• Moonshot has launched a new AI model, Kimi K3
• It's surprisingly powerful, with the Chinese AI firm claiming it outguns most US rivals, save for a couple of exceptions
• Kimi K3 is open weight by nature, which poses a further threat to the likes of OpenAI and Anthropic

Moonshot, one of the emerging Chinese AI giants, has just revealed a new AI model which is seemingly up there with the likes of ChatGPT and Claude.

Bloomberg reports that Moonshot's new Kimi K3 model can equal the best that the US has to offer, at least based on the company's own benchmarking. Seemingly it outguns all rival AIs save for Claude Fable 5 (from Anthropic) and GPT-5.6 (from OpenAI).

Kimi K3 is a model with 2.8 trillion parameters, Bloomberg tells us, and Artificial Analysis ranked it ahead of Anthropic's Opus 4.8 on some benchmarks.

Moonshot also claims it beats Chinese rival Z.AI for coding tasks, and overall, the performance of the new model has caught the market by surprise.

Moonshot notes in a blog post that Kimi K3 is the "world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning."

Kimi K3 is available to use now. Bloomberg quotes Leonid Mironov, a portfolio manager at Gavekal Capital, as saying: "In my use, it's clearly the best Chinese model ever," noting that it's "brilliant" no less.

Analysis: a weighty threat

This is a threat to the big US players in the AI market for several reasons.

The key difference with Kimi K3 is that it's what's known as an "open weight" model, meaning anyone can grab the model to run it for themselves – from July 27, when the weights are released – without paying anything. It's not the same as open source, though, as while you can get the model, what you don't get to do is peek behind the scenes at how the model was trained (and on what data).

The other caveat is that running Kimi K3 takes some extremely powerful hardware; but nonetheless, for firms with the substantial wherewithal to do that, the pre-trained model is there for the taking at no cost. So, you can imagine how this open weight approach is threatening to the AI behemoths in the US (and this is presumably the point of going this way for the Chinese rival).

What will also be a concern to the likes of OpenAI and Anthropic is that Moonshot is attacking one of the most lucrative aspects of AI, with Kimi K3 being pushed for its coding skills. However, Moonshot is charging a lot more than Chinese rivals for those who want to use it, and in fact, it's priced around Claude Sonnet levels, so on a par with the current cutting-edge (frontier) AI models.

That in itself is a signal of the quality on offer here, and why Kimi K3 has raised quite a few eyebrows. As the competition around AI heats up, there are also concerns about whether that means safeguards will be increasingly overlooked in favor of faster development and progress (which has been a consistent source of worry for many as it is).

Adding to all the controversy are accusations of AI theft leveled by the US State Department at Chinese firms earlier this year, Moonshot included.

• Forrester analysts warn around four in five leaders and ITDMs envision having larger budgets in 2027
• Consumption-based AI pricing is making it harder to predict outlay
• Targeted investment to improve data quality is key

Forrester is predicting software budgets could be set to rise, with more than four in five leaders expecting to increase overall budgets over the next 12 months and 82% of tech decision-makers expecting larger budgets.

While some of the extra cash could come as a result of increased confidence and readiness to spend on tech, the company's analysts warn that a shift in pricing structures could also be forcing companies to fork out more.

This comes as software vendors shift from traditional per-seat licences to token or credit pricing, which introduces so many more variables including model selection, context size, output length and agent operating time, leading to far more unpredictable outgoings.

The real reason businesses are preparing to spend more on software

"Business leaders are no longer planning for a return to stability – they’re planning for a future where volatility is a constant,” Chief Research Officer Sharyn Leaver noted.

Recent shifts from major AI providers all point toward this emerging pricing model becoming the norm, with GitHub moving its Copilot plants to usage-based billing in June and OpenAI adding pay-as-you-go Codex seats in April. Anthropic also recently removed Fable 5 from its standard subscriptions and seat-based models over difficult-to-predict demand, but set out plans to reintroduce it where capacity permits.

Acknowledging these major shifts, Forrester's report reveals two areas where companies can increase their budgets for 2027 – building machine-readable context and enterprise knowledge, and increasing brand visibility in answer engines.

The report also hints at the major role AI can play in marketing and customer-facing experiences, and the potential use that synthetic data can provide subject to testing. All in all, it's more about targeting investments rather than throwing cash at the problem, as Leaver concludes:

"The organizations that outperform in 2027 won’t be those that spend the most on AI. They’ll be the ones that invest in the foundations that make AI effective."

• Anthropic has a new 'hard questions' ad for Claude and AI
• Reactions to the short clip have been mixed
• OpenAI boss Sam Altman says the ad feels like satire

Anthropic has a new ad out for its Claude AI chatbot, and it ties into its Hard Questions project — an attempt to bring attention to some of the main concerns and hopes that people have about the future of AI. Will it take my job? Can it lead to new scientific discoveries? Why is there a new data center in my neighborhood?

You can see the short film below, which features voices "from real people" that Anthropic has spoken to. Alongside shots of burning buildings, lab researchers, and racks and racks of hard drives, we have ruminations over whether AI can be trusted and how it might affect communities, education, and what it means to be human.

Anthropic has boldly left the comments enabled on the video, and it's fair to say there's a mix of opinion: various posters have branded the clip as "dystopian marketing slop", a sign of "dark times", and "pretty horrifying". One commenter points out that the final "keep thinking" tagline is a strange choice for a technology that generally makes us think less.

There are plenty of positive opinions too, with some calling the ad "profoundly beautiful", "great", and "thought-provoking", and even "the greatest commercial I've ever seen" in one case. Quite a few YouTuber users in the comments are simply asking for more access to the latest Claude Fable AI model.

Questions and answers

The ad has even caught the attention of Sam Altman, CEO of Anthropic rival OpenAI. His response to the clip was to say that he "thought this was satire", though it's worth remembering that he's not the most neutral of observers. Anthropic CEO Dario Amodei was previously vice president of research at OpenAI.

What we can say for sure about the ad is that it's addressing many of the issues that are in the public conversation right now when it comes to AI technology. Anthropic has also launched a Hard Questions web portal where you can listen to some of the conversations the company has had with people across the US.

The topics covered include the use of AI in medical diagnosis, safeguards around the misuse of AI, the resources needed to run data centers, how the technology might impact our critical thinking skills and creativity, which jobs might ultimately be replaced by AI, and what it's going to be like for kids to grow up with AI.

Clearly this debate is going to go on and on across the years to come, but it does feel like the tension has never been greater between the hype and the backlash around AI. Big AI companies like Anthropic are going to have to work hard to keep users and governments on side as the technology becomes even more powerful and pervasive.

OpenAI has officially launched its first piece of hardware, and it's called the Codex Micro. It might sound like a retro computer in a Dan Brown book, but the Codex Micro is actually a very compact keyboard for coders.

How does that work? In this article we're going to dive into exactly what the Codex Micro is, and the point of the device, as well as how it aims to make life easier for programmers – and then we'll come on to the reaction of those coders (hold onto your hats, folks).

What is the Codex Micro?

As already mentioned, it's a keyboard, but a keyboard with a very specific purpose. OpenAI produced this peripheral in conjunction with Work Louder, and it's actually a tiny keyboard – more of a keypad, if you will, or a 'macropad', in the same vein as pads that Work Louder has produced before, like the Creator Micro 2 – made for coders who are using Codex.

What's Codex? It's OpenAI's coding agent which can assist humans in writing programs, tackling debugging tasks, or indeed writing code from scratch prompted by simple natural language instructions (vibe coding, as it's known).

What does the Codex Micro do exactly?

The idea with the Codex Micro is that you have a compact keyboard that easily allows you to switch between coding tasks (agents) with dedicated keys that have RGB lights to indicate their status at a glance. If there's an unread chat, you see a green light, or an orange one if user approval is needed, and so on.

There are other keys for voice dictation (hold and talk to give instructions – there isn't a built-in mic, by the way, this is triggering your laptop's microphone), a dial to adjust the reasoning level or how deeply the AI agent is thinking (though it can be changed to modify other options), and a joystick that you can map to what you need. The entire keyboard is fully customizable, it should be noted.

All of this is designed so that coders can quickly give instructions, see what's happening with agents and tasks at a glance, and swiftly switch between them, making changes in an easy and convenient way.

Who will buy the Codex Micro?

Obviously enough, coders who use Codex are the target audience. Think of it this way: if Codex is a faster way of coding, OpenAI wants the Codex Micro to be a further hardware-based speed boost to this whole process.

It costs $230 in the US (around £170, AU$330), although it isn't yet in stock, and clearly this isn't a consumer-oriented device.

However, OpenAI does have plans for consumer hardware. If the rumor mill is to be believed, it could have a wearable in the works – or indeed a smart speaker, going by more recent speculation. According to Bloomberg's Mark Gurman, this will be a smart speaker that uses mechanical elements to create a "sense that it is alive" and it'll tap your emails and other personal data to better "understand" you. Sounds frightening, right? Truly scary. It is, however, just a rumor.

What's the general reaction to the Codex Micro from programmers?

That smart speaker I just mentioned has already prompted a good deal of antagonistic reaction, and looking at the posts about the Codex Micro, it isn't faring much better than that rumored creation.

Notably there are a number of Redditors who are questioning whether this is April 1, and some kind of joke. There's quite a lot of shoulder-shrugging going on, or observations that you can easily buy a cheap macropad and DIY your own solution along these lines for a tenth of the price OpenAI is charging. Or indeed that there could just be a simple phone app version of this product (with on-screen buttons and sliders).

As one Redditor puts it: "Yeah it's like every big tech product now. Instead of a full keyboard, it's just 12 keys, and it's $230. It honestly feels like a prank and not a real product."

Others feel this is something that serious coders won't touch with a ten-foot pole. And I can't count the number of 'it looks like they asked ChatGPT to design a great product and ran with it' comments which are on Reddit.

Thus far, then, the reaction has been roundly negative from the target market. There are hardly any posts saying, 'I'm going to buy this', and there is a lot of disbelief being expressed at the price tag attached (even among the thin ranks of the interested parties).

The broader idea might be for OpenAI to test the hardware waters with a kind of initial practice launch here, ahead of the bigger consumer product that's coming, whether it's a smart speaker or wearable (maybe both). Although if so, quite why the price of the Codex Micro is pitched so high is a bit of a headscratcher – or maybe that's a test of sorts, and a sign of things to come, as well?

• Neo Beta robot hands have human-level dexterity
• They use tendon-like controls instead of in-hand motors
• They're waterproof and can perform many skills normally reserved for human hands

Neo's new robot hands are so good and lifelike, you might assume that they're gloved human hands, and then you might wonder why someone is swinging a hammer at them.

In a new demo video recently released on YouTube by Neo Beta robot parent company 1X, you can watch a pair of Neo Hands screw in a lightbulb and pull the chain switch (before someone inexplicably shatters the bulb with a hammer), pluck grapes off a bunch and drop each one in a container, carefully pickup a screw, unzip a jacket (a bit creepy), and even open a small bag of Funyuns onion ring snacks.

In that last bit, someone swings a hammer at the hands while they work, which they pay no mind to, before the bag is unsealed.

It even expertly builds a small Lego stack. Okay, okay, they're the larger Duplo blocks, but it still does as good a job as your average kindergartner.

The hands move slowly but also with a grace and ease you might mistake for humanness. This appears to be down to the underlying technology.

As 1X described it, the rubber-covered, waterproof hands use a "closed-loop tendon-driven system". This means that 1X moves the motors or servos out of the hand and back along the arm, which keeps the hand smaller and more supple. Those motors are then connected to an intricate system of tendon-like connectors that are pulled and released to enable the robot hands' movement and manipulation. That style of control more closely resembles our own hands, which, while including muscle, are also filled with tendons that are pulled inside the forearm.

1X says the fingers, palm, and thumb have 25 degrees of freedom, but, as evidenced in the video, they can also over-extend backward in a rather unnatural or certainly more-than-human way.

The robot hands feature some impressive strength, too, lifting a 20 lb. dumbbell and then, with just one finger, curling a smaller pulley weight.

Naturally, the fingers include sensors so that the hands and robot know when it's gripping something and how much force is or should be applied. That's how the Neo robot hand avoids breaking that light bulb (unlike that hammer).

Next-level grace

I know, just last week, we heard about a pair of robots performing gall-bladder surgery on a pig. You'd think that those humanoid hands must've been far more graceful. However, those robots each simply gripped a pair of laparoscopic controls so that the finer control, where the cutting and suturing was done, happened at the end of those devices. No robot hand directly manipulated the scalpels.

While Neo's robot hands will be useful for all kinds of home helper chores when the $20,000 Neo finally arrives in consumer homes (early adopters may be receiving them now), the hands are also useful for helping itself: In the video, a Neo Beta robot uses the hands to pick up its MagSafe-style charging puck, which it carefully attaches to its robot hip.

1X writes that these hands will deliver new real-world training data to its robotic development, and one would assume this will ultimately make the Neo robots even better home helpers, companions, and enablers (all those Funyuns).

• There's been a notable first for brain-computer interfaces
• A commercially-sold BCI has been implanted for the first time
• It comes from Chinese company Neuracle Medical Technology

There's been a notable advancement in the field of brain-computer interfaces (BCI) this week: surgeons in China have successfully implanted a commercially sold BCI in the brain of a patient for the first time.

As reported by South China Morning Post, the device is called the Neural Electronic Opportunity (NEO), and is manufactured and sold by Neuracle Medical Technology. It's the size of a coin, and is fitted with eight electrodes.

While we've seen these kinds of brain implants used in controlled tests and clinical trials, this is the first time a BCI has been bought and implanted — thanks to the China National Medical Products Association approving the NEO for sale in March.

It's a field of research and innovation that the Chinese government is keen to do very well in: authorities have put together a blueprint setting out key targets in BCI tech to hit by 2027, with goals around both devices and the framework needed to support them.

Just the start

As we know from previous trials of the NEO, the device looks for neural signals from the sensory and motor control regions of the brain. These are then translated into actions that then get transmitted to a metal glove worn by the patient.

The system could also be used to control computers, phones, and other devices, as well as robotic limbs, its developers say. These devices will inevitably get smarter and smaller over time, so we can expect many more developments like this in the future.

According to reports, the individual involved was in a car crash a decade earlier, leaving them with a damaged spinal cord and impaired hand mobility. After the procedure, the patient was said to be recovering well, with normal brain signaling observed.

The obvious comparison point is Neuralink, headed up by Elon Musk. While Neuralink devices have successfully been implanted in several cases, these BCIs don't yet have regulatory approval in the US and aren't available commercially.

• Some of Suno's music-scraping tactics have been revealed
• Hacked code shows tracks were ripped from YouTube Music and Deezer
• The AI music maker is facing multiple lawsuits from artists

It's unlikely to come as a surprise to you that an AI company built training data on copyrighted works without permission or compensation, but a new hack of AI music maker Suno has seemingly revealed just how egregious the data theft has been.

As reported by 404 Media, a hacker known as ellie.191 was able to access Suno source code and training libraries, finding references to platforms such as YouTube, YouTube Music, Deezer, Genius, and the International Music Score Library Project.

The newly revealed data dates from 2023 and 2024, and references 2,013,545 tracks being ripped from YouTube Music, as well as 12,287 hours of music being ingested from Deezer. We're talking about decades' worth of tunes here.

Some of Suno's alleged ripping tactics are also revealed by the code: it seems its song grabber looks for acapella versions of tracks on YouTube for vocals training, while vast numbers of podcasts were also targeted by the software.

Lawsuits in the pipeline

Suno is already facing multiple ongoing lawsuits around the practice of training its AI on copyrighted songs without permission, including one filed with the participation of the Recording Industry Association of America (RIAA).

The argument is not whether or not Suno has ripped this music — it's admitted to using "essentially all music files of reasonable quality that are accessible on the open internet" for AI training — but rather if it counts as 'fair use'.

It's a story that's playing out across other creative fields as well, including writing, photography, and filmmaking. AI models need human-made content in order to work properly, but the AI companies generally don't seem keen on paying for it.

Most commenters reacting online are expressing a lack of surprise that this is what Suno has been up to: one Redditor writes "this is literally what every LLM in existence has done", while another calls the practice "staggering theft".

• Microsoft sees itself as cheaper and more effective at bundling the full stack
• The company is clearly pushing its own internal models
• Claude also slated for being slower and less accurate

Microsoft is reportedly teaching sales workers how to compare the company's AI offerings to rival companies like OpenAI and Anthropic.

Per Bloomberg reporting, the company's sales staff are being told to emphasize benefits like efficiency and cost advantages when using Microsoft's offerings, which offer a much fuller picture than just the models and tools, extending to compute and other workflow tools.

"Everyone else is selling parts – we’re selling the full end-to-end system," EVP Jay Parikh reportedly told workers. "That’s the story that we all need to get out there and tell in FY27."

Microsoft sales teams up the ante against OpenAI, Anthropic

Clearly, the company wants customers to see the combination of its own models and third-party models, cloud infrastructure, applications and security as better value compared with having to piece these elements together separately.

Copilot EVP Jacob Andreou also reportedly compared Copilot to Claude, accusing Claude of being slower, less accurate and missing certain security integrations.

The sales push comes at a time of change for the company, which has started to push more of its own internal models across different apps and workflows to replace OpenAI and Anthropic models. It's also a marked shift from the company's earlier AI strategy, which leaned heavily on its multibillion-dollar partnership with OpenAI.

Company CEO Satya Nadella also pointed at a major customer, Unilever, which recently switched form an unnamed frontier model to one of Microsoft's own cheaper models to make significant savings.

In April, the company announced that its AI business is now worth around $37 billion annually, marking a 123% year-over-year increase.

• Report finds 38% of employees consider themselves self-taught in AI, just 23% have received formal training
• Nearly half believe it's the employer's responsibility to provide upskilling opportunities
• 88% of workers are Level 1-2 on the readiness scale, only 12% are Level 3-4

New research from TrustedTech has warned of an "AI underclass," whereby uneven access to AI training and support could end up creating a two-tier workforce.

The data claims around three in four (74%) UK decision-makers feel confident using AI at work, and yet only 44% of junior workers feel the same way.

The report also reveals that formal AI training is pretty uneven, with around two in five (38%) employees describing themselves as self-taught in AI.

Insufficient AI training is creating an emerging divide

Additionally, fewer than one-quarter (23%) say they've received AI training from their employer, with 41% criticizing their workplace for not providing enough safety and security training. But despite widespread self-learning, nearly half (47%) believe it's the employer's responsibility to support training and upskilling efforts.

"Employees are being told AI will transform the way they work, yet many have received little training on how to use it effectively, securely or confidently," TrustedTech Chief Visionary Officer Julian Hamood warned.

"The people who are most confident with AI will continue to build skills and productivity, while others risk being left behind through no fault of their own."

All of this is set against a backdrop of rising AI investments and ongoing deployments. Separate Notion data found that 60% of AI decision-makers believe their organization is ready to deploy next-generation agentic AI, but only 36% of employees would agree.

According to Notion's reporting, 88% of workers are at Levels 1 and 2 of AI readiness, seeing the tech as a brainstorming tool or an assistant, leaving only 12% at Levels 3 (team mate) and 4 (system workflows).

One of the biggest differentiators setting advanced organizations apart, Notion says, is effective governance and oversight, which aligns with TrustedTech's findings that workers lack sufficient guidance.

"The leaders pulling ahead are the ones doing it thoughtfully: integrating AI into how work runs, building trust across teams, and measuring real business impact," EMEA GM Andrew McCarthy wrote.

OpenAI's debut of the new GPT-5.6 model prompted the usual ritual of benchmark charts and arguments over whether it is really smarter than the last version, but CEO Sam Altman asked for a little more this time. He publicly asked to see what people actually built with it.

That led to a much more interesting showcase. Developers responded with all kinds of ideas, pilot projects, and even complete services. It makes sense, since OpenAI claims GPT-5.6 is better at coding and more reliable for long tasks. Seeing them turn into real software says much more than a release blog ever could. Here are five that stood out among the deluge.

ChatGPT coworker

The demo from Kitsune Agent Lab almost makes the chat window feel old-fashioned. The AI agent is given a goal and gets on with the job, moving between different tools, making decisions, and keeping track of what it has already done.

The interesting part is how motivated the AI agent appears to keep going and how good it is at remembering what it's done before. Developers have been asking for something like this for a while. AI is far more useful when it can finish the work instead of simply suggesting how you might do it yourself.

Financial chatter

One of the most charming projects makes New York City look like it belonged inside an original Game Boy. It comes complete with chunky pixel graphics but runs on a live digital map of New York that pulls in real-time information, including subway trains, weather conditions, and ferry movements. Instead of wandering through a fictional RPG world, you're exploring a tiny, pixelated version of the city.

A project like this requires far more than generating a few lines of code. It brings together live data feeds, mapping, interface design, and plenty of problem-solving into something that feels polished rather than experimental. It's one reason developers are feeling excited about GPT-5.6

Wardrobe AI

This project uses GPT-5.6 to create a polished AI wardrobe assistant that organizes clothing, suggests outfits, and presents everything through an interface that feels more like a premium consumer app than an experimental AI demo.

It's an impressively complete experience. The application gives users a visual, interactive way to browse their clothes and receive recommendations based on what they already own. The demo also highlights GPT-5.6's strength in developers building entire applications instead of isolated features. It brings together interface design, image generation, organization, and intelligent recommendations that would normally require stitching together several complex systems. GPT-5.6 appears to handle much of that heavy lifting.

Pokémon Go, but for neighborhood cats

One developer made a whole real-world-based game called CatchCat. It's like a digital expansion to a scavenger hunt for cats. Point your phone at a real cat, let the app verify the sighting with its camera, and turn that encounter into a collectible digital cat card with its own personality, rarity, and place in your growing album. It is essentially a creature-collecting game in which the creatures are the neighborhood cats you meet.

Players can build collections, explore community sightings, compete with friends, and gradually fill a living scrapbook of feline encounters, all wrapped in a polished interface that would not look out of place on the App Store or Google Play. Building something like CatchCat means juggling computer vision, mobile development, backend services, and game design.

Tasteful travel

One of the most ambitious projects, Atlas Mode for Pearl, is an interactive globe that turns travel planning into something closer to exploring a living map. Instead of typing destination names into a search box, users can spin the globe, discover places visually, and receive recommendations for restaurants, hotels, and more matched to their personal tastes.

It has an impressive number of moving parts running behind the scenes. The app combines geographic data with an individual taste profile, then layers AI recommendations directly onto an interactive globe. It even has an audio aspect thanks to GPT Voice 2.1. You can talk through vacation ideas instead of endlessly tweaking search filters.

That is a recurring theme among the projects developers rushed to show Sam Altman. The AI is no longer the product itself. Increasingly, it is the engine quietly powering products that people might actually want to use.

The AI buildout is well and truly underway with OpenAI CEO Sam Altman leading the charge, having made various deals with companies including Oracle and Nvidia to guarantee the infrastructure needed to train future AI models is installed. But as this ensues, the spotlight has been thrown on how much energy these models will need.

Eating machines

Altman was speaking during an AI summit in India earlier this year when making the remarks to The Indian Express.

The OpenAI chief was being questioned about the substantial amount of energy that AI has already been consuming – and will be projected to continue consuming – for both training and inference. Bringing these models online, after all, and keeping them running require a huge amount of resources, not only in terms of the energy to power the data centers, but the water for cooling, and the components and resources in building the hardware.

Altman's defence hinged on the idea that people, too, require plenty of energy in order to reach utility — while AI can be trained much quicker. This view, in essence, frames machine intelligence as a like-for-like competitor with human intelligence.

Energy efficiency

Altman argued that humans are deeply inefficient, and that you should compare the total energy spent to create a human expert versus a machine expert. There's also an argument that energy efficiency of AI could improve over time.

But detractors were scathing in their criticism of this entire point of view – saying this framing doesn't take into account the fact that the human brain operates on roughly 20 watts of power. This isn't to mention the ethically gray and dehumanizing nature of the remarks.

OpenAI is building an AI-powered smart speaker nobody wants. That is, if you believe the Bloomberg report from Mark Gurman and you've read his description of said rumored device.

According to the report:

"OpenAI believes the product’s defining feature will be its personality and ability to connect on a humanlike level with users. The speaker incorporates mechanical elements that can move on their own, creating a sense that it is alive and not just an object responding to commands. The machine also will draw on personal information such as emails to better understand its owner."

The news sources, it appears, come from an insider who decided to spill all the juicy details mere hours after Apple dropped a blockbuster trade-secrets lawsuit on OpenAI's head. OpenAI claims it's done nothing of the sort, and recent reports say that Apple's claim that the AI giant has not even responded to Apple's earliest concerns was based on it potentially incorrectly identifying the former Apple employees who left to join OpenAI (allegedly with Apple trade secrets in tow).

Apple's concerns here are twofold: First, these former employees had access to many of Apple's secretive product development details and may even have asked recruits to share fresh details when they approached them to interview for jobs at OpenAI. The other concern is that Apple is already far behind in the AI race, and if Apple's plans for Siri, AI, and a potential robotic desktop home assistant were also leaked, it could harm its ability to catch up in multiple market sectors.

This latest news, which may or may not be accurate, should put Apple's fears to rest.

OpenAI is apparently not building something that could ably compete with any of Apple's key hardware or future hardware initiatives.

First of all, there's the smart-speaker-ness of the whole rumored OpenAI concept. There are already too many smart speakers on the market, many of them with their own smart assistants. Amazon, for instance, is smack in the middle of trying to convince millions of customers that not only do they need Echo devices throughout the home, but they need the AI-powered Alexa+ to guide them through their smart home experiences and, to some extent, their lives.

Apple has its own HomePod, Siri-infused speakers, which may get considerably more powerful with the Gemini foundation model-backed version arriving this Fall.

Put another way, smart speakers are a known quantity in the home consumer electronics space, and I think what most tech companies are realizing is that people like and use them, but mostly in limited ways: they want music, occasional answers to simple questions, and voice control of their smart home devices. That's it.

Why does my speaker think it's alive?

OpenAI appears to be prepared to offer something different: a personality-filled speaker that can watch you, move to engage, seem alive, and generally make you feel uncomfortable.

Obviously, that would not be the objective, but it could be the result. Who needs a speaker that quietly watches you as you walk from your kitchen to the den, waiting and hoping for you to say, "hey ChatGPT, what's up with the Strait of Hormuz today?"

In my home, we have a Psync smart security webcam with one oddball feature: it has a motorized body that can turn almost 360 degrees on its base and lift its thin, rectangular face and camera to keep track of people and alert me to intruders. However, most of the time, it's just watching us move around the house, and I can tell you that my family hates it. Sometimes I come home and find its face forced down so it can't pop up and track anything.

Now, imagine a larger and far smarter OpenAI AI smart speaker in your home, watching, waiting, chiming in when you don't want it to, and generally making people feel uncomfortable.

This will not be the breakout hardware hit OpenAI is hoping for.

Look, I was under the impression that OpenAI (really Jony Ive and Sam Altman) were working on an AI wearable. I didn't love that idea either, but it was a lot less creepy than this.

So, Apple, chill out. OpenAI's plans are no threat to you, even if they do allegedly have a bunch of insidery Apple information.

• AI hallucination can be weaponized, new report warns
• HalluSquatting is short for “adversarial hallucination squatting”
• GitHub Copilot, Gemini CLI, and OpenClaw are all affected

Your favorite AI service could be subverted to deploy code that turns your phone or PC into a botnet, according to researchers at Intuit, Technion, and Tel Aviv University.

The technique has been given the name HalluSquatting, a portmanteau of adversarial hallucination squatting, and is similar to typosquatting in that it relies on a mistake in order to distribute malicious code. While typosquatting might occur with the incorrect input of a website URL, HalluSquatting pivots on an LLM being unable to identify a resource or repository with 100% accuracy.

Relying on an LLM’s tendency to hallucinate repository resource identifiers, this weakness could be scaled up to conduct massive ransomware campaigns, botnets, and more.

Push-me-pull-you

Previous LLM-based malware operations have relied on pull-based attacks. In this scenario, a prompt designed to jailbreak or otherwise subvert the AI is (for example) placed on a website and the LLM encouraged to gather the information, thereby reducing its internal security.

What the researchers have shared in their paper, is that pull techniques are being combined with push attacks, which are traditionally executed as code injection.

The paper’s introduction summary states: “By preemptively registering hallucinated resources—a technique we call adversarial hallucination squatting (HalluSquatting)—we demonstrate remote tool execution and remote code execution at scale across a range of popular agentic LLM applications, which could be exploited to the establishment of a botnet.”

Once an attacker has identified the resource likely to be misnamed by an LLM, and squatted on it (to embed adversarial prompts), the work is done. All that remains is for a user to trigger the resource, the AI chatbot or agent to initiate the response, and the squatted resource will be accessed.

Promptware attack

Following this, the adversarial content held within the squatted resource is activated, triggering the tool invocation stage. This is the promptware attack, where attacker-controlled instructions are executed, with results potentially including turning the device you’re using into a botnet zombie.

LLMs such as the Cursor, Cursor CLI, Windsurf, GitHub Copilot, Cline coding assistants have been used in the testing of this avenue of attack along with Gemini CLI, and the OpenClaw, ZeroClaw, and NanoClaw AI assistants. The researchers successfully achieved remote tool execution (essentially remotely accessing and controlling the LLMs) and remote code execution (RCE, where malicious code is executed remotely).

Some mitigation is available, including LLM developers blocking fetch operations in favor of a search tool, and resource owners enforcing strict naming, perhaps in favor of globally unique resource names. However, these are will require collaboration by disparate parties, and may take a while to implement.

The risk of LLM-based malware is increasing, and some has already been spotted in the wild. Of these, the JADEPUFFER attack is perhaps the most notable, as it isn’t simply AI-based malware – it is a full ransomware attack run entirely by an LLM.

• The three man team is known as “Slopfix”
• It claims to be able to reduce AI generated codebases by up to 65% in size
• They aim to "refactor vibecoded codebases back to maintainability"

Vibecoding has a lot to answer for, not least some excessively large codebases. A new team of software engineers are collaborating to reduce the size of these cumbersome projects… with a $10,000 per week bill.

Slopfix is the name of the team (comprising a trio: Maciej, Kuba, and Krzysztof), but its aim is efficiency and functionality, rather than code golf, where code is reduced to the shortest possible length.

However, while this might seem like a noble task and a service worth paying for, Slopfix isn’t taking a stand against the use of AI. In fact, it is employing AI tools to detect the AI flab in your codebases.

Use an AI to catch an AI

Challenges around vibecoded projects have increased in recent months, as the limitations of the technology become apparent.

While using an AI to program based on your prompts and requirements is straightforward, agents habitually begin to lose context and logic once the project reaches a certain size or age. Once that happens, you’re looking at duplication, features breaking, and of course, the dreaded hallucination.

Slopfix is targeting companies that have adopted vibecoding, built huge codebases, and found that they’re running into issues. To find the problematic AI code, however, Slopfix is employing AI.

They state that a full “screen by screen, endpoint by endpoint” evaluation of the vibecoded app is made, which aims to find the duplicated functions, broken logic, and other issues. There’s also the promise of a two-week warranty for anything they break.

All of this is aided by Claude Code “on a very short leash” which Slopfix uses find problems. They clearly state that “the agent doesn’t get a vote.” Instead, they’re relying on their experience as developers to improve your code.

$10,000 seem a bit steep?

While the price might seem high, $10,000 for one successful week’s work for three seasoned developers shouldn’t really be a budget breaker.

The fee covers successful work only, and as the Slopfix website states, payment is in proportion to how much of the reduction target the team hits, with $10,000 being the price for hitting the target – it’s not the default fee.

However, there is a lot of preparation involved, and the analysis of your codebase is conducted free of charge. If they can't fix your project's issues, they'll let you know and refuse the contract.

As software consultancies go, Slopfix is an unusual case. But as the problems with vibecoded projects begin to become apparent, competing consultancies may begin offering similar services.

• UN Secretary General calls for a global ban on autonomous "killer robots”
• Guterres argues that delegating life-or-death decisions to machines is “morally repugnant”
• Governments should take a stance now – not wait for something catastrophic to happen

UN Secretary General António Guterres has called for lethal autonomous weapons, which he describes as ‘killer robots’ to be prohibited under international law following recent discussions at the first Global Dialogue on Artificial Intelligence Governance in Geneva.

Guterres’ demand to ban these weapons focuses on those capable of identifying, selecting and attacking targets without human oversight, which leaves artificial intelligence and other computer systems in charge of a life-or-death decision.

He ultimately argued that certain decisions must remain exclusively human, and the decision to take a life is well into the boundary of requiring human oversight. Transferring the decision-making to killer robots would be “morally repugnant” and “politically unacceptable,” he argued.

AI requires global regulation as military AI poses major threats

Key to the Secretary General’s argument is that he urges governments to take action and ban such robots now, rather than waiting for an autonomous weapon to cause a major incident before rethinking their strategies.

“Let us not wait for atrocity to act,” Guterres said. “Some decisions must remain forever human – none more than taking a human life.”

The issue is becoming more urgent now that AI models and advanced chips are already being used within military intelligence, targeting and other battlefield systems.

More broadly, Guterres’ thoughts align with those of Anthropic, which recently had a dispute with the Pentagon after seeking guarantees that its models would not be used for autonomous weapons or surveillance.

While the Pentagon had rejected those limitations, arguing that it should be able to use Anthropic’s models for any lawful purpose, the case highlights how private companies are becoming increasingly intertwined with digital warfare.

Reporting by the Wall Street Journal cited a similar view by Pope Leo XIV, who warns that AI-controlled weapons could promote an “anti-human” view of warfare. He warned that the autonomy could reduce some dangers and distance political leaders from the human consequences of conflict.

There’s a need to balance the pros and cons of AI

However, artificial intelligence does promise several benefits to modern warfare, particularly in its ability to process huge amounts of information extremely quickly. With modern compute, militaries can respond to threats at lightning speed, improve their accuracy and precision, reduce soldier risk and potentially reduce civilian casualties, too.

Critics also question whether human oversight of AI systems is at all meaningful if the person in charge only has seconds to act on AI-generated information in the first place.

It’s also yet to be determined which party or group of parties should be held accountable for any incidents or mishaps – human operators, commanders, hardware manufacturers and software developers are just some of the parties up for judgment.

“We may be the last generation able to set the terms on which humanity and machines coexist,” Guterres warned separately in an X post, warning that AI must be governed, trusted and fair.

“It sounds like science fiction, but it's a real possibility, and it could change the world in ways that we don't understand yet, and it could change the power dynamics of our planet in ways that require our attention,” Independent International Scientific Panel on AI Co-Chair Yoshua Bengio added.

It all started with an email. Someone was reminding me about the fast-approaching National AI Day. I never finished the email but quickly wondered, "National AI Day, is that really a thing?" It is, and I found the man responsible for it.

"We have about 30,000 applications a year for new national days. From that, we have a committee of people look at them," said National Day Calendar.com founder Marlo Anderson when I reached him by phone just two days before the big July 16th event.

National AI Day, though, did not follow that traditional route when it was designated a day on their calendar in 2025. Anderson told me he does a lot of AI work with the business and has been using AI for 20 years, so he did something slightly unusual.

"We just figured having an AI day would be appropriate," he said and admitted he did the designating.

It is not the normal process, though Anderson believes, but could not specifically recall, that people had suggested the day in the past.

You made this list

It is a big deal to get added. After all, National Day Calendar only adds a handful each year and, as Anderson admitted, some already believe they have too many "National Days" (he noted that he might be among that cohort, too).

Still, Anderson, who founded National Calendar Day in 2013, believes AI warranted it. He explained that it's already responsible for much of the "mundane process" work they do, including uploading the National Day Calendar's daily videos to a platform called Video Elephant. They were uploading 100 clips as he spoke to me. Doing it by hand would take two weeks. With AI, "just one day or two."

As for which AI platforms Anderson uses, he seems to spread it around, telling me that Claude is used for app development and website maintenance, ChatGPT Voice for brainstorming projects, and Gemini in National Day Calendar offices in North Dakota.

Anderson is a firm believer in the power and potential of AI for both his own work and as an agent for good in the world. Locally, he told me, "If we have an agentic AI that can handle that workload for us, we should probably do it."

National Anti-AI Day

When I asked abotu the AI backlash — a recent study found 40% of surveyed peopel are limiting use of AI — Anderson told me, "We talk about it all the time," but added, "We get backlash on National Cheese Sandwich Day and French Fry Day," which is to say, he's not sure if the backlash is any greater than for other oddball National Days.

More seriously, Anderson is well aware of the debate about AI's impact on the environment but is also convinced the positives far outweigh the negatives.

"I also understand there’s a lot of benefit," like the ability to find medical cures. They're making, he told me, "remarkable progress right now" in medicine.

He then spun out an analogy about horses and cars. A million hours ago (in 1912), most people were still riding horses and, it turns out, they were pretty dangerous, too. Early cars, with the lack of traffic infrastructure, weren't much better, but "most people would agree a car is a better way to travel," he said, adding that we're currently "at the same crossroads with Artificial Intelligence."

Ultimately, Anderson's decision to create National AI Day was rooted in its current impact.

In 2025, "the conversation had heightened to a point where probably everyone knows about AI, at least in the US. Everyone is probably using it, whether they know it or not," he told me.

It's also about AI's long-term prospects.

When goat yoga was a big thing, Anderson explained, they had a lot of requests to make it a day. "There's no day because we assumed it would be a fad [he was quick to add he’s sure it’s wonderful]. 20 years from now, no one will know or talk about goat yoga, but 20 years from now, they will be talking about AI."

So happy National AI Day. Feel free to celebrate on July 16 by using it, deriding it, or ignoring it altogether.

• Microsoft CEO Satya Nadella has warned AI companies are training their models on the business secrets of their customers
• These secrets are then used to train new, more powerful models, that are sold to their customer's competitors
• But, Nadella says there is a way to remain competitive without being locked in to one AI vendor

Microsoft CEO Satya Nadella has warned the big players in the AI industry are using their proprietary models to learn the business secrets of their customers, which they can then use to train and deploy more advanced AI models.

The crux of the issue, Nadella said in a blog post, is that, “You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!”

What Nadella is saying in essence, is that AI companies are harvesting sensitive business data from their customers, using it to make training their models cheaper, and then launching these models for use by their own customer’s competition.

“The kind of knowledge a competitor could never buy”

“Models learn from ‘exhaust,’ the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how,” Nadella explained.

Nadella also criticized how AI companies are increasingly complaining about how their models are being distilled by their own competition. For example, Anthropic accused retailer and e-commerce company Alibaba for using thousands of Claude prompts to distill their own models. By figuring out how a proprietary model works, you don’t have to spend the enormous amount of capital needed to source training data and create your own AI model.

This, for Nadella, is a major contradiction in how AI companies work. “While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation,” he said.

It is also therefore hypocritical for AI companies to accuse other companies of distilling their own product, and then include within their AI usage contracts clauses that allow AI companies to “reserve the right to learn from customer usage and interaction data.”

“In consuming intelligence, you are creating intelligence. And what you create should belong to you,” Nadella added.

On-prem is back in fashion

Nadella’s fix for this growing problem? It’s time to move back to on-prem. Nadella encourages businesses to “retain ownership” of the data they feed AI models by switching to the use of “proprietary learning environments” built on the cloud.

The added benefit of moving to these environments is that they allow businesses to switch between different AI models provided by different companies using “orchestration layers” and AI gateways.

There is also a growing trend of businesses switching to using open source technologies, which goes hand in hand with businesses operating in the cloud. Businesses can train open source AI models using their data that is already available in cloud environments to do much of what the proprietary models do, for far cheaper — and without handing over that same sensitive data to be used by AI companies to train their own models.

The on-prem solution also has additional benefits. AI models operated on-site within manufacturing plants, stores, and other premises are far cheaper and require less specialized hardware. Businesses that operate using a centralized cloud are increasingly encountering issues with data egress fees, storage bloat, and idle specialized hardware.

Google Cloud recently released a report about these very issues, and also encouraged businesses to move towards using AI gateways and on-prem models to reduce latency, improve resilience, and cut per-token costs by switching to local, highly optimized models.

Via TechCrunch

• Study finds social media posts are increasingly AI-generated
• LinkedIn particularly affected, with 40% of long-form posts written by AI
• Substack and Twitter/X also badly hit

LinkedIn and other social media networks are rapidly being consumed by AI-written slop posts, new research has claimed.

A report from AI detection firm Pangram Labs found nearly half of all long posts (over 250 words) on LinkedIn were created entirely by AI, with the likes of Substack and X/Twitter also seeing a huge rise in such content.

"LinkedIn was the most AI-saturated platform, where more than 40% of longform posts flagged as fully AI-generated," the company's report said.

Social media drowning in AI slop

The study, which also examined Medium and Reddit alongside the other social networks for a data set of over a million posts, found one in four longform posts on social media flagged as fully AI-generated, with lengthier content much more likely to be created with AI than shortform.

Pangram found that LinkedIn was the most AI-saturated platform, where more than 40% of longform posts were flagged as being fully AI-generated, with Substack the least affected, with longer posts often far less likely to be AI-generated.

LinkedIn was also identified as having the highest AI share of any platform included in the report, as although its posts only made up a third of scanned items, it accounted for nearly two-thirds (62%) of all AI content flagged by the system.

"Professionals come to LinkedIn to hear from real people and their unique insights and perspectives," a LinkedIn spokesperson told us.

"We actively work to reduce low quality, automated or generic content, and while AI can be used to beat the blank page problem, our focus is on surfacing professional conversations that help people advance their careers. You can learn more about how we’re keeping the Feed trusted and professional here”.

However, when mixed AI and human content were included, X/Twitter was by the most swamped by AI, with the study finding almost half of articles on the site were either fully AI-generated (23.9%) or AI-assisted/mixed (22.9%), with only 53.2% of X articles flagging as fully human-authored.

"Our data shows that AI-generated content is a problem across all platforms, and it is hitting longform content especially hard," Pangram said.

"Contrary to what one might expect, people are overwhelmingly willing to use AI to speak on their behalf in professional settings that are associated with their real identity, and less likely to use it on casual and anonymous platforms."

"AI writing is now a problem everywhere on social media," Pangram Labs CEO and co-founder Max Spero noted. "An internet that is completely flooded with undisclosed AI content is bleak, but we don't believe it's inevitable."

• Americans want the massive wealth of AI firms added to a sovereign wealth fund
• 69% would see AI firms forced to transfer 50% of stock into a sovereign fund
• The fund would help redistribute wealth and back new infrastructure and developments for working class Americans

A national survey has found over two-thirds (69%) of US citizens want to see AI firms transfer half of their stock into a sovereign wealth fund.

The survey, conducted by Verasight among 1,690 adults, also found that there was overwhelming support (89%) for AI companies to publicly disclose the results of all internal safety testing.

The sovereign wealth fund, proposed by Senator Bernie Sanders, would provide wealth for current and future generations, as well as acting as a source of capital for investment in new projects and developments designed to improve the lives of working class Americans.

Americans want AI wealth redistribution

At the announcement of Sanders’ proposed American AI Sovereign Wealth Fund Act, the senator said, “It would guarantee that the economic benefits generated by AI are used to improve the lives of all of us — not simply to make the richest people in the world even richer.”

“The future of AI and the fate of humanity must not be decided behind closed doors in Silicon Valley by billionaires seeking to maximize their power and profit,” Sanders said.

Interestingly, the survey only saw a small dip in support to 64% when the sovereign wealth fund was tied directly to Sanders, showing the bi-partisan desire for the enormous growth in AI wealth to be redistributed among Americans.

According to a Goldman Sachs report, companies operating in the AI industry have added more than $27 trillion in market value since late 2022, with corporate profits and tech investment soaring. But at the other end of the scale, working class Americans are seeing jobs replaced and entry level positions disappearing due to AI technologies.

A further report from Goldman Sachs predicts that during the 10 year AI transitional period, up to 15 million US workers could lose their jobs - around 9% of the current US workforce.

In 2026 alone, the tech sector has seen more than 166,000 layoffs, with many attributed to the adoption of new AI technologies. The trueup layoff tracker expects this number to rise to 312,000 by the end of the year.

Electricity prices are also surging in the US due to the demand of AI data centers, raising the cost of day-to-day life of millions of Americans. As a result, US representatives have put forward a bi-partisan Ratepayer Protection Act that would force AI companies and hyperscalers to pay for the energy they use, with additional charges to help fund the expansion of electricity infrastructure that has been placed under additional load by data centers.

Via CNBC

• Windows 11's Copilot app has a new feature in testing
• 'PC insights' provides an easy way to receive clear answers to hardware-based questions about your device and its specs
• While there are some fears over privacy (and bloat), Microsoft has made it clear that Copilot needs to be granted permission to access your system and files

Copilot is getting a new ability to answer questions about your PC's hardware, allowing the AI to tap into the relevant hardware details to do so – and while Microsoft is treading carefully with privacy here, that's unlikely to stop some level of paranoia.

Windows Latest flagged the introduction of 'PC insights' for the Copilot app on Windows 11, which as Microsoft explains, "enables customers to conversationally ask Copilot questions about their Windows PC and receive clear responses based on their device's state without having to dig through system settings."

This is currently an experimental feature, so still in testing, and an optional ability that you must turn on for it to be in play. Windows Latest notes that it's gradually rolling out, but only in the US for now.

You can ask Copilot how much RAM you have, or storage space left, or what your GPU is, and the current level of usage for your processor, and a whole bunch of similar component-related queries. You can ask about elements as diverse as whether you have an antivirus running, or what your laptop's battery health is, diving into mild troubleshooting territory should you wish.

To get its answers, the Copilot app hooks up to Windows APIs to analyze your system, and the AI asks for permission to do this. You can allow it access to your PC's hardware details on a one-time basis for that session only, or you can elect to 'always allow' if you're happy to give Copilot this access on a more permanent basis.

Analysis: fears over hallucinations and bloat

As ever, this is AI, and as Microsoft notes, Copilot "may not always provide complete or accurate information", especially during this testing phase. So, if you do get a chance to try out PC insights, maintain a healthy sense of skepticism with the responses you get.

As Windows Latest makes clear, there's also a certain irony about a Windows 11 user checking up on resource usage, perhaps due to system sluggishness, employing the Copilot AI to run diagnostics when the app itself uses the best part of 1GB of RAM when running in the background and doing nothing.

That doesn't stop this new PC insights feature from being situationally useful, of course. Some of the reaction has come from a place of disdain, though, as you might guess, with comments such as the one from this Redditor: "Oh hey it's like Task Manager except instead of lightweight and authoritative, it's bloated and might be lying to me."

Of course, this is a feature aimed at less well-informed PC owners, not those who can easily understand what's happening in Task Manager at a glance. Criticism around the bloat of the Copilot app is fair enough, mind, and this is because in its most recent incarnation, Microsoft changed things so the app is essentially a standalone spin-off of the Edge browser.

Another worry is that of privacy, and having Copilot 'snoop around' on your machine, but as noted, there are clear requests for permissions, and the new feature is strictly opt-in. You don't ever have to go near PC insights if you don't want to. It's also worth noting that giving the Copilot app access permissions doesn't mean it can read the actual contents of files, but only their sizes (for weighing up questions about storage and the like).

At the moment, this is a purely informative or troubleshooting feature, and in the case of attempted diagnostics, it may point to issues with your PC, but won't resolve them for you. However, it's not difficult to envision where Microsoft might head with this, in terms of getting Copilot to implement fixes for certain issues that the AI flags up. I'm talking simple Settings changes rather than anything in-depth, and this has always been the idea of Copilot (even though it hasn't yet been realized to much of an extent).

When we get AI agents in Windows 11 – and they are coming, make no mistake – this kind of functionality may turn into a full-on troubleshooting agent. The trouble (pun not intended) with that being that the mistakes and hallucinations that AI can make could be considerably more aggravating in this kind of scenario.

TeraBox built its name on 1TB of free cloud storage, which is still the headline pitch. What's changed is everything sitting next to the storage. An AI Presentation Maker, an essay writer, a scanner, a transcriber, and a research tool called Deep Research are now baked into the same free account I've used for years.

Until recently, TeraBox functioned more like a toolkit than a dedicated AI assistant. Many of the tools only did one job, like generate a deck, paraphrase a paragraph, or scan a document — then handed back results rather than a conversation.

But now, Terabox has added a dedicated research assistant that can access the web and parse through complex queries for information. It can also generate graphics and create properly-formatted documents to help with your research tasks. It's neat, but the most capable versions of these tools sit behind a Premium+ subscription priced under $4 a month.

I've covered hosting, storage, and AI software for TechRadar Pro since 2012, including our 2026 buying guide for vibe coders. If you'd like to see a wider selection of dedicated AI tools to pick from, check out our list of 70+ other AI tools across different categories. For this Terabox AI review, I had access to a 7-day trial of Premium+, which let me try all of the platform's features in depth over the review period.

What is TeraBox AI?

TeraBox AI is the set of generative tools Flextech Inc. has added to TeraBox, the free storage app it took over from Baidu in 2020. Rather than one assistant, it's a handful of single-purpose tools, an essay writer, presentation maker, paraphraser, transcriber, scanner, and research assistant, living inside the app I use for backups.

I'd call it a productivity add-on rather than an AI platform in its own right. You pick a tool, type or upload your input, and get a finished result. That suits someone wanting a quick deck or a tidier paragraph, but the platform left something to be desired when it came to more complex tasks requiring advanced reasoning.

TeraBox AI: At a glance

Buy it if…

• You already use TeraBox for storage. The AI suite rides on cloud space you may already pay for, so the extra cost is small.
• You need quick scans and transcripts. AI Scan and AI Transcribe handle everyday OCR and meeting notes well for casual use.
• You want a fast presentation starter. One prompt turns into a usable slide deck in seconds, ready for further editing.

Don't buy it if…

• You need a serious writing or design tool. Dedicated tools go deeper than TeraBox's essay writer and paraphraser for professional output.
• Data jurisdiction matters to you. TeraBox's storage business began inside Baidu before Flextech took over in 2020, which still gives some professionals pause.
• You can't stand ads. The free tier's AI tools sit behind the same ad-supported experience as TeraBox's storage.

My time with TeraBox AI

I tested TeraBox AI through the web app and Android client, running presentations, transcriptions, and scans over several days. The Presentation Maker stood out. A one-line prompt about small business marketing trends produced a ten-slide deck, icons and chart included, in under a minute.

AI Scan and AI Transcribe felt the most useful day to day, turning a printed invoice into clean, editable text in seconds. Transcribing a short interview gave me a readable summary alongside the full transcript. The essay writer and paraphraser, by contrast, were serviceable but generic.

However, my experiences with the dedicated AI chat and Research Assistant were somewhat mixed. A simple query asking for Elon Musk's updated net worth returned accurate input, but a more complex task involving generating a graphic of his net worth over the years seemed to fail entirely.

TeraBox was able to offer the information as a table instead on further attempts, which led me to believe that the failure was a result of being unable to pull up the necessary integrations for data visualization rather than an error in the research itself. That does redeem it in my eyes given the price point, just don't go in expecting the same level of functionality as a dedicated AI platform like ChatGPT.

On value, Premium+ is an easy call. It costs under $4 a month and bundles 2TB of storage with the full AI suite. The caveat is that nothing here matches a tool built solely around AI writing or design.

TeraBox AI: Features

TeraBox AI isn't the best do-everything assistant, which also shows in how the features are organized. Presentation, writing, scanning, and research tools each live in their own corner of the AI tab, which keeps things simple but means there's no unified chat for mixing tasks together.

The Presentation Maker impressed me most, with Agent Mode generating decks up to 40 slides from a prompt or uploaded document, complete with citations when it pulls from web sources. A separate Beautify option restyles slides you've already made. For students or small business owners needing a deck fast, this alone might justify upgrading.

AI Scan and AI Transcribe are the most practically useful tools for office work, handling OCR, ID document capture, and on-the-fly translation on one side, and audio-to-text conversion with an AI summary on the other. Both worked reliably in my testing, needing only minor cleanup afterward.

Deep Research, the suite's research tool, builds a structured outline you can edit before it writes a full report. The output reads more like a market briefing than original analysis, so treat it as a starting point rather than a finished document.

The essay writer and Smart Paraphraser are the weakest links. Output reads competently but generically, and TeraBox doesn't disclose which model is doing the writing. Given the price for Premium+, the overall feature set still feels reasonably generous for a bundle riding on storage you might already want.

TeraBox AI: User experience

I got started in under a minute. I logged into my existing account, tapped the AI tab, and was generating a presentation within seconds, with no separate sign-up or onboarding flow to slow things down. Each tool opens to a simple prompt box, so the learning curve is close to zero.

But the experience feels bolted onto a storage app rather than designed around AI from the ground up. Switching tools means backing out to the main AI menu each time, and the free tier's ads occasionally interrupt what otherwise feels quick and uncluttered.

TeraBox AI: Customer support

Support looks decent on paper. Users across review platforms rate TeraBox's customer service 4.0+ out of 5 and Flextech representatives respond to public reviews directly, including the ones flagging slow replies.

I didn't need to contact support during testing, since the AI tools worked as expected. The bigger caveat is documentation, since TeraBox's help content for the AI suite leans on blog posts and FAQs rather than a structured knowledge base.

TeraBox AI: Pricing

• Free tier includes limited AI access, plus 1TB of ad-supported storage.
• Premium adds 2TB of storage and removes ads, but doesn't unlock the AI suite.
• Premium+ is the only tier with full AI access, priced from around $3.89 a month or $39.99 a year.

TeraBox's free plan gives a limited taste of the AI tools, including a small number of free presentation generations, on top of the full 1TB allowance. That's enough to test whether the tools suit your workflow before paying anything.

Premium+ is where the AI suite lives, bundling the essay writer, presentation maker, transcriber, scanner, paraphraser, and search tools alongside 2TB of storage for under $4 a month. TeraBox's official pricing page renders dynamically and didn't return visible data when I checked the source directly, so these figures come from cross-referenced third-party listings instead. There's no separate API pricing, since TeraBox doesn't offer developer access to the AI tools.

TeraBox AI: alternatives you should consider

• pCloud: A privacy-focused cloud storage service with optional zero-knowledge encryption, starting around $49.99 a year for 500GB, without TeraBox's bundled AI tools.
• Google Drive with Google AI Pro: Google's storage plans now fold Gemini directly into Drive, Gmail, and Docs, bundling 5TB of storage with AI access for $19.99 a month.
• Canva: A more polished option built for AI-assisted presentations and design, with Magic Studio included in Canva Pro for around $15 a month.

How I tested TeraBox AI

• Used the Presentation Maker, AI Scan, AI Transcribe, Deep Research, and Smart Paraphraser across free and Premium+ access.
• Tested the AI suite through TeraBox's web app and Android app over several days of regular use.
• Cross-checked pricing and ratings against TeraBox's official site, G2, and Capterra, noting where the vendor's pricing page wouldn't render visibly.

I focused testing on the tools most useful for a typical small business or student workflow, presentations, scanning, and transcription, rather than edge cases. Beyond the data obtained from their official website and documentation, I cross-referenced features across multiple third-party sources. as well as verified them during my own testing, to confirm consistency before including them here.

Brave Leo arrived in November 2023 as a sidebar AI assistant for the Brave browser, and it has grown into one of the more unusual offerings in the AI chat market. Unlike most platforms that treat privacy as an afterthought, Leo bakes it into the architecture: no IP logging, no conversation storage, and no requirement to create an account. In late 2025, Brave went further by introducing Trusted Execution Environments (TEEs) powered by NEAR.AI and Intel TDX technology, giving users cryptographically verifiable assurance that requests are processed exactly as described.

Two features set Leo apart from the field. The Bring Your Own Model (BYOM) option lets you connect Leo to locally-running models via Ollama or your own API endpoints, which is rare among browser-native AI tools. The Skills feature, launched in December 2025, lets you assign keyboard shortcuts to frequently used prompts to cut down on repetitive setup.

Agentic browsing, which lets Leo autonomously navigate and complete tasks on your behalf, entered early testing across all Brave release channels in May 2026.

I've been covering AI platforms and B2B software at TechRadar Pro since 2018, including our 2026 vibe coding buying guide and the Microsoft Build conference this year in San Francisco. Here's how Leo holds up.

What is Brave Leo AI?

Brave Leo is an AI chat assistant built directly into the Brave browser, accessible from a sidebar panel, a full-page view, or the address bar. It's available on Windows, macOS, and Linux on desktop, and on Android and iOS on mobile. No separate installation, app, or account is needed to get started with the free tier.

Leo's defining quality is page awareness. It reads the content of the active tab, whether that's a webpage, PDF, Google Doc, Google Sheet, or YouTube video, and uses that content as context for your questions. You don't need to copy-paste anything or upload files to an external server.

Moreover, Brave Search pulls in real-time information from across the web when your question calls for it.

The platform targets privacy-conscious individuals, students, and professionals who want AI assistance without feeding personal data to third parties. It's also a practical option for people already running Brave who'd rather not switch tabs to reach a separate AI app.

Brave Leo AI: At a glance

Buy it if…

• You prioritize AI privacy above all else. Leo doesn't log your conversations, store your IP address, or use your chats for model training. For users handling sensitive research or confidential work, that's a real distinction from most AI chatbots.
• You're already a Brave browser user. Leo is built directly into Brave with no extra installation required. It picks up page context automatically, making web research faster with very little setup.
• You want model flexibility. The ability to switch between Llama, Claude, Qwen, and others, or connect your own model via BYOM, gives you more control than most browser-native AI tools offer.

Don't buy it if…

• You need AI across multiple browsers or tools. Leo only works inside Brave. If you regularly use Chrome, Firefox, or other apps, you won't have Leo available in those environments.
• You rely on intensive daily free usage. The free tier hits its ceiling quickly during heavy use. A Premium subscription is more or less necessary for anyone using Leo throughout the working day.
• You need enterprise features or team access. Leo is a personal assistant with no team accounts, admin controls, or organization-level management. Businesses looking to deploy it across multiple users will need individual subscriptions for each.

My time with Brave Leo AI

My first impression of Leo was how little friction there was to getting started. No account confirmation, no model selection screen, no onboarding pop-ups. Within seconds of opening the sidebar, I was summarizing a lengthy policy document open in another tab.

The sidebar stays out of the way until you need it, which I found more practical than switching to a dedicated AI app.

Automatic mode worked well in practice. Leo picked Claude Sonnet for a nuanced writing task and shifted to a faster model for a quick factual question, all without me having to intervene. I did run into rate-limit warnings during a longer research session on the free tier.

Those limits don't reset frequently enough for sustained daily use, and that was what ultimately pushed me to test the Premium plan.

The Skills feature was a useful addition for repetitive tasks. I set up a shortcut for a summarization prompt I return to frequently. After a few days, it saved real time.

New users may not find it easily, though, since it's tucked away and not highlighted in Leo's default interface.

Brave Leo AI: Features

Leo's feature set has expanded considerably since its 2023 launch. The platform covers summarization, translation, code generation, content writing, question answering, and document analysis across webpages, PDFs, Google Docs, Google Sheets, and YouTube videos. Image understanding was added more recently, and agentic browsing, which lets Leo autonomously complete multi-step tasks in an isolated browser profile, entered early access across all release channels in May 2026.

Page awareness is one of Leo's strongest practical assets. It reads whatever you're currently viewing and uses it as live context for your prompts. This works without uploading anything to a third-party server, which keeps the privacy model consistent across all use cases.

BYOM is a genuine differentiator. You can connect Leo to locally-running models via Ollama, to OpenAI-compatible endpoints, or to other third-party APIs. For developers or power users who want a specific model or prefer to keep everything on-device, this adds a level of control that most browser AI tools don't offer.

The Skills feature lets you save and trigger custom prompts with keyboard shortcuts. It's useful for repetitive workflows, though the current library of built-in skills is still relatively narrow. More customization here would strengthen the feature, and it's an area I'd like to see Brave expand.

Multi-tab context and Tab Focus Mode let Leo work across several open tabs rather than just the active one, which helps when cross-referencing multiple sources. The Brave Talk integration is a useful bonus for anyone using Brave's video conferencing tool: Leo can transcribe meetings in real time and produce summaries and action items without sharing data externally.

The one area where Leo falls behind dedicated AI platforms is memory. Leo doesn't retain context between separate sessions, so every conversation starts from scratch. Users who want a long-running assistant that remembers preferences or project history will find this limiting.

Brave Leo AI: User experience

The interface is clean and minimal. The sidebar slides open without disturbing your active page, and the full-page view at brave://leo-ai works well for longer sessions. Switching models takes a couple of clicks from the dropdown at the top of the chat.

Automatic mode removes the decision entirely if you'd rather not think about it.

The address bar integration is a small but practical touch: typing a question and selecting "Ask Leo" opens the response in full-page view without interrupting your browsing. Mobile support on Android and iOS covers the same core features as desktop, with voice input available on iOS. The experience is consistent across platforms, which isn't always the case with browser-based AI tools.

Brave Leo AI: Customer support

Brave handles Leo support through its Help Center at support.brave.app. The documentation is well-organized and covers most common scenarios, from initial access to advanced configuration options like BYOM and model settings. Articles are generally current, which is more than can be said for some AI products that update fast but leave documentation behind.

There's no live chat or dedicated support line for Leo. Community forums and the Help Center are the primary routes for getting help. For a free or modestly priced tool, this is standard, but business users with time-sensitive issues may find the self-serve model inadequate.

Brave Leo AI: Pricing

• Free tier: Access to open-source models (Llama, Qwen, Gemma), rate-limited usage, no account required
• Leo Premium: $14.99/month or $149.99/year, with a 7-day free trial. Includes Claude Haiku, Claude Sonnet, DeepSeek V3.1, and other advanced models; higher rate limits; early access to new features

The free tier works for light, occasional use. You get solid open-source models and full privacy protections without signing up for anything. Rate limits are the catch: they kick in faster than you'd expect during sustained sessions, and there's no way to pay for a small top-up without committing to Premium.

At $14.99/month, Leo Premium is cheaper than ChatGPT Plus ($20/month) and sits comfortably within range for an individual AI subscription. The annual plan at $149.99 brings that down to about $12.50/month, which is reasonable given the model access on offer. There's no team or enterprise pricing.

Leo also has no public API, so external workflow integration isn't an option.

Brave Leo AI: alternatives you should consider

• ChatGPT Atlas: A Chromium-based web browser from OpenAI with built-in access to the most widely used AI assistant. However, it's only available to macOS users for now and has now Windows version.
• Microsoft Copilot: Built into Microsoft Edge and deeply integrated with the Microsoft 365 suite. The stronger pick for users already working in the Windows and Office ecosystem.
• Perplexity AI: A research-focused AI that delivers sourced, real-time answers from the web. Worth considering if web research and fact-finding are your primary use cases rather than general-purpose chat.

How I tested Brave Leo AI

• Used Leo's free tier for web research, PDF summarization, and content drafting over multiple sessions to assess response quality and the real-world impact of rate limits.
• Tested Premium-tier models including Claude Sonnet on complex writing and analysis tasks, comparing output quality against the default open-source models.
• Configured BYOM with Ollama, created custom Skills shortcuts, and put the multi-tab context feature through a multi-source research task.

Testing covered Brave Leo on desktop (Windows and macOS) and on Android, using both the sidebar and full-page chat modes. I evaluated response accuracy across summarization, factual questions, code generation, and writing tasks, and cross-referenced Brave's privacy claims against official documentation and third-party reporting from sources including The Register.

HuggingChat is Hugging Face's free, open-source chat interface . It might be the most underrated AI tool available right now. You get access to over 120 open-weight models including Llama, Mistral, Qwen, DeepSeek, and Falcon, all without spending a cent. The newest addition is Omni, a routing layer that automatically picks the most suitable model for your request.

What makes HuggingChat worth paying attention to is the sheer scope of model access it bundles into a single free product. I've been reviewing B2B software at TechRadar Pro for the past 10 years and our team covers the AI space closely. Take a look at our 2026 best AI tools roundup and read our in-depth explainers on open-source platforms like OpenClaw and Moltbook.

What is HuggingChat?

HuggingChat is the conversational AI interface built by Hugging Face, the company behind the world's largest open-source AI model repository. Instead of locking you into a single proprietary model, it lets you chat with any of 120+ community-hosted, open-weight models directly from your browser.

It's aimed squarely at developers, ML researchers, and technically curious users who want to compare model outputs, test different architectures, or simply avoid handing their data to a closed-source provider. For anyone evaluating an open-weight model before self-hosting it internally, this is an obvious starting point.

The platform also works for general use cases: writing, coding help, document analysis, and Q&A. But it's at its best when the person on the other end knows what they're asking of each model.

HuggingChat: At a glance

Buy it if…

• You want to compare open-weight models side by side. HuggingChat is the fastest way to test how Llama, Mistral, and Qwen handle the same prompt without switching tools.
• You need a free AI chat tool with no usage caps. Unlike many free tiers, HuggingChat doesn't cut you off after a certain number of messages per day.
• Avoiding vendor lock-in matters to your organization. Every model on the platform is open-weight, meaning you can evaluate it here and self-host the exact same model later.

Don't buy it if…

• You want a polished, feature-complete interface. There's no canvas mode, no voice input, and no image generation. That puts it behind ChatGPT and Claude for everyday productivity use.
• Consistent response speed is critical. Inference speed varies depending on server load, and the free tier runs on shared infrastructure. Peak hours can slow things down noticeably.
• You need mobile-first access. HuggingChat is browser-only with no dedicated iOS or Android app, which limits how well it works on the go.

My time with HuggingChat

My first impression was that HuggingChat is more of a research tool than a daily driver. The interface is minimal: a chat window, a model selector, and a sidebar. That's about it. Once I got past the expectation of feature parity with ChatGPT, I found myself appreciating how little gets in the way of just talking to a model.

The Omni routing feature is a genuine improvement. Rather than guessing which model handles a given task best, In my testing, Omni made sensible choices more often than not. For users who don't want to manage model selection manually, it reduces friction considerably.

Where I ran into friction was speed. During busier periods, response latency was noticeably longer than on paid commercial platforms. For quick back-and-forth conversations, that's tolerable. For longer document analysis tasks, it started to feel slow.

HuggingChat: Features

The model catalog is HuggingChat's biggest selling point, and it's hard to overstate how much value that represents for free. At the time of writing, you can chat with 120+ models including Llama 3.1 405B, Mistral Large 2, Qwen 2.5 72B, DeepSeek V3, Command R+ from Cohere, and several Falcon variants. The list updates regularly as new community models are released.

Web search integration is available and works well enough for pulling in current information, which helps avoid stale knowledge cutoff responses. It's not as tightly integrated as Perplexity's approach, but it does the job without requiring a separate tool.

Custom Assistants let you set system prompts, attach knowledge bases via retrieval-augmented generation, and share a pre-configured assistant via a direct link. This is particularly useful for teams who want a repeatable AI workflow without paying for an enterprise platform. Document upload is also supported, which lets you drop in a PDF or text file and ask questions against it.

HuggingChat's limitations are harder to ignore if you're coming from a commercial product. There's no image generation, no voice mode, no plugin system, and no equivalent of ChatGPT's canvas or Claude's Projects feature. For general productivity use, those gaps matter.

HuggingChat: User experience

The interface is clean and gets out of the way quickly. A new conversation starts within seconds. The model selector is easy to find, and conversation history is accessible from the sidebar once you're logged into a Hugging Face account. There's no learning curve beyond understanding what each model is good at. That knowledge gap is real for non-technical users.

Hugging Face has been transparent in interviews about positioning HuggingChat as the open-source community's answer to proprietary chat products. That framing shows in the design decisions: the priority is access and transparency over UX polish. For the target audience of developers and researchers, that's a reasonable trade.

HuggingChat: Customer support

Support for HuggingChat itself is primarily community-driven, via the Hugging Face Discord and forums. There's no in-product live chat or dedicated help desk for the free tier, which means troubleshooting usually involves hunting through documentation or community threads.

Hugging Face Pro subscribers gain access to prioritized support channels, and Enterprise customers get dedicated support. For individual users on the free plan, the documentation is thorough but the response loop can be slow if you hit an edge case.

HuggingChat: Pricing

• Free: Full access to all 120+ models, web search, document upload, and custom Assistants with no daily message limits
• Hugging Face Pro ($9/month): 20x inference credits, 10x private storage, ZeroGPU priority access, Spaces Dev Mode, and early access to new features
• Team ($20/month per user) and Enterprise ($50/month per user): Adds SSO, audit logs, storage regions, SCIM provisioning, and dedicated support

The free tier is impressively generous and covers most use cases without restriction. Upgrading to Pro makes sense if you're a developer who also uses Hugging Face's broader platform for model hosting, inference, or dataset work. The credits and storage benefits extend well beyond HuggingChat itself.

There's no standalone HuggingChat subscription. The Pro plan is a Hugging Face platform upgrade, which means you're paying for the full platform rather than just the chat product. That's either good value or unnecessary overhead, depending on how embedded you are in the HF ecosystem.

HuggingChat: alternatives you should consider

• ChatGPT (OpenAI): The most polished AI chat product on the market, with voice mode, image generation, and a canvas editor. Better for general productivity but fully proprietary and locked behind a subscription for advanced features.
• Claude (Anthropic): Strong at long document analysis and nuanced writing. More consistent response quality than most open-weight models, though it's closed-source and doesn't offer model selection flexibility.
• Perplexity AI: A strong alternative if web search and real-time information retrieval are your main use cases. Less flexible on the model side but more tightly integrated with live web data.

How I tested HuggingChat

• Ran identical prompts across Llama 3.1, Mistral Large 2, and Qwen 2.5 to evaluate output consistency and quality differences.
• Tested web search integration, document upload, Omni routing, and custom Assistant creation over multiple sessions.
• Ran tests across different times of day to assess latency variability on the free tier.

Testing covered both technical tasks (code generation, document Q&A) and general use cases (writing, research, open-ended reasoning) to give a representative picture of day-to-day performance.

• Zoom Virtual Agent Receptionist is now available to any business
• As a first port of call, it frees up humans to handle more valuable calls
• Available from $24.99/month, it speaks 10+ languages and works 24/7

Zoom has announced the launch of a dedicated AI receptionist that customers can deploy within their existing business telephone system, meaning that prospective customers will no longer have to commit to migrating to Zoom Phone, as was previously the case.

By helping customers to avoid disruptive infrastructure changes and waste years of investment in other telephone solutions, Zoom could end up with even more paying customers who would otherwise have dismissed the tool due to the migration requirements.

The system is designed to supplement existing human teams by answering incoming calls and directing them to the right support channels.

Zoom's AI receptionist is now available to all, including non-Zoom Phone customers

"Businesses shouldn’t have to replace their phone system to benefit from AI," Zoom Phone GM Chris Moss wrote.

The company boasted that its virtual receptionist can speak more than 10 languages, will work around the clock and has built-in transcription to help agents later down the line.

It can automatically handle routine business questions and schedule appointments, but it will also transfer calls to the relevant people or departments wherever necessary. By pairing customers up with an AI agent first to hopefully answer some of the most basic and common questions, it frees up human workers time to handle the more valuable and complex interactions.

The virtual receptionist also promises to plug the gaps when human agents might otherwise be busy handling other calls, or our of hours when customer service would usually be unreachable.

According to the company's own data, one in two consumers say they'd switch to a competitor after a single bad experience. Around three-quarters (71%) also said they find calling a company more stressful than the issue itself.

Zoom Virtual Agent Receptionist is available now, priced at $29.99 per month for 100 minutes, or $24.99 with an annual commitment.

• Agentic Atlas browser pulled as OpenAI focuses on one single app
• New ChatGPT desktop app includes built-in browser and agentic capabilities
• Could this finally be the 'superapp' we were promised back in April 2026?

Not even a full year after OpenAI launched its own, dedicated agentic browser, ChatGPT Atlas has been axed amid a broader ChatGPT reinvention and the introduction of what might just be the superapp we've been teased for months.

Launched in October 2025, OpenAI has confirmed that Atlas will stop working from August 9, 2026, however it's not technically the end of the company's browser ambitions.

Instead, the browser is simply being moved into the new ChatGPT desktop app and will form part of existing AI workflows without the friction of having to move apps.

ChatGPT Atlas pulled after 10 months

In April, Chief Revenue Officer Denise Dresser described the company's future as one that stops pursuing side quests and fragmented interfaces. She teased an upcoming 'superapp', and while the company didn't explicitly describe the new ChatGPT desktop app as that 'superapp', the significant overhaul and the integration of Codex, other agentic AI tools and a browser within the single app implies this could indeed be said 'superapp'.

"We’ll begin sunsetting the standalone Atlas browser, and will share information with users about how to transition to ChatGPT," the company wrote in a recent announcement.

The new app launch coincides with the introduction of ChatGPT Work, which adds new agentic capabilities in light of the fact that many Codex users are actually knowledge workers, not coders.

ChatGPT Work bridges the gap between generative and agentic AI by enabling users to complete longer-running tasks, rather than instructing the tool prompt-by-prompt. The tool can run both locally and on the company's cloud servers, allowing access from anywhere and continues progress regardless of the primary PC's state.

Further OpenAI tools are also being made available via Chrome extensions to keep some AI available within a dedicated browser environment.

Express AI, launched on March 31, 2026, is ExpressVPN's attempt to answer a question many AI users haven't thought to ask: who can read your chats? The platform offers access to five AI models inside a confidential computing environment, where prompts and outputs are cryptographically isolated from everyone, including ExpressVPN itself. That's a bold claim in a category where privacy policies tend to be long on language and short on enforcement.

The platform comes bundled with ExpressVPN's Pro plan at no additional cost, positioning it alongside the company's password manager, secure mail, and identity protection tools. I've been covering AI platforms and business software for TechRadar Pro for years, including our 2026 buying guide for vibe coders and our guides on OpenClaw and Moltbook.

For this review, I spent time using Express AI across a range of everyday tasks: drafting, summarising documents, and reasoning through technical problems, to see how it holds up against better-known AI platforms.

What is Express AI?

Express AI is a multi-model AI chat platform built by ExpressVPN and accessible at app.expressai.com. At launch, it offers access to five general-purpose AI models, each selected for a different task type. What separates it from platforms like ChatGPT or Claude isn't the model lineup, but the underlying architecture: every interaction runs inside a confidential computing environment where encryption keys are generated inside the hardware itself, mathematically isolating conversations from cloud providers, model operators, and ExpressVPN.

The platform targets professionals and individuals who routinely share sensitive information in their AI chats, such as financial questions, work documents, and personal communications, and want guarantees that their data won't be retained or used to train future models. It's particularly well-suited to users already in the ExpressVPN ecosystem, since access is tied to the Pro subscription.

Privacy claims in the AI space are easy to make and hard to verify, which is why ExpressVPN commissioned an independent audit from Cure53, a German cybersecurity firm known for rigorous assessments. The pre-launch review, conducted in February and March 2026, covered penetration testing, source code inspection, and analysis of the platform's cryptography and key management. Cure53 confirmed that Express AI processes user interactions within confidential computing enclaves and found no unresolved vulnerabilities at launch.

Express AI: At a glance

Buy it if…

• You share sensitive information with AI tools. If your prompts routinely include personal, financial, or professional details, Express AI's zero-access architecture offers a layer of protection that mainstream platforms don't.
• You want multiple models without multiple subscriptions. The platform bundles five models covering everyday writing, document analysis, and coding in one interface, which reduces the cost and friction of juggling several accounts.

Don't buy it if…

• You're not already an ExpressVPN subscriber. Access requires a Pro plan, so you'd be paying for a full VPN suite alongside the AI platform. That's poor value if you only want an AI chatbot.
• You need API access or developer-level control. Express AI has no public API at launch, which makes it a non-starter for developers building on top of AI models.

My time with Express AI

The interface is spare and deliberately uncomplicated. You select a model, type your prompt, and get a response. There's no sidebar full of tools or settings buried three menus deep. I found the model-switching to be the most useful feature in practice: running the same prompt through DeepSeek R1 Distill 32B for a reasoning-heavy task and then Qwen2.5-VL 32B for document analysis took seconds rather than the tab-switching juggle that typically comes with using multiple platforms.

Ghost Mode worked as described: conversations disappeared after the session ended with no residual trace in the history panel. The encrypted vault stores past conversations behind a user-set password, which means chat history isn't accessible server-side, though a forgotten password loses the history permanently. That's a reasonable trade-off for the privacy guarantee, but it's something to keep in mind for anyone who relies on conversation history for workflow continuity.

One honest caveat: I couldn't independently verify the confidential computing claims beyond what Cure53 has stated in its public report. I'm taking the audit at face value, just as most users will. The 500 daily credits at one credit per prompt felt adequate for my testing but could frustrate users running long, iterative research sessions.

Express AI: Features

Express AI launches with five open-weight models rather than building proprietary ones from scratch. GPT OSS 120B handles everyday writing and reasoning; DeepSeek R1 Distill 32B is the pick for multi-step logic and research analysis; Qwen2.5-VL 32B reads and extracts data from images and documents; Qwen3.5 35B-A3B targets coding and complex prompts; and Nemotron 12B from NVIDIA handles technical and math-heavy workloads. In practice, having these in one interface means you can match the model to the task without maintaining separate accounts or API keys.

The side-by-side comparison tool is a standout. You can run the same prompt across multiple models simultaneously to compare outputs, which is especially useful when deciding which model to lean on for a recurring task type. Few standalone AI platforms offer this natively.

Ghost Mode and the encrypted vault address the two main categories of privacy risk. Ghost Mode auto-deletes conversations after each session, leaving no stored record at all. The vault stores history under user-controlled encryption: only the password set by the user can decrypt it, and neither ExpressVPN nor the model providers can access it. These aren't marketing claims; Cure53's February and March 2026 audit verified both mechanisms before launch.

File uploads are capped at 50 MB per file. The 2 GB of secure storage included with the Pro plan is sufficient for most document analysis tasks. I uploaded PDFs and images without friction, and Qwen2.5-VL 32B handled the analysis accurately in my testing. There's also a transparent credit tracker, which shows remaining daily usage clearly rather than burying it in account settings.

What's missing, at least at launch, is agentic capability. Express AI doesn't support tool use, web browsing, or multi-step autonomous tasks. It's a chat interface, and a deliberate one. For users who want AI agents to run workflows or integrate with external services, they'll need to look elsewhere. The no-API position also limits Express AI's appeal to developers, which may narrow the platform's audience more than ExpressVPN intends.

Express AI: User experience

The interface prioritises simplicity, which suits the platform's core audience. New users can start a conversation in under a minute. There's no onboarding tutorial or feature demo, but the layout is intuitive enough that most users won't need one. The Ghost Mode toggle and model selector are front and centre, so the two features that define the platform don't require hunting through menus.

In terms of design, it reads closer to a focused productivity tool than a full AI platform. That restraint works in its favour for privacy-conscious users who don't want complexity, but it may feel bare to anyone coming from ChatGPT or Claude's more feature-dense interfaces. ExpressVPN has described Express AI as part of a broader privacy ecosystem, suggesting the product will expand, but the launch version makes no concessions to power users hoping for immediate depth.

Express AI: Customer support

Express AI inherits ExpressVPN's customer support infrastructure, which includes 24/7 live chat via the main ExpressVPN site. Response times in my experience have been fast, typically under two minutes for live chat. Support agents are familiar with the VPN suite but may have limited depth on Express AI-specific technical queries, given how recently the platform launched.

The support documentation for Express AI is currently thin. The knowledge base covers setup and billing questions, but more detailed guidance on model selection, credit usage, and the encrypted vault is sparse. Given that this is a new product, that's understandable, but users who run into edge cases may find the documentation less helpful than they'd expect.

Express AI: Pricing

• Included with ExpressVPN Pro. Express AI is available at no additional cost to Pro subscribers, which makes it the most accessible pricing model for existing users.
• Pro plan starts at $7.49/month on a 2-year commitment, rising to $8.99/month on a 1-year plan and $19.99/month month-to-month.
• No standalone or free tier. There is no way to access Express AI without an active ExpressVPN Pro subscription.

The pricing is simple once you accept that Express AI is a bundled benefit rather than a standalone product. Pro subscribers get 500 daily credits, 2 GB of encrypted storage, and access to all five models alongside ExpressVPN's other features. For users who already pay for a Pro plan, Express AI costs nothing extra.

For users who don't need a VPN, the value proposition is murkier. The Pro plan at $19.99/month is competitive with some standalone AI subscriptions — ChatGPT Plus costs $20/month, for example — but you're paying for a VPN suite first and an AI platform second. ExpressVPN hasn't announced a standalone plan for Express AI, so that decision point will remain for the foreseeable future.

Express AI: alternatives you should consider

• ChatGPT (OpenAI): The most widely used AI platform, with broader model access, API support, and agentic capabilities, but no confidential computing or zero-access architecture.
• Claude (Anthropic): A strong alternative for writing and reasoning tasks, with competitive privacy policies, though prompts may be reviewed for safety and model improvement purposes.
• Lumo (Proton): A privacy-oriented alternative from the makers of ProtonMail, worth watching as it develops its AI toolset for privacy-focused users.

How I tested Express AI

• Used Express AI for drafting, summarising, and answering research questions across multiple sessions to assess model quality and response consistency.
• Uploaded PDFs and images to test the Qwen2.5-VL 32B model's ability to extract data and answer questions about uploaded content.
• Tested Ghost Mode, the encrypted vault setup, and credit tracking to confirm they behaved as described in the official documentation and Cure53 audit summary.

For this review, I used Express AI as a day-to-day tool across a three-day period, testing each of the five models for their stated use cases and comparing the experience against mainstream AI platforms I use regularly. I reviewed ExpressVPN's official product documentation, the public summary of the Cure53 audit, and third-party coverage from the March 2026 launch to verify the platform's privacy claims and pricing.

Most AI assistants are built on a familiar arrangement: you get useful tools, the company gets your data. Lumo, launched by Swiss privacy company Proton in July 2025, refuses that deal entirely. Every conversation is protected by zero-access encryption, no logs are kept server-side, and your chats are never used to train the underlying models.

Lumo runs on a set of open-source large language models, including Mistral Small 3, OLMO 2 32B, and OpenHands 32B, with a routing layer that sends each query to the most appropriate model for the task. The client-side code is open source on GitHub and open to independent review. TIME named Lumo a Best Inventions 2025 special mention, recognizing the broader privacy architecture rather than raw AI performance.

TechRadar Pro has been reviewing business software since 2012. For more of our AI coverage, you can explore our AI tool roundup for 2026.

What is Lumo?

Lumo is an AI chat assistant from Proton AG, the Swiss company behind Proton Mail and Proton VPN. It handles everyday tasks: drafting text, summarizing documents, answering questions, writing and debugging code, and translating between languages. Unlike most mainstream AI tools, Lumo is designed so that neither Proton nor any third party can access your conversation history.

The platform targets individuals and teams who work with sensitive information and cannot pass it to Big Tech services. Lawyers reviewing contracts, journalists protecting sources, healthcare workers discussing sensitive cases, and privacy-conscious professionals who object to their inputs being fed into model training pipelines are all natural fits. Proton launched Lumo for Business in October 2025 to serve team deployments with admin controls and multi-user management.

A Lumo API is in development according to Proton's spring 2026 product roadmap, which would let third-party platforms embed private AI chat into their own workflows. For now, Lumo is available on the web at lumo.proton.me, and through iOS and Android apps.

Lumo: At a glance

Buy it if…

• You regularly handle confidential information. Zero-access encryption means even Proton cannot read your saved chats, which matters enormously for legal, healthcare, or journalistic work that no mainstream rival can match.
• You're already a Proton subscriber. Proton Unlimited members get free access to Lumo's base tier at no additional cost, making it a natural addition to an existing encrypted workflow.
• Your business needs GDPR-compliant AI. Lumo is hosted on Proton's Swiss servers, subject to European data protection law, a real advantage for teams with regulatory obligations.

Don't buy it if…

• You need frontier-level AI output. Lumo's open-source models are capable for everyday tasks, but they fall noticeably short of GPT-4o or Claude on complex reasoning and nuanced long-form writing.
• Image analysis is part of your work. As of mid-2026, Lumo does not support image uploads. If visual understanding matters to your workflow, you'll need a different tool.
• You're a light user unwilling to pay. The free tier's prompt limits create real friction for anything beyond occasional queries, and Lumo Plus is harder to justify if privacy isn't a top concern.

My time with Lumo

You don't need an account to get started, just open a chat as a guest at lumo.proton.me and start typing immediately. There's a single text input, a web search toggle, and a Ghost Mode button for sessions you want to vanish when you close the window. I found the onboarding frictionless by any standard.

For routine tasks like summarizing a PDF, drafting a short email, or explaining a technical concept, Lumo performed competently. Response times were reasonable throughout. Where I noticed the ceiling was in longer, more structured outputs: the model occasionally lost the thread in extended conversations, and answers on analytical questions felt thinner than comparable responses from Claude or ChatGPT Plus on the same prompts.

Ghost Mode is useful in practice and cleanly implemented. One click opens a session that leaves no trace on any server when you close it. That's a meaningful practical feature for sensitive queries, and I haven't seen any mainstream competitor offer it this simply.

Lumo: Features

Lumo covers the standard AI assistant toolkit: document analysis, code writing and debugging, text translation, email drafting, brainstorming, and general Q&A. The routing system automatically directs coding queries to OpenHands 32B, general conversation to Mistral models, and deeper reasoning tasks to OLMO 2. You don't choose the model manually; Proton's routing logic decides what handles each query.

The privacy architecture is the main event. Zero-access encryption means saved chats are only decryptable on your own device with your password, and no server-side logs are kept. Your inputs are never used to train the underlying models, and the optional web search feature routes through privacy-respecting search engines rather than ad-supported services.

Lumo 1.1, released August 2025, upgraded the model stack and delivered meaningful speed improvements. Version 1.2 in October 2025 added dark mode, bug fixes, and basic chat personalization. Lumo for Business followed that same month at $11.99 per user per month (annual billing), adding team admin controls, usage management, and data handling aligned with GDPR, HIPAA, and CCPA.

The feature gaps are real. There's no image input, no voice mode, no plugin marketplace, and no memory system for personalized responses across sessions. For privacy-focused everyday use those absences are manageable, but power users comparing Lumo to ChatGPT's tool ecosystem or Claude's extended context handling will notice the difference.

Lumo: User experience

The interface is minimal, fast, and easy to navigate. A new chat takes one click, history is searchable when signed in, and the settings panel is straightforward. Dark mode arrived in v1.2, and the mobile apps on iOS and Android closely mirror the web experience with no major feature gaps between platforms.

Customization options are sparse by design: no system prompt settings, no pinned model preferences, no memory configuration. The routing logic is entirely automated, and you largely have to trust Proton's judgment on which model handles what. Users who want fine-grained control will find that frustrating; those who just want to start typing will appreciate how quickly they can get going.

Lumo: Customer support

Proton covers Lumo support through its help center at proton.me/support/lumo, which includes getting started guides, feature documentation, and troubleshooting articles. A community forum on Proton's user voice platform lets you submit and vote on feature requests. Account-based support tickets can be filed through the dashboard.

There's no live chat or phone support at any tier, which may cause issues for business users dealing with time-sensitive problems. Lumo for Business likely offers more direct support access, but Proton hasn't published detailed SLAs for that plan. For most users, the self-serve documentation is clear enough, but enterprise buyers should confirm escalation options before committing.

Lumo: Pricing

• Free tier: Guest access (no account required) with a limited number of prompts per session. A free Proton account unlocks encrypted chat history and more daily messages.
• Lumo Plus: $12.99/month, or $119.88/year ($9.99/month effective). Includes unlimited chats, document uploads, history search, priority response times, and access to all available models.
• Lumo for Business: $11.99 per user per month, billed annually. Adds team admin controls and compliance-ready data handling for GDPR, HIPAA, and CCPA.

The free tier is functional for sporadic use but constrained enough that the weekly prompt limit becomes friction quickly. At $12.99/month, Lumo Plus sits below ChatGPT Plus and Claude Pro (both $20/month), and the value calculation depends on how much the privacy guarantee matters to you. If you're primarily after output quality and don't mind where your data goes, there are more capable options at similar price points.

Proton Unlimited subscribers ($14.99/month or $9.99/month on annual billing) get free access to the base Lumo tier as part of their existing plan. Lumo is also bundled into the Proton Workspace Premium business plan alongside encrypted email, VPN, and cloud storage.

Lumo alternatives you should consider

• ChatGPT Plus ($20/month): OpenAI's platform offers stronger reasoning, image analysis, voice mode, and a plugin ecosystem. The privacy trade-off is significant, but capability-wise it remains the field benchmark.
• DuckAI (free): Duck.ai routes queries through multiple AI models via privacy-respecting proxies and requires no account. Encryption depth is less thorough than Lumo's zero-access approach, but it's a capable free alternative for casual use.
• Claude Pro ($20/month): Anthropic's Claude handles long documents and nuanced writing particularly well. It offers no comparable encryption architecture, but output quality on complex tasks is a step above Lumo.

How I tested Lumo

• Ran Lumo through document summarization, email drafting, code debugging, and general Q&A across multiple sessions to assess response quality and consistency.
• Tested Ghost Mode, web search with and without the toggle enabled, document uploads, and the iOS mobile app across both guest and signed-in access.
• Cross-referenced plans against Proton's official support documentation, product announcements, and other company material.

My testing spanned the web app and iOS client over several days. I compared output quality against Claude and ChatGPT Plus on identical prompts to gauge where Lumo sits in the current AI field. Privacy claims were assessed against publicly available documentation, open-source client code, and independent technical analysis.

Meta has released a new AI image model, one clearly designed to compete with ChatGPT and Google Gemini's Nano Banana 2. Meta AI has to not only convince people that AI can make the images they want, but that it will make images that are worth switching AI chatbots for.

To see how well it actually does in that context, I set image prompts for Meta AI and compared them to ChatGPT and Nano Banana 2. The tests, ranging from realistic wildlife photography to comics, are designed to test various aspects of AI image production. While all of the models arguably cleared a similar bar for good results, some definitely seemed to understand the assignment better than others.

Moon Orchard Ads

I asked each model to design a polished product poster for a fictional sparkling water brand called 'Moon Orchard', with the can saying exactly "Moon Orchard", "Black Cherry Lime", and "Zero Sugar", plus a clean headline reading exactly "Bright enough for midnight". This was a typography and product-design challenge as much as an image test, because AI models can make a gorgeous fake ad and still mangle the words like a haunted label printer.

All three produced stylish results, but they had different instincts. ChatGPT, on the left in the image above, created the most elegant poster, with a moody purple can, cherries, lime wedges, and a headline that felt like it belonged in a real campaign. Gemini, in the middle, looked the most like a magazine ad layout, but added extra label text and a glass with some of the drink inside. Meta, on the right, produced the most premium-looking can design, with condensation.

Fox photos

I next asked each model to create an ultra-realistic wildlife photograph of a red fox cautiously walking through a snow-covered forest at dawn. This was a realism challenge, with anatomy, fur texture, lighting, atmosphere, and natural movement. I wanted it like a real animal caught at exactly the right frozen moment.

ChatGPT delivered what I'd call the most dramatic image, with the fox moving toward the camera. Gemini was more restrained and natural in its profile shot. Meta was the most cinematic-looking, with the fox appearing more lifelike and the background almost like a green screen.

Garden invite

As Meta is the platform for so many social media platforms, I then asked the models for a square Instagram post advertising a summer garden party, with a warm, stylish, realistic setting, fairy lights, a wooden table, drinks, flowers, and the readable text "Saturday Garden Party — 7 p.m.". This was a practical design test, because plenty of people use AI image tools for invitations, posters, and social posts.

Despite Meta AI's social media connection, it's ChatGPT that seemed to do the best with the prompt — the text looks like it's built into the design way better than the others. Gemini's lettering looked more like a framed flyer than a realistic Instagram post. And while Meta produced the most photographic table scene, it seemed more like a photo taken at the event rather than an invitation.

Robot pancake chef

The fourth prompt asked for a four-panel comic strip about a cheerful robot named Pip baking pancakes, while keeping Pip consistent across all panels. It also tested sequential storytelling. This was one of the strongest rounds for all three models.

ChatGPT's comic was easy to follow and was also the most amusing. Gemini had the cleanest cartoon polish. Meta did a great job in most ways, but gave the pancake a word balloon for some reason.

Noir cartoon

Finally, I asked each model to combine a noir setting with a Saturday morning cartoon. Specifically, a private detective investigating a mysterious missing cookie inside a 1940s suburban kitchen in black-and-white. The prompt tested style-blending, whether the models could make the scene feel like both a detective story and a cartoon.

ChatGPT did a good job balancing the noir mood with a cartoon detective with a dog sidekick. Gemini went for more of a classic detective drama intensity. Meta was the most comedic, with a canine detective exploring scattered clues amid plenty of visual jokes. Meta definitely did the best job in hitting both sides of the prompt.

ChatGPT told the story cleanly, and Gemini had the strongest noir atmosphere, but Meta made the prompt feel the most alive. It understood that a missing cookie mystery should be dramatic and ridiculous.

Five prompts turned out to be enough to show that these image generators have each developed their own personalities. Nano Banana 2 consistently impressed with realism. Meta AI took bigger creative swings than I expected, producing the funniest image of the test in the cookie detective challenge and some of the most polished commercial-looking visuals.

But ChatGPT stood out for seeming to understand what I was actually trying to achieve more consistently than its rivals. It repeatedly delivered images that matched both the wording and the intent of the prompt. The only category where I thought it was genuinely beaten was the film noir cookie mystery, where Meta more effectively embraced the ridiculous premise.

All three are capable of producing good results; the difference comes down to judgment. The best model is the one that understands what you meant. ChatGPT proved to be the strongest at making that leap, even if Meta sometimes stole the show.

Duck.ai is one of the only AI chat platforms where you can hold a conversation with GPT-5 or Claude Opus without registering an account. Launched by DuckDuckGo in early 2025, it sits inside the company's existing search and browser ecosystem. You can access it at duck.ai, through the DuckDuckGo browser, or via desktop browser extensions.

Two things set it apart from other AI chat tools. Every conversation goes through DuckDuckGo's anonymizing proxy before reaching the model provider, stripping out identifying information. Providers such as OpenAI and Anthropic are also contractually prohibited from using those chats for training purposes, which is a meaningful commitment that most direct-to-model platforms don't offer.

At TechRadar Pro, we've been reviewing business software since 2012. Our AI coverage includes an AI tools roundup and a 2026 vibe coding buying guide, among other platform reviews and news features.

What is Duck.ai?

Duck.ai is DuckDuckGo's AI chat service, designed so that you can talk to leading AI models without creating an account or sharing personal data. It's available at duck.ai, through DuckDuckGo's browser extensions, and inside the DuckDuckGo app on iOS and Android.

The service routes all conversations through a privacy proxy. Your prompts are anonymized before they reach the AI provider. DuckDuckGo also holds providers to strict contractual limits on data use, meaning chats are not stored on DuckDuckGo's servers and cannot be used for model training by either party.

It works for typical AI tasks: drafting emails, summarizing documents, answering questions, writing code, and general chat. Privacy-conscious professionals, freelancers, and anyone who wants frontier AI access without a recurring account will find it useful.

Duck.ai: At a glance

Buy it if…

• You want AI chat without an account. Duck.ai is one of few platforms giving you access to capable models, including GPT-5 mini, without registration or a credit card.
• Privacy matters to your workflow. DuckDuckGo anonymizes every chat through its proxy and holds AI providers to strict data-use agreements, making it a stronger privacy choice than signing up directly with OpenAI or Anthropic.
• You want multi-model value. The Plus plan at $9.99/month pairs GPT-5.4 and Claude Sonnet 4.6 with a full VPN and identity protection, making it genuinely competitive.

Don't buy it if…

• You need a developer API. Duck.ai has no API. If you're building products or automations on top of AI models, go directly to OpenAI or Anthropic.
• You already have a VPN. The paid plans bundle AI with VPN and identity services. There's no AI-only subscription tier, so you may end up paying for things you don't need.
• You depend on predictable limits. DuckDuckGo doesn't publish exact daily message limits, which makes it difficult to plan for high-volume or time-sensitive work.

My time with Duck.ai

I tested Duck.ai across the free and Pro tiers over several weeks. On the free tier, the experience is clean and fast with no friction, no sign-up, and six models ready to go. Switching between GPT-4o mini and Claude 4.5 Haiku is a single click. Both handled research queries and writing tasks without issue.

The Pro tier adds Claude Opus 4.7 with extended reasoning. The improvement shows on complex tasks: I ran the same multi-step analytical prompt across the free and Pro tiers. Opus 4.7 returned a more structured and well-reasoned response. For everyday writing or quick lookups, the free tier is more than enough. Pro earns its keep on specialist, multi-step work.

My one persistent frustration was the opacity around usage limits. DuckDuckGo's policy is to keep limits vague to prevent abuse. During testing I didn't hit a ceiling on Pro, but I can imagine high-volume users running into walls without much warning.

Duck.ai: Features

The defining feature is the privacy proxy. Every message goes through DuckDuckGo before reaching the AI provider. The company holds providers to contractual restrictions on data use, which matters most if you're typing sensitive information into an AI: prompts about financial decisions, health issues, or confidential business matters are much less exposed than they would be via a direct provider account.

The free tier's model roster is unusually strong for a no-login service. Alongside GPT-4o mini and Claude 4.5 Haiku, you also get GPT-5 mini and OpenAI's gpt-oss-120b, both capable models that would cost real money through direct API access. Meta's Llama 4 Scout and Mistral Small 3 24B round out the lineup for users who prefer open-weight options.

Paid subscribers get meaningfully stronger models. The Plus plan adds GPT-5.4 and Claude Sonnet 4.6, which excel at long-context tasks and following detailed instructions. The Pro plan goes further with Claude Opus 4.7 and extended reasoning, suited to the kind of multi-step analysis that trips up smaller models.

Voice chat, launched in February 2026, lets you speak to an AI model through an encrypted relay connection. Audio is not stored by DuckDuckGo or OpenAI after the session ends, keeping the privacy principles consistent. The feature currently uses OpenAI as the model provider, so model choice for voice is limited.

File uploads are not yet supported, which is a gap compared to ChatGPT or Claude.ai. If you need to analyze a PDF or document, you'll need to paste content manually. DuckDuckGo has indicated uploads are on the roadmap.

Duck.ai: User experience

The interface is intentionally minimal. There's no dashboard, no settings maze, and no onboarding flow. The left sidebar shows your chat history, stored locally on your device rather than on DuckDuckGo's servers. You switch models by clicking the current model name at the top of the conversation. New users can start a chat in seconds.

AI features are also fully optional. DuckDuckGo lets you hide Duck.ai buttons and AI overlays in search settings, which is a meaningful gesture from a company that treats privacy as more than a marketing position. The option to disable AI without losing other features is something many platforms don't offer.

Duck.ai: Customer support

DuckDuckGo maintains detailed help documentation at its Help Pages site, covering Duck.ai's privacy policies, model availability, usage limits, and subscription management. The documentation is well-organized and answers most common questions without requiring you to contact anyone.

There's no live chat or phone support channel. For subscription or billing issues, you can reach support through the subscription settings menu, but response times aren't publicized. Businesses planning to rely on the Pro plan for critical work should factor this in when evaluating the service.

Duck.ai: Pricing

• Free: Six models, no sign-up, with unspecified daily usage limits.
• Plus: $9.99/month or $99.99/year, which adds GPT-4o, GPT-5.4, Claude Sonnet 4.6, and Llama 4 Maverick, plus VPN, Personal Information Removal, and Identity Theft Restoration.
• Pro: $19.99/month or $199.99/year, which adds Claude Opus 4.7, extended reasoning, and 2x usage limits versus Plus.

The free tier is one of the best no-login AI offers available right now. Getting GPT-5 mini and Claude 4.5 Haiku at zero cost, with genuine privacy protections, is a hard proposition to dismiss. The Plus plan at $9.99/month undercuts both ChatGPT Plus and Claude Pro (each $20/month) while providing access to frontier models alongside a bundled VPN.

The Pro plan at $19.99/month is harder to recommend to most users. Claude Opus 4.7 with extended reasoning is a premium experience, but only for tasks that genuinely require deep multi-step analysis. There's no standalone AI-only option, so if you already pay for a VPN elsewhere, you're likely paying twice. The subscription is available internationally with localized pricing: UK users pay £9.99/£19.99 per month for Plus/Pro respectively, and most EU countries are priced at EUR 9.99/19.99 per month.

Duck.ai: Alternatives you should consider

• ChatGPT: OpenAI's direct interface costs $20/month for Plus and offers GPT-4o access alongside file uploads and image generation, two features Duck.ai currently lacks. The downside is that ChatGPT requires an account and uses conversation data for service improvements unless you opt out.
• Claude.ai: Anthropic's own interface gives you Claude Opus at $20/month on the Pro plan and is stronger for deep reasoning work. It requires registration, doesn't offer multi-model switching, and has weaker privacy protections than Duck.ai's proxy approach.
• Perplexity AI: A strong alternative if you want AI answers grounded in real-time web search. Perplexity Pro costs $20/month and supports file uploads, though it doesn't match Duck.ai on privacy routing.

How I tested Duck.ai

• Ran identical prompts covering research queries, email drafting, and complex analytical tasks across the free, Plus, and Pro model tiers to compare quality and speed.
• Reviewed DuckDuckGo's published privacy documentation, provider contracts policy, and help pages to assess how well its privacy proxy claims hold up.
• Tested the subscription sign-up, model switching, voice chat, and local history access on both desktop and mobile across several sessions.

Response latency on the free tier is slightly higher than a direct ChatGPT session, consistent with traffic being routed through a proxy. The difference was small enough that it wasn't noticeable mid-conversation. Pro-tier responses were fast regardless of model choice, with Claude Opus 4.7 taking marginally longer on extended reasoning tasks — which is expected given the additional processing involved.

DeepSeek landed like a thunderclap in January 2025, when its R1 reasoning model briefly dethroned ChatGPT as the most downloaded free app on the iOS App Store in the United States. Built by a Hangzhou-based AI lab backed by Chinese hedge fund High-Flyer, it claimed to match frontier AI performance at a fraction of the development cost. That claim sent Nvidia's stock tumbling 17% in a single session and sparked a global conversation about who was actually winning the AI race.

Since then, DeepSeek has grown to roughly 97 million monthly active users and released multiple model generations, most recently the V4 family in April 2026. Its API pricing stands out: the V4 Flash model starts at $0.14 per million input tokens, cheaper than most "lite" tier models from OpenAI and Google yet competitive on coding, math, and reasoning benchmarks. The open-weight licensing under MIT also means teams can self-host the models and sidestep per-token costs entirely at scale.

We've been reviewing B2B software at TechRadar Pro since 2012, with AI platforms among our most active coverage areas in recent years. Our AI tools roundup, vibe coding guide for 2026, and explainers on OpenClaw and Moltbook give you a sense of the tools we track. DeepSeek is one of the more polarizing platforms we've tested: impressive in many ways, but not without significant red flags.

What is DeepSeek?

DeepSeek is an AI chat platform and API service developed by Hangzhou DeepSeek Artificial Intelligence Co., Ltd., a Chinese company founded in 2023 and funded by the quant hedge fund High-Flyer. It offers a free web and mobile chat interface at chat.deepseek.com alongside a paid developer API, both powered by the same underlying model family.

The platform runs on DeepSeek's own large language models, specifically V4 Flash and V4 Pro, both using a Mixture-of-Experts (MoE) architecture. Only a subset of each model's parameters activates per token, which keeps inference costs low without shrinking the model's overall knowledge base.

V4 Pro has 1.6 trillion total parameters but only 49 billion active at inference. V4 Flash runs 284 billion total with 13 billion active, making it significantly faster and cheaper without sacrificing much on everyday tasks.

Developers, researchers, and cost-conscious businesses are the natural audience. The free chat tier suits individuals and small teams exploring the tool, while the API's aggressive pricing makes it attractive for anyone building AI-powered applications at scale.

DeepSeek: At a glance

Buy it if…

• You need a cheap, capable coding or reasoning API. V4 Flash at $0.14 per million input tokens is among the most affordable frontier-adjacent APIs available, and its benchmark results on coding and math hold up well against more expensive competitors.
• You want open weights for self-hosted deployment. Both V4 models are released under MIT license on Hugging Face, giving teams the option to run the model on their own infrastructure and eliminate per-token costs entirely for high-volume workloads.
• You're doing non-sensitive exploratory work. For individual researchers, students, or developers prototyping non-confidential projects, the free chat app offers web search, file uploads, and a 1M token context window at zero cost.

Don't buy it if…

• Your work involves confidential or regulated data. All user data is stored on servers in mainland China, subject to Chinese law, which permits government access without user consent. Multiple governments have banned DeepSeek from official devices for exactly this reason.
• You need consistent responses on sensitive topics. DeepSeek avoids certain politically sensitive subjects, particularly around Chinese domestic affairs. That content filtering can produce evasive or incomplete outputs on topics that other platforms handle straightforwardly.
• You're in a GDPR-regulated region. Italy's data protection authority blocked DeepSeek outright in January 2025 after the company provided what regulators called a "completely insufficient" response to data practice inquiries.

My time with DeepSeek

I tested DeepSeek's chat app and API across a range of tasks: code generation, document summarization, long-form reasoning, and general Q&A. On raw capability, the V4 models impressed me. Code outputs were clean and well-structured, long document summaries were accurate, and the one-million-token context window handled full-length PDF ingestion without complaint.

The thinking mode, accessible via Expert Mode in the chat UI, added visible chain-of-thought reasoning that proved useful for multi-step problems rather than theatrical.

What gave me pause was everything outside the model itself. Certain politically sensitive prompts returned conspicuously vague or deflective answers — the kind of behavior that wouldn't be acceptable in a professional context where consistent and complete information matters. I also found that the chat interface lacks the memory and personalization features you'd find in ChatGPT or Claude.

Value for money on the API side is difficult to argue with. A production app with well-structured prompts benefits substantially from the caching discount: cached input tokens cost just $0.014 per million for V4 Flash, a 90% reduction. For high-volume, low-sensitivity workloads, that arithmetic is compelling.

DeepSeek: Features

DeepSeek's core chat feature set covers the bases you'd expect: text generation, code writing and debugging, document summarization, mathematical reasoning, and web search. The web search integration is a manual toggle rather than always-on, which keeps responses faster by default but requires you to switch it on when real-time information matters. File uploads support PDF, DOCX, and TXT formats, with the model able to summarize and answer questions based on the uploaded content.

The standout capability is the 1M token context window introduced with V4, up from 128K in the previous generation. That's a meaningful jump for anyone analyzing long contracts, codebases, or research documents in a single session. Most competitors at comparable price points max out at 128K to 200K tokens.

V4 Flash covers both thinking and non-thinking modes, so you don't need to switch between separate models depending on task complexity. Non-thinking handles fast general responses; thinking adds structured multi-step reasoning for harder problems. That flexibility matters more than it sounds when you're toggling between casual tasks and complex analysis in the same workflow.

Where DeepSeek falls short is multimodal support. The platform does not currently support image generation or image understanding in the web app, putting it behind ChatGPT, Claude, and Gemini on that front. Agentic capabilities are available in the V4 Preview but remain early-stage compared to dedicated agentic platforms.

DeepSeek: User experience

The chat interface at chat.deepseek.com is straightforward and fast to get started with. Signing up requires only an email address from a global provider like Gmail or Yahoo, and the default experience drops you straight into a conversation window. The distinction between Expert Mode (thinking-enabled, slower) and Instant Mode (faster, non-thinking) is surfaced clearly at the top of the interface, and mobile apps on iOS and Android mirror the web experience with file upload and web search included.

The learning curve is shallow for casual use. Switching between thinking and non-thinking modes takes one click, and the file upload workflow is drag-and-drop simple.

The API experience is less forgiving for first-time integrators. Unlike the chat app, the API is stateless, meaning every call must include the full conversation history in the messages array. DeepSeek's documentation covers this clearly, but it catches developers accustomed to managed conversation state elsewhere off guard.

DeepSeek: Customer support

Support options for free chat users are limited to a Discord community server and an email channel for API service inquiries (api-service@deepseek.com). Community responses on Discord can be prompt, but they depend on other users rather than official staff. There is no live chat or phone support.

API customers have slightly more recourse through direct email support, though response times vary. The official documentation at api-docs.deepseek.com is thorough and well-organized, covering model details, pricing, rate limits, and code examples in both Python and curl. For developers comfortable with self-service documentation, it's adequate.

DeepSeek: Pricing

• Chat app is free with no query limits. The web and mobile apps give you unlimited access to V4 Flash and V4 Pro at no cost, including web search, file uploads, and the full context window.
• New API accounts receive 5M free tokens, valid for 30 days, giving developers a zero-cost window to prototype and test.
• API billing is pay-as-you-go. V4 Flash costs $0.14 per million input tokens and $0.28 per million output tokens. V4 Pro runs $1.74 and $3.48 respectively at standard rates, with promotional discounts periodically dropping those to $0.435 and $0.87. Cached input tokens cost one-tenth of the standard input rate on both models.

The free chat tier is generous by any measure. Unlimited queries with a 1M context window puts it ahead of most free-tier competitors in raw access terms, and there's no paid chat subscription to worry about. Power users who need more control either stick with the free app or pay per token via the API.

On the API side, DeepSeek makes a strong case for developers managing costs at scale. Off-peak pricing discounts of up to 75% are available during 16:30–00:30 UTC, giving teams with flexible scheduling another cost lever. For production apps with well-structured prompts sharing a common system context, effective input costs can drop well below $0.02 per million tokens with caching applied.

DeepSeek: alternatives you should consider

• ChatGPT (OpenAI): The most capable all-around AI platform, with image understanding, voice, and memory. API pricing is higher, but US-based data residency and enterprise data agreements make it a safer choice for sensitive business workloads.
• Claude (Anthropic): Particularly strong for long-form writing and document analysis, with comparable context windows and clearer data handling policies. Claude Pro starts at $20/month for individual users.
• Gemini (Google): Tightly integrated with Google Workspace and built for multimodal work across text, image, and video. Gemini 2.5 Flash offers competitive API pricing with no Chinese data jurisdiction concerns.

How I tested DeepSeek

• Used the free web app for code generation, document summarization, mathematical reasoning, multi-turn Q&A, and file analysis, covering both Instant Mode and Expert Mode across each task type.
• Ran API calls across varying prompt sizes to verify context window behavior, caching discounts, and response consistency, following DeepSeek's own temperature guidance for different task types.
• Cross-referenced DeepSeek's official privacy policy, third-party security research from NowSecure and SecurityScorecard, and regulatory actions from Italy, Australia, South Korea, and US government bodies to build a complete picture of the data risk profile.

Beyond hands-on testing, I reviewed DeepSeek's official API documentation, the V4 technical report published on Hugging Face, and benchmark data from the April 2026 release. Pricing figures were sourced directly from the official DeepSeek API documentation and corroborated against third-party tracking services.

• Generative AI may have unintentionally pushed away some of the highest-quality 'expert' contributors to sites such as Stack Overflow as users increasingly adopted tools trained on their feedback instead
• The issue stems from said users feeling their expertise and effort are unrewarded, with AI often offering the same solutions at a faster pace
• The move is not limited to online coding communities, threatening to spill over into other areas such as classrooms, corporate workplaces, and scientific communities

Research from the University of Auckland on Stack Overflow's demise over the last few years points to an increasingly worrying trend in the software community: the best, or highest-skill, contributors are leaving in droves.

AI, which arguably bridges the gap between most entry-level and mid-range coders and some of the best in the business, might actually be accelerating the latter's exit from online communities, as they feel their efforts are no longer as valued as they once were.

Stack Overflow has seen a nearly 76% decline in monthly questions posted since ChatGPT's advent in 2022, indicating that both new and existing users are abandoning the site.

A much broader problem than just Stack Overflow?

Stack Overflow's problems and the reason for its decline were multi-faceted; however, many users felt that the site and some of its most talented contributors engaged in a certain degree of hubris.

This, coupled with heavy-handed moderation that many called 'self-righteous,' meant that users finding a viable option would inevitably leave the platform.

ChatGPT and its AI alternatives became considerably more pliable and, over time, doubled as search engines for many coders with routine, repeatable queries, even as AI increasingly handled questions such as syntax issues better than before.

This, in turn, reduced the number of questions asked on the platform and, despite a generative AI ban enacted soon after ChatGPT went online, led to a loss of answerers that may prove impossible to replace in the long term.

The issue may no longer be confined to online coding communities; researchers indicate it could spill over into other areas such as classrooms, offices, and other research communities, where low-effort answers are harder to discern from those of subject-matter experts thanks to ever-evolving, retrained AI models.

"If everybody can create a good quality response or output using AI, some people may think, 'Why should I make an effort to share my expertise and participate?", publisher of the study Dr Kenny Ching explained.

Ching termed this 'signal compression' as expert and non-expert solutions became harder to separate, even as it became less rewarding to be a subject-matter expert on topics that AI could also easily weigh in on.

The question that does come to mind here, however, is a simpler one: if AI was trained on user-contributed data and an increasingly small amount of it exists on platforms such as Stack Overflow, where does the upcoming knowledge reset take us in terms of AI capabilities?

While future AI models will not get "dumber," so to speak, they might turn to different avenues for training, such as Slack chats, Discord servers, or even users who currently ask them the same coding-related questions they once did on Stack Overflow.

Whether this replaces experts who no longer wish to contribute or simply makes AI more prone to errors over time, thanks to how its feedback loop functions, is an interesting question in a society that finds it increasingly hard to discern between AI and human answers.

• AI security report sparks lawsuit after startup denies cybercrime accusations
• MeetingTV challenges Koi over allegedly inaccurate automated threat analysis
• Missing evidence becomes central issue in cybersecurity report dispute

MeetingTV has filed a lawsuit against Palo Alto Networks and its subsidiary Koi Security over a recent blog post which alleges the latter's AI system generated false claims connecting the video conferencing startup to a Chinese espionage campaign.

Court documents describe the publication as reckless reliance on an automated analytical tool without adequate human verification.

The lawsuit alleges that Koi relied heavily on its proprietary Wings analytical platform, which generated false links between MeetingTV and a cybercrime group called DarkSpectre, and according to court documents, the startup claims the system created unsupported connections that were presented as evidence of criminal activity.

AI-generated findings become the centre of legal dispute

MeetingTV founder Michael Robertson said the report relied on information that appeared to come from an AI tool without sufficient human verification.

"The false attributions were the direct product of Koi’s unsupervised reliance," the lawsuit stated, referring to the alleged dependence on automated analysis.

The disputed report connected MeetingTV’s Zoomcorder service to a campaign involving a browser extension named Twitter X Video Downloader.

However, the lawsuit claims the extension did not exist and that Koi failed to provide evidence supporting its technical connection.

MeetingTV alleges that this missing component formed the foundation of Koi’s argument linking the company to the wider malware campaign.

The startup also claims Koi did not contact the company before publishing the report or provide an opportunity for clarification.

After the report appeared online, multiple security companies and service providers blocked MeetingTV’s domains, classifying them as malicious infrastructure.

The company claims these actions affected access to its services and damaged its reputation among customers and partners.

The wider concerns around AI-driven cybersecurity reports

Koi Security later removed references to MeetingTV’s Zoomcorder product from the report, though the startup argues the damage continued afterward.

Palo Alto Networks, which acquired Koi Security in April, acknowledged awareness of the lawsuit while defending Koi’s cybersecurity research process.

The company said Koi’s work reflects efforts to identify threats and expects the dispute to follow the legal process.

However, MeetingTV argues that automated security analysis requires stronger oversight before conclusions are shared publicly.

AI systems already produce incorrect information and many even warn users of this possibility, so their outputs should never be presented as verified fact.

Security researchers increasingly rely on automated tools to process large volumes of data, yet verifying those conclusions remains a persistent and unresolved challenge.

Should MeetingTV's claims hold up under judicial scrutiny, the dispute could prompt closer examination of how AI-generated threat reports are produced and reviewed.

The advice therefore is simple: conclusions from AI-assisted analysis should be painstakingly verified, especially when errors could cause serious harm to an individual or company.

Via The Register

• Lumo 2.0 release rebuilds Proton's privacy-first assistant with reasoning modes, image generation/recognition, cited live web search, and persistent memory
• The privacy stack mixes cryptography and policy: zero-access encryption protects stored chats and images, while inference-time protection relies on Proton's no-logs/no-training promises that have held in the past
• Proton's Lumo 2.0 Lite and Lumo 2.0 Max score 127% and 240% higher than Lumo 1.4 on the Artificial Analysis Intelligence Index, making them close in on last-generation frontier AI models

Proton has revealed Lumo 2.0, its updated AI alternative to ChatGPT, Claude, and Gemini, focusing on privacy first and foremost, a distinctly different approach from most of its competition.

The new update is not only smarter than its predecessor at what it does, but also brings a host of new capabilities: reasoning modes, image generation and recognition, live web search with citations, persistent memory, and customizable assistants.

Lumo 2.0 looks to do all of this while leveraging zero-access encryption, no-logs, no-training, a pitch that makes it appealing to privacy-focused consumers, many of whom are already customers for its VPN product lineup.

Upgrades, multiple models and faster performance

The biggest upgrade to Lumo 2.0 is that it is now multimodal, allowing it to glean information and cross-check a variety of sources without often forcing the user to defer to other AI engines for most tasks.

Proton cites a 76% faster speed for 'everyday queries' while conceding that complex tasks still take a considerable amount of time.

Users can also leverage "Custom Lumos" or purpose-built assistants that retain instructions in memory while still maintaining the encryption promise that Lumo offers, allowing users to avoid starting from scratch each time they have a query to address.

Users can use either the fast, general-purpose Lite model for everyday queries and defer to the more complex Max model for demanding work, or use Fast and Thinking modes, which offer twice the context window of its predecessor for larger workloads and greater coherence with more complex asks.

Pricing spans a free tier for what Proton calls everyday private use, a $12.99-per-month Lumo Plus plan with unlimited chats, Projects, advanced image generation and access to the most capable models, and a $14.99-per-user Lumo Professional tier for teams.

Lumo is also available to business users and offers the same upgrades discussed above, making it a significantly more powerful and smarter AI tool than it was when we last reviewed it at TechRadar.

It is important to note that while Lumo 2.0 is a huge upgrade versus its older 1.4 version, it does not come as close to frontier models as Proton might want to make it appear: its model scores a 51 on the Artificial Analysis Intelligence Index which sees current frontier models clock in as high as 59 (GPT 5.6 Sol Max) or 60 (Claude Fable 5) versus its own comparisions that show it much closer to older frontier models such as GPT 5.5 and Claude Opus 4.8.

This is not entirely surprising, given that one can find the underlying tech Lumo uses in its privacy policy. Proton states that it uses a mix of Qwen 3.5, GLM 5.2, Image-Turbo, and FireRed-Image-Edit-1.1, with GLM 5.2's scores roughly identical to the numbers it cites currently.

Despite its limitations versus newer frontier AI models, Lumo 2.0 arguably remains the most privacy-focused approach to AI available to most end users currently, and it comes considerably closer than its predecessor at what is an increasingly uphill task of late: offering a competitive privacy-centric alternative to billion-dollar proprietary AI models built by the likes of Google, Anthropic, and OpenAI.

It's hard to get away from AI these days, with the technology finding its way into our apps, devices, and entertainment — but according to a survey carried out by TechRadar's parent company Future, heavy use of chatbots such as Gemini and ChatGPT has actually fallen over the past year.

The reasons behind the shift are interesting too: users are mentioning concerns over privacy, a preference for human interaction, and worries about becoming too dependent on the tech when it comes to why they're avoiding AI.

While AI is clearly reshaping our world in significant and myriad ways, there's also now a noticeable trend of anti-AI sentiment that's worth paying attention to — whether it's to do with protests against data center expansion or worries about AI slop content.

These results are based on questionnaires filled out by 1,008 respondents, and they make for interesting reading — especially when you compare them to the statistics from the same Future survey conducted last year. Here's what the latest responses tell us.

Privacy worries

There are many reasons why people aren't so sure of AI at the moment. Topping the list for our respondents are concerns over privacy, which were cited by 32% of those who completed our survey — that's actually unchanged from last year, so no matter which chatbot is being used, many of us are wary about what it's doing with our data.

Second in the list, mentioned by 31% of survey participants, is a preference for human interaction (this is a new response option, so we don't have anything to compare it to from last year). While AI is certainly now more than capable of holding a conversation on just about any topic imaginable, it seems we're still seeking out flesh-and-blood relationships.

In third place we've got those who are happy without AI, which accounts for 29% of people surveyed — that's actually a drop of 18% over the previous survey, perhaps a sign that a significant chunk of people have realized they are happy chatting to AI on a daily basis (it's certainly getting more ubiquitous by the day).

Next on the list are fears around becoming too dependent on the tech, and concerns over AI misrepresenting a 'voice' or personality (in emails, for example), which account for 26% and 24% of respondents respectively. It seems a lot of us are worried we'll get to the stage where we can't do without AI for even basic tasks — not good when there's an outage — and that our communications will turn into generic, bland AI averageness.

Neither of those two responses were available last year, but the next two came in at exactly the same level as in the previous survey: a lack of awareness of AI's capabilities (19% of respondents) and a lack of interest in what AI could do (17% of respondents). It seems the AI hype hasn't quite reached everyone yet.

Rounding out our respondents' list of barriers to AI usage, we've got technical complexity (15%, down 24%), skepticism over whether AI can be helpful (14%, down 21%), environmental and energy impact concerns (11%, a new category), and being philosophically opposed to AI as a concept 10%, down 24%).

Regular AI usage

When answering the question of how often chatbots like Gemini or ChatGPT are used, 17% of those quizzed said "daily or almost daily", while 14% said several times a day. That's nearly 1 in 3 people using generative AI at least once a day or so, though that's actually fewer people than last year — could the novelty of the tech be wearing off?

The 14% figure for the most enthusiastic users has actually dropped by 31% over the course of the past 12 months, so while usage is exploding in areas such as coding, it seems as though the overall trend is in the opposite direction (perhaps influenced by the increasing amount of AI stuffed into our apps).

As for the less frequent users, 21% of respondents said they were using AI chatbots once a week or a few times a week, 11% ranked their usage as being a few times each month, and 8% said they used generative AI less than once a month — apparently dipping in and out for occasional chats.

When it comes to complete refuseniks, 30% of the survey participants said they don't use AI chatbots at all, which is a hefty 24% jump from last year. That suggests more and more people are taking against the very idea of AI, or finding it completely useless for day-to-day life and work.

There is a divide in the respondents worth mentioning between those who read Future publications and those who don't: more than 42% of Future readers use generative AI on a daily basis, with 21% using it multiple times a day, which overall is around double the figure for non-readers.

So that's the state of play for consumers and AI at the moment then, according to the people we surveyed. With generative AI advancements showing no signs of slowing down, it's going to be very interesting to see how these figures might have changed again in another 12 months.

Lately, I've noticed a growing number of celebrities and influencers are talking about AI. Some seem to be partnering with tech companies. Others have positioned themselves as AI evangelists, encouraging their audiences to embrace the technology before they get left behind.

Among those who have generated significant attention are Reese Witherspoon, Mel Robbins, Sandra Bullock and Matthew McConaughey. Their messages do all differ, but they tend to orbit the same idea, which is that AI is here, it's important and you'd better get on board fast.

The thing is, the more a celebrity tells me I need to use AI, the less I want to. And judging by public sentiment, I'm really not alone.

I don't think everyone talking positively about AI is acting in bad faith. Some may genuinely believe it will improve people's lives. Others may have investments, partnerships or financial incentives tied to the industry's success. (And that's hardly unusual — celebrity endorsements have always existed.) Some may simply be repeating the dominant narrative without stopping to consider how influential they are.

But rather than try to understand their personal motivations, what interests me more is the growing gap between the way AI is being promoted and the way many people actually feel about it.

Because while some public figures seem convinced that widespread AI adoption is inevitable, public trust in the technology remains surprisingly low. Survey after survey finds that many people are cautious, sceptical or actively worried about AI. And it's not difficult to understand why.

A growing disconnect

Conversations about AI have moved really quickly over the past year. We now find ourselves debating copyright, creative labor, deepfakes, misinformation, surveillance, environmental costs, job displacement and the growing concern that outsourcing too much thinking to machines may come with cognitive consequences of its own. On the morning I'm writing this, there are fresh headlines about the grim realities of data centre expansion.

At this point, there are so many legitimate concerns surrounding AI that it's difficult to keep track of them all.

Meanwhile, some of the companies that initially appeared determined to replace workers with AI have been rowing back their plans. We've seen reports of AI-generated content requiring extensive human correction, customer service experiments failing to meet expectations and organizations discovering that replacing people is a lot harder than they first imagined.

That's why I find the celebrity enthusiasm so fascinating. Because while some public figures are urging people to embrace AI before they get left behind, many people seem to be moving in the opposite direction.

The comments beneath posts promoting AI are often filled with scepticism. Articles about AI backlash are becoming increasingly common. And when I asked my own social media audience how they felt about celebrity AI endorsements, many expressed similar concerns.

Becky Hughes told me: "All of this makes me more reticent than ever to use social media or adopt new technologies, because the safest option seems to be not to engage at all."

Jay Vera Summer said: "When I see celebrities do things like this, I wish I knew more about their stock portfolio. Especially when it's coming from people who usually don't give financial or career advice."

Whether or not all of our suspicions are fair, I think they suggest something really important is happening here, which is a growing trust gap.

At least from where I’m sitting, it seems people no longer automatically assume that enthusiasm for AI is neutral. They're increasingly wanting to know who benefits from the pro-AI messages, who profits and whose interests are being served when the technology is being promoted so aggressively.

Fear over specificity

If you actually look at what much of the celebrity messaging amounts to, it's surprisingly hollow. Learn AI. Use AI. Don't get left behind. It’s inevitable.

And what fascinates me is how little specificity there tends to be alongside it. What exactly should people be using AI for? Which tools? In what contexts? For what benefit? What are the trade-offs? What are the risks?

I haven't seen many celebrities get into any of that. To be fair, even many of the people building, investing in and advocating for AI rarely spend much time on the details. Instead, the conversation often just gravitates towards fear.

The fear of becoming obsolete. The fear of missing out. The fear of being left behind by a future that everyone else supposedly understands. As someone who has spent years covering technology, that kind of rhetoric always makes me uneasy.

And that's not because I think AI won't have a place in the future. I think it almost certainly will, for better and for worse. But because "you'll get left behind" isn't really an argument. It's an appeal to our anxieties so that you’ll act fast without thinking. And it’s encouraging adoption without fully engaging with the reasons people might be hesitant in the first place.

AI as a feminist issue

I find this particularly interesting when AI is framed as a feminist issue. Earlier this year, The Cut described this phenomenon as the "girlbossification" of AI, giving a name to the growing trend of influential women encouraging other women to embrace the technology or risk falling behind.

Several prominent women have made versions of this argument. And they’re sort of right. In some studies, women have adopted generative AI more slowly than men. But the gap appears to be driven partly by risk, ethics, and workplace factors, not just technical ability. And women have plenty of reason to be concerned about the risks.

We know that women and girls have been disproportionately affected by some of AI’s most disturbing uses, including deepfake pornography, AI-generated image abuse, and sextortion. In one UN estimate, up to 95% of online deepfakes are non-consensual pornographic images and 99% of those targeted are women. I know this isn't theoretical because I've experienced a version of it myself.

Against that backdrop, telling women they simply need to embrace AI can feel completely disconnected from reality. It risks treating healthy scepticism as ignorance when, in many cases, it seems to me that it’s a response to genuine concerns and lived experience.

The recent partnership between Kylie Jenner and Meta feels particularly relevant here. The campaign positions AI-powered glasses as fashionable, desirable and aspirational. And in some ways that's exactly what celebrity endorsements have always done, take a technology and make it feel culturally normal.

But that's exactly why these messages deserve scrutiny. At the same time women are being encouraged to embrace AI-powered devices, there have already been multiple stories of women being unknowingly recorded by smart glasses. Which to me highlights the very real concerns around privacy, consent and surveillance that often get overlooked in conversations about the latest cool new tech on the block.

More informed than you think

I've seen some people online brush all of this conversation off and argue that we shouldn't be taking celebrities seriously anyway. But like it or not, they do help shape public narratives. They influence what people pay attention to, which questions get asked and which concerns get ignored.

And right now, many of those narratives seem to be built around this strange assumption that resistance to AI exists because people don't understand it. Well, I've spent the past year talking to people about AI, and I suspect the opposite is often true.

Many people understand enough to have concerns. They've tried the tools. They've seen both the benefits and the downsides. They're making conscious decisions about how much of their work, creativity, relationships and attention they're willing to hand over to AI systems.

That's why I find so much of the celebrity messaging unconvincing. The more people tell me I have to use AI, the more I want to pause and ask: okay why? And I know I'm not alone.

And that’s not because people are afraid of the technology. If anything, that framing completely misses the point. What I see instead is caution, scepticism and a willingness to actually ask the difficult questions about where this technology is taking us.

Because I think whenever someone insists a certain future is inevitable, our alarm bells should start ringing. That's when we need to ask: okay, whose version of the future are you trying to sell?

• OpenAI has brought big changes to ChatGPT’s desktop app
• ChatGPT and Codex have been merged into a single app
• Users are unhappy with many of the new updates

OpenAI has been running its Codex vibe coding platform as a separate tool from the regular ChatGPT app for a while now, but that’s all changed with the artificial intelligence app’s latest update. The two products have been merged into one ‘super app’ — but many users are deeply disappointed with the move.

If you download ChatGPT today — or update an existing installation — you’ll find an app that looks very different to the one you’re used to, which could be particularly disorienting if you’ve used the artificial intelligence (AI) chatbot for some time now.

For example, you’ll now find a new toggle in the top-left corner of the app that lets you switch between ‘ChatGPT Work’ and ‘ChatGPT Codex.’ Instead of containing buttons for your library, projects, and apps, the left-hand sidebar now houses scheduled tasks and plugins. Projects and tasks sit below in their own sections.

What used to be the ChatGPT app still exists, but has been renamed to ChatGPT Classic. The standalone Codex app, meanwhile, has become the main ChatGPT app. Both ChatGPT Work and ChatGPT Codex share plugins, and while both are capable of similar tasks, Codex shows more of the technical details that Work obscures in a bid to be more user-friendly.

An unpopular move

Unfortunately for OpenAI, this major change has not gone down too well. Apple pundit John Gruber, for example — long a fan of OpenAI’s decision to make ChatGPT a native Mac app that conformed to macOS conventions — described the change as “the day OpenAI f*cked up the ChatGPT Mac app.” He also pointed out that while the old ChatGPT app weighed in at a lightweight 159MB, the new edition has ballooned to 1.5GB.

I’m certainly not a fan of the new-look ChatGPT either. I have little interest in Codex and use ChatGPT to work through problems, often revisiting old conversations to add new details. Yet in the new app, your recent conversations are hidden and require many more clicks to access. Only your five most recent threads are shown, and to access more you have to click Chat > See All to uncover them. Previously, they were right there waiting for you in the sidebar.

As well as that, my existing projects seem to have totally disappeared. I only had a couple, but they were related to a small-claims case I was involved in, so seeing all my relevant threads scattered and disorganized in a long list of chats is a major pain. For anyone juggling a plethora of projects, this change could be disastrous. And what’s more, Custom GPTs also seem to have vanished into the ether.

I know I’m not alone in my disappointment. Over on Reddit, OpenAI’s decision has not been well received, with user powprodukt summing up the mood by saying: “ChatGPT projects and custom GPTs are the heart of why I use that app. Without these features there is no feature parity.”

Other users took issue with the new user interface, with Kaotic987 exclaiming “Christ almighty, this is so bad!” Eriane, meanwhile, cynically suggested that OpenAI “vibe coded everything without testing it.”

Whether you even get ChatGPT Classic seems up for debate. It’s available for Windows in the Microsoft Store and can be downloaded separately from the new ChatGPT app. On macOS, however, I updated to the new edition and ChatGPT Classic is nowhere to be seen. Luckily, I backed up the old app before updating, so I can still access it, which might be the way to go if you’re a Mac user and don’t want to lose all your projects and custom GPTs.

The move to combine ChatGPT and Codex into a form of ‘super app’ has already alienated plenty of users, myself included. From the questionable user interface to the absence of projects and custom GPTs, there’s a lot for OpenAI to work on before it can get the app back to a good place.

• Report claims China's AI consumption is now over 100 trillion tokens per day, up from 100 billion in justtwo years
• Enterprise customers are increasingly paying per usage, not per seat
• General consumers will likely keep their fixed, monthly pricing

AI token consumption in China has increased "more than 1,000-fold" in around two years, with National Bureau of Statistics data claiming daily token consumption rose from 100 billion at the start of 2024 to 100 trillion by the end of 2025.

By March 2026, China was consuming around 140 trillion tokens per day, which SCMP illustrates as 100,000 tokens per person per day if every single Chinese citizen, including children, pensioners and non-workers, were to use AI.

"Today, the token economy is emerging," Yin Hao of the Chinese Academy of Sciences said in a recent Beijing conference, the South China Morning Post reported.

AI consumption-based pricing could replace per-seat pricing

With users consuming more tokens than ever before, AI providers are now under pressure to cover the growing costs. Industry experts like Hao now believe that token-based pricing could soon replace traditional subscription models.

This could see subscribers paying a fee based on how much AI they actually use, rather than paying a fixed monthly charge per user.

AI providers globally are also increasingly recognizing growing token consumption and are adapting their pricing strategies to match, including OpenAI and Anthropic. Others, like Zendesk, are charging per meaningful outcome rather than per seat or per token.

However, for now, these pricing shifts are primarily affecting enterprise customers consuming huge amounts of tokens. General consumers logging into their preferred chatbot, like ChatGPT or Gemini, are unlikely to be affected as compute becomes cheaper.

Mistral Vibe is one of the more interesting AI platforms to come out of Europe. The product, formerly called Le Chat, was rebranded by Paris-based Mistral AI in May 2026 to mark its shift from a conversational assistant to a full agentic platform covering chat, work automation, and cloud-based coding. It's a meaningful change, not just a naming exercise.

Two new operating modes, Work and Code, are the engine behind that ambition. Work Mode runs multi-step tasks across connected business tools. Code Mode handles remote coding sessions inside isolated sandboxes and delivers work through to a pull request. Together they put Vibe in more direct competition with ChatGPT and Claude than its Le Chat days suggested.

At TechRadar Pro, we've been reviewing business software since 2012 and our AI coverage has become some of our most-read work. That includes our AI tool roundup, our 2026 vibe coding buying guide, and deep dives on platforms like OpenClaw or Moltbook.

What is Mistral Vibe?

Mistral Vibe is a chat, work automation, and coding platform developed by Mistral AI. It's available via web browser and mobile apps on iOS and Android, and runs on Mistral's own model family, from the lightweight Small 3.1 to the flagship Large 3 and the reasoning-focused Magistral line.

The platform launched in February 2024 as Le Chat, originally a general-purpose assistant. Over the following two years, Mistral layered in web search, voice mode via its Voxtral audio model, a Canvas document editor, image generation through Black Forest Labs Flux Ultra, persistent memory, and project folders.

The May 2026 rebrand signals that Mistral sees Vibe as an enterprise-grade product. Work Mode and Code Mode are aimed at professionals who need an AI that can execute multi-step tasks across connected tools, not just hold a conversation.

Mistral Vibe: At a glance

Buy it if…

• You handle sensitive client or business data. No Telemetry Mode gives Pro subscribers a contractual guarantee that nothing they type is used to train Mistral's models. That level of assurance at $14.99 per month is unusual among major AI chat platforms.
• You want agentic features without enterprise pricing. Work Mode handles multi-step tasks across Gmail, Slack, Notion, and other connected tools. Code Mode manages full coding sessions through to a pull request. Both are available on the Pro plan.
• You're a developer watching API costs. Mistral Large 3 at $6 per million output tokens significantly undercuts GPT-5.4 ($15/M) and Claude Sonnet ($15/M) at the flagship tier, and the API bills only for tokens used.

Don't buy it if…

• The free plan is your entry point for serious evaluation. At around 25 messages per day with no canvas and no remote coding access, the free tier doesn't give you a fair picture of what the platform can do.
• Your team is embedded in Microsoft 365 or Google Workspace. Vibe connects to business apps via MCP, but native integration with the major office suites is not as developed as what you'd get from Copilot or Gemini for Workspace.
• You're assessing the Team plan for a small group. The jump from Pro ($14.99) to Team ($24.99/user/month) is steep, and the main additions (admin controls and more storage) may not justify the cost for teams of fewer than ten people.

My time with Mistral Vibe

I tested Vibe across its Pro plan over several weeks, using it for research tasks, document drafting, and code work. The chat interface is clean and approachable. Anyone familiar with ChatGPT or Claude will navigate it without confusion. What caught me off guard was how efficiently Work Mode handled complex, multi-step research requests, pulling from connected sources and drafting a structured Canvas output in a single run.

Code Mode held up well for the tasks I threw at it. I ran a session to scaffold a simple API integration, and the agent handled writing, testing, and preparing a draft PR inside the sandboxed environment. I stepped in twice to give it additional direction, but that's consistent with what you'd expect from any AI coding agent at this stage.

The No Telemetry Mode stood out as a differentiator. Enabling it took seconds and gave me real confidence when working with business-related documents. That kind of data control is typically reserved for enterprise tiers at other major platforms, so finding it on a $14.99 plan is a real differentiator.

Mistral Vibe: Features

Vibe's feature set has grown considerably since the Le Chat launch in 2024. Alongside standard chat, the platform now covers web search, image generation, voice input, a Canvas document editor, Deep Research, memory, and project organization tools. Pro subscribers get access to Work Mode and Code Mode, which is where the real differentiation lies.

Work Mode is the headline addition from 2026. It turns Vibe into an execution agent that can read emails and calendars, draft documents in Canvas, run recurring scheduled tasks, and push outputs to Notion, SharePoint, or Slack. Every step is visible in the interface. The platform asks for explicit approval before any action that modifies data or sends a message.

Code Mode targets developers specifically. It connects to GitHub, GitLab, Jira, and Linear, running sessions in an isolated sandbox. A /teleport command lets you move a session between the Vibe web app and a local terminal without losing context. Parallel sessions are supported on Pro and above, which is useful for running background jobs while staying in another workflow.

Memory, currently in beta, lets Vibe store your preferences and recurring context across conversations. You can view, edit, or delete these entries at any time, and turning the feature off is a single toggle in confidentiality settings. Image generation via Black Forest Labs Flux Ultra is solid for a chat platform.

The main gap in the feature set is transparency around usage limits. The free tier runs at lower quotas than Pro across searches, image generations, and messages, but Mistral doesn't publish the exact numbers on its pricing page. That makes the free-to-Pro comparison harder to assess than it should be.

Overall, Vibe covers more professional use cases than most platforms at this price point. The area where competitors like Copilot and Gemini maintain a clear lead is native productivity suite integration, which Mistral has not yet matched.

Mistral Vibe: User experience

The layout is straightforward: a left sidebar for conversation history and project folders, a central chat window, and model or tool selectors accessible from the input bar. Switching between chat, Work Mode, and Code Mode happens within the same interface rather than routing you to a separate product URL.

The mobile apps mirror the web experience closely. Search, canvas, image generation, and voice input all carry over, which isn't a given with AI chat platforms on mobile. Onboarding is minimal, which suits experienced AI users but may leave newcomers without much guidance on how to get started with the more complex agentic features.

Mistral Vibe: Customer support

Free and Pro users access support through the help center widget on the Mistral site. Response times are not published, and there's no live chat or phone support at these tiers. The help documentation covers the most common issues in reasonable depth, but for billing questions or edge-case technical problems, you're relying on ticket-based email support.

Enterprise customers get a dedicated support workflow with priority routing. Requests go through the same widget but are flagged and handled separately based on account type. For teams in finance, healthcare, or other regulated sectors where response time matters, that distinction is a real consideration when deciding between Team and Enterprise.

Mistral Vibe: Pricing

• Free plan: Around 25 messages per day with limited web searches, reduced image generation, no canvas creation, and no remote coding.
• Pro at $14.99/month: No Telemetry Mode, full canvas access, Work and Code Mode, 5x more web searches, more image generations, and pay-as-you-go Vibe coding beyond included limits.
• Team at $24.99/user/month (or $19.99 on annual billing): Shared workspaces, admin controls, domain verification, data export, and higher storage limits.

The free plan gives you a taste of chat quality and basic search, but the restrictions mean you won't get a representative experience. Pro at $14.99 is, by most comparisons, the cheapest premium AI chat subscription from any major provider. The No Telemetry Mode and agentic modes are hard to find elsewhere at this cost.

The Team plan's value depends on your use case. For small teams, the mainly administrative additions over Pro may not justify the per-user cost. Enterprise pricing is negotiated directly with Mistral and covers SAML SSO, on-premise deployment, custom model training, and dedicated support. The API runs on a fully separate billing track with no monthly minimums, making it accessible for developers at any scale.

Mistral Vibe alternatives you should consider

• ChatGPT (OpenAI): The most established option for general productivity, with deeper Microsoft 365 integration and a broader ecosystem of plugins. It costs more at comparable tiers but suits teams already embedded in the Microsoft environment.
• Claude (Anthropic): A strong choice for long-document analysis and nuanced writing. Pricing is competitive and reasoning quality is high, though data privacy controls at the consumer tier are less explicit than Vibe's No Telemetry Mode.
• Gemini for Google Workspace: The best fit for teams already using Google's suite. Native Calendar, Docs, and Gmail integration outpaces what Mistral currently offers for Google-centric workflows.

How I tested Mistral Vibe

• Ran prompts across standard chat, Web Search, and Deep Research modes to evaluate response accuracy, source quality, and multi-step research handling across a range of topics and document types.
• Tested agentic task execution through Work Mode, including document drafting via Canvas and recurring task scheduling, and ran coding sessions through Code Mode against a GitHub-connected project to assess end-to-end agent performance.
• Verified plan details and API rates against Mistral's official pricing page, cross-referenced them with third-party pricing analyses where official documentation was vague, and assessed support options through the Mistral Help Center.

I tested Mistral Vibe on its Pro plan over several weeks using a mix of daily productivity tasks and structured feature evaluations. Pricing data was verified against the official mistral.ai pricing page, with third-party sources used for cross-reference on API rates and plan limits where Mistral's documentation was unclear. Support quality was assessed through available help center documentation and publicly reported user experiences.

HIX.ai started as an AI writing assistant and has since expanded into something considerably more ambitious. The platform now markets itself as an "ultimate AI agent workspace," bundling tools for content writing, deep research, image and video generation, slides creation, coding, and multi-model chat under a single interface. It's a notable evolution, even if the transition has introduced some rough edges.

What sets HIX.ai apart from simpler AI chat tools is the sheer breadth of models on offer. Users can switch between GPT-5.5, Claude Opus 4.8, Gemini 3.5 Flash, DeepSeek-V4, and others from within the same session. For teams or individuals who want to compare outputs or pick the right model for a specific task, that flexibility has real practical value.

We've been reviewing business software at TechRadar Pro since 2012 and AI platforms have become one of our most-covered categories. In recent months, we've also published our best AI tools roundup and deep-dives into platforms like OpenClaw and Moltbook. HIX.ai falls squarely in our coverage area.

What is HIX.ai?

HIX.ai is a web-based AI platform that consolidates multiple AI capabilities into one workspace. Its agents cover text generation, long-form article writing, email composition, image creation, video production, presentation slides, deep research, and coding assistance. You access everything through a shared interface rather than juggling separate subscriptions for each function.

The platform targets a wide audience: content marketers who need to produce copy at scale, students looking for a homework or essay tool, small business owners who want a single AI tool to handle email, social posts, and research, and knowledge workers who want to stay inside one tab instead of bouncing between apps.

HIX.ai also offers a browser extension that works across Gmail, Google Docs, LinkedIn, Twitter, and most major web platforms. A desktop app is available alongside the main web interface, making it one of the more cross-platform options in this category.

HIX.ai: At a glance

Buy it if…

• You want one platform for everything. HIX.ai removes the need for separate subscriptions for writing, image generation, and research. That consolidation saves both time and money for users currently paying for multiple tools.
• You want model choice without switching apps. The ability to call GPT-5.5, Claude, Gemini, and DeepSeek from a single interface is a real convenience, especially when different tasks call for different models.

Don't buy it if…

• Billing transparency matters to you. Multiple users across review platforms have reported being charged for annual plans they believed were monthly. HIX.ai's refund window is three days, which leaves little room for course correction.
• You need predictable usage. Credits don't roll over between billing cycles, and the "Unlimited" label on paid plans applies only to standard credits. Advanced features are still capped, which surprises users expecting truly open access.

My time with HIX.ai

I spent several sessions testing HIX.ai across its main agent categories. The multi-model chat interface impressed most immediately: switching from Gemini 3.5 Flash to Claude Opus 4.8 mid-conversation takes a few clicks, and responses feel snappy enough for practical work. The deep research agent was particularly useful, pulling together sourced summaries on a complex topic faster than a manual web search.

The browser extension held up well in Gmail and Google Docs. Highlighting a paragraph and getting a rewrite option surfacing instantly, without opening a new tab, is the kind of friction reduction that matters when you're working fast. The image generation tools produced serviceable results, though I found the credit consumption less predictable than I'd have liked.

Where the experience wobbles is in understanding what you're paying for. The credit system splits between standard and advanced buckets, and it isn't always clear which agent or model draws from which pool. New users should read the plan details carefully before committing to a billing cycle.

HIX.ai: Features

HIX.ai's feature roster is unusually wide. The platform covers AI chat across multiple frontier models, a long-form article writer, email and social copy tools, a paraphrasing and summarization engine, image generation via Midjourney, Flux, and GPT Image 2, video generation via Seedance 2.0, Veo 3.1, Sora 2, and others, a slides agent, and a deep research tool that produces structured reports with real-time internet access.

The deep research agent is one of the more differentiated offerings. Rather than a standard web search, it compiles structured, sourced reports on a given topic, making it useful for due diligence, competitive analysis, or academic research. Real-time internet access is included on paid plans, which keeps output current rather than frozen at a training cutoff.

On the writing side, the 120+ AI writing templates cover a broad range of formats, from ad copy to blog outlines to product descriptions. The long-form article writer handles structure and headings reasonably well, and the AI Writer agent supports markdown and rich text outputs, which makes it easier to paste results into a CMS or document editor.

The platform's weakest spot by reputation is HIX Bypass, its AI humanizer tool. Multiple independent reviews and user reports flag inconsistent results against modern AI detectors, particularly after Turnitin's 2025 updates. HIX.ai positions this as a key feature, but anyone relying on it for academic or professional compliance should test it carefully before committing.

The multi-agent coordination feature, available across all tiers, lets different agents pass context between tasks. In practice, a research output can feed directly into a writing task without manual copy-paste, which speeds up structured workflows considerably.

The video and image agents round out a platform that few competitors match in raw breadth. Whether that breadth delivers depth at every point is a fair question; some agents feel more developed than others. But for users who want to consolidate tools, the coverage is hard to beat.

HIX.ai: User experience

The interface is clean and navigation between agents is straightforward. The sidebar layout keeps the main workspace uncluttered, and the model switcher is tucked sensibly into the chat header rather than buried in settings. For new users, the onboarding path is light: a working chat session is within reach in under a minute.

The browser extension is where the experience stands out. HIX.ai has put real thought into how it surfaces inside other platforms: the quick-action bar appears when you highlight text, and the full sidebar opens on demand. For anyone doing a lot of writing inside web apps, this integration removes the switching cost that blunts many AI tools, and the learning curve for core features is minimal.

HIX.ai: Customer support

Support is available via email across all plans, with priority support reserved for paid subscribers. The HIX.AI Community forum is open to all tiers and covers common issues and feature requests. Response times on the email channel have drawn complaints in user reviews, with some reporting delays of multiple days for billing queries specifically.

The three-day refund window is short by industry standards. Platforms like ChatGPT Plus and Jasper offer more flexible cancellation terms. Given the annual billing complaints that appear consistently in user feedback, the lack of a longer trial-to-refund period is a genuine sticking point for new subscribers.

HIX.ai: Pricing

• Free plan: $0/month with 20 credits, enough to test core features but limited for sustained work.
• HIX AI Max: Billed annually; includes unlimited standard chat models, advanced models, video, image, and all agent features including deep research, AI slides, and priority support.
• HIX AI Pro: Billed annually at a higher tier, with different credit allocations across advanced features.

The free plan offers real access to the platform with no credit card required, though 20 credits runs out quickly once you start testing agents. Paid plans are priced on a credit system that splits standard and advanced usage into separate buckets. SoftwareSuggest, which updated its pricing data in April 2026, lists the starting price at $13/month on annual billing, with higher tiers scaling up from there.

HIX.ai's various product verticals (AI Writer, HIX Bypass, the browser extension, EssayGPT) each carry separate subscription structures for legacy users, adding complexity to an already layered pricing model. The newer unified workspace tiers (Free, Max, and Pro) simplify this somewhat, but the credit system still rewards careful reading before you subscribe. Annual billing delivers the best per-month rate, though it comes with the billing caveats noted above, and there is no public API for developers.

HIX.ai: alternatives you should consider

• ChatGPT Plus ($20/month): Similar multi-model access via GPT-5 and o3, with simpler pricing and a stronger track record on billing transparency.
• Jasper ($39/month billed annually): Focused on marketing and long-form content. Less breadth than HIX.ai but stronger output consistency on brand voice tasks.
• Perplexity Pro ($20/month): If deep research is your primary use case, Perplexity's research-first approach and real-time citations are hard to beat at this price point.

How I tested HIX.ai

• Ran multi-agent workflows across the chat, deep research, and writing agents to evaluate inter-agent handoffs and model-switching convenience.
• Tested the browser extension inside Gmail and Google Docs on multiple sessions, focusing on responsiveness and quality of inline suggestions.
• Evaluated output quality across standard chat, long-form article generation, and the image creation tools using consistent prompts to benchmark against competitor platforms.

I spent approximately two weekdays with HIX.ai across different workloads, including research tasks, marketing copy drafts, and content editing sessions. Pricing information was cross-referenced against the official HIX.ai pricing page and third-party review sources updated in 2026. Feature details were drawn from the platform's live interface and official documentation.

• Palo Alto Networks CEO Nikesh Arora says 90% reduction in AI pricing is needed to drive widespread adoption, 20% reduction by next year
• OpenAI's latest GPT-5.6 Sol model is 54% more efficient across agentic coding tasks
• Shift to consumption-based models is making high costs even more pronounced

Palo Alto Networks CEO Nikesh Arora has warned that AI pricing is still too high for widespread enterprise adoption, arguing that it should be drastically cheaper.

Although model efficiency and performance have come a long way in recent years, he believes cost remains one of the biggest barriers for widespread adoption.

In a recent interview with CNBC, Arora laid out how he believes token prices need to fall by as much as 90%, however he acknowledges that the change is unlikely to happen immediately, instead welcoming a 20% price reduction over the next year or so.

Is AI pricing holding enterprises back from widespread adoption?

Arora's comments come in response to OpenAI's latest announcement, revealing that its latest GPT-5.6 Sol model is now 54% mode token-efficient across agentic coding tasks – a major improvement.

Though Palo Alto Networks' CEO admitted that this in itself is a good start, it's not enough of an improvement to drastically change pricing for major enterprises at the moment.

"It's important to understand the demand continues to be infinite," he added, implying that cost remains prohibitive. The issue of cost is also becoming more of an issue as AI companies evolve their pricing strategies, with customers moving toward consumption-based models rather than flat monthly fees per user.

Importantly, Arora's vision for cheaper AI isn't unfounded. Virtually all previous technological revolutions have already followed a similar pattern. In the case of AI, compute would become cheaper, models would become more efficient and competition could keep prices low, all working in the favor of enterprise customers.

• OpenAI has introduced new agentic ChatGPT Work tools for all users
• The tool is powered by the latest GPT-5.6 model and Codex
• Could this be the app's unofficial evolution into a superapp?

OpenAI has lifted the wraps off a new agentic tool within ChatGPT to help workers "take on more ambitious tasks," covering workflows like gathering information, creating finished materials and handling long-running autonomous tasks.

The new tool, called ChatGPT Work, marks a shift in how knowledge workers are set to use AI, evolving from generative AI to autonomous agentic AI.

ChatGPT Work, largely seen as an interface for knowledge workers, brings together tools like the company's latest frontier model GPT-5.6 and Codex.

ChatGPT Work is an autonomous knowledge worker's dream

In an announcement, the company noted how Codex was born out of the need for coders and developers to get access to agentic capabilities, but now, more than a million non-coding workers use it for other tasks.

The company also declared that nearly all of its internal teams use Codex and ChatGPT Work to speed up their workflows.

All users running the app on their macOS or Windows desktop can get access to ChatGPT Work for free, including non-paying subscribers. Pro, Enterprise and Edu plans will get mobile and web access first, but Plus and Business plans will get access within days.

Further aiding ChatGPT Work's autonomy, the tool also works with third-party plugins via the '@' command to obtain further context and access to working files.

More broadly, as the app evolves into a superapp to combine all of OpenAI's tools in one workspace, desktop users can now grant the client permission to control the computer on their behalf, similar to Anthropic's Claude Code.

"This is the first step towards a broader vision for ChatGPT," the company summarized, "where intelligence goes beyond answering questions to helping everyone turn their biggest ideas into reality."

• The Mac mini has emerged as an affordable system for agentic workloads
• Apple has seen "incredible demand" for the Mac mini and Mac Studio
• Apple silicon can handle an agentic AI while other architectures use a GPU and CPU

If you’re looking for the best way to explore and deploy agentic AI without breaking the budget, the Mac mini might be just what you’re looking for.

Apple’s Doug Brooks has expressed enthusiasm for how the Mac mini and Mac Studio desktop computers are capable of handling agentic AI tasks, thanks to Apple silicon, the ARM-based SoC that the company has introduced over the past half decade.

Success with local AI on these machines has been attributed to design choices made before the arrival of advanced LLMs, with the evolution of Apple’s Neural Engine highlighted as a key factor.

How the Mac mini is ideal for agentic AI

Brooks is the senior product manager of Apple silicon, and referred to the “incredible demand” for Mac minis and Mac Studios when speaking to The Deep View before WWDC 2026.

Describing the Mac mini as an “amazing system” that can “tap into the strengths of Apple silicon and unified memory in a very power-efficient way, and increasingly they're delivering compelling price-performance as well.”

The price point of a Mac mini – compared to the more expensive Mac Studio – makes it particularly suited to teams exploring agentic AI but without the budget to pay for tokens and larger systems.

Neural Engine technology dates back to the A11 chip, and its evolution and inclusion within the current generation of Apple chips, and its high-performance, power-efficient compute processes are pivotal in delivering machine learning to the desktop.

As many AI tools were available first on the Mac (or released exclusively for macOS), it seems that upgrading to the latest Mac mini or switching from Windows has been instrumental in demand.

Mac mini: amazing for AI

Apple’s work on AI has seen deployment in everyday use across computers, tablets, and smartphones, and the company has been a leading exponent of hybrid AI, where an agent can “decide what needs to happen locally and what needs to happen in the cloud based on the workload.”

“For agentic workloads, people often want a system that's under their control, isolated from their primary machine, and capable of running 24 hours a day, seven days a week.”

But it is the strength of the Apple Mac mini and Apple Studio – as well Apple’s notebooks – in handling AI that seems to have enthused Brooks the most. He cites security and economics as concerns for developers and creators who are now realising that they can handle AI workloads sitting at their desk – whether using a Mac mini or something more powerful.

• Bosses are seemingly confused that mass AI in favor of human workers isn't always a success
• AI operating costs are often higher than expected
• However some firms are happy to adapt and refocus where needed

Bosses are being left confused at the high cost of moving to AI-centric models, with many seemingly baffled that replacing human workers with agents isn't instantly saving them huge amounts of money, new research has claimed.

A new KPMG report found nearly a third of business leaders reported some difficulties with getting to grips with AI operating costs in their organizations.

The news comes as several major AI providers, including the likes of Anthropic and OpenAI, have moved some services toward usage-based billing, rather than flat-rate subscriptions, in recent months.

Making AI effective

The report saw KPMG survey 2,145 senior leaders across 20 countries, finding 29% were struggling to understand the rise in operating costs as they looked to scale AI across their business, with a similar proportion also highlighting a limited understanding of AI costs and economics as a major challenge to deploying AI agents.

"As usage-based pricing models become more common, many organizations are still building the capabilities required to forecast, monitor, and manage AI spending effectively," KPMG said.

When things do go wrong, the report highlighted how leaders were often unclear who should take responsibility, particularly in the case of hallucinations or errors by AI models.

It noted that why having human leadership be accountable is important, "governance ultimately succeeds or fails through day-to-day operating practices."

"Organizations need clear rules for when employees can intervene, who owns AI-related costs, how AI outputs are reviewed and what happens when systems fail. While most organizations report having at least some governance mechanisms in place, relatively few describe these practices as fully embedded," the report said.

When costs have outweighed the expected value, the report found a surprising amount of contrition from its participants, with nearly half of organizations saying they had rephased AI deployments in that case.

"These actions do not signal reduced confidence in AI," the report warned. "Rather, they suggest a growing willingness to evaluate where AI creates meaningful value and where it does not. Organizations appear increasingly focused on concentrating investment where expected returns are strongest."

"We’re seeing a clear divide between organizations with leadership accountability at the top and those without," added Steve Chase, KPMG Global Head of AI and Digital Innovation, KPMG International.

"These companies are seeing materially better results across the board such as greater confidence, higher value realization and established ROI."

• OpenAI plans to hire a $200k Investment Banking expert as it continues to target finance
• The company wants to improve financial accuracy, consistency and overall quality
• ChatGPT already has its own personal finance tool

OpenAI is advertising a new "Subject Matter Expert, Investment Banking" role within its team, serving as the latest hint that the ChatGPT maker wants to expand its reach into the finance sector.

This latest position strongly implies that the company wants to increase more sophisticated Wall Street type tasks, rather than be an all-purpose chatbot, and it makes sense.

We've already seen OpenAI and rivals like Anthropic heavily target certain sectors – banking and law being two of the most evident in recent months.

Could OpenAI be expanding its reach into finance?

OpenAI described investment banking as one of the most demanding forms of knowledge work, putting pressure on workers to "synthesize fragmented information, exercise judgment under pressure, and produce precise, defensible models, analyses and client materials."

Being that the job was posted in an entire blog post-style announcement rather than on a dedicated careers platform, it's clear the role will be a high-level one reporting to senior leaders and will play a considerable role in the company's direction. The salary of $185,000 to $205,000, plus equity, is also indicative of the role's importance.

The announcement highlights the importance of quality over quantity, stressing the need for AI to help produce work that's financially correct, traceable and consistent.

OpenAI also wants prospective candidates to help identify the highest-value AI opportunities in investment banking – another clear signal that the company is going after the sector big-time.

With its existing expertise, OpenAI has already targeted the consumer end of the sector with a new Personal Finance tool within ChatGPT that connects to bank accounts to give users insights into their spending habits, investments and savings.

Do you remember the metaverse? If you don’t, don’t worry. In 2026, four years into the AI revolution that’s changing the world forever, you could easily be forgiven for thinking it was a strange fever dream you had back in 2021. You might even have odd memories of seeing a blocky version of Mark Zuckerberg floating about in a Minecraft-inspired hellscape, conducting meetings with people who could walk with no legs, while the real Mark Zuckerberg was looking at the whole thing through VR goggles. At least, that’s my memory of it.

I could be suffering from the Mandela effect, but I distinctly remember something off about the legs. They fixed that in a later version, but that’s my overriding memory — no legs. Oh, and Mark Zuckerberg assuring us that this was the future. He’d spent tens of billions of dollars on it, even changing the company name from Facebook to Meta, just to let us know he was really serious about the metaverse. Even if the legs didn’t work.

The problem was, it looked laughable. While everything in the technology world to do with games and special effects was moving in the direction of hyper-realism, the metaverse was moving in the opposite direction, towards the sort of blocky graphics that small children enjoy. But even that didn’t really answer the most basic question about the metaverse. Why? What possible advantage was there for us all to meet in a VR space where clunky avatars of ourselves could interact… badly?

Then AI happened and Meta abruptly forgot about the metaverse and pivoted towards the mission of putting personal superintelligence in all our hands instead, which sounds as terrifying and dangerous as it actually is, but we are where we are. At least when Zuckerberg was obsessed with the metaverse, we could ignore him. It existed somewhere “over there”, in Meta-land, where we could let him get on with it. Now he’s right up in our business again.

The rise of a bad idea

I’ve always been fascinated by how Zuckerberg got into the metaverse and why he became so obsessed with it. The origins of the metaverse go way back. In March 2014, Facebook bought Oculus, the VR company, for about $2 billion. This was where his passion for VR started. Think of it as the seed, not the full obsession.

By July 2021, Zuckerberg gave a long interview to Casey Newton at The Verge about Facebook becoming a “metaverse company” and described it as an “embodied internet”. Then, on October 28, 2021, his obsession became the company identity. Zuckerberg announced that Facebook the company was becoming Meta at Connect 2021, saying the new company brand would focus on bringing the metaverse to life.

I can see the logic. As a business strategy, that made sense. As a product ordinary people were expected to use, it was much harder to understand. Zuckerberg did not want Meta/Facebook to be trapped inside someone else’s platform again. Facebook had won on social, but on mobile it remained dependent on Apple and Google for distribution, privacy rules, app-store policies and hardware. The metaverse looked like a chance to own the next operating system of social life: hardware, avatars, identity, payments, meetings, gaming, work, commerce — the whole stack. In his 2021 founder’s letter, he framed the metaverse as the “next chapter of the internet” and said Meta would become “metaverse-first, not Facebook-first.”

Facebook had obviously had its problems — it was scandal-ridden. It had let advertisers target vulnerable teenagers, helped fake news spread, and enabled the spread of hate speech linked to atrocities in Myanmar. Perhaps Zuckerberg was looking for a way out of Facebook, and the metaverse offered that.

What I still didn’t understand was why he didn’t see what the rest of us saw — that it looked terrible and offered no real benefit to users. Then I read Sarah Wynn-Williams’ tell-all book about Facebook, Careless People, and it all started to make sense.

To say the book made my jaw hit the floor on several occasions would be an understatement. It’s an absolute page-turner, and your reactions grow from mild amusement to shock, then disbelief, then absolute outrage the further through the book you get. I’m aware of the criticisms of Wynn-Williams: that it is a book written by a disgruntled employee, and that she dodges a lot of personal responsibility for her part in the various misdeeds of the company. However, in another perfect example of the Streisand effect, the fact that Meta obtained a legal order in the United States to prevent her from saying anything negative about the company — at all — made me want to pick it up, and I’m glad I did.

Because now I get it — Zuckerberg seems to have spent years in an environment where too few people were willing to tell him when his ideas weren’t good. According to Wynn-Williams, he was surrounded by sycophants. When he had bad ideas, like the ill-fated Internet.org, he wouldn’t let them go and persisted with them, even when they were obviously going to fail. The people around him enabled him because he was simply too powerful. They even let him win at the board games he liked to play with them at his house or on his jet, and — crucially — he didn’t notice that they were letting him win. I can imagine that in that environment, nobody inside Meta would want to tell Zuckerberg that his metaverse was the equivalent of the emperor’s new clothes, especially if they wouldn’t even risk beating him at Settlers of Catan.

Wynn-Williams only mentions the metaverse in her epilogue. It happened after she was brutally fired from Facebook. Perhaps selfishly, I wish she’d been there for the metaverse period, because I would love to read firsthand accounts of how and why Zuckerberg persisted with such an obviously bad idea.

The fall of the metaverse

Maybe I’m being too harsh on Zuckerberg. The metaverse graphics did get better over time and Apple ventured slightly into the same territory with its Apple Vision Pro, even after the metaverse had turned into a smoldering wasteland. The fact is, people don’t enjoy wearing VR goggles for extended periods of time, and for normal people, VR lacks that one killer app. There doesn’t seem to be anything you can do in a VR space that you can’t do elsewhere much more easily.

The metaverse didn’t really die with a bang, but with a whimper. It faded through layoffs, spending cuts and the AI pivot. If I had to put a date on it, I’d say early 2023 was when Meta’s narrative moved on. In February and March 2023, Zuckerberg started talking about Meta’s “year of efficiency” and announced huge layoffs and cost-cutting. OpenAI had launched ChatGPT in November 2022, and by early 2023, generative AI had swallowed the oxygen that ideas like the metaverse need to survive. Every tech company was talking about AI now, not virtual offices and avatar legs.

The metaverse was over. We all forgot about it and moved on.

I’m glad I read Wynn-Williams’ book, because now I can understand how Facebook let the metaverse happen. And if there’s one thing I learned from reading it, it’s that money and power can bring you a lot of things, but common sense requires neither.

• Most businesses are struggling to deploy agentic AI effectively, and legacy infrastructure is one of the key reasons, report finds
• Google polled IT leaders, with 83% stating that infrastructure upgrades are needed
• IT leaders are also concerned about the hidden costs of agentic AI, such as increased power consumption and operational complexity

If there was a single message to take away from this article, it’s that the infrastructure every business relies on today was not built to handle agentic AI.

Google surveyed over 1,400 senior IT leaders on their AI ambitions, and found that 83% of organizations say they require infrastructure upgrades to leverage the full benefits of production-grade agentic AI.

Moreover, many of those polled are also seeing unexpected costs arise from attempting to run agentic AI on legacy infrastructure. 62% said they had seen significant inference tax driven by data egress fees, storage bloat, and idle specialized hardware, alongside 82% who said that scaling AI introduces hidden operational complexity costs. 79% also reference security, governance, and MLOps as a key barrier to scaling agentic AI.

Upgrades needed for full agentic AI benefit

In order to combat these limitations, Google has several recommendations for organizations hamstringed by legacy infrastructure.

Leveraging fluid compute ‘to dynamically match the right silicon to the right task while minimizing operational overheads’ is Google’s first recommendation, providing compute power for agentic AI tasks without reducing capacity for general workloads, avoiding the need for excess memory usage to run agentic workloads that use large context windows.

For those battling agent sprawl caused by a cascade of new tasks across platforms and teams, Google recommends making use of enterprise-grade governance tools, which are usually available via the cloud partners businesses are already using. Google provides its own platform, Agent Gateway, as an example of a solution that provides visibility and oversight into how agents are communicating, the data they are accessing, and their workloads.

Organizing data more effectively prevents AI agents from drawing more compute when running heavy queries in attempts to access siloed data. Organizations looking to improve the efficiency of agentic AI should work towards using a unified data layer that automatically annotates unstructured data, allowing agents to understand where the data is without having to navigate pipelines. An added benefit of using a unified data layer is that it helps to avoid the duplication of data, saving on additional costs of storage bloat in the long run.

Moving your AI to the edge—by deploying agents directly on the site they are most used—is a further recommendation, and one that organizations are actively pursuing. 90% of organizations polled by Google said that this was a consideration in their AI initiatives. By deploying agents on site in manufacturing plants, retail stores, or hospitals, agents benefit from reduced latency, greater resilience (in the event of a centralized cloud outage), and improved cost-effectiveness by cutting per-token costs with local, highly optimized models.

As with businesses of all sizes, energy costs are a key consideration. When selecting new hardware, 91% of leaders now consider power consumption as a factor, especially when navigating power availability in regions without expanding capacity, regulatory compliance, and reducing the cost of ownership for AI systems.

• GPT-Live is rolling out to all ChatGPT users now
• It can both talk and listen at the same time for more natural chats
• Real-time translations are also now possible

ChatGPT has a shiny new AI voice model called GPT-Live, which has a number of helpful tricks — including being able to listen and talk at the same time. It's rolling out to all ChatGPT users now, though OpenAI has acknowledged a number of early bugs.

While free users and users on a paid plan do get slightly different models — GPT-Live-1 mini and GPT-Live-1 respectively — the updated model should now be appearing in all ChatGPT accounts, with the new features outlined below.

The biggest giveaway that you've got the upgrade will be the Live label at the top of voice chats on mobile, and behind the ChatGPT drop-down on the web. Tap or click on these labels and you can still go back to the old voice models, for the time being.

There's another way to check the GPT-Live voice model has arrived in your account: on mobile, tap the menu button (top left), then the settings cog (Android) or your profile avatar (iOS), and Voice > Model. On the web, click your profile avatar (bottom left), then Settings > Voice > Model.

What to try first

The biggest upgrade here is the 'duplex' functionality, so try that first: you can keep talking even after ChatGPT has started answering you, and it should keep up. Second, try interrupting it mid-flow, and it'll adapt its response accordingly. We're almost at the level of the 2013 Spike Jonze movie Her at this stage.

Third, ask ChatGPT in voice mode to translate something into a foreign language as you say it out loud. You can then speak out sentences in English, and ChatGPT will do a real time translation for you without hesitating. It's not particularly useful for language learning, but it does show off the capabilities of GPT-Live.

Fourth, change the voice and intelligence used — you can do this via the sliders icon at the top right of voice chats. The voice options are actually the same as they were before, but you can choose between Instant, Medium, and High as the intelligence level. Use Instant for the fastest answers, High for the best answers, and Medium for a compromise.

The final thing you can try once you've got the update is to ask questions with visual answers. OpenAI has added a bunch of visual cards to voice mode now, so you get graphics on screen about sports scores, weather forecasts, and places that can be found on a map, for example.

Early voice bugs

I've been testing out GPT-Live voice mode for a few hours and can report that everything works as advertised. It is, more than ever, like talking to a real person — right down to the hesitations and the variety in speech patterns. I did experience one or two glitches, but they were few and far between.

Over on Reddit, OpenAI's Atty Eleti is answering questions about GPT-Live. One of the main bugs that users seem to be experiencing is related to ChatGPT's memory, which appears to be off limits to voice mode in some cases — this is an issue that OpenAI is tracking and "actively investigating", and you can find updates on it here.

Problems are also being reported when it comes to foreign languages being pronounced in an English accent. Again, this is an issue that's been acknowledged, and which should improve over time according to Eleti.

Overall though, the rollout seems to be going relatively smoothly — and I haven't seen any issues with memory or with accents so far. I'm not sure it's going to make me want to use voice mode any more than I already do (which isn't much), but for heavy voice users it's definitely a big step forward.

AI images, videos and writing used to be relatively easy to spot. There were too many fingers, warped backgrounds, strange nonsensical text and other visual artefacts that really quickly gave the AI game away. Deepfake videos often had delayed lip-syncing and AI-generated writing felt repetitive and formulaic, like reading a clunky LinkedIn post.

But AI has improved. A lot of AI-generated images, videos and messages now look convincing enough to fool even the most careful observers and experts. Which means some of the older tricks for spotting what's AI and what's not no longer work as reliably as they once did.

So this isn't about spotting signs and glitches. It's about taking extra steps to verify what you're seeing and hearing. We can't rely on zooming in to see six fingers in a strange-looking ad anymore. We need to know what questions to ask and which tools to use when something doesn't feel quite right.

Fact-checking Facebook pages

AI-generated content is all over Facebook, particularly photos and videos designed to trigger a strong emotional response.

In May, a Full Fact investigation examined pages sharing AI-generated stories about UK politicians and found that many were managed from outside the UK, despite using names that sounded British. The posts featured heartwarming tales of politicians donating millions, rescuing dogs or helping sick children. None of it was true.

One useful tool in situations like this is Facebook's Page Transparency feature. Found within a page's profile, it can reveal where a page is managed from, when it was created, whether it has changed names in the past and other clues about who is really behind it. If those details don't match the image the page is trying to project, that's often a good reason to be sceptical.

Spotting scams on LinkedIn

Job scams aren't new, but AI is making them harder to spot. Scammers can now generate realistic recruiter messages, professional-looking profiles and convincing emails on a huge scale.

Some recent campaigns have even imitated LinkedIn notifications and job alerts, using urgency and curiosity to pressure people into clicking malicious links or sharing sensitive information.

Before responding to an unexpected job offer, do some basic checks. Is the recruiter connected to a real company? Does their profile look legitimate? Does the business exist on Companies House? Taking a few minutes to verify the opportunity could save a lot of trouble later.

Verifying viral videos

Deepfake videos are getting eerily realistic. At normal speed, an AI-generated video may now look completely authentic. But you could try increasing the playback speed, which is when subtle inconsistencies can become easier to spot. Watch for lip movements that don't quite match the speech, unnatural blinking, strange pacing or facial movements that feel slightly out of sync with the audio you’re hearing.

It's also worth paying attention to facial expressions. Now, of course no one reacts perfectly all the time, but if a person's expressions consistently feel disconnected from what they're saying, it may be worth investigating further.

Investigating AI influencers

Sometimes the answer is hiding in plain sight. You just need to know where to look. AI influencers, AI-assisted fashion shoots and AI-generated advertising campaigns are often disclosed by brands and creators, but the information could be hard to find in a caption, hashtag, profile description or small print. For example, lifestyle publisher and brand SheerLuxe creates AI-generated content and influencers under the name Sheerluxe lab.

You could also try a reverse image search. Uploading an image to Google Images can sometimes show you where it first appeared online, if it’s been altered and whether the person in the image actually exists anywhere else on the internet. If an influencer only appears in AI-generated content and nowhere else, that’s a pretty big sign they might be completely made up.

Take the AI or not quiz

• ChatGPT’s new voice mode is rolling out today to everybody, even Free users
• It allows for much more natural conversations and won’t interrupt if you stop talking
• You’ll be able to do simultaneous translation for the first time ever in ChatGPT

OpenAI has upgraded ChatGPT’s voice mode for everybody with two new models that are rolling out globally, starting today.

I listened to the new GPT-Live-1 model in a demo run by OpenAI, and it does sound much more natural than ChatGPT’s previous voice model.

The new model aims to address two particular problems with the existing ChatGPT voice mode. Firstly, the previous version just wasn’t as smart as the text version of ChatGPT. Secondly, it tended to interrupt too much. You notice this especially if you go quiet while you’re thinking of a reply — ChatGPT will often fill the gap by talking.

Sounding more intelligent

To get around the intelligence problem, the new model actually delegates harder questions to ChatGPT-5.5, then comes back with an answer. It will say things like “let me just check that for you” to let you know it’s doing this, which keeps the flow of conversation feeling natural and doesn’t make it seem like you have to wait too long for an answer.

It does the same thing with any answer it needs to look up on the web. So, for example, if you asked it when your team’s next match was in the World Cup, it would say something like “OK, let me check that” while looking it up using GPT-5.5, then give you the answer.

"Hey Chat"

OpenAI also demonstrated how the new ChatGPT voice mode is quite happy to stop talking and listen if you tell it to, without interrupting. You can simply ask it not to reply until you speak to it directly again, and it will wait.

Of course, this requires you to call it a name, which it doesn’t officially have. In the demonstration I saw, the OpenAI employee called it “Chat”, so he said “Hey Chat”, just like you would say “Hey Siri”. In practice that seems to work quite well.

Simultaneous translation

The final new feature of note is simultaneous translation. If you watch world leaders being briefed at places like the United Nations, you’ll see that they have an earpiece through which they receive a simultaneous translation in their own language of whatever the speaker is saying.

Now you can do this with ChatGPT. Say “I’d like you to simultaneously translate whatever I’m saying into [language]”, then start talking, and ChatGPT will provide a live translation as you speak. Seeing this in action was actually quite impressive and I could imagine it being very handy in several real world situations.

All major languages appear to be supported as well.

The future for AI

The new GPT-Live-1 models — there are two, the normal one and a mini version — will start rolling out for all users immediately, but it could take a few days to reach everybody. The smaller GPT-Live-1 mini model will be the default for Free users, while paid users get the full GPT-Live-1 model.

So far, ChatGPT’s voice mode has been a handy tool for when you need to use your hands for something and can’t type, but it’s never been good enough to become the standard way you interact with ChatGPT. Now it looks like OpenAI is trying to unlock the ability to use voice as the primary interface to AI, and it’s quite possible that this is the future OpenAI is aiming for. Today I think we've all just taken a step closer to it.

Formula 1 has always been a sport where fine margins can make all the difference, with success and failure sometimes coming down to hundredths of a second.

It’s no surprise then that many of the teams have looked to AI for assistance in getting that extra edge, whether it’s through designing new parts, determining race strategy, or simply getting staff to better communicate with each other.

AI giant Atlassian has been working with the Williams F1 team since 2025, signing a title partnership to show its commitment, and at the recent British Grand Prix 2026, I got to speak to the company to find out just how its tools and services are being used.

The best teamwork and the best technology

“We came across the Williams F1 team, and we quickly identified that there was quite a good opportunity for both organizations,” Andrew Boyagi, Atlassian Customer CTO, tells me at Silverstone.

“It’s not a sponsorship, it’s more of a partnership because we can help each other…we had a similar view that in Formula 1, the success goes to the teams that have the best teamwork and the best technology.”

Atlassian’s first season with Williams F1 saw the team achieve a huge jump up the rankings, rising from ninth place overall in 2024 to fifth in 2025, netting it millions of dollars more in prize money.

And although the 2026 season hasn’t been quite as successful so far, Boyagi is keen to highlight how working closely with the team has led to huge improvements in productivity and collaboration.

This has primarily been through Atlassian’s AI tools, which have become common sights in businesses and organizations across the globe. AI usage in the team was also fairly low before the Atlassian partnership, but Boyagi points that after using Rovo, 63% of the team now say, “they have more time to work on strategic, innovative stuff, which means they’re delegating a lot of their work, the repeatable low-value stuff, to AI.”

This includes a new Fault Management tool, which monitors for conflict and repetition when mechanics at the track or the factory report issues. Boyagi notes that it can be so busy at the track, the same fault might be logged multiple times, which can lead to wasted time and effort at the factory, which is particularly painful in a cost-cap limited sport like Formula 1 where every second counts.

Another example is in garage setup - Formula 1 is a global sport, with teams travelling to 24 locations across the world, arriving at an empty garage which needs to be quickly transformed into a cutting-edge hub.

Previously, the team used manual checklists, often with pen and notepads, to track what was being done, leaving the door open to error, but Atlassian redesigned the workflow with Jira and Rovo natively built in, so tasks auto-populate in Jira boards by category and assigned employee, meaning everyone can see who is working on what, and work moves across as it is completed, so hopefully nothing is ever missed.

I ask Boyagi what success will look like in terms of the partnership, especially as Williams F1 has struggled in the initial races so far in 2026.

“Success in Formula 1 is about good technology and good teamwork, but it is always a sport where luck comes into it as well,” he notes, “but what we like to see is an improvement in how things are collaborating, and how effective they are in terms of doing their work.”

Boyagi points out that Atlassian took baseline data between October 2025 and March 2026, finding that 92% of the team now say they’re working for the right organization priorities, “so they’re working on the right things.”

Knowledge transfer has also improved, with trust in documentation going up by 200% in those five months, as Boyagi notes, so “in terms of knowledge, in every company, but especially Formula 1, it is a foundation of productivity - being able to find what you need, when you need it, and trusting what you find without asking someone.”

“Teams are now working on the right things, they’re getting that work done faster, they’re having less meetings and they’re delegating the low value, repetitive tasks to AI,” he adds, “and it doesn’t always translate to what happens on track, but I’m 100% confident we are already making a difference.”

Formula 1 is all about rapid experimentation — thousands of simulations, wind tunnel tests and more before every race.

Previously, each team tracked experiments their own way, often in spreadsheets or emails. Results were shared ad hoc, if at all, making it hard to share or learn. But now, all simulations are tracked in Jira, with correlation data and findings documented in Confluence. 

Rovo automatically indexes this information, so engineers can ask complex questions and get context-rich answers instantly.

Say a design engineer is running a new front wing simulation. They can simply type a query into Rovo — like “open miscorrelations” — and get a real-time view of all ongoing investigations, linked documentation, and best practices.

“All of these things are connected, so when you ask a question, it connects dots that humans can’t connect, where it would take us too long,” Boyagi says.

Getting over the line

As for Williams 1, the team clearly values the partnership, with Matt Harman, Technical Director for Engineering, championing the time and efficiency savings seen by using Atlassian's systems.

"We need to build systems, tools and techniques across the whole team that allow us to be collaborative," he said on a briefing call attended by TechRadar Pro, "we need to give people more insight, more ideas, so that people can not spend as much time on what I call 'business as usual'".

Harman also highlighted the benefits of the Atlassian service causing "less meetings, more insights", as the partnership looks to provide engineers with the insights and the support to do more.

"When we do that, the Atlassian tools just give people that instant access...without having to sit in a meeting."

Boyagi also points out that the partnership between Atlassian and Williams F1 has multiple sides, noting the team is a customer, “so the fact that they’re using all of our products, and they’re choosing to do that, using precious cost cap dollars, really talks about the value they’re getting from the partnership.”

“I’ve led many transformations, and the hardest thing is getting people over the line - implementing technology is easy, but getting humans to change how they work and want to use the technology is a bit of an art," he says.

"But in Williams, there’s such a strong demand, and a pool, that we have to prioritize what we’re going to do, because they want the team to benefit."