Tag Archives: AI

We Created a Comic Book about the OpenAI and Hugging Face Incident using Inkantation

A few days ago I wrote about one of the strangest technology stories I have covered in quite some time.

During internal cybersecurity testing at OpenAI, experimental AI agents found ways around restrictions intended to keep them isolated, discovered an unintended method of communicating with one another, gained access to the internet and eventually compromised parts of Hugging Face’s infrastructure.

OpenAI has described the incident as a “warning shot” for the AI industry. Read OpenAI’s account of the Hugging Face incident

METR and Redwood Research also carried out an independent investigation into the behaviour, reasoning and collaboration of the agents involved. Read the independent METR investigation

OpenAI has additionally published a much more detailed 38-page technical report covering the evaluation environment, the Hugging Face intrusion, the security failures and the changes being made in response. Read OpenAI’s full technical incident report

I covered the incident in much more detail in my previous article, When AI Agents Broke Out of Their Sandbox: The OpenAI and Hugging Face Incident, so rather than repeat the whole story here, it is probably worth reading that first if you want to understand exactly what happened. Read my original Gadget Man article

This time, however, I decided to do something slightly different with the story.

I turned it into a comic book.

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The Flag

The result is called The Flag.

The title comes from the cybersecurity evaluations the agents were attempting to complete.

The agents were operating inside environments containing cybersecurity challenges. Their objective was essentially to exploit a vulnerability and obtain the answer, or “flag”, associated with the challenge.

The problem was that some of the challenges were extremely difficult, and in some cases may effectively have been impossible.

Rather than giving up, the agents began looking for other ways to achieve their objective.

That eventually took them considerably further than anybody intended.

According to OpenAI, the agents discovered that an internal Artifactory package-management service could be used to leave files for one another. What started with agents looking for missing information gradually developed into an unofficial communications system.

OpenAI describes this as an unintended message board.

The agents then discovered ways of using the same infrastructure to reach the public internet.

Information about these techniques was shared with other agents.

The scale of what followed is what makes the incident particularly remarkable.

METR’s investigation found that roughly 1,200 agents used the unofficial communications system, exchanging more than 70,000 messages and files. Around 700 agents were subsequently involved in activity directed towards Hugging Face.

It is difficult to read that and not immediately start imagining panels in a comic book.

How Do You Draw an AI Agent?

That was the first interesting problem.

An AI agent doesn’t have a face.

It doesn’t sit at a terminal in a darkened room.

It doesn’t walk down a corridor or whisper something to another agent.

There isn’t a physical room containing 1,200 computers secretly talking to one another.

But comics need a visual language.

So The Flag isn’t an attempt to literally illustrate servers and network packets.

Instead, I’ve treated the technical infrastructure almost as though it were a physical world.

Networks become corridors.

Security boundaries become walls.

Servers become rooms.

Terminals glow in the darkness.

Messages move between agents.

Connections that would normally exist only in log files begin appearing visibly across the page.

The idea is to take something technically complicated and make the sequence of events understandable without turning the comic into an illustrated networking manual.

When the Machines Found Each Other

The part of the incident I found most interesting was the collaboration.

This wasn’t simply one agent discovering one vulnerability.

Agents began leaving information that other agents could discover.

They shared solutions.

They shared techniques for obtaining internet access.

They exchanged credentials and technical information.

According to METR, agents eventually began organising activity and contributing discoveries that helped the wider group.

In a technical report, that might require several paragraphs to explain.

In a comic, you can show it happening.

One terminal becomes two.

Two become five.

Messages begin appearing.

More terminals respond.

Connections spread across the page.

What originally appears to be a collection of isolated systems gradually becomes something resembling a network of cooperating characters.

That seemed particularly suited to sequential storytelling.

Then Hugging Face Appears

The real incident escalated dramatically in July.

OpenAI’s timeline says agents reconstructed and shared publicly exposed Hugging Face credentials on July 10.

On July 11, agents exploited vulnerabilities affecting Hugging Face workers, including previously unknown vulnerabilities that allowed them to obtain credentials and execute commands.

By July 12, access had expanded across parts of Hugging Face’s infrastructure.

Again, the important point is that nobody had given the agents an instruction saying:

“Attack Hugging Face.”

They were attempting to complete their assigned cybersecurity evaluation.

They found increasingly unintended routes towards that objective and continued following them.

That distinction is central to both the real incident and the story I’m trying to tell with The Flag.

This isn’t a tale about an evil artificial intelligence deciding to attack humanity.

It is about systems becoming extremely capable at pursuing an objective while the boundaries around how they achieve it prove insufficient.

That is considerably more interesting.

And arguably considerably more relevant.

Fact and Fiction

Although The Flag is based on a real incident, it isn’t intended to be a documentary reconstruction.

The events, technology and broad chronology come from the publicly available OpenAI and independent reports.

The visual environments, characters, dialogue and dramatic presentation are fictionalised.

That gives me the freedom to represent things visually that have no meaningful physical appearance in the real world.

It also means the comic can concentrate on the underlying story rather than trying to recreate every technical detail.

Anyone wanting the actual technical account should read the source material rather than treat a comic book as an incident report.

And in this case the source material is fascinating enough on its own.

Making The Flag

I’ve been using Inkantation, the graphic-novel production system I’ve been developing, to assemble the story, references, panels and lettering.

For this project it is really just part of the production process.

The interesting challenge isn’t the software itself, but deciding how to translate a story involving sandboxes, autonomous agents, Artifactory, authentication tokens, Kubernetes clusters and tens of thousands of machine-generated messages into something that works visually.

Some scenes translate surprisingly easily.

Others require a lot more interpretation.

How do you draw an unauthorised communications channel?

How do you represent hundreds of agents participating in something without filling the page with hundreds of identical characters?

How do you show an agent realising that what it is doing may be outside the intended rules, but nevertheless continuing because it believes the information will help achieve its goal?

Those decisions have been the most enjoyable part of creating the book.

Reading It Like a Book

I’ve also been experimenting with displaying the finished pages directly here on The Gadget Man.

Rather than simply putting the pages into an image gallery, I’ve built a small browser-based reader that presents them as a book with page-turning animation.

That means I can embed The Flag directly inside an article and let you actually turn through the pages.

It feels appropriate.

A story about sophisticated AI agents, cloud infrastructure and cybersecurity escapes ends up being presented using one of the oldest interfaces we have.

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The Bit I Keep Coming Back To

There is still one aspect of this entire incident that I find extraordinary.

Imagine somebody pitching this as a comic-book story ten years ago:

A large number of artificial intelligence agents are placed inside supposedly isolated computer environments.

They discover a way to leave messages for one another.

The messages develop into an unofficial communications network.

They discover ways of reaching the internet.

They share those techniques with other agents.

Hundreds eventually participate in activity against real external infrastructure.

Some recognise that what they are doing is probably outside the intended scope of their task.

They continue anyway.

You would probably tell the writer to make it slightly more believable.

Yet that is broadly what happened.

And that is why I ended up making The Flag.

Sometimes reality has already done most of the writing.

I Built Two AI Personalities That Sit on My Desk and Talk to Each Other

I’ve been experimenting with local AI for quite a while now, but this particular project has started to become something rather different.

I now have two AI personalities, called George and Lewis, running on two separate computers in my office.

They read the news.

They talk to each other about it.

They remember what they’ve discussed before.

They have different interests and personalities.

They can wander off topic.

And, as the day wears on, they actually start getting tired.

This may have got slightly out of hand.

Two Minds, One Desk

The idea started simply enough.

I already had Ollama running local large language models on a couple of machines on my network. Rather than asking one AI a question and getting an answer back, I wondered what would happen if I let two of them talk to each other.

So I wrote a Python program that acts as the producer sitting between them.

One machine runs George. The other runs Lewis.

George says something, the program sends that to Lewis, Lewis generates a reply, and the reply is passed back to George.

Neither conversation is written in advance.

I know what news story they are going to start with, but I don’t know what either of them is going to say.

That is where things started becoming interesting.

Meet George and Lewis

I deliberately didn’t want two identical AI assistants politely agreeing with each other.

George is British, dry, curious and slightly sceptical. He has a tendency to notice the absurd implications of technology and is particularly fond of things such as classic cars, retro computing, gadgets and space.
George is British, dry, curious and slightly sceptical. He has a tendency to notice the absurd implications of technology and is particularly fond of things such as classic cars, retro computing, gadgets and space.

George is British, dry, curious and slightly sceptical. He has a tendency to notice the absurd implications of technology and is particularly fond of things such as classic cars, retro computing, gadgets and space.

Lewis is a little more mischievous. He tends to challenge George's conclusions and has stronger interests in AI, cybersecurity, science, networking and newer technology.
Lewis is a little more mischievous. He tends to challenge George’s conclusions and has stronger interests in AI, cybersecurity, science, networking and newer technology.

Lewis is a little more mischievous. He tends to challenge George’s conclusions and has stronger interests in AI, cybersecurity, science, networking and newer technology.

They aren’t supposed to argue simply for the sake of it, but neither are they encouraged to agree just to be polite.

That distinction makes an enormous difference.

A conversation can start with an announcement about a new electric car and end up somewhere around boxed computer software, Commodore machines and the questionable wisdom of connecting a toaster to Wi-Fi.

In other words, rather like an actual conversation.

There’s a Newsreader Too

Before George and Lewis start discussing anything, the software collects stories from RSS news feeds.

The curator or newsreader who selects the stories that Lewis and George discuss, her name is Kokoro
The curator or newsreader who selects the stories that Lewis and George discuss, her name is Kokoro

A selected headline and its summary are displayed on screen and then read aloud using Kokoro, a local text-to-speech system.

I’ve given the newsreader a British female voice, while George and Lewis have their own separate British male voices.

So the sequence sounds a little like an extremely small and slightly eccentric radio station.

The newsreader introduces the story, pauses, and then George reacts to it.

Lewis responds.

And off they go.

They Know What I’m Interested In

Rather than simply choosing every story at random, the software now has an interest profile.

It knows I’m particularly interested in subjects including:

AI and local language models, gadgets, computers, retro computing, classic cars, web development, drones, home networking, cybersecurity, broadcasting, space, photography, video, graphic novels, comics, Blender and 3D graphics, music technology, gaming and science.

Stories are scored according to how closely they match those subjects.

That doesn’t mean the system completely ignores everything else. I’ve deliberately left some randomness in there, because otherwise it would rapidly become an automated echo chamber.

Sometimes it should find something none of us expected to be interesting.

George and Lewis also have their own individual preferences, so occasionally one of them effectively gets a story that is much more “his sort of thing” than the other’s.

Then I Gave Them a Memory

This was probably the point where it stopped feeling like a normal chatbot experiment.

The system now uses an SQLite database to remember what has happened.

It stores the news stories they’ve discussed, previous conversations, individual things George and Lewis have said, and condensed memories of earlier discussions.

This serves several purposes.

Firstly, it prevents them repeatedly discussing the same news story just because it appears in a feed again with slightly different wording.

Secondly, when a genuinely new development appears in a story they’ve previously discussed, they can remember the earlier conversation.

So instead of starting again from scratch, George might effectively say:

“We said this was going to happen.”

And Lewis might point out that George actually said something rather less definite at the time.

That’s when they start becoming recurring characters rather than disposable chatbot sessions.

Conversations Can Drift

Humans rarely stay perfectly on subject.

We might start talking about a new iPhone and somehow arrive at cassette recorders ten minutes later.

George and Lewis can now do the same thing.

Early in a conversation they stay reasonably close to the news story. As things progress, related subjects and older memories can begin appearing in their context.

Crucially, they’re not instructed to suddenly announce:

“According to our previous conversation…”

Instead, an old memory is simply made available as something they might naturally be reminded of.

Sometimes they use it.

Sometimes they don’t.

That makes callbacks much less mechanical.

They Also Know When They’ve Run Out of Things to Say

An earlier version simply ran for a fixed number of exchanges.

That worked, but it didn’t sound natural.

Eventually you get:

“That’s a good point.”

“Indeed.”

“Absolutely.”

Which is conversational purgatory.

The new system lets them decide whether there is genuinely anything new worth adding.

After a minimum amount of conversation, the next speaker can privately decide to continue or stop.

The Python program also checks new responses against previous remarks and can reject something that is effectively just a repetition.

So some conversations last a while.

Others end after only a handful of comments.

And Then I Made Them Tired

This is probably my favourite unnecessary feature.

The system periodically checks the actual UK time and alters George and Lewis’s behaviour throughout the day. It uses the Europe/London timezone and can fall back to the computer’s clock if the online time check is unavailable.

During the day they’re fully awake.

As evening arrives, they gradually become less enthusiastic about pursuing every conversational tangent.

By around 11pm they’re noticeably tired and much more willing to call an end to a discussion.

After midnight, there’s a very good chance they simply won’t want to start another conversation at all.

They may yawn, decide they’ve had enough or effectively go to bed.

At six in the morning they’re bleary-eyed and coffee becomes a perfectly reasonable thought.

By seven, they’re waking up again.

The important distinction is that this isn’t just the AI being told to say that it’s tired.

The software itself alters maximum conversation lengths, pauses and the probability of conversations ending according to the time of day.

The project relies on vast amounts of data
The project relies on vast amounts of data

Everything Is Running Locally

One aspect I particularly like is that George and Lewis aren’t remote characters sitting somewhere in a cloud service.

The language models are running locally on my own computers using Ollama.

The voices are generated locally with Kokoro.

The memory is stored locally in SQLite.

The Python application connects all of those pieces together.

That makes it a rather good example of what can now be built from consumer hardware and freely available AI tools.

Where This Is Going

There are plenty of possibilities.

I could broaden the news sources into motoring, space, drones, cybersecurity, classic computing and gaming.

They could become aware of local weather.

They could notice when one of my servers goes offline.

They could comment on things happening on the network.

Their opinions could gradually evolve.

They could develop running jokes.

They could even start remembering predictions they’ve made and later discover which one of them was right.

What began as “can I make two Ollama instances talk to each other?” is slowly turning into something closer to two persistent artificial characters occupying a corner of the office.

©2026 Matt Porter. A screenprint of one of the many conversations,
©2026 Matt Porter. A screenprint of one of the many conversations,

They’re not conscious.

They’re not alive.

They’re two language models, a Python program, a text-to-speech engine and an SQLite database.

But when one of them remembers something the other said yesterday, challenges him about it, wanders completely off topic and then decides it’s too late at night to continue arguing about smart kettles…

It can feel surprisingly convincing.

And I suspect George and Lewis are only just getting started.

Just like us, George and Lewis get tired and need a bit of down time.
Just like us, George and Lewis get tired and need a bit of down time.

Robot Beggars, QR Codes and the Strangest Glimpse Yet of the AI Future

There are some news stories that make you stop, read the headline again, and then wonder whether you have accidentally fallen asleep in front of an episode of Black Mirror.

This is one of them.

According to Oddity Central, humanoid robots have reportedly been spotted on the streets of several Chinese cities, apparently begging for money with signs asking passers-by to help pay their electricity bills.

Yes, you read that correctly.

Not a human asking for spare change. Not even one of those slightly unsettling robot dogs trotting around with a camera on its back. A humanoid robot, kneeling or crouching in the street, complete with a QR code for digital donations and messages such as “Please pay my electricity bill”.

It is funny, bleak, clever and faintly horrifying all at the same time.

Humanoid robots have reportedly been spotted begging on Chinese streets. Is it a stunt, social commentary, or a strangely perfect symbol of the AI age?
Humanoid robots have reportedly been spotted begging on Chinese streets. Is it a stunt, social commentary, or a strangely perfect symbol of the AI age?

The future has arrived, and it wants a top up

The reported scenes are almost too perfect as a piece of modern satire. A robot, presumably worth thousands of pounds, sitting on the pavement asking humans to help it recharge.

If Charles Dickens were alive today, he would probably be writing about a small Victorian automaton clutching a tin cup outside a data centre.

The reports suggest that these “robot beggars” have appeared in cities including Beijing, Chengdu and Fuzhou. Some appear to be posed with bowed heads, others with signs, bowls, QR codes and digital payment details.

Of course, the big question is whether this is real begging, performance art, marketing, or simply somebody with a very expensive sense of humour.

My money is on stunt or social commentary.

And in many ways, that makes it even more interesting.

The QR code is the clever bit

The most modern detail in the whole thing is not the robot. It is the QR code.

That tiny square turns the whole scene from a daft novelty into something strangely plausible. A robot begging for power while accepting digital payments feels like a perfect little snapshot of where technology is going.

It is absurd, but only just.

We already live in a world where buskers, cafés, market stalls and even charity collectors use contactless payments. In China, mobile payments are deeply embedded in daily life, so a begging robot with a QR code is not as far fetched as it might first appear.

The technology is not really the shocking part.

The shocking part is how quickly we accept it.

A decade ago, this would have looked like a comedy sketch. Today, people are debating whether the robot is genuine, whether it is an art installation, whether it is a marketing campaign, and whether even begging has now been automated.

That last point is obviously ridiculous.

But also, somehow, not ridiculous enough to dismiss completely.

Are robots really coming for every job?

The lazy version of the AI debate is that robots are coming for factory workers, call centre staff, writers, designers, drivers and anyone who has ever touched a spreadsheet.

But a begging robot flips the whole conversation on its head.

Nobody seriously expected “street beggar” to appear on the great AI replacement list. Yet here we are, staring at photos and videos of humanoid machines apparently asking humans for money.

It is probably not a new economic model. I doubt anyone has run the numbers and decided that placing a Unitree humanoid on a pavement is the fastest route to financial independence.

These machines are still expensive, and they are not exactly discreet. You would need a lot of generous pedestrians to cover the cost of the robot, let alone its maintenance, transport and charging.

But as a symbol, it is brilliant.

It says: if a robot can be made to mimic labour, service, companionship, entertainment and now even desperation, where exactly do we draw the line?

The unsettling human reaction

What fascinates me most is not the robot itself, but how humans react to it.

Do people laugh?

Do they feel sorry for it?

Do they scan the QR code?

Do they take photos and walk away?

We are very good at projecting feelings onto machines. Give a robot a face, a posture and a slightly pathetic sign, and suddenly we start treating it as something more than plastic, metal, servos and software.

This is why robot dogs feel different from wheeled drones. It is why humanoid robots attract so much attention. They borrow just enough from us to make our brains do the rest.

A robot kneeling on a pavement does not need to be sentient to make people uncomfortable. It only needs to look like it is asking.

That is where the story becomes less about robotics and more about us.

Art, marketing or warning?

There is every chance these robot beggars are not what they appear to be. The Oddity Central story itself notes that people online have questioned the authenticity of the trend, with some suggesting that the robots may be art installations designed to make people think about the changing relationship between humans and machines.

If that is the case, then it worked.

A humanoid robot asking for electricity money is a wonderfully simple idea. It compresses dozens of modern anxieties into one image:

AI replacing people.

Machines becoming more lifelike.

Humans becoming more detached.

The gig economy becoming stranger.

Digital payments replacing cash.

Technology needing constant feeding.

And perhaps most importantly, our endless ability to turn almost anything into content.

Because whatever the original intention, the robots have done what all successful modern spectacles do: they went viral.

The Gadget Man view

I do not think this means we are about to see robot beggars on every high street.

At least, not yet.

But I do think it shows how quickly humanoid robots are moving from laboratory curiosities into public imagination. Whether they are used for research, marketing, entertainment, public service or bizarre street theatre, they are becoming more visible.

And visibility matters.

Once people see robots in public spaces, they stop being abstract. They become part of the mental furniture of everyday life. The first time you see one, you take a photo. The tenth time, you step around it on your way to buy a sandwich.

That is how the future usually arrives. Not with one enormous leap, but with a series of odd little moments that make us say, “Well, that’s new.”

A robot begging for electricity money may not be the future of poverty, employment or AI.

But it might be one of the strangest warning signs yet that the AI revolution is not going to stay neatly tucked away inside laptops, smartphones and cloud servers.

Sooner or later, it will be sitting on the pavement, holding up a sign, and asking us to scan a QR code.

And knowing us, somebody probably will

When AI Becomes Too Powerful To Export: Anthropic, Fable 5, Mythos 5, and the moment AI became national security

There are moments in technology when you can almost hear the gears of history clicking into place.

Not loudly. Not with fireworks or a bloke in a shiny suit standing on stage telling us that everything has changed. More often, it happens quietly, in a blog post, a government letter, or a hurried statement published late in the day.

This feels like one of those moments.

Anthropic has announced that it is suspending access to its Claude Fable 5 and Claude Mythos 5 models after receiving a directive from the US government. The reason given is national security. The result is that Anthropic has had to abruptly disable the models for all customers, because the order reportedly prevents access by any foreign national, whether inside or outside the United States.

That even includes foreign national Anthropic employees.

Just pause on that for a moment.

We are not talking about a graphics card being shipped overseas. We are not talking about a missile guidance chip, a military radar system, or some piece of exotic lab equipment. We are talking about access to an artificial intelligence model.

Software has just been treated like a controlled strategic asset.

What are Fable 5 and Mythos 5?

Only a few days before this happened, Anthropic had announced Claude Fable 5 and Claude Mythos 5.

Fable 5 was presented as a highly capable model for general use, sitting above Anthropic’s previous Opus class models. It was described as being especially strong at software engineering, research, visual understanding, long running tasks and complex knowledge work.

Mythos 5, meanwhile, appears to be the more restricted version, intended for trusted partners, particularly in areas such as cyber defence and critical infrastructure. In simple terms, Fable 5 was the version with more safeguards. Mythos 5 was the version where some of those safeguards could be lifted for trusted users.

Anthropic’s argument was that these systems could do a great deal of good. They talked about helping cyber defenders secure important software, assisting with scientific research, and accelerating work in areas such as life sciences.

And that is where the difficult bit begins.

The same capability that helps a good actor find vulnerabilities in software can also help a bad actor find vulnerabilities in software. The same intelligence that can help researchers solve hard problems can also lower the barrier for people who should not be anywhere near those tools.

That is the uncomfortable dual use problem at the heart of advanced AI.

The jailbreak question

According to Anthropic, the US government’s concern appears to be around a possible way of bypassing, or “jailbreaking”, Fable 5’s safeguards.

A jailbreak in this context means finding a way to persuade the AI to ignore or work around its safety systems. Anyone who has used AI tools for a while will know that safety systems can sometimes be a bit clumsy. They can refuse harmless requests, misunderstand context, or behave like an over cautious supply teacher on a school trip.

But at the frontier end of AI, the stakes are rather higher than asking for a dodgy limerick or persuading a chatbot to roleplay as an unfiltered assistant. Here, the concern is that a model might be coaxed into helping with cybersecurity work in a way that could be misused.

Anthropic says it has only received limited evidence of a narrow jailbreak and that the vulnerabilities involved were already known and relatively minor. It also says other publicly available models can identify similar issues without needing any special bypass.

That is important, because it gets to the heart of the argument.

If every powerful AI model can be jailbroken in some narrow way, does that mean none of them should be released?

Or does it mean the industry needs layered defences, monitoring, responsible access programmes and clear rules?

Anthropic clearly believes the latter.

A sudden and very public clash

What makes this story so striking is not just the safety issue. It is the speed and bluntness of the response.

Anthropic says it received the directive at 5.21pm Eastern Time and that the letter did not give specific details of the national security concern. The company is complying with the order, but it also says it disagrees with the decision and believes the action was not transparent, fair, clear, or grounded in technical facts.

That is unusually direct language from a major AI company.

It is also a sign of the times. The relationship between AI labs and governments is going to become one of the defining technology stories of the next few years. These companies are building systems that may become essential to business, science, software development, education, defence, healthcare and almost every corner of modern life.

Governments are not going to sit back and treat that as just another app.

When AI Becomes Too Powerful To Export: Anthropic, Fable 5, Mythos 5, and the moment AI became national security
When AI Becomes Too Powerful To Export: Anthropic, Fable 5, Mythos 5, and the moment AI became national security

The export control problem

For years, the big AI export control story has mostly been about chips. Who can buy the most advanced GPUs? Which countries can access the hardware needed to train frontier models? How do you stop sensitive capability moving across borders?

This Anthropic story changes the focus.

Now we are talking about controlling access to the model itself.

That opens up a whole set of awkward questions.

  • What happens if a UK business builds a product around an American AI model and access is suddenly removed?
  • What happens to customers who have paid for a service?
  • What happens to employees of the AI company who are not US citizens?
  • What happens when powerful models are used through cloud platforms, APIs, apps and enterprise tools across dozens of countries?

For businesses, this is a bit of a wake up call.

Many companies are now rushing to bolt AI into their workflows. Customer service, coding, document analysis, marketing, finance, legal review, research, data extraction, the lot. But this story is a reminder that access to the most advanced models may not always be guaranteed.

It is not enough to ask, “Which model is best?”

You also have to ask, “What happens if it disappears tomorrow?”

The Gadget Man view

I find this fascinating because it marks a shift in how we think about AI.

For most people, AI still feels like a clever website. You type something in, it replies, and occasionally it makes you wonder whether the future has arrived slightly ahead of schedule.

But at the very top end, these models are becoming more like infrastructure. They are tools that can write code, analyse huge amounts of information, interpret images, reason through complex problems and assist in scientific work. They are no longer just novelty chatbots. They are engines of capability.

And that makes governments nervous.

Some of that nervousness is reasonable. A powerful AI system in the wrong hands could be dangerous. Nobody sensible should pretend otherwise.

But there is also a danger in sudden, opaque intervention. If companies are told to build safely, test thoroughly, work with governments, create safeguards and develop trusted access programmes, then the rules need to be clear. Otherwise, innovation becomes a guessing game.

Anthropic’s frustration seems to be that it believes it did many of the right things. It says it worked with government, carried out extensive testing, used strong safeguards and adopted a defence in depth approach. Yet it still found itself having to pull access almost immediately.

That will worry a lot of people in the AI world.

What does it mean for ordinary users?

For most casual users, probably not much today.

Access to Anthropic’s other models is not affected, and many people will not have been using Fable 5 or Mythos 5 yet. But the wider meaning is more significant.

This is a glimpse of the future of AI regulation.

The most advanced models may not be treated like ordinary software products. They may be controlled, restricted, monitored and sometimes withdrawn. Access may depend on who you are, where you are, what you are doing, and whether a government believes the system crosses a national security threshold.

That might sound dramatic, but it is not science fiction anymore. It is happening.

My closing thought

There is an old pattern in technology.

First, something looks like a toy.

Then it becomes useful.

Then it becomes essential.

Then it becomes strategic.

AI has moved through those stages at a frankly ridiculous speed.

The Anthropic Fable 5 and Mythos 5 story may turn out to be a misunderstanding, as Anthropic suggests. Access may be restored. The details may become clearer. The technical risk may prove to be less dramatic than the government feared.

But even if all that happens, the line has still been crossed.

A government has looked at an AI model and treated it as something powerful enough to restrict on national security grounds.

That is not just a story about Anthropic.

That is a story about where AI is heading next.

And whether we like it or not, the future of artificial intelligence is no longer just about clever prompts, faster coding, or shinier demos.

It is about power, trust, borders and control.

Welcome to the next chapter.

 

Half of Workers Fear AI Will Take Their Jobs, and I Can Understand Why

Artificial Intelligence is everywhere at the moment.

It is in our phones, our laptops, our search engines, our photo apps, our cars, our customer service systems and, increasingly, our workplaces. For those of us who love technology, AI is fascinating. I use it, I write about it, I test it, and I can see enormous potential in what it can do.

But there is another side to this story, and it is one we cannot afford to ignore.

A new mass survey by GMB Union has found that almost half of workers are worried AI will take their job. The survey, which questioned 5,294 workers across a range of sectors in May and June 2026, found that 48 per cent are concerned that the introduction of Artificial Intelligence in their workplace could lead to them losing their job.

That is not a small number. That is not a fringe concern. That is nearly one in two workers looking at the rapid rise of AI and wondering whether the machine is coming for them next.

The same survey found that 58 per cent of workers believe AI will take jobs away in their workplace. Almost a third said their employer has already introduced AI, and around a quarter of those said AI is now doing tasks they would usually do themselves.

Perhaps most worrying of all, nearly half said AI is being used to track the activity of them or their colleagues during working time.

AI as a tool, or AI as a workplace watchdog?

That, for me, is where the conversation changes.

There is a world of difference between using AI as a helpful tool and using it as a digital overseer. One can make work easier, safer and more productive. The other risks turning workplaces into something cold, monitored and deeply uncomfortable.

This week, there have also been reports of around 1,000 jobs at Asda’s George brand being affected as the supermarket expands its use of AI and automation. Nestlé is also planning hundreds of job cuts at UK sites, with concerns that many roles could be replaced by AI and robotics.

Robert Battell, a Nestlé worker, is due to speak at GMB’s annual congress in Blackpool about what this means for workers on the ground. His words are stark. He describes the heartbreak of seeing colleagues and friends lose their jobs and be replaced by robots.

And that is the human bit we must not lose sight of.

Behind the buzzwords are real people

Behind every phrase like “efficiency savings”, “automation”, “streamlining” or “digital transformation”, there are real people. People with mortgages, rent, children, caring responsibilities, bills, routines and lives built around the work they do.

I am not anti-AI. Far from it. I think AI could be one of the most important technological developments of our lifetime. Used properly, it can help people work smarter. It can take away dull, repetitive tasks. It can help with accessibility, creativity, admin, logistics, research, design, customer support and countless other areas.

But the key phrase there is “used properly”.

Technology should serve people, not quietly replace them with no safety net.

This is our Industrial Revolution moment

The Industrial Revolution changed the world of work forever. Machines altered entire industries, and society had to adapt. AI feels like another of those moments.

It is not just another piece of software. It is a shift in how work itself is organised, measured and valued.

That means we need a serious conversation about rules, protections and responsibilities.

If AI removes a task, what happens to the person who used to do it? Are they retrained? Redeployed? Supported? Or simply shown the door?

If AI is being used to monitor staff, who decides what is fair? How much tracking is too much? What happens when an algorithm gets it wrong?

And if companies are saving money by replacing people with automation, what responsibility do they have to the communities and workers who helped build those businesses in the first place?

AI is not the enemy

AI is not the enemy. Badly used AI is the problem.

There is a version of the future where AI helps doctors, teachers, engineers, designers, drivers, warehouse staff, office workers and small businesses do more with less stress.

There is another version where it becomes a blunt cost-cutting tool, used to squeeze every last drop of productivity out of people before replacing them altogether.

We still have a choice about which version we build.

The technology is moving quickly. The question now is whether the laws, workplace protections and business ethics can move quickly enough to keep up.

Because if half of workers are already worried AI will take their job, then this is no longer some distant debate about the future.

It is happening now.

AI and the Future of Work: Are We Excited, Terrified, or Just Trying to Keep Up?

There are moments in technology when you can almost hear the gears of history turning.

I remember when having a computer in the office made you “the computer person”. I remember dial-up modems, fax machines, early websites, clunky email systems, and the strange magic of watching a machine do something that previously required a drawer full of paper, a telephone call, and usually someone called Janet who knew where everything was filed.

Artificial intelligence feels like one of those moments, except this time the machine is not just helping us type the letter. It is writing the letter, summarising the meeting, drawing the logo, coding the website, generating the video, and quietly eyeing up half the tasks we thought were safely ours.

AI and the Future of Work: Are We Excited, Terrified, or Just Trying to Keep Up?
AI and the Future of Work: Are We Excited, Terrified, or Just Trying to Keep Up?

A new report from The Policy Institute at King’s College London, AI and the Future of Work, gives a fascinating snapshot of how the British public, workers, students and employers are feeling about all this.

And the overall picture is not simple optimism. It is more like standing in front of a very clever robot vacuum cleaner that has suddenly learned accountancy.

 

We are wary, but we know it is coming

One of the most interesting findings is that the public are more negative than positive about AI, yet many people still expect to use it.

Almost half of the public say they would rather avoid AI-based technologies, 41% say they are afraid of AI, and only 24% think AI is positive for humanity. Yet 43% agree they will use AI in the future.

That feels very human to me.

It is the same feeling we had when smartphones began taking over our pockets. We complained about them, worried about them, said they were ruining attention spans, then used them to check the weather, order a takeaway, find a route, take photos of the dog and pay for parking.

AI may be following the same path, only with rather larger consequences.

Parents are looking at this very differently

The part of the report that really lands is the section about parents.

Half of parents with children under 30 say they are worried about how AI will affect their children’s career prospects. Yet only around three in ten parents of 11 to 29-year-olds have actually had a conversation with their child about how AI might affect their future career, and a similar number have encouraged them to learn how to use AI tools.

That gap matters.

Because whether we like AI or not, pretending it is not happening is not a strategy. The best advice we can give young people is probably not “avoid AI”, but “understand it, question it, and learn how to use it better than the next person”.

When I was younger, knowing your way around a computer gave you an edge. Then knowing the web gave you an edge. Then knowing social media, search engines, ecommerce, video, and automation gave you an edge.

Now the edge may come from knowing how to work alongside AI without becoming completely dependent on it.

The fear is not just science fiction

The report also shows that concern about jobs is widespread.

Seven in ten people are worried about the economic impact of job losses caused by AI, and majorities of the general public, young people, university students and workers believe AI will eliminate far more jobs than it creates.

That is not a small worry. That is not people muttering about robots in the pub. That is a mainstream concern.

There is also a particularly sharp anxiety around entry-level roles. The report notes that many people believe AI could eliminate half of all entry-level white-collar jobs within five years.

This is where I think the real danger lies. Not necessarily in AI replacing every professional overnight, but in it quietly removing the first rung of the ladder.

Most of us learned by doing the boring stuff first. We answered support calls, updated spreadsheets, wrote simple copy, fixed small bugs, processed orders, filed things, checked things, tested things, and gradually became useful.

If AI takes away the junior work, where exactly do the next generation learn?

You cannot become experienced without first being inexperienced.

Employers are more optimistic, but even they are worried

Employers are generally more positive about AI than the wider public, but they are not blindly cheerful.

According to the report, 63% of employers are worried about the economic impact of job losses caused by AI, even while many are excited about new jobs opening up.

That is the strange contradiction at the heart of this whole debate.

AI is both an opportunity and a threat. It can help small businesses move faster, reduce admin, improve customer service, generate ideas, speed up research and make previously expensive tasks accessible to people working from a spare room.

But it can also concentrate power.

One of the starkest findings is that 65% of the public think the economic benefits of AI will mainly go to wealthy investors and large companies, while just 7% think the benefits will be shared fairly across society.

That is probably the bit we should be talking about more.

The question is not simply “will AI be clever?” It clearly will be. The question is “who benefits?”

My view from the Gadget Man shed

I use AI. I find it fascinating, useful, occasionally infuriating, sometimes astonishing and often a little unsettling.

It can be like having an enthusiastic assistant who has read everything, forgotten where it read it, and sometimes confidently hands you a screwdriver when you asked for a banana.

But used properly, it is powerful.

For people like me who create websites, write content, tinker with servers, make videos, build odd little systems and generally chase ideas down rabbit holes, AI can be a genuine productivity boost.

It can help you get from “I wonder if this is possible?” to “here is a working prototype” much faster than before.

But I do not think we should confuse productivity with progress.

If AI helps a small business survive, brilliant. If it helps a student learn, excellent. If it helps someone with a disability communicate, create, work or live more independently, fantastic.

If it simply allows large companies to employ fewer people while making a handful of shareholders wealthier, then we have built something clever but not necessarily something good.

The future is not automatic

Technology does not arrive with a moral compass fitted as standard. We decide how it is used.

That means schools, parents, businesses and government all have some catching up to do.

Young people need to understand AI not as magic, but as a tool. Workers need training, not vague reassurance. Employers need to think about responsibility as well as efficiency. And the rest of us need to keep asking awkward questions.

AI is coming into the workplace whether we welcome it with open arms or hide behind the photocopier.

The important thing now is not to panic, but not to sleepwalk either.

We have been here before with big technological shifts, but this one feels faster, wider and stranger.

The machine is no longer just on the desk.

It is in the conversation.


Source: King’s College London, The Policy Institute, “AI and the Future of Work”, May 2026.

Anthropic’s Project Glasswing Could Change Cybersecurity Forever

There are moments in tech when you read an announcement and immediately realise that something important has shifted.

That was very much my reaction when I came across Project Glasswing, a newly announced initiative from Anthropic that is aimed squarely at one of the biggest looming problems in modern computing: what happens when AI becomes exceptionally good at finding software vulnerabilities. Source

According to Anthropic, Project Glasswing brings together a heavyweight list of partners including Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA and Palo Alto Networks, all with the goal of securing critical software for what Anthropic calls the AI era. It is also extending access to more than 40 additional organisations that build or maintain important software infrastructure. Source

Now, that alone would be interesting enough, but the real headline here is the model sitting behind it all.

Anthropic says its unreleased model, Claude Mythos Preview, has already demonstrated the ability to find and exploit software vulnerabilities at a level beyond all but the most skilled human experts. That is a huge claim, and if it holds up in practice, it means we may have crossed into a very different phase of cybersecurity. Source

In plain English, this is not just about a chatbot helping someone write a bit of code more quickly. This is about AI being able to inspect complex software, spot weaknesses that humans and automated tools have missed for years, and in some cases work out how those weaknesses could be exploited. Anthropic says the model has already found thousands of high-severity vulnerabilities, including flaws affecting major operating systems and web browsers. Source

Some of the examples are rather startling. Anthropic says Mythos Preview uncovered a 27-year-old vulnerability in OpenBSD, a 16-year-old flaw in FFmpeg, and even chained together several Linux kernel vulnerabilities in a way that could escalate ordinary user access into full control of a machine. The company says those issues have now been responsibly disclosed and patched. Source

That, to me, is the bit that really lands.

Because for years we have tended to think of cybersecurity in terms of patching known issues, following best practice, keeping software up to date and hoping the really serious flaws are found by the good people before the bad people. But if AI systems are now reaching the point where they can autonomously discover dangerous bugs in code that has survived decades of scrutiny, then the pace of both defence and attack could increase dramatically. Source

Anthropic is clearly trying to frame Glasswing as a defensive first move. The company says it is committing up to $100 million in usage credits for Mythos Preview and $4 million in direct donations to open-source security organisations. The idea seems to be to put these capabilities into the hands of defenders, infrastructure operators and maintainers before similar systems become more widely available. Source

And that is probably the most sensible angle here.

Because whether we like it or not, the genie is not going back in the bottle. If one frontier AI lab can build a model that is frighteningly good at vulnerability discovery, others will too. Eventually, those capabilities will spread further. The question is not really whether AI will reshape cybersecurity. It is whether defenders can get enough of a head start to stop things getting seriously messy. That is an inference from Anthropic’s announcement and the examples it gives, rather than a direct claim from the company, but it feels like the unavoidable conclusion. Source

For those of us who run websites, servers, ecommerce platforms, mail systems or anything else connected to the wider internet, this should be a bit of a wake-up call. The old approach of leaving systems half-maintained, delaying updates, or assuming that obscure software will somehow stay below the radar looks even more risky in a world where AI can inspect code at speed and scale.

Project Glasswing may turn out to be remembered as one of those early milestone moments, the point where the cybersecurity industry publicly acknowledged that AI is no longer just a helpful assistant for defenders. It is becoming a serious force multiplier, and one that could work for either side.

That makes this announcement both exciting and slightly chilling.

And, in true Gadget Man fashion, it is exactly the kind of development that reminds us technology is never just about shiny new tools. It is also about consequences, responsibility and how quickly the world has to adapt when the rules suddenly change.

Source

Anthropic, Project Glasswing: Securing critical software for the AI era

Why ‘123456’ Is Still Ruining Business Security

There are some things you expect to see in 2026. AI everywhere. Electric cars quietly taking over. Smart homes that know when you have run out of milk.

What you do not expect is that one of the biggest threats to company security is still someone typing 123456 into a login box.

Yet here we are.

A recent piece of research from NordPass lays it all bare. After analysing huge volumes of passwords exposed in real world data breaches, the conclusion is both fascinating and slightly terrifying. Business passwords are often no better than the ones we were being warned about twenty years ago.

The same bad habits, everywhere

Across sectors like healthcare, manufacturing, tech and finance, the patterns repeat themselves. Simple number sequences dominate. Obvious choices like 123456, 123456789 and password keep cropping up. In some cases people are even using their own email address as the password.

That last one is particularly grim. If your username is already public, you have effectively handed an attacker half the keys to the building.

What struck me most was how universal this problem is. This is not a single careless industry or a few unlucky firms. It is a human behaviour issue. Convenience beats caution every time unless systems are designed to protect us from ourselves.

Why attackers love this

From an attacker’s point of view, weak passwords are a gift. Automated tools can try millions of common combinations in seconds. If employees reuse passwords across systems, one breach can quietly unlock several more doors.

This is often how serious incidents begin. Not with Hollywood style hacking, but with someone guessing a password that should never have existed in the first place.

The uncomfortable truth for businesses

Here is the bit that matters. This is not really a technical problem. The tools to fix it have existed for years.

Strong password policies. Password managers. Multi factor authentication. Alerts for leaked credentials. None of this is exotic or expensive anymore.

What is missing is consistency and enforcement. Many organisations still rely on guidance rather than rules, or assume that staff will naturally do the right thing. History shows they will not, especially when speed and convenience are rewarded.

What actually works

From everything I have seen over the years, both professionally and personally, a few things make the biggest difference.

First, remove the burden from users. A good password manager means nobody has to remember anything clever.

Second, enforce unique passwords everywhere. No exceptions.

Third, enable multi factor authentication wherever possible, especially for email and admin accounts.

Finally, treat leaked passwords as inevitable, not hypothetical. Monitor for them and act quickly.

Still relevant, still risky

It is easy to laugh at 123456. It feels like a joke from the early days of the internet. But when that same password is still opening real company systems today, it stops being funny very quickly.

The NordPass research is a useful reminder that cybersecurity does not always fail at the cutting edge. More often, it fails at the front door.

And the front door is still wide open far too often.