AI + a16z

• Enabling Agents and Battling Bots on an AI-Centric Web

  Arcjet CEO David Mytton sits down with a16z partner Joel de la Garza to discuss the increasing complexity of managing who can access websites, and other web apps, and what they can do there. A primary challenge is determining whether automated traffic is coming from bad actors and troublesome bots, or perhaps AI agents trying to buy a product on behalf of a real customer.Joel and David dive into the challenge of analyzing every request without adding latency, and how faster inference at the edge opens up new possibilities for fraud prevention, content filtering, and even ad tech.Topics include:Why traditional threat analysis won’t work for the AI-powered webThe need for full-context security checksHow to perform sub-second, cost-effective inferenceThe wide range of potential actors and actions behind any given visitAs David puts it, lower inference costs are key to letting apps act on the full context window — everything you know about the user, the session, and your application.Follow everyone on social media:David MyttonJoel de la Garza Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

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• Giving New Life to Unstructured Data with LLMs and Agents

  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.Follow everyone on X:Anant BhardwajGuido Appenzeller Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

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• Beyond Leaderboards: LMArena’s Mission to Make AI Reliable

  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.Follow everyone on X:Anastasios N. AngelopoulosWei-Lin ChiangIon StoicaAnjney MidhaTimestamps0:04 -  LLM evaluation: From consumer chatbots to mission-critical systems6:04 -  Style and substance: Crowdsourcing expertise18:51 -  Building immunity to overfitting and gaming the system29:49 -  The roots of LMArena41:29 -   Proving the value of academic AI research48:28 -  Scaling LMArena and starting a company59:59 -  Benchmarks, evaluations, and the value of ranking LLMs1:12:13 -  The challenges of measuring AI reliability1:17:57 -  Expanding beyond binary rankings as models evolve1:28:07 -  A leaderboard for each prompt1:31:28 -  The LMArena roadmap1:34:29 -  The importance of open source and openness1:43:10 -  Adapting to agents (and other AI evolutions) Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

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• 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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