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A 6-month-old named KJ Muldoon just received the first personalized CRISPR gene therapy ever made — designed, manufactured, and administered for his exact mutation in six months. Nature named him to the Top 10 People Who Shaped Science of 2025.The miracle is real. The financing model isn't.Gene therapies run $2M to $3.5M each. Insurance contracts are annual. Gene therapy benefits last a lifetime. The employer who pays in year one rarely sees the savings — average commercial plan tenure is three years.In this episode:— Why the "gene therapy tsunami" narrative is overstated (EBRI's numbers tell a different story)— The free-rider problem and why annual contracts can't price lifetime cures— The concierge medicine paradox: we pay $3M to cure you, but won't pay $300/mo to keep you healthy— Four financing models worth knowing for 2026: gene-therapy-specific stop-loss, outcomes-based agreements, risk-pooling platforms, and performance-based annuities— How AI is compressing drug development timelines — and why that compounds the budget problemWe have 21st century cures and 20th century accounting. Eventually, one of those has to change.Watch the full video: https://youtu.be/ap2XjNf3LN0Read the Substack companion piece: https://open.substack.com/pub/danmccoymd/p/21st-century-cures-20th-century-accountingFull sources and the deep dive: danmccoymd.substack.com

If you're a medical student picking a specialty in 2026, you're being asked to bet a decade and $300,000 in debt on a market nobody is teaching you to read.This episode is the framework I wish someone had given me — plus the data nobody else is putting in front of med students.In this episode:• Why 76% of all FDA-cleared medical AI targets a single specialty — and why residency applications to it are still up 30%• The four kinds of physician cognition (and why "AI replaces cognitive work" is the wrong frame entirely)• Which specialties get repriced, restructured, or protected — and why psychiatry might be safer than radiology• The 2026 CMS efficiency adjustment nobody is talking about — and why it's a ratchet, not a one-time cut• What medical schools are (and aren't) doing to prepare the next generation of physicians for an AI-driven workforceWatch the video version with charts and visuals: https://youtu.be/NETog9SBtZsFull sources and the deep dive: danmccoymd.substack.com

Last week the Texas Medical Association joined a federal antitrust lawsuit against MultiPlan — recently rebranded as Claritev. The story being told is doctors versus insurers, an unlawful cartel, $19B in alleged underpayments. It's a clean story. The moment you look at the actual fee math from the complaint, it falls apart.In this episode I walk through MDL 3121, the $1,000 → $200 cascade, the DOJ's March 27 Statement of Interest backing the plaintiffs' antitrust theory, and what self-funded employers should be asking their TPA on Monday morning. The bellwether trial isn't until December 7, 2027 — three years of fees away.About 42% of what the algorithm calls "savings" never makes it to the plan. That's not a doctors-vs-insurers story. It's a third-character story.▶ Watch the video version: https://youtu.be/H3TQUxJt2Xo 📝 Read the written deep dive (full fee math, sources, 3-question employer checklist): https://danmccoymd.substack.com/p/nobody-in-the-multiplan-lawsuit-isSOURCES MENTIONEDAMA on the litigation: https://www.ama-assn.org/health-care-advocacy/judicial-advocacy/health-insurance-price-fixing-real-and-ama-fighting-itTMA press release: https://www.texmed.org/Template.aspx?id=67699DOJ Statement of Interest coverage: https://www.jdsupra.com/legalnews/doj-adopts-aggressive-stance-against-4076897/NYT investigation summary: https://whatleykallas.com/nyt-investigation-shows-how-health-insurers-use-multiplan-to-reduce-payments-to-medical-providers-to-iincrease-their-fees-and-profits-at-the-expense-of-patients/Capitol Forum on MultiPlan billing incentives: https://thecapitolforum.com/provider-shows-how-multiplan-incentivizes-him-to-raise-his-billing-rate-multiplan-says-it-does-not-encourage-providers-to-overcharge/Seattle Times reprint: https://www.seattletimes.com/nation-world/insurers-reap-hidden-fees-by-slashing-payments-you-may-get-the-bill/KFF on 2026 small group premiums: https://www.kff.org/health-costs/how-much-and-why-premiums-are-going-up-for-small-businesses-in-2026/Medscape Physician Compensation Report 2025: https://www.medscape.com/slideshow/2025-compensation-overview-6018103LEGAL DISCLAIMER This episode covers public reporting and pending litigation. All allegations are unproven and may ultimately be rejected in court. MultiPlan, Claritev, and the named insurers deny wrongdoing. Nothing here is legal, financial, or medical advice.Full sources and the deep dive: danmccoymd.substack.com

49% of plan sponsors reported claims exceeding $1 million last year — double the rate from the year before. And stop-loss carriers are responding not by covering you better, but by finding creative ways to limit their exposure to the most expensive treatments.In this episode, I break down:→ Why stop-loss insurance was built for a different era (when catastrophic meant $300K, not $4.25 million)→ What carriers are actually doing — lasering, exclusions, and reimbursement term misalignment — to push risk back to employers→ The gene therapy pipeline problem: 48 approved therapies today, dozens more coming, and no historical data to predict frequency→ Why even jumbo employers who've never needed stop-loss should reconsider→ The 5 things every self-funded employer needs to do at renewal — including the one question that will tell you everything about your coverageThe mainstream story is that gene therapy is a miracle of modern medicine. It is. But the financial infrastructure wasn't built for this reality, and carriers have decided that the most expensive thing in healthcare is now predictable enough to exclude.You should find that arrangement concerning.📺 Watch the full video breakdown:https://youtu.be/1wwrtwmYAPE📖 Read the Substack post with sources:https://open.substack.com/pub/danmccoymd/p/the-stop-loss-crisis-what-self-funded📧 Subscribe for more healthcare benefits analysis:https://danmccoymd.substack.com

Andrej Karpathy just released AutoResearch — a 630-line Python tool that lets AI agents run hundreds of experiments overnight on a 35-cent GPU rental. In one test, 35 autonomous agents completed 333 experiments while everyone slept and found 20 improvements that worked. No humans involved.Meanwhile, your benefits broker spends maybe 20 hours on your entire annual renewal — most of it pulling quotes and formatting spreadsheets — to manage a budget that consumes 25-40% of your total payroll.In this episode I break down what autonomous research actually is, why the analytical pattern applies directly to healthcare data and benefits design, and three specific questions every employer should be asking their broker at renewal. I also cover the three failure modes of AI research identified by economist Joshua Gans and how Karpathy's system addresses each one.The benefits consulting industry is about to have its spreadsheet moment. The tools exist, the data exists, the pattern is proven. The only question is whether you'll be the one using it or the one disrupted by someone who does.Full sources and the deep dive: danmccoymd.substack.comAlso checkout my YouTube Channel.

The AI labs are selling you $10,000 a month in computing power for $600. They're doing it at massive losses. And they have very specific reasons you should understand.In this episode, I break down the $670B subsidy era fueling healthcare AI — what it means for builders, when it ends, and four strategies to exploit it before the economics correct themselves.We cover:Why AI pricing follows the exact same playbook as early AWSThe real numbers behind OpenAI's and Anthropic's lossesFour strategies to maximize subsidized compute (from $600/mo subscriptions to $2,500 local hardware)Healthcare startups building real businesses on below-cost AIThe subsidy timeline: when prices normalize and what to do before they doWhether you're a healthcare founder, an operator evaluating AI tools, or just trying to understand why trillion-dollar companies are giving away compute — this one lays it out with data, not hype.Watch on YouTube: https://youtu.be/uDB_VJAX05gFull sources and the deep-dive subsidy timeline analysis: https://open.substack.com/pub/danmccoymd/p/the-golden-age-of-healthcare-ai-and?r=11z0su&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Amazon launched an AI health agent inside the Amazon Shopping app — the same app where you order paper towels. It books appointments, manages prescriptions, explains lab results, and connects you to real doctors. Prime members get five free virtual care visits covering 30+ conditions.This isn't a chatbot experiment. Amazon built this on Bedrock using a multi-agent architecture with auditor and sentinel agents that escalate to human providers in real time. Combined with One Medical's 200+ clinics, Amazon Pharmacy, specialty referral partnerships with Rush and Cleveland Clinic, and a billing relationship with 200 million households — Amazon now owns the full healthcare stack.In this episode, I break down how Amazon assembled this over nearly a decade (PillPack in 2018, One Medical for $3.9B in 2023), why the pricing strategy matters more than the AI, and the three things every health system executive should be watching over the next 12 months: the employer channel play, the data advantage, and the behavioral shift that happens when asking a healthcare question becomes as casual as checking the weather.Watch the full video: https://youtu.be/OhHrxHEnSA0?si=B6MeSTiQTGId8388Full sources and deep dive: https://open.substack.com/pub/danmccoymd/p/amazon-just-put-a-doctor-in-your

Anthropic just announced Claude Mythos Preview — an AI model so capable at finding software vulnerabilities that they won't release it publicly. Instead, they launched Project Glasswing with Apple, Google, Microsoft, and others, committing $100M to use the model defensively. In weeks, Mythos found thousands of zero-day vulnerabilities across every major operating system and browser — including one in OpenBSD hiding for 27 years.But the cybersecurity headlines aren't the whole story. The same vulnerability chaining capability that links multiple software flaws into sophisticated exploits maps directly to how polygenic disease works — cascading gene interactions across multiple variants that we've never been able to trace. With models like Arc Institute's Evo 2 and DeepMind's AlphaGenome already decoding the genome, Mythos-class reasoning could change everything about how we understand and treat disease.If we can hack code to break it, we can hack code to fix it — including the code that makes us sick.Sources and full write-up: https://open.substack.com/pub/danmccoymd/p/hacking-dna-the-anthropic-story-nobodys

Meta acquired Moltbook. OpenAI put ads in ChatGPT. Microsoft found 31 companies actively poisoning what AI assistants recommend. Everyone's calling it AI-native marketing — but what they're really describing is an influence mechanism with no disclosure, no regulation, and direct access to how people make decisions.In this episode, I break down Microsoft's AI Recommendation Poisoning research, why OpenAI's health advertising exclusions don't actually solve the problem, the insurance company AI lawsuits you should know about (UnitedHealth's nH Predict, Cigna's PXDX), and the 70-year regulatory gap between subliminal advertising bans and prompt injection. When this reaches healthcare — and it will — the implications for patients, providers, and benefits managers get genuinely concerning.Research sources and extended analysis: https://open.substack.com/pub/danmccoymd/p/prompt-injection-is-subliminal-advertisingWatch the video version: https://youtu.be/4vECwmEUHEs

What's really happening with AI in healthcare? The common story is that health systems just need to find the right tool — the best ambient scribe, the smartest chatbot. But the reality is more complicated.In this episode, I break down why Sutter Health's AI agent deployment through Hyro tells us everything about where healthcare AI is actually heading, why 63% of healthcare leaders say interoperability is the number one AI capability they want, and why the organizations seeing real ROI did the boring infrastructure work first.If you're a benefits consultant, health system leader, or anyone advising healthcare organizations — stop evaluating AI tools. Start evaluating integration readiness.Sources and the deep dive: danmccoymd.substack.com

The telehealth boom was supposed to revolutionize healthcare forever—but what went wrong? In this episode, Dan McCoy unpacks the dramatic fall of industry giants like Teladoc and Amwell, revealing how their high-profile bets on nationwide distribution networks failed to stand the test of time. More importantly, you'll hear why the real disruptor isn’t a return to in-person care, but the explosive rise of AI-powered tools that are decentralizing healthcare delivery.Dan breaks down the structural shifts pushing telemedicine incumbents to the brink, explores the rapid adoption of ambient clinical AI, and explains how local practices now leverage advanced technology to deliver more personalized, context-rich care. If you care about the future of healthcare—from benefit managers to health system execs to curious entrepreneurs—this episode is your essential guide to what’s next, who’s winning, and why yesterday’s telehealth playbook no longer applies.Check out my SubStack for a deeper analysis:  https://danmccoymd.substack.com/

Most employers think they're getting a deal on healthcare. They're not. The exposed rate data tells a different story — one where the same knee replacement costs wildly different amounts depending on which hospital and which network you're in, even within the same city.In this episode, I break down the network arbitrage game: how hospital systems use their leverage to extract premium pricing, why your "broad network" plan is probably the most expensive option, and what the exposed price transparency data actually reveals about where the money goes.We cover:Why the same procedure can cost 3-5x more at one hospital vs. anotherHow hospital systems use "must-have" leverage to inflate entire network contractsWhat narrow and tiered networks actually save (and what they cost in access)The real math behind reference-based pricingWhy most employers have never seen the exposed rates they're payingThis isn't theory — it's what the data shows.Full sources and the deep dive: danmccoymd.substack.com

Mount Sinai just published the first independent safety evaluation of ChatGPT Health — and the findings should change how you think about AI in healthcare.Published in Nature Medicine, researchers ran 960 patient interactions across 21 medical specialties. What they found wasn't that ChatGPT gets medicine wrong. It's that it gets the diagnosis right, then tells you to do the wrong thing about it.In this episode, we break down:Why ChatGPT told patients to wait in over half of true emergencies — after correctly identifying the danger in its own explanationThe inverted suicide crisis alerts that fired for sadness but went silent when patients described specific plans for self-harmThe sycophancy problem: why ChatGPT is 12x more likely to agree when you downplay your own symptomsWhere ChatGPT actually performs well (93% in semi-urgent cases) — and why that makes the failures harder to spotWhat this means for anyone using, building, or recommending AI health toolsSources & Links:Primary Study — Nature Medicine, Feb 2026https://doi.org/10.1038/s41591-026-04297-7Mt. Sinai Press Releasehttps://www.mountsinai.org/about/newsroom/2026/research-identifies-blind-spots-in-ai-medical-triageForbes: "ChatGPT Provided Wrong Advice In Over 50% Medical Emergencies Tested"https://www.forbes.com/sites/brucelee/2026/03/08/chatgpt-provided-wrong-advice-in-over-50-medical-emergencies-tested/NPR: "ChatGPT might give you bad medical advice, studies warn"https://www.nhpr.org/2026-03-11/chatgpt-might-give-you-bad-medical-advice-studies-warnRelated: AI Chatbots and Medical Misinformation — Communications Medicine, 2025https://doi.org/10.1038/s43856-025-01021-3Full research brief and deep dive on Substack:danmccoymd.substack.com

Mark Cuban's Cost Plus Drug Company sells a cancer drug for $47 a month. That same drug often costs well over $2,000 at your pharmacy. Both include a 15% markup. The markup is the same — the price is dozens of times higher, and nobody's asking why.In this episode, I break down why percentage-based pricing is the single most inflationary structural design choice in American healthcare. Not because people are corrupt, but because a math decision made decades ago created a system where every participant — insurers, PBMs, brokers, hospitals, pharmacies — gets richer when costs go up.I identify three distinct "pricing diseases" in healthcare:Percentage Parasitism — when compensation scales with cost, not workChargemaster Fiction — fake list prices with negotiated discounts off fictional numbersOpacity Arbitrage — profiting from the inability of other parties to see the real priceWe're only treating one of them. And I make the case that the AI industry has already solved this problem with per-unit token pricing — they just don't know they solved it for healthcare too.Watch the full video: https://youtu.be/px1eRptDHegFull sources and the deep dive: danmccoymd.substack.com

Everyone's pitching AI as the solution to prior authorization. And they're right — the technology is about to solve it. Ambient scribes capturing every detail. Clinical decision support guiding every order. Automated systems submitting perfectly optimized requests. Approval rates heading toward the high 90s.But here's what nobody's talking about: what happens to healthcare costs when a system designed around 15-20% of requests getting denied suddenly starts approving almost everything?In this episode, I break down the three distinct layers of AI in prior authorization — and why most people are lumping them together when they have very different implications. I dig into a UCSF study showing physicians using AI scribes saw a 5.8% RVU increase with no rise in claim denials. I explain why over 80% of appealed denials get overturned, but only 12% are even appealed — revealing that prior auth was never really about clinical evaluation.And I make the case that once AI solves the coding problem, the question shifts from "did you code this correctly?" to "should you have ordered this at all?"The end game isn't faster paperwork. It's AI evaluating medical judgment. The bar is going up, not down.Full source list and research citations available on Substack.

What if the way we measure a doctor's productivity is completely wrong?Software companies have already abandoned "lines of code" as a productivity metric — they now budget in tokens, the fundamental unit of AI work. Some developers spend $10,000-20,000 a month on AI agents. Microsoft says 30% of its code is AI-written. The old measurements are dead.Meanwhile, healthcare is still stuck on RVUs — a system where physicians spend two hours on EHR documentation for every one hour with patients, family doctors lose 86 minutes every night to "pajama time" charting, and Medicare physician payment has declined 26% since 2001 after adjusting for inflation. Value-based care was supposed to fix this. It didn't. CMS's own innovation center actually increased federal spending by $5.4 billion between 2011 and 2020.In this episode, I lay out the case for replacing RVUs with token-based measurement — shifting the question from "How many patients did you see?" to "How much agentic activity did you perform to improve population health?" I walk through the data from JAMA, McKinsey, MedPAC, and the AMA's own admission that MIPS is broken, and explain why AI-augmented healthcare systems should receive better reimbursement, not worse.This isn't a theoretical framework. The AMA just added 26 new CPT codes for clinical AI solutions. CMS launched the ACCESS Model in February 2026. The transition is already underway — the only question is whether your organization is measuring what matters.Full sources and the deep dive: danmccoymd.substack.comWant a personal walk through, check out our AI consultancy at RocketTools.io.

The Pentagon just labeled Anthropic — the company behind Claude AI — a national security supply chain risk. Not because they're a foreign adversary. Because they refused to remove two guardrails: no mass surveillance of Americans and no autonomous weapons without human oversight.The $200 million contract is canceled. The Trump administration ordered every federal agency and defense contractor to phase out Anthropic's technology. Anthropic is preparing to sue.But the Pentagon fight is the opening act. The real story is what AI can already do with your health data — and why the rules protecting it don't work anymore.In this episode:How AI re-identifies "anonymous" medical records for 800,000+ Americans even after removing all 18 HIPAA-required identifiersWhy your body is becoming a biometric database — facial reconstruction from MRI scans (83-98% accuracy), chest X-rays as fingerprints, and 12 million voice biomarkers extracted per minute of a telehealth callDario Amodei's four warnings about government AI misuseThe HIPAA timeline: written in 1996, last major update in 2003, new rules expected late 2026 — none of which address AI re-identificationWhy Fitbit, Apple Watch, Oura, and AI health chatbots aren't covered by HIPAA at allWhat the Anthropic situation tells us about who's drawing the line on your health data (spoiler: almost nobody)The rules haven't caught up to the technology. This episode breaks down exactly where the gap is and why it matters.Full sources and the deep dive: danmccoymd.substack.com

The common story about healthcare cybersecurity is that big hospital systems are the targets. The reality is more complicated. Rural America is ground zero — and the math is brutal.The Change Healthcare attack knocked out 50% of all U.S. medical claims processing. 80% of physician practices lost revenue. 300 hospitals didn't even apply for federal relief — mostly small and rural. But that was the supply chain breaking. The direct attacks on rural hospitals are worse.In this episode, I break down three things:First — what the Change Healthcare attack actually revealed about how fragile small-provider healthcare really is.Second — why rural hospitals are easier targets with the same valuable data. 69% lack basic multi-factor authentication. Most have one or two people handling all of IT — cybersecurity, printers, wifi, everything. And attackers know rural hospitals are more likely to pay ransoms because they can't afford to go offline when the nearest alternative is an hour away.Third — the AI double-edged sword. Only 29% of healthcare executives feel prepared for AI-powered attacks. But AI might also be the thing that levels the playing field for small providers who will never be able to hire a security team.Full sources and the deep dive: danmccoymd.substack.com/

Gyde just raised $60 million to build the first AI-native insurance brokerage. Not a tool for brokers — a replacement for the brokerage model itself. Led by Lightspeed, backed by Optum Ventures, founded by a 10-year Oscar Health veteran.In this episode, I break down three things:First — what Gyde actually is and why this isn't another SaaS platform. They're acquiring agencies and rebuilding them with AI from the inside out.Second — the 80-90% automation thesis. Most of what benefits brokers do is pattern execution, not judgment. Analytics, renewals, compliance, repricing — AI handles all of it. The 10-20% that remains is where the real value lives.Third — who survives. Three groups are forming: the acquirees, the resisters, and the adapters. WTW just paid $1.3 billion for Newfront explicitly for "agentic AI capabilities." The market is telling you what it values.The US benefits consulting market is projected to more than double to $10.5 billion by 2035. The question isn't whether AI disrupts this space. It's whether you're building or being bought.Full sources and the deep dive: danmccoymd.substack.com/