The Defaults Your Power Users Set

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The signals

• Markus: The people who bring the tool in set the terms everyone else lives under. Very often it's the more technical people who bring an AI tool into the organization, for their own use, and they set the terms it operates under. When the tool rolls out further, those permissions stay what they were and nobody reviews them again. His sharpest example is recruitment: a technical team brings in an HR tool, signs the terms, and as the agentic piece improves they've effectively signed a new recruitment policy that updates itself in the background.
• Sam: Confidence scores are defaults too. More of the AI being bought returns an answer and a number next to it, and somebody decides where the line goes. In testing, a classifier returned a wrong answer at 95% confidence, and moving the cutoff three points would have made every test pass, so it stayed where it was. A 95% system gets 5 of every 100 wrong while working exactly as designed.

The subtractions

• Markus: The overfocus on one number in an experimental environment. An insurance company set out to automate part of its claims under $150. The kickoff target, finger in the wind, was 15 to 20%. They built a rules-based automation, not an LLM, ran it three weeks in parallel with the human team, and landed, highly confident, at 7 to 8%. Someone clever pointed out that was half the goal. They celebrated anyway, correctly: upskilled, nobody let go, ready for the next 10%. A target in an experiment is a destination, not a pass or fail line.
• Sam: Reaching for a large language model by default. Much of what goes to generative models is sorting, which is classification. Sam tested TypeSafe's Jev, a small classifier,: it matched 15 of 15 questions to research categories. It also failed, missing 3 of 22 on whether a legal source supported a claim: that job looked like a sort and was a judgment. A rule goes to code, a sort to a classifier, a judgment to a general model or a person.

About this episode

Sam's analogy for sequencing the three jobs: there's more than one right way to eat an orange, peel first, slice first or not at all, and the way you choose depends on who's eating it. What matters is that the team knows which way it's doing it.

Markus's larger point near the close: a clever idea used to save a team time, and now it mostly creates homework, because every idea has to be tested against the workflow. If everyone is using the same tools, the competitive edge is in the homework rather than the tool. Sam's version is that the bottleneck is the speed at which an organization learns, not the supply of ideas.

The episode's takeaway is The Default Ledger: one row per AI default in force, ending in the name of whoever set it. For the longer version of Markus's argument, including who holds authority behind a default nobody chose, he points to his brief The Decisions Your AI Is Making Without You at Endeavor Intelligence, and to the Endeavor Report, his non-sponsored publication of AI use cases told by the people who implemented them.

Mentioned on air, unpaid: TypeSafe's Jev, which Sam tested on his own work, and PAICE.work, whose error checking Sam described as an elaborate classifier. Disclosure: Sam founded PAICE.work. No money changed hands.

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From Markus Bernhardt

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Issue 70 · Sunday 27 September 2026 · sent on Substack and LinkedIn.

One signal, one subtraction, one analogy, in five minutes. It draws on this episode but stands on its own. Every issue.

The method from this episode is one page with its copy-paste prompt and schema: The Default Ledger, linked under the decision above. Every takeaway.