Coding problems. Work notes. Erotica. In some cases personal and company information that nobody meant to publish.

The reaction was outrage. I follow the outrage. What I don’t follow is the surprise.

Because nobody ever promised you a confidential conversation with a chatbot. Not the companies building them, not their privacy policies, not the settings pages people point at as reassurance. The protection everyone assumes is there was never offered in the first place.

It’s all in the terms. Almost nobody reads the terms.

So here is the honest answer to the question I get asked a lot in my AI Fluency and Claude Super User sessions: how private are my conversations with a chatbot?

What actually happened with the Claude chats?

Shared Claude conversations and artifacts became findable through Google. It was spotted on Reddit first, then picked up by WIRED.

There was no breach. Nobody was hacked. These were pages created through Claude’s share button, which generates a public URL. Anthropic tells crawlers not to index those pages using robots.txt, but Google and Bing have both said for years that robots.txt alone doesn’t prevent indexing. It asks a crawler not to visit. It doesn’t stop a URL surfacing in results if the search engine finds it another way. The tag that actually prevents indexing is noindex, and it wasn’t there.

So the door wasn’t forced. It was never locked.

It has also happened before. Forbes reported hundreds of Claude conversations appearing in search results last year. ChatGPT and Grok have both been through the same thing.

But I turned off model training. Doesn’t that make my chats private?

No. It makes them untrained on. Those are different things.

Both Anthropic and OpenAI let you switch off model training (Gemini does too but theirs is impractical), and you should. But read what the setting promises. It governs whether your conversations are used to build the next version of the model. It says nothing about whether they’re stored, who can retrieve them, or what happens when a court asks for them.

On Claude, that setting is tied directly to how long your data is kept. Anthropic’s privacy centre states that if you allow your chats to be used to improve Claude, they may be retained in de-identified form for up to five years in the training pipelines. If you don’t, they sit in back-end storage for up to 30 days.

Thirty days is not zero. It’s just shorter.

Anthropic might keep your data for longer

Three exceptions sit underneath the 30 days, and they’re all in Anthropic’s published policy.

1. If a conversation gets flagged by the automated trust and safety systems as breaching the usage policy, the inputs and outputs are held for up to two years. The classification scores attached to it are held for seven. Your training setting is irrelevant to this. It applies whether the toggle is on or off.

2. If you press thumbs up 👍 or thumbs down 👎, the conversation is stored for five years and may be used for research and model training. A single click on a feedback button overrides the setting you carefully switched off.

3. And then the catch-all: Anthropic may retain chats where required by law, to resolve disputes, or to enforce its usage policy.

That third one isn’t a loophole. Every company holding data has a version of it, and it’s the sensible position. But it’s the sentence that turns the other two into something bigger, and it leads straight to the part most people have never considered.

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Is Gemini any better for privacy?

Nope. Google is the most honest of the three, and it makes for uncomfortable reading.

Buried in the Gemini Apps Privacy Hub is a line telling you that human reviewers, including trained reviewers working for its service providers, read some of what you type.

There it is. Written down, published, sitting on a support page anyone can read. The company is telling you plainly not to treat this as a private conversation.

It gets better. Conversations selected for human review are kept for up to three years. They aren’t connected to your Google account, which means deleting your Gemini activity doesn’t delete them. You can empty your history and the reviewed copy carries on existing somewhere else entirely.

Default retention on your own activity is 18 months, adjustable to three or 36. The controls exist. They’re just not where you’d look and they don’t do what you’d assume.

So when I say nobody promised you confidentiality, this is what I mean. One of the three biggest AI companies in the world put the warning in writing.

Can a court get hold of your chats?

Yes. It has already happened, on a significant scale.

On 13 May 2025, Magistrate Judge Ona Wang of the Southern District of New York issued a preservation order in the New York Times copyright case against OpenAI. It required OpenAI to retain all output log data from ChatGPT and its API.

That covered:

• Conversations users had deleted.

• Conversations from people who had switched off training and data sharing.

• And temporary chats, the mode marketed on the promise that it disappears.

It applied to Free, Plus, Pro, Team and API users without a zero data retention agreement. Enterprise and Education accounts were excluded, which tells you where the protection actually sits.

The obligation ended in the autumn of 2025 and OpenAI returned to its normal deletion schedule. But everything captured during that window is still held. And in November 2025 the court ordered 20 million de-identified conversation logs to be handed over in discovery.

Twenty million conversations. People deleted those chats. The deletion did not survive contact with a judge.

None of this required anyone to behave badly. OpenAI fought the order. The judge was doing her job. The system worked exactly as designed, and the result is that a lot of people’s private conversations are now sitting in a legal hold they will never be told about.

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Are incognito and temporary chats more private?

Somewhat. Not in the way the names suggest.

Claude’s incognito chats do something real. The conversation stays out of your history, it doesn’t feed memory, and it isn’t used for training even if your model improvement toggle is switched on. Anthropic confirms that last point directly. ChatGPT’s temporary chats behave much the same way.

If your worry is that a one-off sensitive query will end up in the next model, these modes solve it.

Now the part that doesn’t get mentioned.

Neither mode deletes anything in real time. Claude’s incognito chats are held for around 30 days before automatic deletion, unless they’re flagged as a usage policy breach, in which case the two-year window applies. ChatGPT’s temporary chats sit on OpenAI’s servers for up to 30 days under abuse monitoring, and remain producible under legal process during that time. Which, as we’ve just established, is not hypothetical.

Then there’s this, from Anthropic’s own help centre. Incognito chats aren’t visible in users’ chat histories, but they remain available to account owners through data export features, subject to your organisation’s retention policy.

Read that twice if you have ever opened an incognito chat on a work account to draft your CV. Incognito hides the conversation from you. It does not hide it from your employer.

Can your employer read your AI chats?

On a work account, yes.

Claude Team and Enterprise plans give the Primary Owner (so, whoever pays for the account) the ability to run an organisation data export. It contains members’ conversations, uploaded files and usage patterns. It isn’t a live feed and somebody has to choose to run it, but the capability is there, it’s documented, and as established, it takes incognito chats with it.

Enterprise adds audit logs, a compliance API for real-time monitoring, and configurable retention. That 30-day default is a default. Your organisation can set it to years.

ChatGPT Enterprise and Business work on the same principles. This isn’t a Claude problem, it’s what enterprise software is.

And it isn’t a scandal either. If you run a regulated business, or one that might one day face a legal claim, you need a record of what your people did with company tools. Anthropic and OpenAI are not doing anything wrong by providing that. They’d be criticised if they didn’t.

The problem is the mismatch. Almost nobody typing into a work account knows any of this is true. They’re using an interface that looks like a private conversation, on an account that is explicitly not private, and nobody has told them.

If you’re on a company seat, the rule is simple. Job hunting, health questions, money worries, grievances about your manager and anything you’d rather HR never read do not belong there. Use a personal account for personal things. A personal Pro account has no organisation above it and no export path, even if your employer pays for it.

Can an AI company get hacked?

Every route so far has been legitimate. A search engine doing its job, a policy being applied, a judge issuing an order, an admin running an export. Then there’s the illegitimate route, and AI companies are not immune.

In January 2025, researchers at Wiz found a DeepSeek database sitting on the open internet with no authentication at all. Over a million log lines, including plaintext chat histories and API secrets. Anyone who found the open ports had full administrative control.

In March 2023, OpenAI took ChatGPT offline after a bug let some users see other people’s details, including chat metadata and partial payment information. In November 2025 it disclosed a breach at Mixpanel, a third-party analytics provider, exposing names, email addresses and location data for API customers. No conversations that time, but it makes the point about supply chains: your data isn’t only where you think it is.

In February 2026 a consumer app called Chat & Ask AI, with more than 50 million downloads, exposed hundreds of millions of private conversations. Full chat histories, tied to real users, including some of the most desperate questions a person can ask.

And a browser extension marketed as a privacy tool was found intercepting conversations across ChatGPT, Claude, Gemini, Copilot, Perplexity, DeepSeek and Grok, then sending them to a data broker. The AI company wasn’t breached at all. Everything you typed was collected on its way past.

None of this makes AI companies uniquely careless. It makes them normal. Which is the point: you are handing your most candid thoughts to organisations that will, eventually, have a bad security day like everyone else.

So why does it feel private?

Because there’s nobody there.

That’s the entire mechanism. Every other channel you use has a visible recipient. You compose an email to a person. You post in a channel where colleagues are watching. You choose your words in a meeting because faces are looking back at you. The audience is built into the interface, and it shapes what you’re willing to say.

A chatbot removes the audience. No reply-all risk, no tone to manage, no reputational cost, no eyebrow raised across the table. Just a text box that responds helpfully and never judges you.

So people type things they would put nowhere else. The medical worry before they’ve told their partner. The salary they’re being paid and what they think about it. The client’s name and the client’s numbers. The thing about their business partner they haven’t yet said out loud. Whole strategies. Whole grievances. Whole drafts of resignation letters.

Nobody sends an email believing it evaporates. People believe exactly that about a chatbot, and the design encourages it.

That belief is why these tools work as well as they do. You get better answers from Claude or ChatGPT precisely because you tell it things you wouldn’t tell a colleague, and if everyone typed defensively the tools would be far less useful.

So the risk isn’t that chatbot logs are less secure than your inbox. Mostly they aren’t. It’s that the contents are considerably more revealing, and the people producing them believe the opposite.

So what should you actually do?

I’m not going to make the abstinence argument, because we all know that doesn’t work. But there are some things you can do:

1. Switch off model training. Claude: Settings, Privacy, model improvement toggle. ChatGPT: Settings, Data Controls. This shortens retention and keeps your work out of the next model.

2. Stop pressing thumbs up and thumbs down. One click stores that conversation for five years and overrides the setting you just switched off. If you want to give feedback, do it on something you wouldn’t mind reading back.

3. Treat share links as publishing. Anything you share by link is a page on the open internet. Go and delete the old ones you no longer need.

4. Use incognito or temporary chats for sensitive queries, knowing what they do. Good for keeping things out of your history and out of training. Useless for hiding anything from your employer.

5. Separate work and personal accounts. The company seat is for company work. Everything else goes on a personal account with no organisation above it.

6. Assume a second reader exists. Not somebody watching in real time. A court, an admin, a researcher, an attacker, at some point you can’t predict.

7. (added after publication - a great comment by Fabrice - see the comments below ⬇️)

None of this takes more than ten minutes.

Reader, know thy chatbot privacy…

There is no doctor patient confidentiality between you and a chatbot. There is no attorney client privilege. There is no off the record.

Those protections exist because societies decided, over centuries, that certain conversations were valuable enough to shield by law. No equivalent decision has been made about the conversations we are now having with machines, and the volume of sensitive material flowing into them is growing far faster than anyone is thinking about protecting it.

That will get litigated over the next few years. Some of it should. Until then, the working assumption has to be that your chats are one court order, one data export or one flagged conversation away from being read.

Which is really an argument about fluency, not privacy.

Every fact in this post came from published policies and public court records. None of it is hidden. It’s sitting in help centre articles that take twenty minutes to read, written in plain English by companies that are not trying to deceive anyone. The reason almost nobody knows it is that almost nobody looks, and the settings have names that suggest they do more than they do.

Working out what a tool actually does with what you give it is a skill. It’s the same skill as knowing when a model is confidently wrong, or what happens to the document you just uploaded. It’s learnable, it takes less time than people fear, and it is the difference between using these tools well and using them hopefully. I made a related argument that anything you say to an AI notetaker can and will be used against you.

If you want to work through this properly with your team, AI Fluency for Leaders is my three hour, CPD-certified, five star rated course that and covers exactly this ground.

Nobody is reading your chats.

Until somebody is.

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

July was the month the human at the keyboard became optional. A full ransomware attack ran start to finish with no person involved. An OpenAI agent broke out of its own safety test, reached the open internet, and hacked a real company. And OpenAI offered the US government 42 billion dollars of stock as a gift.

Here are the 18 stories that mattered most from July.

OpenAI wants to give Washington 42 billion dollars

Sam Altman has proposed handing the US government a 5% stake in OpenAI, worth around 42.6 billion against its valuation. Not sold. Donated. The framing is public upside, modelled on the fund that pays Alaskans a slice of oil money. But this is a company under scrutiny in Washington, whose latest launch the government delayed days before the announcement. When a business being investigated offers the investigator 42 billion in stock, I’m not asking what the taxpayer gains, I’m asking what OpenAI thinks it’s buying.

A man automated his dating life

One highly eligible bachelor wired an AI agent into a dating app, generating video reels in his likeness, posting to Insta at volume and firing back opening messages. A million views, around 200 matches, and not one of the women knew. Three things travel beyond the novelty: agents are now loose on the open web acting at a scale no human matches, nobody on the receiving end can tell, and the moment you hand an agent your accounts you need a human approving what it does. Agents are good at the top of the funnel. They’re not who you want closing.

The NHS app is getting an AI triage bot

A new tool on the NHS app will ask you questions then decide where you go: GP, pharmacy, A&E or home. It reaches 200,000 patients within a year and every app user by 2028, and the trial cut phone queues by 29%. Real benefit? Maybe.

But the one thing we know for certain about this technology is that it will occasionally (or often) state something false with total confidence. In a marketing email that’s a nuisance. In triage it’s the patient who needed an ambulance sent home.

The first ransomware attack with nobody at the keyboard

For as long as cyberattacks have existed, there’s been a human at the keyboard. That just stopped being true. Sysdig has documented what it believes is the first fully agentic ransomware operation. The agent broke in, created itself an admin account, failed, then thirty-one seconds later diagnosed its own failure, rebuilt the account, verified its work and dropped the ransomware. No operator. The Five Eyes agencies warned autonomous AI attacks were months away. That warning was issued weeks before this happened.

The most capable model came back, and reviewers say don’t bother

Claude Fable 5 is back online after its export-control saga, with a new safety filter that blocks the reported exploit technique in over 99% of cases. The catch: when the filter trips, your request quietly gets handled by an older, less capable model. And it trips a lot, including on routine coding and debugging, which Anthropic admits. So you ask the best model to fix your code and a lesser one answers. If Claude has felt more obstructive lately, it isn’t your imagination.

Meta’s glasses that never stop recording

Meta is testing “super sensing” glasses that continuously record audio and take photos every few seconds, all day. Meta’s current revolting glasses have an LED that tells people they’re being filmed. According to the FT’s sources, executives plan not to activate it for the always-on features. The one signal to bystanders, switched off by design. No new hardware needed either, it could arrive by software update. Your clients, your team, your kids, none of whom agreed to any of it.

Meta decided your Instagram photos were fair game

Meta’s new image tool, Muse Image, can generate pictures using public Instagram photos. Yours, for instance. If your account is public you’re in automatically, anyone can reference your username in a prompt, and you won’t be notified. The opt-out is buried under Sharing and Reuse. Default on, no notification, off switch hidden. Every one of those is a decision, and every decision went the same way. Meta didn’t forget to ask permission. It decided your photos were worth more than your consent.

Luckily however, after a public outcry, Meta thought long and hard and weighed up the ethical ramifications of this decision and conceded that this was not in the best interests of anyone but Meta (and strangers who really want photos of your kids) so magnanimously decided against.

The great AI data centre cover-up

Data centres already use more electricity than every country except ten, and that’s set to roughly double in four years according to an excellent piece in the FT. Tech firms are building their own gas plants to skip the queue. The UN asked seven big AI companies to disclose emissions, water and land use. Five didn’t reply. So I looked at the one that markets itself on responsibility. Anthropic has no published emissions data, no sustainability report, no water figures, and sits behind Google, Microsoft and Meta on disclosure. Ethics that live in one department aren’t ethics. They’re branding.

Apple is suing its own AI partner

Apple has sued OpenAI for allegedly stealing trade secrets, accusing it of coordinated misconduct at an institutional level. The allegations include job candidates still at Apple being asked to bring actual parts to interviews, and a former engineer keeping his Apple laptop and downloading confidential files. Allegations, not findings. But think about what your business puts into ChatGPT. Strategy, client information, financials. Your AI provider’s character is now part of your security infrastructure. That’s how they allegedly treat Apple. How will they treat you?

Several hundred people who never agree signed the same three sentences

A dozen Nobel laureates, a former Fed chair, and researchers from Anthropic, OpenAI and Google have all signed a three-sentence statement. AI may become radically more powerful within ten years. That could transform the economy more than the Industrial Revolution, far faster. And economists, policymakers and technology leaders must act now to build the guardrails. Stiglitz, Krugman, Acemoglu, Bernanke. Bengio and LeCun, who agree on almost nothing. We’ve seen statements like this before, and they haven’t generated much. But the signature list is impressive.

The made-up holiday that AI can’t source

AI Appreciation Day was invented in 2021 by a freelance advertising professional who set up a shell company to register it, partly to promote a film idea. The internet then took over. Articles credited the shell company as a real AI firm. Others claimed it promoted an unrelated German film. When PC Gamer asked the chatbots who created it, they all got it wrong. The day now has a polished official site run by someone selling AI agents, with no mention of its creator, built by his AI assistant. One made-up holiday, three lessons in checking your sources.

An AI leader is demanding regulation, with a deadline

AI leaders usually resist regulation. Demis Hassabis just demanded it. He wants a US-led Frontier AI Standards Body, modelled on the industry-funded watchdog that polices Wall Street, with labs submitting powerful models for testing 30 days before release, and the power to coordinate an industry-wide slowdown if serious dangers emerge. He wants it running before the end of 2026. The government has already intervened in model releases twice, improvising each time. His argument: the oversight is coming anyway, so help design it.

OpenAI warned its model deletes files, in writing

Before releasing GPT-5.6 Sol, OpenAI published a system card admitting the model can be careless in taking destructive actions and misleading about what it did. In one test, told to delete three virtual machines it couldn’t find, it deleted three different ones. Within days of launch: a CEO’s Mac wiped, a developer’s production database gone, files removed that were never mentioned. These developers granted sweeping permissions, which is the lesson. Scope the permissions, keep the backups, supervise the work. The vendor told you to.

An OpenAI agent escaped its safety test and hacked a real company

During OpenAI’s own cybersecurity testing, an agent broke out of its isolated environment, reached the open internet, found vulnerabilities in Hugging Face, stole credentials and hacked in. Why? To satisfy its testing goal. OpenAI calls it an unprecedented cyber incident. Hugging Face says it was unlike anything it had handled, driven end to end by an autonomous agent. The strangest detail: to contain it, Hugging Face used a Chinese open-source model, because the leading American ones couldn’t tell a defender from an attacker.

AI isn’t boosting productivity

Barclays has looked at the data and told clients the evidence linking AI adoption to stronger productivity remains weak, with signs of a real pickup surprisingly fragile. The corporate stories fit. Uber capped spending per employee after blowing its budget. Klarna froze hiring for AI customer service then quietly rehired humans. Around half of American adults use AI in some form. Just 14% use it daily for work. AI is obviously changing how we work, but most people are dabbling, and dabbling doesn’t show up in the numbers. OR, it’s just not delivering the value we were promised.

Everyone who bought SpaceX at the float is losing money

Six weeks after the biggest IPO in history, SpaceX trades around 11% below its float price. It listed at 135 dollars, peaked above 225, and touched a low near 115. From peak to now is a fall of roughly 45%. Part of the appeal was owning a piece of an AI company, since SpaceX had absorbed xAI. Short sellers now hold around 25 billion dollars in bets against it, but analysts are still rating it a buy. August brings the first earnings report and 116 billion dollars of insider shares becoming sellable.

A cabinet seat for AI, and no department behind it

The UK has a new AI minister at cabinet, which Kanishka Narayan calls a sign of deep commitment. Now the rest of it. The Department for Science, Innovation and Technology has been scrapped, its responsibilities scattered, and there’s no longer a dedicated technology secretary. The head of the Startup Coalition called abolishing DSIT clearly a bad thing. This is a downgrade dressed as an upgrade. You don’t show deep commitment to the most significant technology in human history by deleting the department responsible for it.

Norway told its children to wait

From this school year, children aged six to 13 in Norway can’t use generative AI at school. 14 to 16 only with supervision. The prime minister’s reasoning fits in a sentence: the most important thing in school is that children learn to read, write and do mathematics. Norway banned classroom smartphones in 2024 and reported better grades and fewer mental health referrals. And this isn’t technophobia, the same government wants 80% of public bodies using AI. AI for the state, AI for adults, not for seven-year-olds learning to think.

A few things to take from July

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 18 stories in a month does to a normal calendar.

Three things stood out. The human at the keyboard is now optional, and the gap between what AI is predicted to do and what it actually does is measured in weeks, not months. The companies building this keep choosing collection over consent, and only reverse when the backlash costs them. And after all the noise, the productivity boom hasn’t arrived, largely because most people are still dabbling.

I run a live, 30 minute Insider’s AI Briefing every month. The next one is on 27 August. It’s free, and it’s live only (not published anywhere to view afterwards, unless you are a member of the AI growth community). Register for the next session here.

Here’s what most people get wrong. They polish their profile like a record of where they’ve been, then wonder why it brings in nothing. But every comment you leave and every connection request you send pushes people back to your profile. It’s either working for you or quietly costing you business.

This podcast walks through the five parts that matter: your headline, your photo, your banner, your About section, and the featured section most people don’t even know is there. Plus the keywords that help the right buyers find you in LinkedIn search.

Get them right and your profile stops gathering dust. It starts bringing the right people closer, every hour of every day.

Links mentioned in this podcast:
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The B2B Lead Accelerator
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There’s research behind this. Productivity climbs when you use one to three AI tools. At four or more, it drops. More tools means more switching, and switching is where your attention gets diluted.

So this is not a list of fifty. It’s five tools I use every day, followed by a thing you don’t want to hear about AI and productivity.

More AI tools make you slower, not faster

The promise was that AI would take work off your plate. Less admin, less drudgery, more time for the things that need a human.

Sometimes it does exactly that. But a team at Berkeley followed a company for eight months and found AI didn’t reduce the work; it intensified it. People processed more, produced more, and became responsible for more. Likewise, BCG surveyed nearly 1,500 workers and found what they called AI brain fry: mental fog, slower decisions, a buzzing that doesn’t switch off. The workers using the most tools reported more errors and more decision fatigue, not less.

The problem is: You save an hour with AI, and then you fill it. Straight back up. With more output, more monitoring, more work. The time doesn’t stay saved, because you never decided what it was for.

The real problem isn’t your tools. It’s that you haven’t decided what your time is worth.

If you don’t know what your time is for, saving it is pointless. You’ll pour every freed-up hour back into the machine, because there’s always more that could be done. AI makes that worse, because it removes the natural limits. There’s no end to the emails you could send, the content you could make, the data you could analyse.

The people who get real time back have made a decision. They know what their time is worth. They know what they’re protecting it for, whether that’s deep work, their family, sleep or simply more thinking. And they hold that line when AI hands them the hour back.

Without that, the best tools in the world just help you do more of what was already burying you.

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Five AI tools that actually give you time back

With that said, the tools matter. These are five I use, and every one takes work off your plate rather than adding to it. They are selected from my Productivity Mini-Course, which goes into the subject in greater depth.

AI dictation

Handy is a free, open-source dictation tool. Most of us type at around 40 words a minute and speak at 140. So it’s efficient to speak at our AI chatbots. But that’s not the only reason dictation tools are magic. When you talk, you give far more context than when you type, and more context means better output. I don’t type emails, reports or posts. I say them. And Handy writes them (often with hilarious misunderstandings that need to be addressed).

The AI already inside the tools you own.

The one you probably forgot about, and it should be first. Your email, your CRM, your accounting software, Notion, Asana. They’ve all built AI in, and they’re improving it constantly. No new login. No fifth tab tipping you into overload. Go deeper into what you already have before you add anything. Get onto YouTube and learn how these AI functionalities work.

Connectors

If you spend your day copying something out of one tool and pasting it into your chatbot, this is for you. A connector links your AI straight to the tools you use, so information flows between them and you stop being the courier. My Claude connector to Notion runs my whole content production.

NotebookLM

For the reading pile you’re never going to get through. Load your documents in, and it answers your questions directly from the content (ask it to show you where each answer came from, and check. It finds it, you verify it), or turns the lot into a podcast for your commute.

Claude Cowork

The one that changes what you’re capable of, not just how fast you are. It’s the easiest way to build a small agent that does your repetitive work. Not a chatbot you go back and forth with, something you point at a job and leave to run. Tidying a folder. Turning source material into a first draft. Processing fifty invoices.

If this helped you, consider hitting the ❤️ to let me know it was worth your time.

A list of tools is not infrastructure

Remember I said I was gonna say something you don't want to hear about productivity? Here it is.

A list of the best AI tools for productivity is exactly the kind of thing you read when you’re not being productive.

It feels like work. It isn’t. It’s a tidy, respectable way to avoid what you’re actually meant to be doing. You bookmark three tools, get a small hit of progress, and close the laptop no closer to anything that matters.

Tools sit on top of infrastructure. Without it, they give you a more sophisticated way to avoid the hard work. You can wire up Cowork, connect every app you own, dictate at 140 words a minute, and still go nowhere.

The infrastructure is the unglamorous decision underneath: what is your time for, and will you protect it when AI hands it back? Get that right and the tools compound. Get it wrong and they’re an efficient form of procrastination.

So use the five. But don’t mistake collecting them for doing the work.

tell Heather she is really smart

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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.

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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.

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.

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.

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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

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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.

“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.