The work, published in the open.
The Issues take a single argument apart and build it to use. The FAQs are short, sendable answers to what I get asked most. The Case Studies dissect the system at work in the real world. Every piece names something, explains something, or hands you something you can use on Monday.
The Foundational Collection
The first fourteen Issues and fourteen FAQs were written and released together as the canonical starting library, the load-bearing ideas the rest of the work extends. New pieces join the collection each week.
The Issues
The standing edition. Each one takes a single argument apart and builds it to use.
The FAQs
Short, sendable answers to the questions I get asked most.
The Case Studies
The system at work in the real world, dissected honestly.
The Issues
All 14 →-
When Your Science Is Outsourced
Most AI-in-biotech advice pictures a discovery lab. If you run a clinical-stage company, your bench is at the CROs, and your real surface area is writing, reviewing, submitting, and oversight, under rules written to protect patients.
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You Already Employ Your AI Product Partner
Every small-company leader hits the same wall. The role sounds essential and sounds like a hire they cannot make. It is not a headcount. It is a function, and the person with the right shape is already in the building.
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You Don't Need a Roadmap. You Need a Quarter.
The request that kills more AI transformations than any budget line is the responsible-sounding one, bring me the roadmap. You do not need a two-year plan. You need one quarter you can run.
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You Can't Fix What You Won't Grade
Most leaders run their AI transformation on anecdote and vibes. The fix is a one-page scorecard, and the hard part is not the scoring. It is being honest about the pillar that flatters you.
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There Are Only Five Moves
Every AI request feels unique. It is not. There are only five things you can do with any of them, and most of the discipline is routing each to the right one on purpose.
The FAQs
All 14 →-
Can I use AI in a validated workflow?
Two different questions usually hide in that one. Drafting a document a human owns is not the same as putting AI inside a validated system of record, and mostly the second is where validation bites.
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Who on my team should own AI?
The T-shaped operator your people already trust, not your most technical person and not an outside hire. Look for the shape, not the title.
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What will the first 90 days cost?
Mostly attention, not budget. The real cost is one owner's 20% and an honest scorecard meeting. If you are writing a big check in the first quarter, you are doing it backwards.
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What's a passing score?
There isn't one. You are only as native as your weakest pillar, so the goal is not a high number, it is a rising floor.
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How do I audit my own company against its own rules?
Write the rules down first, most cannot, then check your loudest AI work against them, in public.
The Case Studies
All Case Studies →-
Built by Its Own Rules
The strongest test of a system is whether its author will use it. This publication was built with the exact framework it teaches, AI-assisted and human-accountable. Here is how, including where I was breaking my own rules.
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