The a16z Show

Why hasn't AI transformed everyday work as quickly as coding? Town CEO Jean-Denis Greze argues the bottleneck isn't intelligence, but making AI useful to people who don't have the time or technical skills to build their own automations.

Jean-Denis joins a16z General Partner Alex Rampell to share why he pivoted Town from tax software to a personal AI assistant built around email, calendars, and the workflows that run businesses. They discuss why consumer-grade experiences may win in enterprise AI, how Town discovers and automates tasks users wouldn't think to delegate, and why model neutrality and data ownership matter as AI labs increasingly compete with application companies.

They also explore what happens when agents begin acting on our behalf, from managing everyday work to shopping and commerce, and why the next generation of software may need to adapt to each user rather than the other way around.

Resources:

Follow Alex Rampell on X: https://x.com/arampell

Follow Jean-Denis Greze: https://x.com/jgreze

Learn more about Town: https://town.com

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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.

Resources:

Learn more about EliseAI: https://www.eliseai.com/

Follow Mario Martone on LinkedIn: https://www.linkedin.com/in/mcmartone/ 

Follow MTS: https://www.mts.now/ 

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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.

Resources:

Follow Oliver Kharraz on LinkedIn: https://www.linkedin.com/in/kharraz

Follow Julie Yoo: https://x.com/julesyoo

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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.

Resources:

Follow Jad Naous on X: https://x.com/jadtnaous 

Follow Martin Casado on X: https://x.com/martin_casado

Learn more about Grepr: https://www.grepr.ai/ 

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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.

Resources:

Follow Terry Angelos on X: https://x.com/terryangelos 

Follow Alex Rampell on X: https://x.com/arampell

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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.

Resources:

Learn more about Trillium: https://trilliumlabs.org/ 

Follow Nathan Lambert on X: https://x.com/natolambert 

Nathan Lambert’s Newsletter: https://www.interconnects.ai/ 

Follow Lisha Li on X: https://x.com/lishali88

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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Could AI break the cryptography protecting everything from crypto wallets and banking to private messages? Recent advances in AI-driven mathematical research have sparked new warnings about the security of today's encryption systems, but how much of the concern is supported by evidence?

a16z crypto's Liz Harkavy is joined by Stanford cryptographer Dan Boneh, a16z crypto General Partner Eddy Lazzarin, and Research Partner Justin Thaler to separate what we know from what remains speculation. They explain why modern cryptography relies on mathematical assumptions, what OpenAI's recent results do and don't demonstrate, and how the potential threat from AI differs from quantum computing.

They also discuss why existing security practices and cryptographic alternatives offer reasons for confidence, the importance of building systems that can adapt if an assumption fails, and how AI could ultimately help researchers and developers make software more secure.

Resources:

Watch the original a16z crypto discussion: https://www.youtube.com/watch?v=k_CoOoDsZ5Y

Follow Eddy Lazzarin on X: https://x.com/eddylazzarin

Follow Dan Boneh: https://x.com/danboneh

Follow Justin Thaler: https://x.com/SuccinctJT

Follow Liz Harkavy: https://x.com/liz_harkavy

Learn more about a16z crypto: https://a16zcrypto.com/

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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a16z General Partner Martin Casado sits down with MakerMaker.AI co-founders Dhaval Adjodah and Owen He to unpack recursive self-improvement, or RSI, and separate the increasingly popular term from what it would actually mean for an AI system to improve itself.

Dhaval and Owen distinguish true RSI from simply using AI to automate pieces of AI development. Their goal is a closed loop where the system being improved is also doing the improving, across components including models, harnesses, and eventually more of the AI stack. They explain why closing that loop still doesn’t guarantee runaway “takeoff,” and why measuring real progress requires accounting for both capability gains and the resources required to achieve them.

They also share what they’re seeing in their own research, including early signs that self-improving systems can increase performance while improving efficiency. And they dig into the practical challenges of proving those gains are real, from variance and reward hacking to the enormous difficulty of distinguishing genuine improvement from noise.

Resources:

Follow Dhaval Adjodah on LinkedIn: https://www.linkedin.com/in/rand0m

Follow Owen He on LinkedIn:https://www.linkedin.com/in/owen-he-b7b064279/

Follow Martin Casado on X: https://x.com/martin_casado

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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Nick Bostrom joins Theo Jaffee and Sofia Puccini on MTS to revisit the ideas about superintelligence and existential risk that he helped bring into the mainstream, now that increasingly capable AI systems actually exist.

Nick discusses why reinforcement learning may be pushing models toward more goal-seeking behavior, what recent agent experiments tell us about instrumental convergence, and why he favors preserving the ability to slow frontier development rather than committing now to a broad or indefinite AI pause. He also makes the case that the risks of AI need to be weighed alongside its potential upside, from advances in medicine and longer lifespans to entirely new ways of being human. 

They also get into whether today's models could already have some form of consciousness, the tradeoffs of open-source AI, whether machines can do original philosophy, mind uploading, and what the simulation hypothesis might mean for the superintelligence we create.

Resources:

Nick Bostrom / Macrostrategy Research Initiative: https://nickbostrom.com/ 

Follow Theo Jaffee on X: https://x.com/theojaffee

Follow Sofia Puccini on LinkedIn: https://www.linkedin.com/in/sofiapuccini/

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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a16z co-founder and General Partner Ben Horowitz joins James Norman on the inaugural episode of Vintage x Vintage for a conversation spanning entrepreneurship, investing, hip-hop, and what changes when AI makes building dramatically easier.

Ben reflects on going from Netscape to becoming a first-time CEO, why every founder eventually hits a “we’re fucked, it’s over” moment, and what he learned from building a16z around the needs of entrepreneurs. He explains why great investors should look for world-class strengths rather than an absence of weaknesses, and why expanding the ways we recognize talent can raise the bar rather than lower it.

They also look at the current AI cycle, why Ben believes it is bigger than the internet, how AI changes who can become a builder, and why the infrastructure beneath today’s models may need to be rebuilt from the ground up.

Resources:

Follow Ben Horowitz on X: https://x.com/bhorowitz 

Follow James Norman on LinkedIn: https://www.linkedin.com/in/motown/ 

Learn more about Paid in Full: https://paidinfullfoundation.org

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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