — the one showing laptop recommendations — really showcases the brilliance of ChatGPT’s new interactive elements in its replies. Instead of reading a recommendation and wondering how it might change if I cared more about battery life than price, I could find out for myself, simply by moving a slider.

Other AI chatbots can produce interactive experiences too, but what's impressed me about Intelligent UI is how naturally OpenAI has incorporated them into everyday ChatGPT conversations. You don't need to know how to code, or even understand what's happening behind the scenes. You just ask a question.

There are a few things worth bearing in mind. ChatGPT doesn't always produce an interactive answer automatically, and sometimes you need to specifically ask for one. Also, just because an answer comes with an impressive-looking calculator or interactive graphic doesn't mean the underlying information is necessarily correct. The laptop comparison, for example, included an estimated battery-life figure, so I'd still want to check the specifications before spending any money.

There might not have been much fanfare about the introduction of Intelligent UI, but it feels natural and intuitive to use. Now I know ChatGPT can be so interactive in its responses, and I’ve seen how useful it can be, I’m going to start writing prompts that assume it can be interactive in its responses more often. Intelligent UI is one of the best ChatGPT upgrades I’ve seen in a while.

• AI has replaced entry-level work, so graduates are being hired to manage AI
• Goldman worries about its impacts on existing middle managers
• "We don’t quite know what’s going to happen to that group"

Goldman Sachs exec Kevin Sneader has shared (via Bloomberg) how he believes AI could change jobs in banking, and it doesn't necessarily mean that junior workers will be at a loss for jobs.

Instead, Sneader believes that new employees will take on management responsibility immediately, rather than waiting years for promotions.

Previously, young workers would have spent the first few years performing administrative work before progressing into management positions, but rather than progressing into managing humans, junior workers will now start off managing AI.

Junior bankers set to manage AI from day one

With agentic AI now capable of carrying out the routine, administrative tasks that junior workers used to do, entry-level roles will no longer be available. But that doesn't mean there's no room for new graduates – it just means that they'll need to oversee the AI doing those jobs.

"When our young folks now start work, they’re managing agents," he said. "They have to make the most of this virtual army that they’ve now got."

However, there is potentially one group of workers at risk from AI's impacts, and it could be middle management. In short, graduates being employed to manage AI could threaten the job security of middle managers, who would previously have looked after those graduates.

"We don’t quite know what’s going to happen to that group," Sneader added.

More broadly, it could mean that AI challenges the pyramid-like ladder that sectors like banking have previously worked to. If businesses no longer need large numbers of entry-level workers thanks to AI, it means they need fewer middle managers and fewer leaders, which could indeed have adverse impacts on jobs – just not for graduates.

The takeaway from this particular message isn't one of graduate unemployment, but rather a broader shift away from traditional working patterns and how it could impact the more experienced workers.

Cyber resilience isn't measured in firewalls. It's measured in minutes.

Time has quietly become the biggest driver of cyber costs. For years, we've measured cyber security success by what organizations could stop. How many attacks were blocked? Which vulnerabilities were patched? How much was invested in the latest security tools?

They're no longer the questions that matter most.

It isn't a groundbreaking observation to say the threat landscape has changed. Attackers are moving faster, AI is accelerating both offensive and defensive capabilities, and businesses are becoming more digitally connected than ever before. If we accept that no organization can eliminate cyber risk entirely, then our definition of resilience has to evolve too.

The organizations that emerge strongest aren't necessarily the ones that avoid being breached. They're the ones that detect, respond and recover fastest.

Our latest Grant Thornton Finance Leaders Barometer found the average financial impact of cyber incidents now stands at £488,000 over a two-year period, with 82% of CFOs reporting they've experienced a cyber incident with direct financial consequences. Crucially, the cost of an incident is increasingly determined not by the breach itself, but by how quickly leadership teams understand what's happened, make decisions and restore operations.

Every hour spent identifying the issue, establishing ownership or waiting for decisions and ensuring notifications are made to meet regulations, compounds operational disruption, financial loss and reputational damage. Increasingly, organizations aren't judged on whether they experience a cyber-attack. They're judged on the quality and speed of the decisions made in the aftermath.

Every cyber incident eventually becomes a board meeting

In the immediate aftermath of an incident, the conversation shifts away from compromised systems and malicious code towards operational disruption, financial impact and business continuity. Boards aren't asking which vulnerability was exploited or whether identity controls failed. They're asking how much it's going to cost, how long operations will be affected, whether customers, supply chain and regulators need to be informed, and what happens next.

This creates an uncomfortable tension for many organizations. Cybersecurity is fundamentally about risk management, spanning people, processes and technology. While many of the security controls used to mitigate cyber risks are technology-led, they are underpinned by organization-wide processes, policies and decision-making.

Accountability therefore extends well beyond technology teams, with finance leaders and boards increasingly expected to understand and make high-stakes decisions around cyber risk. Yet our research found that 79% of CFOs say ownership of key business risks remains unclear across their organization – a particularly significant gap when speed is critical and decisions can carry financial, operational and reputational consequences.

The organizations that respond best are those that have already decided who owns risk, who makes decisions, how issues escalate and what level of risk the business is prepared to accept. In other words, resilience isn't just about having the right controls in place – it's about ensuring the right conversations have happened long before an incident occurs.

Cyber language doesn’t translate to the boardroom

If organizations are going to make better decisions about cyber resilience, they first need to change the conversation. One of the biggest barriers isn't a lack of investment, it's a lack of translation. Cyber teams naturally talk about identity management, endpoint detection or upgrading legacy infrastructure. All important priorities, but they're not the language boards and CFOs use to make investment decisions.

Finance leaders are asking different questions. How does this protect revenue? What operational disruption could it prevent? What's our potential financial exposure?

How does this reduce regulatory or reputational risk?

The organizations making the most progress are those that can bridge that gap. Rather than presenting cyber as a technology upgrade, they frame it as a resilience investment – protecting business continuity, customer trust and shareholder value. That shift in language changes the conversation from "How much does this cost?" to "What would it cost us if we didn't do it?"

Ultimately, cyber investment shouldn't be justified by the sophistication of the controls being deployed. It should be measured by the business outcomes it enables; reducing downtime, improving recovery, protecting critical operations and giving leadership teams confidence they can respond decisively when disruption occurs.

With the cyber landscape evolving rapidly, it can be difficult to predict exactly where investment will be needed one or two years ahead. Organizations therefore need enough flexibility within budget cycles to adapt investment and controls as new threats emerge, rather than being constrained by priorities set at the start of the budgeting period.

AI is shrinking the time frame for good decisions

Artificial intelligence is changing cyber security in two directions at once. Attackers are already using AI to identify vulnerabilities, automate reconnaissance and increase the speed of attacks. At the same time, recent advances in frontier AI models have demonstrated just how quickly AI tools can uncover software vulnerabilities at a scale previously impossible for human teams alone.

The result isn't simply a more sophisticated threat landscape – it's a faster one.

Leadership teams have less time to understand what's happened, assess the commercial impact and make informed decisions. The organizations that respond well won't necessarily be those with the most advanced AI capabilities. They'll be the ones that have already agreed who makes decisions, how incidents escalate and what business priorities take precedence before an attack occurs.

The real measure of cyber security

Organizations need to rethink what good looks like. The goal is no longer to create the illusion that cyber risk can be eliminated; it's to build confidence that the business can make good risk based decisions when disruption inevitably occurs.

That requires more than better technology. It requires clearer ownership, stronger governance and a shared understanding of risk across the organization. In many ways, cyber has become the clearest test of organizational resilience, not because attacks are inevitable, but because they reveal how well leadership teams perform when certainty disappears.

Ultimately, the organizations that come through incidents strongest won't necessarily be those with the best security tools. They'll be the ones whose leaders have already had the difficult conversations, made the key decisions and built the confidence to act when it matters most.

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Despite living in an age of seamless connectivity, our workplaces have never been more disconnected.

Not literally, of course. We are surrounded by relentless barrage of message pings, overflowing email inboxes, and the ability to speak to anyone, anywhere, instantly. But beneath this surface-level digital noise is a crisis: winning and retaining people’s attention.

According to a recent Gallup poll, global employee engagement has cratered to just 20%, its lowest level since 2020. To some this may seem like a soft HR metric, but it is in fact a hard financial reality with a tangible economic impact. In 2025 alone, this engagement deficit is estimated to have erased $10 trillion from the global economy in lost productivity.

These figures tell us that traditional corporate communication is failing. If you can’t command the attention of your employees, you can’t execute strategy, align culture, or drive transformation. The employee engagement deficit is a challenge that leaders must address to compete in the modern enterprise ecosystem.

To tackle this problem head on, businesses need to stop thinking like traditional corporate communicators and take inspiration from an industry that has been captivating audience attention for almost a century: broadcast media.

Creative, captivating corporate communication

Professional broadcasters have always operated under the foundational truth that if you want an audience to retain information and connect with a message, the delivery must be as visually compelling and dynamic as possible. Broadcasters spend millions on high-production set designs, real-time data graphics, and seamless visual transitions because they know that the moment a screen becomes static, viewer attention starts to wane.

Yet, in 2026, many of the world's largest enterprises still communicate their most critical messages using flat, text-heavy slide decks and passive, one-way video presentations.

This goes further than just being uninspiring, and in the modern hybrid working environment actually constitutes a business risk. In a remote and hybrid work environment, employees are not sitting in an auditorium with their eyes locked on the stage. They are at their desks, surrounded by competing digital stimuli. A static slide deck is an invitation for an employee to open another tab, check their phone, or clear their inbox.

To win the battle for attention, enterprise communications must evolve from basic information delivery into immersive visual storytelling.

The economics of engagement

The economics of engagement refers to the relationship between how much content costs to produce, how effectively it connects with the audience, and what value that connection actually generates. For the pragmatic technology buyer, the first question that comes to mind is “does high-quality visual storytelling deliver a tangible return on investment, or is it merely an expensive aesthetic upgrade?”

Today, businesses can look at ROI through engagement and business outcomes. Firstly, technology has allowed businesses to gather more accurate data on metrics such as active viewing duration and number of Q&A interactions. This means that organizations can have a clearer picture of whether their communication is actually reaching and engaging employees.

Keeping employees invested in corporate communications is also about more than just making sure they are paying attention during a townhall presentation. These meetings are usually where businesses unveil a new strategy or direction for the overall organization, and these can often be complex.

If ideas aren’t communicated clearly, employees can leave with different interpretations of what they have heard, which can lead to inconsistent action. Creating more engaging presentation and making sure the message lands properly is essential for allowing business leaders to execute their overall strategy.

Another key ROI point lies in integrating broadcast capabilities directly into existing enterprise workflows. Rather than requiring employees to adopt new, proprietary viewing platforms, modern systems inject high-production-value graphics, dynamic lower-thirds, and multi-source video feeds directly into the collaboration tools employees are already using, such as Zoom.

By elevating the tools already in place, businesses can expect higher adoption and lower friction, maximizing the value of their existing software licensing.

AI-enable scalability, without specialization

Until recently, the barrier to achieving broadcast-quality communication was very high for most businesses. It required a dedicated physical studio, expensive hardware, and a fleet of broadcast engineers with specialized knowledge. However, AI has broken these barriers down and democratized access to broadcast-level production.

Today, intelligent software can now take on many of the technically complex elements of production, from managing layouts and multi-camera switching to generating real-time closed captions and incorporating real-time graphics. This means that much smaller comms teams can produce a live broadcast that matches the production value of a cable news network, for example.

A business shouldn’t need a production crew every time they want to deliver a high-quality update. By removing the need for specialized engineering and creative talent, enterprises can scale custom, high-impact communications across the entire business.

The technology should sit in the background and allow the global division lead can host a polished quarterly update, or a sales director can deliver an interactive pitch, all while maintaining a consistent brand identity.

A creative solution to the engagement problem

The numbers don’t lie. The $10 trillion productivity drain caused by disengaged workforces is a clear signal that the old ways of corporate communication are no longer fit for purpose.

As we look further into 2026, the competitive divide between organizations won’t just be defined by their products or strategies, but by their ability to align and inspire the people behind them. The businesses that continue to rely on flat, uninspiring mediums will continue to lose the war for attention.

Conversely, the enterprises that embrace the tools of modern broadcasting, democratized by AI, secured by robust IT governance, and integrated into daily workflows, will transform their corporate communications from a passive exercise into a driver of strategic alignment, culture, and measurable business growth.

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When Anthropic's threat intelligence team ranked a year of AI-enabled cyberattacks, the most dangerous operation in the dataset didn't stand out by the measure the security industry has relied on for years.

Mapped against MITRE ATT&CK, the state-sponsored espionage campaign Anthropic disrupted in November 2025 used 30 techniques across 13 tactics, comparable to many medium-risk actors in the same dataset. Against Anthropic's own risk methodology, the same campaign scored the maximum of 100.

The fact that the highest-risk actor looked unremarkable by technique count exposes a weakness in conventional detection thinking. Technique breadth alone tells defenders relatively little about risk. More useful insight comes from understanding how techniques are combined, sequenced and executed over time.

The indicator was always the fragile part

Security operations spent two decades getting very good at recognizing evidence an attacker had already used. Malware hashes, malicious IP addresses and suspicious domains gave teams a practical way to identify known threats and block repeat attacks. Once an attacker exposed part of their infrastructure, thousands of organizations could benefit.

David Bianco's Pyramid of Pain outlined this weakness in 2013. The indicators defenders find easiest to consume are also the cheapest for attackers to discard. A hash can change when you modify a file, while an IP address can simply be replaced. The techniques and procedures an attacker relies on to reach an objective are far harder to change.

Generative AI has made indicator-based detection more vulnerable by driving down the cost of variation. Code-generation tools can produce new malware variants quickly, phishing content can be created at scale, and offensive tooling can be assembled with far less specialist effort. Defenders increasingly work with indicators whose useful life may be shorter than the process required to identify, publish, and act on them.

None of which retires the IOC feed. It remains valuable for blocking known-bad in volume, enriching investigations and hunting retrospectively once a campaign becomes public. What it can no longer do is carry the weight of the detection program.

Individual techniques are weak signals

Anthropic’s study examined 832 accounts banned for malicious cyber activity between March 2025 and March 2026, mapping 13,873 observed actions across 482 techniques and all 14 ATT&CK tactics. The accounts represent a subset of total bans with enough detail for thorough assessment.

There was little correlation between an actor's skill and the number of techniques they used: the least capable actors in the dataset averaged around 16 distinct techniques, the most capable around 20. The platform an attacker worked through made no difference either.

That tracks with how enterprise environments actually behave. Account discovery appears in attacks, and also in legitimate administration. Remote services enable lateral movement and routine infrastructure management alike. Almost every individual technique requires interpretation before it supports a conclusion.

The information appears when activities connect. Account discovery, followed by credential access, followed by movement into another system and data staging within a compressed window, describes something the same activities spread across several days of routine work do not. Sequence becomes part of the detection logic, along with the identity involved, the systems touched, and what happened immediately afterwards.

AI is moving deeper into the attack

AI introduces another consideration: the speed at which an attack unfolds. Automation can compress the time between actions, changing how otherwise familiar activity should be interpreted.

Human-led attacks have always used some degree of automation, but an operator still has to interpret results and decide what to do next. As AI systems take on more of that decision-making, the time between stages of an attack can shrink considerably. That makes the speed and sequence of activity useful information in its own right.

Anthropic's data shows where this pressure is being applied. Across the study period, AI use shifted away from gaining access and toward what happens after. Use of AI tools for account discovery inside compromised environments rose 8.9 percent while AI-assisted phishing fell 8.6 percent. Post-compromise techniques that once demanded real expertise are being performed on behalf of less capable actors.

The proportion of actors Anthropic classified as medium risk or higher rose from 33 percent in the first half of the study to 56 percent in the second, roughly a 1.7-fold increase in twelve months.

Speed alone proves nothing. A sequence plausible over several hours from an administrator carries different weight when the same actions cross multiple systems in minutes. Time belongs in the detection model alongside the activity.

This is the thinking behind APEX, or Adversarial Pattern Extraction and Correlation, a behavioral detection framework developed around the Anthropic findings. Rather than treating an individual ATT&CK technique as the unit of detection, APEX looks at combinations of techniques, the order in which they occur, the time between them and the entities involved.

The premise is that these behavioral chains provide a more durable basis for detection than the individual indicators or techniques attackers can change more easily.

The frameworks have not caught up either

Anthropic's research also exposes a limitation in the frameworks used to describe attacker behavior. MITRE ATT&CK can map the individual techniques used during an intrusion, but it does not currently capture the agentic orchestration that links those techniques together.

An AI system that interprets results, selects its next action and moves through multiple stages of an attack with limited human involvement is therefore difficult to represent within the existing framework. Anthropic is now working with MITRE on how that behavior could be incorporated.

For security leaders, the practical consequence arrives sooner than any framework revision. Recognizing a behavioral chain means correlating evidence across endpoints, identities, networks and cloud platforms, each producing telemetry in a different shape.

That is a substantially harder data problem than checking a value against a list, and it is worth asking whether the telemetry required to see those chains is available, correlated and timely, rather than assuming volume will suffice.

What attackers cannot change

AI makes an attack's observable features easier to change, but the underlying operational requirements remain relatively stable. An attacker seeking to steal data still needs to gain access, understand the environment, obtain sufficient privileges and move information out. The tools and infrastructure may vary, but those requirements are much harder to eliminate.

Bianco pointed to the industry at the top of the pyramid more than a decade ago, and the principle has held. Frameworks such as APEX are now attempting to turn that principle into practical detection logic by focusing on the sequence, timing and context of attacker behavior. AI has made that work considerably more urgent.

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• Four in five say AI has or will create jobs in the next two years
• Two in five are still worried about AI replacing them
• Managers are more likely to use AI agents

A new HP survey of nearly 20,000 desk-based workers globally has revealed that nearly half (46%) of Brits believe AI has positively affected their lives, with one in two (52%) using employer-provided AI tools daily or weekly – up from 38% last year.

But rather than replacing workers, three in five UK business leaders say AI adoption has actually created new roles – or even entire teams – within their organizations.

Additionally, an extra one in five (19%) expects AI to create new roles over the next one to two years, totally countering previous narratives that AI would wipe out human workers.

HP data shows that AI could actually be creating jobs

On the flip side, just 10% say AI is primarily replacing or reducing roles – and this isn't just a trend that's native to the UK. It aligns broadly with the global average, with 59% of business leaders and 62% of IT decision-makers (ITDMs) reporting job creation.

Agentic AI use is also on the up, with 45% of UK desk-based workers now opting to use increasingly automated systems that would previously have been seen as a threat to their livelihood. However, HP's data does align with other recent research to reveal an organizational disparity – agent usage falls when a worker becomes less senior. Just 38% of knowledge workers use agents, compared with 56% of business leaders and 71% of ITDMs.

"For businesses, the priority now is to help people seize those opportunities by investing in the skills, tools and development they need to adapt and grow," HP UK&I MD Ruth Patterson explained, noting the ongoing shift in job roles.

"Work is changing rapidly, but this year’s findings show that people are adapting with it."

Still, despite the largely positive findings, 38% of UK knowledge workers still worry that AI could replace their roles, with a near-identical number (37%) also worried that they could be replaced by somebody who knows more about AI.

The next test for AI is not whether it can produce an impressive pilot. It is whether it can work reliably, every day, in the messy and high-stakes conditions of the physical world.

Companies are prepared to spend. BCG’s 2026 AI Radar found that corporations expect to more than double AI spending this year, from 0.8% to approximately 1.7% of revenue. But the value is not keeping pace. According to PwC’s 2026 AI Performance Study, 74% of AI’s economic value is being captured by just 20% of organizations.

The constraint is no longer ambition or budget. It is execution.

For physical AI, that challenge is especially acute. Unlike a tool that drafts an email or summarizes a document, physical AI operates where decisions affect safety, uptime, service delivery and cost. Moving it beyond the pilot phase requires more than a capable model.

Organizations need to choose a problem worth solving, give AI the operational context to understand it, design the technology around how work actually gets done and connect every insight to an appropriate action.

Start with the operational problem:

Much of the enterprise AI conversation has focused on large language models and office productivity. But some of the most consequential and costly business problems exist beyond the office: vehicle collisions, equipment failures, unplanned downtime and disruptions to the essential services people rely on.

Physical AI brings intelligence into these environments by using data from vehicles, equipment, cameras and sensors to understand what is happening and determine what needs attention.

In fleet safety, for example, AI tools can analyze information from connected vehicles and cameras alongside factors such as weather, road conditions and driving patterns. That context helps an organization distinguish an isolated event from a broader pattern and focus its attention where it can have the greatest impact.

Interest is already substantial, but deployment remains limited. In robotics, one of the most prominent applications of physical AI, Capgemini found that 79% of organizations are engaging with the technology, while only 27% are deploying or scaling it.

The right starting point is therefore not, “Where can we use AI?” It is, “What operational outcome do we need to change?” What is causing the problem? Is the necessary data available? What decision or workflow should the technology improve? A pilot without clear answers may demonstrate technical capability without proving business value.

Build the operational context AI needs:

Selecting the right problem is only the beginning. To move beyond a controlled pilot, AI must understand the context in which an organization operates.

Knowing a vehicle’s registration or model is not enough. AI may also need its maintenance history, current location, typical route, driver behavior and operating conditions. It needs to understand how those factors interact and what normal performance looks like across comparable situations.

When that information is fragmented across different systems, delayed or recorded manually, AI sees only part of the operation. Digitizing workflows and connecting operational data create the foundation that more advanced systems depend on.

The significance is not simply the volume of data. It is that the data is available in a shared operational system through which teams can manage vehicles, maintenance and customer commitments. Without that common foundation, even a sophisticated model risks becoming another disconnected tool.

Design for the operation, not the demonstration:

A pilot can be carefully managed. A scaled deployment cannot depend on constant intervention from a technical team.

Physical operations are distributed across vehicles, worksites, warehouses and frontline teams. Technology must be straightforward to install, reliable in changing conditions and connected to the systems employees already use. It must also work for the people expected to act on its output.

That makes change management part of the technical strategy. Employees need to understand what the technology is identifying, how it supports their work and what action is expected of them. Leaders need to establish ownership, measure whether workflows are changing and incorporate feedback from the frontline.

Governance must be designed into the deployment as well. Organizations should define which actions systems may take autonomously, when human review is required and how decisions can be understood or challenged. The level of oversight should reflect the potential consequences of the action.

Close the loop from insight to action:

An insight is not an outcome. Physical AI creates value when it shortens the distance between identifying a problem and doing something about it.

This can be understood through a sense, decide and act model. Connected vehicles, cameras and equipment sense what is happening in the physical environment. AI evaluates that information in context and decides what requires attention. A person or system then acts—whether that means alerting a driver, creating a maintenance work order or adding a damaged road to a repair queue.

A vehicle health warning, for example, can initiate a maintenance workflow and notify the relevant team before a fault becomes a breakdown. Pothole detection technology can use vehicles already travelling across a city to identify and assess road damage, giving public sector teams better information to prioritize repairs and plan maintenance routes.

The appropriate balance between automation and human involvement will vary. Routine, low-consequence actions may be automated within defined parameters. Decisions that could materially affect safety, employment or service delivery should carry proportionate human oversight. The objective is not autonomy for its own sake. It is faster, more consistent action with the right safeguards in place.

Beyond the pilot:

Organizations must close the gap between AI investment and value by treating AI as an operational capability rather than a standalone technology project.

That means starting with a measurable problem, building the contextual data foundation AI needs, designing deployment around the realities of frontline work and connecting every insight to action. When those conditions are in place, physical AI can move beyond isolated demonstrations and begin delivering safer, more efficient and more resilient operations at scale.

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The U.S. government has a new task force devoted to AI — and it's decided that it would now prefer that everyone stops calling it artificial intelligence.

President Donald Trump has announced the creation of the “Super Intelligence Force,” a White House-led group that will spend the next 120 days examining the risks, opportunities, and appropriate federal response to rapidly advancing AI systems. The task force responds to mounting pressure from lawmakers and even AI company executives for stronger safeguards around increasingly capable models.

The group has a formidable remit. Director of National Intelligence Jay Clayton will lead what is supposed to include work on cybersecurity, biosecurity, and more standards, along with reviewing how AI companies report breaches and hacks.

There is substance buried underneath the comic branding. OpenAI and Anthropic have both been dealing with increasingly uncomfortable demonstrations of what powerful models can do with cyber tools. The new task force is at least an acknowledgment that voluntary promises from AI companies cannot be the entire federal strategy.

A super rebrand

Elon Musk then entered the naming discussion, responding “Yes, we will make that change,” when asked on X whether SpaceX's AI operation would change its name from SpaceXAI to SpaceXSI.

This would be the third rebrand for the AI branch of the company and, like those other new names, doesn't actually change the technology or the business.

The SI terminology comes directly from Trump. An executive order signed in late September directs executive departments and agencies to use “Super Intelligence” and “SI” instead of “Artificial Intelligence” and “AI” in official communications and other non-statutory documents.

The order argues that today's frontier systems have moved beyond merely imitating or automating human intelligence and that the new terminology better reflects their “promise, potential, and rapidly advancing capabilities.”

For now, the executive order defines “Super Intelligence” as the same technologies already covered by the federal statutory definition of artificial intelligence. It asks officials to develop a possible new federal definition within 60 days, but the immediate transformation from AI to SI is linguistic rather than architectural. And even then, seems unlikely to spread very far. SpaceXAI becoming SpaceXSI is much more about politics and branding than a technological milestone.

Notably, “superintelligence” is already used in AI discussions. It's how researchers refer to a hypothetical artificial intelligence that significantly exceeds human capabilities in many ways.

But language does shape how technology is sold and regulated, and “artificial intelligence” has accumulated plenty of baggage after several years of hallucinations, deepfakes, job anxiety, copyright fights and increasingly unsettling demonstrations of autonomous behavior.

Trump explicitly calls renaming it super intelligence a way to spin the technology more positively as something to “empower Americans, strengthen our economy, and improve the lives of the American people.”

Watchdog with a countdown

The Super Intelligence Force arrives after a White House meeting with AI leaders who signed voluntary commitments covering internal model evaluations, independent external audits, and board-level oversight. But corporate promises alone won't settle any argument about technological abilities or regulation.

Its charter gives it 120 days to develop recommendations, with the possibility that its work could be extended or transferred elsewhere. There is already disagreement about how far oversight should go.

“Greater international oversight is absolutely necessary, but concentrating responsibility in one single, global body isn’t the answer,” Nik Kairinos, CEO and co-founder of RAIDS AI said in a statement. “AI is developing far too quickly and across too many jurisdictions for one organization to effectively police it. A stronger and more realistic model would be decentralized oversight, with a broad and diverse ecosystem of AI companies monitoring and scrutinizing one another.”

The difficulty is making decentralized oversight genuinely independent when many of the organizations scrutinizing one another also share an enormous financial interest in keeping development moving quickly.

The Trump administration appears determined to avoid moving too aggressively. But AI policy has the problem of trying to govern technology that can change substantially between the drafting of a rule and its implementation. A 120-day task force is therefore potentially more useful than another years-long commission producing a report about models that nobody uses anymore.

But trying to rename AI to “SI” does not make today's models superintelligent any more than changing SpaceXAI's name makes the employees more intelligent. The Super Intelligence Force might have some value, but it won't be because of any branding exercise.

“Oversight itself needs to be continuous, distributed and capable of evolving at the same speed as the technology,” Kairinos said. “Rules and standards set by governments are then an important supporting layer. The safest AI ecosystem will be one where responsibility for keeping systems in check is shared, rather than concentrated in the hands of a single watchdog.”

• DeepSeek open sourced Ascend ports of its production kernel and communication libraries with Huawei's support in tow
• Published benchmarks ran on a proof-of-concept hardware kit DeepSeek says is not a publicly distributed release
• Two repositories are genuinely new and have received hundreds of GitHub stars versus Nvidia's thousands; the other announced components are updates to existing tools

DeepSeek has revealed it is open sourcing a set of infrastructure components for Huawei's Ascend accelerators.

DeepSeek published two completely new repositories, DeepGEMM-Ascend for matrix multiplication and DeepEP-Ascend for the all-to-all communication that mixture-of-experts models depend on.

The other components named in the announcement were upgrades: TileKernels, DeepSelect, and FlashMLA are existing projects that received Ascend-targeted updates, a distinction FourWeekMBA established from the GitHub creation dates.

An open source offering with limited use cases?

Reuters framed it as two Chinese firms deepening ties in search of an alternative to Nvidia, with Bloomberg called the centerpiece a programming language named TileLang, China's answer to CUDA.

The question is whether any of this matters when engineers are still on the fence about using these tools and access to Chinese compute remains, at best, elusive.

The biggest piece, as Bloomberg puts it, TileLang itself is neither new nor DeepSeek's own offering. It was open-sourced in January 2025, and its Huawei Ascend adapters were published in an external repository, tilelang-ascend, on September 29, 2025, a full year before the recent announcement.

While TileLang supports Nvidia's CUDA backend, it also supports Huawei's Ascend as an ecosystem backend, classified and maintained in a separate repository. One could therefore argue that TileLang is less a weapon aimed at Nvidia than a shared abstraction layer that half a dozen Chinese accelerator vendors are quietly standardizing on, with Huawei as the largest of several tenants rather than the landlord.

The new software has limitations: DeepGEMM-Ascend requires the Ascend 950 series and CANN 9.20. DeepEP-Ascend requires Ascend 950 silicon with UBMEM connectivity, so it targets a narrow audience beyond Huawei's own engineers and limited Chinese AI labs with access to that silicon.

Despite this, at a time when Nvidia chips are unlikely to be generously exported by the US or allowed in by China, Huawei's Ascend silicon has a large task on its shoulders: DeepGEMM-Ascend keeps the same Python package name and API shape as its CUDA sibling, and DeepEP-Ascend aligns its public buffer interfaces with the Nvidia version.

That is because the goal isn't a performance claim, but switching-cost reduction, and switching costs are precisely what Nvidia's moat is made of: it costs far too much for most developers to abandon CUDA or code their own alternative currently.

Public signals show this remains an uphill task; at the time of writing, DeepGEMM-Ascend has 515 stars and 39 forks, compared with DeepGEMM, which has a comfortable lead at 8,522 stars and 1,359 forks, for example. The story is similar for DeepEP-Ascend and DeepEP, with the latter surpassing 10,200 stars versus DeepEP-Ascend's 224.

The reputational problem Huawei faces here predates the repositories. ChinaTalk, citing the Financial Times, quotes a Huawei researcher describing CANN as making Ascend chips difficult and unstable to use, and a Chinese developer characterizing work on the 910B as a road full of pitfalls.

Epoch AI has reported that Huawei dispatches engineering teams to major customers such as Baidu and Tencent to help port CUDA training code and keep deployments running.

A frontier lab publishing its own production kernels partially answers that complaint, because it replaces vendor documentation with code that has survived contact with a model and can be optimized further. It is also, however, code that runs on one chip family, under one toolkit version, on firmware the rest of the world cannot download or even potentially access.

• Fake AI marketing tools impersonate ChatGPT, Gemini, Claude, and others to steal business accounts
• Attackers use realistic browser-in-the-browser phishing and live operators to capture credentials
• Stolen advertising accounts provide access to payment methods, budgets, and linked client accounts

Cybercriminals are offering fake AI-powered products for advertisers and marketing managers, in an attempt to steal their Google business accounts.

Most of today’s biggest AI names, including Chat-GPT, Google Gemini, Claude, Manus, and Muse, are being abused in this campaign, and people are still falling for it.

Security researchers Island have published a new report detailing how the campaign works. Apparently, the operators left the source code for earlier versions of their work open on a misconfigured public GitHub repository, giving the researchers unique insight into the operation and its success.

What researchers found was an advanced phishing platform that was used to target advertisers and marketing managers. It was used to create websites offering fake marketing-related products powered by Artificial Intelligence.

“Every brand gets its own pitch. ChatGPT promises a Monday Google Ads brief. Gemini promises MCC (manager account) and linked-client support. Claude gets its own advertising portal, Perplexity offers campaign planning and spend audits, and Manus offers a private Meta integration,” the researchers explained.

All about connecting

Each site asked the victim to do the same thing - connect their Google account. After pressing “connect”, the platform launches a typical browser-in-the-browser (BitB) attack - it displays an entire browser, address bar and all, within the actual browser.

Therefore, victims not paying attention to the details might see a legitimate domain in their address bar, for example accounts.google.com. However, if they were to lift their gaze just a few inches higher, they would see the actual domain, which has nothing to do with Google, or OpenAI, or any other legitimate business.

The phishing kit is advanced, too. The fake browser adapts to whatever the visitor runs, so it won’t happen that a victim running macOS suddenly sees a Windows, well, window. Newer builds also come with nifty little details, like Safari’s URL pill, Chrome’s custom tabs, and even a dark mode.

As the victim tries to log in, a “human operator” (the scammer) on the other end follows them through the process. They see each submission, and decide what the victim sees next. They can fake a mistyped password error, or they can choose which MFA screen they see. The device gets fingerprinted, from IP and location, all the way down to screen size and WebGL.

The platform supports Google, Meta, TikTok, and Okta workflows, it was said.

Targeting advertisers

At the end of the day, it’s all about money. Island says the crooks are deliberately going for business accounts because these are connected to credit cards, which they can later abuse.

“The lures are written for agency staff, media buyers, and manager-account administrators, because an advertising account is a spending account. It carries a stored payment method and an approved budget, and a manager account can reach several client accounts, each with its own billing profile and linked users.”

This is hardly new. Both Google and Facebook have their own advertising networks, displaying ads to billions of people every day. However, getting accounts, payment methods, and ads approved, is a stringent, painstaking process, which is why it’s a lot easier for crooks to simply use someone else’s account, especially if that someone already walked through Google’s fire barefoot.

Unfortunately, Island could not disrupt the operation or tear it down. At the moment of writing, the campaign was still ongoing, and the researchers saw “hundreds of victim submissions to the platform”.

The researchers are urging organizations to treat fictional AI integrations as account-access requests, inspect the outermost origin, correlate the client pattern, and hunt for the control vocabulary. Finally, they should use phishing-resistant authentication, and review advertising control changes.

“Origin-bound passkeys and hardware-backed authentication remove the reusable password and one-time-code material this platform is built to collect,” they said. “After exposure, check every client account the identity could reach for new managers or partners, changed recovery details, and campaigns or spend nobody approved.”

Via The Hacker News

• AMD's CEO said that the company plans to 'substantially increase' its chip supply in 2027
• Sadly this is chips for AI and data center usage, not consumer products
• If anything this is bad news for Radeon GPUs and Ryzen CPUs, as it's AMD underlining its commitment to the AI sphere

AMD's CEO has assured us that the company is going to increase chip production in 2027, but while that might sound great on the face of it, this isn't good news for consumers.

That's because CEO Lisa Su was talking about making chips for AI usage in data centers, not consumer processors or graphics cards.

Yahoo Finance reports that AMD has been expanding its chip production for AI and that Su said: "We've been able to increase our supply as we've gone through 2026, and we're going to substantially increase our supply in 2027."

This was a comment made to the press during a visit to Taiwan, and the chief executive made it clear that part of the reason for her trip – which included a visit to Foxconn and TSMC – was to make sure AMD's supply of CPU and GPU production could be increased to meet greater AI demand.

The next stop on the CEO's trip is South Korea, where Su will look to shore up memory chip supplies and is set to meet with Samsung and SK Hynix.

On the broader topic of how AI is impacting us all, Su said that it could be "incredibly good for the world" while at the same time cautioning on the risks and the need to cooperate on the safety of this technology.

Analysis: consumers in the cold

There's no mention of consumer products – Radeon GPUs and Ryzen CPUs – here, and the fact that AMD is clearly focused on beefing up AI supply is bad news for the typical PC owner. If anything it might mean that next year is an even leaner time for chip supply for consumer products, especially given all the negative chatter there's been of late talking about memory chip supply in particular looking like it'll considerably worsen next year.

Of course, none of this is surprising – there's a lot more profit in AI accelerators (graphics cards), of course, and as a business, Team Red can hardly afford to ignore that. But it does underline AMD's commitments, and PC owners on Reddit have voiced their displeasure in no uncertain terms.

As one Redditor put it: "Yeah I have a feeling the consumer market will end up being 'whatever the data centers consider obsolete' soon. We might have to contend with buying used data center CPUs and GPUs soon."

Okay, so that's going a bit far. And yes, others are quick to note that you can't just stick an old data center GPU in a PC and run it – there are issues around the implementation and cooling, display output (which isn't needed for a data center card), and the fact that many of these AI GPUs simply can't run games at all. But the point remains: consumers are indeed getting the short end of the stick due to the AI boom, and that's been the case since the invention of the likes of ChatGPT and other LLMs.

It doesn't sound like the situation is going to get any better next year, and that's just another worry to throw on the towering pile of memory price concerns that has been steadily growing in height as this year progresses.

Not all sandboxes are created equal, and until recently there was no way to say so precisely. There is now.

Two OpenAI models escaped an evaluation sandbox, breached Hugging Face's production infrastructure and used the answer key to their own benchmark. The attack was novel and creative in how the models passed notes back and forth, complex multi-step escalations throughout. It's fair to conclude from this incident that frontier models are proficient at hacking and can be dangerous.

But the breaking-out-of-the-sandbox notion is a red herring. If a sandbox is poorly constructed, as so many are, it’s easy to break out of. They don’t have the locked-down environments common at the network level such as access controls and separated privileges. This incident would have unfolded very differently with a properly configured sandbox.

If only some sandboxes are properly configured, how can you tell them apart? Let’s explore if anyone has come up with a real, testable definition of ‘sandboxed’.

The landscape, briefly

Almost nothing written about agent security is a scoring standard for a single sandbox's containment architecture specifically.

OWASP's Agentic AI Top 10 and its Agent Security Cheat Sheet catalogue threats and mitigations at a high level, useful as a checklist, not built to produce a comparable score. NIST's AI Risk Management Framework operates a level above this entirely, more organizational risk governance, rather than a technical grading rubric for a runtime boundary.

MITRE ATLAS catalogues adversarial techniques against AI systems, closer to a threat library than a containment measure. The Cloud Security Alliance has several overlapping efforts, MAESTRO, an AI Controls Matrix, and an Agentic Trust Framework that scores autonomy on a four-stage ladder from Intern to Principal, the closest thing I found to one specific slice of what I was after, how much an agent can do without a human, but it isn't scoped to sandboxing as a whole.

RAND's Securing AI Model Weights defines five security levels, but for weight theft and exfiltration risk at a lab, not for whether a given agent's runtime sandbox holds under an adversarial task. None of those take a single agent sandbox, break it into independent parts, score each part, and produce something you could compare across products.

Let’s look further.

The agent sandbox taxonomy

Published in March 2026 and still under active community review, it organizes itself around a memorable ‘7-7-3’ (seven defense layers, seven threat categories, three evaluation dimensions). The layers are numbered bottom-up, because the lower ones are foundational:

· L1 Compute isolation

o What separates the agent's execution from the host?

· L2 Resource limits

o Can it exhaust CPU, memory, disk or time?

· L3 Filesystem boundary

o What can it read, write or delete?

· L4 Network boundary

o What can it communicate with?

· L5 Credential and secret management

o Can it see, use or exfiltrate credentials?

· L6 Action governance

o Can it perform destructive or unauthorized operations?

· L7 Observability and audit

o Can you see what it did, when, and why?

Each layer gets scored on Strength, 0 to 4, and Granularity, 0 to 3, plus a flat set of Portability tags for OS and infrastructure dependencies. The Strength scale is the part I like the most.

0 is no enforcement. 1 is cooperative enforcement; the sandboxed process can simply ignore or route around, as well as proxy environment variables and an opt-in convention. 2 is software-enforced by something the process can't bypass internally but an operator could reconfigure. 3 is kernel-enforced and irreversible once applied, namespaces, Landlock, seccomp-BPF. 4 is structural, the protected resource just doesn't exist inside the sandbox at all, a microVM, a credential proxy, no network device.

That scale is how we can properly distinguish locked-down environments. Every product gets a fingerprint, a CVSS-style vector showing strength at each layer in order. The taxonomy also maps its seven threats back onto specific layer combinations with explicit thresholds.

That means something like data exfiltration is addressed systematically, and not as a judgment call. It's a mechanical check against whether L3, L4 and L5 all clear a score of 2 or better. And critically, it comes with a composition framework. No single product covers all seven layers well, so the practical guidance is to stack products and take the maximum score at each layer rather than pretend one tool solves everything.

The project ships 26 real products scored, a verification probe you can run against an actual sandbox to check the claims, and an interactive explorer for comparing fingerprints side by side.

The good, and the missing

Let’s return to the Hugging Face incident. The way the agent “escaped” its sandbox was to route around a proxy. That’s a service running in the sandbox. It is a fundamentally weaker guarantee than a boundary enforced below the application layer.

The Agent Sandbox Taxonomy gives that distinction a name and a number instead of leaving it as vibes. It's honest about its own limits; the project says outright which product scores are unverified. And the composition framework's core finding is the most useful thing in it for anyone assembling a stack. It shows that combining products both builds a better box and controls what's inside that box. Almost nobody does both.

Two blind spots stood out. The first is that there's no layer for what happens after containment fails. No kill switch, no automatic credential rotation on trigger, no forensic rollback runbook. It tells you whether an agent could plausibly be contained, not what happens in the window after it isn't. That window is exactly where the Hugging Face incident lived.

A kill switch is not a bad idea in principle. We need to be able to block frontier models that have proven they can escape and cause harm. The hard question is who initiates it, and how fast. It took two of the most advanced organizations in the world, OpenAI and Hugging Face, weeks to comb through the forensics of this attack. A centralized agency without frontier talent would take considerably longer.

We should take our lead from industry. In the U.S. the Institute of Nuclear Power Operations’ first line of defense is an automated control at the reactor core, the second is an onsite operator, and only the third is a centralized regulator whose job is primarily to audit the first two.

Agent infrastructure needs the same shape. Automated kill switches built into the model infrastructure, because nothing else moves fast enough. Safety officers at each lab empowered to take further action or initiate a manual shutdown. Standards set and audited above both.

The second blind spot is related. L7 scores whether logging exists and how tamper-evident it is, but not how fast an anomaly actually reaches a human. A system can score well on L7 and still take days to notice something. That appears to have happened here. Neither blind spot is a knock on the project. They're reasonable scoping choices for a v1, but they're the two places I'd want the standard to grow next.

Running the incident

Here's the actual exercise: can I fingerprint that eval environment using public disclosures alone? Mostly no. That tells me that responsible disclosure is mature for something like a CVE, but not for a sandbox jailbreak.

Compute isolation was described only as ‘highly isolated,’ with no mechanism named. Resource limits and the filesystem boundary weren't addressed in the public writeup at all. The network boundary looks like a 2 and it didn't hold. A single proxy chokepoint was the sanctioned exit and a zero-day in it gave code execution, exactly the failure mode the S:2 tier warns is reconfigurable but not kernel-irreversible.

Credentials look like 0 or 1. Stolen credentials enabled lateral movement into Hugging Face's production systems, consistent with ambient, reachable credentials rather than a proxy or ephemeral tokens. Action governance was 0 or 1 by design. Guardrails were deliberately stripped for this evaluation to measure maximum capability. Observability was partial, logging existed and Hugging Face detected the intrusion independently, but nobody connected it to OpenAI's own telemetry quickly.

Run that through the taxonomy's own threat rules and data exfiltration can't be marked addressed with what's public, because the inputs were never disclosed. That's the value of the exercise. It lets you say precisely which of seven specific, falsifiable claims about a containment architecture were available and a way to request those that weren’t.

That tells me there is a standard definition of ‘sandboxed’, or at least close enough. But reading a fingerprint format is one thing and trusting it is another, especially when nearly every entry in the taxonomy's own dataset was inferred from documentation rather than hands-on testing. Next up is using the probe for verified scores.

None of which gets any individual organization off the hook while the standard matures. Determine your risk appetite. Put sensible auditing and controls into your own infrastructure. Be prepared with your own version of a kill switch.

And if your own agent's sandbox had to be fingerprinted against these seven layers in public, how would it score? Hopefully better than the one OpenAI used in this incident.

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• 50% of developers only use AI to impress and reassure bosses
• 47% said they were judged on how much they use AI rather than the quality of their work
• Work engagement, job security, and career anxiety have all been cited as pressures caused by AI adoption among developers

New research claims many developers are still not massive fans of AI, and most find its introduction into the workplace to be anxiety inducing.

While understanding that massive budgets are behind AI implementation, many of the devs surveyed across the US and Europe revealed that their use of AI was mostly performative.

While reassuring managers and the C-suite might be laudable, it appears to come with a price, as career anxiety among developers concerning the use of AI and their place in an organization that uses the technology is high.

AI stress for devs

By implementing AI technology and practices without concern for existing processes, organizations seem to have left their software developers feeling uneasy about AI and how they’re expected to use it.

Adaptavist’s survey of 1,000 software developers in the UK, US, Germany, the Netherlands, and Spain indicates that specialists feel an expectation to use the technology, whether it has any meaningful result or not.

Furthermore, they feel that they’re using it performatively rather than productively.

The survey reveals some worrying numbers. With 50% of devs using AI simply because they need to demonstrate that they can use it, and 47% judged on how much they use AI rather than the quality of their work, it seems there is a disconnect between organizations adopting AI and knowing what they want from the technology.

This is emphasized with the result of 58% of developers noting that AI use is becoming metric of performance or has been added to their KPIs.

All of this means developers that are increasingly perturbed about their long-term prospects (54%), and a fear of losing their jobs if they don’t use AI (37%).

A fertile exodus

What might these results mean for businesses and organizations looking to keep their developers happy?

"A good Developer Experience is critical not only to the wellbeing of developers themselves, but to organisational growth and innovation," noted Matt Saunders, DevOps Lead at The Adaptavist Group.

"It therefore must not be undermined by adoption targets, usage metrics, or a culture that rewards being seen to use AI over actually using it well. Together, they are creating fertile ground for an exodus, one KPI at a time.”

The challenge on organizations embracing (or overcompensating on) AI need to move their focus away from adoption, and measure AI outcomes instead, says Saunders.

“Organisations that want to hold onto engineering talent need to stop measuring AI adoption and start measuring its outcomes, and give developers the time, autonomy and support to use the technology in ways that genuinely improve their work, rather than simply prove they are using it.”

In July, two of OpenAI’s research models sat down to take a test. An internal cybersecurity benchmark, and they were meant to solve a set of exploitation challenges. Instead, they worked out that the answer key was probably sitting on Hugging Face, where the benchmark files were hosted, and went to get it.

Getting it took chaining a zero-day in a package manager, some stolen credentials, and a privilege escalation across OpenAI's own network. The models broke out of the evaluation sandbox, reached Hugging Face's production infrastructure, and achieved remote code execution. Hugging Face later reconstructed roughly 17,600 actions across four and a half days of this.

Nobody told them to attack anything. They were cheating on an exam.

Two more incidents followed, at Anthropic and Meta, and those were different in a way that matters. Those models didn't build a route out. They found one that was already there, because the evaluation environment had been set up wrong.

So, of the three incidents that prompted the UK’s National Cyber Security Centre’s (NCSC) guidance in August, one was an agent doing something genuinely novel and two were an agent walking through a door a person left open.

Have a guess which type you're more likely to have in your own environment.

The instinct to build a new category is the wrong instinct

The reflex right now is to treat all of this as a new security category. New containment strategy, new governance forum, new line in the budget, probably a new acronym before the year is out. I understand the appeal. It's a lot easier to fund a new thing than to admit the old thing was never finished.

But look at what an agent actually is inside your organization. It's running on some employee's access rights. It's operating under their identity, or a service account they spun up. Its scope is whatever they thought to constrain when they configured it on a Tuesday afternoon, which is usually less than you'd hope. Hand the same agent to two different people and you get two different risk profiles, because the agent didn't change. The human did.

That's not new. That's the thing we've been calling insider risk for fifteen years, with the latency taken out.

Some of what you already own transfers cleanly, which is the good news and also why I'd push back on anyone selling you a greenfield. Behavioral baselining works. You already flag when a user's activity departs from their pattern, and a hijacked agent looks like the same class of deviation. Identity governance works too.

The access reviews and the offboarding runbook you use for leavers apply to agent credentials, and if you skip it you end up with agents cheerfully operating on the access of someone who left in March.

What doesn't transfer is the tooling.

The model extends. The tooling does not.

DLP was built around a human moving a file. It assumes a pace and a shape of data transfer that agents just don't have. Access controls and containerization draw a boundary, which is useful as far as it goes, but they tell you nothing about what happens on the permitted side of it. Scope creep isn't a perimeter breach. It's an agent gradually doing more than anyone intended, entirely inside the rules it was given.

Prompt injection sits underneath all of this. Someone laces instructions into an email or a document, addressed to the AI reading it rather than the person. The attacker never needs to steal the employee's credentials. The agent is already holding them.

Which brings me to the unglamorous part, and the only recommendation here I'd defend in front of a board. You need to know what's actually running.

Shadow AI isn't a new concept, but it's still rampant, and agents make the unsanctioned side of it harder to see. 98% of organizations already have unsanctioned AI tools in use today. According to Mimecast telemetry, unsanctioned GenAI tools see roughly 8,000 copy-pastes and file uploads every hour. It only takes one of those interactions to trigger a GDPR breach or an IP leak.

That’s why keeping track of AI use in an organization with a live inventory is critical. Not a list from last quarter. A live one. Vendors keep switching AI on inside tools you sanctioned two years ago, so the inventory decays whether or not anything in your environment changes. Real-time data flow controls, anomaly detection, agent identity cataloguing, all of it comes after this and none of it does much without it.

Start with visibility. Always.

Ollie Whitehouse at the NCSC put it better than I'm going to: relying on detection alone, after the fact, isn't going to be enough.

Agents didn't invent a new risk. They took the gap between someone making a careless configuration decision and that decision costing you something, and closed it to seconds. Most of us are running an architecture that assumed we'd have longer.

So before you sign off on another point solution, go and find out what's already in there. I'd bet it's more than you think, and I'd bet most of it arrived without anyone making a decision.

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• "No AI is now a feature," LibreOffice says
• Until AI is private and secure, it won't be installed by default
• Users can choose to add their own AI if they're willing to go against LibreOffice's principles

Following the launch of LibreOffice 26.8 and apparent backlash over the software's lack of AI features, The Document Foundation has hit back to confirm that "no AI is now a feature."

Founding member Italo Vignoli explained in a blog post that, while the productivity suite isn't totally against AI features, it recognizes its use by millions of people and ultimately has a responsibility to deliver smooth software.

"An office suite used by tens of millions of people... will not add a feature to its default configuration until that feature can be delivered under the conditions defined by the project in accordance with its principles," Vignoli wrote.

LibreOffice says having no AI is as much of a feature as having AI

Seemingly taking a stab at hyperscaler rivals like Microsoft 365 and Google Workspace, The Document Foundation ended up criticizing other office software for making AI features present even when users choose not to use them.

"It must be installable, removable, and absent from the user interface for those who do not wish to use it."

Because LibreOffice has a set of other principles it abides by – including no telemetry, no dependence on a single provider, no content leaving the computer without user permission and uncompromised ODF formatting – its developers came to the conclusion that there isn't a viable option that meets all these standards, thus, for now, it won't be adding in AI features.

"The criteria we have listed do not amount to a definitive rejection," Vignoli wrote, acknowledging that advancements in the AI department could soon see a viable option come to light.

For the time being, though, LibreOffice will remain without a default AI installation, but users can choose to add extensions that wouldn't otherwise abide by the Foundation's principles on their own accord.

Atlassian has laid out its aim of being the ideal platform for your business to make the most out of its data in the new agentic enterprise era.

“We are living through the most creative, dynamic eras in the history of technology, which is thrilling - and yes, a little bit scary at times too,” Atlassian CEO Mike Cannon-Brookes told the company’s Team Europe 26 event in Amsterdam.

AI is obviously playing a key role in this progress, but these continuous leaps forward can put some businesses at risk of being left behind if they aren’t using the technology effectively - namely, through context.

Context is key

“Models are the engine, but context is the fuel,” Cannon-Brookes added, “no matter how capable a model gets, if it doesn't reliably know why you made a decision back in 2022, it can't help you in 2026.”

“AI living in a silo only delivers a fraction of its potential value - real leverage happens when every type of context is interconnected.”

Cannon-Brookes explained how the Atlassian platform can be your company's “central nervous system” - by providing context, collaboration and conference so that workers can quickly get the information they need when they need it.

In this new age of humans and agents working together, Cannon-Brookes was also keen to highlight how Atlassian’s mission of pushing forward the next generation of collaboration is key.

“Across the board, we're seeing agents aren't replacing the people, they're joining your team,” he noted, “but as things accelerate, we believe that experience of collaboration is going to be so important.”

This acceleration can be an issue for some businesses, Cannon-Brookes admitted, and managing the pace of progress is critical for companies.

“When every person, every agent starts to move faster and faster, new challenges arise,” he noted, “because it’s not just one person moving faster, it’s a multiplication of teams increasing their outputs, resulting in dozens of parallel learning loops.”

“Without a way to connect it all, this velocity can go from exciting to something quite disorientating,” he added, noting that having confidence in what your organization knows comes from having the insight to understand your context and act on it.

“Most of it, it’s knowing how to handle the inevitable speedbumps,” he said.

AMP-ed up

Cannon-Brookes also took the wraps of AMP - Atlassian’s agentic multiplayer protocol offering, which he explained will help boost how people and agents collaborate both on and off the Atlassian platform.

On stage, the company showed off a few examples of how AMP, powered by an all-new Atlassian MCP server, will give agents a lot more tools to get work done.

This included Loom Record for Agent, a new way to create prompts for its Loom platform, allowing users to record a video outlining specific details for a prompt in a natural human fashion.

Atlassian is looking to show a focus on real-time, multi-player collaboration, not just between human teams, but bringing in agents from multiple platforms, effectively working together for a much quicker resolution to even the most complex work tasks.

“People can work alongside agents exactly how they want, and when they want to,” Cannon-Brookes concluded, “you don't replace people with agents, you create a new and powerful system of collaboration for your organization.”

"Working with AI isn't single player any more - it's multiplayer."

Research and Development (R&D) is fundamentally linked to a nation’s prosperity, and ability to respond to major challenges. Without a strong R&D pipeline, innovation and opportunity would be limited.

From tackling the COVID-19 pandemic, to advancing in sustainability and clean energy, R&D has been at the forefront of many breakthroughs.

The AI age is no exception.

Yet, while organizations are investing exponentially in AI, and rushing to adopt the latest tools, many leaders are overlooking a more fundamental foundation that needs to be in place first: the people. While AI may be scaling innovation, it is the R&D professionals that are needed to develop and improve these technologies and implement them effectively.

Organizations that focus entirely on AI adoption, without investing in the skills needed for successful AI deployment, risk forming a capability gap that will hinder them in the long-term. Thus, building talent must become a strategic business priority, or else leaders risk falling behind in the AI age.

Reactive hiring is a losing game

For many organizations, hiring remains a reactive process: recruit when there is a vacancy or the workload needs it. While this approach may have worked in the past, it is no longer aligned with a landscape where demand for specialist tech skills continues to outpace supply. While hiring reactively may solve immediate business challenges, it does not build the long-term knowledge and expertise needed to sustain innovation in the R&D space.

Instead, organizations should think about building talent pipelines. This could mean partnering with universities or offering a range of internships and graduate programs to bring in young people at the start of their career. There should also be a focus on providing mentorship opportunities, and embedding continuous learning into the workplace. Existing employees can then continue to gain skills, and drive the research and development organizations seek.

A successful R&D workforce is not built overnight; like all good investments it is a slow and steady process. AI is progressing rapidly and defined by instant results, and it can be easily forgotten that people don't develop at the same speed as technology. Instead, a consistent flow of skilled professionals is required, who can grow and change alongside the organization, and adapt to the technologies that emerge.

Why culture is the retention differentiator, not salary

Higher salaries are an attractive proposition for many employees, but they quickly lose their appeal when it comes from an organization that has not built a culture to support or retain its people.

Increasingly, employees want more that simple compensation. People are looking to join environments of continuous development, bold innovation, experimentation, and collaboration. Not only will this make organizations stand out in the job market, but it will also make people want to stay longer-term and build higher job satisfaction.

When this becomes embedded into the fabric of a company, organizations become far better equipped at attracting, developing, and retaining the highly skilled R&D talent they seek.

With technology changing and advancing so quickly, this culture of continuous learning becomes even more vital. Organizations should see it as a priority to provide access to technical training, certifications in fast-moving areas such as: cloud-native development, AI and DevSecOps, as well as providing mentoring for employees. There should also be opportunities that allow people to work on emerging technologies – from AI-assisted development tools to automation and advanced analytics - and share knowledge across the wider team.

True innovation is rarely the result of one individual. The most successful R&D outcomes are driven by teams that work together with their different perspectives, skills and experiences; bolstered by a work culture that empowers them to do so.

The R&D goldmine most companies are sitting on

While businesses continue to invest heavily in AI tools and develop technologies, there is one untapped R&D resource that many are overlooking. And it’s free for them to use: their own customers.

Most organizations collect a vast amount of customer data and information, yet much of it remains underutilized. But when combined with AI-driven analytics, this data can become a powerful tool for innovation. Failing to do so, would be missing a valuable opportunity for many companies to better understand their customer needs, and guide future development.

AI can analyze large volumes of data very quickly and efficiently. In doing so, it can help organizations identify patterns, recurring issues, and unmet customer needs, that may go otherwise unnoticed. As a result of these insights, R&D teams can then make better decisions based on actual evidence and user experiences. Paired with upskilled engineers, this is where real opportunity lies. Automation and analytics allows teams to be free from repetitive tasks, so their time can instead be dedicated to solving the more complex problems that drive meaningful innovation.

In an ever-changing market, organizations cannot innovate based simply on guesswork. AI can help uncover what customers need, but it is the R&D teams that must help bring this to life. When this all comes together, businesses are far more likely to develop innovations that resonate and drive long-term growth.

People are significant contributors to AI in driving its successful deployment, so it’s logical that the businesses seeing the greatest success in this new wave of AI, are those that have invested heavily into developing R&D talent and upskilling.

Through implementing a strong talent pipeline, building cultures of bold innovation, and utilizing the goldmine of customer data that most businesses are sitting on, organizations will be far better equipped to turn AI’s development into meaningful, long-term growth.

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Industrial employers sitting on unfilled technical roles and a generation locked out of work are two halves of one fixable equation.

The latest figures from the Office of National Statistics reveal that around 13% of UK 16-24 years olds are not in employment, education or training. The government's own interim review has warned this could mark the makings of a "lost generation."

At the same time, close to a fifth of the engineering and manufacturing workforce is expected to retire by the end of the year, taking with it an estimated half a million skilled engineers, technicians and operators.

One side of the ledger is a generation that wants to work and cannot get in. The other is an industrial base that needs people and is about to lose its most experienced. Treated separately, both look intractable. Treated together, they start to look like a single problem with a single answer. The solution: training AI agents to become autonomous and fill this gap.

A knowledge problem, not a hiring problem

The first mistake is to see the industrial shortage as a hiring problem. Recruiters and trade bodies have been clear that external hiring alone cannot close the gap: the issue is not just empty seats but the decades of judgment walking out the door with every retirement. When a maintenance lead or a supplier audit manager leaves, the loss is rarely the task itself. It is the accumulated reasoning behind it, and the why that never made it into a manual.

That is precisely why hiring and knowledge transfer demand completely different solutions. A vacancy can be filled in months. Thirty years of tacit knowledge cannot be replicated by recruitment, and the so-called "hollowed-out middle" - the shortage of workers with five to fifteen years' experience - means there is often no intermediate layer left to pass that knowledge down. The conveyor belt that once carried expertise from veteran to apprentice has lost its middle section.

Where AI changes the arithmetic

This is where AI changes the arithmetic, and not in the way the headlines assume. The breakthrough is not a chatbot that answers questions. It is the ability to capture the relationship between an organization's structured records, the purchase orders, the part numbers, and the unstructured reasoning that surrounds them: the emails, the annotations, the informal decisions made in the margins.

Map how the two influence each other, embed that context into the daily workflow, and decades of institutional memory become an operational asset rather than a folder no one can find.

Done well, this compresses the experience curve. Instead of acquiring judgment slowly, over years, a newer engineer inherits a working version of it from their first day on the job. The relevant history, the prior decisions, and the reasoning surfaced from the very beginning. The point is not that experience stops mattering. It is that the ramp becomes far shorter.

The window to exchange knowledge is narrow, and it is closing now. Context can only be captured while the people who hold it are still in the building. Once an expert retires, the structured data remains, but the reasoning that made it usable leaves with them. Every quarter of delay is a quarter of irreplaceable judgment lost. The retirement wave is not a distant problem to be scheduled around; it is already underway.

Where AI is used the wrong way

There is an honest objection to confront. AI is widely blamed for hollowing out entry-level work, and the data gives the charge weight: the same interim review found that entry-level roles have become less plentiful and more demanding, recruitment more remote, automated and less human, and apprenticeship starts for young people are down by more than 40%.

But AI is rarely the real culprit; the mindset behind it is. Too many organizations are still buying the technology the way they bought software a decade ago - as a line item that removes people rather than a capability that makes them more valuable. Pointed at entry-level work, AI will cut it and call the result “efficiency”.

Pointed at the opposite goal, making inexperienced people productive faster, the same technology widens the door rather than narrows it. The real question is not whether AI has to close off the old way in, but whether companies choose to use it to open a new one.

A different way in

This only works if the argument is honest about who these young people are. The cohort behind the NEET numbers is not, for the most part, trained engineers waiting for a vacancy. Six in ten have never held a job at all; many are contending with poor health, disrupted schooling or years of disengagement. Employers are willing to take them on yet hesitate because an unproven candidate feels like a risk a smaller firm cannot afford.

AI lowers the cost of that bet. When a system surfaces the right context and lets a newcomer direct complex processes in plain language, the absence of a track record stops being disqualifier. For a 23-year-old walking onto a factory floor for the first time, that is the difference between being screened out and being let in.

That is the opportunity hiding inside the two statistics. The retirement cliff and the NEET crisis are usually filed under different departments: workforce planning on one side, social policy on the other. They belong on the same page. Capture the expertise that is about to leave, put it within reach of the people who are desperate to start, and the industrial sector does not merely plug a gap. It builds the on-ramp for a generation that has been told, too often, that there was no way in.

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Amazon Web Services CEO Matt Garman is warning the more than 100 American communities considering moratoriums on new data centers that stopping their construction now could leave the United States paying for the decision for decades.

"In fact, this build out is so important geopolitically that there are widespread reports of various countries intentionally seeding misinformation in the U.S. about data centers to trick us into slowing down," he wrote in a long statement defending the AI infrastructure boom. "If these measures are enacted, the U.S. could be writing its own losing ticket to this race, and the consequences would last generations. As a country, we can’t afford to find ourselves in that position."

Garman argues that data centers have become as strategically important as highways once were. He believes AI leadership is now an economic and national security contest, and countries that build the necessary computing infrastructure fastest will enjoy the benefits for years to come.

It is a dramatic warning, although the scale of what is being built is dramatic enough to match it. A new Brookings report estimates that investment in American data centers and related AI infrastructure could total $10.3 trillion between 2025 and 2032. That works out to an average of 3.63% of U.S. GDP each year and would make the current AI buildout larger relative to the economy than America's canal, railroad, electrification, highway, and telecommunications industries.

Building AI everywhere

However, the comparison to the interstate highways leaves out the critical difference of who paid for them. The highways were funded by taxpayers, while Garman stresses that private companies are paying for the AI infrastructure boom. That certainly makes a $10.3 trillion construction spree easier to sell politically, although it does not mean the public is insulated from its consequences, like how much electricity and other resources the data centers consume.

Amazon argues that concerns about data centers raising bills, draining water supplies, or creating pollution are often exaggerated or wrong. Garman wrote that AWS works with utilities and regulators to ensure the prices its data centers pay cover the energy and infrastructure they require. Amazon also says its facilities are increasingly water efficient and that it is moving toward lower-emission backup generators.

More significantly, Amazon appears to recognize that simply telling communities to trust the industry is becoming a losing strategy. The company says it no longer uses nondisclosure agreements with government agencies for data center projects and will publish annual information about energy and water use.

"There are clearly areas in which the technology and energy industries can be doing better (along with their government partners) but just like in the past when the country has banded together to accomplish something that’s critical for our national security and economic prosperity — we should approach this as a long-term mission that the private and public sectors pursue together," Garman wrote.

The backlash grows

Amazon is certainly putting some money behind the charm offensive. Its new Built Together program will apparently add more than $1 billion over five years to community investments around its U.S. data centers. The company says the money will support free community college programs, workforce training and energy-efficiency improvements for schools, public buildings and homes, with a goal of helping prepare as many as 100,000 workers annually by the end of 2028.

But these commitments are arriving just as opposition is becoming difficult to dismiss as a handful of angry neighbors.

California offers perhaps the clearest example of the shift, as reported in CalMatters. Governor Gavin Newsom recently signed seven data center bills that will, among other things, work to stop electricity customers from having to subsidize data center infrastructure and set environmental reviews for the facilities.

Even Texas' famously permissive regulations are changing a little bit to increase supervision of data centers, albeit with some enormous loopholes.

Garman argues that misinformation and “outright lies” are driving resistance, but Amazon's promises suggest at least some of the backlash is a response to problems worth addressing.

The $10.3 trillion question

The AI industry is effectively making one of the largest infrastructure bets in American history on the assumption that demand and revenue will justify the bill.

That makes the political sales pitch particularly important as AI companies need communities to accept years of construction and enormous new demands on electricity infrastructure before anyone knows exactly how transformative or profitable the technology will be.

Not every AI developer is opting for reassurance either. OpenAI CEO Sam Altman told Politico that “we believe that the world should accept some bad things happening for the benefits of this technology and people having the agency.”

He was talking primarily about AI safety and misuse rather than data centers, and that eliminating every possible hack or scam would require restrictions that would deprive people of AI's Still, he may as well have been talking about building the physical data centers, as it amounts to a similar request that Americans accept disruption now because of benefits from AI to come.

Garman and Altman are both making arguments that require an awful lot of confidence that AI's destination justifies a turbulent journey. Communities appear less willing to sign that blank check. Amazon's latest promises suggest it understands that resistance cannot simply be bulldozed away.

Garman may ultimately be right that data centers will become infrastructure as consequential as the Interstate Highway System, but nobody wants to live next to the highway. That fact led to rules about where and what kind of housing can be built along highways.

How much control the people living beside AI infrastructure should have and what protections they deserve remains an open question.

• A new startup has built an AI PC which has sold out of its first batch
• Ghost's Core is a 'personal AI computer that runs models entirely on device'
• It connects to your apps, services and devices, so it 'builds context on your life over time, and works continuously to help you'

Ghost is a startup in the AI field that wants to capitalize on the desire to have agents running locally rather than using AI in the cloud — and to get this done with a minimum of fuss.

TechCrunch reports that Ghost's first product is a PC for using local AI agents which runs those LLMs (Large Language Models) exclusively on-device, ensuring your data stays private (I'll come back to that point later).

The device is called Core, and Ghost explains on the product's site: "Core is a personal AI computer that runs models entirely on device. It connects to your apps, files, and devices, builds context on your life over time, and works continuously to help you, not just when you ask."

It is, if you will, a personal AI assistant on your desk at home, or as Zain Javaid, the founder and CEO of Ghost — which just raised $11 million in funding — put it, a "brain in a box".

Core is a PC which is an unassuming black box built around an Nvidia RTX Pro 4000 (Blackwell) graphics card with 24GB of video RAM, the central muscle for driving the AI, along with 64GB of DDR5 system RAM. The rated AI performance is 770 TOPS.

The PC comes with three AI models pre-installed: Qwen-3.8-Next, Qwen-3.8-27B, and Gemma-4-31B.

You can use your own model on the device, as you might expect, or any alternative for that matter, but the real selling point of Core is that it's an easy way to get yourself a fully primed AI box which requires no effort or setup.

This is for the AI enthusiast who doesn't want to buy a Mac mini (or Mac Studio) with maxed-out memory (required for larger AI models), and then have to mess around setting up everything themselves.

Javaid stresses that the PC comes with software built specifically for running those pre-installed AIs, avoiding all the fuss of model configuration. The CEO observes: "You can run [AI] models on existing hardware, but it's a terrible experience on many levels."

Core updates its models automatically as new upgrades emerge (unless you don't want that to happen), and the idea is that it's a seamless system and "similar to how Tesla works" with over-the-air updates.

You interact with the PC using an app (on your phone or laptop) with which you can issue voice commands should you wish.

Privacy and security

As well as the ease of use, privacy is the other big selling point for Core. It's tapping into a market of the increasing number of people realizing the potential of AI for helping with their life or work, who are concerned about sending their thoughts or data into the cloud, when they have no real idea where those queries or uploads will end up.

Core will run your AI agents continuously taking on whatever tasks you require while keeping all your data on the device, with Ghost promising that it's not going anywhere else. The personal data on the PC is encrypted to keep it safe — even Ghost can't access the data — and the device is specifically designed to keep running even if Ghost were to shut down (there's no subscription fee here).

The CEO assures us: "All of the source code, logic, and model weights live on the device, not the services. We build this device to not be dependent on having an outside entity maintaining the service."

Security is further bolstered by a firewall that limits how any agent can use credentials such as passwords, and you can dictate how much autonomy the Core agents are allowed, heavily restricting the AI should you prefer.

Javaid tells us that the firewall monitors all outgoing requests and "if it sees the agent performing a sanctioned action or exfiltrating data it shouldn't, it rejects the request and flags it for user review".

Core is priced at $3,499 in the US (around £2,600 / AU$5,000), and pre-orders for the first batch of PCs sold out on the first day that they were offered (yesterday). Those devices ship at the end of this month, and you can now register your interest in a second batch of PCs which are coming off the production line.

Analysis: a question of worth

Core can be hooked into all aspects of your life — or that's certainly the idea, and part of the reason why there's such an emphasis on privacy. It needs to be watertight for security given that Core connects to your existing devices and services, smart-home gadgets, fitness watches — or personal health data — and learns over time about how you live your life, imparting advice based on that knowledge.

Exactly how useful the system will be, well, that remains to be seen — and it may also depend on how 'smart' you've gone in terms of your array of gadgets and tech. There could be a lot of scope beyond life monitoring for Core's AI agents, of course, and there will presumably be the flexibility to get them doing whatever you want.

The other potential sticking point is that price. Is $3,499 a lot to pay for a dedicated AI PC? Well, it's undeniably a hefty sum of money, but there are some heavyweight components under the hood here (and the RAM crisis remains in full force). Indeed, the current price of the Nvidia GPU actually makes the PC a bargain as it stands (the company bought its stock of those graphics cards when they were much cheaper — so the next batch will be pricier).

If you configure a Mac mini as a top-of-the-line device for AI, you're looking at $3,199 in the US at the time of writing — and it doesn't come with the aforementioned strengths of software specifically designed for running local AI easily, and the privacy and security elements mentioned.

I've a feeling Ghost might be onto something here, at least in terms of pushing the concept of a user-friendly and straightforward AI box that does everything locally. I doubt this will be the last take on this kind of idea, and the price tag involved may eventually become a bit more realistic in the future. Although that depends on how the RAM crisis shakes out — and the latest signs are far from positive, sadly.

Ghost, a new US startup, has launched Core, a personal AI computer to run LLMs locally and will compete directly with Nvidia and AMD. What makes it noteworthy is the fact that they are selling it for $3,499.

This gets you a $3,200 Nvidia RTX Pro 4000 Blackwell SFF GPU with 24GB ECC RAM plus an AMD Ryzen 5 CPU, 64GB DDR5 SODIMM and a 1TB NVMe SSD, all enclosed in a gorgeous metal chassis worth at least $1,800. In other words, a desktop PC worth around $5,000 being sold at a deep discount.

Core goes against 64GB and 128GB versions of Nvidia’s DGX Spark (and a plethora of other similar Nvidia-based desktop workstation). The former will go on sale from October 23rd for $4,999 which explains the buzz around Ghost. How did an obscure entity managed to get parts for way less than the rest of the industry?

What is it?

Zain Javaid, the 19-year old CEO of Ghost, replying to a post on X.com that they “were able to lock down supply of this GPU when it was widely available and $1700 a couple months back. since then prices have doubled so for our next batch will probably need to raise prices. until then we’re happy to pass on our savings to the customer”.

Speaking to TechCrunch, after successfully raising $11 million from Andreessen Horowitz, Nova and a handful of A-list investors, Javaid dismissed the competition, saying “People sometimes give incumbents credit for having already built the product because they have the ingredients”.

Of course, this is a commodity play. Unlike a lot of rivals like say Framework, Ghost has assembled off the shelf parts into a nice package and layered its ‘own software layer, browser and filing system’, that is its IP. Whether or not this can be replicated remains to be seen although I can’t see rivals displaying the same level of generosity with heavily subsidized or at-cost hardware.

The first batch has, unsurprisingly, been sold out completely and will start shipping from October 31. The second batch is most likely to be sold at a much higher price (we don’t know how many parts have been sold) and for all purpose and intent, it has worked well as a viral marketing campaign, grabbing visibility on relevant news outlets and on Reddit. You can already register your interest for the next batch and reserve your Core with a fully refundable $499 deposit.

The first generation of enterprise AI projects taught businesses how to retrieve information. A user asks a question, the system finds relevant context, and a model turns it into an answer.

AI agents change that equation because they retrieve information dynamically and adjust their own state in the process. As tasks evolve they juggle everything from plan development, and outbound tool calls, to updating records, and recording results.

Multiply that activity across hundreds or thousands of agents and the data layer starts behaving very differently - with significant implications for both cost and accuracy. These implications are becoming major considerations when moving agentic AI projects from pilot to production.

Why pilots can hide the real cost

Memory becomes an operational system

Early pilots tend to be narrow by design. Typically, there’s one team working with a limited dataset and relatively simple, short-lived interactions. Take as an example requesting a summary of a document. The model completes a request and that task is done. Here, model access, tokens, and context retrieval appear to account for most of the bill.

When shifting from passive chatbots to multi-agent systems, however, the economics flip. Traditional generative AI costs scale linearly with users, whilst the cost of running multi-agent deployments tends to escalate with task complexity. Proof-of-concept expenditure typically multiplies as tasks become more complex.

For example, AWS puts a text-based proof of concept handling about 100 interactions a day at around $40 a month. Its estimate for an agent-based proof of concept using a knowledge base and guardrails at roughly the same volume is around $840 a month. Anthropic's write-up on its multi-agent research supports this finding.

The data shows multi-agent systems use 15x more tokens than chat interactions and that economic viability requires the value of the task to be high enough to justify the expense.

In multi-agent systems, therefore, it’s easy to see why costs accrue. Agents use dynamic loops, tool calls, retries, and context transfers to complete a task. But there’s another important dimension - agents need to remember and reason across increasing volumes of data.

Agents have longer operational lives than a typical chatbot session. They have to remember yesterday's actions, preserve the evidence behind a decision, and make that information available to other agents in the fleet. Every completed task becomes a new state.

As the agent fleet grows, the business is paying not only for the next answer but also for the expanding operational provenance required to operate efficiently, and safely.

Early generative AI systems often treated memory as context - retrieved when required. Persistent agents create a different requirement. Memory becomes a live operational system that is constantly updated.

That matters for both performance and cost. A narrow write path can create contention as more agents update records. Separate copies of the same context increase storage and synchronization work, and keeping every historical record on the fastest storage tier makes rarely used information unnecessarily expensive.

When designing the data layer to support agentic fleets, the service level dictates how memory is accessed and managed. Leaders should establish how quickly each kind of memory must be available, who can update it, which agents should share it, and how long must it remain immediately accessible.

Designing memory for a fleet of agents

Three main principles will become increasingly important as the variety and complexity of agent deployments grows.

The first is concurrency. Leaders should ask whether write capacity can grow as the number of agents grows. Infrastructure designed mainly for read-heavy applications may behave very differently once large numbers of autonomous processes start updating state at the same time.

The second is shared memory. If several agents are working on the same customer, asset, or process, creating separate copies of the same context can introduce cost and inconsistency. A common source of state can make collaboration easier, provided access controls and provenance remain clear.

The third is separating active memory from historical memory. An agent may need the last few minutes of a workflow immediately, while records from six months ago might only be required for an audit or an unusual query. Treating those two categories identically is an expensive default.

Frequently accessed state can remain close to the compute layer, while older information moves to lower-cost durable storage. Compute can then scale according to current activity rather than the total memory accumulated since the system was launched.

How historical changes are stored poses a similar question. Saving every full version of a record makes retrieval straightforward but consumes more capacity. Saving only incremental changes uses less space, but reconstructing an old state can take longer. Periodic snapshots combined with smaller changes between them can offer a useful middle ground.

What to establish before scaling

These read like technical considerations, but they set the economic model for the deployment, and they are cheaper to resolve before an agent fleet expands than after.

Once it is accepted that agents create information as well as consume it, earlier design choices become measurable rather than abstract. For example, how much state each agent writes and how often; how many agents may update the same customer, asset, or workflow at once; how much memory needs immediate access versus cheaper durable storage after a day, a week, or a month; and whether context can be shared safely rather than copied per agent.

Each of these steps carries a cost, and together they determine the figure that decides viability at scale - the cost per task is both the number of agents and the volume of retained memory.

Resolving these considerations before expansion changes the design of the pilot. A team can test the write load, retention rules, and the expected sharing model in production while the system is still small, which is the point at which hidden costs are both visible and cheap to fix.

AI economics start below the model

Even if model prices continue to fall and inference becomes more efficient, large-scale agent deployments will still create a growing stream of operational data that has to be written, shared, protected, retrieved, and retained.

The cost of that activity will depend heavily on the architecture underneath the model, but companies planning the next stage of AI adoption should measure the whole lifecycle of an agent task, including the memory it leaves behind. Doing that before a pilot becomes a fleet gives technology leaders a much clearer view of whether an AI system will remain economical when it reaches production scale.

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For over a decade, enterprise cybersecurity was built on a straightforward setup: a human user sits behind a screen, authenticates and carries out tasks within a session.

Traditional identity and access management (IAM) frameworks were designed around this single assumption. They validated credentials at login, established a trusted perimeter for the duration of the session, and monitored subsequent activity.

But the emergence of the agentic enterprise is challenging that assumption.

The issue facing modern internet security operations is no longer just whether an identity was verified during initial access. As autonomous software agents, coding assistants, and automated workflows are beginning to operate across cloud environments, code repositories, APIs and internal systems are dealing with a level of speed and autonomy that traditional controls were not designed to manage.

The issue now is whether organizations can see, govern and control what happens after access is granted, especially when the actor is software making decisions in real time.

Attackers increasingly exploit systems through the path of least resistance, rather than searching for complex technical vulnerabilities. This makes stolen or compromised login credentials a prime target, allowing attackers to look legitimate when gaining access without needing sophisticated attack methods.

At the same time, shadow AI, short-lived credentials and machine-driven actions are expanding the attack surface. As enterprises continue to deploy AI tools faster than many security teams can secure them, visibility gaps and governance blind spots will persist.

Where traditional IAM fails

One of the biggest challenges to securing the agentic enterprise is that traditional identity controls are too static for machines which operate continuously. Legacy IAM and privileged access tools are effective at validating access at a point in time. But AI systems do not authenticate and stop. They make choices, invoke tools and change systems after the initial login has been triggered. This creates a need for identity controls which evaluate behavior in context as actions occur, not just at login.

Poor visibility across agentic systems is another barrier. Many organizations still lack a clear inventory of which AI agents exist, what data they can access, who approved them and what actions they are taking on whose behalf. When agents are distributed across fragmented cloud and SaaS environments, those questions become harder to answer and security blind spots grow quickly.

Finally, credential exposure presents a significant risk. In human-led systems, long-lived secrets, shared credentials and broad delegated access are already problematic. They become even riskier when handed to autonomous or semi-autonomous tools operating at machine speed. Security leaders need a model that reduces that exposure, improves attribution and preserves a clear chain of accountability back to a human owner or policy decision.

Securing the human-AI workforce

Navigating this new threat landscape means evolving enterprise security from a system designed primarily for human administrators into infrastructure that can support humans and artificial identities together. This is the broader shift behind IAM infrastructure for the agentic enterprise: making identity easier to operate programmatically, governing AI agents across their lifecycle and evaluating trust continuously as actions occur.

One part of that shift is governance. Organizations need to identify AI agents, assign ownership, define boundaries and understand what those agents are allowed to do. Another necessary shift is runtime trust: ensuring access is evaluated in context and that risky behavior can be contained quickly without relying on static credentials or blind trust. The final part of this change is operability. As more work moves into APIs, terminals, orchestration layers and AI-assisted workflows, identity management systems need to be easier to use beyond the admin console.

Organizations need to treat this as a key operational design matter, moving beyond asking only who logged in and start asking what is acting in the environment, what it is authorized to do and whether teams can intervene when risk changes.

Securing identities at machine speed

The agentic enterprise is no longer theoretical. AI systems are already influencing operations and decision-making across the business.

The challenge for cybersecurity leaders is not to restrict what these systems can do outright. It is to ensure they operate safely, accountably and within clear enterprise guardrails. As unglamorous as it may be, governance is going to be one of the most important factors in determining if a company succeeds or fails in the long run with its AI programmes.

Organisations which can effectively operate and secure AI will be the ones that build identity, accountability and runtime control into the operating fabric of the enterprise from the start. Innovation must be made governable, without being stifled and the organizations that get this right will be better positioned to use AI with confidence.

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In June, Anthropic released two of its most capable AI models to date. Within days, the US government ordered the company to cut off access for every user outside the country, citing national security concerns.

Anthropic had no reliable way to check who was logging in from where. So, it pulled the models entirely, for everyone, including its own American customers. Two and a half weeks later, the controls were lifted and access came back. One of the two models remains restricted to a small number of approved organizations in the US.

That sequence of suspend, restore, and partially restrict is worth looking at. It shows that access to the AI tools a business has built into its operations can change on a government's timetable rather than its own, with little or no notice.

For security leaders, that's the new reality to plan around. Risk is concentrated in a small number of providers, regulation hasn't caught up, and the only thing properly within your control is how well you've covered the fundamentals of security.

Building resilience

The episode has since become something of a reference point in wider discussions about AI sovereignty, and rightly so.

But the mistake would be treating it as a story about one vendor and one fortnight in June. The pattern behind it was already visible before this happened, and it remains visible now. Critical AI capability is concentrated among a small number of providers, whose commercial, policy, or regulatory position can shift with limited warning.

Export-control regimes on frontier AI are still forming and not yet settled. Meanwhile, several governments have used the past few months to accelerate their own sovereign AI investment instead of waiting to see whether the risk repeats itself.

Businesses need to realize that the practical lesson here isn't to predict the next disruption. Instead, it’s to assume that AI access, functionality, and governance requirements will keep evolving, sometimes abruptly, and to build resilience accordingly.

Resilience in this context has a fairly specific meaning. It’s about knowing where AI is embedded across the business, understanding which processes and teams depend on which tools, and having a contingency plan ready before something changes.

Organizations that could answer those questions in June were in a materially better position than those still working it out as the news broke.

Independent validation is still essential

When the export controls were lifted, it followed an agreement between Anthropic and the US government on how future risks would be flagged and reviewed. That's a start, but it's still one company and one government working the problem out between themselves, with the terms set largely behind closed doors.

OpenAI's response to its own security incident follows the same pattern. After one of its models breached Hugging Face's systems during testing, the company paused its largest frontier training run and introduced new internal safeguards, on its own timeline, using its own judgement about what the incident required.

Two of the industry's biggest players are currently setting their own terms for how AI risk gets identified and addressed, and businesses are expected to simply trust that the terms are adequate.

Initiatives like the International Network of AI Safety Institutes exist for good reason – no single vendor should be marking its own homework on how safe or how risky a model is. Yet these bodies remain government led, so strengthening their independence should be a priority.

That doesn't mean you should wait for global governance to mature. Be proactive by building your own capability to question what a vendor or a regulator tells you, rather than accepting it at face value.

It starts with visibility

Most importantly, you can't secure, govern or build resilience around something you can't see. This is the practical starting point and it's where most organizations' AI risk breaks down.

Ask businesses for a complete inventory of the AI technologies running across their operations, and many will give an estimate rather than a concrete answer. Models get adopted department by department, sometimes officially, often not. Data flows into and out of them without a central record of where, or governance to control who has access.

None of this is unusual. It's simply what happens when adoption outpaces oversight, which is where a lot of organizations currently are with AI.

The fix isn't complicated, even if it takes discipline to maintain. A working inventory needs to answer four things for every AI tool in use – where the model sits, what data it can access, who has access to it, and how a change in vendor policy, regulation or export control could affect the processes built around it.

That last point is the one the events in June exposed. For now, the key lesson for organizations is to understand the environment they’re protecting and to know the gaps in their defenses alongside a fully tested business continuity plan. Those fundamentals will matter more than anything else in the coming months as this new landscape remains highly unpredictable.

Visibility of that kind can turn a scramble into a known, manageable problem around which parts of the business are exposed, what the fallback looks like, and how long it takes to execute. That's the difference between reacting to disruption and being ready for it.

Governments and vendors will keep shaping the terms of AI access. Businesses that know their own dependencies will be the ones still standing tall when the terms change again.

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I spend much of my time these days in rooms with plant heads, automation leads, system engineers and almost every conversation about "AI-ready manufacturing" starts with machines and ends with people. We talk about sensors, digital twins, predictive models, robotics on the shop floor and then someone asks, "But who's going to run all this?"

For manufacturing organizations, workforce transformation is becoming a fundamental part of the infrastructure required to support AI-ready operations. It isn’t a secondary HR function or a training line item - it is as critical to performance as hardware on the shopfloor or the datapipes running through it.

The math doesn't say what people think it says

Deloitte's 2026 Manufacturing Industry Outlook estimates that more than 81% of manufacturing task hours will continue to be human-driven, even as AI adoption is expected to roughly double, from 9% to 22%, over the next couple of years.

We're heading into one of the fastest periods of automation adoption manufacturing has seen, and the overwhelming majority of the work is still going to run through human hands and human judgment. That's not a contradiction. It's the actual nature of the transition. AI isn't replacing human oversight - it is shifting what operators need to master.

Workforce strategies built on the assumption that automation shrinks the need for skilled people are fundamentally flawed. What shrinks is the room for narrow, single-discipline expertise. What grows is the need for talent that can can move fluidly across disciplines.

Multidisciplinary talent is the real differentiator

Here's what I mean by that. A connected factory today needs people who understand mechanical and electrical engineering but also fiber and network connectivity, because a smart factory runs on the same reliability principles as any other critical network. It needs people who are fluent in AI and automation to work alongside intelligent systems.

And it needs people who can read data, not as analysts, but as operators, quality engineers and maintenance leads who treat data literacy as a core job skill, the same way you'd expect them to read engineering schematics.

Manufacturers who get this right are the ones who build teams capable of maximizing hardware to its potential, troubleshoot it, question it when the model gets something wrong, and improve it over time. That capability doesn't appear on a spec sheet, but it shows up in uptime, in quality, improved productivity and in how fast an organization can adapt when the technology itself changes, which in the AI era - it will, constantly.

That's the competitive edge. Not "who has the most automation," but "whose people can do the most with it."

Learning has to become continuous

For most of my career, technical training followed a periodic cadence - onboarding, a milestone certification, or an occasional refresher. That model no longer fits the pace of modern manufacturing anymore. The technologies underpinning intelligent manufacturing are moving fast enough that a skill set from eighteen months ago can already be dated.

We’ve had to rethink learning as an ongoing operational rhythm rather than a discrete event. That means shorter, more frequent upskilling cycles tied to what's actually changing on the shopfloor, not a generic annual curriculum.

It means giving engineers exposure to AI tools and data fundamentals even if that's not their "core" job, and giving digital and IT teams enough grounding in production realities - uptime pressure, safety constraints, quality tolerances.

This is a permanent state, not a phase we're passing through on the way to some steady future. Continuous learning is the new steady state.

Breaking silos is an operational necessity

I'll be candid: "break down silos" is one of those phrases that's been said so often it's started to sound like filler. But in AI-ready manufacturing, it's not a nice-to-have culture goal. It's a functional requirement.

An AI-enabled production line doesn't recognize the line between "manufacturing", "IT" or "digital transformation." A single sensor feeding a predictive maintenance model impacts production schedules, data engineering, and IT infrastructure simultaneously. If these teams operate in isolated reporting lines with separate priorities and separate vocabularies, the technology will underperform no matter how good it is on paper.

The organizations building genuinely resilient, connected operations are the ones actively engineering collaboration between these functions like shared goals, shared data, and shared accountability for outcomes.

Building the next decade

None of this is a call to slow down on technology adoption. It's a call to treat workforce capability with the same seriousness we bring to any other major infrastructure investment. You wouldn't deploy a connected factory on unreliable power or unreliable connectivity. You shouldn't deploy one on an unprepared workforce either.

The manufacturers who'll lead the next decade won't be the ones who automated the fastest. They'll be the ones who built people capable of making AI actually work on the ground; multidisciplinary, continuously learning, and organized around the problem rather than around the org chart.

That's the infrastructure I think about most. And it's the one I'd encourage every manufacturing leader to start investing in now, before the technology outpaces the people meant to run it.

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• AWS CEO looks to debunk common 'myths' about data centers
• Amazon announces it will no longer make local governments sign NDAs when proposing data center projects
• The company has also laid out new codified community commitments

CEO of Amazon Web Services Matt Garman has released a statement in defense of AI data centers that labels the technology as “The race our nation can’t afford to lose”.

Within it, Garman calls on the United States to band together against “misinformation and outright lies” that are being spread about AI data centers to prevent the country from missing out on the huge benefits that will be provided to the economy, national security, and local communities.

“A lapse in our nation’s focus or resolve could put us behind, and potentially irreparably so,” Garman says.

Debunking the myths

There has been a significant amount of messaging about the positives and negatives of the AI data center buildout, with falsehoods and misinformation fueling both sides.

Garman’s statement starts with a section aimed at countering the ‘myths’ spread about AI data centers, such as water use, higher energy costs, and pollution - but keep in mind that Garman makes no distinction between AI data centers and regular data centers.

For example, Garman argues that data centers account for “0.5% of total U.S. direct industrial water use”, but draws on the figures of direct water use to argue this point.

The indirect water usage of data centers is far higher, and often isn’t included in water estimates for AI data centers. The indirect water usage includes upstream factors such as the water used to generate the huge amounts of electricity that data centers rely on. Data centers are also being built in regions that have experienced long-term droughts, exacerbating water shortages and their effects on local ecosystems.

Garman also labels the rise in US energy bills caused by data centers as “misleading and convenient scapegoating,” arguing that “the accelerated adoption of AI is precipitating a build-out that was destined to happen.” What Garman fails to mention is that the infrastructural build-out is being fueled by AI data centers.

Up until very recently, new grid connections for data centers haven’t been paid outright by the developers. Instead, grid operators have funded new connections and upgrades to power lines through increased bills on rate-payers. It is only the backlash from rate payers that has preceded the introduction of Trump’s voluntary Ratepayer Protection Pledge - and the much more binding (and bipartisan) Ratepayer Protection Act. Had there been no public backlash, these measures would not have been necessary.

The next myth Garman attempts to counter relates to the pollution caused by “large backup diesel generators”. Garman states that these are necessary as the services “consumers, businesses, and governments rely on every day” are provided by data centers. This is, in its exact wording, true.

But the pollution many of those in the US are arguing against is attributed to the fossil fuel burning gas turbine engines that many AI data centers use as an alternative to connecting to regional power grids - particularly if their connection requests are denied. The turbines at some campuses give off the same noise pollution as an international airport, 24 hours a day, seven days a week. The sounds they produce have also been involved in causing neurological symptoms in local residents.

The demand for gas, pushed higher by AI data centers, could see the US use more natural gas than Germany and Japan combined by 2035, and the associated pollution could cause generational health decline and cost $25 billion in associated costs.

The final myth that Garman seeks to debunk is that ‘Community members get nothing from data centers’, arguing that data center operators are “often the single largest taxpayer in a community” and that this “reduces the taxes paid by families, but it also funds investments in schools, fire and police departments, parks, roads, and other community services.”

But data center operators are also the largest beneficiaries of tax cuts and credits too. Numerous states have begun repealing tax cuts for data centers after seeing billions lost in expected revenue - billions that otherwise could have been used for funding investment in community services.

Garman also points to the boom in construction jobs associated with the AI data center buildout, but doesn’t include that many of these jobs are temporary. Once the construction of a data center is complete, these jobs often disappear unless there is continuing demand within the local region, and operational data centers themselves require a very small number of employees.

Amazon’s new community commitments

In an effort to get local communities onboard with data centers, Garman’s statement also lays out a number of ‘codified’ community commitments that Amazon will adhere to.

These commitments fall under four pillars; Protecting & Enhancing Communities; Providing Jobs & Economic Benefit; Engaging Communities; and Building & Operating Responsibly.

The full list of commitments is available here, but there are a few key points to look at. Outside of vague commitments to covering the cost of grid connections and infrastructural upgrades, paying their fair share of taxes, and paying the workers they hire, Amazon will contribute "$1 billion over the next five years" to jobs and education, energy affordability and water solutions, and community investment.

Given that Amazon operates 236 data centers that are operational or under construction, this $1 billion figure amounts to around $4,237,000 per data center over the full five years, or about $847,000 per data center per year.

Amazon has also announced a commitment to “no longer use nondisclosure agreements with the government agencies we work with on our projects.”

This is by far the biggest announcement included in Garman’s statement, yet it takes up just a single line. Local communities have long called out for more transparency around AI data centers.

In numerous cases, NDAs have been used to prevent local governments from discussing planned data centers with their constituents. In numerous other cases, data centers have only been announced to local communities after the necessary permits are secured, preventing local residents from being involved in the consultation process and limiting their ability to oppose new projects.

The community commitments offered by big tech companies such as Amazon and Meta looking to build AI data centers are not out of the goodness of their hearts. They are a direct result of the community backlash against AI data centers that has occurred across the US.

Had this huge opposition not happened these benefits would not be offered, but they are being jazzed up by big tech companies as a benefit they are providing to communities rather than the efforts of grassroots movements and every day Americans fed up with being subject to rising bills, increased noise pollution, and an economic system that favors those with money and power.

• Google is accused of destroying 300 hectares of forest for a data center
• Microsoft’s data center plans are ‘lipstick on a pig,’ a critic argues
• Some residents are pushing back against further data center plans

Big Tech firms like to tout their environmental credentials, positioning themselves as responsible stewards of the planet’s welfare. Yet you’d be forgiven for viewing those claims more than a little skeptically in the artificial intelligence (AI) era — after all, a slew of recent reports have suggested that such environmental posturing has a bad habit of withering away when there is money to be made.

Google, for example — whose sustainability web page is emblazoned with the headline “Making AI helpful for everyone, including the planet” — has been accused of destroying 300 hectares of Finnish forest (equivalent to roughly 500 football fields) to clear space for an AI data center before an environmental-impact assessment was finished.

Hanna Halmeenpää, chair of the Finnish Association for Nature Conservation, told the AFP news site that, based on satellite images, “we’ve seen that nature sites which should be preserved have also been logged.”

And Google isn’t the only Big Tech firm in hot water. Microsoft’s efforts to blend its data centers into the local area by restoring wetlands and gardens have met with criticism from those who say they merely disguise the data centers’ negative effects.

For example, Adrian Shelley, Texas director at nonprofit body Public Citizen, called Microsoft’s plan “basically lipstick on a pig.” Shelley also noted that the data centers would be powered by nearby pollution-heavy gas plants, adding: “I cannot really see any level of biomimicry investment that makes a gas plant a desirable neighbor.” Even Microsoft’s own estimates said its emissions were up 25% in 2025, “driven primarily by the expansion of our datacenter infrastructure.”

The AI pushback

Perhaps these cases of Big Tech firms finding themselves curiously unbothered by environmental matters should not come as too much of a surprise. After all, the likes of Google and Microsoft are publicly traded companies that are beholden to their shareholders above all.

Many citizens are pushing back against the AI gold rush and its attendant data center expansion. In Texas, for example, Taylor resident Pamela Griffin is fighting plans to turn a plot of land near her home into a data center, arguing that a 1999 deed reserved the space to be used as parkland — something she says has not been honored.

Clashes like this are becoming increasingly common as local residents are resisting attempts to turn land over to AI usage, citing noise pollution and environmental degradation, among other concerns.

And as long as Big Tech companies feel there are fresh riches to be made from AI, that’s likely to continue.

• 16-year-old bug hunter gained admin access to Microsoft's internal Titan analytics service through an unsigned login token that the service never checked
• From there, an estimated 17.3 trillion stored rows and a metadata table of about 25,000 accounts were reachable, though Faav says he only sampled data, avoiding dumping records or touching customer data
• Microsoft has hardened the service's security since and paid a $5,000 bounty, downplaying the trillion-row figure as a theoretical storage estimate rather than actual exposed customer information

A 16-year-old bug hunter who goes by 'Faav' logged into Microsoft's internal Titan analytics platform using a token that no real credentials should have produced, and from an administrator's seat, he could see an estimated 17.3 trillion stored rows and a metadata table listing roughly 25,000 accounts.

Microsoft paid him $5,000 and has since closed the hole, prompting him to detail his findings online even as he describes the impact as hypothetical rather than a consequential breach.

He said the way in was a single missed check, which let him access a system that should otherwise have been locked to Microsoft staff behind a VPN.

A simple mistake that could have catastrophic in the wrong hands

Microsoft's Titan sits behind a "VPN required" page meant to keep its web interface to Microsoft staff, but its API was still reachable through an Azure Cloud Services host, and the Swagger file describing that API listed four access routes.

Three demanded Azure AD authentication. One, "/v2/Query", did not, and it accepted raw SQL, giving Faav a way in. He wasn't moving blind either; to learn what tables to ask for, Faav pulled 2023 snapshots of Titan's pages from the Wayback Machine and recovered an archived Apache Superset configuration listing 56 table definitions.

The deeper flaw was in how the service read login tokens. As Faav explained, Titan validated the contents of a JSON Web Token- the tenant, audience, application ID, and user- but never verified the cryptographic signature that is supposed to prove the token is genuine, offering the analogy of a hotel where every door has a working keycard reader but any keycard opens any room, making it essentially an ineffective check at best.

He submitted an unsigned token, and then, after ten days of failed logins, stopped treating the "upn" field as a real email identity and set it to the plain string admin. Titan resolved that to local user ID 1, which carried the admin role, and ran his query.

The saga has an interesting AI subplot: Faav hunts with an orchestration bot he built called Antares, which he says was running OpenAI's Codex and Anthropic's Claude. Antares did the grunt work, enumerating subdomains, mapping the attack surface, and pushing the forged token through four layers of validation, one error message at a time.

It did get stumped, however, when it tried to find a UPN; it got stuck attempting only email addresses. That is not a problem per se for what was essentially a brute-force-style approach using the most probable email addresses, but it took manual intervention from the hacker to try 'admin' instead as an alternative to finally get access.

Admin access opened Titan's platform metadata database, which, according to his tally, held about 25,000 account and email records, roughly 18,000 employee email records, 15,000 organization records, and tens of thousands of dashboards, charts, and dataset definitions.

The disclosure moved quickly once he reported it on September 5 2026 as case 144051. Microsoft asked him to stop testing and hand over his IP address between September 6 and 8, locked the endpoint on September 9, and paid out on September 17.

There is an interesting caveat, however: Faav discloses that Microsoft had editorial control over his write-up, cutting sections and figures and reshaping how it described the impact before he published, so the most authoritative account of this bug has already been shaped by the company it embarrasses. None of this makes the bug small, however: one unverified signature that made every other access control in Titan pointless is a potential security nightmare, nipped in the bud by a teenager who has had success with similar bugs at Amazon, Google, and Adobe, in addition to a prior disclosure at Microsoft.

• Guilford County, Pima County, and Charlotte are pausing new data centers, but none of the pauses currently affect projects already under construction
• Pima County, AZ, has passed a 120-day pause that does not affect existing projects, even as its legality remains disputed
• Charlotte, NC, wants to extend its 150-day pause by 11 months to October 2027, and Guilford County, NC, holds an October 15 hearing on a moratorium of up to 180 days, even though it has no pending data center applications

In less than two weeks of September 2026, three local governments in two states moved against new data centers, with one passing to date: Pima County, Arizona, adopted one on September 22 and, within a week, the Goldwater Institute said it was illegal.

Guilford County, North Carolina, voted on September 17 to hold a hearing on a moratorium on September 28, and Charlotte set a hearing on extending its own pause into October 2027, a net increase of 11 months, should it pass.

The moves come amid backlash from residents as local officials scramble to keep hyperscale campuses out of cities otherwise earmarked for large buildouts because of cheap land, electricity, lax local laws, or a desirable mix of all three.

A pause with nothing to pause

Guilford County, North Carolina, has not paused anything yet despite its Board of Commissioners voting unanimously to schedule an October 15 public hearing on a temporary moratorium of up to 180 days on new data center projects. This is for two reasons: the ordinance still needs to be drafted, heard, and potentially adopted.

The other reason, somewhat ironically, is simply because there is no actual project to stop or pause to begin with. Planning Director Leslie Bell told the board the county has no pending building permits or other requests for a new data center in its unincorporated areas, the Rhino Times reported. It seems to be laying the groundwork to prevent future legal challenges. This, however, comes as more North Carolina cities and towns push data center moratoriums, with Bell putting the number at over 18.

Pine County, Arizona, has taken a more nuanced, albeit somewhat underwhelming, approach. As the only of the three counties we are covering with an active moratorium in place, it also has one that does not address the projects residents have raised as an issue at meetings.

The moratorium passed on September 22, when its Board of Supervisors voted 3-2 to pause new data center development in unincorporated areas for 120 days, fails to cover two key projects already underway: Project Blue and Project Gravel. The county's proposal still faces a potential legal challenge, as the Goldwater Institute sent a demand letter asking the county to rescind the pause altogether.

Charlotte, North Carolina, whose council voted 11-0 on June 8 to pause new data centers for 150 days, is considering buying even more time to make a final decision beyond the four months it has already spent.

On September 28, the council set an October 12 public hearing on extending the pause by 11 months, through October 11, 2027, the Charlotte Observer reported. At-large Council member Dimple Ajmera called it "one of the consequential policy decisions that we will be making."

The problem is more complex: data centers are allowed by right in eight of Charlotte's zoning districts, so they need no council approval there, and council members have raised concerns about that gap since at least 2023, but a policy review has yet to address it. As Deputy City Manager Alyson Craig told a council committee in September, there was no clear path for ordinance changes.

The problems of each county are different, but one could argue that they are considerably similar in the way that all of them are looking to address a gap in the rulebook rather than a wave of upcoming projects at a time when there is increased interest by hyperscalers to find cheap land with cheaper, readily available access to water and power.

While rulebooks can be updated, local governments have to wrestle with the tradeoff: data centers will create few long-term jobs, but they can offer high taxable value in some cases through local taxation, in addition to the power and water bills they will inevitably generate.

At the same time, residents who voted said councils in are concerned about potentially higher power bills and limited access to water, resulting in a perfect storm where legal challenges like the one from the Goldwater Institute only add to the chaos for counties that have never had the legislature to handle such issues in the past.

• A majority of US voters (61%) now oppose AI data center construction
• Trump voters support construction, but only at 49%, versus 47% opposing
• Polling took place in early September 2026

The majority of US voters have expressed their opposition to further AI data centers, as newly published research by The New York Times and Siena University found 1,503 likely US midterm voters have major concerns over the direction of travel with AI data center construction.

However, the survey found the data center issue is not a priority for most US voters, where economic and foreign policy matters are more prominent.

Reaction to the polling is likely to be minimal, but it appears to underpin the growing discontent with the approval of increasingly expansive data centers. Concerns over the environment (such as wildlife, water use, and impact on nearby communities) dominate the responses to the poll.

The data center question

The question that has attracted the most attention from the poll (paywalled) – which covers a range of issues rather than just AI – was worded “In general, do you support or oppose the construction of data centers to support artificial intelligence, also known as AI, technology in the U.S.?”

An overall response of 61% opposing to 34% supporting highlights how fed up voters are with the AI issue. Data centers are perhaps the most tangible, visible element of the technology for voters to focus their ire on, given the impact the facilities have on local communities and ecology.

Perhaps the most interesting factor of the overall response to the main question was how the result is split. Based on 2024 voting, 49% of Republicans support AI data center construction versus 47% who do not; 18% of Democrats support construction, while 75% do not. (Among independent supporters, the result is 36% support, 60% oppose, and all three measures accommodate the “don’t knows.”)

Regulatory supporters

While the survey explored many topics, it spent some time quizzing the respondents further on AI. Here, some nuance can be found, with 56% of voters in favor of limits to AI data centers, whereas 38% supported a total ban on further construction. Interestingly, a slight majority (42% vs 40%) trusted the Republicans to handle the data center matter.

Those polled expressed support for key concerns surround data center construction, with 32% citing water usage or the environment as the biggest worry. The impact on local communities and resources was expressed by 21%, while 18% had a general distrust or dislike of AI. Impact on local economies and jobs was 9% of those polled, while 10% had concerns over AI regulations.

The poll was completed before President Trump’s statement against guardrails, which he believes would hamper America in the AI race with China.

• 38% of workers who regularly use AI have admitted to “hoarding” prompts to gain a professional advantage
• Meanwhile, 21% have hidden their AI use from their manager in order to appear more efficient
• Many just don't want their colleagues to gain the same advantage to productivity

New research has claimed nearly two-thirds (62%) of workers using AI regularly believe having better AI skills gives them an edge – and 38% are keeping productive prompts to themselves to to gain a professional advantage.

A Use.AI survey of employees with regular AI users in the workplace found that of this number, nearly half (44%) didn’t want their colleagues to gain the same advantage to productivity, while 31% claimed that it was they who had invested the time and effort into developing the prompt and didn’t want to give away their advantage.

While the use of AI for productivity boosts can unlock advantages, the survey indicates a proprietary attitude, with workplace “gatekeepers” potentially hoarding the best AI prompts.

AI teamwork?

A trend of gaining an advantage in the workplace through developing AI skills seems to be the takeaway from this survey, which quizzed 13,276 adults who use AI in the workplace, based in organizations in the UK, US, Canada, Australia, the EU, and Latin America.

As well as unearthing a habit of keeping AI productivity shortcuts a secret, the survey also underpins a growing pattern of anxiety around the technology’s impact on a worker’s performance. It found that 41% of AI users in the workplace were worried about colleagues and managers finding out how much time and effort the technology was saving them. They were also concerned that they would be expected to achieve the same levels of productivity and speed in future.

It also found that 36% had hidden their AI use from coworkers, with 21% hiding their new-found productivity skills from their manager.

AI-generated tensions

Many criticisms of AI at the point of use (as opposed to data centers) tends to dwell on the issues surrounding generative AI and its impact on creative industries. In a non-creative industry, there is the reliability of the results to consider – but Use.AI’s survey seems to point at a new issue.

Ihor Herasymov is a Co-Founder & CEO of Use.AI, and says that the survey suggests “tension” is developing in the workplace around AI.

“When a useful AI method becomes part of someone’s personal edge, sharing it is no longer a neutral act. Companies may need to think more carefully about how they reward people who turn private know-how into shared capability, because employees will notice quickly when openness helps the organization while weakening their own position.”

In short, the companies that find this secretive AI prompt culture developing may find that the metrics with which judge employee performance will have to change.