A running file on healthcare companies and the industry around them.
I want to understand how a scientific idea turns into something a patient actually receives, and where that process breaks. Some of what follows is drug discovery, some is software inside hospitals, some is hardware people put in their skulls, and a lot of it is the money and policy moving underneath.
Current company memos, past research cases, a founder log and a dated record of what I am following.
Companies
Companies I would want to own, written up as full memos.
Founder log
Conversations with founders and operators building healthcare companies.
Following
A dated record of what happened across the sector and what I made of it.
Past research
Earlier cases and what I learned from their outcomes.
I want to own the part that turns a scientific capability into something a patient can actually receive.
In September I organised the thesis around who pays for healthcare software. I still think that is a useful question. It is too narrow for the companies I most want to understand, which are trying to make a new treatment possible rather than sell a tool inside an existing workflow.
The working idea now is that the valuable step is often the one after the impressive demonstration. Can a designed molecule survive development? Can a device be manufactured, placed safely and used long enough to matter? Can the company keep funding the work until someone will pay for the result? I find that space more interesting than another comparison of model benchmarks, partly because it connects the science to a business I can try to reason about.
The recent disclosures give me reasons to keep looking, not a reason to declare the field solved. Xaira has shown two antibody-design case studies. Precision has raised more capital and published a much larger recording footprint. Each makes a company easier to investigate. Neither settles the eventual clinical outcome.
My preference is private biotech, medtech and life sciences, but that is a research preference rather than a rule about what can work. BioAge stays because public trial designs let me check my own reasoning. Software can still belong when the evidence on adoption, retention and payment is better than the story. I want this collection to show the quality of the questions and how the answers change, not how consistently I can defend a category.
Wet-lab results should change the case before model scores do
I give more weight to a designed molecule tested against relevant controls than to a benchmark alone. Xaira’s disclosure helps, but company-selected examples are still a limited test. Inceptive’s paid partnership tells me a qualified buyer values the capability; it does not tell me a medicine will work.
Independent replication, disclosed development economics or repeated clinical failures could change the relative weight I put on model capability and experimental evidence.
In medtech, durability and delivery are part of the product
The BCI question is not settled by electrode count. I want to know how the signal holds up, which clinicians can place the device, what manufacturing costs and whether payment supports a service. Precision and Synchron let me follow different routes through that problem without pretending either route has won.
Long-term home use with reproducible functional benefit, acceptable safety and a workable payment path would matter more than another recording demonstration.
Read the endpoint before interpreting the result
BioAge exposed a mistake in my own work. I described a biomarker study as if it could answer a cardiovascular outcomes question. Acumen's Phase 2 Alzheimer’s trial makes the distinction harder to miss: sabirnetug reduced amyloid plaque on PET, while cognition and function did not improve against placebo. A positive result is only useful relative to the trial that produced it. I need to carry that discipline into lupus, RNA design and neural interfaces as well.
A surrogate reliably predicting patient benefit across independent trials would justify more confidence in it. Until then I will keep the two claims separate.
Runway and value capture belong in the scientific case
I want to ask early who owns the eventual asset, who funds the next experiment and who receives the upside. Turn Bio and EvolutionaryScale stay visible as past cases. I can learn from their direction of travel without inventing investor returns that were never disclosed.
Licensing businesses repeatedly producing durable revenue and attractive returns without owning therapeutic assets would make me less insistent on asset ownership.
I am still more comfortable reading an experiment than pricing its commercial value. The preference for biotech and devices could be partly taste: I find the mechanisms interesting, which is not a reason they are good investments. The next useful step is to compare the cases against alternatives and entry price, including businesses I find less exciting.
Sources for this view · Updated October 31, 2026
De Novo Design of Progressable Antibodies to Therapeutic Targets · Xaira TherapeuticsPrecision closes $250M Series D · Precision Neuroscience / GlobeNewswirePrecision releases a human nervous system atlas · Precision Neuroscience / GlobeNewswireQUELL-CV trial design and primary endpoint · BioAge LabsAlnylam and Inceptive collaboration terms · AlnylamAcumen announces topline results from Phase 2 ALTITUDE-AD · Acumen PharmaceuticalsPrevious thesis · Rewritten September 2026
Whether a healthcare software company works comes down to which budget pays for it. I can only find three that hold up: pharma marketing, a cut of the money you collect, and a billing code.
People keep arguing about whether clinical AI is a feature or a real company. I do not think the products can settle it, because they are getting harder to tell apart. Epic put its own AI charting into general release in February. Freed sells a scribe for $99 a month and Abridge sells one for about $2,500 per clinician a year. On an annual basis that is roughly a 2.1-fold price difference; it does not establish equivalent functionality.
So I stopped comparing products and started asking a duller question. Whose budget does the invoice come out of? Hospital operating margin was 1.7 percent year to date in March, and it fell about 5 percent nationally across the first five months of 2026. Sell into that budget and you are competing with nurse staffing, you go through procurement, and you have to show a return inside the same fiscal year. I had written a version of that sentence about aging products, then about stroke software, then about hospital finances, before I noticed I had been circling the same idea for nine months without ever saying it plainly.
The first budget that works is pharma marketing. OpenEvidence charges doctors nothing and sells access to verified prescribers instead. It went from about $150 million annualised at the end of 2025 to roughly $300 million by the middle of 2026, at close to 90 percent gross margin. No hospital had to approve that spend, because there was nothing to approve.
The second is a cut of the money you collect. Revenue cycle software is priced at three to five percent of what it brings in, against six to twelve percent for doing the billing in house. It pays for itself, so it never queues behind the IT budget. That is why revenue cycle was the most heavily consolidated category of the half year, why Ensemble traded at around $12 billion, and why Abridge, Nabla and Commure all moved into it. Abridge won Best in KLAS for Ambient AI within the Revenue Cycle Management category; that does not measure standalone RCM revenue.
The third is a billing code. In January the AMA gave AI services Category I CPT status for the first time, which sounds like a general opening until you read which services actually got one. Coronary plaque assessment. Cardiac risk from the fat around the arteries. Burn wound classification. AI that returns a result a hospital can bill for now gets paid for it. AI that returns a note still does not.
The part that should worry anyone holding the pharma-funded companies, me included, is that the best of the three budgets is an advertising budget. Doximity is the only public company making money from doctors the same way. It grew 13 percent last fiscal year and has guided investors to 4 percent this year, saying demand for HCP digital pharma advertising is soft and that clients are committing for shorter periods. If that pool is growing at 5 percent or less and OpenEvidence doubled inside it, then OpenEvidence is taking Doximity's share rather than opening a new market. It is still the best business I follow. But the ceiling is set by somebody else's ad budget, and I have not seen anyone price it that way. That is also the answer to the OpenEvidence question I said in August I could not work out.
Retired when the thesis broadened in October 2026. The monthly-versus-annual price comparison and KLAS award wording have been corrected.
Why the memos look like memos
Every company below runs through the same sections: problem, product, why now, market, traction, competition, team, risks against mitigants, open diligence questions, and what would change my mind. I did not invent that structure and that is the point. It is roughly what a real investment memo covers, and using it stops me writing three paragraphs about the science and forgetting to ask who pays.
Which numbers I trust
The ones that are hard to manufacture. Revenue growth with a margin attached. Patients dosed. Regulatory decisions. Whether a sophisticated partner paid real money. I discount registered users, market size projections, and anything a company can improve by changing a definition. Where a figure is company-reported and unaudited, the memo says so.
Nothing here is investment advice, and I am not managing money. These are companies I would want to own if I were, written up in memo form as a way of forcing myself to think clearly.