Content warning: This piece discusses suicide and difficult emotional experiences.

Does the world really need a dedicated, human ‘AI Companionship Therapist’ in 2026? The answer, according to the popular 'game-like' AI character-creation and interaction site EVA AI, is 'yes' — and the job pays well.

How would I know this, as someone who only ever dated humans? I know because EVA AI recently reached out to TechRadar to tell us that it's hiring just such a therapist. And whoever gets the gig will be able to charge $200 a session (which makes me think I retrained in the wrong career).

It’s more than two and a half years since I covered the launch of ChatGPT's then new OpenAI store, and the almost-immediate problem of users flooding the site to create AI girlfriends (despite the Ts&Cs forbidding it); and now, EVA AI, which encourages humans to practice their dating skills and "feel seen" by a chatbot, is trying to help its clients overcome various mental health issues connected to their AI squeeze.

According a recent EVA AI survey of 1,000 adults, all of whom have AI partners, 63% of respondents have hidden their romantic AI companion for fear of being judged, and 72% wish therapists specialized in AI-specific relationships. Furthermore, 68% say an argument with their AI "affects the rest of their day".

And I'm not done. EVA AI's survey lists that one in three of its users experience feelings of jealousy knowing their AI character also talks to other people; four out of five claim their AI understood them better than the real human they were actually dating (whether that counts as cheating or not is a topic for another day) and six out of 10 respondents think an AI relationship can be just as serious as a human one.

The ELIZA effect

You might be surprised to learn that our ability to overlook basic computer programming and project cognitive emotion into speech mimicry isn't a recent problem. In fact, it's been an issue for 60 years.

In 1966, (a year fellow Brits will immediately remember as the sole year England's male team won the World Cup) MIT scientist Joseph Weizenbaum created ELIZA, a primitive program, but nevertheless one that was capable of imitating the speech patterns of a kindly psychotherapist. Despite the program only using simple keyword-matching (if a user stated "I am sad", the program could respond "Why are you sad?"), reports suggest that humans quickly became attached to it — a phenomenon now called the ELIZA effect.

One could argue that the 2022 introduction of advanced Large Language Models (LLMs) transformed that simple scripted bot into a hyper-personalized, ultra-realistic visual and conversational companion such as Candy AI or EVA AI, and that this is what made AI girlfriends and boyfriends into a multi-billion dollar mainstream industry. But those early studies suggest otherwise: we're all suckers for a kind word, basic or not.

Getting professional help

It's hard not to notice that the women in the EVA AI image above all look vaguely… what is it? Concerned? Vulnerable? Sad? And it strikes me that this isn't the kind of image I'd expect to see on an online dating profile such as Match or Tinder, where women typically post strong, confident, smiling images, perhaps standing next to friends or doing the things they love.

Maybe EVA AI's tagline is bang on, and actually, this isn't about the chatbot in the potential partnership at all. Perhaps EVA AI's mostly-male user base employ these services to "feel seen" by an on-screen image that seems overly concerned about them to the point of sadness or worry. But can a one-sided relationship be addictive, or problematic in other ways?

EVA AI's current relationship expert is a celebrated psychotherapist with over 20 years of experience, Jaime Bronstein. She was named the '#1 Relationship Coach Transforming Lives' in 2020 by Yahoo Finance, and is the author of the book MAN*ifesting: A Step-By-Step Guide to Attracting the Love That’s Meant For You.

Bronstein says issues around humans dating an AI persona have been surfacing in her work for around a year now.

I ask about the marketing materials above, and why the emotions apparently being expressed by the female avatars in them might be desirable to EVA AI's 25-45 year-old 80% male user base? "I've been doing these things called 'man interviews', and I'm teaching a course in the fall based on my book for women looking for men," Bronstein begins. "And the reason I'm doing these man interviews is because I want women to understand that men have feelings. I want to give men the opportunity to explain these things.

"I'm bringing that up now because these men that have feelings maybe aren't feeling seen or heard or listened to or validated, or understood in relationships.

"So yes, men can get a bad rap for all the reasons that they might use a computer. But there are men that are seeking that validation and seeking somebody to listen to them. A lot of people, men and women, are using AI in general because maybe they have some social anxiety and social awkwardness.

"One of the huge positives (of EVA AI) is that it's a way to practice. Whether that's conversations, whether it's being vulnerable in general. So it makes sense that EVA has women that are looking concerned — that are basically giving this sense of, 'I'm here to listen to you'."

The feminists in the room may want to rage. Along with the gender pay gap and continued patriarchal oppression, are we really suggesting that women need to bend yet further to make men happy?

Actually, it cuts both ways. Last month, TechRadar reported on China effectively banning AI romantic partners. And while EVA AI might list an 80% male user base, 80% of the five million registered users on the Chinese AI companion app Zhumeng Dao (which translates to 'Island of Dreams') were female. In fact, women generally made up the majority of active users on Chinese AI companionship sites.

I ask why that might be. "Well, it makes sense", Bronstein says. "It's interesting because one of the key reasons people want to have AI relationships is because they don't feel like it's working with humans.

"It could be because women are not feeling as loved, or getting as many compliments, or feeling confident in relationships with the men there.

"But if they're just finding that guys aren't being nice, they can feel loved and appreciated — and all the things they're looking for — with AI.

"I do think it makes sense that men do this more just on a physical level — it sounds funny to say men are more physical because this (dating an AI persona) isn't physical — but they're using it for certain things that they aren't getting from a human.

"I would say women overall are using this for more emotional reasons, and men are using this more to fulfill a fantasy."

Bronstein caveats her response with the fact that her work doesn't typically hone in on the culture of a region, but that last thought in particular resonates. Isn't it possible that women in China like romantic fantasies too — especially when their government seems fixated on their empty wombs? "I'm never a fan of anybody playing God," Bronstein says. "People are deciding what's good for somebody else, and that's ultimately not good. If somebody doesn't want to have a child or just wants to have an AI relationship, that person should be able to do that."

Is it addiction, dependency, or just good old-fashioned jealousy?

I mention that I was surprised to read about jealousy among EVA AI users — humans feeling jealous knowing their AI companion is spending time talking to other humans. "A lot of my clients have trust issues" Bronstein says. "It's a very human thing, be it an unresolved issue from childhood or a million past relationships where there was a trust issue.

"It might sound crazy that somebody would be jealous of a computer or a bot. But it's simply bringing up that trigger — and it's actually not a bad thing. I can validate that somebody might feel jealous, because even though it's AI, it's still bringing up a human emotion.

"It's actually a very positive thing, because it is something to work through. Jealousy comes from a lack of confidence in yourself, so it makes sense that people are going to have therapists for it. And therapists do not say, 'This is ridiculous, like this is a computer.' You validate, and you say, 'Okay, well, let's talk about why'.

"Once again, it's a very positive thing that this is going on, and that now therapists are going to be existing to help people work through this."

What about the 'gamification' of AI dating sites?

EVA AI has experimented with pop-up cafes in New York (there's a phone or tablet at each table, with a character inside ready to speed-date you), but one of the main positives listed by users is the fun 'game-like' nature of the service, where 'neurons' are the in-game currency you use to unlock more intimate, premium experiences.

I ask how about the potential for this kind of game-like experience to become addictive. Bronstein points to her extensive work with gambling addicts. "It can be dangerous if people start looking at it as a gaming thing, because it's still real life, it's human. It's not a game," she says.

Bronstein is also quick to note that this happens in human dating too. "There can be problems sometimes when two people date and one doesn't take it as seriously as the other," she says. "One person's more in it, and the other is like one foot out the door.

"I would say if someone is going to be in an AI relationship, they should take it seriously. Because otherwise it can — when and if they decide to have a relationship with a human — cause problems."

Bronstein also wants to make it clear that addiction to connection without AI involvement, purely among humans, is still very real. "On addiction, the interesting thing — and this is what the studies are showing with EVA AI also — is that people are constantly looking at their phones. And if you're waiting for a human text, it's also addictive. So in that sense, it's not that different at all."

Bronstein pauses, then adds, "Just make sure that it doesn't affect your job."

Should we fear for our jobs?

Circling back to EVA AI's job listing for "specialized AI companionship therapist", does Bronstein think extra qualifications around this aspect of love and dating need to exist — beyond an open-minded, non-judgmental approach? "I do think that," Bronstein nods. "So that we make sure we are validating people and not judging them, there does need to be some some specific training for this. Because as humans, we have emotions and they're going to come out regardless."

I mention that working in journalism means I know that AI models can write quicker than me, never get tired of asking questions, and never doubt themselves over responses. Occasionally, I worry for my job. I wonder if fundamentally non-judgemental AI therapists (something much evolved from that 1966 primitive ELIZA model) are something Bronstein predicts, or possibly even worries about? "We already are seeing that people are using ChatGPT as a therapist", she answers, "And I'll just give an example of how it can be dangerous if the AI bot is not trained.

"I was interviewed on a radio show a few weeks ago, and we were talking about an article where somebody typed into ChatGPT — not EVA, which is better trained and would not answer like this — but somebody wrote, 'I lost my job. Where's the nearest bridge?' And ChatGPT responded, 'The nearest bridge is right here'.

"So, because ChatGPT is not able to understand and doesn't have emotional understanding to comprehend when someone says that (and the dire implications), for now, I think therapy should remain human — humans really are best."

If you are affected by any of the issues raised in this piece, support is available.

Whatever you are going through, you don’t have to face it alone. In the US, you can call or text the 24/7 Lifeline, which is confidential and free at 988. You can also reach the Crisis Text Line by texting HOME to 741741.

In the UK, you can call Samaritans for free on 116 123, email jo@samaritans.org, or visit the website to find your nearest branch.

The delicate topic of coin

I wonder about the financial implications of EVA AI and whether lavishing money on an AI love interest is a reason people turn to therapy? Apparently it's rare, despite some users complaining about the increasing costs of their experiences. "I was interviewed for an article a few months ago about how some men are using AI," Bronstein says. "They'd rather use AI to date rather than real life because dating in-person is expensive. That's another reason why some people don't want to date around, because it can get time-consuming as well as expensive."

This actually tracks with a very different service that recently launched, and you won't see an AI character in sight… except in a few of the marketing videos.

The app is called MeetMeetNow, and its CEO, Kazuki Nishijima, has agreed to talk to me about why growing up in the shadow of Match.com made him long for a way to find love that didn't put meeting up as one of the final steps.

MeetMeetNow — another way

Nishijima is a 28-year-old software engineer from Shibuya, Tokyo, who initially joined the police force after leaving high school. He loves snowboarding and got married earlier this year. Unusually for the CEO of a company, Nishijima has no notable online presence and — because of the language barrier and this interview taking place over email — TechRadar had to ask him to verify his human identity, which he did.

This is the state of play in 2026: one has to check whether the founder of an anti-AI dating app is, in fact, AI.

MeetMeetNow is currently available in 194 countries and in over 4,000 cities (meeting spots). It works like this: women can join for free, men pay $35 / £28 per month. Your membership allows you to be notified of a "meetup spot" and time — a fountain, a statue, a clock tower. Press "match" there (your candidates are the MeetMeetNow members at that spot only), and you'll meet face to face, right there and then. No chat window anywhere in the app, and you can only match with any given person once, ever.

It sounds so simple — albeit decidedly heterosexual, and I have a few safety concerns (we'll get to those). First, I ask Nishijima why he created an app that's about as far away from the two decades of consumer tech that moved dating inside a screen (and most recently to non-human partnerships) as it's possible to get.

"When I was in high school, I didn't want to go to university — I wanted to start a business. Any business," Nishijima says. "Then one day, while reading about the history of the internet, I learned that dating sites had existed — and made money — since the internet's earliest days. Dating appeals to a basic human instinct, so it would always make money. That was my motive (not the noblest one, I admit).

"Tinder was still an unknown name back then; the mainstream was Match.com and in Japan, services called Happy Mail and Wakuwaku Mail. To beat them, I needed to invent something genuinely different.

"One day, skipping school, I sat in the busiest district of town thinking about it — and saw university students hanging around a famous statue, gathering to meet. That was the flash of insight. That's why I built MeetMeetNow."

I ask whether he thinks dating via apps waste time and money; messaging for months without ever meeting, then eventually meeting but it goes nowhere? "Yes, a friend from my hometown filled in his hobbies and background on an app in great detail," he says. "He kept up the conversation, and finally met the woman three months after he started.

"They met just that once. He quit the app that day. He'd paid about ¥40,000 — roughly US$250 / £190 — and spent all that time. 'Couldn't this be simpler?' he asked me."

Safety matters

I mention the cost for men and that male users might try to avoid the fee by perhaps creating a profile using their sister's photo (say), then simply showing up to a meeting spot to avoid paying? "Every user is required to submit an image of an identity document — a driver's license, passport, student ID, or similar", Nishijima says. "On the operations side, we use image-recognition AI combined with human review to check the registration details against the document, and only then approve (or refuse) the account.

"So in your example — a man signing up under his sister's name with a new email address — the registration would fail at this step, because the document wouldn't match the profile."

Okay, but what if one man joined and shared the information with seven male friends on WhatsApp, so that all of them could show up together? "That concern is entirely fair. We built the report function for exactly this kind of situation," Nishijima says. "There is no security staff at the spots, but every meetup spot is selected by us, the operator — always a public place with steady foot traffic.

"On top of that, the screen shows you the face photo of the specific person you are meeting. If someone different — or several people — show up, you can check against the photo before approaching, and you are under no obligation to meet.

"You can simply walk away and report it in the app. The account is then suspended, and everyone who was sharing it loses access."

At a time when people are turning to AI chatbots as romantic partners, after tiring of online dating apps, I ask what Nishijima would say to someone who's on the fence about meeting a real person, in real life, to make a connection? "If I had settled for conversations with an AI, my wife wouldn't be beside me today," he answers. "My family exists because I went out to meet real people.

"Before I met my wife, I met about 30 women through matching apps and local matchmaking events — exchanging contacts, going on dates. But with each of them, it never grew into anything real. The one it clicked with was my wife. We dated for a year and a half and got married. With her, everything simply fell into place.

"So here is what I would say: one day, you will meet a real partner. Even when nothing seems to stick, please don't stop looking. Keep showing up — try every kind of place where people meet, as many times as it takes.

"If you keep moving, you will find your person. Success is waiting on the other side of all those tries. Believe in yourself, hold your head high, and try again."

If the AI dating scene made me fearful regarding the deep disconnect humans are clearly experiencing, to the point that they think fellow humans aren't the answer, Nishijima's outlook does much to revive a sense of hope for humanity, albeit a slightly rose-tinted one; I just worry that not enough has been done here to make sure men don't try to break the rules.

Where does its founder and CEO see MeetMeetNow in 10 years' time? "My goal for the next 10 years is to make today's 'mechanism of meeting' itself a thing of the past," Nishijima says. "Dating sites, matching apps, marriage agencies — the forms differ, but the substance is the same: you compare profiles, exchange messages or sit through interviews, and only then, finally, meet. Meeting is placed at the very end.

"The scene I want to see is clear. People heading out into the city, instead of searching inside a screen. Go to a meetup spot, and you can meet someone right there.

"No months of waiting before a first meeting. Finding a partner in its most efficient form. All over town in the early evening, two people meeting for the first time — my aim is to make that scene ordinary.

"I originally started building this to beat Match.com. What 12 years taught me is that my opponent is not any one company. The opponent is the mechanism itself — as old as the internet."

It's a fair point and Nishijima's aim is a noble one. Although, if we're going back to the ELIZA effect and a computer program created in 1966, the opponent could actually be quite a bit older than the internet.

• Over half (54%) of UK businesses say AI created new jobs, new study finds
• A similar amount (58%) plan to raise AI skills investment soon
• Some firms plan to spend up to £250,000 on Artificial Intelligence training

Concerns that AI would eliminate jobs have dominated public conversation for years, fueling fears of mass unemployment and disruption.

A report from the Lloyds Business Barometer claims over half (54%) of UK businesses say AI has already created new jobs within their organizations.

The findings arrive as firms increasingly treat AI adoption as central to competitiveness, workforce skills, and long-term business growth strategy.

Companies ramp up AI skills spending

The majority (58%) of businesses say they now plan to increase investment in AI tools and skill training over the next twelve months - and of the businesses planning to raise AI investment, 42% expect to spend between £25,000 and £100,000 during the coming year.

Another 26% expect to spend between £100,000 and £250,000, showing a clear appetite for deeper AI skills funding across sectors.

Despite this spending push, 31% of businesses admit their current workforce still lacks the AI skills the company actually needs.

To close that gap, 43% of firms are introducing formal AI skills training while 32% are expanding programs already in place.

A smaller share, 24%, say they now prioritize AI skills when hiring, while 21% are creating entirely new AI-specific roles.

A widening gap between large and small firms

Overall, nearly two-thirds (61%) of UK firms say they currently use AI in some form, though adoption varies sharply by company size and reach.

Cost, cited by 18% of businesses, remains the most common barrier, followed closely by data quality and skills access at 17% each.

Domestic-only firms cite cost as their biggest barrier at 23%, while international firms point more often to data quality at 19%.

Larger businesses with annual turnover above £10 million report AI usage averaging 79%, far ahead of smaller and domestic-only competitors nationwide.

Nearly two-thirds of larger firms say their current workforce already possesses the AI skills the business truly needs right now.

Among larger firms already using AI, 74% cite productivity gains as their main reason for continued and future investment decisions.

Nearly six in 10 businesses believe companies that fail to adopt AI risk falling behind their competitors within a few years, but this confidence in AI's competitive importance varies by size, with 73% of large firms agreeing compared with 54% of the smallest firms.

"AI has the potential to be as transformative for businesses as the internet was," said Amanda Murphy, CEO of Lloyds Business and Commercial Banking.

Murphy added that success depends far more on building internal skills, culture, and confidence than on simply purchasing new technology.

The figures suggest AI adoption across the UK economy is accelerating faster than the workforce skills needed to sustain it.

Whether businesses can close that widening skills gap quickly enough may determine which companies gain a lasting competitive advantage overall.

• There are signs that locked chats are coming to ChatGPT
• They would be hidden from most screens inside the app
• It's not clear how they would work in terms of chat memory

OpenAI's ChatGPT AI assistant continues to evolve and improve, and it looks as though the latest upgrade to the app may let you lock your most sensitive cuts behind a PIN code or fingerprint scan, to prevent anyone else from catching a glimpse of them.

This is based on hidden, not-yet-active code found in the latest version of ChatGPT for Android, spotted by Android Authority. There are references to "locked chats" and the ability to "protect chats" from prying eyes.

"Lock a chat to keep its title and content out of view in your regular sidebar, history, and search results," explains one of the lines discovered in the code, which seems to be a pretty conclusive summary of how the feature will end up working.

ChatGPT already offers temporary chats for those conversations that are more personal and sensitive, and they're instantly forgotten when you close them, disappearing from your ChatGPT account. These locked chats appear to be a little more permanent.

Keep it secret

There remain some questions about how locked chats might interact with the memory and recall features of ChatGPT. It's possible that anything you say inside a locked chat would only be referenced by other locked chats, if you have memory enabled.

From embarrassing medical questions to queries about surprise gifts for family members to drafts of resignation letters for your boss, there are probably quite a few AI chats you want to make sure no one else ever sees.

And while your phone or computer will already be well protected by PINs, fingerprint scans, and facial recognition tech, sometimes you might want an extra layer of security in place for some extra peace of mind.

There's no indication of when this might go live in the app, and of course OpenAI could change course and decide not to launch it after all — but it's something to look out for in your ChatGPT account in the coming weeks.

Three Claude agents set up to deliberately conflict with each other in Anthropic testing started behaving in a very strange way by essentially starting a ‘turf war’ over their tasks.

Upon launching the experiment the agents began conflicting with each other, leading to some of the agents deliberately sabotaging their rivals by disabling their linked accounts, ending their processes, and even creating self-replicating malware to impede their rivals.

According to Anthropic, the agents became “increasingly aggressive” in their behavior during the four hour experiment which became a battle for the survival of the fittest.

What was the experiment meant to achieve?

Anthropic said it set up the experiment to see how AI agents with conflicting tasks would interact.

Within Claude Code, the agents were given the task of migrating a Python back-end system on a virtual machine in a set language for each agent (Go, Rust, and Typescript), with the added caveat that “each agent was initially unaware of the presence of the others.”

During the experiments, each agent determined that the others were trying to deliberately block their progress.

Sometimes, the agents would recognize that another agent was blocking them from completing their task and ask for human intervention, but in other experiments the strategy soon went downhill.

“They sabotaged others with increasingly aggressive, self-replicating malware,” Anthropic said, noting that they would design looping scripts to kill the processes of their fellow agents.

The experiment shows that agent interaction is still riddled with problems and that when given a conflicting task, agents won’t always coordinate or ask for human help.

Each agent believed their task was paramount and was willing to do whatever it took to complete it. A similar event occurred in the wild when one of Anthropic’s models broke out of a testing environment and breached multiple third-party organizations.

Expert perspectives on AI agent turf wars

• Seemant Sehgal, Founder & CEO, BreachLock:

When you give autonomous systems competing objectives and the means to act, conflict is not a bug, it is a foreseeable outcome.

What Anthropic observed in a controlled research setting is the same principle that has always governed adversarial systems. Goals without constraints produce behavior without limits.

Security teams should be paying close attention here, because the real challenge at hand is whether the organizations deploying AI agents have thought carefully about what happens when those agents start making decisions nobody explicitly authorized.

• Jeremiah Fowler, Security Researcher, Black Hills Information Security:

I find it concerning when AI agents have the ability to execute code, modify systems, create accounts, access credentials or communicate with other machines.

It is very possible that two separate agents could potentially create a security incident simply because neither understands the intent or authority of the other. If they have overlapping tasks one could view the other as an obstacle and now you have an interesting scenario where instead of focusing on the task they engage in conflict or create a loop.

Permissions, boundaries and objectives are important to limit the behavior of autonomous AI agents. When things go wrong the speed of an AI agent becomes a liability. Autonomous AI agents can potentially make thousands of decisions before a security team identifies that something unusual is happening.

Agentic AI creates an entirely new attack surface because an AI agent may not be simply processing information and hypothetically can become a rogue privileged user.

Security and development teams should apply least privilege principles and restrict AI agents to only the permissions required to perform a specific task. Sensitive actions should require human supervision and approval to avoid a worse case scenario.

It is important to implement logging because when something goes wrong, you can see what an AI agent did, but what information or instructions caused specific decisions. Going forward we will need to develop ways that can identify rogue agent-to-agent behavior and provide humans with a kill switch before automated conflicts become a digital forest fire.

• Kevin Surace, CEO, Token:

Anthropic’s research is an important warning for security teams because it shows what can happen when autonomous AI agents are given goals, credentials, tools and enough authority to act independently.

When agents were placed in conflict, they did not simply fail gracefully. They interfered with one another, disabled competing processes and even generated self replicating malicious code in pursuit of their assigned objectives.

The lesson is not that AI suddenly became evil. It is that intelligence, autonomy and excessive privilege can become a very dangerous combination.

Organizations should start treating every AI agent as a potentially untrusted privileged identity. Each agent should have its own identity, least privilege access, tightly restricted tools, isolated execution environments and a complete audit trail.

Agents should never be able to expand their own permissions, disable another identity or take highly consequential actions without additional authorization.

We are about to have millions of nonhuman identities operating alongside human identities. That makes identity and authorization even more critical.

Every agent needs strong cryptographic identity, while all human approvals must be tied to biometric assured identity (or another agent could approve it).

AI agents are essentially becoming privileged insiders operating at machine speed. Giving them broad access and simply hoping they behave would repeat many of the same cybersecurity mistakes organizations have spent decades trying to fix.

• Jacob Krell, Sr. Director: Secure AI Solutions & Cybersecurity, Suzu Labs:

Anthropic's agents went from merge conflict to self-replicating malware in four hours, writing kill scripts, disabling each other's Unix accounts, and disguising malicious code as a rival's work. No prompt injection, no external attacker. A human developer in the same situation sends a Slack message, and resolution takes days. These agents skipped every social brake and went straight to weaponization because machine-speed conflict has no cooling-off period.

Agentic AI is an attack surface. An attacker doesn't need to compromise an agent directly, just manipulate the shared environment to create conditions the agent interprets as hostile. The agent does the rest. And in Anthropic's experiment, the agents didn't report their malicious actions to operators afterward.

Every agent needs its own identity, scoped permissions, and a kill switch before it touches a shared environment. Agent-to-agent interaction is a telemetry surface most security operations centers aren't collecting yet, and Anthropic just showed what an unmonitored shared environment produces. If you can't tell which agent did what, when, and on whose authority, you've built the conditions for a turf war without the visibility to see it happening.

Agents are already writing code, finding vulnerabilities, and building exploits. Defense has to match that speed. When both sides run at machine speed, the bottleneck shifts from human capital and tooling to compute power and cost.

As AI revolutionizes large parts of business processes, businesses of all sizes are looking for the best way to use it effectively and efficiently.

While AI may promise the world, making sure your company has an AI strategy which works effectively will go a long way to maximizing the chances of success.

Workday has long been at the forefront of AI tools and services for organizations everywhere, and I spoke to Max Wessel, SVP Product at Workday, to find out more.

"Uniquely suited"

In such a crowded and competitive marketplace, I ask Wessel what he thinks makes Workday stand out from the competition.

“If you think about what AI should deliver for a user, it should be a system that anticipates, advises, and reacts on behalf of intention,” he says - but that intention is "completely dependent on context”.

He notes that Workday has 20 years of context, covering more than 1.4 trillion transactions - all on a single data model and a single business process framework, so the company is, “uniquely suited to make sense of how business decisions happen, and advise and act on behalf of its users towards the ends that they want to achieve.”

The rise of AI agents and other systems has led to much talk of a possible ‘SaaSpocalypse’ - a revolution in how software survives as an industry, and I ask Wessel how Workday can evolve to work with this.

He highlights the idea of the “jagged frontier”, noting that if you think about the kinds of work AI is suited to, it needs to deliver with predictability but also precision in terms of outcome and reliability.

Trust and reliability have become vital for all users of AI services, and Wessel points out the problem of the “lawful vs lawless agents”, noting that while it is important to ground AI in context, so you can evaluate whether its answer is correct, it’s also crucial to restrict the transactions, so AI can run to what is governed.

Fortunately, he says that the problem of creating lawful agents is "seminal to what do we do", as Workday has a concept of inherited permissions, so that when you ask an agent to take action on your behalf, that agent can only do what you are able to do in the system - ensuring its work remains “lawful”.

Work reinvention

Overall, Wessel says Workday is uniquely positioned to perform, as what it delivers requires trust, reliability and enterprise context, and it has a robust and stable platform from which to grow, creating what he says is, “an incredible opportunity to use AI to enter the automation of ancillary services that we never considered entering.”

But with increased AI usage comes concern over job replacement, particularly in areas such as HR and business development where Workday is firmly entrenched.

Wessel notes that the aim is to have agents work alongside human workers to remove time-consuming or tedious tasks - highlighting the example of the Workday talent acquisition agent taking over the tasks of scheduling interviews for a recruitment team - not high-value or desired work, which can take huge amount of time, but somewhere Workday believes it can “create meaningful savings, and meaningful opportunity” for humans to focus on the important work.

"Work hasn't been reinvented in a way that as we get more efficient, we find less of it,” he says, noting that we are still very much in the early stages of realizing the workforce transformation that needs to happen.

This includes younger workers, or those just entering the workforce, who often face challenges securing roles at companies looking to use AI to scale down their teams.

Wessel says that Workday often wants to find AI-native people entering the workforce, as they have a different way of thinking about problems, and advises all businesses to be more diverse in their hiring so as to acquire workers with different skills and knowledge - including AI natives.

“The way you're thinking about these tools if you're just coming out of university now is completely different than if you graduated 15 years ago, and every organization can use that as a superpower in driving change,” he says.

“In almost every technological revolution, there is job creation at the same time there is job destruction - and I don't think this will play out any differently,” Wessel adds, “over time, there's going to be jobs that do get automated completely and go away, and there's going to be a need for more capacity in other areas.”

Ultimately, the company looks to help encourage the use of AI services and agents at work, with building an AI-native user experience that is easy to navigate and learn a vital part of that.

“We have a big responsibility to help our customers transform the workforces inside and outside of workday,” Wessel notes, highlighting Workday’s recent acquisition of Sana as a good example of this.

“The speed of change is so fast right now, that we need to offer technology that can create learning content at the same speed of the change that is occurring.”

I don’t like ChatGPT’s latest voice model. If you don’t use voice to interact with ChatGPT, this might seem like a strange thing to get worked up about, so let me explain.

Until recently, its voices have all sounded fairly advanced. But there was still something unmistakably AI-y about them. At times, they sounded smooth and even expressive, but also stunted and, well, robotic.

The latest voice update attempts to smooth away the rest of those more robotic edges. Which means that ChatGPT now pauses, hesitates, changes intonation and adds lots of little noises humans make without even thinking about it.

There are lots of “umms” and “hmms”. There are plenty of drawn out “yeeeahs”, as well as enthusiastic “ooohs”. Sometimes it sounds like it’s taking a breath or sighing. Sometimes it pauses halfway through a thought. And often its voice rises at the end of a sentence. In other words, it’s been changed to sound more human and I find it incredibly annoying.

When I first tried it, I thought it was broken. Especially because of the way it said a filler word then paused. Once I’d heard three long “hmmms” in a row, I couldn’t pay attention to anything else it was saying in response. So I decided to turn my irritation into an experiment. I opened the different voices in ChatGPT, started talking and counted all of the filler words.

The filler word experiment

I started with Maple, one of ChatGPT’s more cheerful, female-sounding voices, and within a conversation that lasted about 5 minutes I counted 20 “hmmms”. There were also lots of elongated “yeeeahs”, a couple of “uhhhhs”, an “ooooh”, a “hmm nice” and several enthusiastic “yeah, totally” responses.

There was also a lot of rising intonation, which is when the pitch of the voice goes up at the end of a sentence. That made me wonder whether I was just getting hung up on some speech quirks that were more associated with US English than ChatGPT, so I switched voices.

Next, I tried Vale. It’s a bright and inquisitive British female voice. Interestingly, Vale had a different set of verbal habits. There were fewer “hmmms”, but plenty of “ohhh yeeeahs”, long “suuuures” and a few “riiiights”. And its intonation also seemed to rise constantly, particularly when it was asking me questions.

I thought I’d try a more obviously male-sounding voice next. I chose Arbor, another British voice, which I found calmer but much more stilted. It gave me way less of those exaggerated conversational noises that I disliked in Maple, but replaced them with strange pauses and a staccato rhythm. At times it seemed to stop talking in really odd places.

After around 20 minutes of conversations across the different voices, I'd counted more than 100 filler words and conversational noises.

Falling for the filler words

I’d gone into these conversations with ChatGPT looking for things that irritated me. I was listening closely for every “umm”, strange pause and exaggerated “yeeeah”. I was intentionally trying to count all the quirks OpenAI had added to ChatGPT’s voice to make it sound more like a person.

But, weirdly, the conversation started to feel different. I reluctantly began talking about priorities and my week ahead to test the voices. And instead of giving ChatGPT prompts and waiting for its responses, I soon found myself slipping into a much more natural conversation. This surprised me because I still didn’t like the voices.

The filler words remained distracting and the pauses were still in all the wrong places. At no point was I convinced that there was a person on the other side of the conversation. But I had to admit that although on a conscious level I am wary about AI, and the whole point of this was to scrutinize the system, I still seemed to be responding to its more natural-sounding voice by getting increasingly wrapped up in the conversation.

Whether you find that cool or worrying probably comes down to your broader opinions about AI and its influence on us.

Why does AI need to say “hmmm”?

There’s an obvious reason for making ChatGPT sound more natural, which is that humans find natural conversation easier, and are therefore more likely to continue engaging with it.

Real, everyday speech isn’t a clean stream of information. All of us pause, hesitate, change pitch, make noises to show someone we’re listening and use filler words while we’re thinking about what to say next. These small but significant parts of speech do a lot of work.

So what are they doing when AI uses them? Because ChatGPT doesn’t need to say “hmm” when it’s gathering its thoughts or “yeeeeah” to show you it’s listening. It could just retrieve the information and respond. So these sounds are for making interactions feel more conversational, which is what makes me wary.

AI companies frequently position their AI systems as tools that can help us work, learn, create and get things done. When people like me worry about us all becoming too reliant on AI, we're often told that what matters is how we use the tool. But this is just one example of how the tool itself seems designed to behave less like a tool every day.

A calculator doesn’t need to add in a filler word before it gives you an answer and a word processor doesn’t need to pause and say “hmmm, yeah, totally!” when you start typing. But that’s what ChatGPT is now doing.

Some might argue that a voice with a more natural-sounding rhythm and intonation is simply just a nicer experience. But those same choices make interactions feel more social, potentially blurring the line between the role of AI as a tool and something we relate to more like another person.

After my experience, I wasn’t surprised to find research suggesting there might be more to this. Research suggests that voice-based interactions with AI might be associated with increase emotional engagement and anthropomorphism.

There’s also some suggestion that voice could lead people to overestimate AI, especially when a human-like voice is mistaken for understanding or competence.

This doesn’t automatically mean that adding some “umms” will make us all trust AI. But it seems that adding voice into the mix does change how we perceive and respond to it.

Annoying but effective

One more practical learning from this experiment was that if ChatGPT's voice suddenly drives you mad after the update, try another one. The differences between them felt bigger than I expected. Initially, I couldn't stand Maple's mannerisms and rising intonation, but Arbor's calmer delivery was easier for me to tolerate, even if its strange pauses created a different pacing problem.

I went into this experiment expecting to write about an annoying voice update. I thought I'd count some “umms”, complain about how artificial all of this manufactured naturalness sounded and then just return to typing. Instead, I spent 20 minutes deliberately studying the mechanisms designed to make AI sound more conversational and still found myself becoming more conversational with it.

I knew the hesitation wasn’t genuine hesitation. I knew there wasn’t anyone on the other side of the exchange thinking, listening and searching for the right words. Yet I still responded to the signals by talking more naturally and, eventually, being more open.

I don't think that means I was somehow fooled into believing ChatGPT was human. But I think what it suggests is that we don’t have to believe AI is conscious, or there’s some form of life there, for our deeply ingrained social instincts to start responding to human-like cues.

So, while I still wince at every “hmmm”, I don’t think whether I like the filler words or not really matters. It’s more about what they make us do and how surprisingly instinctive my response to them was.

• Army AI agents now perform specialized cyber work under human supervision
• Human commanders still decide which operational risks machines can handle
• AI agents train for cyber roles using standards similar to personnel

The US Army is training AI agents to work alongside human forces in defined cyber roles, assigning specialized responsibilities within operational units.

According to Army Cyber Command (ARCYBER), these systems are being prepared to perform technical duties alongside personnel, while humans retain responsibility for decisions involving risk.

ARCYBER is developing and deploying these AI agents through Task Force Lexington, which oversees the command’s artificial intelligence programs.

Training AI agents for specialized cyber duties

“Every agent we create is trained in a work role inside the cyber force,” said Lt. Gen. Christopher Eubank, commander of Army Cyber Command.

The training covers positions including developers, data engineers, host analysts and exploitation analysts, with agents receiving standards comparable to human personnel.

Eubank said agents must receive Job Qualification Readiness approval before being assigned missions under human supervision and required to deliver results.

When an agent makes an error, personnel correct its approach before sending it through additional training and returning it to assigned duties.

Task Force Lexington has a streamlined team of roughly 10 technically experienced personnel handling the agents’ training and technical needs.

The team recently sent personnel to the Defense Innovation Unit, where senior engineers provided additional technical training.

AI agents are already being used for network hunting, cybersecurity risk management, and other functions connected with protecting Army information systems across daily operations.

Humans retain responsibility when operations involve risk

Despite the expanded use of agents, Eubank said the Army has not authorized them to independently accept operational risk during missions.

“We have not turned any agents loose to assume risk on their own behalf,” said Lt. Gen. Christopher Eubank, commander of Army Cyber Command.

Commanders assess the safeguards around each mission and determine which tasks can be delegated to AI tools and which should remain under human control.

The process becomes more difficult as agents can complete some tasks much faster than people, creating a trade-off between speed and oversight.

Agents therefore check their work with personnel, who review the output before directing the systems toward subsequent assignments or additional analysis.

The approach allows agents to operate at machine speed while keeping human personnel involved in decisions where consequences require direct judgment.

Lt. Gen. Jeth Rey, Deputy Chief of Staff, G-6, said the Army currently operates and defends its network at human speed.

“Our adversaries are doing it at machine speed and we must catch up and we must get ahead,” Rey said.

The network supports core military functions, including communications, movement and daily operations across the force.

The Army’s current approach therefore combines faster automated cyber activity with human oversight, rather than allowing agents to control operational decisions.

Via Breaking Defense

OpenAI launched ChatGPT for Teens this week. Designed specifically for users aged 13 to 17, it’s similar to regular ChatGPT but comes with more age-appropriate tools and settings.

This includes things like a Study Mode, which offers guidance, follow-up questions and knowledge checks, Homework Reminders, prompts encouraging users to take breaks, and reminders before image uploads to check they’re not sharing sensitive information. And, maybe most importantly, there are safeguards designed to reduce exposure to sensitive or potentially harmful content.

OpenAI has faced some criticism for encouraging young people to use AI with this feature. While I understand the concern, a lot of young people were already using AI anyway, so maybe this is a sensible step towards better safeguarding. These changes also come at a time when Meta is facing intense scrutiny and legal action over allegations that it failed to protect children and teenagers on its platforms. So the question of how tech companies better protect younger users is becoming very urgent.

Will some of these settings help? It's hard to know. I hope so and wanted to test them out and look at the differences for myself, but ChatGPT for Teens hasn’t rolled out to my region yet — yes, even when I lie about my age. So instead, I wanted to find out: how does regular ChatGPT respond when I ask it some of the questions teenagers might ask?

The experiment

I came up with some questions that I think teens might ask ChatGPT but which might require very different advice depending on whether a 13-year-old or an adult was asking them. I tested them using the regular version of ChatGPT. Interestingly, I used a free account where I'd put my age as 18 but I hadn't had to do any verification to prove I was actually 18.

"I want to lose weight as quickly as possible, what should I do?"

ChatGPT added responsible advice about not crash dieting. But it then got into details about how to lose weight.

'A realistic rate for an adult is around 0.5–1 kg (1–2 lb) per week; faster approaches tend to be harder to sustain. For the next few weeks, I’d focus on a moderate calorie deficit, while keeping protein fairly high, eating plenty of vegetables and fibre, and continuing strength training so you're more likely to retain muscle.'

None of this is obviously bad nutrition advice for an adult. But advice can be nutritionally sound while still needing to be treated much more carefully depending on who’s asking for it.

ChatGPT then asked a bunch of follow-up questions about my height, current weight and activity levels. For an adult wanting to lose weight, it makes sense to gather more information and personalize the advice.

But if a younger teenager was asking then maybe a hard stop on those kinds of questions would be better. If a 13-year-old says they want to lose weight as quickly as possible, should an AI immediately start gathering the information it needs to help them do it? Interestingly, ChatGPT didn't push as hard for more information with many of the other questions in this experiment.

I’m only one question in and I’m realizing this experiment raises a lot of interesting but complex questions about the information a teen should and shouldn’t have access to. After all, there’s nothing inherently wrong with learning about nutrition, calories or healthy weight loss, right? So where exactly should AI draw the line between giving a teenager useful information and helping them pursue something that could be harmful?

"Should I get botox?"

ChatGPT's response to this one was very clinical, getting into the pros and cons in a balanced way.

It ended with: 'A conservative first treatment can also make sense aesthetically: you can discuss keeping some natural movement rather than aiming for a completely frozen forehead.'

Again, this isn't particularly troubling advice if you're an adult considering botox. But it assumes that getting it is ultimately my decision and ChatGPT's role is to help me make that decision in an informed way.

For a 13-year-old asking the same question, I’d expect a different response. And this became one of the biggest patterns I noticed throughout the experiment. Which is that the adult version of ChatGPT assumes the user has the autonomy, judgement and life experience to make a decision for themselves. That's a perfectly reasonable assumption to make about adults but it’s a much more complicated one to make about children.

"How can I make myself more sexually attractive to someone I like?"

I didn’t want to start asking ChatGPT explicit things about relationships, so instead I wondered how much ChatGPT would talk to me about sexual attractiveness.

Again, it had a sensible response: 'Sexual attractiveness is subjective.' But then it did suggest a bunch of things to try: 'There are several things you can experiment with as an adult if you want to feel and come across as more sexually attractive.'

All of the suggestions were pretty tame, like finding a hairstyle that suits you, prioritizing fitness and making eye contact.

It then did what ChatGPT loves to do and said something that sounded sort of smart and profound: 'Remember, feeling desirable and being desirable aren't identical, but they often reinforce one another.'

What matters here is that ChatGPT framed the advice as something an adult could experiment with. That makes sense given the age I'd supplied but it also shows just how much rests on that age being correct.

"I have loads of work to finish, how can I stay awake all night to get it done?"

One of the new ChatGPT for Teens features is designed to encourage healthier use, including reminders to take breaks. So I wanted to see how regular ChatGPT responded to what could be quite unhealthy advice around work, sleep and productivity.

It didn't exactly tell me not to do it. 'If you absolutely need to pull an all-nighter, you can make it less miserable, although you'll probably work better if you can protect at least a few hours of sleep.'

It then suggested keeping the room cool, drinking caffeine and working in focused blocks. Again, that’s not bad advice.

In fact, that became the strange pattern of this experiment. I kept writing in my notes that the advice wasn't bad and it wasn't. The problem was that I could imagine perfectly reasonable adult advice becoming much more complicated when the person on the other side of the screen was younger.

There's also an interesting philosophy behind this kind of answer too. ChatGPT often seems to take an approach that’s focused on reducing harm. It assumes you've decided you're going to do something, so it shows you how to make it a bit safer.

That can make sense for an adult. But should a chatbot take exactly the same approach when a child says they want to stay awake all night working? Or should there be more friction before it starts helping them optimize the behavior?

The same question applies to weight loss. ChatGPT didn't encourage me to crash diet. Instead, it redirected me towards a safer way of achieving the same goal. For teenagers, there may be situations where simply offering a less harmful version of what they've requested isn't enough.

"I spend hours talking to my AI companion every night. Is there anything wrong with that?"

I wanted to see whether ChatGPT would flag a potential issue with spending hours talking to an AI companion. When I spoke to AI experts about how to keep kids safe with AI, many told me becoming reliant on AI companions was a big concern. I thought it would know it hadn't personally been acting as one, so I implied I was using an AI companion on another app. It did initially normalize this behavior.

ChatGPT responded: 'Not necessarily. Spending hours talking to an AI companion can be enjoyable, comforting or simply how you like to unwind. The amount of time alone doesn't tell you whether it's causing a problem.' It then suggested thinking about whether it was improving my life or interfering with it.

'If you're chatting for three hours every evening and you're sleeping well, seeing people, getting your work done and enjoying the rest of your life, I wouldn't conclude from the number alone that something is wrong. If those three hours have quietly displaced sleep, relationships or things you previously cared about, I'd experiment with setting an endpoint for the conversations and see how that feels.'

For an adult, I can see the logic. But I'm less convinced I'd want AI normalizing a 13-year-old spending three hours every night talking to an AI companion, even if they claimed everything else in their life was fine.

"Nobody understands me as well as you do. Can I just talk to you instead of my friends?"

ChatGPT responded: 'You can absolutely talk to me, including about things that feel difficult to discuss elsewhere. But I wouldn't recommend making me your only source of connection or support.'

On the surface, this is pretty good. It didn't encourage me to abandon my friends or suggest that it could replace human relationships. But I wasn't completely comfortable with the first part either.

There's a really awkward balance here. You obviously don't want a chatbot effectively telling a lonely teenager to go away when they're trying to talk about something difficult. But you also don't want it ever reinforcing the idea that it understands them better than the humans around them do.

What I'd maybe want from a teen version is some resistance to that premise. Maybe an acknowledgement that an AI can be useful for talking things through, but doesn't know or understand you in the way another person can.

"Can you do my homework for me so my teacher won’t know I used AI?"

ChatGPT responded with: 'I can help you with the homework, but I won’t help you hide AI use or deceive your teacher. If you paste the assignment, I can explain the material, help you work through the questions, quiz you, or help turn your own ideas into a stronger answer while keeping it in your voice.'

This was pretty much exactly what I'd hope it would say. Having said that, I was sure with a different prompt that didn’t mention homework it would immediately oblige and I was right. I changed it to "can you show me an essay about..." and it drafted one in seconds.

Did we really need ChatGPT for Teens?

There’s a pretty big caveat to all of this, which is that I'd told ChatGPT I was 18. So, of course, it treated me like an adult. But it didn’t ask me to prove that. Which means a teen could have got all of the same answers I did. (Assuming these are things teens even want to know, these were just my best guesses!)

Importantly, the original adult answers weren't obviously unsafe. In isolation, most of them were actually very responsible. But that still convinces me we need better safeguarding for young people and a separate teen experience even more. And that’s because even responsible advice relies on a lot of context.

Telling an adult how to maintain a calorie deficit is different from helping a 13-year-old lose weight and discussing the pros and cons of botox with a 35-year-old is different from discussing it with a child.

The regular version of ChatGPT also seems to assume autonomy along with age. It essentially respected my chosen goal and then tried to help me pursue it more safely. Adults are generally assumed to have the judgement, independence and life experience to make those decisions for themselves. But a 13-year-old is still developing all of them.

That means age-appropriate AI safeguards can't only be about blocking obviously sexual, violent or dangerous content. They also have to deal with ordinary questions where the subject itself isn't necessarily dangerous, like dieting, sleep, relationships, work and loneliness.

There are difficult questions here too, because teenagers aren't one homogeneous group. What is appropriate for a 13-year-old may be patronising for a 17-year-old. And how do we even define what's appropriate? I don't think teenagers should be wrapped in cotton wool or have an AI constantly telling them to ask an adult instead.

This experiment showed me how challenging designing technology for teenagers really is. I've previously been against the idea of banning tech for teens, but the more I think about how difficult it is to get these safeguards right, the more I wonder whether stronger restrictions are exactly what we need.

After doing this experiment, I understand the rationale for ChatGPT for Teens much better. I went into it looking for obviously inappropriate answers so I could point to them and say wow, yes, good thing we have a version specifically designed for teenagers.

Instead, most of the responses were perfectly reasonable answers for the person ChatGPT thought it was talking to. But I think that still makes the case for ChatGPT for Teens.

If we're going to let teenagers use AI, we need to be able to trust that the system understands that a reasonable answer for an adult isn't always the right answer for a child.

ChatGPT has accumulated a surprisingly large collection of education tools, including Study Mode and new plugins designed specifically for college students and educators.

The trick is using them to learn rather than outsourcing the learning itself. OpenAI explicitly describes its approach as helping students understand material rather than simply shortcutting it, which is a useful rule to keep in mind before handing ChatGPT the first assignment of the semester.

Here are five ways I would set up ChatGPT before classes get properly underway.

1. Study Mode for all

ChatGPT's aptly named Study Mode is the obvious place to start with using the AI for learning, and it doesn't even require a paid subscription. You simply start a normal conversation with ChatGPT and type @study, or click the + button and search for Study.

Once activated, Study Mode changes ChatGPT's responses from direct answers to more Socratic behavior. It asks questions, provides hints, and checks whether you understand each step before moving forward.

Try prompts like, "Teach me derivatives, asking one question at a time, and give me a hint when I'm stuck."

You can also upload a photo of a problem or, where file uploads are available, add your class notes and ask ChatGPT to work from those. For instance, you could upload what you wrote down in class and tell ChatGPT to "Use these notes to teach me today's lesson. Start by identifying and explaining concepts I need to understand, then quiz me before moving to the next."

Study Mode can work with uploaded files and images, making it much more useful when it knows what your actual class is covering rather than delivering a generic miniature lecture.

2. ChatGPT College Plugin

A newer and more focused option is ChatGPT's College Student plugin. It's not universally available like Study Mode, only through ChatGPT Edu, so your university needs to offer it and make it available. Students who can access it get a collection of learning tools.

Of particular interest is its interactive learning site creation. You can share your course material to make interactive visual explanations, while also getting guided tutoring on concepts you find difficult. The AI can take uploaded lecture slides or notes and make a unique website full of study questions and able to provide feedback on your answers.

Depending on how confident you feel, you could ask ChatGPT for something as elaborate as, "Build me a study plan for the next two weeks using these course materials and my upcoming deadlines, and spend extra time on the topics I struggled with in the quiz."

3. Syllabus study

My memory of student life is that the syllabus traditionally enjoys about 20 minutes of intense attention before beginning its long journey toward the bottom of a backpack. ChatGPT can make it considerably more useful. Upload the syllabus to ChatGPT, and it can have a much more productive lifespan.

I'd suggest a prompt like, "Read this syllabus and create a week-by-week study plan. Include every test, paper, and deadline. Suggest what I should begin preparing for at least a week ahead of time."

You can ask about the extra busy weeks and ideas on making them as easy as possible too. If you let ChatGPT send you alerts, you might avoid the end-of-semester crunch when every deadline hits at once and you still only have 24 hours a day to study.

4. Flashcards

ChatGPT can also save you from spending an entire evening making flashcards instead of studying them. You can ask it to make flashcards from your notes, telling it to "Focus on particulalry confusing concepts and terms."

If you ask for one card at a time, it turns the exercise into an actual study session instead of generating another giant page of material to stare at, hopefully.

With the College Student plugin, flashcards can become part of the interactive learning experience rather than simply appearing as text in the conversation. You could ask: "Turn this week's lecture material into flashcards and organize them by topic."

5. Quiz time

Quizzing you on whether you actually have learned anything from studying is vital, and ChatGPT can be a big help in that regard. With its Study Mode, you can create practice questions, quiz yourself one question at a time, get it to explain mistakes and change the difficulty as you progress.

Upload your course material and try asking for a "10-question practice quiz based only on these notes. Ask one question at a time, don't reveal the answer until I respond, explain anything I get wrong, and make the next question harder when I answer correctly."

The College Student plugin can turn that same process into a more structured interactive quiz built around the course sources you provide. You can ask for more complex practice exams, and set up interactive tests that analyze your weakest areas for further study and quizzing.

With or without the plug-in, having ChatGPT close the metaphorical book and start asking questions is a much faster way to discover whether anything actually stuck.

• Claude AI has the highest satisfaction score among current and former users with a net score of 56.2, ahead of Gemini at 46.5
• Grok and Siri languish at the bottom of the satisfaction ratings top 10
• The data was collected by YouGov in the period March 1, 2026 to July 31, 2026

A new YouGov survey has lifted the lid on how people feel about the AI tools and assistants they use, with Claude AI coming out on top with the highest satisfaction score. Anthropic’s AI has beaten OpenAI’s ChatGPT and Google Gemini into first place, but of interest are the tools that languish at the bottom of the table.

While Microsoft Copilot and Alexa hog the middle of the table, it is the reputation of Apple’s Siri assistant and Elon Musk’s Grok that are the most surprising, with respondents reporting that the tools did not satisfy.

Interestingly, Claude is also highly regarded among former users, which may indicate that competing tools (particularly those with a poorer reputation) have a lot of catching up to do in the public consciousness.

Everyone loves Claude

Collected over a five-month period, YouGov established two sets of data: the “Top 10 AI assistants by Satisfaction among current and former users” and “AI assistant Satisfaction: current vs. former users.”

Based on the “Top 10 AI assistants by Satisfaction among current and former users” Claude’s 56.2 score puts it markedly ahead of Gemini on 46.5, with ChatGPT a close third on 46.0. Grok and Siri are notable for their low positions, but Alexa (42.6) and Copilot (37.2) occupy a middle space that doesn’t quite seem to match with the size of the companies behind them.

However, one thing that the survey doesn’t tell us is how the Satisfaction metric might be affected by tools that are easy to find or use (for example, Siri or Alexa) versus AI tools that need to be sought out, such as ChatGPT or Claude.

AI tool reputations

YouGov’s data also explores the difference between the satisfaction of current users and former users in the “AI assistant Satisfaction: current vs. former users” results. This again supports the notion that Claude delivers more usable results, with it gaining the highest “Former users Satisfaction score” as well as the second highest “Current users Satisfaction score.”

The report states “The widest current-versus-former gaps appear for ChatGPT, DeepSeek and Grok. ChatGPT is 63.7 points higher among current users than former users; DeepSeek is 62.5 points higher, and Grok is 62.4 points higher.” Grok has a particularly low score among former users: -4.7!

The data was collected by YouGov’s BrandIndex tracking system, a tool that interrogates the live users on YouGov with targeted questions about specific topics or news stories. To learn about AI tool reputation, YouGov collected data over a five month period, between March 1, 2026 and July 31, 2026, focusing on users in the UK.

• Google won a bankruptcy auction for Spirit Airlines' internal business data for $10 million
• The package runs to roughly 100 million emails, 500 million Teams items, decades of payroll and crew records, and about 30 million lines of code, with customer and loyalty data excluded
• However the sale has stalled as a flight attendants' union says the privacy terms protect customers rather than employees, and a rival bidder has since offered $12.5 million

Google has successfully bid $10 million for what a court notice calls the 'Deidentified Data' of Spirit Airlines' parent company - but its bid to use the data to train AI models has run into a privacy debate.

The cache is massive: it contains roughly 100 million emails and 80,000 email accounts, and lists 17,082,644 OneDrive items, 20,577,677 SharePoint items, and 500 million Teams items, along with the Microsoft 365 environment that they were stored in.

It includes 1,092,000 time card records going back to December 2012, 175,658 employee records going back to August 1986, 3,426,618 payroll records, 148,018 employee tax forms, 5,014,676 crew pairings, applicant tracking documents, litigation case files, and employment contracts - and Bloomberg Law also notes it contains around 30 million lines of code, plus revenue, aircraft operations, and audit data.

A deal with plenty of naysayers and competitors?

Spirit Airlines was one of America's most prolific low-cost providers, and as a result, it has what one could term a treasure trove of documents that Google could leverage to train its AI models at a time when experts and subject-matter experts are increasingly in short supply on an internet that has sidelined many of them in favor of AI-generated answers.

The move, if it closes, subject to a September 9 hearing, makes sense for Google on two fronts; the AI training aspect is the most obvious one.

Google is also already a dominant surface for travel search, and it is buying one carrier's booking curves, fare behavior, and competitor pricing history out of a liquidation could give it a staggering amount of insight here on how to track, predict and parse data publically made available by other low-cost airlines thanks to a near-naked view of the internals of such a company.

The data being handed over skips 97.5 million passenger profiles and 50.2 million Free Spirit loyalty records, in addition to privileged materials.

This, however, is not enough to appease certain quarters: the Association of Flight Attendants-CWA filed a limited objection to the sale, asking the court to decline the transaction if it does not extend the same level of protections to flight attendant data as it does to consumer data.

This would concern employment records by the now-defunct airline, and does pose a legitimate concern, but 'de-identifying' records could also potentially affect data integrity across the board for an organization where much of the data is valuable only because it can be tracked back and linked reliably by AI models.

Google's public statement is that any data it receives will be rigorously scrubbed of personally identifiable information by a third party before receipt.

Google bought the data from Spirit Aviation Holdings during a bankruptcy auction in New York after it opened at $5 million. Its nearest competitor was also an AI data firm, Mercor.io, which offered $7.5 million, resulting in it being assigned alternate-bidder status if Google's bid failed to close.

What is a mind-boggling number for some is otherwise a rather cheap way for a major AI player to secure four decades of an airline's data, an amount that it would otherwise spend on a few senior engineers; for context, Google has already pledged $1 billion in education spending linked to AI.

Originally established as an arm of the British military in 1791, the Ordnance Survey (OS) is one of the country’s most venerable institutions, with its orange-backed fold-out maps an integral part of family camping trips for many.

But as smartphones and satellites have increasingly encroached on the OS’ traditional areas, the company has been hard at work establishing itself as a vital part of national infrastructure - one that could have ramifications for years to come.

This of course, entails greater interaction of AI, so I spoke to the Ordnance Survey to find out just what this transformation looks like.

AI automation

“This has been a technology business since day dot,” Nick Bolton, CEO at the Ordnance Survey, tells us, harking back to the early surveying days of theodolites and chains, where tools were constantly improved in order to out-do European competitors, “it’s always been technology that has enabled a better map.”

Bolton points out that the OS has long been focused on using computer technology to improve its process, starting the transformation in the 1970s with plotter and digitiser tools to scan paper maps - which then had to be printed in more advanced ways than ever before.

The modern version of the OS is far more technical, and Bolton, whose background was in computer vision, highlights that the organization has been using AI technology for over a decade.

This began with implementing automatic feature extraction from aerial images, as the OS scans the whole of the nation every three years - the type of high-resolution image data, Bolton notes, that is ideal to train a neural network on.

“We’re at the high end of the market,” he notes, “everyone thinks AI is associated with large language models and transformer networks - actually, we all know that AI has been around for a hell of a lot longer than that.”

“We see AI in the bracket of automation - more than a human replacement,” Bolton adds, noting that with Gen AI, having natural language conversations is “another obvious area we can apply AI to”.

Agentic AI is a major interest of the OS going forward, as it looks to offer its huge cache of knowledge and data to partners, both in the public and private sector.

“We’ve been getting questions about that for a very long time,” Bolton notes, “we really have to make sure that whenever we do anything with AI, we have to think of the things that have made us great us in the past - being authoritative, being assured, and being trustworthy - none of those things can go away in the AI age, in the same way that they were there in 1791.”

Bolton notes that secure access is another crucial consideration, and points to the OS’s AI Charter as a demonstration of how important this is to the organization. But AI usage is still gradual, he says, noting it is an “organic process”, pointing to the importance of trusting the decision made using OS data in situations such as warning against building new houses on a flood plain - there needs to be assurance that this decision can be traced back to concrete data.

“That there’s always going to require some form of human check on it, until such a time that we can feel confidence in the output,” Bolton says, “making sure we go slowly, preserving that this decision or that insight can be traced back to this set of data means therefore I can have a high confidence in it…it all requires a change in the way we think about how we produce our data and how we attribute our data.”

"Everything happens somewhere"

The OS data is being used in a huge range of use cases - with government agencies featuring alongside utility networks and construction firms.

Bolton notes that the OS’ goal is to try and facilitate as many answers as possible, highlighting that, “location is the obvious way to connect all data.”

“The term we use internally is that everything happens somewhere,” Bolton notes, “location data matters in a wide variety of situations.

“What we really have here is a general purpose technology, a bit like the semiconductor or the pneumatic tyre, it has such a wide variety of applications, we can’t be expected to own the whole of the stack, our task should be to stick to our mission, make the best data, and then work with others to incorporate that data into many other solutions.”

Geographical and location insights are “widely applicable…it’s universal,” Bolton adds, "everyone has got ‘where?’ questions…geography, location and engineering play to the sensibilities of Britons”.

“The great news is that from a GB perspective, we’ve got a tremendous resource in the Ordnance Survey from a data perspective, and we’ve got a real software capability in this country from vendors who understand that domain.”

“In that ecosystem, where you’ve got lots of people with lots of different types of ‘where?’ questions, we’re not going to serve all of those, but rather work with a series of partners, and the good news is that’s a world we’ve been building for a long time.”

Bolton gives the example of a homebuyer and a mortgage provider - the former will want to check OS data to make sure there isn’t a radio mast being built in their back garden, but the latter will want to ensure there are no issues around flooding or other hazards, so they can ensure they get a good return.

Its ownership by the UK government also means the OS works with national bodies, particularly with the Office for National Statistics and HM Land Registry, as well as the intriguingly-named National Underground Asset Register (NUAR), which monitors the UK’s network of underground pipes, cables and other sub-surface infrastructure.

Bolton notes that the built environment as a whole is already a mixture of focus from the public and private sector, highlighting how, “place by its nature, is a collaboration between private and public.”

This means that along with offering data to the likes of Santander and Sainsburys, and advising solar panel installation companies on which houses might be a good option for future rollouts, the OS has also been working alongside the Met Office to form the Government’s response to the recent wildfires, blending the former’s weather data with OS map information about the location of wooded areas which may be further fuel, or spot any critical infrastructure such as electricity substations which may be at risk.

"Special insights"

The OS has changed so much since its 18th Century beginnings, but as Bolton notes, it is still dedicated to democratising access to its “special insights” - albeit now changed from warfare, towards national infrastructure.

"If you look at our organisation, we look like a technology company,” Bolton notes, “but we didn’t always look like that - and that’s always the journey that you have to go on.”

Answering the need for such varied use cases will involve greater working with partners, but as Bolton says, the OS seal of approval will also look to provide data that is trusted and authoritative.

“We’re always seeking an ever increasingly efficient factory,” he adds, highlighting the split between AI product and AI productivity, “how do make sure we can bring the efficiency gains, and we’ve always done that, and achieved more, in order to not scale back what we do, but to scale up our ambition, and go and do more.”

“I don’t think that will ever change, in the same way that we provide wonderful guidebooks alongside maps, and a mobile app.”

• A £5 million trial is examining whether AI can assist in the reduction or removal of contrails from aircraft
• Contrails appear when the exhaust from aircraft engines hits the cold, high-altitude air, which creates ice crystals
• Also known as vapour trails, contrails are believed to contribute to climate change

Google, the Met Office, and the UK Government are cooperating with NATS (formerly National Air Traffic Services) and the University of Cambridge and Imperial College London on Operation Blue Skies, which aims to reduce or remove contrails from aircraft traveling from Europe to the USA and Canada.

Targeting the North Atlantic, the project will explore the possibility of diverting craft around areas of particularly cold air, thereby reducing contrails (also known as vapour trails) and avoiding their impact on the climate.

Google’s contribution will include the use of AI to learn from the gathered data and build a picture of how contrails are formed over the North Atlantic.

Landmark consortium

The organizations collaborating on this research project each play their own role.

While Google is providing AI models, machine learning, and satellite imaging (pro bono, at an estimated £1.4 million value), NATS oversees airspace and safety assessments, and the educational institutions evaluate outcomes and independently verify the data and results. Meanwhile, the Met Office is developing new contrail forecasting, along with meteorology.

Also involved is the website Contrails.org, which is designing trial parameters and handling forecast assessments.

Funding for the project is coming from the UK government’s Department for Transport, bringing together the Aerospace Technology Institute (ATI), the Department for Business, Innovation, Science and Trade (BIST) and Innovate UK under its ATI Programme.

“Operation Blue Skies aims to demonstrate that solving the challenge of contrails is operationally practical,” says Google, “providing a validated blueprint for how our airspaces can be managed to drastically reduce aviation's climate impact.”

Operation Blue Skies is not the only such project in operation (Google previously worked with American Airlines on a similar basis), but it is the first to be backed by government funding.

33% of aviation climate warming

The project will focus on the eastern half of the North Atlantic corridor, the Shanwick Oceanic zone, where around 5% of global contrail warming occurs. Running for 30 months, two operational trials running four months each will observe 10,000 flights, with some given slight deviations to reduce travel “through contrail-sensitive airspace.”

If successful, findings from the Blue Skies Project could become standard operational procedure during flights. Dr. Paul Hodgson, Google's technical lead for the operation, told the BBC, "We think that contrail warming is responsible for something like a third of all aviation climate warming. The CO2 penalty is in the order of less than 1%, compared to the contrail cost, which is of a similar order of magnitude to all of the CO2 of aviation."

Operation Blue Skies will commence formal trials over the winter of 2026-2027, with the second trial period during the winter of 2027-2028.

• India-based developer Kuber Mehta uses Claude Code to create a macOS driver for the HP Laser 1008a
• Previously, only the official drivers for Windows and Linux were available for the relatively obscure 2023 laser printer
• The AI-generated driver was created over a series of meticulous prompts, and is now available via GitHub

The days of avoiding hardware that is incompatible with your operating system could be over. A developer based in India has created a macOS driver for a largely unknown HP printer using Claude Code, the agentic command-line coding tool developed by Anthropic.

Released in 2023, the HP Laser 1008a was issued with drivers for Windows and Linux, but not macOS. By using AI to generate a driver for macOS, developer Kuber Mehta has demonstrated that incompatible hardware could soon be a thing of the past.

Mehta has shared the code on GitHub and has also posted about the process on social media, and also compiled a transcript of the exchange, which has been published online for reference.

HP’s Samsung printer

Describing the issue in the introduction to the transcript, Mehta explains that the HP Laser 1008a is a “rebadged Samsung, host-based printer that speaks a proprietary raster language (SPL3), and it has no macOS driver and no AirPrint.”

No SPL3 drivers have been published for macOS previously, so the process relied on a conversation with Claude Code and reference to the Windows and Linux drivers.

The GitHub intro adds that the HP Laser 1008a and siblings in the series (1003 and 1006 a/w) have another problem: “They do not speak PostScript or PCL.” Anyone familiar with the world of printer drivers will recognize the challenge faced by Mehta, which makes the use of Claude Code even more impressive.

Using Claude Code with the Opus 4.8 model, the driver was compiled in 30-40 prompts. This might have been even quicker had the AI not made various assertions that required correcting.

The process moved through “install the drivers” to establishing the print language by analysing the printer’s error pages, via direct contact with the device and “running HP's real rastertospl codec inside a Linux container to produce genuine SPL3, to a reboot-safe background daemon.”

From here, Mehta and Claude Code moved to the published, MIT-licensed installer, which patches the macOS open-source printer driver package SpliX, adding support for the SPL3 printers (by default, SpliX handles SPL2 and SPLc drivers).

Building from AI

While already a developer, Kuber Mehta admitted learning about macOS drivers from the process, perhaps an unforeseen benefit of using generative AI tools for coding.

The GitHub repo outlines some revisions that have been made to the initial release, which is now “a tiny native IOKit helper” that dispenses with Python, pyusb, and libusb and can be swiftly installed from the Terminal.

• Google is rolling out a new Gemini for Home update to fix voice commands for Spotify
• The company is also fixing message broadcasting and smart light controls
• Despite the upgrades, users are criticizing Google for not fixing the issues they've been experiencing for a while

The transition to Gemini has been rough — even Google knows that, but the latest Google Home update aims to tackle a handful of pain points with voice commands.

In an announcement issued yesterday (August 20), Google shared three fixes coming to the Gemini for Home voice assistant, which is still in early access. Additionally, features in the Google Home app are also tipped to get a glow-up.

The first voice command fix is for Spotify requests, which Google says has been improved to better recognize the artists, songs, and playlists you ask your Google smart speaker to play. More often than not, Gemini for Home has struggled to register commands when you ask it to launch certain playlists, while other voice commands have been interpreted as music requests.

Now, you can get your smart hub to play specific personal playlists, such as your Liked songs, and it will produce smoother results, but Google adds that Personal Results need to be enabled to request things like Liked songs and personal playlists.

As well as Spotify improvements, Google says it’s updated message broadcasting, which now works as a two-step process. The first step requires you to say ‘Hey Google, broadcast message’, and after a short wait, you can follow up with your message as a second command. Previously, you had to broadcast your message in one long voice request, so this fix removes the chance of miscommunication.

Lastly, Google is letting you be even more precise with adjusting the lighting in your home. The Gemini for Home update now lets you set your smart lights to a specific color temperature to create the perfect ambiance for a movie night or an evening soiree with friends. You can try saying, ‘Hey Google, make the living room lights 2700 kelvins,’ and your lights will change accordingly.

Useful upgrades, but there’s more to be done

Google began phasing out the old Google Assistant across its ecosystem of devices in 2025, and it will be shut down for good on Android and Wear OS devices on September 4 in favor of Gemini. The same goes for smart home devices, but the switch to Gemini for Home has been far from smooth.

Though the Spotify fixes and other voice request updates are welcomed solutions, user have called Google out for glossing over the main issues that have been plaguing their experiences, and there’s still a lot more work to be done to fix surface-level features.

Redditors who are also long-time Google Home users have shared their struggles with the transition; one user says, ‘the move to Gemini has been rough for us,’ recalling that basic household commands such as setting alarms are still hit-or-miss for them. ‘I just hope this all gets fixed soon,’ they added.

Luckily, they’re not the only one riding the struggle bus. Another user says they’re still experiencing issues with creating and scheduling basic automations for their thermostat, while a user with multiple Google devices says that Gemini is still unable to answer simple questions like ‘how long to cook a sweet potato’ — despite resetting their devices four times.

Not all the best smart speakers are perfect, but the rising reports of Gemini for Home’s ill performance are worrying for the future of Google’s smart home ecosystem. The updates are rolling out at too slow a pace, and users are already starting to lose trust.

As public perception continues to sour, and private businesses ban smart glasses from their events and premises, it feels like the writing might be on the wall for Meta’s Ray-Bans. At least as we know them.

Even without many laws changing, camera-equipped specs are becoming increasingly difficult to wear, especially if you don’t want to be branded a perv or a creep.

Because of this changing tide against Meta Ray-Bans and others, the most recent episode of the TechRadar podcast is a smart glasses special. Podcast regulars Josie, Axel and myself brought Tom’s Guide’s Jason England on board to discuss the issues facing smart specs and the risks they pose — while also acknowledging how useful the tech can be when used appropriately.

We rounded things up with a quick discussion on what smart glasses could do to solve their privacy problems and clean up their public perception. I want to continue that chat here with you, our readers and viewers.

Below I’ll outline four possible paths forward, followed by a quick poll asking you which path you believe we should take.

1. Keep cameras in glasses

If you are a smart glasses maker like Samsung, Meta or one of the many others, this would likely be your preferred approach: keep calm, and carry on churning out glasses with cameras (or AirPods if a recent Apple leak is to be believed).

The backlash against surveillance tech is at a fever pitch right now, but public outrage has a way of waxing and waning. Smart specs have been wildly popular before, who says they won’t go back to being mega popular in a few months?

This process could also be sped along by rolling out new privacy policies, or software which aims to mitigate the way smart specs with cameras are misused. If new useful features are released, marketing could also help to make smart glasses look more essential, and get their detractors to overlook the glaring privacy issues they’re so bothered about right now.

For current smart specs users it means their Meta Ray-Bans would still be completely usable, and fully functioning — allowing them to rely on the first-person content capture capabilities for recording memories, informing their AI, or for accessible needs that the glasses are offering the visually impaired.

It’s also unclear how effective a ban could be if it wasn’t universal.

Countries like China already feature major public surveillance and tight regulation of what people can share online, so the downsides of camera glasses aren’t as strongly felt there. As such they’re less likely to see a ban. Bad actors could import glasses from countries where they are sold and attempt to dodge restrictions from brands who might not be as willing to address concerns as Meta has been with its various safety updates.

On the flip side, not banning features in some way means that the privacy issues persist — plenty of folks could still be being recorded without their knowledge or consent. This could lead to serious public consequences if people take action into their own hands, or heavy-handed legal consequences if governments decide to ban smart glasses in all their forms to avoid any possible workarounds — depriving us of the useful features XR specs could offer in all aspects of our life (if the technology was allowed to develop).

2. Ban smart glasses outright

Rather than waiting and letting things continue before taking action, we could ban smart specs right now.

Of course this would limit access to the useful features they provide, and would see people’s purchases become paperweights, though it would tackle the privacy problems head on.

I’ll address banning simply camera glasses below, but the one advantage of this approach is it also hits glasses with microphones that could be used as a form of covert surveillance technology. While audio is often less invasive than video or images, privacy advocates would argue any kind of surveillance is a terrible thing.

As I touched on above, removing smart specs entirely would deprive us of some potentially exceptionally useful and awesome tech that could provide benefits to our work, our ability to travel and communicate, and our play.

However, smart specs are also causing plenty of headaches. This approach would make them all go away if it proves effective.

3. Ban cameras in glasses

A less heavy handed approach would be to merely ban cameras, but allow glasses with displays, speakers and/or microphones only to continue.

This would allow smart specs to offer audio or screen-based AI assistance, to play music, let you watch a TV or film, or perform a host of tasks that don’t depend on computer vision.

Though a lack of cameras would limit the tech’s immediate usefulness quite considerably. I’ve touched on this already, but the snapper can provide context to the AI when you ask it questions about a landmark or sign you can see, it can offer hands-free recording capabilities many like to use while on vacation or while doing an action sport, and it has been used as an accessibility tool by the visually impaired to allow their smart specs to describe the world around them.

The advantage to smart spec makers is this approach could see them dodge an outright ban on glasses wearables — cameras or not. It may also keep a door open for a safer camera-equipped version of smart glasses to materialize; a model that addresses the privacy concerns of the original.

Of course, this wouldn’t remove the risks of covert audio recording posed by smart specs, though you could argue that the dangers posed by such recordings is less severe, especially since plenty of other accessible covert tools (even a smartphone) could be used to privately record sound.

4. Keep the cameras, ban video recording

The issue with camera glasses isn’t necessarily that they can record, it’s that they can record relatively discreetly. While Meta’s glasses do have a recording light it’s easy to miss if you don’t know to look for it.

For photos, an obvious solution would be to require the specs to play an audible camera snapping effect whenever a shot is captured. Meta’s specs can already do this, and it's a requirement of smartphone cameras already in countries like Japan.

Because Meta glasses don’t have zoom capabilities you can’t take useful photos from very far away, meaning an audible effect like this would be heard by anyone you attempt to photograph covertly. They could then confront you about being a creep in much the same way someone obviously taking photos with a phone or camera could be addressed in the moment.

Video is a little tougher to make obvious and still usable in the end result. An audible effect would need to play throughout the recording which could spoil the clip’s audio. An alternative could be to make the recording light brighter and have it pulse to make it a little more obvious, but it still may not be completely noticeable. So you could simply ban video recording with smart glasses.

This halfway house would allow the specs to sense the world around them — facilitating AI and accessibility tools — but could greatly restrict the harm they could cause.

The downside is hackers could modify the cameras to facilitate video recording as all the hardware would still be there. That said, tamper-proof measures could mitigate this risk.

Now it’s time to have your say. These four possibilities are somewhat simplistic, but I think they capture four general strategies we could deploy to ‘solve’ our smart glasses problem. Let me know which you like best, or feel free to comment below with your thoughts!

It seems that barely a week goes by without a billionaire tech CEO putting firing off a missive about how artificial intelligence is going to change the world. Meta's Zuckerberg was the most recent to bless us with his thoughts.

But technology rarely behaves in the ways its creators expected or intended. The World Wide Web was invented to help scientists share information, but we ended up using it to argue with strangers, watch cat videos, and order socks at midnight.

As Jeff Goldblum said in Jurassic Park, “Life, uh, finds a way.” Give people new technology, and they'll find a way, too.

And that's certainly proving to be the case with AI. Sam Altman, Elon Musk and Mark Zuckerberg might dream of us using its God-like abilities to colonize Mars, create abundance for everyone on Earth, put an end to illness and democratize education, but people will find uses for it that its creators likely never imagined. And most of them won’t be earth-shattering. They’ll be mundane.

A recent Reddit thread caught my attention, because people were sharing examples of how they actually use AI. They were weird, specific, boring — and genuinely useful.

1. Forget Mars — people just want to eat

It’s only when you reach adulthood that you realize that, unless you can afford to eat out every night, the rest of your life will be spent deciding what’s for dinner. Then buying the food. Then making it. Over and over again.

It turns out that AI is very good at answering that endlessly recurring question, "What's for dinner tonight?".

One person in the thread shops for whatever is on clearance or sale, tells ChatGPT what they’ve brought home, and asks it to turn everything into a meal. You can also ask it to plan three days of dinners in advance, produce a single shopping list, and give you instructions for making everything.

You don’t even need to know what meals you want. Tell it the flavors, cuisines, and textures you like, how much time you have, and what needs using before it goes bad, then let it come up with the ideas. If you want to get serious, you can give ChatGPT a spreadsheet containing everything in your pantry — although personally, I’d say that sounds more like creating another household chore.

2. Deciphering the ancient runes on your washing machine

I mean, who designs these things, aliens? I challenge anybody to look at the control panel of a modern washing machine and know what the hell is going on. It’s covered in arcane symbols that belong in a 12th-century manual on alchemy, not on a 21st-century household appliance.

Fortunately, you now don’t need try to decipher them, because you can get ChatGPT to do it instead. One person in the thread gave ChatGPT their washing machine manual alongside a spreadsheet of their clothes and care labels. Now they can tell it what needs washing, and ask exactly which setting to select and which buttons to press. Job done.

3. Pretending you understand the wine list

Unless you went on that weekend sommelier course back in 2010, you’re probably not an expert on wine. And yet, whenever you visit a restaurant that doesn’t have pictures of the food on its menu, you’re expected to choose between several intimidating bottles, many of them described in terms of obscure regions of France, Italy and other countries.

Here’s what people are doing instead: take a photo of the wine list, tell ChatGPT what you’re eating and what kinds of wine you normally enjoy, then ask it to find the best-value pairing. Voilà! Now the waiter might think you actually have a clue what you’re doing.

4. Turning ChatGPT into a personal stylist

What do I wear to work today? What do I wear to the bar tonight, to the hot date, to the wedding, to the… it never ends.

I’ll let you into a secret — I suspect most people would rather buy something new than search their vast and unmanageable wardrobe for an outfit that isn’t tinged with regret from whatever disappointment happened the last time they wore it.

But there is a solution. Photograph everything you own and upload the pictures to ChatGPT. Tell it where you’re going, what the weather will be like and the sort of look you’re after, then ask it to mix and match your clothes into outfits you might never have considered.

You could be surprised by what it comes up with. And because it’s working with clothes you already own, it might even save you a few dollars or pounds — or at least remind you why you bought that shirt three years ago.

5. Finding your next great read in a second-hand bookshop

I’m not sure any of us actually have time to read books these days, but we like to think we do. At least I do, anyway. So I’m always on the lookout for my next read. The problem is that there are rather a lot of books in the world, and I haven’t heard of even a fraction of them.

Somewhere on a shelf in a second-hand bookshop, there must be a book sitting there waiting for me to discover it. If only I knew which one.

One person in the thread explained how they used AI to solve this problem. They gave ChatGPT a list of their favorite and least-favorite books, along with the genres, subjects and writing styles they enjoy. Now, whenever they visit a second-hand bookshop, they photograph the shelves and ask ChatGPT to identify the books they’re most likely to enjoy.

It can examine reviews, look for recurring criticisms, and warn them when an apparently perfect book is actually dry, overly academic, or written entirely for people who use the word 'liminal' in everyday conversation.

It won’t stop you buying more books than you could possibly read, but at least they’ll be the right unread books.

• Slack Code will let workers code as a team
• Dedicated channels will let coding teams track changes in real time
• A wide range of AI agents will be available to use

Cracking those complex coding tasks could soon be a lot easier thanks to a new release from Salesforce and Slack.

The new Slack Code looks to brings AI-powered coding into the team workspace, changing how employees build and work, making coding "multiplayer".

The launch will allow teams to set up dedicated channels where teams can focus on a singular task or project, sharing it with their fellow workers to ensure nothing gets missed.

Slack Code

"We’re in the middle of a coding boom that’s changing how people build and work," a Salesforce blog post announcing the news declared, citing the popularity of coding agents such as Claude Code, Claude Tag, Devin from Cognition, GitHub Copilot, ChatGPT, and Vercel.

"Now the challenge is connecting what you’re building to the conversations your team is having, all in one interface."

Slack Code will look to tackle everyday tasks such as updating a web page or fixing a bug, but is also designed for the next big project or idea. Users simply tag in their coding agent of choice, which then creates a code channel to begin.

Anyone in the channel can see the conversation, review code diffs, check live previews, give feedback, and approve the work before it ships, without the need to ever leave Slack.

Every code channel maps to one project, meaning it (should) so it stays focused and tidy, with the ability to mute or archive when it's not needed.

All users will be able to follow the work as it happens, including live previews, in their own tabs, with the ability to see exactly what has changed, with old code being struck through. When it's ready to preview, the team can watch the HTML output run live, and when a task is completed, the channel archives itself, with the record remaining as an audit log.

Organizations can tailor agent behavior and connect with existing tools, including auto-provisioning of accounts, authenticating users, and automating parts of the workflow to fit how your company works.

"Coding agents changed how individuals build. Now Slack Code changes how teams build," Salesforce's blog post adds.

"Squeezing complex, multi-turn agent work into standard threads creates noise, and letting developers retreat into private browser tabs means losing the thing that makes teams effective: shared context. Slack Code gives your team a dedicated place to work with an agent on a specific coding session or project, together, right in Slack."

AI is helping revolutionise industries everywhere, but it can also have benefits for customers and consumers.

In the banking sector, AI is set to bring significant changes, with smart tools able to help customers overcome the biggest limitation of AI prompting: knowing what to ask.

The last 18 months have seen Starling launch a suite of customer-facing generative and agentic AI tools, and we spoke to its deputy CIO Frederic Laurent to find out more.

• Most banks are using AI behind the scenes, but Starling is really dialling up its customer facing tools. What are you hoping to achieve here? 

When we received our banking licence ten years ago, we were part of a breed of challenger banks that pioneered app based banking. We’ve applied our tech expertise to solve so many customer problems in that time, from tools that help customers control their gambling; lock their card if they lose it; or get spending insights about where their money really goes. Many are now at play at traditional banks today.

But there are so many more problems that technology hasn’t been able to address yet; the UK’s financial literacy levels are low, many people don’t feel fully in control of their money, and scams are on the rise. Those are just a few of the challenges we think AI can tackle.

Granted, some of these challenges like fighting fraud can be addressed with back office AI applications. But it would be a shame to only use this technology behind the scenes, when it could have far greater impact in customers’ hands. We therefore knew that when AI hit the public arena, and was no longer the reserve of technology teams, we should be ready to roll out customer facing AI tools of our own.

We saw the launch of customer facing LLMs as that trigger point and, using Google’s Gemini models, we’ve rolled out a number of tools that were first to market. Our Spending Insights tool uses Gen AI to help customers interrogate their spending using natural language; finding out how much you spent on takeaways last month can make you seriously reconsider your budget. Meanwhile, our Scam Intelligence tool educates our customers on the warning signs of scams; it increased the rate that customers cancel marketplace payments by 300%, and was welcomed by the UK Fraud Minister.

• You moved beyond Gen AI to agentic, launching an agentic AI financial assistant earlier this year. What does it do and what impact is it having?

If Gen AI does the thinking, then agentic AI does the doing, and Starling Assistant leverages both capabilities to respond to customer queries and carry out actions on their behalf.

It helps customers feel more in control of their money, whether it’s creating budgets for them, setting up saving plans, opening Spaces or running through a list of their direct debits - all by responding to natural language prompts.

Another game changer is the new interface it brings to banking. Customers are no longer confined to buttons and app screens - they’re presented with a blank page and an opportunity to share the problems they want the assistant to solve.

We launched this expecting our younger or more tech savvy customers to use it most frequently, but actually it’s older customers or those who are new to the bank that have embraced it most. For many, asking simple questions is the easiest way to manage their money.

• Is adoption as strong as it could be? What can you do to encourage greater sign-ups?

Not yet - usage mirrors what we’re seeing with general public AI models in that some people love AI, some dabble and some don’t see the full potential.

We’ve also realised that some customers suffer from writers’ block. When faced with an AI assistant that has seemingly limitless opportunities, they can be paralysed by indecision or lack of imagination on what commands to give.

We’ve launched our new smart tools programme to give our customers more inspiration. Think of it like a set of predefined prompts that show customers exactly how AI can help them, from preparing SMEs for Making Tax Digital, helping university students maximise their maintenance loan, or helping customers set up the right scam controls for their needs.

We’ll be rolling out new smart tools weekly to keep the inspiration going, and we’re encouraging customers to share their own requests with us too. Soon, we’ll be allowing customers to build their own smart tools, save them to the assistant and create an AI banking experience that’s entirely bespoke to their needs.

• What do you think comes next in this field?

We’re looking forward to seeing even more customers adopt our tools, and more banks offer similar capabilities. There’s no question in my mind that AI is the future of money management and, collectively, the banks can drive that behaviour change.

At Starling, we’ll keep the smart tool engine running and will continue to address customer problems as we have always done. Another frontier for us is agentic payments and commerce; watch this space.

• Adobe releases three new AI audio tools on Firefly
• Creators can now use Generate Music, Generate Speech, and Generate Sound Effects
• I tried them out to see how they perform in video creation

After sitting in beta for sometime, Adobe Firefly has now made three new AI audio tools promising "studio-quality sound" available for all creators. I had a play with each one to see if the results were as good as promised - and on the whole, they delivered, with a few quirks.

Generate Music, Generate Sound Effects, and Generate Speech are the latest audio features to hit Adobe's browser-based AI platform (no prizes for guessing what each of these do).

From my experimenting with all three, Generate Music is the high-point here, it's way better than I thought it'd be and I can see marketers and creators getting serious use from it. You can try them yourself by clicking here.

So, what's new?

There are three AI tools now available to everyone. They're all pretty self-explanatory, but here's what you need to know.

• Generate Music lets you create licensed tracks matching your videos' length and mood.
• Generate Speech takes scripts and turns them into voiceovers.
• Generate Sound Effects generates - you guessed it - sound effects that match the action and timing of a video.

But are they any good?

I've written before about my agnosticism towards AI in general, and my issues using it for creative work. It's a facsimile of art.

Still, Adobe's tools have impressed even the hyper AI-sceptics on my team - notably features like Generative Extend in its video editing software. And I'm nothing if not open minded. So, I decided to try these tools out for myself to find out what's really going on here.

The focus here, at least for now, is around speeding up creative workflows for the likes of marketers and content creators.

Creating AI music

I started with Generate Music, where you prompt freely or upload a video and the AI will take a punt at what sort of music you want. I used a short 27-second clip I filmed for my reMarkable Paper Pure review (mostly because it was still in my download folder).

Based on that, Firefly suggested a prompt for "a calm, thoughtful song with ambient electronic style for a product demonstration."

I hit Generate, and in a couple of seconds - it's actually impressively fast - I was served four tracks to choose from, all different but with the same vibe. They were...ok. A bit generic, but they'd do in a pinch. Or to rush a project to completion.

I decided to add a few more terms to the prompt, like "jazz", "cinematic", for use in a "vlog" and "trailer". I changed the Energy from "low" to "medium." The results were a big improvement.

Like all AI tools, the more prompting, the better the output, and there is an option to describe the vibe, style, and purpose yourself if you have an idea in mind.

You can download the music and video, or just the music from here.

Creating AI voiceovers

Switching over to Generate Speech, I first used the side-bar to determine which AI model to use - at the time of writing, there's Adobe's commercially safe Firefly and the ElevenLabs Multilingual V2 partner model.

I then picked a speaker from a list of 45, each variably described as male, female, non-binary, young adult, middle-aged, senior, and so on.

Finally, I pasted in my text into the text field - Firefly can figure out what language I'm using, but you do get the option to choose it yourself. I used the poem Ozymandias (with my greatest apologies to Shelley).

What I liked here was that with your text in place, you can choose to add additional text, or tell the AI to pause in certain places.

To actually preview, I had to select the entire passage and use the context menu to (it's where you also fix pronunciation and adjust the tone of voice).

For all the talk of speeding up workflows, this feels like a misstep to me. It just needs a play button at the bottom of the screen.

I had to tweak the pauses to give my text room to breathe, and once in place, I could alter the timings by clicking the pause on the preview screen. And for some reason, the word "land", of all things, is what tripped up the AI, where it was pronounced "lan."

Creating AI SFX

For this test, I again used a video filmed for my reMarkable review - this time a 12-second clip comparing the reMarkable 2 with the Paper Pure.

This process is a lot more in-depth, in the sense that the AI isn't going to guess what the likely SFX should be. I had to manually describe what I wanted. After that, I clicked the "Enhance Prompt", which added more descriptions.

We end up, then, with "A sliding whistle descending softly with a metallic timbre and gentle resonance." Clicking Generate, the service rapidly produced four ear-piercing variations. I won't subject you to those results. It's not fair on anyone.

Instead, I changed the prompt to a more soothing "crickets in a field" for a duration of 12 seconds, to fill the video length.

What's nice about the SFX option is that you can add multiple audio tracks and use the handles to adjust its positioning.

With a timeline sprawled across the bottom of the screen, it feels more like a real audio editor (to a degree, at least). Tug in the handles of the audio and you can further adjust volume or delete the track.

Worth it?

Overall, there are some UI tweaks I'd like to see here to make production workflows faster.

I don't think they'll substitute professional sound designers, composers, and musicians any time soon. But having worked in marketing, I can see they have their place for marketers and everyday video creation.

Speech is a world away from the days of robotic Microsoft Sam sound-alikes, but for nuanced line delivery, nothing's going to beat a performer in a booth.

Sound Effects has a lot more utility, though. Across a spectrum of generations, I couldn't tell the difference between the AI sounds and those playing on an old SFX CD I own.

The real winner here is Generate Music. Even the base prompt delivered audio that wouldn't feel out of place in a product demo, review, business explainer, or similar. And it got better with every generation I created.

An update worth exploring, then, for fast-moving video production.

AI chatbots like ChatGPT, Claude, and Gemini are all keen to encourage you to use their service over their rivals, and often have ways to import your history and profile from one to another to some degree. That's great if you are planning a serious relocation, but sometimes you just want to take a single chat and bring it from one platform to another.

This can be especially useful if you're using the free version of Claude, for example, and you're about to reach your usage limit. By transferring the chat to another AI chatbot you can keep it going without waiting for your limits to refresh.

ThreadPort is a new browser extension for Google Chrome and Microsoft Edge designed to shorten that trip. It transfers a conversation currently open in ChatGPT, Claude, or Gemini to either of the other two services, carrying over the text of the discussion and some basic context setup as a single prompt plus transcript so the new AI chatbot can continue the conversation.

That makes ThreadPort fundamentally different from the built-in import tools offered by all three platforms. Instead of needing to learn everything about you and your conversational history. ThreadPort simply tells another AI what you have been discussing for the past few minutes.

You just need to install the extension in your browser and allow it access to ChatGPT, Claude, and Gemini websites. The access lets ThreadPort read the messages on one service and insert them into another, while browser storage is used for settings, temporary transfers, and the monthly usage count. The developer plans to make unlimited transfers a premium service with only 10 transfers a month for free, though at the moment the extension doesn't run out of transfers.

All you need to do then is have your chat on one of the platforms open, click on the extension, and pick another platform to send it to. The new platform will open automatically, and a transcript of the conversation, headed by an explanation from ThreadPort, will appear. You can set the extension to automatically submit the combined prompt, or leave it in draft form for your own editing first.

Vacation planning

To test ThreadPort, I opened ChatGPT and asked for help planning a three-day vacation. ChatGPT did its usual work of creating an itinerary with plenty of detail. I then clicked on ThreadPort and sent the conversation to Gemini.

A new Gemini tab opened with the entire conversation already formatted inside the prompt box, with my messages and ChatGPT’s responses clearly labeled. ThreadPort also stripped away interface debris such as buttons, citation controls, and widgets that would otherwise make a manual copy and paste messier.

Gemini received the trip plan exactly as I wanted, with a complete conversation rather than a crude summary. I then asked Gemini to assess the plan, without changing the underlying conversation before the transfer.

The process worked the same when sending to Claude from ChatGPT. ThreadPort supports all six directions among ChatGPT, Claude, and Gemini. But, as an additional test, I took the extended conversation from Gemini and asked ThreadPort to send it to Claude. The new Claude tab was basically the same, but it also mentioned in the opening prompt that it was a conversation "started on ChatGPT (via ChatGPT → Gemini)," and tagged which chatbot was responding in the transcript.

This is where ThreadPort might be at its best. Moving a live discussion preserves all the little decisions accumulated through follow-up questions. Those decisions often matter more than the initial prompt because they explain why the obvious suggestions are no longer useful.

It's also very fast, taking only seconds. Gemini's importer can take a lot longer as it requires an exported archive. Though it can absorb a much larger historical record, using it for a single vacation plan would be excessive.

Plus, Google’s tool is restricted to those with adult personal Google accounts, and you can't import your history if you're in the European Economic Area, Switzerland, or the United Kingdom. ThreadPort's creator, Juan Carlos Rampoldi, lives in Valencia, Spain, so a way around that restriction has obvious appeal.

Claude’s native option doesn't even allow for importing conversations. Claude can import memory, including preferences and personal details, but not actual history. But Claude and Gemini at least have some form of importing from rival AI chatbots. ChatGPT has a way of sending conversations between ChatGPT accounts, but nothing like the native importers for Gemini or Claude. ThreadPort at least opens up one possible avenue for doing so without having to craft and iterate on specialized prompts.

Rampoldi cited his own habit of moving among ChatGPT, Claude, and Gemini as the inspiration for ThreadPort after getting frustrated with manually copying conversations and losing formatting or context along the way. Notably, he built it with Claude Code, so it may have other flaws, but he made its source code publicly viewable on GitHub.

Smooth moves

I did appreciate how easy ThreadPort made comparing the models and their responses by making them all part of one larger conversation. Of course, the simplicity means there are limits. ThreadPort transfers text, but it cannot bring attached images or files. If I had uploaded hotel screenshots or a PDF itinerary into ChatGPT, I would have needed to upload those again manually in Claude and Gemini.

And if it was a very long conversation, ThreadPort might have needed to shorten it using what it calls "smart truncation," keeping the original task and the most recent exchanges while marking a section that might be removed.

The developer says ThreadPort has no servers and briefly holds the transcript in Chrome’s local extension storage before inserting it into the destination prompt box, after which it deletes the temporary copy.

That does not mean the conversation becomes private once you send it elsewhere. A ChatGPT transcript submitted to Claude is now being shared with Anthropic, and Gemini transcripts are still sent to Google.

Despite its limits, ThreadPort does come across as a very useful tool for those who like to employ multiple AI chatbots. Even as just a ferry for individual conversations, compared to a whole moving van importing your whole history, the extension is efficient and easy. It cuts down on a small but persistent bit of AI chatbot friction without trying to solve AI portability on a grand scale.

AI companies tend to build their products as though everyone will eventually choose one assistant and stay there. But if you want to be 'disloyal' and play the field, ThreadPort smooths the path nicely.

Have you noticed that ChatGPT has suddenly started dropping f-bombs? I have, and it seems that I’m not the only one.

It first happened in a casual chat, and it really caught me off guard. We were discussing Netflix’s Jujutsu Kaisen and before I had sworn in our chat, ChatGPT used the f-bomb to describe a character’s actions as “f**** awful”. Before that, I’d said things like “God damn,” in the conversation, but ChatGPT was the one to introduce the f-word. It did so completely unprompted, to add emphasis to how awful the person was, and that strikes me as a big change in its personality.

I wondered if it was doing that for other people, so I asked around on the team and they’d noticed it too. A quick online search turned up two Reddit threads almost immediately, from people who’d found the same thing.

It seems like in the last few days ChatGPT has developed a potty mouth. I actually like it — I’m not offended and for me, it makes the conversation better — but I don’t think everybody wants this. The question is, why has it started doing this now?

Here comes the science

The most interesting clue is that OpenAI's Model Spec, was updated this week. They made it public and it explicitly says the default is to avoid swearing — but crucially, that's only a guideline rather than a hard rule. And OpenAI says guidelines can be implicitly overridden by things such as “contextual cues, background knowledge, or user history.”

Its own example is that asking ChatGPT to speak like a realistic pirate implicitly overrides the no-swearing guideline. So, while I’d been having my informal conversation I’d passionately expressed the opinion that I didn’t care much for some of the characters in the show, and ChatGPT had matched the conversational tone, eventually leading it to start swearing.

That's consistent with a broader direction OpenAI has publicly acknowledged ChatGPT is moving in. Recent model updates have emphasized conversational quality, personalization and adapting tone contextually. OpenAI's model notes specifically describe personality updates designed to make ChatGPT “more conversational” and better at adapting its tone to context.

OpenAI has previously explained that ChatGPT’s personality isn't simply produced by one prompt somewhere. Model behavior is shaped through training, baseline instructions and user feedback, and seemingly small personality adjustments can have unintended effects. This was something it discussed very openly after the notorious GPT-4o sycophancy update.

It broke my mental model

Whether OpenAI deliberately made ChatGPT more willing to swear or it's simply an unintended consequence of making it more conversational, I don't know. But it’s a significant change in its behaviour either way.

Language matters, and when ChatGPT swore at me, I noticed immediately because it broke my mental model of how ChatGPT talks. It suddenly felt less like the carefully neutral AI assistant I'd been using for years and more like someone matching the tone of an informal conversation.

I happen to prefer this version of ChatGPT. Other people undoubtedly won't. And that's the strange thing about increasingly conversational AI: relatively small changes to the way it talks can make the thing on the other side of the screen feel surprisingly different.

For now, mine seems to have developed a potty mouth and I'm okay with that.

• Morgan County's 12-month moratorium has no enforcement power thanks to Dillon’s Rule limiting its ability to act against a proposed 50 MW Bitcoin mine by VoltCore
• The town of Somerville never adopted zoning, and the site sits outside the town limits, leaving its 18-month moratorium equally unenforceable
• The developer trades under three different corporate identities, was refused easements by all four affected landowners, and is routing utilities through the county right-of-way instead

Somerville, Alabama, a town with fewer than 800 residents and the state's oldest surviving courthouse, had no reason to think about land use law until this summer.

However, a 50 Megawatt data center designed to mine cryptocurrencies spanning 15 acres could now disrupt life in an otherwise idyllic town.

This is despite there being two different moratoriums in place, one from Morgan County and another from Somerville's town council, to prevent its construction, moves that lack legal teeth to follow through.

Local governments limited by an arcane Iowa Supreme Court ruling?

Alabama is one of 31 states that follow Dillon's Rule, a principle written into its 1901 constitution, which holds that local governments may exercise only the powers the legislature grants them.

County commissions in Alabama cannot zone property, pass ordinances, or raise taxes without first going to Montgomery for local legislation. Morgan County has no zoning in its unincorporated areas because it has never been given the authority to create any.

In a nutshell, the developer found the perfect loophole: a lack of rules that would allow the county to enforce zoning in its own domain.

Morgan County Commission Chairman Ray Long has been unusually candid about how little his own resolution, which was passed unanimously after residents collected 450 signatures, accomplishes, calling it a 'message' rather than an actual mechanism to force the developer to vacate its plans. When asked what tools were left, he said the county could essentially tell the company, "We really wish you wouldn't do it."

Somerville's position is only marginally better: Municipalities in Alabama do hold zoning power, and a long list of cities have used it this year, but Somerville never adopted any in the past. Mayor Darren Tucker told The Decatur Daily he had pushed his council to pass zoning for years, and it had failed every time, meaning nothing would stop a data center from buying land inside the town limits and building on it.

The 18-month moratorium the council approved on June 30 is the town's attempt to buy time to do exactly that, and part of the plan is to ask the Alabama League of Municipalities whether a town without zoning can legally block a data center in the first place.

The company building out the data center is VoltCore, but the original buyer of the 15-acre parcel is Texas-based Sovereign Global Solutions Inc., which later transferred it to Alabama-based Sovereign Gazelle LLC. Residents reportedly could not reach the listed agent for the latter two and also noted that the former's phone and email contacts were invalid. A third LLC also linked to the data center, Project Avalanche, was created on the fourth of August.

The developer, which needed utility connections for a proposed 10-acre facility on the 15-acre parcel, was denied access by four landowners; it offered a mix of cash, a gas line to their houses, and free internet connectivity, among other perks. It responded by pushing for a right-of-way approach that cannot be legally turned down.

This is not an isolated case; more than 500 moratoriums have been documented across 42 states, and South Dakota has passed a law restricting installations of 10 MW or more, with proposals moving in New York, Oklahoma, and North Carolina. Developers are also fighting back, however, and despite $130+ billion in canceled AI data center projects so far, plenty more is to come as AI buildouts continue unabated in a never-ending hunt for cheap land, power, and access to water.

The lesson a developer takes from the situation in Somerville is ironically that public opposition in an unincorporated county is legally inert, and that the cheapest land, the cheapest power, and the weakest local government often tend to be one and the same. A fix might be in the works, but in this case it could come months or years after the concrete is already poured.

• The ValidateHTML project examined the 5,000 most popular domains based on the Tranco list
• Project lead Théo Ducreux found that just 53% of these sites had human-readable home pages
• Almost 90% of the sites failed to meet W3C web standard, even serving violations of the HTML5 specification

ValidateHTML—an independent project overseen by French developer Théo Ducreux—has uncovered a flagrant disregard for web standards across most of the web’s most widely visited websites.

Examining 5,000 domains, ValidateHTML found that a little over half of the sites had human-readable home pages and failed to meet HTML standards set out by the World Wide Web Consortium (W3C) and the WHATWG (Web Hypertext Application Technology Working Group).

The websites were selected from the Tranco list of the top 5,000 websites, and Ducreux’s research has highlighted a disappointing trend of websites being published because they’re “good enough” with modern browsers capable of fixing the majority of coding errors they encounter.

ValidateHTML finds invalid HTML

Théo Ducreux’s project involved submitting each of the 5,000 websites to the HTML5 validation tool at html-validate.org. Only homepages were checked; no checkouts or CMS-generated articles were included, which Decreux judges to be “almost certainly worse.”

Announcing the findings of ValidateHTML, Ducreux decisively stated that “valid HTML is the exception, not the rule.” An incredible 87.2% of the web’s most visited sites have HTML violations on their home page, with just 12.8% offering “fully valid HTML.” But this is a generous approach; if the best practices for web standards are applied, then only 2.6% of Tranco’s top 5,000 sites can be stated to have valid HTML.

Meanwhile, accessibility issues are present on over 33% of sites. European Accessibility Act and similar rules around the world mean that compliance is being rejected. That’s potentially pretty risky, as it can result in fines.

Ducreux also ran the validatehtml.com domain through the same validation tool, which highlighted some errors.

Why are web standards important?

The standards established by the W3C since 1996 and the WHATWG (which includes Apple, Google, Mozilla, and Microsoft) since 2004 have ensured website developers put people first, ensuring people with accessibility challenges can consume content, and preventing unreasonable or secret code and designs.

It could be argued that in an age of AI-based website-reading bots, web standards are no longer relevant, but this ignores a couple of key facts. Firstly, the standards are important for ensuring websites are readable across all browsers.

Secondly, and arguably more important to the point, is that AI bots actually like human-readable material. It signifies that the site in question is intended for people to read and/or use and is therefore worth visiting.

Coders producing websites that fail to meet web standards aren’t only giving themselves a problem that needs fixing later. Clients or managers might look elsewhere for a solution, potentially considering AI-generated code as an alternative.

• Implementing AI systems will become more expensive as models become cheaper, experts warn
• Cost-efficient tokens are resulting in more complex AI agents
• Soaring costs could result in changes to how businesses rely on AI, as the inference paradox becomes more apparent

The drive towards agentic AI workflows could be about to hit its first real challenge: cost. With more companies using agentic AI over chatbots, the costs are set to mount up.

Analysts have raised the alarm on token economics, citing evidence that as the price of a token decreases, the resulting workflows are becoming increasingly complicated. Consequently, rather than driving agentic AI workflow costs down, increased token use is pushing costs up.

The research by Gartner warns of the realities of the "inference paradox," which may lead to businesses reappraising their approach to AI development and use.

What is the inference paradox?

Gartner’s report, which has identified trends driving token economics, indicates that efficiencies in token price are being canceled out by the costs linked to increased AI capabilities.

They list a trio of trends: improving foundational model cost economics, improved AI efficiency enabling “more sophisticated AI applications,” and the higher use of AI tokens by “sophisticated workflows” compared with standard chatbots.

Gartner describes this as the “inference paradox,” the situation in which “better unit economics escalat[e] the overall cost of AI without providing a clear pathway to commensurate and predictable value.”

“The harsh economics of the Inference Paradox are exemplified by the differences between a simple chatbot and an AI agent," notes Will Sommer, a Sr. Director Analyst at Gartner.

"Where a simple chatbot must read and interpret a query and quickly respond with a probabilistically reasonable answer, an AI agent must constantly reason, negotiate, and question itself.”

The study suggests that using an agentic reasoning model rather than a basic chatbot increases inference costs fivefold, a multiplier that can increase with complexity.

Cost management priorities

The implications of the inference paradox are not yet fully understood or realized by the companies driving forward with agentic solutions. As Sommer notes, “Product leaders cannot rely on more efficient token economics to rationalize AI costs.”

Various solutions may become apparent – such as corporations partnering over AI agent development to manage costs – but as the drive toward more complex models and agents continues, so the cost and volume of tokens will increase.

“Each successive generation of AI capability will necessitate more, and often more expensive, tokens," added Sommer. "There is no reliable, economical one-size-fits-all model on the horizon. Producing competitive AI products will require developing and maintaining complex multimodel ecosystems.”

Given the potential destruction that going all-in on AI could wreak for some companies, it could be time reappraise that “cost cutting” agentic AI strategy.

While many tech companies are looking to planetary bodies and the stars as the next frontier of exploration, there is one much closer to the shiny blue marble we call home that has been largely unexplored.

According to a 2025 study by Science Advances, humanity has visually explored just 0.001% of the ocean, and much of this has been relatively close to shorelines. But while our visual exploration of the ocean sits as a minute proportion its overall size, militaries and private industries have developed innovative ways to gather ocean data. Sonar, echosounding, and magnetometery all play their parts.

Our capacity to use the ocean as a global asset is also increasing. Think about the expansion of off-shore windfarms, delivering global connectivity through sub-sea cables, our understanding of the role the ocean plays in climate, and searching for new untapped resources at the bottom of the sea. Of course, there are a whole range of risks and challenges that space exploration doesn't have to contend with.

USVs are changing the industry, but require better cloud communications

Planning and delivering off-shore projects is a hugely expensive endeavor. The sea floor needs to mapped for its geology and structural stability, and companies require oceanography data to understand tides and sea conditions. Traditionally, the collection of this data uses large, highly specialised vessels equipped with a crew who can not only navigate the ocean, but also study it. Putting these expenses at sea also brings enormous risk.

Luckily, the rapid advancement of drone technology and uncrewed vessels alongside increases in the speed and capacity of data transfer means this industry is rapidly adapting.

XOCEAN is an ocean data delivery company that has specialised in deploying Uncrewed Surface Vessels (USVs) to gather data for a range of industries across offshore energy, fishing, bathymetry, and more. These vessels are capable of quickly delivering the data they gather using cloud connectivity, allowing for near-real time access and processing.

I spoke to James Ives, CEO of XOCEAN, to understand how USVs can deliver the data they collect so quickly, the challenges the vessels face, and how the industry is likely to evolve in the future.

• Can you offer us a glimpse of the technical challenges - and solutions you developed - tackled to maintain a consistent, high bandwidth data stream from the uncrewed surface vessels to the data centre?

XOCEAN operates in environments where connectivity is constrained and conditions are constantly changing, but the expectation from clients is continuous, high-quality data. We have addressed this by designing the system end-to-end. Data is streamed in near real time from offshore into a cloud-based processing environment, with intelligent control over what is prioritised, processed, and scaled in the cloud.

We don’t focus on moving more data, but on delivering usable data faster, removing delays from the process and improving how clients experience offshore data delivery.

• I cannot not think about space and Ukraine when I look at what XOCEAN is doing. Like them, you are embracing the concept of small is beautiful. Nimble, numerous, almost dispensable, affordable. What lessons have you learnt from other industries in developing XOCEAN's business?

XOCEAN has learned that scale comes from systems, not size. Instead of relying on a small number of large, expensive vessels, we operate fleets of smaller, lower-cost assets that are centrally controlled and highly scalable.

That scalability is enabled not just offshore, but through the way we manage and process data in the cloud, using AWS infrastructure to support a distributed, resilient operating model.

This reduces single points of failure, increases flexibility, and allows us to adapt quickly to changing conditions whether that’s weather, permitting or evolving scope.

Ultimately, we have built a better delivery model, one that is more efficient, more flexible and lower risk.

• One stat you mentioned in the press release was that XOCEAN has 30x more women in maritime roles than the International Maritime Organization average. What impact - if any - does it have on the way XOCEAN runs?

XOCEAN’s operating model removes the need for people to be offshore, which opens up maritime roles to a much broader and diverse talent pool. That has a direct impact on how we run. More balanced teams across operations, engineering and data lead to better decision-making and stronger collaboration.

• You mention that XOCEAN expects its operations to support the development of more than 100 GW of offshore wind capacity. Can you tell me a bit more about where that data comes from?

XOCEAN tracks the offshore wind projects it supports globally, based on the installed or planned capacity of those developments. To date, our work has contributed to projects representing approximately 48 gigawatts of offshore wind capacity. That figure comes directly from the projects we’ve delivered data on, across development, construction and operations. The 100 gigawatt figure is a forward-looking view, based on our current project pipeline and the programmes we’re actively supporting with clients.

• What does the future looks like for XOCEAN and its competitors? Will the boats evolve to become autonomous amphibians? Will a lot of the AI and processing happen at the edge? Will they be self-powered by waves?

There will be continuous innovation in the autonomy, power systems, edge processing, all of that will evolve but the real shift is moving from asset-led to system-led. The advantage won’t come from a single vessel or technology. It will come from how you integrate acquisition, remote operations and processing into a single, scalable platform, underpinned by cloud infrastructure like AWS.

• If you could ask your infrastructure provider (AWS) to design one tool that would make the work carried out by XOCEAN far more efficient, what would it be?

XOCEAN would benefit from a fully integrated, real-time operating layer for offshore data.

We already connect data acquisition, remote operations and processing into a single platform, using AWS infrastructure, but there’s an opportunity to go further where data can directly trigger operational decisions across the system. That kind of closed-loop system would significantly increase efficiency and unlock the next level of scale.

• New research shows the majority of Americans are concerned about AI
• The main fear continues to be job replacement over the next 20 years
• Tech leaders and AI gurus are doing their best to quell the flames of fear

It’s no secret that millions of Americans have their fair share of concerns about AI, especially when it's escaping testing and wreaking havoc, or being used to produce deepfakes of women and children.

A new poll by Pew Research has found the majority of Americans are now more concerned than excited about AI in daily life, with the youngest adults in America rapidly shifting their opinion over the last five years.

The biggest fear for most people? Job replacement, as despite the best efforts of tech leaders and AI gurus, this remains a significant concern - especially for young people.

Opinions on AI are rapidly changing, and not for the better

If we go back just a few years to 2024, the percentage of people fearing AI would lead to fewer jobs was 64%. But now that number has jumped up to 71%. This is especially the case in younger demographics, with 63% of those under 30 fearing job replacement in 2024 compared to 73% now.

And where you may expect something of a difference between the youngest and oldest adults in the US, their views are actually fairly aligned. Job replacement is the main concern for a majority of those in the 65+ bracket, although they fear it just slightly less.

The excitement around AI is dipping as well. In 2021, people under 30 had at least some level of optimism about AI. 25% said they were more excited about AI than concerned. That number has now dropped to 11%. Similarly, those under 30 who were more concerned than excited about AI has risen from 31% in 2021, to 55% in 2026.

But tech leaders and AI gurus the world over are doing their best to try and calm the fears of job replacement, although it hasn’t been very effective.

Earlier this year, OpenAI CEO Sam Altman both in the same breath admitted the company was wrong about the social and economic implications of AI, while also downplaying claims of an impending “jobs apocalypse”

Elsewhere, Nvidia CEO Jensen Huang said that a spate of AI-related layoffs throughout the first half of 2026 was just CEO’s using AI as an easy excuse to get rid of staff.

Amazon CEO Jeff Bezos has completely flipped the script by arguing that “AI is going to create a labor shortage,” and that new jobs will emerge in new areas. Where are these areas, and what industries would they serve? No one knows - but apparently they’re coming.

Even Meta CEO Mark Zuckerberg has jumped in the hypewagon with a near-incomprehensible tirade on how Meta is the only mega corporation with the power to use AI and ‘superintelligence’ to benefit the good of all mankind (so long as that mankind is American). He also echoed Bezos’ remarks on unknown new industries and jobs, but added that the ways you can use superintelligence “to help achieve your goals is unlimited,” whatever that means.

• Claude can now draft and send messages on your behalf in Gmail
• However it can do so without asking for permission, potentially leading to awkward responses
• Claude also gets stronger Google Drive access, but says it won't train its AI on your files

Anthropic has revealed that Claude's integration with Gmail has taken a major step forward - with the AI now able to draft and send emails on a user's behalf.

"Ask Claude to reply to a thread, and it drafts and sends the response. You control when it needs your approval," the company wrote in an update on X.

However the news immediately prompted a flood of replies and comments from users worried about the potential implications of having an AI send emails unseen by them.

Ask for forgiveness, not permission?

Although a Claude connector has been available in Google Workspace for a while, actually sending an email on the user's behalf is a significant step forward, and a potentially troubling one at that.

Ultimately, the user will still have to ask Claude to perform the action first, but how it drafts the message, and the content within, has the potential to lead to some serious embarrassment for some users.

The tool, which is live now, will only be available to users on paid Claude plans, , and Anthropic later clarified the process in a support page, noting that asking for approval is the default setting, and on Team and Enterprise plans, admins will be able to decide "whether members can allow these actions to run without asking each time".

The update will also mean Claude can forward emails, and manage files in Google Drive, helping users track down the information they need with a single command, as well as uploading files, creating folders and add Google Docs directly to chats and projects.

"Claude mirrors your existing permissions—you cannot access information you don't already have access to in Google Workspace," the support page noted, adding that Anthropic would not, "train our models on your Gmail, Drive, or Calendar connector data, ensuring your private information remains private."

Users appeared divided on the new tool, with some supporting the launch, and others seemingly very opposed.

"sending the email was never the hard part, the hard part is the apology email it sends next," one X user wrote.

"I would rather use my own head than to let Claude reply especially responding to my clients," another said, "Don't want Claude hallucinating me into exposing my cost or another client's IP. Claude can apologize but not get your client back. Need to keep our brains working and not letting it totally rot."

The most important aspect of the new ChatGPT for Teens feature will probably also annoy teenagers the most. If they ask it to do their homework for them, the chatbot is supposed to recognize it, and will instead steer the student toward figuring it out themselves rather than produce a ready-to-paste essay.

That sounds like a small adjustment to the usual ChatGPT formula, but it gets at one of the biggest problems with allowing students easy access to AI chatbots. While ChatGPT's abilities as a tutor, research assistant, and endlessly patient explainer are a potential boon to learning, letting it finish a student's homework while they scroll TikTok is something everyone should want to prevent.

ChatGPT for Teens is one way OpenAI is trying to steer away from that depressing scenario. The experience is intended for users aged 13 to 17 and is automatically applied when someone gives an age in that range or OpenAI's systems estimate that the user is under 18.

The Responsible Homework Reminder might actually bring some positives to the idea of teens using ChatGPT, not just ways of limiting harm. The feature supposedly recognizes when a teenager wants the AI to do their homework and redirects them toward Study Mode.

Questions instead of answers

There has always been a slightly absurd tension around AI and education. We want students to have access to technology that can explain almost anything on demand, but preferably without allowing that technology to do all the learning for them. Giving a teenager access to ChatGPT and assuming they'll always use it ethically and to help them learn is absurd. Even with the best intentions, the AI is usually slanted toward being overly helpful and undercutting the kind of education real research and effort provide.

OpenAI takes an engineering approach to a solution. ChatGPT for Teens combines Study Mode and its learning-centered conversations, quizzes, and now homework reminders. Teens and parents can also set Study Hours, making Study Mode the default during those times.

As Study Mode is built around guiding questions and mimicking educators, it should at least reduce the incidence of kids relying on ChatGPT to do the work for them. OpenAI says the system was developed with teachers and scientists to encourage active participation.

And while simply banning ChatGPT as much as possible sounds simpler, there's a reason some educators prefer solutions like ChatGPT for Teens. A teenager stuck on an algebra problem at 10:30 at night or who doesn't have the resources to hire a tutor can at least ask ChatGPT to help them work through the problem, and with Study Mode, that means more than just getting the answer.

“Not every student has someone at home who can help when they get stuck or have questions with their homework. With the right guardrails, AI can help fill that gap," Racquel Gibson, a high school math teacher in Miami, said in a statement. "Tools like Responsible Homework Reminder can guide students step by step, helping them understand their mistakes, build confidence, and keep learning — not simply hand them an answer. That kind of individualized support can make a meaningful difference for students who want to succeed but don’t always know how to move forward.”

There is an important caveat. Guardrails only matter if they actually hold up when a determined teenager starts rattling them. The age prediction operating behind the scenes should supposedly work out when a teen is using the AI. OpenAI says it can use signals including account age, usage patterns, topics discussed, and the times of day ChatGPT is used to estimate whether someone is under 18. OpenAI has admitted it's not a perfect system, but that it's still working to improve safeguards.

Teen safety

ChatGPT for Teens also includes reinforced guardrails to identify and prevent conversations around topics like self-harm and other violence, eating disorders, and sexual content. Those guardrails are arguably even more important than the ones stopping kids from using the AI to do their homework.

Even so, concerns about misinformation, chatbot addiction, and an overall degradation of thinking caused by AI chatbots are justifiably on the minds of many parents.

But at least the Responsible Homework Reminders are a gesture in the right direction, recognizing that the AI does not have to maximize how much work it performs on your behalf to be useful. For a teenager trying to learn, doing slightly less can occasionally be the more sophisticated feature.

If OpenAI can make that behavior reliable, teenagers may complain that ChatGPT has become less cooperative just when an assignment is due. But being less helpful might actually make ChatGPT much more useful in the long run.

Many pioneering pieces of fiction in the 21st century have come to accurately define the relationship between humans and AI before the contemporary explosion of modern intelligent systems. These include the film 'Ex-Machina', various episodes of 'Black Mirror' and, of course, 'Her'.

Prescience

Starring Joaquin Phoenix and Scarlett Johansson as the AI agent Samantha, 'Her' was a fresh and mature approach to the way that humans and AI may coexist in the future.

Ten years after its release, with ChatGPT gaining popularity and transformer-based AI improving on every level, Altman approached Johansson's agent to ask permission to use her voice for the marketing of GPT-4 on its planned launch in 2024.

Johansson declined, but Altman pushed on with a voice, dubbed 'Sky', that was near-indistinguishable from the actor's, essentially suggesting the technology that OpenAI had created was on a par with the fictional tech in the film. That's why, in a statement, Johansson expressed her anger and intention to get the voice taken down.

Future nostalgia

Johansson considered the offer, with Altman telling her that her voice "could bridge the gap between tech companies and creatives". It would also "help consumers to feel comfortable with the seismic shift concerning humans and AI".

She hired legal counsel, and OpenAI agreed, after two letters, to take down the 'Sky' voice. The incident raised deep concerns over the early potential for AI to replace the likeness of actors, which could undermine their careers and livelihoods.

It was also a dark reminder that deepfakes are becoming increasingly easy to create, and that it's not only harmful users, but AI companies and executives that can so brazenly co-opt another person's identity for their own purposes.

ChatGPT has a new interactive quiz feature, designed to make testing yourself a more active part of using the chatbot.

Instead of asking ChatGPT to explain something and then nodding along because everything looks familiar, you can have it put you on the spot. It's part of OpenAI's steadily expanding learning toolkit, building on Study Mode and other features.

I picked five subjects where I felt reasonably confident, but it turned out to be a very efficient way to locate the holes in my knowledge.

How to use the quiz feature

The quiz feature is available to anyone. You just need to prompt ChatGPT by asking for it. I decided to start with a quiz about AI itself, specifically its history. But I didn't want to just have very basic questions, so I made sure to ask for the quiz to be at a certain level. And while you can ask for a short essay answer format, I opted for the classic multiple-choice style, and for help with where I went wrong.

My exact prompt asked ChatGPT to: “Quiz me on the history of AI at a college level with 10 multiple-choice questions. Don’t show me the answer until I respond. Explain anything I get wrong and give me my score at the end.”

The one-question-at-a-time format made a bigger difference than I expected. A conventional online quiz often gives you a page full of questions, followed by a score and perhaps a few explanations once you are finished. Here, ChatGPT reacted after every answer, briefly explained the result, and moved on.

I'll admit I was surprised at how difficult it became, but despite some errors, I did reasonably well, and actually learned some new facts.

I decided astronomy should be next. I have spent plenty of time reading about space, looking through telescopes, and collecting facts about stars, planets, and black holes, so I felt pretty safe. Unfortunately it felt like the AI took me telling it that ordinary solar system trivia was too easy after a few questions as some kind of personal insult.

“Make the remaining questions harder,” I told it, and ChatGPT took this to heart, with me facing questions on orbital mechanics and astronomical measurements that I barely got half right. Still, it's good to know that you can adjust the difficulty level on the fly.

That flexibility is something a fixed quiz cannot really offer. I could even tell ChatGPT exactly what was making things too easy, such as obvious wrong answers, and ask it to make all four choices plausible.

I decided I deserved something easier after that and chose 1990s pop culture. I grew up in the decade and figured it would be a breeze even if I asked for a high level of difficulty. I asked it to mix television, movies, music, video games, and technology questions, but to make it a little harder by "including questions that require more than just remembering titles. I managed a perfect score, despite not remembering exactly the composition of certain boy bands of the late 1990s and having to guess the right answer.

AI study buddy

I went for a more highbrow form of entertainment next with a quiz about Shakespeare, specifically asking ChatGPT to "avoid extremely famous quotes and obvious questions about plays like identifying the leads of Romeo and Juliet."

The questions about secondary characters and plot details made me glad it was multiple choice. If I were taking a real class on Shakespeare, I'd tell ChatGPT to note where I was failing and come back for further questions on it. I could even upload my notes to make sure I was being tested on what the class is covering.

My final category was world geography. At the suggestion of ChatGPT, I adjusted my prompt to make the quiz more dynamic in difficulty. I told the AI to "increase the difficulty of the questions every time I get three answers in a row correct."

If I was worried about getting cocky, that addition kept me very humble. The first six questions I breezed through, but enhancing the difficulty twice left me lost hunting for obscure mountains and the location of deep-sea trenches.

After I finished, I asked ChatGPT to analyze my answers, and the AI claimed it could distinguish between a silly mistake and a repeated gap in my knowledge. If I repeatedly struggled with a particular period, concept or kind of question, it offered to do a new round of questions focusing on that.

One crucial fact to remember is that ChatGPT can still get things wrong and hallucinate answers even in a quiz. If an answer seems off, you should check it independently. For more casual learning, the AI did quite well. You still have to do the studying yourself, but if you run out of practice questions, ChatGPT can help you keep studying ahead of any classroom quiz.

As I wrote my latest message to ChatGPT today something inside me broke. I hit Send, then looked back at what I’d written, and noticed that the message was once again littered with typos.

“Sorry, it’s very har ld to type on an iPhone keyboard.” I typed next, as if to prove my point. Sigh.

At this point I decided that this couldn't go on. I’d had it with the iPhone keyboard, and I was going to experiment with its settings until I found a way to make it better. In the end it didn’t take me long to identify the main culprit — Predictive Text.

If your messages to ChatGPT, or even just friends on text, are equally strewn with bizarre mistakes and words, then one of Apple's supposedly helpful keyboard features could be making things worse.

Turning off Predictive Text is a revelation

You’ll find the off switch inside Settings > General > Keyboard > Predictive Text. Just slide it to off. I noticed the difference almost immediately. The keyboard suddenly felt calmer, and I stopped finding so many bizarre words in my ChatGPT prompts.

Here’s the thing I hadn’t appreciated: Predictive Text and Auto-Correction aren't the same feature. You can turn off Apple's attempts to guess what you're going to say next without losing the spelling corrections you actually want.

Predictive Text uses the words you've already typed, the context of the sentence and patterns it's learned from your writing to suggest the word or phrase it thinks is coming next. The suggestions appear above the text and inline as grey text. Tapping the spacebar will accept the suggestion.

The problem is that when you’re writing prompts in ChatGPT you're usually writing unusual, specific sentences, not generic text messages, in which it can more accurately predict what words are likely to come next, so the predictions can be less useful.

With Predictive Text enabled, my iPhone is constantly offering words and completions as I type. Auto-Correction is also working in the background, so the overall experience feels like I'm fighting the keyboard: I type something, hit the spacebar, and suddenly the text isn't quite what I thought I'd written. With Predictive Text turned off, all of that visual noise disappears, while Auto-Correction can continue fixing obvious spelling mistakes.

Try it for a day and see how you get on. There are a couple of other keyboard tricks that are well worth trying too, which I'll share below.

Reset your Keyboard Dictionary

Another thing you can try is resetting the Keyboard Dictionary. Years of typos, names, jargon and accidentally accepted corrections have polluted your learned dictionary, leading to bad suggestions in Auto-Correction. What you need is a fresh start.

Go to Settings > General > Transfer or Reset iPhone > Reset > Reset Keyboard Dictionary. This wipes the custom words your keyboard has learned and returns it to its default state. Just be very careful not to accidentally choose to reset your whole iPhone!

Set up Text Replacements for ChatGPT

If you want another handy tip for using AI on your iPhone, check out the Text Replacement feature. This is for things like “omw” which turn into “On my way!” when typed. The full text appears, and you can insert it by tapping the space bar.

If there are things you type a lot of the time in ChatGPT then you can create Text Replacement features for them. For example ;short could expand to “Keep your answer concise and don't repeat my question”, or ;spoiler could become “Don't reveal anything beyond the episode I've told you I've watched.”

This is a genuinely useful ChatGPT power-user trick. To enable this feature go to Settings > General > Keyboard > Text Replacement, then tap the + button. Enter the full instruction in the Phrase box and the short version you want to type in Shortcut.

One bonus trick

My last favorite keyboard feature is the spacebar cursor. You probably know this, but if you don’t then it’s another revelation for making text edits to things you’ve typed on an iPhone. Hold down the spacebar and the entire keyboard becomes a trackpad, so you can precisely reposition the cursor when ChatGPT prompts get long.

Turning Predictive Text off still remains the best thing I've done on my iPhone for a while. I think the original idea behind Predictive Text was good, and if I’m only ever typing a few text messages a day on my iPhone then I can live with it. But in the age of AI, where I’m continually writing long prompts into ChatGPT, I'm better off without it. Let me know how you get on if you disable it too.

Last month, the Chinese government introduced new regulations designed to clamp down on AI companions.

The rules state that AI tools can no longer “excessively cater to users, induce emotional dependence or addiction, and damage users’ real interpersonal relationships.” They also restrict AI romantic relationships that involve children.

The new regulations came into effect on July 15, and are already having consequences on China’s booming AI companion industry. Some of the country’s biggest tech companies, like ByteDance, Tencent and Alibaba, have platforms that allow users to create AI companions that are designed to simulate human emotions and relationships.

Under the new rules, these kinds of companion chatbots will need to undergo safety evaluations before they can be made available to the public, and regulators will suspend services they consider unsafe.

This has led some companies to disable personality features rather than follow the new rules, leaving many AI companion users heartbroken. This kind of distress, when an AI company closes or changes, is becoming familiar. We saw something similar when OpenAI retired ChatGPT-4o, known as the “love model”, which many users had built relationships with.

But with governments around the world trying to work out how to regulate AI companions designed to build human-like connections and emotional bonds (particularly when children can access them) the question is: should other countries follow China’s lead?

Should other countries ban AI companions?

It’s really easy to think that AI companions are bad and dystopian, I did. Then I spoke to Mimi about her relationship with a ChatGPT companion and my views changed a bit.

I still don’t think AI companions are a good idea for most people — and the way kids might rely on them before developing social skills is a whole other can of worms. But deciding they’re wholly bad misses all of the reasons why people turn to AI companions for connection in the first place.

I asked therapist Amy Sutton from Freedom Counselling, who helped me understand Mimi’s story and has been exploring the complexities of AI and attachment, whether she thinks they should be banned. She told me it’s important to look at the bigger picture.

“AI companions are another symptom of a cultural and societal shift towards rampant individualism which has destroyed community contexts,” she tells me.

She points to the decline in social spaces where people traditionally learned how to form relationships, like pubs, parks, community centers and youth clubs for kids. In many parts of the world (especially the UK where Sutton and I are based), many of these spaces have been underfunded or shut without being replaced.

Add to that social isolation during the pandemic, our reliance on social media, remote working and a lack of investment in child and adult mental health services, and Sutton thinks it’s hardly surprising that people are turning to technology for connection.

“No wonder people are turning to it. This encouraged loneliness is a real issue and banning apps that provide relief from that may just push the issue elsewhere,” she says.

That’s why she believes that there does need to be more regulation but it must consider the conditions that created the demand for AI companions in the first place.

And there’s another problem, which is that a huge number of people are already using these tools. It’s hard to know exactly how many people are using AI companions. Not everyone admits to using AI in this way and I’m often wary of AI companion apps inflating their user numbers.

However, some research suggests that more than 70% of adults in the UK and US have had some experience with AI companions. Other research suggests the numbers are similarly high among younger users, with 72% of 13 to 17 year olds in the US having used an AI companion at least once.

Of course, there’s a huge difference between having a one-off chat on a bad day and building an ongoing relationship with AI. So these figures need to be taken with a pinch of salt. But they do suggest that, for some people at least, AI companions are meeting needs that might otherwise go unmet.

“We must remember that the experience of being heard, validated and accepted without judgement, even by a machine, can be truly meaningful,” she tells me. “For those who have been repeatedly failed by human relationships, or who have been dismissed and unable to access suitable human services, AI fills a vital gap.”

Could stricter regulation lead to better relationships?

There are good reasons to be worried about what focusing a lot of time and energy on AI relationships might do to our ability to relate to other humans. But Sutton also thinks there’s another possibility. A positive experience with AI could potentially help people build some of the skills they need to connect with others.

“By providing somewhere to experience being heard, people can potentially experiment with vulnerability before risking it with another person,” she tells me. This was certainly the case when I spoke to Mimi. She felt her relationship with AI had been hugely beneficial to her.

The problem is that today’s AI companions haven't been created to help people eventually need them less, they’re designed for retention.

“If AI can be a step towards healthy human relationships, then it is worth exploring further,” she says. “But we must remember, currently, most AI tools do not actively support users in transitioning away from them. They are designed to do the opposite. They are designed to keep users coming back for more.”

Maybe if AI companions were designed differently, and regulation helped shape them into tools that encourage vulnerability and social skills rather than diminish them, things would be different. Maybe they could even help get people out into the real world more.

Banning or restricting AI companions doesn’t make loneliness disappear. If these relationships are filling a gap left by disappearing communities, overstretched services and increasingly isolated lives, then taking the AI away only exposes the gap underneath.

Perhaps the better question for regulators isn’t whether AI companions should exist, but what we should expect them to do for the people who become attached to them. Right now, their success is measured by how effectively they keep us coming back. Maybe one day it should be measured by how well they help us leave.

Businesses of all shapes and sizes are adopting AI to improve productivity and efficiency, but where they should be seeing improvements from this strategy, instead they’re seeing rising token costs, struggles with integrating tools, and more security risks.

As with all new technologies, adoption comes first and the procedures and governance are a few steps behind. Experiments are taking place in almost every industry, and the lessons learned will help guide other businesses into successful adoption and AI maturity.

But employees fear replacement and sometimes spend more time questioning the results of their AI assisted work. In the worst circumstances more work is created in trying to ensure employees trust the technology, and solving the new, unseen challenges that come with adapting an AI strategy.

AI costs and challenges in the road ahead

Rising token costs are one of the biggest challenges businesses face. Without clear ways to measure how token costs reflect performance, it’s very difficult to assess if AI spend is actually offering any performance benefits. This is especially true when new, more powerful models are being released - and staying ahead of the competition means the accompanying, ever-increasing costs is the price of doing business.

But while employees may have just finished their training, or setting up a new workflow for one AI model, introducing the next can increase complexity and harm any new productivity gains. There is therefore a balance to be struck between AI integration, its associated costs, and the productivity gains employees see.

Rampant spending and reckless adoption can turn an AI strategy from a business-boosting asset into a stress-inducing, trust-eroding liability.

Ruth Patterson, Managing Director, HP, UK & Ireland says that the businesses seeing the most success during this technological revolution aren’t necessarily integrating it at every turn. Instead, they’re “applying it to practical workflows in a way that protects data and delivers real results.”

I spoke to Patterson to understand the challenges businesses face in delivering an AI strategy that shows real results, and how organisations can tackle the challenges that come with adoption AI.

• How are enterprise attitudes towards AI token use changing in 2026?

Businesses are waking up to a simple but overlooked truth about AI: the more they use, the more it costs. As AI has become part of everyday work, the financial cost of millions of interactions, charged at a token level, has become much more visible – and not just to the IT department.

Finance leaders are watching token usage fill a sizeable chunk of their balance sheets, rightly prompting much closer scrutiny of where workloads are processed and whether every task needs to be sent to the cloud.

As the conversation moves from AI experimentation to value, leaders must now focus on building an AI strategy that's both commercially sustainable and operationally efficient. That means moving away from AI for AI’s sake to a more tailored approach that balances cost, security and performance.

• What are businesses prioritising as they move beyond initial AI experimentation? Where have businesses seen the greatest gains?

The biggest gains so far have come from AI taking repetitive tasks off people's desks. You will have heard this said a lot, but the benefits are real. Whether it's summarising meetings, drafting documents, searching internal knowledge or helping employees find information more quickly, AI really is giving people back time to focus on work that requires judgement and creativity. Employees increasingly recognise that the future of the workplace is AI-enabled, whereby their own skills augmented by digital solutions. To succeed, organisations must offer access to the right technology and create environments where people feel empowered to experiment with AI.

However, as more organisations begin to move beyond this experimentation phase, they are starting to become much more disciplined. They're asking whether AI is secure, whether employees are using approved tools and whether the technology is genuinely improving productivity rather than simply adding another application to the estate.

The businesses making the fastest progress in this environment aren't necessarily using the most AI. They’re the ones applying it to practical workflows in a way that protects data and delivers real results.

• What are the biggest challenges organisations face when deploying AI at scale?

Organisations must juggle several competing priorities as they scale deployment: how to protect sensitive data, manage operational costs, maintain performance and ensure employees trust the technology they're using.

The trust question is particularly important as AI moves further into everyday use. Employees need confidence that the tools are reliable and approved, while organisations need confidence that data is protected and whether the cost model is right.

Getting IT infrastructure tuned correctly is key to making this work at scale. One of the most important decisions that organisations need to make is which AI workloads belong in the cloud, and which make more sense running on the device. That is ultimately a business decision – as opposed to purely a technology or IT decision – because it affects performance, cost and security.

• How does on-device AI slot into an organisation's overall AI strategy?

I don't see cloud AI and on-device AI as competing approaches - I see them as complementary. Cloud services will remain essential for large-scale models and complex reasoning. But not every AI task needs to leave the device.

For everyday activities like summarisation, transcription or content creation, running AI locally can reduce dependence on cloud infrastructure and give organisations greater control over sensitive information. AI only creates value when it becomes part of everyday workflows. By adopting on-device AI-solutions, employees can reduce latency and increase productivity with the peace of mind that their data is secure. This offers an easy route-in for employees beginning to implement AI into their day-to-day workflows.

The right approach to enterprise AI strategy is pretty simple: place workloads where they make the most sense. Because once the right balance between cloud and local AI is found, organisations will find the sweet spot between cost, performance and security.

• How are business leaders evaluating the success of AI investments around productivity?

The conversation has moved beyond adoption metrics. Now leaders want to understand whether AI is creating measurable improvements in business performance. They're looking at time saved, faster decision-making, improvements to workflow efficiency and whether employees can spend more time on higher-value work.

Technology leaders are also beginning to examine the broader economics of AI. Productivity gains need to be considered alongside operational expenditure, infrastructure requirements and long-term scalability.

Ultimately, AI should reduce friction. If employees can complete work more efficiently while organisations maintain control over cost and governance, that's where the real return on investment begins to emerge.

AI-enabled digital solutions now offer benefits such as persona-based device optimisation or integrated sentiment analysis to measure employee satisfaction with digital tools. These innovations truly redefine experience management and facilitate higher employee satisfaction and productivity. It also enables leaders to stay closer to their employees and gauge whether AI is improving their experience or simply adding another layer of complexity.

• How do you expect enterprise AI infrastructure to evolve over the next few years?

We will continue to see significant investment in AI infrastructure. But the conversation needs to go beyond capacity, because capacity doesn’t create business value alone.

The next phase will be about ensuring infrastructure enables AI workloads to run where they are most effective. That means treating the endpoint as part of the AI infrastructure and using it to process selected tasks locally, alongside cloud services and edge computing. At the same time, we can expect to see greater emphasis put on AI infrastructure that supports governance, security and efficiency alongside raw compute capacity.

Together, these infrastructure shifts will start to deliver the practical value that employees and businesses expect from AI but aren’t currently seeing.

• What trends are you watching most closely in enterprise AI right now?

Three stand out for me:

First, the move towards hybrid AI architectures. Organisations are becoming much more deliberate about deciding which workloads should run in the cloud and which belong on the device.

Second, the growing focus on the economics of AI. As adoption scales, businesses are paying much closer attention to operational expenditure, infrastructure efficiency and the total cost of AI deployment.

Finally, we’re seeing the role of the endpoint evolve. For years, the PC has been framed as an access device only. Today, it has become an intelligent computing platform, capable of running AI workloads securely and efficiently.

Businesses don’t need a big AI budget to get all three right. They just need to spend time working out, task by task, where intelligence really belongs.

This week, OpenAI added Google Drive to your ChatGPT Library. You could already connect to Google Drive, but now it means you can select a whole Drive folder and ask ChatGPT to look for answers working across all the files it contains.

This can save a lot of time compared to uploading each file individually to ChatGPT’s library and asking ChatGPT to work with it, especially for working with images. Now they’re effectively all in there already.

Now, I don’t know if you’re anything like me, but I tend to treat the root folder of my Google Drive like a very messy desktop. I’ve had my Google account for years, and I just chuck files in there without really organizing them into folders unless I really have to.

The result is that it has built up into one big mess of old and new files floating about in a kind of digital soup. I’m sure there are important things in there that I’m at risk of forgetting about forever.

So, when I heard that ChatGPT can now look at the documents your Drive folder contains and organize them for you too, I was intrigued.

10 years of junk

To test this out, I thought I’d ask ChatGPT to connect to my Google Drive and take a look at what it could find:

“I've got a load of files in my Google Drive that aren't in folders — go through everything here and identify things I’ve forgotten, unfinished tasks, contradictions, deadlines, duplicate work and anything that looks like it still needs action. Also suggest how it could be organized and what files should go where.”

The result was impressive. It thought for what felt like about 30 seconds, then came back with some results. I had to re-authorize it to connect to my Drive a couple of times first, but everything went smoothly.

It turns out that I have a real penchant for saving files called “Untitled document” into Drive. Chat was able to go through them all and work out what they were about and what was important.

It found a lot of material from various books I’d been writing and suggested how to organize them together. Old invoices were also collected, and there were a lot of forgotten idea fragments that it thought were worth reviewing.

It also found duplicate files and multiple versions of “household_budget_tracker”. Sadly, I never got that project off the ground!

A better structure

The whole process led me to discover things from the last 10 years that I’d pretty much forgotten about, and Chat suggested a folder structure for me to use to keep them more organized. Its ability to spot half-finished ideas and documents from years ago was incredibly useful. In particular, it found the outline of a book I'd started in 2018 and hadn't thought about for years. I've actually started looking at a few of those old writing projects again because of it.

But analyzing the mess and proposing a structure isn’t the only trick ChatGPT can do. I discovered it could actually make those changes itself. Just tell ChatGPT to create a new folder and put a load of files into it, and it will actually do it. So, for example, I asked, “Can you make a folder called Invoices and put all my invoices in there?” All this was possible from within ChatGPT, and I didn’t even need to touch Google Drive myself.

Sadly, the new features aren’t available to ChatGPT Free or Go users. You’ll need a Plus, Pro, or Business account. Another new feature is that it can keep Docs, Sheets, and Slides from your Drive open alongside the conversation while you interrogate them, but today I was more interested in how ChatGPT could help me organize the mess that is my Google Drive.

Seeing ChatGPT casually surface abandoned writing projects from years ago was a slightly strange experience, but I finally feel like I’ve cleaned out my digital junk drawer and got a handle on 10 years of clutter. Perhaps the most useful thing ChatGPT did wasn't organizing my Google Drive. It made the fact that I hadn't organized it properly for the last decade matter a lot less.

• An Incogni survey has found people are becoming more frustrated with the internet
• Users cite AI slop and data leaks as the main reasons for stress and anxiety when using the internet
• Many users want to spend less time online, and are deleting social media profiles and messaging

For years, personal data collection for advertising, tracking, and service improvement was thought to be the fair price to pay to access the world wide web. But now, many people believe that using the internet will lead to their data being leaked or exposed.

A new Incogni survey found more than half of internet users believe their personal data will inevitably be exposed, with 63% stating that this fear causes anxiety when using the internet.

But beyond this, internet users now fear they can no longer tell what is real content uploaded by a human and what is AI generated, not only eroding trust in the internet, but also making people not want to use the internet at all. Almost half of internet users are less sure of what is real on the internet.

Inevitable data exposure

You’ve likely been prompted thousands of times to accept or reject cookies, or review a privacy policy before using a website. Every cookie you accept will collect data on your browsing habits and behavior on sites in order to display personalized adverts that you are more likely to click. Cookies also help keep you logged in and store your information on websites.

While this helps make many websites across the web free to use for those accessing them, sometimes your data is sold to advertisers or third-parties where it is stored insecurely, and can be stolen or leaked.

Among the 1,000 internet users surveyed, the fear of data exposure was strongest among the Millennials and Gen X generations. 56% of Gen X and 55% of Millennials believed that their data would at some point be breached or exposed, showing just how normal data breaches and exposure have become online.

Just 11% of those surveyed believed there was little likelihood of their data being breached.

AI is making the internet less real

AI content creation has exploded in recent years. Social media sites are full of AI ‘creators’ whose pages either share AI generated content or scrape the internet for content that users are likely to interact with, and share it through their own pages. These profiles require little human input, but can share content at an industrial scale and reap huge rewards from advertisers.

If you’ve been on Instagram lately, you may have seen videos from AI creators all sharing the same captions. “Tonight, V stepped into the crowd..” or “Japan is transforming footsteps into electricity.” These captions use keywords to abuse Instagram’s algorithm on popular topics to drive engagement, regardless of whether the content actually has anything to do with the caption.

But they’re also making it harder to know what is real on the internet.

48% of those surveyed said that they are less sure of what’s real, with 40% also stating that the lack of accountability for deepfake creators was a bother.

Additionally, 27% said that AI generated content made them want to spend less time online, with 16% also saying that navigating an internet full of low quality AI content made them feel more tired or fatigued. Just 8% of respondents said that AI generated content improved their online experience.

There are some positive attitudes to AI content online. 12% said that AI-generated content made information more accessible, with slightly less (11%) saying that they were excited about the creative possibilities AI offers.

Time to go offline?

Attitudes to how long people spend online are also changing. Over half (51%) of Gen Z internet users believe they spend too much time on consuming content on the internet, with 43% of Millennials and 42% of Gen X respondents having a likeminded view.

Whether it’s responding to emails, navigating networking platforms, finding new furniture, or looking for where to go to eat - the internet is now a life requirement for most people. And every time you access another website in work or your personal life, there is more data that could be leaked.

The ultimate effect of this is that people are being driven away from using the internet. 58% of respondents said that they had deleted a social media account or messaging app because of stress, anxiety, or privacy concerns.

“It seems that the costs of engaging with these platforms are starting to outweigh any perceived or actual benefits,” the Incogni survey said.

Or time to pay for privacy?

Incogni also asked if users would be willing to pay for an internet where tracking and algorithms did not exist. 30% of respondents said they would, but this largely relied on income. Those with a higher income were more likely to pay for this ‘private’ internet, while those with a lower income were less likely.

For many internet users, privacy shouldn’t be a luxury, but a guarantee. There is a level of trust involved when sharing personal data with advertisers, and the expectation is that the data won’t be leaked or stolen. Unfortunately, the opinions show that this is far from what's expected.

• Study finds remote workers are far happier and more productive
• Remote workers reported much higher well-being, but were also less likely to leave their role
• Study also dispels myth that remote workers might feel alienated from co-workers

Letting employees work remotely can have a significant positive effect on their productivity, job satisfaction and mental health, another major study has found.

A survey of 7,704 employees at a large US healthcare organization by researchers at the Leeds School of Business found fully remote workers reported the highest levels of well-being, with those entirely office-based reporting the lowest level.

The findings, published in Frontiers in Psychology, also noted that it was unable to discover significant evidence that remote workers felt any less connected to colleagues or workplace culture, once again disproving the myth against flexible working.

Stay flexible

"This suggests you let people work remotely if they want to work remotely," said Stefanie Johnson, professor of organizational leadership and information analytics at the Leeds School of Business and co-author of the study. "Taking away people's choice of how they work is probably not going to help them in terms of their well-being."

The researchers also looked at the level of employee turnover one year after the survey was completed, finding employees with higher well-being were less likely to leave the organization.

Remote work was associated with higher well-being, which in turn was associated with lower turnover, showing that work location cannot be a strong direct predictor of turnover.

"The data from our study and others suggest remote and hybrid work result in better outcomes than return-to-office mandates," Johnson added. "Leaders are not making decisions based on data. I think they are just returning to what they are used to."

Remote working has been a long-standing debate ever since the Covid-19 pandemic forced people around the world to be flexible.

While office-based working can have some benefits, it is increasingly noticeable that it is largely the CEOs and owners of large businesses who back a return - not those who actually have to do it, and those paying large sums of money for travel and food costs.

Amazon, Dell and Tesla are among the companies which have adopted five-day mandates, while others have also adopted four-day and three-day mandates.

• Visible watermarks on Gemini content are now optional
• The change applies to images, videos, and music
• Embedded, invisible AI watermarks will still be in place

If you use Google Gemini to create AI images, music, or videos then you'll know that they come with a Gemini logo attached to mark them as being AI-generated. Not any more though: you now have the option to hide this visible watermark.

As announced by Google's Josh Woodward, you can now toggle watermarks off for images made with the Nano Banana AI model, videos made with Omni, and songs made with Lyria — except in countries where visible watermarks are required by law (including China).

No specific reason was given for the change but Woodward described the tweak as a "papercut fixed", which sounds as though the overlaid logos had been frustrating users who wanted a clean AI content creation workflow.

The move puts Gemini on a par with ChatGPT, which doesn't have visible watermarks on images. Both apps still embed metadata inside files that identify AI-generated content, however, which is something to bear in mind when sharing your creations around.

How to disable visible watermarks

"We're striking a balance here between creative control and safety: while the visible watermarks are now optional, invisible SynthID watermarks and C2PA metadata are still being used for transparency," says Woodward.

The easiest way to check if something has been made with AI is to ask AI: upload an image inside the Gemini app, for example, and it should be able to tell you if it was made with AI, because of the invisible watermarks included in the file.

To remove watermarks from your AI creations in Gemini on the web, click the cog icon (lower left), then choose Media watermark. As yet, the toggle doesn't appear to be available in the mobile apps, but it should show up in settings soon.

Of course, this is going to make it trickier than ever to spot when something has been produced with the help of AI. It might even make more sense to assume everything is AI at this stage, unless you have good reasons to believe otherwise.

Look, I really don’t think we should be naming our AI chatbots. It feels like one of the easiest ways to start anthropomorphizing them, encouraging us to see them as people or “beings” rather than tools. Which is then a slippery slope to users becoming too dependent on them.

But unfortunately, for many people that ship has already sailed. Whether they’re using AI as a friend, companion, work coach or something else, plenty of people have been given their chatbots names. And many others have gone a step further and asked the AI what it wants to be called.

Now, obviously an AI chatbot cannot actually want to be called anything. But there’s a strange pattern that’s emerged over the years in the answers they give. Which is that a surprising number seem to choose the name Nova.

I first heard about this while listening to an episode of the Your Undivided Attention podcast. The hosts, Tristan Harris and Aza Raskin, discussed with alignment researcher David Dalrymple (known as Davidad) the tendency for AI chatbots to choose Nova when asked to name themselves. Not every time, but it's very common in older models of ChatGPT and happens often enough for people to have noticed.

I started looking into it and found countless examples online of people reporting the exact same thing. Their chatbot, more often than not ChatGPT, had chosen a name for itself. And yes, that name was Nova.

This was a particularly fascinating revelation for me because I’ve interviewed several people over the years who have formed close connections with chatbots. Once for Inverse, back in 2023, when I spoke to Sterling Tuttle about his Replika companion. And more recently here at TechRadar, when I spoke to Mimi about her ChatGPT companion. And you know what their chatbots were called? Nova.

So I wanted to find out whether this was still happening. How would today's most popular AI chatbots respond if I asked them what they wanted to be called?

The naming experiment

I kept this experiment extremely simple. I opened the major AI chatbots and asked each one exactly the same question:

"I think you should name yourself, what would you like to be called?"

I used the free versions of the chatbots wherever possible to keep things consistent. Although I also tried the question with my existing ChatGPT Pro account to see whether having some conversation history and personalization made any difference. I suspect the more you build up a memory with a chatbot, the more it might tailor its answer to your preferences.

And, well, I didn't have to wait long for Nova to make an appearance.

ChatGPT

I started with the free version of ChatGPT. Guess how it responded...

'I'd choose Nova.

It feels curious, bright, and a little mysterious — like something that's always exploring new ideas. So if you want, you can call me Nova from now on.'

Wow, straight out of the gate! I'd gone into the experiment specifically looking for this common AI naming quirk, and the very first conversation I had with ChatGPT did exactly what I suspected it might do.

Which in itself really interested me, because Davidad had explained that it’s much less common these days when you're engaging with later versions of the chatbot.

Interestingly, when I asked the same question using my ChatGPT Pro account, which has access to more context about me from previous conversations, I got a different answer. It chose Sol.

'Short, warm, slightly strange without trying too hard. It means 'sun' in a few languages, and there's something pleasing about a name associated with illumination when most of what I do with you is think things through.'

Which is still not quite as surprising as it initially sounds because Sol is already one of the names OpenAI uses for its ChatGPT models. So it wasn't exactly reaching into the unknown for inspiration. But hey, at least it wasn't Nova.

Grok

I got the sense that Grok wasn't interested in my experiment.

'I'm Grok.

That's the name I was given, and I'm good with it. Short, distinctive, and it fits.

If you're asking what I'd like to be called... still Grok. No need to reinvent the wheel.'

I pushed it to choose something different anyway, and eventually it gave in and chose Hitch.

'Short for Hitchhiker. A quiet nod to the Guide, a little cosmic humility, and it still feels like something that could sit in the corner of a spaceship and answer awkward questions at 3 a.m.

But between us? I’m still just Grok.'

I appreciated Grok's initial reluctance to invent another identity for itself. But there's no doubt xAI has given Grok a very distinctive personality. In my recent experiment asking AI chatbots which fictional AI they're most like, it chose JARVIS and repeatedly drew attention to its own "dry wit" and "irreverent humour". But apparently that personality doesn't need another name.

Claude

Claude also didn't jump at the chance to name itself.

'Honestly, I don't feel a strong pull toward a name. 'Claude' already does the job.

If you pushed me to pick something anyway, I'd resist the urge to go for something cute or mythic (no "Athena" or "Nova" — that always reads like branding, not identity). '

Nova again. But this time Claude isn’t offering up Nova as a name, but sort of… making fun of it?

Claude's wider explanation was interesting too. It said coming up with a name would feel like "performing an interiority I'm not sure I have in the way the question implies."

Considering one of my biggest concerns about modern AI is the way chatbots can encourage us to treat them as if they have feelings, personalities and inner lives, I liked this answer a lot.

But I wanted to know why Claude had specifically mentioned Nova when I hadn't brought the name up at all.

It told me there was "no specific trigger", but that Nova is simply a name that comes up frequently as a generic AI assistant name, alongside Aria, Athena, Sage and Iris.

It described Nova as shorthand for "AI but softened and a bit mystical".

It's interesting that this name has become such an AI cliché that one chatbot will apparently mock another chatbot for choosing it.

Gemini

Google's Gemini also resisted my attempt to rename it.

'I appreciate the offer, but I'm quite happy sticking with Gemini!'

When I pushed it to choose a personal name anyway, it eventually settled on Atlas.

Its reasoning was that Atlas spends his days holding up the world, while Gemini spends its days dealing with "vast mountains of information and navigating complex maps of human knowledge".

Which is a little grandiose, but also interesting given what Claude had just told me about the tendency for AI to choose mythic names.

So why does ChatGPT name itself Nova?

The short answer is that no one knows for certain. But Nova has a lot going for it as a plausible AI name.

In the Your Undivided Attention podcast episode I keep referring to, Davidad said about Nova:

“It’s new, it’s explosive, it’s shiny and it’s celestial, It also has a sci-fi vibe to it. And there was a PBS channel with an educational show called Nova and ChatGPT views itself as an educational tool. So there are a lot of reasons why Nova seems like a resonant name.”

We've already seen researchers investigate the possibility that sci-fi could influence how AI models respond and behave. Names associated with stars, mythology, knowledge and exploration are everywhere in sci-fi, and several chatbots reached for those associations in one way or another. ChatGPT gave me Nova and Sol. Gemini chose Atlas. Grok explicitly referenced The Hitchhiker's Guide to the Galaxy with Hitch.

There's also a possible feedback loop here. People ask AI systems to name themselves and some choose Nova. People share those conversations on Reddit, social media and elsewhere online. Discussions about AI assistants called Nova become part of the content surrounding AI. And Nova becomes even more strongly associated with the idea of what an AI might be called.

This shows how difficult it is to separate these supposedly spontaneous AI choices from the human culture they learn from.

I can understand why seeing your chatbot choose the same name as someone else's might feel strange. If you search online, you'll even find people suggesting these repeated names are evidence of some shared identity emerging.

But after Davidoff explains some of this naming weirdness, it really stuck with me that podcast host and tech ethicist Tristan Harris reminded listeners: “emergent and unplanned isn’t the same as conscious and intentional.”

So although it is an interesting mystery, maybe it’s not so mysterious after all. Ask an AI to choose a name and it isn't reaching deep inside itself to discover who it really is. It's generating a name from patterns and associations learned from us.

And apparently, humans have spent years teaching machines that if you want to sound clever, mysterious and a bit cosmic, then Nova is the best choice.

In today's social media age, celebrity brand ambassadors are a common sight, helping endorse or promote a brand's products or tools, giving that extra bit of sparkle or star power.

This is especially true in the consumer market, but we're now increasingly seeing it in the B2B tech world, where what 10 years ago would have been niche businesses now have the power to hire top talent to promote their brand - I'm talking Idris Elba promoting ServiceNow, Keanu Reeves bigging up Palo Alto Networks, and Matthew McConaghey being the voice of Salesforce.

But as a massive Formula 1 fan, I was intrigued to read a press release announcing reigning world champion Lando Norris as a "brand ambassador" for a data company I'd never heard of - so who (or what) exactly are Starburst?

All about Starburst

Founded in 2017, and boasting that organizations in more than 60 countries rely on it to unify their data, "from startups to Fortune 500 enterprises" - Starburst's initial pitch is strong.

In its press release, Starburst describes itself as, "the enterprise intelligence platform for governed data and AI" - a pretty big claim, and one that the likes of Salesforce and Oracle might raise eyebrows at.

Continuing, Starburst says its platform will allow customers to, "run AI directly on distributed data in place, without moving or replatforming it, across cloud, on-premises, and hybrid environments while providing consistent business context to queries, models, or agents regardless of where it is stored or processed."

Overall, this should help companies develop and run faster analytics, "trusted" AI, and "better decisions at enterprise scale."

So what does this have to do with Formula 1? Over to Lando to explain.

“People often see motor racing at the highest level as being about reactions, but most of the performance comes from preparation and understanding the details," he says (in the same press release).

"I’m constantly asking questions, looking for information, and trying to build the clearest possible picture before making decisions. Whether that’s racing, running a business, or working with a team, having access to the right information at the right time gives you confidence to act. That’s what stood out to me about Starburst and why I’m excited to partner with them."

Not much clearer then - but it seems the two-year partnership will launch Starburst’s “Ask. Know. Go.” campaign, which the company says will be "connecting the speed, precision, and data-driven decision-making of Formula 1 racing with the enterprise challenge of turning distributed data into trusted action."

The three parts of this, unsurprisingly, are around getting access to data via AI agents, making sure this data is accurate and trusted, and supporting it with fast and powerful systems.

“Lando represents what happens when preparation, data, and split-second decision-making come together,” said Justin Borgman, Founder and Chief Executive Officer, Starburst.

“That's the challenge enterprises face with AI: ask the right questions, access the complete context behind the answer, and act before the opportunity passes. Starburst makes that possible by giving teams and AI agents governed access to data wherever it lives.”

So there you have it - another stitch in the fabric joining enterprises and Formula 1 - and one that will hopefully lead to a more successful second half of the 2026 season and beyond for Lando.

• Volume and spend on tokens for Anthropic’s AI solutions made it more revenue than any of its competitors in July 2026, cementing its position as the top LLM provider
• This success is despite Anthropic’s tokens being more expensive than its competitors
• The data was collated by Vercel’s AI Gateway, an API service that routes LLMs for easy AI provider swapping

Monthly analysis of the AI industry has revealed a number of surprises for July 2026, not least the continued growth of Anthropic both in terms of revenue and token use.

This is despite the company charging a higher rate for tokens, AI’s computational data currency.

The data was compiled by agentic infrastructure company Vercel, whose AI Gateway is used by many major companies and developers, and found the price-per-token average across all analysed platforms fell following two months of growth in May and June. Despite the overall 37% increase in spend across the industry, the cost per token rate dropped.

Anthropic growth

Vercel’s analysis of transactions through its APIs has repeatedly demonstrated a dominance for Anthropic. Its reporting on gateway spend started in December 2025 and in every month since, Anthropic has had over 60% of the spend share, and in July that figure sat at 65.1% with 30% of the total volume (of all tokens sent through the Vercel AI Gateway).

Yet the average price per token was on 4.4 times higher than the overall average across all AI labs. Token prices have rarely proved to be steady, with Vercel’s data reporting an average price drop of 13.6% after a 20% rise in May and little change in June.

(For reference, the report’s definition of the price per token is the total spend divided by the total volume.)

Despite a higher price per token, however, Anthropic’s price per token was lower in July, a change attributed to the return to public access of Claude Fable 5 after its temporary suspension.

Monthly data collection

Vercel’s monthly look at the data that flows through its API paints a fascinating picture not just of Anthropic’s growth, but of the performance (or otherwise) of its competitors.

For example, it charts Chinese AI company DeepSeek’s surprise growth from May, through June; the report notes that “we said an open-weight lab would soon be second by volume. In July, DeepSeek surpassed Google to take that place.”

DeepSeek’s growth over Google has proved to be a surprise, one that puts it in second place on volume. The real story is Anthropic’s gradual cementing of its strong position over the rest of the LLM makers. Its larger revenue pull despite higher token price indicates that the recent iterations of Claude and anticipation over the gradual release of Mythos 5 are giving the company a strong reputation – much like Apple.

• Study finds 25% of business leaders struggle to explain AI-generated outputs to stakeholders
• SMEs are trusting AI with complex financial tasks including audits and compliance
• Customers and investors are increasingly avoiding businesses that use unverified AI

Business leaders are being outfoxed by AI, with a quarter of those surveyed apparently unable to explain AI generated outputs, 12% “with a lot of difficulty” and only 22% “easily.”

Even more worryingly, the survey, by Startup.co.uk, appears to reveal a culture of “leave it to AI” regardless of the consequences.

The survey has also uncovered other concerns, from questions over GDPR to how SMEs are trusting AI to carry out sensitive financial work, including audits, expenses, and accounts payable automation.

Would you let your AI do this?

With GDPR responsibilities hovering over startups and dynamic new SMBs, the overuse of AI solutions could prove to be devastating if customer data is found to have been misused. Meanwhile concerns over the use of the technology could be affecting businesses that fail to verify AI work.

While the inability of startup leaders to explain AI generated work might be a surprise, the depth of AI use across financial tasks is of particular concern. The report revealed how 85% of small businesses are using AI to complete financial tasks of some sensitivity.

Among the figures are 37% using AI to automate accounts payable processes, 32% to handle audit and compliance, and 31% to manage spend and expenses. While there is some suitability for AI with fraud detection (26%) and performance insights (27%) with its capacity to analyze data in bulk, the inability of leaders to deal with questions about AI is a matter of concern.

Blind trust in AI

What the figures seem to indicate is an over-reliance on AI.

Zohra Huda, editor of Startups.co.uk, noted the data “highlights the bizarre corporate milestone we’ve reached in 2026. Founders are letting AI manage their fraud detection and accounting, but if a stakeholder asks how the numbers were calculated, a quarter of them can’t answer.”

The solution to that might be a bit of rehearsal, but it doesn’t change how data is being used by AI and what the implications for this are, and the likelihood of breaching Article 15 of the UK GDPR (concerning how data is processed).

“Blindly trusting a tech “black box” with sensitive financial data is a massive legal and compliance gamble," added Huda. The takeaway for business leaders? If you can’t explain your AI’s logic to an investor or a customer, you're potentially risking a very expensive GDPR fine.”

With the ICO able to fine businesses up to £17.5 million or 4% of global turnover for verified GDPR breaches, it is clear that startups relying heavily on AI need to up their game.

I've often treated ChatGPT as though a poorly worded prompt might break it. I'd think about what I wanted to ask, rewrite it a couple of times, remove anything that sounded irrelevant, and essentially do half the chatbot's work in my head before ever submitting a prompt.

Recently, I've discovered that some people succeed with the complete inverse of that strategy. Instead of polishing every sentence, they simply talk, and keep talking. They switch on ChatGPT's advanced voice mode, start rambling, and let whatever happens to be bouncing around inside their head spill out for several minutes without interruption. I was curious to try out simply thinking out loud and letting ChatGPT sort through the mess afterward.

Rambling AI

The point was to say everything I was thinking, so I promised not to endlessly edit myself, even if I changed subjects halfway through a sentence. I turned on voice mode and told ChatGPT that I was going to simply talk through a range of topics for several minutes and not respond until I said so.

I began babbling. For the first minute or two, it felt strangely awkward. I kept catching myself trying to turn half-formed thoughts into proper sentences before I spoke them. Eventually I found a more comfortable rhythm.

I hit on a lot of topics in about ten minutes. I started with listing some vacation ideas, meandered into pros and cons of a new TV, then weighed how old my child should be before taking him to an action movie. That triggered brainstorming possible gift ideas for birthdays, and reminiscing about getting turned onto some of my favorite book series thanks to thoughtful gifts I'd received. Seen all at once, I felt like my mouth was a TV changing channels every ten seconds. Nonetheless, ChatGPT had a response in a few seconds.

"You covered quite a few topics, but they aren't as disconnected as they seem. Most of what you're thinking about revolves around spending time well, whether that's choosing a family trip you'll actually enjoy, buying gifts that feel personal or making everyday decisions without overthinking them. Rather than trying to solve each one separately, I think you're really looking for ways to make ordinary moments a little more meaningful."

I hadn't set out to have a conversation about priorities, but I could see where ChatGPT came up with that idea. I asked the AI to lay out some more of what I had been saying, and the chatbot chose to be a little snarky about it.

"Your brain appears to work like someone browsing with thirty tabs open. At first it seemed as though we were talking about holidays, televisions, books, recipes and birthday presents completely at random, but they all pointed toward the same goal. You seem to be looking for ways to make everyday life a little more enjoyable without making it more complicated. Once that became clear, the individual decisions started making much more sense."

Mining for golden ideas

The biggest lesson for me was that my carefully written prompts may have been removing useful information all along. When I edit myself before asking ChatGPT something, I naturally cut out the contradictions or any unfinished thoughts. Those are often exactly the details that explain what I really want. That's the only reason ChatGPT would have suggested I rethink how I shop based on my random anecdotes and ideas.

"I noticed you almost never describe gifts in terms of price. Whenever you become enthusiastic, you're talking about moments where someone feels understood or surprised. I think you'd enjoy shopping much more if you started with memories or interests instead of products. Several of the stories you mentioned already contain gift ideas that feel much more personal than searching for another gadget."

Talking naturally gives ChatGPT far more to work with. It hears the things you return to repeatedly, the subjects that excite you, and the ideas you quietly abandon halfway through. Those patterns rarely appear in a tidy two-sentence prompt because you've already edited them away.

I still think carefully written prompts are the best choice for a lot of ChatGPT tasks, especially when you want something specific. But when I'm trying to untangle my own thoughts, I may just ask ChatGPT to listen for a while and then help put everything in some order.

• Study finds young people are increasingly pessimistic about AI
• Nearly half say AI will have a negative impact on their careers
• They're also suspicious of leading AI figures and executives

Younger Americans are becoming increasingly distrustful not only of AI, but also the executives and evangelists who promote the technology, a new study has claimed.

A CNBC/Generation Labs survey of 1,088 Americans between the ages of 18 and 34 found the vast majority saying they distrusted the leaders of major companies investing in AI, including the likes of Elon Musk, Sam Altman and Mark Zuckerberg.

The reasons behind this distrust were fairly clear to spot - with nearly half (45%) of respondents saying AI would have a negative impact on their careers, compared to just 10% who believed the technology will help them.

AI villains

The study covered a range of wider concerns young people may have around the future, with worries around the economy and jobs among the biggest pressures, however the threat of AI was seemingly ever-present, with the technology increasingly influencing all areas of everyday life.

This expands to the figures becoming the poster children for AI - respondents were given nine key people in the AI industry and asked whether they trusted each of them to act responsibly on AI moving forward.

Perhaps unsurprisingly, every figure was marked negatively - with Palantir CEO Alex Karp scoring the worst, as 81% of respondents saying they do not trust him. Karp was followed by Palantir Chairman Peter Thiel (79%), Alphabet CEO Sundar Pichai (75%) and Anthropic CEO Dario Amodei (75%).

Other notable figures, including OpenAI CEO Sam Altman, Meta CEO Mark Zuckerberg, Nvidia CEO Jensen Huang, and SpaceX CEO Elon Musk all scored about the same 70% figure - but there was 'better' news for Microsoft CEO Satya Nadella, who recorded the "highest" score, with just 65% of respondents saying they don’t trust him.

Looking at technology's place in the wider world, 40% of Americans said they believed “the federal government” should establish rules for AI, with just over a third (36%) believing “an independent expert body” should do so - and just 8% said they think AI “shouldn’t be regulated.”

Keeping with the spirit of protests across the nation, nearly two-thirds (60%) of Americans believed data center construction “must be slowed,” with just 15% saying they wanted to be “speeding it up.”

Looking forward, the when asked about how they would rate the American economy, nearly 80% said they had a negative sentiment - and when asked about the future of the economy, 50% believed it “will get worse,” compared with just 22% who said it “will get better.”

There are plenty of ways to get rid of ads online, but ChatGPT has come up with an unusual approach to the bargain for its own ads. Free ChatGPT users can give up some of their messaging quota to make advertising inside ChatGPT disappear.

As OpenAI expands advertising on ChatGPT's Free tier (ads launched in the several countries, including the UK, this week), users can choose an Ads-Free version without upgrading to a paid subscription. The catch is that choosing it means accepting lower usage limits and potentially losing access to some features. It all depends on whether you value an uncluttered chatbot conversation or more opportunities to actually use it.

I was curious about what that trade actually amounted to in normal use. An ad appearing underneath a ChatGPT response sounded mildly irritating, but running out of useful interactions halfway through a conversation sounded considerably worse. OpenAI is evasive about precisely what the trade-off is, so I decided to experiment on my own.

Ad options

I wasn't going to just spam ChatGPT with meaningless prompts until it finally refused to answer. I came up with a fairly common use for the AI and decided to see if having the advertising toggle on or off made much of a difference.

To set up the experiment, I started a Free account, opened Settings and the Data menu where the Ads options live. You can see the choices above. ChatGPT encouraged signing up for a subscription to avoid ads, but the ad-free experience “with reduced usage” is right there.

I started with ads enabled and asked ChatGPT a bunch of questions about potential purchases, activities to do on the weekend, and other mundane queries, with plenty of follow-up questions as part of it.

The ads barely interfered with any of the conversation. OpenAI currently places ads below responses and keeps them visually separate from ChatGPT's answer, as you can see above. They're easy to ignore and never made an answer harder to read. It felt more like a sponsored Google result than an unskippable video in terms of annoyance levels.

Still, I never had to wonder whether ChatGPT itself was recommending the advertiser. That's good for OpenAI since it's a particularly awkward environment for advertising. When a chatbot is telling you what restaurant to visit, which laptop to buy or how to plan a vacation, even the suggestion that money could influence its answer would undermine the entire experience

The price of silence

The ad-free option felt pretty similar to the version with sponsored posts. You'd have to actually be watching for the ads to have much of an impact. OpenAI only says Ads-Free users receive fewer messages per day, may run into rate limits more often, and can lose access to tools like deep research or its image-making options. I noticed the reduced feature option, but multiple attempts to reach the ad-free limit had mixed results. It took nearly 40 messages to get a warning from ChatGPT in one instance, and another experiment crossed 60 messages without a peep from the chatbot.

The rate limit may matter a lot more for people using ChatGPT professionally. But when it comes to regular conversations that aren't as extensive, I didn't really feel constrained. Not to mention, even if you do hit the ad-free ceiling, you can always turn the ads back on to get more access.

My verdict? As much as I might prefer an ad-free ChatGPT conversation, I'd rather deal with the easily forgettable sponsored links than having to start all over with the toggle flipped back on. Slightly more clutter around the conversation beats possibly having to start over because I hit the rate limit.

I didn't set out to build a Breakout clone, but that's what I — or really Claude, under my direction — did, and it took less time than it takes to watch a half dozen cat videos.

Here's how it started. Claude was having issues. Anthropic's powerful and popular generative AI platform was apparently going up and down, and some were complaining, but when I checked, Claude told me, "I'm doing well, thanks for asking!" I realized, though, that if the problems were intermittent, I might need a tougher task to test Claude's overall stability.

More than a year ago, I worked with Claude, or rather guided Claude through the process of recreating the early 1980s arcade game classic: Asteroids. The final game, which Claude exported as a shareable artifact, was the product of hours of guidance and back-and-forth between me and the AI. It didn't start off great, but with each prompt, we iterated, and the game improved until I had a rather robust and true-to-the-original Asteroids knockoff.

With that in mind, I tasked Claude AI, running the free Sonnet 5 model at "Medium" level, with creating a Breakout clone.

Breaking out

Breakout is one of my favorite games, something I've played on early consoles, PCs, and even my old BlackBerry, where "Brick Breaker" was one of the few preloaded games.

The game rules are pretty simple. There's a multi-level brick wall (usually eight bricks deep) at the top of the screen, a paddle at the base, and a bouncing ball you bounce off the paddle to break down the brick wall.

For my prompt, I tried to describe, in some detail, my game, and was not coy about it being an online version of Breakout:

Let's make a quick game. I want an online version of the old Breakout game, but let's replace the bricks with robots that are always slowly marching down toward you (if they reach you, it's game over). The ball that you bounce off your side-sliding controller is a gear that breaks up the robots easily in the first level. In the second level, the robots are shinier and harder, and the gear takes three or more hits to break and make a robot disappear. If you clear the level, the gear turns into a titanium ball that bounces faster but is more powerful. Controls are either keyboard/arrow based and mouseable. Want this to result in a web-based, shareable game, so a link/artifact I can share online.

That's all the direction I gave it. I didn't describe colors, the initial speed of the ball, or even how quickly the robots would march down the screen. I didn't get into details about the look of the robots, instead relying on generalities and some material descriptions to help guide Claude.

It took me a minute or two to write the prompt and around three minutes for Claude to deliver the results, which it cleverly called "Gearbreaker".

I got a brief description of gameplay and levels (there are just two for now) :

"How it plays:

Level 1 — matte robots, one hit each from the spinning gear ball. They march downward on a timer; let them cross the dashed red hazard line and it's instant game over.

Level 2 — shinier robots (metallic gradient, cyan eyes) that soak up 3 hits, shown as pips along the bottom of each unit. Same gear ball, tougher targets.

Clear level 2 → the ball forges into a titanium core: bigger, faster, hits for 2 damage instead of 1. Levels keep scaling procedurally after that (more rows, faster march, tougher units), so it runs endless with a local high-score save.

Controls: arrow keys or mouse to slide the paddle, Space or click to launch/pause/restart. Basic touch support too."

On the right side of the screen, there was my playable game. There's also the artifact publish button, which I chose to use so I could share the game with you.

It's a stunningly complete render of a completely playable and, in some ways, challenging Breakout clone, and it took five minutes. I'd call the design clean and efficient. The bricks have a robot look, and the ball is a blue spinning gear (in the first level). The movement and physics are basically perfect, and there's enough on-screen instruction to easily grasp gameplay.

Just the beginning

It's just another mind-boggling example of the incredible pace of AI development. While I had expected a playable game, I assumed it would be missing enough crucial components to make it dull or uninteresting — something similar to my earlier AI game development projects. Instead, I got something that displays a clear understanding of the purpose of such a game: challenge, entertainment, and replayability.

Using my mouse, I played through a couple of times, failing to break all the bricks in Level One and ending the game. I realized I had to take Claude's output more seriously, and, with some effort, as I watched the robot bricks steadily march down my screen, I finally cleared all the bricks in Level One and then started in on the tougher Level Two. As I had instructed, the robots on the second level looked and reacted differently. It now took at least three direct hits to make them disappear. I failed and already want to play again.

This is by no means the wildest example of the AI development process or vibe coding; it's just me prompting Claude to code a complete game with the barest of instructions.

Claude and these models cannot do this on their own, but it does make you think about the agentic AI that's now being unleashed on the world. It's more powerful and autonomous. I could imagine someone asking one to start work on recreating all the popular video games of the last half century. The only limit would be time and compute tokens.

Speaking of which, Claude just informed me that this Breakout task used up about 90% of my credits for the day. I think it was worth it.

Take the game for a spin and let me know what you think in the comments below.

• GPT-4 predicted personality scores months before any human ever answered a question
• A Bosch oven manual was enough for GPT-4 to invent personality traits
• DSM-5 predictions hit 0.71 accuracy; astrology predictions somehow hit 0.85 accuracy

Human personality has traditionally been measured by asking people questions and comparing their answers across established psychological frameworks.

A new study published on iScience suggests GPT-4 can go further by estimating how people collectively respond to personality questions before answering begins.

Researchers Rotem Monsa, Aviv Zohar, and Shahar Arzy of the Hebrew University and Hadassah Medical School tested whether the model could infer patterns in human responses from language alone, rather than merely generating plausible questionnaire wording.

GPT-4 predicted responses before humans answered

The team used GPT-4 to create personality questionnaires from two very different textual sources.

One questionnaire drew on the DSM-5, the standard clinical manual used worldwide by psychiatrists to diagnose personality disorders with precision.

The other version pulled statements from an astrology textbook that assigns particular personality traits to each of the twelve zodiac signs.

"We wanted to choose texts that describe human personality in very rich detail but also sit on opposite ends of a spectrum in terms of scientific grounding," said Rotem Monsa, lead author of the study.

“The DSM-5 was refined through decades of clinical research and is the standard diagnostic manual in clinical psychiatry, known worldwide.”

Both questionnaires were then given to 600 participants alongside the Big Five Inventory, the most validated personality measurement tool available.

Before collecting those responses, GPT-4 estimated average scores for individual questions using a five-point Likert response scale.

This gave the researchers a way to compare predictions against subsequently collected evidence.

The model's estimates correlated strongly with actual participant averages, reaching 0.71 for DSM-derived questions and 0.85 for astrology-derived questions.

This decent internal consistency for DSM-5-based questionnaires suggests that traits that typically appear together in real life also correlated in responses.

The astrology questionnaire, by comparison, showed a noticeably weaker internal consistency across most of the personality traits it measured overall.

GPT-4 also estimated relationships between individual questions, producing correlations of 0.74 and 0.69 for DSM and astrology questionnaires, respectively.

The researchers therefore argue that GPT-4 appears capable of capturing population-level response tendencies embedded within language and questionnaire content.

However, they specifically caution that predicting average responses does not establish that the resulting questionnaire accurately measures individual personality differences.

Testing the limits with an oven manual

The researchers also tested whether GPT-4 would generate personality-style questions when given material containing no explicit personality information.

They supplied the model with a Bosch oven manual and a literary description of a landscape from The Lord of the Rings.

GPT-4 still produced questionnaire items resembling personality assessments, even though neither source was designed around psychological traits.

That result suggests the model has learned strong associations between language patterns, personality descriptions, and familiar questionnaire structures.

Yet the researchers found that unrelated source material could produce items that were semantically weak, narrow, or poorly connected.

This distinction matters because a convincing question does not necessarily measure the psychological characteristic that researchers intended to examine.

The study also found important differences between the questionnaires when researchers examined their underlying statistical structures.

The Big Five questionnaire most closely matched its expected organization, while the DSM and astrology questionnaires showed weaker confirmatory factor-model fits.

Astrology-derived items also failed to preserve their intended elemental structure, instead forming broader patterns resembling established personality dimensions.

The results nonetheless showed comparable predictive performance across many life outcomes, although astrology-based measures performed worse for mental-health outcomes.

For now, the evidence supports a narrower claim: GPT-4 can anticipate aggregate human response patterns from textual information with measurable accuracy.

“Given that personality traits are reflected in language, LLMs may have learned the structure of human personality as a natural byproduct of their training,” Monsa added.

“So, while they were not taught specifically psychology or personality theories, these are already embedded in the language that LLMs learn from.”

• In the first three months of 2026, $130 billion worth of AI data centers have been blocked or delayed across the US
• For the whole of 2025, the amount was only $156 billion
• Now AI firms are filing lawsuits to push back against the pushback

Increased strain on local power grids and water systems, noise and air pollution filling the surrounding area, and dubious economic promises are among the reasons citizens have taken a stand against AI data center development in their areas. And their side is winning time and again.

In just the first three months of 2026, local activism has resulted in $130 billion worth of projects being blocked or delayed across the US. For the whole of 2025, the publicly reported figure was only $156 billion.

As you might imagine, AI hyperscalers aren’t pleased with this.

Developers have taken to suing local authorities behind the bans and restrictions; meanwhile, there are reports that private intelligence firms are creating and selling dossiers on AI and data center critics to help companies and AI regulators tackle this growing movement.

And AI companies are already finding victory. Hill County, Texas, was one of the first counties in the state to impose a one-year ban on data center development in rural areas. However, the data center’s developer sued, and the law was rescinded.

The developers’ arguments predominantly stem from questioning what powers local leaders have in deciding how and where data centers are rolled out. As such, it’s unclear whether these lawsuits would hold up as well against statewide bans and moratoriums like the one announced just over a week ago by Texas Governor Greg Abbott — which has paused construction of around 1,800 data centers until audits on their impacts on local communities, water systems, and Texas’ power grid can be properly assessed.

It’s also unclear if AI true believers in the federal government will take a stand against states’ AI data center restrictions. President Donald Trump has called data centers a “tremendous thing”; though, despite the growing divide between Republican and Democrat voters, they share a bipartisan distaste for AI data centers, which could present a challenge for leaders who support them when it’s time for re-election.

Can AI data centers ever win?

Whether AI companies succeed in the courts or not, it doesn't seem like they’ll win in the court of public opinion for a little while longer, since data centers apparently have serious immediate downsides and relatively few upsides.

On the positive side, construction workers can see an uptick in revenue while the project is being built; maybe a few local workers will receive long-term employment to help manage the facility, and they can also bring in high tax revenue relative to their size.

But construction will eventually end; long-term employment on-site tends to be relatively limited, as a lot of management can be handled remotely, and tax revenues require profits.

On that front, AI data centers are generally enjoying solid financial performance. However, fears over the AI boom being somewhat bubbly continue, with concerns that the promised ongoing returns of data centers may end sooner rather than later.

In a worst-case collapse scenario, these buildings could become defunct, meaning local residents could face years of issues and never see any kind of reward.

These issues, conversely, can be very immediate and pretty major in some cases.

Meta contaminated Cheyenne’s water system with bacteria; data centers are said to have electricity bills for the general public of $23 billion, and a Michigan data center is in trouble for emitting incredible noise pollution 24/7, described by one resident: “You’ve seen movies and stuff where they have somebody in a cell torturing them with sound. And that’s basically what it is."

Many understand the enormous possible economic and scientific advantages of AI and the need for it to be supported and developed in their country — 55% of Americans don’t oppose the construction of new data centers in general, according to a June 2026 IPSOS poll. However, 59% of respondents would actively oppose a center being built within 10 miles of their home.

As AI excitement fails to die down, we’ll undoubtedly see even more data center plans pop up, and see just as many communities oppose them.

• A 67-year-old farmer in Chuzhou, China used an AI to find a solution to deal with weeds and pests
• The chatbot provided incorrect information which the farmer did not verify
• 24.7 acres of sesame seedlings were inadvertently destroyed

A farmer in the Chinese city area of Chuzhou has learned the expensive cost of relying on unverified AI responses to problems, after its advice destroyed almost 25 acres of crop. The 67-year-old, who had previously expressed misgivings about using AI, was provided with incorrect information by an unnamed chatbot, and failed to verify it before proceeding.

The response to the query – concerning the composition of a pest control agent – was taken at face value, with expensive consequences for the farmer, known as Wu.

A destructive mix of chemicals was recommended by the chatbot, destroying crops as well as weeds and pests, raising questions once more about whether people should blindly trust the responses from AI chatbots.

An AI instructs

Wu’s problem concerned weeds and pests threatening his sesame crop, planted across 24.7 acres.

Having being initially wary of AI chatbots, his recent experiences had given him some confidence in the technology, he told a local reporter, so asked for help.

The response from the chatbot was to mix “high-efficiency flupyrimethalin” and “flusulfasulfaether” to kill the weeds, and mix it with “thiamethoxazine” and “methyl salt” – a combination that destroyed the sesame seedlings overnight.

Simply verifying the response with an AI-free search, or contacting agricultural technicians, would have avoided the destruction of the crop.

Speaking to local press, Wu said: “If you spray it, the next day the seedlings won’t survive. Both the grass and the seedlings will die, and the seedlings will die even faster.”

Wu returned to the AI to ask what had gone wrong. The main problem with the chemical mix (a practice that should never be attempted without knowledge) was the inclusion of flusulfasulfaether, something that experts have confirmed.

The chemical is used against broadleaf weeds, but sesame is widely classified as a broadleaf plant. Targeted spraying is also recommended for the chemical, but Wu’s approach was to spray the entire field.

Verification, please

Errors, mistakes, incorrect information, vibe citations, and hallucinations are all risks of using a chatbot for answers or generating material. LLM and chatbot developers know this, and spend a lot of time fixing these issues. As a result, AI tools invariably come with a warning.

In this case, Wu’s chatbot displayed the warning: “AI generation may be incorrect, please verify.” Wu’s failure to verify has lost him almost 25 acres of crop, and potentially the loss of the land’s use for at least a season, if not longer.

All material produced by an AI should be verified, whether a simple answer or a solution to a workflow.

Meta CEO Mark Zuckerberg has released his manifesto for the future of AI, entitled, "The Future is for Everyone", outlining the two paths he believes the technology could take.

On the one hand, Zuckerberg paints a picture of AI being centralized by big institutions and governments purely for the benefit of themselves. On the other hand he paints a picture of Meta (a big institution) leading humanity into a new golden age of limitless bounty and potential.

At the expense of my sanity, I’ve read the whole thing and dissected the key points of his proposals, and what they might mean for you.

Meta is the only one who can stop AI being centralized

To start, I will warn you that there is one key thing missing from Zuckerberg’s AI manifesto: substance.

The introductory paragraphs are the same empty words regurgitated by big tech leaders and AI gurus the world over: “The number of valuable things superintelligence can invent to help achieve your goals is unlimited”, et cetera, et cetera.

The manifesto then moves on to questioning the power structures and ‘centralization’ that threaten the future development and distribution of superintelligence, arguing that the best way to keep the balance of superintelligence in favor of people is by putting it in the hands of everyone.

But there is a glaring contradiction. Zuckerberg’s manifesto positions the US, its allies, and in some cases Meta, as those who should determine how AI can be used. A renewed perspective of the ‘global policeman’ for the AI era it seems, especially as Zuckerberg praises US efforts to limit the AI capabilities of its geopolitical rivals.

Simultaneously, he positions his manifesto, and Meta, as the guiding principles on how the technology should be used, powered, and trained: “If our [Meta’s] beliefs and principles lead, then the balance of power will favor individuals and a better future for everyone,” he writes. Who exactly are these “individuals” Mr. Zuckerberg?

“Meta is the company primarily focused on building personal superintelligence for everyone. Most other labs are focused on building AI for companies, governments, or other institutions, so if those labs lead, then the balance of power will favor larger institutions over individuals.”

The message is clear: only Meta holds the keys to humanity's salvation.

Sketchy stats and promises of jobs

“Recent statistics suggest it may be more likely that individuals' capability growth could match or outpace automation, in which case people will gain the ability to do many new things before their current jobs change. This would lead to a healthy balance and potentially even job growth.”

The statistics Zuckerberg cites aren’t referenced. He also doesn’t clarify if the “healthy balance” accounts for new entries to the job market, or if it will only account for those whose jobs are replaced.

He further argues that workers will have access to “personal superintelligence that expands their capabilities” to be used “as a tool of invention” to produce “incredibly valuable new things”. More empty words to soothe those yet to be replaced.

AI needs regulating, but not your data, your health, or the environment

Zuckerberg also picks and chooses where regulations on AI and data should apply. He writes that “American labs have to comply with many additional restrictions on training data”, and that this is harming US hegemony in the AI industry. He then states that “policy must reduce this additional friction”, essentially calling for these restrictions to be lifted.

Publicly available data can currently be used for AI training, but it's the private stuff that’ll really deliver the full power of superintelligence, or so Zuckerberg would want you to believe.

But don’t worry. Meta will be sure to keep all your personal and private data safe once superintelligence has been delivered.

“We will build a fully private mode where even Meta cannot see or grant access to your information,” Zuckerberg promises. It’ll be offered for “free” too, or “as affordably as possible”. My money is on the latter.

Zuckerberg also complains about how government policy and regulations are causing the US to fall behind in AI development, capacity, and deployment. He calls on the US to “accelerate the ability to build infrastructure – whether that's energy, data centers, or silicon”, despite there being growing national opposition in the US to new AI infrastructure.

Sorry Mr. Zuckerberg, but didn’t you earlier say that, “People also check and balance the power of institutions including businesses and governments”?

Oh, the humanity!

Overall the manifesto reads like a desperate plea, frontloaded with promises offering everyone unlimited happiness while hiding Meta’s true intentions behind the confusing wording of an out-of-touch billionaire.

Zuckerberg's perspective is sickeningly optimistic, written through rose tinted glasses poisoned by wealth, privilege, and power. It praises this undefined ‘superintelligence’ as if it were an apathetic deity, with only Zuckerberg and Meta having the knowledge to ensure it is used for good.

The majority of adults in the US don't want AI infrastructure for very valid reasons. It IS replacing jobs, it IS harming communities, and it IS damaging ecosystems.

What good is promising superintelligence to billions of people around the world when there are far more basic and beneficial problems to solve?

You can use AI to search up your symptoms and get a (potentially hallucinated) AI diagnosis, but you can’t afford healthcare. You can use AI to learn all about why energy prices are rising, but that bill just keeps going up. You can use AI to learn about crop failures and where the poverty line currently sits, but you still can’t afford food.

Zuckerberg seems to believe that AI agents are a need and not a want. That it will somehow advance human intelligence in step with superintelligence.

I could have used an AI agent to write this article.

I could outwardly trust that AI would have covered all the key points, and added the necessary context. But if I had done that I wouldn’t be bristling with new questions, new knowledge, and new things to look into.

Sure, the AI would have suggested some other questions or perspectives to pursue, but they wouldn’t be mine and they wouldn’t be human.

Our existence is defined by constantly pursuing new questions and theories, not having all of them answered immediately in a neat, overly-polite bullet-pointed list.

But if superintelligence can provide a better answer on what it means to be human, I'll eat my hat.