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Andreessen Horowitz

The a16z Show discusses tech and culture trends, news, and the future – especially as ‘software eats the world’. It features industry experts, business leaders, and other interesting thinkers and voices from around the world. This show is produced by Andreessen Horowitz (aka “a16z”), a Silicon Valley-based venture capital firm. Multiple episodes are released every week; visit a16z.com for more details and to sign up for our newsletters and other content as well!

a16z Podcast

Recently, Marc Andreessen joined Lenny Rachitsky on Lenny's Podcast. They talked about why 2025 may be the most significant year in tech history, how AI is reshaping the future of product managers, designers, and engineers, and what founders need to understand about building in this moment—from where moats actually exist in AI to what the most AI-native companies are doing differently to the skills Marc is teaching his own kids to thrive in what comes next.

 

Resources:

Follow Marc Andreessen on X: https://twitter.com/pmarca

Follow Lenny Rachitsky on X: https://twitter.com/lennysan

Check out Lenny’s Podcast: https://www.lennysnewsletter.com/podcast

 

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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.

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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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Marc Andreessen on Why This Is the Most Important Moment in Tech History

Can a country be built from the internet up? Not as a metaphor or an online community, but as a system that replaces institutions we usually think of as fixed, money, law, and governance.

In this conversation taken from The Network State Podcast, a16z cofounder Ben Horowitz joins Balaji Srinivasan to explore how internet native institutions are beginning to mirror and challenge traditional state structures. Drawing parallels to China’s early special economic zones, they discuss how constrained experiments like Shenzhen tested new rules without rewriting the entire system, and why similar experimentation is now happening online.

The discussion examines crypto, digital identity, and network states as attempts to turn code into coordination and coordination into legitimacy, while grappling with a core tension. Code is deterministic, but societies are not. Ben and Balaji explore where these systems work, where they break, and whether network states are a curiosity or the next phase of governance.

 

Resources:

Follow Ben on X: https://x.com/bhorowitz

Follow Balaji on X: https://x.com/balajis

Listen to more from The Network State: https://ns.com/podcast

 

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Follow our host: https://x.com/eriktorenberg](https://x.com/eriktorenberg

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.

 

 

 

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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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Ben Horowitz and Balaji Srinivasan on Netscape and Network States

Out-of-Pocket is a healthcare education company founded by Nikhil Krishnan that helps people understand how healthcare works and how to navigate it in practice. In this episode, a16z investing partner Jay Rughani and Nikhil discuss why health insurance is losing its role as the default way people access care. They explain how rising costs are pushing more consumers to pay out of pocket for diagnostics, preventive care, and navigation. The conversation also looks at what this shift means for startups, AI-powered tools, regulation, and access as healthcare continues to move beyond insurance.

Resources:

Follow Jay Rughani on X: https://twitter.com/JayRughani

Follow Nikhil Krishnan on X: https://twitter.com/nikillinit

Read Out of Pocket’s 2026 Predictions: https://www.outofpocket.health/p/out-of-pockets-2026-predictions

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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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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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Healthcare 2026: AI Doctors, GLP-1s, and Insurance Defection

In this episode, Jen Kha, Head of Investor Relations, and David George, General Partner, discuss how late-stage private markets are evolving as AI reshapes scale, capital intensity, and growth timelines. They explain why AI-driven companies are staying private longer, how infrastructure spending is changing return profiles, and what this moment means for durability, value creation, and long-term outcomes in private markets.

Timecodes:

0:00 — Introduction

04:21 — The Market Opportunity for AI

26:48 — Pricing, Monetization, and Cash Burn

43:15 — Companies Staying Private Longer

51:30 — Portfolio Composition and Construction

57:18 — Team Culture and Collaboration
 

Resources:

Follow Jen Kha on X: https://x.com/jkhamehl

Follow David George on X: https://x.com/DavidGeorge83

 

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Follow our host: https://x.com/eriktorenberg

Not an offer or solicitation. None of the information herein should be taken as investment advice; Some of the companies mentioned are portfolio companies of a16z. Please see https://a16z.com/disclosures/ for more information. A list of investments made by a16z is available at https://a16z.com/portfolio.

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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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The Hidden Economics Powering AI

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.

Follow Jennifer Li on X: https://twitter.com/JenniferHli

Follow Yoko Li on X: https://twitter.com/stuffyokodraws

Follow Han Wang on X: https://twitter.com/handotdev

Follow Hahnbee Lee on X: https://twitter.com/hahnbeelee

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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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How Mintlify Is Rebuilding Documentation for Coding Agents

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.

Follow Matt Bornstein on X: https://twitter.com/BornsteinMatt

Follow Simon Mo on X: https://twitter.com/simon_mo_

Follow Woosuk Kwon on X: https://twitter.com/woosuk_k

Follow vLLM on X: https://twitter.com/vllm_project

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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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Inferact: Building the Infrastructure That Runs Modern AI

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.

Watch more from Six Five Media: https://www.youtube.com/@SixFiveMedia

 

Resources:

Follow Martin Casado on X: https://twitter.com/martin_casado 

Follow Patrick Moorhead on X: https://twitter.com/PatrickMoorhead

Follow Daniel Newman on X: https://twitter.com/danielnewmanUV

 

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Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX

Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711

Follow our host: https://x.com/eriktorenberg

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.

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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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Martin Casado on the Demand Forces Behind AI

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:

Follow Beyang Liu on X: https://x.com/beyang

Follow Martin Casado on X: https://x.com/martin_casado

Follow Guido Appenzeller on X: https://x.com/appenz

 

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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.

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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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From Code Search to AI Agents: Inside Sourcegraph's Transformation with CTO Beyang Liu

The a16z AI Apps team outlines how they are thinking about the AI application cycle and why they believe it represents the largest and fastest product shift in software to date. The conversation places AI in the context of prior platform waves, from PCs to cloud to mobile, and examines where adoption is already translating into real enterprise usage and revenue. They walk through three core investment themes: existing software categories becoming AI-native, new categories where software directly replaces labor, and applications built around proprietary data and closed-loop workflows. Using portfolio examples, the discussion shows how these models play out in practice and why defensibility, workflow ownership, and data moats matter more than novelty as AI applications scale.

 

Resources:

Follow Alex Rampell on X: https://twitter.com/arampell

Follow Jen Kha on X: https://twitter.com/jkhamehl

Follow David Haber on X: https://twitter.com/dhaber

Follow Anish Acharya on X: https://twitter.com/illscience

 

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Find a16z on X: https://twitter.com/a16z

Find a16z on LinkedIn: https://www.linkedin.com/company/a16z

Listen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX

Listen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711

Not an offer or solicitation. None of the information herein should be taken as investment advice; Some of the companies mentioned are portfolio companies of a16z. Please see https://a16z.com/disclosures/ for more information. A list of investments made by a16z is available at https://a16z.com/portfolio

.

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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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The AI Opportunity That Goes Beyond Models

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.

 

Follow Omar Khattab on X: https://x.com/lateinteraction

Follow Martin Casado on X: https://x.com/martin_casado

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.

Stay Updated:

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Follow our host: https://twitter.com/eriktorenberg

 

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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How Foundation Models Evolved: A PhD Journey Through AI's Breakthrough Era

From glittery reaction gifs modded by grandparents to rage faces on Reddit, stickers (gifs and other layered images) and emotive “biaoqing” have taken over messaging culture in China and beyond. Stickers are tied to filter culture, too — whether originating in real life as purikura photo sticker booths in Japan or digitally as Snapchat filters.

Why are these forms of social communication so popular? Because sometimes you just want to say “I feel totally Nicki Minaj side-eye dot-GIF about this”, and no one can give a side-eye as good as Nicki Minaj can. But it’s not just about isolated expressions, celebrity stickers like Kimoji, or personalized bitmoji; stickers are shaping and codifying the way people talk to each other online in new and multi-layered ways.

It’s even connected to mobile livestreaming, a phenomenon that’s taking off in China right now, in the most mundane (food eating streams) to subversive (seductive banana eating streams) ways. And how are all these memes tied to monetization and payments? In this episode of the a16z Podcast, ROFLCon co-founder and human-centered researcher/writer Christina Xu and Connie Chan in conversation with Sonal Chokshi take us on a wild tour of cultural messaging memes and messaging tech in China and beyond.

Stay Updated:

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Follow our host: https://twitter.com/eriktorenberg

 

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.

 
Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

a16z Podcast: Stickers! Filters! Memes! Livestreams!

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