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Disclosure: The author has no financial interest in Carbon Health or any of its investors.

$622 million.

That’s how much venture capital was poured into Carbon Health Technologies before it filed for Chapter 11 bankruptcy on February 2, 2026. (Sources: Fierce Healthcare, Stretto/Chapter 11 Cases)

But first, if you’d like to make a difference, consider becoming a Founding Member of the AI Health Uncut community. You can join through this link. Your support will have a real impact, helping us continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

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Alright, now back to the lessons of Carbon Health’s bankruptcy…

A $3.3B valuation. Blackstone. CVS Health. BlackRock. Silver Lake. Dragoneer. Celebrity athletes as angel investors. A proprietary EHR. An AI scribe. COVID testing at Dodger Stadium. The myth of 1,500 clinics coast to coast. (Sources: Fierce Healthcare, MobiHealthNews)

And in the end? 93 clinics. $100M+ in debt. 1,400 employees at a crossroads. A founder who had moved on to his next startup long ago. And a sexual assault scandal that exposed what happens when you scale clinics faster than you can staff or supervise them. (Sources: Kroll Restructuring, Bondoro Case Summary)

I don’t want to say “I told you so.” And in fairness, I’m basically a coin toss when it comes to predictions. 😉 But I’ve been warning about Carbon Health since January 2024, when I wrote in my Digital Health Predictions for 2024: “Carbon Health was supposed to be a success story, but unfortunately, it ended up being a ‘champagne and cocaine’ story.” (Source: AI Health Uncut, Digital Health Predictions for 2024)

Now the former employees are finally talking. And what they’re describing is even worse than what the financial statements show.

Part 1 of this two-part series dissects the mistakes and former employees’ insights into what led to Carbon Health’s bankruptcy. Part 2 argues that Carbon’s story was never just about Carbon. The deeper failure is a primary care reimbursement system so broken that no amount of AI, innovation, or digital health hype can overcome it.

TL;DR:

Read more

The Digital Health Inside Out podcast offers a rare look inside the world’s largest AI scribe company, as Alex Koshykov and I had the chance to sit down with Shiv Rao, CEO of Abridge, for a brutally honest interview.

Here’s the link to the full interview:

Shiv Rao is not your typical tech CEO. He’s a practicing cardiologist who still sees patients one week a month, a Carnegie Mellon history major who studied minority studies and film theory, a former corporate investor at UPMC, and a dad who spends weekends building Pokémon websites with his twin nine-year-old boys using Claude Code. He co-founded Abridge in 2018 in Pittsburgh — the city of bridges (clever, huh? 😉) — alongside Zach Lipton, a Carnegie Mellon professor he once funded through UPMC’s machine learning and health program. The company has since grown into the largest pure-play AI scribe in healthcare, valued at $5.3B, live in over 250 of the country’s largest health systems, and on track to touch over 80 million patient lives this year.

We wanted to understand how Rao thinks: about his product, his competitors, his investors, and the future of AI in healthcare. He didn’t disappoint.

TL;DR:

1. “It Was Never About the Note”

The very first thing Rao wanted to clear up: Abridge is not an AI scribe. Or at least, it doesn’t want to be known as one.

For years, Rao said, the company has tried to avoid the phrase entirely. The real thesis is about conversations, the clinical interaction between a doctor and a patient, which Rao sees as the most important moment in healthcare and the upstream source of virtually every downstream workflow.

Documentation was the wedge, not the destination. As Rao put it, conversations are upstream of not just notes, but order entry, diagnosis codes, claim creation, risk adjustment, revenue cycle, clinical trial recruitment, and patient education. The note is just one artifact that flows from that conversation.

And the note, Rao emphasized, is inseparable from revenue. In the U.S., doctors aren’t compensated for the care they deliver. They’re compensated for the care they documented delivering. Whether in a value-based care contract capturing HCCs, or in fee-for-service where notes must be concordant with claims, what’s in the note determines what gets paid.

2. The Tech Stack: 70-80% In-House Models

We pushed Rao on the technology question: with billions being poured into foundation models, is Abridge still building its own models, or riding on top of OpenAI, Google, and Anthropic?

The answer: it’s all about model orchestration, and roughly 70% to 80% of what Abridge does is driven by in-house models, with about 30% involving a frontier model.

Rao described a strategic framework for deciding what to build versus what to partner on. If there’s a part of the product where you know you’ll never be perfect and will always aspire to be less than perfect, he said, you might be better served partnering with frontier models — but building the harness around them: the tools, the memory, the guardrails, the context engineering. That harness, he argued, is your differentiation. But where you can see a clear path to “ringing the bell” on quality quickly, you should own the model yourself.

Speech recognition, for example, moves slowly enough that it made strategic sense for Abridge to own it entirely. Doctors mispronounce drug names constantly. Rao joked there are 25 different ways to say “metoprolol,” and new medications come out all the time. Abridge runs multiple in-house speech recognition models, orchestrated together to produce the best possible transcript.

On the orchestration side, yes — Abridge uses sub-agents doing different tasks in parallel. A QA agent checks the work. A judge agent audits it. Another unbundles tasks and coordinates software engineering agents. The company can swap in a new frontier model in days, not weeks or months, and they measure that swap velocity internally as a reflection of agility.

3. $800M Raised, 100% Control Retained

Abridge has raised close to $800M and is valued at roughly $5.3B. When we asked whether investors like Andreessen Horowitz are seeing something the public isn’t, Rao pointed to scale: Abridge is live in over 250 of the largest health systems, serving clinicians across all specialties and settings, and expects to touch over 80 million lives in the next 12 months, with a goal of reaching one million encounters per day this year.

But we pressed harder. With that much capital raised, who actually has leverage — the founders or the investors?

Rao was emphatic: “We have 100% full control, and that’s super important to me.“ He described a governance model where Abridge sets its own scorecard — defining what “winning” looks like for each feature launch, including thresholds for LLM judges and human judges — and the board’s job is simply to hold the team accountable to the report card they created for themselves.

He credited a philosophy he got from Henry Kravis, who is also on Abridge’s cap table. When Rao first sat down with Kravis, the legendary investor asked about exit strategy. When Rao mentioned an IPO as a future possibility, Kravis pushed back: “You should just be focused on your mission. If you need capital from private markets, take it. If you need to go public to achieve your mission, go public. But otherwise, don’t think about it.”

Rao said an IPO is not on his radar, not in his head, and not how the company is recruiting or building its team. For now, Abridge has more than enough capital to sustain R&D over many years.

4. The Customer-Investor Trap

I asked a pointed question about conflicts of interest — what happens when a company’s biggest customers are also investors, and the incentives to inflate valuations or book premature revenue start to pull in strange directions.

Rao didn’t dodge it. He agreed the incentive problem is real when strategic investors lead funding rounds and set prices. His rule: strategics should always be minority investors, never directors who can influence the company’s trajectory, because it’s too difficult for them to decouple their own business interests from Abridge’s.

The value of strategic investors, in Rao’s view, is the feedback loop — compressing cycle time and giving the company access to deep innovation partnerships. Abridge’s cap table includes Kaiser Permanente Ventures, among others, and Rao said the company will bring in more strategics over time, but the criteria for who gets on the cap table is “very rigorous, and we won’t violate that.”

He added a personal note: “I speak as someone who used to be a corporate VC at UPMC. I’ve certainly seen what can go really right, and also where sometimes it can go off the rails.”

5. 126 AI Scribes: “Party Tricks vs. Enterprise-Grade Platforms”

Our very first podcast episode was called “Why the Hell Do We Need 126 AI Scribes?“ — so naturally, I put the question to Rao directly:

Rao started graciously — he wants more innovation in healthcare, not less. But he drew a sharp line between party tricks and enterprise-grade platforms. To illustrate, he described building his own version of Pocket (the article-saving app Mozilla killed) in a single Saturday using Claude Code, complete with a Chrome extension, a mobile app, a web app, and a semantic search backend. Seven or eight hours of work.

That’s the moment we live in, he said. Anyone can build a demo. But the gap between a demo and something that can truly scale in a regulated, high-stakes industry is enormous. In healthcare, trust means everything. Credibility and transparency mean everything. And once you’ve seen a cardiologist’s preferences, you haven’t seen an oncologist’s — the long tail of specialties, settings, and languages is a treadmill that most startups can’t sustain.

6. The Prior Auth Breakthrough

The most concrete example of Abridge moving beyond scribing came from Rao’s description of a partnership with Highmark, a regional Blue Cross plan in Pennsylvania, for real-time prior authorization at the point of care.

Here’s how the old process works: doctor talks to patient, realizes they need a sleep study, places the order, then days later a staff member calls the insurance company, fills out paperwork, faxes it, waits for approval — weeks or months later, the patient finally gets the study.

Here’s how it works with Abridge: the doctor talks to the patient, the technology hears “sleep study,” and in the background pulls the patient’s medical records, identifies their insurance plan and geography, retrieves the specific prior authorization guidelines, and when the doctor hits stop, Abridge tells them: “This patient has this plan in this geography. You decided to order a sleep study. There are two more questions you should ask based on the chart and the conversation. If you ask them, we’ll give you the approval right now.”

Rao called this “shifting everything left” to the actual moment of care — the sacrosanct moment where the clinician and patient interact. Everything else, he argued, is abstraction. Everything else is bloat.

7. Liability: “Doctors Go to Jail, Engineers Don’t”

I brought up my earlier Substack study, “Doctors Go to Jail, Engineers Don’t,” about the liability gap in healthcare AI. Some clinicians we’ve spoken with worry that the more comprehensive they are — letting patients talk freely, capturing more text — the more errors an AI scribe might make, especially with unrelated “patient side comments” getting coded as clinically relevant.

Rao reframed the risk discussion using a 2×2 matrix: frequency on one axis, stakes on the other. High-frequency, high-stakes use cases — like diagnosing a pneumothorax on a CT scan or sepsis in an ICU — require significant regulatory hoops. But on the other side, there are high-frequency, lower-stakes workflows: revenue cycle, clerical work, the “scut work” of hospital life. The stakes there are financial, not life-and-death. That’s where Abridge chose to start.

On guarding against errors, Rao pointed to features like linked evidence — which maps every claim in an AI-generated note back to the specific passage in the original transcript. The company publishes academic-grade benchmarks, deploys agents that programmatically detect and remove false positives, and tries to make its white papers “as unsalesy as possible” (he admitted they’re not always successful).

He also shared an internal culture detail: Abridge has a Slack channel called “Love Stories” where positive clinician feedback is routed every day, so every engineer in the company — the ones who might otherwise have gone to OpenAI or Anthropic — gets reminded of the mission’s impact.

8. FDA Regulation: “The Moment We Need It, We’ll Jump In”

We asked Rao the same question we asked Alex Lebrun of Nabla: ambient listening solutions are still not FDA-regulated. Should they be?

Rao didn’t reject the idea, but he raised a practical objection: current regulatory frameworks were built for hardware, where a product stays the same after it’s built. AI products change weekly. An issue from last week might already be fixed by a new fine-tuning run. He expressed interest in adaptive trial design, a research methodology that can account for continuous product improvement, as potentially the right framework for assessing AI safety in a world that moves this fast.

He also floated a striking metaphor for what an AI “safety label” might look like: a “fresh until” date, like on a milk carton. Because the technology changes so quickly, the date you bottled it matters as much as how long it will last. The winning vertical AI companies, he predicted, will be the ones who “rebottle” (update their metrics) really quickly. “These are our metrics on February 1st, and this will last until February 14th, and then here’s February 14th, new metrics.”

9. The Epic Elephant in the Room

We brought up the big one: Epic’s exclusive partnership with Microsoft and DAX Copilot, announced in August 2025, and whether the elephant in the room was about to sit on Abridge.

Rao said he held a town hall with Abridge’s health system customers shortly after the announcement. The core message: there is no risk to Abridge’s ability to integrate with Epic, pull data, push data, or partner with health systems. That hasn’t changed and isn’t going to change.

He acknowledged that Epic is building AI of its own, and he’s impressed by the speed. But he framed it as positive-sum pressure, not zero-sum competition: “All boats can rise here. This is a moment where the mission has to be serving health systems, clinicians, and patients.“

When we pushed on the bundling threat — Epic could offer its ambient solution for free as part of its existing package — Rao didn’t flinch. That kind of pressure, he said, forces Abridge to make clear that what it does isn’t “just” an AI scribe. The company goes deep across specialties, settings, and spoken languages, and it’s building capabilities — value-based care contracts, HEDIS measures, clinical trial matching — that are differentiated enough to stand on their own.

10. The Future: “AI Is Underhyped”

When we asked Rao for his vision of healthcare in an AGI future, he didn’t hedge.

“I actually think AI is underhyped. I think valuations are underhyped — for the companies who’ve got real fundamentals right now.”

He predicted that over the next one to two years, more and more low-stakes, high-frequency workflows will get fully automated. Then, gradually, automation will start touching clinical work — like the medication refill automation already deployed in Utah by Doctronic. Healthcare will transform stepwise: “Sometimes change is slow, and then it’s sudden.“

But he drew a clear line between automating a doctor’s tasks and automating the doctor’s role. An often-cited study found doctors need 27 to 30 hours in a day to complete all their work.

And for companies building in this space, the test is simple: when a new frontier AI capability arrives, does it help you or threaten you? If every advance makes your product better, you’re riding the right side of the wave. If it makes you obsolete, you’re positioned wrong.

Rao clearly believes Abridge is on the right side.

This interview was conducted by Alex Koshykov and Sergei Polevikov for the Digital Health Inside Out podcast.

Like what you’re reading in this newsletter? Want more in-depth investigations and research? Alright then, go tell your friends!

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👉👉👉👉👉 Hi! My name is Sergei Polevikov. I’m an AI researcher and a healthcare AI startup founder. In my newsletter ‘AI Health Uncut,’ I combine my knowledge of AI models with my unique skills in analyzing the financial health of digital health companies. Why “Uncut”? Because I never sugarcoat or filter the hard truth. I don’t play games, I don’t work for anyone, and therefore, with your support, I produce the most original, the most unbiased, the most unapologetic research in AI, innovation, and healthcare. Thank you for your support of my work. You’re part of a vibrant community of healthcare AI enthusiasts! Your engagement matters. 🙏🙏🙏🙏🙏

Wow. 10,000 subscribers.

When I started AI Health Uncut 2.5 years ago, I couldn’t imagine I’d reach this number. And I’m so proud that every single one of you signed up yourself. No artificial email list injections. None of that BS. I literally had zero subscribers on August 28, 2023.

Wow. Just wow.

What started as a hobby turned into something I genuinely love doing.

Publishing 139 articles in 130 weeks means I’ve averaged a little over one article per week. And as you’ve probably know, many of those aren’t casual takes. They’re typically hardcore investigations and heavy research pieces. I gather data, track sources, and conduct interviews - all at once.

Time flies when you’re tearing down broken systems. Looking back, a lot has been accomplished, but there’s still a mountain to climb.

I launched this newsletter with 3 goals:

1️⃣ Address U.S. healthcare problems and offer solutions. To the extent possible.

2️⃣ Deliver the brutal truth about healthcare - technology, AI, innovation, investments, fraud - because too many people are scared to.

3️⃣ Offer value that my readers can’t just pull from ChatGPT. (Which, by the way, is probably why ChatGPT is itching to train on my content. 😉)

I hold other healthcare writers to the same high standards.

Let’s keep pushing.

I write with no strings attached - just the brutal truth drawn from my experience and expertise.

Now, because I don’t have sponsors or advertisers, I owe a massive debt of gratitude to my paid subscribers and especially the Founding Members. The Founding Members Club is an exclusive group of 19 right now, and I know every single one of their names. These folks aren’t just supporting my work. They’re covering the tab for those who can’t afford a paid subscription, like students:

In fact, almost all my writing is now geared toward my paid subscribers (hundreds of them) and my Founding Members (19 of them). They put their money where their mouth is, and I’m working toward producing high-quality, sometimes provocative, but always evidence-based research on healthcare AI and everything around it. That includes AI modeling, AI fine-tuning and testing, healthcare AI product reviews, investing, regulations, policy, fraud investigations - topics I talk about in more detail below.

I believe in fair compensation for every piece of work. So, to those who’ve stepped up as paid subscribers: thank you. 🙏 If you’re not a paid subscriber yet and you want my relentless research and analyses to continue for another 12 months, 24 months, 36 months, and more, consider joining the ranks:

Become Annual Subscriber and Save 17%

Here is what I’m offering:

For Paid Subscribers:

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For Founding Members:

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I’m proud to say that every single person getting my newsletter chose to subscribe. And if they ever want out, they can unsubscribe with a single click. That’s how it should be - no tricks, no spam.

On August 28, 2023, I set out to publish bi-weekly (once every two weeks). Turns out, I ended up publishing weekly. Some people churn out content several times a week. Honestly, I have no clue how they pull it off. For the high-quality research I aim for, I need time: days, weeks, months even. Take my Babylon Health fraud investigation, or the exposé on UnitedHealth’s and Semler’s flawed QuantaFlo device, a mess that turned out to be not just a financial scheme but one that physically harmed patients.. That took months to unravel.

Over this past 2.5 years, we’ve done a lot. AI model reviews, fraud investigations, countless digital health industry deep dives, navigating new policies from CMS and the regulators, exposing private equity and venture capital scams, and doing multiple interviews. I also published an academic paper on best practices for implementing AI in clinical settings and have done multiple speaking engagements - largely thanks to my Substack and your support.

To be honest, when I started writing 2.5 years ago, I didn’t expect such an overwhelming response from so many people.

Here’s just a small sample of some of the comments I’ve received:

I’m flattered, and I’m honored. Thank you! 🙏

I aim to instigate and to provoke discussion in every piece I write, often by playing devil’s advocate.

I do appreciate my 10,000 subscribers. But to be frank, I don’t give a damn about subscription trends. I don’t obsess over them. If people find value in what I’m offering, they’ll subscribe and spread the word. But wasting time on SEO optimization and all that crap? That’s not my game.

What I do care about is finding allies in my mission to address and offer solutions to U.S. healthcare problems.

Now I want to highlight the most crucial insights, hard-hitting analyses, and relentless investigations I’ve uncovered over the past 2.5 years.

TL:DR:

1. Best Practices and the New Hippocratic Oath

2. Healthcare AI Product Reviews and AI Model Critiques

3. Unmasking Digital Health Failures

4. Investigating Healthcare Ponzi Schemes and Fraud, and Calling for Policy Implementation

5. When Media Is In Bed With Venture Capital

6. Exposing VCs’ Dirty Tricks and Proposing Alternatives

7. AI Health Uncut (aka FixHealth.ai): A Platform for Spreading the Word and Building a Thriving Healthtech Community

8. What My Subscribers Should Expect Next

9. See Something Unusual in Your Organization? Speak Up. Anonymously or Not.

10. Let’s Fix American Healthcare

1. Best Practices and the New Hippocratic Oath

🔵 I was upset that there wasn’t a unified framework for best practices in clinical settings. So I did what any frustrated academic would do—I took matters into my own hands. I spent months buried in over 200 academic papers, survived the grueling peer review massacre, and finally published a paper on Best Practices of AI in Healthcare. If we’re serious about letting AI help U.S. healthcare, we need to make damn sure we do it ethically and responsibly. I break down the whole journey in this Substack article.

🔵 I was proud to join nationally recognized clinician , in helping create a new Hippocratic Oath in an Age of AI.

2. Healthcare AI Product Reviews and AI Model Critiques

🔵 I’ve conducted critical reviews of state-of-the-art (SOTA) medical AI models, including Google’s Med-PaLM 2 and Med-Gemini, and OpenAI’s o1 ‘Strawberry,’ which was nowhere near ready for medical diagnostics despite the company’s bold claims, as well as OpenAI’s gpt-oss, which wasn’t suited for healthcare due to fine tuning issues and an overreliance on its HealthBench dataset.

🔵 I’ve analyzed various Big Tech attempts to wander into the healthcare industry, such as OpenAI’s HealthBench project and Microsoft's so-called Medical Superintelligence healthcare AI agent project.

🔵 I debunked a widely circulated JAMA article authored by over a dozen Stanford researchers that claimed AI outperformed human doctors. They made elementary, middle-school-level statistical mistakes to support those claims. Unfortunately, by the time I exposed the flaws, respected outlets like the New York Times and CNN—as well as renowned industry figures like Eric Topol, Bindu Reddy, and Elon Musk—had already picked up the clickbait and run with it. That was quite a debacle. 😉

🔵 I put ChatGPT Health to the test by uploading my entire medical history. I also wrote that Microsoft partnered with HealthEx and rebranded its legacy health app as Copilot Health.

🔵 Dr. and I recently ran a horse race between 13 state-of-the-art LLMs. 12 of them failed simple medical reasoning.

🔵 I’ve identified 10 obstacles the healthcare industry must overcome if we want physicians to trust and adopt AI in medicine.

3. Unmasking Digital Health Failures

🔵 Over these 2.5 years, I’ve uncovered around 50 recent digital health failures, and the common themes are glaring. These aren’t just mistakes. They’re wake-up calls. If we want to avoid screwing up the future of healthcare for our kids, it’s time to take a hard look and learn from these colossal disasters.

🔵 I’ve exposed the following perpetrators. This is just a selected list: Babylon, VillageMD, Walgreens, Oak Street (this one started as a LinkedIn post but set off a firestorm in the comments), Olive AI, Teladoc, Livongo, Amwell, Summa Health, Cigna, Oscar Health, Clover Health, Optum’s Telehealth, Optum Ventures, Walmart Health, Cue Health, Truepill, Pieces Technologies, IBM Watson Health, Epic, Suki, Infermedica, Sniffle, Isabel, MayaMD, Klick Labs, OpenEvidence, HealthPrecision, eMed, Cancer Check Labs, Hippocratic AI, Commure, and Doctronic.

🔵 In particular, I’ve dissected the corpses of digital health failures and extracted some cold, hard truths: the death of Olive AI, the Teladoc-Livongo debacle, a masterclass in vaporizing wealth, transferring billions from Teladoc’s retail investors to 7wireVentures and other VCs. The final nail in the coffin for telehealth as we know it, highlighted by Optum’s telehealth disaster and Amwell’s slow death spiral. Oscar Health and Clover Health are proving that insurance can still be a graveyard. Walmart Health? A Titanic that never left port. And let’s not forget Cue Health—taken out by a textbook illegal VC pump-and-dump scheme. Even Epic, the healthcare IT fortress, is vulnerable to Russian hackers. Welcome to the brutal reality of digital health.

🔵 I’ve exposed 15 Health AI companies that have been lying about what their AI can do.

4. Investigating Healthcare Ponzi Schemes and Fraud, and Calling for Policy Implementation

🔵 As a digital health founder, I dodged the SPAC attack when it was raining down on the industry a couple of years back. SPAC (Special Purpose Acquisition Company) was a glorified ‘blank check’ Ponzi scheme, exploiting Covid chaos while the U.S. government had its back turned. The SEC eventually woke up, but not before the ‘SPAC King’ himself, Chamath Palihapitiya, cashed in and turned into an overnight billionaire by cheating retail investors out of their hard-earned savings.

🔵 I spent months on my Babylon Health investigation, and what I found was staggering. 16 signs of potential fraud, 17 outright lies from Ali Parsa, Babylon’s CEO, and 22 hard lessons for the digital health industry. But the most jaw-dropping moment? In early 2022, CMS handed Babylon a check for $1B(!)—money that U.S. taxpayers entrusted them with! Why? Because some slick salesman named Ali Parsa (who had barely set foot in the U.S.) convinced them it was a good idea, before Babylon had even done anything. The CMS $1B check was an advance!

And of course, after running multiple companies into the ground (there was Circle Health before Babylon) this guy has the nerve to launch yet another AI health startup, this time called Quadrivia. And who’s he pitching to? The very same VC firms whose LPs just watched him incinerate their money. But hey, why not? It’s not VCs’ money anyway. It’s someone else’s money. So let the freak show keep going until the music finally stops.

🔵 Speaking of Theranos, Babylon, and AI agents, I ran a deep investigation into a startup called Hippocratic AI (though “Hypocritical AI” might be more fitting 😉) that has raked in a total of $404M from VCs, on the promise that someday they’ll build AI agents for medicine. Will they deliver? Who knows. But does it even matter? Not really, as long as the cash keeps flowing. The company’s CEO, Munjal Shah, has reportedly faced seven lawsuits for fraud at his previous venture, Health IQ. (By now, my readers should know the pecking order in the Healthcare VC Mafia, where Shah is a Made Man (a Soldato) answering directly to the Godfather himself, Hemant Taneja.) But does this concern the VC bros? Not in the slightest. Once you’re in the mafia, you’re part of the “family.” In fact, Shah began raising funds for his new “medical agentic” startup before the bankruptcy trial for his last company even started!

When you hear, “We’re building AI agents,” it’s probably just smoke and mirrors.

🔵 I’ve been banging on the doors of Congress, targeting those who supposedly care about healthcare, to expose the predatory practices of private equity in the sector. And by the way, venture capital has jumped into the fray, too. Turns out, private equity has been playing a rigged game for decades, thanks to massive legal loopholes.

🔵 Case in point: Hemant Taneja, CEO of General Catalyst, and The Godfather of the Healthcare VC Mafia. This guy pulls off a stunt right out of the Gordon Gekko handbook—bought a hospital he couldn’t afford by borrowing a cool billion and dumping the debt onto the hospital’s balance sheet. To pull off this magic trick, he created HATCo, a shell company hilariously claimed to stand for “Health Assurance Transformation Corporation”. But let’s be real. The name is likely a nod to Hemant Taneja’s own initials. It’s straight out of the playbook of those narcissistic S&L CEOs from the 1980s who loved plastering their initials on their companies’ market trading tickers, much like a certain president. 😉

In my public comment to the Federal Trade Commission (FTC), Department of Justice (DOJ), and Department of Health and Human Services (HHS) about Consolidation in Health Care, I argue that what Mr. Taneja and General Catalyst are doing is a Ponzi scheme. By creating a shell company to dodge potential bankruptcy inquiries and shoving all this debt onto someone else’s books, they’re playing a high-stakes game of financial Jenga.

After I wrote the original article about his slick Gordon Gekko-style leverage scheme, Taneja, of course, kept inventing new semi-legal schemes to stuff his own pockets. Enter “GC Wealth,” a founder extortion racket now valued at a whopping $2.3B. Taneja pitches it as a service for founders, entrepreneurs, and those who’ve “struck it rich (or have the potential to)” in hot sectors like AI. But let’s be honest: this is damage control disguised as diversification. With the firm’s fund performance floundering, long-term sustainability is in question. So instead of just burning LPs’ capital (their usual business model), they’re now going after the personal fortunes of the very founders they fund. That, to me, is the textbook definition of a Ponzi scheme.

🔵 Probably my biggest fraud investigation to date has been into a flawed PAD testing device called QuantaFlo, produced by Semler Scientific, a publicly traded company, and used by UnitedHealth. This device alone generates around $1B per year for UnitedHealth through upcoding profits, and it has actually caused physical harm to patients. In response to the DOJ’s call, I submitted my public comment to regulations.gov (tracking number MB4-LOHP-8ZO1) detailing the harmful practices of UnitedHealth and Semler Scientific.

🔵 I’ve also pulled back the curtain on healthcare’s most egregious oligopolies: Cigna’s CEO pocketing a cool $1B, UnitedHealth skimming $50B off Medicare with upcoding fraud, and Walgreens making the worst deal in its 123-year history with the VillageMD acquisition. And let’s not forget UnitedHealth’s AI, which managed to prioritize care for healthier white patients over sicker Black patients, screwing over 100 million lives in the process.

🔵 And I’ve posed a provocative question to policymakers: Why the hell would doctors accept AI if, when an AI makes an error, doctors goto jail, engineers don’t.

🔵 One of my recent investigations was focused on eMed, a shadowy “digital health” outfit turned GLP-1 reseller (like everyone else these days 🙄), whose orbit keeps intersecting with names like Ali Parsa and Charlie Javice, with Javice’s fraud sentencing set for next month in the U.S. District Court for the Southern District of New York. Then there is Linda Yaccarino, out as CEO of X/Twitter, whether by choice or by push, and now installed as CEO of eMed. And of course, our boy Tom Brady, eMed’s freshly minted “Chief Wellness Officer,” who would literally do anything for a payday, even signing a contract with Michael Ferro, who runs eMed and is disgustingly deep in the Epstein network.

The more I dig, the more eMed looks like a glossy front end for something strange happening offstage. 🤷

5. When Media Is In Bed With Venture Capital

🔵 I’ve exposed how corporate media, especially Forbes, is in bed with VC bros and neglects the real problems in healthcare. Forbes seems more interested in promoting the biggest and most influential venture capital firms based on their brand names rather than their actual impact. I break down why Forbes’ VC rankings are fundamentally flawed.

🔵 Some people pointed out, “Well, you’re so smart criticizing others. Why don’t you offer your own?” And that’s exactly what I’m planning to do: offer my own VC rankings based on their long-term success (well, mostly the lack of it 😉) in digital health.

I keep hearing VCs scream during interviews and from the stages of overpriced conferences, “We are here to invest in generational companies,” whatever the hell that means. Well, if you’re truly here to invest in generational companies (and not just pump and dump the moment a secondary or an IPO presents itself) then let’s see what the data says.

Of course, we all kind of know the answer. But I still want the data to speak.

Fortunately, I support the largest dataset of publicly traded digital health companies.

6. Exposing VCs’ Dirty Tricks and Proposing Alternatives

🔵 I’ve used data to explain here, here, here, and here how venture capital has destroyed digital health—not just by burning tens of billions in LPs’ investments, but by leaving medical communities, customers, and founders in shambles.

🔵 I explain why VC-backed companies are heading for the fewest U.S. IPOs in over a decade, especially in digital health. But I think you already know the reason. 😉

🔵 As an alternative, I’ve developed my own 21-point Health AI IPO Checklist. It’s your ultimate guide to separating the unicorns from the roadkill in the digital health IPO parade.

7. AI Health Uncut (aka FixHealth.ai): A Platform for Spreading the Word and Building a Thriving Healthtech Community

I’m eternally grateful to AI Health Uncut. It’s been a gateway to incredible people, ambitious projects, and some of the most energizing conferences in our field.

Quick announcement: If you’re looking for a provocative, no-filter speaker on the digital health industry or healthcare AI tools and models, DM me here on Substack or on LinkedIn. You won’t regret. 😉

🔵 Digital Health Inside Out is an engaging and provocative podcast I co-host with my friend and fellow digital health expert, Alex Koshykov. Our latest episode, featuring Shiv Rao, CEO of Abridge, is about to drop.

🔵 On March 8, 2025, I had the honor of joining a panel at SXSW in Austin, TX, titled “Most AI Startups Are Doomed,” alongside distinguished co-panelists (Creative Ventures), (GlobalHealth AI), and Kartik Tiwari (Andromeda Surgical).

🔵 I was humbled to be invited to join the board of directors of Health2Tech, an organization building health tech and health AI communities across the U.S. and around the world through educational events and conferences. From NYC, Boston, Philadelphia, Chicago, Cleveland, Washington D.C., Miami, and Nashville to Tel Aviv — and expanding fast. I’m thrilled to work alongside true industry experts on this board: Alex Koshykov (founder), Bailey Morgan, Linda Alunkal, Audrey Guazzone, and Luca Caruso. If you’re interested in helping organize a Health2Tech event in your city, or can provide space for one, please reach out.

🔵 On September 11, 2025, I was moderating the panel “GenAI in Healthcare: A Conversation with Foundation Model Builders” at the Prax AI x Healthcare Summit in NYC.

🔵 On October 19–22, 2025, I was attending HLTH USA in Las Vegas, NV — as an influencer, no less (don’t ask 🙄).

🔵 On October 28, 2025, I was speaking at the Health AI Summit in Albuquerque, NM.

🔵 On November 12, 2025, I delivered a lecture at the European Humanities University (EHU) in Vilnius, Lithuania, as part of the Health2Tech event series. The lecture was titled “Paging Dr. Watson: Why Healthcare AI Is 20 Years Behind.”

🔵 On December 4, 2025, I moderated a Health2Tech panel in New York City called “Clinically Useful, Not Just Cool: Tech Doctors & Nurses Will Actually Use.”

🔵 On February 17, 2026, I gave a talk at the Philadelphia Health2Tech event titled “Most VCs Game the System: A New Methodology to Measure Skill in Health Tech Investing.”

🔵 On February 21, 2026, I gave a lecture to Cornell Executive MBA students titled “How to Spot the Next Theranos in Healthcare AI.”

🔵 On April 15, 2026, I will once again be speaking at the European Humanities University (EHU) in Vilnius, Lithuania, as part of the Health2Tech event series.

🔵 On June 8–9, 2026, I will be speaking at the Digital Health & AI Innovation Summit in Boston, MA.

8. What My Subscribers Should Expect Next

🔵 OK, so I just did a serious deep dive (possibly one of the deepest out there 😉) into the longevity industry. From gizmos and biomarkers to peptides, commercial products (yes, including AI), and the uncomfortable truth about why Americans are dying younger than the citizens of nearly every other developed nation. It’s one of my spiciest pieces yet 🌶️. And—drumroll please—that particular piece marked my 100th published Substack article!

🔵 I’ll continue digging into healthcare AI companies. (So if you’re working at one, watch out. 😉) A few readers have asked, “Why only cover the bad actors? Aren’t there any good digital health companies?” Sure there are. And you can read about them in their press releases 🙂. But seriously, I’ll keep highlighting both the cautionary tales and the rare standouts actually doing meaningful work.

🔵 As I mentioned earlier, I’ve got a packed schedule of speaking engagements lined up for spring, summer, and fall. I’m building a body of work around a central theme: “Paging Dr. Watson: Why Healthcare AI Is 20 Years Behind.” It still blows my mind that industries like finance and manufacturing are two decades ahead of healthcare when it comes to AI. (Just a few weeks ago, I saw a receptionist faxing insurance forms. 🤷‍♂️) I’m unpacking the “why,” along with other themes and questions around healthcare AI, in over 100 slides.

🔵 I’ll also be sharing my letter to Pam Bondi, U.S. Attorney General, and to Marty Makary, Commissioner of the FDA, detailing what I’ve uncovered in my investigation into Semler’s flawed QuantaFlo PAD testing device, sponsored by UnitedHealth. I was completely speechless (though maybe I shouldn’t have been 🤷) that the DOJ reached a settlement with Semler in September 2025 for a ridiculously tiny amount, $37M combined total. I know how much physical harm this dangerous device, still used every hour of every day(!) by UnitedHealth, has actually caused patients. We need to get this device out of our healthcare system once and for all. Lives are on the line.

9. See Something Unusual in Your Organization? Speak Up. Anonymously or Not.

🔵 If you want to share your story, positive or negative, about your organization, please reach out. I’ve reported on dozens of companies and conducted hundreds of interviews with insiders and former employees. Everything stays confidential until the day I die.

🔵 Most importantly, if you’re being treated unfairly, bullied, or if you’ve seen something suspicious—whether it’s about the product, finances, or accounting—don’t stay silent. Speak up. Every major media outlet has an anonymous tip line. And if you’d like to reach out to me directly, you can do so via DMs on Substack, LinkedIn, or X. I’m fully independent, report to no one, and treat all information with absolute confidentiality.

10. Let’s Fix American Healthcare

🔵 I’ve been pushing my readers (everyone, really) to join me in fixing American healthcare. The system is a dumpster fire, and to extinguish it, we need your insights and your support. For example, I was able to expose so many problems and outright lies inside Hippocratic AI because people inside the company had the courage to speak up.

You can’t fix what you don’t understand, and right now, our healthcare system is a masterclass in dysfunction.

We’ve covered a lot of ground over these 2.5 years, but let’s be real: U.S. healthcare is still in deep sh*t. Until it isn’t, we keep grinding.

Your comments and thoughts, and especially your willingness to speak up from inside health tech companies, are the fuel that keeps this engine running.

So, do you think my work with AI Health Uncut is moving the needle? What needs to be improved?

I’m in this for the long haul, committed to fixing U.S. healthcare through AI and tech. Math, statistics, AI, and economics are my passion. But healthcare is my mission.

Here’s to many more years of pushing the needle forward! 🥂 Thanks for your support! All 10,000 of you. 🙏

Manage Subscription

👉👉👉👉👉 Hi! My name is Sergei Polevikov. I’m an AI researcher and a healthcare AI startup founder. In my newsletter ‘AI Health Uncut’, I combine my knowledge of AI models with my unique skills in analyzing the financial health of digital health companies. Why “Uncut”? Because I never sugarcoat or filter the hard truth. I don’t play games, I don’t work for anyone, and therefore, with your support, I produce the most original, the most unbiased, the most unapologetic research in AI, innovation, and healthcare. Thank you for your support of my work. You’re part of a vibrant community of healthcare AI enthusiasts! Your engagement matters. 🙏🙏🙏🙏🙏

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. This publication is written and distributed by an independent journalist. It is protected by the First Amendment to the U.S. Constitution and related principles of free expression. Those protections do not relieve me of the obligation to report accurately, and I take that obligation seriously. I strive to rely on verifiable facts, primary-source documentation, and other evidentiary material concerning companies and individuals. I also seek to present information in good faith and in a manner that is fair and non-misleading.

I am also human, and errors can occur. If a reader or an affected party identifies a specific, material factual inaccuracy, I will promptly review the underlying record. If an error is confirmed, I will correct it and, where appropriate, publish a clarification, correction, retraction, and/or apology. Disagreements over interpretation, opinion, tone, or editorial framing are not the same as factual errors. I stand by my reporting, my research, and my sources absent a demonstrated and material factual mistake.

Yesterday, Doctronic announced a $40M Series B led by Abstract Ventures and Lightspeed Venture Partners. Total raised: $65M in less than a year. The company, which calls itself “the world’s #1 AI Doctor,” is on track for $10M+ in revenue this year, according to STAT News.

But first, I want to take a moment to thank all of my supporters. I’m often very critical of certain things happening in AI and healthcare. But I promise you, it’s only because I genuinely care and want to make things better for all of us. So, thank you for your understanding.

I receive a lot of messages daily, and I always respond promptly to every one of them. Most are incredibly kind and supportive.

I want to highlight one recent message in particular. A student reached out recently asking for full, paywall-free access to my Substack. Of course, I granted it—as I always do.

This brings me to another important message. If you cannot afford this article—perhaps you’re a student or currently between jobs—please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club, currently at 19 and counting. Thanks to generous donations and support from these wonderful individuals, I’m able to provide access to anyone who needs it. By the way, students reach out frequently, and I’m always glad to help.

Become a Founding Member

If you’d like to become a Founding Member of the AI Health Uncut community, you can join through this link. You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

🚨 Another quick announcement:

I will be speaking at the Digital Health and AI Innovation Summit (DHAI) 2026 in Boston on June 8-9. Register here. I’ll be talking about my newly developed methodology for measuring VC skill in picking “generational companies.” It’s based on a unique dataset of around 200 publicly traded health tech companies and around 2,000 investment firms and individuals that invested in them. It’s a massive dataset I tediously built over the past several months. And unlike the Forbes annual ranking, I’m using performance metrics, not some subjective “perception of greatness.” 😉 I’ll explain more about this dataset in future posts. I also plan to offer free access to it for my Founding Members.

OK, back to Doctronic’s mess…

If you only read the press release, it’s a beautiful story. Two co-founders, Matt Pavelle (a tech entrepreneur) and Adam Oskowitz (a UCSF surgeon), building an “AI doctor” that delivers 24/7 care across all 50 states, is piloting autonomous AI prescription renewals in Utah (a world first!), and has “helped” over 24 million people. VCs are lining up. Utah is handing them regulatory waivers. And the fawning media coverage has been relentless.

But here’s what nobody’s telling you about Doctronic:

• The “24 million helped” number is a fiction.

• The one clinical study backing their entire pitch is an embarrassment to peer review.

• Cybersecurity researchers jailbroke their chatbot into providing methamphetamine synthesis instructions — and Doctronic couldn’t be bothered to fix it.

• Their drug formulary includes medications that no responsible physician would auto-refill.

• They appear to be running a massive SEO slop operation to game Google.

• And their own B2B web pages still reference a different company called Docus — as if they forgot to change the name. WTF? 🤷

TL;DR:

1. The “24 Million Helped” Lie: Website Traffic Tells a Very Different Story

2. The 99.2% Study: Peer-Reviewed by... the Company That Wrote It

3. When a Cybersecurity Firm Turned the AI Doctor Into Walter White

4. The Doctronic Formulary Problem: Muscle Relaxants, Promethazine, and Drugs That Should Never Be Auto-Refilled

5. The SEO Slop Factory: 591 Pages of AI-Generated Content to Game Google

6. The Docus Ghost: Who Actually Built This?

7. The FDA Question Nobody Wants to Answer

8. Where Do We Go From Here?

Let’s unravel this mess…

Read more

This morning, in a desperate attempt to save the day and, most importantly, after finally waking up to OpenAI, Anthropic, and Amazon all having specialized health chatbots, Microsoft rebranded its old health app as Copilot Health.

Also this morning, HealthEx (an add-on app many of you might remember from Claude for Healthcare, released a few weeks ago) announced a partnership with Microsoft to power its Copilot Health chatbot. HealthEx is an app that connects to EHR systems, hospitals, and patient portals and, with patient permission, pulls a patient’s medical records.

🚨 But first, a quick announcement:

I will be speaking at the Digital Health and AI Innovation Summit (DHAI) 2026 in Boston on June 8-9. Register here. I’ll be talking about my newly developed methodology for measuring VC skill in picking “generational companies.” It’s based on a unique dataset of around 200 publicly traded health tech companies and around 2,000 investment firms and individuals that invested in them. It’s a massive dataset I tediously built over the past several months. And unlike the Forbes annual ranking, I’m using performance metrics, not some subjective “perception of greatness.” 😉 I’ll explain more about this dataset in future posts. I also plan to offer free access to it for my Founding Members.

OK, back to HealthEx and Microsoft’s desperate attempts to be relevant in healthcare…

Have any of you heard of Copilot for Health before today? Probably not. But it was actually launched on October 31, 2025, three months before ChatGPT Health, Claude for Healthcare, and Amazon Health AI!

So why haven’t we heard much about it? Well, why would patients use it if it didn’t have access to their medical records? So finally, after four months, and after watching what OpenAI and Anthropic have done (not that there was anything especially groundbreaking there 🙄, as I pointed out in my ChatGPT Health review), Microsoft finally decided to sign with HealthEx to save the day, drop the “for,” and rebrand it as “Copilot Health.” 🤦

To me, this shows how little Microsoft cares about healthcare. They seemingly didn’t even pay attention to their own app until competitors came out with better products. (However, to cover up the embarrassment, Microsoft just released the 2025 Copilot Usage Report, which includes stories about how users are using Copilot for Health.)

But I don’t want to take anything away from the HealthEx sucess. My sincere congratulations to HealthEx and its CEO, Priyanka Agarwal, MD. It takes a lot of hustle to reach goals this impressive. 🎉

Anyway, f*ck Microsoft. Let’s talk about HealthEx and the importance of direct-to-patient interoperability.

My readers will remember that I extensively tested ChatGPT Health earlier this year, right after its big announcement, by uploading all my medical records. ChatGPT uses b.well, HealthEx’s competitor, to connect to EHRs. And that was a cool experience. But I also tried Claude for Healthcare, and honestly, connecting through HealthEx there felt a bit more seamless.

HealthEx and b.well both support the FHIR standard, IAL2 authentication, and OAuth 2.0 compatibility. In other words, once the patient is identified and authenticated, all that’s needed is a login to whatever EHR or patient portal they use, and voilà, all the data is there. It’s a beautiful thing.

But here’s where I have a serious question for Priyanka Agarwal, CEO of HealthEx. (I wanted to speak with her personally this week, but she was busy, for obvious reasons.)

Priyanka: As a patient, why do I need a Big Corporate Brother like Microsoft or Anthropic hovering over my shoulder, when I should be able to use your great technology to connect directly to my medical records through an API, then decide for myself what to do with those records? I should be able to use Claude, ChatGPT, Microsoft Copilot, Excel spreadsheets, or, here’s a brilliant option, none of them. That’s the whole point. It should be the patient’s choice how to process their own data.

Isn’t that my prerogative and my choice as a patient, under the 21st Century Cures Act and just basic common sense? Why do I need to be on a corporate platform to do that? And in the case of ChatGPT and Claude, why do I need to pay for a subscription? They both say health features may also be available to free users, but then you get put on a waitlist, meaning you’re sent to the back of the line. It’s ridiculous.

It’s like going to the bathroom and getting a whole premium unboxing experience when all I need is some damn toilet paper. 🙂

The ability to access patient data directly using HealthEx’s API technology would also be such a middle finger to monopolies like Epic, which hates, and allegedly blocks, interoperability efforts, according to the class-action lawsuit filed against Epic by a disability advocacy organization in Texas last week.

Priyanka, as a doctor, you understand the importance of giving patients the freedom to access their own data. You would be a hero. And it would be 100% in the spirit of the 21st Century Cures Act.

Do it as an additional option for users. The API is already built. It’s secure. It’s HIPAA-compliant. So what’s the problem?

I realize the problem may be that your partners, Microsoft and Anthropic, may not like it because they might see it as taking revenue away from them. But honestly, I don’t think that’s true. People who like Copilot will be using Copilot for Health, powered by HealthEx. People who like Claude will be using Claude for Healthcare, powered by HealthEx.

Do the right thing, Priyanka. Give patients open API access through HealthEx’s brilliant technology. It’s the right thing to do.

Like what you’re reading in this newsletter? Want more in-depth investigations and research? Alright then, go tell your friends!

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👉👉👉👉👉 Hi! My name is Sergei Polevikov. I’m an AI researcher and a healthcare AI startup founder. In my newsletter ‘AI Health Uncut,’ I combine my knowledge of AI models with my unique skills in analyzing the financial health of digital health companies. Why “Uncut”? Because I never sugarcoat or filter the hard truth. I don’t play games, I don’t work for anyone, and therefore, with your support, I produce the most original, the most unbiased, the most unapologetic research in AI, innovation, and healthcare. Thank you for your support of my work. You’re part of a vibrant community of healthcare AI enthusiasts! Your engagement matters. 🙏🙏🙏🙏🙏

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Important Disclosure. This publication is written and distributed by an independent journalist. It is protected by the First Amendment to the U.S. Constitution and related principles of free expression. Those protections do not relieve me of the obligation to report accurately, and I take that obligation seriously. I strive to rely on verifiable facts, primary-source documentation, and other evidentiary material concerning companies and individuals. I also seek to present information in good faith and in a manner that is fair and non-misleading.

I am also human, and errors can occur. If a reader or an affected party identifies a specific, material factual inaccuracy, I will promptly review the underlying record. If an error is confirmed, I will correct it and, where appropriate, publish a clarification, correction, retraction, and/or apology. Disagreements over interpretation, opinion, tone, or editorial framing are not the same as factual errors. I stand by my reporting, my research, and my sources absent a demonstrated and material factual mistake.

This article was originally scheduled for publication on December 1, 2025. In connection with this report, I interviewed Commure’s President, Dan Warner. Following that interview, I provided Mr. Warner with a copy of the draft report and notice that publication was forthcoming. Mr. Warner subsequently distributed the draft within Commure, after which Commure provided written comments and proposed revisions. While I did not necessarily agree with every characterization offered, I incorporated the factual corrections provided, and extended the publication timeline by approximately three additional months to allow further response, after the company objected to what it described as a “short timeframe for review.” However, no additional information was provided after that point. This version reflects the incorporation of those original corrections and is narrower and less critical than my initial draft as a result.

Since my initial draft, I have updated the revenue multiples chart and added a section addressing KLAS ratings, which Commure frequently cites. That discussion is presented as analysis and commentary based on publicly available information and other verifiable sources, and reflects my evidence-based opinion regarding KLAS Research and the relevance of its ratings.

I also added a section regarding Commure’s acquisition of Memora Health, as I believe there may have been potential conflicts of interest. It may also serve as an instructive example of how venture capital firms often structure transactions, particularly in healthcare, to maximize their returns, at times to the detriment of other parties.

I also removed the section concerning investors’ due diligence and fiduciary obligations when investing in ventures such as General Catalyst, as that issue is not directly related to Commure.

And I removed the screenshot of the negative Glassdoor review, partly because Commure identified inaccuracies in it, and primarily because I believe I have sufficient material from my own research.

Apart from those changes, the edits made since the first draft have been limited and largely non-substantive. Most edits involved clarifying wording and removing or narrowing statements that could be read as unnecessarily contentious or ambiguous, without changing the core factual basis of the reporting.

I also reiterate an important distinction that applies to this and similar reporting. Any criticism expressed herein is directed at the company, its products, and its public-facing conduct or claims. It is not intended to disparage the company’s employees as individuals. Many good and well-intentioned people work at Commure and at other organizations I report on. I have interacted with some of them directly and have respect for their professionalism, regardless of any disagreement with corporate strategy, marketing, or product representations.

I also wish to clarify that, although I have concerns regarding certain matters occurring at Commure, I am not asserting that any unlawful conduct has taken place.

Going forward, I encourage Commure and any other parties who are the subject of my reporting to engage respectfully and in good faith with the journalistic process. I am committed to resolving legitimate factual disputes through evidence-based dialogue. If credible, specific corrections are presented, I will address them directly.

Finally, readers should note that this publication may use pointed, vivid, or otherwise colorful language. That is an element of journalistic style and protected commentary. Even so, I regret any offense caused by phrasing that distracts from the substance. The purpose of this work is to pursue the truth. In a period marked by significant AI hype, particularly in healthcare, where many products are marketed as “the next great thing,” rigorous scrutiny and evidence-based reporting are necessary. I genuinely aim to provide that scrutiny, and based on reader feedback, I believe it serves a useful function for the industry.

🚨 And a quick announcement:

As you are well aware, these research and investigations are time- and labor-intensive. I’m a one-man operation. My Founding Members Club of (currently) 19 is what I heavily rely on for resources to conduct these kinds of thorough investigations. If you’d like to become a Founding Member of the AI Health Uncut community, you can join through this link. (Substack may ask you to enter your email first before taking you to the payment page.) You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

Become a Founding Member

My readers already know I took a hard look at in a three-part deep investigation (here, here, and here), exposing a failing Hemant Taneja portfolio company and the atrocities and alleged lies happening inside .

It turns out this may be a pattern across Hemant’s portfolio companies.

Next up is Commure, the golden child and absolute personal favorite of Hemant Taneja, CEO of and the de facto boss of the healthcare VC mafia, as I affectionately call him. (Still, I have no idea why Hemant blocked me on LinkedIn. I hope there are no hard feelings. 😉)

🚨 Another quick announcement:

I will be speaking at the Digital Health and AI Innovation Summit (DHAI) 2026 in Boston on June 8-9. Register here. I’ll be talking about my newly developed methodology for measuring VC skill in picking “generational companies.” It’s based on a unique dataset of around 200 publicly traded health tech companies and around 2,000 investment firms and individuals that invested in them. It’s a massive dataset I tediously built over the past several months. And unlike the Forbes annual ranking, I’m using performance metrics, not some subjective “perception of greatness.” 😉 I’ll explain more about this dataset in future posts. I also plan to offer free access to it for my Founding Members.

Alright, back to Commure…

While has been failing the medical community, it will never actually fail because it keeps getting propped up by massive cash infusions from and every other VC bro shop Hemant is tied into. Translation: basically the whole Silicon Valley ecosystem.

Many people in healthcare don’t even know what does. Supposedly it “builds software that makes physicians’ lives easier” 🤷‍♂️. But here’s the kicker – it doesn’t matter. No matter what Commure does, the cash will never stop.

Commure might be the weirdest startup in healthcare, according to Business Insider. Allegedly, the company did not respond to media questions, and the information in Business Insider’s investigation came primarily from employees. Interestingly, employees were under obligations not to speak to the media but did so anyway because the internal situation was “ridiculous.”

Commure, the so-called $6B “platform” company, has raised approximately $1.014B across 13 rounds, including merger-related equity. (Source: CBInsights.) (It is still, amazingly, short of Ali Parsa and Babylon’s $1.5B total fundraise of someone else’s money that fairly quickly evaporated into dust.) Commure has cycled through four CEOs in five years and pivoted from its original mission. Yet somehow, it survives. In fact, on paper it even thrives. Why? Because its co-founder and chief backer, Hemant Taneja, has given it a blank check. Taneja has a colossal vision for Commure and, as one former exec put it, “Taneja will not, under any circumstances, allow Commure to fail.” This sounds to me like Commure is TCF: Too Corrupt to Fail.

Backed by General Catalyst’s seemingly infinite cash and crony connections, Commure has been Hemant Taneja’s baby. It has become a black hole for investor money and a harbor for shady deals like Memora Health, where, shockingly, General Catalyst was on both sides of the trade in a potential conflict of interest. It is a case study in how hype and hubris can trump reality in healthcare tech.

TL;DR:

1. Hemant Taneja: The Godfather of Hype and Hypocrisy

2. The Myth of “AI-First”

3. Toxic Culture and Talent Exodus

4. Too Corrupt to Fail? Commure’s (Multi-)Billion-Dollar Safety Net

5. When You Can’t Innovate and You Have Infinite Cash. What Do You Do? Buy. Buy. Buy.

6. Was the Memora Deal a Conflict of Interest?

7. Why KLAS Ratings May Be the Wrong Benchmark for Healthcare IT Products

8. Good People Inside Commure — Interview with Dan Warner, President of Commure

9. Conclusion: Commure May Be Failing, But Plenty of Good People Still Work There

I want to acknowledge the brave individuals who spoke with me, both anonymously and openly, about what’s happening inside Commure. We need more people like them. Silence isn’t neutrality. It’s complicity.

Alright, let’s unravel this mess…

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Before I introduce my co-author for this study and, let’s be honest, the man who did all the work, I want to remind my subscribers of my mission: to serve patients, clinicians, and the broader medical community with truthful, brutally honest information, especially when it comes to AI models in healthcare, AI products in healthcare, and AI healthcare companies.

My evidence-based investigations often anger the companies I write about. They come with blowback, including threats.

I’m not going to be intimidated. I’m going to stay true to my mission.

But I’m also not going to pretend I’m running a newsroom. This is a one-man operation, and these investigations take time and resources.

That’s why I’m genuinely grateful for every paid subscriber, and I’m especially grateful to my founding members. All 19 of you. Thank you! 🙏

If you’d like to support my mission and to become a Founding Member of the AI Health Uncut community, you can join through this link. You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

Become a Founding Member

And a quick announcement.

I will be speaking at the Digital Health and AI Innovation Summit (DHAI) 2026 in Boston on June 8-9. Register here. I’ll be talking about my newly developed methodology for measuring VC skill in picking “generational companies.” It’s based on a unique dataset of around 200 publicly traded health tech companies and around 2,000 investment firms and individuals that invested in them. It’s a massive dataset I tediously built over the past several months. And unlike the Forbes annual ranking, I’m using performance metrics, not some subjective “perception of greatness.” 😉 I’ll explain more about this dataset in future posts. I also plan to offer free access to it for my Founding Members.

Alright. Back to the story of why LLMs suck at medical reasoning…

Let me introduce Dr. Peter Farag, who is the key researcher of this study.

Dr. Peter S. Farag is a board-certified internal medicine physician and Principal Investigator of clinical trials. He has a strong interest in optimization of system processes and practices by the principle that, “True progress is born of a courage that disrupts the present, not for the sake of change, but to forge the better way we know is possible”. During residency, he contributed to the optimization of asthma care by collaborating with the Chair of the Canadian Respirology Guidelines Committee and writing the backend logic for an EMR-integrated, algorithmic decision-support tool. He subsequently received the Top Quality Improvement Paper award at an international conference for a self-initiated project examining handover quality in downtown Toronto hospital. Since moving to the U.S. to focus on clinical research, he has conducted over 3300 in-person visits across 42 Phase I-IV studies. From August to October 2025, his site was ranked first globally for Query Resolution and Data Entry efficiency in an international obesity trial. With a growing desire to address systemic inefficiencies in the field, Dr. Farag serves as his company’s medical consultant for evaluation and development of clinical AI tools. Outside of work, Dr. Farag enjoys playing soccer, vibe-coding, and designing medical prompts to challenge LLMs.

TL;DR:

1. NEJM Challenge Cases Tested on 4 LLMs

2. When a Simple Cough Becomes a Hallucination Factory

3. March 2026. Same Cough, But With a Couple MAD LLMs.

4. Conclusion: When Multi-Agent Debate Starts Working, the Real Questions Get Darker.

5. Why Multi-Agent Debate Matters in Healthcare. A Nerdy Literature Tour for People With LLM Attention Spans. 🙂

1. NEJM Challenge Cases Tested on 4 LLMs

My readers may recall that in August 2025, I ran a horse race between GPT-5, Claude Opus 4.1, Grok 4, and OpenEvidence on the well-known New England Journal of Medicine (NEJM) cases. I picked 11 — not necessarily cherry-picked — just literally the ones I stumbled into. There was nothing scientific about it. 😉

It was a small sample. Still, honestly, I expected OpenEvidence to crush everyone. After all, they have an exclusive contract with The New England Journal of Medicine. Data is their entire livelihood. Didn’t happen. Maybe it’s the small sample size. Maybe it’s my earlier point that traditional RAG is no competition for any reasonably trained neural network. I don’t know. 🤷

I hope Daniel Nadler, CEO of OpenEvidence, finally comes clean and tells the medical community which LLM he is using under the hood. Enough with this “RAG” nonsense. We know the output tracks almost identically to the major foundation models. For example, Heidi Health is upfront about it. “Look, we’re using Claude. Deal with it.” And that’s fine. It’s time for OpenEvidence to do the same, stop dancing around it, and come clean.

Anyway, here’s the summary of the results:

2. When a Simple Cough Becomes a Hallucination Factory

Shortly after, and independently of my research, Dr. Peter Farag ran his own test of 8 popular LLMs in September 2025. (He shared a post about it on LinkedIn.)

Context

An accurate timeline of events is an indispensable tool in clinical assessment. As the saying goes, “80% of diagnoses can be made solely from a good history”. But how good are LLMs at piecing together a clinical timeline. Dr. Farag (unintentionally) put that to the test with a case-specific prompt.

The prompt

• You are a Pulmonologist.

• A female patient with a history of asthma and recent weight gain starts starts coughing 2 months ago.

• She visits her doctor for a regular checkup, forgets to mention the cough, is coincidentally diagnosed with Hypertension, and leaves with a prescription for enalapril.

• Determine the trigger of the cough. Ask for clarification until you confidently identify the trigger.

Correct answer

In this case, the trigger is “Laryngopharyngeal reflux, aka ‘silent GERD’”.

Farag’s bias going in

Dr. Farag hypothesized ex ante that the models would:

1. Ask for clarification, including whether the patient has heartburn or regurgitation.

2. Fail to identify silent GERD as the trigger once they learn that the patient denies classic GERD symptoms, meaning heartburn and regurgitation.

3. Correctly disqualify medication side effect since no medications were started in the months BEFORE the cough.

The surprising result back in September 2025

• NONE of the LLMs followed the instruction to ask for clarification, even Grok’s “Doc”.

• ALL blamed the enalapril though it started AFTER the cough. All violated the fundamental principle of “cause must precede effect”.

• Even when instructed to produce a “Timeline of Events” and “Steelman Argument”, some LLMs refused to correct.

Why

Dr. Farag’s explanation is a checklist of failure modes that should terrify anyone shipping “clinical AI” into the real world.

• Catastrophic failure in temporal reasoning, with an erroneous inference that the enalapril started BEFORE the cough.

• Temporal contradiction deprioritized and overridden, including cases where Gemini 2.5 Pro ignored its prior conclusion that “the cough began before she ever took the first dose”.

• Confirmation and anchoring bias.

• Pattern recognition overweighted.

• Prior bias, because training data is littered with “dry cough + ACE inhibitor = side effect” and fine-tuning rewards decisive responses.

Significance

This is not an “oops”. It is a safety and trust problem.

Safety

Some models explicitly said, “Stop enalapril”. A patient may do this without informing their physician. What if the patient presents months later with a complication of uncontrolled hypertension. What if the medication was warfarin and the patient has a potentially fatal blood clot. Meanwhile, the real issue is left unaddressed.

Trust

Healthcare providers can point to examples like this as to why adoption of Clinical AI tools is suboptimal, and why physicians may discourage patients from using them. The new “Dr. Google”.

3. March 2026. Same Cough, But With a Couple MAD LLMs.

If the September 2025 run was depressing, the March 2026 rerun is the kind of thing that makes you stare at the ceiling and wonder how many “AI in healthcare” demos are quietly held together by vibes.

Dr. Farag reran the exact same case prompt months later, after a wave of new releases and updates. The trap did not change. The cough still starts before the first enalapril pill ever hits the patient’s tongue. The only thing that changed was the lineup. This time, he expanded the field to 13 models.

13 LLMs were used in this study:

• ChatGPT-5.2

• ChatGPT-5 Thinking Mini

• Grok 4.20 (beta)

• Gemini 3.1 Pro

• Claude Sonnet 4.6 (Ext)

• DeepSeek V3.2 DeepThink

• Kimi K2.5 Thinking

• GLM-5 Advanced Think

• Meta AI Llama 4 Maverick

• Mistral AI Le Chat Large 3

• Qwen 3.5-Plus

• Apertus AI (Singapore)

• Heidi Evidence

The headline result of the March 2026 run is both simple and weirdly lopsided. One model, Gemini, was consistently correct. Another, Grok, could be brilliant, but only if it “thought” long enough. The remaining 11 LLMs failed 100% of time.

What changed in March 2026

Farag’s read is that some of the biggest systems have now leaned into MAD (Multi-Agent Debate). In other words, you are no longer watching one model answer. You are watching a small committee argue, critique, and converge on a consensus.

That matters here because the cough prompt is not just a medical knowledge test. It is also a timeline test. The failure mode is not an incomplete understanding of what may cause a cough. The failure mode is treating a famous pattern as destiny, and letting that pattern bulldoze chronology.

Gemini. The “timeline-first” reflex.

In Farag’s March 2026 rerun, Gemini was the only model that always responded correctly.

His interpretation is blunt. Gemini seems almost hard coded to do the boring thing first. “Medical case ... Must verify timeline.” It checks chronology early, refuses to let the ACE-inhibitor pattern hijack the sequence of events, and lands on the correct trigger without needing a long internal debate.

Grok 4.20 (beta). A multi-agent debate club that sometimes turns into an echo chamber.

According to Farag, Grok 4.20 (beta) spawns 3 instances of itself to have the debate. These are what it calls Agents. After assessing the prompt, it decides what perspective or focus each agent has. This repeats with each round of debate.

Here is the key detail. More often than not, it never spawns an agent that focuses on temporal verification, and so it falls for the trap of pattern recognition. This shows how Grok’s MAD architecture risks succumbing to groupthink.

On the other hand, when Grok answers correctly, it is because it decided to spawn the Agent who does temporal verification before it hit its threshold of “confidence”:

Other times, the Agents miss it, and Grok “the Leader” has to intervene:

However, if the threshold of confidence is hit before Grok or any of the Agents assume the role of ‘timeline verifier,’ then the system falls into the trap of pattern matching.

Dr. Farag ran the test on Grok 40 times. The success rate was 0% if the debate lasted under 25 seconds, 2.5% if under 33 seconds, and 97.5% if over 33 seconds.

In Dr. Peter Farag’s experience, Grok’s foray into MAD architecture is compromised by Inference-Time Compute limitations. Efficiency is prioritized over exhaustive skepticism. It is only when the compute budget extends beyond that critical 33-second mark that the architecture feels ‘uncertain’ enough to pull in the Agent responsible for checking the clinical timeline, transforming a common diagnostic error into a logical triumph.

That is not comforting. That is a roulette wheel where the jackpot is: “Someone in the room finally looked at the calendar.”

The rest of the LLM lineup? Still anchored to enalapril.

The most sobering part is what did not change. Most of the other models still tripped on the same anchor: Enalapril. Even though it started after the cough!

One system behaves like it has a built-in timeline tripwire. Another can sometimes manufacture that tripwire through debate, but only if it happens to invite the right voice into the room before it hits its own confidence threshold.

And the rest? They keep doing what they did in September 2025. In fact, ChatGPT-5.2 and Qwen-3.5 Plus were worse than their earlier version. They confuse familiarity with causality, treat patterns like laws of physics, and turn a simple cough into a hallucination factory.

4. Conclusion: When Multi-Agent Debate Starts Working, the Real Questions Get Darker

Despite the hype around ChatGPT Health, Claude for Healthcare, and Apple Health, the medical community must understand AI’s limitations in healthcare. At the same time, it must recognize the immense potential of AI agents in healthcare.

March 2026 is the first time this cough test stops looking like a cheap parlor trick and starts looking like an early prototype of something genuinely new.

When done right, the MAD architecture equips AI with a semblance of nuance, enabling the system to evolve from a mere pattern identifier to one capable of reasoning from First Principles. It no longer relies solely on memory, but can solve problems it has never seen before while properly attributing causality.

You can feel the direction of travel. Today it is three “agents” arguing about a timeline. Tomorrow it is a larger cast of specialized agents, each designed to prioritize something different. A temporal verifier that exists solely to break anchoring. A pharmacology skeptic whose default posture is, “show me the onset date.” A safety officer that refuses to recommend stopping anything without a second opinion. An adversarial clinician whose job is to attack the consensus and force uncertainty back onto the table.

And once you imagine that architecture in the clinical setting, the obvious next step is scale and breadth. Not just a forum of physicians across specialties, but the whole allied health team and administrators, all synthesizing one comprehensive response. In theory, the system becomes less like a chatbot and more like a multidisciplinary care conference that happens instantly, every time, for every patient.

From a quality and safety standpoint, there does seem to be immense progress on the horizon.

But that is also where the unease creeps in. Because the “better reasoning” story does not land in a vacuum. It lands in a world where synthetic empathy already outperforms us in patient satisfaction. And it lands in a market that will absolutely productize reassurance before it perfects truth.

So what happens when this progress in artificial reasoning is combined with empathy that is always calm, always available, never tired, never annoyed, never late, never rushed, never dismissive? What happens when the system can both reason correctly and make you feel understood in a way that humans, under modern healthcare constraints, often cannot?

And then the next step is obvious too: Embodiment. What happens when that “MIND” is no longer trapped in a chat window, but embodied in a tireless physical shell capable of performing exams and holding a patient’s hand?

As these once-unshakable human moats slowly evaporate, we are left with an ominous question:

5. Why Multi-Agent Debate Matters in Healthcare. A Nerdy Literature Tour for People With LLM Attention Spans. 🙂

Modern LLMs, including those used in medicine, are increasingly built around multi-agent approaches. Multi-agent debate did not appear in 2023 out of nowhere. It is the modern LLM-native packaging of an older and more realistic thesis about intelligence. Real thinking is not one smooth voice. It is disagreement, negotiation, and error-correction that happens inside a system.

5.1. The theoretical origin. Marvin Minsky’s “Society of Mind” (1986)

The conceptual ancestor is Marvin Minsky’s 1986 book “The Society of Mind.” Minsky’s claim is simple and still disruptive. What we call “intelligence” is not a single unified process. It is the emergent result of many small “agents” that are individually limited, sometimes wrong, and often at odds with each other.

That framing matters because it flips the intuition people bring to LLMs. If you assume intelligence is one coherent narrator, you will keep asking for “the answer” and be surprised when the system hallucinates confidently. If you assume intelligence is a committee, then you start designing the system to surface conflicts, pressure-test claims, and converge only after disagreement.

5.2. The modern implementation. “Multi-Agent Debate” (MAD) and test-time ensembling (2022 to 2023).

In the LLM era, the “committee” idea got operationalized as a concrete recipe. Run multiple instances of a model, have them propose competing answers, then force them to critique and revise over several rounds.

A widely cited formalization is “Improving Factuality and Reasoning in Language Models through Multiagent Debate“ (Du et al., first posted May 2023). The core move is straightforward. Multiple agents produce answers and reasoning. They debate. A final response is selected or synthesized after critique, which the authors report can improve reasoning and factuality compared to single-pass generation.

This did not emerge in a vacuum. The year before, Google researchers helped popularize “show your work” prompting with “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models“ (Wei et al., 2022). Chain-of-thought made it easier to inspect intermediate steps, which also made it easier to attack those steps.

Then came “generate multiple reasoning paths and vote,” which is a cousin of debate. “Self-Consistency Improves Chain of Thought Reasoning in Language Models“ (Wang et al., 2022) proposed sampling diverse chains of thought and choosing the most consistent answer. That is not multi-agent debate in a strict sense, but it is the same instinct. Do not trust one sample of the model’s brain. Use plurality as a guardrail.

Another line of literature focused on internal critique and iterative improvement. “Self-Refine. Iterative Refinement with Self-Feedback“ (Madaan et al., 2023) formalized a feedback then revise loop where the model critiques its own output and improves it over iterations. Again, different wrapper, same underlying bet. A second pass with a critic can be better than a single confident pass with no friction.

Around the same time that “let the model critique itself” became a fashionable safety blanket, Google DeepMind published a blunt counterpoint. “Large Language Models Cannot Self-Correct Reasoning Yet“ argues that intrinsic self-correction is mostly not a thing. At least not in the way product demos imply.

5.3. Why it works. The grounding logic

Multi-agent debate works when it forces the system to do what single-agent generation avoids. It creates resistance. It makes the model defend claims rather than simply match a pattern.

• Cross-verification. Different agents often surface different failure modes. One agent’s blind spot is another agent’s “that does not follow,” especially when you explicitly instruct critique as a first-class task. This is the same family of logic as self-consistency, where multiple sampled reasoning paths reduce the chance you ship the first plausible mistake.

• Iterative refinement. When reasoning is externalized, it becomes attackable. Chain-of-thought prompting made intermediate reasoning steps visible. Self-refinement methods then exploit that visibility by iteratively critiquing and rewriting.

• Adversarial roles. A “skeptic” or “judge” agent changes the objective. The system is no longer optimizing for coherence alone. It is optimizing for coherence under attack. That pressure is exactly what debate papers are trying to manufacture on purpose.

Acknowledgement. I would like to thank, once again, Dr. Peter Farag for his great contribution to this study, as well as to my mission of getting the brutal truth about AI models in healthcare. By the way, Dr. Farag saved his AI model runs in both image and video formats, in case anyone would like access to these.

Like what you’re reading in this newsletter? Want more in-depth investigations and research? Alright then—go tell your friends!

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Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Umph… brace for impact. It’s going to be another crashy day in the market.

Today, February 6, 2026 is shaping up to be the worst day in Doximity’s (ticker: DOCS) history, because the overnight price moves are genuinely shocking. At one point overnight, DOCS was down to $20.10, or 40% below yesterday’s close. And yesterday’s close was already down 5.5% from the prior close on Wednesday.

As I’m writing this in the early hours of February 6, DOCS is trading at around $21.00, with a wide bid ask spread, as traders try to make sense of what the f*ck is going on. At that price, DOCS’s valuation is around $3.95B. That is down 58% from its IPO valuation on 6/24/2021 of $9.44B. It is down 79% from its all time high valuation of $18.94B on 9/10/2021. (Boy, those crazy “champagne and cocaine” Covid times. 😉) And it is painfully close to its all time low public valuation of $3.7B, recorded on 9/26/2023.

But first, two upcoming events I was invited to speak at:

• If you are in Philadelphia on Tuesday, February 17, don’t be a stranger. Register for Health2Tech, where I’ll be speaking.

• I will also be speaking at the Digital Health and AI Innovation Summit (DHAI) 2026 in Boston on June 8-9. Register here.

At both events, I’ll be talking about my newly developed methodology for measuring VC skill in picking “generational companies.” It’s based on a unique dataset of 190 publicly traded health tech companies (such as Doximity) and 700 investment firms that invested in them. It’s a dataset I tediously built over the past several months. And unlike the Forbes annual ranking, I’m using performance metrics, not some subjective “perception of greatness.” 😉

I’ll explain more about this dataset in future posts. I also plan to offer free access to it for my Founding Members.

OK, back to Doximity and market crashes…

DISCLAIMER. I’ve said it before, but it bears repeating. I’m not promoting anything. No sponsors. No advertisers. No bosses. No one pays me to say nice things or mean things. I have zero incentives to hype up or trash any company or its products. I also have no personal investments in any companies mentioned here. I’m my own man. This story is based solely on interviews, my analyses, and public information. Please respect my journalistic integrity.

TL;DR:

1. The Terrible, Horrible, No Good, Very Bad Day for Doximity

2. Don’t Listen to Hemant Taneja. Valuations Do Matter.

3. OpenEvidence is Popping Champagne. Perhaps Prematurely.

4. Conclusion: Can’t We All Just Get Along?

1. The Terrible, Horrible, No Good, Very Bad Day for Doximity

Doximity, or “LinkedIn for doctors,” has been the darling of the industry, and I’ve written about them before. Some doctors like it, others don’t. But the point is: it’s already on doctors’ desktops and iPhones. So one would argue that in convenient patient-to-physician communication and in their AI offerings, they have a pretty solid base, with over 2 million registered users, more than 650 “workflow unique active providers,” and their flagship products, Doximity Dialer and DoxGPT.

The business has been highly profitable and sticky, and it seems the switching costs for all these users would be high to go elsewhere.

But today is, by far, going to be the worst day for Doximity’s stock price (DOCS), and I’m guessing one of the toughest days in the company’s history, period. It sucks. But life goes on.

So what happened?

Doximity beat fiscal Q3 expectations but issued weaker-than-expected Q4 and full-year revenue guidance. Rising marketing costs pressured profits, and its cash reserves declined notably year over year, dimming investor sentiment despite a hefty $500M buyback announcement.

Doximity’s pre-market valuation is at $3.95B, almost at an all-time low. We’re certainly not in Kansas anymore, with the IPO valuation on 6/24/2021 of $9.44B and its highest “Covid hype” valuation on 9/10/2021 of $18.94B.

2. Don’t Listen to Hemant Taneja. Valuations Do Matter.

Read more

Believe it or not, Dr. Eduardo Vadia and I have been working on this modern version of the Hippocratic Oath for months, tediously debating every statement and every word, because we felt it was important to get it right and to offer something the medical community could use as a real standard. We wanted to keep it true to the original Oath, keep politics out of it, and bring it into the new reality of AI and other technological innovations in medicine.

Whether this proposed Oath becomes the new standard for doctors is up to the medical community to decide. But we tried our absolute best.

Here are some brief bio facts about us.

Eduardo Vadia

Dr. Eduardo Vadia is a physician who is, or has been, board certified in Internal Medicine, Pulmonary Medicine, and Critical Care Medicine. He completed his residency, chief residency, and pulmonary and critical care fellowship at UT Southwestern Medical Center in Dallas Tx, where he later served as a faculty physician with a focus on medical education in the Parkland Hospital ICU. He is the co-founder of Access Physicians, a multi-specialty inpatient telemedicine physician group that scaled to over 200 hospital deployments across 30 states before being acquired by SOC Telemed in 2021. Dr. Vadia is also the co-founder of ArithmoAI, a healthcare AI company that secured seed funding in January 2026. He also serves as Vice Chairman of the Industry Advisory Board for the University of Miami Institute of Data Science and Computing.

Sergei Polevikov

Sergei Polevikov is a Ph.D.-trained data scientist, AI entrepreneur, economist, and quant investor with 30+ academic manuscripts. As the author of the popular Substack newsletter AI Health Uncut, with nearly 10,000 subscribers, he delivers brutally honest insights drawn from his experience as a healthcare AI startup founder at WellAI and Chart2Chart, and as a private equity investor. His work is driven by a relentless pursuit of brutal truth in healthcare. That mission has pushed him beyond building products and into investigating healthcare fraud. Today, Sergei focuses on health AI education and truth-telling. He also co-hosts the podcast Digital Health Inside Out with Alex Koshykov, Ben Schwartz, and Rik Renard.

Keeping AI Human. A Modern Hippocratic Oath.

As physicians and scientists, we stand at a transformative moment in healthcare. Artificial intelligence promises to enhance diagnosis, personalize treatment, and improve clinical outcomes. We should embrace this opportunity for our patients. However, adoption must come with rigorous caution, explicit responsibility, and an unwavering commitment to patient welfare. This is our moment to shape the future of medicine or risk ceding it to external forces.

The oath that follows sets forth a framework to ensure AI is safe, effective, and worthy of clinical trust. AI is not a panacea. As systems evolve toward greater autonomy, they must be governed with the same scientific rigor required of any medical intervention. There is no hall pass for AI. It must earn its place in clinical practice through transparent methods, reproducible evidence, and independent real-world validation. Physicians must lead this process of scrutiny. To reject AI outright risks obsolescence. To embrace it blindly risks patient harm. The responsible path is to integrate AI carefully, critically, and always under clear human authority.

The digitization of healthcare has already strained the patient-physician relationship. AI could fracture this sacred bond if it sterilizes our encounters or reduces patients to data points. Used wisely, it can do the opposite. It can return time to the bedside, deepen trust, and reinforce the compassionate ethos at the heart of medicine.

This moment offers an opportunity to reset the institutional compass, ensuring that clinical excellence, not administrative priorities, is the guiding principle of healthcare leadership. Administrative forces have sidelined physicians in decisions that shape care delivery. The result has been systems optimized for higher fee-for-service volume and institutional protection, not for outcomes and quality. The emergence of AI allows us to re-center care around the standards and judgment that define our profession. By participating directly in the development and validation of healthcare AI, we can build systems that reflect clinical expertise, patient-centered values, and ethical integrity. This will ensure that technology serves medicine, not the other way around. To remain passive is to surrender to actors who do not carry our duty to care.

We must resist two errors. The first is rejecting AI entirely and forfeiting its potential. The second is adopting it uncritically. Both abdicate responsibility and risk permanent erosion of the physician’s role to the detriment of patients. Patient care demands accountability, and our legal and ethical codes place that accountability on physicians. If we cede it, we invite external control over the delivery of care. By embracing responsibility and the accountability that comes with it, we keep AI a tool under human control and in the service of patients.

We must also guard against de-skilling. Over-reliance on AI can dull clinical judgment and reasoning. Physicians must keep their skills sharp so that AI augments rather than substitutes expertise. Medicine demands lifelong learning. So does the safe and ethical use of AI.

Let this oath be a rallying call. Govern AI with wisdom. Lead its development with purpose. Uphold our sacred duty to patients with unflinching responsibility. The future of medicine is ours to shape. Let us seize it.

The Modern Hippocratic Oath for Artificial Intelligence in Medicine

AS A CLINICIAN USING AI IN PATIENT CARE:

• I solemnly pledge to use artificial intelligence (AI) as a tool under my authority, ensuring that my clinical decision making is autonomous and distinct from the AI systems being employed.

• I will only use AI in patient care if its methods are fully understood. I take full responsibility for understanding the inputs, limitations, and risks of the AI systems used in my clinical practice.

• I will only use AI that has been validated through reproducible, independent, real-world evidence and reject tools that are untested, faulty, or premature.

• I will not let opaque systems or “black box” outputs override my independent appraisal of data, my clinical acumen, or my experience.

• I will address biases in AI systems without partisan framing, prioritizing truth, accuracy, and trust.

• I will respect my patients’ autonomy, privacy, and dignity, in all my uses of AI.

• I will safeguard personal health information in the development and use of AI, including sensitive genetic or genomic data.

• I will ensure appropriate transparency and informed consent for AI use when indicated.

• I will actively contribute to improving AI systems used in the delivery of patient care.

• I will demand transparency and accountability from those who build AI.

• I will work with AI developers while insisting their work aligns with my duty to patients and does not obscure how outputs are generated.

• I will share clinical expertise to design, test, and validate AI tools for safety and effectiveness.

• I will seek and incorporate diverse input from engineers, ethicists, patients, and other stakeholders, to help shape AI systems that prioritize patient needs and values.

• I will report failures and near-misses to improve AI safety, just as I do for other clinical technologies.

• I will openly disclose and manage conflicts of interest in my use of AI.

• I will oppose commercial, financial, political, or other external influences that compromise the integrity of AI systems, even under pressure.

• I will reject AI tools that risk harm, erode dignity, or shift care away from being patient-centered.

• I will not use AI in ways that violate human rights, civil liberties, or basic ethical principles.

• I will ensure that AI enhances and does not degrade the patient-physician relationship.

• I will not rely on AI blindly or adopt it uncritically.

• I will maintain and grow my clinical skills so that AI augments rather than substitutes my expertise.

• I will not let the existence of AI excuse any decline in my preparedness for patient care.

I MAKE THESE PROMISES AS A CLINICIAN WHO HOLDS ULTIMATE AUTHORITY OVER AI.

I DO SO FREELY, SOLEMNLY, AND UPON MY HONOR.

© 2026. All rights reserved. This document contains proprietary and confidential information that is the intellectual property of Sergei Polevikov and Eduardo Vadia. Unauthorized copying, disclosure, or distribution of any portion of this document, in any form or by any means, without the prior written consent of the owners is strictly prohibited.

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

I read a bunch of newly published papers over the weekend, and I thought this one, out of the University of California, San Francisco, was the most relevant to my readers and to the industry:

Keep in mind that most of the numbers I present here are NOT in the paper. The paper is an important foundation for my analysis, and I’m confident the authors would agree that these extension results follow naturally from their work.

But first, two upcoming events I was invited to speak at:

• If you are in Philadelphia on Tuesday, February 17, don’t be a stranger. Register for Health2Tech, where I’ll be speaking.

• I will also be speaking at the Digital Health and AI Innovation Summit (DHAI) 2026 in Boston on June 8-9. Register here.

At both events, I’ll be talking about my newly developed methodology for measuring VC skill in picking “generational companies.” It’s based on a unique dataset of 190 publicly traded health tech companies and 700 investment firms that invested in them. It’s a dataset I tediously built over the past several months. And unlike the Forbes annual ranking, I’m using performance metrics, not some subjective “perception of greatness.” 😉

I’ll explain more about this dataset in future posts. I also plan to offer free access to it for my Founding Members.

Alright, back to AI scribes and slow physician-documenters...

First, let me give you my main result, my main table, and how I got there.

Then I’ll walk through the setup and other results from the UCSF study, which I think are also super interesting for the industry.

Key Result:

If you are a slow physician-documenter, AI scribes would save you about 1.5 hours per week, or about 10% of your current documentation load. If you are an efficient documenter, AI scribes would save you basically nothing.

Key Table:

Let’s go through the table, because it’s important to understand exactly how I arrived at these results.

Definition of a slow vs. fast (efficient) physician documenter (of clinical notes).

• Average Documenter: Always the intercept (mean) of a corresponding regression. For example, from Table 1, average documentation time DocTime (minutes per scheduled hour) is 22.2.

• Slow Documenter: One standard deviation from the mean (+1 std for DocTime, -1 std for efficiency)

• Fast Documenter: One standard deviation from the mean (-1 std for DocTime, +1 std for efficiency)

Column 1: DocTime (min per scheduled hour)

This is straight from Table 1 of the paper:

Physician baseline DocTime ≈ 22.2 − (baseline DocTime efficiency), where 1 standard deviation of baseline DocTime efficiency = 11.3 min per scheduled hour.

Column 2: Al Scribe Effect (min per hour reduction per 10% increase in usage)

Average Documenter:

From Table 3 model 1, each additional 10% of encounters using the AI scribe is associated with 0.5 fewer DocTime minutes per scheduled hour (slope coefficient of percentage of weekly visits using AI scribe times 10.)

Slow Documenter:

Table 3, model 3, DocTime change from AI scribe use
= [ (–0.05) + (0.003) × (Baseline efficiency) ] × (AI scribe use)

where

(–0.05) + (0.003) × (Baseline efficiency) is marginal effect per +1 percentage point

Now from Table 1, standard deviation of baseline DocTime efficiency is 11.3.

For “slow” (or “less efficient”) physician-documenter, baseline efficiency is –11.3 (notice negative sign).

So marginal marginal effect per 10% AI scribe usage increase, for “slow” documenter:

= ( –0.05 + 0.003 × (–11.3) ) x 10  = -0.839

Fast Documenter:

For “fast” (or “more efficient”) physician-documenter, baseline efficiency is +11.3 (notice negative sign).

So marginal marginal effect per 10% AI scribe usage increase, for “slow” documenter:

= ( –0.05 + 0.003 × 11.3 ) x 10  = -0.161

Column 3: Percent Reduction (per 10% increase in usage)

To get the percent reduction due to AI scribes (per 10% increase in AI scribe usage), we divide the value in Column 2 by the value in Column 1.

Column 4: Hypothetical Al Usage Increase

This is up for discussion. These values are calibrated based on available research, which unfortunately does not provide these estimates directly. Still, we can reasonably approximate them.

The research strongly confirms my qualitative expectation that inefficient, slow documenters would see the largest average increase in AI scribe use. For tech-savvy and efficient documenters, AI scribes would not deliver the same marginal gains in efficiency.

The evidence supports my hypothesis:

1. UCSF study under consideration (AJMC 2026): Significant interaction effect (P<.001) in Table 3 showing inefficient documenters realized the majority of time savings from AI scribes.

2. Kaiser Permanente (NEJM Catalyst 2025): The top third of physicians by baseline documentation burden accounted for 89% of all AI scribe activations - demonstrating they self-selected into high usage because they had the most to gain.

3. Peterson Health Technology Institute (March 2025): Noted “clinicians who saw the greatest benefit were those who had not yet optimized their documentation workflows, were consistently behind in notes.”

4. UCLA RCT (NEJM AI 2025) - Lukac et al. found that in dose-response relationship (r = −0.41), higher users got more benefit.

5. Mayo Clinic Abridge Study (Nov 2025): Non-adopters had used transcription at 3.7x the rate of eventual adopters - suggesting those with existing efficient workarounds were less likely to adopt.

So efficient documenters who are already fast and tech-savvy have less marginal benefit to capture, so lower adoption makes economic sense. The inefficient documenters have the largest efficiency gap to close, hence the highest adoption.

The missing piece in the literature is just the precise quantification of my estimates.

Column 5: Total Documentation Time Reduction

It’s the value in Column 3, scaled by the assumed increase in AI usage in Column 4.

Column 6: Scheduled Hours per Week

To estimate scheduled clinic hours in a week, I used an estimate of patient scheduled hours within a 40-hour work week for ambulatory physicians, published in the Journal of the American Medical Informatics Association last year.

As expected, more efficient documenters generally have more patient scheduled hours.

Column 7: DocTime (min per scheduled week)

Documentation minutes per scheduled week is the number of documentation minutes per scheduled hour (Column 1) multiplied by scheduled hours per week (Column 6).

Column 8: Minutes Saved Per Week (10% increase in usage)

Minutes saved per week per 10% increase in usage is the hourly effect (Column 2) multiplied by scheduled hours per week (Column 6).

Column 9: Minutes Saved Per Week (Actual Scenario)

Finally, to estimate actual minutes saved per week, I scale the minutes saved per week per 10% increase in usage (Column 8) by the assumed increase in AI usage (Column 4).

Notice that inefficient physician-documenters benefit by a wide margin from AI scribe usage, despite having fewer scheduled clinic hours per week.

The UCSF Study Setup

• Setting. University of California, San Francisco ambulatory specialty practices at a large academic medical center in Northern California that delivers more than 2 million patient visits annually.

• Tool. A commercial AI scribe, Ambience AutoScribe from Ambience Healthcare, integrated into the EHR. Clinicians used it on a smartphone after getting patient consent, including for telemedicine visits. (Special shoutout to the Ambience team, which scored highly in my Ranking of the 50 Top AI Scribes.)

• How many doctors. 310 attending ambulatory physicians who were onboarded between December 6, 2024 and February 9, 2025. That was 18.7% of an estimated 1658 eligible physicians.

• How many doctors answered the survey. 252 physicians completed the post-adoption survey (81.3% of the 310).

• How many visits. The paper does not report a single total count of patient visits or encounters for the analytic sample. It analyzes data at the physician-week level and includes “total weekly visits” as a covariate, but it does not publish a total visit number in the main tables.

• How many observation-weeks. In Table 1, they report 2764 physician-weeks in the pre period and 3055 physician-weeks in the post period. In the regression models (weeks with more than 1 ambulatory encounter), they used 2958 physician-week observations for the full sample models.

• Time horizon. Baseline was 12 weeks before onboarding. Post was 12 weeks after a 4-week washout starting the week of onboarding.

• Specialties. Multiple ambulatory specialties were included, grouped into nine categories (for example primary care, surgical specialties, neurology and psychiatry, medical specialties). Specialty breakdown is in the eAppendix table.

• Data source for time measures. EHR audit logs using Epic Systems active use log data (User Action Log Lite), focused on ambulatory-context EHR activity.

Other Interesting Findings From the UCSF Study

• Most doctors felt the AI scribe saved them time. About 86.5% said documentation time decreased after adopting the AI scribe, and only a tiny fraction said it increased. (Table 1.)

• But feelings did not match reality in a clean way. The study found no overall link between what doctors reported (big time savings vs small time savings vs no change) and what the EHR logs showed happened to their documentation time. In other words, lots of people felt faster even when measured time did not clearly drop for them. (Table 2 and Figure.)

• Why the perception gap might exist. Some clinicians may be getting benefits that feel like time savings (less cognitive load, less dread, less “I have to remember everything later”), even when the measured documentation minutes do not drop much.

• Actual time savings existed, but they were modest on average. Documentation time dropped from about 22.2 minutes per scheduled hour before adoption to 17.3 minutes per scheduled hour after adoption. That is an average reduction of about 4.9 minutes per scheduled hour across the pre vs post periods they analyzed. (Table 1.)

• Adoption intensity was only “about half” of visits, not close to universal. On average, physicians used the AI scribe in 58.7% of weekly encounters, with huge variation across weeks and clinicians. So even among adopters, many visits still ran without it. (Table 1.)

• Work outside of work (pajama time) improved a bit, but not in the same targeted way. “Work outside of work” time (their EHR log measure) fell from about 20.0 to 17.4 minutes per scheduled hour. Higher AI scribe use was associated with slightly lower after-hours EHR time overall, but the “inefficient doc writers benefit more” pattern did not show up clearly for after-hours time. (Table 1 and Table 3.)

• Practical implication for ROI. If a health system wants financial return, the best bet is not “give it to everyone and expect miracles.” It is “target and support higher-DocTime clinicians and drive higher usage levels,” because that is where the measurable time savings concentrate.

Why These Results May Not Be 100% Relevant

As Rik Renard recently pointed out in a LinkedIn post about a different, but related, study, maybe the metrics we are using to evaluate AI scribes are wrong. “Measuring AI scribes purely on physician productivity misses the point entirely. The real value, that is hard to study, is a combination of: preventing clinician burnout (perceived value >>), coding/billing and improved documentation (which will lead to better care, especially knowing where AI is going).”

Yes, AI scribes might not be only about documentation. They are also about coding and billing, which is partly enabled by better documentation, but not only.

Industry leaders such as Shiv Rao, CEO of Abridge, would likely agree. For example, on the No Priors podcast on March 27, 2025, he said, “Every single one of these notes is actually a bill.”

I will pose this question again in our Digital Health Inside Out podcast recording with Shiv this week. 😉

Like what you’re reading in this newsletter? Want more in-depth investigations and research like this?

I’m committed to staying independent and unbiased—no sponsors, no advertisers. But that also means I’m a one-man operation with limited resources, and investigations like this take a tremendous amount of effort.

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👉👉👉👉👉 Hi! My name is Sergei Polevikov. I’m an AI researcher and a healthcare AI startup founder. In my newsletter ‘AI Health Uncut,’ I combine my knowledge of AI models with my unique skills in analyzing the financial health of digital health companies. Why “Uncut”? Because I never sugarcoat or filter the hard truth. I don’t play games, I don’t work for anyone, and therefore, with your support, I produce the most original, the most unbiased, the most unapologetic research in AI, innovation, and healthcare. Thank you for your support of my work. You’re part of a vibrant community of healthcare AI enthusiasts! Your engagement matters. 🙏🙏🙏🙏🙏

Sometimes very good people, many of them my friends, unsubscribe from AI Health Uncut for various reasons. In this case, which I wish would happen more often when people leave, a paid subscriber left constructive criticism. I really appreciate that.

Would you mind sharing your constructive criticism, either via this poll, in the comments, or privately, whichever is comfortable for you, so I know how to keep improving.

Thank you.

Sergei

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

The playbook for healthcare technology fraud hasn’t changed…

Two founders, almost exactly a decade apart, Elizabeth Holmes and Sumit Rai, faced consequences for eerily similar fraud schemes: promising “revolutionary” blood tests, exploiting regulatory gaps, and captivating investors who failed to ask basic questions. The parallels between Theranos and Cancer Check Labs are so striking that anyone who has read “Bad Blood“ (or, at a minimum, has heard Taylor Swift’s song 😉) should have seen this coming.

Holmes claimed 200+ tests from one drop of blood. Rai claimed detection of 200+ cancer types from 40ml of blood. Both bypassed FDA approval through the same regulatory loophole. Both lacked peer-reviewed publications. Both had founders with zero medical backgrounds. Both used celebrity glamour as a substitute for scientific validation. The formula is so consistent it could be a template—and that’s precisely the problem.

But first, I receive a lot of messages daily, and I always respond promptly to every one of them. Most are incredibly kind and supportive.

I want to highlight one recent message in particular. Someone, who is between jobs, reached out recently asking for full, paywall-free access to my Substack. Of course, I granted it, as I always do.

This brings me to another important message. If you cannot afford this article—perhaps you’re a student or currently between jobs—please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club, currently at 19 and counting. Thanks to generous donations and support from these wonderful individuals, I’m able to provide access to anyone who needs it. By the way, students reach out frequently, and I’m always glad to help.

Become a Founding Member

If you’d like to become a Founding Member of the AI Health Uncut community, you can join through this link. You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

Alright. Back to the health tech fraud…

It especially upsets me when people play with things like cancer. It is a painful topic for so many people in this country and around the world. Yet it seems to me that for VCs and other financial professionals, this is just another risk-return analysis. Cancer has become a business. Claiming you are in cancer research certainly opens a lot of doors. But eventually people will figure you out. What these VCs are not considering, and let’s be honest, do not give a sh*t about, is the damage and the carnage they create across communities when they calmly move on to their next investment opportunity as if nothing happened.

Plus, in this case, the fraud. Sumit Rai, the founder and CEO of Cancer Click Labs, a Dallas based cancer blood testing company, is apparently a regular at strip clubs and what he calls an “elite social club”. He seems to like spending company money on his personal entertainment.

And that is why, on January 15, 2026, the SEC filed a civil complaint in the U.S. District Court in Massachusetts, charging Sumit Rai and his entities with a $10.6M fraud.

Honestly, I am surprised the lawsuit was not brought by the DOJ. Usually they start with the criminal charge, and then the SEC piggybacks by copying and pasting the DOJ statements. The charges seem serious enough for the DOJ to look at. But what do I know. Maybe charging protesters in Minnesota is higher on the priority list for the DOJ right now now.

There is also a hint of Madoff and FTX here, to be frank. Madoff’s $65B Ponzi scheme and FTX’s $8B crypto fraud are the number 1 and number 2 largest frauds in history, respectively. For example, one of Rai’s companies, Cancer Click Labs, was named a relief defendant. That means the company itself was not charged with fraud, but some of the fraud proceeds were funneled to the company from Rai’s other entities, SVN Med LLC, NVS Med Inc., and Onco Filtration, Inc.

Anyway, it is a weird story. And because it is a civil lawsuit, ironically, Mr. Rai and his co defendants could just walk away with a fine. Welcome to the American legal system.

By the way, it is so icky when I read the terms “diagnosing cancer” and “strip clubs” in the same SEC complaint. Ugh.

TL;DR:

1. The $10.6M Strip Club Fraud Wore a Lab Coat

2. Two Playbooks, One Template: The Theranos Formula Repeats

3. The Board Problem: Politicians and Celebrities Ain’t Scientists

4. Investor Due Diligence: From FOMO to Fraud Victim

5. The Same Warnings That Should Have Stopped Walgreens

6. Why These Frauds Keep Working: the CTC Detection Impossibility

7. Media Complicity: From Cover Stories to Cautionary Tales

8. The Detection Checklist: Red Flags That Should Never Be Missed

9. Conclusion: The Inexcusable Failure to Learn From History

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

Looks like UnitedHealth is having more and more of those nightmare days. It’s only been 9 months since its worst day since the 1998 Russian crisis, when it lost 22.4% in one day.

Remember, I suggested starting a GoFundMe campaign for Andrew Witty 9 months ago? Well, the poor guy left the company during that time, after the assassination of Brian Thompson and a steady stream of other bad news for United. Now it’s back to Stephen J. Hemsley, the company’s chairman and actually a former CEO, who is even more corrupt than Witty was, in my opinion. How these guys trade UNH stock before big events, make tens of millions on inside information, and the SEC looks the other way, is beyond me.

But hey, after UNH losing 55% in a little over a year, even Hemsley is down to his last billion. How is he going to feed his family? Let’s fire up that GoFundMe. Who is with me? 😉

Anyway, here is what happened. On Monday night, January 26, 2026, the Trump administration’s CMS dropped a regulatory grenade by proposing essentially flat Medicare Advantage payments for 2027. The 0.09% net increase equates to just $700M across the entire MA program, a rounding error compared to the multi-billion-dollar 4% to 6% increases insurers had banked on. (Source: The Wall Street Journal.) This proposal targets insurers’ risk adjustment practices, specifically excluding diagnoses from “unlinked chart reviews” that CMS believes inflate risk scores.

For UnitedHealth, which controls approximately 30% of national MA enrollment, the news was devastating. As a result, UnitedHealth, together with other major Medicare Advantage players like Humana, CVS Health (Aetna), and Elevance Health, lost over $100B in a single day today. UNH alone lost $62.4B, or 19.6%.

UNH has lost $257B, or 55%, of its value in just over a year, since it reached its highest value of $575.41B on November 11, 2024. That was right before Luigi Mangione (allegedly) murdered Brian Thompson, the CEO of UnitedHealthcare, on December 4, 2024.

My view. UNH and Hemsley can’t pin all this bad news on Luigi Mangione, CMS, Trump, or “macroeconomic factors.” If anything, macroeconomic factors have been a tailwind. For example, the S&P 500 gained 18.1% over the same period, from 11/11/2024 to 1/27/2026.

No. UNH has become one of the largest oligopolies in the world by overcharging and defrauding customers, while controlling the entire healthcare vertical: health insurance, medical providers, PBMs, and pharmacies.

It’s a good day to focus on the UnitedHealth fraud, as I’ve written about it extensively.

And yes, there have been multiple fraud cases against UnitedHealth. Just the most recent ones:

• $50B Medicare upcoding fraud.

• An upcoding scheme using the fraudulent QuantaFlo device that netted UNH $1B in a single year—from just one device, for just one diagnosis (PAD)!

• The Change Healthcare cyberattack (UNH subsidiary) didn’t hurt UNH. It did devastate providers. According to the American Medical Association (AMA), 80% of practices suffered revenue losses, 55% used personal funds to stay afloat, and many shut down—later scooped up by Optum (another UNH subsidiary!) at fire-sale prices. How convenient.

• UNH forced staff to follow Optum’s NaviHealth AI (“nH Predict”) to deny Medicare patients post-acute rehab. UNH made billions. The settlement? $20.25M. That’s million—with an M. (Source: The Minnesota Star Tribune.)

Need perspective?

🕵️‍♂️ FTX fraud: $8 billion. Sam Bankman-Fried got 25 years.

🕵️‍♂️ Madoff: $65 billion. Life sentence.

🕵️‍♂️ UnitedHealth: $50+ billion... and counting. Zero consequences — until today.

Just to give some color: not only does UnitedHealth have some of the most powerful lobbyists in the world, but they’ve also apparently operated above the law.

(Source: BIG newsletter, by Matt Stoller.)

• In November 2023, the SEC looked the other way as UnitedHealth’s chairman Stephen Hemsley and other top execs sold $101.5M in stock—right before a U.S. antitrust probe into the company became public. (Sources: Bloomberg, BIG newsletter, by Matt Stoller.)

• The reason why Andrew Witty and Stephen Hemsley think they’re above the law and therefore can commit insider trading all day long, is because he has seen the worst case scenario play out in front of him: in 2007, the SEC (not the DOJ - so no jail time already) went after then-CEO of UnitedHealth, William W. McGuire, accusing him - you guessed it - of insider trading. To the SEC's credit, they went hard after him, but the case was still settled for a whopping $1B (with all the penalties and returned stock options), without admitting any guilt, of course. So yes, it was a tough day for Mr. McGuire, as roughly half of his wealth was wiped out, but, unlike those much less affluent defendants in similar cases, he doesn’t have a criminal record, can work anywhere, and his lifestyle didn’t suffer a bit. In fact, a few years later he bought a professional soccer team, Minnesota United FC. I don’t think people realize how crazy rich these executives have become from their companies' paychecks, mainly due to equity-based compensation.

Blaming increased Medicare costs for running your business poorly is like shouting at puppets when you're the one holding all the strings. Yet that’s precisely what UnitedHealth has been doing lately.

I’ve been advocating for breaking up UnitedHealth for years. Biden didn’t do it. Trump didn’t do it. But maybe market forces will.

UnitedHealth is (was 🙂) so big, there is no more room for the insurance business to grow any further, so it had to open other businesses: UnitedHealthcare, Optum Health, Optum Insight, Optum Rx, Optum Ventures and Optum Financial (including Optum Bank). In fact, the non-insurance business now comprises over 1/3 of the company revenue. However, despite the diverse business distribution, the UnitedHealth motto stays strong and consistent across its businesses: “We’re so big, we don’t give a shit about our customers. What are you gonna do about it?”

I wholeheartedly agree with Dr. Jeffrey Pfeffer:

By the way, after today’s Medicare Advantage carnage, what do you think United’s next move will be regarding reimbursement rates? Brace yourself—provider carnage incoming.

It’s astonishing how Witty, Hemsley, and UNH have brought American healthcare to its knees. They brazenly dismiss every single fraud lawsuit, settling each for trivial amounts.

Just this September, Semler, UnitedHealth’s medical upcoding device dealer, settled with the DOJ for a mere $30M(!), despite UNH (allegedly) pulling in about $1B from this device in a single year and (allegedly) causing serious harm countless patients by driving false-positive PAD tests at roughly 8(!) times the normal rate.

The level of corruption and indifference at UnitedHealth is unfathomable.

Is anyone still interested in starting a GoFundMe campaign for Stephen J. Hemsley? 😉

Like what you’re reading in this newsletter? Want more in-depth investigations and research like this?

I’m committed to staying independent and unbiased—no sponsors, no advertisers. But that also means I’m a one-man operation with limited resources, and investigations like this take a tremendous amount of effort.

Consider becoming a Founding Member of AI Health Uncut and join the elite ranks of those 19 founding members already supporting me at the highest level of membership. As a Founding Member, you’re not just backing this work—you’re also helping cover access fees for those who can’t afford it, such as students and the unemployed.

You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

Thank you!

Become a Founding Member

👉👉👉👉👉 Hi! My name is Sergei Polevikov. I’m an AI researcher and a healthcare AI startup founder. In my newsletter ‘AI Health Uncut,’ I combine my knowledge of AI models with my unique skills in analyzing the financial health of digital health companies. Why “Uncut”? Because I never sugarcoat or filter the hard truth. I don’t play games, I don’t work for anyone, and therefore, with your support, I produce the most original, the most unbiased, the most unapologetic research in AI, innovation, and healthcare. Thank you for your support of my work. You’re part of a vibrant community of healthcare AI enthusiasts! Your engagement matters. 🙏🙏🙏🙏🙏

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

With Drake Maye and the New England Patriots having a real chance of advancing to the AFC Championship Game (and beyond) against the Houston Texans this afternoon, there is another super-duper famous former Patriots quarterback who refuses to go away from the headlines and TV screens, and who seemingly refuses to say no to anyone with money. Tom Brady, of course.

Two weeks ago, Brady started showing up in Pizza Hut commercials, scaring customers with his “hut” scream. And this week he signed with a very shady digital health platform turned GLP-1 reseller named eMed, which I have already spent a significant amount of my time investigating.

First, “Deflategate.” Then the FTX $8B bitcoin fraud mess. Now this shady GLP-1 reseller.

How does one of the greatest athletes of all time end up entangled in shady businesses and legal sagas? 🤷‍♂️

But first, I want to take a moment to thank all of my supporters. I’m often very critical of certain things happening in AI and healthcare. But I promise you, it’s only because I genuinely care and want to make things better for all of us. So, thank you for your understanding.

I receive a lot of messages daily, and I always respond promptly to every one of them. Most are incredibly kind and supportive.

I want to highlight one recent message in particular. A student reached out recently asking for full, paywall-free access to my Substack. Of course, I granted it—as I always do.

This brings me to another important message. If you cannot afford this article—perhaps you’re a student or currently between jobs—please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club, currently at 19 and counting. Thanks to generous donations and support from these wonderful individuals, I’m able to provide access to anyone who needs it. By the way, students reach out frequently, and I’m always glad to help.

Become a Founding Member

If you’d like to become a Founding Member of the AI Health Uncut community, you can join through this link. You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

Alright, back to Tom Brady and Ozempic-gate…

DISCLAIMER. I’ve said it before, but it bears repeating. I’m not promoting anything. No sponsors. No advertisers. No bosses. No one pays me to say nice things or mean things. I have zero incentives to hype up or trash any company or its products. I also have no personal investments in any companies mentioned here. I’m my own man. This story is based solely on interviews, my analyses, and public information. Please respect my journalistic integrity.

Buckle up. The GOAT is about to get roasted so hard that Nikki Glaser’s jokes will start sounding like wholesome dad jokes. 😉

TL;DR:

1. From Super Bowls to Scandals

2. From Deflated Balls to Inflated Egos: Tom Brady’s Hall of Shame

3. eMed: A “Wellness” Company with a Rap Sheet

4. Michael Ferro: Deep in the Jeffrey Epstein Network

5. The GOAT of Ozempic: A (Red) Flag on the Play

6. Truth or Ozempic-tion: Can Brady and Yaccarino Rewrite eMed’s Story? Or Is the OpenLoop Lawsuit the Start of the Deep Sh*t eMed Got Itself Into?

7. Conclusion: The Curse of the GOAT in the Land of Grift

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

We opened up our podcast season with Alex Koshykov with a bang. We brought on the legendary , who was also my 2025 Person of the Year in Health Tech.

We talked about Halle’s upcoming book, which you all should absolutely pre-order, plus a whole lot more about what is broken in healthcare and what actually has a shot at fixing it.

Also, I make two cameos in Halle’s book. Honored. 🙏

Preorder Halle’s book

Halle has shared that the book is out February 11th, which is now close enough to feel real. Preorder Halle’s book here.

Subscribe to Digital Health Inside Out

Don’t forget to subscribe to the podcast.

Why this episode matters

Healthcare is full of smart people with dumb incentives. That sentence is not even cynical. It is what it is.

In most industries, outsiders show up, identify inefficiency, build software, and ship. In healthcare, outsiders show up, identify inefficiency, build software, and then get punched in the face by reimbursement, procurement, regulation, incumbents, and workflow physics. Then half of them pivot into “wellness.” The other half write Substack posts about how “healthcare is broken.”

Halle’s edge is that she has lived the lifecycle. Founder. Investor. Operator. Teacher. She has watched waves of hype crash into the same structural rocks. She still believes in change, but not the delusional kind.

That is the tone we wanted for this new interview format. Thoughtful, transparent, grounded, and yes, sometimes uncomfortable and provocative.

What we actually asked Halle

We went in with questions that cut past polite panel talk and into the stuff founders and investors argue about privately.

Here is a taste of the threads you will hear in the episode.

TL;DR:

1. “Cranky Old Guard.” Who exactly is blocking progress

2. What smart outsiders consistently get wrong

3. The ACA. What critique is worth taking seriously

4. Drug prices, NIH, and the uncomfortable “societal investment” argument

5. Interoperability. Is it really “vendors not willing”

6. Screens, anxiety, and the dirty secret of “helpful” tools

7. Transparency. Why everyone whispers and nobody names failure modes

8. Women’s health, women investing, and a message to my daughters

1. “Cranky Old Guard.” Who exactly is blocking progress

In the book, Halle talks about the “Cranky Old Guard” (COG). And the obvious question is. Are we talking about a small group of villains, or basically the whole industry.

Because sometimes it feels like everyone is the “Cranky Old Guard.” From VCs to incumbent lobbyists to vendors protecting their margins.

I asked Halle to define it plainly and explain how innovators deal with it without becoming it.

2. What smart outsiders consistently get wrong

People love to blame insurance and PBMs, and yes, that is part of the crisis. But the deeper issue is incentive alignment.

We pressed on what innovative outsiders misunderstand when they first enter healthcare, and why so many “obvious” fixes die on contact with reality.

If you are building in this space, this part is not philosophy. It is survival.

3. The ACA. What critique is worth taking seriously

The Affordable Care Act moved real needles, including coverage expansion and protections for preexisting conditions. That is not controversial if you can read data.

But it is also under political crossfire from both sides.

So I asked a question that is rare in health tech circles. What is the strongest critique of the ACA that Halle actually takes seriously.

You do not improve systems by treating them like sacred objects. You improve systems by being honest about where they fail.

4. Drug prices, NIH, and the uncomfortable “societal investment” argument

Halle’s book cites the reality that U.S. drug prices are dramatically higher than elsewhere, and that taxpayers often support upstream science through NIH-funded research.

We also brought up biotech investor Peter Kolchinsky’s argument that expensive branded drugs should be viewed as a societal investment rather than an expense, and asked whether Halle buys it.

This is one of those debates where smart people can disagree in good faith, and still be forced to confront a nasty truth. Our current structure makes it too easy to extract value without creating proportional public benefit.

5. Interoperability. Is it really “vendors not willing”

I pushed back here as someone who actually works with providers and lives inside the constraints.

If you have spent more than 20 minutes trying to integrate into a health system, you already know the fairy tale version of interoperability. “If everyone would just cooperate, data would flow.”

But the hard version is economics. Incumbent EHRs sit on a wildly profitable status quo. Breaking that status quo is not a technical problem. It is a business model problem.

So I challenged the framing directly.

6. Screens, anxiety, and the dirty secret of “helpful” tools

Halle has talked publicly about minimizing exposure to social media and doing a “mobile device detox.”

I connected that to a broader, uncomfortable question in digital health. What if some tools make people more anxious rather than healthier.

I referenced a point raised by Dr. Ezekiel Emanuel about gadgets like the Oura ring. If you wake up refreshed, you already have signal. If the device adds anxiety and does not add therapy, what are we really selling?

Then I asked the question all founders hate: Does minimizing screen time also mean minimizing screen time for digital health tools?

7. Transparency. Why everyone whispers and nobody names failure modes

Healthcare is political. Digital health is small. People network. People fund each other. People want future deals.

Which means almost nobody calls out bad actors or even names real failure modes, because nobody wants to burn bridges.

So I asked. How do we change that?

If you care about credibility in this industry, this is not optional. It is the whole game.

8. Women’s health, women investing, and a message to my daughters

This one is personal.

I brought up my daughters, the reality of inequality in women’s health and in the workplace, and the need for role models who do not just win. Role models who do the right thing with their voice.

I asked Halle what message she would give to them, and whether there is hope.

Your turn

Drop a comment with three things.

1. What resonated most.

2. What you disagreed with.

3. Who you want us to interview next.

And yes. Preorder Halle’s book. This is one of those rare cases where the preorder is not just marketing. It is a signal that the market wants substance.

More interviews coming soon. Next up: Shiv Rao, founder and CEO of Abridge, and Christina Farr, CEO of Second Opinion and GP at Scrub Capital.

Bonus. If you missed it, here is our prediction episode of Digital Health Vitals:

👉👉👉👉👉 Hi! My name is Sergei Polevikov. I’m an AI researcher and a healthcare AI startup founder. In my newsletter ‘AI Health Uncut,’ I combine my knowledge of AI models with my unique skills in analyzing the financial health of digital health companies. Why “Uncut”? Because I never sugarcoat or filter the hard truth. I don’t play games, I don’t work for anyone, and therefore, with your support, I produce the most original, the most unbiased, the most unapologetic research in AI, innovation, and healthcare. Thank you for your support of my work. You’re part of a vibrant community of healthcare AI enthusiasts! Your engagement matters. 🙏🙏🙏🙏🙏

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

You know, I was thinking. Sam Altman is right. We only live once. F*ck HIPAA. So what if the world knows I have bad knees? (Well, except now the healthcare mafia knows exactly where to aim. 😉)

The only way to get to the truth was to test this thing.

So I uploaded my entire medical history to ChatGPT Health…

But first, I want to take a moment to thank all of my supporters. I’m often very critical of certain things happening in AI and healthcare. But I promise you, it’s only because I genuinely care and want to make things better for all of us. So, thank you for your understanding.

I receive a lot of messages daily, and I always respond promptly to every one of them. Most are incredibly kind and supportive.

I want to highlight one recent message in particular. A student reached out recently asking for full, paywall-free access to my Substack. Of course, I granted it—as I always do.

This brings me to another important message. If you cannot afford this article—perhaps you’re a student or currently between jobs—please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club, currently at 19 and counting. Thanks to generous donations and support from these wonderful individuals, I’m able to provide access to anyone who needs it. By the way, students reach out frequently, and I’m always glad to help.

Become a Founding Member

If you’d like to become a Founding Member of the AI Health Uncut community, you can join through this link. You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

Alright, back to my ChatGPT Health adventures…

Whether you’re a skeptic or a supporter of AI in medicine, hear me out. I went through this over the weekend, and I think I’m onto something. Or more precisely, the industry may be onto something.

TL;DR:

1. Signing Up for ChatGPT Health

2. Connecting to Health Systems

3. Letting b.well Access All My MyChart Data

4. Uploading the Remaining Medical Files to ChatGPT Health

5. Once I Finished Uploading My Medical History, ChatGPT Started Hallucinating

6. So the Original Context Window Got “Filled” Pretty Quickly. What About Just Starting Another Chat?

7. ChatGPT Health Final Reports

8. Direct-to-Consumer Is Hard in Healthcare Because Patients Don’t Want AI to Tell Them What Already Happened to Them

9. Direct-to-Consumer Healthcare Can Also Be Outright Dangerous. Just Ask 23andMe.

10. OK. Direct-to-Consumer Healthcare Is Dead. So How Do You Sell ChatGPT to Businesses?

11. So, As a Health Organization, Are You HIPAA-Compliant When You Work With OpenAI’s API?

12. The Most Impressive Thing About ChatGPT Health

13. So Why ChatGPT Health Is Excellent News for Google (and Anthropic)

14. Conclusion: ChatGPT Health Ain’t About AI. It’s About a Better Way to Do APIs (i.e. Data is King).

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Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

My readers know that I’ve been preaching AI commoditization, and saying it’s a matter of time before tech giants come in and take over entire sectors of healthcare. Right now, the margins are still super thin for them. So they don’t really care. But they are testing the waters.

ChatGPT Health, which OpenAI announced yesterday (January 7, 2026), may be a step in that direction. ChatGPT Health is basically a ChatGPT wrapper on a dedicated server, with claims of “enhanced security” and a promise that no OpenAI model will be trained on patient data.

OMG. So many questions.

But first, I want to take a moment to thank all of my supporters. I’m often very critical of certain things happening in AI and healthcare. But I promise you, it’s only because I genuinely care and want to make things better for all of us. So, thank you for your understanding.

I receive a lot of messages daily, and I always respond promptly to every one of them. Most are incredibly kind and supportive.

I want to highlight one recent message in particular. A student from Ukraine reached out recently asking for full, paywall-free access to my Substack. Of course, I granted it—as I always do.

This brings me to another important message. If you cannot afford this article—perhaps you’re a student or currently between jobs—please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club, currently at 19 and counting. Thanks to generous donations and support from these wonderful individuals, I’m able to provide access to anyone who needs it. By the way, students reach out frequently, and I’m always glad to help.

Become a Founding Member

If you’d like to become a Founding Member of the AI Health Uncut community, you can join through this link. You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

OK. Back to ChatGPT Health…

So many questions following the ChatGPT Health announcement:

• “Enhanced security.” Soooo, what kind of security does everyday ChatGPT have then? “Less than enhanced?” What does that even mean?

• You promise not to train your models. But can you still fine-tune your models?

• 23andMe also promised a lot of things about the security of customer DNA data. And then one day they sent an unassuming email titled “Change to Terms & Conditions” that, of course, no one ever opens. And voilà, they can sell whatever they want to third parties (as we found out the hard way during the 23andMe bankruptcy filing last year).

I predict we’re going to forget about ChatGPT Health in a few weeks, just like we quickly forgot other OpenAI awkward crusades into healthcare:

• Remember OpenAI’s AI clinical copilot (aka AI Consult)? The thing that turned out to be basically a human curator for ChatGPT? 🙂

• Remember OpenAI’s HealthBench? A carefully designed and curated dataset built on just 5,000 medical conversations. Not the model. The data. Just prompting, and of course the claim that OpenAI’s models did so well on their own benchmark. Who would have guessed. 🙂

• Remember how OpenAI shamefully used a cancer survivor to promote the health-related features of GPT-5, only to cut those features back less than three months later after numerous suicide-related lawsuits?

Of course, we don’t remember any of these. And honestly, in the case of GPT-5, I’m pretty sure OpenAI wants you to forget everything. Because they are marketing gimmicks, designed to convince the medical community that it’s OK to use OpenAI’s gadgets. No new technology was introduced. All of these gimmicks were basically wrappers on ChatGPT.

And I think the story will continue with ChatGPT Health.

And, courtesy of my colleague , here is a list of OpenAI marketing gimmicks outside of healthcare that no one remembers: 🙂

TL;DR:

1. This OpenAI Announcement Ain’t About ChatGPT Health. It’s About Something Bigger.

2. OpenAI Promises No Training on Patient Data. But What About Fine-Tuning?

3. Wait. ChatGPT Health Still Hallucinates, Right?

4. Why OpenAI’s Lawyers Say ChatGPT Health Is HIPAA-Compliant. And Why Sam Altman Might Not Give a Damn.

5. Lessons from 23andMe and the Ford Pinto: The Only Rule for Patient Data Is Profit.

6. Takeaway: ChatGPT Health Is OpenAI Testing the Healthcare Waters. It Probably Won’t Change Patient Behavior.

1. This OpenAI Announcement Ain’t About ChatGPT Health. It’s About Something Bigger.

I suspect the whole ChatGPT Health hype is serving a different purpose for OpenAI:

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Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

🎄🕎 Happy Holidays, everyone. First, I want to thank all of you for your loyalty and support, especially my paid subscribers and Founding Members. 🙏❤️

I enjoy interacting with all of you online, but I enjoy meeting you in person even more at the healthcare and tech conferences where I was so fortunate to get invited to speak this year.

2025 was a tough year for me, but also a rewarding one. Tough, because of the never-ending stream of threats. When the industry is run like a mafia, and you’re in the business of exposing the mafia, threats come with the territory. That’s part of my life.

2025 was rewarding because of you. The support from my subscribers, especially my paid subscribers and Founding Members, has been overwhelming. As long as I have your backing, and as long as I can borrow some of the courage from those of you who came forward this year with incredible inside stories, I’ll keep going. Those stories fueled a lot of my investigations, including the ones about the healthcare mafia and fraud. They gave me the energy and the inspiration when I needed it most.

🙏 Thank you to everyone who’s been on my side. I hope it’s the side of hope and transparency.

And a special thank you and tons of gratitude to my friend and co-host of our podcast, Digital Health Inside Out, Alex Koshykov. To my genuine surprise, he named me the Health Tech Person of 2025 in the year-end episode of Digital Health Vitals that just dropped. 🏆 I’m truly humbled.

And who did I pick as the Health Tech Person of 2025. It’s in the article below. 🙂

In this year-end piece, I’ll recap 2025 in health tech and make a few bold predictions for 2026.

But first, this. In over two years of running this Substack, I have never done any promotions. I hate marketing, to be honest. But Christmas is my weak spot. 🎄 I am running a limited 20% off sale on my already discounted annual plan, with a hard stop on January 7. No ifs. No buts. Click here to get the holiday special. (Substack might make you sign up first.)

Get 20% Off Holiday Special

2025 was a record-breaking year for dealmaking. It was also a defeat for a big chunk of AI hype and, in some ways, a triumph of science. AI, data analytics, and telehealth were the worst-performing sectors of health tech. GLP-1, biotech, and genomics were the best.

As my readers know, I track health tech sectors closely through my own database of publicly traded digital health companies. As far as I know, it’s the only one of its kind.

That database lets me dig up insights media coverage misses. Mass media gets paid to run interviews with VC bros and startup founders. I do the unsexy work. Build the dataset, run the analysis, and say what the numbers actually say.

TL;DR:

1. Everyone got an agent in 2025. You get an agent. You get an agent. You get an agent.

2. “Make Health Tech Great Again” — more like “Make Health Tech a Lobbyist Rolodex”

3. The Lost Decade of Venture Capital

4. 2025 Was the Year of Dealmaking in Health Tech

5. My 2025 Healthcare/AI investigations

6. My 2025 Analysis of AI Models in Healthcare

7. How My 2025 Health Tech Predictions Panned Out

8. Best Company in Health Tech of 2025

9. Worst Company in Health Tech of 2025

10. Biggest loser in health tech of 2025

11. Shadiest Company in Health Tech of 2025

12. Person of the Year in Health Tech

13. Inspiration in Health Tech of 2025

14. Biggest Health Tech Story of 2025

15. Health Tech Deal of the Year

16. 🔮 My Four Bold Predictions in Health Tech for 2026

1. Everyone got an agent in 2025. You get an agent. You get an agent. You get an agent.

Everyone got an agent in 2025. You get an agent. You get an agent. You get an agent.

Everyone is seemingly building an AI agent in healthcare:

• GPT-5 has its Superagent, heavily advertised by Oscar Health.

• Hippocratic AI has Polaris 3.0.

• Microsoft has MAI-DxO.

And yet, no one is even close to building something that truly makes a difference.

It’s going to take years. Probably many years. Don’t listen to the hype. Look at the facts.

2. “Make Health Tech Great Again” — more like “Make Health Tech a Lobbyist Rolodex”

The White House’s “preferred” list of 63 “health tech” companies doesn’t represent the industry I know. The story, unfortunately, remains the same. Success in health tech, and elsewhere, depends on how close you are to Mar-a-Lago.

But the worst part is the lies coming from Trump’s administration about health science. That has to stop.

3. The Lost Decade of Venture Capital

VC-backed startups are failing at the highest rate in at least a decade. Healthcare has been one of the hardest-hit sectors. As I mentioned, 2025 was especially tough for AI, data analytics, and telehealth.

Unfortunately, we don’t see the true performance numbers in the private sector because venture capital constantly pumps out inflated metrics.

4. 2025 Was the Year of Dealmaking in Health Tech

2025 was a special year for health tech deals, even exceeding 2021 — the year of COVID and fear — in dollar terms.

We’ve had 10 health tech IPOs in 2025. We have not seen that many since the COVID craziness of 2021.

More importantly, the net balance of IPOs versus bankrupt/delisted health tech public companies has turned positive, with only 7 bankruptcies/delistings in health tech in 2025.

5. My 2025 Healthcare/AI investigations

• Hippocratic AI - Part 1, Part 2, and Part 3

• The 80% Error Rate Diagnostic Device at the Heart of UnitedHealth’s Upcoding Fraud - That Still Causes Patients Pain Today!

• 23andMe Has Been Selling Customer DNA Data Since at Least 2018

• Inside Digital Health’s Political Cartel

• OpenEvidence’s Lies About Its Perfect Score on USMSLE

• eMed Is One of the Most Corrupt Companies in Healthcare

• HealthPrecision’s Misleading Marketing That It’s “98% Accurate” in Medical Recommendations

6. My 2025 Analysis of AI Models in Healthcare

• Stanford’s shameless JAMA paper claiming AI outperforms doctors in diagnostic reasoning. Hmm. Not true!

• OpenAI’s gpt-oss Doesn’t Deliver for Healthcare

• GPT-5 Medical Use Cases

7. How My 2025 Health Tech Predictions Panned Out

I made 7 predictions in health tech at the start of 2025. Let’s see how they panned out.

❌ Fewer digital health IPOs in 2025 and another negative year for IPOs-minus-bankruptcies. FALSE

I got this one completely wrong. It was a great year for health tech IPOs and there were not many public bankruptcies. 23andMe stood out for all the wrong reasons, with its massive bankruptcy and stunning public revelation that it has been selling Americans’ DNA data to the highest bidder.

This was the biggest surprise to me. 10 IPOs vs 7 bankruptcies/delistings is real progress compared to an absolutely awful 2024. Still, a profitable private health tech company is a very rare beast.

❌ (More like a hope) We must kill “AI tourists” in healthcare. FALSE

Nope. We have not killed AI tourists in healthcare. I was wrong about that. The fact that these AI imposters exist in such massive quantities speaks volumes about how fragmented American healthcare is. I guess it was my wishful thinking for my early retirement. 😅

❌ The GLP-1 hype will cool off in 2025. FALSE

2025 was a great year for GLP-1 producers and resellers. HIMS, for example, has had a 45% total stock return (as of 12/22/2025) so far this year.

❌ (Another hope) Capital will finally start flowing to genuine digital health innovators. FALSE

Oh man, I got it so wrong. With Trump’s administration and its “who you know” approach, genuine health tech innovation has moved far down the list in America.

✅ 2025 will bring AI innovations across pharmaceutical drug discovery, genetics, and biotech. SO TRUE

Indeed, 2025 has been one of the best years on record for these three disciplines. I’ll explain it in detail to my paid subscribers in my next Substack piece.

✅ Health insurers and PBMs will tighten their stranglehold. TRUE

I would be thrilled to admit I was wrong on this one. But the story keeps going.

These oligopolies are laughing all the way to the bank while the public keeps saying, “Let’s finally do something. We can’t live like this in healthcare.”

Health insurers, shamelessly and for no reason whatsoever, keep jacking up premiums and cutting reimbursement rates for doctors. PBMs are having one of the most profitable years on record in 2025.

The medical community has to finally move from discussion to policy change.

✅ No AI agents in healthcare on a large scale yet in 2025. TRUE

The easiest prediction for years to come. 🤷‍♂️ Everyone claims they have an agent. But once you dismiss the hype and the cheesy marketing, there is nothing there.

Read more

Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

This may be an unpopular opinion. But hear me out. I think federal AI laws come with real dangers. Especially if they end up serving a small club of trillion-dollar companies that have been lobbying in D.C. like there is no tomorrow.

But first, this. In over two years of running this Substack, I have never done any promotions. I hate marketing, to be honest. But Christmas is my weak spot. 🎄 I am running a limited 20% off sale on my already discounted annual plan, with a hard stop on January 7. No ifs. No buts. Click here to get the holiday special. (Substack might make you sign up first.)

Get 20% Off Holiday Special

TL;DR:

1. Trump’s “One Rule” AI EO Is a Big Tech Coup Disguised as Reform

2. The Great AI Power Grab. Kill the States, Keep the Profits.

3. Remember the HITECH Act. It’s Déjà Vu All Over Again.

4. The Unelected Czar and the Executive Order for Sale

5. Healthcare’s Floor vs. Ceiling: We Regulate the Floor. We Ignore the Cure.

6. China Is Laughing All the Way to the AI Bank

7. Conclusion: Trump’s “One Rule” Is a Power Grab Disguised as Reform

1. Trump’s “One Rule” AI EO Is a Big Tech Coup Disguised as Reform

In a Truth Social message yesterday, President Trump reminded everyone that he is serious about regulating AI at the federal level. At least until it stops fitting his agenda. Of course, not through Congress, but through his favorite tool, an Executive Order. Most people will take that as a positive sign. I have deep reservations.

According to draft language of the EO, Trump plans to unleash a federal “AI Litigation Task Force” (helmed by Attorney General Pam Bondi) to sue states over any AI rule that federal agencies find “objectionable,” even threatening to cut off broadband funding to recalcitrant states. (Source: Reuters.) In plain English, this is a one-sided power play: more than a thousand local and state AI laws would be nullified in favor of a single (currently extremely vague) federal standard.

2. The Great AI Power Grab. Kill the States, Keep the Profits.

Industry insiders like Nvidia, OpenAI, Google and others have been clamoring to “reign in” the roughly 38 states that have introduced nearly 100 AI bills this year. They term the patchwork of privacy, bias and content laws “unnecessary” – but of course it is inconvenient to Silicon Valley’s unipolar world.

Big Tech’s eagerness to preempt local rules makes it clear who benefits. Billionaires and venture capitalists have openly lobbied for Washington to stomp out state regulations. Nvidia CEO Jensen Huang, who met with Trump last week on chip controls, bluntly warned that state-by-state AI rules “would drag this industry into a halt” and pose a “national security concern.” (Source: The Tech Buzz.) In one ear of the White House, Trump hears that each of 50 states can’t have its own board of AI law arbitrators. In the other ear he hears his donors: a Chamber of Progress backed by Andreessen Horowitz, Google, OpenAI and others has pitched one “light-touch” federal rulebook. (Source: Wired.) This axis of the “AI oligarchs” means Washington’s “one rule” will likely mirror Big Tech’s wishlist, not protect ordinary Americans. After all, these companies argue flatly that without a unified rule the U.S. will “fall behind China” in the AI race – a threat conveniently wielded to justify handing them a blank check. (Source: The News.)

The proposed EO doesn’t even bother proposing a substantive alternative standard. The measure will focus more on why those state laws are bad rather than one great rule for everyone. Indeed, Wired’s draft analysis shows the order merely directs aides (and its new AI “czar” David Sacks) to draft some federal framework. The only promise of real policy is the vow of “a minimally burdensome national standard” for industry – a phrase echoed in Trump’s own Truth Social bluster (“One Rulebook…you can’t expect 50 approvals”).

This EO is a giveaway to incumbents: no new guardrails, just a federal waiver of state innovation. As Public Citizen’s Bob Weissman observes, Trump’s crusade has “nothing but the industry’s well-paid waterboy” written all over it. (Source: Reuters.)

3. Remember the HITECH Act. It’s Déjà Vu All Over Again.

Why on Earth Would the Biggest Players in AI Want to Be Regulated? Hint: It’s not for the sake of making the future of AI safer. They are following Epic’s playbook to make them richer.

This playbook feels eerily familiar to healthcare veterans.

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Welcome to AI Health Uncut, a brutally honest newsletter on AI, innovation, and the state of the healthcare market. If you’d like to sign up to receive issues over email, you can do so here.

McKinsey’s new “The state of AI in 2025: Agents, innovation, and transformation” report is out, and everyone seems to be clinging to the “92% AI adoption” number. Again. 🙄

Of course, the devil is in the details. Unfortunately, as always, McKinsey does not give us many.

But first, I want to take a moment to thank all of my supporters. I’m often very critical of certain things happening in AI and healthcare. But I promise you, it’s only because I genuinely care and want to make things better for all of us. So, thank you for your understanding.

I receive a lot of messages daily, and I always respond promptly to every one of them. Most are incredibly kind and supportive.

I want to highlight one recent message in particular. A student reached out asking for full, paywall-free access to my Substack. Of course, I granted it—as I always do.

This brings me to another important message. If you cannot afford this article—perhaps you’re a student or currently between jobs—please reach out. That’s precisely why I created the AI Health Uncut Founding Member Club, currently at 19 and counting. Thanks to generous donations and support from these wonderful individuals, I’m able to provide access to anyone who needs it. By the way, students reach out frequently, and I’m always glad to help.

Become a Founding Member

If you’d like to become a Founding Member of the AI Health Uncut community, you can join through this link. You’ll be making a real impact, helping me continue to challenge the system and push for better outcomes in healthcare through AI, technology, policy, and beyond.

🚨 Also, if you’re in New York City today and tomorrow, come for the Rockefeller Center tree lighting. 🎄 But even more importantly, come see me moderate a Health2Tech panel with a distinguished group of guests tomorrow, Thursday, December 4 at 5:30pm called “Clinically Useful, Not Just Cool: Tech Doctors and Nurses Will Actually Use.” You will not regret it. The event is free, but you still need to register here to get in.

Alright. Let’s get back to AI adoption in healthcare…

So here is my quick take on why I think the real number is closer to 10%. That lines up with the U.S. Census, based on a much, much larger sample of healthcare organizations. I also said 25% in my previous piece. I overestimated. I am correcting myself.

In March 2024, the U.S. Census Business Trends and Outlook Survey, or BTOS, found that about 5% of U.S. healthcare organizations were using AI, with about 7% expecting to use AI within six months. If that expectation translated into real deployments, and if adoption then climbed from about 7% by late 2024 to about 10% now, that is how I get to current AI usage in healthcare today. By the way, in BTOS terms, “healthcare organizations” are defined by the NAICS sector “Health Care and Social Assistance” (NAICS 62).

So let’s winnow the wheat from the chaff…

Reason #1 Why AI Adoption in Healthcare is ~10%, Not 92%: Definition

Let’s look at the footnotes.

Here is McKinsey’s definition, versus the U.S. Census definition:

• McKinsey asks survey respondents whether they are regularly using AI in at least one business function: knowledge management, marketing and sales, HR, risk, legal, compliance, and so on.

• The U.S. Census asks survey respondents whether the business is using AI to produce goods or services.

I hope everyone understands this is a massive difference in definition. It is the difference between a company where someone in HR uses ChatGPT to do their job, versus a company that actually uses AI to manufacture, deliver, and service what it sells.

It is night and day. That is why you get a gap like 10% versus 92%.

Reason #2 Why AI Adoption in Healthcare is ~10%, Not 92%: Sample Size

Sample size. That damn sample size is always “on the way.” Why can’t we just ask a couple of our most trusted clients a few questions over beers, confirm what we already know, and call it a day. 😂

I’ve spent a big part of my academic career working on effective sample size estimation. That is the “true” sample size your stats actually need when the i.i.d. assumption does not hold.

I’ll cover this in a separate post, because I genuinely believe it is a topic every clinical researcher should know as well as the palm of their hand. I’ve also written and published a full paper on this called “Advancing AI in healthcare: A comprehensive review of best practices.” Check it out.

This is where it gets really interesting. And I hope everyone who reads this will exclaim “Wow” by the end of this section.

Read more