The a16z Show

What changes when an AI mistake doesn’t just hurt a benchmark score, but affects someone’s housing or healthcare? EliseAI Head of Applied Research Mario Martone argues that deploying AI in essential industries requires a very different standard of reliability. Mario joins MTS to explain how EliseAI handles roughly 5 million phone calls a month, why LLMs aren’t the right solution for every problem, and how the team combines language models, traditional machine learning, neural networks, and internally built evaluations to decide what actually works in production. Drawing on 15 years studying theoretical physics, Mario also explains how the scientific method shapes EliseAI’s research culture, from testing new models against its own benchmarks to fine-tuning open-source models and automating increasingly complex workflows across housing and healthcare. And he makes the case that applying AI to the real world can produce problems every bit as intellectually difficult as the ones he encountered studying string theory.

Why is booking a doctor's appointment still so difficult, and can AI finally change that? Zocdoc CEO Oliver Kharraz argues that the real problem isn't finding doctors, but connecting patients to accurate availability and making appointments bookable wherever they start their search. Oliver joins a16z General Partner Julie Yoo to discuss how Zocdoc spent nearly 20 years building the infrastructure behind online scheduling, and why it's now opening that technology to AI agents, insurers, and other platforms. They explore Zocdoc's partnership with Gemini, why patients shouldn't be locked into a single healthcare platform, and how surfacing last-minute availability can reduce appointment wait times from weeks to days. They also discuss what happens when AI becomes a new front door to healthcare and why Oliver believes the future of Zocdoc extends far beyond its own app.

a16z General Partner Martin Casado sits down with Grepr CEO Jad Naous to ask what comes after AI learns to write code: can it also debug and operate the systems that code creates? Jad shares how AI has transformed engineering inside his 10-person team, cutting some projects from six months to just 10 days. But operating production systems is a harder challenge, with unpredictable workloads, failures, and countless possible states. They discuss whether AI can learn to diagnose problems, test hypotheses, and debug systems like a human engineer. They also explore AI SREs, security, query optimization, and why the best engineers may be gaining the most leverage from coding agents.

a16z General Partner Alex Rampell sits down with VGS Executive Board Member, Terry Angelos, to unpack what happens to payments when AI agents go from recommending what to buy to actually making the purchase. Terry explains the infrastructure required to safely give agents access to payment credentials, why wallets like PayPal, Shop Pay, and Link are competing to become the default payment layer for agents, and how new agentic tokens could preserve the chargebacks, identity checks, and merchant relationships that make today's payment networks work. VGS, which already secures payment credentials across a significant share of e-commerce, is now provisioning millions of these agentic tokens each week. They also discuss what happens when merchants don't want to transact with agents, how delegated authority changes liability when an agent makes a mistake, and why AI could simultaneously increase consumer spending while eliminating the subscriptions, fees, and other forms of economic "breakage" that depend on people not paying attention.

a16z Partner Lisha Li sits down with AI researcher and Trillium founder Nathan Lambert to ask how durable the gap really is between frontier AI labs and open models. Nathan argues that OpenAI and Anthropic have structural advantages in compute, capital, and the ability to pioneer new training paradigms, but open-model labs, particularly in China, have proven remarkably capable of following close behind. They unpack how much of that comes from distillation versus technical talent, why compute remains a major constraint, and how data, RL environments, and post-training are increasingly shaping model progress. They also discuss Nathan’s new nonprofit research lab, Trillium, and its plans to open up parts of AI research that are increasingly difficult to study from the outside. That includes releasing RL environments and documenting how AI is actually being used to automate AI research, with the goal of demystifying recursive self-improvement and giving researchers and policymakers a clearer picture of what frontier systems can and cannot do.