OpenAI’s Codex has already shipped hundreds of thousands of pull requests in its first month. But what is it really, and how will coding agents change the future of software?

In this episode, General Partner Anjney Midha goes behind the scenes with one of Codex’s product leads- Alexander Embiricos - to unpack its origin story, why its PR success rate is so high, the safety challenges of autonomous agents, and what this all means for developers, students, and the future of coding.

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

Models, Modalities, and Memes: Creating Compelling AI Characters
In this episode of AI + a16z, Hedra founder and CEO Michael Lingelbach joins a16z partners Justine Moore and Matt Bornstein to talk about building AI-native video — and why the next wave of generative content is all about characters, not just clips.
They discuss how Hedra’s expressive, full-body, dialogue-centric video models are powering everything from viral meme content to enterprise training tools. Michael explains why “character” is the core design primitive in Hedra’s architecture, how consumers are leading the charge in discovering new use cases, and what it takes to productionize those behaviors for real-world applications.
Along the way, they explore what makes multi-modal generation uniquely hard, the role of user control in shaping believable AI performances, and why being a founder sometimes means responding to thousands of support emails — at 6 a.m.

Key takeaways:

• How Hedra’s real-time video model blends audio, image, and character control
• Why generative content is shifting from static avatars to programmable personas
• The surprising crossover between consumer creativity and enterprise adoption
• Where existing LLMs fall short in generating emotionally authentic characters
• What vibe coding, hands-on design, and founder obsession look like in practice

For anyone curious about building AI characters, scaling creative workflows, or the future of human-computer interaction — this one’s not to be missed.

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

If you've been experimenting with image, video, and audio models, the chances are you've been both blown away by how good they're becoming, and also a little perturbed by how long they can take to generate. If you've been using a platform like Fal, however, your experience on the latter point might be more positive.

In this episode, Fal cofounder and CEO Burkay Gur and head of engineering Batuhan Taskaya join a16z general partner Jennifer Li to discuss how they built an inference platform — or, as they call it, a generative media cloud — that's optimized for speed, performance, and user experience. These are core features for a great product, yes, and also ones borne of necessity as the early team obsessively engineered around its meager GPU capacity at the height of the AI infrastructure crunch.

But this is more than a story about infrastructure. As you'll hear, they also delve into sales and hiring strategy; the team's overall excitement over these emerging modalities; and the trends they're seeing as competition in the world of video models, especially, heats up. 

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In this episode, a16z partner Joel de la Garza sits down with Socket founder and CEO Feross Aboukhadijeh to dive into the intersection of vibe coding and security. As one of the earliest security founders to fully embrace LLMs, Feross shares firsthand insights into how these technologies are transforming software engineering workflows and productivity — and where there are sharp edges that practitioners need to avoid.

The TL;DR: Treat AI-assisted programming the same way you'd treat other programming, by vetting packages, reviewing code, and generally make sure you're not sacrificing security for speed. As he explained, LLMs can make developers more productive and even make their software more secure, but only if developers do their part by maintaining a safe supply chain.

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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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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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Labelbox CEO Manu Sharma joins a16z Infra partner Matt Bornstein to explore the evolution of data labeling and evaluation in AI — from early supervised learning to today’s sophisticated reinforcement learning loops.

Manu recounts Labelbox’s origins in computer vision, and then how the shift to foundation models and generative AI changed the game. The value moved from pre-training to post-training and, today, models are trained not just to answer questions, but to assess the quality of their own responses. Labelbox has responded by building a global network of “aligners” — top professionals from fields like  coding, healthcare, and customer service, who label and evaluate data used to fine-tune AI systems.

The conversation also touches on Meta’s acquisition of Scale AI, underscoring how critical data and talent have become in the AGI race. 

Here's a sample of Manu explaining how Labelbox was able to transition from one era of AI to another:

It took us some time to really understand like that the world is shifting from building AI models to renting AI intelligence. A vast number of enterprises around the world are no longer building their own models; they're actually renting base intelligence and adding on top of it to make that work for their company. And that was a very big shift. 

But then the even bigger opportunity was the hyperscalers and the AI labs that are spending billions of dollars of capital developing these models and data sets. We really ought to go and figure out and innovate for them. For us, it was a big shift from the DNA perspective because Labelbox was built with a hardcore software-tools mindset. Our go-to market, engineering, and product and design teams operated like software companies. 

But I think the hardest part for many of us, at that time, was to just make the decision that we're going just go try it and do it. And nothing is better than that: "Let's just go build an MVP and see what happens."

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In this episode of AI + a16z, dbt Labs founder and CEO Tristan Handy sits down with a16z's Jennifer Li and Matt Bornstein to explore the next chapter of data engineering — from the rise (and plateau) of the modern data stack to the growing role of AI in analytics and data engineering. As they sum up the impact of AI on data workflows: The interesting question here is human-in-the-loop versus human-not-in-the-loop. AI isn’t about replacing analysts — it’s about enabling self-service across the company. But without a human to verify the result, that’s a very scary thing.

Among other specific topics, they also discuss how automation and tooling like SQL compilers are reshaping how engineers work with data; dbt's new Fusion Engine and what it means for developer workflows; and what to make of the spate of recent data-industry acquisitions and ambitious product launches.

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Arcjet CEO David Mytton sits down with a16z partner Joel de la Garza to discuss the increasing complexity of managing who can access websites, and other web apps, and what they can do there. A primary challenge is determining whether automated traffic is coming from bad actors and troublesome bots, or perhaps AI agents trying to buy a product on behalf of a real customer.Joel and David dive into the challenge of analyzing every request without adding latency, and how faster inference at the edge opens up new possibilities for fraud prevention, content filtering, and even ad tech.Topics include:

• Why traditional threat analysis won’t work for the AI-powered web
• The need for full-context security checks
• How to perform sub-second, cost-effective inference
• The wide range of potential actors and actions behind any given visit

As David puts it, lower inference costs are key to letting apps act on the full context window — everything you know about the user, the session, and your application.

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Instabase founder and CEO Anant Bhardwaj joins a16z Infra partner Guido Appenzeller to discuss the revolutionary impact of LLMs on analyzing unstructured data and documents (like letting banks verify identity and approve loans via WhatsApp) and shares his vision for how AI agents could take things even further (by automating actions based on those documents). In more detail, they discuss:

• Why legacy robotic process automation (RPA) struggles with unstructured inputs.
• How Instabase developed layout-aware models to extract insights from PDFs and complex documents.
• Why predictability, not perfection, is the key metric for generative AI in the enterprise.
• The growing role of AI agents at compile time (not runtime).
• A vision for decentralized, federated AI systems that scale automation across complex workflows.

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Guido Appenzeller

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