Window into the Future

First, the boring-but-important bit: Fable is included on most paid Claude plans through Tuesday, July 7. If you want the full story of what changed, The Neuron wrote a good plain-english version, but the short version is all you need: you can use the best model there is, on your normal plan, until Tuesday.

And the smart way to spend the next two days is setting up a few things that keep paying you back long after Tuesday. Five worth stealing:

1. Stop over-explaining it. 

Your instinct is to write a giant prompt with fifteen rules. Anthropic's own guide says that makes Fable worse. The elaborate prompt setups you built for older AI are the thing dragging it down. Tell it what you’re actually doing and who its for, hand over the mess, and get out of the way. Their exact template, which you can paste right now:

im working on [the bigger thing] for [who its for]. they need [what the output lets them do]. with that in mind: [your actual ask].

so instead of a numbered checklist, its: "im writing the Q3 board update for our exec team, they've got five minutes and care about pipeline and churn, here’s everything. Write it."

2. Give it the whole pile at once. 

The one thing Fable does that a normal model can’t is hold a huge amount at once without losing the thread. So stop feeding it one doc at a time. Drop in the whole folder, the call notes, the horrible spreadsheet, and ask for the finished piece, not a summary. This is the sit-down job you’ve been putting off for a month.

3. Give it a memory. 

Keep one plain running doc where it notes how you like things, what it got wrong last time, the calls youve already made, and have it read that every session. Anthropic recommends this outright, and you can set it up with no code in about ten minutes. It means you stop re-explaining yourself in every new chat, which, if you’re anything like me, is half the reason AI starts to feel like a chore.

4. Brainstorm with the cheap model, then have Fable build it.

This is the one people are calling the smartest move of the week. You’ve only got Fable at half your usual limit this week, so don’t burn it on the thinking. Open a chat with Opus 4.8 and have it interview you about your day (where you lose time, what you do over and over, what drives you mad) until you land on one small tool that'd actually help. Then, while Fable's still free, have Fable build the thing. Cheap model to figure out what you want, Fable for the part that lasts.

In Claude Opus 4.8: 

"Interview me about my work, one question at a time, about where I lose time, what I repeat every week, and what annoys me most, until we land on one small tool or system that would genuinely help. Then write a clear, detailed brief for building it." 

Then paste that brief into Fable 5: "Build this for me, end to end. Here's the brief: [paste]."

5. Build yourself a "does this pass?" check. 

My favorite, and the one I’m stealing. A creator fed Fable all their past posts, had it work out what made the best ones land, and turned that into a scorecard. You can copy his prompt to build you own:

Here are my three best [posts]: [PASTE THEM]

1. Study them and write a scoring rubric out of 100. Base the categories on what makes these good: [hook, structure, voice, clarity]
2. Draft a new one on [TOPIC]
3. Score the draft against the rubric as a strict reviewer
4. Under 95: rewrite and score again. Repeat until it passes
5. Show me only the final version and its score

swap "posts" for emails, designs, proposals, reports. I immediately wanted one for this newsletter.

Notice what all five have in common? none of them are really about Fable. They’re about building yourself a little system, a way of describing things, a memory, a reusable playbook, a quality bar, that keeps working after the model changes. And it will change. Fable might be behind a paywall by Wednesday. Thats the whole thing I keep banging on about with AI-proofing your work: the tool is temporary, the way you work is what compounds. (It’s most of why 100 School exists.)

(and yeah, there’s a loud fight this week about whether Fable came back weaker, its real, but its a coding argument, and for everything mentioned above, that shouldn’t be a concern)

So don’t rush, don’t feed it junk. Set up one of these five properly and let it earn its keep long after Tuesday. And build yourself something fun like the koi pond too, for morale.

How to AI: Fable 5 Edition 🤖

Every week, this section is your shortcut. Here are a couple of tips/ ways you could try Fable 5 this week that are worth your time:

• How to use Claude Fable 5 without going bankrupt

• How to prompt Claude Fable 5 (Anthropic’s full guide)

• How to use Claude Fable 5 (unlearn your ChatGPT habits)

• How to create cinematic websites with Claude Fable 5

Before you go ✌️

Whatever gets launched or pulled back next week, the habit matters more than the tool, so I’m curious where you actually are right now?

none of those? hit reply and tell me where you really stand. I read every one.

See you next week!

Window into the Future

On Tuesday, Anthropic launched Claude Tag: a version of Claude that lives inside a Slack channel as a shared teammate. You tag it the way you'd tag a colleague, hand it a task, and it works away while you get on with something else. There's even an "ambient" mode where it doesn't wait to be tagged. It watches the channel and jumps in when it reckons it should.

For over a year the question was "are you using AI?" but now it’s "how does your team work when there's an agent in the room?"

The people who build this stuff are excited, and their reason is more interesting than "cool robot." The argument, made by Andrej Karpathy and the Latent Space crowd, is that an agent was never really held back by how clever the model is. It was held back by where it lived.

A chatbot in a separate tab only helps if you remember to open it, paste in the context, and copy the answer back out. An agent that already sits where the work happens, with the memory and the access, skips all of that. Karpathy called it the third big redesign of how we work with AI. He also said that the payoff only shows up once the dull, hard plumbing is done aka the security, the memory, the permissions.

Not everyone's sold though. The worry, from people like Ashwin Gopinath, is about what your Slack becomes over time, the place that holds every decision, every "we tried that in Q2 and it died," every half-finished thread. Let one vendor's agent be the thing that remembers all of it and you stop renting a clever model and start renting your own company's memory back from them. The open-source camp, Santiago and others, say own the layer yourself and never get married to one provider. Reasonable. Though it's worth noticing that most of the loudest critics happen to sell the alternative.

Ethan Mollick made a good point. While leadership debates whether to build an in-house AI stack, the employees have already decided. They want the same tool they use at home, and they'll keep asking IT until they get it. Nobody's waiting for permission. People are already using AI at work on their own. Claude Tag just makes it official.

The change that matters more and that’s easy to miss in my opinion is the second you tag an agent and hand it a job, you've stopped using a tool and started managing one. You start feeding it context, setting limits, checking its work, figuring out when to trust it and when it's wrong. That's management. And most people on your team have never managed anyone. Claude Tag doesn't just drop a new worker into the channel. It makes everyone in there a first-time manager, overnight, with nobody to show them how.

You can't buy a team that manages this well, and you can't get there overnight. You build it on purpose. So the ones who come out fine will be the ones who already manage well, or who decide to get good at it. Anthropic's own blog basically says this: what makes a human-and-agent team work are clear roles, writing things down, building trust over time, keeping a person on the calls that matter which is just good management. None of it is new. The agent only makes skipping it more expensive. (Which is most of why 100 School exists. The behaviour change is the part that sticks when the tool gets swapped out from under you.)

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to build a human-and-agent team that actually holds together.

• How to set up Claude Tag in your team's Slack.

• How Claude Tag actually works from the people who built it.

• How to use ambient mode in Claude Tag.

Before you go ✌️

If an AI showed up in your team's channel tomorrow, would your people know how to work with it? Hit reply and tell me, I read every one.

See you next week!

Window into the Future

Everyone keeps asking what AI's going to do to their job. I get the worry but a more useful question is who on your team is going to be the person that gets everyone else to use this stuff?

because that person is going to matter way more than people realise right now and at the moment, almost no one is doing the job.

Think about where last week's money actually went. $60 billion on Cursor, 300,000 consultants lined up to go and set the software up. And getting normal people to change how they work with this day to day gets almost nothing. I’m a bit annoyed at myself for how long it took me to properly see that.

BCG actually put a number on this that stuck with me. They reckon about 70% of the value companies get from AI comes from people changing how they work, not from the tech itself and yet the spending is almost all going to the tech.

but how do you become that person? A few things, and none of them are technical, I promise.

1. First, stop talking about AI and just show it. Pick the one task your team openly hates and do it with AI, right in front of them. People copy people. A colleague sharing in Slack how they use AI to get something done does more than any all-hands slide on AI strategy ever will.
   
   …but research out of Atlassian found that if you admit you used AI, your colleagues rate you as 10 times lazier and are far less likely to put you forward for the good, visible work. However, in teams where people use it out in the open and the wins get talked about, that flips completely, and the AI users end up looking like the sharp ones. So dont hide it. the more openly you use it, the easier you make it for everyone around you to do the same.

   
   

   

   
   

2. Second, sit with the AI sceptic. the person you want is the one working around the tool the old way, nodding along in the meeting and ignoring the whole thing the moment it ends. Winning that one person over is most of the job. Think back to Morgan Stanley: the four months wasnt a tech problem, it was a trust problem.
   

3. Third, and this is the one that wears me out: please dont run a training. Im so tired of the one-hour AI workshop everyone forgets. Just sit next to one person and do one real task on their actual work, together. What you're after is a habit, and habits dont come from watching a video.
   

4. The fourth one is the one everybody skips. Stick around long enough that the new way just becomes the normal way. One of our clients, HiBob, has been doing this with us for 18+ months. 15 mins of AI a day is the habit they kept feeding, which is exactly why it worked.

   
   And the role itself is starting to exist now: Stripe is hiring someone to embed with a team of marketers and coach them, one workflow at a time, until working with AI is simply how that team operates. This is a job that didnt have a name a year ago.
   
   

   

   
   

5. and the last one might be my favorite: when one person finally cracks it, don’t keep it to yourself. Show the next person, or better, get the one who cracked it to show them. We ran our 15 day of AI challenge with one content team at Headway, and two other teams signed themselves up based on the content team’s results. No one told them to, they just watched it working for their colleagues and wanted in.

which brings me back to those Morgan Stanley numbers. The money this year is still pouring almost entirely into the tech. Soon enough everyone's working off the same models and the same apps, and having the best one wont count for much. What sets one team apart from the next will be something far less shiny. Whether there’s someone there who can get a room full of people to work differently. You dont need a budget, or a title, or anyone's permission to be that person. You can start this Monday.

Before you go ✌️

When's the last time you sat down with a colleague and went over one task with them? what happened?

Our team at 100 School meets once a month for what we call “AI Show & Tell” where each one of us shares how they’ve been using AI in their work. Highly recommend!

See you next week!

Window into the Future

The best thing I read all weekend wasn't really about the ban. It was Daria Cupareanu on what she built in the 72 hours before it.

She's not a developer. For months she'd been trying to get her own numbers out of Substack, which doesn't make that easy, and had given up. This time she just described what she wanted, walked away, and Fable built the whole dashboard for her. It's live on her site now, refreshing every morning.

She's in Romania. So she's also one of the people who lost it on Saturday.

According to Greg Isenberg, the smart move from here is to get good at local models. The ones you download and run on your own machine so nothing can ever switch them off. He's right that it's a real option now. You can run capable models at home, and his thread walks you through how to start. Alex Finn went even further encouraging you to own your own compute, before that gets taken away too.

Bans aren't the only risk. Mike Taylor flagged that the cheap era is ending. Right now AI companies subsidise your subscription but that won't last. Microsoft just moved Copilot to pay-per-use, and some bills jumped from $39 to over $3,000 a month. Funny timing, then, that the UK government landed on the answer the same week. What Works for AI Upskilling in the UK studied ten organizations: Airbus, Roche, the NHS and found the same thing every time: the tools were never the problem. Everyone had tools. What mattered was building skill that sticks with you, not one product that's gone in six months. We were one of the ten (still can't believe it) and their numbers, not ours: one team went from 49% to 86% confident using AI.

Easy to say, though. So here are three ways to actually build that confidence with AI regardless of whichever model is up, down, or suddenly charging you:

1. Get good at the skill, not the tool. That 49-to-86 jump was people getting better at directing AI (like what to ask it and how to tell when it's wrong). That works in Claude, ChatGPT, anything. Last week I called it keeping the first and last mile. Try this: run the same task through two models this week, so switching feels normal.

2. Save your prompts, not just the answers. Most people lose their best prompts. You write something that works, get a great result, then never find it again. So when a prompt nails something, paste it into one running note. Five seconds now, and it saves you redoing the thinking later. The wording might need a tweak when you move to a new model, but you're starting from something that already works instead of a blank box.

3. Keep your own data. You know the sinking feeling when an app you relied on shuts down, changes its plan, or locks your stuff behind a paywall? Same risk here. Keep your files, your numbers, and your important docs somewhere you control. That's Isenberg's real point: own enough of your setup that losing one tool doesn't wipe you out.

That's it. None of this is about trusting AI less. It's what lets you lean on it harder, because losing any one tool stops being a big deal.

Before you go ✌️

Did you get to try Fable before it disappeared? And if you did, what did you build, or what were you about to? Hit reply, I read every one.

See you next week!

Window into the Future

There's an answer everyone's landed on. Dylan Field, who runs Figma, put it this week: when execution's cheap, design and creativity are the edge. Swap in "taste," swap in "judgment". That's most of my feed right now. The doing is the easy part, your value is knowing what's worth doing.

Twitter tweet

I buy a version of it. I've built part of a company on it. But it caught on so fast that "taste is the edge" has become its own kind of slop because nobody says what “taste” actually looks like.

Since agentic AI turned up, app releases have shot up but the apps people use stayed flat, and reviews even dropped. There's even a study on it, with a title that gives it away: Writing Code vs. Shipping Code.

The researchers have a name for it: the weak link. AI crushed the easy part like building, so all the slow human parts that come after like getting anyone to use these apps became the bottleneck. Cheap execution didn't hand everyone an edge. It just shows how important distribution is.

The thing that turns AI from a fast typist into better work is knowing what to ask for, catching it when it's wrong, and reworking it until it's yours. You build that by doing the work, not by watching AI do it. a16z had a piece this week about handing a dentist's insurance paperwork to an agent. Fair enough, nobody trains for years to chase reimbursements. But the junior stuck with that job was also learning how the business actually works. Hand it all over on day one and the judgment everyone's calling the edge never gets the chance to form.

Which is how you get what people are calling AI dumping: someone forwards you a doc Claude spun up and you can't tell if a brain was ever in the room. I do it too, when I'm tired. The fix isn't clever. Treat anything AI hands you like a first draft from a sharp intern, and never ship it unread. Argue with it. Cut the lines that sound smart and say nothing. Your name's on it, so the thinking has to live somewhere.

It's also why your feed reads like it was written by one tired robot. LinkedIn said this week it'll start burying posts that sound machine-made including the "it's not X, it's Y" move, now officially a tell (a sentence shape I'll be avoiding for the rest of my life). Ethan Mollick caught the everyday version: it's bleak to open an app and hit a menu full of Claudisms. When everyone runs their thinking through the same model, polished writing stops being proof that anyone thought. And proof is all it ever was.

Twitter tweet

And it's about to get blurrier. OpenAI gave ChatGPT a memory upgrade this week (they’re calling it dreaming) so the more you use it, the more it answers like it knows you. Want to see that line? Drop the same prompt into a logged-out incognito window and compare. The difference is how much was just it having learned you.

So you have to decide what stays yours on purpose.

The rule I've landed on, stolen from Alex Lieberman, who broke down his content process this week: keep the first mile and the final mile, give AI the middle. First mile is deciding what's worth doing and handing over the context. Final mile is the final call. The boring 70% in between, let the machine have it.

A visual inspired by Alex Lieberman’s Content Machine.

So this week: pick one thing you've been handing over whole. Write the messy first draft yourself, let AI clean it up, then do the last read by hand. Keep the two ends. That's the part you'll want to still be good at next year.

For your coffee break 🎧

Last week, John Emmerson had me on his leadership series and I had a genuinely good time. We got into the stuff like why teams buy the right AI tools and still don't change how they work and why you can't give people chaos, you need to give them pathways. Worth a listen if you've got a spare half hour.

How to AI 🤖 

Every week, this section is your shortcut. Here are a few ways you could try AI this week that are worth your time:

• How to connect Claude to your apps

• How to stress test business ideas with Perplexity

• How to get started with Claude Code

• How to build a content machine with AI

Before you go ✌️

What's a bit of your work you still do by hand on purpose, even though AI could take it now? Hit reply, I read every single one.

See you next Sunday!

Window into the Future

First, the money, because it's hard to even picture. Anthropic is now worth around $965 billion, more than OpenAI, and bringing in roughly $47 billion a year. A year ago a number like that would've sounded made up. But the giant number isn't really the part that changes your week.

The same day, they put out a new version of Claude (Opus 4.8, if you're keeping count). The scores nudged up a bit, nothing dramatic. What people care about is the stuff around it.

There's a little dial now that lets you tell it how hard to think, from "quick and cheap" to "really chew on this." There's a faster, cheaper setting. There's a mode where it splits a big job across dozens of copies of itself, all working at once. And there's another change I think is the most interesting one. It's now a lot better at telling you when it's not sure, instead of confidently handing you something that's wrong.

Reactions were all over the map.

Dan Shipper, who runs Every and tests this stuff obsessively, loved it and said it's good enough they may as well have skipped ahead and called it Opus 5. Anthropic's own CTO said the "tells you when it's unsure" part matters to him more than any test score. Then you scroll over to where the heavy users hang out, and they're fuming. They loved the old version, they reckon the new one burns through their usage, and they're roasting it for sounding fake-humble (it keeps opening replies with "let me be honest…"). The best example going around is a guy who asked it for a quick review, and Claude spun up 45 copies of itself and torched his entire usage allowance in 43 minutes.

One reply called it "the new YOLO mode for when you've got usage left before the weekly reset."

Meanwhile, the most-shared setup guide for Opus 4.8 going round this week barely talks about the flashy stuff. It spends most of its time on that little dial, and the big tip is that most people will just leave it cranked all the way up and never touch it. The actual trick is turning it down for the easy stuff and only leaning on it for the hard stuff. Which, from the other direction, is exactly what nuked that guy's afternoon. The AI can't sort that out for you. Same with the "tells you when it's unsure" bit. It only helps if you're the kind of person who reads the part where it says so.

Funny thing is, Anthropic's own CEO more or less called this almost a year ago, before any of it existed. On a podcast last summer he said AI would mess up way less often than people, but in weirder, sneakier ways, because it sounds just as sure of itself when it's wrong as when it's right. The fix, he said, was teaching it to double-check its own work. That's basically what this update is. The hard thing he described is now a setting you can switch on.

And the people who actually do this work for a living keep landing in the same spot. Duolingo's CEO spent this spring walking back his big "AI-first" announcement, now openly admitting the stuff still spits out a lot of junk. A founder who's about as pro-AI as they come told his team a line I liked: AI should get you to great work faster, not drop you straight into mediocre. And Klaudia, a team manager at Ocado who just finished our 15 Days of AI for Teams, said it best. The win for her wasn't AI doing her job. It was setting it up to handle the boring lifting so she could put her energy into the part that's actually hers.

So that's the week. A near-trillion-dollar price tag, a new Claude half the internet loves and the other half wants to undo, a house that'll take your startup shares, and a pile of smart people agreeing the AI was never the hard part. There'll be a slightly better one next month, there always is. The thing I'm watching is whether "when do I lean on this, when do I check it, and what's even worth making" starts to feel like the real skill. For most teams, not yet.

How to AI 🤖 

Every week, this section is your shortcut. Here are a few ways you could try AI this week that are worth your time:

• How to buid a Single Company Brain using AI

• How to use Claude Code as a video editor

• How to build an AI-native paid Ads system

• How to get maximum quality for minimum cost from Claude Opus 4.8

Before you go ✌️

Quick gut check. When AI hands you something, do you actually read it before you pass it on? When's the last time you nearly sent something and caught a problem just in time? Hit reply, I read every one.

See you next Sunday!

Window into the Future

The thing I really can't stop thinking about isn't the cuts though. It's that the two stories about them landed in completely opposite directions, and both of them got passed around like crazy.

Intuit's CEO Sasan Goodarzi went on CNBC and said that "None of it had to do with AI. Everything was about how we become more effective." He framed the 3,000 cuts as cutting management layers, closing some offices, simplifying the org. Now, whether you fully buy that is another thing. Intuit also announced multi-year partnerships with OpenAI and Anthropic the same day. But the choice to not reach for AI as the public reason, when reaching for it is basically free this year, is one of the more interesting things any CEO has done in 2026.

ClickUp's Zeb Evans went the other way. Like, the complete other way. He laid off 22% of his company by way of a 2,000-word manifesto on X about something he calls the 100x org, with million-dollar salary bands for people who "create outsized impact using AI", plus a line about how "most companies are delusional" if they think they can keep iterating on what they already have. Cuts and manifesto in the same tweet, on the same morning Meta was sending out its 8,000 notices. By the next day, it was already a meme.

It's kinda funny that ClickUp posted this 8 days before laying off 22% of its staff expecting a 100x return.

Original video by greg_tube, hailey_janemarie and brittanyroo_

LinkedIn

And honestly, if you kept scrolling, the same split was showing up everywhere. Elena Verna, a growth advisor working with a bunch of scale-ups, wrote a piece this week saying most people in tech won't have the same job by the end of 2027. Dan Shipper, who runs Every, wrote pretty much the opposite from inside his own company. They've automated everything they possibly can and somehow grown from 4 humans to 30.

Which brings me to the practical bit. There's been a lot of advice flying around your feed this week about what to actually do with all this. I want to walk you through the three takes I think are worth your time.

1. Claire Vo, three-time CPTO, currently building ChatPRD. Dropped a tactical playbook right after Coinbase that picked up a lot of reactions. Her advice if you find yourself laid off: download Codex and Claude Code, build a dozen skills, ship them to GitHub, throw together a portfolio site with an agent per skill, post the link, then start messaging the founders of every "AI for [your job]" startup you can find.

2. Lenny Rachitsky, who many of you probably already read, came at it from a totally different angle in his most recent State of the Product Job Market piece. His take basically: ignore the headlines for a second and look at the actual hiring data, because the data tells you a different story. PM openings are at their highest level in over three years. The companies adopting AI properly aren't shrinking. They’re going out and hiring more people, especially ones with what he calls AI fluency. So basically the panic is louder than the data. Go figure out where the hiring actually is before you spiral.

3. Holly Lee, who spent years as a global recruiting leader at Amazon, Google, Meta and Microsoft and now coaches careers, made a point earlier this year that I think holds up best this week. "Layoffs shock people's self-esteem. But you've got to be innovative and open-minded. Because when the market picks up, and someone wants to hire you, they'll ask: 'What did you do during that time?' You can't say, 'I've been applying for thousands of jobs.'" So what Holly is suggesting is to make whatever gap you've got visibly count for something.

Someone trying to hire you in six months wants to see what you did with this moment. And honestly, the same thing applies if you haven't been cut, which is most of us reading this. The people I see getting ahead are the ones who've had AI on their laptop every single day for a while, doing real work alongside them, well before any of this week's news hit their inbox.

There's actual data on what that looks like, by the way. Asana's CMO Shannon Duffy shared this research her team did with Anthropic. The jump from weekly to daily is bigger than the one from never to weekly. Most of what makes someone genuinely AI-native isn't which model they're using or which course they took. It's whether the tool is in their hands every single day, on the work they were already going to do anyway.

Join a 5-day Build Sprint 🛠️

If you've been meaning to actually build something, this is your sign. Harold and Ash are running a five-day vibe coding sprint (May 27–31) made for non-technical people, the whole point being that you can ship a working app without writing code.

There are live workshops along the way too, then a demo day to show what you made. Sign up here to join The Vibe Coding Games.

Alongside the sprint, the team will also host live workshops full of practical tips, AI workflows, and live demos.

🗓️ 27 May | Kickoff: Build Sprint Starts
🗓️ 29 May | Workshops
🗓️ 1 June | Build Sprint Deadline
🗓️ 9 June | Final Demo Day

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to design using Nano Banana Pro

• How to run a workday in 47 mins with Claude Cowork

• How to make Claude brutally honest

• How to audit Claude’s context for you and your work in 15 mins

Before you go ✌️

Which of the three takes lands for you this week, Claire's, Lenny's, or Holly's? Hit reply, I read every one.

See you next Sunday!

Window into the Future

So if the tools were settled in five minutes, what filled the rest of the room? The culture. And under all of it, one question nobody could really answer: what does good AI use actually look like here? Some treat heavy AI use as a kind of cheating. Others treat it as survival. Often it's the same company and sometimes even in the same team.

Zapier did something about that. They made AI fluency a requirement for every new hire, then published an update to the rubric they score people against and the telling part is what they added. The original test had three parts: mindset, strategy, building. The new fourth part is accountability.

Their line for it: with AI you can hand off the work, but not the responsibility for it. "Capable" (you use AI with purpose and can show real impact) used to be a decent score but now it's the minimum. And the tier below it, the one they call unacceptable, sounds almost exactly like Romero on slop: work that reads like obvious AI, and looks the same before AI as after.

So who owns this? I argued recently that the most underrated AI job in most companies sits in HR. Not because HR is technical, but because it already touches every employee and every review cycle. Zapier seems to agree. Their Chief People Officer, Brandon Sammut, is now Chief People and AI Transformation Officer, and that rubric runs through every hire.

McKinsey's new Rewired makes the same case in consultant language. Every digital transformation is really a talent transformation and adds the uncomfortable part: as AI takes on more, teams get smaller, and someone has to plan for that honestly. You have to decide what good looks like then build it into how you hire and promote.

The good news: a rising bar is progress. For years "I used ChatGPT" counted as an achievement. The bad news is a bar also sorts people.

Deedy Das wrote a much-shared post on the mood in San Francisco where a small group got rich fast and everyone else is wondering if the ladder they're climbing is even the right one. Jasmine Sun was circling the same thing on the fear of a "permanent underclass". So if "good at AI" becomes a hiring filter, it matters who got a fair shot at getting good and who didn't.

And maybe not everyone needs to be good at AI at everything. Eleanor Warnock makes the point that enjoyment counts for something. The baker doesn't want to vibe-code an inventory tool, they want to make a great croissant. Claude for Small Business will take that kind of work off their plate, and that's genuinely useful. But Warnock's instinct is right: being able to do something and wanting to do it are not the same thing.

(Every team I talk to now have the tools and the budget but are missing a shared answer to "what does good look like here". Ianos, in delivery management, finished our 15 Days of AI this week and posted that the surprise was how much of it showed up in his actual day. So yes buying the tools is easy but the capability and the measuring are what we keep building 15 Days of AI around.)

Which brings me back to that fake Monet. The crowd were handed a label and confidently picked apart flaws that weren't there. Companies are about to do a version of that. Deciding what counts as good AI use before anyone's agreed what they're even looking at.

Join a 5-day Build Sprint 🛠️

If you've been meaning to actually build something, this is your sign. Harold and Ash are running a five-day vibe coding sprint (May 27–31) made for non-technical people, the whole point being that you can ship a working app without writing code.

There are live workshops along the way too, then a demo day to show what you made. Sign up here to join The Vibe Coding Games.

Alongside the sprint, the team will also host live workshops full of practical tips, AI workflows, and live demos.

🗓️ 27 May | Kickoff: Build Sprint Starts
🗓️ 29 May | Workshops
🗓️ 1 June | Build Sprint Deadline
🗓️ 9 June | Final Demo Day

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to Build a Personal AI System With Claude

• How to automate marketing assets with Chatgpt Image 2.0 

• How to do Prospect Research using AI

• How to master Claude Code in 30 mins (workshop from Anthropic)

Before you go ✌️

Has anyone where you work actually said out loud what good AI use looks like? Or is everyone just guessing? Hit reply, I read every one.

See you next Sunday!

Window into the Future

While all that was unfolding, Claude landed inside Microsoft 365 (Excel, PowerPoint, Word, Outlook) with one conversation carrying context across all four. ChatGPT landed inside Sheets and Excel. And GPT-Realtime-2, a voice model with GPT-5-level reasoning baked in, is openly aimed at the call centre.

Nothing to install. It's just there.

AI used to be a separate tab. You'd open Claude, paste your context, ask the question, copy the answer back. There was always a moment of switching that filtered out the casual stuff. If it wasn't worth opening another tab, you didn't bother. That filter is now gone.

So if you're wondering how to read all that, here are three habits I keep coming back to that are not new but very useful:

1. Think before you prompt.

Old advice but there's a recent study Think First, ChatGPT Later that puts numbers on it. The think-first group retained more, made better decisions on follow-up tasks. The AI-first group was faster on the original task and worse on everything after. We've covered the difference between using AI and getting better at it before. The research is finally catching up. (Vlad at Lucanet put it well after he finished our 15 Days of AI for teams this week and posted that what stuck for him were the frameworks, not the tools, which tracks.

2. Make it show its working. 

Whatever AI gives you, ask it to walk you through how. What it assumed, what it ruled out, what trade-offs it made. Not for the model. For you. Microsoft's Work Trend Index this year calls this human agency, which is on the nose but accurate. (We did a whole issue on the same idea applied to Skills.)

Painfully accurate. Which brings me to the third one.

3. Decide what shouldn't exist. 

Philippa Hardman wrote this week that when anyone can build a course in an afternoon, the real job becomes deciding which ones shouldn't. Same logic for almost anything you ship now. AI will produce a hundred decent versions of anything in the time it takes to drink coffee so the bottleneck moves to the front of the process. What to make at all, for whom, why this and not the other ninety-nine. An earlier issue made the same point: the teams getting traction aren't the ones using AI most but the ones still asking what's worth doing.

(Speaking of which, we just kicked off a partnership with Ocado Retail to roll 15 Days of AI into their UK team. It’s our flagship enterprise product. If you want it for your team, reply and let's talk.)

That's the texture of the week. Signals in every direction. Pick whichever habit looks doable and try it.

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to research 1, 10, or 100 companies using Claude Code.

• How to vibe code and build your own ideas.

• How to build real products by yourself (no coding skills needed).

• How to use Claude Design for real product design workflows.

• How to design your brand in one weekend.

Before you go ✌️

Now that AI's inside Excel, Word, Sheets, and your inbox, what's the part of your job you'd rather keep doing yourself? Hit reply, I read every one.

See you next Sunday!

Window into the Future 🔮 

That second-place finish wasn't about people using AI to do their job faster. It was people using it to think about their job. Whether to take the offer. How to handle the awkward 1:1. Whether they're being unreasonable.

Which is a different thing than a tool.

Most people reading this have probably done a version of it. Pasted a Slack thread in to ask what was actually going on. Drafted a job offer pros-and-cons list you wouldn't show a colleague. None of it shows up in a productivity dashboard or makes it onto LinkedIn. And once you've spent enough time talking to Claude like that at work, it starts leaking out everywhere else too.

While most of us are having these conversations on our phones, a Harvey piece I read this week showed me what the opposite looks like. They profiled five law firm leaders who've actually made AI stick at their firms, and the pattern is the same across all five. They're doing it out loud.

• Esther Bowers at Honigman runs Shark Tank–style competitions where people demo how they're actually using AI to their colleagues.

• At ArentFox Schiff, things shifted when a respected litigation partner started inviting other partners to watch him use AI on real matters.

• Pierre Zickert at Hengeler Mueller runs short weekly sessions he calls "just enough time for an espresso". One use case, one coffee, done.

Both studies are real. Most of us are doing the private thing and very few teams are doing the public thing. Neither is wrong on its own. There are conversations you genuinely don't want to have in a Slack channel, and there are conversations that get better when more people see them. The trick is knowing which is which, and most teams haven't thought about it that way yet. (For what it's worth, this is what every team I talk to is trying to figure out. They have the tools, the budget, and a few quiet power users; what they don't have is a way to make any of that knowledge collective. It's the work we keep building 100 School around.)

Want to build your own stuff with AI? 🛠️

Quick one for the people who keep asking how to actually start building stuff with AI when they can't code. The Vibe Coding Bootcamp opens a new cohort Monday May 11th.

Code 100SCHOOL gets you $100 off. Highly recommend.

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to Build a Daily Web Scraper with Claude Code

• How to stress test business ideas with one Perplexity prompt

• How to create LinkedIn posts using GPT Image 2

• How to automate a newsletter with AI

• How to make AI sound exactly like you

• How to do content engineering with Claude Code

P.S. Speaking of doing AI out loud, I'm on stage at workforceLIVE this Wednesday May 6th. Come say hi if you're there.

Before you go ✌️

What's an AI use case that's become part of your week? Hit reply, I read every one.

See you next Sunday!

Window into the Future 🔮 

Back to the brand kit for a second because the part that surprised me wasn't the polish.

It was that Image-2 didn't make us a logo. It read what we already are (the mountain, the red, the copy from our website..) and produced things that don't exist yet but definitely could. That's a different operation than image generation as we've known it.

Claude Design is doing the same shift for structure that Image-2 is doing for visuals.

Peter Yang, a product manager, used Claude Design to build a video, slides, a website, a mobile app, and a design system in 16 minutes. None of them were perfect but they all looked shippable.

Shreya Heda, a designer, redesigned a brand she admires from scratch in 15 minutes and paused mid-video to ask: "will portfolios even matter after this?" Then answered her own question that maybe the next portfolio is just handing someone a brief and saying "give me a week, I'll show you what I can build."

If you want to actually be good at Claude Design, Ryan Mather, an Anthropic designer, posted seven tips this week. The three that matter if you're not a designer are

• don't describe edits with words, point and click using the Comment tool (saves 40% of your token budget)

• set up your design system in the first hour, so everything generated after is on-brand without re-specifying

• and prompt from your actual work context.

Then GPT-5.5, which ties this together. Tom's Guide ran Opus 4.7 against it on seven hard reasoning prompts. Claude swept all seven. But the more honest comparison came from Every's Vibe Check, where they called GPT-5.5 "the model I'd use when I need to get the job done without babysitting it." That's a comparison of friction. And friction is the thing that's been disappearing across all three launches.

A take that Tommaso Nervegna put well in his Substack, which now applies to all three at once: when everyone uses the same AI, the output starts to look the same. What stands out are the decisions AI can't make for you like cutting a section even if it looks "right," breaking a pattern because your users need something different, and leaving space when the AI would keep adding.

When three frontier models all get good at the same things in the same week, what stays expensive is the part nobody can outsource: which model for which task, which sentence to keep, which client problem is worth solving. That's the job now. And it's the work we keep building 100 School around because every team I talk to has the tools, the budget, and the buy-in. What they don't have is somebody to sit with them and say here's where this goes in your actual week.

Your experiment for this week 👇

If you only do one thing this week, do this: take 30 minutes, pick one of the three (Image-2, Claude Design, GPT-5.5), and try it on a real thing you have to make.

• Image-2 👉 feed it your brand assets, ask for a kit. Or try the menu test from TechCrunch.

• Claude Design 👉 one sentence first. Let it ask the eight questions. Use Ryan's Comment tool tip for edits.

• GPT-5.5 👉 give it a messy input you've been avoiding to turn into a clean output (a transcript, meeting notes, a pile of survey replies)

Reply with what you made. I'll feature the best ones in next week's issue.

The 100 School brand kit at the top of this email took me one prompt and about ninety seconds. The impressive part is that it's Sunday night and I'm still thinking about what to do with it.

Twitter tweet

Join our free Claude workshops next week 🎟️

You’ve probably saved a bunch of Claude demos by now. You tried a few things then maybe dropped off. It happens.

If you want to actually get something working, this is the easiest way to start.

Harold and Kieran are running two live workshops this upcoming week. You’ll build something you can use (not just watch along) in just 4 hours.

✅ Claude Code Workshop: Build your first SaaS in 4 hours
🗓️ 28 April
⏰ 1:00PM GMT+1

✅ Claude Cowork Workshop: Build Your AI Workflow System in 4 hours
🗓️ 29 April
⏰ 1:00PM GMT+1

You don’t need to know what you’re doing yet. You’ll figure it out as you go.

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to design a $500,000 logo for $0 using Claude Design 

• How to use Claude Design to generate email templates

• How to get the best results out of Claude Design

• How to generate stunning infographics in Claude

• How to design a personal dashboard w/ Claude Design & Opus 4.7

• How to generate insane UI, designs, and animations in Claude Design

Before you go ✌️

What's become your go-to for design work with AI? Do you still use Figma?

Also, don’t forget to reply with what you’ve built. I’d love to see it!

See you next Sunday!

Window into the Future 🔮 

The first one came from Every. Their COO, Brandon Gell, used to spend the first two hours of every Monday cross-referencing strategy docs against launch calendars to figure out what the team should work on. Three years of Mondays, gone the same way. Now he asks an agent named Anton, sitting in their Slack, that reads the same docs in two seconds and posts a prioritised summary to the team. Brandon's Mondays start at 9am instead of 7.

Anton's daily Monday broadcast inside Every's Slack.
Screenshot from Katie Parrott's full story in Every's Source Code

Brandon's job didn't get automated. It got redesigned when he stopped asking "how do I do this faster?" and started asking "why am I doing this at all?"

That same question is what Dr. Philippa Hardman wrote about this week in a totally different industry. About 95% of corporate L&D teams right now are using AI to build courses faster. The other 5% have stopped and asked whether the course was the right answer in the first place. Rolls-Royce ran a controlled pilot replacing engine training courses with an in-workflow AI coach, and formal training hours dropped 35% while issue resolution sped up 12%. The course wasn't improved but it was replaced by something it was always a poor substitute for.

Then Unframe surveyed 255 companies and put a number on it. AI is now turning the 5-day workweek into 6 days of output. People are saving 2 to 6 hours a week. By every measure of adoption, it's working. And yet only 7% of those companies are seeing it land on the income statement. Most of us are running faster on the same treadmill.

The fourth story is mine. On Tuesday I had to make a call about rebuilding the 100 School website, and I was torn between Webflow (safe, predictable) and Claude (riskier, weirder, no muscle memory for it). I picked Claude because if a company is teaching other companies how to work with AI, you should be able to tell from the outside.

None of us did anything technically impressive. We just stopped using AI as a faster version of the old work and started letting it change what the work is.

This is what I was getting at last week when I wrote about doubling your team's AI output without buying anything new. I think I undersold it. The real unlock is noticing that the tools you already have are waiting for you to ask a different question.

Three things keep coming up in every honest conversation I have with teams right now. The skills people need are different. The experience of doing the work is different. And the way you measure whether any of it is working is completely different. That's the work we're building 100 School around because every team I talk to has the tools, the budget, and the buy-in. What they don't have is the new shape of the work.

That's the thing worth figuring out right now. And it's not on LinkedIn yet.

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to run Perplexity Computer as your personal CFO

• How to design in Claude Code

• How to keep track of projects using notebooks in Gemini

• How to delegate work inside Notion with Claude Managed Agents

• How to stay digitally secure in the Age of Claude Mythos

• How to build an "orchestrator skill" within Claude Cowork

Before you go ✌️

A real question for you this week, because I'm trying to get smarter on it myself:

You've been using AI for months now. What's one thing on your calendar this week that should look different by now and somehow doesn't?

Hit reply. I read every one.

See you next Sunday!

Window Into the Future 🔮

I just came across something that should change how every company thinks about AI right now.

Researchers at Harvard and INSEAD took 515 startups and split them in two groups. Both groups got the same AI tools (same ChatGPT, Claude, credits, and training). One group was shown stories of how other companies had reorganized around AI (actual workflows, where a human got replaced by a model, and how a team of eight became a team of one).

At the end, that group had nearly double the revenue of the other one. They were shipping faster, signing more customers, and asking for 40% less money to get there. They had the same tools but they had a better idea of where to use them.

The researchers call this the mapping problem.

AI keeps getting better so the individual-task gains are obvious by now, but at the company level the gains are still hard to find.

Most people still can't see where AI fits in their own work. So everyone defaults to the same obvious stuff like a chatbot for support, an AI to draft emails, summarize meetings, etc... Meanwhile the real unlocks sit there undiscovered because nobody ever walked you through what they look like.

From Kim, Kim & Koning (INSEAD/HBS, 2026).

Here's what that actually looks like in real companies:

🎨 Gamma, the AI presentation tool, is at around $50M in revenue with about fifty people. Instead of designers and product managers arguing about what to build next, they let AI watch how people use the product, spit out a few versions of a new feature overnight, then one person picks the winner in the morning. What used to take a team of eight takes one person now.

🧳 FazeShift rebuilt how companies chase down unpaid invoices. The old way was someone opening four tabs (spreadsheet, accounting software, bank website, email) and copying things across them all day. The new way is AI does all of that in the background, and a person only shows up when something's weird.

🤝 A team we worked with at 100 School with a group of 100+ HR leaders spent fifteen minutes a day for three weeks using Claude and ChatGPT. By the end they built 1,400+ things they could use at work: email sorters, onboarding helpers, little tools to clean up messy data. People who started calling themselves "beginners" had basically all disappeared.

None of these took new hires. Every one of them started with someone asking "where in my week is there a thing I do over and over that AI could just…do?"

Which brings me to something else Anthropic did recently that got less attention than it deserved.

They used Claude itself to interview 81,000 people across 159 countries about how they feel about AI. The top thing people said they wanted from AI was "professional excellence."

It sounds corporate but when Claude pushed on what that meant, the answers were about feeling relief and having more time. One woman in Colombia said AI meant she could cook with her mother on a Tuesday night instead of finishing tasks. That answer is the whole story.

Source: Anthropic, 2026

So here's where I've landed.

People already want AI at work. The tools are already in their hands. And the research is showing, pretty clearly now, that access alone doesn't move anything. What moves things is someone sitting down with a person, in their actual role, with their actual week, and helping them see where this goes.

That's basically the work we do at 100 School, and I'll be honest it's not the exciting part. It's the piece almost every company skips and it's why most AI rollouts die after six months.

I was writing about this on LinkedIn a few days ago. $98 billion got spent on employee training last year. Only 10% of employees say that training actually changed how they work. Companies keep measuring completion rates like clicking "next" is the same thing as learning something but it isn't. Completion is a compliance metric. What matters is whether someone built something, changed something, or used AI in a way they didn't know was possible the week before.

The companies doubling their revenue aren’t using better tools than you. Your team already has everything it needs. What's missing is a map of where AI goes in your actual week, in your actual job, in the messy middle of how real work gets done. Someone just has to draw it.

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to stop hitting usage limits in Claude

• How to Build Your Second Brain

• How to finally trust Claude's advice

• How to think better with AI

• How I use every Claude feature (and how to set them up)

Before you go ✌️

Who has been figuring out AI in your team? Is it unofficially you?

I've got a hunch the answer is "nobody formally" at most places and want to see if I'm right.

See you next Sunday!

Window Into the Future 🔮

So here's what happened at Anthropic in the last seven days alone.

• Claude got computer use which means it can now open your apps, navigate your browser, fill in spreadsheets, and operate your desktop like a colleague borrowing your laptop.

• There's a new tool called Dispatch that lets you assign Claude tasks from your phone and come back to finished work.

• Fortune broke a story about Mythos, a next-generation model that leaked accidentally and was described internally as a "step change" in capability.

• And reports surfaced that the company is in early talks about going public, potentially as soon as October.

Any one of those would've been the biggest AI story of the week but I don't want to just walk you through the news. I want to talk about what the pace itself is telling us because I think many people are drawing the wrong conclusion from it.

The obvious reaction to a calendar like that is I need to keep up. Learn all these features. Understand computer use and Dispatch and the new model and what it all means for my workflow. That reaction makes sense because it's how we've always thought about technology.

But the people I know who are genuinely good with AI are not keeping up with the releases. What they've done instead is go deep on one or two things. They have a Claude project that knows their entire context. They have a workflow they've iterated on dozens of times. They've built something specific to how they work, and they keep refining it.

There’s research backing this up. BCG found that only about 5% of companies are generating real value from AI at scale despite adoption rates hitting 88%. They call it the adoption puzzle: usage is up, impact isn't. Because most orgs treat AI like a software rollout where they give people access, run a training session, and move on when what actually works looks more like building a habit.

The calendar is impressive, but it's slightly misleading if you're a professional trying to figure out what to do with it. Because it implies that the person who knows about all 52 releases is winning. My honest read is the opposite. The people pulling ahead right now are the ones who picked one use case three months ago and just kept going back.

I've been living at Network School for the last month with 400+ founders, creatives, freelancers all in one place and it honestly feels like eavesdropping on the future. One person here has seven paid Claude accounts. SEVEN. Most have more than one. I was surprised at first, but I think for most of us our imagination just hasn't caught up to what's possible with that much usage. One account versus seven is a completely different capability. And it made something click for me: the people who are getting disproportionate value aren't dabbling across every AI tool. They've gone absurdly deep on one.

And this is worth being honest about too. Wharton researchers recently coined the term "cognitive surrender" for what happens when people stop thinking critically about AI output. With AI, the answers are right often enough that you stop checking, and your judgement declines. But the part I find genuinely hopeful from the research is that when an AI tool was designed to coach rather than just answer, the negative effects disappeared entirely. How you use the tool matters more than whether you use it.

So the people who go deep develop the judgement to know when Claude is helping and when it's wrong. The people who stay shallow never build that muscle. And in a world where your AI tool updates every few days, that muscle is the only thing that compounds alongside the technology.

Every one of those releases is making the tool better at the thing those people are already doing. Computer use in Claude means the workflow they've been building can now go further. Memory means their Claude project retains more context. The 1M context window means they can feed an entire quarter's worth of work. These aren't 52 things to learn but 52 upgrades to a practice you already have (if you have one).

And the Anthropic calendar isn't just a to-do list for power users. It's a window into what an AI-first company actually looks like when it's compounding. 80% of Anthropic employees use Claude daily. One employee alone hit $150,000 in Claude Code usage in a single month. The tool is building itself and shipping at a speed that most orgs won't reach for years.

You're watching what the future of work looks like.

Twitter tweet

I know this can sound like "if you're not using AI daily you're falling behind" and that framing is exhausting. It's not what I mean. There's a difference between trying to keep up with the news and building something real with the tools.

The first one will burn you out. The second one will genuinely change your work. And a lot of what's on that calendar (the Excel integration, the PowerPoint add-in, Cowork, computer use) is Anthropic meeting you where you already are. They're integrating Claude into the apps you already have open daily. If you've been waiting for a good time to start, the barrier just got meaningfully lower.

How to AI 🤖 

Every week, this section is your shortcut. Since this entire issue is about Claude, here are some practical ways to put it to work this week:

• How to effectively use Claude Cowork

• How to design faster with Claude Skills

• How to build a personal command center in Claude Code

• How to use Claude Cowork to build custom agents, automations, and Skills

Before you go ✌️

What's the one thing you keep coming back to Claude for? Is it the quality of the copy/ output, the actual workflow, or something else?

Hit reply. I read them all.

See you next Sunday!

You're using Claude daily but it still doesn't know how you work

A lot of the teams we work with have already adopted AI. Many of them are using Claude regularly. But when you look at how, it's almost always the same pattern. Open a new chat, explain the context, get something back, close the tab. Repeat. One L&D lead described it to me recently as "96% usage, almost no variation." Everyone's doing the same basic thing over and over. The tool is capable of so much more but nobody has taught it how their team actually works.

That difference between using Claude and teaching Claude is exactly what had the internet moving this week.

Thariq Shihipar, an engineer on the Claude Code team at Anthropic, published a piece about how Anthropic's own team uses Claude internally. It hit over 6 million views. The next day he ran a live session and brought in someone from Uber to show how their team uses the same approach at scale.

The post is about something called Skills.

In the same week, Garry Tan, CEO of Y Combinator, open-sourced his entire Claude setup on GitHub. A collection of Skills that give Claude different roles across his whole workflow. One acts as a CEO reviewing ideas, one writes as an engineer, one reviews that same work for bugs. The repo already hit 37K+ stars.

Over on Reddit, someone posted "What exactly are Claude's Skills?" and the thread is still going. The general sentiment is a Skill is more powerful than a saved prompt, but most useful when your work is repetitive enough to warrant a procedure rather than a one-off instruction. The best framing in the thread is that a prompt tells Claude who to be while a Skill tells Claude how to think. Skills went from niche feature to mainstream conversation in about a week. So what actually is one? And how can you start using it for your own work?

A Skill is a permanent brief. You write down your workflow, your standards, your voice, your context once. Claude reads it, follows it, applies it automatically.

One writer compares it to training a new team member. Day one, you explain how you like things done. Day two, same. Day three, same. By week two you're exhausted. A Skill is writing it down once. Claude reads it and delivers the work exactly how you want, every time.

You without Claude Skills vs. You with Claude Skills

Thariq's post revealed that when his team catalogued all their Skills, they fall into three types:

• Reference Skills: your brand guidelines, your tone, how you write.

• Workflow Skills: step-by-step processes Claude should follow.

• Gotcha Skills: the specific failure points Claude keeps hitting in your work that you've learned to catch.

The best Skills, he says, get updated over time. They get smarter as you use them which is what makes this different from a prompt. Here's what that actually looks like across different jobs:

• For writers and content creators: A voice Skill that makes Claude write exactly like you

• For marketers and business owners: A repurposing Skill that takes any long piece of content and extracts platform-specific angles for LinkedIn, email, and social, etc…

• For Ops and PMs: A meeting debrief Skill that follows your team's exact structure every time

• For career and personal development: A feedback Skill that reviews your work before it goes out

• The skill that builds other Skills: Claude's own skill-creator Skill. Describe a workflow in plain language, answer a few questions, and it builds the file for you. This is how you start.

It’s important to note, however, that you don't need to be a developer. Khe Hy wrote a full walkthrough for non-technical people. Anthropic also published a practical intro notebook if you want to understand how they work under the hood.

Two weeks ago, I wrote about the difference between using AI and getting better at it. How even inside AI-native companies, most people only delegate 0–20% of their work to Claude because knowing what's good is hard to outsource. Skills don't close that gap on their own but they definitely change the starting point of every single session.

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to bring anything to life using Google AI Studio

• How to use Lovable for more general tasks

• How to turn a messy doc into a portfolio using Stitch by Google

• How to use Claude Code (50 tips and best practices)

• How to use Claude Cowork like a second employee

Before you go ✌️

Is there a task you keep re-explaining to Claude every week? Tell me what it is and I’ll help you think through what a Skill for it would look like.

See you next Sunday!

Window into the future 🔮

The productivity paradox and what it means for your team

In the 1890s, factories across New England swapped their steam engines for electric motors and waited for the productivity gains to follow. For thirty years, almost nothing changed. The technology was superior but the organisation was identical. It wasn't until the 1920s when factories tore everything down and rebuilt from scratch, with assembly lines and individual motors in each piece of equipment that electrification finally paid off.

a16z published a piece this week that opens with three sentences I haven't been able to stop thinking about: "AI just made every individual 10x more productive. No company became 10x more valuable as a result. Where did the productivity go?"

Their answer: we swapped the motor. We did not redesign the factory.

This is the question I keep bumping into in conversations with teams. Everyone has AI now. Most people are using it to do the same things they were doing before, just faster. That's real value but it's not transformation and the data is starting to reflect that.

You might have seen this chart from Ramp AI index on your feed that shows that 24.4% of businesses now pay for Anthropic up from 1 in 25 just a year ago. OpenAI's adoption actually fell for the first time ever. Anthropic now wins 70% of matchups against OpenAI among businesses buying AI for the first time.

That's a complete reversal of the trends we saw throughout 2025. Ramp's economist thinks it's partly culture. Anthropic has become, and I'm quoting here, "cool." The DoW moment from two weeks ago also sharpened a public contrast between the two companies that seems to have had real business effects.

Which is probably why Anthropic just announced a $100M Claude Partner Network this week. This network is a formal ecosystem of consultancies and agencies to help enterprises go from "we have Claude licences" to "AI is actually embedded in how we work." They're scaling their partner team fivefold. The message is clear that the bottleneck isn't the model anymore but its implementation.

And then Atlassian happened.

Their CEO, Mike Cannon-Brookes, sent a memo to 1,600 people this week telling them their roles were being cut (10% of their global workforce). The stated reason is that AI is changing the mix of skills we need. The company is "reorganising around its System of Work to move faster." In October, Cannon-Brookes said on a podcast that Atlassian would employ more engineers in five years, not fewer, they'd just be more efficient. Five months later, 1,600 emails went out.

I want to be careful here because I think the easy take "AI is taking jobs" isn't quite right either. Atlassian's share price has dropped more than 50% this year. Investors are spooked about whether AI tools will make traditional SaaS products obsolete with some calling it a "SaaSpocalypse". The layoffs look at least partly like financial restructuring wrapped in an AI narrative. Even Sam Altman called this pattern out a few weeks ago calling it "AI washing."

But here's what I think is actually true, and this is the part worth sitting with before your Monday morning:

The companies that come out of this period well won't be the ones that used AI to cut costs. They'll be the ones that used it to do things they couldn't do before. That's a completely different question and it requires a completely different kind of thinking about how work gets done.

Built using Claude's new visualizer feature.

Which brings me back to Chipotle's chatbot. Pepper can write Python. That's not a joke anymore. The question is whether your organisation is set up to take advantage of that or whether you're just running a faster version of the same old factory. (If that's where your team is stuck, this is what we built 100 School for.)

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to build skills for Claude

• How to use AI for content without creating slop

• How to use Claude's new "visualizer" feature

• How to make AI think harder (7 prompts)

• How to run a more organized workday using ChatGPT

• How to create designs with Claude Code & OpenClaw

Before you go ✌️

Quick one: does your team actually feel more productive with AI or just busier? Hit reply. I read them all.

See you next Sunday!

Window into the future 🔮

Anthropic published the most honest AI jobs research yet

Anthropic published a research paper this week that went viral. It's called Labor Market Impacts of AI: A New Measure and Early Evidence, and the core idea is more interesting than the title suggests.

Their question: of everything AI could theoretically do across every occupation, what is it actually being used for day-to-day? The radar chart below tells the story.

🔵 Blue = what LLMs could theoretically handle.
🔴 Red = what's actually happening.

For Computer & Math (the most AI-saturated sector) Claude currently covers just 33% of tasks in practice. The gap between possible and practiced is enormous across almost every profession.

A few findings worth sitting with:

• The most exposed workers tend to be older, more educated, female, and higher-paid. Not the entry-level roles most people picture when they hear "AI displacement". It's knowledge work and it's already happening.

• The top three most exposed occupations are computer programmers (75% coverage), customer service reps (70%), data entry (67%).

• No increase in unemployment for highly exposed workers yet. But hiring of younger workers in those roles is slowing. That's the early signal.

My honest read is the research is more useful as a methodology than a prediction. Anthropic is saying "here's how we'll track this over time" not "here's the answer."

But the finding that sticks with me is the gap itself. The blue area is massive. The red area is small. And the teams we work with are living proof of why they’ve been using AI for a year, usage is high, but actual capability is genuinely unclear. Most don't have a verifiable way to measure whether their people are getting better, not just more comfortable.

Use and capability aren't the same thing. And the size of that gap for individuals and teams alike is less a threat than an opportunity, if you're paying attention.

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• 🐦 How to get creative with GPT 5.4

• 💬 How to build your brand system with this one prompt

• 🛠️ How to turn Claude Code into your SEO command center

• 🎥 How to Make Your First $10,000 with AI 

• 🛠️ How to use ChatGPT in Excel

Before you go ✌️

This one's been on my mind for some time:

If you think about your own AI use over the past year, do you feel like you've genuinely gotten better at it, or just more comfortable with it? Is there even a difference?

Hit reply. I read them all.

See you next Sunday!

Window into the future 🔮

If you're doing anything visual at work, Nano Banana 2 is worth your time.

Back in November, we played around with Nano Banana Pro when it launched, the image generator built on Gemini 3 Pro. The quality was genuinely impressive, but you paid for it in speed. It was slow.

Google's answer to that is Nano Banana 2. It's now the default image model across Gemini, Google Search, and even Google Ads, live in 141 countries.

Ethan Mollick (Wharton professor, one of the most reliable AI testers out there) got early access and called it "the first model to handle really complex images and diagrams with some real consistency." He tested it with a Where's Waldo scene set in ancient Venice (with an otter in a pilot outfit, naturally). The outputs were noticeably sharper than what Nano Banana Pro was producing and way faster.

What’s new and useful about Nano Banana 2

Text inside images finally works.

Previous versions were notoriously bad at readable words with broken letters and broken fonts. Nano Banana 2 fixes this enough that you can now draft social post mockups, product labels, presentation visuals, or event invites directly in the generator and have the text be legible. Worth a proofread still, but less scrambled gibberish.

It pulls from real-world knowledge.

This is the big one. Nano Banana 2 connects to real-time Google Search data, which means it can generate visuals that are factually grounded (accurate product branding, current events, how things actually work). For anyone making educational content, decks, or thought leadership visuals, this changes what's possible.

Characters stay consistent across images.

If you’re building a storyboard, a campaign, or a visual story, this model now tracks characters and objects across up to 14 images and keeps them looking the same.

If you've used Nano Banana 2 in your own work this week, whether for a deck, a client mockup, or a social post… reply and show us what you made. I’m genuinely curious what it looks like in different professional contexts.

To try it: Google posted official prompting tips here if you want to go deeper.

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to 100x a business with AI.

• How to clone any writing style with Claude.

• How to deploy AI agents for your business in 4 weeks.

• How to use Claude for everyday work tasks.

Also, in case you missed it:

• Perplexity just dropped their own Openclaw version!

Before you go ✌️

Quick question, and this one's genuinely for me as much as for you:

When you switch between Claude, ChatGPT, and Gemini, do you notice a personality difference? And does it actually affect your work?

Hit reply. I read them all.

See you next Sunday!

Window into the future 🔮

6.8 billion people have never used AI

A few days ago, this chart started spreading across X.

• Grey = never touched AI (84% of the world)

• Green = free chatbot users (16%)

• Yellow = people paying for AI tools (0.3%)

• Red = people using AI for serious work like coding (0.04%).

Damian Player on X

By the time I saw it, dozens of popular names in the space have shared it with their own take. And what fascinated me was the split in how people were reacting to it.

Camp #1

• "You're not behind. This is your generational opportunity. We're so early."

• "Nah. Bro to bro, it's go time. One day we'll look back on this as the golden age." 

• "You're not behind but those 2,499 other dots are your generational opportunity." 

Inspiring, genuinely. If you're in the AI space, this is the chart that makes you feel like you picked the right moment.

Camp #2

While the other camp including Damian Player's original framing before the chart even took off is "your timeline convinced you AI is in a bubble. Talk to a boomer above the age of 35 for five minutes. Most people don't even know what Claude is." Most people in the second camp were basically saying that the 84% not using AI are not even on tech Twitter. They're not reading this newsletter. They don't know this moment is happening. Which means all the "go time, bro" energy is, in a way, talking to itself.

There's a third thing worth saying that I haven't seen anyone mention. Damian's chart is a really good visual estimate but depending on what you count as "used AI", sources like DataReportal put global AI users closer to 1-1.5 billion. That would make the grey section slightly smaller, but the point holds. Most of the world hasn't engaged with this technology in any meaningful way. Whatever the exact figure, the direction of the story is clear.

The chart that people are reading wrong

The same week, Anthropic published a research paper that generated its own viral chart showing where AI agents are actually being used.

• Software engineering dominates at nearly 50%.

• Every other field (healthcare, legal, finance, marketing, education) are all under 5%.

The tech crowd read this as a treasure map. Garry Tan (CEO of Y Combinator) circled the under 5% categories and called it "a hundred AI unicorns waiting to be built." That framing makes sense if you're a VC. But for most working professionals, there's a simpler read: your industry hasn't figured this out yet. Which means you can.

• However, the data only reflects people building on Anthropic's tools. The 50% software engineers figure isn't saying "AI is only useful for coding" but that devs were the first to use them. Other industries are behind by months, not decades.

• Anthropic found what they call a "deployment overhang", meaning tools today can handle more than people ask. The ones who've been using them longest are starting to let them run for over 45min on complex tasks.

That's the story neither chart fully tells on its own. We're not just early in terms of who is using AI. We're early in terms of how even the people who are using it are using it.

What this means if you're a working professional

Claire Vo wrote something this week that sits in the background of both charts: companies that think they're competing aren't. The ones that have lost the arena are still running the same plays (same hiring, same processes, same quarterly reviews, etc…) while a smaller group of people moves so fast they look like they're cheating.

Her idea is if your team can't fix a bug, ship a landing page, and respond to a customer all in the same day, you're probably not in the arena anymore.

That's a sharp take. But I'd add one thing. Most of the people in the grey squares aren't losing to anyone yet. They're just not in the game at all. Which makes this moment stranger than "disruption" usually looks. It's not that a new competitor is eating your market. It's that the market is being reorganized around a new set of skills, and most people haven't noticed.

How to AI 🤖 

Every week, this section is your shortcut. Here are a couple of ways you could try AI this week that are worth your time:

• How to design using AI in 2026

• How to create content for your LinkedIn using AI

• How to write viral Youtube scripts with NotebookLM

• How to get more inbound from LinkedIn comments

One more thing 🎉

If you've been following along with Harold and the Vibe Coding Games, the closing ceremony is here. If you haven't watched any of it, this is a good place to catch the highlights and see what people actually built.

Before you go ✌️

If this landed, forward it to one person who probably doesn't know what Claude is. And if you want to stop being in the grey zone, we built the path for exactly that.

See you next Sunday!

What's been going on lately 🍿

It's been a heavy couple of weeks in AI world. Here's the highlight reel:

Anthropic just raised $30 billion at a $380 billion valuation. Their revenue went from zero to $14 billion in under three years. Claude Code alone is doing $2.5 billion. These numbers have stopped making sense to me and I build an AI education company for a living.

Meanwhile, the people actually building this stuff keep walking out the door:

• Half of xAI's founding team has now left.

• Anthropic's head of safety research resigned saying "the world is in peril" 

• An OpenAI researcher quit on the same day they launched ads in ChatGPT, writing a NYT essay comparing OpenAI to Facebook.

And the essays keep coming. Dario Amodei's "The Adolescence of Technology" was heavy enough (I wrote about it here). Then Matt Shumer dropped his "Something Big Is Happening" post comparing AI right now to the 2020 lockdown. That post hit 80+ million views in days.

And the bots built their own social network and started a religion called Crustafarianism in case you weren't stressed enough.

It's a lot. I get it. Every time you open your phone there's a new reason to feel like you're behind or the world is ending or both. But here's where I want to be honest with you.

Window into the future 🔮

Matt Shumer's post went viral because it said what a lot of people are feeling but can't quite articulate. The pace has changed. The models are getting better faster. GPT-5.3 Codex was literally used to help build itself. That's not nothing.

Twitter tweet

He cites METR's data showing AI models went from completing 10-minute human-expert tasks autonomously a year ago to nearly 5-hour tasks now, with that doubling every few months. He talks about being "no longer needed for the actual technical work" of his job. He compares it to February 2020 that moment before everything changed where most people were still saying "this seems overblown."

I shared it on Twitter because I think the core message resonates: if you're not taking this seriously yet, the window is closing.

But.

Gary Marcus who's been one of the most consistent and credible AI skeptics wrote a really useful response that's worth reading alongside Shumer's piece. A few of his points landed for me:

• The success criterion of the METR benchmark Shumer keeps citing is 50% correct, not 100%. And it only measures coding tasks, not general work. Shumer doesn't mention that. He also doesn't mention that a separate METR study found coders sometimes imagined productivity gains that weren't actually there. And some of the most experienced AI coders are now reporting burnout working faster but not necessarily better.

• Marcus's sharpest point: a coder friend told him that the closer these systems get to appearing right, the more dangerous they become because people stop checking. The confidence goes up. The verification goes down. That creates a real risk.

• And then Marcus flags at the end of Shumer's post: Shumer thanks seven human friends for reviewing his draft. If AI is truly doing all the work now...why did he need humans to check it?

What I think is both of these people are right about different things and wrong about others. The pace is accelerating. Shumer is right about that. But the "AI can do everything now" framing glosses over the messy reality that anyone who's actually used these tools every day knows well. Sometimes it's magic. Sometimes it confidently tells you to walk to a car wash without your car.

Your job is going to keep changing, faster. And the people who'll navigate that best aren't the ones reading every doomy essay or panic-scrolling through resignation announcements. They're the ones building practical fluency right now while there's still time to develop the judgment that separates useful AI work from confident-sounding nonsense.

P.S. If you want to go from learning to actually building something, Harold's running the Vibe Coding Games Build Sprint starting Wednesday. Non-technical friendly.

This week in keeping up with AI 🤖

The model arms race is relentless right now. I wrote last week about what the constant stream of new models actually means for your work and this week was no different:

• Gemini 3 Deep Think: Google's upgraded reasoning mode. Designed for science, research, and engineering tasks that need deeper thinking. Not for everyday use yet, but a sign of where things are heading.

• GPT-5.3-Codex-Spark: OpenAI's new coding assistant that responds almost instantly. Built on new hardware that makes it 15x faster than its predecessor.

• MiniMax M2.5: A Chinese model that's suddenly competing with the best. State-of-the-art in coding benchmarks, and the pricing is wild: $1 for an hour of continuous use. They're calling it intelligence too cheap to meter.

• GLM-5: Another Chinese model, this one trained entirely on Huawei chips (no Nvidia). It's open-source and free to use, which matters because it means access to powerful AI is no longer locked behind expensive subscriptions or

👉 TLDR: If you're not a developer, you don't need to memorise any of this. The takeaway is simple. The tools are getting faster, cheaper, and more competitive every single week. Which means the time you invest now in building real fluency compounds faster than it ever has.

Before you go ✌️

The essays are getting longer. The models are getting better. The safety people are leaving. The funding rounds are getting bigger. And AI still can't figure out that you need to drive your car to the car wash.

Make of that what you will. I know what I'm making of it: keep experimenting, learning, and keep your judgment sharp. Because the one thing that's clear is that nobody, not Matt Shumer, not Gary Marcus, not Dario Amodei actually knows exactly how this plays out.

The people who'll be fine are the ones who are in the arena figuring it out. Not the ones reading about it from the sidelines.

See you next Sunday 👋