Notes from the studio, newest first. Written when there is a lesson or an example worth passing on, not to a schedule. RSS.
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Three essays on the company brain: why every company will build one, what it takes to make a company readable, and why a model cannot supply the company itself.
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Why every company will build a brain
On the company brain - why twenty years of trying to build one failed, what changed, and what it means for the shape of the firm.
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A company your model can read
A company knows far more than it can say. Until that knowledge is made legible, a model cannot do anything useful inside it.
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Models compress. They cannot invent your company.
A language model is brilliant at turning many facts into one clean answer, and unreliable at turning too little into the truth. That asymmetry - not the model's cleverness - is the real constraint, and it is why grounding matters more than scale.
Every essay, newest first
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The median is free
A language model trained on everything returns the most likely answer to everything. That is the average, and everyone gets the same one, whichever model they use. The way off the middle of the curve is not a cleverer prompt or a bigger model. It is context only you have.
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The AI you didn't buy into
You have started on AI. Your team is further ahead. Most of what they do runs outside any licence, pilot or policy, so the business carries the risk without owning the benefit, and the know-how leaves when they do.
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Automation makes the wrong work faster
Automation makes work faster. It does not ask whether the work should happen - and in most organisations the larger cost is not work done slowly, but work that should never have started.
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Why every company will build a brain
On the company brain - why twenty years of trying to build one failed, what changed, and what it means for the shape of the firm.
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Models compress. They cannot invent your company.
A language model is brilliant at turning many facts into one clean answer, and unreliable at turning too little into the truth. That asymmetry - not the model's cleverness - is the real constraint, and it is why grounding matters more than scale.
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Most AI spend never reaches the P&L
The failure rate isn't a model problem. It's an operating-model problem - and it has a specific cause.
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What ai-native actually means
The market defines ai-native by birth certificate - the firms born with the model. The truer definition is structural, not chronological, and it is fully available to the company you already run.
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What stays human
As working intelligence becomes abundant, the scarce thing is no longer analysis. It is judgement, taste, and the will to mean it.
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Decisions with the model in the loop
AI as a tool you pick up, or an agent that replaces you. Both pictures miss the real change. The shift is to the loop.
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A company your model can read
A company knows far more than it can say. Until that knowledge is made legible, a model cannot do anything useful inside it.
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The end of scarce intelligence
Every company alive today was built around the assumption that working intelligence is scarce. That assumption has quietly stopped being true.
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