AI + a16z

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

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AI Is Upending SaaS Pricing

dharris@a16z.com (Scott Woody, Martin Casado) — Fri, 18 Jul 2025 14:00:00 +0000

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 design

If you’re selling software today, you don’t want to miss this discussion.

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The AI Infrastructure Stack with Jennifer Li

dharris@a16z.com (Jennifer Li, Patrick McKenzie) — Fri, 11 Jul 2025 14:00:00 +0000

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.

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AI's Unsung Hero: Data Labeling and Expert Evals

dharris@a16z.com (Manu Sharma, Matt Bornstein) — Fri, 27 Jun 2025 14:00:00 +0000

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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AI, Data Engineering, and the Modern Data Stack

dharris@a16z.com (Tristan Handy, Matt Bornstein, Jennifer Li) — Fri, 20 Jun 2025 14:00:00 +0000

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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Enabling Agents and Battling Bots on an AI-Centric Web

dharris@a16z.com (Joel de la Garza, David Mytton) — Fri, 13 Jun 2025 14:00:00 +0000

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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Giving New Life to Unstructured Data with LLMs and Agents

dharris@a16z.com (Anant Bhardwaj, Guido Appenzeller) — Fri, 6 Jun 2025 14:00:00 +0000

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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Beyond Leaderboards: LMArena’s Mission to Make AI Reliable

Fri, 30 May 2025 15:46:18 +0000

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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A Deep Dive Into MCP and the Future of AI Tooling

Timestamps

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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Building AI Systems You Can Trust

dharris@a16z.com (Scott Clark, Matt Bornstein) — Fri, 23 May 2025 14:00:00 +0000

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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Who's Coding Now? AI and the Future of Software Development

dharris@a16z.com (Guido Appenzeller, Matt Bornstein, Yoko Li) — Fri, 16 May 2025 14:00:00 +0000

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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MCP Co-Creator on the Next Wave of LLM Innovation

dharris@a16z.com (David Soria Parra, Yoko Li) — Fri, 2 May 2025 14:30:00 +0000

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:

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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Benchmarking AI Agents on Full-Stack Coding

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What Is an AI Agent?

dharris@a16z.com (Guido Appenzeller, Matt Bornstein, Yoko Li) — Mon, 28 Apr 2025 20:00:00 +0000

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:

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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Introducing Fullstack-Bench

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Benchmarking AI Agents on Full-Stack Coding

dharris@a16z.com (Sujay Jayakar, Martin Casado) — Fri, 28 Mar 2025 14:00:00 +0000

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

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What is AX (agent experience) and how to improve it

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Automating Developer Email with MCP and Al Agents

dharris@a16z.com (Yoko Li, Zeno Rocha) — Fri, 21 Mar 2025 14:00:00 +0000

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:

A deep dive into MCP and the future of AI tooling

Dracula theme

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Zeno Rocha

Crossing the uncanny valley of conversational voice

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The Future of Digital Workers

dharris@a16z.com (Prabhav Jain, Joe Schmidt) — Thu, 20 Mar 2025 17:00:00 +0000

In this episode of AI + a16z, a16z Partner Joe Schmidt sits down with 11x CTO Prabhav Jain for an inside look at how AI-powered digital workers are reshaping sales and revenue operations. They discuss the evolution of agentic AI, the trade-offs between orchestration and autonomy, and the technical innovations driving 11x’s products, Alice and Mike.

Prabhav breaks down the challenges of real-time voice AI, the complexities of multimodal agent interactions, and why the future of enterprise AI is about delivering measurable customer outcomes—not just automation. They also dive into the fast-moving landscape of model providers, the impact of open-source AI, and how startups can stay ahead in an environment of constant technological change.

Plus, they explore 11x’s bold decision to re-architect its platform from the ground up, the lessons learned from scaling AI-powered sales automation, and what it takes to build truly effective digital workers.

Key Takeaways:

The difference between true AI agents and complex orchestrations—and why it matters.
How 11x built Alice and Mike to deliver human-like sales performance at scale.
The cutting-edge advancements shaping AI voice assistants and real-time multimodal interactions.
Lessons from rebuilding an AI platform while supporting a fast-growing customer base.
How AI startups can balance rapid iteration with long-term strategic bets.

For anyone interested in AI-powered automation, enterprise sales, or the future of digital work, this episode offers a front-row seat to the latest innovations pushing the boundaries of AI agents.

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11x

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Building the Next Generation of Conversational AI

dharris@a16z.com (Ankit Kumar, Anjney Midha) — Fri, 14 Mar 2025 16:00:00 +0000

In this episode of AI + a16z, Sesame Cofounder and CTO Ankit Kumar joins a16z general partner Anjney Midha for a deep dive into the research and engineering behind their voice technology. They discuss the technical challenges of real-time speech generation, the trade-offs in balancing personality with efficiency, and why the team is open-sourcing key components of their model. Ankit breaks down the complexities of multimodal AI, full-duplex conversation modeling, and the computational optimizations that enable low-latency interactions.

They also explore the evolution of natural language as a user interface and its potential to redefine human-computer interaction.
Plus, we take audience questions on everything from scaling laws in speech synthesis to the role of in-context learning in making AI voices more expressive.

Key Takeaways:
How Sesame AI achieves natural voice interactions through real-time speech generation.

The impact of open-sourcing their speech model and what it means for AI research.
The role of full-duplex modeling in improving AI responsiveness.
How computational efficiency and system latency shape AI conversation quality.
The growing role of natural language as a user interface in AI-driven experiences.

For anyone interested in AI and voice technology, this episode offers an in-depth look at the latest advancements pushing the boundaries of human-computer interaction.

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The Maya + Miles demo

Sesame CSM 1B model

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Introducing AX: Why Agent Experience Matters

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Agent Experience: Building an Open Web for the AI Era

dharris@a16z.com (Matt Biilmann, Martin Casado) — Fri, 7 Mar 2025 15:00:00 +0000

In this episode of AI + a16z, Netlify CEO and Cofounder Matt Biilmann joins a16z General Partner Martin Casado to explore how AI is reshaping web development — not just through faster code generation, but by fundamentally shifting how we think about building for the web. At the center of this shift is Agent Experience (AX), a new paradigm where AI agents aren’t just tools, but active participants in development, shaping both the creative process and the underlying infrastructure.

Matt shares how Netlify is evolving to meet this future, why the next 100 million web developers will collaborate with AI, and what’s at stake if the web doesn’t adapt — will we see a thriving, open, AI-powered internet, or a future dominated by walled gardens?

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What Are the Security Risks of Deploying DeepSeek-R1?

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What DeepSeek Means for Cybersecurity

dharris@a16z.com (Joel de la Garza, Dylan Ayrey, Ian Webster, Brian Long) — Fri, 28 Feb 2025 15:00:00 +0000

In this episode of AI + a16z, a trio of security experts join a16z partner Joel de la Garza to discuss the security implications of the DeepSeek reasoning model that made waves recently. It's three separate discussions, focusing on different aspects of DeepSeek and the fast-moving world of generative AI.

The first segment, with Ian Webster of Promptfoo, focuses on vulnerabilities within DeepSeek itself, and how users can protect themselves against backdoors, jailbreaks, and censorship.

The second segment, with Dylan Ayrey of Truffle Security, focuses on the advent of AI-generated code and how developers and security teams can ensure it's safe. As Dylan explains, many problem lie in how the underlying models were trained and how their security alignment was carried out.

The final segment features Brian Long of Adaptive, who highlights a growing list of risk vectors for deepfakes and other threats that generative AI can exacerbate. In his view, it's up to individuals and organizations to keep sharp about what's possible — while the the arms race between hackers and white-hat AI agents kicks into gear.

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Research finds 12,000 ‘Live’ API Keys and Passwords in DeepSeek's Training Data

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Agents, Lawyers, and LLMs

dharris@a16z.com (Aatish Nayak, Kimberly Tan) — Fri, 21 Feb 2025 15:00:00 +0000

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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Reasoning Models Are Remaking Professional Services

dharris@a16z.com (George Sivulka, Alex Immerman) — Fri, 14 Feb 2025 18:38:36 +0000

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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Alex Immerman

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Data Management for Enterprise LLMs

dharris@a16z.com (George Fraser, Guido Appenzeller, Derrick Harris) — Fri, 7 Feb 2025 15:00:00 +0000

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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Task-Oriented Dialogue with In-Context Learning

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From NLP to LLMs: The Quest for a Reliable Chatbot

dharris@a16z.com (Martin Casado, Alan Nichol) — Fri, 10 Jan 2025 17:30:00 +0000

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:

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

CALM Summit

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RIP to RPA: The Rise of Intelligent Automation

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Best of the Year: Building AI Companies

Fri, 27 Dec 2024 17:00:00 +0000

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

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Can AI Agents Finally Fix Customer Support?

dharris@a16z.com (Jesse Zhang, Kimberly Tan, Derrick Harris) — Wed, 18 Dec 2024 17:00:00 +0000

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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REPLAY: Scoping the Enterprise LLM Market

dharris@a16z.com (Naveen Rao, Matt Bornstein, Derrick Harris) — Sat, 30 Nov 2024 15:00:46 +0000

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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Building Developers Tools, From Docker to Diffusion Models

dharris@a16z.com (Ben Firshman, Matt Bornstein, Derrick Harris) — Fri, 15 Nov 2024 15:00:00 +0000

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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The Best Way to Achieve AGI Is to Invent It

dharris@a16z.com (Martin Casado, Pedro Domingos) — Mon, 4 Nov 2024 15:30:00 +0000

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."

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Neural Nets and Nobel Prizes: AI's 40-Year Journey from the Lab to Ubiquity

dharris@a16z.com (Anjney Midha, Derrick Harris) — Fri, 25 Oct 2024 18:30:00 +0000

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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How GPU Access Helps AI Startups Be Agile

dharris@a16z.com (Anjney Midha, Derrick Harris) — Wed, 23 Oct 2024 14:00:00 +0000

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."

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Chasing Silicon: The Race for GPUs

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DisTrO and the Quest for Community-Trained AI Models

dharris@a16z.com (Anjney Midha, Jeffrey Quesnelle, Bowen Peng) — Fri, 27 Sep 2024 18:39:16 +0000

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?"

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Balancing AI Expertise and Industry Acumen in Vertical Applications

dharris@a16z.com (Nikhil Buduma, Derrick Harris) — Fri, 13 Sep 2024 16:15:00 +0000

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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AI, SQL, and the End of Big Data

dharris@a16z.com (Jennifer Li, Jordan Tigani, Derrick Harris) — Fri, 30 Aug 2024 14:00:00 +0000

In this episode of AI + a16z, a16z General Partner Jennifer Li joins MotherDuck Cofounder and CEO Jordan Tigani to discuss DuckDB's spiking popularity as the era of big data wanes, as well as the applicability of SQL-based systems for AI workloads and the prospect of text-to-SQL for analyzing data.

Here's an excerpt of Jordan discussing an early win when it comes to applying generative AI to data analysis:

"Everybody forgets syntax for various SQL calls. And it's just like in coding. So there's some people that memorize . . . all of the code base, and so they don't need auto-complete. They don't need any copilot. . . . They don't need an ID; they can just type in Notepad. But for the rest of us, I think these tools are super useful. And I think we have seen that these tools have already changed how people are interacting with their data, how they're writing their SQL queries.

"One of the things that we've done . . . is we focused on improving the experience of writing queries. Something we found is actually really useful is when somebody runs a query and there's an error, we basically feed the line of the error into GPT 4 and ask it to fix it. And it turns out to be really good.

". . . It's a great way of letting you stay in the flow of writing your queries and having true interactivity."

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The Researcher to Founder Journey, and the Power of Open Models

dharris@a16z.com (Anjney Midha, Robin Rombach, Andreas Blattmann, Patrick Esser) — Fri, 16 Aug 2024 23:51:40 +0000

In this episode of the AI + a16z podcast, Black Forest Labs founders Robin Rombach, Andreas Blattmann, and Patrick Esser sit down with a16z general partner Anjney Midha to discuss their journey from PhD researchers to Stability AI, and now to launching their own company building state-of-the-art image and video models. They also delve into the topic of openness in AI, explaining the benefits of releasing open models and sharing research findings with the field.

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Why Computer Science Subsumed Biotech

dharris@a16z.com (Vijay Pande, Derrick Harris) — Fri, 9 Aug 2024 14:00:00 +0000

In this episode, a16z General Partner Vijay Pande walks us through the past two decades of applying software engineering to the life sciences — from the Folding@Home project that he launched, through AlphaFold and more. He also discusses the major opportunities for AI to transform medicine and health care, as well as some pitfalls that founders in that space need to watch out for.

Here's an excerpt of Vijay discussing how AlphaFold and other projects revolutionized biology research not just because of their algorithms, but because of how they introduced software engineering into the field:

"I think the key thing about AlphaFold that really got people excited was not just the AI part, because people have been using machine learning. And so that part was there. I think it was how fast, at least to me, an engineering approach could make a big jump in this field. Because this was a field largely addressed by academics, and academics would have a lab of maybe 20 [or] 30 people — some of the bigger ones, maybe slightly bigger. And of that, these are graduate students working on their PhDs. It's very different than having a team of professional programmers and engineers going after the problem.

"And so that jump in team ability, plus the technology, I think was very critical for the jump in results. And also, finally, I think having a company like Google say, 'You know, this is a problem we're excited about and we're interested in,' and that AI and biology is something that is an area of great interest to them . . . was a huge flag to plant."

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Democratizing Generative AI Red Teams

dharris@a16z.com (Ian Webster, Anjney Midha) — Fri, 2 Aug 2024 14:00:00 +0000

In this episode of the AI + a16z podcast, a16z General Partner Anjney Midha speaks with PromptFoo founder and CEO Ian Webster about the importance of red-teaming for AI safety and security, and how bringing those capabilities to more organizations will lead to safer, more predictable generative AI applications. They also delve into lessons they learned about this during their time together as early large language model adopters at Discord, and why attempts to regulate AI should focus on applications and use cases rather than models themselves.

Here's an excerpt of Ian laying out his take on AI governance:

"The reason why I think that the future of AI safety is open source is that I think there's been a lot of high-level discussion about what AI safety is, and some of the existential threats, and all of these scenarios. But what I'm really hoping to do is focus the conversation on the here and now. Like, what are the harms and the safety and security issues that we see in the wild right now with AI? And the reality is that there's a very large set of practical security considerations that we should be thinking about.

"And the reason why I think that open source is really important here is because you have the large AI labs, which have the resources to employ specialized red teams and start to find these problems, but there are only, let's say, five big AI labs that are doing this. And the rest of us are left in the dark. So I think that it's not acceptable to just have safety in the domain of the foundation model labs, because I don't think that's an effective way to solve the real problems that we see today.

"So my stance here is that we really need open source solutions that are available to all developers and all companies and enterprises to identify and eliminate a lot of these real safety issues."

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Security Founders Talk Shop About Generative AI

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Augmenting Incident Response with LLMs

dharris@a16z.com (Dean De Beer, Derrick Harris, Joel de la Garza) — Fri, 26 Jul 2024 14:00:00 +0000

In this episode of the AI + a16z podcast, Command Zero cofounder and CTO Dean de Beer joins a16z's Joel de la Garza and Derrick Harris to discuss the benefits of training large language models on security data, as well as the myriad factors product teams need to consider when building on LLMs.

Here's an excerpt of Dean discussing the challenges and concerns around scaling up LLMs:

"Scaling out infrastructure has a lot of limitations: the APIs you're using, tokens, inbound and outbound, the cost associated with that — the nuances of the models, if you will. And not all models are created equal, and they oftentimes are very good for specific use cases and they might not be appropriate for your use case, which is why we tend to use a lot of different models for our use cases . . .

"So your use cases will heavily determine the models that you're going to use. Very quickly, you'll find that you'll be spending more time on the adjacent technologies or infrastructure. So, memory management for models. How do you go beyond the context window for a model? How do you maintain the context of the data, when given back to the model? How do you do entity extraction so that the model understands that there are certain entities that it needs to prioritize when looking at new data? How do you leverage semantic search as something to augment the capabilities of the model and the data that you're ingesting?

"That's where we have found that we spend a lot more of our time today than on the models themselves. We have found a good combination of models that run our use cases; we augment them with those adjacent technologies."

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Scaling AI for the Coming Data Deluge

dharris@a16z.com (Robert Nishihara, Derrick Harris, Jennifer Li) — Fri, 19 Jul 2024 14:00:00 +0000

In this episode of the AI + a16z podcast, Anyscale cofounder and CEO Robert Nishihara joins a16z's Jennifer Li and Derrick Harris to discuss the challenges of training and running AI models at scale; how a focus on video models — and the huge amount of data involved — will change generative AI models and infrastructure; and the unique experience of launching a company out of the UC-Berkeley Sky Computing Lab (the successor to RISElab and AMPLab).

Here's a sample of the discussion, where Robert explains how generative AI has turbocharged the appetite for AI capabilities within enterprise customers:

"Two years ago, we would talk to companies, prospective customers, and AI just wasn't a priority. It certainly wasn't a company-level priority in the way that it is today. And generative AI is the reason a lot of companies now reach out to us . . . because they know that succeeding with AI is essential for their businesses, it's essential for their competitive advantage.

"And time to market matters for them. They don't want to spend a year hiring an AI infrastructure team, building up a 20-person team to build all of the internal infrastructure, just to be able to start to use generative AI. That's something they want to do today."

At another point in the discussion, he notes on this same topic:

"One dimension where we try to go really deep is on the developer experience and just enabling developers to be more productive. This is a complaint we hear all the time with machine learning teams or infrastructure teams: They'll say that they hired all these machine learning people, but then the machine learning people are spending all of their time managing clusters or working on the infrastructure. Or they'll say that it takes 6 weeks or 12 weeks to get a model to transition from development to production . . . Or moving from a laptop to the cloud, and to go from single machine to scaling — these are expensive handoffs often involve rewriting a bunch of code."

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ARCHIVE: The Dream of AI Is Alive in AlphaGo

dharris@a16z.com (Sonal Chokshi, Frank Chen, Steven Sinofsky) — Fri, 5 Jul 2024 14:00:00 +0000

In this archive episode from 2015, a16z's Sonal Chokshi, Frank Chen, and Steven Sinofsky discuss DeepMind's breakthrough AlphaGo system, which mastered the ancient Chinese game Go and introduced the public to reinforcement learning.

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Beyond Language: Inside a Hundred-Trillion-Token Video Model

dharris@a16z.com (Jiaming Song, Anjney Midha) — Wed, 3 Jul 2024 23:06:22 +0000

In this episode of the AI + a16z podcast, Luma Chief Scientist Jiaming Song joins a16z General Partner Anjney MIdha to discuss Jiaming's esteemed career in video models, culminating thus far in Luma's recently released Dream Machine 3D model that shows abilities to reason about the world across a variety of aspects. Jiaming covers the history of image and video models, shares his vision for the future of multimodal models, and explains why he thinks Dream Machine demonstrates its emergent reasoning capabilities. In short: Because it was trained on a volume of high-quality video data that, if measured in relation to language data, would amount to hundreds of trillions of tokens.

Here's a sample of the discussion, where Jiaming explains the "bitter lesson" as applied to training generative models, and in the process sums up a big component of why Dream Machine can do what it does by using context-rich video data:

"For a lot of the problems related to artificial intelligence, it is often more productive in the long run to use methods that are simpler but use more compute, [rather] than trying to develop priors, and then trying to leverage the priors so that you can use less compute.

"Cases in this question first happened in language, where people were initially working on language understanding, trying to use grammar or semantic parsing, these kinds of techniques. But eventually these tasks began to be replaced by large language models. And a similar case is happening in the vision domain, as well . . . and now people have been using deep learning features for almost all the tasks. This is a clear demonstration of how using more compute and having less priors is good.

"But how does it work with language? Language by itself is also a human construct. Of course, it is a very good and highly compressed kind of knowledge, but it's definitely a lot less data than what humans take in day to day from the real world . . .

"[And] it is a vastly smaller data set size than visual signals. And we are already almost exhausting the . . . high-quality language sources that we have in the world. The speed at which humans can produce language is definitely not enough to keep up with the demands of the scaling laws. So even if we have a world where we can scale up the compute infrastructure for that, we don't really have the infrastructure to scale up the data efforts . . .

"Even though people would argue that the emergence of large language models is already evidence of the scaling law . . . against the rule-based methods in language understanding, we are arguing that language by itself is also a prior in the face of more of the richer data signal that is happening in the physical world."

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Developer Tool UX in the Age of Generative AI

dharris@a16z.com (Alasdair Monk, Yoko Li, Derrick Harris) — Fri, 21 Jun 2024 14:00:00 +0000

In this episode, design engineer Alasdair Monk joins a16z's Yoko Li and Derrick Harris to discuss how generative AI is changing how developers — and the those building for developers — interact with the tools of their trade. Alasdair’s journey includes stints at dev-centric companies such as Heroku/Salesforce, and he's presently designing the user experience for Poolside, an AI programming startup.

Here's a sample of Alasdair discussing the future of the prompt bar in generative coding tools:

"When interacting with machine learning models, we've almost thrown away 30 years of human-computer interaction knowledge and kind of reverted to using a terminal circa 1980 to interact with computer, or the prompt bar. This very plain-text way to interact with AI is really interesting.

"I think it's very different when you can't predict what a user interface is going to look like. What an LLM can spit out is basically unpredictable or non-deterministic, and so how do you design for that or how do you design around the guardrails for that are the really interesting things that I think everyone who works in the industry right now is trying to figure out. And I think it's pretty clear to a lot of people that sometimes you want to chat to the computer as if it's like the rubber duck.

"I think a lot of where AI is going to really help us, particularly with engineering, is going to be in the interactions that aren't that at all, and will actually probably look much more like interacting with traditional software today, where I interact with it via windows and buttons and all sorts of GUI elements."

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Building Production Workflows for AI Applications

dharris@a16z.com (Tony Holdstock-Brown, Yoko Li, Derrick Harris) — Fri, 14 Jun 2024 19:25:45 +0000

In this episode, Inngest cofounder and CEO Tony Holdstock-Brown joins a16z partner Yoko Li, as well as Derrick Harris, to discuss the reality and complexity of running AI agents and other multistep AI workflows in production. Tony also why developer tools for generative AI — and their founders — might look very similar to previous generations of these products, and where there are opportunities for improvement.

Here's a sample of the discussion, where Tony shares some advice for engineers looking to build for AI:

"We almost have two parallel tracks right now as, as engineers. We've got the CPU track in which we're all like, 'Oh yeah, CPU-bound, big O notation. What are we doing on the application-level side?' And then we've got the GPU side, in which people are doing like crazy things in order to make numbers faster, in order to make differentiation better and smoother, in order to do gradient descent in a nicer and more powerful way. The two disciplines right now are working together, but are also very, very, very different from an engineering point of view.

"This is one interesting part to think about for like new engineers, people that are just thinking about what to do if they want to go into the engineering field overall. Do you want to be on the side using AI, in which you take all of these models, do all of this stuff, build the application-level stuff, and chain things together to build products? Or do you want to be on the math side of things, in which you do really low-level things in order to make compilers work better, so that your AI things can run faster and more efficiently? Both are engineering, just completely different applications of it."

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Denoising Diffusion Probabilistic Models

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The Future of Image Models Is Multimodal

dharris@a16z.com (Jennifer Li, Mohammad Norouzi, Derrick Harris) — Fri, 7 Jun 2024 16:00:00 +0000

In this episode, Ideogram CEO Mohammad Norouzi joins a16z General Partner Jennifer Li, as well as Derrick Harris, to share his story of growing up in Iran, helping build influential text-to-image models at Google, and ultimately cofounding and running Ideogram. He also breaks down the differences between transformer models and diffusion models, as well as the transition from researcher to startup CEO.

Here's an excerpt where Mohammad discusses the reaction to the original transformer architecture paper, "Attention Is All You Need," within Google's AI team:

"I think [lead author Asish Vaswani] knew right after the paper was submitted that this is a very important piece of the technology. And he was telling me in the hallway how it works and how much improvement it gives to translation. Translation was a testbed for the transformer paper at the time, and it helped in two ways. One is the speed of training and the other is the quality of translation.

"To be fair, I don't think anybody had a very crystal clear idea of how big this would become. And I guess the interesting thing is, now, it's the founding architecture for computer vision, too, not only for language. And then we also went far beyond language translation as a task, and we are talking about general-purpose assistants and the idea of building general-purpose intelligent machines. And it's really humbling to see how big of a role the transformer is playing into this."

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ARCHIVE: Open Models (with Arthur Mensch) and Video Models (with Stefano Ermon)

dharris@a16z.com (Anjney Midha, Arthur Mensch, Stefano Ermon) — Fri, 24 May 2024 14:00:00 +0000

For this holiday weekend (in the United States) episode, we've stitched together two archived episodes from the a16z Podcast, both featuring General Partner Anjney Midha. In the first half, from December, he speaks with Mistral cofounder and CEO Arthur Mensch about the importance of open foundation models, as well as Mistral's approach to building them. In the second half (at 34:40), from February, he speaks with Stanford's Stefano Ermon about the state of the art in video models, including how OpenAI's Sora might work under the hood.

Here's a sample of what Arthur had to say about the debate over how to regulate AI models:

"I think the battle is for the neutrality of the technology. Like a technology, by a sense, is something neutral. You can use it for bad purposes. You can use it for good purposes. If you look at what an LLM does, it's not really different from a programming language. . . .

"So we should regulate the function, the mathematics behind it. But, really, you never use a large language model itself. You always use it in an application, in a way, with a user interface. And so, that's the one thing you want to regulate. And what it means is that companies like us, like foundational model companies, will obviously make the model as controllable as possible so that the applications on top of it can be compliant, can be safe. We'll also build the tools that allow you to measure the compliance and the safety of the application, because that's super useful for the application makers. It's actually needed.

"But there's no point in regulating something that is neutral in itself, that is just a mathematical tool. I think that's the one thing that we've been hammering a lot, which is good, but there's still a lot of effort in making this strong distinction, which is super important to understand what's going on."

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Stefano Ermon

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Open Models and Maturation: Assessing the Generative AI Market

dharris@a16z.com (Guido Appenzeller, Matt Bornstein, Derrick Harris) — Fri, 17 May 2024 14:00:00 +0000

a16z partners Guido Appenzeller and Matt Bornstein join Derrick Harris to discuss the state of the generative AI market, about 18 months after it really kicked into high gear with the release of ChatGPT — everything from the emergence of powerful open source LLMs to the excitement around AI-generated music.

If there's one major lesson to learn, it's that although we've made some very impressive technological strides and companies are generating meaningful revenue, this is still a a very fluid space. As Matt puts it during the discussion:

"For nearly all AI applications and most model providers, growth is kind of a sawtooth pattern, meaning when there's a big new amazing thing announced, you see very fast growth. And when it's been a while since the last release, growth kind of can flatten off. And you can imagine retention can be all over the place, too . . .

"I think every time we're in a flat period, people start to think, 'Oh, it's mature now, the, the gold rush is over. What happens next?' But then a new spike almost always comes, or at least has over the last 18 months or so. So a lot of this depends on your time horizon, and I think we're still in this period of, like, if you think growth has slowed, wait a month and see it change."

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Security Founders Talk Shop About Generative AI

dharris@a16z.com (Dean De Beer, Kevin Tian, Travis McPeak, Derrick Harris) — Wed, 15 May 2024 14:00:00 +0000

In this bonus episode, recorded live at our San Francisco office, security-startup founders Dean De Beer (Command Zero), Kevin Tian (Doppel), and Travis McPeak (Resourcely) share their thoughts on generative AI, as well as their experiences building with LLMs and dealing with LLM-based threats.

Here's a sample of what Dean had to say about the myriad considerations when choosing, and operating, a large language model:

"The more advanced your use case is, the more requirements you have, the more data you attach to it, the more complex your prompts — ll this is going to change your inference time.

"I liken this to perceived waiting time for an elevator. There's data scientists at places like Otis that actually work on that problem. You know, no one wants to wait 45 seconds for an elevator, but taking the stairs will take them half an hour if they're going to the top floor of . . . something. Same thing here: If I can generate an outcome in 90 seconds, it's still too long from the user's perspective, even if them building out and figuring out the data and building that report [would have] took them four hours . . . two days."

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How to Think About Foundation Models for Cybersecurity

dharris@a16z.com (Zane Lackey, Joel de la Garza, Derrick Harris) — Fri, 10 May 2024 14:00:00 +0000

In this episode of the AI + a16z podcast, a16z General Partner Zane Lackey and a16z Partner Joel de la Garza sit down with Derrick Harris to discuss how generative AI — LLMs, in particular — and foundation models could effect profound change in cybersecurity. After years of AI-washing by security vendors, they explain why the hype is legitimate this time as AI provides a real opportunity to help security teams cut through the noise and automate away the types of drudgery that lead to mistakes.

"Often when you're running a security team, you're not only drowning in noise, but you're drowning in just the volume of things going on," Zane explains. "And so I think a lot of security teams are excited about, 'Can we utilize AI and LLMs to really take at least some of that off of our plate?'

"I think it's still very much an open question of how far they go in helping us, but even taking some meaningful percentage off of our plate in terms of overall work is going to really help security teams overall."

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Retrieval Augmented Generation (RAG)

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Securing the Software Supply Chain with LLMs

dharris@a16z.com (Feross Aboukhadijeh, Joel de la Garza, Derrick Harris) — Fri, 3 May 2024 14:00:00 +0000

Socket Founder and CEO Feross Aboukhadijeh joins a16z's Joel de la Garza and Derrick Harris to discuss the open-source software supply chain. Feross and Joel share their thoughts and insights on topics ranging from the recent XZutils attack to how large language models can help overcome understaffed security teams and overwhelmed developers.

Despite some increasingly sophisticated attacks making headlines and compromising countless systems, they're optimistic that LLMs, in particular, could be a turning point for security blue teams. As Feross sums up one possibility:

"The way we think about gen AI on the defensive side is that it's not as good as a human looking at the code, but it's something. . . . Our challenge is that we want to scan all the open source code that exists out there. That is not something you can pay humans to do. That is not scalable at all. But, with the right techniques, with the right pre-filtering stages, you can actually put a lot of that stuff through LLMs and out the other side will pop a list of of risky packages.

"And then that's a much smaller number that you can have humans take a look at. And so we're using it as a tool . . . to find the needle in the haystack, what is worth looking at. It's not perfect, but it can help cut down on the noise and it can even make this problem tractable, which previously wasn't even tractable."

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ARCHIVE: GPT-3 Hype

dharris@a16z.com (Frank Chen, Sonal Chokshi, Derrick Harris) — Wed, 1 May 2024 14:00:00 +0000

In this episode, though, we’re traveling back in time to distant — in AI years, at least — past of 2020. Because amid all the news over the past 18 or so months, it’s easy to forget that generative AI — and LLMs, in particular — have been around for a while. OpenAI released its GPT-2 paper in late 2018, which excited the AI research community, and in 2020 made GPT-3 (as well as other capabilities) publicly available for the first time via its API. This episode dates back to that point in time (it was published in July 2020), when GPT-3 piqued the interest of the broader developer community and people really started testing what was possible.

And although it doesn’t predict the precambrian explosion of multimodal models, regulatory and copyright debate, and entrepreneurial activity that would hit a couple of years later — and who could have? — it does set the table for some of the bigger — and still unanswered — questions about what tools like LLMs actually mean from a business perspective. And, perhaps more importantly, what they ultimately mean for how we define intelligence.

So set your wayback machine to the seemingly long ago summer of 2020 and enjoy a16z’s Sonal Chokshi and Frank Chen discussing the advent of commercially available LLMs.

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Vector Databases and the Power of RAG

dharris@a16z.com (Edo Liberty, Satish Talluri, Derrick Harris) — Fri, 26 Apr 2024 14:00:00 +0000

Pinecone Founder and CEO Edo Liberty joins a16z's Satish Talluri and Derrick Harris to discuss the promises, challenges, and opportunities for vector databases and retrieval augmented generation (RAG). He also shares insights and highlights from a decades-long career in machine learning, which includes stints running research teams at both Yahoo and Amazon Web Services.

Because he's been at this a long time, and despite its utility, Edo understands that RAG — like most of today's popular AI concepts — is still very much a progress:

"I think RAG today is where transformers were in 2017. It's clunky and weird and hard to get right. And it has a lot of sharp edges, but it already does something amazing. Sometimes, most of the time, the very early adopters and the very advanced users are already picking it up and running with it and lovingly deal with all the sharp edges ...

"Making progress on RAG, making progress on information retrieval, and making progress on making AI more knowledgeable and less hallucinatory and more dependable, is a complete greenfield today. There's an infinite amount of innovation that will have to go into it."

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