Instabase founder and CEO Anant Bhardwaj joins a16z Infra partner Guido Appenzeller to discuss the revolutionary impact of LLMs on analyzing unstructured data and documents (like letting banks verify identity and approve loans via WhatsApp) and shares his vision for how AI agents could take things even further (by automating actions based on those documents). In more detail, they discuss:

• Why legacy robotic process automation (RPA) struggles with unstructured inputs.
• How Instabase developed layout-aware models to extract insights from PDFs and complex documents.
• Why predictability, not perfection, is the key metric for generative AI in the enterprise.
• The growing role of AI agents at compile time (not runtime).
• A vision for decentralized, federated AI systems that scale automation across complex workflows.

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Guido Appenzeller

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LMArena cofounders Anastasios N. Angelopoulos, Wei-Lin Chiang, and Ion Stoica sit down with a16z general partner Anjney Midha to talk about the future of AI evaluation. As benchmarks struggle to keep up with the pace of real-world deployment, LMArena is reframing the problem: what if the best way to test AI models is to put them in front of millions of users and let them vote? The team discusses how Arena evolved from a research side project into a key part of the AI stack, why fresh and subjective data is crucial for reliability, and what it means to build a CI/CD pipeline for large models.

They also explore:

• Why expert-only benchmarks are no longer enough.
• How user preferences reveal model capabilities — and their limits.
• What it takes to build personalized leaderboards and evaluation SDKs.
• Why real-time testing is foundational for mission-critical AI.

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Ion Stoica

Anjney Midha

0:04 -  LLM evaluation: From consumer chatbots to mission-critical systems

6:04 -  Style and substance: Crowdsourcing expertise

18:51 -  Building immunity to overfitting and gaming the system

29:49 -  The roots of LMArena

41:29 -   Proving the value of academic AI research

48:28 -  Scaling LMArena and starting a company

59:59 -  Benchmarks, evaluations, and the value of ranking LLMs

1:12:13 -  The challenges of measuring AI reliability

1:17:57 -  Expanding beyond binary rankings as models evolve

1:28:07 -  A leaderboard for each prompt

1:31:28 -  The LMArena roadmap

1:34:29 -  The importance of open source and openness

1:43:10 -  Adapting to agents (and other AI evolutions)

Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

In this episode of AI + a16z, Distributional cofounder and CEO Scott Clark, and a16z partner Matt Bornstein, explore why building trust in AI systems matters more than just optimizing performance metrics. From understanding the hidden complexities of generative AI behavior to addressing the challenges of reliability and consistency, they discuss how to confidently deploy AI in production. 

Why is trust becoming a critical factor in enterprise AI adoption? How do traditional performance metrics fail to capture crucial behavioral nuances in generative AI systems? Scott and Matt dive into these questions, examining non-deterministic outcomes, shifting model behaviors, and the growing importance of robust testing frameworks. 

Among other topics, they cover: 

• The limitations of conventional AI evaluation methods and the need for behavioral testing. 
• How centralized AI platforms help enterprises manage complexity and ensure responsible AI use. 
• The rise of "shadow AI" and its implications for security and compliance. 
• Practical strategies for scaling AI confidently from prototypes to real-world applications.

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Distributional

Matt Bornstein

Derrick Harris

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In this episode of the a16z AI podcast, a16z Infra partners Guido Appenzeller, Matt Bornstein, and Yoko Li explore how generative AI is reshaping software development. From its potential as a new high-level programming abstraction to its current practical impacts, they discuss whether AI coding tools will redefine what it means to be a developer.

Why has coding emerged as one of AI's most powerful use cases? How much can AI truly boost developer productivity, and will it fundamentally change traditional computer science education? Guido, Yoko, and Matt dive deep into these questions, addressing the dynamics of "vibe coding," the enduring role of formal programming languages, and the critical challenge of managing non-deterministic behavior in AI-driven applications.Among other things, they discuss:

• The enormous market potential of AI-generated code, projected to deliver trillions in productivity gains.
• How "prompt-based programming" is evolving from Stack Overflow replacements into sophisticated development assistants.
• Why formal languages like Python and Java are here to stay, even as natural language interactions become common.
• The shifting landscape of programming education, and why understanding foundational abstractions remains essential.
• The unique complexities of integrating AI into enterprise software, from managing uncertainty to ensuring reliability.

Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

In this episode of AI + a16z, Anthropic's David Soria Parra — who created MCP (Model Context Protocol) along with Justin Spahr-Summers — sits down with a16z's Yoko Li to discuss the project's inception, exciting use cases for connecting LLMs to external sources, and what's coming next for the project. If you're unfamiliar with the wildly popular MCP project, this edited passage from their discussion is a great starting point to learn:

David: "MCP tries to enable building AI applications in such a way that they can be extended by everyone else that is not part of the original development team through these MCP servers, and really bring the workflows you care about, the things you want to do, to these AI applications. It's a protocol that just defines how whatever you are building as a developer for that integration piece, and that AI application, talk to each other. 

"It's a very boring specification, but what it enables is hopefully ... something that looks like the current API ecosystem, but for LLM interactions."

Yoko: "I really love the analogy with the API ecosystem, because they give people a mental model of how the ecosystem evolves ... Before, you may have needed a different spec to query Salesforce versus query HubSpot. Now you can use similarly defined API schema to do that.

"And then when I saw MCP earlier in the year, it was very interesting in that it almost felt like a standard interface for the agent to interface with LLMs. It's like, 'What are the set of things that the agent wants to execute on that it has never seen before? What kind of context does it need to make these things happen?' When I tried it out, it was just super powerful and I no longer have to build one tool per client. I now can build just one MCP server, for example, for sending emails, and I use it for everything on Cursor, on Claude Desktop, on Goose."

Learn more:

A Deep Dive Into MCP and the Future of AI Tooling

What Is an AI Agent?

Benchmarking AI Agents on Full-Stack Coding

Agent Experience: Building an Open Web for the AI Era

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Yoko Li

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In this episode of AI + a16z, a16z Infra partners Guido Appenzeller, Matt Bornstein, and Yoko Li discuss and debate one of the tech industry's buzziest words right now: AI agents. The trio digs into the topic from a number of angles, including:

• Whether a uniform definition of agent actually exists
• How to distinguish between agents, LLMs, and functions
• How to think about pricing agents
• Whether agents can actually replace humans, and
• The effects of data siloes on agents that can access the web.

They don't claim to have all the answers, but they raise many questions and insights that should interest anybody building, buying, and even marketing AI agents.

Learn more:

Benchmarking AI Agents on Full-Stack Coding

Automating Developer Email with MCP and Al Agents

A Deep Dive Into MCP and the Future of AI Tooling

Agent Experience: Building an Open Web for the AI Era

DeepSeek, Reasoning Models, and the Future of LLMs

Agents, Lawyers, and LLMs

Reasoning Models Are Remaking Professional Services

From NLP to LLMs: The Quest for a Reliable Chatbot

Can AI Agents Finally Fix Customer Support?

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Guido Appenzeller

Matt Bornstein

Yoko Li

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In this episode, a16z General Partner Martin Casado sits down with Sujay Jayakar, co-founder and Chief Scientist at Convex, to talk about his team’s latest work benchmarking AI agents on full-stack coding tasks. From designing Fullstack Bench to the quirks of agent behavior, the two dig into what’s actually hard about autonomous software development, and why robust evals—and guardrails like type safety—matter more than ever. They also get tactical: which models perform best for real-world app building? How should developers think about trajectory management and variance across runs? And what changes when you treat your toolchain like part of the prompt? Whether you're a hobbyist developer or building the next generation of AI-powered devtools, Sujay’s systems-level insights are not to be missed.

Drawing from Sujay’s work developing the Fullstack-Bench, they cover:

• Why full-stack coding is still a frontier task for autonomous agents
• How type safety and other “guardrails” can significantly reduce variance and failure
• What makes a good eval—and why evals might matter more than clever prompts
• How different models perform on real-world app-building tasks (and what to watch out for)
• Why your toolchain might be the most underrated part of the prompt
• And what all of this means for devs—from hobbyists to infra teams building with AI in the loop

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Introducing Fullstack-Bench

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Sujay Jayakar

Martin Casado

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In this episode of AI + a16z,  Resend founder and CEO Zeno Rocha sits down with a16z partner Yoko Li to discuss:

• How generative AI — powered by agents and, now, MCP — is reshaping the email experience for developers, as well as the overall world of programming. 
• Zeno's obsession with developer experience has evolved into designing for "agent experience" — a new frontier where LLM-powered agents are not only building products but also operating within them. 
• How email, one of the most ubiquitous tools for developers and end users alike, is being reimagined for a future where agents send, parse, and optimize communication. 
• What it means to build agent-friendly APIs. 
• The emerging MCP protocol, and how AI is collapsing the creative loop for prosumers and developers alike.

Learn more:

What is AX (agent experience) and how to improve it

A deep dive into MCP and the future of AI tooling

Dracula theme

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Zeno Rocha

Yoko Li

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In this episode of AI + a16z, a16z Partner Joe Schmidt sits down with 11x CTO Prabhav Jain for an inside look at how AI-powered digital workers are reshaping sales and revenue operations. They discuss the evolution of agentic AI, the trade-offs between orchestration and autonomy, and the technical innovations driving 11x’s products, Alice and Mike.

Prabhav breaks down the challenges of real-time voice AI, the complexities of multimodal agent interactions, and why the future of enterprise AI is about delivering measurable customer outcomes—not just automation. They also dive into the fast-moving landscape of model providers, the impact of open-source AI, and how startups can stay ahead in an environment of constant technological change.

Plus, they explore 11x’s bold decision to re-architect its platform from the ground up, the lessons learned from scaling AI-powered sales automation, and what it takes to build truly effective digital workers.

• The difference between true AI agents and complex orchestrations—and why it matters.
• How 11x built Alice and Mike to deliver human-like sales performance at scale.
• The cutting-edge advancements shaping AI voice assistants and real-time multimodal interactions.
• Lessons from rebuilding an AI platform while supporting a fast-growing customer base.
• How AI startups can balance rapid iteration with long-term strategic bets.

For anyone interested in AI-powered automation, enterprise sales, or the future of digital work, this episode offers a front-row seat to the latest innovations pushing the boundaries of AI agents.

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11x

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Joe Schmidt

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In this episode of AI + a16z, Sesame Cofounder and CTO Ankit Kumar joins a16z general partner Anjney Midha for a deep dive into the research and engineering behind their voice technology. They discuss the technical challenges of real-time speech generation, the trade-offs in balancing personality with efficiency, and why the team is open-sourcing key components of their model. Ankit breaks down the complexities of multimodal AI, full-duplex conversation modeling, and the computational optimizations that enable low-latency interactions. 

They also explore the evolution of natural language as a user interface and its potential to redefine human-computer interaction.
Plus, we take audience questions on everything from scaling laws in speech synthesis to the role of in-context learning in making AI voices more expressive.

Key Takeaways:
How Sesame AI achieves natural voice interactions through real-time speech generation.

• The impact of open-sourcing their speech model and what it means for AI research.
• The role of full-duplex modeling in improving AI responsiveness.
• How computational efficiency and system latency shape AI conversation quality.
• The growing role of natural language as a user interface in AI-driven experiences.

For anyone interested in AI and voice technology, this episode offers an in-depth look at the latest advancements pushing the boundaries of human-computer interaction.

Learn more:

The Maya + Miles demo

Crossing the uncanny valley of conversational voice

Sesame CSM 1B model

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Ankit Kumar

Anjney Midha

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