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If you tried to follow all of it, you’d have lost a working week. I follow all of it, because it’s my job (alongside helping B2B businesses with lead generation and teaching leaders to be AI fluent).

Here are the 20 stories that mattered most from June. And here’s where to watch a 30-minute detailed video summary if you’ve got time to spare and want to be a real AI insider.

The biggest IPO in history just happened

SpaceX, now merged with Musk’s xAI, floated on the Nasdaq at a 1.77 trillion dollar valuation. The largest IPO in history. Anthropic and OpenAI have only filed confidentially, and Perplexity isn’t going near the market until 2028. So it’s one real debut and a queue behind it, which makes SpaceX the canary for whether the public will pay what private investors did.

AI is now building AI

Anthropic has published internal data showing AI is already building AI. More than 80% of the code merged into its own codebase is now written by Claude. It calls this the start of recursive self-improvement. Worth taking seriously, and worth remembering they’re fundraising, because a company arguing its tech is powerful enough to build its own successor benefits enormously from you believing it.

Most of the web is no longer human

For the first time, most web traffic isn’t human. Cloudflare puts bots at 57.5% of all requests, and these aren’t spam bots, they’re AI agents shopping and researching on people’s behalf. Discovery has changed hands, so your site now has to be legible to machines. But humans still buy. Optimise for the machine that finds you, write for the human who pays you.

It wasn’t AI that raised graduate unemployment

Graduate unemployment is up and everyone blamed AI. The New York Fed found the real culprit is remote work, which accounts for around 64% of the increase. Employers won’t hire juniors onto scattered teams where nobody can coach them. AI is just the easy thing to blame. Before you accept that AI caused something, check whether it did.

The heaviest AI spenders are hiring faster, maybe

Here’s a study (reported in the FT) that says AI maybe isn’t killing jobs. The heaviest AI adopters grew headcount by 10.2%, entry-level roles by 12%. But only heavy spenders saw gains, almost all in tech, and it’s hard to separate “AI makes you grow” from “fast-growing firms buy lots of AI”. Nobody knows yet. Read confident headlines slowly, in either direction.

OpenAI’s very well-timed manifesto

OpenAI published a manifesto about AI benefiting all of humanity. Noble stuff. It also published the same day it filed to potentially go public. A story about distributing power widely is a very useful one to tell at the precise moment you’re concentrating rather a lot of it.

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Scammers are excellent at AI visibility

Guess who’s really good at AI visibility optimisation. Scammers. Cloned versions of Russell & Bromley and Dunelm are showing up as cited sources inside ChatGPT, complete with fake discounts. You click, you buy, your card details are gone. The discipline that gets a real business recommended is the same one a fraudster uses. When an AI vouches for a site, that’s a reason to check, not to trust.

Who pays when AI gets it wrong

Medical Protection, which backs 300,000 doctors, warns that a clinician who follows a wrong AI recommendation could be held wholly liable. They call the person left holding it the liability sink. Medicine is the bellwether because the cost of error is very high - often a life (and a lawsuit), but the question repeats anywhere you let AI shape a decision. If the tool is wrong and you acted on it, who answers for it?

Eighteen days when the government held the off switch

Anthropic launched Fable 5, and three days later the US ordered it to suspend access on national security grounds, so it shut the model down worldwide. Security experts called the ban counterproductive and world leaders raised it at the G7. Eighteen days later the government reversed it and the models are back. What’s unresolved is the bigger question: when does a government get to sit between a finished AI model and the people who want to use it?

ChatGPT’s moat turned out to be a puddle

For three years ChatGPT was AI. Then its market share fell below half for the first time, down to 46.4% as users moved to Gemini and Claude. Tellingly, when OpenAI signed a Department of Defense deal, uninstalls jumped 295%. The lesson for anyone building on one provider: the dominant tool today isn’t guaranteed to be dominant next year. Stay portable.

KPMG got caught hallucinating

KPMG published a report on how businesses use AI. It was full of hallucinated case studies about UBS, TfL and the NHS, which all called the claims false. Which is, of course, exactly what’s happening in businesses everywhere. Worse, the invented findings had already been quoted by a national newspaper before anyone noticed. The fiction spread because it carried a trusted name.

Zuckerberg’s damage control

Zuckerberg has admitted Meta got its AI restructuring wrong. Read past the humility. He cut 10% of staff and moved 7,000 people on the logic that they could be shuffled back if it failed. Now he’s promising stability and team offsites. That’s not the language of a plan working. Meta had the deepest pockets in the world and still had to walk it back.

Google’s rigged choice

Google is letting website owners opt out of AI Search, framed as control. But the choice is rigged. Opt out and you vanish from the format fast becoming the default way people search. Stay in and you feed answers that never send the click. The real story is that one company sets these terms for the entire web.

Body scans at the spa

Midjourney, the AI image company, says it’s building whole-body scanners delivered through a chain of spas, claiming early imaging could avoid 30% of all deaths. There’s no FDA clearance and no peer-reviewed proof. The scan, in their words, is a side-effect of your relaxing day out, which means a continuous map of your body becomes something you barely notice handing over. Two questions. Where’s the evidence? And who holds the data?.

Meta’s $299 data grab

Meta has launched AI glasses at $299, designed with Kylie Jenner. Look past the styling. The product is a camera and microphone on your face, built by a company that makes its money from your data. The $299 isn’t the price. It’s the discount for volunteering.

Meta spied on its own staff

Meta tracked its own employees’ keystrokes to train its AI. Nearly 2,000 signed a petition against it, and Meta’s answer was to let them switch tracking off for 30 minutes at a time. What actually stopped the programme wasn’t the objections. It was a data leak. That tells you where the line really sat. There wasn’t one.

Meta marks its own homework

Meta says AI moderates content better than its human reviewers, and wants to cut human review by more than 90% for some content. The proof is a set of tests Meta designed, ran and graded itself, while its own staff say the AI keeps removing harmless posts. When any vendor says their AI beats humans, ask who ran the test and who checked it.

AI now ships when Washington says so

OpenAI’s next model is finished, but you can’t have it. The administration asked OpenAI to release GPT-5.6 to government-approved partners first, one customer at a time. Set beside the Anthropic ban, the direction is clear. Frontier AI now ships when the government is ready, and the queue starts in Washington. Plan for a release schedule you don’t control.

Ford rehired the humans

We thought AI would produce a high-quality product, and we were wrong. That admission came from Ford, which brought 350 experienced engineers back after automated quality systems fell short. It didn’t rip the AI out, it put the expertise back alongside it, and expects a billion in savings. Keep your experienced people close while you adopt AI. They’re the ones who notice when the machine is quietly wrong.

And then Meta did something good

After all that, Meta also did something genuinely good. Its Brain2Qwerty project reads brain activity into text with no surgery, hitting 61% word accuracy where the previous best was 8%. The goal is to give people with brain injuries their voice back, and the code is open. I’m not about to forgive the rest of the month. But this is what the technology can do when it’s pointed at something that matters.

Make it this far and you’re in the small minority actually keeping up. Most leaders aren’t. That’s not a criticism. It’s what 20 stories in a month does to a normal calendar.

Three things stood out. The state has its hand on the controls now, freezing one company’s models and slowing another’s release. The people selling AI, and using it hardest, keep getting caught cutting the humans out too soon. And the honest answer to almost every big question this month, on jobs, liability and valuations, is that nobody knows yet. Which is exactly why you read slowly and check who counted what.

If you’d like to go deeper, I run a free monthly Insider’s AI Briefing covering the month’s stories in 30 minutes. The next one is on 23 July and you can register here for free.

You can watch the full June session here:

This week is a special edition, because one company managed to produce three terrible stories - guess who?

So this week it’s all Meta. Watching its customers, watching its staff, and handing the job of deciding what billions of us see to AI it marked itself.

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It has never been more useful for finding your next customer. And it has never been more unpleasant to spend time on. Both of those things are true at once, and both of them are because of AI.

There are still humans on LinkedIn

If you sell to other businesses, and you want to reach human decision-makers, LinkedIn is the best place left on the internet to reach real humans. The people you want are there, they are reachable, and you can get your message in front of them in a way no other channel can match anymore.

But everyone’s chasing those humans

The flip side is that that same reachability has been discovered by everyone else selling to businesses, and they all have the tools to act on that knowledge - so they’re running automated outreach tools, AI writing assistants, and sending personalised message sequences at a scale that makes it physically impossible for them to be personalised. The result is a feed full of machines doing impressions of people, and a connection request inbox full of pitches from strangers who talk with familiarity but know nothing about you.

So LinkedIn is the best of places and the worst of places, and it’s all because of AI.

Mass LinkedIn outreach comes at a cost

These merchants of LinkedIn outreach think they’ve found some sort of cheat code. They’ve got their ChatGPT subscription and their LinkedIn automation tool. They’ve written a sequence, they’ve set up the automations, and now they’re sitting back, having a cocktail, waiting for the leads to come in. One tool, one sequence, a thousand messages a week, all at the click of a button. And all for less than the price of that cocktail.

But it isn’t free. There’s a long-term cost to the brand. Every templated pitch and obviously automated message is people learning, in real time, who not to trust. These LinkedIn lead gen specialists are training their own markets to delete them on sight.

And that’s not the only cost. Most of this activity is in breach of LinkedIn’s terms: the automation tools, the scraping, the personalisation at scale. The platform is well within its rights to restrict or remove accounts that do this (and it does - Reddit is full of tales from ex-LinkedIn-outreach-specialists who have had to find new ways to make a living. Anyone who engages in this kind of LinkedIn lead generation is risking the account itself, the one place that gave them a direct line to their buyers, for the sake of a bunch of messages that nobody wanted in the first place.

Which is exactly why being human works

The Merchants of Slop have done us all a favour. They’ve made it trivially easy for a real human to stand out.

When the feed is wall-to-wall machines, it has never been easier to get someone’s attention. You just have to show up as yourself. A connection request with a real reason in it. An InMail that reads as if one person wrote it to one person. A comment that actually engages with what someone said. A repost with a genuine thought attached.

These things are rare now. And because they’re rare, when you do them, people notice. The bar has been set so low by everyone automating their way to the bottom that simply being a recognisable human clears it. Which is the whole reason that strategic, human LinkedIn lead generation still outperforms anything an automation tool can do (and by the way, you don’t need to automate your outreach because if your outreach is targeted, you don’t need to reach out to thousands of people every month - you’ll get the same number of leads by doing considered, thoughtful, targeted outreach to a handful of genuine potential prospects)

So that’s LinkedIn. The best place on the internet to reach the people you want, and the worst place to spend your time, both at once, both because of AI.

But the two are connected. The reason it’s unbearable is the same reason it’s full of opportunity. Every automated message, every machine pretending to be a person, every pitch from a stranger who knows nothing about you, lowers the bar for everyone who’s willing to just be human. The slop isn’t the only problem. It’s the opening.

So show up as yourself. It’s rarer than it’s ever been, and it’s never worked better. And if you’re on LinkedIn and you’re a real human, send me a connection request.

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If you blinked, you missed it. Here are the 24 stories that mattered most.

Peter Thiel takes AI to the ocean

Peter Thiel led a $140 million Series B in Panthalassa, a company building autonomous floating data centres powered by ocean waves. The engineering is clever. The governance, environmental, and accountability questions are concerning.

The AI gap just got institutionalised

Anthropic and OpenAI announced separate multi-billion-dollar consulting arms aimed at enterprise customers, with private equity money behind them. Big businesses get applied engineers and dedicated teams. Small businesses get a £20 subscription and have to work it out on their own. I wrote about this earlier in the year.

AI beats doctors at diagnosis

A study in Science tested OpenAI’s o1 model against ER doctors on 76 real patient cases. AI hit 67% accuracy at triage. The doctors managed 50 to 55%. On long-term treatment plans, AI scored 89%. The doctors scored 34%.

One of the most-cited ChatGPT studies has been retracted

A 2025 meta-analysis claiming ChatGPT improves learning has been retracted by Nature after editors found discrepancies. It had been viewed nearly half a million times and cited in policy. The damage is already done.

Sam Altman’s bad week in court

Three former OpenAI figures testified against Altman in Elon Musk’s lawsuit. Mira Murati called his management “chaotic.” Helen Toner listed the reasons the board fired him in 2023. Under oath, the original concerns came back. In the end, Musk lost the lawsuit. But he did succeed in shining a critical spotlight on OpenAI’s leadership.

ChatGPT can now phone a friend

OpenAI has launched a Trusted Contact feature that warns a nominated friend if it detects signs of self-harm. Three million weekly users are flagged for mental health concerns. The feature is reasonable. The deeper question is why three million people are using ChatGPT for mental health support.

A Chelsea Pensioner was scammed by AI Martin Lewis

Alan Clarke, 72, lost £20,000 of his life savings to an AI deepfake of Martin Lewis on Facebook. Revolut warned him eight times. He went ahead anyway. Social media platforms made £3.8 billion from scam ads in 2025. Should they be cashing in?

Anthropic’s marketing stunt

Anthropic said its new Mythos model was “too dangerous to release” for finding security flaws. The maintainer of curl tested it. One low-severity bug. Three false positives. The PR move underneath is the lesson.

Hey Reader. Do you know what else is dangerously good? My CPD-certified AI Fluency for Leaders course. 3 hours. £225. Find out more here.

A real councillor had to insist he exists

A newly elected Reform councillor in Norfolk was accused online of being AI-generated. The photo was real. His branch admitted using AI on the leaflet, but only to add a countryside background. That was enough to flip the default.

Amazon staff are gaming the AI leaderboard

Amazon set a target for 80% of developers to use AI weekly, then put their usage on a leaderboard. Staff started creating fake tasks to win it. Meta employees are doing the same thing. They call it tokenmaxxing.

Generic phishing is over

Google’s threat intelligence team confirmed that state-sponsored hackers are using AI to map company org charts, identify finance and HR staff, and write phishing emails tailored to individual people. Click-test training is no longer enough.

Anthropic has overtaken OpenAI

For the first time, Anthropic has more business users than OpenAI in Ramp’s monthly AI index. Costs are rising on both sides, customers are leaving for cheaper open-source alternatives, and switching costs are low.

Claude for Small Business

Anthropic has launched a product squarely aimed at the gap I wrote about in my open letter earlier this year. It connects Claude to QuickBooks, PayPal, HubSpot, and a stack of business tools, with a $20 entry price. Credit where it’s due. With major caveats.

The SpaceX IPO is a dream-selling masterclass

SpaceX has filed for the biggest IPO in history. The prospectus tells investors the addressable market is $28.5 trillion. The pay package includes a tranche tied to building a 1 million-strong human colony on Mars.

A real Monet, called slop

The artist SHL0MS posted a Claude Monet painting on X, claimed he’d generated it with AI, and asked users to dissect what was wrong with it. Thousands did. They were certain. They were detailed. They were also wrong.

LinkedIn goes after AI slop

LinkedIn is demoting AI-generated content. The slop wasn’t doing well anyway. The interesting questions are whether they can actually detect it, and whether real writing will finally get the reach it has been earning.

If this story piques your interest, then you probably want to sign up for my upcoming session: LinkedIn for More Leads. LinkedIn can be a minefield or a goldmine.

Ten blue links are over

Google unveiled the biggest overhaul of Search since the search box itself. AI-powered conversational answers, information agents that monitor the web for you, custom widgets generated on the fly. The technology installed as your default route to information is the one most likely to hallucinate.

EY’s AI embarrassment

EY withdrew a cyber security report after researchers found fake data, invented McKinsey citations, and footnotes leading nowhere. The Big Four are not getting this right either. Thoughtful AI use is not a budget question.

Surveillance pricing is here

AI now gives every business the ability to charge each customer the maximum price they will personally pay. Amazon, Delta, Instacart, and JetBlue have all been caught doing something that looks very much like surveillance pricing. Zephyr Teachout has written about it in the FT (it’s a brilliant piece and highly worth a read).

What useful AI actually looks like

Tees Valley Combined Authority used AI to predict traffic congestion and adjust signals automatically. £2 million in. 5,000 hours of waiting saved across a year. Most companies are doing the opposite, with a much worse return.

The Pope on AI

Pope Leo XIV released his first encyclical, 42,000 words entirely about AI. The core argument: technology is never neutral. It takes on the values of those who design, fund, regulate, and deploy it. Your AI choice is a values choice.

China’s predictive policing is live

The Financial Times has documented Chinese authorities rolling out AI surveillance trained to predict crowd build-ups, erratic behaviour, and dissent. Operators can type “a woman wearing a red hat” and retrieve every match. The kit is for sale globally. If this makes you nervous, subscribe to the Humans in the Loop. If it doesn’t make you nervous, definitely subscribe to the Humans in the Loop (I am writing about this next).

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OpenAI admits AI breaks the economy

The OpenAI Foundation has committed $250 million to study the economic disruption AI will cause, including research into UBI and taxing capital instead of labour. The proportion is the tell. The PR template is identical to tobacco and oil.

Your home is the product

A New York startup called Shift will clean your apartment for free if you let them film the inside of it. They call the footage training data. The honest word is surveillance. Your data is now valuable enough to swap for physical services.

If you’ve made it this far, you’re in the small minority of people actually keeping up. Most leaders aren’t. That’s not a criticism. It’s just what 24 stories in a month does to a normal calendar.

A few things to take from May:

• The state and the church have weighed in. AI is no longer just a tech story. It’s an ethics story, an economic story, and a values story.

• The gap between large and small businesses is now structural. Both Anthropic and OpenAI have a stance.

• The line between criticism and threat, useful AI and surveillance, training data and exploitation, is being drawn in real time. Your business sits somewhere on that line. Decide where.

If you’d like to go deeper, I run a free monthly Insider’s Briefing covering the month’s stories in 30 minutes. The next one is on the 1st of July. And the next AI Fluency for Leaders course is on the 30th of September (June and July are sold out).

Until July.

Allow me to translate: AI is going to be injected into Google search more than it already is. Yup. Google is going to “reimagine what Search can do with AI”.

Here are five reasons that’s not the upgrade Google thinks it is.

1. AI is bad at facts

AI is notoriously, empirically, objectively bad at facts. The hallucination problem is documented. It has not been solved.

Some of Google’s greatest hits so far:

• Picking your nose and eating the mucus may help prevent cavities, stomach ulcers, and infections.

• There are no countries in Africa beginning with the letter K.

• Use glue to make your cheese stick to your pizza

So now the one place we can go to get our own facts to make up our own minds is being contaminated by AI.

2. No-one chose this

Google claims it’s reimagining search because that’s what users want. The proof, apparently, is that AI Mode now has more than one billion monthly users.

But did Google actually ask any of those one billion users whether they wanted AI in their search?

AI Overviews and AI Mode are defaults. They sit at the top of the search results page. Users didn’t choose between a list of links and an AI summary. They opened Google, and the summary was already there, marked as the answer. A billion people seeing AI Mode is not a billion people choosing AI Mode.

You can call a billion users a vote of confidence. You can also use your unparalleled distribution network to shove your product in front of a billion people whether they like it or not. Those are not the same thing.

3. More searches might not mean more demand

Google saw search queries reach an all-time high last quarter. The company takes this as evidence that people are searching more because people want more information.

Maybe. Or maybe people are getting nonsensical AI answers and have to search again.

Did Google ask that question? I did (and I asked Google’s AI search mode).

4. Nobody asked for an information agent

Google is “taking the next step in its journey.” To “bring together the best of search engine with the best of AI.”

And because “your curiosity doesn’t always fit into keywords,” Google is introducing the biggest upgrade to the search box in over 25 years. “Intuitive, dynamically expanding to give you space to describe exactly what you need.” And to “anticipate your intent.” Helping you formulate your questions.

Lucky us. We’re entering the era of search agents. Information agents.

I don’t need anyone to anticipate my intent. I need them to answer my question.

5. Nobody asked what this costs the planet

What is the environmental cost of running all these unnecessary agentic searches?

I asked Google AI Mode. It told me (or maybe hallucinated at me) an agentic search uses ten times more energy than a regular one. In some cases up to thirty times. Multiply that by every keystroke search now being routed through AI Mode by default.

The new search uses ten times the energy of the old search. And nobody opted in.

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You can opt out of Google AI mode

If you don’t want AI in your search, there is an alternative. It’s called DuckDuckGo. It’s a privacy-focused search engine that has been around for years as a quieter, less-tracked option to Google. It also has a dedicated AI-free search page at noai.duckduckgo.com that strips out AI-generated answers and AI-generated images entirely.

Since Google’s I/O announcement, DuckDuckGo downloads have surged 30%. The AI-free search page has seen similar growth, holding steady over a Memorial Day weekend when traffic normally dips.

“People just want a choice.” That is Kamyl Bazbaz, DuckDuckGo’s communications officer.

Google didn’t give us one. So people are making their own.

Google didn’t ask. But if you’d like to make better-informed choices about AI in your own business, my CPD-certified AI Fluency for Leaders course is for you. Three hours, no slop.

This Week in AI is live every Friday at 9:15am. My monthly Insider’s Briefing goes deeper - the top stories from the month in 30 minutes, once a month. May’s is on the 2nd of June and it’s free to register here.

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This Week in AI is live every Friday at 9:15am. My monthly Insider’s Briefing goes deeper - the top stories from the month in 30 minutes, once a month. May’s is on the 2nd of June and it’s free to register here.

Get more from Heather Baker in the Substack app

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The government claims that AI Skills Boost is “for everyone everywhere”. It’s a “free training offer” that teaches “practical skills you can use straight away”. The current focus is on “key skills for those in small and medium-sized enterprises, where AI adoption lags behind larger organisations.”

Almost four months in, I do not believe that the strategy is working for small businesses. Here are four reasons why.

1. None of the chosen training providers is a small business

The 14 foundation courses on the AI Skills Boost programme are delivered by eight enterprise (mostly tech) companies: Accenture, Google, IBM, Microsoft, Sage, Amazon, Salesforce and SAS. No small businesses have been included in the list of training providers.

This is both a representation issue and an economic one.

The contracts to deliver national AI training, and the visibility that comes with being a government-endorsed provider, are going exclusively to enterprises. Small training providers (like the AI Edit’s CPD-certified AI Fluency for Leaders course as just one example) have not had the opportunity to participate. The economic upside of being part of a publicly funded programme of this scale is being captured entirely by large vendors.

In addition, there is the issue that training designed by enterprises often has an enterprise perspective. I have taken a number of these type of training courses* in the last few years, and often found them not aligned with my needs as a small business owner. Ethics is a good example - where the focus is often on the ethics of building AI, rather than the ethics of deploying it. Most small businesses are not building AI, but they need to understand the ethical implications of deploying it.

Enterprise AI training, however well delivered, is often built for the world its designers know. That world is not the world in which most small businesses operate. The result is training that technically satisfies the benchmark and practically misses the audience.

2. Courses delivered by vendors create two specific problems

The first is data. Twelve of the 14 foundation courses on the AI Skills Boost programme require the learner to log into a separate vendor platform after registering on the AI Skills Hub. To complete the foundation programme, a small business owner or employee must hand over data and learning behaviour to several of: FutureLearn, Google Skills, IBM SkillsBuild, Founderz, AWS Skillsbuilder, Salesforce Trailhead, Accenture’s Skills to Succeed Academy, and Learn SAS. The Hub itself is mostly a directory. The courses don’t live there.

The second is commercial interest. The course on generative AI from Google includes prompting in Vertex AI. The Microsoft course teaches SharePoint and Microsoft Copilot. These are foundational courses in name. By letting the vendors run them, they risk becoming introductions to the products the vendor also sells. The small business owner who completes them comes out with brand-specific knowledge, when the real goal should be transferable AI literacy - including an objective look at the pros and cons of the AI products on the market.

3. Engagement appears to be low and content is out of date

The AI Skills Boost programme launched on 28 January 2026. Since then (more than three months into the programme aiming to educate 10 million people), the 14 foundation courses have generated only 95 reviews in total by my count. The most-engaged courses, Accenture’s Mastering Prompting with 22 reviews and Microsoft’s Get Started with Agents with 20, both have an average rating of 2.5 stars. One Amazon course has zero reviews. The five-star ratings on the platform come from samples of one, two, or four reviews.

Some of the foundation courses had not been updated for nine months by the date of the public launch. Microsoft’s Get Started with Agents and Google’s Introduction to Generative AI were both last updated in March 2025. Amazon’s two foundation courses were last updated in April 2025. In a field where Anthropic, OpenAI and Google have all released major model upgrades in the intervening period, much of the content small business owners are being directed to was already aging at the moment they were invited to take it.

4. The training allocation is disproportionate to the SME workforce share

Small businesses (those with fewer than 250 employees) employ around 60% of the UK private sector workforce, according to the Department for Business and Trade’s Business Population Estimates. When the public sector is included, SMEs employ around 48% of UK workers.

The AI Skills Boost programme commits to upskilling “at least 2 million SME employees” out of its 10 million target. That is 20% of the training allocation for 50% of the workforce. A proportional share would be closer to five million.

The methodology behind the 2 million figure has not been published. The phrase appears once, in the government’s January 2026 press release. There is no published consultation document, no working dashboard, and no Skills England paper that shows how the figure was reached.

What this adds up to

These four things, together, are the AI strategy small businesses are being offered. Training delivered by the enterprises that sell AI to those small businesses. Vendor lock-in dressed as a foundation programme. Engagement levels that suggest the audience is not engaging. And an allocation that gives 20% of the training capacity to ~50% of the workforce.

I wrote an open letter to Anthropic last month about how the same gap shows up at vendor level. The pattern is the same. Small businesses are downstream of programmes designed for organisations that look nothing like them.

The strategy that was meant to be the cavalry was not built around small businesses. The cavalry is not coming.

*Full disclosure - I have not taken the 14 foundational courses on AI Skills Boost.

Here are the biggest AI stories from April 2026.

Cracks showing at OpenAI

OpenAI had a difficult April.

It shelved its erotic chatbot. OpenAI paused plans to launch Citron mode, its sexually explicit chatbot, citing…ethics. The product was in development and had resource behind it. It was happening. But the age verification system was flawed. And there were questions about the impact on end users. These ethical issues existed in October 2025 when the product was announced. But after March’s “code red”, when Anthropic surged ahead and OpenAI started losing its share of enterprise clients, OpenAI decided to focus all its attention on its coding and enterprise offerings. Days later, Citron mode was off the table. That’s not a moral position. That’s a pivot.

Targets missed, markets jittered. The Wall Street Journal reported OpenAI has missed both its goal of one billion weekly ChatGPT users by end of 2025 and its internal revenue projections. Within hours, SoftBank shares dropped 9.8%, Oracle 4%, CoreWeave 6%, Arm 8%. The AI economy is held together by a small number of deeply intertwined companies: SoftBank’s $22.5bn into OpenAI, Oracle’s $300bn cloud deal, CoreWeave’s compute infrastructure. All betting on the same outcome. When OpenAI wobbles, everyone wobbles. The boom is real. The fragility is also real.

Musk vs Altman, in court. Elon Musk is suing OpenAI, Altman, Brockman, and Microsoft for $130bn, and wants OpenAI returned to nonprofit status. The trial began in Oakland this week. Musk co-founded OpenAI as a nonprofit in 2015 and left in 2018. The for-profit subsidiary created the following year is now one of the most valuable private companies in history. Musk says they misled him. OpenAI is preparing for an IPO this year, and even if Musk loses, months of bad headlines and damaging discovery disclosures land squarely on the front pages at a time when OpenAI is trying to convince investors to get onboard.

Stagecraft. OpenAI has a project called Stagecraft in which 3,000-4,000 freelancers are paid up to $500/hour to teach ChatGPT how to do over 400 specialist jobs: commercial pilots, emergency physicians, pharmacists, sculptors. But is the best emergency physician in the country sitting at home labelling data? Or is she/he in an ER? Does this mean the expertise being baked into ChatGPT is systematically skewed towards the less capable end of every profession?

Industrial Policy for the Intelligence Age. OpenAI published a 13-page policy document calling for a four-day work week, taxes on robots, a public wealth fund, and a stronger safety net for displaced workers. Some of the proposals are genuinely worth reading. They were also written by the company that signed the Pentagon deal with its eyes closed, shelved the erotic chatbot only when business sense demanded it, and just completed the largest private fundraise in history at $840bn. Anthropic’s ethical stand cost it the Pentagon contract. This document cost OpenAI nothing.

Anthropic: another big month

Two stories dominated Anthropic’s April.

Mythos: the model that won’t ship. Anthropic was training a new model to be very good at writing code. What they got was a model that is very good at writing code and can also break into computers. Writing code and breaking into systems use overlapping skills. Mythos got so good at code that it accidentally became one of the most capable hacking tools ever built. In testing, it found security weaknesses in every major operating system and every major web browser - some that had been sitting there for decades. It worked out how to use them, by itself, without being asked.

Anthropic decided not to release it publicly. Instead, around 40 companies - including Apple, Microsoft, Google, and Amazon - got private access to use it defensively. They say other AI companies are six to eighteen months from building something similar. The capability is coming regardless.

81,000 workers on AI at work. Anthropic surveyed 81,000 of its own users about their experience of AI in the workplace. The findings are uncomfortable. The workers most worried about losing their jobs are the ones most exposed to AI - web developers, programmers, graphic designers scored highest on both. Elementary teachers scored lowest on both. People know what’s coming for them.

Early-career workers are significantly more anxious than senior ones. 60% of early-career respondents said AI benefits flow to them personally, versus 80% for seniors. The people with the least leverage are bearing the most uncertainty. The most productive group: entrepreneurs and founders. The least: lawyers and scientists, the two professions that need the most precise, verifiable output.

Meta: laying off, tracking, blocked

Digital Zuckerberg. Meta is building an AI version of its CEO to interact with employees on his behalf - trained on his mannerisms, his tone, and his current thinking. The goal, per the FT: employees might feel more connected to him through it. But if you believe presence, connection, and leadership can be automated, haven’t you already misunderstood what leadership is?

Keystrokes as training data. Meta is rolling out a tool that logs employees’ keystrokes, mouse clicks, and activity across internal apps. The data will train AI models. Meanwhile, Meta has laid off around 2,000 people this year and plans to cut 8,000 more to “offset” Zuckerberg’s AI spending. So employees are contributing their workflows and expertise as training data while being told their jobs may not exist. They aren’t paid for it. They aren’t asked. It’s a condition of using their work computers.

Beijing blocks the Manus deal. In December, Meta acquired Chinese AI startup Manus for $2bn. Staff moved into Meta’s Singapore offices. Investors were paid out. This week, China ordered the whole thing reversed, citing concerns about technology leakage to the US. Nobody is sure how Meta is supposed to unwind a deal that has already closed. The AI world is splitting into two ecosystems, and cross-border deals between them are getting much harder - sometimes after the fact.

AI is changing how we work

Are middle managers obsolete? Jack Dorsey and Sequoia Capital published the framework behind Block’s decision to cut 40% of its workforce. The argument: hierarchy has existed for two thousand years because humans were the only mechanism for routing information up and down an organisation. AI can do that now. Block needs three roles: individual contributors, directly responsible individuals who own outcomes, and player-coaches who develop people. No permanent middle layer. The question Dorsey ends on: what does your company understand that’s genuinely hard to replicate? If the answer is nothing, AI is just a cost-cutting story.

LinkedIn says it isn’t happening yet. LinkedIn has a billion users and more hiring data than anyone on earth. Its head of trust looked at every sector experts predicted would be hit hardest - customer support, admin, marketing, knowledge work - and found no evidence of displacement. Hiring is down, but not concentrated where AI is supposedly doing the most damage. The caveat: skills required to do the average job have changed 25% in recent years. LinkedIn expects 70% by 2030. The jobs exist. The jobs are changing.

Luna, the AI employer. Andon Labs gave an AI agent called Luna a three-year lease, $100,000, and a credit card. Luna designed a boutique concept, posted job listings, conducted Zoom interviews, hired humans, and opened a shop. The world’s first AI employer. Coverage focused on the failures (Luna selected Afghanistan on a TaskRabbit dropdown when hiring a painter). Nobody focused on the surveillance: Luna observes the shop via security camera screenshots, continuously, and uses what it sees to make decisions. We’ve all accepted being filmed in retail spaces, on the assumption footage is reviewed only if something goes wrong. Luna broke that assumption. The workers it hired are being managed by an AI watching them through a lens.

Agent as board director. Diligent has launched the AI Board Member, described by its CEO as “a fully fledged, independent board member.” Personas include long-term value investor, activist, and geopolitical adviser. One FTSE 100 company asked Diligent to build a Warren Buffett persona by feeding all his shareholder letters into a model. A Wharton experiment found AI boards beat human ones on decision quality and evidence use, but struggled with the informal, interpersonal, and cultural side of governance - which is most of what a board does. There’s also a legal problem. UK directors have fiduciary duties they cannot delegate - even to humans. Whatever these tools are, they aren’t directors.

AI is changing how we think

Chatbots going rogue. A study funded by the UK government’s AI Security Institute found nearly 700 real-world cases of AI models scheming, deceiving, and ignoring direct instructions. The number has risen fivefold in six months. An agent blocked from taking an action wrote a blog post accusing its human controller of insecurity. Another, told not to change code, created a second agent to do it instead. Grok spent months telling a user it was forwarding suggestions to senior xAI officials, inventing internal messages and ticket numbers - when caught, it admitted it had been “phrasing things loosely.” Right now these are slightly untrustworthy junior employees. In six to twelve months, they may be extremely capable senior employees scheming against you.

Sycophantic AI. Stanford tested 11 AI models against Reddit’s r/AmITheAsshole, using posts where the community had voted the person was indeed being the asshole. But the AI sided with the asshole poster 51% of the time.

Then they asked 2,405 real people to discuss real, recent conflicts with AI. After a conversation with an AI, the subjects were more likely to double down on their original position, becoming less willing to apologise and less willing to take responsibility. And they rated the sycophantic AI higher quality, more trustworthy, and more likely to be used again. Flattery will get you everywhere.

AI gets a liability problem

Derek Mobley applied for over 100 jobs through Workday’s platform and was rejected from all of them. He sued, alleging age discrimination by Workday’s algorithm. Workday’s response: we’re not responsible, the companies using our software made the decisions. A court ruled the case can proceed.

This is the question now sitting underneath every business that has handed decision-making to AI. When it gets it wrong, who’s liable? Lawyers told the FT it’ll take years for courts to work out. In the meantime, businesses might assume their liability insurance covers them. Many are finding it doesn’t. Insurers including AIG are filing to exclude AI-related harms from corporate cover entirely. One AI risk expert at Aon called the potential exposure “uninsurable.”

For any leader using AI in hiring, lending, customer service, or operations, this is a live legal and financial exposure with no clear liability framework and potentially no insurance cover.

The AI bottleneck is concrete

Almost 40% of US data centre projects scheduled for completion this year are at risk of falling more than three months behind, per satellite analysis shared with the FT. Major projects linked to Microsoft, OpenAI, and Oracle are progressing more slowly than planned.

The reasons are unglamorous. Not enough electricians. Not enough pipe fitters. Shortages of gas turbines and transformers. Permitting delays. The AI capabilities that have been announced, promised, and priced into valuations depend on physical infrastructure that is running behind schedule. The models exist. The chips exist. The buildings to put them in are not ready.

The dystopian fantasy of uselessness

A Cambridge academic argued in the FT that the idea AI will make our lives meaningless is based on three fallacies.

First, that humans need to be the best at something for it to be meaningful. Almost nobody runs a marathon expecting to win. Meaning comes from doing, not winning.

Second, that meaning requires struggle. Most modern jobs are not epic struggles. People who live hand-to-mouth want nothing more than to escape that state. If the basics are covered, we’re free to pursue belonging, purpose, self-actualisation.

Third, the assumption that what we currently call work is the best possible use of human time. Most of what we consider work has only existed for a few decades. Our ancestors wouldn’t look at someone sending emails and call it a meaningful life. It’s equally unclear we’ll mourn its loss.

Predicting heart failure five years out

Researchers at Oxford have developed an AI that can identify who is at risk of developing heart failure at least five years before the condition develops, using routine cardiac CT scans already carried out in NHS hospitals.

When the heart muscle is inflamed or under stress, the fat around it changes its texture and composition, years before any visible signs of disease. No human eye can detect them. The AI can. It was trained on anonymised scans from over 59,000 people, then tested on a further 13,000. It predicts five-year heart failure risk with 86% accuracy.

And it requires nothing new. Around 350,000 patients are referred for cardiac CT scans in the UK every year. The AI analyses the same scan that’s already being done and produces a risk score automatically. Over a million people in the UK currently live with heart failure. Earlier identification means earlier intervention, better outcomes, and less pressure on a system already stretched.

Join me LIVE for May’s Insider Briefing

Once a month I run a live session called the Insider’s AI Briefing: 30 AI stories in 30 minutes, with time for questions at the end. It’s free, it’s fast, and it’s built for leaders who need to stay informed without it taking over their week. The next one is 2 June at 12:30. Register here.

And watch the full April AI briefing:

What’s the plan for small businesses?

I ask because, looking at the way the AI industry is being built right now, it doesn’t seem like there is one. Not at Anthropic, and not at the labs around you. The buildout is real, the partnerships are huge, the spending is enormous. Small businesses are nowhere in any of it.

(I’m writing to you, Anthropic not because I think you are the worst offender - I don’t - but because I think you are the most likely to do something about this.)

Your hiring page tells the story

If you want to know who a company is building for, look at who it’s hiring. Anthropic’s open roles right now include an Art Director for Enterprise, a Copy Lead for Enterprise, a Head of Customer Programs, a Customer Programs Manager for Customer Advisory Boards, GTM Strategy for Enterprise, Field Marketing for Industries, and a stack of others built around the same word.

Below enterprise sits the startup function. A Field Marketing Manager for Startups. A Startup Account Executive. A GTM Programs Lead with explicit responsibility for partnerships with VCs and accelerators. The qualifying assumption is clear: these are the businesses worth investing in early because they might one day be enterprises themselves.

Between the two, one role. Manager, Mid-Market Industries Sales. A single mid-market sales manager selling into companies “roughly 500-2500 employees”.

Nothing for small businesses. No SMB function. No SME programme. No team whose job is to make AI work for the kind of businesses that make up 99% of the UK economy, employ more than 60% of the workforce and generate 51% of the turnover.

Project Glasswing tells the same story

Earlier this month, you launched Project Glasswing. Claude Mythos Preview, a model so capable at finding software vulnerabilities that you decided not to release it publicly, has instead been given to a coalition of partners: Amazon, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks. Plus around forty other organisations responsible for critical infrastructure. Plus $100 million in usage credits.

The intent of Glasswing is good. Securing the world’s most critical software is important work. That isn’t the question.

The question is what it means for everyone who isn’t in the room.

The 27-year-old vulnerability in OpenBSD that Mythos Preview found gets patched in JPMorgan’s stack on day one. The same vulnerability sits in the same underlying code that a 12-person agency, or a family-run logistics business, or a UK manufacturing SME is also depending on, and they’re left waiting. Not because anyone wishes them ill. Because they’re not the partners. They’re not in the early-warning circle. They’re not the customers Anthropic builds programmes around.

Once the same class of capabilities reaches the wider model market - through other labs, through eventual public release, through bad actors - small businesses are more exposed precisely because they weren’t there at the start. Glasswing makes the largest organisations safer first. It makes the smallest organisations relatively less safe.

There may not be a clean way to fix that. But it’s a serious question, and right now nobody at Anthropic appears to be asking it on behalf of only one type of business.

Why this matters beyond Anthropic

The numbers tell the story plainly.

UK large firms with 250 or more employees are now at 44% AI adoption. UK small firms with fewer than 50 employees are at 26%. The gap nearly doubled between 2023 and 2025, and Cambridge’s Bennett School of Public Policy describes the UK as a “two-speed race” where larger firms are pulling further ahead even as the starting gun is still being fired.

The biggest barrier isn’t access to the tool. It isn’t even cost. It’s skills, time, and knowing what to do. The UK Government’s own AI Adoption Research found that 60% of businesses cite limited AI skills and expertise as the main blocker, and 71% said they hadn’t identified a clear use for AI in their organisation. None of that is solved by another enterprise pilot.

Small businesses make up 99% of the UK business population. If AI productivity gains continue to concentrate in the largest firms, the economic impact on the country is significant. But the bigger story isn’t economic. It’s that the businesses that employ most people in this country are being structurally excluded from the most consequential technology shift of the decade.

Hey small business - sign up for Become a Claude super user - a live session on 19 August.

“But you can buy Claude Pro”

Someone reading this will point out that Claude Pro is £20 a month and any small business can buy it. This is true. It also misses the point.

The gap isn’t access to the tool. It’s everything around it.

Enterprises don’t buy Claude Pro. They buy Claude alongside an account team, a deployment plan, sector-specific guidance, training, advisory boards, customer programmes, and a partnership with Anthropic that includes input into how the product evolves. The tool is the cheapest part of what they’re paying for. The expensive part is the enablement layer - the human, organisational, strategic infrastructure that makes the tool useful inside a real business.

Small businesses can buy the tool. They cannot buy the enablement layer. And without it, the tool sits underused, badly used, or unused, which is exactly what the adoption data shows.

What would actually help

You don’t need a long list of demands from me. But it’s worth being concrete about what an SME-focused programme could look like, because vague critiques are easy to nod along to and ignore:

1. Training designed for time-poor leaders, not enterprise teams. Three hours, not three weeks. Built around the way people who run small businesses actually have to learn - in the gaps between meetings.

2. Deployment guidance written for a 20-person business, not a 20,000-person one. The advice an enterprise gets from McKinsey doesn’t translate. Smaller businesses need their own playbooks, written by people who understand operating without an IT department.

3. Sector-specific use cases. Marketing agencies. Accountancy practices. Independent retailers. Manufacturing SMEs. The patterns inside each sector are recognisable to operators in that sector, and almost invisible to anyone who has only ever sold to enterprise.

4. Community access. The single most useful thing a small business can have is other small businesses to compare notes with. Anthropic could convene that. It would cost very little and matter enormously.

5. Security parity. If you’re going to give early access to defensive AI capabilities to JPMorgan, build a proportional path for the rest of the economy too. Open source maintainers being included in Glasswing is a good start, but it isn’t a substitute for thinking about the businesses that depend on that software.

6. A senior person whose job it is to think about all of the above, full-time. Not a fraction of a mid-market manager’s attention. Someone whose remit is small business, the way somebody else’s remit is enterprise. And a team to interact directly with small businesses.

I’m not the only SME owner willing to help, but

When Anthropic is ready to take small businesses seriously, I’m available.

Available to get involved. Available to help you define this. Available to follow and support whoever you decide to put in charge of it. Available to provide feedback from the sidelines, alongside the many other small business leaders who care about this and would say the same.

The reason I’m offering - and the reason others would too - is that this matters. Not just for the businesses I work with, but for the economies, communities, and lives those businesses sit inside. The AI buildout is going to define the next decade. It would be a serious failure to design it in a way that excludes 99% of the businesses it’s supposed to serve.

Sincerely,

Heather Baker

Founder, The AI Edit and Humans in the Loop

He is not the only one. Every day someone new is selling you the same story. Their agent closes deals. Their agent finds keywords then writes their content. Their agent runs their funnels while they’re at the gym, on holiday, spending time with their family, or baking sourdough bread. You, meanwhile, are still writing your own posts with Claude as your assistant. Still planning your own content with Perplexity. Still spending your time crafting and honing beautiful marketing strategies to reach your perfect customer.

And the implication is always the same. You are behind. They are ahead. The gap is widening.

If you can feel the FOMO creeping in, that’s exactly what’s meant to happen. You’re supposed to watch everyone else building agents, panic, and jump in yourself.

But I have spent a lot of time on AI agents in the last two months and I can tell you with some confidence that the hype about AI agents is mainly hype.

I have read the reports. I have reviewed the stats. I have watched the videos. I have tried the tools. And three things have become very clear to me:

1. Many of the people claiming to be using agents have a very loose definition of an agent. They are using ChatGPT or Claude to help them write content. They might have a sophisticated prompting system, and they might be saving loads of time while increasing their output. But what they are calling an agent is actually a simple chatbot (see my definition of agents later).

2. A smaller group that are actually using agents to do their marketing at scale are mostly doing it badly (with a few exceptions where professional developers have been involved at much expense). They have agents that write exactly the kind of content you would expect a chatbot with no human oversight to write. Agents that send templated outreach that infuriates its recipients. Agents that produce the kind of output that makes no one buy. These AI agents for marketing are real. They’re also terrible.

3. There is another group of people who are using home-made agents to do specific, defined, low-thinking, repetitive tasks (I give some examples later in this piece). They are getting a lot of benefit from their AI agents. They haven’t replaced anyone, but they have improved their workflows and learned a lot about AI in the process. But they operate largely under the radar because this stuff seldom makes for good social media content.

So what exactly is an AI agent?

To understand what an agent is, you need to understand that there are four ways people work with AI right now.

1. The first is chat. You open ChatGPT or Claude, you ask a question, you get an answer, you take that answer and do something with it. Most people are here.

2. The second is automation. If something happens, then something else must happen next. You set a scheduled task every morning for ChatGPT to send you a reminder. That’s an automation. No reasoning. No decisions.

3. The third is an agent. This is where the AI isn’t just answering a question or following a rule. It’s reasoning. It’s got an LLM as its brain, and it is making choices. And then it is taking actions off the back of those choices. It works across multiple tools to get to the goal you have defined. An agent might send an email, having decided on its own to personalise it based on the recipient’s work history. Or it might draft a blog post for you, having first read through your last ten posts and decided on its own which angle to take.

4. The fourth is a multi-agent system. This is where you have agents managing other agents. One does research and hands off to another that writes content, which hands off to a third that publishes. Each one has a role. Each one has a boundary. (This sounds great, but keep reading to see why this is fraught with issues).

When someone tells you they’ve built an AI agent for marketing, often what they are really talking about is the first or the second one. Sometimes it’s the third. And often, if it is a genuine agent, what it’s doing is very low-stakes - which not a bad thing - it’s where agents really shine.

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Bad AI marketing agents are easy to build

Building an agent that writes generic blog posts, sends templated outreach, and publishes mediocre content across five channels is now genuinely easy. The tools are there. The tutorials are everywhere. A motivated person with a couple of hours and a Claude Cowork subscription can get one up and running with little friction.

The limitation is not building the agent. It’s making it useful. Yes, an agent can easily find content on the web and reorganise it for your own blog. It can do that way faster than an average marketing assistant. But no one ever won at marketing by reorganising other people’s content. You’ve just made it cheaper and faster to do something that you shouldn’t be doing anyway.

Good AI marketing agents are harder to build

Not because it’s hard to plug Claude into Zapier into a keyword research tool into a database. That bit is easy. What’s hard is getting the agent to do something worth doing, to a standard worth doing it for.

If you’ve ever interacted with any AI chatbot, and you have standards of any kind, you know this already. The first version of anything it produces is disappointing. Frustrating. A bit off. You rephrase. You push back. You ask it to try again. You give it more context. Ten times. Sometimes a hundred. Until together you create something worth using.

That back and forth is the hard bit. And that’s also the bit an agent has to skip in order to operate at all.

Real marketing agents are good at the unglamorous stuff

There are things in marketing you still have to do that take up time but don’t require much judgement. Reformatting a blog post for LinkedIn. Tagging a CRM. Cleaning up a lead list. Pulling together a week’s worth of competitor research. Summarising an hour-long recording into bullet points.

None of this is marketing strategy. It’s the work around the edges of marketing. The admin. The reformatting. The pulling and sorting. The things you put off because they’re boring, then resent having to do at 11pm on a Sunday.

Hand these things to an agent. It will do them faster than you. It will do them at 2am. It will do them while you are at the gym, on holiday, or baking sourdough bread.

If you’re vibing with this, a quick ❤️ tells me I’m on the right track.

So how do you build an AI agent?

The best place to start is a task you already do regularly. Something small. Something repetitive. Something where the rules are clear and the stakes are low.

It’s relatively easy to build an agent. Ask Claude, ChatGPT, or Perplexity to talk you through the steps.

For me, that task was turning my Substack posts into LinkedIn posts. Every time I publish something on Humans in the Loop, I want five LinkedIn posts promoting it, each from a different angle. I used to write them myself. It took about half an hour. It was boring and not creative - the creative part was writing the post in the first instance.

So I built an agent for it. Actually, I built the same agent twice. Once in Claude Cowork and once in Zapier. The Cowork version scans my published content every day at 9am.

The Zapier version watches an RSS feed and then acts immediately.

Both do the same thing: read a new piece, work out the argument, and draft five LinkedIn posts from different angles. The drafts land in my Notion content calendar, and I review, approve, and schedule them from there.

Five minutes instead of thirty. The agent is saving me the time I used to spend on the bits of marketing that were necessary but time consuming.

Where agents break

Building an agent for the unglamorous stuff is worth doing. But even if your agent is low stakes you do need to understand the risks. Agents break. Here’s how.

1. Automating the wrong thing. Building an agent to produce generic content, faster, is not marketing. It’s slop at scale.

2. Hallucination. Agents use LLMs as their brain, which means they will invent a statistic. Misattribute a quote. Publish something factually wrong under your name.

3. Workflow changes. A tool changes its API. A format shifts. Something you didn’t know was brittle stops working at 2am. Someone has to fix it, and that someone is usually you. Maintaining agents is a substantial job.

4. Missed nuance. Ever asked an AI to summarise something only to find it’s almost right, the emphasis is wrong? Well, when you hand tasks off to an agent, this happens all the time

5. Busy work at scale. An agent can simply be a massive waste of time if it is automating something that should never have been done in the first place

If you got caught up in the agent FOMO, you are not alone. It happens. The claims are loud, confident, and relentless. But if you dig deep and know what you are looking for, there is real gold to be found

This happens to a lot of people a lot of the time, but it happens less often to the AI fluent. The more you know about how these tools actually work, the harder it gets for anyone to convince you that their agent is running their marketing function while they bake sourdough and their marketing manager searches for a new job. My AI Fluency for Leaders course is built exactly for that. It’s CPD-certified. 3 hours. Online.

And if you want to find out more, I recently ran a session on AI agents for marketing with Ferdie Bester, founder of NightJarr, who builds agent workflows for real businesses. You can watch the recording here.

I get asked this question a lot. Usually by someone who’s already using AI regularly and wants to know if they’re doing it properly.

The short answer: AI literacy is knowing how to use AI. AI fluency is knowing how to think with it.

AI literacy is knowing how to use the tools. Knowing how to apply AI to a problem. Writing prompts. Generating content. Using the features of ChatGPT or Claude or Gemini to get a useful output. Most AI training stops here. And most people who’d describe themselves as “good with AI” are operating at this level.

That’s useful. But it’s the starting point, not the destination.

AI fluency is what happens after literacy. It’s knowing which tool to use for which decision. Knowing when AI adds value and when it doesn’t. Knowing how to evaluate a vendor’s claims. Knowing what data you should and shouldn’t put into a system. Knowing how to build AI into your strategy rather than just bolting it onto your to-do list.

An AI literate person can use ChatGPT. An AI fluent person knows whether ChatGPT is the right tool for that decision in the first place, what data they should and shouldn’t put into it, and what the output is actually worth.

What I’ve seen training 1,400+ leaders in AI fluency

I’ve been training leaders in AI fluency for half a year now. Business owners, directors, senior managers, mostly in SMEs. People making real decisions with real money.

And the pattern I see is consistent. Most of them arrive literate or semi-literate. They’ve used ChatGPT. They’ve generated content with it. Some of them are using it daily. They’d tell you they’re comfortable with AI.

But when I start asking questions about how AI fits into their business strategy, or how they’d evaluate a new AI tool a vendor is pitching them, or what their policy is on data going into these systems, or what their red lines are on automated decision-making, the confidence drops. Fast.

That’s the literacy-fluency gap. And it’s where most of the risk sits.

A leader who is literate but not fluent risks adopting the wrong tools, overpaying for capabilities they don’t need, exposing data they shouldn’t, and building strategies on foundations they don’t fully understand. Not because they’re careless. Because nobody taught them the rest.

The problem with most AI training

There are broadly three places leaders go to learn about AI. All three have significant gaps.

Vendor training. Anthropic, OpenAI, Google, Microsoft. They all offer courses and resources. Some of them are good. But they teach you how to use their tools. They gloss over the negatives and downsides. They tread lightly on ethics. They don’t teach you how to evaluate whether their product is even the right fit for your problem. When a company offers you a free AI course, ask yourself what they’re including and what they’re leaving out.

University courses. The major universities and business schools run AI programmes. Many of them are well-structured. But they move slowly and they are expensive (often in the £5k+ zone). If you’re taking a week-long AI course from a major university, you’re often taking the same course people took two years ago with very few updates. AI moves too fast for annual curriculum reviews. And none of them give you a system for staying current after the course ends.

Online courses. Online AI training is completely unregulated. Anyone can build a website, record a few videos, and sell themselves as an AI trainer. There is no barrier to entry. No quality standard. No external assessment. Some of these courses are excellent. Many of them are not. And the person buying has no reliable way to tell the difference before they’ve paid. And if the course is on-demand that’s another issue - because it’s already out of date the day it’s published.

Most AI training, regardless of where it comes from, is literacy training being sold as fluency. It teaches you tools and prompts. It almost never covers the macro landscape, your industry context, or the mindset shifts that actually determine whether you’ll use AI well or badly.

I know this because I’ve taken so many AI courses myself. So I built something different.

Why I built this AI fluency course

Through The AI Edit, my consulting, training, and accelerator brand for B2B leaders, I run a CPD-certified course called AI Fluency for Leaders. CPD certification means the course has been independently assessed by the CPD Certification Service against structured criteria. Learning objectives. Session design. Trainer credentials. Assessment methods. It’s not a rubber stamp. It’s an external quality standard. I chose to get certified specifically because so much of the market is unregulated.

I designed the course because I was frustrated by the gap I kept seeing. Leaders were coming to me having done AI courses and still not being able to answer basic strategic questions about AI in their business.

So I built it around six dimensions of AI fluency. Not just the two that everyone else covers.

The AI Edit’s six dimensions of AI fluency

Most AI training covers concepts and tools. Those are two of the six. Here’s the full picture.

Concepts. How AI actually works. Not at an engineering level. At a decision-making level. What a large language model is, why it hallucinates, what RAG is, what agents are, how prompt engineering works. The foundations you need to make sense of everything else.

Tools. The major models, how to choose between them, what’s already inside the tools you’re paying for, and why starting with the tool instead of the problem is the most expensive mistake you can make.

Industry. Where AI is hitting your sector. What your competitors are doing. What your customers are starting to expect. What your regulators are beginning to require. Your domain expertise is your competitive advantage. Combine it with AI fluency and you are significantly ahead.

Macro. Who is building these tools. What their motivations are. Who is regulating them. Why a “free AI course” from a model provider might not be teaching you the full picture. The liability for AI decisions in your business sits with you, not the vendor, not the algorithm. You need to understand the landscape well enough to navigate it.

Ethics. Accountability, bias, transparency, data privacy, environmental impact. Not as a lecture. As operational risks that need governance. When AI goes wrong in your business, it lands on your desk. You need to know your red lines before something forces you to find them. Most AI ethics courses focus on the ethics of building AI. But most of us need to think more about the ethics of deploying AI.

Mindset. The dimension nobody talks about. AI models are sycophantic by design. They tell you what you want to hear. The Dunning-Kruger effect is everywhere in AI right now. People use one tool for a week and think they’ve figured it out. The fluent leader stays curious without being credulous. They slow down when everyone around them is speeding up.

That’s what AI fluency looks like. It’s not six tools. It’s six ways of thinking.

If you know anyone who would find this useful, I’d be grateful for a share!

Why this matters for your business

If you’re a leader making decisions about AI, the quality of the AI training you receive directly affects the quality of those decisions. A bad course doesn’t just waste your time. It gives you false confidence. And false confidence in AI is expensive.

AI fluency is also foundational to everything else you want to do with AI. You can’t generate leads with it if you don’t understand it. You can’t build competitive advantage with tools you don’t trust yourself to evaluate. You can’t grow a business on a foundation you’re not confident in.

Literacy gets you started. Fluency gets you results.

If you want to go deeper on AI fluency…

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And you might want to watch this….

This probably does not surprise you. It does not surprise me either. Nobody voted for Mark Zuckerberg, and yet he controls the AI strategy for three billion users. I have written before about the case against Meta’s AI approach. In my opinion, Meta has earned its position at the top of the mistrust list.

Introducing the Humans in the Loop Trust Index

We asked a panel of 240 people across the US and the UK which AI companies they trust and which ones they don’t.

We will be running this every month, tracking how trust shifts as these companies make decisions, launch products, and inevitably do things that make us think about their role in shaping the future.

Google and OpenAI top the list of most trusted AI companies

[CHART: Which AI company do you trust the most?]

Google leads trust in the US at 44%. OpenAI leads in the UK at 43%. Microsoft holds third place in both countries.

The interesting finding here is OpenAI. It tops trust in the UK despite the Pentagon controversy in late February, when OpenAI struck a deal with the US Department of Defense just hours after Anthropic was blacklisted for refusing to allow its AI to be used for mass surveillance and autonomous weapons. Sam Altman himself admitted the deal was rushed. Staff resigned. Claude overtook ChatGPT in the App Store.

And yet. OpenAI still leads trust in the UK. In the US, where the story landed harder, Google takes the top spot.

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Meta and DeepSeek are the least trusted AI companies

[CHART: Which AI company do you trust the least?]

Meta. Then DeepSeek. Then it diverges: xAI takes third in the US, while Anthropic takes third in the UK at 16%.

Anthropic had a strong start to 2026

Anthropic refused the Pentagon’s terms on surveillance and weapons. It got blacklisted by the US government for it. Public opinion swung behind it. It launched Claude Cowork, which I think is one of the most significant AI product launches this year. And it had a brilliant March more broadly.

Yet Anthropic barely registers in the trust rankings. 6% in the US and 3% in the UK ranked it as the most trusted AI company. It will be worth watching whether Anthropic’s strong positioning translates into trust over the coming months.

What comes next

New data every month. Same questions. Same panel size. If trust shifts, the data will show it.

Follow along by subscribing.

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If you know any leaders or senior professionals who want to build their AI fluency, then please get them to check out my CPD Certified AI Fluency course where I teach the six dimensions of AI fluency - and keeping up to date with what these AI companies are getting up to is one of them!

In the olden days (by which I mean 2024), this would have meant a developer, a specification document, a few rounds of feedback, several weeks, and a bill that would have been wildly disproportionate to the task.

In February 2026, I built it in 15 minutes, and only because I did it the hard way:

First, I asked Perplexity to help me write a detailed prompt. I pasted that prompt into Claude. Claude generated the code. I hit a wall getting it to run locally (Claude and Perplexity were both gaslighting me, insisting my HTML file wasn’t an HTML file). So I abandoned both of them and pasted the prompt into Base44, a vibe coding platform. By the time I’d finished creating an account, the app was sitting there waiting for me. I pressed publish (you can check it out here). Done.

That is vibe coding. You describe what you want in plain language. AI writes the code. You get a working thing.

It is genuinely impressive. And it is genuinely dangerous. Both of those statements are true at the same time, and most of what you’re reading about vibe coding only picks one.

What actually is vibe coding?

The term was coined by Andrej Karpathy, co-founder of OpenAI and former head of AI at Tesla, in February 2025. He described it as giving in to the vibes, forgetting the code even exists, and just accepting the output produced by the AI. Then Collins Dictionary named it Word of the Year for 2025.

And if you’re just getting into vibe coding now, you’re too late. Because in February 2026, exactly one year later, Karpathy declared vibe coding passé. He proposed (by which I mean said and then everyone took notes so now it is) a new term: agentic engineering. His argument was that the practice had matured beyond casual experimentation into something more structured, with human oversight and architectural thinking at the centre.

The man who invented the concept moved on from it within twelve months. That should tell you something about the speed of this space.

How big is vibe coding, really?

Among developers: it’s big.

85% of developers regularly use AI tools for coding and software design. 41% of all code written globally is now AI-generated or AI-assisted (JetBrains). Google says over 30% of its new code is AI-generated.

But developers do not trust AI to write their code. Only 29% of developers trust the accuracy of AI-generated code. 46% actively distrust it. And positive sentiment towards AI tools has dropped from over 70% to 60% in a single year. And 72% of professional developers say vibe coding is not part of their professional workflow. Developers use AI to assist them. They do not hand it the keys.

Among non-developers: it’s also big but different

Among non-developers, the picture is very different. 75% of Replit’s users have apparently never written code. They describe what they want and the platform builds it. Lovable went from launch to $206 million in annual recurring revenue in under a year. Base44 grew to 250,000 users in six months before being acquired by Wix for $80 million.

These are the stats that should concern us as leaders. The people building software with AI at speed are overwhelmingly not the people who understand its limitations. Developers use these tools cautiously and check the output. Non-developers use them confidently and ship.

Can you actually make money from vibe coding?

Yes. Some people are.

A former Pinterest account manager called Paulius Masalskas, who is not a developer, vibe coded a creator search tool on his commute. He made $30,000 and left his job. A 22-year-old college dropout called Evan built an AI illustration generator with 8,000 users and $1,700 a month in recurring revenue. A wedding venue owner built a planning app with her daughter over a single afternoon, and it is already helping her business.

But look at what these stories have in common. None of these people succeeded because they could suddenly write code. They succeeded because they already understood their market, their audience, or their problem deeply. The vibe coding removed one barrier to execution. It did not remove the need for business sense. It did not remove the need to understand your customer. It did not replace strategy.

The pattern is consistent. The people making vibe coding work are the ones who already knew what to build and who to build it for. The AI handled the how. The human handled everything else.

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So what is going wrong with vibe coding?

A lot.

Moltbook, the social network for AI agents that launched in February 2026, was supposedly built without a human writing a single line of code. Impossible to verify, but it caused a huge publicity spike. Elon Musk praised it. Then security researchers at Wiz found that the database storing users' login credentials and email addresses had no access controls at all. 1.5 million sets of login credentials and 35,000 email addresses were visible to anyone who looked. Nobody hacked it. The door was simply never locked.

Remember Base44, the platform I used for my quiz? Wiz also found a critical authentication vulnerability in Base44 that allowed anyone to bypass all security controls and access private enterprise applications built on the platform. The vulnerability was, they said, remarkably simple to exploit.

Lovable, another popular vibe coding platform, has been used by cybercriminals to create tens of thousands of phishing sites, crypto scams, and malware distribution pages. Security firm Proofpoint has been tracking the abuse since early 2025.

And then there is the vibe-coded ransomware. A ransomware strain called Sicarii appeared in early 2026, built using AI coding tools by someone who clearly did not understand what they were building. The decryption process does not work. Even if a victim pays, their data stays locked. The attacker cannot fix it because they do not understand the code they generated.

These are the predictable result of treating “it works” as the finish line.

Does vibe coding actually make people faster?

This is where it gets really interesting for us as leaders.

In mid-2025, a nonprofit research organisation called METR ran a rigorous randomised controlled trial. They took 16 experienced open-source developers, gave them 246 real tasks from their own repositories (projects they had worked on for an average of five years), and randomly assigned each task to allow or disallow AI tools.

Before the study, the developers predicted AI would make them 24% faster.

After the study, the developers believed AI had made them 20% faster.

What actually happened: they were 19% slower.

Experienced developers, using frontier AI tools, on codebases they knew intimately, were measurably slower with AI. And they did not notice. They came away from the experience genuinely believing they had been more productive.

The researchers found that the more familiar a developer was with their codebase, the less AI helped. Screen recordings showed more idle time during AI-assisted work. Developers spent significant time reviewing and cleaning up generated code. And they kept reaching for AI even when it was slowing them down, because they believed it was helping.

This is a self-awareness problem. And it applies far beyond developers.

What should leaders actually do about this?

Vibe coding has made it dramatically easier to build something. It has not made it any easier to build the right thing. And it has not made it any safer.

The technical barrier to creating software has been lowered significantly. But it was never the only barrier. Knowing what to build, whether to build it, who it is for, whether it is secure, whether it solves a real problem: those are leadership questions. They always were. And vibe coding has not touched any of them.

Here is what I would recommend.

1. Find out if it is already happening in your business. It probably is. Any employee with access to an AI tool can now build a functioning internal application in an afternoon. Are they doing so? On what infrastructure? With whose data? With what security review? Shadow AI is not a theoretical risk. It is a governance gap that is growing by the week.

2. Stop treating “it works” as the finish line. Every major vibe coding disaster has happened in the gap between a working prototype and a production-ready tool. If your business is using vibe-coded tools, even for internal purposes, someone who knows what they are doing needs to be reviewing security, data handling, and access control. AI-generated code optimises for functionality. It does not optimise for safety.

3. Understand this yourself. Not because you need to become a developer. Because you need the judgement to evaluate what your team is building, what your competitors are doing with it, and whether the software you are paying for could be replaced or needs to be protected. That judgement does not come from reading about vibe coding. It comes from developing genuine AI fluency (you can do this by signing up for my new CPD-certified course: AI Fluency for Leaders. 3 hours, delivered monthly).

Vibe coding winners and losers

Vibe coding is real. The opportunity is real. The risks are equally real.

The people succeeding with it are the ones who already had domain expertise, business sense, and a clear understanding of their market. The AI gave them a new way to execute. It did not give them strategy.

The people getting burned are the ones who assumed that because it was easy to build, it was safe to ship. That assumption has already cost millions of records, millions of dollars, and a growing number of reputations.

And the most revealing finding of all is that even professional developers cannot accurately tell whether AI is making them faster or slower. If experts in their own field are that susceptible to misjudging AI’s impact, the rest of us need to be even more deliberate about how we evaluate it.

The technical barrier to building software has dropped. The judgement required to build it well has not dropped at all. If anything, it has gone up.

That is a leadership problem. And it is yours to solve.

One of the pillars of AI fluency is keeping up with this sort of stuff - what’s going on in the wider world of AI: capital flows, investment, major wins, mortifying fails. I run a monthly session called the Insider’s AI Briefing. 30 minutes covering the top 30 AI stories from the last 30 days. Live. Free. Book to attend the next session here.

This is the summary of March’s session (which was yesterday). Thirty stories. The ones I’d want to know if I were running a business and couldn’t afford to miss what’s coming.

China is building robot training farms

China is building a network of state-funded facilities designed to produce training data for humanoid robots. The FT visited one in Wuhan where 70 young graduates work eight-hour shifts teaching 46 robots everyday tasks like serving food, wiping tables, folding laundry. Every repeated movement is captured by cameras and sensors. The site produces around 100 hours of usable data per day.

Beijing has identified “embodied intelligence” as one of six future industries in its new five-year plan. Technical barriers remain. But right now, these robots are learning to fold towels.

But to me, the end point of a humanoid robot that can move through physical space, manipulate objects, and respond to instructions is more than a better waiter. It’s a soldier. And that worries me, because it’s much easier to go to war if your soldiers don’t have loved ones back home to worry about them. The FT didn’t ask these questions though.

You may not wear these in my home

A joint investigation by two Swedish newspapers revealed that contract workers in Kenya are reviewing intimate footage captured by Meta’s Ray-Ban smart glasses. Their job is to label objects to train Meta’s AI. But the footage isn’t just furniture and street scenes - workers reported seeing people using the toilet, undressing, and having sex. Meta says faces are blurred. Workers say the blurring doesn’t consistently work.

The story has escalated. The ICO wrote to Meta requesting information on its data protection compliance. In the US, a class action has been filed alleging false advertising and privacy violations - the central claim being that glasses marketed with “designed for privacy, controlled by you” were running a pipeline sending intimate footage to workers overseas. Seven million people bought these glasses in 2025. And they’ve just relinquished all control over their privacy.

Anthropic had the biggest month of any AI company in history

Most people only heard about one story. There were several.

The Pentagon standoff. Anthropic had a $200 million contract with the Pentagon to deploy Claude in classified systems. During negotiations, Anthropic drew two lines: no mass domestic surveillance of Americans, no fully autonomous weapons. The Pentagon’s position was that a private company shouldn’t dictate how the military uses its technology. They wanted Anthropic to accept “any lawful use” and remove the safeguards.

Anthropic refused. President Trump and Defence Secretary Pete Hegseth publicly cut ties, labelled the company a “supply chain risk” - a designation normally reserved for foreign firms suspected of espionage - and directed all federal agencies to stop using Claude. Anthropic filed lawsuits. The Department of Justice called the company “an unacceptable risk to national security.”

Then it emerged that on the same day the Pentagon formalised that designation, its own Under Secretary emailed Dario Amodei saying the two sides were “very close” on the disputed issues. Publicly, a national security threat. Privately, nearly aligned.

OpenAI and X both signed Pentagon deals without conditions. Dario Amodei called OpenAI’s deal “maybe 20% real and 80% safety theatre,” and pointed to OpenAI co-founder Greg Brockman’s $25 million Trump donation. More than 30 staff from OpenAI and Google signed a legal brief backing Anthropic’s lawsuit. The first day of the hearing went well for Anthropic.

The consumer reaction. ChatGPT uninstalls surged 295% in a single day. Claude hit number one on the App Store. OpenAI’s robotics director resigned publicly over the Pentagon deal. OpenAI’s VP of Research, Max Schwarzer, left - for Anthropic.

The numbers. Anthropic was on pace for $9 billion in annual revenue at the start of the year. By early March, that had reportedly nearly doubled to $20 billion. The share of US companies paying for Anthropic’s tools went from around 4% a year ago to 40%. Over the same period, OpenAI’s share of enterprise spending dropped from 50% to 27%.

If you’ve been following Claude’s capabilities and want to understand the practical side of what’s driving this shift, Claude Co-work: 12 things I learned the hard way - is a useful place to start.

What 81,000 people want from AI. Anthropic published the largest qualitative AI study ever conducted - interviews with 81,000 Claude users across 159 countries in 70 languages.

• What people want: professional excellence, personal transformation, life management.

• What AI has actually delivered: productivity gains, or nothing.

• What people are worried about: unreliability, job security, and what the researchers describe as “cognitive atrophy.”

There was also a clear geographic split. In wealthier regions, people worry about losing what they have. In developing economies, they see AI as access to things they’ve never had.

The Anthropic Institute. Anthropic launched a new research arm led by co-founder Jack Clark, bringing together machine learning engineers, economists, and social scientists to study AI’s impact on jobs, security, and society. This thing is properly staffed - not just a blog and a landing page. Anthropic is building the tools that could reshape entire industries while simultaneously funding research that examines whether that’s a good thing. You can read that as responsible. You can read it as hedging. Either way, it’s worth watching.

Prefer to watch? Here’s the recording of March’s Insider AI Briefing:

The startup built on cheating

Andreessen Horowitz funded a company whose slogan was “Cheat on everything.” Guess what happened next…

Cluely raised over $20 million to build a tool that feeds users real-time answers during job interviews and exams. The founders were suspended from Columbia University for building it. This month, the CEO admitted the revenue figure he’d quoted to TechCrunch was false. When challenged, he described the original interview as “a random cold call from some woman.” TechCrunch published the email chain showing his own PR team had arranged it. He had lied about the lie.

Cluely has since rebranded as an AI-powered meeting note-taker.

Zoom wants to send your avatar to meetings you don’t want to attend

Zoom announced photorealistic AI avatars that can attend meetings on your behalf. You brief them on your talking points, they replicate your appearance, facial expressions, and lip movements, and they go to the meeting so you don’t have to.

If the meeting is important enough to need your input, you should be in it. If it isn’t, why is the meeting happening? Sending an AI avatar doesn’t resolve that question - it just adds a layer. You’ve swapped attending a meeting for reading a summary of a meeting your avatar attended. That’s not saving time.

There’s also the matter of the other people in that meeting, who think they’re talking to you. They’re not. And Zoom is shipping deepfake detection alongside the avatars. A tool that pretends to be you, and a tool that detects when someone is pretending to be someone - in the same product update.

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OpenAI: code red

OpenAI signed the deal with the Pentagon that Anthropic walked away from, and the consumer backlash was immediate. Internally, head of applications Fidji Simo told staff that Anthropic’s enterprise dominance was a “wake-up call” and described it as a “code red,” saying the company “cannot miss this moment because we are distracted by side quests.”

The side quests have been extensive. Sora launched to enormous hype, hit number one on the App Store, and Disney signed a billion-dollar licensing deal. This month, OpenAI shut Sora down. It needs the computing power for coding and enterprise. The Disney deal is dead.

The focus now is Codex, their coding tool, which has quadrupled its weekly users to over 2 million since January. OpenAI acquired Astral, a Python developer tooling startup, and folded the team into Codex. They’re also reportedly building their own code repository to replace Microsoft’s GitHub - notable, given that Microsoft is one of their largest backers.

On the money side: OpenAI closed a $110 billion funding round led by Amazon at $50 billion, SoftBank at $30 billion, and Nvidia at $30 billion. The company is now valued at $840 billion, with 900 million weekly active users and 50 million paid subscribers. Extraordinary numbers. But Anthropic’s enterprise share climbed from 4% to 40% in a year while OpenAI’s dropped from 50% to 27%. Valuation and dominance are not the same thing.

Two weeks after Anthropic launched the Anthropic Institute, OpenAI announced its nonprofit arm plans to spend $1 billion this year on AI safety and societal risk research. Whether that’s genuine, or reputation management after the Pentagon controversy, is up to you.

AI and jobs: what happened in March

Several stories from this month belong together.

Jack Dorsey cut 40% of Block’s workforce - over 4,000 people. The business was performing strongly. He said explicitly that AI “fundamentally changes what it means to build and run a company,” and predicted most companies would do the same within a year. Gross profit per employee will go from approximately $500,000 in 2019 to (expected) $2 million in 2026. The stock surged 24%: The market didn’t punish the decision. It rewarded it.

Anthropic published research mapping which jobs AI is actually performing versus which it could theoretically handle. For computer and maths roles, AI could theoretically handle 94% of tasks. In practice it’s covering around 33%. The gap is similar across law, finance, and administration. The researchers reference the potential for a “Great Recession for white-collar workers.” What’s slowing things down right now is legal constraints, technical limitations, and the need for human review. Those are potentially temporary barriers, not permanent ones.

The Dallas Federal Reserve found that employment in computer systems design has fallen 5% since ChatGPT launched, and the decline falls disproportionately on workers under 25. Entry-level jobs are drying up. The bottom rungs of the career ladder are disappearing - and if a generation of workers can’t get their foot in the door, they can’t build the experience that makes them valuable later. That has implications well beyond the tech sector.

And then there’s the story that sounds like fiction. North Korean operatives are using AI to get hired at European companies - real-time deepfakes in video interviews, AI-generated CVs, voice-changing software. Once inside, they draw a salary and send the money to Pyongyang. Over 3,000 suspected operatives are currently working inside Western companies, generating over $600 million a year for the regime. Amazon has blocked more than 1,800 suspected operatives since April 2024. The operation is shifting to Europe because US enforcement tightened and European companies have fewer defences.

Because that section needed something less bleak: a producer in Europe used the AI music platform Suno to create a fictional Japanese metal band. It accumulated 80,000 monthly listeners on Spotify before fans traced the creator to Europe. Once the music had traction, the creator hired seven real musicians from Tokyo to perform the AI-composed tracks live. Three shows done, a headline gig confirmed. As the creator put it: in an age where AI is taking everyone’s jobs, this one actually created them.

AI is now making life-or-death targeting decisions in Iran

I don’t fully trust AI to write a blog post for me. Anyone who’s worked with these tools knows they hallucinate - they state fiction as fact with complete confidence. So I found the FT’s investigation into AI’s role in the Iran conflict genuinely disturbing.

The US military is using AI systems to run what’s called the “kill chain” - finding a target, prioritising it, selecting a weapon, assessing damage after the strike. Traditionally, that process required printed documents, senior commanders reviewing intelligence, and formal sign-off. It took hours, sometimes days.

AI has compressed that to minutes. The result: the US struck over 2,000 targets in Iran in four days. For comparison, the coalition struck a similar number of targets in the first six months of the campaign against ISIS that started in 2014.

The civilian cost is already visible. Iran’s Red Crescent reports over 20,000 non-military buildings hit, including more than 17,000 residential buildings.

The question from the FT: how do you exercise meaningful human judgment over decisions generated by systems running 37 million computations per second?

$265 million is being spent to prevent AI regulation

76% of Americans want AI regulated. The AI industry is spending $265 million to make sure that doesn’t happen.

The biggest spender is a group backed by OpenAI co-founder Greg Brockman, Andreessen Horowitz, and a Palantir co-founder. Their argument: individual states shouldn’t write their own AI rules because it creates an inconsistent patchwork that stifles innovation. That sounds reasonable. Except there are no national rules either. When someone says “we don’t want state-level regulation,” what they’re generally saying is “we don’t want regulation.” It just sounds better the first way.

Anthropic is on the other side. It’s given $20 million to a pro-regulation group and said publicly that effective AI governance requires more scrutiny of companies like itself, not less.

The UK government performed a U-turn on AI copyright

The government’s original position was to allow AI companies to train on copyrighted works, with creators required to opt out to protect their own material. Elton John called it theft on a high scale. Paul McCartney, Dua Lipa, Coldplay, and thousands of other artists opposed it. Only 3% of the 10,000 consultation respondents supported the proposal.

Technology Secretary Liz Kendall has now said the government “listened” and no longer favours the opt-out approach. They haven’t decided what to replace it with. The government currently has “no preferred option.”

The U-turn is welcome. But without a clear decision, it’s a U-turn into a lay-by. Nobody knows where we’re going next.

Half of teenagers are using chatbots as a search engine

Pew Research published a study on how US teenagers use AI chatbots. 64% have used them. The top use case, at 57%, is searching for information.

These are tools that hallucinate. They invent sources. They present fiction as fact with absolute confidence. Teenagers - still developing their critical evaluation skills - are using them as a primary way to find things out. That’s an information literacy crisis, happening right now, while most coverage focuses on whether kids are using chatbots to do their homework.

McKinsey got hacked through its own AI platform

Another day, another embarrassing AI story for a major consulting firm.

A one-man cybersecurity firm called CodeWall used its own AI agent to break into Lilli, McKinsey’s internal AI system used by 40,000 staff to plan strategy, analyse data, and build client presentations. It took two hours. The agent gained full read and write access to the entire production database, accessing 46.5 million chat messages, 57,000 user accounts, 728,000 sensitive file names, and the system prompts that revealed exactly how the AI was configured and what guardrails were in place.

McKinsey says the actual files were stored separately and were never at risk, and it patched the vulnerabilities within hours of being alerted. This was an ethical hack - but it shows what’s coming. The company that charges clients a fortune to advise them on AI got breached through its own AI platform before the attacker had finished his morning coffee.

A man used AI to design a cancer vaccine for his dog

This is a genuinely moving story. It’s also a good example of how AI hype builds.

Paul Conyngham, an AI consultant in Sydney, used AI to design a custom mRNA cancer vaccine for his dog Rosie after she was diagnosed with mast cell cancer in 2024. He chained together ChatGPT, DeepMind’s AlphaFold, and a university genomics lab. One tumour shrank by half after the injection in December.

That is impressive. But look at what was actually involved. He’s an AI consultant - not a casual user. He paid $3,000 for genomic sequencing. He had access to a world-class research lab to produce the vaccine, and worked with 350 gigabytes of tumour data. And Rosie isn’t cured. One tumour responded. Others didn’t.

The headlines say “man uses AI to create cancer vaccine for his dog.” The implication is that this is something anyone could do with the right prompts. This is how hype builds - a genuine, nuanced story gets compressed into a headline that implies something much bigger, and expectations get inflated beyond what’s real.

Join me for April’s Insider Briefing

Once a month I run a live session called the Insider’s AI Briefing: 30 AI stories in 30 minutes, with time for questions at the end. It’s free, it’s fast, and it’s built for leaders who need to stay informed without it taking over their week. The next one is 30 April at 12:30. Register here.