From program manager to published AI scientist for biotech

0.1 · The honest answer to your question

You asked how a program manager with 25 years and strong AI fluency can walk into a biotech company and sell something he built, in 20 weeks, like a pro. He cannot, and the v4 plan was wrong to imply it. Here is what is true:

1. You will not sell biology. You will sell measurement. Biotech founders know their biology better than you ever will. What they do not have is someone who can turn their experiments into structured data, models and decisions, and who can prove it with a paper. That person is rare and is paid accordingly.
2. Credibility in science has only one currency: things other scientists can check. A repo with a DOI, a dataset other people download, a preprint with a method they can reproduce, a co-authored paper with a scientist who has a lab. Not talks, not posts, not certificates. Posts only amplify what the currency already bought.
3. The bridge from "outsider with a tool" to "peer" is a co-author. Most GridX founders are CONICET scientists. When one of them puts their name next to yours on a preprint, you are no longer a consultant pitching them; you are a collaborator they introduce to others. Everything in this plan is arranged so that this happens between week 16 and week 32.
4. Twenty weeks is enough for the first two rungs, not for the pro. At week 20 you will have one bioRxiv preprint (a dataset and a method), one endorsed arXiv preprint (a modeling result), three instruments with numbers, and a reading level in biology and machine learning that lets you follow a lab meeting. That opens the employee track at scientific-data companies and makes founders answer your messages. The paid consulting starts in Half 2, after the third paper is in motion, around week 28–32.
5. A plan cannot guarantee a discovery. It can guarantee the artifacts that make one likely. Three of the four papers in this plan are of a kind that reliable work produces (a dataset, a benchmark, a reproducible modeling study). The fourth (the enzyme paper, Half 2) is the one with a real chance of a finding, and it is designed with a founder's lab as the place where the finding gets tested.

0.2 · The cockpit: five files, nothing else

Create a private repo cockpit on day 0. It holds five files. You open it at the start of every block and close it at the end. Nothing about the plan lives in your head.

0.3 · The rituals (this is the ADHD armor)

0.4 · Weekly shape (never changes for 40 weeks)

Every week in Part 6 gives you: the goal in one line, the exact Tuesday and Thursday material with links and units, the three build tasks with "done when", the Sunday output, and the "if you only do one thing" line. G = GitHub, L-Sun = LinkedIn build log, L-Wed = Wednesday note, M = Medium, Z = Zenodo DOI, HF = Hugging Face, X = preprint.

The four papers

Why this thesis and not another

• It uses what you already have (agents, RAG, orchestration, evals) as the method, so the science is where you are strong, and the biology is where you are learning.
• It needs no wet lab until Paper 3, where the wet lab belongs to the co-author.
• Every piece is public data: Europe PMC open-access full texts, IndPenSim, FLIP and Meltome.
• It fits 6 GB of GPU: extraction runs on APIs and local 7B models; soft sensors train on CPU; ESM-2 650M embeddings run in fp16 on the 3050.
• It produces things founders in archetype A (25 companies in the GridX list) will want to read, and things scientific-data companies (DataHow, Aizon, TetraScience) hire for.

Risks and what you do about them

4.1 · Track B — Biology from zero (Tuesdays, ~30 h in Half 1)

4.2 · Track M — AI science from the basics (Thursdays, ~30 h in Half 1)

Deferred to Half 2 (Part 7): Karpathy videos 2–8 as needed, fast.ai if you want image models for trialkit, causal inference (Brady Neal's free course) for Paper 3's analysis, CS336 lectures 1–2 and 10 only if labdocs needs them.

4.3 · Track D — Domain and craft (inside build blocks, no separate hours)

5.1 · Repositories

9.1 · Profiles (week 0, updated at each gate)

• At week 16 replace the middle with: "Author of FermLit (bioRxiv): 2,000 fermentation papers, one validated dataset". At week 20 add the arXiv paper. At week 32 add "co-author with [lab]".
• About: the thesis statement of Part 3.2 in plain words (3 lines), the gauge level with links, one line on the on-prem angle, one line on what you are looking for (collaborators and, from week 27, Loop Audit clients).
• GitHub: real photo, one-line bio, fermlit and batchlens pinned, profile README with the headline numbers and DOIs.
• Google Scholar profile at week 16 (preprints count). ORCID at week 0 (free, 5 minutes, needed by bioRxiv and journals).

9.2 · Content pillars and cadence (same as v4, re-pointed)

Rules unchanged from v4: one number or one admitted failure per post; archetypes, never named weaknesses; zero EY data.

9.6 · Templates (reuse v4 section 9 for build log, gap note, video, audit one-pager — see Appendix C)

Only one template changes. Targeted message (weeks 16+):

Hi [NAME],
I read [their paper]; [one specific line showing it].
I just put out a preprint on [FermLit / hybrid soft sensors]: [one number]. Code and data: [DOIs].
[Their organism / process] appears in [N] of the papers in the dataset; I attached the figure.
If it is useful, I would like 30 minutes to hear how you [monitor batches / record trials] today. No pitch.
[NAME]

Tier the 64 non-AI companies by what you need from them

Not by what they can pay. Click a dot when a partner says yes.

Beyond the list: who actually pays high ticket

Not on the list on purpose: Big Four and large IT consultancies' life-sciences practices. Same culture you are leaving, different logo.

Culture filter: ask before signing anything

Two bad answers out of six: walk away. Calibration point: ask your friend at Sumble to answer these six about their own workplace so you know what "good" sounds like.