Miju LabsWet lab is the niche where the arithmetic is fine and the room is full.
The wage arbitrage works: BLS puts biological technicians at $57,510/yr, $27.65/hr (BLS Occupational Outlook Handbook; no URL was captured in the research), against Mercor's Biology Expert (PhD) listings at $65–$70/hr, 15–25 hrs/week, requiring a PhD in molecular biology, microbiology, virology, genetics, biochemistry, bioinformatics, synthetic biology or immunology, "deep familiarity with modern laboratory and computational techniques", and "sound judgment regarding biosecurity and dual-use information handling" (Mercor). That is a 2.3x multiple on a low floor — easy recruiting, healthy spread.
Now count the capital in the room. Edison Scientific, the FutureHouse spinout, raised a $70M seed in December 2025 (Endpoints). Lila Sciences raised a $350M Series A from Flagship Pioneering (FinSMEs). Periodic Labs raised a $300M seed to build "a large physical lab to run experiments" for closed-loop RL (Maginative). That is roughly $720M standing between you and the customer, before you count Medra's robotic lab or the acquisition below.
What the data actually is
Two layers, and the whole page turns on which one you can afford.
The affordable layer is protocol judgement. Troubleshooting a failed western blot from the description alone. Choosing between three purification strategies and saying what each costs you. Reading a methods section and naming the step that will not reproduce. No bench required — it is expert reasoning about published artefacts, the same construction that makes Law and Defensive security buildable.
The differentiating layer is the closed loop. The model proposed an experiment; here is what happened when someone ran it. That is the asset Periodic Labs raised $300M for and the thesis Medra is built on [WEAK] (SemiAnalysis).
Proof scores 2, because the affordable layer already has its canonical artefact and someone else owns it. LAB-Bench 2 — Edison Scientific with FutureHouse and Broad Institute contributors — is 1,900+ tasks across literature retrieval, database access, protocol troubleshooting, molecular-biology assistance and experiment planning, with model performance down 26–46% versus the original LAB-Bench (arXiv 2604.09554). Unsaturated, yes. But built as a moat by a company with $70M, and built the same way you would build it: the tasks were generated by "contracted domain experts who hold or are in the process of obtaining PhDs in biology" through "a purpose-built web-based platform" with multiple review rounds.
That sentence is worth reading slowly. Edison is already running your business as a cost centre inside its own. BioLP-bench and BioProBench also exist [WEAK — existence verified, scores not] (bioRxiv, arXiv 2505.07889).
Is anyone buying
Budget scores 3. Real, intermediated, and narrower than it looks.
Mercor's PhD biology listings are live postings with bands, including an explicit AI-safety and biosecurity variant. That is a buyer paying today. But GDPval excludes biology lab work from its 44 occupations (OpenAI), and no frontier lab posts a biology subject-matter-expert req of the kind OpenAI has for investment banking.
SemiAnalysis frames the constraint from the buyer's side: "a single biological experiment can cost hundreds to thousands of dollars and take hours to complete, compared to a coding task" (SemiAnalysis). Labs know this. It is why they buy reasoning about experiments rather than experiments, and why the four well-capitalised players are all trying to make the physical loop cheap enough to sell.
No system card, model release or disclosed contract cites purchased wet-lab expert data. The demand case is Mercor's recruiting pages plus the inference that four funded companies would not exist without buyers. That inference is stronger here than on most pages in this set — $720M is a lot of people betting the same way — but it remains an inference.
Getting the experts
Reach scores 4. The population is large and academically organised, but there is no single register: PhDs are not licensed and there is no NASBA equivalent.
protocols.io is the obvious channel — funded by Moore, Open Philanthropy and CZI, positioned as "a secure platform to develop, share, and discover reproducible research methods", with HIPAA and 21 CFR Part 11 compliance for industry customers (protocols.io). It publishes no user counts and its AI and data-licensing terms are not stated on the homepage [UNVERIFIED]. Then Addgene, where depositors are named; ResearchGate; r/labrats; ASCB and ASM meetings; and the postdoc population generally, which is the cheapest highly-trained labour in any domain in this atlas.
Note who else is standing in that channel. Benchling serves more than 1,300 biotechs and is building "AI Scientist", a gated loop that designs experiments, drafts notebook entries, pauses for human execution and resumes when data returns, plus Benchling Automation connecting instruments to records, a Model Hub, MCP connectors and one-click CRO ordering (R&D World). Benchling is accumulating the richest wet-lab execution corpus in existence. Whether it will ever license it is [UNVERIFIED], and structurally it is far more likely to build than to sell.
What it costs to run
Cost scores 1 — the lowest in this set alongside Chip design and EDA and Skilled trades and field service, and the reason the stance is what it is.
The affordable business needs no capital and no differentiation follows from it. Protocol judgement runs on published papers and PhD hours at $65–70/hr. You can start tomorrow. So can anyone, and Edison, Mercor and Lila already have.
The differentiated business needs a building. Not a machine, not a licence — a bench, a fume hood, freezers, reagent supply, a biosafety programme, waste handling and insurance, plus the running cost that SemiAnalysis states plainly: hundreds to thousands of dollars and hours per experiment. Compare Mechanical CAD and manufacturing, where the equivalent physical loop is one CNC mill, an operator and a small space, and where the marginal verification costs the price of a billet.
That is the whole distinction. A capital line that upgrades a viable labour business is a strategy; a capital line whose absence leaves you undifferentiated against $720M is a disqualifier. This is the clearest capital-intensity failure in the research, and it is not close.
Who is already there
Room scores 1. It is the only 1 in this set, and it is earned by an acquisition rather than a competitor.
Sepal AI — the science-environment specialist — was acquired by Mercor in February 2026 (Orrick). The incumbent horizontal has already bought the vertical in your target domain. That is a categorically different competitive fact from "a startup exists here": it means the largest expert-data marketplace in the market now has in-house science-environment capability, the supply network, and the lab relationships, simultaneously.
Around it: Edison Scientific with $70M and the benchmark, Lila with $350M, Periodic with $300M and a physical lab, and Medra building a robotically automated biology lab [WEAK].
Defense scores 4 despite all of it — and the score is about the domain, not your position in it. A biology PhD takes six years and cannot be faked; biosecurity screening is now mandatory and deters casual entrants; protocols, reagents and techniques churn continuously so the data decays on its own. Whoever holds this position holds something durable. It just will not be you.
What would kill it
You are fifth, and the four ahead of you have $720M. Room is 1 for the same reason Design and UI/UX's is: every defensible position is held by a funded company less than two years old.
Edison's benchmark becomes the standard. LAB-Bench 2 is already the reference artefact for exactly the layer you can afford. A benchmark you publish alongside it competes with a $70M company's marketing budget.
Biosecurity screening. Mandatory screening and dual-use review are now table stakes; one mishandled protocol is an existential regulatory event rather than a data-quality problem, and the compliance overhead is a fixed cost you carry from day one.
Institutional IP. Unpublished protocols belong to the university or the company. Your experts' best work is the work they cannot show you — the same wall Defensive security hits with detection rules.
The first ninety days here
Spend them somewhere else. Nothing about biology as a domain is wrong — the arbitrage is good, the pool is deep, the channels are real — it is simply the one niche in this set where the competitive field is already fully capitalised and the differentiating asset is a building.
If a biology-adjacent business must be built, the opening is at the seam the four incumbents skipped: negative results and failed protocols, which nobody publishes and nobody has collected, and which are the cheapest possible signal about what an experiment planner gets wrong. That is a narrow product and a real one, and it does not require a bench. See When to stop for the general form of deciding this quickly, and The first ninety days if you decide to proceed anyway.
Where the record is thin
protocols.io's AI and data-licensing terms are unread. If this niche advances at all, that document decides whether the largest public protocol corpus is available to you or foreclosed — it is the single highest-value diligence item on this page.
Benchling's position on licensing customer data is unverified and inferred from an article that does not discuss it. Sepal AI's acquisition price is undisclosed. The BLS technician figures arrive without a captured URL. And no system card in any of these eight domains attributes purchased expert data, so the demand case rests on rate cards and on other people's fundraising.