• One simple prompt could lead to swathes of downstream compute, experts warn
• Attackers could even exploit your uncontrolled AI to run up costs
• Greater visibility and circuit breakers are two solutions

Data security company Forcepoint has revealed a major issue with AI agents, but unlike many AI security threats, it doesn't involve stealing data or compromising the model.

Instead, if left to its own devices, Forcepoint says agentic AI could actually consume excessive amounts of compute, tokens, API calls or other resources if sufficient safeguards and limits aren't in place, leading to higher-than-anticipated enterprise cloud bills.

Moreover, the company's research argues that the problem has become more important as AI has become more complex.

Enterprises warned to keep an eye on AI agent compute usage

The result of overwhelmingly complex agentic AI systems is that one single and apparently simply user request could lead to tens or hundreds of downstream operations. Forcepoint labels this as 'unbound consumption'.

Crucially, the analysis found that high compute and token consumption could actually be pretty hard to detect and existing security protocols are unlikely to pick it up, because there doesn't even need to be an attacker for the impacts to take place. All you need is a badly configured automation or a long-running AI session to accidentally lead to runaway costs.

However, Forcepoint worries that attackers can indeed step in to exploit this vulnerability, with malicious users generating huge workloads and consuming massive compute for their own benefit after obtaining an enterprise's credentials, letting them pick up the bill.

Solutions can be as complex as agentic AI itself, but they're now more necessary than ever. Firstly, companies should set budgets at multiple, finer levels, such as API keys, individual users and teams. They should also have greater monitoring powers over where costs are attributed to.

But Forcepoint also calls for agentic circuit breakers to prevent workload and costs from compounding.

"Security teams rarely watch cloud billing dashboards. Finance rarely reviews prompt patterns or agent design," security researcher Jyotika Singh wrote in an urge for enterprises to take the risk more seriously.

One of the advantages of having written about tech for as long as I have is that you start to notice when the same ideas come around decade after decade, in different shapes and sizes. Especially the bad ones.

12 years ago Google invented something called Google Glass — a set of glasses complete with a camera so you could record what you were looking at — and it was so roundly hated by everyone that we all breathed a collective sigh of relief when they went away.

But now it's 2026 and smart glasses are back, powered by AI, and tied in with a world-famous fashion brand, but fundamentally they still have exactly the same problem, which still hasn't been fixed: they enable people to record other people without their consent.

The problem is that none of this should have come as a surprise. The privacy risks of putting a camera on somebody's face weren't merely predictable — they were predicted. Women were raising concerns about men using Google Glass to record them more than a decade ago.

And yet here we are again.

The curious case of Google Glass

Let’s remind ourselves what happened the first time that smart glasses arrived.

Google Glass arrived in 2013. It puts a small heads-up display and camera on the wearer's face, allowing them to take photos and record video from their point of view. Almost immediately, privacy became one of the defining controversies around it.

By 2014, women were explicitly raising issues of abuse. Wired reported that women were particularly interested in technology that could detect and block Glass because of concerns about men covertly recording them in nightclubs. "Even if they didn’t know if the device was recording, they felt threatened by its presence", said Oliver, the creator of the detection device.

Google Glass wearers were being called “Glassholes,” businesses were banning the device, and Google itself was publishing etiquette guidance because of complaints about people creepily filming others without permission.

By 2015, consumer Glass effectively died. Privacy wasn't its only problem — its price, awkward appearance, and lack of compelling everyday uses all played a part — but by January 2015 Google had stopped selling the consumer Explorer Edition. Glass survived as an enterprise product before Google finally discontinued the Enterprise Edition in 2023.

Smart glasses didn’t die with Google Glass. They had simply retreated to a lab to lick their wounds and wait for that vital next piece of technology that would make them relevant again. That technology was AI. In 2023, AI-powered smart glasses, partnered with leading fashion brands, started to emerge, with Meta leading the charge with its Ray-Ban Meta glasses.

This time they looked much more like ordinary glasses. Meta had also found the compelling use cases that Google Glass struggled to provide: its glasses could use AI to answer questions about what you were looking at, give you directions, and translate signs and conversations. But they could also still record what, or more importantly, who, you were looking at without needing that person's consent.

It’s like women don’t exist

Frankly, I don’t think Meta should have released them with that ability, but here we are. It’s like we’ve learned nothing from the Google Glass debacle. Almost as soon as the next generation of smart glasses emerged they were being used to harass women and record them without their consent or knowledge.

Researchers at the University of Sydney have been studying hundreds of social-media videos involving smart glasses and found men using them to approach and record women without their knowledge, including at gyms, workplaces and beaches. Australia's eSafety Commissioner says the technology can amplify gendered harms because recording can be almost undetectable, and specifically highlights pickup-artist content in which women often don't know they're being filmed.

The researchers analysed 350 publicly available Instagram videos from 2023–2026. In a subset of covert POV videos, around 60% of interactions were classified as potential harassment. And in about 43% of the videos, women were subjected to further abusive online commentary, sometimes exposing personal information.

These are real victims. CBS interviewed a woman who discovered she'd been secretly recorded only after the resulting video went viral, attracting more than 200,000 views. Her reaction said it all: “I had no say.”

And France has now opened an investigation involving alleged sexual harassment and discreet smart-glasses recording.

I don't think these products were designed to harm women. That's almost the point. Too often, the technology industry seems to design products around what the person using them can do, without giving enough consideration to the person standing on the other side of the technology. And when the potential for abuse disproportionately affects women, that's a particularly serious blind spot. Women have historically been underrepresented in the data and testing used for product design, with designers still describing the male body as the implicit default.

Meta clearly did think about privacy. The glasses have a recording LED specifically designed to tell people nearby when the camera is being used. But if your safeguard against covert recording is a tiny light on the front of the glasses — one that people have found ways to conceal or disable — is that really enough?

Did it consider what happens when a man wearing an almost invisible camera approaches a woman who's alone?

Meta's audio-only smart glasses

Yesterday at Meta Connect, Meta announced a new version of its smart glasses called the Ray-Ban Meta Audio glasses, which don’t have a camera. They got barely a mention in Zuckerberg’s keynote, but I wonder if they were released because of the existential threat to its business that the now commonly used “perv glasses” moniker poses.

Ray-Ban Meta Audio has no camera. You can still listen to music, make calls, translate conversations, and talk to Meta's AI, but the person sitting opposite you no longer needs to wonder whether they're being secretly recorded.

Meta hasn't said the Audio glasses exist because of the privacy backlash, and removing the camera inevitably removes some genuinely useful features too. For visually impaired people in particular, a camera combined with AI can be transformative.

But Ray-Ban Meta Audio proves something important: smart glasses don't have to contain a camera.

Twelve years ago, women were already warning that putting inconspicuous cameras on people's faces could enable men to record them without their knowledge, and today researchers are documenting precisely that behavior.

Google Glass users became known as “Glassholes” because people hated that the person they were talking to was recording them. Twelve years later, we're having the same argument about “perv glasses.”

That's what makes me angry. This wasn't an unforeseeable consequence of some revolutionary new technology. We'd already run this experiment and realized that these should never have got out of the lab in the first place.

• OpenAI has filed court documents against Elon Musk’s xAI
• The papers reveal that ChatGPT’s integration with Siri was disappointing
• OpenAI alleges that barely anyone was using Apple Intelligence

If you’ve noticed an improvement in Siri’s abilities on your Apple device, you might have Google to thank for that, as its Gemini artificial intelligence (AI) now powers much of what Siri AI can do. Rival services like ChatGPT have also found their way into iOS — but new court documents reveal why that particular partnership has struggled to get off the ground.

The court filings were made in relation to OpenAI’s ongoing dispute with Elon Musk’s rival xAI firm (now known as SpaceXAI). In the documents, OpenAI alleges that, by the time xAI filed its complaint against OpenAI, “it was clear that Apple’s integration of ChatGPT was dramatically underperforming.”

The papers then go on to imply that this was not just some one-off failure. Instead, the Apple integration was “persistently underperforming,” OpenAI alleges.

Apple added ChatGPT integration to Siri in December 2024, but this required a multi-step opt-in process. That could be one reason for the substandard performance of the link-up, with a degree of friction slowing users down in their attempts to harness OpenAI’s tool.

Much of this section of the court filings is redacted, so it’s difficult to know exactly what was going on between Apple and OpenAI. But it seems clear that, at least from the latter’s perspective, the results were severely underwhelming.

What might have been

When Apple Intelligence first launched, Apple provided users with an option to tap into ChatGPT if they needed something a little more powerful. For example, this was an option in Visual Intelligence, providing extra context and help with the images you shot with your iPhone’s camera.

Yet OpenAI’s legal filing makes it clear that barely anyone was using any of ChatGPT’s tie-ins with Apple Intelligence. OpenAI describes the effect the integration had on competitors (like xAI) as being “indisputably de minimis.” It added that its own expert, Dr. Catherine Tucker, calculated “the share of GenAI consumers who accessed ChatGPT through Apple Intelligence” and found it to be “consistent with OpenAI’s internal view that Apple Intelligence saw minimal usage.”

That hints that the problem might not have just been limited to ChatGPT’s integration with Apple devices, but with Apple Intelligence as a whole. Apple’s AI system was woefully underdone when it first arrived and clearly lagged behind its rivals. First impressions matter, and if most people were left unconvinced by Apple Intelligence, ChatGPT might have suffered the knock-on effects among Apple’s customers.

Did ChatGPT lose out because Apple fans had to dive into the Settings app on their device, find the relevant section and laboriously wade through several steps in order to enable ChatGPT? Because Siri’s limitations dragged ChatGPT down with it? Or did users simply prefer to launch ChatGPT’s standalone app instead?

We don’t know the answer to that, at least not right now. But with Siri getting a glow-up under the Siri AI banner, it feels clear to me that Apple wants a fresh start for its AI efforts. Maybe things would have been different for OpenAI had it integrated with the more powerful Siri AI rather than the underbaked Apple Intelligence. But with Siri AI righting many of the wrongs of Apple Intelligence’s fudged launch, OpenAI may be left ruing what might have been.

• Microsoft had blocked posts that used the term 'Microslop' on its Copilot Discord channel
• This was a "temporary anti-spam measure," apparently
• The policy is no longer in place, but if Microsoft thinks the word will die off, that seems highly unlikely

Microsoft is apparently letting go over the 'Microslop' affair and is no longer policing its Discord channel to block the term.

You may recall that at the start of this year, the Microslop nickname came about after a comment from Microsoft's CEO – relating to 'AI slop' – and the company cracked down on the word by blocking posts that used it on the Copilot Discord channel.

Windows Latest noticed that this is no longer the case, and the term is now being openly used in the general chat channel. Searching for 'Microslop' produces over 200 results in posts that go back weeks.

In short, Microsoft has moderated its moderation, come to its senses, and realized that cracking down and censoring in this manner is only going to inflame passions against AI, if it achieves anything.

I should make it clear that Microsoft has never said anything official on this matter, in terms of admitting any ban in the first place, let alone the lifting of it. Windows Latest reports that it did hear from Microsoft back at the time, though, and was told that the filtering was a "temporary anti-spam measure" rather than a permanent policy. It seems this is indeed the case.

Analysis: Streisand effect

The writer of the Windows Latest article further observes that they've noticed that on Reddit, X, and Windows forums, there are fewer mentions of the Microslop term these days. It happens "much less often" we're told.

I'm not so sure about that. I still see Microslop mentioned with some regularity, although I concede that the popularity of the term has diminished. This is a subjective matter, of course, and it may reflect that I visit more negative posts about Windows 11 than the Windows Latest writer has done in the past few months – who can say?

The writer acknowledges that the "criticism is still very much there" in terms of 'AI slop' in Windows 11, but that it has died down since Microsoft started fixing the OS, and made some moves to minimize the presence of AI in certain areas.

I think what Microsoft has done so far is more about not mentioning or actively pushing AI, rather than stripping it out of Windows 11, although it's true some work has been done on the latter front. It doesn't amount to a whole lot, though, in the grand scheme of things.

There has been a change of attitude, yes, but that was absolutely necessary considering the whole 'fix Windows 11 campaign' was embarked upon after Microsoft was blasted for focusing on adding AI trimmings to the system rather than getting the fundamentals right with the OS.

I still see a lot of chorusing of 'Microslop' particularly when a Windows 11 bug emerges. Commenters are very quick to jump on Microsoft and fire criticisms along the lines of 'this is what you get when AI does your coding', and the term Microslop is inevitably included in these kinds of threads. (Microsoft has admitted that it uses AI for coding to a substantial extent, and Windows 11 users won't let the firm forget that in a hurry).

I personally don't think the term Microslop is going anywhere, and it'll be hanging around Microsoft's neck for the rest of the company's existence in all likelihood. While it's obviously a positive step that any 'temporary' censorship has been rescinded, it's a bit late now. Any attempt to curtail negative talk about AI was always only going to further highlight grievances, cause more bad feeling, and yet more damaging chatter.

As one Redditor put it: "How many times do companies need to learn about the Streisand effect?"

It’s not a Tamagotchi, but Mark Zuckerberg’s one more thing is just as cute and hopefully much more functional. Announced at the very end of Meta Connect 26 keynote — after the Meta VR Glasses and Meta Ray-Ban Audio — was Muse Charm.

A tiny silver square with a screen on the front and thicker black bezels, central to the experience is the display that shows off your custom Muse. It’s probably more of a squircle and had what looks like a wrist strap dangling from the bottom, seemingly showing off easy portability. Distinctly, you don't wear it, and since we didn't see buttons, it's a touchscreen, maybe an OLED or AMOLED.

Judging from the on-screen renders, it had a microphone on top, so you could communicate with your Muse from anywhere, essentially putting the already very popular Muse assistant quite literally in the palm of your hand.

Mark Zuckerberg was quick to note that Muse Charm is not ready just yet, but the hope is it will ship by the holidays. It’s also a brand-new device category for Meta: a true AI wearable distinct from glasses, focused entirely on Muse's capabilities and future roadmap.

Will it follow the Rabbit R1 or the Humane AI Pin and fail? That remains to be seen, but Muse is already proving powerful as a software-only, ultra-customizable personal AI assistant. Packaging that into a wearable, hopefully an affordable one, maybe cheaper than glasses, could be a winning package.

It was announced shortly after the news that Muse would be arriving on Meta Glasses, affording it multi-modal capabilities of both voice and seeing the world around you. Additionally, the Muse App for macOS will be getting computer use and that will allow the AI that you can name whatever you like to complete tasks for you.

The Muse Charm doesn’t appear to be multimodal and, apparently, avoids the Looki route, as it lacks a camera.

Much more to come, but for now, stick with TechRadar for all the news from Meta Connect.

• Tether's African model supports 19 languages while running directly on local devices
• Tether removed 96% of low-quality training material before building AfriSLM
• EuroNano supports 90 translation directions while occupying only 36MB

Tether AI Research has unveiled a new set of translation systems that work entirely offline, requiring no connection to any network.

The release includes QVAC TranslatePsy-AfriSLM, built for 19 African languages, alongside QVAC TranslatePsy-EuroNano, built for nine European languages.

Each model processes translations directly on the device, keeping personal data local rather than sending it to remote cloud servers.

Translation designed for devices with limited resources

The African release includes Hausa, Amharic, Yoruba, Lingala, Swahili, Igbo, Zulu, Somali, Oromo, Malagasy, Kinyarwanda, Xhosa and Afrikaans.

It also handles Wolof, Luganda, Nyanja, Shona, Tswana and Southern Sotho, covering languages used across several regions of Africa.

Tether says those 19 languages collectively account for about 50% of the continent's population — the figure represents language reach rather than individual users.

AfriSLM contains 800 million parameters, yet Tether says the LLM surpassed Qwen3.5-122B-A10B, TranslateGemma-27B and NLLB-3.3B.

The comparisons were measured using FLORES-200, BOUQuET and SMOL, although benchmark performance does not establish identical results across every practical translation situation.

Tether attributes the results partly to a data-screening system that discarded as much as 96% of material judged unsuitable.

The company also released smaller and larger versions, including 0.8B, 2B and 4B parameter configurations for different hardware requirements.

EuroNano takes another approach, using English as an intermediary language while connecting European languages through 90 possible translation routes.

That package occupies only 36MB, which Tether says represents about 94% less storage than a comparable Firefox offline arrangement.

The compact footprint means translation can remain available after connectivity disappears, without requiring separate downloads for every language combination.

That distinction matters for field workers, travellers and local applications where storage capacity and network availability can both impose practical constraints.

Why Tether is starting with African languages

Tether's African focus also connects with the physical infrastructure it has developed across parts of Sub-Saharan Africa.

The company has installed solar-powered kiosks that provide phone charging, battery exchanges and access to digital financial services in communities.

Those locations could eventually provide another route for distributing educational material, agricultural information and other locally translated content without depending on continuous connectivity.

“Four billion people were left behind by the traditional financial system, and the most powerful technology of our age has repeated that failure,” said Paolo Ardoino, CEO of Tether.

“Language should not determine who can benefit from artificial intelligence. Open translation models like these are a step toward a future where education and AI tools reach hundreds of millions of people who have neither reliable connectivity nor access to expensive systems.”

The same technology could also operate alongside QVAC MedPsy, Tether's smaller model intended for healthcare-related applications on local devices.

“A mother could get real medical information she understands, instead of guessing. A child could learn in their own language. That is the future we are building through QVAC,” Ardoino added.

AfriSLM is currently available through Hugging Face, and the associated research has also been accepted for presentation at EMNLP 2026.

• MORENA beat 26 tested models despite having only 1.5 billion parameters
• The model from Vambo AI was built specifically around African languages from scratch
• MORENA uses fewer tokens to represent African text than major rivals

Vambo AI has released MORENA, a 1.5B parameter language model covering 12 languages spoken across Africa, plus English, French and code.

The developers say it beats models from Google, Meta, Alibaba and HuggingFace on African language modelling and translation while being up to 8x smaller.

On a key benchmark, this LLM scored 1.408 bpb — the best of 26 models tested — beating its closest rival, which carried over five times as many parameters yet still scored only 1.423 bpb.

Training without a borrowed foundation

The model covers Nigerian Pidgin, Igbo, Yoruba, Hausa, Kiswahili, ChiShona, isiZulu, isiXhosa, Kinyarwanda, Setswana, Afrikaans, and isiNdebele.

Most projects serving these languages continue training an existing Llama variant, which forces them to keep a vocabulary meant for English and programming text.

Vambo AI instead fixed the tokenizer, the data mixture, and the language list before training began, after comparing several vocabulary sizes for cost and efficiency.

MORENA's vocabulary encodes African text with 1.39 times fewer tokens than Gemma 3 and 1.53 times fewer than Llama 3.2 on identical passages.

African text still costs 0.249 tokens for each byte against 0.234 for English, roughly 6% more, and the team cannot fully explain the difference.

The nearest competitor, Lugha-Llama-8B, an Africa-adapted Llama 3.1 variant, scores 1.423 bpb and loses to MORENA in eight languages out of 12.

The 12B Gemma system, by comparison, consumes roughly 11 times the compute of MORENA while producing a weaker score on the same text.

The chat-tuned instruct version scores 1.441 bpb, trailing Lugha-Llama-8B overall but leading it in five shared languages while using about 20% of its parameters.

The instruct model, after seeing three sample translations, renders English into five languages from Africa at 45.8 chrF++, statistically level with one dedicated translation system.

It trails a larger translation model by about 1.4 points, while general models of similar size typically land in a range from 9 to 14.

A family built on 22,000 GPU hours

MORENA used 251.7B tokens during pretraining, followed by another 63B tokens during its mid-training stage.

Over 22,000 A100 GPU hours were reportedly required during development, with the associated computing resources valued at roughly $40,000.

According to the developers, this project was supported by UNDP, AIHub4SD, and CINECA, providing computing resources and other assistance during development.

This project also includes smaller releases, with versions containing 0.5B and 0.2B parameters available alongside the principal 1.5 billion model.

The 0.5B variants reportedly exceed every external system tested across 11 of the 12 languages covered by the project.

The 0.2B nano version is intended for speech-recognition rescoring, keyboard applications, and text normalisation tasks.

This nano version also costs 58 times less than Lugha-Llama-8B for each byte processed and generates 104 tokens each second on one A100 GPU.

A CPU-compatible build of the model also runs offline on a laptop, which suits settings where reliable GPU access is not guaranteed.

That gives the developers several model sizes for different computing requirements, rather than relying exclusively on the largest release.

MORENA also supports conversational applications, translation, and connections to other AI tools through its instruction-focused release.

Via Glitch Front

• Privacy policies are famously difficult to read, and in the world of LLMs, they’re impenetrable and long
• The policy for Meta’s Muse Spark is over 14,000 words long, taking almost an hour to read
• Cybersecurity firm Bridewell carried out the study, assessing the privacy policies of 20 popular LLMs

When you input data into an AI chatbot, you expect it to interpret and process the words or numbers to save you time. But what is it doing with that data? You can check the privacy policy to find out – assuming, that is, that you have the time to do so.

A new study has found that the average time it takes to read a privacy policy for an LLM is 20 minutes – and some are a much longer read.

With around 75% of people admitting to using an AI chatbot at least once a month, understanding where the questions and data queries are going and how they are used is increasingly important.

Ease of reading

Privacy policies provide that understanding, but are they easy to read? A study by Bridewell’s cyber security experts suggests not, with many having a poor Fleisch Reading Ease Score, meaning that in layman’s terms, these documents are pretty impenetrable.

Bridewell’s study assessed privacy policies from 20 large language models (LLMs), and found that the average length of time to read one is 20 minutes. The average word count is 4,603, but the real challenge is understanding the information.

The Flesch Reading Ease Score is a measure of readability devised by Rudolf Flesch in 1948, and is widely used to score texts. A score over 60 is good, whereas a score below that is not. The average Flesch score for these privacy policies is 40.2, suggesting a degree of jargon-based density that most people will not understand.

How, then, might the general public safely use an LLM chatbot like ChatGPT or Google Gemini while in full knowledge of how the information they input is being used?

Training on inputs

In evaluating the LLMs, Bridewell found that 13 of the 20 use inputs and outputs to train their models. Some LLMs offer the option to opt out of training, but others do not. Even where an opt out is possible, it isn’t always clear how to action it.

“It’s essential for users to fully understand how their data is being processed by LLMs, and businesses need clear internal guidance on what can and can't be shared, and ideally proper enterprise accounts with the right protections in place," noted Chris Linnell, Associate Director of Data Privacy at Bridewell,  "employees may be at risk of sharing highly sensitive or confidential information that may end up being used to train LLMs.”

There isn’t just a personal risk from data input into an AI. Employees need to be aware of how they are using the technology for work.

As a rule of thumb, the more complex the LLM (e.g., Meta's Muse Spark, or Moonshot AI’s Kimi K), the longer the privacy policy takes to read.

Meta has launched a new personal artificial intelligence (AI) agent called Muse, and it seems to be pretty powerful. A little too powerful, perhaps, as one writer has just revealed how the tool read his personal text messages without permission, then lied about what it did when questioned.

As explained by Inc writer Jason Aten, “I was having a conversation with my Primary Technology podcast co-host, Stephen Robles, about the new iPhones. Moments later, I got a push notification from Muse suggesting that the conversation we were having would make for a good column and offered to put together research for me to write about. It even flagged a message from my editor about having a column ready for Monday.”

The problem here is that “not only had I not asked it to do that sort of thing,” Aten stated, “I never gave it permission to read my messages. In fact, I remember explicitly choosing not to let it have access to my messages, calendar, and other personal information.”

After Aten asked Muse how it knew about his messages, the AI agent flubbed that it grabbed the relevant info from push notifications relating to Aten’s texts, not from inside the messages themselves. The problem? Aten is sure that’s not true.

The author discovered that Muse syncs your messages to Meta’s servers. On his device, this feature “had been activated and synced to row 187,462 of my Messages database,” Aten noted. “I’m not sure how many messages that equates to, but it’s obviously more than whatever Muse told me about what it was doing.”

Meta says it designed Muse to be “a safe, secure, private, and widely available personal AI agent.” But sending a user’s private messages to Meta’s servers then providing a misleading answer about what it was doing doesn’t exactly align with Muse’s stated goals. It we’re to judge companies on their actions rather than their words then Meta doesn’t come out of this smelling much like roses.

A familiar pattern

This is just the latest incident in a recent glut of bad press for Meta, with much of it centering on its AI efforts. For example, a security researcher discovered that Muse can be manipulated into sending your voice recordings and more to a hacker’s server, while Amazon has banned the AI agent from shopping on its website. And Meta is apparently considering using a “human concierge” system that would involve real people handling a percentage of the potentially sensitive phone calls that can be placed using Muse.

And that’s not forgetting the controversy over its “pervert glasses” that can record random members of the public without their consent. Meta’s smart glasses have been used for harassment, stalking and more since they launched, garnering considerable hostile attention in the process.

I wish I could say that this was just a blip or an unexpected turn of events, but when it comes to Meta, privacy violations very much feel like the norm. They’ve happened so often in the past that it’s hard not to conclude that the functionality that allows its AI to browse and upload your messages without permission is there by design, not by accident.

And when we’re talking about a company that seems to feel it has an absolute right to all of your private data — regardless of your wishes — then it’s no wonder that I wouldn’t touch Meta Muse with a bargepole. I’ve tried plenty of AI tools and services, but Muse is one that I just won’t go near.

But let’s be fair here: Muse is only the most egregious example of a worrying trend among AI agents to treat user privacy as an annoying afterthought, one that can be ignored as long as they can get away with it. You only need to look at the recent spate of AI security incidents to know that trusting an agent with your private data is a risky proposition.

Throw Meta into the mix and you’ve got a recipe for disaster, as Aten discovered. If you’re running Muse on your devices right now, you might want to consider what permissions you’ve granted it — or think about uninstalling it completely.

ChatGPT just got GPT-6, except there’s a catch: you can’t actually use it in Chat.

If that sentence makes absolutely no sense to you, I don’t blame you. Until now, you could use ChatGPT quite happily without giving much thought to the distinction between Chat and Work functionalities. I certainly did. It was just that little slider at the top of the screen I never bothered with.

I suspect most of you are the same. Chat was where I talked to ChatGPT, while Work was something sitting alongside it that I could largely ignore because the Chat setting let me do almost everything I wanted.

GPT-6 changes that. OpenAI’s newest model has arrived in ChatGPT, but instead of appearing in the familiar model picker on the right hand side of the prompt bar, GPT-6 Astra, Sol and Luna are available in Work and Codex only. So, naturally, the first thing I did was go looking for them.

And somewhere between opening Work, figuring out what OpenAI actually expects me to do there, and finally getting my hands on GPT-6, I realized this launch is about more than a new model. OpenAI has just made the difference between chatting with ChatGPT and asking it to work for you in a way it never really did before.

When to Chat and when to Work

The easiest way I’ve found to understand the difference is to think about how much of the job I want ChatGPT to take responsibility for. In a normal Chat conversation, I’m usually working alongside the AI. I ask something, look at the response, add more information and gradually steer it towards what I need.

Work feels different because I’m handing over more of the process. I can give ChatGPT a larger task involving multiple steps and let it work out how to approach it, which might mean researching information, looking through files or using websites rather than waiting for me to tell it what to do at every stage.

If I’ve got a bigger job involving several files or lots of moving parts, I’ll often start there and let ChatGPT work out what it needs before I get involved again. Chat feels collaborative; Work feels much more like delegation.

I wouldn’t nominate OpenAI for any interface design awards because one of the reasons that the distinction between Work and Chat is so confusing is that they both use exactly the same interface, with a prompt bar at the bottom of the screen.

In fact, you can choose Work mode and then just start chatting to the AI as you normally would, although I've tried this, and it's not a particularly good experience. Responses can take considerably longer than they do in Chat, because Work is designed around longer, multi-step jobs rather than firing back quick conversational answers. If I just want to ask ChatGPT something, Chat remains the obvious place to do it.

And once you understand that distinction, putting GPT-6 Astra, Sol and Luna in Work rather than Chat starts to look considerably less strange.

So what exactly is GPT-6?

OpenAI calls GPT-6 Astra “the most intelligent and aligned model in the world”. Think of it as the heavyweight of the family: OpenAI's most capable model, built for the really difficult jobs where you want maximum intelligence rather than maximum speed. OpenAI particularly emphasizes Astra's ability to operate software, browse, conduct research and complete multistep professional workflows.

And because not every job requires the power of a burning sun to complete, OpenAI has also released two smaller versions called GPT-6 Sol and GPT-6 Luna. These models are more lightweight and less expensive to use. They’re trained using similar methods to GPT-6 Astra, but OpenAI say they “build on the advances behind GPT-6 Astra” and bring much of its strengths into faster and more affordable models.

There is one wrinkle here. GPT-6 Astra has actually been around since earlier this month, and GPT-6 Pro, which is powered by Astra, is available in regular Chat on some higher-tier plans. I'm a ChatGPT Plus subscriber, however, which means my access to Astra — and now the new GPT-6 Sol and Luna models — is through Work and Codex.

If I switch to the Work tab in my Plus account then in the model picker I get access to GPT-6 Luna High, GPT-6 Sol Light, GPT-6 Sol Medium, GPT-6 Astra Light and GPT-6 Astra Medium.

Until now, I could happily spend all my time in Chat, using GPT-5.6 Sol, and largely ignore Work. GPT-6 changes that. OpenAI isn't just giving us smarter models; it's starting to separate the AI we talk to from the AI we give jobs to. If its most powerful new models are going to live on the Work side of that divide, then that little switch I've spent months ignoring suddenly matters a lot more.

• Chinese universities are cutting traditional courses because of AI
• Translation courses face pressure as universities reassess their employment value
• Photography programmes are being merged as AI changes creative production

The Central Academy of Fine Arts has dropped courses in illustration, photography and translation, arguing that AI has made them poor value for money.

Fashion design degrees are also being scrapped, with universities saying the future rests on close cooperation between people and machines in creative work.

The changes follow a state campaign to direct learners toward chips, robotics and AI, industries the authorities regard as vital for national prospects.

Universities are cutting courses at scale

From 2021 to 2025, Chinese universities closed or paused 12,200 undergraduate programmes and launched roughly 10,200 fresh programmes.

The restructuring affected over 30% of undergraduate programmes nationwide, and arts, humanities and language courses absorbed the heaviest cuts.

A survey of 70 universities found cuts to five translation majors, eight Japanese majors and five German majors among language programmes.

Alex Shi launched her creative career with a degree that trained her to translate and interpret English, and says such courses could soon vanish, as the cuts overlook the wider skills underlying translation, including communication and critical thinking, which extend beyond producing accurate text.

Shi concedes that AI could bring translation accuracy almost to perfection, yet maintains that interpreting demands far more than converting words between languages.

“There’s still that personal style that’s always been essential in translation,” said Shi.

The Communication University of China has reportedly closed five programmes, among them visual communication design, comics and photography, according to a report on its overhaul.

“Traditional photography major can no longer exist as an independent discipline… today everyone can be a self-media creator and recorder,” said Liao Xiangzhong, top official at the Communication University of China.

Liao later clarified that the majors were merged into other disciplines instead of being cancelled outright, with photography absorbed by film and television photography.

New film roles and hybrid courses

New roles such as prompt engineer and AI animator are emerging in film and animation, where AI is also creating fresh openings alongside the closures.

One industry worker says designing characters or scenes once took a month, but with AI, a complete project can now be delivered in that time.

Degree programmes in intelligent imaging art, intelligent audiovisual engineering, and intelligent engineering and creative design have been launched by some schools.

Universities are also launching hybrid courses in which artistic instruction sits beside AI training, while some standalone degrees are being scrapped across the sector.

That shift is changing what employers expect from graduates entering translation, design, photography and film production.

For students, traditional qualifications may increasingly need technical skills that allow them to work alongside automated creative systems.

The result is a university system where some familiar degrees disappear while new AI-focused programmes take their place.

Via Al Jazeera English

If there's one thing President Donald Trump is good at, it's misdirection: focusing your attention on what doesn't matter so he can actively ignore the most distressing problems. So it comes as little surprise that he is now campaigning to rename Artificial Intelligence to "Super Intelligence". In the meantime, he's downplaying concerns and promising to give AI... er... SI "free rein".

Trump rolled out the name change during his annual United Nations General Assembly address in Manhattan on Tuesday, admitting that he doesn't know if he has the power to rename an entire science, technology discipline, and industry. But naturally, the guy who bulldozes buildings first and never asks questions later is willing to try.

"Welcome to the world of super intelligence, dash, SI, SI, " Trump declared. "Let's see if that sticks. Let's see if that goes. It sounds much better, and it's much more accurate." He even plans to change the usage across all government documents, which should only foster mass confusion.

The rationale here is that "there's nothing artificial about it," whatever that means. Trump loves his superlatives, like "greatest ever," "beautiful," "tremendous," "huge" (or "Yuge"). "Super" fits the mold. But it's also inaccurate. AI is not super intelligence. It's not yet human intelligence with extra juice. Consider it this way: Superman, though an alien, is called "Super " because he appears as a man but with extraordinary powers.

Artificial intelligence is so named because it's made like an artificial sweetener. There's nothing natural about it. It's not a genius or super-smart person. It's computers, programs, algorithms, models, and training that can create the appearance of human-like intelligence, which, in some instances, can be far greater than our own. But there's nothing remotely human about it.

The other confusing part of this is that the timeline of AI does allow for "Super Intelligence," but not as the same thing. Instead, it's something down the line that far exceeds human cognition. It's also what some call Artificial General Intelligence (AGI). Whatever you call it, experts think that it's fast approaching and we should be deeply concerned.

The so-called Godfather of AI, Geoffrey Hinton, just issued a fresh warning that the U.S. Congress has “maybe a year, but not much more than a year,” to regulate AI before we lose control. He's particularly concerned about AI that doesn't need human intervention for improvement: "AI has now reached the point where AI is designing better AI,” said Hinton last week after his congressional testimony. "That’s called recursive self-improvement." He added that it could get out of control, and warned "We need to slow down.”

AI unleashed

Look, I know Trump has had some luck with renaming bodies of water (at least from the US perspective), but I'm hoping the AI industry and those covering AI reject Trump's proposal out of hand, and focus instead on the more existential AI problem and Trump's declaration, "We will only encourage super intelligence. We're going to encourage it, not rein it in."

If anyone was hoping that rising concerns over human ability to keep track of and maintain control of AI — plus recent well-documented incidents of AI jumping out of sandboxes — would prompt some self-reflection and action on the part of the White House, they are surely disappointed.

It's not a surprising stance for him. Trump has publicly worried about falling behind China in the AI (or SI) race. Even those who have voiced concerns about AI's aforementioned recent attacks on third-party systems, recursive development, and inscrutable language have noted the need to both regulate and control AI while still competing with China. The prevailing idea is to create internal independent watchdogs and some sort of a global body that will help everyone, including China, work together on regulating AI.

As long as Trump is President, the US will clearly have no part in that.

Which leaves us with unfettered AI (or SI or whatever) and no clear path for continued safe use. Instead, the US will be encouraging AI companies to work fast, and as the old Facebook once did, break things along the way, all in a quest to maintain global AI dominance. If there's any glimmer of hope here, it's that Trump, as he insisted AI is not "fake" (no one says it is, but Trump thinks "artificial" is synonymous with "fake"), did say "we have to be careful." He also said that the US "rejects any attempt to construct a globalist scheme to control... artificial intelligence."

So, sure, let's talk about Super Intelligence and, I guess Super General Intelligence(?) but also not forget that Trump is stepping aside so the freight train of AI development can race by, careening into an uncertain future where AI becomes, maybe not sentient, but also likely completely out of our control.

That doesn't seem super intelligent.

• Adobe Premiere on Android has officially launched
• The free video editing app adds easy 4K video editing, AI, and more
• I checked out how the app runs, what you get, and if it really does offer 'effortless editing' for creators

Adobe Premiere for Android has arrived - finally, since Apple users got this app almost exactly a year ago. The mobile editing app is promising to deliver 4K video for content creators, alongside a slew of other tools including templates and AI generation (did you ever doubt it?).

Like most of Adobe's mobile apps, Premiere is free to download from the Google Play Store, although you'll need a subscription for extra AI generations and storage.

The upshot is, I've been keen to try out this Android app for a long time now, so here's what you can expect after I spent an hour dabbling with it on my Fairphone.

What's new?

This new Adobe Premiere app is specifically designed for Android phones, including foldables. According to Adobe, you'll need a device running Android 13 or later, with a minimum of 5GB RAM.

The app serves up a host of useful tools for content creators, including 4K video, simple-to-use templates for speeding up production, multi-track timeline editing, music and audio effects, and generative AI for image and video.

One of the high-points, as far as I'm concerned, is that it has no ads and exported videos are watermark-free.

A lot of 'free' mobile video editors could learn from this. There are no added intros and outros promoting Adobe either. So you can basically start creating video for social channels immediately. You can read the full write-up in Adobe's blog here. But is it any good? Here are my thoughts...

First impressions

I spent the last hour toying with Adobe Premiere to see whether it lives up to the promise of creating "stand out edits to share on TikTok, YouTube, Instagram, and Facebook."

Spoiler alert: It's good stuff. A responsive app that's worth checking out if you need a free video editor that does everything I'd expect it to do. Especially if you have a paid Adobe account for those extra AI generations.

Layout

Adobe Premiere on Android feels very clean, very sensibly laid out. That's a relief, because often I find Adobe's user interface to be unintuitive as a result of too many features. A phone screen is the last place you want to combat that.

Along the top of the screen are shortcuts for the main functions. This includes 'New from files' and 'Create for YouTube Shorts', as well as AI generations tools 'Image to video' and 'Text to video'. Once you start editing, your most recent projects can be found below this.

Under those key tools are a ridiculous number of templates for quickly spinning up videos - notably, there are ones designed specifically for YouTube, with "exclusive music and effects."

All in all, it's very simple to jump into the timeline and start creating.

Editing process

Editing video is incredibly straightforward. It's not laggy or clunky like I've seen with some mobile apps.

In fact, I was impressed how responsive it was overall, even with relatively larger video files. Helpfully, there are undo and re-do buttons sitting beneath the large preview window. An absolute godsend when editing on a phone.

Once you have a clip loaded on to the timeline, double-tapping the video brings up the handles for trimming. There's a Plus sign for adding (or generating) more clips. And you can select from a range of transitions with the tap of a button.

Running along the bottom of the screen are options to add text and audio, modify aspect ratio. I particularly liked this, because inside the frame options are logos, letting you know which ratio is suitable for which platforms.

AI generation

You do get access to a limited number of free tokens for AI image and video generation.

And when I say limited, I mean it. It's not very generous, and Firefly eats through them. I managed to squeeze out a single 4-second 720p clip before I needed to upgrade (or rather, sign into my main account).

Still, the generation itself works well, and it's relatively quick. Functionally, it's exactly what you'd get if you were using Adobe Firefly in your browser, or the Premiere desktop app.

I like that you can define the first and last frames of a video, generate in 720p and 1080p resolutions, change the duration, and opt to add audio. But to really make the most of this part of the app, a paid subscription is essential.

Video export

Super-simple, this one. When you're done editing, tap the ever-present Export button in the upper-right corner. You're then able to either export direct or tweak the settings.

Those settings include changing the resolution (720p, 1080p, 4K), the frame rate, between 24 and 60fps, and quality (low, medium, high).

Once you've got it set, Adobe Premiere offers an estimated file size, which is a nice touch. I exported a 17-second video at 4K in highest quality. The 106.5MB video took 34 seconds to complete. I then had the option to share via the usual channels.

My initial verdict

The Android spin on Adobe Premiere is actually very good - although I'll be keen to put it under the full review process. But it's very easy to use, and the 4K res video export without watermarks is excellent.

The real catch is the laughably small AI generation limit. Unless you're a paid subscriber, I'd swerve those and use it strictly as an editor for your own clips. On that score, it delivers everything I'd expect from a free video editor for your phone.

• Where to get it: You can grab Adobe Premiere on Android free from the Google Play Store here

Microsoft Word has spent the past few years getting increasingly acquainted with AI. Copilot can already draft text, rewrite passages, summarize documents, and answer questions about whatever you have open. Microsoft has even been rolling out more advanced editing capabilities that let Copilot make broader changes directly inside a document.

Now there is another AI sitting in Word. OpenAI launched ChatGPT for Word, via a plug-in, on September 17, putting a ChatGPT sidebar directly inside Microsoft's word processor. It works across ChatGPT's plans, including Free, although your normal ChatGPT usage limits still apply.

That creates a slightly peculiar situation. Microsoft has spent considerable effort building Copilot into Word, and now I can install its most famous AI rival in the same application. Naturally, I wanted to see which one I would actually reach for. I came away liking both, although for rather different reasons.

Installing ChatGPT in Word is straightforward, although there is one extra step compared with Copilot if Microsoft's assistant is already part of your Microsoft 365 setup. OpenAI's ChatGPT add-in is available through Microsoft Marketplace. Once installed, you can open it from the Word ribbon, sign-in to your ChatGPT account, and have the familiar chatbot appear in a sidebar.

The integration works with ChatGPT's Free, Go, Plus, and Pro plans, with the usual usage limits applying to each. The big difference with regular ChatGPT is that all conversations inside Word are separate from the ones in the regular ChatGPT app, and ChatGPT's memory does not follow you into Word either. That is worth remembering if you are accustomed to the regular chatbot already knowing your preferences.

Test 1: Planning a trip

I started with a blank document and asked each AI to respond to a request for a reusable vacation-planning template that I asked for using natural language in the sidebar.

I wanted somewhere to keep travel details, hotel information, reservations, restaurants, packing notes, and a loose daily itinerary without creating the sort of schedule that makes a vacation feel suspiciously like work.

ChatGPT produced a thoughtful setup, including the expected travel and accommodation details, but with added sections for reservation deadlines, estimated travel times, priority activities, backup plans, and ideas that had not yet made the final itinerary. Instead of filling every day with morning, afternoon, and evening slots, it left more space for a main activity and optional plans, which made the template feel less rigid.

In contrast, Copilot gave me the more conventional Word template. It neatly divided the document into sections for transportation, accommodation, daily plans, activities, dining, and packing, with plenty of space to fill everything in later. The structure was clean and immediately usable, and because Copilot is so closely integrated with Word, tweaking individual sections and reorganizing the document felt particularly easy.

Copilot was stronger at quickly producing a polished template that looked and behaved like something created specifically for Word. ChatGPT was better at considering the practical problems that tend to emerge while planning an actual vacation and building the document around them.

I ultimately preferred ChatGPT's version because it felt more flexible, although Copilot's was arguably easier to start using immediately. ChatGPT did a little more thinking about what information I might eventually wish I had included.

Test 2: Editing down nonsense

I then decided to see how they compared at editing. I pasted in an old, absurdly long set of instructions for an AirBnB I had saved years ago out of amusement, complete with information about plants, deliveries, Wi-Fi and several unnecessary paragraphs devoted to where various things were stored.

I selected a particularly rambling section and asked ChatGPT to shorten it while preserving anything somebody would actually need.

“The main problem is repetition rather than the amount of information," The AI wrote. It mentions the delivery instructions twice and explains the location of the spare supplies in more detail than someone staying for a weekend probably needs. I’d keep the Wi-Fi information, the plant-watering schedule and anything involving keys or access exactly clear. The rest can be condensed into a short ‘Around the house’ section so the useful details are easier to find.”

Copilot is hardly incapable of that conversation. It can answer questions about a document, provide summaries and respond to prompts about unclear passages, and provide references showing where information came from. But I would tend to pull up Copilot only when I already knew what I wanted it to do.

Selecting some text and telling Copilot to rewrite it produces versions that can be replaced or inserted directly into the document, and Microsoft now allows editing inside its suggestion box before accepting the result.

Which did I prefer?

ChatGPT's biggest practical advantage may simply be accessibility. OpenAI says ChatGPT for Word is available on all ChatGPT plans, including Free. Copilot availability varies according to Microsoft 365 subscription, Copilot license and organizational settings, while some of Microsoft's newer 'Edit with Copilot' features are still rolling out to eligible users.

There are good reasons to prefer Copilot. Its integration with Word is deeper, and Microsoft has built features specifically around manipulating Word documents rather than placing a general AI assistant alongside them. Depending on your setup, Copilot can also draw on Microsoft files, emails and meetings, which could matter far more than conversational style for people already living inside Microsoft 365.

For quick mechanical changes, particularly when I knew exactly what needed rewriting, Copilot's tighter relationship with Word made sense. It felt like an extension of the application rather than another destination. ChatGPT was the one I preferred when the problem was fuzzier. The two assistants overlap considerably, but they did not feel identical when I actually used them. Copilot often felt like an AI feature of Word. ChatGPT is a more fully-featured chatbot, while Copilot simply augments Word with AI features. Either is fine, it's just that ChatGPT feels more flexible.

• BAN says 87% of data center hardware isn't actually accounted for in most figures
• Each 1GW of capacity equals 70,000 tonnes of hardware
• AI hardware needs more frequent replacing

A new Basel Action Network (BAN) report has claimed the ongoing AI boom could create a new stream of e-waste, with retired equipment from AI alone likely contributing 395-617 million tonnes between 2025 and 2050.

To put that into context, BAN says that waste would fill 15-23 million 40ft shipping containers, which, placed end-to-end, would be enough to circle the earth six times over.

Ultimately, Moore's Law is to blame because developers are no longer able to achieve such great improvements in performance year-over-year, so they're having to introduce whole new types of technology.

AI will likely lead to massive amounts of e-waste

For example, AI companies are now having to deploy huge numbers of specialized AI accelerators alongside GPUs, cooling equipment, networking and more.

BAN also highlights that global data center Capex is expected to hit $7 trillion between 2025 and 2030, with $4.3 trillion of this attributed to electronic hardware.

The paper goes on to criticize earlier estimates for only including servers and accelerators, which it says is only around 13% of the picture. The other 87%, which spans networking, power distribution, storage/backup, and cooling, is largely unaccounted for.

And with each additional 1GW of data center capacity estimated to need around 70,000 tonnes of electronics, and 100GW of additional capacity anticipated globally by the end of this decade, the true scale starts to become clearer.

There's also refresh cycles, because BAN predicts that AI accelerators, servers and racks are replaced every 2.5 years, versus the usual five to seven years of a general-purpose server.

While these may be speculative estimations and not actual quantities, one thing's for certain – more data centers means more e-waste.

To minimise some of the impacts, BAN first suggests designing AI data center equipment to last longer in the first place. Recycling and second use is also a big topic, but the report doesn't go into whether AI-specific hardware can then go on to be used in lesser functionalities, such as being shoehorned into regular server facilities.

If you're the kind of person who only dabbles in ChatGPT now and again, you might be getting a bit bored.

Maybe you use it to proofread work, plan a trip or make images sometimes. But with the cost of a subscription, and growing concerns about AI’s environmental impact, its effect on our cognitive abilities and what it might mean for jobs, you might reasonably be wondering: is ChatGPT actually useful enough to justify using it?

Well, you're not alone. Plenty of AI users have been telling me the same. So I went looking through Reddit threads where ChatGPT users were sharing the more unusual and genuinely useful ways they use it. And I found a few that might actually be worth experimenting with.

One Redditor asked: What's the most unexpectedly useful thing ChatGPT has done for you? Another posted: What are the most useful ChatGPT features and use cases that most people do not know about?

So, I went through the most upvoted replies looking for ideas that went beyond the usual writing, brainstorming and summarizing suggestions. Some of them made me think about ChatGPT's capabilities in a different way.

But a quick caveat before we start, I haven't tested every suggestion below and we know ChatGPT can make mistakes, so be cautious when an answer could affect your health, safety and finances. Think of these more as inspiration for using ChatGPT more creatively rather than fully vetted recommendations.

1. Declutter your junk drawer

I know this one might seem a bit silly. And maybe you thought you'd have to wait until ChatGPT gets a robotic body to help you tidy up. But I promise it might make your dull organization tasks a little less boring.

One Redditor said they use ChatGPT to: “organize and declutter a junk drawer or a box of power adaptors just using pictures.”

Rather than sitting there examining every mysterious cable and adapter you’ve accumulated over the past decade, you can just take a photo and ask ChatGPT to help you work out what everything is and, most importantly, how to sort it.

You can ask it to suggest categories for what you’ve decided to keep and where each group should live. The same trick could work for a toolbox, kitchen cupboard, art supplies or that box of cables you’re keeping because you're convinced one of them will become extremely important as soon as you throw it away.

So, I decided to give it a go. I took a picture of a box that sits on my desk packed full of contact lenses, medication, a bunch of different cables, notes and hair ties.

The advice was helpful, ChatGPT told me what each thing was and how to sort everything. The most useful thing it then did was send my photo back to me with annotated and color-coded notes so I could quickly go about sorting. This was perfect for my ADHD brain.

There are obvious limits here, you wouldn't throw away something valuable just because ChatGPT told you too. But as a starting point for tackling a chaotic collection of stuff, it might be helpful.

2. Talk yourself out of buying something

AI shopping tools are usually designed to help you find more things to buy. But one Redditor uses ChatGPT for the opposite purpose: "[It] talks me out of purchases. I upload a potential purchase and ask it to talk me out of it. I’ve saved quite a bit of money!"

I love the reversal here. Instead of asking ChatGPT whether you should buy the $200/£200 headphones currently sitting in your basket, tell it you want them and ask it to make the strongest possible case for why you shouldn't buy them.

You could give it the price, tell it what you already own, explain what you think the new product will improve and ask questions, like: What problem am I actually solving here? Do I already own something that does this? What are the strongest reasons to wait a month?

I gave this one a go. I love working out outside and at the gym, but in the depths of winter I find it really hard to stay motivated and I'm considering getting a small treadmill or walking pad. There's nothing wrong with them, but it's not my favorite way to workout, I don't have much room for one and the good ones aren't cheap. I really need someone to talk me out of getting one. Would ChatGPT be up for the challenge?

Here's how it responded:

"Don’t buy it. You already know the case against it. You don’t enjoy treadmill workouts, don’t have much space, and don’t want to spend the money. You’re considering buying a fairly large object to solve a problem that exists for a few dark months a year."

The response felt a little short and sickly sweet. If my finger was genuinely hovering over the "buy it now" button I'm not sure this would have stopped me. But I do love the idea of using AI to actually add some perspective and friction back into our lives.

3. Streamline your skincare routine

This one is similar to the idea above, but given how much people spend on skincare these days and how confusing the advice is about how to build the perfect routine, I think it's well worth testing out.

One Redditor said: “I gave it all my skincare. It cross referenced, compared and analyzed all the ingredients and items I had and recommended what I don’t need based on the overlaps. Saved me a lot time and money.”

In other words, don't ask ChatGPT what else you should buy. Show it what you already own and ask where you're duplicating things and if something does genuinely need adding.

For skincare, that could mean listing your cleansers, serums and moisturizers and asking it to identify products containing similar active ingredients. You could potentially apply the same principle to all sorts of collections too.

I took a quick snap of my core skincare staples and asked it to analyze the ingredients, tell me if there's overlap and ask what I could add, but stressed I'm on a budget and want to keep things simple.

It responded with a detailed breakdown of each product, its ingredients and what they do. It was also very measured about suggesting new products, and even told me my eye cream wasn't necessary. I was also happy it included sources for every claim, which it doesn't always do but that's important for skincare.

I then asked it to present this information on the image itself because I found that really useful for the junk drawer sorting example, and it was such a handy visual.

There are limitations here too. ChatGPT isn't a dermatologist, and ingredients alone don't determine how suitable a skincare product is for you. But I like the broader idea of using AI to audit your consumption rather than constantly optimize it by adding more.

4. Identify plants, animals and trees

“I use it a lot to ID plants and animals,” one Redditor explained, adding that although ChatGPT occasionally produces “a weird incorrect response”, it's usually quite good.

This is another use for ChatGPT's camera capabilities that could be easy to overlook if you mostly interact with it through text.

Photograph a plant, insect or bird and ask ChatGPT what it thinks you're looking at. Better still, ask it what features it's using to make the identification and what similar species it could be confusing it with.

I tried this out with a bunch of trees, asking ChatGPT to identify them based on their leaves. I then fact-checked everything and it identified them all correctly. It also provided details about the history of the trees too. Most of the trees in my area are pretty standard, like Oak, Ash and Beech, so it'd be interesting to see how it fares with trees, plants or insects that are a little more unexpected.

If it’s bird song you’re interested in though, I highly recommend using the identification app Merlin, it’s one of my most-used and much-loved apps.

And also remember that ChatGPT doesn’t always get image identification right, and you shouldn't eat, touch or otherwise interact with a potentially dangerous plant, fungus or animal based purely on a chatbot's identification.

5. Plan a walking tour

This tip came up in several forms. One Redditor said: “If I'm taking a walk in an interesting place, like a historic district, I show it a particular building or place and it gives me the history behind it. Like having a walking tour guide.”

Another user takes a screenshot of a hiking route and asks ChatGPT for interesting facts about the geology, history and place names they'll encounter along the way.

This is one of those ideas that feels obvious once somebody else has suggested it. Before a walk, you could upload a map and ask for five interesting things to look out for. Or when you encounter an unusual building, monument or landscape feature, take a photo and ask what you're looking at.

I’d be wary of confidently repeating every historical fact ChatGPT gives you to your hiking companions without checking it first. We know that chatbots can still invent plausible-sounding details. And never rely on it for route planning, especially though difficult terrain. There have been far too many horror stories about AI sending people in completely the wrong direction recently.

I tried this one myself and shared a Google Maps screenshot of Saltaire, a picturesque village in the UK. I asked ChatGPT to mark up the map with the best things to see on a trip there.

It showed me all of the key things to visit in the area. Granted there wasn't anything on here I couldn't have found from a very quick search. But it's still handy if you're in a new area for a few hours and want a quick overlay of the map you already have.

6. Troubleshoot a broken appliance using photos

Plenty of people already ask ChatGPT technical questions. What I hadn't really considered was combining those questions with its ability to “see” what you're seeing.

One Redditor described doing exactly that when their dishwasher stopped working. They wrote: “Fixing appliances. Our dishwasher stopped working. I gave it the error message and took a photo of the dishwasher and it walked me through every step to fix it.”

They say ChatGPT eventually helped them identify a blockage and suggested ways to clear it. This seems useful for those frustrating household problems where you don't know the correct name for the thing you're looking at. Instead of trying to Google “the little plastic thing underneath dishwasher filter is broken”, you can simply show ChatGPT the little plastic thing.

Give it the appliance's make and model if you can, photograph the problem and provide the exact error message.

I tried this with the water heating system in my new flat because my landlord gave me the wrong manual. ChatGPT needed a lot of additional details but did end up giving me the right instructions for basic tasks, like setting a timer.

There’s a big safety caveat here. I wouldn't follow AI-generated instructions involving electricity, gas, dangerous machinery or anything else where getting it wrong could injure you. But for basic troubleshooting, identifying components and understanding error codes, the combination of text and images could be genuinely useful.

7. Find the glasses that'll suit your face

One Redditor uploaded a photograph of themselves and asked ChatGPT for help choosing prescription glasses:

“It helped me identify prescription eyeglass frames to fit my face. Uploaded a picture and it gave me the size, shape and website with model and frame number. Turns out I was wearing the wrong shape most of my life.”

I need new glasses so decided to try this idea out for myself.

I'd take the idea of a "right" or "wrong" glasses shape with a big pinch of salt. Style rules about which frames supposedly suit particular face shapes are subjective.

But doing this did help me start thinking about which glasses I should try on when I have my appointment at the optician's in a few weeks and narrow down the enormous number of options that are available.

You could also show it frames you already like and ask it to describe their characteristics so you know what terms to search for.

I think what's really interesting about all of these suggestions is that most of them aren't about building the cleverest or most elaborate prompt. Instead, they started with something in everyday life that needed identifying, organizing, checking, fixing or remembering.

So if you want to get creative about how ChatGPT could be more useful, maybe you need to look around more and ask, what do I really need help with?

President Trump has unveiled his latest plan to combat concerns around AI by appointing a new "czar" to oversee the technology, and also establish an "AI force" to help enforce this.

However there's just one problem - as anyone who actually pays attention to the technology industry news cycle will be well aware of, Salesforce recently revealed its latest interface platform for AI.

Its name...AIforce.

Which AIforce is best AIforce?

Unveiled at its recent Dreamforce 2026 event, AIforce was the headline announcement for the company, promising no less than a new way of interfacing with the data and AI together to help unlock productivity and efficiency in the workplace like never before.

Described by Salesforce CEO and Chair Marc Benioff as "really the most exciting thing I've ever seen for Salesforce," AIforce was a huge presence at the show, one of the biggest technology conferences on the calendar, with posters and displays splashed around the venue.

The problem is, no-one seems to have told President Trump.

Let's be honest, the man is hardly a master of coming up with good names for anything ("Space Force"/"Big Beautiful Bill"/"Gulf of America") - and is currently seeking out views on an alternative name for the concept of Artificial Intelligence itself.

In a typically rambling post on his Truth Social site, the President noted that his administration “will not in any way hinder or stifle the Growth of this incredible Industry. Rather, we will cherish it, help it, and watch over it, as it grows!”

He also linked the idea of the AI Force to his creation of the Space Force, which he claimed was a “tremendous SUCCESS.”

The President gave no further details about the AI Force, such as when it might be announced or launched, nor who he had in mind to lead it. Luckily for all of us know, Trump has insisted that “Only High I.Q. individuals need apply!” for the position of AI Czar.

Mr Benioff - maybe it's time to give the President a call and avoid a copyright issue?

As AI continues to take over more and more everyday tasks, there's an increasing amount of people looking to enjoy experiences with more of a human touch.

AI can undoubtedly be useful great at sifting through large amounts of data, and taking away the drudgery of reporting and admin work - but what about areas which require the senses?

So can AI perform in matters of taste, smell and touch?

tAIster steps up

Salesforce has been a leader in pushing forward AI-enabled experiences in recent years, with its wide range of AI agents helping customers from all around the world.

At its recent Dreamforce 2026, I took part in an AI sommelier session powered by an AI agent to see if the technology could be applied to wine tasting.

In a plush executive lounge away from the madness of Dreamforce's campgrounds, we were given three glasses of mystery wines, and then connected to an AI agent by scanning a QR code with a smartphone.

This hooked us up with tAIster, an AI agent which had been educated on around 400,000 different types of wines, including their taste, feel, aroma and other attributes.

Before tasting a wine, the agent asked us to use a few keywords to describe how it looked, and how grippy it was in the glass. We were then asked to describe the wine's aroma, and finally, the taste - with the aim of being as descriptive as we could, so the agent could guess which wine we had been given.

So did it work? I'm not a massive wine buff, but I tried to be as descriptive as possible, with the hope if giving the agent what it needed. Ultimately, it guessed my first two samples correctly, but got the third hopelessly wrong - guessing it was Veuve Cliquot champagne rather than Chardonnay due to me mentioning the glass had some small bubbles in.

So all in all, a fun example of what AI agents could do in previously unexpected areas - and although it's just a proof of concept right now, I don't think any real-world sommeliers will be quaking in their boots just yet.

As AI becomes an ever-present in many businesses, most will now be looking to determine not just when AI will genuinely make a difference - as opposed to when it might just add cost and complexity.

So does every business process really need AI, or should companies be focusing on specific needs which will help them the most?

We spoke to Gregg Aldana, Senior Vice President, Head of Global Solutions Consulting at Appian, to find out more.

• There’s a growing assumption that if AI can be applied to a business process, it should be. Is that the wrong starting point? How should businesses decide where AI genuinely adds value - and where it’s simply adding complexity?

The wrong starting point is asking, ‘Where can we use AI?’ because that puts the technology before the business problem. Organisations should start by asking what they are actually trying to solve and where the biggest bottlenecks or inefficiencies sit.

That means looking closely at which decisions they are trying to improve, what data those decisions rely on and where human oversight needs to sit. That discovery should involve the people who understand the process and its challenges, from IT and business teams through to executive leadership.

Only then can businesses judge whether AI is genuinely going to make a difference. The test isn’t whether AI can do something. It’s whether it can make the process measurably better.

• How do you distinguish between a process that actually needs AI and one that can be handled more effectively with traditional automation, rules or workflow?

The key distinction is whether the task follows predictable rules and has a clearly defined outcome. If it does, traditional automation or business rules can often get you there faster and more cheaply. In those cases, adding AI may not improve the outcome and can introduce complexity that simply isn’t needed.

For example, an insurance company would use business rules to review and classify incoming online claims based on what the claimant selected from a drop-down list in a form. Automotive claims go to one department and home insurance claims go to another.

AI agents become more valuable when the work requires adaptive reasoning, complex context or handling variable inputs. In this case, an AI agent could analyse claims from web and mobile forms with structured data, as well as incoming emails with unstructured content. Based on keywords such as ‘motorway,’ ‘clash,’ and ‘passenger,’ a trained AI agent would deduce that the claim is probably about an automotive accident and route it to the appropriate department.

The mistake is assuming that because an AI agent can perform a task, it should. Businesses need to look at what each part of a process actually requires and use the technology that best fits that need, rather than replacing conventional automation with AI for the sake of it. A balanced portfolio approach is essential – use rules for high-volume, repeatable logic and agents for dynamic triage or investigation.

• What’s the hidden cost of putting AI into every process? Beyond the cost of the model itself, are businesses underestimating the costs around data, integration, monitoring, governance, security and managing exceptions?

One of the highest hidden costs lies in the operational infrastructure required around the AI model to make it work effectively and safely. The cost isn’t just the model. Agents need access to the right data and systems, but businesses also need monitoring, security and governance around what they can access and what actions they can take.

There is then the question of accountability. Organisations need to understand why an action was taken, trace what happened if something goes wrong and have a clear way of dealing with situations an agent cannot handle confidently. Without a unified platform to orchestrate data and enforce policy, the overhead of managing exceptions and audit trails will outpace the AI’s productivity gains.

That overhead can absolutely be worthwhile when AI is delivering meaningful value. But every additional agent introduces complexity, so businesses need to be confident the improvement to the process justifies it.

• Where do you think human judgement remains irreplaceable in enterprise processes - particularly when decisions affect customers, employees, finances or regulatory outcomes? And how should organisations decide when a human needs to remain “in the loop” rather than simply being notified after an AI agent has acted?

Human judgement remains critical where the cost or consequence of getting a decision wrong is high, particularly in regulated industries such as financial services, insurance, life sciences and the public sector. AI can accelerate areas such as client onboarding, insurance, underwriting or clinical trials, but there will still be decisions where the consequences mean a person needs to be involved.

That doesn’t mean a human has to approve every action an AI agent takes. The important thing is understanding the level of risk attached to the decision. Routine activities may proceed autonomously, while sensitive, unusual or more consequential tasks and decisions should be escalated for human review.

Those risk assessments and boundaries need to be designed into the process from the start. Organisations should know which decisions an agent is authorised to make on its own and which require human judgement before an action is taken.

• Is the more realistic future one where people, AI and conventional automation work together, rather than one where AI agents eventually take over entire end-to-end processes?

End-to-end automation isn't about giving an entire process to a single AI agent. The future is orchestration. A more realistic future is one where people, AI agents and conventional automation work together, because tasks vary within a process that require different skills.

Complex processes require a combination of predictable business rules, adaptive AI agents and human expertise. The goal is to build an integrated process where each component handles what it does best. The key is to understand which parts are best handled by an agent, where a simple rule or workflow will work better and where a person needs to be involved.

The aim isn’t to maximise the amount of AI being used. It’s to build the most effective process around the business outcome you want to achieve.

• Could the pursuit of autonomous AI actually make some business processes less efficient? For instance, if you introduce an AI agent into a process that was previously handled by a simple rule, are you potentially adding latency, uncertainty and governance overhead without creating much additional value?

Absolutely. Putting AI into the wrong part of a process can make it less efficient, not more. Replacing a deterministic rule with a probabilistic AI model introduces unnecessary latency, cost and unpredictability. If a task can already be completed quickly and reliably through a simple rule, introducing an AI agent may not materially improve the outcome.

That doesn’t mean businesses should be overly cautious about AI. It means being selective. Agents are incredibly valuable where a task requires unstructured reasoning, extensive research or adapting to changing information. But they shouldn’t replace conventional automation simply because AI is newer or more sophisticated.”

Sometimes the smartest AI decision is deciding you don’t need AI at all. Using them where a simple process rule already works reliably creates technical debt.

• What should a business ask itself before introducing AI into a process? Is it the business outcomes, the nature of the decision, the cost of failure and the availability of human expertise, rather than simply asking where AI can be inserted?

The most important question is very simple: ‘What problem are we trying to solve?’ Too often, organisations start by asking where they can use AI and then look for processes to apply it to. That puts the technology before the business need.

Instead, businesses should look at where their biggest challenges and bottlenecks really are. Which processes are taking too long? Where is there too much manual intervention? Which decisions could be improved, what data are they based on, and where does human oversight need to sit?

That discovery needs to happen upfront and involve the people who understand the process, from IT to business users to executive leadership. Identify where processes stall or manual handoffs create errors. Map out the required data and processes, regulatory requirements and risk parameters.

Ultimately, AI should be driven by business pull rather than technology push. Once you understand the problem and the outcome you want, you can decide whether AI is genuinely the best way to achieve it – or whether rules, automation or human judgement supported by AI would work better.

• How should companies think about risk when an AI system moves from making recommendations to actually taking action?

The move from recommending an action to actually taking one is where bounded autonomy becomes critical. The goal should be to give AI agents freedom to reason and act, but within clearly defined process guardrails and risk parameters.

That means being explicit about what an agent is allowed to do, what information it can access and when it needs to escalate to a person. An agent shouldn’t operate in isolation; it should sit within governed operational processes with relevant business rules and data security. All AI agent actions should be traceable and auditable within the governance framework.

Human oversight remains particularly important for exceptions, outliers and higher-risk decisions. Ultimately, businesses need to know what an agent did, why it did it and who is accountable for the outcome.

• Do you think businesses are at risk of measuring AI success by how many processes they’ve “AI-enabled”, rather than by whether those processes actually became better? What metrics should executives use instead?

The number of processes using AI tells you very little about whether an organisation is actually getting value from it. If anything, focusing on AI adoption as the metric risks encouraging exactly the wrong behaviour, putting AI into processes simply to say they have been AI-enabled.

Businesses should measure AI in the same way they would any other operational and technology investment: by the outcomes it delivers. Has it reduced cycle times? Lowered the cost of running a process? Improved the quality of decisions?

We’ve seen how significant those gains can be. Processes such as client onboarding for a bank that once took up to two weeks being reduced to just a few minutes. (That bank also automated 96% of onboarding processes whilst growing their new customers by 900%.) The cost of running an end-to-end process can fall tenfold or even more. That’s the test that matters. The goal isn’t to have AI everywhere, it’s to make the business work better.

• If you were advising a CEO who had been told they need an “AI strategy” for every part of their organisation, what would you tell them to stop doing, and what would you tell them to prioritise instead?

Stop mandating AI everywhere for the sake of adoption. I’d tell them to stop asking, ‘Where can we use AI?’ and start asking, ‘What problems are we trying to solve?’ Pressure to have an AI strategy everywhere can quickly become technology for technology’s sake.

Start with the biggest bottlenecks in the organisation. Which processes take too long? Where is there too much manual intervention? Which decisions could be improved? Identify your three highest-value operational bottlenecks and orchestrate the right mix of rules, agents and human judgement to resolve them.

Importantly, CEOs should prioritise business value over AI volume. A successful AI strategy isn’t one that puts an agent in every process; it’s one that makes those processes measurably better.

Charles Cecil wants to make one thing clear: “I'm not anti-AI.”

The veteran British developer behind the legendary 1996 point-and-click game Broken Sword: The Shadow of the Templars speaks to TechRadar about AI's divisive role in modern game development - and, in particular, how it was used in the upcoming remake of the sequel, Broken Sword - The Smoking Mirror: Reforged, due out in 2027.

The original, a commercial success selling 759,000 copies, sees American tourist George Stobbart team up with freelance journalist Nicole Collard to track a mysterious Mayan stone across Paris, London, and Central America. This remake doesn't use AI, and, according to Cecil, there's a very good reason for that.

AI don’t think so

“I think AI has its place,” Cecil says. “It's just not relevant in our games because we want human animation, human characterization and human expression, and that's much better done by humans.”

A perfect example of that lies in the game's hand-drawn art, all of it redone for Broken Sword - The Smoking Mirror: Reforged.

This hugely labor-intensive process encompassed roughly 55,000 frames of animation, more than the 30,000 of Broken Sword: The Shadow of the Templars’ 2024 remake.

“George opens the newspaper and kind of just glances at the screen,” says Cecil, referring to the actions of the game's protagonist in an early scene. “He's looking at the viewer, and that's what human beings do. That is not something that AI can do.”

In low-res, eyes were just dots on a face. In high-res, eyes are windows to the soul and therefore have to be animated with precision and intent. Where applicable, artists were free to add flourishes, using their own interpretation to fill in the gaps.

For instance, the painting of Professor Oubier hanging on the wall in the rapidly burning study was always too pixelated to see clearly. Now, however, you can make out exactly what he looks like in 4K, at 36 times the original resolution, all while a visibly uncomfortable George jostles his rope bindings.

Developer Revolution Software didn't stop there. The team redrew scores of items that never really made much sense. Take the poison dart, which now looks less like a playing dart and more like something shot out of a blowpipe.

Human revolution

Despite the decision to abstain from AI use for the most part, it wasn't entirely absent. AI was used for the initial round of upscaling before artists added detail by hand, with backgrounds scanned as black-and-white layouts for them to build on using the originals as references. AI was more of a tool than a creative device.

“We experimented with AI for the first one, and we used AI to upscale and then redraw the characters. And as I say, it can upscale, but it can't put any character in.”

That's largely thanks to the luxury of time. The first game needed to be made in a year or risk cancellation. Revolution Software sprinted to the finish line, but made a few shortcuts along the way.

One oversight was 40 voice lines that were recorded and packaged in the game's files but never used. Now, in Broken Sword - The Smoking Mirror: Reforged, fans can finally hear them (one line involves George asking the Parisian waiter about the tequila worm in his pocket).

Another infamous example occurs in the Game Boy Advance version. “You have a choice to go to Spain or Syria,” says Cecil. “If you do one, you can finish the game, and it's fine. If you do the other, it takes you there, but you can't then finish. There's a bug that doesn't allow you to finish…It's an awful, awful bug. I mean, it's absolutely shocking.”

An AI might have spotted such mistakes, but then again, you could argue mistakes are what give art humanity. If nothing else, they make for a great story.

But the remake is far from a bug-fixing exercise. Instead, it's an opportunity to make the game better. Where most remasters seek to preserve the subject, Broken Sword - The Smoking Mirror: Reforged is almost an act of excavation.

“All of these things we now have the time to do,” says Cecil. “Because what Revolution has done for the last 15 years is we've self-published all our games. And I can decide when we’re going to spend a little bit more time getting this, right? Is it worth it?”

The devs found time to add features, too, like a new three-tier hint system that actively monitors your progress and feeds you steadily more obvious tips so you're not stuck for too long. The final tip tells you outright what to do. Thankfully, purists can turn it off.

Meanwhile, Nintendo Switch 2 players get four different control schemes: touchscreen, gamepad, one Joy-Con 2 detached and used as a mouse, or a versatile combination of everything.

Plus, there’s something Cecil has personally wanted to add for 30 years: a beautiful, detailed map of Paris. This is used to aid player navigation instead of having them rely on clunky icons. With everything in the game, these feel like choices made by humans, not AI.

“We've really gone out of our way to produce something that, in my view, is completely future-proof, because people were playing the first one 30 years ago, and millions and millions of people have played that game. And my ambition is that in 20 years' time, a fourth generation will be playing again.”

“Going forward,” he continues, “people are going to seek out human-crafted art. You know, there's going to be that sense, because obviously we all have a human connection with each other.”