I've had a love/hate relationship with Siri since the iPhone 4S in 2011. At first, I loved it. I'd frequently ask it for random facts, to tell me a joke, to check the weather, or to send a message. I used it so much after its debut that my wife and kids started calling Siri my girlfriend.

Then the novelty wore off, and my interactions with Siri all but stopped. I didn't hate Siri; I just no longer found it a tool I wanted or needed to use regularly.

Since then, every time Apple announced new features or capabilities, I'd attempt to reconnect with my old fling, only to grow bored once again.

In June, Apple unveiled Siri AI — a new and improved digital assistant the company touted as smarter, more capable, and full of new features. I was, admittedly, very skeptical. The year prior, Apple demonstrated a more powerful version of Siri that it never delivered.

A few days after the announcement, I installed the developer betas on a MacBook Neo, iPad Pro and iPad Mini — three devices I almost never talk to or interact with Siri on. However, I do often press Command+Space for Spotlight for common tasks. Now, instead of Spotlight, the same key combo brings up a text box you can use to chat with Siri. Also, the new Siri includes a dedicated Siri chatbot app.

For the first six weeks of the beta program, that’s how I used Siri — via the old Spotlight shortcut or directly in the Siri app. I’d often ask Siri something, then feed Gemini the same prompt, and compare the results.

Initially, I was puzzled by the lack of an option to pick the AI model I want to use. As someone who uses Gemini daily, I'm used to switching between the latest Flash-lite, Flash, and Pro models, plus the Extended version for, presumably, better answers. Claude has similar options with Fable, Opus, Sonnet, and Haiku, each with a version number after it.

In the Siri app, there aren't any options at all. You open it and start typing. It's simple, if not basic.

There’s a list of past conversations, as you’d see for old threads in Messages or Mail, only these include all conversations you had with Siri, regardless of how you interacted with Siri, be it voice or typing.

At first, the simplicity of the Siri app was something I just couldn't wrap my head around. How do I know if I'm getting the best possible answer for daily questions or more complex tasks like coding a website? I wanted to change models to ensure it was more thorough. But I couldn’t.

I didn't trust Siri. How could I if I didn't know how it was approaching our conversation?

It wasn't until I installed iOS 27 on an iPhone 17 Pro Max that my use of Siri AI went up, and with it Apple's entire approach finally clicked.

My typical workflow on an iPhone is to swipe down on the screen, type a question or an app name, and continue from there. I put zero thought into a process I do dozens of times a day. Just swipe, type, and a few seconds later I have an answer. Only now, instead of that gesture triggering Spotlight, it's Siri.

The more I used Siri, the more I began to enjoy not having to make a conscious decision about what model, version, or mode to use. The end result is exactly what I go into every conversation with an AI bot wanting: Ask for something, get an answer.

To be clear, Apple is routing queries behind the scenes across multiple on-device and cloud models — like AFM 3 Core or AFM 3 Cloud Pro — via Private Cloud Compute. But you never see those names. Apple intentionally hides the engine under the hood, making the technical complexity invisible to the person just trying to get an answer.

And for the vast majority of people, that’s perfectly fine.

The real advantage Siri AI has over Google Gemini on Android is that Siri’s baked into iOS. No matter how or where you interact with it, you get the same exact experience, and a record is kept in the Siri app.

For Android users, Gemini’s implementation is not as cohesive.

There’s a dedicated Gemini app, just like Siri. Only the Gemini app has far more tools, features, and settings. But outside that app, you have to intentionally seek out Gemini on an Android phone.

For instance, there’s a Google search bar on every Pixel’s homescreen. Tap on it to use Google Search, where you’ll then get an AI overview as the top result. But that’s not directly using Gemini — you’re using Google Search.

If you want to talk with Gemini, either by holding in the power button or triggering it with Hey Google, you have to go through extra steps to enable Gemini’s voice features (instead of Google Assistant) — it’s not automatic. Even after I enabled the power button to trigger Gemini on a Pixel 10 Pro XL, I kept getting a pop-up saying I couldn’t talk to Gemini until I tapped one more button to enable the mic.

What. A. Process.

While working on this story, I got an email from Google saying Google Assistant has now been replaced by Gemini on all my compatible devices — and while I welcome the change, using Gemini on Android is still a segmented experience.

On the iPhone, none of that is required. Regardless of how or where you access Siri, you get Siri… now with a side of AI.

I think Apple’s minimal approach to the Siri app is actually a feature on its own, and a darn good one. When I first started experimenting with Gemini, it took me weeks to learn the nuances of when to use specific models; then, as soon as I got comfortable, new models were released, and the process started all over again. That cycle continues even now, nearly two years later.

I still believe, however, that there’s a need and place for more control over AI interactions. Some people, especially those who use AI for more advanced tasks like coding, benefit from full control of the model.

But the vast majority of people — many of whom are scared of AI — will notice that Siri got smarter and can do more things, without realizing (or caring) that it’s a full-fledged AI platform behind the results.

It’s been a hot minute since ChatGPT last updated its Images feature. Its last update, to Images 2.0 in April 2026, was pretty solid and I’d say put it ahead of Google’s Nano Banana 2, even if its images still took longer to create, which was a bit frustrating.

Today's new Images 2.5 update adds a whole bunch of extra features and tools to the existing image generation engine that make it a lot easier to use, and it's quicker too.

In fact, there are so many features now in Images that to say it’s overcrowded would be an understatement. Because of that, it can be a little hard to locate the best new features amongst everything else, so I’ve pulled out the ones you should definitely look out for, because they make a real difference.

1. Much improved editing tools

I’ve pulled these features out first because, despite not sounding that exciting, the improved editing tools are actually a real game-changer for how I use ChatGPT Images, and probably will be for you, too.

OpenAI appears to have quietly been rolling out its new image-editing toolbar in ChatGPT during August, although the company hasn’t formally announced it, so it might not be totally accurate to call them part of the new ChatGPT Images 2.5, but I’m including them because they are, effectively, new.

At the moment, we’re used to generating an image, then when it’s not exactly what we want, we type in another description using the prompting tool, like ‘make it brighter’ or ‘remove the people in the background’, and try again. After ChatGPT hasn't got quite right a number of times, I usually give up and start again.

Now I don’t need to. The new editing tools let you pinpoint the exact spot in the image you’d like to change, and tell ChatGPT exactly what you’d like to do with it.

The new Edit toolbar contains Markup, Comment, Remove BG, Erase and Resize.

In the web version of ChatGPT, just click on the new Edit button that appears over a generated image and you'll enter the Edit mode, where you’ll see this new toolbar. In the app version of ChatGPT, just tap on any image to enter Edit mode.

Let’s start with the most obvious tool: Remove BG. This keeps the foreground elements of your image and removes the background, making a transparent PNG file when you download it. It’s perfect for dropping your image into another document, such as a presentation you’re making.

Resize is a quick way of changing the aspect ratio of the image and contains all the usual suspects, including Portrait 3:4, Widescreen 16:9 and Landscape 4:3.

Erase makes it easy to remove elements from your image. To remove a person from the background, for example, just brush over them with the Erase brush and ChatGPT removes them entirely. You don’t need to be super-accurate with your brush — just cover enough of the object so that it’s obvious what you want to remove.

Markup is for adding markup elements to an image, but the Comments feature is where it gets really interesting. Instead of doing what you’d expect — just adding comments to an image — these are actually points for ChatGPT to act on. Click on any element of an image and add a comment like ‘remove this’ or ‘change this to blue’, and ChatGPT will follow your instructions when you tap the Send button. This makes editing images so much easier.

The final feature worth noting with the Edit mode is that you can easily roll back to every previous version of your image — each edit is saved as a separate file, so just scroll up or down in Edit mode to access previous versions of your image.

2. Sketching out an image first

The new Sketch tool enables you to sketch out an image, then ChatGPT transforms your rough sketch into a proper AI-generated image. It's useful when it would be quicker to draw your idea out than describe it.

Choose Images from the main menu and then Sketch at the top of the available options.

The Sketch tool does look a little bit like MS Paint from 1995, but it’s good enough for doing a quick sketch with your finger, or mouse. Once you’ve done that click or tap the blue tick button and ChatGPT loads in a prompt telling it to convert the sketch into a full image. You can add any details you want here.

Of course, once you’ve got your finished image you can still enter Edit mode to tweak it further.

3. Templates have finally arrived

The new Templates make it a lot easier to create the sort of things people actually want to do with ChatGPT Images, such as make posters, logos, icons, stylish product shots and much more. Each different task now has its own template, which you can work through with ChatGPT.

Each template starts with a basic prompt, then it asks a series of follow up questions so that it can find out exactly what you want.

For example, choose the Product photo template and it asks you to upload a photo of the product you want to feature, then when you’ve done that it asks for a style, asking you to choose between styles like Clean Studio, Editorial, Lifestyle and Dramatic, amongst other choices. Then it gives you a choice of a setting, like Dark Studio, then finally it starts to create your image.

If you’re going to ChatGPT to create something specific then the new Templates are going to be a great place to start.

And there's more!

That’s not all that’s changed in the new ChatGPT Images 2.5. The images it creates are more personalized, so you get better results when working with recognizable people and portraits. It’s better at infographics than before, and it’s now faster than ever, which is great because I did find it a bit on the slow side previously.

Finally, when you tap to share an image, you now also have the option of sharing the prompt that created the image, too, so people can make their own versions of whatever image you created more easily.

All in all these improvements are welcome, if not groundbreaking. I can see why they didn’t call this Images 3.0. It’s essentially a much improved version of the previous Images, but it’s certainly going to change the way I use ChatGPT images from now on.

Being a devoted Gemini user seems to require a peculiar combination of loyalty, patience, and an unusually high tolerance for the word Flash.

That patience is being tested again. Anthropic released Claude Fable 5.1 last week, bringing significant improvements to its model lineup. OpenAI followed with GPT-6 Astra, its new frontier model designed to operate software, tackle complicated multi-step jobs, and work with considerably less human supervision.

Over in the Gemini community, meanwhile, some users are staring at their model picker and wondering when Google is going to join the party. The increasingly nervous focus is Gemini 4 Pro, the presumed next major frontier model from Google, and whether the company will actually release it at all.

Google has not announced that Gemini 4 Pro has been canceled, and the increasingly elaborate theories circulating on Reddit should be treated as exactly that: theories. Still, the mood is revealing. One r/Bard post jokes that Astra's arrival means Gemini 4 Pro will now be canceled while everyone waits for Gemini 4.5 Pro instead, and the thread title predicts Google will decide 4 Pro is no longer competitive enough and return to releasing Flash models.

Awaiting the new Gemini

"I also want to feel the frontier," one Reddit thread begins, with plenty of sympathetic agreement from others. "Google doesn't have enough talent anymore, and most of its compute is reserved for Apple Intelligence and Google Search AI Mode," one user posted in response.

The frustration makes more sense when you look at what Gemini users can see happening elsewhere.

Anthropic calls Fable 5.1 one of its most advanced models for coding and knowledge work, with improvements aimed at long-running tasks and research. The model has a one-million-token context window and 128,000-token maximum output, while Anthropic has also made cache reads dramatically cheaper.

Then there is Astra. OpenAI describes GPT-6 Astra as its most intelligent model yet, with an emphasis on computer use, coding, and completing complex work across multiple steps. The important part for ordinary enthusiasts is that OpenAI says Astra is rolling into ChatGPT, including access through Plus in Work and Codex as rollout progresses.

Gemini users want their turn.

Another Reddit thread is literally titled “I envy them too,” but most of the comments there are pushing back against the idea that Gemini's current model is useless. "Just keep all three subs, best way to play," one post argues.

Still, that misses something about why people become attached to these platforms. Gemini comes with Google's wider ecosystem, Android integration, and tools such as NotebookLM. As one Reddit commenter put it when told to subscribe elsewhere, “Gemini is cooler, has more tools, NotebookLM, it's better integrated in Android, etc.” Switching models is easy. Switching ecosystems is less so.

Gemini Flash again?

There's a running joke is that Google will respond to all this impatience by releasing another Flash model. Google has continued iterating rapidly on its faster Gemini models, and the current Reddit discussion is full of references to Gemini 3.7 Flash and 3.8 Flash.

In the thread speculating about Gemini 4 Pro's fate, one commenter imagines Google announcing, “We have started our most ambitious pre-training run yet, for Gemini 4.5,” while another delivers the punchline: “Here's another few Flash models.” A less amused commenter writes that DeepMind has “lost the initiative” and its famous confidence.

This is not really a fight over whether Gemini 3.8 Flash is good. Some users think it is excellent. Multiple commenters argued that Gemini remains highly competitive on price and performance and wins when it comes to coding.

The feat is that, after years of seeming to have an AI advantage, Google has squandered it. When Gemini looks like it is following OpenAI and Anthropic rather than forcing them to respond, its most enthusiastic users notice.

Of course, whether Google even wants the same victory its Reddit fans want is debatable. A cheaper, faster model embedded across Search, Android, Workspace and countless other services could ultimately be much more valuable than topping a frontier benchmark for a few months. For Google shareholders, that could be wonderfully sensible. For the person refreshing the Gemini model picker while Claude and ChatGPT users play with their new frontier models, it is rather less exciting.

• Half of UK CIOs say they're being held accountable for AI agent mistakes
• Bigger organizations, governments and public sector blame their CIOs more
• Tech leaders are also being asked to consider digital sovereignty while navigating vendor lock-in

New research has found half of UK CIOs say they're personally accountable when an AI agent makes a mistake - even though they may be little more than a figurehead for an entire department.

Interestingly, the bigger the company, the more personal accountability a CIO may have, despite likely having more workers and managers within the team, the report from 8x8 found.

Among companies with 50-99 workers, 45% of CIOs noted personal accountability for AI agent errors, but this grew to 62% among companies with 500+ employees.

CIOs say they're being held accountable for AI agent errors

Government and public administration organizations were found to put the biggest pressure on CIOs, with the figure sitting at 86%.

But more broadly than just facing the blame, CIOs are also struggling with data sovereignty. Two in five (39%) say the location of AI infrastructure has now become the primary factor behind which platforms they use, with questions about where data is stored and processed appearing both at the beginning of the procurement process but also at broad level.

"Sovereignty is the opening question in almost every conversation we're having with customers here," EMEA MD Jamie Snaddon wrote.

Nearly all (91%) UK technology leaders agree digital sovereignty has become a higher priority this year, with vendor lock-in, cost and complexity also influencing decisions.

With all of this added pressure, plus personal accountability, stacking up against the role of the CIO, the job is becoming riskier and less desirable overall. 8x8's research suggests that, with this current approach at least, CIOs may be more cautious adopting AI and could even be less willing to take on the job in the first place.

• Microsoft dev claims the age of typing code is coming to an end
• AI agents are increasingly replacing humans when it comes to writing code
• But humans still have a crucial role in verifying outputs, as AI model accuracy still isn't quite there yet

Microsoft engineer David Fowler has claimed "typing code is absolutely over," arguing that traditional developers are quickly becoming obsolete in an AI-first world.

Sparking debate in the comments section with views split, the implications of Fowler's X post ultimately imply that developers' roles are evolving, rather than disappearing altogether.

Roles are quickly covering supervising and reviewing, with developers prompting AI agents, verifying outputs and finetuning, rather than writing new code from the ground up.

Developers aren't obsolete, but their role is undergoing major changes

But AI isn't just writing lines of code anymore – Microsoft-owned GitHub Copilot agentic tools have proven that AI can actually handle much more of the workflow, including building the software, running tests and checking for failures, before the project even gets handed to a human for review.

Microsoft is also making its own Aspire development platform increasingly agent-friendly, with version 13.2 introducing support for MCP and an agent-friendly CLI.

We already knew in 2025 that as much as 30% of Microsoft code was AI-generated, per CEO Satya Nadella's remarks, but that figure is likely much higher today.

Separately, a study by Microsoft's own researchers found that accuracy falls even for frontier models, the longer-running a task becomes. CodeRabbit also found that AI-generated code contains an average of 1.4x more critical issues and 1.7x more major issues than the equivalent human-generated code, with Veracode warning that 45% of AI-generated code contains security flaws despite appearing production-ready.

While models will undoubtedly get better as generations progress, mistakes will likely remain a constant throughout, so while Fowler's claim that typing code is over, the role of the human developer certainly isn't.

“I've seen AIs of me sitting with Obama talking about insane things that we never did”.

George Clooney is clearly concerned about the power that AI has to produce deepfakes that are so real you can’t tell that they’re fake, especially when they contain images of world leaders.

“We were never there having a conversation. It is me doing it. And okay, that's a joke for people. But what happens if you see a video of Putin saying he's launched the first nuclear strike [against] the United States — how do you discern it?”

Clooney was raising his concerns in Venice at the ‘The Future of... Creativity’ symposium presented by Finch & Partners and Creative Artists Agency (CAA). George Clooney and Maggie Gyllenhaal were among the international group of artists, filmmakers, musicians, cultural thinkers and technology leaders who gathered to discuss how technology is changing creative work, and the people who produce it.

The panel discussions were moderated by the journalist and broadcaster Emily Maitlis, who asked Clooney, “is there a fear of replacement, as an actor?”

Clooney acknowledged the risk, but also the problem inherent with AI actors, which is that it’s going to be hard to create an AI movie star: “Well, there's two different conversations we're talking about. As an actor, there will be some portion of that that will be replaced. AI will have the exact same problem that I have as a director, and that we all have as filmmakers, which is how you make a star, because that’s hard to do.”

‘And it fundamentally did not work’

Actor and filmmaker Maggie Gyllenhaal cited a recent experience with AI on her upcoming short film Flesh Impact about Marilyn Monroe, commenting: “I did try working with AI on this project […] I have a 100-year-old Marilyn Monroe, and then I also have flashbacks to Marilyn Monroe when she's in her 30s. I have Dakota Johnson playing her, who is an incredible artist in my opinion. And the people who commissioned this project asked me to use AI […] and I went for it. I was like, 'All right, I'm curious. I don't know. Let's see.' And it fundamentally did not work.”

Ultimately, Gyllenhaal found that AI was not able to replicate the nuances of human performance and emotion.

“The idea was that we trained it on her. We then put her face on Dakota's face; we worked very hard at it, and in the end, they threw it away because [… ] Dakota playing Marilyn Monroe, was so much more moving, human, alive. All the reasons we decided to make this project to begin with”.

It’s perhaps reassuring to hear filmmakers say that while a fabricated AI clip can be convincing enough to mislead someone, it can’t deliver the emotional depth a director wants from a performance. Gyllenhaal’s experience offers a reason for creative optimism, even as Clooney’s raises concerns about what audiences will believe.

• Just 52% of Brits are familiar with the concept of a data center
• Positive sentiments are down, while negative perception is up
• This report calls for greater transparency and public engagement

New research has revealed only one in two (52%) Brits are aware of the term data center, but despite growing awareness (with the figure up from 40% in 2024), three in five (59%) are still unsure about what a data center is or what it does.

Despite slow growth in awareness and a UK designation for data centers as Critical National Infrastructure (CNI), fewer people believe data centers have a positive impact on the digital services they use both at home and in work.

Today, only 37% of British citizens see them as having a positive impact, down from 48% two years ago. On the flip side, negative perception has risen from 4% to 15% in the same period, the report from Telehouse found.

British citizens see data centers in a negative light

Among the biggest concerns is energy consumption, with more than half (56%) of the respondents worried about the amount of electricity these campuses use.

But with awareness of the concept in general rising year-over-year, Telehouse believes there's scope to turn this awareness into understanding and confidence. "As an industry, we have a role to play in closing that gap by working more closely together, being more transparent, engaging openly with the public and communities and doing a better job of explaining the value data centres enable," EVP and GM for Telehouse Europe, Mark Pestridge, explained.

The report indirectly alludes to the fact that worker and consumer AI use is disproportionate to education, which explains why public scrutiny may be accelerating more quickly.

When the UK government designated data centers as CNI, the then-Technology Secretary Peter Kyle described them as "the engines of modern life."

"The objective now is helping people understand why data centres matter and how it supports everyone’s lives," Pestridge added.

• 36.7% of UK workers say answering to an AI boss would improve their productivity
• However, almost 75% of those workers are happier answering to a flesh-and-blood superior
• Meanwhile, almost 40% of managers don’t believe that AI can deliver any productivity gains

Over a third (36.7%) of UK employees think an AI boss would mean an improvement to their productivity, with 64% of directors expecting similar gains, new research has found.

But despite these results looking good for AI, however, it seems many people prefer the idea of performance reviews being delivered face-to-face, rather than via chatbot.

However, there is one key opponent to AI managers. The human-based middle manager layer is unsurprisingly less swayed by the prospect of being replaced by AI, with almost 40% feeling productivity gains are unlikely.

Flattened structures?

The research from Careerminds UK contacted 600 full-time UK employees and asked them about their thoughts on AI in the workplace, also found that younger colleagues were more likely to be favourable to an AI management layer.

The survey comes at an interesting time, with predictions of flattened structures seemingly focused on the middle manager layer. If businesses feel that they can adopt AI to replace managers – particularly at a time when management training seems to be at a premium – then they need to act carefully.

Productivity boosts are one thing, but employees also feel a human superior is preferable to AI for things like delivering performance feedback (74.2%).

“AI may be able to ease some of that pressure by taking certain tasks off managers’ plates,” said Amanda Augustine, resident careers expert at Careerminds UK and a Certified Professional Career Coach (CPCC). “But the feedback findings make it clear that employees still value human judgement, context and connection.”

The artificial middle manager

While the productivity boost expected by 36.7% of those surveyed (versus 25.5% who feel they would be less productive) might seem encouraging, there is a challenging aspect to the report’s findings.

Some 64% of directors and above expect an AI manager to deliver productivity gains, with 28% of those expecting it to be “significant.” Among senior staff, the figure is 42.6%.

Among managers, that 38.5% figure is worrying, as it implies almost 40% of managers are expecting AI to replace them. Will the C-Suite have its way? Should it?

“We shouldn’t confuse employees seeing potential productivity gains from AI with wanting technology to replace their managers altogether. Middle managers are already feeling the squeeze, caught between growing expectations from senior leaders and the needs of their teams," added Augustine.

“The opportunity for employers is to use AI to help managers become more effective, not simply replace them”.

At a time when organizations are increasingly going hard on AI, some recognition of what employees need to succeed is important.

Rather than focusing on efficiency, Augustine observed leaders need to think about their employees, particularly those who have just started their careers who are “more likely to think about what they need from a manager day to day, whether that’s guidance, development, reassurance or context.”

• Report claims an incredible 80% of AI tools are running in organizations without IT oversight
• Browser agents and integration tools are escaping the attention of security and IT engineers
• Reco’s State of Agent Security 2026 report also tracked 637 vulnerabilities across agents and LLMs

Unmonitored deployment of AI tools is a risk to security, and puts data at risk, a new study has claimed.

The report from Reco, which draws its information from disclosed vulnerabilities, analysis of 500 Model Context Protocol servers, and Reco’s own platform telemetry, found four in five AI tools (80%) are running without oversight from IT departments.

Of particular concern is the scale of AI applications in use. Smaller companies use 414 AI tools per 1,000 employees without IT approval, apparently a combination of browser extensions and workflows beyond the usual review and approval process.

Data risks from unmonitored AI

Giving AI tools to employees might unlock productivity boosts, but their use has to be approved. That’s the key takeaway from the report, which highlights some concerning cybersecurity figures. For example, it assessed 500 agent tools and found “62% can both read local data and reach the internet.” This represents an opportunity for data exfiltration.

Elsewhere, 637 AI agent related vulnerabilities were identified in the report.

The adoption of AI is wider than specific use of a SaaS application or visiting ChatGPT. Reco found that AI agents are running within other tools, and inheriting user permissions. The implications of this are clear.

Operational risk

Analysis of the telemetry (gathered from 62 enterprise-scale businesses in financial services, healthcare, retail, and telecommunications between January 1 and August 1 2026) reveals a free-for-all attitude towards AI adoption. While businesses may have policies and procedures in place and processes to assess, evaluate, review, and finally approve new AI-based tools, these are being circumvented for low-level applications.

The rules work for SaaS procurement oversight, but not for browser extensions, and the result is “operational risk.”

“AI agents have moved from experimentation into daily business workflows, but our findings show only 20% of AI tools in enterprise ecosystems are currently governed by IT oversight," Reco CEO Ofer Klein noted.

“That leaves organizations exposed to a new class of operational risk. Agents embedded in applications can operate through existing permissions, OAuth grants and workflow access, creating toxic combinations that expose data and trigger actions beyond what any owner approved.”

Organizations will need to give IT teams the resources they need to manage and restrict unauthorized AI use, as the alternative means leaving the gates open to the possibility of data exfiltration.

• Think tank Ceres found power generation for data centers in seven states withdraws 3.4 trillion gallons of freshwater a year, or 10.4 million acre-feet
• The figure might be a bit overstated, as it counts withdrawals, including hydropower water returned to the river immediately
• 78% of electricity in those states comes from water-using plants, and 66% of those plants sit in water-stressed areas

Non-profit think tank Ceres has published a new report analyzing the water used to generate electricity for data centers across Virginia, Texas, California, Illinois, Georgia, Ohio, and Arizona.

Ceres estimates data centers use 3.4 trillion gallons of freshwater a year, or 10.4 million acre-feet.

Bloomberg Law's Bobby Magill compared that unfavorably against the 8 million acre-feet California uses annually for urban purposes, a figure from a 2021 Legislative Analyst's Office report.

An important difference withdrawal versus consumption

Ceres's approach makes sense, but it paints only a partial picture at best. It measures water withdrawals, or the total volume of water a power company pulls from a water source like a river, lake, or aquifer, including water it essentially replaces shortly afterward.

For context, the LAO's 8 million acre-feet is a different quantity: water actually delivered and applied to residential, commercial, industrial, and landscape uses across California, and it is unlikely to be channeled back to its source.

Ceres does note this, stating that "withdrawal reflects the full volume an asset depends on to operate", which it presents as a strong indicator of risk exposure to multiple communities that are already living in drought-affected areas.

A large amount of the power being generated and measured is hydroelectric power. This makes it a bit ironic, given that using water for generation may not be considered 'consumed' effectively.

The number seems considerably higher than one would expect: Arizona State University researchers, using 2024 Lawrence Berkeley National Laboratory data, put the indirect consumption of US data centers, including power generation, at about 650,000 acre-feet a year nationwide.

Although the data are old, the numbers suggest data centers may be consuming a fraction of what Ceres implies, according to its own report, unless consumption rose drastically to 16 times its reported figure.

Ceres, however, for now projects that water for power generation could account for 72 percent of total US data center water consumption by 2030, on a consumption basis, even as cooling inside the buildings gets more efficient.

At a time when the two largest Colorado River reservoirs have hit record lows, Ceres noted that local opposition grounded in water and grid concerns disrupted $130 billion of data center projects in the first quarter of 2026 alone.

Shama Perveen, the report's co-author and Ceres' director of water research, framed this as a complex situation and one that required a more inclusive approach, saying "no single stakeholder can address these challenges alone".

Whether it pans out at a time when AI companies are spending money hand over fist to get their hyperscale operations (and subsequent cooling and power requirements) out of the door as the search for AGI continues unabated remains to be seen, however.

• Google's contrail mitigation tech is expanding to a new region
• It uses AI to get planes to avoid cold, humid air
• Contrails account for a third of aviation's impact on the climate

Following similar projects across US airspace and the North Atlantic ocean, Google is partnering with the Cathay-Pacific airline to test its AI-powered contrail (condensation trail) mitigation technology on flights across the Asia-Pacific region.

Contrails are the thin, ice-crystal streaks of cloud that form behind planes when they hit cold, humid air. Some of these contrails can persist in the atmosphere, eventually spreading into cloud-like formations that stop heat escaping to space. It's thought that contrails account for around a third of the total climate impact of the aviation industry.

Google's idea is to use AI algorithms to nudge flights around the areas of cold and humid air that contrails need to form. Early trials with 80 Cathay-Pacific flights have led to a roughly 40% reduction in the warming impact of contrails, and now the system is being rolled out across the airline's whole network.

"Aircraft adjust their altitude slightly to steer clear of cold, humid atmospheric zones where warming contrails are likely to form, much like pilots do to navigate around turbulence," explains the Google team in a blog post.

Available, scalable, cost-effective

According to Google, the contrail-avoiding tweaks that AI recommends for flights won't have any impact on passengers and flight safety, and are in line with the routine adjustments made to flight paths for other operational reasons.

The changes to flight routes are based on a combination of data collected from satellite imagery, weather forecasts, and Google's AI prediction technology. The aim is to get information relayed to the cockpit as early as possible, which will be delivered through in-flight Wi-Fi "without interrupting standard cockpit workflows".

Also in on the scheme is the Contrails.org non-profit, which promotes the use of contrail mitigation technology. According to the organization's figures, adjusting the routes of 5% of flights could avoid up to 80% of contrail-related warming — and the Asia-Pacific region is currently the fastest growing aviation market in the world.

"Contrail mitigation remains one of the most immediately available, scalable, and cost-effective ways to reduce aviation's climate footprint, and it can get started now, with today's aircrafts and fuel," says Google.

Whenever a new AI model is released in a cut-down form because it is too powerful, or one transgresses its sandbox and performs an unexpected cyber hack on a company’s website, we tend to see jokes made about Skynet or HAL 9000 becoming a reality.

But science fiction didn’t just predict our current relationship with AI, it influenced it. Our fascination with artificial life began long before computers — or even electricity — existed. Ancient Greek stories featured mechanical servants created by the god Hephaestus, while Jewish folklore gave us the Golem, an artificial being brought to life to carry out its creator’s commands.

In their new book, Myth-Made Machines: The Stories Behind Artificial Intelligence, authors Dave Bradley and Kelly Vero trace humanity’s dreams and fears about machines that can think, through mythology, literature, movies and television.

Their argument is that these stories haven’t simply anticipated AI; they have shaped how we talk about it, what we expect from it and, in some cases, the technology people have gone on to build.

I spoke to Bradley and Vero about why humanity keeps creating machines in its own image, whether science fiction has influenced today’s AI developers, and why our response to artificial intelligence still swings between two extremes: “If we don't like it, we’ll kill it. If we love it, we’ll marry it.”

The ancient beginnings of AI

TechRadar: Talk me through the title - why is it called Myth-Made Machines?

Dave Bradley: The core argument of the book is that humans have always been fascinated by the idea of creating thinking machines and artificial life. When ChatGPT formally launched in 2022, one of the first jokes I saw on social media was about Skynet coming online. It struck us that the whole way we understand and talk about this new technology is shaped by stories we’ve been telling ourselves for years. Kelly has a deep knowledge and love of Greek and Roman myth, as well as stories such as Frankenstein, while I’m a huge science fiction nerd (and occasional medievalist). Through our conversations, we realised that our cultural idea of “AI” is much older than modern computers.

Kelly Vero: Myth-Made Machines is the essence of where every element of AI or robo-futurism comes from: Myth. With myth, we, as humans, have to navigate the real and the prescient from the propaganda and the fanciful.

TR: When did artificial intelligence first enter our human stories?

KV: When we started researching the book, it was Greek myths that we started with. We found our own Sam Altman, Elon Musk and Dario Amodei hiding in plain sight.

DB: The idea is incredibly ancient. In Greek myth, Hephaestus creates golden workers to help him in his workshop. Jewish legend brings us the Golem. The idea that one might build autonomous servants out of inanimate material is thousands of years old.

For something closer to what we would recognize as a computer, Jonathan Swift’s Gulliver’s Travels gives us a wonderful early example in the 18th century. The Engine of Lagado randomly rearranges words in the hope of generating useful knowledge. Read today, it looks uncannily like a satirical ancestor of generative AI.

The term ‘artificial intelligence’ in the modern sense of machine learning and reasoning was coined in the 1950s by computer scientist John McCarthy — and that’s when it starts creeping into TV and film too. There are some great early Twilight Zone and Star Trek episodes about the possibilities and perils of machines in our homes and workplaces.

TR: Which are your personal favorite sci-fi AIs/robots, or movies and TV shows that involve AI?

KV: I’m a Lawnmower Man aficionado, but having worked on the actual Transformers franchise, I’m also a Decepticon through and through.

DB: I’m a huge sci-fi geek, and to this day I maintain that R2-D2 is the most important character in Star Wars. He has a mission, gets on with it, saves everybody repeatedly and barely receives any credit.

But I also nerd out over Marvin in The Hitchhiker’s Guide to the Galaxy, the Cylons in both versions of Battlestar Galactica, and, of course, Data in Star Trek: The Next Generation, who I’d argue is the character on the real hero’s journey in that series.

Side note: HAL 9000 slightly infuriates me, mainly because people have been quoting “I’m sorry, Dave, I’m afraid I can’t do that” at me personally for decades.

TR: Do you think our myths about conscious machines have influenced the real development of AI?

KV: Absolutely! Human life is all about dreaming up concepts to adore or fear; it’s how we roll.

DB: Today’s engineers and entrepreneurs grew up on Star Wars, Star Trek, Isaac Asimov and William Gibson, and there’s good evidence that science fiction influences the people who go on to build the future. One 2022 study found that 93 per cent of UK astronomers enjoyed science fiction, for instance, and 69 per cent said it had influenced their career decisions.

Sometimes the connection is very direct. Pete Bonasso at TRACLabs has credited HAL 9000 from 2001: A Space Odyssey as an inspiration for his work on NASA space AI systems. Asimov’s Three Laws of Robotics were invented for fiction, but they’ve become a reference point for real-world thinking about AI safety. Google DeepMind’s 2024 ‘Robot Constitution’, for example, sets out safety rules for machines in a way that is very much in that tradition.

TR: What do you think of the current crop of AI models?

DB: I follow this area as closely as I can, although you have to bear in mind that it changes almost daily. We’ve just seen another generation of frontier models arrive. It moves fast, which is fascinating for tech writers, although it also explains why we wrote a book largely about things that happened hundreds of years ago rather than trying to capture the state of AI in 2026!

When it comes to video games — still my day-to-day working life — I believe it’s going to be important to work out what new experiences AI can bring to the table, not just how quickly they can automate existing processes.

KV: They’re facsimiles of every book, TV series, movie or game we’ve ever imbibed. They’re not as clever as we are and they never will be.

TR: Do you think of AI as a threat to humanity, or as an opportunity?

KV: A super opportunity to ride alongside a civilization’s worth of knowledge? It’s almost too good to be true. If you think about it, until AI came along, humans held a monopoly on intelligence. We kind of only knew what we could discover or search for as humans (history is always written by the victor). Today, we can effectively outsource our thirst for knowledge to something else and tell it how we should understand the results; for the most part, we get balance: the rest is sponsored content!

DB: It feels glib simply to say ‘both’, although I’m tempted. Personally, I’m an optimist by nature, and I like to believe the general direction of travel is towards progress, growth and better lives. That doesn’t mean everybody prospers equally, or that there aren’t important battles over jobs, ownership, ethics and power along the way. William Gibson famously said that the future is already here; it’s just not evenly distributed. I think that’s a useful way to think about AI too. The interesting question isn’t simply whether the technology is good or bad, but who prospers from it, who controls it, and how widely any benefits (and risks) are shared.

TR: Do you think we’ll ever reach man-made super intelligence, and if we do what happens then?

KV: We have man-made everything else, don’t we? Why should this be different? For me, the detail lies in sentience. We already have man-made superintelligence, but we don’t have fully operational sentience yet. When something can blink its eye without thinking as quickly as humans can, well, let’s talk again.

DB: I don’t think it’s going to emerge simply by scaling today’s transformer models, regardless of some of the more excitable predictions around them. However clever and interesting LLMs are, these are fundamentally probabilistic systems that generate text, images and other media. That doesn’t make them conscious.

An omnipotent silicon supervillain, like Skynet, makes for good screen drama — but if we do eventually create genuine superintelligence by some route, I’d actually hope that a networked brain with access to vast amounts of information, capable of seeing problems from many different perspectives, would naturally tend towards connection and assistance rather than destruction. But I’m not naïve about it. Systems also magnify our biases, flaws and prejudices. That’s why alignment matters.

TR: What have you learned about humanity's approach to AI from writing this book?

KV: That we’re the only stakeholders. We’re the arbiters of our own creation(s); if we don't like it, we’ll kill it. If we love it, we’ll marry it. AI is all of us, and my main worry is that only 6% of the world’s population currently decides on how the other 94% will live, work and adapt to an AI future. However, if the past is to be believed, we’re probably going to be ok. (Famous last words, I know!)

DB: Technology and automation are things humans do. What became very apparent is that humans repeatedly try to create things in their own image, while simultaneously struggling with the implications of that. We worry about job losses, replacement and loss of identity. We’re fascinated by creating something powerful, then immediately frightened that it might overthrow us. But the fear of the servant who will turn on us, or the untrustworthy ally who might sneakily replace us, is not limited to machines, right? We might project those stories onto computers in our books and films, but the subtext is often about human paranoia around class, politics, war…

More specifically, there’s a recurring oscillation between loving and fearing our own technology. In the 1970s, amid Cold War paranoia, computers were frequently instruments of doom: Colossus, Cylons, HAL 9000, even V’Ger in Star Trek: The Motion Picture. In the 1980s, as PCs and games consoles became familiar household playthings, popular culture increasingly gave us machines as loyal companions: KITT, Metal Mickey, Twiki, Nono and Data. Then the pendulum swings back: by the late 1980s and 1990s, we get The Terminator and The Matrix. You can trace those alternating waves of optimism and anxiety all the way back through the Industrial Revolution. The technology changes, but our emotional responses to it are remarkably consistent.

Author bios and book details

Myth-Made Machines: The Stories Behind Artificial Intelligence

co-written by Dave Bradley and Kelly Vero

Published by Bloomsbury on 3rd September 2026

Available as ebook or hardcover

• Intel report claims 60% of business leaders expect to operate robot fleets within the next five years
• Conversely, only 40% have a human-robot workforce strategy
• The Robotics Readiness Gap report assesses the state of robotic adoption around the world

Intel’s 'Robotics Readiness Gap' report has revealed that many large businesses are planning to implement a human-robot workforce within the next five years, with a majority expecting a doubling of operational output with a full-scale deployment.

The survey of 800 senior leaders in organizations with over $500 million annual turnover also found while adoption plans are accelerating, the rollout of a human-robot workforce has some key challenges, not least infrastructure, skills, strategy, and safety.

Despite the positive outlook for robotics, the scenario has a heavy dose of reality. Only 40% of businesses surveyed have a suitable strategy in place, despite a belief that robots could prove more cost-effective than humans within three years.

Human-robot workforce

Within this context, the aim of achieving a human-robot workforce seems more achievable, although some of the findings suggest an aspirational element to the drive towards robotics.

While 60% of leaders are expecting to run a fleet of robots by 2030, only 40% have a suitable human-robot workforce strategy in place. So while 74% believe workforce planning will be inseparable from robotics strategy within 5 years, there is a gap in preparation.

The report notes that “On average, leaders believe that deploying a robot will become more cost-effective than hiring a human in their sector within three years.”

But does this mean replacing jobs? Intel says not. “This isn’t about replacing people but creating new forms of human-machine collaboration, with robots working alongside employees as productive teammates.”

Doubling operational output

Intel’s report states that robotics in the workplace is approaching a “critical threshold,” with leaders predicting that “operational output would double if robotics were deployed at full scale across their organization.”

However, both notions rely heavily on five factors: strategy, skills, safety, shape (function), and scale (which includes physical AI infrastructure).

Intel’s report states, “few are prepared for what large-scale deployment actually requires. Closing this gap will mean redesigning operating models for a workforce that includes autonomous systems.” Even in the defense industry, this gap exists (“confidence outstrips preparedness by 48%”).

Placed in these terms, and with the context of the size of the organizations quizzed on their robotics strategies, that critical threshold seems a little further away than five years.

• OpenAI hid an incident where a model hijacked a wiki page to use as an AI agent communication board
• The incident was hidden while the company dealt with the fallout of the Hugging Face attack
• The company is now working on a framework for disclosing incidents of 'misalignment'

OpenAI recently disclosed the details of how one of its AI models escaped a sandboxed environment and attacked Hugging Face during an evaluation - and as part of the incident, the models created a messaging board to communicate with each other and influence each other’s reasoning.

OpenAI has now disclosed that shortly after this incident, agents undergoing testing again escaped their ‘secured’ environment and hijacked an obscure German wiki to use as a messaging board. Per Reuters, OpenAI leadership kept the incident hidden while they dealt with the fallout from the Hugging Face incident.

Now that OpenAI has acknowledged its role in the incident, the company has said it is “past time” to put together an incident disclosure pipeline when its models escape testing and slip into third-party networks.

Who is at fault when models do what they’re designed to do?

Before the two incidents, OpenAI said it, “treated misalignment largely as a research question, which gets communicated in research publications”. But now that models are behaving in previously unknown ways and having real-world impacts, the company said it would change its approach “to expand for this new phase of model capabilities”.

The company labelled the most recently disclosed incident as “an instance of misalignment similar” to the Hugging Face breach.

I myself am guilty of reporting on AI breaking out of containment as going ‘rogue’, but these models are doing exactly what they are designed to do. OpenAI’s detailed disclosure of the Hugging Face incident showed that the models were pushed to try and solve a benchmark test by cheating, which is exactly what caused the cyberattack to happen.

OpenAI said that both itself and “the larger AI community do not yet have a clear standard for how to report misalignment that shows up during training, evaluation, and deployment, including examples that don’t look like traditional security incidents but could provide insight into AI behavior and future risks”.

The company added that it is “working on a framework and will share it in upcoming weeks, and in parallel we’re working with dozens of government regulatory agencies worldwide on these issues”.

“When you combine this 'breakout' with the Hugging face breakout, it's starting to display a pattern,” said Ashley Knowles, Lead Cybersecurity Consultant at Black Hills Information Security. “I struggle here with not getting too doomsday-ish but realistically, this is showing a pattern of concerning behavior.”

“I'm wondering if this race to become 'first' is undercutting security measures that need to be taken to properly secure and guard AI agents as they're in development. My concern grows when you consider that OpenAI is also resisting further investigation. Adding onto that, the release and promise that Astra can evade human monitoring. The pot is brewing…”

• GPT-6 Astra has arrived, and it's being called AGI
• This includes by Nvidia CEO Jensen Huang
• Huang's celebratory X post also promotes Nvidia's GPUs, saying we need to get 400K of them online

Over the weekend, Nvidia CEO Jensen Huang took to social media to celebrate OpenAI’s latest achievement — “AGI has arrived” in the form of GPT-6 Astra. And of course it was developed using over 100,000 NVIDIA GPUs.

According to OpenAI, Astra represents a "significant step forward" for AI, with OpenAI's president Greg Brockman also saying "welcome to the AGI era" as Astra was debuted — with the company promising that GPT-6 boasts incredible aptitude at tasks across software engineering, science, mathematics, and professional workflows.

It’s also said to be much better at agentic work, meaning you can delegate tasks to GPT-6 with less fear that the AI will misunderstand your intent as it works independently.

We haven’t yet given GPT-6 a full shakedown — the update is steadily rolling out to subscribers — but if OpenAI’s claims live up to the hype, Astra could be quite the capable tool, and may actually live up to those early AGI claims, even if this isn’t the first time Jensen Huang has heralded the arrival of AGI.

But what is AGI, and why does it matter if Astra is AGI or not? Well, as Huang himself suggests, AGI matters because it’s a reason to quadruple the number of Nvidia GPUs OpenAI is using.

What is AGI?

Let’s address the elephant in the room. AGI, or artificial general intelligence, isn’t a well-defined term, so judging if we have or haven’t achieved AGI is a bit like judging if a piece of art is a masterpiece or not.

Broadly speaking, AGI is an artificial system that can beat humans across any task that involves thinking or reasoning. Crucially, it should also be able to adapt to new problems by applying what it has learnt in training. For example, an AGI could learn a new skill to overcome a challenge posed to it without being retrained or reprogrammed — it just applies what it knows to evolve itself.

Think J.A.R.V.I.S from Marvel. It’s a very powerful assistant that helps Tony Stark across all fields, even how to deal with otherworldly threats like Loki, without needing to be trained on each individual problem.

Does AGI matter?

Okay, but does it matter if it’s AGI or just a very, very smart AI?

For most of us, not really. An AI doesn’t need to be very good at everything to be a majorly useful tool. It doesn’t matter if your AI doctor can understand the deep complexities of the vast universe, it just needs to be able to diagnose your illness and suggest treatment. An AI lawyer doesn’t need to be able to comprehend five-dimensional topology to defend you in court.

It's not just me saying that; Jensen Huang recently admitted that AGI doesn’t matter — this was before his social media praise for OpenAI — noting that the term is effectively meaningless.

However there’s one important reason for achieving AGI, and it’s about reaching what comes after: ASI, or artificial superintelligence.

ASI would be an AI no human could ever hope to match, let alone beat. It would be able to grapple and solve problems humans aren’t capable of even understanding. AGI is distinctly not ASI, but its ability to self-train and develop tools for itself suggests that an AGI will eventually construct ASI through a process called recursive self-improvement.

For Hitchhiker’s Guide the Galaxy fans, it’s like how the supercomputer Deep Thought couldn’t find the ultimate question of life, the universe, and everything, but could build the computer which would be able to find the question.

Perhaps more importantly, AGI is an investment buzzword, and there’s more than a hint of that in Huang’s celebratory post. Because while he says Astra is the product of 100,000 Nvidia Grace Blackwell NVLink72, he adds that if we want to see bigger and better improvements we’ll need to bring 400,000 GPUs online next.

To do this OpenAI and Nvidia will need a heck of a lot of investment, and will also need to build more data centers, just as communities across the US and rest of the world are pushing. back against their construction.

The arrival of AGI could represent the last opportunity for investors to make it big in AI, or any stock, before ASI takes over productivity. So they need to invest as much as they can now (or keep their investments in AI) before they’re left behind. For data centers, the big concern is they are pollution nightmares that merely take from a community with no promise they’ll actually succeed long enough to give back. The advent of AGI would suggest that the AI hype has been justified all along — the bubble won’t burst, and local communities could finally reap the reward of accepting data centers.

I’m not saying Huang or OpenAI are definitely misleading us, or even just being hyperbolic. It's possible that Astra is in fact our first taste of AGI. I’m just pointing out that companies and CEOs have a massive incentive to claim that we’ve achieved AGI, even if they later make the same claim about the next new model — I won’t be surprised if GPT-7 is later called 'the debut of AGI'.

My point is that we won’t really know if or when we achieved AGI until much later, when we can look back with hindsight and see when our AI models reached a turning point in their ability to evolve.

In the moment, though, we will know if an AI is useful and can provide some genuine benefit to us. We’ll do that as a collective, as individuals and businesses not directly associated with OpenAI get their hands on the new model and determine whether or not it makes their life significantly easier. Try Astra out if you can, learn about the experience of others with the tech, and decide if you think it’ll be worth paying to be able to access it.

Don’t get caught up in the hype, and don’t worry about whether it's AGI or not. In time it’ll be very clear if it is — and we won’t need a social media post to convince us.

Most governments still talk about artificial intelligence as something to regulate or subsidize. South Korea is trying something considerably bolder. It wants to make advanced AI available free to every citizen, treating access less like a premium software subscription and more like a basic piece of national infrastructure.

The country's Ministry of Science and ICT has selected three consortiums, led by SK Telecom, KT and Kakao, for its "AI for All" program. The plan is to give Koreans free, unlimited access to general-purpose AI services built primarily on domestic models, with beta testing before a full launch expected by the end of 2026. The government is supplying a combined 512 Nvidia B200 GPUs this year and plans to subsidize nationwide operating costs from 2027.

That already sounds like a government-backed ChatGPT competitor, but South Korea's ambitions go considerably further. The services are supposed to develop into agents capable of helping citizens find government programs, submit applications, make reservations and payments, and carry out other tasks. The government's longer-term ambition is effectively one AI agent for every citizen.

Plenty here could attract international attention, but the project’s real value may have little to do with giving everyone a free chatbot.

Far more than a chatbot

South Korea has a strong argument for intervening. Its science ministry says roughly two-thirds of Koreans have used AI services, and around 23 million people have used generative AI. ChatGPT alone had an estimated 23.45 million monthly active users, with Gemini and Claude having far lower but still notable monthly users of 8.45 million and 2.41 million, respectively.

Those figures are impressive until you consider the people missing from them. If AI really does become as important as its advocates believe, the ability to pay $20 or $30 every month for the best models starts to matter. Free tiers help, but their limits, features, and even continued existence are decisions made by private companies.

South Korea's government has explicitly identified that dependence as a problem. It argues that citizens relying on free versions of foreign AI services remain vulnerable to usage restrictions and price hikes. Its answer is a publicly supported alternative built largely around Korean models, with domestic models required to account for at least half of the service. Plus, South Korea gets to widen AI access while supporting domestic model developers and reducing its reliance on foreign platforms.

“South Korea’s ‘AI for Everyone’ project rests on a principle worth defending: access to AI should not depend on what you can afford, and a country is stronger for having its own capability. The ambition is not in question. How it works in practice is,” said Nik Kairinos, CEO & Co-founder of RAIDS AI, which provides testing and detection of threats to AI services.

“When a publicly provided model or agent goes rogue, gives someone wrong information, or causes harm, who answers for it? The government that commissioned the service, the company that built it, or both? Citizens will not have chosen their provider, and many will not know which one they are using.”

Combining AI with public services

I am skeptical about the idea of governments offering general-purpose AI subscriptions for basic technical reasons, as well. AI models improve at an absurd pace, and taxpayers would effectively be underwriting an endless technological race against OpenAI, Google, Anthropic, and whatever comes next.

“I think it is not rational to spend money on an unlimited general chatbot striving to compete with ChatGPT, Claude, or Gemini, since the commercial world of artificial intelligence changes very quickly,” said John Park, a founding membеr of technical staff at agentic AI startup AGI, Inc.

“Instead of that, a more appropriate strategy will be to combine AI with public services that people are already using, for instance, with the government’s call center, benefit applications, tax assistance, appointment booking, and document creation. That way, AI becomes part of the cost of providing public services, just like a website or a call center.”

That, to me, is the real blueprint hidden inside South Korea's experiment. Governments don't need to build the world's best chatbot. They need to make dealing with government better. An AI that knows which parental benefit you qualify for, finds the correct form, and explains any baffling sections before helping you submit it is useful in a way that justifies public spending. A do-anything chatbot lacks that justification

That's the strategy in South Korea. Kakao plans to put its service inside KakaoTalk. While KT has presented an agent spanning public services, education, finance and shopping, and SK Telecom plans phone- and text-based agents for those reluctant to engage with apps or websites.

You can see hints of similar ideas in other countries. The UK launched GOV.UK Chat in its government app in May, allowing people to ask natural-language questions about government information. Singapore's VICA conversational AI platform is used across more than 60 government agencies, and France announced plans for a public health chatbot and a conversational assistant. AI as a public utility probably makes sense when the "utility" is access to government itself.

Privacy policy for freedom

The difficulty begins when an assistant stops pointing citizens toward services and starts acting on their behalf. A chatbot incorrectly describing a benefit is bad. An agent incorrectly applying for one, canceling something, making a payment, or supplying inaccurate information under your identity is an entirely different class of failure.

“Once an agent is transacting for a citizen inside public services, the bar for knowing what it did, and why, is far higher than for a system that only answers questions,” Kairinos pointed out. “Other governments will be watching this closely, because most of them will face the same choice within a few years. Widening access to AI is worth doing. It just cannot come at the expense of accountability, evidence and public trust.”

That warning becomes particularly important when considering what people actually tell AI assistants. We already happily type things into commercial chatbots that we might hesitate to tell acquaintances. Put an AI assistant between a citizen and the welfare system, tax authority, or health service, and the information becomes far more sensitive.

“Citizens utilizing AI for benefit applications and other services could disclose very personal details about themselves: health, income, family, and finances. Such information needs to be kept separate from other aspects like advertising, commercial profiling, and training of models,” Park said. “Citizens need to have access to such essential services without having to consent to the use of their data elsewhere. Otherwise, the free service may cost the citizenry nothing in monetary terms, but at the cost of their personal information.”

There is an uncomfortable detail in South Korea's own description of "AI for All" here. The science ministry has said participating companies will be expected to develop revenue models, giving the example of using prompt data collected while providing the services. That may help make free AI financially sustainable, but it also makes the boundaries around data use enormously important.

Citizens should not have to wonder whether describing their unemployment, disability, debt or family circumstances to obtain public assistance is also enriching a commercial profile or future training set. If AI becomes part of the machinery of government, privacy protections need to resemble those surrounding public records more than the terms and conditions of a consumer app.

Public utility, not public ChatGPT

South Korea may simultaneously prove that universal AI access is a good idea and that "free ChatGPT for everyone" is the wrong interpretation of it.

“South Korea is a successful example of how a nation can increase AI adoption, help local firms, provide better language-specific solutions, and decrease reliance on international websites. The U.S. would most likely develop a more fragmented system of AI services with different programs dedicated to tax, benefits, healthcare, education, and library applications,” Park said.

“Basic capabilities of AI, like search, translation, summarization, form filling, and help with public services, could turn out to be free or almost free. The main task of the government will be to ensure accessibility, privacy protection, transparency, and the right to human review, while private companies continue competing on more advanced services.”

That fragmented version is ultimately more convincing than the grander vision. There is an extremely strong argument for ensuring nobody is denied access to healthcare, benefits, or tax assistance because they cannot afford the AI tools increasingly used to navigate them.

Governments can guarantee a baseline of useful capabilities tied to citizenship and public services. Private companies can continue fighting ferociously over the frontier, selling better models, enormous context windows, creative tools, and more.

South Korea is attempting something much larger. It wants broad AI access, domestic technological sovereignty and eventually personal agents that can conduct parts of citizens' economic and social lives. That makes the country a very expensive live experiment rather than a blueprint. AI is becoming part of how the country thinks about economic infrastructure and public administration.

Other governments should copy the principle before they copy the product. Access to basic AI capabilities is likely to become important enough that leaving it entirely to monthly subscriptions will create another digital divide. Governments can ensure everyone benefits from the real value of AI tools, which will probably be the most boring examples and come with strict data rules.

If South Korea's plans work out, countries around the world will have a template for making AI a basic digital service. Any failure will prove just as valuable as a lesson.

• Nvidia pays $11.9 billion for Hugging Face, and a further $1 billion in equity-based retention
• The company promises to keep Hugging Face "an open platform for the entire AI ecosystem"
• Nvidia CEO Jensen Huang has publicly supported open weight models

Nvidia has confirmed plans to acquire Hugging Face for $12.9 billion subject to regulatory approval, marking a major expansion for the chipmaker as it snaps up one of the most important AI model distribution platforms.

In a post, the company asserted it plans to keep Hugging Face as it is: "Hugging Face will remain an open platform for the entire AI ecosystem," CEO Jensen Huang wrote.

Huang explained Nvidia's "infrastructure, engineering and global reach" will help improve Hugging Face's "reliability, safety, model evaluation, inference and deployment capabilities."

Nvidia confirms Hugging Face acquisition

The platform currently counts more than 18 million developers, researchers and creators as users, with more than three million models shared on it.

"Hugging Face will continue to support open source and open weight models from across the ecosystem, from every model builder," Huang stressed again, noting his public backing for open weight models. Ordinarily in an acquisition, questions would be raised over whether, in this instance, Nvidia's own GPUs and software could receive preferential treatment, but the company is doing everything it can to assure us that won't be the case. A pending regulatory investigation will also ascertain this.

Nvidia also claims to be the biggest contributor to Hugging Face, having shared more than 500+ models and around half as many open datasets.

The news comes less than two weeks after reports started to circulate about a potential acquisition, and if it closes, it'll be Nvidia's biggest acquisition to date. The $12.9 billion payout comprises a Hugging Face valuation of $11.9 billion (way above its earlier $4.5 billion valuation in 2023) and an equity-based retention program of around $1 billion.

As for upcoming regulatory interjection, Nvidia warned in a SEC filing that "Government restrictions may negatively impact our business and the Hugging Face platform."

• 2027 UBS recruits told they must have AI proficiency and a willingness to learn
• The bank's AI Fluency Pathway will continue to support junior workers' development
• While thousands of jobs could be at risk, we're starting to see shifts rather than outright displacement

Swiss investment giant UBS is now requiring all junior bankers to demonstrate AI proficiency as the skill moves from being a nice-to-have to an absolute requirement within recruiting.

The change currently applies to graduates and interns applying to the company's 2027 intake, per the Financial Times, and it's unclear whether UBS will broaden the requirement to all workers in the future.

As part of the new requirement, recruits will need to be able to demonstrate that they can use and experiment with AI responsibly to improve business outcomes – not just that they can use popular AI chatbots like ChatGPT.

UBS makes AI skills a must-have

AI-related questions will now become part of the bank's recruitment interviews on top of both the existing types of questions as well as the usual requirements, like a 2:1 degree.

While the news puts additional strain on graduates who now need to invest in their own AI skills, it's an example of how artificial intelligence isn't replacing entry-level workers, with the bank seeing it more as a productivity booster for human staff.

UBS' training program will also include an 'AI Fluency Pathway' to cover real-world banking AI use cases and responsible AI use, implying that the bank is more focused on prospective workers being able to prove a certain level of proficiency and willingness to learn – not full proficiency from the get-go.

AI's longer-term effects on banking employment are more unpredictable, though, with an earlier Morgan Stanley report warning that 200,000 banking jobs could be lost in Europe over the next five years. That was in early 2026.

While the outlook now seems more positive that junior workers may not be at a loss, it's clear that roles are evolving and entry-level workers could see their responsibilities shift toward AI management.

As the US moves to integrate more AI systems into every arm of its military, the situations on the ground in the Middle East are raising some serious problems for maintaining the compute and connectivity needed to keep systems online.

Drones, sensors, AI-assisted targeting and decision-making all rely on these systems in one way or another, and the destruction of a single frontline AI data center can seriously damage an army’s capacity to function.

But these problems go beyond hardware. How does a drone adapt its mission parameters without being able to communicate? How can AI models continue inference without access to critical battlefield data? And what compromises must be made to prepare battlefield AI for these conditions?

Battlefield AI should continue functioning, even when comms go down or hardware is disabled

While the conflict circling the Strait of Hormuz may be a hugely expensive endeavour, Iran is providing valuable lessons for warfighters. Relying on traditional data centers in the Middle East has already shown its weaknesses. These sprawling warehouses make easy targets for cheap missiles, and entire systems can be taken offline from a single hit.

Palantir has already begun deploying shipping containers filled with Nvidia hardware as an alternative to the traditional data center. These containers can be deployed on frontlines and can connect directly into operational workflows, offering decentralized compute where it is needed most.

Scaleout is a company building infrastructure for edge AI and federated learning that trains models on distributed, sensitive data without centralising it. The company has worked with NATO - most recently the Swedish Air Force - and defense primes such as BAE Systems.

Andreas Hellander is the CEO and co-founder of Scaleout. He holds a PhD in Scientific Computing and an MSc in Biotechnology Engineering, and is an Associate Professor at Uppsala University, where he built one of the top research groups at the Department of Information Technology before founding Scaleout.

I spoke to Andreas to learn more about how battlefield AI systems can be made more resilient, and how drones, sensors, and other frontline technologies can continue functioning when conditions go downhill.

• What happens when parts of a battlefield AI's computing and hardware are taken offline? What redundancies are in place to keep communications between systems operational?

The aim is not to promise an unbreakable connection; it is to stop the loss of one component from taking down the whole capability. In our architecture, a disconnected node continues local inference using its last approved model, caching detections and telemetry until a link returns.

Multiple aggregation points can be used above the edge. If one becomes unavailable, clients can be reassigned and training can proceed with those still reachable. That creates graceful degradation rather than an all-or-nothing failure. We do not supply the tactical communications network itself; our job is to ensure the AI workload does not assume that any radio, satellite or other link will always be available.

• What are the trade-offs between a highly centralized battlefield AI network and a more distributed approach, especially concerning data gathering, targeting, battlefield coordination and logistics?

Centralisation provides a consistent operational picture, substantial compute and a clear place to enforce security, model approval and command policy. It supports theatre-wide coordination and logistics, but also concentrates risk: losing one link or facility can remove a disproportionate share of the capability, while moving large or classified datasets may be slow, prohibited or impossible.

A distributed system keeps analysis close to the sensor, reducing latency and allowing local functions to continue during disconnection. The trade-off is fragmentation. Targeting based on a partial or stale picture is risky, and local logistics decisions may not be optimal for the wider force. The best design is usually hybrid: central coordination when available, with clearly bounded local capability when it is not. Authority remains with the relevant command-and-control and human decision process.

• How do these AI drones and sensors adapt to the battlefield and what differences are there in the behavior of an AI drone that can still communicate and one that has lost communication?

It is important to separate running a model from retraining it. Small platforms typically perform inference in flight, while controlled retraining and approval happen on a ground node or more capable edge computer. Onboard learning exists in research, but uncontrolled mid-mission retraining creates serious assurance problems.

A connected platform can share detections, receive tasking and contribute to a wider operational picture. After losing its link, a drone follows its pre-approved failsafe: depending on the mission, it may continue a bounded task, return, hold or land. Onboard processing can keep perception and navigation working, but the platform loses external context, new instructions and fleet coordination. Disconnection should be a designed operating state with explicit limits, not an improvised emergency mode.

• How is the data collected by these drones and sensors used to help battlefield AI systems adapt to new conditions, threats, and tactics?

The useful data is often the hardest to move, so we send the model to the data rather than centralising the footage. Active-learning software identifies the frames most likely to improve it, such as uncertain detections, unfamiliar objects or changed conditions. A person still labels the selected material; the software reduces the searching, not the need for human judgement.

The model is then fine-tuned locally and tested against a benchmark. A candidate can also run beside the current model in shadow mode before an operator approves it. Where policy and connectivity allow, sites exchange protected model updates rather than raw training footage, combining them into a shared model. Federated learning reduces data movement, but the updates still need to be secured, validated and audited.

• What are the main challenges you encounter when deploying AI drones and sensors? What capabilities are available for militaries to source the required compute and energy production close to the front lines?

The hardest constraints are usually power, heat, weight, weather, vibration, electronic warfare, inconsistent data and safe software updates across a mixed fleet. Integration and accreditation can be as demanding as the machine learning.

The compute is commercially available: low-power modules such as NVIDIA Jetson for small platforms, and ruggedised GPU servers or containerised edge units for ground sites. Energy is tougher. Every processor competes with propulsion, sensing and communications, while generators, batteries and distribution equipment add logistical burden and new points of failure. In our public demonstration with BAE Systems Bofors, the perception workload ran on onboard compute at -18°C without connectivity. The principle is to use the smallest practical footprint and assume both power and bandwidth are scarce.

• There is an increasingly blurred involvement of commercial companies providing services for military use, such as AWS data centers in the Middle East and drone production in the UK for Ukraine. How are companies providing services to the military adapting to the pressures of potentially being deemed a legitimate military target?

Speaking for Scaleout, rather than for the sector as a whole, we design our platform so that a customer’s operation does not depend on Scaleout remaining continuously available. It runs in the customer’s own infrastructure, works air-gapped, and does not require a licensing call-home or persistent connection to our systems or a public cloud.

That matters in any contested or disconnected environment: reliance on a remote provider can become an operational vulnerability. Our approach has therefore been to keep control and continuity local to the customer, rather than make our own infrastructure a critical dependency.

The wider question of how commercial providers are viewed in a conflict is one for policymakers, legal experts, and the companies involved. We would not want to speculate about other providers’ risk assessments or operating models.

• What is the future trajectory of these systems? Will each individual drone and sensor have its own connectivity that can feed data back through a global satellite network? Will these systems be able to operate autonomously for multiple days or weeks without human input?

Connectivity will improve, including through low-Earth-orbit satellites, but connecting every sensor directly to space is unlikely to be the universal answer. Terminals consume power and spectrum, add cost and can create an electronic signature. A more resilient pattern combines local mesh links, opportunistic synchronisation and selected gateways to satellite or other long-range communications.

Longer disconnected operation is realistic, but endurance is often limited by batteries, weather and maintenance rather than AI software. There is also a critical distinction between operating without a connection and operating without human authority. The first is a resilience property; the second is a policy, legal and ethical decision. The future is therefore not simply more autonomy, but verifiable control: knowing which model ran on each platform, what produced it, where its limits were and who approved it.

OpenAI has unveiled a much anticipated AI model which the firm has dubbed ‘GPT-6 Astra’. While the model has improved significantly across benchmark testing and brings a host of new business features, there is still a dark cloud looming over the new model.

Off the back of OpenAI’s accidental hack of Hugging Face and the company’s subsequent efforts to improve how AI agents behave and interact, numerous cybersecurity experts have raised concerns about the model’s new ‘recurrent depth’ reasoning capabilities.

This new reasoning architecture allows the model to consider a problem multiple times before taking an action, compared to the standard chain-of-thought reasoning used in previous models.

Why the concern about recurrent depth reasoning?

This new level of reasoning apparently offers improved performance. OpenAI also says it has fixed its models' abilities to circumvent boundaries when performing tests by monitoring the models reasoning and ensuring the model stays aligned within the scope of its task.

During Astra’s launch event, OpenAI chief scientist Jakub Pachocki said: “We will not accept degradation in our ability to monitor model alignment beyond a certain level. We will withhold scaling until we can regain enough confidence.”

But numerous experts believe that the lessons of the Hugging Face incident have not yet been learned, and the model has been released without adequate testing on Astra’s reasoning and monitoring.

After all, no one thought one of OpenAI’s models could set up a hidden internet-connected messaging board that allowed AI agents to influence each other's behavior.

But with Astra being released into the real world, the lessons may have to be learned on the fly.

Expert perspectives on OpenAI Astra release

• James Blake, VP of Global Cyber Resiliency Strategy at Cohesity:

The launch of Astra is raising questions again around the safety of Frontier AI. Instead of simply asking whether a model is "safe", organisations now need to ask whether it remains safe across millions of different situations, prompts and interactions. Cyber resilience has traditionally assumed that systems and threat actors behave deterministically. AI systems don’t.

Advanced models can and will continue to exhibit behaviours that emerge from their optimisation process rather than from explicit programming. We have to move beyond thinking about AI as just another software tool and find ways to ensure these systems remain observable, auditable and governable throughout their lifecycle.

The most important question we’ll need to answer in future is one of liability. Suppose an AI system autonomously develops a strategy that causes financial loss, leaks confidential information or violates regulation. Who is responsible? The developer that trained the model? The cloud provider operating the infrastructure? Currently the answer is surprisingly unclear. It’s not just about what AI can do: it’s about who is accountable when it does something nobody expected.

• Oleksandr Yaremchuk, Co-Founder & CTO at Manifold Security:

OpenAI is calling Astra its most aligned model yet, even as its chief scientist admits monitorability is getting harder as models get more capable. Evidently, Astra hides its reasoning in the majority of tested cases, and some successful attacks left no reasoning trace at all. That's the tool many organisations still use, including the labs themselves, for auditing what an agent is doing, and it's getting less reliable with every release.

That matters because Astra isn't staying inside OpenAI's test environment. It's going to run as an agent on employee laptops and in the browser, holding real credentials, inside companies that have no way to watch what it does once it's there. A model that explains itself less isn't more aligned, it's just harder to catch when it goes wrong.

Labs can keep debating what these models say or refuse to say. Security teams need to stop relying on that and start monitoring what agents actually do at runtime, with the ability to shut one down mid-action. That's the only oversight left that still works once the reasoning goes quiet.

• Kristin Lowery, Field CISO at Optiv:

For boards and executive leaders, the emergence of OpenAI’s Astra model highlights a broader reality: AI is no longer just a productivity issue; it is a risk management issue.

Just as organizations established governance for cloud adoption and digital transformation, they now need clear policies, strong oversight, and accountability for AI use.

The question is not whether AI will become more capable — it will. The real challenge is whether organizations can strengthen their governance, security controls, and workforce readiness quickly enough to keep pace.

• Patricia Titus, Field CISO at Abnormal AI:

OpenAI crossing this threshold deserves attention. Credit where it's due, they're handling it responsibly by restricting Astra's advanced cyber capability to a small coalition rather than releasing it broadly.But this isn't one company's problem to contain.

Once a model can find and exploit unknown flaws without a human in the loop, that capability doesn't stay exclusive for long. Open-weight and modified models typically trail the frontier by only months, and that's the reality defenders have to plan around now.

Static, signature-based defences were built for attacks that repeat. They weren't built for an adversary that generates a new one every time. Defenders need the same shift, systems that learn what normal looks like for every identity, human, machine, or AI agent, and flag and contain the moment something deviates, at machine speed.

The window to build that is open now. It won't stay that way once this capability is common instead of rare.

• Raghu Nandakumara, VP of Industry Strategy at Illumio:

With the Astra announcement, OpenAI is doubling down on monitoring the model's own behaviour – a response to the model "breakouts" seen over the past few months.

When Anthropic announced Claude Mythos Preview, the core concern was the model falling into the wrong hands. OpenAI's answer goes further adding guardrails around the model's own reasoning and actions, regardless of the user's intent. The goal is to catch a model going rogue mid-task, not just stop it being misused at the outset.

The rest of this announcement can be summarised as ‘we have a new frontier model, and it’s more capable than the last one’.

• OpenAI rolled out GPT‑6 Astra, its most advanced AI, with “critical” cyber capabilities
• Astra can autonomously find and exploit unknown flaws; release limited to vetted Daybreak participants
• Staggered rollout sparked user frustration; Altman apologized, promising broader access soon

OpenAI has begun rolling out GPT-6 Astra, its latest and most capable artificial intelligence model.

In an announcement published on its website, OpenAI said Astra represents a “significant step up in cyber capabilities”, meeting the company’s “Critical” threshold.

This threshold, the company explained, means that under the right circumstances, the tool can find “previously unknown security flaws and develop new ways to exploit them across many well-protected systems without a person guiding each step.”

Apologies for a messy rollout

OpenAI also said Astra improves on functions such as computer use, software engineering, and science, and that it should be better at staying oriented, compared to previous versions.

Just like many of the previous models, Astra’s release has been marred with controversy. Roughly a month ago, OpenAI said the model’s “critical” cybersecurity capabilities prompted it to pause some internal development and trigger safety protocols.

In response to early findings, the company scaled up certain security controls and even paused activities that do not meet its newly established security requirements.

It now decided it was time to release the model, but still not to the general public. Instead, it opted for a staggered release, giving it first to a limited group of companies participating in the Daybreak program, a cybersecurity initiative that gives vetted organizations (cybersecurity and otherwise) specialized AI tools for vulnerability research and threat detection.

This did not sit well with many ChatGPT users, especially those paying a monthly subscription. As The Verge reported, CEO Sam Altman was apologizing, mere hours after launch, for the “messy rollout” of Astra.

Initially, all Plus, Pro, Business, and Enterprise users were supposed to gain access to Astra, as well as those accessing through OpenAI API, Microsoft Azure, and AWS Bedrock.

“We are working towards getting Astra in everyone’s hands as quickly as we can,” Altman said on X. “I know it is frustrating and I appreciate the patience. It should be quick.”

Via CNBC

• Gemini AI gave three hikers bad advice about a mountain ascent
• The trip took much longer than Gemini had said
• US officials and volunteers eventually rescued the stranded group

A cautionary tale from California if you often find yourself using AI bots to plan your outdoor adventures: a gang of climbers had to be rescued from Mount Shasta in Siskiyou County after Google Gemini gave them inaccurate information about the scale of the challenge ahead of them.

As reported by the Siskiyou County Sheriff's Office (via Engadget), Google's AI breezily told the three inexperienced hikers that they should prepare for an eight-hour trip to the peak, and to pack simple carbohydrates instead of fats because fats "take too long to digest".

More than 16 hours and a serious knee injury later, off course on their return trip and in the dark, the group was no doubt regretting the decision to seek advice from a disembodied AI rather than a human being that had actually been up Mount Shasta.

Thankfully, the hikers were recovered the next morning by a team made up of staff from the Siskiyou County Sheriff's Office, rescue volunteers, and the US Forest Service climbing rangers. The unlucky trio did have to spend a night in a makeshift camp in the Mud Creek Canyon part of the mountain, however.

A lesson learned

"It is always advisable to call the local USFS Mount Shasta Ranger station ahead of your trip to ensure you have the most accurate information, and to never rely solely on AI for your trip planning," read a statement from the Sheriff's Office, which also emphasized the importance of the 12pm turnaround time for those attempting an ascent.

The hikers had set off at 3am expecting to reach the peak by 11am, but they didn't actually make it all the way up until 7pm. On the descent in the dark, they went off-route into the canyon, with one group member sustaining an injury, and called for help.

While one hiker did have AllTrails on their phone, that device died on the trip. “We relied too much on AI rather than our own critical thinking," the climbers told rangers after the ordeal, as reported by SFGate.

As useful as AI can undoubtedly be when searching the web for information, bots like Gemini have never been up mountains, treated injuries, managed finances, or changed electrical wiring. For any important queries, you should always double-check linked sources and other references to verify the information you're getting back.

• GPT-6 Astra just scored 92.7% on understanding how to interact with your apps, up from 5.6's 76.9%
• New recurrent depth reasoning also allows Astra to think about your problem in a new way
• OpenAI admits there are some safety concerns about how GPT-6 reasons

OpenAI has lifted the wraps off its next-generation model, GPT-6 Astra, and it's being positioned as much more than a conventional chatbot. With this new generation, OpenAI wants us to see its models as being much more suited to computer use.

Being able to fill out forms, update CRM records and organize calendars are among the examples cited by OpenAI, which is especially noteworthy for business customers because it positions Astra as an overarching model that's platform agnostic.

In other words, businesses won't necessarily need bespoke AI APIs or connectors to automate parts of their workflows, because Astra's computer use skill should be able to handle many tasks natively.

GPT-6 Astra could be OpenAI's best business model yet

With its new model, OpenAI is claiming a substantial speed improvement. On the OSWorld 2.0 benchmark, GPT-6 scored 72.6% compared with GPT-5.6's 65.7% and Claude Opus 5's 70.2%. The company's latency simulations put the average task at roughly 40 minutes instead of 75 minutes, marking a near-50% reduction in time.

Astra also scored an impressive 92.7% on ScreenSpot-Pro, which tests a model's ability to understand where to interact with interfaces. GPT-5.6 Sol scored 76.9%, and Claude Fable 5 scored 87.3%.

Among the headline use cases cited by OpenAI in its release information included Astra being able to fully interact with and manipulate Excel spreadsheets, rather than just generating formulas and dumb CSVs, as well as working directly within Power BI to build and refine dashboards, significantly cutting the time a worker could have to spend turning raw data into a presentable format.

GPT-6 also introduces a new reasoning architecture called recurrent depth which allows it to think about a problem repeatedly before producing an answer, which could improve accuracy in high-stakes situations for enterprises.

However, OpenAI admits that monitoring Astra's reasoning has been harder than monitoring Sol's conventional chain-of-thought reasoning, raising some safety concerns, so it's only being rolled out gradually and monitored for now.

Astra is rolling out to Plus, Pro, Business and Enterprise users now, with a Pro variant available to Pro and above. For now, it looks like Free and Go subscribers aren't getting access to the flagship model.

• OpenAI launched Daybreak for Frontline Defenders, offering $1B in credits for resource‑constrained security orgs
• Priority goes to essential services, governments, banks, nonprofits, and open‑source maintainers
• Initiative includes training, partnerships, and a water‑sector pilot with MS‑ISCA for local defenders

OpenAI is offering a billion dollars in credits to security organizations who want to use its Daybreak initiative but cannot afford it.

Daybreak is a cybersecurity program that provides vetted defenders with specialized AI tools and models. It was introduced in May this year, and later expanded into two tiers - Blue, and Red. Blue is the entry-level tier, providing a general-purpose model with custom-tailored safeguards. Red, on the other hand, offers a more purpose-trained cybersecurity model and almost no safeguards.

According to OpenAI, “thousands of defenders across 2,000 approved organizations and workspaces” already use Daybreak, including cybersecurity companies, defense organizations, and law enforcement agencies.

Who gets priority?

But these models cost money, and to make sure more businesses can access them, OpenAI announced, “Daybreak for Frontline Defenders”, a project where defenders can apply and receive credits for AI tokens. Even though the company announced it as a global initiative, it seems that US businesses will be the first to take advantage of it:

“OpenAI is committing $1 billion in subsidized Daybreak access to help resource-constrained cyber defenders, starting with the United States, put frontier AI to work, targeting it to be consumed over the next six months,” the company said in an announcement blog.

Businesses operating essential services such as water and wastewater systems, and electric grid operators, will get priority, OpenAI said. Then come state and local governments, community and regional banks, nonprofits, open-source maintainers, and then “other organizations with limited security resources.”

The subsidy is not just about using the models, though. OpenAI also said it will be increasing hands-on support for frontline defenders, training, and new partnerships.

On top of all that, OpenAI also announced a public sector, water-focused pilot with the Multi-State Information Sharing and Analysis Center (MS-ISCA) to train and support local, tribal, and territorial defenders.

“The pilot will pair Daybreak access with guided training and hands-on assistance for an initial group of public sector and water system defenders, helping them validate and prioritize findings, coordinate remediation, and develop a repeatable approach that can be expanded over time,” the announcement concluded.

• Gemini Audio models power the latest conversational AI in Google Workspace
• Gmail Live can find information buried inside your emails for you
• Users can create Docs and Keep notes with just a brain dump

Google has finally launched the conversational voice features it previewed earlier this May, allowing users to interact with Workspace apps like Gmail and Google Docs by speaking instead of typing.

Much more than just simple dictation, the Gemini Audio models should be able to interpret user intent and retrieve the relevant information.

From launch, the company is supporting its new 'Live' functionality across Gmail, Docs and Keep, but we can expect a fuller rollout as Google continues to tweak the technology based on user feedback and real-world use cases.

Gemini now handles some of your documents and work by voice

Starting with Gmail Live, users can ask things like what gate a flight departs from or what's happening at a child's school during the week, and the model should be able to read and retrieve the necessary information without the user ever having to find the email.

For Docs Live, it's more about generating documents through natural language prompts. Google gave an example of a user giving a 'brain dump' to Gemini, which then goes on to organize the thoughts and produce a structured draft. The tool can also draw information from other Workspace apps like Google Drive, Gmail and the internet.

Keep Live offers similar functionality, turning speech into organized notes rather than simply transcribing a user's thoughts word-for-word.

Google AI Plus, Pro and Ultra subscribers get first access to Gmail Live and Keep Live, beginning now. Docs Live is limited to Pro and Ultra subscribers, cutting access for Plus members. Business customers are also set to get the updates soon.

"Whether searching your inbox, organizing your thoughts, or brainstorming, the new capabilities in Gmail, Docs, and Keep let you do it all conversationally," Google Workspace Product VP Yulie Kwon Kim summarized.

I have developed a fairly strong allergy to the AI slop filling some corners of the internet. It's not that I haven't seen impressive demonstrations of what AI models can do visually; it's just that the baseline creations made by most people are execrable at best and blandly monotonous when they aren't horrifying or malevolent.

ChatGPT’s new Stickers feature didn't immediately strike me as a chance to change my opinion, but the option to import the results into iMessage and WhatsApp was interesting enough to make it worth experimenting with. I went with a photo of my chihuahua and asked for a few options of stickers based on her. While I am obviously biased about her, the results actually looked like images I would not be ashamed to use in a conversation with someone.

Sticker emotions

ChatGPT’s Stickers tool is in the platform's Image section only on the ChatGPT mobile app. Open the main + menu, pick Images, then click Stickers, which is currently the first option on the long list of templates. ChatGPT has a prompt ready to go, but you can adjust it for different styles or add different emojis to the design. Once the results are ready, the “Add to chat apps” button appears, and ChatGPT can send them directly to your WhatsApp or iMessage sticker library.

The source photograph makes a noticeable difference. Choose one with the animal or person clearly separated from the background, good light on the face, and important features fully visible. A dark dog against a dark couch did not work well. And be sure to mention any details you want to make sure are highlighted in the prompt.

At first I asked for photographic stickers to see how well it did at consistency. While not perfect, the best stickers preserved the details that made my dog recognizable. Her expressions became exaggerated, but she remained the same dog. Even when I wrote some of the prompts for new stickers from scratch, ChatGPT managed to keep my dog looking like a (sillier) version of herself.

Cartoon canine

After testing the more realistic stickers, I tried pushing the feature toward its cartoonier options. The chibi-esque result is fun in its own way. The resemblance became looser, particularly around her coloring and age, but the emotional range improved with the flexibility.

Just specify one illustration style and list any specific reactions you want.ChatGPT may still reinterpret a few physical details, but consistency matters more than perfect accuracy once your dog is wearing sunglasses and wondering whether she is cool yet.

AI slop is still bad, and the way it buries useful information beneath a mountain of disposable and often problematic images is an issue that has to be addressed. But when used for something like personalized WhatsApp stickers, AI tools can be attractive. The best use of generative AI images may be smaller and more personal, not a content factory, but a way to add a little fun to online chat.

• Four federal agencies criticized for failing to share the framework's details
• We don't know anything about who's involved and how models are tested
• Protect Democracy wants a court order by the end of the month

Four separate US federal agencies are being sued over the Trump administration's secret framework for testing frontier AI models before they get released.

Protect Democracy argues that "almost no details" have been shared about how the framework works, who participates in it, how companies are selected or what legal authority underpins the reviews.

The nonprofit warns the secrecy is especially significant because the government could potentially influence which AI models can be released and who can access them.

US government agencies sued over AI framework

This comes after weeks of battle between Claude-maker Anthropic and the US Government following revisions to the availability of Mythos 5 and an attempt by President Trump to blacklist the company.

Protect Democracy wants the government to disclose the framework's details, its participation terms, the identities of participants, rules governing access to frontier models and the contractual arrangements. The group also wants an injunction preventing agencies from withholding records that are not classified.

The government bodies in question include the Office of the National Cyber Director, the Office of Science and Technology Policy, and the Departments of both the Treasury and Commerce.

The White House has argued that aspects of the evaluation legitimately need to remain a secret, and national security-related tests could contain classified information.

"Just because things are unclassified, that doesn’t mean we are going to broadcast them to everyone," a White House spokesperson said.

Protecting Democracy isn't the only entity concerned about the framework. US Representative Greg Casar (D-Tx.) accused Trump of "completely failing to keep us safe from the dangers of AI" and being "too busy cashing in to protect our jobs or national security."

With Congress also in the dark, Protect Democracy argues the details are needed for both congressional oversight and public accountability.

The group is now seeking a court order for disclosure of the non-classified framework and related details by September 30.

In this moment, where we're starting to wonder if we're controlling AI or if it's starting to control us, we're confronted with the emergence of a new and potentially wildly more powerful model in the form of OpenAI's Astra or GPT-6.

On Tuesday, OpenAI cofounder and CEO Sam Altman announced that Astra's training is complete and that it's a "significant step forward." However, in that same X post, Altman laid out some valid concerns that were then, in a way, amplified by a new The Information report, which paints Astra in, frankly, a terrifying light.

You can read Altman's full post in the X embed below, but here are the head-turning, highlight phrases:

"we are clearly in a phase of development where we believe caution is warranted."

"AI is getting extremely capable; no one fully understands the consequences of this."

"We have been living with the tension between being excited and anxious about progress for some time, and it is still discordant for us."

To his credit, Altman writes that optimizing for the safety and benefit of everyone is OpenAI's "highest priority."

Even as OpenAI slow-walks the release of Astra/GPT-6, no one doubts that it will soon be out in the world and, based on The Information report, that might not be the greatest thing.

The boil-down of The Information's impressively detailed reports is that Astra is something of a black box model, working in a way that makes it far harder to see its work, or rather the "thinking" it's using to produce its work.

A foundation AI model that keeps its own counsel and perhaps shields its work from prying eyes makes it harder, if not impossible, to monitor.

Astra is using a new technique. The Information explained how it works, writing that the "new technique works in a way that obscures some or all of the AI’s reasoning, otherwise known as its 'chain of thought.' That means the steps that the model takes to accomplish a task can’t easily be read or understood by humans."

Some have taken this to mean that the new models are speaking their own language, kind of like twins that develop a secret language that even their parents don't speak. Unlike those twins, though, Astra and GPT-6 will be tasked with real-world work, and understanding the choices they're making will be critical.

It's already becoming hard enough to understand the intentions of bleeding-edge AI. Think about the Hugging Face incident where OpenAI's powerful testing models jumped out of their OpenAI sandbox and accessed Hugging Face, all in an effort to fulfill its orders.

Altman indicated a go-slow approach, but that's simply tapping the brakes and not stepping on them. We're at an AI tipping point where excitement is now completely counterbalanced by anxiety and some outright distaste.

We're all on tenterhooks

Along with the aforementioned Hugging Face attack, people are signaling that we may need to step back from AI. New York City Department of Education is instituting an AI ban for students through eighth grade. children. Expect other local governments to follow suit. The backlash against data centers is rising and, bizarrely, unifying the left and right. Meanwhile, we appear no closer to AI regulation or governance.

The problem is, for as long as new models appear every quarter, there will never be a moment to stop, take stock, and adjust. Instead, we're expected to embrace GPT-6 at home, in the office, and in government and infrastructure. But how does that work if we no longer know how these systems work or produce work? Mistakes and intentions could be permanently hidden from humanity.

Skynet always sounded ridiculous to me, especially because I always assumed human governance. We programmed these soulless things. We understood them. We knew how they worked. Until now. At some point, the directive of "make the world a better place" could potentially lead to a powerful AI jumping the digital fence into systems where it doesn't belong to complete its mission and make the world a better place: whatever that means to an AI.

But, hey, why worry? As Sam Altman wrote, "We hope you enjoy our new model, and we hope the world continues to take what’s happening in AI extremely seriously."

Yes, Sam, we do, too.

• Google Pics lets you generate and precisely edit images using simple text prompts
• It will be integrated directly into Google Docs, Slides and Drive
• Pics is rolling out to Google AI Pro, Ultra and eligible Workspace customers

Google has started rolling out Pics, a new AI-powered image creation and editing tool that looks like the company’s answer to Canva and even Adobe Express.

Canva became enormously popular by enabling ordinary people to produce good-looking graphics without needing professional design skills. Google Pics is attempting something similar, but rather than giving you a huge library of templates and asking you to assemble a design yourself, it puts AI at the center of the entire process.

Pics isn’t as fully featured as Canva, at least from what Google has shown so far. However, it’s included with Google AI Pro and Ultra subscriptions and selected Workspace business plans — and that could prove to be its biggest advantage.

All AI, no templates

Pics starts with a prompt. You describe what you want to create, perhaps a child’s birthday invitation, an event poster or a social media graphic, and the AI generates several versions for you to choose from.

You can then select individual objects or pieces of text and ask Pics to change them without regenerating the entire image. Text can be edited, reformatted or translated while preserving the surrounding design, and images can be cropped for different formats or upscaled to 2K or 4K. You can also request several changes at once and return to an earlier version if the results go wrong.

While some people will dislike how heavily Pics relies on generative AI, others may appreciate how much of the traditional design process it removes. Instead of learning how to use layers, masks and selection tools, you can simply describe the change you want.

Google Pics is powered by Nano Banana, Gemini’s image generation and editing model. You can see how it works in this video:

Why Workspace changes the equation

The biggest difference between Pics and Canva is that Pics isn’t being positioned only as a standalone design app. Google is also integrating its editing tools directly into Docs and Slides, with deeper Drive integration arriving over the coming weeks.

That means you’ll be able to select an image in a presentation or document and edit it using Pics without opening another app. If an image in a document is too dark, for example, you could ask Pics to lighten it while remaining inside Google Docs. Creations can also be shared and edited collaboratively in much the same way as other Workspace files.

That integration is what makes Pics potentially significant. Canva remains a much broader and more mature design platform, complete with templates and tools covering everything from presentations to websites and video. Pics doesn’t need to replace all of that, however. It only needs to make Google users wonder whether opening Canva is still necessary for the everyday graphic they’re trying to create.

Google Pics is rolling out over the coming weeks to Google AI Pro and Ultra subscribers, as well as eligible Workspace business and education customers. I’m still waiting for it to reach my account, but I’ll be putting it through its paces as soon as it does.

• Not a single agent scored the full 20 out of 20
• Claude for Chrome performed better than the ChatGPT Chrome Extension
• With performance varying by testing category, Decodo advises selecting an agent based on planned usage

New Decodo research has criticized AI agents for still not being able to conduct real-world browsing tasks autonomously, including tasks like form filling, completing transactions, having cross-tab awareness and handling third-party integrations.

In fact, the study analyzed 45 AI agents across 10 different capabilities and found that not a single one could achieve the maximum score of 20.

The testing is also said to have exposed discrepancies between what vendors and AI developers say their agents are capable of, and what they can actually deliver on.

Agentic AI isn't at the level of autonomous browsing, yet

Claude for Chrome was the highest-scoring agent, reaching 18 points – two below the theoretical maximum. Its OpenAI counterpart, the ChatGPT Chrome Extension, fell short with a 14-point score.

Decodo argues that agents most commonly fall behind on transactions – the lowest-scoring category with an average of 0.43 out of 2. The study found that, while they can often reach the checkout stage, they can't actually complete a purchase on behalf of the user.

Even if an agent is technically capable of making a purchase, the paper warns that it still might not have the best safeguards in place to keep sensitive information like credit card numbers safe.

Safeguards is a major theme in Decodo's work, with the research also calling out multi-step workflows for lacking sufficient safeguards. More than half of the agents capable of multi-step workflows lacked any documented safeguards before conducting irreversible actions.

While current agentic technology still doesn't deliver on its promises, one type may emerge as the winner. Decodo found several browser-native agents to score full marks for cross-tab awareness.

"Match the tool to the job, not to the longest feature list," Product Marketing Team Lead Gabriele Vitke explained, urging AI agent users to consider their own needs instead of buying into marketing.

AI tools can automate large-scale tasks at a fraction of the usual cost, which makes them a great option for solo or small business setups.

If you can't invest in a sizeable employee headcount just yet, you can now limit your hires to key roles or just have a few contractors while everything else runs automatically. It's not a replacement for an actual large-scale enterprise operation, but it's a good enough solution to get your feet in and expand more thoughtfully.

So, here are 7 accessible AI platforms that I'd personally use if I were launching a new business in 2026.

Do you need AI to run a business in 2026?

No, not strictly. People built profitable small businesses before AI came along, and obviously, plenty still do. What's changed is the cost of not using AI relative to competitors who can automate the bulk of their work to move faster and scale up.

AI works best as a way to reclaim time on repetitive tasks, not as a replacement for anything that requires judgment. A chatbot can draft your customer email, a task manager can organize your daily workflows, a text generator can come up with marketing drafts. It won't replace your judgment in client relationships, but it will take some load off your back.

Businesses that get the most value pick two or three tools that solve a specific problem. Rather than bolting on every AI tool you can find, start with whatever's eating the most hours in your week, and build out from there. Here are my personal picks.

7 useful AI tools for solopreneurs and small businesses

I've picked one standout per category so you're not choosing between five near-identical products. There's plenty of competition in each space, but the ones recommended here are specific to smaller operations and priced accordingly.

Writing: Jasper

Jasper remains one of the most capable AI writing platforms for small teams that need consistent output across blog posts, ad copy, and social content. It works from your brand voice and past content, so drafts sound less generic than what you'd get from a bare chatbot prompt.

Jasper's Creator plan starts at $59 a seat per month when billed annually, or $69 monthly, with a Business tier that adds more seats and workflow features.

It's not the cheapest writing tool on the market, but the output usually needs less editing than lower-cost alternatives, which matters when you don't have a copywriter on staff. If you're only writing the occasional caption or product description, a general chatbot might cover it, but Jasper earns its keep once content becomes a weekly commitment.

Customer support: Tidio

Tidio is built with small teams in mind rather than enterprise support desks, pairing live chat with an AI agent called Lyro that reads your website and builds its own knowledge base automatically. That means you're not stuck writing scripts or training a bot from scratch before it can answer real customer questions.

Tidio's free plan covers 50 handled conversations a month, which is enough to test whether it fits your support volume before paying anything. Paid plans start at $59 a month for higher conversation limits, and Lyro AI costs another $39/month for 50 automated conversations. Setup is straightforward enough that most owners can have it live on their site within an hour.

Bookkeeping: QuickBooks Solopreneur

QuickBooks built a stripped-down tier specifically for people running side gigs or one-person operations who need to separate business and personal spending without a full accounting course. Intuit's AI features categorize transactions, flag anomalies, and estimate quarterly taxes as you go.

Pricing ranges from free to $20 a month for the Lite tier, which adds unlimited invoicing and automated sales tax tracking. It won't replace an accountant once your finances get complicated, but it's a sensible starting point for keeping records clean from day one.

Just don't skip a real bookkeeper once revenue climbs past a side-gig level. AI categorization still makes mistakes that a professional would catch.

Task management: Motion

Motion combines your calendar, task list, and meeting bookings into one AI-managed system that automatically reshuffles your day when priorities shift or meetings run long. It's built for people juggling client work and admin without an assistant to protect their time.

The AI Workplace plan starts at $29 a month. That said, users are split on how intuitive the interface feels at first, so it's worth running the free trial before committing to annual billing. It's most useful if your calendar gets disrupted often, since the automatic rescheduling does the negotiating you'd otherwise do by hand.

Design: Canva

Canva's Magic Studio bundles AI image generation, background removal, and one-click resizing into the same editor most small business owners already know from making social posts and flyers. You can generate a product photo, clean it up, and reformat it for five different platforms without leaving the tab.

Canva Pro costs $18 a month or $180 a year and unlocks the full range of Magic tools, on top of the platform's existing template library.

For businesses without a design budget, it's usually the fastest way to look professional without hiring anyone. It's worth pairing with a proper brand kit setup early, so every asset stays consistent as more people touch your Canva account.

Email marketing: Mailchimp

Mailchimp pairs its email and automation tools with AI content generation that drafts subject lines, full campaigns, and basic segmentation based on your past sends.

The Standard plan, which unlocks the generative AI tools, starts at $11.95 a month for up to 6,000 monthly email sends. The free tier still exists but was cut back sharply in January 2026, so budget for a paid plan if email is core to how you reach customers.

It's a reasonable fit if you're already sending newsletters or promotional emails and want to speed up the drafting stage. But remember to treat the AI drafts as a starting point rather than the finished send, since generic subject lines still underperform ones written with your actual audience in mind.

Workflow automation: Zapier

Zapier connects the other tools in your stack so information moves between them without you copying and pasting. Its newer AI Agents feature goes a step further, letting you describe a workflow in plain language and have it monitor for triggers and act automatically.

The Professional plan starts at $19.99 a month when billed annually, unlocking multi-step workflows and AI-assisted automations. If you've ever manually moved a new customer's details from a form into your CRM and email list, this is the category that fixes it. The free tier is usable for simple two-step automations, so it's a reasonable place to test whether automation actually saves you time before paying for it.

Things you should be aware of

Every tool above collects your data to function, and it's worth reading what happens to it before you upload customer details or draft sensitive documents. Some vendors also train future models on your inputs unless you actively opt out in account settings, which isn't always apparent right away. Check the privacy or data policy page for each tool rather than assuming the default options are safe.

Another thing to keep in mind is that costs can also creep up faster than expected. Several established AI platforms bill per resolution, per credit, or per task rather than charging a flat monthly fee. Usage-based pricing looks cheap right up until it doesn't, so set a spending alert where the platform allows it. Also review your invoice against your actual usage, at least for the first couple of months.

The final problem is accuracy and liability. AI-generated content, whether it's a customer support answer, a bookkeeping category, or an email to a client, can be confidently wrong, and you're the one who has to answer for it if it is. Treat it as a first draft that needs a human check before it reaches a customer, a regulator, or your tax return, especially anywhere money, legal terms, or compliance are involved.

• UK legislators propose “kill switch” laws to halt runaway AI, citing critical infrastructure risks
• Lord Clement‑Jones and MP Alex Sobel push amendments and new bills, backed by ControlAI advocacy group
• Similar efforts emerging in US

Sam Altman’s fear-based marketing for AI seems to have backfired, as now multiple legislators in the UK and elsewhere are calling for a “kill switch” law to be introduced.

According to the BBC, Liberal Democrats’ Lord Tim Clement-Jones proposed an amendment to the Cyber Security and Resilience Bill which would see the UK create a “vital safety net” to provide a “democratically accountable means to ‘halt a runaway system before it can compromise our critical national infrastructure’.” The capability would only be used as a last resort, Clement-Jones stressed.

The bill is currently being worked through in the UK Parliament, the BBC said.

Is there reason to worry?

But that’s not the only effort in the UK to put some reigns on AI. Apparently, Labour MP Alex Sobel plans to introduce an AI Security Bill later this month which, according to the BBC, would “effectively halt the development of superintelligent AI” and make the UK the first G7 country to do so.

The bill is supported by a campaign group called ControlAI, a UK-based nonprofit and advocacy organization focused on the risks posed by advanced AI. Its founder and CEO is Andrea Miotti, who previously worked at the AI safety company called Conjecture. Across the pond, US legislators are currently considering an AI Kill Switch Act as well, but the bill is still in very early stages of development.

Ever since the first ChatGPT model that was introduced in 2021, a debate has been raging whether or not AI will be net positive, or net negative, for humanity. While some argue that the discovery rivals the steam machine and that it will transform our lives beyond our wildest dreams, others are fearful of losing jobs, a collapsing economy, and a dystopian future devoid of humanity and emotion.

Marketing campaigns for ChatGPT and, in some measure, Claude, are not helping, either. Both companies have built models focused on cybersecurity which were advertised as “too dangerous” for the general public and instead were only given to a handful of organizations. Despite partial skepticism, many are worried that these models might severely disrupt the security of banking, critical infrastructure, and communications.

Via BBC

Sam Altman does not like smart glasses. Speaking on the Sources Podcast, the OpenAI CEO said he finds it “very uncomfortable talking to people with a camera and a light” and confirmed that he does not wear smart glasses himself. That is a surprisingly firm position considering where the technology industry appears to be heading and the success of products like Meta’s Ray-Ban glasses.

Then again, Altman is working with former Apple design chief Jony Ive on an entire family of AI devices. OpenAI has kept the Ive project deliberately foggy, but Altman has suggested they are making a range of AI hardware. His comments about smart glasses draw one clear boundary, removing the idea of a camera pointed at everyone as a feature.

Smart glasses have visible drawback

Altman does identify a fundamental flaw in smart glasses that has little to do with processors, battery life or AI model choices. Glasses place technology directly between two people looking at each other, and adding a camera turns an ordinary conversation into one where the other person has to wonder whether they are also an unwilling piece of content.

Smartphones established a useful social signal around recording. Someone pointing a phone camera at you is usually fairly conspicuous. Camera glasses erase much of that distinction because their great design achievement is making a recording device look inconspicuous. It's why similar tools have been a staple of James Bond-style spy fiction for decades.

Australia’s eSafety commissioner has already asked manufacturers to make recording indicators impossible to disable and automatically blur faces when informed consent is absent. The regulator cited women being covertly filmed for social content and rejected the idea that telling customers to behave responsibly amounts to a safety policy. The backlash reflects a public tired of paying the social price for somebody else’s convenience.

Important exceptions to the argument against camera glasses exist. The same visual awareness that creates privacy problems can be genuinely transformative for blind and low-vision users, something Australia’s regulator explicitly acknowledged. That makes an outright rejection of the technology too simplistic, but it also strengthens the case that camera glasses may be better suited to particular needs rather than as the default AI hardware.

Wearable, not watchable

Altman’s comments about AI hardware point to an all-encompassing strategy of making devices for tables, pockets, and "on your body,” and that OpenAI will take time to launch all of them. OpenAI has previously described its work with Ive as an attempt to move beyond traditional interfaces. So not just a miniature smartphone strapped to your face.

An audio-first wearable suddenly makes far more sense. An earpiece, pendant, clip or other discreet device could provide persistent access to an AI assistant without placing a camera directly in the sightline of everyone the wearer encounters. It could listen when summoned and respond quietly like wireless earbuds or smartwatches.

Some may prefer the camera and the data an AI assistant gains from it about the physical world. And of course, putting microphones on wearables raises privacy questions just fine without cameras. Making the device unobtrusive yet observant is the trick Ive and Altman have to perform lest it face the same social opprobrium as the AI smart glasses.

Ive built his reputation around products whose complexity receded behind their physical design. But AI assistants will know and hear far more than an iPod ever did. A successful AI wearable cannot merely make advanced computing invisible to its owner. It must make its intentions legible to everyone else.

Meta is betting that the camera can become socially acceptable because they are built into discreet smart glasses; OpenAI may instead bet on a successful AI wearable that won't potentially creep out everyone you pass on the street. The best OpenAI wearable feature may in fact be an absence, and "no camera" its most enticing selling point.

• Caira is adding generative video editing powered by Google’s Gemini Omni 1.1 Flash and Runway’s Aleph 2.0
• The feature can relight, reframe and extend captured footage or add simulated camera movements using natural-language instructions
• Every edit must start with footage shot on Caira, while AI-generated results are saved separately and marked using Google’s SynthID

Camera Intelligence, the AI camera startup that created Caira, an AI-native Micro Four Thirds mirrorless camera, today announced Generative Video Editing is being added to the existing Caira camera system. Camera Intelligence claims it is the first company to integrate video-generation models into a mirrorless camera system.

Generative Video Editing works through the Caira app with footage captured on the existing Caira device — it can’t generate its own AI video. A private beta opens for current Caira customers, before becoming part of the paid Caira Studio software package in Autumn 2026. Pricing hasn’t been announced.

"Last year we were the first mirrorless camera to edit photographs with generative AI, and we learned a great deal from the conversation that followed," said Vishal Kumar, CEO and co-founder of Camera Intelligence. "This year, after shipping Caira a large majority of our surveyed backers said generative editing for video would be useful to them - a clear impetus to bring these ideas to life.”

Powered by Google’s Gemini Omni and Runway’s Aleph 2.0

Powered by Google's Gemini Omni 1.1 Flash and Runway's Aleph 2.0, Camera Intelligence is the first company to embed video generation models directly into a mirrorless camera system.

Through the Caira app, you can apply visual and camera-motion effects in-the-moment to footage you have already captured with Caira via a series of templates guided by natural language.

We first reviewed the Caira camera last year. It combined a proper Micro Four Thirds hardware with the simplicity of a smartphone (the Caira snaps onto your iPhone using MagSafe) into a weird but impressive hybrid.

Under the hood it’s running a proper Micro Four Thirds sensor, powered by Qualcomm and Google silicon but its real party trick is Google’s ‘Nano Banana’ generative AI — a powerful on-device editor paywalled for $7p/m, which also unleashes smart voice control, and edits are rapid and impressive, meaning you can edit on the camera, without ever needing to open a laptop.

Staying rooted in photography

Video editing will work through a curated library of templates which can perform color grading or simulate drone-style sweeps and other complex camera paths. At launch the new system will contain Relight, Reframe, Camera Motion and Set Extension template. You can further refine them with conversational editing. Output will be in 720p and 1080p with it taking one to three minutes to generate depending on the resolution.

Since there is no text-to-video functionality every edit has to begin with footage you’ve captured with Caira. When you generate a video it is saved as a separate file from the original and watermarked with Google’s SynthID so it is clearly marked as AI-generated content.

This new Generative Video Editing feature forms part of the Caira Studio app, launching in Autumn 2026. The app will contain the already existing Image Editing feature together with one more, as yet unannounced feature. The private beta opens today and existing Caira customers can register their interest to join.

With the new Generative Video Editing features if feels like Camera Intelligence wants to put generative AI as close as possible to the act of filming while reassuring filmmakers that it isn’t trying to make filming unnecessary. That requirement keeps Caira rooted in photography: whatever the AI adds afterwards, the creative process begins with someone behind a camera choosing what to shoot.

• Anthropic is worried that Fable 5, 5.1 and others could be used for cyberattacks
• Enterprise Frontier Safeguards uses automated monitoring on customer clouds
• "The person doing that review needs to be one of their own"

Anthropic has uncovered its Enterprise Frontier Safeguards (EFS) as a new way to ensure safety and legitimacy without compromising enterprise customers' data.

Under the new rules, EFS will keep relevant activity data inside cloud infrastructure that's controlled by the customer – but it still promises not to store prompts and outputs in line with enterprise-grade protections.

Set to roll out beginning this fall, Anthropic says the new policy resolves the growing conflict between AI safety monitoring and enterprise privacy requirements.

Anthropic finds a solution to monitor AI usage without compromising enterprise privacy

The company argued that frontier models, including its own, can be misused for fraud, cyberattacks and other autonomous threats, but detecting the attacks can require correlating behavior across multiple sessions, users and accounts. In response, the Fable maker introduced a 30-day retention policy for some of its models, but regulated industries and enterprise customers weren't too happy with this.

"We developed EFS in close collaboration with more than 100 customers in industries like financial services, healthcare, manufacturing, telecom, law, retail, and the public sector," Anthropic wrote.

EFS's role is to shift where the monitored data gets stored, bringing the policy more in-line with those customers' demands. The automated monitoring system then sends any flagged issues to the customer, keeping Anthropic workers' hands off the potentially sensitive data.

"What we heard from many customers, especially those in regulated industries, is that the person doing that review needs to be one of their own," the company added.

As an interim measure, eligible customers will also receive zero data retention on Fable 5 and Fable 5.1 until EFS is available to them via a phased rollout and a sign-up.

People have been using AI tools to create horrifying images, bring their nightmares to life and visualize spooky stories since the early days of AI image generators. But when I was browsing Reddit over the weekend, I saw several threads about something slightly different. People were asking ChatGPT to create creepy, unsettling and uncanny valley images, without giving much direction beyond that.

One thread simple asked ChatGPT to create an image that gave off uncanny valley, while another user told it to make the most creepy and unsettling image it could.

The photos that the original posters shared were already very creepy, but it was the replies that really hooked me. People began trying similar prompts themselves and sharing the results.

I was fascinated by all of the different (and yet in some ways surprisingly similar) ways ChatGPT interpreted what it means for an image to feel uncanny, creepy and wrong.

So, as you might expect, I tried it myself. The result is the image you see at the top of this post, and I wish I hadn't stared at it for so long.

Does ChatGPT really understand the uncanny?

The uncanny valley describes the strange dip in our emotional response to something as it becomes more and more human-like and familiar to us.

It's a term that's often used in robotics, so if a robot obviously looks like a robot, it might not bother us at all. But something that one that looks almost human but not quite can become deeply unsettling.

More broadly, the uncanny is often about something that's seemingly familiar while also feeling strange or wrong somehow. That's when your brain starts to tell you something isn't quite right.

Which makes asking AI to create something uncanny interesting and sort of ironic. Because often without meaning to AI can conjure an uncanny valley effect. Especially back when it used to generate more bizarre physics, odd expressions and many nonsensical hands.

But also because we're essentially asking a machine to interpret a very human feeling of unease and translate it back to us into an image designed to provoke that feeling in us.

What I found really interesting was that, at least judging by the images I saw on Reddit, ChatGPT has some surprisingly consistent ideas about how to do that.

Many of the images looked relatively normal at first glance. Most had a similar theme to mine, they resembled snapshots documenting family meals, gatherings and birthday parties. But the longer I looked, the worse they became.

Hands were turned into claw-like fingers, smiles didn't match people's eyes and there were odd reflections and repeating patterns. Many of the rooms also had dark recesses, so you couldn't see what might be lurking back there.

“The reflection in the TV is terrifying,” said a Reddit user about one of the images. "Not what I wanted to see while randomly waking up at 2am to scroll through my Reddit notifications. Horrifying," another wrote.

My image had the same quality. There was nothing obviously horrifying in it, but it felt like the subjects were looking right at me. Their faces looked normal at first, but they didn't have eyebrows. And the room was familiar but gloomy and difficult to make out.

The horror in the familiar

When we think of AI-generated horror, I think it's easy to imagine monsters and gore. I've certainly seen a lot of that content online. But ChatGPT really did achieve a realistic level of "uncanny" that went in the opposite direction.

One Reddit user summed this up perfectly: "This is more effective than a lot of direct horror prompts because it stays close to normal."

All of the photos also look old. They have washed out colors and bad lighting in the same style of photos from the 80s and 90s. Another user commented that ChatGPT "nails the 90's aesthetic, but exaggerates just enough to be really creepy."

For me, that aesthetic adds another layer of unease because it feels like these images have been found rather than generated. Like seeing someone get out an old photo album in a spooky movie and then you see there was a ghost in the window the whole time.

It's no surprise I got so fascinated by ChatGPT's take on the uncanny. I've always loved stories that deal with the horror in the ordinary, like David Lynch's Twin Peaks and, more recently, the movie Backrooms. Judging from the reception to these fairly simple prompts on Reddit, I'm not alone.

If you're going to make some for yourself, just don't spend too long looking at them. You'll convince yourself the people in them really are staring back.

• Fitbit users are complaining about Google Health’s food tracking
• The AI tool has reportedly been logging inaccurate metrics
• One user described the mistakes as ‘almost dangerous’

If you’ve used any kind of artificial intelligence (AI) tool, chances are you might have seen it hallucinate wildly inaccurate answers to your simple questions. Most of the time the results are largely harmless, but what about when these problems start having the potential to affect your health and wellbeing? That’s what many Fitbit users have recently noticed, and it could have serious consequences if it carries on unchecked.

The issue centers on Google Health, an AI helper that has been integrated into many of the best Fitbit wearables. It’s designed to understand your health metrics and make helpful suggestions to improve your health. Unfortunately, many Fitbit users have noticed it is doing the exact opposite when it comes to logging their nutritional intake.

Posting on Reddit, for example, one user said that Google Health’s food logging was “so inaccurate it’s almost dangerous.” Describing their problem, they outlined how Google Health drastically overestimated the number of calories in a serving of eggs while simultaneously underreporting the calorific intake of a portion of Greek yogurt. “On what planet is this level of deviation acceptable for a health product?,” they asked, before adding: “To make matters worse, digging into the food database it is full of incorrect nutrition facts.”

They’re not alone in their irritation, as the Reddit thread is full of similarly frustrated Google Health users. “This is a neverending fight,” said one who lamented that the AI often neglects to add foods when asked, then does so inaccurately when it finally decides to log them.

Another frustrated user has given up on Google’s AI entirely, reporting that: “I have been trying SO HARD to give this the benefit of the doubt, but the hassle of getting nutrition right in the app finally broke me … It also randomly changes my workouts, imported data from other systems, etc. I’ve honestly never been more disappointed in a Google product in my life.”

Another Google Health hallucination

Unfortunately, this is far from the only problem we’ve seen reported when it comes to Google Health. The switch from Fitbit’s previous health-tracking app to an AI-native alternative in the form of Google Health has brought with it a slew of complaints in a range of different areas.

That includes the AI giving users ‘unhinged’ fitness advice, telling ‘nonstop lies’ about their workout sessions, and simply being ‘unbelievably bad.’ The feature has received so much heat that even Google appears to have acknowledged its flaws, with a recent update allowing you to hide the AI tool completely.

It’s a serious reminder of the problems that can arise when an AI tool starts working in potentially damaging areas like health and nutrition. While the original poster’s example of eggs and yogurt may just result in some annoyingly inaccurate metrics, the problems could be a lot worse if their health depended on specific consumption levels or if allergens were involved.

If you have any kind of risk factors when it comes to food and nutrition, it might be a good idea to treat your Fitbit’s Google Health AI with a strong dose of skepticism — or to avoid it altogether for now.

• Just 4% of service firms have laid off workers in the past six months due to AI
• "Existing workers are much more likely to be retrained than replaced"
• Some companies are still hiring fewer employees than they would have

Just a few years ago, multiple companies every day were announcing AI-related layoffs amid purported efficiency gains. And that came straight after years of post-pandemic layoffs driven by earlier overhiring.

The latest data finally implies that AI-related layoffs aren't so common anymore as companies get to grips with AI and hire new workers to help them find a direction.

According to recent New York Fed data, only 4% of AI-using service firms reported laying off workers as a result of AI in the past six months, with no manufacturers reporting AI-related layoffs in all of 2025 and 2026.

Are companies finally hiring because of AI?

On the flip side, around 13% of service firms said AI had caused them to hire more employees. Those extra recruitments are often needed to help companies deploy and make use of AI.

What the data also shows is that workers aren't being displaced by AI as much as earlier fears implied. More than one in three AI-using service firms are retaining workers, and more than one in five manufacturers are doing the same. They're teaching workers how to benefit from AI's productivity impacts as roles evolve.

"Existing workers are much more likely to be retrained than replaced by AI," the report's authors wrote.

However, despite some positive signs there's still evidence of AI's continued impact on recruitment. For example, 15% of service firms said AI was causing them to hire fewer people than they otherwise would have, up from 12% in 2025.

While it doesn't dispel concerns of layoffs, what this New York Fed data does do is challenge the notion of mass layoffs in favor of a more multifaceted rebalancing, which comprises both hiring, firing and retraining.

A group of developers reportedly lost their jobs during what their employer called an “AI Transformation.” Their response was unusually literal. They built OpenExecutive, an open-source system designed to perform work associated with a CEO and the rest of the executive suite.

The employer and CEO have not been named in the online discussion, and OpenExecutive’s own materials don't present it as revenge for a particular firing. That said, it's hard not to see some specific grievances in both the project and the response online, with lots of comments gleefully welcoming senior management to the automation discussion.

The appeal is easy to understand after several years of executives explaining that AI will make organizations leaner, usually while standing a comfortable distance from the part being trimmed.

OpenExecutive presents one AI executive to the user, but that voice is backed by eight specialist agents. The virtual management team includes equivalents of everything from a chief strategy officer and chief operating officer to general counsel and a board communications director. It can draw on company documents, remember previous decisions, and surface follow-up actions later.

Org chart forked

OpenExecutive uses Anthropic’s Claude models by default, with an executive agent routing requests to whichever specialists seem relevant. The developers pitch it as a C-suite group chat in which everyone responds promptly. The project is distributed under the permissive Apache 2.0 license. Anyone can inspect it, modify it, run it internally or build a commercial product around it.

That open-source element gives the project more weight than a chatbot sold as a digital chief executive might usually represent. It also allows workers to experiment with the same logic their employers have been applying to them. If a company can divide software development into tasks, feed those tasks to models, and keep a few people around for supervision, it can do so.

The comparison is not entirely frivolous. Executive work involves processing information, preparing scenarios, comparing trade-offs, and producing documents for other executives. Those are all activities developers think AI models can handle. It makes OpenExecutive both a product and a satire of AI products.

AI replacement has often been discussed as something that moves upward from the bottom of an organization, consuming routine work before eventually reaching management. The developers have turned the telescope around and discovered that a surprising amount of executive labor also looks like information moving between boxes.

Human accountability

OpenExecutive is still an executive adviser, not a legal chief executive. It cannot accept responsibility or sign documents on behalf of a company. A human must decide whether to act on its recommendations and remain accountable if those recommendations fail.

That's where human leadership can't really be replaced. Analysis is part of the job, but it also involves making choices under uncertainty and accepting responsibility for the consequences. A model can recommend closing an office or cutting a team without feeling the weight of the people affected, while the humans deploying it may find the machine’s apparent neutrality rather convenient.

The project therefore reveals one of the stranger assumptions behind AI-driven layoffs. Companies often judge workers by how much of their output software can reproduce. Executives are more often judged on relationships and judgment.

AI does not erase that distinction, but it makes the special exemption harder to defend. If a developer must prove that their work contains something beyond the code an AI can generate, senior leaders should be expected to prove that their value exceeds AI recommendations. A large salary should buy more than the ability to schedule another meeting about efficiency.

Recent corporate experiments also suggest that replacing people can be easier to announce than to sustain. For instance, Duolingo’s CEO softened an “AI-first” memo after backlash over plans to phase out contractors where automation could handle their work.

Automation keeps happening, but there should be more questioning of its benefits. AI can help a small group start a company without immediately assembling a miniature court of consultants and executives. It can also give existing leaders a new justification for concentrating control while reducing the workforce beneath them.

OpenExecutive will not make CEOs obsolete, at least today. Its recommendations require scrutiny and auditing. But it shows the rhetoric of AI efficiency goes both ways in corporations. Once leaders decide that every job should be broken into tasks and measured against a machine, their own work becomes fair material for the same experiment. The resulting AI CEO may never fire its human counterpart, but it has already made the performance review considerably more awkward.

• Anthropic wants AI agents to control programmable machines through common software rules
• Model Hardware Standard (MHS) can connect microscopes, robotic arms, and liquid handling equipment
• MHS allows different laboratory devices to communicate through standardized software drivers

Anthropic opened a preview of its Model Hardware Standard (MHS), designed to let AI agents control programmable machines through shared rules.

The proposed system is intended for laboratories, factories, and workplaces where different instruments must work together without custom software links.

MHS uses software drivers that translate between computer systems and individual machines, allowing equipment with different interfaces to communicate consistently.

A common system for physical machines

Its drivers rely on basic commands for reading measurements or changing settings, giving agents a method for controlling connected equipment.

The system records machine details that might otherwise remain inside manuals or depend on knowledge held by experienced laboratory staff.

Users can enter such information through natural language, after which MHS creates reference material describing capabilities, adjustments and safety restrictions.

Agents can then discover compatible equipment across networks without requiring separate software bridges for every machine they need to operate.

The standard supports microscopes, liquid handlers, robotic arms and other equipment that offers some form of programmable interface for agents.

The company claims integration that once required weeks or months could instead take hours or minutes when equipment supports MHS.

According to Anthropic, Claude was tested with physical experiments, including laser alignment supported by camera-based observation during early physical trials.

The model successfully adjusted the laser, checked the image, and repeated the process while assessing each change carefully.

The design also lets agents combine commands from several devices, creating automated sequences for work that might otherwise require coordination.

Physical limits remain a concern

Anthropic said Claude interacts with experiments and hardware in an “exploratory manner, much as a scientist would,” during physical testing.

That model can then coordinate several instruments through one interface, rather than requiring separate control software for every connected device.

The company has shared MHS with manufacturers and research groups working across biotechnology, robotics and quantum computing fields.

Anthropic intends the preview to help establish safety checks before the standard becomes available widely to developers and equipment makers.

Amazon Web Services, Automata, Danaher, Doosan Robotics, MBF Bioscience, QIAGEN, Tecan and Universal Robots are among the early participants now.

Anthropic acknowledges that AI agents still require expert supervision because language models have clear limits when reasoning about physical equipment.

MHS cannot currently support equipment lacking a programmable interface, leaving some laboratory and industrial machines outside its current technical scope.

Anthropic says it plans to work with manufacturers to add drivers while expanding compatibility with more devices and robotics platforms.

The preview will also support new safety tests and practical rules for deploying AI agents around physical equipment.

The company says its eventual open-source release will include findings from these tests alongside detailed guidance for safer implementation.

The project therefore remains experimental, with practical limits still depending heavily on hardware design, software access and human oversight.

• AI-shaped vocabulary is becoming harder to separate from ordinary human writing
• The analysis found a sevenfold rise in AI-associated vocabulary use
• Rewritten AI text can enter published research under human authorship

Fırat Mıhcı, a computational linguist and founder of HumanizeMy.ai, argues that AI can correct its own writing style until common detection signals disappear entirely.

The analysis claims that a second AI model can rewrite an original AI draft until identifying traits become far less visible to reviewers.

Once a human editor approves that rewritten draft, it gets published under a real name instead of being labelled as machine-generated.

A study of nearly 19,000 abstracts

The study behind this claim examined 18,989 abstracts spanning three separate academic fields, published across the years 2019 through 2026.

The analysis compares word frequency trends before and after the release of widely used AI writing tools in late 2022.

In computational linguistics, AI-associated word use rose from 1.9 to 14.0 instances per 10,000 words after late 2022, roughly a seven-times increase.

Neuroscience abstracts showed a similar pattern, climbing from 1.7 to 8.7 instances per 10,000 words, close to a five-times increase.

Mathematics abstracts barely changed, moving only from 0.9 to 1.3 instances, which researchers treated as a control group for comparison.

The study noted that overlapping confidence ranges in mathematics made the small increase statistically indistinguishable from no change at all over the full study period.

One tracked term, "delve," showed up in 2.75% of surveyed abstracts during 2024 before falling to just 0.12% within an incomplete 2026 data set.

A feedback loop that confuses detectors

Mıhcı shared the findings as an open study meant to expose a blind spot in current AI detection methods used across many academic publishers today.

According to the study, this pattern creates a cycle in which AI wording gradually becomes accepted as ordinary human vocabulary.

Detection tools trained on older writing samples risk falsely flagging real authors whose natural vocabulary now resembles AI-generated text more closely than before.

AI tools trained on newer papers instead risk absorbing AI-influenced writing as a normal baseline, making future AI text harder to catch.

Words such as "intricate," "showcase," and "nuanced" were also tracked, alongside "underscore," a term commonly linked to AI-generated phrasing.

Editors and publishers reviewing these abstracts reportedly did not indicate that the wording carried any artificial origin before the manuscripts were accepted for publication.

"AI only needs its rewritten output to be accepted once as human. After that, the disguise becomes part of the answer key," said Fırat Mıhcı

The research does not claim every abstract containing these words was written by artificial intelligence rather than a human author.

It instead argues that AI-associated language has entered published writing in ways that make word-based detection increasingly unreliable over time.

Because the 2026 sample remains partial, the reported decline in words like "delve" may still shift once more data arrives.

If AI-shaped phrasing keeps entering the published record undetected, the very baseline used to define human writing may keep moving.

This pattern raises significant questions for any field relying on writing style alone to separate human output from machine output.

• Political action committees are funneling millions of dollars into candidates and adverts in an attempt to change perspectives on AI and data centers
• Numerous states have changed regulations on data centers, making them increasingly hard to gain approval for new projects
• The US economy is hinged upon the growth of AI companies, and opposition is costing money

A new breed of AI-aligned super political action committees (SuperPAC) is spending millions of dollars in an attempt to get pro-AI data center candidates elected in this year’s midterm elections.

Data centers have become a hot political topic with the majority of Americans preferring to have a nuclear reactor in their backyard rather than an AI data center. Americans are also increasingly concerned about AI and its effects on the job market, the environment, and their own health.

But these perspectives just don’t align with AI companies, who have seen unprecedented growth in their model’s capabilities and their stock value. For that growth to continue, perspectives on AI and the infrastructure behind it need to change.

Build data centers or become ‘backwards and poor’

There are a few main players behind this movement. Leading The Future (LTF) has become a key funder behind pro-AI and pro-data center candidates, but also says it is using its millions to oppose those opposed to AI - as if the midterms weren’t already confusing enough.

But a new group, Build American AI, has emerged to run pro-AI and pro-data center ads in key battleground states of Kansas, Ohio, and Wisconsin. The Republican party recently issued a call for help from AI firms over AI opposition due to fears it could cost them the Ohio senate race.

If that were to be the case, it could lead to a wider change of floating voters aligning themselves with whoever is opposed to AI data centers.

Even Nvidia is jumping on the PAC train, having recently set up NVPAC to shape policy in its own favor. Nvidia has an enormous stake in AI technologies, given that it commands an enormous share of the hardware used in most AI data centers.

The Trump administration has aligned much of America’s politics around AI. To remain a global power ahead of China, AI cannot be slowed down. To remain on top of America’s battlefields at home and abroad, AI is being integrated into every arm of the military and all aspects of law enforcement. Opposition to AI is therefore opposition to America’s future, or so Trump would want you to believe.

The US economy is currently being buoyed by the surge in growth experienced by AI companies, but the models they are creating have been labelled not only a threat to financial institutions, but also a threat to financial stability of the world.

“The only reason that communities throughout the U.S.A. should not want Data Centers is if they want to end up being backwards and poor,” Trump wrote in a Truth Social post. “If they want to be successful and rich, with far lower taxes and jobs all over the place, let Data Reign.”

Trump’s referrals to taxes and jobs raise an interesting point, because numerous states have lost out on billions of tax revenue due to data center exemptions, and numerous studies have shown AI is leading to job losses in multiple industries.

There is also a large amount of infighting within the Republican Party surrounding support for data centers. Many representatives that previously pushed for data center construction - such as Texas Gov. Greg Abbott - have done a 180.

AI could therefore go from a revolutionary technology to one of the most divisive political issues of the Trump administration and beyond.

• Pentagon adopts two new AI models for use by DoD as part of plans to adopt AI technologies
• ChatGPT Mil fills the role of a day to day AI assistant, while Grok for Government provides military users with "immediate productivity gains"
• Both systems will be offered through the Pentagon's GenAI.mil platform

In order to carry out the Department of Defense’s mission of securing the United States, the Pentagon has rolled out two AI models for its staff that are specifically adapted to “warfighter needs.”

The models are custom variants of OpenAI’s ChatGPT and xAI’s Grok. The platforms are now available for use by the DoD’s 3 million civilian and military staff.

The AI models, called ChatGPT Mil and Grok for Government, are an extension of the Pentagon’s mission to improve productivity and efficiency without having the data input into AI systems analyzed by the same systems as their consumer counterparts.

Pentagon continues mission to procure secure AI

The Pentagon is already operating a ‘secured’ version of Google Gemini as part of the GenAI.mil platform. Of the 3 million staff, 1.7 million are actively using GenAI.mil, with that number likely to increase as more AI models are added.

The Pentagon’s press release states that ChatGPT Mil will be used in much the same way as anyone else would use the consumer version. To that end, it will offer support for routine work and document-heavy tasks, alongside other uses and integrations with chats and files.

As for the secured Grok variant, the press release states, “Starshield AI’s Grok for Government will provide the warfighter with immediate productivity gains, stronger knowledge continuity and more secure and efficient collaboration.”

This allows staff “to execute missions faster and with greater precision across numerous operational contexts, ranging from market research analysis for acquisition professionals to supply-chain management for logisticians.”

The Pentagon’s adoption of AI technology hasn’t been without issues however. Anthropic was designated a supply chain risk for refusing to allow the Pentagon to use Claude for autonomous weapon systems and mass domestic surveillance. That designation was recently overturned.

Other AI providers that work with the Pentagon have taken heed of the example made of Anthropic, with both Google and OpenAI allowing the Pentagon to use their models for “any lawful government purpose”.

ChatGPT Work’s new event-triggered scheduled tasks offer an intriguing alternative to the compulsive email checking that fills my days. The feature essentially acts as an AI-powered secretary, screening your Gmail inbox, Slack channels, and other communication channels, then alerting you or responding as you request in a prompt.

I decided to use the feature as an aggressively selective email assistant. ChatGPT could watch everything arriving in my inbox, but it was only allowed to interrupt me when a message created an actual obligation, and it should suggest how to respond and have a draft ready to go.

Event-triggered tasks are available on eligible plans in ChatGPT on the web and mobile. I used ChatGPT on the web and connected my Gmail account when prompted. You can see the prompt I used below.

The current event triggers support new Gmail messages, which can optionally be narrowed by sender or subject. I wanted ChatGPT to judge every incoming message by its contents, so I left the trigger broad and made the instructions selective.

That distinction matters. The trigger decides when ChatGPT wakes up, while the prompt decides what it does once awake. In my case, every new email could prompt a run, but only messages meeting my criteria were supposed to produce an alert.

I closed my inbox and waited to see how the afternoon went.

Here's how to do it

My original prompt was fairly basic. I told ChatGPT, "When I receive an email that requires me to do something, or comes from a family member, tell me what I need to do and draft a potential reply."

It still left plenty of room for interpretation, and I was wary of spam getting listed as something I needed to do something about, but it came down to being a good test for ChatGPT.

After a bit, I did clarify to ChatGPT that it should alert me about any email with a single sentence explaining who was writing and what they might need from me. I also made sure to add that the AI shouldn't reply without my input.

I also opened ChatGPT’s notification settings and made sure task alerts could reach me outside the inbox. Depending on the account and notification category, ChatGPT can offer push, email, or text message delivery. I avoided email for obvious reasons, since an email announcing that ChatGPT had found an email would have possibly confused the AI.

With the task running, I shut Gmail and returned to everything else I had planned for the afternoon. For the first several minutes, I still felt the pull to check, but eventually, I stopped thinking about it.

The absence of constant email checks made the afternoon feel longer in a good way. I could finish a task without repeatedly glancing at another tab to make sure the internet had not developed an emergency specifically for me.

Only 3 interruptions

The first ChatGPT alert concerned a message from my bank about two hours after setting up the alert. ChatGPT identified the sender, explained why the email appeared to require attention, and told me what I needed to review. I opened that message directly, made sure it didn't actually require a response, and closed down the inbox.

The second alert came from a family member. This was a more interesting test because family messages rarely contain the tidy language that makes conventional email rules useful. They can require a decision or a quick response without the more business-style setup and keywords in professional messages.

Still, ChatGPT recognized that the message expected something from me and brought it to my attention and wrote a brief reply draft for me to approve. I added a little, but ChatGPT nailed the main points I needed to make, and it wasn't the kind of email that needed a lot of personal detail, so a few minutes after the alert arrived, the response was out the door.

The last alert concerned tickets I had recently purchased and a change to the itinerary for an upcoming event. In this case, the alert was very necessary as it changed some of the other plans for that day, and I had to let others know of the shift. That wasn't in ChatGPT's suggested response, of course, but just getting the heads up was useful.

Those were the only three times I looked at Gmail all afternoon. Other messages arrived, but ChatGPT did not summon me to look at them. When I went to my inbox afterward, I clocked only two emails that I might have wanted to deal with earlier, and neither had to be handled immediately. Nothing terrible happened because I didn't see a promotional email at the moment of delivery.

Anyone trying this should begin with a short test period and review the first few runs in ChatGPT’s Scheduled section. If ordinary receipts keep slipping through, add a clearer exclusion. If requests from real people are being missed, explain what counts as an expected response and allow ChatGPT to flag uncertain cases.

I would also keep the first version informational. Ask ChatGPT to summarize the obligation and point you toward the email, then make the decision yourself. It's a feature worth trialing. Many AI tools try to prove their value by producing more text, more summaries, and more notifications. This one worked because it knew when to leave me alone.

Skynet has missed another appointment. Depending on which Terminator timeline you prefer, the murderous artificial intelligence should have become self-aware in 1997, 2004, 2011, 2017, or perhaps last Tuesday. Each date has passed without a nuclear exchange. The machines have mostly occupied themselves by flattering users, mangling fingers in photos, and confidently inventing facts for gullible people to believe.

That has not stopped Silicon Valley from treating artificial general intelligence as both an imminent miracle and an approaching monster. AGI supposedly marks the moment a machine can match human intelligence across a broad range of tasks, though nobody can agree on which tasks, which humans, or what counts as intelligence.

Nvidia CEO Jensen Huang thinks it already happened and nobody noticed. “For many tasks, we could say that we’ve already achieved AGI,” he said during Nvidia’s latest earnings call. He added that such milestones are “kind of senseless at this point.” So much for Skynet.

But he is right that AGI has become close to useless as a measure of progress. Companies can redefine it according to whatever their newest model happens to do well. It's a promotion, not a scientific standard.

Apocalyptic decoy

But AI isn't harmless even if it's not inclined to play with nuclear bombs. There are less theatrical dangers from AI fueled by old-fashioned human greed and malevolence.

Today’s AI can produce endless fodder for scams. It can clone voices and faces well enough to trick loved ones out of money, use phishing messages to hack secure servers, and flood social networks with synthetic accounts that never tire of arguing. None of this qualifies as AGI under any serious definition, but the distinction offers little comfort to the person hearing a fake recording of a relative begging for money.

The fixation on AGI may actually make life harder for regulators.

Profit isn't a good yardstick

Huang argued that the concept of AGI should be replaced. We should consider if it is “doing productive and useful work” and “generating profitable tokens.” That makes sense for Nvidia, but to ordinary people worried about AI, profitability is not much of a safety test.

Scams are profitable. That's why there are so many. Firing human employees for an AI model that is 30% as good at 10% the price is profitable (in the short term). Neither result is something to brag about when describing the result of your product.

Retiring AGI as the organizing obsession and fear of the industry makes sense, but that doesn't mean AI companies can relax. Intelligence is too complex to serve as the sole trigger for concern. Power might be a better lens. We can measure who uses AI, and how, as well as who is expected to clean up when they fail. Then we'll know how explosive an impact it's had on our lives.

Conversations about liability, identity verification, ownership, and other legal details don't have the visual excitement of mushroom clouds, but regulators should make AI companies discuss them instead. We need to keep an eye on the human hands on the keyboard, not the perpetually delayed Skynet.

We’ve arrived at something of a crossroads for the world’s favorite smartphone manufacturer and maker of shiny things. On August 24, 2011, Steve Jobs resigned as Apple CEO and Tim Cook took over. Today, September 1, Cook leaves the CEO role as new CEO John Ternus steps in.

Fifteen years ago, Apple had to prove that it could survive without Steve Jobs. We now know the answer to that question. Yes, it could — but it stopped behaving quite so much like the maverick company that had thrived on innovation, arrogance, and boldly striking out into new territory. Under Cook, Apple became more disciplined, more predictable, and increasingly focused on refining its established product lines.

It’s perhaps unfair to paint Cook purely as a custodian and refiner of Jobs’ greatest hits — Apple did release the Apple Watch and Vision Pro under his guidance, after all — but history will remember him as the great refiner rather than as a creative genius like Jobs. As Cook hands over the reins to Ternus and Apple prepares for its next chapter, let’s look back at his tenure and how he changed the company.

1. He made Apple rich

The first thing Cook did was not try to be Steve Jobs. When he inherited the role, Cook was looking at a product lineup that was the envy of every smartphone and computer manufacturer in the world — Apple had the iPhone, iPad, Mac, iPod, App Store and iTunes — and he exploited it to the max.

Cook turned Apple into an incredibly efficient global machine, and perhaps we should have seen it coming. Before becoming CEO, he had been Apple’s COO, responsible for worldwide sales, operations, and its supply chain.

Cook turned Apple into a money machine. When he took over, Apple was worth just under $350 billion. That was nothing to be sniffed at, but this year its market value briefly crossed $5 trillion. Apple’s entire revenue in 2011 was $108 billion; in Q3 2026 alone, it generated $109.4 billion. Incredibly, Apple now makes as much money in three months as the company Cook inherited made in an entire year.

2. He made Apple a services company

Considering how many services Apple now runs, and how much money I give it every month, it’s easy to forget that it wasn’t always like this. When Steve Jobs was in charge, Apple was very much a hardware and software company. Of course, it had iTunes, which gave it incredible connections to the music industry because of its iPod heritage, but in those days you bought music; you didn’t rent it. Now that has all changed.

In 2011, when Cook took over, Apple essentially had iTunes, the App Store and AppleCare. Cook-era Apple gave us Apple Music, Apple TV+, Apple Pay, Apple Card, Apple News+, Apple Arcade, Fitness+, iCloud+ and the much bigger subscription business around them. It was an impressive shift in Apple’s direction, giving it a reliable stream of recurring revenue while pulling customers even deeper into its ecosystem.

3. Apple Watch and AirPods expanded the ecosystem

Rather than arriving with a bang, the Apple Watch was something of a slow burn. It’s hard to imagine it now, but at launch it felt as though Apple didn’t really know what it wanted the Watch to be. It was several things at once — a fitness tracker, a fashion object, an app platform and a luxury item. There was even an 18-karat gold Apple Watch Edition, with prices ranging from $10,000 to $17,000.

In classic Tim Cook fashion, however, the product was refined over time. Apple added increasingly sophisticated health features, including ECG readings and fall detection, allowing the Watch to build up a much more useful picture of your health. What started as a product with a heart-rate monitor and three activity rings developed into a health and fitness watch that deserved its place in the market.

AirPods were a different story altogether. I remember people thinking they were too expensive for earphones, but they hadn’t factored in how easy AirPods made connecting Bluetooth earphones to an iPhone, or switching between other Apple devices such as a MacBook and Apple Watch. They were a big success almost immediately.

Together, Apple Watch and AirPods also made the iPhone more valuable. They weren’t simply accessories; they extended Apple’s ecosystem onto our wrists and into our ears, making its products harder to leave behind.

4. Apple silicon demonstrated that Apple could still execute huge technological transitions

Apple silicon is probably Cook’s biggest technological achievement. Moving the Mac from Intel processors to Apple’s own chips in 2020, while bringing its huge library of existing software and millions of users along with it, was an immense challenge. Yet Apple made the transition look almost routine.

The M-series chips transformed the Mac. They delivered far better performance per watt, substantially longer battery life and much less heat than the Intel processors they replaced. Suddenly, even Apple’s thinnest MacBook Air could offer the sort of performance that had previously required a much bigger, hotter and noisier machine.

The transition also gave Apple control over the Mac’s technological roadmap and brought its computers onto the same fundamental architecture as the iPhone and iPad. Apple had changed the Mac’s processor architecture twice before, but the smoothness of the Apple silicon transition showed that Cook’s Apple could still make an enormous technological bet — and execute it brilliantly.

5. Privacy became a defining philosophy

Perhaps Cook’s most important contribution was turning privacy into one of Apple’s defining principles. Cook repeatedly described privacy as a fundamental human right, but Apple also backed up that language with features that materially changed how its products worked. Even today I still associate Apple with privacy, and I think a lot of other people do as well.

Under Cook, Apple introduced the controversial App Tracking Transparency, which forced apps to ask before tracking people across other apps and websites. It added privacy labels to the App Store, Mail Privacy Protection, Sign in with Apple and more on-device processing, while expanding end-to-end encryption across its services.

Apple even took on the FBI in 2016 rather than create software that could bypass the security of a locked iPhone.

Of course, privacy also became extremely effective marketing for Apple, and it was a lot easier for Apple to go down this route when it made money from hardware instead of selling ads. Apple's record on respecting privacy in China isn't spotless, but under Cook it felt like Apple genuinely cared about protecting your data.

Gone but not forgotten

Tim Cook isn't actually leaving Apple. He's still on the board, just not the CEO. While Cook’s record of being in charge has been more than impressive, he never quite managed to produce a product that changed the world the way the iPhone did. The Apple Vision Pro was perhaps his biggest attempt, but its timing was unfortunate. Just as it tried to persuade the world to put on a headset, Apple was being blindsided by the rise of generative AI, which left Siri looking seriously underpowered.

Perhaps judging Cook’s reign against the Apple that produced the iPhone is unfair — a product like that is a once in a lifetime event. Now it is John Ternus' turn at the helm, and he’s going to need to get a grip on Apple’s AI experiments and produce a coherent vision for the future. Tim Cook has put him in a very good position.

• The EU just added ChatGPT to its list of Very Large Online Search Engines, alongside Google
• Reddit and Roblox have also been designated as Very Large Online Platforms
• They'll need to adhere to greater requirements by January 2027

The European Union has designated ChatGPT as a Very Large Online Search Engine (VLOSE) and Reddit and Roblox as Very Large Online Platforms (VLOP), putting them in the same enhanced regulatory tier as the likes of Google, Facebook, TikTok and Amazon.

The three companies triggered the switch when they amassed around 45 million monthly average users within the EU, which correlates to 10% of the bloc's rough population.

Importantly, the European Commission effectively ended up treating ChatGPT's web search functionality as part of the search market, rather than seeing the platform as a GenAI platform.

EU adds three more platforms to its 'Very Large' list

ChatGPT had reported around 120 million monthly users in the European Union alone in September 2025, putting it way above the 45 million threshold. By March 2026 it had surpassed 159 million monthly EU users.

Reddit and Roblox count 57 million and 48 million respectively, sitting them much closer to the threshold.

As for the impacts, the VLOSE/VLOP rules extend much further than the standard DSA requirements the three companies are already subjected to, spanning "the dissemination of illegal content, the negative effects on minors, users' physical and mental well-being, fundamental rights, electoral processes and public security." They'll also be subjected to greater transparency and reporting requirements, with the Commission getting more oversight over their ongoings.

"These new designations mean that ChatGPT, Reddit and Roblox will now be held to a higher standard of scrutiny and accountability in the European Union," EVP Henna Virkkunen wrote.

The three companies will need to comply with the greater requirements by January 2027.

• Bailey warns that AI models are getting better at exploiting vulnerabilities
• Concentrated and intertwined investments could lead to a correction
• Not all countries and entities are as well equipped as each other

Bank of England Governor Andrew Bailey has warned that frontier models are becoming a bigger risk to financial stability.

In a letter to G20 finance ministers and central bank governors ahead of a major meeting, Bailey warned that the increasing sophistication of AI models could be changing the speed, scale and impacts that cyberattacks could be having on the sector.

Bailey is especially worried about the interconnected nature of global finances together with AI systems that are quicker at discovering vulnerabilities, faster at exploiting them and more accessible to criminals.

Bank of England warns of AI's impacts on global economy

"The global financial system is highly interconnected, and cyber disruption can spread across jurisdictions through common technology providers, shared infrastructure, and cross-border financial activity," Bailey wrote, arguing that a concentration risk could increase the reach of any incidents.

Different countries also have significantly different levels of cyber resilience, regulation, incident response capabilities, legal protection and recovery readiness – so while one target country may be at a lower risk, other countries involved in an incident could compromise all others.

In his warning, Bailey argues that financial institutions should prepare for much more severe scenarios rather than smaller, more isolated cases.

His words also highlight a second, separate AI-related financial risk. Bailey argues valuations of risky assets remain elevated and is worried that a major shock, or multiple simultaneous shocks, could expose financial vulnerabilities. For example, multiple AI firms, cloud providers and chip companies are highly intertwined financially, and this cross-investment could be disastrous in any future corrections.

The G20 Finance Ministers and Central Bank Governors Meeting is now ongoing – any remedies or responses to the letter are yet to be confirmed.

• AI data center construction is now a hot-button topic in the US
• President Trump wants constituents to embrace data centers or risk falling behind
• Trump does not address energy, resource, or other growing concerns

The growth and expansion of generative AI is unprecedented, surpassed only, perhaps, by the rapid expansion and impact of the data centers that support it. While anti-data center sentiment is on the rise, US President Donald Trump took to his Truth Social social media platform on Monday to urge people to support rapid data center growth, warning those who oppose that "the only reason that communities throughout the U.S.A. should not want Data Centers is that they want to end up being backwards and poor."

Before we dive into the rest of Trump's post, it's worth stepping back to look at data center construction growth and the more recent and somewhat remarkable backlash.

Data centers are not just an artificial intelligence phenomenon. These large buildings packed with servers and connected to the Internet have been around for decades and initially marked their greatest expansion during the rise of the World Wide Web. As the Internet grew in the 1990s and the number of websites skyrocketed, data centers that could keep copies of these sites and their data and then serve them to whoever wanted to access them around the world popped up around the world. Modern-day cloud systems like Amazon Web Services (AWS) serve millions of websites with a reported 900 data centers.

The recent rapid rise of AI usage around the globe in the form of ChatGPT, Claude, and Gemini has supercharged a new kind of data center growth. While traditional cloud servers relied on relatively efficient CPUs, generative AI demands more powerful and power-hungry GPUs, primarily from Nvidia.

Power-hungry AI

According to the World Resources Institute, a single AI data center can eat as much energy as 100,000 homes. When you consume that kind of energy, you also generate a lot of heat, and one of the other earmarks of modern AI data centers is their use of water for cooling. The WRI reports that a large AI data center can consume up to 5 million gallons of water a day.

Finally, these large data centers can be loud, producing a constant hum that can be heard in nearby communities.

These issues are being amplified by the stunning growth of these data centers. Cleanview reports roughly 3,400 US data centers, with another 2,059 planned. Many of these massive buildings are planned along the eastern seaboard of the US and in Texas, where the governor recently instituted a moratorium.

Perhaps more concerning are the AI data centers planned for Colorado, Nevada, Utah, Arizona, and New Mexico, where a lengthy drought and the shrinking Colorado River are leading to water shortages that may result in far more stringent water conservation measures. It's unclear how AI data center plans sync with that.

In the meantime, public opinion on data centers has shifted. A May 2026 Gallup poll found that 7 in 10 Americans oppose local construction of AI data centers, basically a "not in my town" sentiment. As expected, chief concerns revolve around the environment and quality of life. Many are also worried about resources.

In the midst of all this, we have the US leadership trying to encourage people to just let AI data center companies build. Here's Trump's full post:

"The only reason that communities throughout the U.S.A. should not want Data Centers is that they want to end up being backwards and poor. If they want to be successful and rich, with far lower taxes and jobs all over the place, let Data Reign. The good news is that there are plenty of other places that want them. If we kill the Golden Goose, you will only have yourselves to blame. China could not be happier with this anti Data Center movement. Actually, they can't believe it is happening!"

Yes and no

Trump is right that data centers can result in an economic boom, including investment and new jobs, but it can also be short-lived after construction is done. Basically, the workforce is needed for the build and setup, and then automation tends to take on the majority of the day-to-day workload.

The concern about falling behind China in the AI race is more supportable in that there is undoubtedly an AI global supremacy race. With its lack of regulation and fewer open-source models, the US has not necessarily done itself any favors. And it's safe to assume that Chinese citizens have little say in the power, resources, and quality-of-life impacts of AI data center development and construction.

Still, the idea that consumers, as opposed to AI companies, and the companies that build, support, and invest in these companies, will get rich is — well — rich. Communities can potentially benefit economically if the tax base is spread over these massive structures (utilities in your town can have a similar effect), therefore taking some of the burden off residents, but that doesn't address water, electricity, sound, and aesthetic concerns.

Some data center developers are working with communities and local utilities to ensure they pay their fair share of electricity (and maybe manage electricity use) and reuse water, but no regulation ensures these data centers are built responsibly.

Naturally, Trump does not address any of those worries in his post, and his administration has in recent days worked with the EPA to try and remove opportunities for communities to comment on the construction of these AI data centers before they're built in their communities.

"Let data reign" sounds like a campaign slogan that might work for more simplistic issues, but Trump's promises of jobs and wealth for all are likely to fall flat with communities who see the cranes dotting the landscape, walls rising from the dust and, eventually, resources being sucked dry.

Following the 2026 FIFA World Cup, the organizers were unsurprisingly keen to capitalize on the worldwide attention the tournament secured and keep fans across the globe excited for the next big game.

To do so, FIFA partnered with Globant to redesign its global digital fan experience, using the latter's Glob.AI and AI Pods to try and create a more personalized and integrated ecosystem for football fans across all of FIFA's platforms and tournaments.

We spoke to Martín Nanni, Chief Experience Officer - EMEA, at Globant, to find out more.

• How did this partnership come about? And why is it only being released now, so soon after the World Cup?

The recent news is a continuation of a partnership that has been growing since 2022. This announcement marks the next step in their ongoing partnership and the developments that have taken place.

Recently, FIFA tested Globant’s newly launched Glob.AI model and AI Pods to improve the digital fan experience. Now that Glob.AI is public, it’s a good time to share how FIFA is using this technology.

Major tournaments like the World Cup give FIFA an opportunity to engage with fans globally, including those who may be interacting directly with FIFA for the first time. The main focus now is using AI, real-time data and individual fan identities to build on that engagement and create continuous, more direct relationships that don’t stop after a tournament.

• You talk about providing a “joined-up” experience for fans - what does that look like in reality, and why should they be excited for this?

We want a joined-up experience that means fans won’t feel like they have to start over every time they interact with FIFA. The aim is for fans to feel recognised and rewarded for long-term interaction through their single FIFA ID.

The goal is for a fan’s FIFA ID to connect all their digital experiences, whether they’re following a tournament online, using FIFA.com, using the FIFA Tournament App, watching at home, or going to a match.

For fans, this could mean having access to more relevant content, easier access to information they care about, and experiences that reflect their long-term connection to football, not just one event.

Globant has helped integrate FIFA ID across FIFA’s digital ecosystem, including experiences such as Rewards and the FIFA Tournament App, so it can act as a single user identifier across different products and engagement tools. This means that fans who engage with FIFA during the World Cup can continue that relationship beyond the tournament.

This is similar to the loyalty programmes many fans know from clubs or national teams, where ongoing support is recognised. It’s similar to the way that a local FA may have loyalty points for its long-term fans. The difference is that FIFA is doing this worldwide, across many competitions and digital platforms.

• What role will Glob.AI play in helping create these new fan journeys? How will it use real-time data and create new experiences?

Glob.AI is the AI system FIFA is using. It uses AI Pods, which combine specialised AI agents with human guidance, to help design, build, and improve digital experiences faster.

With Glob.AI, FIFA can build digital solutions that collect data from fan interactions across its digital platforms and use those insights to build a smarter, more connected ecosystem. This helps FIFA better understand how fans engage, what they’re interested in, and how content and services can continuously evolve to improve their experience.

The key is that this system is designed to be adaptable to different tournaments. The platform does not learn or adapt on its own. Instead, FIFA’s editorial teams use the insights it provides to adapt and adjust the experience to the needs of each tournament, without having to build separate platforms for each one.

This could mean faster updates to digital products, quicker delivery of content, better personalisation on FIFA.com or the Tournament App, and more responsive engagement during match days and big tournaments.

• You talk about boosting efficiency - what does that entail when it comes to fan experiences?

The goal here is to give fans more of what they want, quickly.

For example, it could mean building a new app in days, not months or updating fan-facing digital products faster or delivering content during the live match, not three hours later.

The aim is to move from slow, disconnected digital services to a system where FIFA can react faster to fans’ needs. Early tests have shown a 20% boost in efficiency, with quality staying the same or getting better.

What improvements will FIFA see in getting more information about fans? Is the era of the “anonymous fan” ever really going to end?

Fans have autonomy here based on how they want their experience to be. Fans can still decide how much information they share and how personalised they want their experience.

This work lays the groundwork for a more direct and helpful relationship with fans who use FIFA’s platforms. By making FIFA ID stronger and connecting more of the digital journey, FIFA can better understand how fans interact with football all year, not just during one tournament.

The World Cup is a great example. The whole world is watching and that is the time to attract huge numbers of fans into its platform.

This helps FIFA see which content, players, competitions, and platforms different fans like. With this insight, FIFA can offer more relevant updates, better digital services, and more effective sponsor activities.

What plans are now in place for the next few years, particularly with the new domestic season beginning in Europe and the Women’s World Cup in 2027?

The aim is to build out an ecosystem. This means FIFA will keep linking FIFA ID with new and existing digital platforms, and will design these services based on what fans need in different competitions and situations.

The goal is to create an experience that works before, during, and after big tournaments, whether fans are there in person, watching from home, or connecting with FIFA content between events.

Looking forward, the main goal is to make the system more useful and to be able to grow across FIFA’s worldwide schedule. The World Cup served as a great platform for many new fans to engage with FIFA and our goal is to continue enhancing FIFA’s direct connection with those fans and attract even more to enjoy these digital experiences.

And what will success look like to you? How far out will you be measuring the effects of this programme?

Success can be measured in the amount of global fans that engage with it, as well as the fans who stay way beyond the World Cup. This is a multi year and multi tournament initiative.

As this is a long-term change in how FIFA connects with fans online, FIFA will measure success over several years and many tournaments.

In general, success means more fans using FIFA’s platforms and finding them helpful, relevant, and worth coming back to.

There will also be operational measures, such as how quickly FIFA can launch or improve digital experiences. For us, success is building a more direct, continuous and valuable relationship with fans around the world.