AI in healthcare may be entering a new chapter, one where the biggest question is no longer whether the technology works, but who is willing to deploy it, measure it, and take responsibility for the risk.

This week, Steve sits down again with Eric Larsen to revisit his predictions from last year’s Webby-winning episode on generative AI in healthcare. Eric argues that the first wave of AI has been inflationary, reinforcing the old payer-provider payment model, but that the next wave could be deflationary as automation moves into revenue cycle, administrative work, clinical reasoning, and drug development. They discuss why incumbents still have a narrow window to co-develop the future, why clinical AI may move faster outside the US, and why liability may become the deciding factor in who wins.

Medicaid reimbursements are shrinking, providers are pulling back, and vulnerable populations are losing access to care. Akido Labs is betting that AI can expand care capacity fast enough to reverse that trend.

This week, Halle sits down with Prashant Samant, co-founder and CEO of Akido Labs, to discuss what it actually takes to scale care with AI. They explore why Akido built a full-stack healthcare company, how its AI operates inside real clinical workflows, and why the hardest patients are the best place to test whether this model works.

Forty million people use ChatGPT for health-related questions every day, making it one of the most widely used tools for health information in the world. So what is their team doing to maximize impact and minimize harm?

In this full circle episode, Halle Tecco sits down with her Rock Health co-founder Nate Gross, who also co-founded Doximity (DOCS) and is now VP of Health at OpenAI. They discuss the astonishing speed of AI progress, how models are trained for safety and accuracy, and what this technological evolution means for every part of the healthcare system.

Pete McCanna, CEO of Baylor Scott & White Health, believes that health systems are built around the wrong objective… and he has an ambitious goal to change that.

This week, Halle sits down with McCanna to unpack how one of the largest and most successful health systems in the country is shifting from a supply-driven model to one built entirely around the customer. They discuss why legacy systems operate like “walled castles,” what it takes to redesign care around real conditions instead of departments, and how Baylor Scott & White is testing a model that prioritizes access, personalization, and long-term trust over short-term profit.

When Eric Lefkofsky’s wife was diagnosed with breast cancer, it exposed how little technology and data were shaping cancer care, pushing the serial entrepreneur to build a different model.

Lefkofsky is the founder and CEO of Tempus, now a $10B publicy traded health tech company, and previously founded Groupon. At Tempus, he’s building a tech-first company applying multimodal data and AI to make diagnostics smarter and treatment decisions more tailored, starting in oncology and expanding across disease areas.

On the heels of raising $210 million at a $3.5 billion valuation, OpenEvidence is the fastest-growing physician app in history, now reaching over 40% of U.S. physicians and powers 17 million monthly clinical queries.

Most people spend over 30 hours a year dealing with customer service—on hold, repeating account numbers, and navigating endless phone trees. But what if AI could fix that without losing the human touch?

AI companies are hitting growth milestones in record time—some reaching $100 million in revenue in just two years. But while this pace feels familiar in tech, healthcare has always been slower to adopt new tools. That may finally be changing.

Healthcare costs keep climbing, and yet patients and employers often feel powerless to change the system. What if outsiders—those not steeped in the traditions of healthcare—are actually the ones best positioned to fix it?

Hospitals are under immense pressure: burned-out clinicians, outdated systems, and rising costs have made delivering care harder than ever. 

Three simple questions plague every American seeking healthcare: Where should I go? When can I be seen? And how much will it cost me? Despite seeming basic, these questions have remained largely unanswerable—until now.

Most medical care is backed by varying types of evidence—yet we apply higher standards to digital health tools before they’re trusted, adopted, or reimbursed.

Most AI in healthcare promises superintelligence—but what if that’s the wrong goal entirely?

Over 160 million Americans are served by Optum, yet many still don’t fully understand what it actually does—or why it matters.