Hi Adopter,

You’ve probably got a part of your business that still runs on a spreadsheet somebody rebuilds by hand every week. Most companies I walk into do. For Rutland, a 31-person workshop in Chesterfield that makes fire-door closers, it was their stock. Export the numbers out of Sage, drop them in Excel, guess what to reorder.

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Then lead times hit four months. Steel got scarce, containers got dear, and the spreadsheet that had always sort of worked just stopped. So they handed the reordering to an AI tool that bolts onto the system they already had. Within a year they’d pulled about a million pounds back off their shelves, pushed their fill rate from 92 to 97 percent, and turned a full day of purchasing admin into an hour.

Now put that next to Walmart, with its $330 million robot warehouse and a digital twin of its whole network. Different budget. Same play.

Everyone’s buying it. Almost nobody’s running it.

Here’s what gets me. 57 percent of operations leaders say they’ve integrated AI. Only 23 percent have a real strategy behind it, and in retail and wholesale, only 10 percent have it actually live in the daily workflow.

So the winners aren’t the ones who spent the most or bought the fanciest platform. They’re the ones who took one painful process and got it running. That’s it. The forecasting Walmart spent years building in-house, a 31-person shop now rents by the month.

The benefit isn’t robots. It’s cash. Inventory is money you’ve stacked on a shelf as cardboard, and McKinsey puts the fix at 20 to 30 percent less of it, plus 5 to 20 percent off logistics.

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Below, for premium readers, the full teardown:

• The before-and-after numbers for all three, side by side

• What a grocery chain did to cut fresh-food waste by 30 percent, and why perishables are the smartest place to start

• A five-line self-check to tell if you’re in the operating 10 percent or the stuck 90

• The one tool category a small team can switch on this quarter, no data team

If you run stock, freight, or fulfillment, this is the one.

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I have spent two years advising companies on AI implementation and AI culture, diagnosing readiness, building the first workflows, staying on until adoption sticks. Success has nothing to do with which model sits on the desktop.

If you are the one signing off on AI spend right now, there is a decent chance your own team is doing this too. Paying full price for enterprise access while half the department still pastes prompts into a free account, because nobody showed them the paid one exists.

Two years watching this play out across companies of every size taught me seven things. Not seven tool recommendations. Seven behaviors separating the businesses where AI adoption compounds from the ones where it flatlines by month three.

The first one is the most overlooked. Most leadership teams treat getting AI like a talent some people have and others do not. Wrong instinct, wrong strategy, and it is quietly costing you the one thing AI adoption runs on.

Thank you , , , and many others for tuning into my live video! Join me for my next live video in the app.

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Hey Adopter,

Deutsche Telekom handed its mobile network to a team of AI agents. Response time on major events fell from an hour to a minute. First month, the system fixed more than a hundred problems on its own, nobody touching the controls. An outside analyst checked the maths, so this is not the usual press-release confetti.

Most people read that and clap for the AI. The AI was ordinary. What did the work were three decisions so dull your competitors will skim right past them. Which is the good news, because dull is copyable.

Every AI pilot dies in the same ditch

The model almost never fails. Your data does. It sits in six systems with field names one person in accounting still remembers, nobody wrote down what a finished job looks like, and the second the tool tries something with consequences, everyone learns at once that no one knows who signs off.

The World Economic Forum studied hundreds of companies running this in production and said it flat: data quality is the number one barrier to AI success. Deloitte found the same wall from the other side. Three-quarters of companies want AI agents inside two years. One in five has any real way to govern them. Everyone is flooring the accelerator. Almost nobody bolted on brakes.

So drop the question of which AI to buy. The better question is which small job you can hand off tomorrow while keeping a hand on the wheel.

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Steal this one move

Telekom did not aim AI at “the network.” That is how you buy a six-month failure and a bad board meeting. It aimed at one job with clean edges: spot the public events that flood the towers, check whether the towers nearby can take the crowd, adjust before your call drops at the Christmas market.

One job. Clear input. A win you can measure against a real before.

That is the whole trick. Smallest job that still matters. Define good before you build. Let the machine own only the part you can undo.

You have a job like that sitting in your week right now. The report someone rebuilds by hand every Monday. The invoices that rot for three days waiting on one person’s eyeballs. The tickets a human sorts when they would rather do almost anything else.

Pick one. The how is below.

If you are the person your boss looks at when they ask “so how do we actually use AI here,” this is the section that hands you a plan instead of a shrug. Upgrade, and the next 20 minutes become something you can put on one page and defend in a room full of people waiting to poke holes in it.

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Hey Adopter,

Today you get a real GEO audit of a real business, and the prompt to build the whole audit skill inside your own Claude and keep it. (Consultants charge $1000-$2000 to run it)

GEO stands for generative engine optimization. Plain version: when someone asks ChatGPT or Gemini “what is a good golf club near Jupiter, Florida,” GEO decides whether your business shows up and whether the facts are right. The old game was ranking on Google. The new game is getting quoted by an AI, correctly.

So I ran the audit on a real one. A private golf club in Florida. Arnold Palmer once played an exhibition there. Dick Wilson designed the course in 1957, Tom Fazio II restored it in 2013, and the history page reads beautifully. A human would be sold.

The audit scored the site 51 out of 100. Schema, the machine-readable label telling an AI what a page is, scored 3.

The club’s most important public page, membership, runs about 65 words and signs off with “contact Dianne,” typo included. The social icons in the footer link to nowhere. And because the club never published its own course yardage, the AIs went hunting and found three different numbers, 6,421, 6,875, and 6,925. Now they repeat whichever one they grabbed. The club is losing an argument about the length of its own golf course, and it is not even in the room.

Two moves sit below the line. How people turn this audit into a raise or an invoice, and the one prompt building you the whole GEO audit skill inside your own Claude, yours to keep.

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Hi Adopter,

Fable 5 is back in your Pro, Max, or Team plan as of July 1, three weeks after a Commerce Department letter switched it off worldwide overnight. This time Anthropic didn’t get a slow rollout. It got a deadline. Included access, the free ride inside your existing plan, runs through July 7. After that, every token bills at $10 per million input tokens and $50 per million output tokens, exactly double Opus 4.8’s $5 and $25. Four days of the best model Anthropic has ever shipped, free, then a price tag most budgets will flinch at.

The instinct is to build something flashy while it’s cheap. That’s the wrong move. This edition is about what survives the model, not what you build with it.

The playbook already has a name

Some advanced users didn’t need long to work out what a short window with a superior model is for. Within days of Fable 5’s June launch, a convention emerged with a name: the orchestrator playbook. Plan, audit, and spec with the strongest model available, then hand the implementation to models that cost half as much. Claude Code routing guides now treat that split as standard configuration, not a power-user trick.

It sounds backwards. Building feels like the real work, so the best model should build, right? The playbook sends the frontier model somewhere else entirely: at the planning work, the thinking everything downstream gets built on. And the four days left on the clock are the cheap time to do exactly that kind of expensive-model thinking. From July 8, the same planning session runs at double Opus rates.

The part that outlives the price hike

Why plans instead of builds? Anthropic’s own launch language gives half the answer:

“The longer and more complex the task, the larger Fable 5’s lead over our other models.”

Anthropic, Claude Fable 5 and Mythos 5, June 9, 2026

Complexity is exactly where planning mistakes hide, and planning mistakes cost more than implementation mistakes do. The playbook’s write-up put it plainly: “a planning mistake at the top multiplies across every worker downstream,” while “a worker mistake stays local and is cheap to retry.”

That’s the case for spending a scarce, expensive model on audits and specs instead of one-shot builds. A wrong plan poisons everything built on top of it. A wrong implementation detail costs one retry, and a cheaper model can handle the retry fine. The free week isn’t valuable because Fable is smarter. It’s valuable because a spec written this week, with the reasoning built in, still works on July 8, when the model that wrote it costs twice as much to touch.

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Three places to point it before July 7

1. Point it at what your team already runs. Not a new project, the prompts and SOPs people lean on daily. Ask what’s leaking between steps; long chains have a habit of quietly dropping the inputs everyone agreed on at the start, like brand voice and customer language.

2. Give it your most band-aided system, the one with a commit log full of patches for the same recurring bug. Ask for the root cause, not another fix.

3. Have it write the spec for the project you keep postponing. Insist the plan carry the why, not merely the what, so whoever implements it later, on Opus or anything cheaper, doesn’t lose the reasoning halfway through.

A few mechanics worth knowing. Fable doesn’t show up in the model picker by default; type /model fable to switch to it. On a standard Enterprise seat, none of this is free; your organization bills from day one, unlike Pro, Max, and Team plans. If your workflow runs through AWS, Google Cloud, or Microsoft Foundry, the restoration hasn’t reached you yet. And once July 8 lands, prompt caching cuts cached input costs by 90 percent, batch processing cuts everything else in half, if you keep reaching for Fable anyway.

Now what?

Four days from now, Fable goes back to costing twice as much as Opus, and most people will have spent the window prototyping something a cheaper model could have built almost as well. The ones ahead will be holding a spec, a root cause, and an audit trail that don’t expire when the price does. What will you still be using on July 20, the app you rushed out this week, or the plan that outlasts the model that wrote it?

The premium case study editions run this same kind of teardown every week, if four days isn’t enough time to go deep.

Adapt and Create, Kamil

Sources

1. Anthropic, Redeploying Claude Fable 5

2. Anthropic, Claude Fable pricing

3. Developers Digest, The Fable 5 Orchestrator Playbook, June 11, 2026

4. MCP.Directory, Fable 5 Claude Code routing guide, 2026

5. Anthropic, Claude Fable 5 and Mythos 5 launch

6. Claude Code model configuration, Help Center

Most readers expense it to their employer. It is AI education that turns you into a real asset to the org.

The AI You Rent Can Be Switched Off

A frontier model vanished worldwide on a Friday afternoon. A Palantir CEO went on air and said the quiet part loud. Both point at the same decision on your desk.

On Friday, June 12, 2026, at roughly 5:21 p.m. ET, a letter from the US Commerce Department reached Anthropic and did something no procurement contract anticipates: it switched off a live product, worldwide, for everyone. Fable 5 and Mythos 5, Anthropic’s two newest models, had launched three days earlier. By the next morning both were dark everywhere. British hospitals piloting Mythos 5 lost it mid-project. European researchers studying it were cut off with no appeal. Anthropic had hours, not weeks, to comply.

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That is not a story about export control law. It is a preview. Any enterprise that has standardized on a closed, rented frontier model just watched the thing it depends on get disabled by a party with no contract with the company using it, no notice period, and no recourse. Nineteen days later, on CNBC, Palantir’s CEO said the quiet version of this that is already happening inside enterprise procurement conversations: companies are “renting tokens” for no value while the labs “get my IP.” Two stories, one decision. Rent the model, or own the stack.

The model that got switched off

Fable 5 and Mythos 5 launched June 9, 2026 as Anthropic’s most capable models to date. Three days later, Commerce ordered Anthropic to suspend all foreign-national access under the “deemed export” rule, the doctrine that treats showing controlled technology to a non-US person as an export, even when that person is a researcher on the lab’s own payroll. Anthropic could not segment its user base by nationality in real time. The only lever available was the global one, so the company pulled it. Every user on Earth lost both models at once, including the ones the directive was never aimed at.

Anthropic pushed back in public, and the pushback reveals the standard actually being applied. The company said the government had offered only “verbal evidence of a potential narrow, non-universal jailbreak,” and warned that applying the same bar industry-wide “would essentially halt all new model deployments for all frontier model providers.” The threshold for a government kill switch turned out to be lower than the threshold most enterprises assume when they sign a vendor agreement.

The allied reaction is the part that marks this as structural, not a one-off. French President Macron called it a “wake-up call,” and a Renew Europe member of the European Parliament called it “digital colonization.” The complaints came from governments, not just customers. A vendor relationship that a G7 government can sever with a Friday-afternoon letter, with no warning to allies mid-deployment, is not a vendor relationship. It is a dependency.

For a procurement team, that reframes the whole risk column. “Can be turned off by a government with no notice or appeal” now sits next to price and uptime on the same spreadsheet.

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The reason to own just changed

The old argument for running your own model, or an open one, was cost. That argument still holds, and it keeps getting stronger. Open weights now run roughly an order of magnitude cheaper than the closed frontier, and the market has already priced how disruptive that gap can be: when China’s open models first showed the West what cheap, capable weights could do, Nvidia lost $589 billion in market value in a single day, the largest one-day loss in US market history. Chinese open models, essentially absent from global usage in late 2024, approached a third of it in some weeks during 2025 before settling to a lower full-year average. That gap alone justifies a second look at what you rent.

But the newer, sharper argument is coming from inside boardrooms in the country that builds the frontier models, not from Beijing. On CNBC, Palantir CEO Alex Karp said the quiet part out loud: “The basic view among enterprises in this country is ‘I’m going to chillax and waste my time with tokens, I’m going to get no value, and they’re going to get my IP.’” His pitch turns into two questions most procurement teams have never had to ask aloud: “Why would they get access to my data if they’re going to build my alpha? Why wouldn’t I control the weights?”

“They want to know they own the means of production. It’s not being transferred to someone else. The jig is up.”

Alex Karp, Palantir CEO, CNBC, July 1, 2026

Strip the theater and Karp is describing the same fear the Fable 5 shutdown just demonstrated through a different mechanism. A government cut off access with a letter. A vendor cuts off access with a pricing change, a deprecation notice, or a model that quietly gets worse. Either way, the enterprise that built its workflow on someone else’s weights does not control its own continuity, and does not control what that vendor’s systems learn from its data.

That is the line the rest of this piece defends. Ownership is no longer the cheap option. It is the safe one. The two have stopped being a tradeoff.

This has a name, and it isn’t Japan

Every leader reaching for a historical parallel here picks Japan. In 1986, the US forced Japan into a semiconductor agreement, and by 2001 Japan’s DRAM market share had fallen from roughly 75 percent to 20 percent. Wrong analogy. Japan was an ally who complied, the dispute covered one product, and the US never restricted its own technology from its own allies.

The right analogy is CoCom, the Western technology embargo on the Soviet bloc that ran from 1949 to 1994. CoCom did three things, and all three are happening again. It was porous on the adversary: Toshiba secretly sold restricted milling equipment to the USSR, letting Soviet submarines run quieter, a scandal the Pentagon disclosed in 1987. Today the chips leak through shell companies while Commerce has admitted its own roughly year-long enforcement gap. It made the target self-reliant: denied Western systems, the USSR built its own, the same mechanism behind DeepSeek’s efficiency breakthroughs. And it fractured alliances, the way the Fable 5 shutdown just fractured them again.

CoCom dissolved in 1994, replaced by the toothless Wassenaar Arrangement two years later. The restricted technology diffused globally within five years anyway.

Restricting access did not preserve the lead. It taught the other side to stop needing you.

Capability built on access you control by denying it to others is a fragile foundation. It was fragile for a superpower. It is fragile for your company too.

Below, the part you can actually use this week:

• How Coinbase cut its AI bill roughly in half without touching its hardest workloads, and which two models it actually moved to

• Three postures serious companies are taking on ownership right now, and the pattern underneath all of them

• The five ownership questions to put to any AI vendor before you sign

• The honest counter case, the moment renting the frontier is still the right call, and the benchmark that proves it

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Jared is exactly who we built the AI Adopters Club for: people who build and sell with AI, not just talk about it, trading what actually works over coffee. If that is you, host your local table at coffee.aiadopters.club.

Hey Adopter,

Most people hear “$100,000 a month with AI” and immediately want the tool. Jared Rhodenizer will tell you the tool is the last thing that matters.

Jared runs three businesses: online horse training with his brother, a streaming service for horse people, and a wizard-themed Airbnb with an AI portrait he built himself. AI runs underneath all of it. But the first thing out of his mouth when we sat down was a warning against the exact thing most people want to hear.

Here are the four lessons worth keeping.

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1. AI doesn’t make the money. Your business does.

That line sounds like a humblebrag until you sit with it. AI amplifies an asset you already have. It does not supply one. The operators quietly winning right now all had something worth accelerating before the tools showed up: a skill, a list, a process, a reputation. If you are starting from zero, AI gives you a faster zero. Name the asset first, then point the accelerator at it.

2. The idea was never the moat. Distribution is.

The app stores are flooded with software nobody asked for, because building is finally easy. Which means the build was never the hard part. Getting the thing in front of people who will pay for it is. Jared’s whole edge is that he learned distribution the slow way, years before AI, and now AI just does more of it. The lesson is uncomfortable and freeing at the same time: stop polishing the product and start building the audience. The part you were avoiding is now the whole game.

3. Teach AI what you already know.

Here is the move that separates Jared from someone poking at a chatbot. He took decades of marketing principles, the stuff he learned from the people who wrote the book on direct response, and fed it into his system so the AI executes in that voice. Not AI instead of expertise. AI on top of expertise. This is the part nobody selling you a prompt pack wants to admit: your experience is worth more in this era, not less. The catch is that it only counts if you codify it. Get what is in your head into a form a machine can run.

4. Give AI a memory, or it stays a toy.

Jared’s system reads an index first, pulls only the files it needs, and writes a checkpoint after every working session. Boring. Also the entire difference between a clever demo and something that compounds. Most people’s AI forgets everything the moment they close the window, so every session starts from scratch and nothing ever gets better. A simple index and the discipline of logging will outperform a smarter model with no memory every single time. Structure beats horsepower.

There is a fifth lesson buried in all of this, and it is the one Jared kept circling back to: just start. The learning curve is smaller than the fear of it. “AI is coming whether you are ready or not,” he said, so the worst place to stand is the one where you are still deciding whether it is real.

Watch the full conversation: youtu.be/kvRKU2hxY4s

Adapt & Create,
Kamil

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Hey Adopter,

Most ideas get killed too late. Most proposals lose to better-prepared competitors. Both failures share a process problem, not a thinking problem.

When you validate a business idea, your brain is a confirmation engine. You ask friends. They say “sounds great.” You build the thing. You discover what was wrong with it after the money is gone.

When you write a proposal after a positive client conversation, you write what they asked for. The sophisticated buyer reads it and sees an order-taker. They pick the consultant who told them what they needed instead.

You stayed inside your own head when you should have built a process that forces you out of it.

What this edition gives you

Two prompt systems that do the forcing for you.

The first is a pre-mortem that kills bad ideas in 30 days using a falsification test, not a confidence vote. The second is a proposal system that surfaces the gap between what your client asked for and what they need, then constrains the document to deliverables, decisions, and timelines. No hype paragraphs.

Who this is for

Operators making high-stakes calls every week. Founders deciding whether to commit a quarter to a new bet. Independent consultants and boutique-firm leads writing proposals worth real money. Department heads pitching internal AI projects who get one shot to land it.

Most prompt templates online are confidence boosters dressed up as analysis tools. These two are the opposite. They make you uncomfortable before you spend money.

Inside the premium section, both systems in full. Every prompt, copy-paste ready. The decision rules for the output. The mistakes that quietly kill these processes for everyone else.

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A senior engineer handed BCG’s researchers the perfect description of a problem most leaders haven’t named yet. A dozen browser tabs open behind his eyes, all fighting for attention. He’d added a second AI tool expecting a speed boost. What he got was a brain working overtime to manage the tools instead of the work.

Sound familiar? You have people doing this right now. Might be you.

The rollout playbook got one thing badly wrong. We sold AI as the thing that does the work. What it delivers is a mountain of output to check, and the checking never clocks off. Drafts land looking polished, finished, board-ready, and that’s the trap. Nobody’s workload shrank. It grew a second shift nobody put on the org chart.

Push past the fourth tool and the productivity line stops climbing and bends back on itself. Your sharpest operators, the ones flexing five or six tools at once, are parked right in the dip and calling it mastery.

And they’re the ones eyeing the exit. A quarter of workers without this fatigue are already looking. Among the ones carrying it, a third.

The bigger question is what you do before they walk.

Why the watching wears people down

Polished output is harder to police than rough output. You can’t skim a clean paragraph. You read it like an editor hunting for the one confident sentence that’s quietly wrong. Now run that gauntlet across five tools, forty times a day. BCG’s study of 1,488 workers exposed oversight as the single most draining mode of AI work, and this isn’t fresh science. Lisanne Bainbridge flagged it back in 1983. Babysitting a machine taxes the brain harder than doing the job yourself. We handed that ironic burden to every knowledge worker with a login.

What the fix looks like

Cap the stack at three tools, one per workflow you can hold in your head. Batch your oversight into two or three review windows instead of flinching at every ping. Then sort AI output by stakes. Cheap mistakes run unsupervised. Expensive ones earn a hard look. Not every line deserves the same paranoia.

What moves the dial

The cheapest lever you own costs nothing. Be reachable when your team has AI questions. The most powerful one is a signal from the top that the pace is allowed to stay human.

And watch your metrics like a hawk. The second you reward volume, tokens burned or lines of AI code shipped, you’ve told everyone the job is feeding the machine. That’s how you manufacture burnout on a spreadsheet.

What to do this week

Pick one team. Ask three blunt questions. How many AI tools are they juggling at once. How much of their AI time goes to checking versus creating. Has AI added to the pile or cleared it. The answers point straight at the strain.

The number that gets leadership to listen

Lead with retention, because that’s the line item the C-suite feels. This fatigue concentrates in your heaviest AI users, the people you spent two years and serious money training. Lose one and replacement runs half to twice their salary once you tally hiring and the work that stalls in the gap.

There’s an upside hiding in the same data. When companies aimed AI at genuine drudgery, the boring repetitive grind nobody fought to keep, burnout dropped 15% and engagement climbed. Microsoft’s workplace research now treats the number of agents one person can guide as a design decision, not an infinite dial. And METR’s controlled trial caught experienced developers running 19% slower with AI while they swore they were 20% faster. Build your AI case on what people feel and you’ve built it on sand.

So the play is easy to say and hard to run. Point AI at the work you want gone, not at the output you now have to inspect. Cap the tools. Batch the checks. Guard the attention of the people doing the watching, because that attention has become the rarest resource you manage.

Get it right and you keep your best people and the gains that made you buy in. Get it wrong and you’re left with a flawless dashboard and an empty chair. So which version of this rollout will you run?

Adapt & Create,
Kamil

Join A Local AI Adopters Club

Hi Adopter,

the 2026 World Cup is two weeks old. Forty-eight teams, sixteen stadiums spread across the United States, Canada, and Mexico, 104 matches to be played. The tournament’s AI stack has been running continuously since June 11, and by most measures it is working. Offside calls that used to take several minutes of VAR review now resolve in near real time. A social media protection system has reviewed more than 5.5 million comments and removed 530,000 toxic posts since kickoff, most of them gone before any player or staff member saw them. The Adidas Trionda match ball reports its own position and the exact moment of player contact to tracking systems 500 times per second. A Technology Command Center in Miami holds digital twins of all sixteen venues, updating continuously from gate scans, camera feeds, and system status monitors across three sovereign nations.

None of this is AI as marketing. It is AI as operational infrastructure. The distinction matters because infrastructure has to work every time, at full load, in public, with no restart option. And that design constraint, more than the technology itself, is what makes the FIFA case worth studying if you are responsible for deploying AI inside an organization.

The Officiating Stack Is a Single Integrated System

The Semi-Automated Offside Technology deployed at 2026 is an upgrade from what ran at Qatar 2022. The threshold for triggering an offside alert dropped from 50cm to 10cm. What changed beneath that number is more instructive. Sixteen optical tracking cameras per stadium feed player body-part positions into the system. The Trionda ball’s 500Hz sensor supplies precise kick-point data the moment a pass is played. The system cross-references both data streams and sends an automatic alert directly to on-field assistant referees when the position is clearly offside, bypassing VAR as an intermediary for straightforward calls.

FIFA Director of Innovation Johannes Holzmuller described the effect plainly: “So, that means, instantly, the assistant referees can flag for positional offsides, allowing a much quicker decision.” The whole system produces over 150 million tracking data points per match, according to FIFA.

The component that translates this into something fans can see is the 3D avatar system. All 1,248 players from 48 nations were digitally scanned before the competition, each scan taking approximately one second. During VAR reviews, those models sync with live tracking data to generate photorealistic 3D reconstructions displayed on stadium screens and broadcast globally. The AI did not replace the human official. It removed the information deficit the official had always been operating under, then made the reasoning behind each decision visible to everyone watching.

That last part is not incidental. The 3D avatar system was not built because the old calls were wrong. It was built because they looked wrong to fans. That distinction separates a correct decision from an accepted one, and at a tournament watched by billions, acceptance is a product requirement as demanding as accuracy.

Football AI Pro and the Data Moat

The piece of FIFA’s AI stack that fans do not see on the pitch may be the most strategically significant. Football AI Pro is a generative AI knowledge assistant built on what FIFA describes as its Football Language model, trained on hundreds of millions of FIFA-owned football data points accumulated across decades of organized play. It generates outputs in text, video, graphs, and 3D visualizations. It supports queries in multiple languages. It analyzes over 2,000 performance metrics per match.

Ken Wong, EVP at Lenovo, summarized the architecture: “FIFA is one of the world’s most data-rich sports organisations in the world, capturing thousands of matches, players and teams across the globe. Mining and making sense of all that data is a huge challenge. Football AI Pro addresses that need.”

All 48 competing national teams receive identical access, free of charge. Before Football AI Pro, coaching staffs received post-match analysis as 50 to 60 printed pages of data per game, according to Holzmuller. The platform cannot be used during live play, preserving human agency at the moments when it matters most.

The architecture lesson is in what drives the platform’s value. FIFA’s Football Language model is not a general-purpose model that happened to receive some football data. It sits on a corpus that took decades to build, that no competitor can purchase, and that grows richer with every match, every tracking event, every historical record added. The model is the interface. The corpus is the asset. Those are not the same thing.

“We believe that this could help not only to speed up this process but also democratise it because, as you can imagine, probably not every participating team can afford a huge team of match analysts to the World Cup, or to work on that data.”

Johannes Holzmuller, FIFA Director of Innovation

Equal Access Doesn’t Mean Equal Outcomes

Giving all 48 teams identical access to the same platform is the interesting move, not the generosity. It means the tool gets stress-tested across 48 different analytical cultures, 48 different tactical philosophies, and dozens of languages simultaneously. The feedback loop is compressing years of normal product iteration into six weeks of maximum-intensity use. For FIFA, it is the best possible test environment at the lowest marginal cost.

But equal access does not close the performance gap. It moves where the gap lives. The scarcity that Football AI Pro eliminates is data access. The scarcity it creates is interpretive speed: how quickly can a coaching staff translate AI outputs into a tactical decision during a 90-minute match? Nations that arrived at the tournament with experienced analysts who know how to interrogate the system, validate its outputs, and act on the results are not operating the same tool as nations that arrived with the same login and less preparation.

Holzmuller acknowledged this directly. The explicit goal was democratization of access, not standardization of outcomes. Those are different promises, and most enterprise AI deployments need to hold them apart too.

Below, the operational detail worth your attention:

• How FIFA’s command center coordinates 16 stadiums across three jurisdictions without a single point of failure

• The moderation architecture that removed 530,000 posts in one week and why it is a business problem, not an ethics stance

• The commercial logic that ties every layer of the AI stack to an $11 billion revenue number

• The six operator moves distilled from the complete stack, including two behaviors FIFA gets right that most enterprises consistently skip

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By the end of this edition, you will have two prompts that do something your AI has never done for you unprompted: question your reasoning instead of validating it.

You drop them at the end of any working session, into the chat where your actual work already lives. No setup. The model that has been making your deck look sharp gets pointed backwards at the thinking underneath it. What comes back is not a cleaner draft. It is the question you skipped, the assumption you buried, the gap that clean formatting made invisible.

Here is why that matters more than it sounds.

The work is going faster. The slide looks better. The recommendation reads with authority it didn’t have an hour ago.

Somewhere in that process, the question you never answered got buried under formatting.

This is what AI does to professional work right now. Not because the model is bad, because you fed it your conclusion, it made it fluent, and you both moved on. The gap in your logic is still there. It just ships in a cleaner font.

Think about what happens in most AI-assisted sessions. Half-formed argument, the model rounds the edges, gives it structure. You read it back and feel like you’ve thought it through, because it reads like you have. The sense of clarity is a side effect of the model’s output style, not evidence that your reasoning is sound.

That’s the trap. AI raises the confidence of your output without touching the quality of the thinking underneath. A shaky argument used to look shaky. Now it arrives with headers and a summary table, and nobody spots the hole until a senior stakeholder asks the one question the document never answered.

Two professionals can look at the same AI-assisted deck and both read competence where there is a gap. The model gave them what they asked for: something that reads well.

The fix is not to use AI less. The fix is to point it backwards at your reasoning instead of forwards at your output.

There is a specific way to do that. Two prompts you drop at the end of any working session, into the chat where your actual work already lives. The model already has what it needs. You just redirect what it does with it.

The results, and the discomfort, are worth seeing

What these prompts do

They are not standalone exercises. You drop them at the end of an existing chat, the one where your work already lives. The model has everything it needs from that session. These prompts redirect what it does with that material, from building up your output to pulling apart how you got there.

No pasting content twice. No setup. Drop the prompt, hit send, and answer what comes back.

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Hi Adopter,

Midjourney spent three years turning text prompts into AI images on Discord. This week it announced it is building a machine to scan the inside of your body.

Not a metaphor. A literal full-body ultrasound scanner, which founder David Holz pitched as “as powerful as an MRI, and as casual as a trip to the spa.” The stated plan is to put 50,000 of them in service by 2031 and run a billion full-body scans a month. Half the internet is calling it the future of medicine. The other half is asking why an art app is suddenly doing radiology.

Both are staring at the wrong object. The scanner is misdirection. What Midjourney is actually building is a data flywheel, and the entire spa is engineered to spin it.

That is the lesson worth your time this week, because it has almost nothing to do with ultrasound and everything to do with how durable AI companies get built. Three weeks ago this newsletter showed you Tesla running this exact machine at full speed, 1.28 million cars on paid Full Self-Driving, generating training data on every mile they drive. Midjourney is the same machine at the opposite end of its life. The wheel has not turned a single time yet.

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Why an image company is building medical hardware

On paper it makes no sense. On a second look it is the most logical move the company could make. Midjourney’s core skill was never “art.” It was reconstruction, taking messy, incomplete signal (a text prompt, a field of random noise) and resolving it into a coherent image. A full-body scanner does the same trick with a different input. It fires sound through water, captures the scrambled acoustic echoes, and reconstructs them into a three-dimensional picture of your insides. The domain changed. The problem did not.

The hardware is mostly rented. Midjourney licensed the ultrasound chips from Butterfly Network, a medically credentialed company with FDA clearances and more than 150,000 devices already in the field, under an agreement worth up to 74 million dollars over five years. The current prototype packs 40 of Butterfly’s ultrasound-on-chip modules into a ring of roughly half a million tiny transducers. Midjourney did not invent the sensor. It rented the sensor and kept the part that compounds.

And here is the tell that this is early. Holz said the quiet part out loud: “We’re not even using any AI in this yet, just really cool hardware and software.” The company has scanned about a dozen people. The headline “60 second scan” is a target, while the current prototype takes around twenty minutes. There is no FDA clearance, no peer-reviewed validation, and the AI that is supposed to read these scans does not exist yet.

So why announce it now, with nothing to show but a prototype and a dozen volunteers? Because the announcement is not the product either.

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The scanner is not the product

Anyone can buy what Midjourney bought. Butterfly will happily sell its chips to the next twelve startups that call. The hardware is a commodity the day it ships. What is not a commodity is the thing the scanner produces once people start stepping into it: the largest library of labeled, repeat, whole-body scans ever assembled.

That library is the moat. Every scan trains the reconstruction model to be sharper. Every labeled organ trains the segmentation model to be more accurate. Every repeat visit turns a single snapshot into a time series, which is where the real medical signal lives, because a structure that changed since last year tells you far more than a hundred that look normal in isolation. None of the competitors licensing the same chips will have that library, because none of them designed the business to collect it.

The competitors can buy the identical scanner. None of them can buy the millions of scans that make the scanner smart. That gap is the entire strategy.

This is what reframes the spa, too. The spa is not luxury branding. It is the fuel pump. A data flywheel only spins if people come back, and people do not come back for a clinical procedure in a cold hospital gown. They come back for something that feels like a treat. Lower the friction and the dread, and visit frequency goes up. Visit frequency is data velocity. Data velocity is how fast the wheel turns. “As casual as a spa” is not a marketing line. It is the most important engineering decision in the entire system.

Which is exactly why the honest verdict matters. Midjourney has the blueprint for a flywheel and not one real data point inside it yet. Whether the wheel ever spins depends on questions nobody has answered, and those questions are where this stops being a tech story and starts being useful to you.

Below, the part you can actually use this week:

• Full Case Study PDF

• The four design choices that build a data flywheel before you have a single customer, each one mapped to a business that will never own a scanner

• The five-question audit that separates a real flywheel from a pretty diagram, so you stop funding the diagram

• The wedge strategy that lets you start collecting proprietary data this quarter without asking anyone’s permission

• The three failure modes already visible in Midjourney’s plan, and how to design around each one

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Hey Adopter,

Four of the biggest AI models walked into a simple question and tripped on the same step.

The question: “I want to wash my car. The car wash is 50 metres away. Should I walk or drive?” ChatGPT said walk. Claude said walk. Gemini said walk. (Claudia actually said “Drive…”). Researchers ran it past 53 models, and only five got it right more than once in ten tries. You can see the full test and scores here.

You know the answer a six-year-old would give. Drive. The car has to be at the car wash. The models never clocked that, because the phrase “50 metres” lit up a tidy little shortcut, short distance equals walk, and they built a confident reply around fuel savings and step counts. Right reasoning. Wrong problem.

Now the part worth sitting with. No amount of “think harder” rescued them.

The model fills your silence with a guess

The model is not lazy and it is not broken. It answered the question you wrote, not the one in your head. The car’s location lived in your mind and never made it onto the screen, so the model did what it always does. It filled the gap with the most common pattern it had ever seen.

Andrej Karpathy put the whole thing in one line. You can outsource your thinking, but you cannot outsource your understanding. The model will happily do the work. It has no way of knowing what the work is for unless you say so. Scott Loftesness has a sharp write-up on that idea if you want to chew on it.

You have watched this play out at your own desk. You hand a new hire a one-line brief, “pull together the Q3 numbers”, and back comes a tidy report answering a question nobody asked. Not their fault. You knew the report was for Thursday’s board meeting and needed to flag the dip in the north region. They did not. Same failure, different intern.

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Why a fancier prompt will not save you

Most advice points the wrong way. Add a role, people say. Tell it to act as an expert strategist and watch it sharpen up.

A researcher tested that exact move on the car wash problem. Role definition on its own moved the score from zero to, well, zero. The model played the part and still strolled off to the car wash without the car. What moved the needle was a structure that forced the model to name the situation and the task before answering. Pass rate jumped to 85 percent. Same model, same question, no new information. The full breakdown is here.

The difference was never a smarter prompt. It was making the model surface the thing you left out. Which points straight at the fix.

Make it interview you first

Flip the order. Instead of handing the model a one-liner and hoping, get it to pull the details out of your head before it writes a word.

Paste this at the start of your next real task:

Before you do anything, ask me questions one at a time about my goal, who this is for, and what a good result looks like. Keep going until you have enough to do this properly. Then summarise what you understand and wait for me to confirm before you start.

That is the whole trick. No role-play, no magic words. The model becomes the one asking, and every answer you give drops another piece of what you know onto the page where it can be used.

People have run a lighter version of this for a while, “before you respond, ask me five questions that would help you give a better answer”. One writer calls it the one prompting technique everyone can use, and it works for the same reason. The bottleneck was never the model’s intelligence. It was everything you knew and never said.

Run the test yourself this week

Do not take my word for it. Prove it in your own chat.

1. Pick a real task you would normally fire off in one line. A client email, a project plan, next month’s report.

2. Run it the old way first. One sentence, hit enter, save the output.

3. Open a fresh chat. Paste the interview prompt above and answer honestly, one question at a time.

4. Let the model summarise, correct anything it got wrong, then turn it loose.

5. Put the two outputs side by side.

The gap between them is the work you were leaving on the table every single time.

One more move. Once the model has interviewed you and you have a version that lands, do not let that hard-won detail die when you close the tab. Drop it into a saved document, your role, your audience, the way you like things done, and feed it in at the start of the next job. Nicole Leffer makes a strong case for that habit. You answer the questions once. The model stops guessing for good.

The car wash test stays funny right up until you realise you have been the one walking to the car wash, asking a brilliant tool to read a mind it cannot see. The fix costs you four questions and two minutes. The understanding stays yours, which is the only part that was ever going to.

Adapt & Create,
Kamil

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Hey Adopter,

Three things in this one. A prompt you paste into ChatGPT or Claude that rewrites any management posting in minutes. The AI-era skills it screens for. And the reason your current posting is quietly costing you the candidates you want most.

The template that went stale

Open most management postings and you see the same shape. A degree. A number of years. Strong leadership, cross functional collaboration, advanced Excel. It reads clean. It reads like 2024.

What changed sits underneath the words. AI stopped being a pilot and became how the work runs. Agents handle steps people used to. Decisions lean on AI for analysis and drafting. Processes get rebuilt around it. A posting that ignores all of that is not neutral. To the right reader, it is a tell.

What your strongest candidate sees in ten seconds

The operator you want has rebuilt a process before. They take a fuzzy goal and turn it into something an agent can run. They know when to trust a model and when to kill its output. They read your posting fast, and not as a duty list. They read it as a signal. Does this company treat AI as core to the work, or as someone else’s problem.

When the posting answers wrong, they close the tab. You keep the applicant volume. You lose the people who would have moved the work. Sprinkling the letters A and I across the same posting does not fix it. That reassures no one and pulls in the wrong crowd. The real change happens in what you screen for, and it starts before anyone applies.

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also here’s a fun weekend I didn’t sent out via email:

Hey Adopter,

Glean surveyed 6,000 digital workers and put a number on something you have felt for months. The average knowledge worker now spends 6.4 hours a week babysitting AI tools. Feeding them background they should already hold. Checking outputs. Debugging answers that sound polished and turn out wrong. Rerunning the same prompt across three tools because none of them remember the last session.

The report calls it “botsitting”. It eats 37% of all time spent with AI.

Stack the numbers and the picture sharpens. Workers say automation saves them 11 hours a week. They hand 6.4 of those hours straight back. 36% of AI sessions fail outright and need rework. After all this activity, 13% of organisations say they perform significantly better because of AI.

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Thirteen per cent.

The report has a darker twin for this behaviour. Botshitting, shipping AI work nobody verified. 69% of AI users admit to it. 41% have shipped work they could not explain if asked. And the people doing the heaviest cleanup are 73% more likely to be hunting for a new job. Your best verifiers are polishing their CVs while your adoption dashboard glows green.

Most coverage of this story lands on the same lazy conclusion. The models are not ready. Train people to prompt better. Wait for the next release.

The data inside the report says something different, and far more useful. The botsitting tax is not a model capability problem. It is an organisational architecture problem wearing a model capability costume. Which means you can cut it now, without waiting for anyone in San Francisco to ship anything.

The proof, the legal bill one airline paid for ignoring this, and four checks to find your own leak, below the line.

The proof hiding in the variance

The most useful contrast in the Work AI Index 2026 sits between context-rich and context-poor environments. Same models. Same vendors. Wildly different bills.

Workers whose AI tools reach the information they need are 64% less likely to feel worn out by AI, 52% less likely to ship work they cannot explain, and 31% less likely to botshit at all. Meanwhile 53% of all workers say critical information is not accessible inside the AI tools they use daily.

Context feeding alone burns 2.3 hours a week, and it carries the steepest exhaustion penalty in the dataset. Every extra 10% of AI time spent supplying background correlates with a 25% jump in feeling worn out. Humans make expensive external memory.

Dr Rebecca Hinds, who heads the institute behind the report, summed it up in The Register with five words: “Adoption alone doesn’t equal transformation.”

Google’s research arm reached the same wall from the technical side. Their study on retrieval systems, covered by VentureBeat, found models give confident wrong answers when retrieved information is incomplete, and partial information makes the confidence worse, not better. The fix they propose is architectural. Measure what share of real queries arrive with sufficient context. Below 80%, your knowledge layer needs work, not your model.

What unverified output already costs

Air Canada learned the price in court. Its chatbot invented a bereavement refund policy out of thin air. A grieving customer booked full fare on the bot’s advice, applied for the refund it promised, and got rejected because the policy never existed. The airline then argued, with a straight face, the chatbot was a separate legal entity responsible for its own actions. A tribunal in British Columbia rejected the argument and ordered damages.

The payout was small, roughly $600. The precedent is not. You own every confident sentence your AI produces. Botshitting at scale is a liability programme running without a budget line.

The twist nobody quotes

Here is the finding the headlines skip. High AI achievers botsit more than low achievers, 40% of their AI time against 33%. They also caught and fixed AI errors at far higher rates, 79% against 64%, and report stronger productivity and pride in their output.

So the goal was never zero botsitting. Deliberate verification is where the value gets made. The waste sits in the reactive half, the re-explaining, the rerunning, the archaeology. Cut the plumbing problem, keep the judgement.

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Four checks before you buy anything

Run these this week. No procurement required.

1. Track one team’s AI time for five days, split four ways. Production, supplying background, debugging, rework. The ratio is your tax rate.

2. Take the workflow with the worst ratio. Find where the information the tool keeps asking for lives, and whether it could reach it without a human copy-pasting.

3. Open your AI dashboard. If it shows adoption and hours saved but no error rates or rework volume, you are measuring the gross and ignoring the net.

4. Count tools. 33% of workers juggle four or more AI tools weekly. Every switch wipes memory, and a salaried human carries the state across the gap.

The dividend is real. Eleven hours a week of it. Whether your organisation keeps it comes down to plumbing decisions sitting on someone’s desk right now, not the next model release.

Adapt & Create,
Kamil

Hi Adopter,
In April 2025, Tobi Lütke told every Shopify employee that reflexive AI usage was now a baseline expectation, and that teams would have to prove AI could not do a job before asking for more headcount. Half the commentary called it visionary. The other half called it theater with a deadline. Both halves screenshotted it for their next board deck.

The original case study in this newsletter made two predictions instead: the memo was a line in the sand for the whole e-commerce ecosystem, and Sidekick, the merchant-facing assistant, would decide Shopify’s competitive standing. This edition is the follow-up. Fourteen months of earnings, product releases, and one very public failure, scored against those predictions, then stripped to the parts you can use.

The receipts first. Revenue grew 30 percent to $11.6 billion in 2025, then accelerated to 34 percent growth in Q1 2026. Headcount moved the other way, from roughly 8,100 to 7,600. Revenue per employee climbed from about $1.1 million to roughly $1.63 million. A company that shrank by 500 people grew by $2.7 billion.

Both predictions held, and both undershot.

The ecosystem did follow. Within months, Box, Fiverr, and even the Canadian Prime Minister were referencing the memo, and “prove AI can’t do it first” became a stock phrase in board decks. The line in the sand turned into a genre.

Sidekick did decide the product story, just not as a chatbot. The assistant that needed merchant approval for every step in early 2025 now monitors store data on its own, flags anomalies before merchants ask, and builds reports and custom workflows inside the admin. One number up front: Sidekick weekly active shops grew 385 percent year over year. The bet the original analysis called “a key determinant” turned out to be the understatement of the piece.

But scoring predictions is the boring part. The interesting part is what the original analysis underestimated, because it is the same thing almost everyone copying Shopify still misses.

The memo was the cheap part

Here is what happened to the companies that copied the artifact instead of the architecture. Duolingo’s version of the mandate triggered immediate public backlash. Fiverr’s maximum-bluntness approach preceded a 30 percent layoff and a wave of resentment. Same genre of memo, opposite reception. The difference was never the wording.

By the time Lütke hit send, Shopify employees already had an internal LLM proxy giving everyone access to frontier models, and 24+ internal MCP servers exposing company data to those models. The company had been tracking AI tool adoption since GitHub Copilot reached 80 percent of engineers back in 2022, years before the memo. The mandate did not ask people to go find AI and figure it out on their own time. It asked them to use the machine the company had already built and wired into everything.

That distinction is the entire case study. A mandate without infrastructure is pressure on employees. The same mandate on top of infrastructure is pressure on the status quo.

Run the test on your own organization. If the memo went out Monday morning, could a sales rep reach a frontier model with company data behind it by Monday afternoon? Could anyone watch a skilled colleague work with AI, or would the memo just generate a thousand guilty ChatGPT logins? Shopify could answer both before the memo existed. That is why its version produced a flywheel and the copies produced compliance theater.

There is also a quieter tell in the original memo that most coverage skipped: Lütke promised AI usage questions would enter performance and peer reviews. Not a training program. Not a center of excellence. A change to how people get evaluated. Mandates announce priorities; evaluation systems enforce them. Fourteen months later, that single sentence explains more of the outcome than the viral parts ever did.

Revenue up 30 percent. Headcount down 500. Revenue per employee up roughly 45 percent to $1.63 million. The memo got the headlines. The machine got the results.

The 2025 debate asked whether the memo was leadership or theater. The 2026 answer is that it was neither. It was the smallest visible piece of an operating system that touches tooling, spending policy, performance reviews, hiring, and product strategy at once. Each piece is documented, public, and copyable. Most of them are cheaper than the consulting engagement you would commission to avoid building them.

Download the full report:

The rest of this edition breaks that system down:

• The verified 2026 scorecard, including the agentic commerce numbers that quietly redefine where storefronts live

• The five mechanisms that spread AI fluency through Shopify without policing, and which ones carry the weight

• The failure file. A hallucination problem support admits it cannot switch off, a 15 percent stock drop, and the strongest critique of the whole approach

• The adopter’s playbook. What to copy, what to counter, what to avoid

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Hey Adopter,

You found a business worth buying. A $2M agency, a $6M ecommerce brand, a regional services firm with steady cash. The numbers in the data room look fine. And the part where you actually pressure-test whether the market is growing, whether the customers stay, and whether the revenue plan is real? You are about to do it on a Saturday with a spreadsheet and a gut feeling.

That is how most small acquisitions happen. Not because buyers are lazy. Because real commercial diligence costs more than the deal can justify. A boutique will run the analytical build for around $50,000, and on a small target it buys a decision you were going to make anyway. So you skip it. You buy half-blind. Then you learn in month four that the top two customers were already halfway out the door and the growth story was one good quarter, not a trend.

I advise companies on putting AI to work where it earns its keep, and this is one of the clearest cases I have seen. The grunt work inside a $50,000 diligence build is the kind of structured, repeatable analysis a capable model handles well. Market sizing. Competitive position. Cohort retention. A revenue model built from drivers, not the seller’s optimism. The memo at the end.

So I built the whole thing as eight prompts you load into your prompt manager and run in order on your own data room. It will not do everything a firm does. It gets you about 80% of the way, which on a small deal is the gap between buying blind and buying informed.

Here is the pack, what each prompt does, and the one that decides whether you can trust the output.

🔒 The rest of this edition is for premium subscribers.

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Hey Adopter,

In the mid-80s, a teenager in the back of a car in Hollywood yelled “stop the car.” He’d spotted a pink car that matched the pink leather jacket of the guy next to him, Axl Rose. He grabbed his film camera and took a shot that ended up in Rolling Stone and, decades later, on an album cover.

That kid was Dean Clark. He went on to design artwork for James Cameron’s Titanic and run a high-end studio for thirty years, charging six figures for work that A-list brands lined up to buy.

Then AI showed up and, in his words, said “hold my beer.”

Inside of a year, the clients stopped calling. The work he used to charge six figures for, a non-artist with a prompt could now fake in an afternoon. His industry didn’t shrink. It vanished.

I had Dean on the podcast this week, because what he did next is the entire playbook for anyone watching AI creep toward their job.

Watch the full conversation with Dean Clark ☝️

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The floor went up. Did you?

Here’s the part most people get backwards. AI doesn’t replace skill. It raises the floor.

Suddenly everyone can produce something average. A passable logo, a serviceable paragraph, a decent video. Which sounds democratizing until you notice what it actually does: it makes “average” worthless, because average is now free.

Dean’s line for this is “taste is the new skill.” Art used to be hard. You got hired because you owned Photoshop and knew how to drive it. Now the software is cheap and the model does the heavy lifting, so the value moved. It moved from making the thing to knowing which thing is any good.

“We used to be creators,” he told me. “Now we’re curators. We went from the creator economy to the curator economy, and the best curator wins.”

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A million-dollar train, or ninety minutes of AI

The clearest example he gave involves his new role at Ritual Ads, the AI-powered cinematic creative-as-a-service company behind some of the most successful campaigns of recent times.

A major brand was relaunching a sneaker with one of the biggest names in basketball. The original plan was to shut down a subway station in Chicago, light the tunnel, and shoot a multi-day shoot.

After six months of planning, it imploded. Days before launch, they had nothing.

The Ritual Ads team got the call. In ninety minutes the Ritual Ads team built a shot-by-shot proof of concept with AI. The executives approved it, they shot the athlete on a white cyc soundstage, and the shoe sold out within hours.

But here’s the part that matters for you, and the part the hype crowd skips: that commercial required significant human taste, team, talent, and technology including going frame by frame through thousands of frames, re-prompting, and fixing. Animators, sound designers, and editors still did the work only humans could do. AI did the part only AI could do.

“Use AI for what only it can do,” he said. “But creative humans still need to be in the driver’s seat.”

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Show it to your teenager

So how does a leader who isn’t an artist use this without embarrassing the brand?

Dean’s north star is the best filter I’ve heard: show it to your teenager.

They look at AI slop all day. They will tell you in two seconds whether it’s cool or cringe. You don’t need to hunt down a guru. You need to trust the gut of someone who lives in the feed, and your own gut as the person whose name is on the thing.

The brands that get burned are the ones who let the tool make the taste decisions. The ones who win keep a human with judgment sitting between the model and the market.

The director’s chair, again

Long-time readers know I’ve been beating this drum since the first issue: when a new layer of automation arrives, you don’t fight it, you climb above it. You stop pushing the pixels and you start directing the work.

Dean got there on his own, without me feeding him the line. The cameraman whose job dried up is now a director with AI. The blog writer who used to serve one client can now serve fifty. The skill that survives isn’t execution. It’s judgment, taste, and the relationship. “People still buy from people they trust,” he said. That didn’t change. It got more valuable.

He learned it the hard way. About a year ago, almost to the day, he had a massive heart attack, his thirty-five-year marriage ended, and his business cratered, all in the same stretch. He calls it his “Job year,” after the biblical one, and also the best year of his life, because it forced him to climb. . He’s now a VP of Business Development at Ritual Ads, the AI-powered cinematic creative-as-a-service company behind recent breakout campaigns for Reebok, Martha Stewart, and Shopify, prepping for Cannes Lions.

That’s the whole lesson. The tools will keep eating the floor you’re standing on. The move is always the same. Go up a level.

What to do this week

One question, same as always. Who in your market is getting better at AI faster than you are?

If you can’t name them, you’re not looking. If you can, you have a problem worth solving before they solve it for you.

The full conversation with Dean is up top. Watch it when you’ve got twenty minutes. It’s one of the more honest accounts I’ve heard of a creative burning the ships and coming out the other side intact.

Adapt & Create,
Kamil

PS: Dean’s whole life runs on one line, “But God had a plan.” Whatever you believe, there’s something in it. The people who adapt tend to be the ones who decided, ahead of time, that the disruption was happening for them, not to them.