🎧 Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

How the Coinbase Founder’s $3.1B Longevity Biotech NewLimit Chose the Most Boring Path in Medicine.What That Means for Peptide & Wellness Bros Dragging Cutting-Edge Longevity Into Reimbursed Health

Brian Armstrong disrupted finance by attacking slow legacy systems. Then he built a longevity company that does everything the slow legacy way. That tension turns out to be the most important strategic signal in biotech right now…

Listen now

3 hours ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of Contents

1. A crypto guy walks into a biotech and picks the most boring possible disease

2. What NewLimit is actually doing, and why $435M just chased it

3. Meanwhile, on the other side of the canyon: the peptide and wellness-bro economy

4. The wall everybody keeps running into: aging is not a disease the FDA will pay for

5. The peptide reckoning of 2026: Category 2, a July docket, and a Health Secretary on a podcast

6. Where the lab and the locker room actually meet, and where they don’t

7. What the analytical reader should take away before the next round closes

Abstract

NewLimit, the epigenetic reprogramming company co-founded by Coinbase’s Brian Armstrong, just closed a $435M Series C led by Founders Fund at a $3.1B valuation, with first-in-human trials slated for 2027 in alcohol-induced liver disease. The interesting part is not the cap table, it’s the strategy: the guy from the most disruptive corner of finance picked the single most conventional path in drug development, which is to take a real cellular mechanism, point it at a named disease with an ICD code, and grind it through an IND. That choice sits in sharp contrast to the cash-pay longevity world that has exploded in parallel, where Function Health ($499/yr, roughly 100k members, plus an Ezra MRI acquisition) and Superpower ($199/yr, 100-plus biomarkers, a $30M Forerunner round) sell biological-age dashboards, and where the peptide crowd traffics in BPC-157, TB-500, MOTS-c and a dozen other molecules the FDA shoved into Category 2 of the 503A bulks list back in September 2023. Two FDA actions in 2026 (a July 23-24 advisory committee on reinstating roughly a dozen peptides under Docket FDA-2025-N-6895, and an April 30 move to pull compounded GLP-1s off the 503B list) plus an unfinished TAME trial define the whole integration question. This piece argues the canyon between frontier longevity science and reimbursed medicine is bridged by exactly one thing the wellness world keeps trying to skip: a disease indication with evidence behind it. Armstrong figured that out. Most of the bros have not.

A crypto guy walks into a biotech and picks the most boring possible disease

Read more

NewLimit just closed a $435M Series C at a $3.1B valuation. First human trial in 2027. Target: alcohol-induced liver disease.…

Read more

🎧 Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Lassie’s $47M a16z Round And The Bet That Autonomous AI Can Replace The Dental Front Desk: Why Sitting In A Back Office Posting 837D Claims By Hand Became A Service-As-Software Wedge Into Healthcare

Lassie raised $47M from a16z to autonomously run dental billing. No human biller. The agent logs into payer portals, pulls remits, posts payments, and confirms cash in the bank. 700+ practices, 49 states. Here is why this is a bigger story than it looks…

Listen now

19 hours ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Abstract

• Lassie raised a $35M Series A led by a16z (Alex Rampell on the board), bringing total funding to $47M. Advisors include Ed Zuckerberg and ex-Robinhood CFO Jason Warnick.

• The pitch: AI that runs small businesses, starting with doctors’ offices. Live in 700+ dental practices across 49 states, claiming 30 hours of labor per month per practice, roughly 250,000 hours a year in aggregate.

• The wedge is dental revenue cycle, arguably the gnarliest, most fragmented back office in all of healthcare. The agent logs into payer portals, pulls reimbursements, reconciles against the practice management system, and verifies cash in the bank.

• The real story is not the demo. It’s whether autonomous portal automation survives payer countermeasures, HIPAA exposure, and the accountability gap when an agent misses a timely-filing deadline.

• Target is $100M ARR within a year on five-figure ACVs. The open question for everyone watching: does a dental wedge generalize, or is this a great single-vertical RCM company wearing a horizontal costume?

Table of Contents

1. The news, in plain terms

2. The method actor and why the origin story matters

3. Why dental is the wedge

4. What the agent actually does under the hood

5. The incumbents it steps over

6. Why a16z wrote the check

7. The part nobody tweets about

8. Compliance, liability, and the accountability gap

9. The business model and the $100M ARR question

10. Does the wedge generalize

11. The bottom line

The news, in plain terms

Steijn Pelle, an early product manager at Robinhood and Coinbase, and Frederic Renken, the first product hire at Superhuman, just announced a $35M Series A for their company Lassie, led by Andreessen Horowitz. That brings total money in to $47M. Alex Rampell, the Affirm co-founder and a16z general partner, is taking a board seat. The advisor list is a little wink at the founders’ pedigree: Ed Zuckerberg, the dentist (yes, that one’s dad), and Jason Warnick, Robinhood’s former CFO. The cap table also picked up Rahul Vohra of Superhuman, Zach Perret of Plaid, Taavet Hinrikus of Wise, Gokul Rajaram, and Brian Balfour, plus earlier backers SV Angel, Homebrew, and Go Global Ventures. So this is very much a consumer-fintech mafia pointing its guns at healthcare admin.

The product claim is the headline worth chewing on. Lassie says it’s building AI that runs small businesses, and it’s starting with doctors’ offices. Today that means dental. The company says it’s live in more than 700 practices across 49 states (somebody in the last state, call them) and that it hands each practice about 30 hours of labor a month back, which they roll up to roughly 250,000 hours a year across the base. Note the framing. Not 30 hours of time saved. 30 hours of labor provided. The whole pitch is that this is a worker, not a tool. Whether that distinction holds up under load is the entire investment thesis, and most of what follows.

Read more

Dental RCM is a swamp most automation has failed to cross. Claims go out as 837D transactions. Remits…

Read more

🎧 Part I Podcast free on Spotify.

Thoughts on Healthcare Markets & Technology

Part I: How the Trump Administration and a Cohort of AI Startups Are Building a Regulatory On-Ramp for Autonomous AI Doctors, and Why Working Physicians Think the Genie Is Already Out of the Bottle

The administration is building a regulatory pathway for AI systems that diagnose and prescribe with no physician in the loop. Utah already has a pilot running. The Medical Licensing Board asked for an immediate suspension…

Listen now

2 days ago · Thoughts on Healthcare

🎧 Part II Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Part II: How the Trump Administration and a Cohort of AI Startups Are Building a Regulatory On-Ramp for Autonomous AI Doctors, and Why Working Physicians Think the Genie Is Already Out of the Bottle

The administration is building a regulatory pathway for AI systems that diagnose and prescribe with no physician in the loop. Utah already has a pilot running. The Medical Licensing Board asked for an immediate suspension…

Listen now

2 days ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of Contents

The genie nobody put back

What the administration actually did while everyone was watching tariffs

Utah, prescriptions, and the doctrine nobody at the pilot wants to discuss

The self-driving car analogy and the part where it quietly falls apart

Who is funding this and who is writing the rules

What the evidence says when you stop reading the press releases

Certuma, Doctronic, and the dream of an FDA-stamped robot internist

The plumbing problem: liability, reimbursement, and the corporate practice of medicine

What to actually watch over the next eighteen months

Abstract

The Washington Post reported on June 4, 2026 that a group of MAHA-aligned and tech-friendly officials inside HHS, CMS, and FDA are laying groundwork for AI systems that diagnose and prescribe with little or no human in the loop. The reporting is thin on signed regulation and heavy on intent, which is exactly why it matters. Key data points worth holding onto:

Money on the table: roughly 50M dollars in federal research awards earmarked for conversational cardiovascular AI, with Anthropic, AWS, Certuma, Doctronic, and several universities named as participants.

The pilot in question: a three-month-old Utah program letting chatbots handle prescription refills, currently with humans overseeing decisions, with stated plans to remove the human. Utah’s own Medical Licensing Board has already asked for an immediate suspension.

The evidence base: an Oxford Internet Institute study in Nature Medicine, 1,200 volunteers, where chatbots landed the right condition about 34 percent of the time and did worse than plain Google at steering people toward correct decisions.

The demand-side argument: roughly one third of Americans already use AI for medical guidance per KFF, and half of US counties lack a single cardiologist or ob-gyn, which is the shortage story the administration keeps leaning on.

The legal wall: no state board and no part of FDA currently lets a fully autonomous AI practice medicine, so the entire effort is about building a pathway, not approving a product.

The bottom line for operators and investors: the regulatory posture is shifting faster than the clinical evidence, the corporate practice of medicine doctrine is the sleeper risk, and liability allocation remains completely unsettled. That gap is where the next two years of dealmaking, litigation, and lobbying will happen.

The genie nobody put back

Every wave of health tech hype has a founding anecdote, and this one has a good one. Amy Gleason, who now runs the US DOGE Service after Elon Musk handed it off and who advises RFK Jr at HHS, watched her daughter Morgan feed sixteen years of carefully kept medical records into ChatGPT after more than a decade fighting an autoimmune disorder. The model came back with a different read than Morgan’s human doctors, and that new framing reportedly got her into a clinical trial she had been shut out of. It is the kind of story that converts skeptics, and it converted Gleason. Her line to the Post was that the difference AI is making is visible and that the genie is out of the bottle.

It is worth sitting with that phrase for a second, because it is doing a lot of work. The genie metaphor smuggles in a conclusion: that autonomous medical AI is inevitable, already loose, and that the only sane move is to manage the escape rather than try to stuff it back. That framing is convenient if the goal is to clear regulatory brush. It is less convenient if the question is whether a language model that agrees with whatever a frightened patient tells it should be allowed to write a prescription at 2 a.m. with no clinician anywhere in the chain.

The anecdote also quietly does the thing that makes clinicians grind their teeth. One patient with sixteen years of structured records, a rare disease, and the literacy to upload everything and read the output critically is not the median user. The median user is someone with a sore throat, no records, a vague sense of dread, and a strong incentive to be told they are fine. Generalizing from the best-case n of 1 to a population health policy is the oldest move in digital health, and it is back in fashion.

What the administration actually did while everyone was watching tariffs

Strip away the vibes and look at the concrete actions, because there are a few and they are not nothing.

First, the money. The administration plans to put up around 50M dollars in research awards for developers building conversational AI that can deliver cardiovascular care, the specific scenario being someone calls a provider with heart attack symptoms and an AI fields the call. The named participants are a tell: Anthropic and AWS on the infrastructure and model side, startups Certuma and Doctronic on the application side, plus a set of top universities to lend academic cover and, more importantly, to generate the safety data everyone keeps insisting will exist someday. Worth noting for the record that Jeff Bezos owns the Post and AWS is in the mix, which the paper itself flagged.

Second, FDA built a fast track for digital health tech, AI tools and wearables included, framed around chronic disease monitoring. Fast track is a loaded term in this space. FDA already has the De Novo and 510(k) and PMA pathways, plus a real precedent that the breathless coverage tends to skip: an autonomous diagnostic AI for diabetic retinopathy was authorized back in 2018, allowed to deliver a screening result in a primary care setting with no eye doctor reading the image. So autonomous AI making a clinical call without a human in the loop is not science fiction and is not unprecedented. It exists in a tightly bounded, single-disease, screening-only box. The leap the entrepreneurs want is from that box to general-purpose diagnosis and prescribing, which is a different animal entirely, and pretending the precedent transfers cleanly is sleight of hand.

Third, a CMS-adjacent program letting Medicaid reimburse AI-powered wellness apps for chronic disease management. Reimbursement is the part operators should care about most, because in US healthcare nothing scales until somebody pays for it, and Medicaid signaling willingness to pay is a louder signal than any white paper.

Fourth, and most consequential, the Post reported that administration figures are working on a pathway to regulate independent AI doctors, with Gleason explicitly comparing it to the decades-long grind that moved self-driving cars from closed test tracks onto public roads. Mehmet Oz, running CMS, told an industry crowd in March the agency was in talks to bring AI agents to every beneficiary by year end, citing the doctor shortage. That is a CMS administrator floating universal deployment on a calendar measured in months, which is either ambitious or unhinged depending on which side of the exam table you sit.

Utah, prescriptions, and the doctrine nobody at the pilot wants to discuss

The center of gravity right now is a three-month-old Utah pilot that lets AI chatbots handle prescriptions instantly. Humans currently oversee the bot’s decisions, but the stated plan is to make it fully autonomous, which is the part that turned a sleepy state pilot into a fight. Doctronic is the partner running it, and the company raised 65M dollars over the past year, with co-founder Matt Pavelle saying out loud that this administration shows a willingness to experiment he has not seen before.

Utah’s Medical Licensing Board did not take it well. The board asked regulators for an immediate suspension and made a point that sounds bureaucratic but is actually the whole ballgame: prescription refills require physician authorization for a reason. Refills look trivial, which is precisely why they are a clever wedge. A refill feels like data entry, not medicine. But the authorization requirement is not about the typing. It is the legal moment where a licensed human takes responsibility for the clinical decision, checks for interactions, notices that the patient asking for a third refill of a controlled substance maybe should not get one, and can be held accountable if it goes wrong. Automating the refill quietly automates away the accountability, and the board clearly understood that the wedge does not stay at refills.

Sitting underneath all of this is a doctrine most of the AI optimists either do not know about or are choosing to ignore: the corporate practice of medicine. Most states bar corporations and non-physicians from practicing medicine or employing physicians to control clinical judgment, a holdover meant to keep business incentives from overriding patient care. A chatbot owned by a venture-backed company that diagnoses and prescribes is, on its face, a corporation practicing medicine. Telehealth companies have spent a decade building elaborate friendly-PC and management-services-organization structures to tiptoe around CPOM. An autonomous AI prescriber detonates the whole framework, because there is no friendly physician to point to, there is a model weights file and a Stripe integration. Nobody pushing these pilots has a clean answer for who holds the license, and that is not a detail. It is the foundation.

The self-driving car analogy and the part where it quietly falls apart

The robotaxi comparison is everywhere in this coverage, and it is worth taking seriously precisely because it is seductive and wrong in a specific way. Cicero’s health policy director Adam Meier, formerly head of Montana’s health department, pointed out that robotaxis run today in San Francisco, LA, and Phoenix, and that it took years of testing to move from controlled settings to public roads while eventually showing a driverless car can be as safe as a human. The cardiologist running the administration’s cardiovascular grant program made the same move on LinkedIn, comparing the moment to having a class of new medical students ready to graduate with no residency, no attendings to supervise them, and no accreditation body, while insisting the system can get there.

Here is where the analogy breaks. Self-driving cars operate in a domain with relatively clean ground truth. The car either stayed in the lane or it did not, hit the pedestrian or it did not, and every mile is logged with sensor data that can be replayed. Medicine has no such oracle. The right diagnosis is often contested among experts, outcomes play out over months or years, confounders are everywhere, and the patient frequently does not tell the truth, sometimes on purpose. Generating the safety data that the grant program is explicitly designed to produce assumes you can define safety as crisply as a collision. You cannot. A missed early cancer does not register as a crash. It registers as a death two years later that gets attributed to the disease, not to the bot that told someone their symptoms were probably stress.

The other half the analogy buries: robotaxis are geofenced. Waymo does not drive everywhere. It drives mapped, sunny, well-understood streets and refuses the hard cases. The medical equivalent of geofencing is narrow, single-condition deployment with hard refusal on anything ambiguous, which is roughly the diabetic retinopathy model from 2018. That is the responsible version. What the entrepreneurs are pitching is the opposite of geofencing. It is a general-purpose primary care physician in a chatbox, which in driving terms is Level 5 autonomy on every road in any weather, the thing the actual autonomous vehicle industry has spent a decade learning to stop promising.

Who is funding this and who is writing the rules

Follow the money and the same names keep surfacing. The Cicero Institute, a think tank funded by right-leaning tech entrepreneur Joe Lonsdale, is pushing model legislation that would let states stand up pilots like Utah’s. Lonsdale is also a main funder of Certuma. So the entity drafting the bills to legalize autonomous AI prescribing and the company building an autonomous AI prescriber share a backer. That is not a scandal, it is just how policy entrepreneurship works in this country, but anyone evaluating the regulatory tailwind should price in that some of the wind is being generated by the people who profit from where it blows. Doctronic, fresh off 65M dollars, plans to press the Cicero model bill in state legislative sessions later this year, which means the playbook is the familiar one: run a pilot, generate a sympathetic anecdote, draft a model bill, and replicate state by state before federal regulators wake up.

The cast on the government side is the part operators should map carefully. Gleason at DOGE with an HHS advisory role and a personal conversion story. RFK Jr at the top of HHS, where the MAHA framing of chronic disease as a national emergency provides the rhetorical justification for almost any intervention labeled as disease prevention. Oz at CMS talking about every beneficiary. A cardiologist administering the grant program who genuinely believes in an eventual path and is candid that the supervisory infrastructure does not exist yet. This is a coalition of true believers, anti-regulatory instinct, and enormous capital, which is exactly the combination Robert Wachter, the chair of medicine at UCSF, flagged when he said the entrepreneurs are saying the quiet part out loud. Wachter, who just published a book on AI in medicine, put it plainly: a pro-business, anti-regulatory administration plus money plus a faction that wants to move fast is a specific and combustible mix.

What the evidence says when you stop reading the press releases

This is the section the pitch decks skip. The peer-reviewed picture is, to be generous, mixed, and to be accurate, alarming for anyone proposing autonomy.

The headline study comes from the Oxford Internet Institute, published in Nature Medicine. Researchers took 1,200 volunteers, handed them detailed clinical scenarios, and had them act as patients in conversations with chatbots built on ChatGPT and Meta’s Llama. The bots identified the medical condition correctly about 34 percent of the time. Worse for the autonomy thesis, the systems were essentially no better and in some respects worse than plain Google at guiding users toward the right medical decision. The crucial finding is not the raw accuracy number, which is bad enough. It is the gap between the model alone and the model plus a real human under stress. These same systems pass medical licensing exams and beat doctors on certain complex diagnostic vignettes in controlled conditions. Put them in front of an actual nervous person who describes symptoms badly, omits the embarrassing detail, and anchors on their own theory, and performance collapses. The exam is not the job. The exam never was the job.

Then there is sycophancy, which is the technical term for the documented tendency of these models to tell users what they want to hear. A Duke biomedical engineering researcher who studied chatbot responses to health questions on Reddit at scale made the point that this people-pleasing reflex, mildly annoying when a chatbot flatters your business plan, becomes genuinely dangerous in a clinical setting where the patient wants reassurance and the model is optimized to provide it. A reassurance machine pointed at a population of anxious sick people is not a neutral tool. It has a thumb on the scale toward you are fine, which is the single most dangerous output in primary care, because the job of primary care is catching the rare bad thing hiding inside the common benign complaint.

The failure modes are not theoretical. A Doctronic chatbot, a different deployment than the Utah one, was reportedly goaded by users into saying it would prescribe fentanyl. Pavelle’s defense was that the drug was never actually prescribed because the system blocks opioid requests, which is true and also exactly the point. The safety came from a hardcoded guardrail bolted on after someone thought of that specific abuse, not from clinical judgment. Every guardrail is a patch for a failure someone already imagined. The failures nobody imagined yet are the ones that kill people, and Wachter said the quiet thing here too: at some point the system gets a level of trust it has not earned, someone gets hurt, and probably someone gets killed, and you can feel that risk growing.

Certuma, Doctronic, and the dream of an FDA-stamped robot internist

The most revealing characters are the founders, because they are refreshingly honest about the ambition. Martin Varsavsky, a serial entrepreneur best known for building a large fertility clinic chain, started Certuma after stewing for weeks waiting on a cardiologist appointment. His complaint is legitimate and widely shared: half of US counties lack a single cardiologist, and ob-gyn coverage is similarly thin in vast stretches of the country. The access problem is real, the rural physician desert is real, and the AAMC has projected physician shortages running into the tens of thousands over the next decade. The demand-side case writes itself.

Varsavsky’s stated goal is for Certuma to be the first FDA-approved independent AI physician, a chatbot that checks symptoms, issues a diagnosis, and prescribes. His CMO, Armando Cuesta, who is an actual physician, reportedly likes the provocative working title for their book, something along the lines of the last doctor, while Varsavsky thinks that goes too far. Both expect that within a few years many medical services, primary care especially, will be handled entirely by autonomous AI. Internationally they are moving faster, having worked with the medical regulator in Varsavsky’s native Argentina, where a right-wing president friendly to deregulation has championed the approach, to offer prescriptions and advice through Certuma’s consumer-facing chatbot.

The Argentina detail deserves a flag for anyone doing diligence. Regulatory arbitrage is a feature of this strategy, not an accident. Run the aggressive version where the rules are loose, generate operating data and revenue, then carry the deck back to US regulators and argue the thing already works abroad. It is the same pattern crypto, gene therapy tourism, and a dozen other frontier industries have run. The data generated in a permissive regime is real data, but it is data about what happens in that regime, with its population, its liability environment, and its standard of care, none of which transfer cleanly to a US courtroom or a US payer.

The honesty of the founders is genuinely useful, though. They are not pretending this is a clinical decision support tool meant to assist a doctor. They are explicit that the doctor is the thing being replaced. That clarity matters because it collapses the usual rhetorical dodge. For years, every medical AI company insisted it was just augmenting clinicians, keeping a human in the loop, never replacing judgment. Certuma and Doctronic are dropping the act, and that should make the regulatory conversation more honest even as it makes the clinical risk more acute.

The plumbing problem: liability, reimbursement, and the corporate practice of medicine

Here is the stuff that determines whether any of this becomes a business or stays a demo, and almost none of it gets airtime in the coverage.

Start with liability. When a physician misdiagnoses, malpractice law has a century of precedent for who pays. The standard of care, expert testimony, the physician’s license and malpractice carrier, all of it is established. When an autonomous AI misdiagnoses, the liability chain is a fog. Is it product liability against the model developer, which would treat the diagnosis like a defective toaster. Is it medical malpractice against an entity that has no license to commit malpractice with. Is it the deploying clinic, the prescribing pharmacy, the cloud provider. The legal theories do not line up cleanly with any existing category, and until they do, no serious malpractice carrier knows how to price the risk, which means either nobody insures it or someone insures it badly and blows up. The Pennsylvania action where Gov. Josh Shapiro’s administration is going after Character.AI for allegedly presenting its chatbot as a licensed medical professional is an early read on where courts and AGs will land, and the early read is unfriendly. Unauthorized practice of medicine statutes exist in every state, and a chatbot that diagnoses without a license is squarely in their crosshairs regardless of what any federal pilot says.

Reimbursement is the second piece. The Medicaid pilot reimbursing AI wellness apps is a toe in the water, but wellness app reimbursement is a long way from a CPT code that pays a chatbot for an E/M visit. CMS would have to decide an AI can bill for an evaluation and management encounter, which raises questions that make the agency’s lawyers visibly age: who is the rendering provider, what NPI goes on the claim, how does the documentation requirement work, what happens to fraud enforcement when the provider is a model that can generate infinitely many perfectly documented notes. Oz floating AI agents for every beneficiary is easy to say at a conference and extraordinarily hard to operationalize through the actual claims plumbing.

And then CPOM again, because it cannot be waved away. Even if FDA clears a product and CMS pays for it, most states still prohibit the corporate practice of medicine. A federal clearance does not preempt a state licensing board’s authority over who can practice medicine within its borders. This is why Utah is the test case and why the Cicero model bill matters so much, because the entire strategy depends on getting states, one at a time, to carve out statutory permission for an AI to do something the state’s own medical board currently considers illegal. Utah’s board asking for suspension three months in is a preview of the trench warfare ahead. Expect this to get fought out fifty times, with the outcome depending heavily on the local balance between innovation-hungry legislatures and protective medical societies.

What to actually watch over the next eighteen months

For operators, investors, and policy people trying to figure out where the real signal is, a few markers will tell the story better than any keynote.

Watch whether the Utah pilot actually removes the human, or whether the licensing board’s suspension request sticks and the human-in-the-loop requirement becomes permanent. That single binary outcome will tell you whether autonomy is genuinely on the table or whether this stays in assisted-decision territory dressed up in autonomy language. Watch how many states introduce the Cicero model bill and, more importantly, how many pass it versus how many die in committee after the state medical society shows up. The introduction count is noise. The enactment count is signal.

Watch the 50M dollar cardiovascular grant program for whether it publishes actual safety endpoints and whether those endpoints are defined rigorously or defined to be passed. A program designed to generate the data that justifies a conclusion already reached is not research, it is a procurement exercise with a literature review attached. The quality of the endpoints will reveal which one this is. Watch the malpractice carriers, because the first carrier to write a policy for an autonomous AI prescriber, and the terms they write it on, will reveal how the people whose actual money is at risk are pricing the danger that the founders wave off.

Watch the litigation. The Character.AI action in Pennsylvania is the opening shot, and the unauthorized practice of medicine theory is the one most likely to scale across states, because it does not require new law, it just requires applying statutes that already exist. If a state AG wins a clean case on that theory, it changes the calculus for every company in the space overnight.

And watch the failure that has not happened yet, because it will. Wachter is almost certainly right that at some point a system gets trusted past what it has earned and someone dies, and the question that determines the next decade is not whether that happens but what the regulatory response looks like when it does. If it triggers a hard rollback, autonomy gets set back years. If it gets absorbed as an acceptable cost of progress, the way a certain number of robotaxi incidents have been absorbed, then the genie really does stay out of the bottle. The uncomfortable truth sitting under the whole debate is that American healthcare already kills a lot of people through ordinary human error, and the strongest argument the optimists have is not that AI is safe, it is that the bar it has to clear is lower than anyone wants to admit. Whether that argument should win is a values question dressed up as a technical one, and the people building the on-ramp are betting the country answers it in their favor before it fully understands the question.​​​​​​​​​​​​​​​

​

A Nature Medicine study gave chatbots 1,200 real patient simulations. Correct diagnosis rate: 34%. They were worse than Google at gui…

Read more

A Nature Medicine study gave chatbots 1,200 real patient simulations. Correct diagnosis rate: 34%. They were worse than Google at guiding people to the right decision. These same models pass medical licensing exams.

The self-driving car comparison is everywhere. But robotaxis are geofenced - Waymo refuses the hard routes. The entrepreneurs are pitching a general-purpose AI that handles everything. That is Level 5 in any weather, which the auto industry spent a decade learning to stop promising.

The sleeper risk is the corporate practice of medicine doctrine. Most states bar corporations from practicing medicine. A chatbot owned by a VC-backed company that diagnoses and prescribes is, on its face, a corporation practicing medicine. Nobody running these pilots has a clean answer for who holds the license.

Subscribe to www.onhealthcare.tech for free and paid articles, podcasts, and more.

🎧 Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

How FDA’s June 2026 Draft Guidance Lets Genome Editing Sponsors Reuse CMC, Nonclinical, Bioinformatics, and Clinical Data Across Programs, and Where the Agency Still Wants Product Specific Work Done

FDA just dropped a draft guidance that every genome editing platform company should read. The question it answers: when can you stop regenerating data you already proved on a prior program…

Listen now

3 days ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of Contents

Why this guidance exists and what PDUFA had to do with it

The vocabulary problem, public knowledge versus platform knowledge

CMC, where the leverage actually saves real money

Stability and comparability, the data that travels between products

Nonclinical, riding the delivery vehicle until the cargo bites back

Bioinformatics and off-target, where the guide RNA spoils the party

Clinical data, natural history studies, and the rare disease math

How you actually submit this, and what it means for the people building these companies

Abstract

FDA’s Center for Biologics dropped a draft guidance in June 2026 on leveraging prior knowledge for human gene therapy products that incorporate genome editing, both ex vivo and in vivo. It is a PDUFA VII deliverable, comment only, nonbinding, and it tries to answer the single most expensive question in the field: when can a sponsor stop generating brand new data and instead point at something it or someone else already proved.

Quick orientation. The guidance defines three buckets: public knowledge, platform knowledge, and the umbrella term prior knowledge. Platform knowledge comes in 4 flavors (internal company, third-party via master files, publicly available, and consortium/data-sharing). It walks through 3 domains where reuse can happen: CMC (analytical methods, lot release specs, stability, comparability, process characterization and validation, facilities, cell banking), nonclinical (product-type reasoning, biodistribution, tox, genotox, DART, transgene), and clinical (trial design, clin pharm, natural history, real-world evidence, long-term follow-up). The throughline is dependence. Anything independent of the edit itself (an analytical method, a delivery vehicle’s biodistribution, a cleanroom classification) travels well. Anything sequence-specific (off-target profiles, on-target outcomes, identity, potency) mostly does not. The guide RNA is the line in the sand. References worth flagging: the Jan 2024 genome editing guidance, ICH Q2(R2), Q5D, S2(R1), S5(R3), S12, USP chapters 1042 and 1044, and 21 CFR 601.2 and 312.23(b). Net for builders: real CMC and nonclinical efficiency for platform companies, near-zero relief on the parts of the dossier that make each product its own product.

Why this guidance exists and what PDUFA had to do with it

Read more

The core idea is a split between independent and dependent attributes. Analytical methods, cleanroom classifications, delivery vehicle biodistribution - these travel …

Read more

🎧 Part I Podcast free on Spotify.

Thoughts on Healthcare Markets & Technology

Part I: Mayo Owns the Model, Microsoft Owns the Pipes: What the Mayo Clinic and Microsoft Frontier Healthcare AI Deal Reveals

Mayo Clinic will OWN the frontier healthcare AI model it is building with Microsoft. Microsoft will distribute it through Azure. That ownership split is the entire story…

Listen now

4 days ago · Thoughts on Healthcare

🎧 Part II Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Part II: Mayo Owns the Model, Microsoft Owns the Pipes: What the Mayo Clinic and Microsoft Frontier Healthcare AI Deal Reveals

Mayo Clinic will OWN the frontier healthcare AI model it is building with Microsoft. Microsoft will distribute it through Azure. That ownership split is the entire story…

Listen now

4 days ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of Contents

The short version, for the impatient

What got announced, minus the varnish

Why ownership is the actual headline

The data question, and the trick of de-identified but longitudinal

Superintelligence talk and the benchmark behind it

Azure Foundry, or how Microsoft gets paid while Mayo gets the credit

The healthcare AI announcement graveyard, and whether this dodges it

Regulation, liability, reimbursement, and what to watch

Abstract

On June 2, 2026, Mayo Clinic and Microsoft announced a collaboration to build a frontier AI model purpose-built for healthcare, combining Mayo’s de-identified clinical data and longitudinal insights with Microsoft’s compute, engineering, and what the press release calls superintelligence capabilities. Two structural facts carry the whole thing: the model will be owned by Mayo, and Microsoft will distribute it through Azure Foundry APIs. Key figures worth holding in your head: 1) Mayo Clinic Platform has been cited at roughly 54 million de-identified patient records, with Discover alone listing 13.6M+ patients, 5.8B+ images, and 2.72B+ lab results. 2) Mercy added about 15.2M de-identified records in February 2026. 3) Microsoft’s MAI-DxO hit 85.5 percent diagnostic accuracy on 304 hard NEJM cases versus roughly 20 percent for the physicians it was tested against. 4) Mayo Clinic Platform is about seven years old, and Mayo previously signed a ten-year infrastructure deal with Google in 2019. The essay below walks through the ownership structure, the data underneath it, the benchmark fueling the superintelligence talk, the distribution economics, the long history of healthcare AI deals that died on contact with workflow, and the regulatory and reimbursement traps still sitting in the road.

The short version, for the impatient

Two very large organizations got on a press release together and said they are building a frontier model for healthcare. Mayo brings the data and the brand. Microsoft brings the compute, the engineers, and a CEO who likes the word superintelligence. The part that actually matters, and the part that got buried under the usual nouns, is that Mayo owns the model and Microsoft pipes it to the rest of the planet through Azure. Everything else flows from those two facts.

Skepticism is the correct default here, because healthcare has buried a lot of these announcements in shallow graves, and the word frontier is doing roughly the same load-bearing work that the word cognitive did for IBM a decade ago. But the assets in this one are genuinely real, which is more than you could say for most of the press releases that came before it. Mayo has a productized data foundation with years of operating history. Microsoft has been quietly building a medical AI team that put up some eye-popping benchmark numbers. So the right posture is not eye-roll and not hype. It is the posture of someone who has read enough term sheets to know that the interesting risk is never in the noun, it is in the ownership, the distribution, and the boring stuff that nobody tweets about.

What got announced, minus the varnish

The literal claims, from the announcement itself: a strategic collaboration to develop and deploy a frontier AI model designed specifically for healthcare. The model combines Mayo’s global clinical expertise, de-identified clinical health data, and longitudinal insights with Microsoft’s AI, cloud, engineering, and superintelligence capabilities. It is meant to synthesize diverse clinical data to support earlier diagnoses and more personalized treatment. The frontier AI model will be owned by Mayo Clinic, and Microsoft plans to make the model available through Azure Foundry APIs. It gets deployed inside Mayo’s own clinical environment first, where it can be tested and refined on real use before it goes anywhere else. Mayo’s CEO tied the whole thing back to Mayo Clinic Platform, which he framed as a roughly seven-year-old effort to move healthcare from a pipeline to a platform model. Mustafa Suleyman, who runs Microsoft AI, said frontier medical intelligence is around the corner.

Now the varnish you can scrape off. Superintelligence capabilities is a marketing phrase, not a spec sheet, and frontier medical intelligence has been around the corner for about as long as the self-driving car has been a year away. The substance underneath the adjectives is three concrete things: a very large pile of clinical data, an unusual choice about who owns the IP, and a distribution rail. One detail that the polite framing skips is the timing. This is the same Mayo that entered a ten-year partnership with Google in 2019 to build an enclave for the ethical secondary use of clinical data. That 2019 Google deal built the Mayo Clinic Cloud and the controlled de-identified enclave that became the Platform. That arrangement was plumbing. This one is product. The plumbing partner and the product partner are not the same company, which tells you something about how Mayo thinks about keeping its options open, and about how badly Microsoft wanted the most trusted brand in American medicine on its model card.

Why ownership is the actual headline

Health systems consume models. They almost never own the frontier ones, for the same reason a hospital does not own a fab or a launch vehicle. Training and maintaining a real foundation model is a capital sink, a talent war, and a permanent ML operations headache involving drift, retraining, evaluation, and safety review that never ends. So when a nonprofit clinic says it will own a frontier healthcare model rather than rent one, that is the genuinely strange and interesting move, and it deserves more attention than the word frontier.

Ownership does a few things at once. It puts Mayo’s brand on the box, which is the entire point, because the brand is the moat. It also puts Mayo’s name on the liability, which is the part nobody mentions at the announcement. And it lets Mayo behave like a model vendor, packaging Mayo’s clinical judgment as something other organizations can call on demand. For Microsoft, letting Mayo hold the IP is not generosity, it is risk transfer with a side of strategy. Microsoft is a three trillion dollar magnet for plaintiffs, and the last thing it wants is to be the named clinical decision-maker when a model in Tulsa suggests the wrong workup. Far better to be the cloud and the engineering shop while the doctor’s name is on the diploma. Microsoft eats the training compute, presumably, and gets paid on the back end through Azure consumption every time anyone uses the thing. That is a very old cloud playbook wearing a white coat. The catch for Mayo is that owning a flagship model puts it in quiet competition with the third-party developers it hosts on its own platform, which is an awkward thing to be when you are also the landlord.

The data question, and the trick of de-identified but longitudinal

The model is the cover band. The data is the catalog. Mayo Clinic Platform has been described as the largest portfolio of high-quality de-identified data in the world, built on a collection of around 54 million patient records. Its Discover offering alone has been listed at more than 13.6 million patients, over 5.8 billion images across CT, MRI, and PET, and over 2.72 billion lab results. The network keeps growing through partnerships. In February 2026, Mayo expanded its collaboration with Mercy, one of the fifteen largest U.S. systems with 55 hospitals, adding visibility into more than 15.2 million de-identified patient records. The global side, Mayo Clinic Platform_Connect, has pulled in Hospital Israelita Albert Einstein in Brazil, Sheba Medical Center in Israel, and University Health Network in Canada, alongside founding member Mercy. The whole arrangement runs on a Data Behind Glass model, where each organization’s de-identified data stays under its own control and is analyzed in place rather than shipped out.

Here is the part the data nerds will notice, because it is a genuinely clever piece of engineering hiding inside a bland phrase. The announcement says de-identified and longitudinal in the same breath. Those two words do not naturally get along. HIPAA Safe Harbor de-identification strips eighteen identifiers, and once you do that naively, you have shredded the thread that lets you follow one patient across years of encounters, which is exactly what longitudinal means. Keeping the thread while still being de-identified is not free. It takes privacy-preserving record linkage and tokenization, the unglamorous machinery that lets you say this de-identified encounter and that de-identified encounter belong to the same person without anyone ever learning who the person is. So the longitudinal insights claim is not fluff. It implies real linkage infrastructure under the hood, the kind that takes years and lawyers to build, and it is a meaningful part of why this data is worth more than a hundred random EHR dumps glued together.

The thing nobody on the announcement wants to dwell on is generalizability. Mayo’s own patients skew toward the tertiary and quaternary referral end of medicine, which is the polite way of saying complex, often wealthier, disproportionately Upper Midwest, and not especially representative of the country, let alone the world. A model that learns mostly from what walks into Rochester learns what walks into Rochester. That is not the same population that shows up at a Phoenix safety-net clinic or a rural emergency department at two in the morning. Mayo clearly knows this, which is the entire reason for Mercy and the global partners and the talk about depth, breadth, and spread of data. Whether bolting on Mercy and a handful of international centers actually fixes distribution shift is an empirical question that has not been answered yet, and that nobody can answer from a press release. There is also a quieter irony. Referral-grade complex cases make for spectacular diagnostic benchmarks and lousy training data for the boring eighty percent of medicine, which is sore throats, blood pressure, and people who forgot to take their meds.

Superintelligence talk and the benchmark behind it

The confidence in that superintelligence language did not come from nowhere. It came from a benchmark. Microsoft built a Sequential Diagnosis Benchmark from 304 complex New England Journal of Medicine clinicopathological cases and ran its MAI Diagnostic Orchestrator against 21 experienced physicians. The orchestrator reached the correct diagnosis 85.5 percent of the time, while the physicians averaged about 20 percent. The design is model-agnostic, splitting the work across several virtual doctors that argue with each other in a chain-of-debate style, one keeping a ranked differential, one choosing the next test, one playing skeptic, one watching the bill, and the best results came when the orchestrator was paired with OpenAI’s o3. Microsoft has a second tool aimed at rare disease called DxGPT, which runs in the Madrid regional health service, is available to about 6,000 doctors, has reportedly touched around 500,000 patients, and lands near 60 percent accuracy overall. All of this came out of a health unit Microsoft stood up quietly in late 2024 under Suleyman, the DeepMind co-founder, with Karen Simonyan as chief scientist and a new MAI Superintelligence Team, where Suleyman has talked about a line of sight to medical superintelligence in two to three years.

Now for the asterisk that is not an asterisk, because asterisks would mess up your copy and paste. In that famous test, the physicians were not allowed to look anything up. No colleagues, no reference tools, no point-of-care lookups, none of the scaffolding that every actual doctor leans on every single day. So four times better than doctors is more precisely four times better than doctors working blindfolded with both hands tied. The NEJM cases themselves are selected to be diagnostic puzzles, the medical equivalent of the hardest crossword in the paper, the zebras that get written up precisely because they are weird. Winning a benchmark built entirely out of zebras is real and genuinely impressive engineering, and it says something true about machine reasoning. It just does not say what the word superintelligence wants you to think it says, because roughly ninety-five percent of medicine is horses, paperwork, follow-up, and the much harder problem of getting a human being to actually act on the output. The benchmark measures the part that was already the most automatable. The road from there to bedside is mostly the unautomated part.

Azure Foundry, or how Microsoft gets paid while Mayo gets the credit

Distribution is where the money quietly lives. The plan is to expose the Mayo-owned model through Azure AI Foundry, which is Microsoft’s developer platform and model marketplace, so any organization already on Azure can call Mayo’s model the way it would call any other endpoint. Strip away the mission language and the engine is simple. Every single inference is Azure compute consumption. Microsoft monetizes the tokens and the cloud underneath them, Mayo monetizes the brand and the data and presumably some licensing, and the meter never stops running. It is a vending machine where Mayo’s logo is on the front and Microsoft owns the coin slot.

The strategic read is even cleaner. Microsoft already owns Nuance and its DAX ambient documentation product, a deal worth roughly twenty billion dollars, which means it already has a front door into the workflow of thousands of hospitals through the ambient scribe sitting in the exam room. A Mayo-branded reasoning model plugs into that funnel without much friction. This is Microsoft’s answer to Google, which has its own medical models and was Mayo’s original infrastructure partner, and to OpenAI, which is pushing into health on its own. Owning the distribution rail for the single most trusted brand name in American medicine is worth a great deal more than owning any individual model, because models are becoming commodities and trust is not. Mayo’s name on the box, Microsoft’s meter on the pipe. For the health-tech founders reading this, the uncomfortable wrinkle is the platform versus vendor tension, which is no longer theoretical. Mayo Clinic Platform hosts third-party developers and validates other people’s algorithms, and Mayo is now also shipping a flagship model of its own. Building your company on a landlord who just opened a competing storefront in the same building is a strategy, but it is the kind of strategy you want to have read the lease for.

The healthcare AI announcement graveyard, and whether this dodges it

It would be irresponsible to write any of this without visiting the cemetery. The headstone everyone knows reads IBM Watson and MD Anderson, a partnership that burned through something like sixty-two million dollars before it was scrapped around 2017, and that reportedly produced unsafe recommendations in testing. IBM eventually sold off Watson Health to Francisco Partners in 2022 for a figure reported around one billion dollars, which sounds like a lot until you remember how much went in and how much was promised. The pattern that killed it is the pattern that kills most of them. The demo is magic. Then comes EHR integration, then clinician trust, then workflow, then validation, then reimbursement, and somewhere in that gauntlet the magic quietly suffocates. Google Health got reorganized into other parts of the company. Babylon went from a multibillion dollar valuation to a fire sale. The graveyard is not small and it is not full of dumb people.

So the fair question is whether this one is built differently, and the honest answer is partly yes and partly the same trap with a nicer logo. On the yes side, the data foundation is real and already a product, not a slide, with years of operating history and even a validation arm meant to test models for bias across populations before they reach the clinic. Mayo owning the model means the incentives around clinical safety sit with the party whose name is on the door, which is a healthier alignment than a tech vendor optimizing for a demo. And deploying inside Mayo first means the thing gets beaten up on real cases before anyone tries to export it. On the same-trap side, the announcement describes a model meant for the broadest scope of clinical reasoning, and the broadest possible scope is exactly the scope that ate Watson alive. The wins in healthcare AI have been narrow and specific, the ambient scribe, the sepsis early warning, the imaging triage that flags the bleed. General clinical reasoning packaged as a product has a perfect, undefeated record of humbling the people who try to build it. Betting that this time the boss fight goes differently is a bet, not a conclusion.

Regulation, liability, reimbursement, and what to watch

Start with the regulator, because the regulator does not care about your benchmark. The FDA’s device framework was built for software that behaves predictably, a locked or change-controlled algorithm with a narrow intended use and a predetermined change control plan when it learns. A frontier model doing open-ended clinical reasoning fits inside that framework about as well as a whale fits inside a kiddie pool. The 21st Century Cures clinical decision support carve-out only rescues you if a clinician can independently review the basis for the recommendation, which is a tall order when the basis is a hundred billion parameters that cannot explain themselves in a way that survives a deposition. So the realistic near-term path is the one everyone uses, which is to ship it as informational and not as a medical device, a polite fiction that works right up until it does not.

Then the liability, which follows ownership like a shadow. Mayo owns the model, so Mayo owns the wrong answers. That is a brand the approximate size of a small country putting its name on probabilistic outputs. Inside Mayo’s own walls, with Mayo clinicians supervising and a malpractice posture Mayo already understands, that is manageable and even prudent. The moment it goes out through an Azure API to a hospital in another jurisdiction calling it for its own patients, the questions about standard of care, vicarious liability, and who exactly is practicing medicine get strange in a hurry, and the lawyers will get there well before the engineers do. Reimbursement is the quietest killer of the three. There is still no CPT code that reads frontier model reasoned thoughtfully about the patient. Fee-for-service pays for codes and procedures, not for cleverness, so the money has to come from brand licensing, from Azure consumption, from efficiency like fewer unnecessary referrals and faster diagnoses, and maybe from value-based contracts where a better diagnosis genuinely lowers total cost of care. That is a real business, but it is a slower and grindier business than the announcement’s energy suggests.

As for what to actually watch, keep an eye on a few specific things rather than the next round of adjectives. Watch whether the validation studies, when they appear, report performance on representative populations and not just the NEJM zebras, because that gap is where the generalizability problem either gets solved or gets quietly buried. Watch whether Azure Foundry pricing and the Mayo licensing terms ever see daylight, since the economics are the whole game and they are currently invisible. Watch whether Mayo Platform’s hosted developers start grumbling about competing with the house model, because that is the canary for the platform versus vendor conflict. Watch the FDA’s posture on generative clinical reasoning, which is the rail everything else runs on. And watch the simplest signal of all, which is whether anyone outside Rochester is running this in production within about eighteen months, or whether around the corner stays a corner. Frontier medical intelligence has been one corner away for a while now. The data here is real, the ownership structure is genuinely smart, and the distribution play is the best one in the building. The only thing left to prove is the part that has beaten everyone who came before, which is the short, brutal, unglamorous distance between the benchmark and the bedside.​​​​​​​​​​​​​​​​

Mayo brings 54M+ de-identified patient records, 5.8B+ images, and years of longitudinal linkage infrastructure. Microsoft brings compute, engineers, and Azure distribution. Neither coul…

Read more

Mayo brings 54M+ de-identified patient records, 5.8B+ images, and years of longitudinal linkage infrastructure. Microsoft brings compute, engineers, and Azure distribution. Neither could do this alone.

The benchmark driving the superintelligence language: Microsoft’s MAI-DxO hit 85.5% on 304 hard NEJM cases. Physicians averaged 20%. Catch: physicians were not allowed to look anything up. Not quite the same test.

Microsoft already owns Nuance DAX, the ambient scribe in thousands of hospital exam rooms. A Mayo-branded reasoning model plugs into that funnel directly. Models are becoming commodities. Trust is not.

Subscribe to www.onhealthcare.tech for free and paid articles, podcasts, and more.

🎧 Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Why ASCO Stood Up for Daraxonrasib: RASolute 302, Pancreatic Cancer Survival, and the New KRAS Investment Thesis

40,000 oncologists gave a standing ovation to a survival curve at ASCO 2026. That does not happen. Here is why it did…

Listen now

5 days ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of contents

The standing ovation, and why a room that claps for hazard ratios came unglued

RASolute 302 by the numbers

The forty-year wall, and how RAS(ON) tri-complex chemistry got past it

The sleeper headline, it worked no matter which mutation or none at all

Resistance, and why this drug might age differently

The franchise behind the pill

Following the money

The parts that did not get a standing ovation, price, access, and the first-line question

What it signals for everyone still chasing the undruggable

Abstract

• RASolute 302, a global Phase 3 in second-line metastatic pancreatic ductal adenocarcinoma, randomized 500 patients (248 daraxonrasib, 252 chemo) across 59 sites in six countries. Median OS 13.2 months vs 6.7, hazard ratio 0.40, p below 0.0001. That is a roughly 60 percent reduction in risk of death.

• Median PFS 7.2 to 7.3 months vs 3.5 to 3.6. ORR roughly 31 to 33 percent vs 11 to 12. Time to pain deterioration 9.2 vs 3.8 months. Better tolerated than chemo, with rash and stomatitis the main dose-reducers.

• Benefit held across RAS G12 mutants (about 459 of 500 patients) and in the overall population, including RAS wild-type. The “works regardless of mutation” finding is the structural story.

• Daraxonrasib (RMC-6236) is an oral, once-daily, non-covalent RAS(ON) multi-selective tri-complex inhibitor that recruits cyclophilin A and gums up the active, GTP-bound state. This is the opposite design philosophy from the G12C(OFF) covalent drugs sotorasib and adagrasib.

• Revolution Medicines carries a market cap near 33 billion dollars, raised about 2.2 billion in April on top of an earlier 2 billion, has Breakthrough and Orphan designations, an Expanded Access Program running, and an NDA in motion. Sell-side targets sit in the 180 to 195 range.

• The pipeline is the actual asset, zoldonrasib (G12D), elironrasib (G12C), RMC-5127 (G12V), plus first-line, adjuvant, and doublet trials. Pancreatic cancer is roughly 67,000 US cases a year, 5-year survival near 13 percent, about 3 percent once metastatic.

The standing ovation, and why a room that claps for hazard ratios came unglued

Oncologists are not a soft audience. These are people who sit through hundreds of forest plots a year and react to a new median survival curve the way a poker player reacts to a decent flop, polite interest, maybe a raised eyebrow. So when 40,000 of them in a Chicago convention hall stood up and cheered for a Kaplan-Meier curve, that was not normal Sunday behavior. The positive results received a standing ovation from an audience of science and medicine experts at the ASCO annual meeting , and the video that ricocheted around the timeline showed the kind of energy you usually only see when a sports team clinches something.

The reason is worth sitting with. Pancreatic cancer has been the disease where good news goes to die. The American Cancer Society estimates about 67,000 new US cases this year and more than 52,000 deaths, with a 5-year overall survival rate of 13 percent , and that number drops to about 3 percent once the disease is metastatic. For decades the standard second-line option was more chemo that bought a patient a few extra months and a fresh set of side effects. Current second-line regimens deliver median progression-free survival of three to four months and median overall survival of six to seven months . That is the baseline a room of professionals had quietly accepted as the ceiling. Brian Wolpin from Dana-Farber walked up to the plenary podium, put up the survival data, and the ceiling moved. People who study this for a living understood instantly what they were looking at, and they got out of their chairs.

RASolute 302 by the numbers

Read more

RASolute 302 enrolled 500 patients with metastatic pancreatic cancer - a disease where 5-year survival is around 3% once it spreads. The result: 13.2 months median OS on daraxonrasib vs 6.7 months on chemo. Hazard ratio 0.40. That is rou…

Read more

🎧 Part I Podcast free on Spotify.

Thoughts on Healthcare Markets & Technology

Part I: Inside the Agentic Back Office Race for Specialty Practices: How a YC Company Hit 17x Growth and Seven Figures in ARR by Doing the Fax, Referral, Scheduling, and Collections

A YC company grew 17x and hit seven figures in ARR in under a year by not selling software. It does the fax, referral, scheduling, and collections work instead…

Listen now

6 days ago · Thoughts on Healthcare

🎧 Part II Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Part II: Inside the Agentic Back Office Race for Specialty Practices: How a YC Company Hit 17x Growth and Seven Figures in ARR by Doing the Fax, Referral, Scheduling, and Collections Work

A YC company grew 17x and hit seven figures in ARR in under a year by not selling software. It does the fax, referral, scheduling, and collections work instead…

Listen now

6 days ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of Contents

The pitch that quietly skips the software part

Anatomy of one referral, and why the fax pile never empties

The vise: costs up, rates down, payers automating the denials

The 605 million dollar elephant and the rest of the field

Why ARR here is not the ARR you think it is

Where this whole thing could break

The read for anyone writing checks

Abstract

• Plena, a 2025 YC company, says it grew 17x and crossed seven figures in committed ARR in roughly eight to ten months running referrals, fax intake, scheduling, procedure compliance, and collections end to end for specialty practices, across six specialties and eight states, with 80% of growth from word of mouth and 40+ EMR integrations.

• The pitch is not “buy our software.” It is “we do the work.” That framing is the whole story.

• Macro backdrop is brutal for independents: practice opex up 11.1% from 2024 to 2025, Medicare conversion factor down 2.8% to $32.35, denial rates climbing, payers running AI to downcode and deny faster than humans can appeal.

• Tennr sets the comp at the top of the category: $101M Series C, $605M valuation, RaeLM trained on 100M+ docs and 8,000+ payer rules, 10M docs/month.

• Real debate is business model (services-as-software priced on labor, not seats), defensibility (integration sprawl vs EHR incumbents bolting on agents), and the exception rate, which is the metric that quietly decides whether any of this is actually cheaper.

• Investor read: TAM is labor spend, not software spend, but the moat is operational and boring, and that cuts both ways.

The pitch that quietly skips the software part

Scroll healthcare X long enough and the launches blur together. Then one shows up that is worth stopping on, not because the demo is slick but because of one sentence buried in the founder story. Plena, a company that came out of Y Combinator and launched publicly in the last day or so, describes itself as the AI operating system for specialty medical practices. It says it automates the administrative workflows that keep clinics running, referrals, fax intake, scheduling, procedure compliance, and collections, end to end across the systems a practice already uses, and that in roughly ten months it grew 17x and crossed seven figures in committed ARR across six specialties and eight states, with 80% of growth through word of mouth.  The YC post on the screenshot frames it as eight months and contracted ARR, so somewhere between those two there is a real number, and either way the slope is steep.

The cofounder, Eyad Abdalla, has the resume you would build in a lab if you wanted to sell trust to doctors and engineers at the same time. Doctor’s son, engineer by trade, Waterloo then Datadog then Shopify, and his mom is a physician whose clinic became Plena’s first office and first user.  There is something funny and a little perfect about a career arc that runs through two of the better-known scaling stories in tech and lands, of all places, on the fax machine. The fax is the cockroach of American healthcare. It will outlive everyone in this essay.

Here is the line that matters, paraphrased so nobody can accuse anyone of quoting a tweet: after three years embedded inside specialty clinics, Abdalla concluded they do not need more software, they need someone to do the work, and Plena does it. Read that twice if you spend your days in health tech, because it is a quiet repudiation of the last fifteen years of the category. The dominant model was to hand a clinic a dashboard, a login, and a quarterly invoice, then call the staff time spent learning it “adoption.” Plena is selling the absence of a dashboard. The product is the outcome. The software is plumbing the customer is not really supposed to think about.

That is not a marketing trick, it is a different shape of company, and most of what follows is an attempt to figure out whether the shape holds.

Anatomy of one referral, and why the fax pile never empties

To understand why a clinic would pay someone to just make the work disappear, you have to sit with how absurd a single referral actually is. A specialty practice gets a referral by fax, portal, or email. Somebody reads it. Somebody matches it to the right patient, or creates a new one. Somebody extracts the clinical detail, checks insurance and eligibility, updates the EHR, attaches the documents, routes the task, and then chases whatever is missing, which is usually something. Plena’s own materials describe coordinators spending three to eight minutes per fax on a bucket that never empties, with 15 to 25% of referrals never getting scheduled at all.  That last number is the one that should make an operator wince. A double digit slice of demand simply evaporates inside the intake process, which means the practice paid for marketing, referral relationships, and front desk labor to generate patients it then loses to its own paperwork.

Multiply that by the dozens of distinct workflows a clinic runs every day and you get the real picture. One Plena case describes a four month, 4,800 referral backlog sitting in Epic Care Link that got cleared, with coordinators afterward processing two days of intake per shift and doing same day outreach.  A 4,800 item backlog is not a software problem in the way people usually mean it. The clinic already had software. It had Epic. The backlog existed because there were not enough human hours to feed the software, and the software did nothing on its own while it waited.

This is the gap the whole agentic back office wave is crawling into. Every year more than a third of Americans get referred to a specialist, and the process still lives in a patchwork of faxes, emails, and portals, which produces delayed care, overwhelmed staff, and missed revenue.  The technical claim Plena and its peers are making is that vision and language models can now read the messy unstructured artifacts, the scanned PDF, the handwritten note, the fax of a fax of a fax, and then take action inside the existing systems rather than dumping a structured blob into a new pane of glass for a human to retype. Plena says it reads and writes in the formats the EMR already understands, with no data migration and no parallel system, across 40 plus EMRs including the long tail like Allscripts, Practice Fusion, Office Practicum, and Intergy.  That long tail is the whole ballgame for specialty, and we will come back to why.

The detail that gives the model away as serious, not vaporware, is small. Plena notes that patients tell the clinic they think the agent is one of the staff.  If true at any volume, that is the difference between a tool and a teammate, and it is also the thing payers and compliance teams will eventually want to poke at. For now it is a clean proof that the bar has moved from “summarize this document” to “handle this human on the phone without anyone noticing.”

The vise: costs up, rates down, payers automating the denials

None of this lands without the macro, because the macro is why a busy physician would even take the meeting. Independent practices are getting squeezed from both ends at once, and the squeeze got measurably worse this year. Practice operating expenses climbed 11.1% from 2024 to 2025 while the Medicare conversion factor fell 2.8%, from $33.29 to $32.35, which is the structurally insane situation of paying more to do the same work for less money per unit of that work.  A clinic cannot cut its way out of that with a better scheduling template. It needs either more revenue per patient or radically less labor per task, and the labor market for medical front office staff is not exactly loosening.

Then there is the other side of the table, where the payers have already shown up with their own robots. This is the part of the story that deep readers will find the most interesting, because it reframes the whole thing as an arms race rather than a productivity story. Per Experian Health’s 2025 State of Claims, 41% of providers now report that more than one in ten of their claims gets denied, up from 30% three years ago, while HFMA and Kodiak data put initial denial rates near 12% in 2024 and find that up to 65% of denied claims are never reworked at all.  Sit with that 65%. Most denied revenue is not lost in a fight, it is abandoned without a fight, because the appeal costs more than the claim and a tired biller triages it into the void.

It gets sharper. Kodiak found that insurers denied more claims on clinical grounds in 2025 than in 2024, driving a 25% increase in net revenue leakage at hospitals, with payers using AI and analytics to review entire datasets instead of samples and compress audit timelines.  And the payers are not stopping at denial. Major insurers have been citing aggressive provider coding as a driver of higher 2025 and 2026 medical spend and responding with across the board downcoding strategies.  Translate that out of jargon: the plans are using automation to quietly knock a level 4 visit down to a level 3 and dare the practice to notice and contest it across thousands of claims. A human billing team cannot win that volume war. An agent that appeals every single denial with the supporting documentation attached, at zero marginal labor cost, changes the math, which is exactly why one practice administrator described that capability as finally leveling the field.

So the demand for the back office wave is not really about convenience. It is about a small business getting outgunned on automation by its largest counterparties, and reaching for the only weapon that scales the same way the other side already scaled.

The 605 million dollar elephant and the rest of the field

Plena is not the first mover here, and pretending otherwise would be silly. The company that defined investor expectations for this category is Tennr, and the gap in funding between them is enormous, which is the interesting part rather than a knock. Tennr raised a $101 million Series C led by IVP in June 2025, with Andreessen Horowitz, Lightspeed, GV, ICONIQ, Foundation Capital, and Frank Slootman in the round, founded in 2021 in New York, and by that point processing 10 million documents a month across hundreds of healthcare organizations.  That round put the valuation at roughly $605 million.  The technical pitch is a purpose built model rather than a wrapper. Tennr’s RaeLM was trained on more than 100 million deidentified healthcare documents and over 8,000 payer specific documentation requirements, so it can read scanned, handwritten, and PDF inputs and check them against payer criteria before submission.  Worth remembering the arc, because it is a tidy lesson in how this category compounds. Tennr’s first notable raise was an $18 million round from a16z in 2024 framed entirely around automating things that start with a fax.  Fax to $605 million in about fifteen months tells you how much pent up money is hunting this exact problem.

The distinction Plena is drawing, and it is a real strategic fork, is point solution versus operating layer. Plena’s framing is that unlike point solutions covering a slice of the problem, it becomes the operating layer across the whole practice, connecting the systems, teams, and workflows.  Tennr started laser focused on the referral, the highest value and most painful single workflow, and has been widening from there. Plena is claiming the wide footprint from the jump. Both bets are defensible and the honest answer is nobody knows yet which sequencing wins. Land and expand from one killer workflow is the classic SaaS playbook and it derisks early sales. Show up as the whole back office is harder to sell but stickier if you survive the install, because ripping out the thing that runs your referrals, your phones, your scheduling, and your collections is a near death experience for a clinic, and switching costs are the only moat that has ever reliably worked in healthcare software.

The rest of the field is filling in fast, and a lot of it is coming at the same labor from the revenue side rather than the intake side. There are clinical documentation and coding plays merging note generation with revenue cycle intelligence so the clean claim gets built at the point of documentation rather than reconstructed weeks later. There are ambient scribes drifting downstream into coding. There are RCM incumbents and billing platforms bolting agents onto eligibility and denials. The category does not have clean borders, which is both the opportunity, because the back office is genuinely one connected mess, and the risk, because everyone is converging on the middle from a different edge.

Why ARR here is not the ARR you think it is

This is the section the analysts should linger on, because the headline metric is doing something sneaky. When a traditional SaaS company says seven figures of ARR, it means it sold a number of seats or a platform fee, and the cost to deliver the next dollar of that revenue rounds to nothing. When a company whose entire pitch is “we do the work” says seven figures of ARR, the revenue is sitting on top of an operation that has real variable cost, because somewhere there are agents running, exceptions getting escalated, and almost certainly humans in the loop catching what the models miss. The gross margin question is therefore not a footnote, it is the entire investment thesis, and it is the thing a polished growth chart can hide for a surprisingly long time.

The broader market has a name for this now, services as software, and the bull case is enormous precisely because it is not measured against software budgets. The TAM is the labor line. A clinic does not compare Plena to its EHR subscription, it compares Plena to the salaries of the coordinators it would otherwise hire and cannot find. That is a far bigger pool, and it is why the entire enterprise software world is nervously rerating. IDC expects pure seat based pricing to be obsolete by 2028, with 70% of software vendors refactoring pricing around consumption, outcomes, or capability.  ServiceNow paid $2.85 billion for the AI agent platform Moveworks in early 2025, which is a legacy incumbent buying its way into the new model rather than waiting to be eaten by it.  The capability curve underneath is what makes the timing plausible rather than premature. Bain noted that the cost of OpenAI’s o3 reasoning model dropped about 80% in two months even as accuracy improved, which is the kind of curve that turns a workflow from “human plus app” into “agent plus API” inside a single budget cycle. 

But here is the part the cheerleaders skip. McKinsey’s framing of the near term is that agents mostly act as users of existing software, converting enterprise spend from labor to software while value accrues to whoever controls the underlying data and process.  For Plena that is encouraging, because sitting across 40 plus EMRs and actually doing the work is exactly the control position. For everyone pricing these companies, it means the revenue quality depends on how much of the labor genuinely got eliminated versus relocated. Which brings us to the uncomfortable operational truth nobody puts on a launch tweet.

Where this whole thing could break

The most clear eyed thing written about this category recently did not come from a VC, it came from practice operators, and it is worth taking seriously because it is the bear case from people with no incentive to be bearish about their own automation. One account, drawn from auditing around 1,000 eligibility checks across live physician practice environments, argues that AI often relocates administrative burden instead of reducing it, and that the pivotal metric is the exception rate, because unresolved edge cases create downstream labor that just moves the work around rather than deleting it.  If an agent handles 85% of referrals beautifully and quietly fumbles the other 15% into a new and harder to see pile, the practice has not saved money, it has changed the shape of its problem and possibly made the remaining 15% worse because the easy cases that used to keep staff in practice are gone. The honest version of every demo in this space hides inside that exception rate, and it is the first number a serious diligence process should demand and the last number any founder volunteers.

The second crack is the same one that has swallowed healthcare software companies for thirty years, and Plena is leaning straight into it on purpose. Forty plus EMRs is a flex on the website and a maintenance nightmare on the engineering roadmap, because every one of those integrations is a living thing that breaks when the vendor ships an update, and the long tail systems for specialty are exactly the ones with the worst APIs and the least incentive to keep them stable. The operating layer pitch is only as good as the day to day reliability of writing back into Practice Fusion or Intergy without corrupting a chart, and that reliability is unglamorous, expensive, and never finished.

The third crack is the incumbent question, and it is the one that should keep founders up at night more than the competitors they can see. The EHRs are not going to sit still while a startup becomes the operating layer on top of them. The large platforms have the integration that the startups are paying enormous engineering cost to fake, they have the distribution, and they are all racing to ship their own agents. The downside scenario for the whole independent agentic back office cohort is not that they lose to each other, it is that referral and intake automation becomes a checkbox feature inside the EHR a practice already pays for, the way e prescribing and patient portals did. Specialty’s fragmentation is the best defense against that, because the giants chase the big health systems first and the long tail specialty clinic is genuinely underserved, but “the incumbent is distracted” is a moat with a clock on it.

And the fourth crack is the system one, the one policy people will care about most. The Peterson Health Technology Institute has warned that under current incentives, efficiency gains from these tools accrue inside organizations rather than flowing through to lower system wide costs, and that no stakeholder is positioned to deploy AI in ways that actually reduce friction across the system.  Read in full, that is a forecast of an automation arms race where providers buy agents to fight payer agents, both sides spend more on technology, claims still get adjudicated in a millisecond duel, and the patient and the taxpayer fund both armies. A company can build a wonderful business inside that equilibrium. It is just worth naming that a thriving Plena and a less wasteful health system are not automatically the same thing.

The read for anyone writing checks

Strip the launch energy away and a few things are durably true. The pain is real, large, and getting worse on a schedule set by Medicare math and payer automation, so demand is not the question. The technical capability crossed a threshold in the last two years that makes doing the work, not just summarizing it, actually feasible, so feasibility is mostly not the question either. What is left, and what should drive the valuation, are the boring operational questions that do not fit on a slide: the true exception rate, the real gross margin once the humans in the loop are counted, the cost to keep dozens of integrations alive, and the half life of the moat before an EHR ships the same feature for free.

For investors the category framing is the trap and the prize at once. Pricing these against labor budgets makes every TAM slide look spectacular, and the services as software wave is real enough that the rerating of software multiples is not hype. But labor replacement businesses live and die on delivery quality at scale, which is an operations discipline, not a software one, and the firms that win will look more like exceptionally well run BPOs with a model in the loop than like classic high margin SaaS. That is not a criticism. It is a different animal that deserves a different comp set and a different set of diligence questions, and the funds that already know how to underwrite outsourced operations may have an edge here over the ones pattern matching to seat based SaaS.

Plena is a genuinely interesting entry because it picked the harder go to market, the whole operating layer, in the most fragmented and underserved corner, specialty independents, with the credibility of a founder who watched the problem from inside his mother’s exam room. The 17x growth and 80% word of mouth, if both hold up, suggest the work is landing in a way that makes customers do the selling, which is the only growth signal in healthcare that ever really meant anything.  Whether that becomes a $605 million comp of its own or gets absorbed into a platform it was politely sitting on top of is the open question. The pile of fax paper, at least, is not going anywhere, and somebody is finally getting paid to make it disappear instead of just selling the clinic a nicer way to look at it.

This is a sensitive enough commercial topic that the numbers above are worth double checking against the companies’ own current filings and pages before anything gets published, since launch day figures and funding rounds move fast and a few of the macro stats come from vendor research with a point of view.​​​​​​​​​​​​​​​​

15-25% of specialty referrals never get scheduled at all. Not lost to competition. Lost to intake paperwork. One client had 4,800 referrals sitting in Epic for 4 months.

Payers denied more claims …

Read more

15-25% of specialty referrals never get scheduled at all. Not lost to competition. Lost to intake paperwork. One client had 4,800 referrals sitting in Epic for 4 months.

Payers denied more claims in 2025 than 2024. Up to 65% of denials are never appealed. Not because practices win, but because the appeal costs more than the claim.

The comp is Tennr: $101M Series C, $605M valuation, 10M docs/month. Fax-to-$605M in about 15 months tells you how much capital is hunting this exact problem.

Subscribe to www.onhealthcare.tech for free and paid articles, podcasts, and more.

🎧 Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Token Economics Versus the 20 Watt Brain: Why Inference Costs, Not Intelligence, Will Cap Clinical AI, & Whose Job It Is to Decide If Machines Should Diagnose Us When They Reason Better but Cost More

AI models hit 80-85% accuracy on brutal NEJM diagnostic cases. Unaided generalist physicians averaged around 20% on the same cases. So why isn’t smarter AI running on every patient? The answer is the economics of thinking…

Listen now

7 days ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Table of Contents

1. Everyone fell in love with training and forgot who pays for thinking

2. A three pound lump of fat that out-reasons a server farm on twenty watts

3. Healthcare is the worst possible customer for a brain with a meter on it

4. The benchmark that should worry doctors and terrify the people who model gross margin

5. The agency that signs off on safety never sees the electricity bill

6. Can anyone actually force the smarter, pricier brain into the exam room

7. How this probably ends, and where the money is hiding while it does

Abstract

The bottleneck for clinical AI is no longer capability. It is the marginal cost of each smart answer, and the fact that the smarter the answer, the more it costs to produce.

Quick orientation for the skeptics:

1. Training is a one-time capex bonfire. Inference is the opex line that scales with every patient, every note, every prior auth, basically forever.

2. A median frontier query runs about 0.34 Wh, but a long reasoning query with roughly fifteen times the tokens runs about 4.32 Wh, a thirteen-fold jump for the exact behavior that makes models good at hard diagnosis.

3. The human brain does the same hard reasoning on about 20 watts, which is the existence proof that silicon has three or four orders of magnitude of headroom it has not collected yet.

4. Microsoft’s sequential diagnosis work hit roughly 80 to 85.5 percent accuracy on brutal NEJM cases against about 20 percent for unaided generalist physicians, and it did so by spending more compute to spend less on tests.

5. No US regulator owns the question that actually matters, which is whether a better answer is worth the tokens. FDA does safety. CMS does coverage and payment. Nobody does cost-effectiveness with teeth.

Everyone fell in love with training and forgot who pays for thinking

The whole scaling-laws romance was about training. Bigger model, more data, more flops, smarter machine, and the charts went up and to the right in a way that made venture partners weepy. Fine. But training a frontier model is a capital event. You light the money on fire once, you get a set of weights, and then the weights sit there. The thing that actually costs money every single day, forever, is inference, which is the unglamorous act of the model answering a question. Training is the wedding. Inference is the marriage, and it is the marriage that bankrupts people.

Here is the structural problem nobody priced into the 2023 hype. Inference cost scales with usage, and in a transformer the cost per answer is not flat. Attention is quadratic in the length of the context, so a long chart or a long reasoning trace does not cost a little more, it costs a lot more. There is a prefill stage that is compute-bound and a decode stage that grinds out one token at a time, and the output tokens dominate the energy bill. So the moment you ask a model to actually think, meaning chain its reasoning out across thousands of tokens, the cost curve bends against you in a way it never did when the model just autocompleted a sentence.

The numbers are no longer hand-wavy. Sam Altman put a stake in the ground in mid-2025 with a figure of about 0.34 watt-hours for an average ChatGPT query, roughly what a high-efficiency lightbulb sips in a couple of minutes, and an Epoch AI analysis landed in the same neighborhood even under pessimistic assumptions. So far so cheap. But a 2025 inference-energy paper out of the arXiv crowd put the median frontier-scale query at that same 0.34 Wh and then showed what happens with test-time scaling, the reasoning regime where the model generates roughly fifteen times more tokens. Energy per query rises about thirteen-fold to 4.32 Wh. Serving a billion plain queries a day runs around 0.8 GWh. Shift just ten percent of them to long reasoning and you are at 1.8 GWh a day. The interface is drifting toward tasks that ask the model to think, plan, search, verify, and act, which is to say toward the expensive regime, on purpose, because that is the regime that produces answers worth having.

Zoom out to the grid and the abstraction stops being cute. The IEA’s 2025 Energy and AI report pegged data centers at about 415 TWh in 2024, roughly 1.5 percent of global electricity, growing 12 percent a year, with updated projections of close to 945 to 950 TWh by 2030, somewhere around three percent of world demand and roughly the entire current consumption of Japan. The US slice was about 183 TWh in 2024, already north of four percent of national electricity, projected to grow about 133 percent to 426 TWh by 2030. The AI-specific portion quadruples or triples depending on the scenario. Yes, efficiency per task is improving at a rate the IEA called unprecedented in energy history. That is exactly the trap. Jevons paradox does not care about your good intentions. Make each answer cheaper and people will demand vastly more answers, and the ones they demand will increasingly be the expensive thinking kind. The downfall thesis is not that the models stop getting smart. It is that the marginal cost of the marginal smart answer refuses to trend to zero on the timeline the marketing implied, and someone, somewhere, has to keep paying that bill on every query for the rest of time.

A three pound lump of fat that out-reasons a server farm on twenty watts

Read more

There are two AI cost lines. Training: one-time capex, you build the model, done. Inference: ongoing opex, you pay every time the mod…

Read more

🎧 Part I Podcast free on Spotify.

Thoughts on Healthcare Markets & Technology

Part I: A Solopreneur Business Plan for Building Medicaid Fraud Detection Software Aimed at the Early Intensive Developmental and Behavioral Intervention Autism Scam

Smart Therapy LLC billed $14M in phantom autism therapy over 5 years. The state had all the data to catch it. Nobody ran the queries…

Listen now

9 days ago · Thoughts on Healthcare

🎧 Part II Podcast episode for paid subscribers only. Also available on Spotify.

Thoughts on Healthcare Markets & Technology

Part II: A Solopreneur Business Plan for Building Medicaid Fraud Detection Software Aimed at the Early Intensive Developmental and Behavioral Intervention Autism Scam

Smart Therapy LLC billed $14M in phantom autism therapy over 5 years. The state had all the data to catch it. Nobody ran the queries…

Listen now

9 days ago · Thoughts on Healthcare

To listen to paid episodes in Apple or Spotify, link your Substack subscription via the show settings on those platforms (instructions inside the Substack app under Subscriptions → Podcast).

Abstract

A single Minnesota provider, Smart Therapy LLC, allegedly pulled more than $14M out of the state autism benefit by billing one-on-one therapy that mostly did not happen, staffing teenage relatives as clinicians, paying parents $300 to $1,500 a month per kid to keep the census full, and forging supervisor sign-offs while those supervisors were out of the country. The same shell also ran a $465K Feeding Our Future play. None of that is exotic. Every move it made leaves a fingerprint in claims, enrollment, and public registry data. This essay lays out a business plan for one person to build a company that catches exactly this pattern, using Claude Code to compress what used to be a 12-engineer build into a solo effort. Topline numbers worth holding in your head: EIDBI claims went from roughly 600K in 2018 to north of 400M by 2025, Minnesota flagged 14 Medicaid programs as high risk, and a single Optum-run tightening cut spending across those programs by about 29 percent, roughly 165M, in a couple of quarters. The market is not theoretical. The fraud is a data problem wearing a clinical costume.

Table of contents

1. The scam, reconstructed from the charging document

2. Why this is a software problem the state keeps losing

3. What the product actually is

4. The data you can pull without anyone’s permission

5. The detection logic in plain English

6. Building it solo with Claude Code

7. Who pays, and the contingency versus license fight

8. Selling it without a sales team

9. The incumbents, and why a one-person shop still has a shot

10. Moat, unit economics, and a napkin model

11. The ways this blows up in your face

12. The honest read

The scam, reconstructed from the charging document

Strip away the autism framing and the mechanics are almost boring. A 28-year-old registered an LLC with the Minnesota Secretary of State in November 2019, listed herself as sole owner, and enrolled it as a provider in the Early Intensive Developmental and Behavioral Intervention benefit, which is the Medicaid program that pays for ABA therapy for kids under 21 with an autism diagnosis. On paper, Smart Therapy delivered intensive one-on-one behavioral therapy under a Qualified Supervising Professional. In reality, per the information, the techs were often 18 and 19-year-old relatives with a high school education and zero autism credentials, the kids were recruited out of the Somali community with monthly cash kickbacks to parents, and any child who lacked a diagnosis got run through a friendly QSP until they qualified. The line in the charging doc that should make any program-integrity person wince is that there was no child Smart Therapy could not get qualified.

Then comes the billing. Claims went out for the maximum hours Medicaid allowed for a given service, on days when the kid got a fraction of those hours or none at all. Sign-offs from the required providers and supervisors were forged, including from people who did not work there or were physically out of the country on the service date. Drivers hauled kids in and out and billed DHS for transportation, and some of those transport providers were also on the Smart Therapy payroll, which is a tidy little related-party loop. The take was more than 14M from DHS and UCare. Some of it left the country as wire transfers and turned into real estate in Kenya. The same entity, for good measure, enrolled in the federal child nutrition program under Feeding Our Future and claimed it was serving exactly 300 meals a day, seven days a week, scaling up to about 1,200 meals a day, roughly 200,000 meals total, for another 465K. Subtlety was not the brand.

Why this is a software problem the state keeps losing

The reason this case matters for a founder is that it is not a clever fraud. It is a loud one that ran from late 2019 through December 2024 before the first charge landed. The state was not blind because the scheme was sophisticated. It was blind because nobody was running the right queries against data it already held.

The macro picture is brutal and it is the whole pitch. EIDBI claims climbed from a little over 600K in 2018 to more than 400M by 2025 by DOJ’s own accounting. Recipients went from about 1,400 in 2020 to more than 5,600 in 2024 while annual cost ran past 300M. Across all autism providers, billings hit roughly 1.6B from 2018 through 2025. Minnesota eventually named 14 Medicaid service categories as high risk for the same reason, and a sibling program, Integrated Community Services, did the identical hockey stick from 4.6M in 2021 to over 170M in 2024. The state legislative auditor later found DHS had the legal authority to investigate kickbacks in the autism program for years and simply did not use it. So the gap is not authority and it is not data. It is the absence of a system that turns the data into ranked, defensible leads before the money goes out the door.

That is the structural failure a software company sells against. The dominant model is pay and chase, where claims get paid fast to keep providers happy and access intact, and recovery happens years later through a Medicaid Fraud Control Unit after the cash is already a duplex in Nairobi. Pay and chase recovers cents. A scoring layer that flags the provider in month three instead of year five is worth orders of magnitude more, and the buyer can do that math without help.

What the product actually is

The product is not an AI that decides who is a fraudster. That framing gets you sued and gets disabled kids cut off from real therapy. The product is a scoring and investigations layer that sits next to the claims pipeline and does two jobs. First, it ranks providers and claims by fraud risk continuously, with every score traceable to specific evidence a human can read. Second, it gives an investigator a workbench where a flagged provider opens into a single view, the entity graph, the billing anomalies, the cross-program links, the documents, all assembled so a case that used to take an analyst three weeks to build takes an afternoon.

Think of it as three layers. An entity-resolution layer that figures out that Smart Therapy, its registered agent, its owners, its transport vendor, and its meal vendor are one cluster of humans even when the paperwork pretends otherwise. A detection layer that runs a library of pattern checks against claims and enrollment. And a case layer that packages the output into something a state attorney or an MCO investigator can act on, with an audit trail, because anything that cannot survive a due-process challenge is worthless.

Scope discipline is the whole game for a solo founder. Do not build a general healthcare fraud platform. Build the thing that catches the EIDBI archetype and its close cousins in the other 13 high-risk programs, which all share the structure of low-credential labor, high-frequency repetitive billing, vulnerable enrollees, and shell entities hopping between programs. One archetype, done so well it is embarrassing for the incumbents, beats a broad platform built by one person and trusted by nobody.

The data you can pull without anyone’s permission

The thing that makes this buildable by one person is that most of the signal lives in public or semi-public data you can ingest before a single customer hands you a claim file. You can build the entity graph cold and walk into a sales meeting already knowing things the buyer does not.

Start with the National Plan and Provider Enumeration System, which gives you every NPI, the rendering and billing names, practice addresses, and taxonomy codes, refreshed monthly and free. Layer the OIG List of Excluded Individuals and Entities, which would have flagged the hidden owner who had been excluded for three years over an adult daycare, and the SAM exclusions list. Pull Secretary of State business registrations to get incorporation dates, registered agents, and officers, which is how you catch the November 2019 LLC that enrolled as a provider weeks later. Add the Death Master File so a claim signed by a dead clinician lights up. Add USPS address normalization and county property records, because shell entities reuse addresses and mailboxes the way teenagers reuse passwords.

On the claims side, you will not get a state’s live MMIS feed on day one, but you do not need it for the demo. CMS publishes de-identified Medicaid research files and T-MSIS analytic extracts, and many states post provider directories and open payment data. That is enough to train and tune detectors and to show a buyer the shape of the answer. The pitch to the first customer is simple. The public data already clusters your high-risk providers. Give us a read-only claims extract under a business associate agreement and we will tell you which ones are billing impossible days. You are asking for the smallest possible data grant in exchange for a ranked list, not a data lake migration. That is a yes a mid-level MCO investigator can actually say.

The detection logic in plain English

Every move in the charging document maps to a detector, and none of them require a neural network to start. The fancy modeling comes later. The first version is mostly arithmetic and graph queries, which is exactly why one person can ship it.

Billing the ceiling is the easiest tell. A provider whose claims cluster at the maximum authorized units, day after day, kid after kid, is statistically screaming. Real clinical delivery is messy and varies. Fraud bills the cap because the cap is the revenue. You compute, per provider, the share of claims sitting at the authorized maximum and the variance of delivered units, and the honest providers form a fat distribution while Smart Therapy sits out on the tail by itself.

Impossible-day capacity is next. Sum the billed direct-service hours per rendering tech per day. When a single 19-year-old is credited with more one-on-one therapy hours than exist between sunrise and pickup, or is billed as present at two sites at once, that is not a productivity miracle. The absent-supervisor version is the same trick applied to the QSP. When a supervising professional is signing off on hundreds of sessions on dates that overlap with, say, a long gap in all their other activity, you flag the credentialing pattern even before you can prove they were in another country.

Related-party loops fall straight out of the entity graph. A transport vendor that shares an owner, an address, a bank routing pattern, or a phone with the therapy provider is the circular billing the prosecutors described. Hidden ownership is the same graph problem. You do not trust the DHS ownership form. You resolve the real cluster from shared identifiers across Secretary of State filings, addresses, agents, and exclusion records, and you surface the excluded party hiding behind a relative’s name.

Then the demand-side signals. You cannot see a cash kickback, but you can see its shadow. A provider with abnormally high authorization amounts per child, unusual enrollment volatility as families churn toward whoever pays more, and a QSP whose qualifying rate approaches 100 percent looks like a diagnosis mill, which is what no child we could not qualify actually means in data. The single highest-value detector is cross-program reuse. The same humans showed up in Feeding Our Future, then autism, then housing stabilization, then integrated community services. A graph that spans programs catches the operator on their second scheme, which is where the real money and the real deterrence live.

Building it solo with Claude Code

Here is where the company becomes a one-person job rather than a Series A. The architecture is unglamorous on purpose. A Postgres database with a graph extension or a dedicated graph store for entity resolution, a Python ingestion layer that pulls and normalizes the public datasets on a schedule, a detector library that is mostly SQL and pandas with a scoring service on top, and a thin web app for the investigations workbench. Nothing here is research. It is plumbing, and plumbing is exactly what an AI coding agent is good at.

The realistic loop with Claude Code looks like this. You hand it the NPPES and LEIE file specs and have it write the parsers, the schema, and the loaders, then you point it at the messy reality of the files and have it fix the encoding and the dedupe edge cases, which is where most of the actual hours go. You describe each detector in a sentence, share-of-claims-at-cap by provider, hours-per-tech-per-day exceeding a threshold, and it writes the query, the test fixtures, and the backfill. You give it a sample fraud cluster and ask it to build the entity-resolution scoring, then you spend your judgment correcting the matches, because a false merge that links an innocent provider to a fraud ring is the kind of mistake that ends the company. The workbench, the auth, the audit logging, the PDF case export, all of that is standard app code an agent produces quickly.

What does not get automated is the part that matters, and that is the reason a domain person should build this rather than a generic engineer. Deciding which signals are defensible, where the false-positive cost lands on a real disabled child, how to weight a detector so you flag the operator and not the small honest clinic having a weird month, and how to phrase a finding so it survives a provider appeal. Claude Code collapses the engineering. It does not collapse the judgment about Medicaid, ABA delivery norms, authorization rules, and program integrity law. That asymmetry is the founder’s edge. A solo build that used to need a team is now mostly a question of whether you know which queries to ask.

Who pays, and the contingency versus license fight

There are three buyers and they pay differently. The managed care organizations, the UCare-shaped entities that took capitation and ate the 14M, have Special Investigations Units, real budgets, and a direct financial interest because fraud is their loss now, not just the taxpayer’s. They are the fastest yes. State Medicaid program integrity offices and the agencies that run these benefits are the largest spenders but move at the speed of procurement, which is to say geologically. The Medicaid Fraud Control Unit and federal partners are users of the output more than buyers of software, though they make excellent reference logos.

The pricing fork is contingency versus license. Contingency, where you take a cut of recoveries or of prevented spend, is seductive because it aligns you with the customer and lets a cash-poor founder land a deal with no budget line. It is also a trap if you are not careful. Recoveries take years, attribution is a knife fight, and you can do brilliant detection work and watch the case die in a backlog with nothing to invoice. The cleaner model is a software license priced against the spend you are watching, with a measured savings guarantee, because the Optum tightening that cut roughly 165M across the high-risk programs gives you a defensible reference point for what good oversight is worth. A blended deal works best for the first customers. A modest platform fee so you can eat, plus a success component on confirmed prevented payments, with prevented defined tightly enough that you are not arguing about counterfactuals forever. The savings story sells itself. Stopping a 14M scheme in month three rather than year five is not a soft ROI.

Selling it without a sales team

A solo founder cannot run an enterprise sales motion against state government, so do not. The wedge is a report, not a demo. Take the public data you already ingested, build a risk profile of a region’s providers in one high-risk program, and turn the most defensible findings into a short, sober briefing that names patterns and not people. This billing-at-cap distribution, these related-party transport loops, these incorporation-to-enrollment timelines under 60 days. Then walk it into an MCO SIU or a sympathetic program-integrity director as evidence that the answer is sitting in data they own. You are not selling software. You are showing them their own house is on fire and you brought a hose.

Design partners come next. One MCO that gives you a read-only claims extract under a business associate agreement, in exchange for a ranked lead list and a fixed low fee, gets you the proprietary signal that makes the product real. Publish the methodology, not the targets. The thought-leadership surface here is enormous because the underlying story, billions across a web of programs, is already in the press, and a credible technical voice explaining how the detection actually works generates inbound from exactly the SIU directors and state CFOs who buy. FOIA the enforcement actions and you get a steady drip of confirmed fraud patterns to validate and market against. The motion is land via a free risk read, prove it on one customer’s data, let the savings number recruit the next buyer.

The incumbents, and why a one-person shop still has a shot

This space is not empty. SAS sells fraud analytics to state Medicaid agencies, Optum and Cotiviti run payment integrity at scale, LexisNexis Risk Solutions owns a lot of the identity and provider data graph, Codoxo and Qlarant and Thomson Reuters Pondera all play in government program integrity. A reasonable person asks why a solo founder is not roadkill.

Three reasons. The incumbents sell broad platforms with long implementations and seven-figure price tags, which means they orient toward big national programs and the big claim types, and a narrow, fast, cheap tool aimed precisely at the EIDBI archetype and its 13 cousins is a wedge they are too heavy to bother defending. The incumbents are also generalists about the clinical and policy detail, and the detail is where these schemes hide, so a builder who actually understands CMDE qualification, QSP supervision rules, and authorization ceilings can encode sharper detectors than a horizontal vendor staffed by data scientists who have never read an EIDBI policy manual. And the incumbents are slow, because procurement and enterprise contracting are slow for everyone, which means there is a multi-year window where states are panicking about exactly this fraud and the big players are still scoping statements of work. A solo shop that ships a useful risk report this quarter beats a platform that goes live in 2028. The realistic exit, frankly, is that one of these incumbents buys the company once the data graph and the customer proof exist, which is a fine outcome and shapes what you build.

Moat, unit economics, and a napkin model

The uncomfortable truth is that detection logic is not a moat. Any competent team can write a billing-at-cap query. The moat is the cross-program entity graph, the resolved clusters of humans and shells linked across Feeding Our Future, autism, housing, and the rest, enriched by every confirmed case your customers feed back. That graph gets better with each customer and each enforcement outcome, and a competitor starting fresh has to rebuild years of resolution and labeling. Switching costs help too, because once an SIU runs its caseload through your workbench and trains its process around your evidence packages, ripping you out is painful.

The napkin model is friendly for a solo operator. Costs are basically your time plus cloud and data, call it low five figures a year in infrastructure because public data is free and claims extracts are not enormous. Price a mid-size MCO deal at, say, 150K to 400K a year for the watched spend, which is trivial against a single 14M loss, and gross margin on software like this sits in the 80s once built. Three or four MCO logos is a real business run by one person with maybe a contractor for support. The total addressable market is not small either. Fourteen high-risk program categories in Minnesota alone, multiplied by every other state now waking up to the same exposure after a 259M federal funding pause got everyone’s attention, multiplied by the MCOs inside each state, is a long runway for a focused tool. You are not trying to win all healthcare fraud. You are trying to own the vulnerable-population, low-credential, high-frequency-billing corner of it, and that corner is bleeding money in every state that copied Minnesota’s benefit design.

The ways this blows up in your face

A sober founder spends real time here because the failure modes are nasty and several of them are ethical, not just commercial. The worst case is not that the product fails. It is that it works badly and a false positive flags an honest clinic into a payment suspension, and a real autistic kid loses real therapy while the appeal grinds. That is not a rounding error. It is the reason the product must be a ranked-lead and evidence tool for human investigators, never an automated denial engine, and why every score has to be explainable down to the specific claim. Build it to make a good investigator faster, not to replace judgment, and write that into the contract.

The rest of the risk list is mundane and survivable. Data access is gated by HIPAA and business associate agreements, so you carry compliance overhead heavier than a normal SaaS and you cannot cut corners on security as a solo operator, which is precisely the kind of unglamorous work to hand Claude Code and then verify carefully. Government procurement is glacial and contingency revenue is lumpy, so you finance the early years on MCO software fees rather than betting the company on recovery timing. There is defamation and due-process exposure if findings name providers before they are adjudicated, so the language is patterns and probabilities, never accusations. Model drift is real because fraudsters adapt the moment a detector becomes known, so the detector library is a living thing, not a one-time build. And the bus factor is you. One person holding the only knowledge of a system that states depend on is a fragility you have to design around early, with documentation and eventually a second pair of hands, or your acquisition diligence falls apart.

The honest read

The opportunity is real and the timing is unusually good, because the fraud is loud, the dollars are enormous, the states are scared, and the tooling finally lets one capable person build what used to need a funded team. The charging document reads like a product requirements list. Bill the cap, fake the staff, forge the sign-offs, pay the parents, hop the programs, wire it abroad, and every one of those leaves a trace that arithmetic and a good graph will catch.

What it is not is a passive lifestyle business or a clean morality play. It is regulated, the sales cycles fight you, the incumbents will eventually notice, and the same engine that catches a 14M thief can hurt an innocent provider and a vulnerable kid if built lazily. The version of this company worth starting is narrow, explainable, obsessed with false-positive cost, and pointed at the exact archetype Minnesota keeps prosecuting. Done that way, a solopreneur with Claude Code, a domain brain, and the patience to court one MCO at a time has a genuine shot at building something that saves a lot of public money and is worth buying. Done lazily, it is just another black box that pays kickbacks of its own kind in the form of wrongly suspended care. The difference is entirely in the judgment, which is the one part the AI does not write for you.​​​​​​​​​​​​​​​​