The a16z Show

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 The conversation also covers why docs go out of date, what “self-healing” documentation requires to actually work, and how serving fast-moving customers has shaped both their product priorities and their pace.
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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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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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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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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.
 
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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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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.
 
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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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