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."

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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.

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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 web
• The need for full-context security checks
• How to perform sub-second, cost-effective inference
• The wide range of potential actors and actions behind any given visit

As 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.

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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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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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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)

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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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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.

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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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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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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

Learn More:

Introducing Fullstack-Bench

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