Bread makers don't knead dough the way a human baker does. They get to the same result via a completely different route — and that turns out to be a pretty good map for what's actually happening with AI right now.

We start with brioche, end up in a crisis of professional tacit knowledge, and find a surprisingly useful frame for thinking about what machines can and can't do — and what that costs us in the long run.

• Why "harness the model" is the real skill — and what that actually looks like in practice
• The 50/30/10 split: surprisingly good, fine, and catastrophically bad — in the same tool (and no the maths doesn't add up)
• Why test-driven development went from "boring best practice nobody does" to "the only way this works at all"
• The tacit knowledge cliff: what happens in 20 years when there are no senior lawyers who did the grunt work
• Explicit → explicable → tacit → relational: a spectrum that explains where AI taps out
• Why scarcity and skin in the game are the two things a language model structurally cannot fake
• Artisanal lawyers, peak athletes, and the industrial revolution: why commodification always leaves a pocket for the handmade

For anyone trying to think clearly about AI without falling for either the hype or the backlash.

References

• Luca Dellanna's piece on what makes humans different from AI
• Nonaka & Takeuchi (1995) – the bread maker as an example for how knowledge is encoded in processes and organisations https://www.scribd.com/document/258487259/Nonaka-I-and-Takeuchi-H-1995
• (Found after we recorded) a critique of Nonaka & Takeuchi's work on bread making machines - bread maker not as incorporation of tacit knowledge, but as fitting a social prosthesis into a rearranged world: https://www.researchgate.net/publication/215439406_The_Bread-Making_Machine_Tacit_Knowledge_and_Two_Types_of_Action

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When a hiring manager asks an unanswerable question, what if that's the whole point?

In this one, Corissa brings along a Reddit thread that stopped her in her tracks. A candidate who discovered, mid-interview, that the interviewer had never once rejected someone based on their actual answer. What they were watching for was whether you'd sit with the discomfort of having no right option, or immediately reach for a safe response ... one where you try to please the interviewer.

That question spirals us into the Kobayashi Maru, the art of giving feedback (and filtering feedback), why founders hold back from the conversations that would actually tell them the truth, and what it means to stop preparing the right answers and start trusting your own judgement.

• The Star Trek test designed to be unwinnable — and the eejit who won anyway
• Why the interviewer never rejects based on the answer itself
• The golden rule for making sense of feedback that's almost always true
• Why founders often make every move except the one that would actually test the idea
• Why when you hear "other people would ..." it should set off alarm bells
• The Red Queen effect in interviews: why every clever question eventually gets gameable
• How to prepare for interviews by stopping preparing for interviews

This one's for anyone who's noticed that the world keeps getting less predictable, and that "sitting with discomfort" is somehow both obvious advice and surprisingly hard to take.

References

• The Iron Triangle (good / cheap / fast ... pick two)
• Mike Haber's Inverted Iron Triangle (bad / late / over-budget ... you can have all three!)
• Neil Gaiman's feedback rule (attributed, then immediately un-attributed, could be apocryphal)
• Star Trek's Kobayashi Maru scenario
• The Red Queen Effect

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Everyone's got a brilliant value proposition. And most of them fail.

Tom's been reading a book that argues the problem isn't your messaging, your features, or your market research. The problem is that most people — most of the time — simply don't care.

Indifference is the default. And you can't overcome indifference by being more persuasive. (Nor by shipping faster, dear LLM code wrangling friends.)

In this one, we dig into why "unmet needs" is a nearly useless frame, what authentic demand actually looks like in the wild, and how a trucking startup found the wrong signal in all the right places.

• The "not-not" principle: why "it would be nice" is almost worthless, and what you're actually hunting for
• Why a massive client said "when can it be ready?", started talking money, all the signals you'd be excited about ... and still never signed the contract
• Our old buddy Ignatz Semmelweis. He was right in a way that was socially unacceptable, and so he was thrown out of the establishment. 50 years later, everyone else finally got it, but only because it became socially unacceptable not to.
• What six weeks of not talking to truckers reveals about the psychology of founders (and possibly all of us)
• An example based on a fish finder and an accidental twist that created a breakthrough in product demand, from meh to three or four on each boat.
• Sturgeon's Law, the Mom Test, and why all the right signals can still point you in the wrong direction

This one's for anyone who's built something sensible that people said they wanted — and then didn't buy.

References

• Heart of Innovation — Merrick Furst, Matt Chanoff, Daniel Sabbah & Mark Wegman (founders of Damballa and Flashpoint incubator) http://theheartofinnovationbook.com/
• Cedric Chin's writing on Heart of Innovation — https://commoncog.com/the-heart-of-innovation-why-startups-fail/
• Rob Fitzpatrick — Write Useful Books — https://writeusefulbooks.com
• Sturgeon's Law — https://en.wikipedia.org/wiki/Sturgeon%27s_law
• Goodhart's Law / Strathern's reframing — https://en.wikipedia.org/wiki/Goodhart%27s_law
• The Mom Test — Rob Fitzpatrick — https://www.momtestbook.com
• Innovation Tactics (Tom's card deck via Pip Decks) — https://pipdecks.com/products/innovation-tactics
• Multiverse Mapping — https://multiversemapping.com

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