The Issues.
The standing edition. Each one takes a single argument apart and builds it to use. Newest first. The first fourteen were written and released together as the Foundational Collection, the canon this publication stands on; new Issues join from there.
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The Contours of an AI Team
There are two jobs hiding inside the decision to build an AI team, and they call for different investments. One IT can handle. The other, changing how the company works, needs a small team of four built in a shape that barely existed five years ago.
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Write the Role Before You Fill It
You cannot hire, grow, or even fractionally assign a job you have never written down. Defining the AI Product Partner is the first real act of the transformation, not the paperwork after it.
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Inside the Role
We had engineers. We had data scientists. The hard part was not what we could build. It was whether people would trust it enough to use it.
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The Demo Is a Question, Not an Answer
Ninety-five percent of AI pilots are said to fail. They fail because companies invest before they prove adoption. Reverse the order, and let the demo find the truth instead of selling a future.
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People First, or Not at All
A company can deploy every AI tool and change nothing. Deployment is not adoption, and the gap between them is people. You close it people-first, or the transformation does not hold.
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Buying AI Is Easy. Becoming a Different Company Is the Hard Part.
The founding welcome to Translational Intelligence, an open operating system for building an AI-native biotech, because the technology is the easy part and the human part is most of the job.
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A Policy Is Only as Good as It Is Understood
Writing an AI policy is the part everyone expects to be hard. It is not. The hard part is teaching it, in person, until a company understands it well enough to act, and can eventually teach it to itself.
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Delete It by Default
AI meeting transcripts are useful and frightening for the same reason, that they stick around. Govern them by making the record delete itself, and making the act of keeping it the moment a named person becomes accountable for it.
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Don't Default AI to IT
Ask most biotechs who owns AI and the answer is IT, a function trained to say no. Traditionally IT in a regulated company is a risk-management shop. Today it has to be a foundational enabler of AI, and that shift is a culture change at its core.
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No Final Form
AI-native is not a milestone you reach and bank. It is a standing capacity to keep re-translating as the technology keeps moving. The company that is never finished, on purpose.
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The Blank Page Is the Expensive Part
A regulatory team said no to AI for twelve months. Then a three-day demonstration on their own documents did what a year of argument could not. The lesson is not about drafting. It is that a real demonstration beats a year of no.
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The Product Is Belief
The real output of a first AI win is not the hours it saves. It is a believer. Start at the individual, because belief is the thing that actually moves a company, and it travels through people, not licenses.
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Don't Point AI at Your Best Work
The instinct is to aim AI at your hardest, most important work. Often backwards. Point it at the work that is high value to the business and low value as a use of your best people, and spend the bandwidth you free on the work only they can do.
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The Fast No
Everyone asks AI to find the next drug. Wrong question. Its highest-value job in discovery is to find you a reason to stop before you spend a scientist's year, and to know that its silence is never a reason to go.
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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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An Afternoon, or a Restructure
The word transformation hides five different sizes of change, from a habit you can build before lunch to a company you have to take apart and rebuild. Naming the size is most of the work.
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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.
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Audit Yourself in Public
The strongest test of a system is whether its author will use it, in the open, with the gaps left in. So I ran this publication against its own rules.
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Arguing About the Wrong Thing
The meeting is about which model and which vendor. Those choices matter less than the room thinks. The scarce resource was never the technology.
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The One-Lever Mistake
A company gets serious about AI and reaches for one lever. A year later the lever moved and the company did not. It takes three pillars, and they grow together or not at all.
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Your Edge Was Never the Model
Whichever model leads this quarter, you rent. The thing worth owning sits underneath it, the capacity to turn whatever arrives into how your company works.
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Excellent and Generic
You can think hard, land a real argument, and still produce prose that sounds like a machine made it. Polish is one thing. Whether the writing could only have come from you is another.
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Don't Automate the Struggle
The fear that AI will atrophy how we think is real, but only if you use it to replace the struggle. Update your mental model instead, and the same tool makes you think harder, not just write faster.
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Stop Swinging for the Fences
In the AI era the build-versus-buy line leans toward build, and the temptation is to only build big. But adoption is the game, and the smallest builds are how you win it.
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There Is No AI Work Product
The first thing every leader asks is what their AI policy should be. The whole answer fits in one sentence, and it moves accountability to exactly one place.
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The Biotech of Tomorrow
You cannot build the biotech of tomorrow by making the biotech of yesterday slightly more efficient. The founding argument of Translational Intelligence.