AI + a16z

What if the hardest part of building a company isn’t the product, but knowing exactly who it’s for?In this episode, a16z General Partner Martin Casado sits down with Abhishek Agrawal, Cofounder and CEO of Material Security, to discuss how an ideal customer profile is discovered, how to manage any kind of customer, and how frothy markets can distort real signal.Follow Martin on X: https://x.com/martin_casadoFollow Material Security on X: https://x.com/material_secFollow Abhishek on LinkedIn: https://www.linkedin.com/in/abhishek--agrawal/ Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI is transforming both sides of the cybersecurity cat-and-mouse game. Attackers are using LLMs to scale impersonation, phishing, and even deepfake fraud—while defenders are racing to automate detection and takedowns at the same speed.In this episode, a16z partner Joel de la Garza talks with Kevin Tian, cofounder & CEO of Doppel Security (and former Uber engineer), about building in this new landscape. They cover:Why outsider founders sometimes build the most effective security companiesThe “3 V’s” framework for today’s social engineering attacks: volume, velocity, varietyHow Doppel uses reasoning models and reinforcement fine-tuning to cut false positives and improve precisionSimulation tools like “vibe phishing” to train employees on real attacker tacticsThe shift from manual cyber-intelligence services to AI-driven, software-margin businessesWhy the biggest bottleneck now isn’t model cost—but engineering time to deliver the right contextIf you’re building security products or exploring how AI can automate tough edge cases, this is a ground-level look at what’s working—and what comes next. Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What if you could retake your favorite memories years after they happened, fixing the lighting, catching the smile, or even opening your eyes?In this conversation, a16z General Partner Martin Casado and Partner Yoko Li sit down with scientist and Lytro founder Ren Ng along with Phota Labs cofounders Cecilia Zhang and Zhihao “Zach” Xia to explore the past, present, and future of computational photography. They trace the story from the invention of light-field cameras and the evolution of smartphone photography to today’s AI powered retakes that preserve identity and context in ways filters never could. Together they reflect on how AI is changing what it means to capture a moment, why authenticity matters as much as aesthetics, and how the future of photography may no longer depend on a lens at all but on models that know you.Resources: Find Cecilia on GitHub: https://ceciliavision.github.io/Find Zach on GitHub: https://likesum.github.io/Find Ren on LinkedIn: https://www.linkedin.com/in/renngFind Yoko on X: https://x.com/stuffyokodrawsFind Martin on X: https://x.com/martin_casado Timecodes:00:00 The Decisive Moment in Photography00:33 Introduction to Computational Photography01:05 Personal Histories and Connections02:27 Evolution of Computational Photography04:15 The Birth of Light Field Photography07:28 From Hardware to Software Innovations08:52 Founding of Photo Labs11:10 Generative AI in Photography13:54 The Future of Photography14:47 Personalized Visual Gen AI16:27 User Reactions and Real-World Applications17:44 Technical Innovations and Challenges24:11 New Use Cases and Exciting Prospects25:34 The Essence of Slide Photography26:16 The Future of Photography: Generative AI28:58 Authenticity in Photography32:11 Generative AI and User Behavior34:39 The Impact of Generative AI on Photography37:02 The Evolution of Photography Styles46:20 The Future of Computational Photography  Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

OpenAI’s Codex has already shipped hundreds of thousands of pull requests in its first month. But what is it really, and how will coding agents change the future of software?In this episode, General Partner Anjney Midha goes behind the scenes with one of Codex’s product leads- Alexander Embiricos - to unpack its origin story, why its PR success rate is so high, the safety challenges of autonomous agents, and what this all means for developers, students, and the future of coding.Resources: Find Alex on X: https://x.com/embiricoFind Anjney on X: https://twitter.com/AnjneyMidha Stay Updated: Find a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://x.com/eriktorenbergPlease 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 Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Models, Modalities, and Memes: Creating Compelling AI CharactersIn this episode of AI + a16z, Hedra founder and CEO Michael Lingelbach joins a16z partners Justine Moore and Matt Bornstein to talk about building AI-native video — and why the next wave of generative content is all about characters, not just clips.They discuss how Hedra’s expressive, full-body, dialogue-centric video models are powering everything from viral meme content to enterprise training tools. Michael explains why “character” is the core design primitive in Hedra’s architecture, how consumers are leading the charge in discovering new use cases, and what it takes to productionize those behaviors for real-world applications.Along the way, they explore what makes multi-modal generation uniquely hard, the role of user control in shaping believable AI performances, and why being a founder sometimes means responding to thousands of support emails — at 6 a.m.Key takeaways:How Hedra’s real-time video model blends audio, image, and character controlWhy generative content is shifting from static avatars to programmable personasThe surprising crossover between consumer creativity and enterprise adoptionWhere existing LLMs fall short in generating emotionally authentic charactersWhat vibe coding, hands-on design, and founder obsession look like in practiceFor anyone curious about building AI characters, scaling creative workflows, or the future of human-computer interaction — this one’s not to be missed. Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

If you've been experimenting with image, video, and audio models, the chances are you've been both blown away by how good they're becoming, and also a little perturbed by how long they can take to generate. If you've been using a platform like Fal, however, your experience on the latter point might be more positive.In this episode, Fal cofounder and CEO Burkay Gur and head of engineering Batuhan Taskaya join a16z general partner Jennifer Li to discuss how they built an inference platform — or, as they call it, a generative media cloud — that's optimized for speed, performance, and user experience. These are core features for a great product, yes, and also ones borne of necessity as the early team obsessively engineered around its meager GPU capacity at the height of the AI infrastructure crunch.But this is more than a story about infrastructure. As you'll hear, they also delve into sales and hiring strategy; the team's overall excitement over these emerging modalities; and the trends they're seeing as competition in the world of video models, especially, heats up.  Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, a16z partner Joel de la Garza sits down with Socket founder and CEO Feross Aboukhadijeh to dive into the intersection of vibe coding and security. As one of the earliest security founders to fully embrace LLMs, Feross shares firsthand insights into how these technologies are transforming software engineering workflows and productivity — and where there are sharp edges that practitioners need to avoid.The TL;DR: Treat AI-assisted programming the same way you'd treat other programming, by vetting packages, reviewing code, and generally make sure you're not sacrificing security for speed. As he explained, LLMs can make developers more productive and even make their software more secure, but only if developers do their part by maintaining a safe supply chain.Follow everyone on social media: Feross AboukhadijehJoel de la Garza Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, a16z GP Martin Casado sits down with Metronome CEO Scott Woody to unpack how AI is fundamentally changing the value proposition of software—and why that shift demands a rethink of the traditional SaaS business model.They explore how, in the cloud era, value scaled with the number of users accessing a shared system (think Salesforce). However, in the AI era, value shifts to the work the software performs on your behalf, automating tasks such as writing code or resolving support tickets. As a result, the old value metric of “users” is being replaced by “output,” and it’s upending how companies monetize.This conversation goes deep on:What new pricing models will emerge in an AI-native world Why usage-based billing is gaining ground—and where it breaks How to align GTM teams and customer success orgs with evolving value metrics Strategic advice for SaaS founders navigating hybrid business models and incentive designIf you’re selling software today, you don’t want to miss this discussion.Follow everyone on social media:Scott Woody Martin Casado  Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, which originally aired on the Complex Systems Podcast, a16z General Partner Jennifer Li discusses how AI is reshaping every layer of the software stack, creating demand for new types of middleware. Jennifer talks about emerging infrastructure categories and why the next wave of valuable companies might be the unsexy infrastructure providers powering tomorrow's intelligent applications.Subscribe to Complex Systems:SpotifyApple Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Labelbox CEO Manu Sharma joins a16z Infra partner Matt Bornstein to explore the evolution of data labeling and evaluation in AI — from early supervised learning to today’s sophisticated reinforcement learning loops.Manu recounts Labelbox’s origins in computer vision, and then how the shift to foundation models and generative AI changed the game. The value moved from pre-training to post-training and, today, models are trained not just to answer questions, but to assess the quality of their own responses. Labelbox has responded by building a global network of “aligners” — top professionals from fields like  coding, healthcare, and customer service, who label and evaluate data used to fine-tune AI systems.The conversation also touches on Meta’s acquisition of Scale AI, underscoring how critical data and talent have become in the AGI race. Here's a sample of Manu explaining how Labelbox was able to transition from one era of AI to another:It took us some time to really understand like that the world is shifting from building AI models to renting AI intelligence. A vast number of enterprises around the world are no longer building their own models; they're actually renting base intelligence and adding on top of it to make that work for their company. And that was a very big shift. But then the even bigger opportunity was the hyperscalers and the AI labs that are spending billions of dollars of capital developing these models and data sets. We really ought to go and figure out and innovate for them. For us, it was a big shift from the DNA perspective because Labelbox was built with a hardcore software-tools mindset. Our go-to market, engineering, and product and design teams operated like software companies. But I think the hardest part for many of us, at that time, was to just make the decision that we're going just go try it and do it. And nothing is better than that: "Let's just go build an MVP and see what happens."Follow everyone on X:Manu SharmaMatt Bornstein Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of AI + a16z, dbt Labs founder and CEO Tristan Handy sits down with a16z's Jennifer Li and Matt Bornstein to explore the next chapter of data engineering — from the rise (and plateau) of the modern data stack to the growing role of AI in analytics and data engineering. As they sum up the impact of AI on data workflows: The interesting question here is human-in-the-loop versus human-not-in-the-loop. AI isn’t about replacing analysts — it’s about enabling self-service across the company. But without a human to verify the result, that’s a very scary thing.Among other specific topics, they also discuss how automation and tooling like SQL compilers are reshaping how engineers work with data; dbt's new Fusion Engine and what it means for developer workflows; and what to make of the spate of recent data-industry acquisitions and ambitious product launches.Follow everyone on X:Tristan HandyJennifer LiMatt Bornstein Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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 itA deep dive into MCP and the future of AI toolingDracula themeFollow everyone on X:Zeno RochaYoko Li Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.