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.

Learn more:

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."

Learn more:

RIP to RPA: The Rise of Intelligent Automation

Big Ideas in Tech for 2025

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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.

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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

Learn more:

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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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."

Learn more:

They trained artificial neural networks using physics

They cracked the code for proteins’ amazing structures

Notable AI models by year

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Derrick Harris

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In this episode of AI + a16z, General Partner Anjney Midha explains the forces that lead to GPU shortages and price spikes, and how the firm mitigates these concerns for portfolio companies by supplying them with the GPUs they need through a program called Oxygen. The TL;DR version of the problem is that competition for GPU access favors large incumbents who can afford to outbid startups and commit to long contracts; when startups do buy or rent in bulk, they can be stuck with lots of GPUs and — absent training runs or ample customer demand for inference workloads — nothing to do with them. 

Here is an excerpt of Anjney explaining how training versus inference workloads affect what level of resources a company needs at any given time:

"It comes down to whether the customer that's using them . . .  has a use that can really optimize the efficiency of those chips. As an example, if you happen to be an image model company or a video model company and you put a long-term contract on H100s this year, and you trained and put out a really good model and a product that a lot of people want to use, even though you're not training on the best and latest cluster next year, that's OK. Because you can essentially swap out your training workloads for your inference workloads on those H100s.

"The H100s are actually incredibly powerful chips that you can run really good inference workloads on. So as long as you have customers who want to run inference of your model on your infrastructure, then you can just redirect that capacity to them and then buy new [Nvidia] Blackwells for your training runs.

"Who it becomes really tricky for is people who bought a bunch, don't have demand from their customers for inference, and therefore are stuck doing training runs on that last-generation hardware. That's a tough place to be."

Learn more:

Navigating the High Cost of GPU Compute

Chasing Silicon: The Race for GPUs

Remaking the UI for AI

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Anjney Midha

Derrick Harris

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In this episode of AI + a16z, Bowen Peng and Jeffrey Quesnelle of Nous Research join a16z General Partner Anjney Midha to discuss their mission to keep open source AI research alive and activate the community of independent builders. The focus is on a recent project called DisTrO, which demonstrates it's possible to train AI models across the public internet much faster than previously thought possible. However, Nous is behind a number of other successful open source AI projects, including the popular Hermes family of "neutral" and guardrail-free language models.

Here's an excerpt of Jeffrey explaining how DisTrO was inspired by the possibility that major open source AI providers could turn their efforts back inward:

"What if we don't get Llama 4? That's like an actual existential threat because the closed providers will continue to get better and we would be dead in the water, in a sense. 

"So we asked, 'Is there any real reason we can't make Llama 4 ourselves?' And there is a real reason, which is that we don't have 20,000 H100s. . . . God willing and the creek don't rise, maybe we will one day, but we don't have that right now. 

"So we said, 'But what do we have?' We have a giant activated community who's passionate about wanting to do this and would be willing to contribute their GPUs, their power, to it, if only they could . . . but we don't have the ability to activate that willingness into actual action. . . . The only way people are connected is over the internet, and so anything that isn't sharing over the internet is not gonna work. 

"And so that was the initial premise: What if we don't get Llama 4? And then, what do we have that we could use to create Llama 4? And,  if we can't, what are the technical problems that, if only we slayed that one technical problem, the dam of our community can now flow and actually solve the problem?"

Learn more:

DisTrO paper

Nous Research

Nous Research GitHub

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Jeffrey Quesnelle

Anjney Midha

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In this episode of AI + a16z, Ambience cofounder and chief scientist Nikhil Buduma joins Derrick Harris to discuss the nuances of using AI models to build vertical applications (including in his space, health care), and why industry acumen is at least as important as technical expertise. Nikhil also shares his experience of having a first-row seat to key advances in AI — including the transformer architecture — which not only allowed his company to be an early adopter, but also gave him insight into the types of problems that AI could solve in the future.

Here's an excerpt of Nikhil explaining the importance of understanding your buyer:

"If you believe that the most valuable companies are going to fall out of some level of vertical integration between the app layer and the model layer, [that] this next generation of incredibly valuable companies is going to be built by founders who've spent years just obsessively becoming experts in an industry, I would recommend that someone actually know how to map out the most valuable use cases and have a clear story for how those use cases have synergistic, compounding value when you solve those problems increasingly in concert together. 

"I think the founding team is going to have to have the right ML chops to actually build out the right live learning loops, build out the ML ops loops to measure and to close the gap on model quality for those use cases. [But] the model is actually just one part of solving the problem. 

"You actually need to be thoughtful about the product, the design, the delivery competencies to make sure that what you build is integrated with the right sources of the enterprise data that fits into the right workflows in the right way. And you're going to have to invest heavily in the change management to make sure that customers realize the full value of what they're buying from you. That's all actually way more important than people realize."

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Fundamentals of Deep Learning

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Nikhil Buduma

Derrick Harris

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