In this episode of AI + a16z, Aatish Nayak, head of product at Harvey, sits down with a16z partner Kimberly Tan to share his experience building AI products for enterprises — including the legal profession — and how to address areas like UX, trust, and customer engagement. Importantly, Aatish explains, industries like law don't need AGI or even the latest and greatest models; they need products that augment their existing workflows so they can better serve clients and still make it home for dinner.

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BigLaw Bench

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Aatish Nayak

Kimberly Tan

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In this episode of AI + a16z, a16z partner Alex Immerman sits down with Hebbia founder and CEO George Sivulka to discuss the potential for reasoning models and AI agents to supercharge knowledge-worker productivity — and the global economy along with it. As George explains, his customers are already saving significant time and and effort on important, but monotonous, tasks, and improved models paired with savvy users will continue to reshape how industries including finance, law, and other professional services operate.

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George Sivulka

Alex Immerman

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In this episode of AI + a16z, Fivetran cofounder and CEO George Fraser and a16z partner Guido Appenzeller discuss how LLMs fit into the data management picture within large enterprises. In order to take advantage of a potentially revolutionary technology, organizations don't need to rip out their existing infrastructure, but they do need to rethink their data hygiene so language models can understand it.

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George Fraser

Guido Appenzeller

Derrick Harris

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In this episode of AI + a16z, a16z General Partner Martin Casado and Rasa cofounder and CEO Alan Nichol discuss the past, present, and future of AI agents and chatbots. Alan shares his history working to solve this problem with traditional natural language processing (NLP), expounds on how large language models (LLMs) are helping to dull the many sharp corners of natural-language interactions, and explains how pairing them with inflexible business logic is a great combination.

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Task-Oriented Dialogue with In-Context Learning

GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Application

CALM Summit

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Alan Nichol

Martin Casado

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A 2024 highlight reel, featuring founders sharing their insights, advice, and experiences building AI companies — from foundation-model labs to vertical applications. Topics include:

• Building AI tools for developers
• Getting into AI as a systems expert
• The researcher-to-founder journey
• Founding AI companies in specific industries
• Early lessons from selling AI agents
• And more

Companies include:

• Ambience
• Anyscale
• Black Forest Labs
• CommandZero
• Databricks
• Decagon
• Ideogram
• Inngest
• Replicate
• Socket

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

In this episode of the AI + a16z podcast, Decagon cofounder/CEO Jesse Zhang and a16z partner Kimberly Tan discuss how LLMs are reshaping customer support, the strong market demand for AI agents, and how AI agents give startups a a new pricing model to help disrupt incumbents.

Here's an excerpt of Jesse explaining how conversation-based pricing can win over customers who are used to traditional seat-based pricing:

"Our view on this is that, in the past, software is based per seat because it's roughly scaled based on the number of people that can take advantage of the software.

"With most AI agents, the value . . . doesn't really scale in terms of the number of people that are maintaining it; it's just the amount of work output. . . . The pricing that you want to provide has to be a model where the more work you do, the more that gets paid.  

"So for us, there's two obvious ways to do that: you can pay per conversation, or you can pay per resolution. One fun learning for us has been that most people have opted into the per-conversation model . . .  It just creates a lot more simplicity and predictability.

. . .

"It's a little bit tricky for incumbents if they're trying to launch agents because it just cannibalizes their seat-based model. . . . Incumbents have less risk tolerance, naturally, because they have a ton of customers. And if they're iterating quickly and something doesn't go well, that's a big loss for them. Whereas, younger companies can always iterate a lot faster, and the iteration process just inherently leads to better product. . .  

"We always want to pride ourselves on shipping speed, quality of the product, and just how hardcore our team is in terms of delivering things."

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RIP to RPA: The Rise of Intelligent Automation

Big Ideas in Tech for 2025

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Jesse Zhang

Kimberly Tan

Derrick Harris

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This is a replay of our first episode from April 12, featuring Databricks VP of AI Naveen Rao and a16z partner Matt Bornstein discussing enterprise LLM adoption, hardware platforms, and what it means for AI to be mainstream. If you're unfamiliar with Naveen, he has been in the AI space for more than decade working on everything from custom hardware to LLMs, and has founded two successful startups — Nervana Systems and MosaicML.

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

In this episode of AI + a16z, Replicate cofounder and CEO Ben Firshman, and a16z partner Matt Bornstein, discuss the art of building products and companies that appeal to software developers. Ben was the creator of Docker Compose, and Replicate has a thriving community of developers hosting and fine-tuning their own models to power AI-based applications.

Here's an excerpt of Ben and Matt discussing the difference in the variety of applications built using multimedia models compared with language models:

Matt: "I've noticed there's a lot of really diverse multimedia AI apps out there. Meaning that when you give someone an amazing primitive, like a FLUX API call or a Stable Diffusion API call, and Replicate, there's so many things they can do with it. And we actually see that happening — versus with language, where all LLM apps look kind of the same if you squint a little bit.

"It's like you chat with something — there's obviously code, there's language, there's a few different things — but I've been surprised that even today we don't see as many apps built on language models as we do based on, say, image models."

Ben: "It certainly maps with what we're seeing, as well. I think these language models, beyond just chat apps, are particularly good at turning unstructured information into structured information. Which is actually kind of magical. And computers haven't been very good at that before. That is really a kind of cool use case for it. 

"But with these image models and video models and things like that, people are creating lots of new products that were not possible before — things that were just impossible for computers to do. So yeah, I'm certainly more excited by all the magical things these multimedia models can make."

"But with these image models and video models and things like that, people are creating lots of new products that were just not possible before — things that were just impossible for computers to do. So yeah, I'm certainly more excited by all the magical things these multimedia models can make."

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Matt Bornstein

Derrick Harris

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Replicate's AI model hub

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Longtime machine-learning researcher, and University of Washington Professor Emeritus, Pedro Domingos joins a16z General Partner Martin Casado to discuss the state of artificial intelligence, whether we're really on a path toward AGI, and the value of expressing unpopular opinions.  It's a very insightful discussion as we head into an era of mainstream AI adoption, and ask big questions about how to ramp up progress and diversify research directions.

Here's an excerpt of Pedro sharing his thoughts on the increasing cost of frontier models and whether that's the right direction:

"if you believe the scaling laws hold and the scaling laws will take us to human-level intelligence, then, hey, it's worth a lot of investment. That's one part, but that may be wrong. The other part, however, is that to do that, we need exploding amounts of compute. 

"If if I had to predict what's going to happen, it's that we do not need a trillion dollars to reach AGI at all. So if you spend a trillion dollars reaching AGI, this is a very bad investment."

Learn more:

The Master Algorithm

2040: A Silicon Valley Satire

The Economic Case for Generative AI and Foundation Models

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Pedro Domingos

Martin Casado

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In this episode of AI + a16z, General Partner Anjney Midha shares his perspective on the recent collection of Nobel Prizes awarded to AI researchers in both Physics and Chemistry. He talks through how early work on neural networks in the 1980s spurred continuous advancement in the field — even through the "AI winter" — which resulted in today's extremely useful AI technologies.

Here's a sample of the discussion, in response to a question about whether we will see more high-quality research emerge from sources beyond large universities and commercial labs:

"It can be easy to conclude that the most impactful AI research still requires resources beyond the reach of most individuals or small teams. And that open source contributions, while valuable, are  unlikely to match the breakthroughs from well-funded labs. I've even heard heard some dismissive folks call it cute, and undermine the value of those.

"But on the other hand, I think that you could argue that open source and individual contributions are becoming increasingly more important in AI development. I think that the democratization of AI will lead probably to more diverse and innovative applications. And I think, in particular, the reason we should expect an explosion in home scientists — folks who aren't necessarily affiliated with a top-tier academic, or for that matter,  industry lab — is that as open source models get more and more accessible, the rate limiter really is on the creativity of somebody who's willing to apply the power of that model's computational ability to a novel domain. And there are just a ton of domains and combinatorial intersections of different disciplines.

"Our blind spot for traditional academia [is that] it's not particularly rewarding to veer off the publish-or-perish conference circuit. And if you're at a large industry lab and you're not contributing directly to the next model release, it's not that clear how you get rewarded. And so being an independent actually frees you up from the incentive misstructure, I think, of some of the larger labs. And if you get to leverage the millions of dollars that the Llama team spent on pre-training, applying it to data sets that nobody else has perused before, it results in pretty big breakthroughs."

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They trained artificial neural networks using physics

They cracked the code for proteins’ amazing structures

Notable AI models by year

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Anjney Midha

Derrick Harris

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