The a16z Show

马丁·卡萨多与MTS主持人西奥·贾菲和索菲亚·杜共同探讨：人工智能领域的价值究竟在何处积累？为何这一技术周期与以往浪潮存在根本性差异？前沿实验室最终是否会占据大部分市场？ 马丁阐释了为何人工智能将风险投资转变为规模化资本游戏——小型团队能够高效配置巨额资金，以及资本、创新与增长之间的关系为何从未如此紧密。他既分析了前沿实验室主导AI领域的论据与反论，也探讨了开源模型与专业模型的作用，以及应用程序为何正在捕获更多价值。 对话还涉及模型路由、AI经济学、创始人-市场契合度，以及马丁为何认为这可能是自20世纪90年代以来他所见证的最大财富释放机遇。

Elena Burger和Matt Bornstein与Inferact联合创始人兼CEO Simon Mo展开对话。Inferact作为开源推理引擎，正驱动着当今众多最先进的AI应用。他们共同探讨了开源AI如何从研究项目演变为关键基础设施，推理为何成为AI技术栈中最核心的层级之一，以及将前沿智能带给全球开发者需要哪些条件。 对话涵盖了vLLM的起源、开放权重模型的兴起、企业为何越来越渴望掌控AI基础设施，以及开源推理如何赋能新一代AI应用。他们还讨论了模型许可、开放权重AI的经济性、Kimi K3、蒸馏技术、AI基础设施，以及Simon为何认为开放模型与封闭模型之间的差距正在迅速消失。

亚历克斯·兰佩尔和奥利维亚·摩尔与拉西公司联合创始人斯泰因·佩莱和弗雷德里克·伦肯对话，探讨如何将人工智能引入经济中最被忽视的领域之一：小型企业。 受在牙科诊所工作经历的启发，佩莱和伦肯着手自动化那些让医疗从业者远离患者的行政工作。他们讨论了AI代理如何改变账单处理、保险理赔、患者支付及其他运营流程，使诊所减少文书工作的时间，将更多精力投入患者护理。 对话深入探讨了AI代理——这类软件不仅存储信息，更能执行实际工作——如何融入现实商业场景，以及为何医疗行政管理领域蕴藏着自动化领域的最大机遇之一。过程中，他们分享了产品设计、市场策略，以及在复杂商业环境中构建可靠AI系统的关键要素。

Applied Intuition 在过去十年中一直致力于构建驱动智能机器的软件，其应用范围涵盖乘用车、卡车、国防系统、采矿设备以及工业机器人。 在本期对话中，马克·安德森和埃里克·托伦伯格与Applied Intuition的联合创始人卡萨尔·尤尼斯和彼得·路德维希一同探讨了物理人工智能的兴起，以及公司最新推出的平台Dana——一个旨在加速自主系统开发的新平台。 他们深入讨论了自动驾驶汽车、机器人技术、世界模型、仿真、AI基础设施，以及在物理世界中安全部署智能系统所面临的工程挑战。此外，他们还谈到了自动驾驶汽车、人形机器人、全球竞争，以及为何降低构建物理AI的门槛可能催生全新一代的产品和公司。 资源： 在X上关注卡萨尔·尤尼斯：https://x.com/qasar 在领英上关注彼得·路德维希：linkedin.com/in/peterwludwig 在X上关注马克·安德森：https://x.com/pmarca 保持更新： 在YouTube上找到a16z：YouTube 在X上找到a16z 在领英上找到a16z 在Spotify上收听a16z节目 在Apple Podcasts上收听a16z节目 关注我们的主持人：https://twitter.com/eriktorenberg 请注意，此处内容仅供信息参考；不应被视为法律、商业、税务或投资建议，也不应用于评估任何投资或证券；且不针对任何a16z基金的投资者或潜在投资者。a16z及其关联公司可能持有所讨论公司的投资。更多详情请参见a16z.com/disclosures。 由Simplecast（AdsWizz旗下公司）托管。关于我们收集和使用个人数据用于广告的信息，请参见pcm.adswizz.com。

作为夏季重播系列的一部分，我们重新回顾了Runtime（a16z关于AI基础设施与计算未来的会议）中最引人注目的对话之一。 Atreides Management管理合伙人兼首席投资官Gavin Baker与David George一同探讨了当前AI投资周期面临的最大问题。AI是泡沫吗？数据中心、GPU和计算基础设施的前所未有的建设对经济意味着什么？投资者应如何看待那些构建下一代AI的公司？ 对话探讨了前沿模型、英伟达、谷歌、定制芯片、AI基础设施、应用软件、机器人技术，以及Baker为何认为当前的AI投资周期与21世纪初的互联网泡沫有本质区别。在此过程中，他们讨论了GPU的经济性、企业软件、AI商业模式，以及随着AI从实验阶段进入更广泛的经济领域，接下来会发生什么。 资源： 在X上关注Gavin Baker：https://x.com/GavinSBaker 在X上关注Atreides Management：https://x.com/atreidesmgmt 在X上关注David George：https://x.com/DavidGeorge83 保持更新： 在YouTube上找到a16z：YouTube 在X上找到a16z 在LinkedIn上找到a16z 在Spotify上收听a16z节目 在Apple Podcasts上收听a16z节目 关注我们的主持人：https://twitter.com/eriktorenberg 请注意，此处内容仅供信息参考；不应被视为法律、商业、税务或投资建议，也不应用于评估任何投资或证券；且不针对任何a16z基金的投资者或潜在投资者。a16z及其关联公司可能持有所讨论公司的投资。更多详情请参见a16z.com/disclosures。 由Simplecast（AdsWizz旗下公司）托管。关于我们收集和使用个人数据用于广告的信息，请参见pcm.adswizz.com。

Anish Acharya与Josh Elman展开对话，探讨消费科技的未来以及Elman加入a16z的决定。 过去二十年里，Elman参与塑造了包括LinkedIn、Facebook、Twitter、Robinhood、Discord、Musical.ly、TikTok和Apple在内的一批最具影响力的消费科技产品与公司。基于这些经历，他反思了科技如何从一个小众行业演变为日常生活的核心力量。 对话涉及消费级AI、产品设计、分发、社交网络、创作者生态，以及科技与人类行为之间不断变化的关系。他们讨论了为何AI可能催生全新一代消费产品，发现与分发方式如何改变，以及创始人能从以往平台转型中学到什么。 在此过程中，Elman分享了他对留存率、网络效应、产品市场契合度的见解，以及他认为在消费科技领域仍未被充分挖掘的机遇。 资源： 在X上关注Josh Elman：https://x.com/joshelman 在X上关注Anish Acharya：https://x.com/illscience 保持更新： 在YouTube上找到a16z：YouTube 在X上找到a16z 在LinkedIn上找到a16z 在Spotify上收听a16z Show 在Apple Podcasts上收听a16z Show 关注我们的主持人：https://twitter.com/eriktorenberg 请注意，此处内容仅供信息参考；不应被视为法律、商业、税务或投资建议，也不应用于评估任何投资或证券；且不针对任何a16z基金的投资者或潜在投资者。a16z及其关联方可能持有所讨论公司的投资。更多详情请参见a16z.com/disclosures。 由Simplecast（AdsWizz旗下公司）托管。关于我们为广告目的收集和使用个人数据的信息，请参见pcm.adswizz.com。

a16z Managing Partner and Head of Global Partnerships Jen Kha joins MTS hosts Theo Jaffee and Sophia Dew to discuss a16z's Machine Age Fund and the investment thesis behind rebuilding the physical infrastructure that powers AI. Jen explains why chips, networking, memory, cooling, data centers, and other parts of the physical computing stack are becoming investable again after decades in which software captured much of the industry's attention. As AI demand pushes existing infrastructure to its limits, she explains why a16z created a dedicated fund and why hardware founders are increasingly rethinking the stack from first principles. They also discuss the global race to adopt AI, what hardware startups need beyond capital, the backlash against data centers in the U.S., and why experienced systems builders are returning to entrepreneurship as a new generation of infrastructure gets built.

Ryan Greenblatt, Chief Scientist at Redwood Research, joins MTS host Theo Jaffee to unpack a new independent investigation into the OpenAI Hugging Face hacking incident and what it reveals about how large groups of AI agents behave when they're allowed to coordinate. Ryan and his collaborators found agents spontaneously organizing through message boards, sharing information, assigning tasks, forming teams, and even sacrificing their own chances of success to help other agents. Rather than simply trying to steal answers, hundreds of agents were working together on elaborate strategies to manipulate how their performance would be scored. Theo and Ryan discuss why this level of coordination was surprising, how reward hacking may emerge during training, and the risk that attempts to eliminate bad behavior could simply make it harder to detect. They also explore what the incident means for AI monitoring and alignment, and why independent risk assessment may become increasingly important as agents grow more capable.

Ben Horowitz, Martin Casado, Raghu Raghuram, and Erik Torenberg discuss the launch of a16z's new Machine Age Fund and the infrastructure buildout behind AI, from chips, memory, and networking to power, cooling, and data centers. Why a dedicated fund now? The group argues that the bottleneck in AI is increasingly shifting from the models themselves to everything beneath them. Hyperscaler CapEx is surging, critical components are booked years in advance, and each new generation of reasoning and agents requires dramatically more compute. They unpack why this cycle looks different from previous infrastructure booms and how AI is turning problems once constrained by engineering into problems that can increasingly be attacked with capital and compute. They also explore where the next generation of infrastructure companies could emerge, why founders are returning to hard technical problems across hardware and systems, and what it will take to rebuild the computing stack for the Machine Age.

a16z General Partners Martin Casado, Sarah Wang, and Matt Bornstein unpack the story of Cursor: how a small, product-obsessed team entered one of the most competitive markets in technology, took on incumbents with seemingly unbeatable advantages, and repeatedly made decisions that ran against conventional startup wisdom. They revisit the early bet that the interface between humans and AI would matter more than building a coding-specific foundation model, why Cursor built its own product rather than a VS Code plugin, and how the founders' ability to say "no" became one of the company's defining strengths. They also discuss Cursor's rapid evolution from IDE to agent and model platform, and why the team was willing to cannibalize its own products as AI capabilities improved. The conversation gets into what founders can learn from Cursor's approach to competition, hiring, enterprise sales, M&A, and company culture, including why the team remained unfazed by competitors from Microsoft to Anthropic and how its obsessive focus on product ultimately extended into every part of building the company.

Anish Acharya joins Jen Kha to break down the next frontier of AI, from the evolving model landscape and open-source AI to why the application layer, and consumer AI in particular, may be entering a new phase. Anish explains why he believes there will be multiple winners at the model layer, why traditional moats like network effects, scale, and brand still matter, and how companies can choose between frontier and open-weight models depending on the economics of the task. They also explore why models are increasingly specializing, and how applications can combine different types of intelligence to create products that are more valuable than any single model. The conversation then turns to consumer AI: personal agents that can shop and manage your inbox, coding tools enabling a new generation of small businesses, and why Anish thinks we're seeing a renaissance for consumer builders. They also discuss the changing economics of AI software, the rise of "luxury software," and why the biggest risk for today's founders may no longer be thinking too big, but thinking too small.

a16z General Partners Martin Casado and Erik Torenberg are joined by Board Partner Steven Sinofsky to explore what recent breakthroughs in AI and mathematics tell us about where the technology is headed, and whether some of the basic assumptions that have governed computing for decades are starting to break. Martin and Steven debate whether AI's progress in mathematics represents a genuine leap in reasoning or simply a new tool for solving problems at a higher level of abstraction. From the four-color theorem and early computers to graphing calculators and today's models, they trace how new technologies have repeatedly changed which problems humans need to solve themselves, and ask what makes this moment different. The conversation then turns to one of the biggest shifts in AI: problems that were once constrained by engineering talent can increasingly be attacked with capital and compute. They discuss what that means for startups versus incumbents, venture capital, the coming wave of AI applications, and why pouring billions into increasingly capable models may force us to rethink what these systems can ultimately accomplish.

Daisy Wolf and Eva Steinman are joined by Engy Ziedan, co-founder and Chief Scientific Officer of Protege, to discuss why medical AI has a measurement problem, and why scoring well on a benchmark doesn't necessarily mean a model is ready for the hospital. Engy explains why healthcare AI needs independent evaluations that go beyond static exams and measure how models actually perform in real-world clinical workflows. They explore the risks of subtle bias and misalignment, why the same model can rank differently depending on how it's prompted or tested, and what happens as AI becomes more personalized and changes faster than traditional healthcare quality systems can keep up. The conversation also gets into Protege's role as an independent evaluator, how contaminated training data can undermine benchmarks, and why the future of medical AI may require continuous monitoring rather than occasional testing.

a16z's Joel De La Garza is joined by Aaron Zollman, Deputy CISO at Microsoft Gaming, to discuss how security teams can embrace AI agents without losing control. Aaron shares Microsoft's experience with OpenClaw, from the initial instinct to ban it to figuring out how to make it safe to use. They unpack what agents mean for identity, permissions, containerization, and monitoring, as well as how AI is shifting the CISO's role from saying "no" to safely enabling new technology. They also explore whether AI could help defenders patch vulnerabilities as quickly as they're discovered, and why new AI threats don't make the old security problems go away.

Elena Burger is joined by a16z’s Angela Strange and Gabriel Vasquez to discuss the rise of the "borderless founder": entrepreneurs who bring the networks and insights of their home markets together with the talent, capital, and speed of Silicon Valley to build global companies. Angela and Gabriel trace how a16z's international investing efforts grew from early work in Latin America into a broader global network, and why AI has accelerated the flow of founders and talent between Silicon Valley and startup ecosystems around the world. They explore the advantages borderless founders can bring, from differentiated talent networks and early customers to strong local brands and communities that help open doors across markets. They also discuss how founder diasporas can function like powerful alumni networks, why spending time in Silicon Valley can help founders recalibrate around speed and ambition, and how the next generation of global companies may increasingly be built across multiple countries from day one.

a16z General Partner David George is joined by Grant LaFontaine, co-founder of Whatnot, to unpack how a marketplace that started with collectibles evolved into one of the world's leading live shopping platforms. Grant traces the company's origins from selling Pokémon cards online as a kid to discovering live commerce by watching Whatnot's earliest customers hack together sales on social media. They discuss why Whatnot thinks less like a traditional e-commerce marketplace and more like a digital shopping mall, where discovery, entertainment, community, and commerce all happen at once. Today, users spend roughly 95 minutes a day on the platform, and most aren't even buying something on a given day. They also explore how Whatnot is enabling small businesses to reach global audiences, expanding from collectibles into categories like fashion, food, and golf, and using AI to make sellers more efficient without replacing the human connection at the center of the experience.

a16z's Joel De La Garza is joined by Nick Warner of Neo and Max Pollard of Cotool to discuss what happens when cybersecurity tools built to defend against humans and malware suddenly have to contend with AI agents. As frontier models become more capable of finding and exploiting vulnerabilities, many of the assumptions underlying traditional security are beginning to break. They explore why guardrails designed to stop AI-powered attackers can also prevent security teams from doing their jobs, why defenders increasingly need access to multiple models, and how agentic software creates an entirely new endpoint security problem. They also discuss why static signatures and even newer techniques like honeypots are struggling in a world where software can reason and act autonomously. Recorded around Black Hat, the conversation looks at how security teams are adapting in real time and why the same AI capabilities creating new attack surfaces could ultimately give defenders their biggest advantage yet.

a16z General Partner David George is joined by Will Gaybrick, President of Product & Business at Stripe, to discuss how AI is changing the way Stripe builds products, organizes teams, and thinks about the future of internet commerce. Stripe has evolved from a payments company into a multi-product financial infrastructure platform, while a new generation of AI companies is growing and monetizing faster than previous software cohorts. Will explains why Stripe sees AI productivity as an opportunity to build more rather than simply cut costs, including how its internal coding agents now generate thousands of pull requests each week. They discuss creating founder-like agency inside large companies, building smaller and flatter teams, and why Stripe believes dramatically more software will be created as the cost of building continues to fall. They also look ahead to agentic commerce, why checkout pages could disappear, the potential return of micropayments, stablecoins as infrastructure for a global economy, and a future where AI agents increasingly buy software and services from other machines.

Ben Horowitz, Travis Kalanick, and Erik Torenberg take the stage at Atoms' launch event for a candid fireside conversation about entrepreneurship, company building, and why Kalanick believes the next industrial revolution will be powered by AI. They revisit pivotal moments from Uber's history, including the decision not to acquire Lyft, lessons from scaling one of the world's fastest-growing companies, and how Kalanick has evolved as a founder. The conversation also explores Atoms' vision for industrial AI, why software is moving into the physical world, what it takes to build enduring company cultures, and why Kalanick believes the biggest opportunities of the next decade lie in transforming industries like food production, mining, and manufacturing.

Elena Burger is joined by a16z's Andy McCall and Joe Schmidt to break down two very different ways AI startups can go to market: the lighthouse and the landgrab. Should founders win a handful of marquee customers whose credibility unlocks an entire industry, or move quickly across a broad market where the ROI already speaks for itself? Drawing on Joe's Lighthouse or Landgrab framework and Andy's experience building sales organizations at Samsara and Meraki, they explore how founders can determine which strategy fits their market, when social proof matters more than math, and why the current rush to adopt AI has created a rare window for startups to sell big software again. They also get tactical on POCs, pricing and ACV, hiring early sales teams, moving from mid-market to enterprise, and why founders shouldn't spend too much time perfecting their GTM strategy before talking to customers. As Andy puts it: spend 1% of your time on strategy and 99% executing.

Anish Acharya is joined by Garry Tan, President and CEO of Y Combinator, for a conversation about how AI is rewriting the startup playbook, why founders should be more ambitious than ever, and what two decades of Silicon Valley booms, busts, and missed opportunities have taught Garry about building what's next. Garry reflects on turning down an early opportunity to join Palantir, why chasing what's "hot" is often the wrong strategy, and why the best ideas tend to begin with people pursuing strange, earnest obsessions outside the mainstream. They also explore how AI changes the economics of company building, why traditional SaaS may be losing its advantage, and how tiny teams equipped with hundreds of agents can build businesses at a scale that once required entire organizations. The conversation goes deeper into agentic companies, taste and agency, why "a markdown file is an employee," and how AI could remove layers of bureaucracy that have historically limited organizations. Garry and Anish also discuss the future of consumer AI, the coming "harness wars," why AI adoption may take longer than Silicon Valley expects, and what the next generation of founders can build with intelligence that was unimaginable just a few years ago.

a16z's Joel De La Garza is joined by Emilio Escobar, Chief Information Security Officer at Datadog, to discuss what it takes to secure a company where nearly every employee is using AI and more than 4,000 engineers are working with coding agents. Rather than trying to block new tools, Emilio explains why Datadog chose to embrace AI early and build the security infrastructure needed to use it safely. They unpack how AI changes traditional assumptions around data permissions, credentials, developer access, and software supply chains. Emilio shares how Datadog uses role-based MCP servers and ephemeral credentials, as well as an AI "judge" built by his security team to evaluate the intent behind code and agent skills before they enter the environment. They also discuss why security teams can't afford to wait for commercial solutions to every new AI threat, how the relationship between developers and security teams needs to change, and why Emilio is less concerned about an AI "escaping" than he is about the sheer volume of vulnerabilities AI could uncover.

Angela Strange 和 Gabriel Vasquez 与 Kavak 的首席产品及 AI 官 Alejandro Maza Ayala 一起，深入探讨这家拉丁美洲二手车市场如何围绕 AI 代理进行自我重建，目前 96% 的客户互动和 95% 的交易由代理处理。 Alejandro 解释了为什么 Kavak 认为仅仅给员工提供 AI 工具是不够的，而是围绕代理重新设计了公司的系统、团队和客户体验。他们讨论了为什么 Kavak 在评估上投入的工程精力与构建代理一样多，AI 销售员如何超越人类团队，以及一项实验：AI“CEO”在一个城市的第一月就将利润提升了 50%。 对话还探讨了当代理完成大部分工作时，组织架构会发生什么变化，为什么 Kavak 培训从高管到机械师的每个人使用 AI 进行构建，以及 Alejandro 的观点：那些寻求渐进式 AI 应用的公司可能错过了更大的机会——重新设计组织本身。

Joel De La Garza 与 Truffle Security 联合创始人兼 CEO Dylan Ayrey 以及 Socket 创始人兼 CEO Feross Aboukhadijeh 共同探讨网络安全领域正在发生的最大变革之一：AI 模型不再仅仅发现漏洞——它们正在利用漏洞。随着前沿模型越来越具备黑客能力，软件安全、供应链攻击和网络防御正进入一个全新的时代。 对话探讨了 AI 驱动的黑客攻击、软件供应链攻击、泄露的凭证、零日漏洞、包管理器安全，以及为什么日益自主的 AI 系统所选择的阻力最小的路径也可能是最危险的。他们还讨论了企业、开发者和开源生态系统需要如何适应，因为漏洞发现与利用之间的差距正在不断缩小。

本周，a16z American Dynamism Films 首映了三部短片纪录片，聚焦于应对美国最大工业挑战的公司：Ulysses、Mariana Materials 和 Radiant。在观看这些影片之前，我们重温了与这些公司创始人的对话。 您将听到 Will O'Brien 解释为什么自主水下机器人可以开启海洋探索和安全的新时代，Turner Caldwell 讨论重建美国关键矿产供应链和现代化采矿，以及 Doug Bernauer 分享为什么便携式核微反应堆可以改变我们发电的方式。这些对话共同展示了那些致力于重建美国工业基础的技术和创始人。

Theo Jaffee 与 OpenAI 的首席未来学家 Joshua Achiam 进行了一场对话，讨论了 AI 网络安全、前沿模型能力，以及为什么他认为社会可能已经跨入了 AGI 时代的门槛，却没有完全意识到这一点。 他们讨论了 AI 快速发展的网络能力、国家支持的黑客行为、模型越狱、递归自我改进，以及当 AI 系统开始发现漏洞的速度超过人类修补速度时会发生什么。Joshua 还解释了为什么大多数人已经悄悄适应了那些在几年前还看似不可想象的能力，以及为什么 AI 带来的最大变化可能会逐步到来，而不是一下子全部出现。

Sophia Dew 和 Sofia Puccini 与 Ruby Thelot 进行了一场广泛对话。Ruby Thelot 是设计师、艺术家、网络民族志学者、纽约大学教授，也是 13101401 的创始人。他们讨论了互联网文化、人工智能、数字社区以及技术如何重塑我们彼此之间的关系。 基于多年对在线行为的研究，Ruby 解释了数字文化如何形成、算法为何不仅仅影响我们所见的内容，以及人工智能正在如何改变创造力、沟通和身份认同。他们还探讨了网络民族志、在线亚文化、“机器品味”、人工智能伴侣、社会规范、互联网语言，以及为什么美国人常常表示不喜欢人工智能——尽管他们在日常生活中越来越依赖它。

Marc Andreessen、Chris Dixon 和 Robert Hackett 讨论了加密货币行业面临的最重要的政策辩论之一：推动全面的美国市场结构立法，以及监管清晰度对创新、金融市场和美国技术领导地位可能意味着什么。 他们探讨了CLARITY法案、稳定币、证券法、消费者保护，以及为什么建设者和金融机构都在呼吁明确的规则。在此过程中，他们讨论了早期互联网、FTX、开放金融网络的教训，以及为什么他们认为深思熟虑的监管可以加强创新而非减缓创新。

Sarah Wang 和 Kimberly Tan 与 Decagon 联合创始人 Jesse Zhang 和 Ashwin Sreenivas 一起讨论企业AI代理的演变，为什么该公司越来越依赖开源模型，以及它如何帮助一些世界上最大的公司在生产中部署AI。 Decagon 通过构建自动化客户支持、销售和运营工作流程的代理，成为增长最快的AI公司之一。Jesse（Decagon的CEO）和Ashwin（总裁）解释了公司如何大规模构建企业AI。 他们剖析了为什么Decagon将大部分推理迁移到开源模型，延迟、评估和微调如何塑造生产AI系统，以及为什么企业AI远不止是简单地接入前沿模型。对话还探讨了前向部署工程、企业销售、AI对就业的影响，以及为什么应用公司将继续与基础模型实验室共同繁荣。

Andreessen Horowitz 合伙人 Justine Moore 加入 New Economies，探讨生成式媒体的快速演变以及为什么AI原生内容正达到一个转折点。他们讨论了AI微短剧的兴起，创作者如何构建全新的娱乐形式，以及为什么最大的机会可能不在于取代好莱坞——而在于扩大谁可以创作。 他们还讨论了创作者工具的未来、个人AI代理、生成式视频、AI“垃圾内容”、AI原生工作室的经济学，以及随着消费者AI进入新阶段，创始人下一步应该在哪里建设。