Christopher Mims and Tim Higgins of the Wall Street Journal sit down with a16z General Partner Martin Casado on WSJ?s Bold Names to ask whether the AI spending boom is a bubble waiting to burst. Martin explains why the fundamentals differ dramatically from the dot-com era?when WorldCom had $40 billion in debt versus today's tech giants with hundreds of billions on their balance sheets?and why a speculative valuation correction shouldn't be confused with systemic collapse. They also discuss where a16z sees opportunity in the "long tail" of AI companies beyond the state-of-the-art large language models.

 

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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In this feed drop from The Six Five Pod, a16z General Partner Martin Casado discusses how AI is changing infrastructure, software, and enterprise purchasing. He explains why current constraints are driven less by technical limits and more by regulation, particularly around power, data centers, and compute expansion.

The episode also covers how AI is affecting software development, lowering the barrier to coding without eliminating the need for experienced engineers, and how agent-driven tools may shift infrastructure decision-making away from humans.

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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Mintlify is a documentation platform built by cofounders Han Wang and Hahnbee Lee to help teams create and maintain developer docs. In this episode, Andreessen Horowitz general partners Jennifer Li and Yoko Li speak with Han and Hahnbee about how coding agents are changing what ?good docs? mean, shifting documentation from a human-only resource into infrastructure that powers AI tools, support agents, and internal knowledge workflows. They share Mintlify?s early journey, including eight pivots, the two-day prototype that landed their first customer, and the ?do things that don?t scale? sales motion that helped them win early traction. The conversation also covers why docs go out of date, what ?self-healing? documentation requires to actually work, and how serving fast-moving customers has shaped both their product priorities and their pace.

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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Inferact is a new AI infrastructure company founded by the creators and core maintainers of vLLM. Its mission is to build a universal, open-source inference layer that makes large AI models faster, cheaper, and more reliable to run across any hardware, model architecture, or deployment environment. Together, they broke down how modern AI models are actually run in production, why ?inference? has quietly become one of the hardest problems in AI infrastructure, and how the open-source project vLLM emerged to solve it. The conversation also looked at why the vLLM team started Inferact and their vision for a universal inference layer that can run any model, on any chip, efficiently.

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

 

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To Regulate AI Effectively, Focus on How It?s Used

A conversation with Martin Casado on learning from past computing platform shifts, understanding marginal risk in AI, and why open source matters for US competitiveness.

One of the core pillars of our roadmap for federal AI legislation makes clear AI should not excuse wrongdoing. When people or companies use AI to break the law, existing criminal, civil rights, consumer protection, and antitrust frameworks should still apply. Enforcement agencies should have the resources they need to enforce the law. If existing bodies of law fall short in accounting for certain AI use cases, any new laws should be evidence-based, clearly defining marginal risks and the optimal approach to target harms directly. 

In this conversation, we go deeper on what that principle means in practice with Martin Casado, general partner at a16z where he leads the firm?s infrastructure practice and invests in advanced AI systems and foundational compute. Martin has lived through multiple platform shifts?as a researcher where he worked on large-scale simulations for the Department of Defense before working with the intelligence community on networking and cybersecurity, a pioneer of software-defined networking at Stanford, and the cofounder and CTO of Nicira, which was acquired by VMware?giving him a rare perspective on how breakthrough technologies are governed as they develop and scale. 

Martin joins Jai Ramaswamy and Matt Perault to discuss how decades of technology policy can inform addressing harmful uses of AI, defining marginal risk in AI, the importance of open source for long-term competitiveness, and more. 

 

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When four MIT grads decided to build a code editor while everyone else was building AI agents, they created the fastest-growing developer tool ever built. 

Cursor CEO Michael Truell joins a16z?s Martin Casado to discuss the deliberate constraints that led to breakthroughs: why they rejected the "democratization" narrative to focus on power users, how their 2-day work trials test for agency over credentials, and the strategic decision to own the editor when conventional wisdom said it was impossible.

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Nvidia?s $5 billion investment in Intel is one of the biggest surprises in semiconductors in years. Two longtime rivals are now teaming up, and the ripple effects could reshape AI, cloud, and the global chip race.

To make sense of it all, Erik Torenberg is joined by Dylan Patel, chief analyst at SemiAnalysis, joins Sarah Wang, general partner at a16z, and Guido Appenzeller, a16z partner and former CTO of Intel?s Data Center and AI business unit. Together, they dig into what the deal means for Nvidia, Intel, AMD, ARM, and Huawei; the state of US-China tech bans; Nvidia?s moat and Jensen Huang?s leadership; and the future of GPUs, mega data centers, and AI infrastructure.

Resources: 

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This episode is a special replay from The Generalist Podcast, featuring a conversation with a16z General Partner Martin Casado. Martin has lived through multiple tech waves as a founder, researcher, and investor, and in this discussion he shares how he thinks about the AI boom, why he believes we?re still early in the cycle, and how a market-first lens shapes his approach to investing.

They also dig into the mechanics behind the scenes: why AI coding could become a multi-trillion-dollar market, how a16z evolved from a small generalist firm into a specialized organization, the growing role of open-source models, and why Martin believes AGI debates often obscure more meaningful questions about how technology actually creates value.

 

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X: https://x.com/mariogabriele

https://www.generalist.com/

 

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What if the next leap in artificial intelligence isn?t about better language?but better understanding of space?

In this episode, a16z General Partner Erik Torenberg moderates a conversation with Fei-Fei Li, cofounder and CEO of World Labs, and a16z General Partner Martin Casado, an early investor in the company. Together, they dive into the concept of world models?AI systems that can understand and reason about the 3D, physical world, not just generate text.

Often called the ?godmother of AI,? Fei-Fei explains why spatial intelligence is a fundamental and still-missing piece of today?s AI?and why she?s building an entire company to solve it. Martin shares how he and Fei-Fei aligned on this vision long before it became fashionable, and why it could reshape the future of robotics, creativity, and computational interfaces.

From the limits of LLMs to the promise of embodied intelligence, this conversation blends personal stories with deep technical insights?exploring what it really means to build AI that understands the real (and virtual) world. 

 

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

 

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a16z's Martin Casado sits down with Shikhar Shrestha, CEO and cofounder of Ambient, the company bringing agentic AI to physical security.

Shikhar shares how a traumatic armed robbery at age 12?and a security camera that no one was watching?sparked his mission to make every camera intelligent.

They discuss how Ambient's AI monitors camera feeds in real-time to detect threats and prevent incidents before they happen, navigating COVID as a physical security company, building their own reasoning VLM called Pulsar, and why the future of security is AI not just detecting threats but automatically responding to them.

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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Matt Knight spent five years as OpenAI?s CISO. Now he runs what colleagues call ?the most interesting job at the company?: leading Aardvark, an AI agent that finds security vulnerabilities the way a human researcher would?by reading code, writing tests, and proposing patches. It recently found a memory corruption bug in OpenSSH, one of the most heavily audited codebases in existence.

In this conversation with a16z?s Joel de la Garza, Matt traces the evolution from GPT-3 (which couldn?t analyze security logs at all) to GPT-4 (which could parse Russian cybercriminal chat logs written in slang) to today?s models that discover bugs humans have missed for decades. They also discussed the XZ Utils backdoor that nearly compromised half the internet and why 3.5 million unfilled security jobs might finally get some relief, and how Aardvark could give open source maintainers a fighting chance against nation-state attackers.

If you enjoyed this episode, please be sure to like, subscribe, and share with your friends.

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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Most companies still rely on dashboards to understand their data, even though AI now offers new ways to ask questions and explore information. Barry McCardel, CEO of Hex and former engineer at Palantir, joins a16z General Partner Sarah Wang to discuss how agent workflows, conversational interfaces, and context-aware models are reshaping analysis. Barry also explains how Hex aims to make everyone a data person by unifying analysis and AI in one workflow, and he reflects on his post about getting rid of their AI product team and the process behind Hex?s funny launch videos.

Timecodes: 

0:00 ? The problem with dashboards
1:20 ? The evolution of data teams and AI?s role
2:05 ? Democratizing data: challenges and opportunities
3:45 ? The rise of agentive workflows
9:48 ? Threads and the changing UI of data analysis
13:16 ? Building AI agents: lessons from the notebook agent
16:12 ? Model capabilities and the future of AI in data
19:10 ? The importance of context and trust in data analysis
24:34 ? Semantic models and context engineering
29:27 ? Data team roles in the age of AI
31:52 ? Accuracy, trust, and evaluating AI systems
37:43 ? Building Hex: embracing AI as core, not an add-on
48:48 ? Pricing, value capture, and the future of SaaS
55:55 ? The modern data stack and industry consolidation
1:04:26 ? Acquisitions and owning the data insight layer
1:06:46 ? Lessons from Palantir: forward-deployed engineering
1:13:11 ? Commitment engineering and customer collaboration
1:17:25 ? Brand, launch videos, and having fun in SaaS

Resources:

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details, please see http://a16z.com/disclosures.

 

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

 

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Ian Webster built PromptFoo after watching 200 million Discord users systematically dismantle his AI agent?now Fortune 10 companies pay him to break theirs before customers do. The "lethal trifecta" sounds academic until you realize it's already happening: untrusted input plus sensitive data plus an exfiltration channel equals the security incident that just cost a SaaS company its multi-tenancy guarantees. Webster's red-teaming agents don't use signatures?they have 30,000 conversations with your system, socially engineering their way past guardrails the same way a teenager with emojis convinced ChatGPT to leak data, except his tools find the vulnerability before your users become the pen testers.

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Netlify's CEO, Matt Biilmann, reveals a seismic shift nobody saw coming: 16,000 daily signups?five times last year's rate?and 96% aren't coming from AI coding tools. They're everyday people accidentally building React apps through ChatGPT, then discovering they need somewhere to deploy them. The addressable market for developer tools just exploded from 17 million JavaScript developers to 3 billion spreadsheet users, but only if your product speaks fluent AI?which is why Netlify's founder now submits pull requests he built entirely through prompting, never touching code himself, and why 25% of users immediately copy error messages to LLMs instead of debugging manually. The web isn't dying to agents; it's being reborn by them, with CEOs coding again and non-developers shipping production apps while the entire economics of software?from perpetual licenses to subscriptions to pure usage?gets rewritten in real-time.

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

 

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Sourcegraph's CTO just revealed why 90% of his code now comes from agents?and why the Chinese models powering America's AI future should terrify Washington. While Silicon Valley obsesses over AGI apocalypse scenarios, Beyang Liu's team discovered something darker: every competitive open-source coding model they tested traces back to Chinese labs, and US companies have gone silent after releasing Llama 3. The regulatory fear that killed American open-source development isn't hypothetical anymore?it's already handed the infrastructure layer of the AI revolution to Beijing, one fine-tuned model at a time.

 

Resources:

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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Ryo Lu spent years watching his designs die in meetings. Then he discovered the tool that lets designers ship code at the speed of thought: Cursor, the company where Ryo is now Head of Design. In this episode, we discuss why "taste" is the wrong framework for understanding the future, why purposeful apps are "selfish," how System 7 holds secrets about AI interfaces, and the radical bet that one codebase can serve everyone if you design the concepts right instead of the buttons.

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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The Stanford PhD who built DSPy thought he was just creating better prompts?until he realized he'd accidentally invented a new paradigm that makes LLMs actually programmable. 

While everyone obsesses over whether LLMs will get us to AGI, Omar Khattab is solving a more urgent problem: the gap between what you want AI to do and your ability to tell it, the absence of a real programming language for intent. He argues the entire field has been approaching this backwards, treating natural language prompts as the interface when we actually need something between imperative code and pure English, and the implications could determine whether AI systems remain unpredictable black boxes or become the reliable infrastructure layer everyone's betting on.

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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If you can?t robustly protect your secrets, you can?t have reliable AI agents.

In this episode, Truffle Security cofounder and CEO Dylan Ayrey joins a16z partner Joel de la Garza to discuss the emergent security stack for AI agents, why leaks are actually getting worse, and how Truffle evolved from an open-source side project to a major VC-backed startup.

 

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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In this episode, a16z General Partner Martin Casado sits down with Ovais Tariq, Cofounder and CEO of Tigris Data, to discuss why independent storage is so hard, what operating your own datacenters is like, and what?s in store for the future of cloud.

Resources

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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Customer support platforms lacked adequate solutions for B2B companies - until Pylon entered the scene.

We sat down with Pylon cofounders Marty Kausas, Advith Chelikani, and Robert Eng to discuss why they went into B2B, how they plan to beat huge competitors, and why they still live together in a windowless apartment and work 9-9-6 hours despite having raised tens of millions.

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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In 2025, we saw the first glimpses of true AI agents. In 2026, every company will be rushing to get them into production, and they?ll need companies like Keycard to manage fleets of agents.

In this conversation, a16z Partner Joel de la Garza sits down with Keycard Cofounder and CEO Ian Livingstone to discuss the continuum from copilots to agents, the security realities of tool-calling, why enterprises will adopt before consumers, and how to control your agents.

 

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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AI coding has emerged as a major market for AI: one that?s already rewriting how software gets built.

a16z Infra Partners Yoko Li and Guido Appenzeller break down how ?agents with environments? are changing the dev loop; why repos and PRs may need new abstractions; and where ROI is showing up first (like legacy code migration). We also cover token economics for engineering teams, the emerging agent toolbox (sandboxes, code search/parsing, agent-optimized docs, orchestration), and founder opportunities when you treat agents as users, not just tools.

Read the blog post here.

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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What if the hardest part of building a company isn?t the product, but knowing exactly who it?s for?

In this episode, a16z General Partner Martin Casado sits down with Abhishek Agrawal, Cofounder and CEO of Material Security, to discuss how an ideal customer profile is discovered, how to manage any kind of customer, and how frothy markets can distort real signal.

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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AI is transforming both sides of the cybersecurity cat-and-mouse game. Attackers are using LLMs to scale impersonation, phishing, and even deepfake fraud?while defenders are racing to automate detection and takedowns at the same speed.

In this episode, a16z partner Joel de la Garza talks with Kevin Tian, cofounder & CEO of Doppel Security (and former Uber engineer), about building in this new landscape. They cover:

Why outsider founders sometimes build the most effective security companiesThe ?3 V?s? framework for today?s social engineering attacks: volume, velocity, varietyHow Doppel uses reasoning models and reinforcement fine-tuning to cut false positives and improve precisionSimulation tools like ?vibe phishing? to train employees on real attacker tacticsThe shift from manual cyber-intelligence services to AI-driven, software-margin businessesWhy the biggest bottleneck now isn?t model cost?but engineering time to deliver the right context

If you?re building security products or exploring how AI can automate tough edge cases, this is a ground-level look at what?s working?and what comes next.

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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What if you could retake your favorite memories years after they happened, fixing the lighting, catching the smile, or even opening your eyes?

In this conversation, a16z General Partner Martin Casado and Partner Yoko Li sit down with scientist and Lytro founder Ren Ng along with Phota Labs cofounders Cecilia Zhang and Zhihao ?Zach? Xia to explore the past, present, and future of computational photography. 

They trace the story from the invention of light-field cameras and the evolution of smartphone photography to today?s AI powered retakes that preserve identity and context in ways filters never could. Together they reflect on how AI is changing what it means to capture a moment, why authenticity matters as much as aesthetics, and how the future of photography may no longer depend on a lens at all but on models that know you.

Resources: 

Find Cecilia on GitHub: https://ceciliavision.github.io/

Find Zach on GitHub: https://likesum.github.io/

Find Ren on LinkedIn: https://www.linkedin.com/in/renng

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Find Martin on X: https://x.com/martin_casado

 

Timecodes:

00:00 The Decisive Moment in Photography

00:33 Introduction to Computational Photography

01:05 Personal Histories and Connections

02:27 Evolution of Computational Photography

04:15 The Birth of Light Field Photography

07:28 From Hardware to Software Innovations

08:52 Founding of Photo Labs

11:10 Generative AI in Photography

13:54 The Future of Photography

14:47 Personalized Visual Gen AI

16:27 User Reactions and Real-World Applications

17:44 Technical Innovations and Challenges

24:11 New Use Cases and Exciting Prospects

25:34 The Essence of Slide Photography

26:16 The Future of Photography: Generative AI

28:58 Authenticity in Photography

32:11 Generative AI and User Behavior

34:39 The Impact of Generative AI on Photography

37:02 The Evolution of Photography Styles

46:20 The Future of Computational Photography

 

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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.

Resources: 

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Find Anjney on X: https://twitter.com/AnjneyMidha

 

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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 controlWhy generative content is shifting from static avatars to programmable personasThe surprising crossover between consumer creativity and enterprise adoptionWhere existing LLMs fall short in generating emotionally authentic charactersWhat 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.

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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. 

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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 webThe need for full-context security checksHow to perform sub-second, cost-effective inferenceThe 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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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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LMArena cofounders Anastasios N. Angelopoulos, Wei-Lin Chiang, and Ion Stoica sit down with a16z general partner Anjney Midha to talk about the future of AI evaluation. As benchmarks struggle to keep up with the pace of real-world deployment, LMArena is reframing the problem: what if the best way to test AI models is to put them in front of millions of users and let them vote? The team discusses how Arena evolved from a research side project into a key part of the AI stack, why fresh and subjective data is crucial for reliability, and what it means to build a CI/CD pipeline for large models.

They also explore:

Why expert-only benchmarks are no longer enough.How user preferences reveal model capabilities ? and their limits.What it takes to build personalized leaderboards and evaluation SDKs.Why real-time testing is foundational for mission-critical AI.

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Wei-Lin Chiang

Ion Stoica

Anjney Midha

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)

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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In this episode of AI + a16z, Distributional cofounder and CEO Scott Clark, and a16z partner Matt Bornstein, explore why building trust in AI systems matters more than just optimizing performance metrics. From understanding the hidden complexities of generative AI behavior to addressing the challenges of reliability and consistency, they discuss how to confidently deploy AI in production. 

Why is trust becoming a critical factor in enterprise AI adoption? How do traditional performance metrics fail to capture crucial behavioral nuances in generative AI systems? Scott and Matt dive into these questions, examining non-deterministic outcomes, shifting model behaviors, and the growing importance of robust testing frameworks. 

Among other topics, they cover: 

The limitations of conventional AI evaluation methods and the need for behavioral testing. How centralized AI platforms help enterprises manage complexity and ensure responsible AI use. The rise of "shadow AI" and its implications for security and compliance. Practical strategies for scaling AI confidently from prototypes to real-world applications.

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Distributional

Matt Bornstein

Derrick Harris

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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In this episode of the a16z AI podcast, a16z Infra partners Guido Appenzeller, Matt Bornstein, and Yoko Li explore how generative AI is reshaping software development. From its potential as a new high-level programming abstraction to its current practical impacts, they discuss whether AI coding tools will redefine what it means to be a developer.

Why has coding emerged as one of AI's most powerful use cases? How much can AI truly boost developer productivity, and will it fundamentally change traditional computer science education? Guido, Yoko, and Matt dive deep into these questions, addressing the dynamics of "vibe coding," the enduring role of formal programming languages, and the critical challenge of managing non-deterministic behavior in AI-driven applications.Among other things, they discuss:

The enormous market potential of AI-generated code, projected to deliver trillions in productivity gains.How "prompt-based programming" is evolving from Stack Overflow replacements into sophisticated development assistants.Why formal languages like Python and Java are here to stay, even as natural language interactions become common.The shifting landscape of programming education, and why understanding foundational abstractions remains essential.The unique complexities of integrating AI into enterprise software, from managing uncertainty to ensuring reliability.

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

 

Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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In this episode of AI + a16z, Anthropic's David Soria Parra ? who created MCP (Model Context Protocol) along with Justin Spahr-Summers ? sits down with a16z's Yoko Li to discuss the project's inception, exciting use cases for connecting LLMs to external sources, and what's coming next for the project. If you're unfamiliar with the wildly popular MCP project, this edited passage from their discussion is a great starting point to learn:

David: "MCP tries to enable building AI applications in such a way that they can be extended by everyone else that is not part of the original development team through these MCP servers, and really bring the workflows you care about, the things you want to do, to these AI applications. It's a protocol that just defines how whatever you are building as a developer for that integration piece, and that AI application, talk to each other. 

"It's a very boring specification, but what it enables is hopefully ... something that looks like the current API ecosystem, but for LLM interactions."

Yoko: "I really love the analogy with the API ecosystem, because they give people a mental model of how the ecosystem evolves ... Before, you may have needed a different spec to query Salesforce versus query HubSpot. Now you can use similarly defined API schema to do that.

"And then when I saw MCP earlier in the year, it was very interesting in that it almost felt like a standard interface for the agent to interface with LLMs. It's like, 'What are the set of things that the agent wants to execute on that it has never seen before? What kind of context does it need to make these things happen?' When I tried it out, it was just super powerful and I no longer have to build one tool per client. I now can build just one MCP server, for example, for sending emails, and I use it for everything on Cursor, on Claude Desktop, on Goose."

Learn more:

A Deep Dive Into MCP and the Future of AI Tooling

What Is an AI Agent?

Benchmarking AI Agents on Full-Stack Coding

Agent Experience: Building an Open Web for the AI Era

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Yoko Li

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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In this episode of AI + a16z, a16z Infra partners Guido Appenzeller, Matt Bornstein, and Yoko Li discuss and debate one of the tech industry's buzziest words right now: AI agents. The trio digs into the topic from a number of angles, including:

Whether a uniform definition of agent actually existsHow to distinguish between agents, LLMs, and functionsHow to think about pricing agentsWhether agents can actually replace humans, andThe effects of data siloes on agents that can access the web.

They don't claim to have all the answers, but they raise many questions and insights that should interest anybody building, buying, and even marketing AI agents.

Learn more:

Benchmarking AI Agents on Full-Stack Coding

Automating Developer Email with MCP and Al Agents

A Deep Dive Into MCP and the Future of AI Tooling

Agent Experience: Building an Open Web for the AI Era

DeepSeek, Reasoning Models, and the Future of LLMs

Agents, Lawyers, and LLMs

Reasoning Models Are Remaking Professional Services

From NLP to LLMs: The Quest for a Reliable Chatbot

Can AI Agents Finally Fix Customer Support?

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Guido Appenzeller

Matt Bornstein

Yoko Li

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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In this episode, a16z General Partner Martin Casado sits down with Sujay Jayakar, co-founder and Chief Scientist at Convex, to talk about his team?s latest work benchmarking AI agents on full-stack coding tasks. From designing Fullstack Bench to the quirks of agent behavior, the two dig into what?s actually hard about autonomous software development, and why robust evals?and guardrails like type safety?matter more than ever. They also get tactical: which models perform best for real-world app building? How should developers think about trajectory management and variance across runs? And what changes when you treat your toolchain like part of the prompt? Whether you're a hobbyist developer or building the next generation of AI-powered devtools, Sujay?s systems-level insights are not to be missed.

Drawing from Sujay?s work developing the Fullstack-Bench, they cover:

Why full-stack coding is still a frontier task for autonomous agentsHow type safety and other ?guardrails? can significantly reduce variance and failureWhat makes a good eval?and why evals might matter more than clever promptsHow 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 promptAnd what all of this means for devs?from hobbyists to infra teams building with AI in the loop

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Introducing Fullstack-Bench

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Sujay Jayakar

Martin Casado

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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:

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

A deep dive into MCP and the future of AI tooling

Dracula theme

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Zeno Rocha

Yoko Li

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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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Joe Schmidt

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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

Crossing the uncanny valley of conversational voice

Sesame CSM 1B model

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Anjney Midha

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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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Introducing AX: Why Agent Experience Matters

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Martin Casado

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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.

Learn more: 

What Are the Security Risks of Deploying DeepSeek-R1?

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

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Ian Webster

Dylan Ayrey

Brian Long

Joel de la Garza

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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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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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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

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Alex Immerman

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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

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Guido Appenzeller

Derrick Harris

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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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

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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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.

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