AI + a16z

• Building AI Systems You Can Trust

  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.Follow everyone:Scott ClarkDistributionalMatt BornsteinDerrick Harris Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

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• Who's Coding Now? AI and the Future of Software Development

  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.

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• MCP Co-Creator on the Next Wave of LLM Innovation

  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 ToolingWhat Is an AI Agent?Benchmarking AI Agents on Full-Stack CodingAgent Experience: Building an Open Web for the AI EraFollow everyone on X:David Soria ParraYoko Li Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

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• What Is an AI Agent?

  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 existsHow to distinguish between agents, LLMs, and functionsHow to think about pricing agentsWhether agents can actually replace humans, andThe 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 CodingAutomating Developer Email with MCP and Al AgentsA Deep Dive Into MCP and the Future of AI ToolingAgent Experience: Building an Open Web for the AI EraDeepSeek, Reasoning Models, and the Future of LLMsAgents, Lawyers, and LLMsReasoning Models Are Remaking Professional ServicesFrom NLP to LLMs: The Quest for a Reliable ChatbotCan AI Agents Finally Fix Customer Support?Follow everybody on X:Guido AppenzellerMatt BornsteinYoko Li Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

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• Benchmarking AI Agents on Full-Stack Coding

  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 agentsHow type safety and other “guardrails” can significantly reduce variance and failureWhat makes a good eval—and why evals might matter more than clever promptsHow 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 promptAnd what all of this means for devs—from hobbyists to infra teams building with AI in the loopLearn More:Introducing Fullstack-BenchFollow everyone on X:Sujay JayakarMartin Casado Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

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