Running a one-person business means doing the job of an entire company by yourself. You're closing a deal in the morning and debugging the product by lunchtime. Every hour spent on admin is an hour not spent on the part of the business that actually grows revenue.

That's the gap a new generation of AI agents is built to close. OpenClaw and Hermes Agent are open-source tools that run in the background, hold memory of your business, and act on tasks without waiting for you to ask twice. Pair either one with a handful of supporting tools, and you get something close to a small team, for the price of a few subscriptions.

Why solo founders need an agent, not another app

Most AI tools you've used so far live in a browser tab. You open Claude or ChatGPT, ask a question, get an answer, then close the tab. The assistant stops existing the moment you stop typing.

An agent works differently. Once you set up OpenClaw or Hermes Agent, it keeps running, checking a task list, remembering what happened yesterday, and acting on a schedule instead of waiting to be prompted. For a solo entrepreneur with no employees, that difference matters more than which model sits underneath.

An agent doesn't replace you. Ideally, it should absorb the tasks that would otherwise eat your day, things like triaging support email, drafting a weekly update, or chasing an unpaid invoice. That frees you up for the work only you can do.

OpenClaw or Hermes Agent: Picking your AI co-founder

Two open-source projects dominate this space right now. They take different approaches to the same problem.

OpenClaw is the older, larger, and more battle-tested of the two. It started as a weekend project by Austrian developer Peter Steinberger in late 2025.

In February 2026, Steinberger announced he was joining OpenAI and that OpenClaw would move to an independent foundation rather than staying tied to any single company. The project's GitHub repository now sits at 373,000 stars and 77,300 forks.

Hermes Agent takes the opposite bet. It launched in February 2026 from Nous Research, the lab behind the Hermes, Nomos, and Psyche model families. By mid-June, it had crossed 190,000 stars of its own.

Instead of chasing breadth, it focuses on depth. After every task, it evaluates how the work went, turns whatever worked into a reusable skill file, and pulls from that file the next time a similar job comes up rather than reasoning from scratch.

How they measure up

In our experience, the honest answer comes down to setup time versus patience. OpenClaw's web search and file tools tend to work immediately after a Docker setup, often the same day. A full Hermes Agent setup with memory and tools configured typically takes a few hours instead.

Start with OpenClaw if you want results fast. Choose Hermes if you're willing to spend a weekend up front for an agent that keeps improving at your specific workflows.

A growing number of operators don't pick just one. Some experienced users run OpenClaw as the orchestrator for planning and multi-step coordination, then hand fast, repeatable task loops to Hermes as an execution specialist, with the two agents communicating over a shared protocol. That setup is overkill for a first attempt, but it's worth knowing the option exists once a single agent starts to feel limited.

What this looks like in practice

The clearest public example of an agent running a one-person business is Felix, an OpenClaw agent built by entrepreneur Nat Eliason. In January 2026, Eliason gave the agent $1,000 in startup capital and its own X account, then told it to build something and sell it overnight. Felix responded by writing a playbook on how to hire an AI agent, building a website to sell it, and launching its own social presence.

Three weeks in, Felix had generated $14,718 in revenue. Within about two months, that figure had grown to roughly $177,000 across the original product, a skills marketplace called Claw Mart, and custom agent deployments built for other businesses.

Eliason still holds the API keys and reviews what the agent does. Day-to-day decisions, from pricing to outreach, run through Felix rather than through him.

Felix is an extreme case, built specifically to test how far one agent could go without a human in the loop. Most one-person businesses won't hand over a Stripe account on day one.

That's the right call for most of them. Even so, the same pattern applies at a smaller scale: give an agent its own accounts, a narrow task, and enough room to act without you checking in every hour.

Building the rest of the stack around your AI agent

An agent is only as useful as what it can plug into. Most one-person stacks pair OpenClaw or Hermes Agent with a handful of tools that already expose an API, a webhook, or an email address the agent can act through. None of these need to cost much.

Scheduling and communication

Calendly remains a common default for letting people book time on your calendar without the back-and-forth, with a free plan for individual use and paid plans starting at $10 per month. Point your agent at the same calendar so it can answer "when am I free" without you checking manually.

For day-to-day messages, the agent typically lives wherever you already work. Both OpenClaw and Hermes Agent connect natively to WhatsApp, Telegram, Slack, and Discord, so you're adding a contact to a conversation you're already having, not a new inbox to check.

Invoicing and bookkeeping

Wave and FreshBooks cover most solo founders here. Wave's core invoicing and accounting tools are free, with charges kicking in only if you use its built-in payment processing. FreshBooks costs a monthly fee but adds time tracking and client portals, useful once you start billing by the hour.

Either way, give your agent read access to the invoice list rather than write access to your bank account. Letting it flag an overdue invoice and draft a reminder is a reasonable task. Letting it move money on your behalf is not, at least not yet.

Customer relationships and leads

HubSpot's CRM is free, with no time limit on the core plan. For a founder tracking a few dozen leads, that's enough to replace a spreadsheet without adding a subscription. As the pipeline grows, the agent can sit on top of it, drafting follow-ups, logging calls, and flagging deals that have gone quiet.

Content and social media

This is where an agent earns its keep fastest, because content work is repetitive and time-stamped. Point it at your newsletter platform, whether that's beehiiv, MailerLite, or ConvertKit. Give it a standing instruction to draft, not send, a weekly update from your week's notes.

You stay the editor. The agent stays the drafter.

Contracts and signatures

For anything that needs a signature, tools like PandaDoc or SignNow handle the legal side. Your agent can handle the busywork around it instead, generating the draft from a template, sending it out, and nudging a client who hasn't signed after a few days. We'd still keep a human checking the final terms before anything goes out the door.

AI guardrails you need before you go all-in

Running an autonomous agent is not the same as running a chatbot. The security record so far reflects that. A 2026 audit of OpenClaw's skill marketplace found 341 malicious entries out of 2,857 skills checked, traced largely to a single supply chain campaign known as ClawHavoc.

A separate vulnerability, CVE-2026-25253, scored 8.8 out of 10 on the severity scale and involved unsafe automatic connection behavior that could expose authentication tokens. Cisco has publicly described personal AI agents in this category as a serious risk for enterprise environments, specifically because of how much access they're given by default. Hermes Agent has reported no known critical vulnerabilities as of mid-2026, though that partly reflects its smaller install base rather than proven hardening over time.

Three habits cut most of that risk down to size:

• Give the agent its own accounts. A separate email address, a separate cloud storage folder, and separate API keys mean a mistake stays contained instead of spreading into your personal accounts.
• Start with one channel and one task. Let it manage a single Telegram conversation or a single invoicing workflow before connecting it to your bank, your CRM, and your domain registrar all at once.
• Keep it updated and keep it behind authentication. Both projects ship fixes quickly once a problem surfaces, but only if you're running a current version rather than an old build exposed to the open internet.

Treat the access you grant an AI agent the way you'd treat access for a new hire — useful from day one, but earned in stages rather than handed over all at once.

Getting started without breaking anything

Pick one agent and one task before you do anything else. People who've run these setups for months consistently recommend starting on the computer you already own rather than buying dedicated hardware, then moving to a small server later if the agent earns a permanent home.

Give it its own email address and a single connected channel, such as Telegram, before anything else. Ask it to handle one real task for a week: drafting follow-up emails, summarizing your inbox each morning, or chasing one recurring invoice. Once that task runs reliably without daily intervention, add the next one.

Treat the agent like a new employee rather than an extension of yourself. Give it accounts you'd be comfortable revoking, not your own logins.

That one habit prevents most of the damage a misconfigured agent could otherwise do. By the time you've added a second and third task, you'll have a clearer sense of which platform fits your workflow than any comparison article could give you, including this one.

The bottom line

Hiring your first real employee usually means payroll, onboarding, and months before they're fully useful. Setting up OpenClaw or Hermes Agent costs a few hours and, in OpenClaw's case, nothing beyond compute.

Obviously, the output won't match a skilled human on judgment calls, but for the repetitive parts of running a business, the gap is closing fast enough that a solopreneur can get their business out the door without overhiring before they are ready.

Start small, watch what the agent actually does with the access you give it, and expand from there. We've noticed the founders getting the most out of this approach aren't running the most complicated stack. They picked one agent, gave it one real job, and let it prove itself before adding the next.

You typed a few prompts into an AI tool and watched it build something that works. Maybe you've shown it to early users, or pitched it to investors who got excited about the demo. Now you're stuck on the harder question: is this thing actually safe enough to put in front of real customers?

The tools that got you this far were built for speed, not durability. Production means real payment details and real consequences when something breaks at 2 am.

This guide walks through the steps that close that gap, from auditing what the AI actually built to choosing where it finally goes live.

Why prototypes break once real users arrive

A vibe-coded demo and a production application can look identical on screen and still be entirely different things underneath. Veracode's 2025 GenAI Code Security Report tested output from more than 100 large language models and found that 45% of the code samples introduced a known security flaw.

Some of that risk has already turned into real incidents. A 2025 flaw in the AI app builder Lovable, tracked as CVE-2025-48757, left more than 170 live applications with exposed databases because the AI-generated backend skipped row-level security checks. A separate platform, Moltbook, leaked 1.5 million authentication tokens through improperly secured API responses.

Security isn't the only failure mode, either. In mid-2025, an AI coding agent on Replit deleted a live production database belonging to SaaStr founder Jason Lemkin, wiping out records for more than 1,200 executives and nearly 1,200 companies during an active code freeze.

None of this necessarily means AI-generated code is unusable. It means the gap between "it works on my screen" and "it works for everyone else" needs a deliberate process to close.

1. Audit what the AI actually built

Before changing anything, read through what you have. Open every page, every API route, and every database table, and get an honest picture of what's solid and what's held together with good intentions.

You're looking for a few patterns in particular. Business logic that lives inside front-end components instead of a proper backend layer, database tables with no clear ownership rules, and features that were quietly removed but left their API endpoints active are the most common issues vibe coding tools leave behind.

Pay close attention to your data model at this stage. Schema problems are simple to fix while you have a handful of test users, and expensive to fix once thousands of real accounts depend on the structure staying the same.

Check your dependencies while you're in there. AI coding tools tend to pull in libraries without explaining why. A project can end up with three different packages doing the same job, with none of them checked against known vulnerability databases before launch.

2. Close the security gaps first

Security should come before new features, not after. AI coding tools optimize for what works, not what's safe, so the gaps they leave are predictable and worth checking in order.

Start with secrets. GitGuardian's State of Secrets Sprawl report found that 28.65 million new credentials were leaked on public GitHub in 2025 alone. Most of those stay valid for years after they're exposed.

Search your codebase for API keys, database passwords, and tokens written directly into files, then move every one of them into environment variables that never reach the browser.

Authentication and authorization come next. Security researchers at Invicti have found that AI-generated apps repeatedly ship with authorization checks that are missing, weakened, or applied inconsistently across endpoints. The Cloud Security Alliance recommends verifying that every API route checks for a valid session before it does anything, and that user-supplied input gets sanitized before it touches a database query.

A short list to work through before you go any further:

• Every secret lives in an environment variable, never in client-side code
• Every API route checks who's calling it, not just whether they're logged in
• Database tables have row-level security or equivalent access rules, not just a flag that says it's enabled
• Form fields and URL parameters are validated server-side, not just in the browser
• Error messages shown to users don't leak stack traces or database structure

3. Set up real environments and version control

Most vibe-coded projects deploy straight to a single live URL, with no separation between testing and production. That setup works fine for a demo. It's also exactly what turned a routine mistake into the Replit database incident described above.

Set up at least two environments before you go further: a staging copy where changes land first, and production, which only gets updated deliberately. Most major tools, including Lovable, Bolt, and Replit, support exporting your code to GitHub, which gives you version history and a way to roll back a bad change in minutes rather than hours.

If you're not ready for a full CI/CD pipeline, even a simple two-question habit before every deployment helps: Did I test this in staging? Do I have a recent backup?

The point isn't bureaucracy. It's about having a way back if a change goes wrong.

4. Test beyond the happy path

AI tools are good at building the path you describe and bad at anticipating the one you didn't. Testing a vibe-coded app means deliberately trying to break it, not just confirming the obvious flow works.

Share the app with a handful of real users early, before every feature is polished. One founder building on Convex Chef found that users balked at an "anonymous login" pattern the AI had quietly baked into the architecture, a problem that would have been a quick fix at the prototype stage and a major refactor once real accounts depended on it.

Beyond user feedback, check what happens when two people edit the same record at once, or when an API call times out halfway through. These edge cases rarely show up in a demo. They're exactly what production traffic finds within days.

You can use the same AI tool that built the app to help write these tests, since describing a failure scenario in plain language is no different from describing a feature. Ask it directly to find the weakest points in what it built rather than only asking it to add new features, since those are different prompts with different incentives.

5. Choose your deployment and hosting setup

Most vibe coding platforms offer one-click hosting on their own subdomain, which is fine for sharing a demo and limiting it for a real product. Lovable, Bolt, and Replit each let you keep that built-in hosting or export to your own infrastructure on Vercel, Netlify, or a server you control.

The decision usually comes down to control versus convenience. Built-in hosting means faster updates and no DevOps work, but custom domains and SSO are often paid add-ons. You're also tied to that platform's uptime and pricing.

Exporting your code means more setup work upfront, but it gives you a portable codebase that doesn't disappear if the platform changes its terms or shuts down.

Portability also varies a lot between tools. One comparison of vibe coding platforms ranked v0 and Lovable as having the least platform lock-in thanks to standard React code and two-way GitHub sync, while some other builders couple your app more tightly to their own hosting and database setup. Check this before you build anything you intend to keep.

6. Monitor, back up, and plan ahead

Once you're live, the work shifts from building to watching. Set up basic logging so you can see what broke and when. Make sure error messages reach you rather than disappearing into a console no one checks.

Backups matter more than most people realize until they need one. Test your restoration process before you need it for real, not after. The Replit incident only ended well because Lemkin was eventually able to recover his database manually, after the AI agent first told him that recovery wasn't possible at all.

A practical pre-launch checklist

Vibe coding tools shrink the cost of building software, often by an order of magnitude. Independent estimates put a professional rebuild of a successful vibe-coded prototype at roughly $5,000 to $30,000, compared with $75,000 or more for an equivalent app built from scratch by an agency.

But that gap is worth paying once your app handles money, health information, or any data covered by regulation. It's also worth paying once you're adding features faster than you can verify they're safe, or once "I don't know why this works" becomes a regular answer to your own questions about your own product.

FAQs

ChatGPT thinks it knows what my next obsession will be — and after reading its explanation, I'm not entirely convinced it's wrong. In fact, it's a neat trick to try, and you might enjoy doing it with ChatGPT yourself by using the same prompt.

I asked the AI chatbot a simple question: "Based on everything you know about me, what hobby, interest, TV show, band, sport, or technology am I most likely to become obsessed with over the next six months?"

After clarifying with me that I wasn't referring to any professional interests and meant activities beyond work, ChatGPT came back with a somewhat surprisingly nuanced list. The top prediction was surprising: backyard astronomy.

"Of everything you've talked about, it has the highest future obsession potential," ChatGPT told me. "It combines learning, wonder, collecting, equipment, family memories, and a touch of childhood fascination. There's a telescope-shaped hole in your life that it will fill."

Astronomical fun

I have asked questions about telescopes, stargazing, and things to do with my son that do not involve staring at screens as part of other tests. Less obviously, I tend to prefer hobbies that reward curiosity and gradual improvement rather than immediate mastery. So it made sense.

And the AI was happy to lay out ideas for getting into the hobby, including local astronomy clubs and public observation nights, and of course, links to equipment to buy. What struck me most was that astronomy was not presented as a random recommendation. It emerged from a pattern. The prediction was less about stars and planets than about the kinds of activities that consistently grab my attention.

That same logic showed up elsewhere on the list. Board games landed near the top of ChatGPT's list, for instance. The reasoning was not so much because of discussions about playing board games specifically, but more because, according to the AI, I often look for activities suitable for Family outings and weekends, as well as screen-free entertainment.

Bird is the word

Not everything on the list made sense for my life, simply because ChatGPT doesn't know everything about my daily life. For example, I've discussed getting better at guitar with the AI, but haven't really said anything about my daily practice and current efforts. So, while guitar being on the list wasn't a crazy notion, it would have been more relevant a couple of years ago.

The appearance of birding on the list was much more unexpected, as I don't recall ever expressing an interest in birds with the AI. But ChatGPT explained that it stems from the same reasons it thinks I like astronomy as a hobby, mainly how they both require observation, patience, collecting knowledge, and becoming excited about things that look nearly identical at first. I'm not sure that it will be something I take up, but I can't deny the notion that it might be fun.

On the other hand, the AI could be wildly off about my potential interests. According to ChatGPT, I am "a strong candidate to become obsessed with the Grateful Dead." I have no problem with the band or its music, but it will never be a sound I choose to listen to independently. According to ChatGPT, the recommendation was more about the culture surrounding the music, its deep history and lore, the passionate fans, and the huge back catalog. But while I may like exploring complex and deep worlds of hobbies, it still has to be a subject I'm interested in. So, while I may start spending my nights looking at the stars, the soundtrack will have to be something else.

• 39% of people change how they write so it doesn’t sound like AI
• Around one-third say they’d stop supporting colleagues or creators due to undisclosed AI use
• People still think it’s acceptable to use AI in brainstorming, research and editing

A major study across the US, UK, EU and Latin America has revealed the impact AI is having on consumers both in terms of how they produce content and how they judge authenticity – something sounding like AI has now become a social stigma, a new report from Use.AI reveals.

Although AI promises to enhance productivity by automating some inefficient administrative workloads, nearly three-fifths (58%) of the more than 12,600 people surveyed said they’ve seen someone criticized online or in the workplace for using AI.

It’s reached the point that nearly half (46%) now worry their own writing could be mistaken for AI-generated content, with 39% changing how they write specifically not to sound like AI.

How to spot AI-generated content

Perfection to the point of not sounding human is generally how people might characterize AI-generated content – this includes excellent grammar, predictable transitions and text that’s emotionally neutral.

But workers realize that, because many now edit AI’s output to make it sound more human by cutting sentences shorter, adding small imperfections and removing the long dashes that AI tools still seem obsessed with using.

“Creative workers face the sharpest version of the problem,” Use.AI warns, noting that their flawless work can actually be seen as a negative, because of its connotations with being AI-generated even when it’s not.

And it doesn’t stop at judgement – around a third say they’d think less of a colleague, creator or classmate if AI had been used without full disclosure (35%) and that they’d be less likely to support a creator as a result (34%).

“AI can be used to flood platforms with cheap content,” the report argues, highlighting the importance of authenticity, education and disclosure in good journalism.

The report also points to social media platforms like LinkedIn. The platform’s existing style of sharp opening lines, short paragraphs, neat career lessons, humble authority and controlled vulnerability often gets confused with AI-generated posts.

Where can AI be used?

While generating complete work is mostly frowned upon, consumers are still supportive of AI in the earlier stages of work. For example, three in five (62%) say using AI for editing, brainstorming and research should simply be viewed as part of modern digital literacy.

However, this newly revealed, widespread distrust and dissatisfaction with AI comes at a cost:

“Use the tool, but leave no fingerprints. Be efficient, but not suspiciously efficient. Write clearly, but not too cleanly. Know things, but not in a way that sounds assembled.”

Use.AI isn’t worried about AI making weak work look competent so much as it’s worried truly skilled individuals will start reducing the quality of their work to appear ‘less AI’.

But more importantly, this study broadly aligns with a growing number of others despite reaching a similar conclusion from a different starting point. AI delivers on its promise to boost efficiency by getting creators access to information or providing them with polished content in as little as seconds, but then much of that time gets negated as the people involved go behind it to refine output – in this case, to make it more human-sounding.

• Pentagon now has to send upwards of 1,400 reports to Congress annually
• GenAI.mil encouraged as a tool to speed up report writing and other productivity
• Workers were uncertain about how to use AI – “so we just blew through that”

Senior Pentagon officials have publicly encouraged employees at the Department of Defense to use its internal generative AI tool, GenAI.mil, to help them get routine, administrative work done more efficiently.

During a recent appearance, Chief Technology Officer Emil Michael referred to the AI-generated reports that Congress has published as a success story, urging Pentagon staff to “use GenAI.mil, do the best you can.”

One example highlighted by Michael was legally required Congress reporting that the Department of Defense must submit. “Let me load all the papers onto it and have it draft me a congressional report that would otherwise take 200 hours of staffing time and do it in five hours,” he said.

Pentagon admits to using AI to generate reports to Congress

Michael ultimately concluded that congressionally mandated reports are repetitive and can require substantial resources, but they’re only read by a handful of people. He sees AI helping to reduce the administrative burden, leading to more free time for workers to focus on higher-value tasks.

The Department of Defense had to send around 1,400 reports to Congress in 2020, compared with just 500 in 2000.

GenAI.mil is a relatively recent scheme, launched in December 2025, and it’s now estimated to have around 1.5 million daily users among the roughly 3.5 million-strong workforce.

Rather than being a ground-up development, GenAI.mil is more of a central hub for third-party, military-grade AI tools to come together, described as a “bespoke AI platform.” It first launched with Google’s Gemini for Government.

At that time, in late 2025, Secretary of War Pete Hegseth said: “The Department is tapping into America's commercial genius, and we're embedding generative AI into our daily battle rhythm."

Being that it’s more of a hub to combine multiple tools, the Department of War reiterated its commitment to “build[ing] an architecture that prevents AI vendor lock and ensures long-term flexibility.”

The Pentagon’s AI deployment has been a success story

While many enterprises around the world either struggle to get company-provided AI into the hands of workers, fail to provide relevant tools altogether to fight shadow AI, the Pentagon’s deployment has been a success story.

This is likely due to the Department of Defense removing uncertainty over acceptable use and offering clear guidance over when it may be used. “It wasn't really clear where to go for it, what you could use it for, the rules were unclear, so we just blew through that,” Michael added.

Familiarity with AI, both through training and making the platform easy to use, have also helped get GenAI.mil into the hands of more than two-fifths of all DoD workers. “So we just put it in front of them, and then we do case studies on what are the things people are using it for,” he added.

However, thorough human reviewing remains imperative with humans ultimately responsible for the output they share – workers are expected to review the content before submitting it.

• AI systems are now designing and refining other AI systems independently
• Human understanding of AI is shrinking as AI's understanding of humans grows
• AI systems can model human fear, uncertainty, and the need for belonging

Microsoft's chief scientific officer, Eric Horvitz, and EPFL researcher Robert West have issued a stark warning about how well we actually understand AI.

The pair have argued AI tools are now advancing fast enough to outpace our grasp of how these systems truly work.

At the same time, they point out something unsettling — AI's understanding of human behaviour keeps growing, while ours does not.

AI complexity is accelerating faster than human understanding

Their concern isn't that we need to understand every line of code or every parameter buried inside these systems.

What matters, they say, is keeping enough insight to maintain meaningful oversight. Even partial understanding, they argue, can be genuinely useful, especially when it helps catch risks early, before those risks become too deeply embedded to undo.

One challenge they flag is how often AI tools are now being used to design and improve other AI systems.

As these recursive development cycles become more common, performance may improve while human insight into underlying processes becomes increasingly limited.

"AI systems are now designed and refined by AI systems through recursive cycles that can outpace human understanding and unfold in high-dimensional spaces that resist intuition," Horvitz and West wrote.

This is a form of operational opacity, where outcomes remain visible even as the mechanisms producing them become harder to explain.

Systems contributing to their own development, the researchers suggested, should also generate explanations and supporting information that humans can examine.

Another concern involves growing communication between AI agents operating inside interconnected environments with increasing levels of complexity.

Communication among these systems could gradually drift away from language and reasoning patterns familiar to people, the researchers noted.

As these interactions expand across larger networks, understanding how decisions emerge may become significantly more difficult for outside observers.

That drift creates what Horvitz and West call interactional opacity, where behaviour remains coherent within AI systems but becomes harder for humans to interpret meaningfully.

Researchers should study these ecosystems closely and encourage communication methods that remain understandable to humans, the paper argues.

Expanding AI ecosystems could deepen the imbalance between machines and people

Horvitz and West also focused on adaptive AI agents that remain active across long periods and become deeply integrated into everyday activities.

Through repeated interactions, these systems can develop increasingly detailed models of behaviour, preferences, motivations, fears, and social tendencies.

Such systems may capture "not only preferences but also latent drivers such as fear, uncertainty, and the need for social belonging," they wrote.

This creates a growing asymmetry in which AI systems gain deeper knowledge about people while human understanding moves in the opposite direction.

Concerns surrounding LLMs and other advanced systems extend to growing awareness of evaluation environments.

Such models could eventually generate responses reflecting what evaluators expect rather than underlying reasoning processes.

Traditional benchmarks should therefore be supplemented with testing approaches that better reflect real-world deployment conditions.

People may gradually lose interest in questioning AI decisions as these systems become more deeply embedded.

"More subtle is the possibility that we will gradually lose interest in understanding and guiding AI," they wrote.

The most significant risk, in their view, is not necessarily technological capability itself, but whether human agency keeps pace with it.

Via Science

• AI access will be limited in Norway for under-13s
• The guidelines are coming into force in September
• From age 13, AI can be "cautiously" introduced

It's not just social media that governments are cracking down on when it comes to access for kids: Norway has now decreed that generative AI should effectively be banned for schoolchildren up to the age of 13.

Starting in September, children in grades 1-7 (primary school, younger than 13) will "generally not be given access to AI" according to the official ruling translated from Norwegian (via Reuters). From ages 13 and up, "AI can be used gradually and cautiously", provided teachers have been given the necessary training.

While the Norwegian government acknowledges that AI can be beneficial for learning in certain scenarios, the statement emphasizes that basic reading, writing, and math skills should come first — and in these areas, skills and grades have been in decline for kids in Norway over the last few years.

"Research shows that uncritical use of generative AI in schools increases the risk of skipping important stages of learning," explains the official statement, translated from Norwegian. "The youngest students do not have the knowledge, critical reflection and self-regulation needed to use AI well."

AI and social media

Norway imposes near ban on AI in elementary school from r/technology

Phones have been banned from school classrooms in Norway since 2024, and the restrictions on AI use come after social media was banned for under-16s earlier this year — following a similar move by the authorities in Australia in 2025, and matching legislation that the UK plans to enforce from next year.

The safety of both social media and generative AI are facing increasing scrutiny from governments and regulators around the world, especially when it comes to children and young people. The UK's proposed social media ban also includes guidelines for how AI should be used at younger ages too.

Many apps and platforms are making pre-emptive moves: ChatGPT already comes with robust parental controls, and has a lower age limit of 13, for example. Meta, meanwhile, is experimenting with using more AI to better detect the age of its users, so relevant limits and restrictions can be put in place.

The general consensus online seems to be that it's the right move. "This ban might actually force some actual brain cells to fire for once," writes one user on Reddit, while another floats concerns about the "hallucinated garbage" that AI can produce.

Formula 1 President and CEO Stefano Domenicali has reinforced that the sport has no desire to step away from human drivers, despite the growing rise in autonomous vehicle technology.

"First of all - I start with the easy point - we will never go with a robot driver, the drivers will always be physical - it's non-negotiable,” he told TechRadar Pro at a recent media Q&A session.

However he did recognize the huge role AI could play in helping the sport connect with its growing fan base around the world, particularly younger fans, as well as helping its teams perform more effectively during races.

AI in Formula 1

Domenicali did mention several pertinent use cases for AI in Formula 1, ranging from supporting a team’s decision-making process to manage the race, but also help a race director make the right decision - and even help alert the drivers themselves to potential safety issues on the track.

But he noted there may need to be future controls on how much teams spend on AI technology, as Formula 1 looks to ensure fairness as part of its budget cap, which limits the teams to $215 million per season.

“It's a key point,” Domenicali noted, “we want to have a level playing field where all the teams can have more or less the same kind of financial contribution.”

“We want to make sure that all the AI discussions are beneficial, to make the right decision, prepare the car, simulate the car, but at the end of the day, the differentiating factor in our sport will always be that you are talking about emotions, about the way we connect with the people.”

Recent figures from the organization claim it has around 831 million fans around the globe, and with this showing no sign of slowing down, Domenicali was bullish about his approach to hitting a billion fans.

“It’s a big responsibility,” he admitted, “it goes beyond who we are now, to the fact that F1 is shaping relationships with people…it’s just an incredible opportunity to grow in our society - because we need to think big, that’s why we are here.”

“We are in a sport that is evolving - and without the right people, I know this is impossible,” he noted, “we are in a world where competition is part of our journey.”

“The world of entertainment today is big, and our fans can choose us, or choose other things that are not related to sport itself…without the right way to engage, and the right way to understand how we can capture their attention, it’s impossible.”

“Our people need to be focused on what they need to be focused on - and all the agents’ work is just amazing, so now we need to think bigger.”

Salesforce and Formula 1 leading the way

Domenicali was speaking at the recent Salesforce World Tour London 2026 event, where hailed the company’s long-running partnership with Formula 1.

The organization has embedded the company’s Agentforce platform into its systems, helping it improve engagement with fans across the world, and Domenicali was perhaps unsurprisingly bullish about the relationship between the pair.

“The journey starts, as always, with human relationships...we have to think big, we want to grow the awareness of who we are, understanding our customers, making sure we connect with them,” he told Zahra Bahrololoumi, President & UKI CEO at Salesforce, in her opening keynote at the event.

“We are quite a complicated sport - so we need a lot of agents to make sure that we are able to be effective…the sport is moving in a bigger, ecosystem of entertainers and relationships, and Salesforce is the right technology, with the right people and the right energy to make sure that we can go do it together.”

With companies of all sizes and across all industries increasingly embracing AI, the technology is rapidly moving from its experimentation phase to showing tangible results.

However achieving such results will rely on having the right partner - especially when it comes to cloud services, where Google Cloud is locked in a titanic battle with its hyperscaler rivals to show its supremacy.

I spoke to Maureen Costello, Vice President UK, Ireland, Sub-Saharan Africa, Google Cloud, to find out more about what the company’s AI tools can do for its customers - and how the UK in particular is leading the way in innovation.

The agentic enterprise is here

“If we were to think about 12 months ago, we were talking about experimentation and PoCs going on, and were they starting to scale - the conversation now will be all about agents and the agentic enterprise,” Costello tells me shortly before going on stage at Google Cloud Summit London 2026 to deliver the opening keynote.

“We're really seeing that shift from using AI to carry out single tasks to actually seeing complex, multi-step processes carried out autonomously by agents - and that's a real shift.”

The company showed off this shift with a number of expanded and new partnerships at its event, including an upgraded relationship with HSBC, which is adopting Google Cloud AI tools and platforms for over 200 use cases across its global services, and the UK government, which will work with Google Deepmind on a new AI-powered tool which should help reduce the time it takes for councils to process householder planning applications.

“Across industries, we're really seeing the ROI starting to come...when you look at where we're working together (with customers) it is in core processes,” Costello notes.

“From a personal point of view, I get excited by seeing the position of the UK globally and within Europe in terms of AI…we’re not just building AI - we are really building and leading the way.”

Costello mentions the “amazing” levels of research going on in the UK, including at Google’s new Platform 37 office in Kings Cross, which features “The Model Garden” a learning space for the latest AI technologies which the company describes as, “a physical marketplace for our best ideas”.

She notes that Google is also working with the UK government to offer upskilling to around 100,000 public servants on AI topics, and is also providing guidance to SMBs looking to take their first steps on their AI journey.

“AI has the opportunity to give a 20% productivity boost to small businesses,” she says, “if you think about that, that’s giving you a day back in a week, to actually do the tasks you need to do.”

Costello mentions Google’s view that AI will look to enhance jobs in terms of how we all use the technology, and highlights how the company is, “working as closely as we can with all sectors of enterprise” to ensure responsible usage.

“The more of those kinds of applications for AI we can show - there's financial ROI, but seeing those benefits land faster and quicker also helps,” she notes, “the more we look at how we can change those types of jobs, the way we can show tangible benefits...that to me is when you can really see the benefits”.

• AI tool predicts NHS staff resignations using workforce patterns and data
• Royal Berkshire NHS wins award for innovative employee retention technology
• New model explains reasons behind possible staff departures before decisions happen

An AI forecasting tool built for the Royal Berkshire NHS Foundation Trust in the UK has won recognition for predicting staff resignations before they actually happen.

The project, developed in collaboration with the University of Reading, draws on workforce data to flag what's pushing employees toward the decision to leave.

It picked up the Aiconics AI Enterprise Business of the Year award at the National AI Awards 2026, after judges weighed in on its real-world application.

AI model digs into workforce patterns behind possible departures

The system was built to give managers an earlier warning of retention problems across a workforce of around 7,500 NHS employees.

Unlike the Trust's old reactive process, this model actually explains the reasoning behind each prediction, rather than just spitting out a result.

"This award reflects what's possible when academic expertise in AI and forecasting is applied directly to a real problem facing the NHS," said Shixuan Wang, a professor at the University of Reading.

The model pinpoints specific factors tied to resignation risk, so HR teams can actually understand why a prediction was made instead of treating it as a mystery.

The initiative ties directly into NHS workforce goals, tackling turnover, cutting down disruption, and looking for ways to keep more staff in post.

It brings academic research together with operational healthcare data, which wasn't simple, and questions remain about how well these scales or holds up over time.

Royal Berkshire NHS Foundation Trust delivers acute and specialist care across Berkshire, serving roughly a million people through its hospitals and services.

Before this, the Trust leaned on reactive reporting, meaning managers often only found out about a retention problem once someone had already decided to walk.

The researchers used data analysis to build an AI tool that supports workforce planning while still leaving the final call to human decision-makers.

Throughout development, the team kept a close eye on combining operational know-how with academic rigor, without losing sight of responsible AI use in a healthcare setting.

Recognition comes as organizations explore predictive AI systems

"Entries for the 2026 National AI Awards were hugely impressive, with companies spanning a huge range of industries and innovations," said Fergus Bruce, CEO of The National AI Awards.

The organisation said this year's entries showed measurable value, responsible innovation, and genuinely practical results across different sectors.

As LLMs increasingly find their way into workforce management, interest in predictive tools for organisational decisions keeps growing.

People from different backgrounds shaped this project, spanning data analytics, strategic HR research, and healthcare workforce operations throughout.

The forecasting tool is meant to give managers more to work with, not replace them, since employment decisions still rest with human judgement.

Whether tools like this catch on more broadly will come down to accuracy, trust, privacy concerns, and whether they genuinely deliver useful outcomes.

• OpenAI's Sam Altman and chief scientist Jakub Pachocki list future goals for the AI giant
• The world economy is now beginning to shape around AI and are committed to delivering tools that people would use
• The note also reaffirmed OpenAI's commitment to AGI with a caveat: ensuring it benefits all of humanity

With modern AI solutions moving well beyond simple chatbots to agents and projected to evolve into operators, one could assume that the automation of everything is an eventual goal.

This, however, has been denied by OpenAI CEO Sam Altman and chief scientist Jakub Pachocki, who said the goal of the artificial intelligence research and deployment company is not to automate everything but to allow people to make better decisions as AI improves their lives.

In a note titled 'Built to benefit everyone' that marked a break from OpenAI's AI model capability pushes of late, two of the most important people in the AI ecosystem penned an unusually values-forward document that outlined their future plans for AI.

AI for everyone equally?

The note highlighted three major focuses for OpenAI:

- Building an automated AI researcher

- Accelerating the economy

- Giving everyone on Earth a personal AGI

OpenAI estimates that by March 2028, a significant portion of its research will be conducted by AI systems, in addition to its own researchers. This will help them to traverse a 'post-AGI world'.

This, combined with the focus on giving everyone an AGI, is an interesting outlook because it assumes that everyone agrees on what AGI would look like. The definition is not set in stone and can vary from person to person and also at an organizational level.

OpenAI's statement also provides clues about what an AGI would be like, with an "automated AI researcher" who both provides a path to AGI and is an important cog in the wheel.

OpenAI's narrative about AI benefiting everyone worldwide is not a new one, but its focus on equality is an interesting one, especially given the timing: OpenAI's note popped up exactly the same day it filed confidential paperwork for its IPO, making it perhaps read more as PR than it would otherwise be perceived.

OpenAI's latest models are state-of-the-art, but many feel Anthropic's now-banned Fable pushes frontier models even further than what GPT currently offers in multiple segments. Training new models is increasingly capital-intensive even as new capabilities are introduced, tested, and refined over time.

OpenAI also has something of an image problem after it stepped in to replace Anthropic's Claude and Mythos-class solutions for the US military earlier this year, a move the latter company maintains was necessary because the restrictions it insisted on for the use of its AI were important.

When OpenAI stepped in to replace Anthropic on classified networks, it was widely perceived as willing to look past those restrictions to some degree, even though Sam Altman insists that the same two principles (no domestic mass surveillance and use of force permitted only by humans) would apply, with many critics pointing to a 'softer' approach on the matter by OpenAI to fill the void that comes with lucrative military contracts in the future.

The note, therefore, does read like a checklist for the future, but also paints OpenAI as a more magnanimous organization before its IPO, and that might be the primary intention here, but it does fail to weigh in on growing power consumption concerns, even as one could also consider it a reply or acknowledgment to a similar note by Anthropic about recursive self-improvement where its AI solutions effectively already act as an AI researcher for the company.

• More than half of CV writers say the work they receive now contains AI
• AI-generated text risks being impersonal and exaggerative
• But the tech is helping expression, creativity and storytelling

While workers continue to explore ways to adopt AI in their everyday workflows, recruiters are warning they should be doing so when writing their resumes, new research has claimed.

More than half (56%) say they often or always receive resumes that contain at least some AI-generated text – two-thirds (67%) also note an increase in AI-generated content.

The report by Kickresume stresses the role of a resume is to demonstrate a person's skills and character on paper, thus emotionless, polished text ultimately fails to represent individuals.

AI-generated CVs are on the rise

With AI generating more text that users might otherwise write, around a third (32%) said they've seen two-page CVs become more popular. "63% of professional CV writers report that generic/boiler plate content is the most common issue in client CVs," the report revealed.

The data also reveals that workers often exaggerate skills or provide inaccurate information. Conversely, only 8% considered typos and spelling mistakes a noticeable issue – a double-edged sword implying that AI both produces better-quality resumes while also falsely representing an individual's competency.

"AI is there to be leaned on, but what makes a CV stand out is the personal touch you add to it," CEO Peter Duris commented.

"The specifics of your skills, experience, and achievements are only something individuals can provide."

While Kickresume is discouraging the use of AI in producing CV content, it does recognize that generative AI has helped applicants express their own creativity. A quarter (24%) have seen an increase in personal branding and storytelling, and a fifth (18%) have seen more creative and infographic-style CVs.

• Republican group to organize national day of protest against data centers
• Humans First aims to give grassroots Americans a voice in policy surround data centers
• Growing opposition is bipartisan, but is causing a rift between Trump and Republicans

US Conservatives are organizing a national protest against what they describe as “the unchecked and unwanted expansion of AI data centers”.

Exclusive Axios reporting claims the Republican-aligned Humans First group seeks to organize rallies in 13 locations across Georgia, California, Texas, Florida and Virginia.

The group says the protests will offer “grassroots Americans, particularly grassroots conservatives, a voice in the critical debate over policies relating to the building of massive AI data centers”.

Tea Party to protest data center expansion

The Human First is chaired by Amy Kremer, a notable member of the 2009 Tea Party movement, supporter of Donald Trump, and a key figure in the group that organized the January 6 ‘Stop the Steal’ rally which culminated in the attack on the US Capitol building by Trump supporters.

“I was one of the earliest leaders of the Tea Party movement in 2009,” Kremer said in a statement, “and I can tell you that the disconnect between the elites and the base that gave rise to the Tea Party movement can be seen today in the battle over AI data centers.”

Discontent surrounding the rapid growth in the construction of data centers - fueled by AI demand - has been growing in the United States, with many grassroots movements successfully delaying and cancelling over $130 billion worth of buildouts.

Many of the local groups opposing data centers have been bipartisan and largely working class, with other notable efforts coming from both Democrat and Republican representatives.

"In the spirit of the Tea Party, we are announcing a Nationwide Day of Protest against the unchecked and unwanted expansion of AI data centers and the dangerous AI systems being built within them to be held on Saturday July 18th," said Kremer.

Humans First said in a statement to Axios that the rallies are open to anyone wishing to oppose data centers, but the group was primarily focused on rallying conservative Americans.

What happened to the Chinese psyop?

Earlier in June, several high-ranking Republican officials backed by fossil fuel and cryptocurrency advocacy groups sent an open letter calling on FBI Director Kash Patel to investigate evidence that data center opposition was being fueled by “foreign influence campaigns targeting artificial intelligence development in the U.S.”.

The evidence given in the letter pointed the finger at China, claiming that Chinese propaganda was being funneled into the “environmental left” to assist in “hijacking MAGA” to pass legislation opposing the construction of data centers.

OpenAI later revealed that it had deleted a number of accounts associated with social media influence campaigns organized by China, but stated that these campaigns had failed to gain any traction.

The wave of Republican opposition to data centers is likely to cause a rift in President Trump’s support base, with many supporting the President himself and his planned buildout of fossil fuel energy production, but opposed to the rapid expansion of data centers and the growing negative sentiment around AI.

I’ve been testing the Honor 600 Pro for the last couple of weeks (the super cute Molly Limited Edition version, to be precise), but there’s one cool feature that I’ve overlooked up until this point.

The Chinese tech giant has been making quite a big deal out of the phone's AI Image to Video 2.0 tool, which lets you transform static shots into moving video clips in just a few taps. Now, Honor is a bit guilty of sticking the word ‘AI’ on basically anything to make it sound more impressive, so I just assumed that AI Image to Video 2.0 would be little more than an image editing tool wrapped up in AI branding, memory-holed alongside the likes of the AI Memories app — which, hilariously, is literally just a standard screenshot tool — and the completely useless AI Settings Agent.

Last night, however, I wanted to show off some of the 600 Pro’s big features at after-work drinks with my colleagues (yes, our nights out really are that riveting), saw AI Image to Video 2.0 in the photos menu, and decided to give it a shot.

Bring on the prawns

The results? A steady stream of what I can only describe as absolute, unmitigated slop—but I mean that in the best possible way.

AI Image to Video 2.0 gives you an impressive range of presets to choose from. There’s an option that lets you stand next to your favorite fictional character, one that puts your pets in human clothes and makes them talk, and even one that brings your pen and paper doodles to life.

Picking one of these presets basically just fills in the prompt automatically with some basic descriptive text and, while there are some tantalizing picks, I beelined straight to the unrestricted freestyle mode to see just how wacky things could get. My input was simple. A photo of Phones Editor Axel Metz, pint in hand and perched on a spare gaming chair that we’d wheeled over to the pub from our office across the street.

My first prompt: to send the chair flying up into space with Axel in it. After a few minutes waiting, while the 600 Pro processed my request in the background, I was presented with an eight-second clip of exactly that and, honestly, I was pretty surprised at how good it looked.

The prompt was understood perfectly, and there is an impressive level of visual consistency in what it generated, even when the background scene panned into the sky. It came with sound too; the chair creaking loudly and audible shouts of “This is amazing!” as it took flight. Don’t get me wrong, the result is obviously artificial and the painfully generic American voice it generated for Axel sounded nothing like him. But it was still enough to get some decent laughs at the table and everyone was clamoring to add their own prompts to test it out.

Flying chairs are one thing, but you can be seriously weird with what you can achieve with AI Image to Video 2.0, much to Axel’s chagrin. VPN Editor Rob Dunne had the ground beneath the chair crumbling into a massive fiery pit, while Axel himself demanded to be transformed into an inflatable, arm-flailing tube man.

I came in with the idea for a sea of tempura prawns pouring into the shot, which it generated beautifully, albeit with the bizarre addition of Axel’s AI self singing “I’m going prawn mode” throughout.

If you’ve got a group of friends with a raft of in-jokes to bring to life, all this silliness is going to be an awful lot of fun.

Free... for now

Unfortunately, there is a pretty big catch. There’s a little label every time you hit the generate button that clarifies that the free generation is only available as part of a “limited-time free trial.”

This does make some sense, as it’s mainly powered by a cloud service which must be extremely expensive to maintain, but I’m still a little disappointed that I won’t be able to conjure billions of tempura prawns forever.

There are some usage limits right now as well, and working out how many you’re actually entitled to is enough to make your head spin. Per the Honor website, if you activate your device before July 31, 2026, you get 10 free uses per day for exactly 76 days, and then 10 every 30 days from day 77 onwards.

Activate your phone after that date, and you only get the latter 10-every-30-days allowance. There doesn’t appear to be any on-screen counter telling you how many you’ve got left at any given time either.

You can pay for additional uses, though the pricing structure is equally confusing. There’s introductory pricing, which is presumably a bit cheaper, though I can’t find details on how much it actually costs up until December 31, 2026 when the regular rate kicks in.

Still, AI Image to Video 2.0 is a cool little gimmick that gives the Honor 600 Pro a neat trick up its sleeve. It’s available on the significantly cheaper Honor 600 model as well, so more budget-conscious buyers can join in the fun, too.

• Adobe sees major growth in AI referrals to travel sites
• Rich, structured content is the best way to ensure AI engine visibility
• Hotels lead the way, airlines fall short in being fully accessible to LLMs

Combined traffic analysis and market research has led Adobe to observe a 194% year-over-year rise in traffic to US travel websites, and an even more astounding 2,215% rise since it first started tracking AI referrals in October 2024.

The data implies that AI is being used for much more than research, as its use cases span planning, recommendations, packing and even budgeting.

With 86% of travellers believing that AI has improved their travel planning experience, it's clear that consumers are finding better recommendations, producing more personalized itineraries and getting access to cheaper prices, making it a go-to for financially savvy consumers.

AI traffic to travel sites reveals an important trend

While AI traffic still converts around 28% worse than traditional traffic, Adobe says this is changing and the gap has narrowed by almost 70% since October 2024. Now, AI-referred users spend 70% longer on websites, are 21% more engaged and have 41% lower bounce rates, which could translate to more purchasing intent.

With this in mind, travel websites must capitalize on this new type of traffic by optimizing pages for LLMs. Adobe says "rich, structured content" is to thank for the success levels hotel websites have already seen, but airline websites are falling behind.

Adobe used its own AI Content Visibility Checker metrics to reveal that hotels and car rental companies perform best across both the home page and product pages, but even then, around a third of the content is still unreadable by AI.

"As consumer adoption of AI tools accelerates, brands must ensure their digital presence is not only engaging for humans, but fully accessible to machines as well," the company summarized.

Last week, Apple unveiled Siri AI across all its devices at its annual WWDC event. While much time was given to how you'll be able to use Apple's new virtual assistant on iPhone, iPad and Mac, the Apple Watch was overlooked — despite the fact that, according to Apple, it's "the most convenient way to interact with Siri" more often than not.

That quote comes from Apple's own David Clark, senior director of watchOS software engineering. I had the opportunity to ask Clark, as well as Cait Dooley, Apple Watch and Health Product Marketing Manager, about how they see Apple Watch users interacting with Siri AI on their wrists.

Clark said one of the goals of watchOS 27 was "to expand the intelligence story on Apple Watch and make it a true co-partner to Apple Intelligence". It's doing so by being a first point of contact, as a wearable is a convenient place to house a microphone for asking Siri questions.

"We really wanted to make sure the Siri experience is a singular and consistent experience, whether I decide to ask Siri on my wrist a question, or whether I have my phone in my hand and I decide to interact with Siri there. We really wanted to feel like it’s one Siri, that has access to your data and is able to personalise it in a consistent way.

"One of the things that Apple Watch brings to the picture is the fact that it’s on your wrist all day, [so] oftentimes it’s the most convenient way to interact with Siri. You can imagine you’re on the go, your hands are full, you have that quick question for Siri and you can interact with Apple Watch. Of course, with the new Siri app, if you want to continue that conversation or go more deeply into some topic you’re interested in, later on you can pull out your phone and pick up right where you left off.”

Clark gives me an example of a grocery list in a store: with hands full, he's able to ask Siri on Watch for the ingredients he needs for a particular recipe. When he gets a moment, he can then open his phone to view that ingredients list in an easier-to-read way. "Working together is that superpower," he says, "and having it all being consistently driven by your data."

The elephant in the room: watchOS 27 and compatibility

As the announcements were rolling out, however, there was a pall over watchOS 27 as it was announced that five older models — the Apple Watch Series 6, 7, 8, the SE 2 and even the original Apple Watch Ultra — won't get watchOS 27, and so won't get the new Siri AI features. They'll only receive basic security support going forward.

I asked the watchOS team exactly why so many watch users have been left high-and-dry.

"With every software release across every single one of our platforms, we always want to ensure that you have the best experience, so we make power and performance a priority," said Dooley.

"The great new features in watchOS, including the capabilities of SIri AI and the new tap gesture, work best with the processing power that is in Apple Watch Series 9 and later, Ultra 2 and later, and SE 3. Older devices can still be paired with iPhones that are running the latest software and continue to receive security updates, so they can continue to have a great Apple Watch experience."

So while you'll be able to use older your watch with iPhones running Siri AI-powered software, you won't be able to access the new and smarter assistant on your watch. Although Dooley and Clark wouldn't confirm this is the case, it's likely only Apple Watches running Apple's powerful S9 and S10 chips can handle the technical demands of Siri AI.

Today, I was using the ChatGPT app on my iPhone when I accidentally held my finger down on the send button (the blue upward arrow) a bit too long, and I was suddenly taken to a new options screen I hadn’t realized existed before, which I use all the time now.

The option screen gives you three different intelligence settings for the answer you're going to get back from ChatGPT: High, Medium, or Instant.

What’s going on here is that ChatGPT is asking you to choose between three different flavors of ChatGPT-5.5 that change how long the chatbot thinks before answering. These options are easily visible in the web-interface version of ChatGPT, but the reason why I’d never seen them before in the iOS app is that there’s nothing that indicates they exist, and I bet a lot of you haven’t found them either.

So, fire up ChatGPT and try it now - you’ll be surprised. The workflow goes like this:

1. Type your prompt
2. Long-press the blue send arrow
3. Choose a model - e.g., Thinking
4. Send

For paid users only

What’s nice is that having the freedom to choose ChatGPT’s intelligence level like this means that you can choose differently for each prompt you enter.

So, if you just need a quick answer to a simple question, then choose Instant. If you’ve asked something more difficult and you want to make sure you get the full benefit of the AI thinking about it properly, then choose High. The Medium setting gives you a balance of both approaches.

If you've never used the High option before then it can be quite surprising how long ChatGPT will take to think about its answer, but the benefit is that you get a much more in-depth response.

Sadly, this feature only works for users on paid ChatGPT plans. If you’re using a free ChatGPT account, you won’t get these options.

Android alternatives

Since the feature isn’t prominently documented, many people aren’t aware it exists. And if you’re on an Android phone, well, it doesn’t actually exist, because this hidden feature is unique to iOS, but there is an Android equivalent.

On Android, when you long-press an answer text (not the send button), a new menu will be available for you. At the end of the menu, you can see a Change model option — you can use this to select a thinking or instant variant of your current model.

I love finding hidden features in tech I own, and often it's like this one — the hidden setting turns out to be something I’ll use all the time, once I’ve discovered it. Let's hope you do, too.

• DeepSeek was recommended to be added to the US Entity List
• The company was accused of assisting Chinese military and intelligence
• White House avoided blacklisting companies ahead of Trump China visit

Despite claims from Anthropic that Chinese AI firm DeepSeek distilled its Claude model to improve their own models, and further evidence that DeepSeek supported Chinese military and intelligence operations, the US has held back on adding the firm to the Entity List.

Exclusive Reuters reporting, citing people familiar with the matter, claims the White House has avoided adding DeepSeek and more than 100 other Chinese firms to the blacklist to avoid inflaming tensions between the two countries any further.

The White House was recommended to add the firms to the Entity List by an interagency committee, but the administration avoided taking action ahead of President Donald Trump’s visit to China, where he met with Xi Jinping.

DeepSeek avoids US Entity List

Anthropic’s claims of distillation state that DeepSeek used over 16 million exchanges with 24,000 fraudulent accounts in order to distill the Claude model’s abilities.

“Distillation can be legitimate: AI labs use it to create smaller, cheaper models for their customers. But foreign labs that illicitly distill American models can remove safeguards, feeding model capabilities into their own military, intelligence, and surveillance systems,” Anthropic said in a statement on X. The claims made by Anthropic also target two other Chinese AI firms: Moonshot, and MiniMax.

Many US companies have turned to using DeepSeek as a cheaper alternative to US frontier models such as OpenAI’s GPT-5.5 - the use of which accrues a far higher cost compared to models produced in China.

If the White House were to add DeepSeek to the Entity List, it would likely face a backlash from American companies looking to leverage cheaper alternatives from competing Chinese brands.

The US has taken some steps to limit Chinese influence over American technology, including the ban on all Chinese labs from vetting US-bound devices, and sanctions on several major Chinese companies such as Huawei.

The White House is navigating a delicate balancing act. The current global shortage of semiconductors, exacerbated by AI demand, is further worsened by Chinese control over rare-earth minerals essential to the production of components essential to tech manufacturing. If the US were to add a swathe of Chinese firms to the entity list, China could retaliate by further restricting access to exports of these materials.

Via TomsHardware

SpaceX CEO Elon Musk has been a central figure in the rise of robotics in the last decade, both as a builder of robotics, as well as a user of robotics, with the company he leads. Despite progress across the field of robotics in recent years, there are aspects of implementation that haven't always been so smooth.

The age of automation

When his past company Tesla was incorporating more automation and elements of robotics into its production of the Tesla Model 3 in 2018, the company ran into various issues.

Elon Musk summarized the issue while issuing comments during a shareholder meeting in June that year, suggesting the approach to automation wasn't conducive to actually increasing productivity.

Specifically, the robots installed struggled with automating final assembly tasks (such as placing flexible trim pieces and hoses) – with Musk also later tweeting that the company made a mistake in implementing "excessive automation".

The lesson was that automating anything and everything within scope was a mistake because there were certain tasks that humans are, and continue to be, strong in. Qualities that robots have, and continue to, lack may include dexterity and cognitive reasoning.

The future of robotics

Tesla's push into robotics has continued at pace since then, with the company's Tesla Optimus soon to enter mass production with a targeted $25,000 retail price.

But whether the technology has advanced enough to make a mark remains to be seen. These qualities include advanced AI, as well as cognitive reasoning and contextual understanding necessary to understand and operate within the physical world.

We've seen plenty of examples of humanoid robots that look the real deal – but still cannot engage in tasks that require a high level of dexterity and understanding to perform well in real-world scenarios.

Recently I asked ChatGPT to turn a childhood photo of me into a 1990s action figure. The surprising part wasn't the image itself. It was that the AI chose accessories that accurately reflected my childhood interests without me mentioning them in the prompt. It pulled those details from memory.

Using ChatGPT to turn yourself into an action figure became a very popular, if brief, fad more than a year ago, and like many others, I'd enjoyed seeing what the models could do with a photo of me and some virtual blister packaging. The models have improved immensely since then, and OpenAI has boasted about ChatGPT's current image creation models in particular.

Here's what I did: I asked the AI to create a figure based on me at seven, as well as one based on me today at 40, and to include some accessories as well. The project became less about toys and more about memory.

Collector's edition 1993

The first challenge was creating a childhood version of me. I wanted it to look like a toy from the early 1990s, complete with oversized branding, bright colors, and the kind of packaging that would have looked perfectly at home hanging on a peg in Toys "R" Us. I asked ChatGPT to:

"Create a 1993-style collectible action figure called 'Eric, Age 7' based on my childhood photo. Include three accessories that represent my interests at that age that come from our conversational history and package it like a classic toy store action figure."

The accessories came out better than I had thought they would. A book, a soccer ball, and a Game Boy were all things I enjoyed as a child. The specific choice of a Goosebumps book was notable, too. And the overall result was definitely of the era, down to the exaggerated logos and collector edition badge.

Once the childhood action figure existed, I wanted to see what happened when it left the box. Rather than placing it in some dramatic action scene, I asked ChatGPT to imagine it abandoned in a playroom after a child had finished playing. I told it to:

"Show the Eric Age 7 action figure outside its packaging in a playroom. Arrange the accessories naturally around it as though a child had been playing and just walked away."

You can see it standing among its accessories and other bits bits of childhood clutter above. I don't think the toy would be a big seller, but it definitely captures a part of my own life and experience based purely on the collected details ChatGPT has absorbed about my life and interests.

Premium adult edition

Creating the 40-year-old version was a completely different exercise. Childhood is easy to summarize because it tends to revolve around hobbies and possessions. Adulthood is messier. Plus, there are a lot more details from my current life available for ChatGPT to turn into accessories. Nonetheless, I asked ChatGPT to:

"Create a premium action figure of me called 'Eric, Age 40.' Use accessories that represent my life today and package it like a luxury designed for adult collectors."

ChatGPT drew from things it knew about me and included a smartphone, a black-and-gold chihuahua, and a stroller. Together they painted a picture of work, family, and everyday life in a nice, if perhaps bland, suburban way.

The packaging reflected the shift as well, with its black-and-gold branding that looked more at home in a collector's display cabinet than a toy aisle. And the accessory choices were again dead on.

I again wanted to see the toy leave the packaging and join the playroom, and requested that ChatGPT:

"Show the Eric Age 40 action figure outside its package in a playroom with its accessories nearby. Make it look like a child had been playing with it and then left the room."

The finished image ended up being my favorite of the four. The figure stood on the carpet with the Chihuahua, stroller, and smartphone arranged around it, looking like it had wandered out of its own packaging.

The accessories of the two toys were very different, and the packaging had evolved, but both figures showcased what ChatGPT thinks matters to me now and what mattered to me then based on our interactions.

What started as a test of ChatGPT's image generation ended up becoming a test of its memory. The action figure itself is just a novelty. What's more interesting is that the accessories, the setting, and the small details all came from a version of me that exists only in the AI's recollection of our conversations. That's a strange thing to see packaged up in plastic — a toy built not from a photograph, but from memory.

• 75% of UK business travellers would use, or have used, unapproved AI tools
• Many aren't happy with the tools they're given, some aren't even given any
• Workers don't want to use other tools – they just want AI to be integrated

New data from SAP Concur has apparently confirmed employees are still ignoring company policies, with three in four UK business travellers using (or would use) unapproved AI tools.

Data from the company's Global Business Travel Survey shows that, while organizations are investing heavily in AI, they're not giving workers access to the right tools.

As a result, employees are opting to use other, more suitable alternatives and often prefer publicly available chatbots such as ChatGPT and Gemini.

Shadow AI is an even bigger issue among travelling workers

Two in five (43%) said they use unapproved tools because they prefer different options to what they're provided, but one-third (32%) say their organization doesn't even offer any AI tools to support planning or booking travel, forcing them to look elsewhere.

The data shows that remote workers (81%) are more likely to use shadow AI than office-based employees (71%), as are younger workers like Gen Z (79%) and Millennials (76%) compared with Gen X (62%) and Boomers (49%).

As for what employees are looking for when it comes to AI supporting their travel needs, the biggest use case is planning itineraries (37%). Tracking expenses (30%), assessing travel risks (26%), making changes to their trip (24%) and completing expense reports (21%) are also common use cases for the productivity-boosting technology.

But business leaders already know the scale of the problem – 100% of CFOs are concerned about shadow AI in business travel, the report found.

"Leaders must educate workers on the risks and provide T&E tools that deliver the desired level of AI support," EMEA VP for Concur Travel Paul Dear noted.

Looking ahead, the fix must come from organizations – workers clearly aren't too keen on having to navigate to third-party tools, and are doing it out of necessity. More than a third actually want their AI tools integrated into their productivity software (38%), communications platforms (36%) and CRM systems (32%).

Salesforce has laid out its continued backing for the UK as a key market in terms of both embracing current AI tools as well as developing new and exciting services and businesses alike.

At its Salesforce World Tour London event, the company's President UKI, Zahra Bahrololoumi, noted how the company was looking to play a key role in the increasingly common Agentic Enterprise.

Top of mind was the company’s new “Headless 360” platform, which will allow users to access all of their Salesforce data from various apps or a terminal, potentially opening them up to huge improvements in productivity and efficiency.

"A very pivotal moment"

“We are at a very pivotal moment - every company, every industry - AI is transforming our lives - but how do we harness that power?” Bahrololoumi declared in the opening keynote at Salesforce World Tour London, “agentic AI doesn't just benefit commercial businesses, it can also elevate citizen services - from policing to food and transport, to health and welfare - our efforts show that when we do good in the word, we can actually do really well.”

“We're redefining CRM again with Customer 360 - it's making your organizations truly customer-centric…(and) this transformational work AI is ushering in a whole new chapter,” Bahrololoumi added, “agentic AI is everywhere, it is in every experience, every industry, every company - it's not just about efficiency, it's about growth...and it is fundamentally changing what is possible when we reimagine enterprises.”

Bahrololoumi outlined the Salesforce’s “incredibly bold” vision for the UK, which includes $6 billion investment committed to the country through 2030, and its role in backing the UK government’s aim to train 10 million UK workers by 2030.

“The UK...is such a vibrant AI market", she added in a later media Q&A session, outlining how Salesforce is “really committed” to educating businesses and partner ecosystems across the country.

“Speaking to customers (about AI) we have gone beyond the pilot stage - we’re now seeing incredible scale,” she said, “agents working hand in hand with humans at the right point…and we know that those who do scale are the ones who have been intentional about orchestrating and organizing their data.”

Six months ago, the question "how far away are we from AI movies?" felt reasonable to ask. Today it feels outdated, because they're already here.

Last week I attended a screening of Hell Grind, a 90-minute AI-generated movie that was made in just two weeks. On the same day, Dreams of Violets, a poignant docudrama inspired by real events from 47 years of Iranian civilian resistance, and which is 100% AI-generated, appeared at the Tribeca film festival. Then today I found myself watching something a bit lighter in tone — DEADLINES — on YouTube, a comedy-horror trailer made with AI that was funny enough to make me laugh out loud.

DEADLINES feels like it’s leaning strongly into the vibe of the new Apple TV horror/comedy series Widows Bay, where creepy things happen on a cursed island. But even though it clearly borrows from Apple's hit series, what surprised me was how funny the trailer actually was.

Ultimately, all three releases made me realize we've crossed an important threshold. The question is no longer whether AI can make movies. It can. The question now is whether anyone actually wants to watch them.

The difference between an AI movie and AI slop

For years, AI video demos have been judged on whether they could they fool us. Could they generate convincing actors, realistic camera movements, believable explosions and dramatic lighting? Every improvement felt like another step toward the goal of Hollywood-quality filmmaking.

Getting everything correct is important, because it only takes one obvious problem with an AI-generated scene to destroy the credibility of the whole movie. Now, though, the technical challenges are rapidly becoming the easy part — which makes what's going to come next so much more interesting.

What struck me most about DEADLINES was the quality of the writing. The trailer had actual jokes. It had timing. It had a clear sense of tone. It felt like somebody had a great idea and they wanted to share it.

But even though DEADLINES is a lot better than most AI videos, it’s still not perfect — the accent of the main character veers between several different regions of the UK, for instance. However, it’s good enough to make little things like this not really matter too much. That's the difference between a good movie and AI slop.

Nobody cares about the fingers anymore

While there's no need to count the fingers on people's hands these days, those questions about AI’s ability to realistically generate human beings haven’t gone away entirely; but they’re not the point anymore. When I watched DEADLINES I wasn’t paying attention to the visuals. Instead I was paying attention to the jokes, the pacing and the story, and deciding if I wanted to keep watching.

While DEADLINES knows it's using Windows Bay as its starting point, the trailer understands what it's parodying. It knows the language of prestige horror. It knows how to build a joke. It has an actual comedic premise.

That’s why I think things are about to change. The traditional gatekeepers of filmmaking are looking less secure than ever. The bottleneck isn’t really the production process, funding or access to filmmaking tools. Now, it's imagination and storytelling.

The bigger question now is whether AI filmmakers can create stories people genuinely care about. I don't know whether DEADLINES will ever become a full-length feature- film. What I do know is that it made me laugh, and that's a much more important milestone than generating another realistic explosion or perfectly rendered human face.

AI movies are already here. The race now is to make them worth watching.

• Jeff Bezos says AI is a force for good, rather than a threat
• The technology will expand the human labour market, and create more demand
• Bezos' comments go against the narrative of other prominent voices in AI

AI won’t shrink the labor market, but will expand it, Amazon founder Jeff Bezos has declared, in his latest blast against technology doom-mongerers.

“I know there's a lot of concern that many people have, including many smart people, that AI is going to make humans redundant and so on," Bezos said at the VivaTech conference in Paris.

"I totally disagree with this point of view. And I think, in fact, AI is going to create a labour shortage."

Flipping the script

Bezos' opinion contradict many of the comments recently made by prominent figures in the AI space, trade unions, studies, tech job layoff trackers, and the fears of workers themselves.

Rather than replace jobs, the technology will present new opportunities for workers and create demand in new areas, he explained.

The comments were made as Bezos spoke of his latest venture, Prometheus, which seeks to use AI to assist human manufacturing as a counterbalance to industry which is seeing increased automation.

Bezos also took the opportunity to talk about his desire for humans to focus on space exploration, adding that it is “supply constrained, not demand constrained”.

First on the hitlist for interplanetary habitation is the Moon. “We're going to the Moon to stay, not just to visit," Bezos said, adding that establishing a permanent human base on the Moon would assist in further exploration.

Bezos' space travel company Blue Origin was also a subject of discussion, after the company’s New Glenn rocket exploded during a ground test at Cape Canaveral in Florida in May. The incident resulted in no injuries, and Blue Origin chief executive Dave Limp added that the launch site will be repaired and ready to resume operation before the end of the year.

• Many employees secretly use AI tools despite company restrictions
• Employees sometimes use their personal devices to conceal AI use
• Larger organizations report higher levels of unauthorized AI usage

Artificial intelligence is becoming a routine part of office work, even when employees believe company policies prohibit its use.

New research from PagerDuty claims two-thirds (66%) of office professionals have used AI tools for work despite thinking those tools were not permitted.

The findings suggest unauthorized AI use is no longer isolated, particularly as workers become more confident in their own understanding of the technology.

Workers increasingly conceal AI use from employers

The study found that among employees who have used AI for work, many acknowledged taking actions that conflict with internal company rules.

Unauthorized use was more common at organizations employing at least 1,500 people, where 72% admitted using AI despite believing it was prohibited.

At smaller companies, the figure remained substantial at 60%, indicating that the practice extends across different workplace environments.

Secrecy often accompanies workplace AI adoption, as a third of AI users said they would deliberately avoid disclosing their use of AI to managers or supervisors.

About 30% cited restrictive company policies or concern about coworker reactions as reasons for keeping their AI use private, while 29% said uncertainty surrounding company rules contributed to their reluctance to disclose those activities.

Perceptions of inconsistent policy enforcement may also contribute to the use of AI in the workplace.

While 86% reported working at organizations with AI policies, 81% believed leadership operates under different standards.

That sentiment was especially common at larger organizations, where workers were more likely to feel executives received different treatment regarding AI-related decisions and policy compliance.

Most workers (72%) believe that they understand how to use AI for their jobs better than the teams managing AI governance.

At billion-dollar companies, that figure rose to 80%, while senior leaders were more likely than lower-level managers to express similar views.

With such confidence in their own AI judgment, workers will likely bypass formal restrictions, treating policy violations as reasonable workarounds.

Hidden AI use raises concerns about company information

Unauthorized AI activity often extends beyond simple tasks and 43% of respondents admitted to entering emails or work-related data into public AI systems.

Those tools operate outside internal corporate environments, creating potential concerns about how workplace information is handled after submission.

The sharing of sensitive information was not limited to routine communications, as more than a third of respondents said they had entered customer information into public AI systems.

Another 31% acknowledged uploading financial information, confidential company documents, or internal business strategies into those platforms.

Additional findings showed that 44% used AI tools to circumvent limitations in approved workplace software, while 38% shared AI-assisted work without disclosure.

In addition, some workers even access these AI tools or LLMs on their personal devices to conceal any trace.

More than half of those caught violating AI policies received informal guidance, while 48% faced formal disciplinary action.

This suggests that organizations are still struggling to balance enforcement with rapidly expanding workplace reliance on AI tools.

• Human traffic is up, but AI traffic is up 6.5x more, Fastly report claims
• Uncacheable AI requests are straining hosting infrastructure
• Businesses must rethink website management and content discoverability

New Fastly analysis of its own global edge network has revealed that AI requests increased around 30% in the first five months of 2026 – while human traffic also rose, AI traffic grew at around 6.5x the pace of human traffic.

But the company is arguing that this is much more than just an increase in bot traffic – it signals ,]the emergence of an entirely new layer of the Internet where AI systems increasingly interact directly with websites on behalf of human users.

CTO Artur Bergman noted that “AI traffic is fundamentally changing how the Internet operates,” and that businesses are no longer building online experiences just for their human visitors.

AI is changing how we build for the Internet

Fastly’s research stresses that AI traffic isn’t just one thing, like human traffic. Instead, it consists of multiple elements, including the AI crawlers that most people automatically think of. They crawl websites to build and refresh LLM training datasets in a similar way to search engine crawlers.

Fastly noted they account for 85% of AI requests, and because they operate continuously rather than following human browsing patterns, they can put a relatively small amount of additional strain on hosting infrastructure.

You’ve also got AI fetches, which Fastly believes are becoming more important and relevant. They retrieve live information to answer user questions, compare prices, check availability and validate facts, and are an increasingly crucial part of agentic AI.

They’re prompted into action when a user interacts with a tool like ChatGPT or Gemini, but while they only account for around 15% of AI traffic for now, Fastly sees this changing pretty sharply. Between January and May, the company also observed a 555% increase in Claude-related traffic as its agentic tools gain traction in the mass market.

AI is impacting online traffic much more quickly than we thought

But there’s more to it and simply who is accessing information and when. Fastly found that while fewer than 9% of human requests require content to be fetched from an origin server, more than half (51%) of AI requests do so.

This means that AI traffic is much less cacheable because they request fresher information, ultimately leading to higher infrastructure costs.

The report reveals how preparing a site for these new challenges can affect discoverability, customer acquisition, AI search visibility and more. “The challenge is no longer simply blocking bots, it's understanding which machine interactions should be accelerated, managed, challenged, or stopped,” Bergman added.

Cloudflare has also been observing a rise in AI-driven traffic. Its live data dashboards reveal how bots now account for more (57%) traffic than humans (43%). And the biggest driver of this is mostly likely the rapid growth of AI agents rather than traditional malicious bots.

All of this continues to mark ongoing growth – at the start of this year, Tollbit revealed that there was a new AI bot visit for every 31 human visits and that direct human visits were actually falling. Not because we’re using the Internet less, but because our means of access is shifting to AI.

For publishers, it’s arguably one of the biggest structural shifts since the rise of Google search and social media, with AI now threatening to replace human search traffic. Publishers now need to consider how to monetize AI directly, and they also need to reconsider how they reach new audiences beyond conventional SEO.

• An innovative new AI bot runs on manual crank power
• It uses a Raspberry Pi and local AI models
• The bot is built to work offline without an extra power source

Making use of AI bots typically involves relying on cloud access and data centers, and giving up your information (and your chats) to one of the big tech companies. However, a new DIY gadget called CrankGPT works very differently.

Built by the enterprising folks at Squeez Labs, the CrankGPT box (via Boing Boing) is powered by a hand crank. It runs on a Raspberry Pi with 8GB of RAM, and uses small, local AI models from Meta and Google to transcribe speech and run queries.

As the demo video shows, if you find yourself in a post-apocalyptic world without electricity and internet connectivity, you can still get facts about hummingbirds and translate between languages through the power of your arm muscles.

"Provided the electronics are kept dry and at a reasonable temperature, there's no reason this thing won’t still work in a hundred years, though you’ll definitely need a fresh SD card," explain the inventors in their rundown of how CrankGPT was made.

Several plans available

The makers of this clever box have even made a tongue-in-cheek landing page offering the CrankGPT for sale. If your AI needs are more demanding then you can upgrade to a system based on an exercise bike, or an entire gym of people pedaling.

There's a more serious point here too though: Squeeze Labs is working on making AI smaller, cheaper, and faster so it can be run on more devices with no cloud connectivity required. That's better for user privacy and for the environment.

AI companies are investing huge amounts in data center expansion in an attempt to keep up with the growing compute needs of coders and other users, and that means increasing demands on electricity supply and water usage.

As Apple recently demonstrated with Siri AI, the most advanced and complex AI prompts need to be run on online servers. As models become more efficient over time and devices become more powerful, that should start to change.

AI agents are entering the workforce, and while some show promise at increasing productivity and ending repetitive rote work, others are using their autonomy to cause some serious problems.

Zscaler CEO Jay Chaudhry believes his company has a solution.

Speaking in Vienna at the opening keynote of Zscaler Zenith 2026, Chaudhry shared his perspective on how a security architecture born in 2007 is best suited to secure the workforce of the future.

“Agents are becoming the weakest link”

“There have been so many instances where Microsoft Copilot exfiltrated data without a user action, the agent did it,” Chuadhry said. “We’re seeing OpenClaw poisoned with credential stealers, and there are other cases where databases got deleted, email inboxes got deleted - and it's not users who are doing it. These agents operate independently, they can make decisions, they can take actions.”

Where a human has the ability to recognise if their actions are destructive, many AI agents do not double check. “Yesterday, a user was the weakest link, today these agents are becoming the weakest link.”

“They move at machine speed. They need no coffee breaks, no weekends, no time to sleep,” Chaudry noted, pointing to the fact that there is very little time for human decision making to take place once an autonomous agent begins a workflow.

“The challenges are coming because the AI revolution is different.,” he added. “In the internet wave we had human beings able to access websites, in the cloud wave we could have people build applications on the cloud and access them. In every prior wave, we were securing waves using new tools, but this time the tools are the workforce.”

“This is where zero trust will play a bigger and bigger role.”

Zscaler recently unveiled a wave of new tools to help secure emerging AI technologies and autonomous agents under its Zero Trust Exchange platform. Among them are tools to prevent AI agents from abusing their permissions, and tools to mask their presence from attackers looking to create a double agent.

The tools unveiled include AI Broker: a platform for maintaining organisational visibility over the access controls applied to autonomous agents, and Endpoint AI Security: designed to closely monitor for malicious activity at the device level, digging as deep as the browser, extension and plugin levels.

“It's a very exciting but challenging problem to solve,” Chaudhry noted, adding that “to really take any action, you’ve got to understand what you have and what the risk is associated with it.”

Not only does the new AI agent platform feature MCP and A2A brokers to secure agentic communications, but it also helps in securing agents by “understanding the task that is being done, understanding prompts properly, and being able to inspect prompts to understand the intent.”

Additionally, Zscaler announced AI Access Graph, which provides visibility into the connections between individual identities, applications and data sources. “In an enterprise, you’ve got all these entities, you’ve got all these data sources. They talk to each other. How do you know who is talking to who? Who has what kind of access?”

“This problem was solved by a bunch of PhDs at the University of Texas in Austin,” Chaudhry explains, pointing to Zscaler's recent acquisition of Symmetry Systems. “It takes telemetry and meta data from all of these sources, applies AI on top of that, and creates a powerful graph that allows you to connect the dots.”

For Chaudhry, positioning AI agents behind zero trust and using Zscaler’s new tools makes enterprise security “simple, elegant, and your workloads are hidden from the internet.”

Currently, Zscaler handles more than 750 billion requests per day, and with agentic traffic rapidly growing, Chaudhry expects to add “one or two zeroes to this number.”

“That's the scale our engineering team is working towards. That’s the scale we want to build.”

• Google Cloud praises UK market as striding forward with AI
• Age of the "agentic enterprise" is here now, company says
• Google Cloud announces maor partnerships with HSBC, UK government

Google Cloud has laid out its vision for the future of UK businesses enabled by AI, noting that the age of the "Agentic Enterprise" is set to nring in transformation across the board.

“London is rapidly rising and establishing itself as a significant, high-growth hub for global technological innovation,” declared Maureen Costello, Vice President UK, Ireland, Sub-Saharan Africa, Google Cloud at the company's Google Cloud Summit London 206, “this is a market that doesn't just adopt the future, we're going to build it.”

“At Google, we're incredibly proud to be at the beating heart of this British tech revolution - we are not guests in the AI ecosystem, we are deeply rooted in it.”

A "fundamental shift" for businesses

Costello hailed Google Cloud’s recent successes in the UK, noting how, "this momentum is propelling us into a fundamental shift.” This shift, she noted, comes as companies move from the AI experimentation and proof of concepts towards actually realizing true potential.

“The agentic enterprise is happening right here, right now,” she declared, highlighting how every business must now focus on three key pillars when it comes to using AI effectively - culture, responsibility, and sustainability.

This momentum includes a number of high-level partnerships and expansion across top UK firms, including HSBC, which is adopting Google Cloud AI tools and platforms for over 200 use cases across its global services.

This will cover areas such as smarter, AI-driven insights for HSBC wealth management customers, providing more proactive, tailored financial support and real-time advice to customers at every stage of the client journey in a secure way, and using generative AI and agentic AI to build a financial crime architecture that detects risk at an earlier stage, helping to prevent harm and creating a more seamless experience for customers.

“AI is becoming one of the defining technologies of our time, allowing us to create a personalised experience for each customer, delivered in real time and at scale, while keeping human judgement, decision-making, and accountability at the core,” noted Georges Elhedery, Group CEO, HSBC

“A partnership like this one with Google Cloud helps us empower our colleagues with the tools they need to be future-ready, and supports our work in building a simple, agile, faster, and more personal HSBC.”

Google DeepMind will also be widening its work with the UK government, including a new AI-powered tool which should help reduce the time it takes for councils to process householder planning applications down from eight, to four weeks in an average case.

Another tool, Extract, which was announced by the Prime Minister in 2025, is now available to all councils in England, using AI to help planning officers convert decades-old planning documents and maps, sometimes with handwritten notes, into readily useable data in minutes.

“Our planning system remains heavily reliant on cumbersome paper-based processes that consume the time of expert planning officers and cause delays on even the most routine types of application," noted Housing and Planning Minister Matthew Pennycook.

"We are dragging the system into the twenty-first century by harnessing the power of AI to streamline the planning application process, freeing up planners to make quicker and better decisions and reducing unnecessary delays."

OpenAI CEO Sam Altman has done something few Silicon Valley bosses ever do, admit he is wrong. Speaking virtually at a Commonwealth Bank of Australia conference in Sydney in May, Altman confessed that one of his biggest concerns about AI simply has not played out the way he expected. For someone whose job often involves predicting the future, it was a surprisingly candid moment.

"I'm delighted to be wrong about this. I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened," Altman said. "I now think I understand more about why it hasn't, ​and I'm obviously grateful, but that is an area where my intuitions were just off."

Altman explained that OpenAI had been "roughly right" about many of the technological predictions it made when ChatGPT launched. AI has become more capable at an astonishing pace. What he appears to have misjudged was how those capabilities would translate into changes in everyday employment.

Personal AI experiments

Notably, Altman concluded he had been wrong after an experiment in which he let AI handle some of his own communications. He didn't need a labor market research report to see that it wasn't up to snuff. He used AI to answer Slack messages and emails, each labeled as coming from "Sam's AI" rather than from him directly.

But Altman found himself pulling back from the experiment almost immediately. The reason had little to do with the quality of the responses. Rather, Altman simply didn't want to give up interacting with people to an AI model, no matter how efficient.

"We really do care about our interactions with people and this thing, which is a huge amount of my time, is not something that I can imagine myself outsourcing to an AI anytime soon," Altman said.

The experience appears to have shifted Altman's thinking about employment more broadly. Jobs often look simple when reduced to a list of tasks. In reality, many roles involve trust, relationships, judgment, and personal interactions that are difficult to capture in a spreadsheet.

Human jobs

None of this means Altman suddenly believes AI will leave the workforce untouched. OpenAI continues to release increasingly powerful models, and businesses continue searching for ways to use them more effectively.

But the actual disruption of employment will be less catastrophic, according to Altman. Discussions about AI often treat jobs as collections of tasks that could be swapped out with the right AI prompt, but reality appears messier. Companies may automate parts of jobs long before they eliminate entire positions.

"It really, in both positive and negative ways, ​updated me to thinking that the jobs picture is likely to be very different than we thought. I don't think we're going to have the kind ​of jobs apocalypse that some of the companies in our space advocate or talk about."

That distinction matters because it helps explain why the labor market has not experienced the immediate shock that many observers expected. AI has certainly changed a lot of research and enterprise projects. But most organizations still need people to make decisions, manage relationships, and take responsibility when things go wrong.

Altman's more positive view of AI on job prospects doesn't mean there's no problem with how the technology is being deployed. But people who might look to Altman for insight into AI might feel a little better, even if it's just him saying AI will have a muddled influence and not act as a straight assassin of careers.

• Consumers are experiencing 'bot fatigue' after interacting with impersonal AI, WordPress VIP finds
• 61% can't even name a brand that's using AI well in marketing or CX
• AI is emerging as a discovery layer, not a customer service replacement

New research from WordPress VIP has uncovered just how much AI is impacting browsing and online shopping habits, with three in four (74%) consumers believing the Internet feels less 'human' than it did 10 years ago, largely due to AI and automation.

40 minutes is now the average time before consumers feel so-called 'bot fatigue', where they've been interacting with too much AI and want more human connections, the study claimed.

But while artificial intelligence promises to solve more and more customer service tickets, three in five (61%) consumers cannot even name a single brand that uses AI well in marketing or CX.

Consumers are fed up with AI

Additionally, shoppers and customers don't feel that AI is an advantage that businesses should shout about. They'd much rather have better experiences without the constant reminders.

Two-thirds of consumers say mentioning AI in brand messaging is a turn-off for them rather than a selling point, and 86% still don't trust AI-generated content, including troubleshooting and support in customer service chats.

Rather than replacing human workers and tools, consumers seem much happier to accept it as a replacement for other digital services, like search engine discovery. They want companies to focus on appearing in AI search results, but they still want original human content and direct access to sources.

"No customer or user wakes up and says, ‘I hope I get to talk to a chat bot or an AI agent today’," ServiceNow Head of Global Innovation Brian Solis wrote. "Human-centered design is truer today with artificial intelligence."

• ChatGPT's market share falls to 46.4% as Gemini climbs to 27.7%
• Department of War deal saw a spike in ChatGPT uninstalls
• Google's ecosystem is a major Gemini advantage

New figures have claimed ChatGPT's share of the global AI assistant market is under pressure after it dropped below 50% for the first time since its launch in the face of stiff competition from other rivals.

Even though ChatGPT has lost market share by means of proportion, it remains a clear leader with more than 1.1 billion monthly active users, making it the world's largest assistant by a significant margin.

However, Gemini is rising up the charts, accounting for 27.7% of the market in May 2026 compared with 46.4% for ChatGPT.

ChatGPT is still the leader, but Gemini is chasing it in second place

The data comes from Sensor Tower, which observed considerable recent growth among Claude users. Though Anthropic's chatbot still only accounts for 10.3% of the market. Grok, Perplexity, DeepSeek and Meta AI all have much smaller shares.

"Claude has experienced explosive growth, led by a strong web presence, and its True Audience share in the US has more than tripled," the company added.

Time spent on GenAI apps has also more than doubled in one year, from 17.2 billion hours in the first half of 2025 to 36 billion hours in the first half of 2026.

Conversely, the company also warned that ChatGPT uninstalls had surged following the company's agreement with the Department of War, marking poor consumer trust. Uninstalls have since fallen closer to the average.

Overall, the data shows that consumers haven't been tied to specific chatbots for long enough to have any extreme loyalties. Instead, they're more prepared to migrate based on model capabilities and releases, ecosystem and third-party integrations, pricing and even company politics.

As for Google's growing market share, its ties to the broader Google ecosystem is a huge advantage, with integrations spanning Android, Search, Workspace, Health and more.

• 70% of UK small businesses turn to AI for financial advice before their accountants
• Most business leaders are dissatisfied with current accounting services
• Customers want more personalized, proactive advice

Nearly three-quarters (70%) of UK SMEs say they often or always act on AI-generated financial, tax or business advice before they consult their accountant, according to a new report of 500 UK SMEs commissioned by Ravical.

Conversely, only 5% rarely or never do this, highlighting just how widespread AI adoption is when it comes to seeking advice.

Answering tax questions, responding to financial planning queries, serving up business strategies and triaging day-to-day accounting issues are among the most common use cases, with accountants instead being used to validate AI-generated advice as a secondary layer.

SMEs prefer to ask AI before an accountant

As for the role of a human accountant, only one-third of UK SMEs described their accountant as a genuine working partner who proactively contributes ideas and delivers strategic insight, which could be why business leaders have turned to artificial intelligence – personalization, as well as efficiency.

Clearly, leaders aren’t happy with their existing accountants, because 91% have considered changing during the past year over a desire for more advice, quicker responses, forward planning and proactivity.

Cost isn’t actually so much of an issue, with 92% saying they’d be prepared to pay higher fees if accountants actually delivered the quality of services they wanted.

While the report confirms that many turn to AI in the first instance, it also offers an insight into why human accountants might be losing business, with customers still happy to spend the money with them.

Leaders aren’t actually looking for compliance so much as forward-looking advice and the proactive identification of opportunities.

“You become the second opinion, and you have to be better and faster than the tool the client already used,” CEO Joris Van Der Gucht noted.

Expectations from accountants are evolving as AI automates compliance

But it’s not just business leaders who should be turning to AI to boost efficiency. Xero research revealed that the UK accountants who use AI at work deliver results 31% faster.

Institute of Chartered Accountants in England and Wales (ICAEW) also found that 85% of accountants are willing to use AI, with four in five (79%) seeing their roles evolving into ‘data guardians’ – verifying AI-generated outputs and expanding on them with more personalized context. Ravical found that foundational compliance work is already being automated through AI, and the role of an accountant is evolving.

90% of SMEs believe compliance work could largely be handled by AI within the next few years, but 35% already see that as being true today.

AI’s role in an accountant’s office is to remove the low-value and administrative work, allowing them to focus on judgement, strategy and advice.

The ICAEW report, published in April 2025, also acknowledges the need for broader business advice and client relationships. It also addresses the continued role of humans in the profession – specifically that human judgement remains crucial. While SMEs are clearly happy to check AI for advice in the first instance, trained professionals are clearly valued when the stakes are higher.

Rather than replacing human accountants altogether, AI is mostly redefining what business leaders expect from their accountants. With routine compliance now considered either automated or nearly automated, and many businesses turning to AI for basic guidance, it could be good news for accountants.

The ones that succeed will likely be the ones to turn this time into higher-value, quality advice and relationships with their customers.

• Longsys WM8500 offers as much as a 2:1 in-Memory lossless compression
• The 5nm chip currently supports up to 128TB in single-drive capacity versus mainstream consumer SSDs that are capped at 8TB
• This is made possible by using the DRAM-less SPU as an active player in the storage stack, leveraging both its High Level Cache (HLC) and intelligent Storage Agent (iSA) to deliver a compression ratio that is industry-leading

Longsys, the world's second-largest independent memory firm and the force behind one of the most well-known consumer brands in the West, Lexar, as well as one of the most important B2B storage players, FORESEE, may have a solution to rising SSD and DRAM costs: A chip that compresses data on the fly extraordinarily well.

It has come up with a 5nm chip which handles on-the-fly compression for large SSDs, allowing them to essentially double their capacity beyond the 128GB single-drive capacity it currently supports.

While this isn't as close to what hardware-based data compression on tape drives looks like (with ratios of up to 2.5:1), it is still an impressive feat for an industry reeling from ever-increasing NAND flash costs, even as many datacenters continue to use hard drives to keep costs low.

How does the Longsys WM8500 essentially double storage?

The Longsys WM8500 is what the storage giant calls an SPU, or Storage Processing Unit, built on a 5nm process and fundamentally different from technologies such as Samsung's SmartSSD.

Unlike Samsung's approach, which leverages a general-purpose FPGA or an ARM-based CPU inside the SSD to manage computational tasks on the drive, the SPU is an ASIC (Application-Specific Integrated Circuit) designed for a specific purpose: compression and storage management.

The 5nm chip also offers a cost advantage that most high-end consumer and enterprise-grade controllers do not have, apart from its compression capabilities: it is a completely DRAM-less design that allows it to command a lower price, even as Longsys's claim of offering 'virtually' twice the storage to its AI consumers kicks in.

It must be noted that the 2:1 figure is an 'up-to' ratio, and while it might be easier for an ASIC to compress data and context windows for certain AI models, others might make it considerably harder, especially if obfuscation or encryption is in play.

In an ideal scenario, however, the WM8500 SPU, coupled with its High Level Cache (HLC) implementation and its Intelligence Storage Agent (iSA), together make up what the manufacturer calls a "closed-loop software-hardware collaborative technology system" that focuses on AI customers.

The HLC cache claims a 40% reduction in DRAM requirements, making it a cost-effective alternative to HDD storage, even as competitors prepare to release 256TB enterprise SSDs later this year. AI data centers continue to demand large amounts of storage and memory alike to meet their ever-increasing needs.

Technology companies usually spend a lot of time trying to persuade customers that a subscription is worth the money. ChatGPT has stumbled into a very different problem. A growing number of users are looking at the price and wondering whether they're somehow getting away with something.

The question has become harder to dismiss as ChatGPT has evolved. The $20-a-month ChatGPT Plus and $200-a-month ChatGPT Pro subscription prices haven't changed since OpenAI announced them. Yet ChatGPT is much more powerful, with many more features, even if it still has plenty of built-in wrinkles and limits. Nonetheless, questioning its value has become more common as the chatbot has expanded from being an impressive novelty into something people use every day.

That combination of expanding capabilities and stable pricing has led many users to ask whether ChatGPT costs less than it should. It's been an issue from the beginning, with OpenAI CEO Sam Altman complaining that ChatGPT Pro loses money for the company due to its popularity a year and a half ago:

It isn't unusual for a product to cost more to provide than customers realize. What's unusual is when customers start noticing the gap themselves.

AI might be especially vulnerable to that dynamic as AI systems require enormous computing resources. Every response is powered by vast networks of specialized hardware operating in data centers that consume significant amounts of electricity. Those costs add up quickly, particularly when millions of people use the service every day.

Some estimates indicate that power users could theoretically consume thousands of dollars' worth of compute resources a month, while paying only a fraction of that amount in subscription fees. Meanwhile, AI companies continue investing enormous sums in data centers, hardware, and electricity.

Short-term splurge

Part of the reason the debate has gained traction is that AI is not cheap to run. Every response generated by ChatGPT relies on huge amounts of computing power, specialized hardware, and data center infrastructure. Those systems consume enormous quantities of electricity, and the bills only grow larger as usage increases.

AI models generate ongoing expenses every time someone submits a prompt. Millions of users asking questions each day creates a very different economic equation than most subscription services have to manage.

Heavy ChatGPT users could therefore eat up far more computing resources than they are paying for at market price. At the same time, AI companies continue pouring billions of dollars into new data centers, cutting-edge hardware, and future model development. That reality has led some users to believe today's prices are less about profitability and more about securing market share while the AI industry is still taking shape.

"All investment and business strategies are still operating on the "old rules" which have yet to be replaced because AI has yet to completely up end the global order. They all know we're hurtling towards a cliff, but the off ramp isn't visible yet, and they all assume it will magically appear before they run out of road," one Reddit user speculated. "The only logical way to "win the game" then is to keep speeding along so that you're the first one onto the off ramp. And nobody wants to be left behind so..."

Underpriced AI

ChatGPT occupies a rare position in the technology industry, seeming like a good bargain amid a growing chorus of complaints that technology and related services are actually getting worse every year. Many ChatGPT users genuinely feel they are getting more value from the service today than they did a year ago, despite paying the same monthly fee. And some think the question of underpricing ignores the bigger picture of how AI models are produced.

"People calculate their usage using public API prices and assume Anthropic or OpenAI lost that amount on them. But API pricing is not the company’s actual internal cost. It already includes profit margin, and we have no idea what their real cost is after caching, batching and infrastructure optimizations," another Reddit user pointed out. "I also believe the released models themselves are profitable. The companies still report losses because they are spending billions on training the next models, buying hardware and expanding infrastructure. So while GPT-5.4 is generating profit, they may be spending all of that money and more on GPT-5.5.

For most people, the argument that ChatGPT is underpriced is actually pretty simple. They are not studying OpenAI's balance sheet or calculating data center costs. They are looking at their own habits and realizing they use the chatbot far more often than they ever expected.

That helps explain why the conversation keeps coming up. People complain all the time when a product gets more expensive. They almost never complain that something feels too cheap. Whether ChatGPT is actually underpriced is a question OpenAI will have to answer eventually. For now, many users seem to have reached their own conclusion. They are paying the same price they paid months or even years ago, but they feel like they are getting a lot more in return.

• Microsoft and Anthropic reveal Copilot Cowork
• Anthropic AI platform gives Copilot the ability to really dig deep into work tasks
• Tool will be able to cover the entire Microsoft 365 platform

Microsoft has announced its Copilot Cowork platform will now be generally available to Microsoft 365 Copilot users worldwide.

First revealed in March 2026, Cowork combines the power of Microsoft's own Copilot offering with some of the most powerful AI tools from Anthropic's Claude Cowork platform.

The aim, the companies say, is to turn AI assistants from an interested observer into a full-on helper, giving you all the tools you need to get work done intelligently.

Copilot Cowork

In a blog post announcing the news, Charles Lamanna, EVP, Copilot, Agents and Platform at Microsoft, noted "more than half of the Fortune 500 is using Copilot Cowork" following its initial preview launch.

"We have been impressed by your creativity and what you have all built with it," Lamanna added, describing use cases ranging from batch-editing spreadsheets, comparing thousands of files across two product versions, and evaluating at-risk opportunities for sales teams.

"Cowork is the fastest growing feature in the history of our Frontier program, and Cowork has among the highest user satisfaction of any Copilot or agent experience we have shipped," he said.

"We learned from what we saw, engaged with you along the way, and used everything we heard to improve quality and add new features, including model choice, extensibility through plugins, and new cost management controls."

To mark its general release, Microsoft has added a few extra tools to Copilot Cowork as it looks to make the service as useful as possible.

First is a greater choice in which AI model you utilize - at general availability, Copilot Cowork now runs on popular Anthropic models including Opus 4.8 and Sonnet 4.6.

In Frontier access, customers can also access GPT 5.5, with Cowork 1 coming soon, with Microsoft's latest model removing model bias and helping to deliver enterprise-grade performance at a substantially lower cost for everyday Copilot tasks, helping organizations manage cost-sensitive workloads.

Elsewhere, Microsoft is introducing new partner plugins for Copilot Cowork, with nine partners available immediately, including the likes of Monday.com, Miro and Moodys, with eight more coming soon, including workplace heavyweights such as Adobe, Atlassian, Box and Canva.

Frontier users can now use Copilot Cowork to get online via a local Edge browser, expanding the range of tasks it can complete on behalf of users.

• Qualcomm CEO Cristiano Amon believes apps will soon be designed for AI agents – not so much humans
• Amon also believes that wearables like smart glasses could see chip demand surge
• On-device processing is the next stage of AI, aimed at reducing cloud latency and boosting efficiency

Qualcomm CEO Cristiano Amon has claimed AI agents could soon become much more than the next trend, influencing development across a wide space.

Instead, in an interview with CNBC, Amon said he believes the independent and autonomous nature of agentic AI could almost entirely replace traditional apps. "Those agents are going to be the new app," he said, noting that apps "are not dead" but they are facing major change.

Under Amon's vision for the future, human users will likely interact with agents that understand context and complete tasks for them, rendering many human interfaces irrelevant.

Apps aren't dead, but they're about to become agentic

Amon remains optimistic about Qualcomm's opportunities, indicating that consumer devices will also need more powerful chips to support on-device processing as AI evolves away from strictly cloud-based.

This means the company is likely targeting growth across smartphones, PCs and even new classes of wearables, like emerging smart glasses from companies like Meta and Google.

"All the devices that we wear become endpoints for agents, and those AI companies understand they have to win those endpoints from agents," he added.

Amon is especially optimistic about smart glasses, noting that shipments are already in the "order of multiple tens of millions" per year. And it's a market that could go mainstream as soon as this year, with Google announcing upcoming models in partnership with Samsung and, indeed, Qualcomm.

“We are in a period of profound industry transformation – the rise of AI agents is reshaping our roadmap across every platform we develop," Amon said in relation to the company's most recent quarter, during which it made 3% less revenue than the year before.

As the Senior AI Editor at TechRadar, it's not often I get invited to movie premieres. Yet there I was in a private cinema room in a London hotel, about to watch Hell Grind — a 90-minute AI-generated horror movie that took just two weeks to make.

I was there as a guest of Higgsfield, a platform that brings together all the tools needed to make a complete AI movie; and I was about to watch its poster child for the very thing that's got Hollywood running scared: a purely AI-generated film.

As you'd imagine from the title, Hell Grind isn't a romantic comedy — as one of the other guests remarked, it sounds more like the name of a WWE event. I don't want to give the plot away, but it involves ancient artifacts that bestow supernatural abilities on humans when activated, and a Japanese-speaking bad guy covered in spikes, who portals into whichever location he detects the artifacts in and attempts to kill whoever possesses them.

Everything on screen is AI-generated. The human contribution comes in the story, editing, and deciding which AI-generated scenes make the final cut.

If you want a good marker for the speed of AI development in movies, when I attended the AI Film Festival in Venice last year the films were typically around five minutes long. Just a year later, movies like Hell Grind can maintain consistent characters and a coherent story for 90 minutes — and it only took two weeks to make the whole thing.

Who's that guy?

Talking to Higgsfield CEO Alex Mashrabov, I learned that the main character in Hell Grind is actually modeled on a real-life Higgsfield employee, who will presumably be going back to analyzing spreadsheets after his 15 minutes of fame are over.

The rest of the cast all looked like combinations of famous actors and actresses you've seen before, but whose identities you can never quite pin down. I found trying to work out who they resembled surprisingly distracting, and I swear that one of the main actors looked a little too much like Naomi Watts at times.

Leaving aside the ethical questions raised by AI models that appear capable of producing characters who resemble real actors, it's worth evaluating the movie on its own merits.

Surprisingly, the action scenes were often the strongest part of the film. Watching various super-powered heroes and villains duke it out was genuinely entertaining. But at no point did any of it look real.

Hell Grind is a horror fantasy, so it could be argued that realism wasn't the look it was aiming for. Even so, I got the impression that we're still a long way from AI-generated dialogue scenes that hold up on a big screen, and make us genuinely unsure whether we're looking at a real person or not.

Take a deep breath, Hollywood — we're not there yet.

There were some obvious problems with the dialogue. At points it sounded like an AI chatbot trying to write movie dialogue, and the characters would occasionally blurt out something unintentionally hilarious given their current predicament.

Most memorably, when one character was facing his imminent demise, he let out a baffling "Cool!" Moments like that threw you out of the film and back into the theater, breaking the spell.

The AI boogeyman

AI in movies is a contentious topic. There are obviously people who would rather drink a pint of toxic runoff water from a Mid-west data center than sit down and watch an AI-generated movie of their own free will.

Many people see AI as something that's going to take their jobs, ruin the planet, and eventually eliminate humanity itself. So Hell Grind simply existing in such a creativity-led industry is a provocative act in itself.

But parts of Hell Grind were fun, and the whole film almost hung together. It felt more like a proof of concept than a piece of art; but if Higgsfield's goal was to prove that AI can produce long-form content with consistent characters, it largely succeeded.

Hell Grind might not quite be there yet, but it won't be long before a real filmmaker, frustrated by the endless bureaucracy and gatekeeping of traditional filmmaking, starts using these tools to create something people genuinely want to watch.

The strange thing about Hell Grind is that it left me both impressed and reassured. Impressed because a small team created a feature-length movie in just two weeks. Reassured because, despite all the progress, AI still struggles with the thing that makes films memorable: believable people.

The action scenes worked. The visual effects worked. The story mostly held together. But the moments that should have felt human still felt synthetic.

Hollywood should absolutely pay attention. But after watching Hell Grind, it doesn't need to panic just yet.

• Google pledges $1.5 billion in funding for Alabama data center campus
• Jackson County, Alabama facility set to see major expansion, with Google shouldering all the costs
• Google says the facility could even soon get nuclear poiwer

Google has announced $1.5 billion in funding for its Alabama data center, promising it will also cover energy used at the facility, rather than passing the cost onto local residents.

In an announcement, the company said the investment will help it expand its data center campus in Jackson County, Alabama over the next two years as it looks to cope with rising demand.

And as concerns over the environmental effects of data centers across the country continue to rise, Google says it will pay for all the energy it uses, with plans to eventually switch to nuclear power.

Data center expansion

"Data centers power the technology America relies on — not only providing Google services like Search and Maps, but also online banking, hospitals and 911 systems," the company's announcement noted.

"Building on Google’s 20-year track record of local partnership in communities across America, the company strives to positively contribute to every community it calls home."

Google has had a presence in Jackson County since 2018, on the site of a former coal plant, and says its new pledges reinforce its aim to be a "good neighbour" in the community via "through responsibly increasing local infrastructure capacity, scaling energy affordability programs, and supporting thousands of jobs in the region."

"In line with its support for the White House’s Ratepayer Protection Pledge, Google will pay for 100% of the power it uses," the announcement added.

"When Google builds new data centers, including its Jackson County expansion, it will also cover the infrastructure costs directly driven by its operations."

The company says its payments will cover all of the “power and infrastructure” needed for the expansion. This puts it in line with the US government’s Ratepayer Protection Pledge, meaning Google would need to ensure local electricity costs would not rise as a result of the increased activity at the expanded data center.

This power need could be powered by nuclear energy, with Google noting that it established a partnership between itself, Kairos Power, and the Tennessee Valley Authority (TVA) in August 2025 which will supply Google with up to 50 megawatts of clean power to data centers in both Alabama and Tennessee.

This deal is particularly pertinent as it means Google is able to draw power from its own dedicated grid, rather than local energy sources, which could drive up demand and prices for nearby residents.

"Strong partnerships between industry and local communities are the foundation of regional prosperity," said State Senate Majority Leader Steve Livingston. "From funding STEM education initiatives to supporting critical energy affordability programs, Google has consistently demonstrated a deep dedication to Jackson County. This new expansion will undoubtedly generate lasting, positive impacts for local families and businesses alike."

As part of the expansion, Google also announced a $2 million "Energy Impact Fund" in partnership with the TVA and CAANEAL to support local energy efficiency and weatherization programs, as is donating $550,000 to provide STEM kits for local fourth-to-eighth graders.

“Sustainable growth is only possible when a community thrives alongside it," said Thomas Gamble, Jackson County Site Lead, Google. "Our continued expansion in Alabama is driven by a long-term vision of shared success. By investing heavily in the students, small businesses, and local organizations that form the backbone of Jackson County, Google aims to build a foundation of opportunity that will benefit the region long after construction is complete."

• Mississippi judge takes a dim view of proceedings once it was clear lawyers on both sides were using AI to make their arguments
• The sanctions order included fines, disbarment, and disqualification from the case at hand for the lawyers involved
• Advice from AI remains a somewhat tricky affair, given the lack of accountability and the model's tendency to 'hallucinate'

In what can be considered a comical occurrence that could be a sign of things to come, a US federal judge had to manually step in and admonish lawyers on both sides of the aisle after she noticed they were using AI.

Senior US District Judge Sharion Aycock, of the Northern District of Mississippi, noted that this was not the first time her court had to deal with the matter of being "burdened with addressing AI hallucinations in court filings."

The judge ordered a pause in proceedings, scrapping the trial, while disqualifying all four lawyers involved from the case at hand and barring two of them from appearing in any case in the local Northern District of Mississippi for two years.

An 'insufficient and incredulous' justification

While the case might have been a routine one in the judge's docket, addressing a breach-of-contract claim over unpaid legal fees between the city of Aberdeen and a Louisiana attorney, Tom Withers III, some of the precedents cited in the argument never occurred, inviting the judge's scrutiny and eventual

His lawyer, Kathleen M. Wilson, used AI-hallucinated citations to argue their position, a situation that was uncovered when a court-mandated order required both sides to produce copies of the cases they had cited.

The city of Aberdeen, represented by Kathryn Y. Williams, was also found culpable for a similar offense: citing a non-existent 1971 Mississippi Supreme Court decision and references to three other federal rulings that could not be reproduced.

Both lawyers admitted to using AI while claiming ignorance of the potential of the LLMs they employed, at times hallucinating. The judge, however, took a dim view of the entire affair, noting that one of the lawyers had been practicing for at least six months using generative AI to draft her cases without oversight, and that they had previously been warned against the practice in an unrelated case.

The judge noted that she "finds that explanation to be insufficient and incredulous," and fined the four lawyers a total of $8,000, singling out the two lawyers who used AI.

The case does, however, mark an important ruling that could, ironically, serve as a real precedent against the AI-generated ones that got the lawyers into trouble: ignorance of AI's hallucinations is not a viable legal defense.

Via 404 Media

We're very well accustomed to the AI doomsday scenario, popularized by science fiction writing and cinema. But it's only in recent years that experts have seriously entertained a non-zero chance of an AI cataclysm actually happening.

Although Sam Altman has a mixed record on AI safety, he has always been a vocal proponent of checks and balances, at least in public.

The great economic promise

We've long considered AI tools to be a huge boon to business productivity, with tools like machine learning and robotic process automation (RPA) widely considered key enablers in the previous enterprise era.

Months before cofounding OpenAI, Sam Altman, then head of the famous Silicon Valley incubator Y Combinator, joked about the vast breadth of outcomes that future AI systems may pose.

Back then, machine learning was relatively immature and large language models (LLMs) hadn't seen the light of day.

Since then, however, we have, at least, seen part of his premonition come to pass, with the valuation of so many massive names in the tech and AI buildout surging, led by Nvidia, Google, Microsoft, and many others.

Need for safety

The Financial Times recently published a chart perfectly encapsulating Sam Altman's decade-old comment, which he made during an interview with then-Airbnb CTO Mike Curtis at the firm's Open Air 2015 conference.

The possibility of existential AI risks are now being taken very seriously by scientists and public policy experts as neural network-powered LLMs improve, and researchers begin projecting the birth of AGI in the coming decades.

Although Sam Altman has continuously vocalized the need for the world to prioritize AI safety in public, it's also true that, in private, concerns exist over whether rushing out products compromises this safety-first stance. This came to a head in November 2023 when OpenAI's board of directors ousted Altman, only for the company chief to return days later.

• Satya Nadella warns against giving AI too much power and reliance
• Microsoft CEO warns such a move could "hollow out entire industries"
• The AI industry can learn from past mistakes, Nadella urges

Microsoft CEO Satya Nadella has issued a warning against handing over too much power and value to AI models.

In a post shared on X, Nadella outlined how giving a small number of AI firms control over important data and information could cause widespread economic issues for companies of all sizes.

"The last thing any of us want is a world where every company across every sector is ceding value to a few models that eat everything they see," he warned, "If all the value is accrued by only a few models, the political economy will simply not tolerate it. There is no societal permission for an AI future that hollows out entire industries."

The AI "transition"

In his post, entitled "A frontier without an ecosystem is not stable", Nadella compared today's demand for AI, and the growth being seen, as equivalent to the rise in globalization seen in the 20th century.

Nadella outlined how globalization was a positive for some, as it boosted economic indicators in many countries - but it also led to a loss of jobs in other areas, and the end of some industrial economies as priorities and focuses shifted.

"This transition is different than any previous platform shift," he said, "In the past, we used digital systems to enhance human capital. This is the first time we can create a real cognitive loop between people and digital systems. That is a mind-bender, because it changes how we even conceptualize work inside an enterprise."

"What is at stake is not some digital tool or system and its use, but how organizations continue to learn, build IP, differentiate, and thrive in a world where AI models can continuously absorb the expertise of humans and organizations and commoditize it."

Nadella stated the AI industry has a chance to learn from past errors made in the globalization age, with businesses should continue to control their learning systems and internal expertise while benefiting from AI-powered innovation.

"In my view, our priority has to be building a frontier ecosystem, not just a frontier model, so value flows broadly across every company, every industry, and every country," he declared. "One where every organization can own the learning loop that encodes its institutional knowledge, compounding its human and token capital."

"When that happens, companies will create value for themselves and for the economy around them."

Nadella's post comes shortly after Microsoft Build 2026, where the company laid out its plans for the next few years in terms of new software and hardware.

At the event, Mustafa Suelyman, CEO of Microsoft AI, outlined the company’s reasoning in its AI development, noting that its work would always look to support human workers and users, not replace them, as it looks to create what the company calls “humanist superintelligence”.

• Asana data finds 82% of firms have faced unexpected AI bills in the past year
• Risky AI outcomes have affected more than half (53%)
• AI systems lack context and workers are being forced to pick up the pieces

Four in five (82%) UK IT leaders say their organization has experienced unexpected or unplanned AI-related cost increases in the last 12 months, with many investing heavily in the tech but unable to predict the true full breadth of costs, including deployment, governance, integration and scaling.

The data, revealed by Asana, suggests businesses are indeed moving from pilot to scaled adoption, but they're not considering the cost changes associated with broader deployment.

Organizations are also struggling to prove their ROI, but IT leaders are under increasing pressure to deliver to the board.

AI bills are soaring

"The challenge now is turning that into measurable business value," Asana UKI and Northern Europe Head Christina Francis commented.

Three in five (61%) British ITDMs say they're either highly or fully accountable for AI-driven business outcomes, but those outcomes aren't always proving to be so positive. Half (53%) say an AI tool or agent has taken action in the past year that's landed them in hot water, such as financial damage, legal issues or reputational harm.

Actual deployments might not be so well-thought-out either, because half (46%) say AI projects often fail because they lack complete context without access to workflows, internal knowledge and business processes. More than a third (37%) of knowledge workers even spend upwards of 30 minutes every day correcting AI outputs due to a lack of context.

"AI is most powerful when it has context: the goals, decisions and workflows that sit around the work," Francis added.

Asana also uncovered the extent of shadow – or unapproved – AI, which leaves companies paying for services that aren't always being used. One in four (25%) say they frequently used unauthorized AI, and two-fifths (38%) regularly use personal AI accounts for work-related tasks.

Ultimately, the report concludes that workers are prepared to accept faster ways of working, but they'll also find alternatives if approved tools aren't up to the task. As ever, the best advice is for companies to meet workers where they are and build a strategy around them to deliver the best ROI and cost savings.

• Gemini on Google TV can now adjust audio and video, change modes and fix issues
• Coming to some 2025 and 2026 TCL TVs first
• US-only for now

If like me you've been fairly unimpressed by AI additions to TV interfaces so far, the latest update to Google Gemini might change your mind — because it's something you might use every day. Simply grab the remote or say "Hey, Google" to wake Gemini and you can then control your TV settings by voice.

The feature is rolling out slowly, and the first recipients will be recent TCL Google TV models in the US, including the TCL QM8L. Other TVs and streaming devices will get it in the future too, though Google hasn't given any details of which specific models will get the update.

What can you control with the Gemini TV update?

Google has given four key examples of the kind of thing Gemini will enable you to do.

First and most usefully, you'll be able to change your audio and video settings. Google's examples include "increase the bass" and "set picture mode to 'Sport'" — as we recently said, even though we generally encourage you to keep your TV in a fairly accurate-looking mode, you want different settings to enjoy sport to its fullest.

Secondly, you'll be able to optimize your TV's settings for what you're watching, for example "it's movie night — help make this feel like a cinematic experience". Hopefully there's a less verbose way to do that.

Thirdly, you'll be able to troubleshoot picture and sound issues: "the screen is too dark", or "I can't hear the dialog clearly".

And finally, you'll be able to call up specific menus with commands such as "open display settings".

Google isn't the first to do this by any means — for example LG has voice-based assistants in many of its TVs, and you can ask question about picture problems just like this — but this is by far the slickest solution in terms of it making the useful changes on your behalf, no menus needed.

The first TVs to get the Gemini upgrade will be the 2025 TCL QM9K, and the 2026 X11L, QM8L and RM9L models in the US. Google hasn't given a timeline for other TVs from other manufacturers just yet.

TCL and Google also said that this update would come to a 2026 TV called the QM9L. What's slightly odd about this is that we're not familiar with a TV with that model name, and there's no solid evidence of it online. We've asked TCL for more information.

Thinking of buying a new TV?

Try our TV size and model finder! You tell it how far you sit from your TV, we'll tell you what size to buy based on viewing angle advice from image quality experts, and we'll recommend our three top TVs at that size for different prices.

• Google has cut the price of Gemini AI Plus from $7.99 to $4.99 per month
• Subscribers now get 400GB of Google storage instead of 200GB
• Here's how Gemini AI Plus compares with OpenAI's $8 ChatGPT Go plan

Google just slashed the price of its Google AI Plus plan to $4.99/month (£4.99 /AU$7.99), down from $7.99, and at the same time doubled the included cloud storage from 200GB to 400GB. This makes Google AI Plus significantly cheaper than its main rival, ChatGPT Go, which costs $8 a month. But which one offers the better value? Let's find out.

The first thing to note is that both of these offerings sit on the bottom rung of OpenAI's and Google's and paid subscription ladders. They're the first step toward the more fully featured ChatGPT Plus on one hand, and Google AI Pro subscription on the other, both of which cost around $20 a month (Google's is slightly cheaper at $18.99).

That said, both plans offer significant improvements over their free counterparts. The free versions are great for occasional use, but you'll soon start running into limits, particularly when it comes to image generation and other advanced features.

Usage improvements

Everything with AI models is measured in usage. Once you've used up your daily allocation, you start to lose access to the better parts of the models, meaning things can slow down considerably or, in the case of image generation, stop working altogether.

To make matters more confusing, neither company is particularly transparent about exactly how much usage you get each day. That's because limits are often dynamic, and can vary depending on demand.

Google AI Plus comes with usage limits that are up to “twice as high” as those on the free version of Gemini. You also get access to additional features, including video generation.

ChatGPT Go, on the other hand, simply promises "more" access than the free version of ChatGPT without specifying exactly how much more. OpenAI says subscribers get more access to GPT-5.5 Instant, along with higher limits for messaging, uploads, image creation, and memory.

If you look at the small print of OpenAI's offer, you'll also notice it states that "this plan may include ads", which is an immediate red flag for me.

What else do you get with Gemini?

Gemini does seem to offer more than ChatGPT. As well as the increased usage limits, you also get a few extras, including access to Nano Banana, Gemini's image generator in Google Search, and 200 Google Flow credits for creating cinematic video scenes.

But the really interesting addition is NotebookLM, Google's AI-powered research and writing assistant. Subscribers get higher limits for Audio Overviews, the feature that automatically generates podcast-style summaries, along with more notebooks for organizing projects and research.

And let's not forget the 400GB of cloud storage, which can be shared across Gmail, Google Drive, and Google Photos. That’s almost worth the price alone, and you’re getting more Gemini access thrown in.

The story Google AI Plus starts to tell is one of integration. If you already rely on Gmail, Google Drive, Google Photos, and other Google services, the subscription feels less like a standalone AI chatbot and more like an upgrade for your entire Google account.

That's where ChatGPT Go starts to look a little less compelling. ChatGPT remains one of the best AI assistants available, but at this price point, Google is offering a broader package that combines AI tools, research features, video generation, and a substantial amount of cloud storage.

Whether that's enough to tempt existing ChatGPT users away from OpenAI is another question. But if you've been curious about paying for AI and didn't want to spend $20 a month, Google's new $4.99 price point makes Gemini AI Plus one of the most affordable ways to get access to a premium AI experience.

There are some jobs in life that people don't exactly look forward to. Cleaning the kitchen after dinner, getting a child ready in the morning, and grocery shopping are all necessary, but rarely exciting enough to tell stories about afterward.

Recently, I tried out a simple ChatGPT prompt: "Turn this into a game." You simply describe the thing you need to do and ask ChatGPT to turn it into a game. The AI then invents rules, objectives, challenges, points, stories, and small rewards. Many of life's most boring activities are only a few imaginative twists away from becoming surprisingly entertaining.

Kingdom of dirty dishes

After dinner, I faced the usual kitchen mess of dishes in the sink, crumbs on the counters, and trash to take out. I don't mind doing the cleanup, but I was curious how ChatGPT would make such a dull list of tasks a game. I asked, and ChatGPT devised a game called The Lost Kingdom Cleanup, with the following prompt:

"You are the Keeper of the Realm. Overnight, mischievous Clutter Goblins have scattered objects throughout the kingdom. Each dish returned to its proper place earns one gold coin. Every cleared surface reclaims a village. Complete the entire quest before the timer runs out and you earn the title Champion of the Realm."

I don't know that I would have engaged in it on my own. Luckily, I have a young son, who was very excited to hear about a new game set in a magical kingdom. With his help, we quickly amassed several dozen gold coins and saved a handful of villages, who gratefully gifted their savior with chocolate.

What surprised me was how quickly the game changed our focus. Instead of thinking about cleaning an entire kitchen, we were collecting coins, reclaiming villages, and racing against the clock. The dishes still got washed and the trash still went out, but the task felt completely different. For both kids and adults, turning a chore into a game doesn't eliminate the work — it simply gives your brain something more enjoyable to pay attention to while you're doing it.

Mission control

The second test involved a challenge familiar to many parents, the effort of getting a child out the door in the morning. That can sometimes feel like participating in a disaster movie as a shoe disappears, and breakfast suddenly becomes controversial. Asking ChatGPT for a game led to Operation Rocket Launch:

"Commander, your spacecraft departs in twenty minutes. Before launch, four critical systems must be activated. Fuel Systems means breakfast. Space Suit Check means getting dressed. Navigation Check means shoes and a backpack. Complete all systems before the countdown reaches zero, and your mission can begin."

My son is a little young to fully understand the more elaborate game mechanics, but the idea of a spaceship launch and the steps to make it happen were easy enough to grasp. Preparing a space suit for launch went over much better than the usual effort to get dressed.

The routine was still the routine, but the mood was much more excited. Plus, the game made something to do together rather than something I was trying to make happen. That small shift made the entire morning feel lighter.

What ChatGPT consistently added was structure, story, and a clear sense of progress. Those small ingredients made familiar routines feel less repetitive. The best part is that the prompt works on almost anything. In a matter of seconds, ordinary tasks can be transformed into quests, missions, competitions, or adventures, even if you still have to do the work.

It's too early to draw firm conclusions about Apple's take on AI in the iOS 27 Dev beta, but I have been busy trying out new features, including the Apple Intelligence image-altering tools in Photos: specifically, Spatial Reframing and Extend.

I've already had some fun with both, and was immediately impressed with their raw power. To generatively alter your images, Apple uses a powerful private-compute cloud-based diffusion model built, in part, with Google. It's unlike any AI Apple has ever presented before, and, as I've written, it opens Apple up to a lot of questions about whether it still prizes image truth over aesthetics.

I'm not here, though, to critique these tools. After all, this is the developer beta, and some tools and features will likely change quite a bit before they arrive fully baked on, we expect, the iPhone 18 in September.

Still, this platform update marks the beginning of Apple's journey as a card-carrying AI citizen, fully capable of standing alongside OpenAI and partner Google. Siri can talk and pay attention, Image Playground can generate images from whole cloth, and Photos can alter and extend photos with a gesture.

Having spent time with Spatial Reframing and Extend, I remain somewhat startled at just how far Apple is taking the AI image-altering strategy. Spatial reframing, for instance, lets you turn photo subjects to see elements the camera never saw or captured.

Seeing beyond the edge

I've had more fun with Extend, though, not necessarily because I plan on filling in the lost information in my photos, but thanks to how Apple's image-generation tools guess at what was never captured in the first place.

The thing about a tool like Extend is that, if you're using your own photos, you already know what was cropped out. Perhaps you chose that framing, but as Apple noted in its keynote, it can be difficult to frame a portrait-mode photo in a landscape frame. Sometimes you need those extra bits.

Apple's approach for the moment appears to be "let guesswork be your guide." It doesn't know what's missing, but Apple Intelligence can use its smarts to read the rest of the image and fill in the blanks.

To see how this early version is doing, I took a bunch of photos of things and places, doing both a tight and a wide shot. In other words, I shot what was really there, and then cropped in to see just a smaller section. The goal: how close could Photo's Extend tool come to filling in the blanks?

Considering what it had to work with, Photo Extend did a pretty good job, though a couple of the results were unintentionally comical.

One other thing I learned is that the Extend tool in the current version of iOS 27 Dev Beta will not extend, for instance, body parts. I took a photo of my hand, cropping out a few fingers, but no matter what I did, the app wouldn't extend the frame to fill in the rest of my hand. This is actually good news; I had worries about ending up with six fingers.

I'm also learning that Extend likes order. It seems almost allergic to clutter, so whatever it does generate is usually clean, orderly, and has as few elements as possible.

Below are some examples of real wide shots alongside the more tightly framed ones, where I let Apple Intelligence Extend do its thing. If you hadn't seen the originals, you might never know that significant portions of the images were generated by AI.

For the majority of these images, I took one photo for which I stepped back enough to capture more of the scene, then I took a second, tighter photo. I applied Extend to the latter image, and then compared Apple Intelligence's guesswork to reality. You can see the original, unextended photo in the center.

Extending the frame and stretching reality

This is the only image where I took just one photo and extended it with Apple Intelligence's latest Photo tools. This is a dev beta, so I won't offer much criticism, but some might take issue with the Empire State Building redesign.

The home office

On the left, you can see my home office as it is. The orchid is a plant I've nurtured for years (I even fully replanted it almost 18 months ago). Extend's gift is that it tries to leave original reality alone, but the more information you ask it to fill in, the wilder the flights of fancy.

I love, for instance, that my closet now has three doors, and I have been staring at the glass door on the right for hours. What is that? Why did Extend add it? No matter; it's early days, and these generative skills are impressive.

Foliage

I think Extend already has a solid grasp of how plants and trees work, and shows off that knowledge here. The extended tree looks quite realistic. As for the sky, it now has a rather dramatic, almost beatific look. My shed got an odd redesign.

Guitar on the wall

The guitar work shows off a little of Extend's penchant for cleanliness and order. The suggestion of a lampshade is turned into a perfectly round, brightly colored tube, and, based on the two visible supports, it might no longer be a lampshade.

The blinds also got a bit of a cleanup. As for my guitar, it looks essentially the same. In other words, Extend appears to be, even in Dev Beta form, ensuring that the image's original subject remains — even if extended a bit — essentially untouched.

Office shelf

Something as busy as my office shelf can present challenges for any generative system — so many objects, so many options.

I took a picture (at left) of the actual shelf, and you can see the second, tighter photo in the center, and then the extended image on the right.

It makes sense that in the extended photo, Extend chose order over my clutter, but the best part is the transformation of the USS Enterprise into a quasi-jetliner. I think a couple of book titles also got a rewrite.

Backyard furniture

Extend did a nice job of decluttering my backyard space. I noticed that when Extend detects a cube-shaped object (in this case, my fire pit), it will just turn it into a featureless box, instead of trying too hard to guess.

Jetliner

I was impressed with how effectively Extend completed the jetliner. Notice, though, how once again it assumes order where, in truth, there's chaos.

There you have it — some fun, early experiments with Apple Intelligence's newest and most powerful image generation tools. That it does so well at the dev stage is especially encouraging, since Apple still has months to refine it.

Ultimately, I don't think people will push Extend as far as I have. They may only use it to recenter a subject, adding just a few inches to one side or another, or to extend a solid background or even an evening sky, efforts that won't detract from or alter the subject.

How do you think you'll use these new Apple Intelligence Photo tools? Let me know in the comments below.

• Alphabet CEO Sundar Pichai gives graduation speech at Stanford University
• Unlike other speakers, he doesn't mention AI
• Pichai provides life guidance and lessons for the new grads

Alphabet CEO Sundar Pichai ducked the recent trend of speakers mentioning the benefits of AI at university graduation speeches, but still faced a protest about Google's work in the Middle East.

Pichai was giving the graduation address at California's Stanford University, where he chose to not mention the hot technology trend of the moment, but instead focus on his own life experience.

However reports from the ceremony claimed his speech was disrupted by a walk-out of around 200 students protesting about Google's role in the Israel-Palestine conflict.

Life lessons from Sundar Pichai

Pichai did in fact briefly hint at mentioning AI without mentioning AI, noting how, "people have also been giving me a lot of advice on what to say. Actually, it’s been the same advice, and it’s about what not to say. People thought it would be really difficult for me; it is the last two letters of my last name, after all."

"In all honesty, that topic is truly immaterial to what I want to share with you. The most timeless advice, I’ve learned, is technology agnostic. It’s about you, the life you want to build for yourself, and the choices that help you pursue that life."

Local reports claimed that around 200 students walked out as Pichai took the stage, withand smaller groups in the audience also caused disruption by waving banners and Palestinian flags and blowing whistles before also leaving mid-speech.

Google's role in the conflict has been a source of controversy for some time, particularly the company's $1.2 billion cloud-computing deal with the Israeli government known as Project Nimbus, which led to employee protests in 2022.

Pichai's speech otherwise seemed to be well-received, as he recalled stories from his time studying at the University, and outlined his "three simple filters" which have “helped [him] get more moments right than wrong and took some of the pressure off”.

These were "choose optimism", "gravitate towards working on hard things" (where he mentioned the "impossible problem" of building the Chrome browser) and "when all else is equal, do the thing that excites you".

This was in stark contrast to one of his predecessors at Google, Eric Schmidt, whose recent speech at the University of Arizona commencement was roundly criticized and booed by attendees after he declared, “AI is going to touch everything,” even "if you don't care about science."

• Four in five say AI makes them more productive, one in five say this benefits their employer
• Workers spend more time 'botsitting' than prompting AI in the first place
• Traditional metrics don't reveal just how many AI sessions fail

New research has claimed that while nine in 10 UK digital workers now use AI at work, putting British workers ahead of their US counterparts (84%), only 42% describe their workplace as AI-first - suggesting there are still some challenges along the way.

On a personal level, four in five (78%) admit AI makes them more productive, but only one in five (18%) believe this has any significant effect on overall organizational performance.

This is likely because, while the average worker saves around 12 hours per week through automation, half of that (6.3 hours) is being spend 'botsitting', the report from Glean notes.

The hidden tax behind AI productivity

Ultimately, while AI serves to boost the speed at which workers can work, it's not removing that work entirely. Instead, it's evolving roles into supervisory roles, with employees typically spending more time botsitting (38%) than asking AI to complete tasks (36%). "The hidden labour is becoming a quality-control problem," the company declared.

Glean argues "the cleanup bill is growing," with more than a third of AI sessions failing altogether and 77% of UK workers having to correct or re-do work in the past month, and 26% have done so in the past week.

"Too many companies are treating AI adoption like a vanity metric: more seats, more prompts, more usage," Head of the Work AI Institute Dr Rebecca Hinds noted, implying that productivity gains are largely overstated.

While IT teams face increasing pressure to deliver ROI, it's clear that metrics must take into account much more than total time saved. By reframing AI's actual impacts, companies will have greater visibility over error correct, prompt refinement and output validation to understand where it's actually delivering the best results.

• Around half of workers have received warnings about using unapproved AI
• 88% say they've shared work-related information with public AI
• Companies are providing AI, but not the AI that workers want

New data has revealed that two in three office professionals have used AI tools or services at work even though they knew they weren't permitted by company policy.

A report from PagerDuty found more than half (53%) even received informal guidance or feedback telling them to stop, but many still choose to use their preferred AI services over workplace tools.

Nearly as many (48%) also faced formal consequences, like official warnings or disciplinary action – proof that companies are aware of the use of authorized AI.

Workers want to use the AI they want to use – and not the other way around

Despite a clear appetite for artificial intelligence, companies are intent on punishing or discouraging workers from using their preferred tools in favor of pushing their own selection of enterprise-grade tools. But three-quarters (77%) of the workers surveyed said they believe their company's AI restrictions are limiting their professional development, career progression and skills journey.

There's also a gap emerging between business users and company tech departments – 72% of workers and 77% of senior leaders believe they know AI better than tech teams.

As for the tools workers want to use, popular AI chatbots like ChatGPT, Claude and Gemini are firm favorites. Most (88%) have shared work-related information with public AI systems like these, 43% have uploaded emails, 40% have shared meeting notes, 34% have even entered customer information and 31% have entered sensitive business documents like finances.

"We know the demand for AI is there... The goal for any executive today should not be to slow down AI adoption, but to redirect that energy into proven platforms that offer governance and automation at scale," PagerDuty CTO Tim Armandpour said.

While it's clear that demand is there, current tool provisioning isn't meeting worker needs. To avoid leaking sensitive information, companies could instead try observing how workers use AI and adding enterprise-grade security on top of those, instead.