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Erik Torenberg 与 Marc Andreessen 探讨了人工智能、媒体的现状，以及正在重塑互联网的广泛文化与经济变革。他们讨论了围绕AI的叙事——从恐惧到炒作——如何影响公众认知，以及为何实际使用情况讲述的是截然不同的故事。 谈话涉及AI对就业和生产力的影响、"AI原生"建设者的崛起，以及为何更强的能力往往会扩大工作而非淘汰工作。Andreessen 还分析了企业如何适应变化，从重组团队到围绕更全能的"建设者"重新定义角色。 他们还探讨了不断变化的媒体格局——从影响力与信息的动态变化，到传统权威的瓦解——及其对信任、文化和代际态度的影响。沿途，他们触及了从机构权力到新兴互联网亚文化等话题，全面审视了技术如何重塑系统与社会。 时间戳： 00:00 - 开场 00:42 - Anthropic 勒索事件与 AI 末日论文学 02:49 - 自杀式共情与 SPLC 指控 16:33 - AI、就业与 AI 吸血鬼的崛起 25:39 - 技术岗位的未来：从程序员到建设者 30:55 - AI 精神错乱、AI 自我安慰，以及模型其实已经很棒了 38:48 - 为何 AI 情绪调查具有误导性 45:28 - UFO：我们所知的事实与政府隐藏的秘密 52:25 - 给年轻人的建议与代际鸿沟 资源： 在 X 上关注 Marc Andreessen：https://x.com/pmarca 保持更新： 如果您喜欢本期节目，请务必点赞、订阅并与朋友分享！ 在 X 上找到 a16z：https://twitter.com/a16z 在 LinkedIn 上找到 a16z：https://www.linkedin.com/company/a16z 在 Spotify 上收听 a16z 播客：https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX 在 Apple Podcasts 上收听 a16z 播客：https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 关注我们的主持人：https://x.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 http://a16z.com/disclosures。

在a16z Runtime大会的闭幕主题演讲中，普通合伙人Erik Torenberg与公司联合创始人Marc Andreessen和Ben Horowitz共同探讨了大会亮点、大语言模型能力的现状，以及为何尽管资本支出巨大，人工智能并非泡沫。 时间戳： 00:00 开场 01:00 人工智能能否真正创造？智能与发明 03:32 混音、原创性与人类创造力的本质 06:20 Ben谈嘻哈、创新与创意天才 09:10 智能、权力与真正的领导者 12:20 超越智商：领导力、情感与心智理论 16:40 具身智能——身心问题 20:14 人工智能在“心智理论”方面的真实水平 23:02 我们处于人工智能泡沫中吗？基本面与炒作 27:58 平台变革、谷歌的警钟与新用户体验范式 31:00 在独特的人工智能时代指导创始人 34:14 人才、芯片与即将到来的过剩周期 37:10 中美人工智能竞赛与机器人未来 38:52 再工业化与下一步发展 保持更新： 如果您喜欢本期节目，请点赞、订阅并与朋友分享！ 资源： 关注Marc的X账号：https://x.com/pmarca 关注Ben的X账号：https://x.com/bhorowitz 关注a16z的X账号：https://x.com/a16z 关注a16z的领英账号：https://www.linkedin.com/company/a16z 在Spotify收听a16z播客：https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX 在Apple Podcasts收听a16z播客：https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 关注主持人：https://x.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。

山姆·奥特曼带领OpenAI从2015年创立之初的非营利研究机构，在十年后成长为全球最具价值的初创公司。 在本期节目中，a16z联合创始人本·霍洛维茨与普通合伙人埃里克·托伦伯格与山姆展开对话，探讨OpenAI多元化战略背后的核心逻辑、发布Sora的原因、内部使用模型的方式、最佳AI评估方法，以及未来发展方向。 时间轴： 0:00 开场 0:41 OpenAI的愿景与基础设施 2:37 商业模式与垂直整合 5:08 AGI、Sora与社会协同进化 8:01 AI交互界面的未来 9:12 AI科学家与科学进步 11:44 对进展与模型能力的反思 16:17 山姆的CEO经历与领导力心得 17:34 战略合作与基础设施规模化 25:05 监管、安全与社会影响 28:33 版权、开源与内容创作 33:15 能源、政策与AI资源需求 37:07 变现模式与用户行为 43:03 人才争夺战与个人思考 45:20 给创始人的建议 资源链接： 关注山姆的X账号：https://x.com/sama 关注OpenAI的X账号：https://x.com/openai 了解更多OpenAI信息：https://openai.com/ 体验Sora：https://sora.com/ 关注本的X账号：https://x.com/bhorowitz 持续关注： 如果您喜欢本期节目，请点赞、订阅并分享给朋友！ 关注a16z的X账号：https://x.com/a16z 关注a16z的领英页面：https://www.linkedin.com/company/a16z 在Spotify收听a16z播客：https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX 在Apple Podcasts收听a16z播客：https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 关注主持人：https://x.com/eriktorenberg 请注意：本内容仅供信息参考，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。更多详情请参阅a16z.com/disclosures。

当人工智能开始为所有人生成内容，却没人愿意看时，会发生什么？ 在本期节目中，MSNBC的克里斯·海耶斯与广告技术资深人士安东尼奥·加西亚·马丁内斯，与a16z普通合伙人埃里克·托伦伯格一同探讨注意力经济的变化：从“AI垃圾内容”和泛滥的信息流兴起，到我们真正想关注的内容与平台强推内容之间的差异。 他们探讨了： - 人工智能如何改变内容的创作与呈现方式 - 为什么互联网广告大多仍然糟糕 - 群聊的回归——以及大众文化的缓慢消亡 基于克里斯的新书《塞壬的呼唤》，本期节目坦诚审视了人工智能可能在我们网络生活中放大或破坏的方面。 时间戳： 00:00 注意力时代与AI垃圾内容 00:40 嘉宾介绍与背景 02:02 注意力的污染与垃圾信息 03:03 AI内容：垃圾还是创意？ 05:14 人类对AI生成内容的反应 07:01 社交媒体、名声与自我认知 09:45 群聊解决方案与社区 14:43 收入模式与有用技术vs.盈利技术 19:22 文化的碎片化与同质化 22:25 媒体、广告与AI的未来 24:35 过度碎片化、同质化与本地文化的消亡 29:54 算法、方言与新的全球单一文化 33:00 从垃圾到实质，还是饱和？ 36:00 AI、界面与人机对话 38:18 将每一秒商品化 41:47 增长、崩溃与开放网络的未来 资源： 在X上关注克里斯：https://x.com/chrislhayes 在X上关注安东尼奥：https://x.com/antoniogm 了解更多关于克里斯的新书《塞壬的呼唤》：https://sirenscallbook.com/ 了解更多关于安东尼奥的著作《混沌猴子》：https://www.harpercollins.com/products/chaos-monkeys-antonio-garcia-martinez?variant=32207601532962 保持更新： 告诉我们你的想法：https://ratethispodcast.com/a16z 在Twitter上关注a16z：https://twitter.com/a16z 在LinkedIn上关注a16z：https://www.linkedin.com/company/a16z 在你最喜欢的播客应用上订阅：https://a16z.simplecast.com/ 关注我们的主持人：https://x.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。

在本期《本与马克秀》中，a16z联合创始人马克·安德森和本·霍洛维茨与a16z普通合伙人、媒体公司Turpentine创始人埃里克·托伦伯格坐在一起，剖析互联网如何粉碎了旧媒体秩序，并重塑了美国权力的运作方式。 这场对话始于对媒体演变的探讨，但很快便扩展为更宏大的主题：关于真相、信任以及机构权威的崩塌。他们探讨了社交媒体如何同时成为X光机和引擎，为何真实感如今胜过精致包装，以及政治与新闻业的规则如何被永久改变。 他们共同剖析了以下内容： - 为何2017年标志着科技界与新闻界之间的结构性断裂 - 客观性、行动主义与“向权力说真话”之间的张力 - 为何播客主而非评论员正在设定议程 - 杠铃策略如何重塑媒体：短视频病毒式传播与长内容深度并存 从水门事件、罗根的崛起、传统守门人的衰落，到奥巴马、特朗普和卡戴珊家族背后的媒体策略——本期节目探讨了我们如何走到今天，接下来会发生什么，以及这对创始人、选民和任何试图构建（或讲述）故事的人意味着什么。 时间码： 00:00 引言 00:40 媒体演变：从集中化到颠覆 02:33 互联网对传统媒体的影响 09:32 现代科技新闻中的行动主义 12:30 机构信任的兴衰 15:07 认识传统媒体的转变 20:51 新闻业：客观性与行动主义之争 25:08 马丁·古里与权威的崩塌 28:30 特朗普时代与媒体范式转变 32:23 去中心化：从谈话广播到社交媒体 37:04 社交媒体是引擎还是摄像机？ 43:50 本对媒体与政治的早期看法 49:49 特朗普作为传统媒体与社交媒体之间的桥梁 55:44 媒体培训与未经修饰的真实性 57:57 真人秀、职业摔跤与新剧本 1:01:04 戏剧性、党派之争与个性时代 1:03:13 有线新闻如何变成24/7娱乐 1:09:27 UFC早期争取媒体准入的挣扎 1:14:44 杠铃效应：短视频与长播客 1:29:30 失势时的颠覆工具 1:32:00 创始人的媒体策略 1:37:16 直接触达的力量：创始人、品牌与受众 收听我们！ Apple：https://bit.ly/3SdsfNt Spotify：https://spoti.fi/3SclPOr 资源： 马克的X账号：https://x.com/pmarca 马克的Substack：https://pmarca.substack.com/ 本的X账号：https://x.com/bhorowitz 埃里克的X账号：https://x.com/eriktorenberg 埃里克的Substack：https://eriktorenberg.substack.com/ 保持关注： 在X上找到我们：https://x.com/a16z 在领英上找到我们：/ a16z 此信息仅供一般教育目的，不构成购买、持有或出售任何投资或金融产品的建议。Turpentine是a16z Holdings, L.L.C.的收购项目，并非银行、投资顾问或经纪交易商。本播客中提及的个人和公司并非认可AH Capital或其任何关联公司（包括但不限于a16z Perennial Management L.P.）。本播客中提及、引用或描述的任何投资或投资组合公司并不代表a16z的所有投资，且无法保证这些投资将盈利，或未来的其他投资将具有类似特征或结果。a16z的投资清单可在https://a16z.com/investment-list/获取。所有投资均涉及风险，包括可能损失本金。过往表现不保证未来结果，所呈现的观点不能视为未来表现的指标。在做出涉及法律、税务或会计影响的决策前，应咨询适当的专业人士。信息来源于发布日认为可靠的来源，但a16z不保证其准确性。

在美国，一座新矿从审批到建成可能需要超过15年——然而，从智能手机到战斗机，再到人工智能数据中心，我们依赖的几乎所有现代技术，都离不开关键矿物的稳定供应。 在本期节目中，埃里克·托伦伯格在演播室与玛丽安娜矿业公司创始人特纳·考德威尔，以及美国活力基金普通合伙人艾琳·普莱斯-赖特和合伙人瑞安·麦肯塔什一同探讨。 特纳在特斯拉工作了近十年，从工厂设计一路向上游发展，涉足电池材料和采矿领域。如今，他正在打造一家新型采矿和精炼公司——垂直整合、软件优先——旨在满足我们工业未来的需求。 我们深入探讨了该行业为何如此问题重重，将矿石转化为可用材料究竟需要什么，以及美国如何重建其开采、精炼和制造最重要物资的能力。 时间码： 00:00 引言 00:53 关键矿物的重要性 01:54 采矿过程解析 04:05 采矿行业面临的挑战 05:46 采矿领域的职业路径 06:40 个人经历与见解 11:16 特斯拉的垂直整合 12:47 地缘政治与市场动态 16:24 采矿技术创新 20:47 行业挑战与机遇 28:19 玛丽安娜在建筑和采矿领域的产品与理念 29:30 在采矿中利用技术 32:02 自动化化学处理与优化精炼运营 35:25 精炼厂规模化与调试的挑战 36:55 决定建设什么及在何处合作 38:05 新技术的供应链与商业部署 40:20 风险投资与采矿行业 42:59 关键矿物及其重要性 47:45 美国的许可与监管挑战 53:46 国际战略与未来目标 54:44 结论：重建基础设施与能力 资源： 在X上关注特纳：https://x.com/tbc415 在X上关注艾琳：https://x.com/espricewright 在X上关注瑞安：https://x.com/rmcentush 保持更新： 告诉我们你的想法：https://ratethispodcast.com/a16z 在Twitter上关注a16z：https://twitter.com/a16z 在LinkedIn上关注a16z：https://www.linkedin.com/company/a16z 在你最喜欢的播客应用上订阅：https://a16z.simplecast.com/ 关注我们的主持人：https://x.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。

人工智能是基础设施的第四大支柱吗？ 基础设施不会消失——它会层层叠加。如今，人工智能正与计算、存储和网络一起，成为新的基础层。 埃里克·托伦伯格采访了a16z的马丁·卡萨多、詹妮弗·李和马特·伯恩斯坦，深入探讨了基础设施在人工智能时代如何演变——从模型和智能体，到开发者工具和用户行为的变化。 我们深入探讨了如今基础设施的真正含义，它与企业级基础设施有何不同，以及为什么软件本身正在被颠覆。此外，我们还探讨了技术用户作为买家的崛起、什么让基础设施公司具有防御性，以及从云计算到新冠疫情再到人工智能的过去几波浪潮如何重塑我们的构建和投资方式。 时间戳： 00:00 基础设施介绍 00:48 定义基础设施及其组成部分 02:27 第四层：人工智能模型 06:34 基础设施的演变 17:46 开发者工具与人工智能浪潮 21:27 数据引擎系统 22:11 人工智能基础设施的防御性 25:28 扩张与收缩阶段 27:09 人工智能与基础设施面临的挑战 28:32 人工智能模型与泛化 30:59 对安德烈·卡帕西关于人工智能演讲的思考 34:09 人工智能与人类期望 36:18 人工智能时代开发者的角色 40:31 合成数据 43:17 人工智能智能体 45:16 垂直整合与水平专业化 资源： 在X上关注马丁：https://x.com/martin_casado 在X上关注詹妮弗：https://x.com/JenniferHli 在X上关注马特：https://x.com/BornsteinMatt 保持更新： 告诉我们你的想法：https://ratethispodcast.com/a16z 在Twitter上关注a16z：https://twitter.com/a16z 在LinkedIn上关注a16z：https://www.linkedin.com/company/a16z 在你最喜欢的播客应用上订阅：https://a16z.simplecast.com/ 关注我们的主持人：https://x.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；并且不针对任何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。

a16z Crypto普通合伙人Ali Yahya、Arianna Simpson和Erik Torenberg深入探讨了当前加密货币领域真正奏效的应用——从稳定币作为现实世界支付层的崛起开始。他们讨论了Stripe和SpaceX等公司如何采用稳定币，监管变化如何为加密初创公司打开新大门，以及人工智能与加密货币如何开始交汇。 他们还涵盖了： - 去中心化社交网络的未来 - 以太坊、Solana及其他公链的现状 - 仍阻碍行业发展的误解 - 一场务实的对话：什么是真实的，什么是炒作，以及加密货币最终在哪些领域找到了突破口。 时间码： 00:00 引言 00:45 比特币的原始愿景与演变 01:47 稳定币：游戏规则改变者 03:02 稳定币的当前应用场景 05:22 金融体系面临的挑战 07:08 稳定币与金融体系的未来 09:02 大公司在加密货币中的角色 18:21 去中心化社交网络与消费者偏好 23:20 人工智能与加密货币的交汇 31:32 关于加密货币的误解 35:54 智能合约平台之争 39:33 政策变化与未来机遇 资源： 在X上关注Ali：https://x.com/alive_eth 在X上关注Arianna：https://x.com/AriannaSimpson 保持更新： 告诉我们你的想法：https://ratethispodcast.com/a16z 在Twitter上关注a16z：https://twitter.com/a16z 在LinkedIn上关注a16z：https://www.linkedin.com/company/a16z 在你最喜欢的播客应用上订阅：https://a16z.simplecast.com/ 关注我们的主持人：https://x.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。

在本期《本周消费》节目中，a16z 普通合伙人 Anish Acharya 和 Erik Torenberg 与 a16z 董事会合伙人、前微软 Windows 部门总裁 Steven Sinofsky 一同深入探讨了当前 AI 热潮如何与过去的计算转型相似（以及不同之处）。 他们探讨了 AI 是处于“Windows 3.1”阶段，还是仍处于最早期阶段；为什么消费者采用速度超过了开发者的准备程度；以及部分自主性、锯齿智能和“氛围编码”等框架如何塑造未来的构建方向。他们还深入分析了真正的瓶颈所在——并非技术本身，而是公司、产品和人员的工作方式。 时间码： 00:00 引言 00:35 讨论 Andrej Karpathy 的演讲 02:17 AI 与工具的早期阶段 03:23 氛围写作与氛围编码 07:33 自动化与人类判断 15:13 产品管理的未来 15:55 平台转型与氛围编码 17:54 编程语言的演变 23:07 AI 在创意写作中的应用 28:06 谷歌在科技行业的地位 资源： 在 X 上关注 Anish：https://x.com/illscience 在 X 上关注 Steven：https://x.com/stevesi 保持更新： 告诉我们您的想法：https://ratethispodcast.com/a16z 在 Twitter 上关注 a16z：https://twitter.com/a16z 在 LinkedIn 上关注 a16z：https://www.linkedin.com/company/a16z 在您最喜欢的播客应用上订阅：https://a16z.simplecast.com/ 关注我们的主持人：https://x.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。

Arcjet 首席执行官 David Mytton 与 a16z 合伙人 Joel de la Garza 共同探讨了管理网站及网络应用访问权限与操作行为的日益复杂性。核心挑战在于判断自动化流量究竟来自恶意行为者与麻烦的机器人，还是代表真实客户购买产品的 AI 代理。 Joel 与 David 深入分析了如何在分析每个请求时不增加延迟，以及边缘端更快的推理如何为欺诈防护、内容过滤乃至广告技术开辟新可能。 讨论主题包括： - 为何传统威胁分析无法适用于 AI 驱动的网络 - 全上下文安全检查的必要性 - 如何实现亚秒级、高性价比的推理 - 每次访问背后潜藏的多种行为者与操作类型 正如 David 所言，降低推理成本是让应用能够基于完整上下文窗口（即关于用户、会话及应用的所有已知信息）采取行动的关键。 关注社交媒体上的各位嘉宾： David Mytton：https://x.com/davidmytton Joel de la Garza：https://www.linkedin.com/in/3448827723723234/ 查看 a16z 在人工智能领域的全部动态，包括文章、项目及更多播客：https://a16z.com/ai/ 时间戳： 00:00 引言 00:34 AI 机器人的兴起及其潜在益处 02:43 良性流量与恶意流量 | 开发人员与安全团队面临的挑战 03:23 该领域的解决方案 04:37 Robots.txt 的作用 06:42 理解 AI 代理及其运作方式 10:55 在互联网规模下控制流量 12:51 构建指纹识别体系 18:45 机器人行为及新一代 AI 带来的可能性 22:39 本地运行 LLM 的未来

在本期《Ben & Marc Show》中，a16z联合创始人马克·安德森和本·霍洛维茨深入探讨了安德森·霍洛维茨创立背后未经修饰的故事——以及他们如何着手重塑风险投资本身。 马克和本首次详细阐述了打造一家面向未来的世界级风险投资公司的起源、战略和理念——而不仅仅是着眼于下一支基金。他们揭示了如何通过大胆的品牌、全栈支持模式以及对支持杰出创业者的长期承诺来打破行业常规——其核心理念是创始人理应获得真正的支持，而不仅仅是支票。 加入他们引导对话的是埃里克·托伦伯格——安德森·霍洛维茨最新的普通合伙人——他首次在《Ben & Marc Show》中担任主持。埃里克是一位科技企业家、投资者，也是媒体公司Turpentine的创始人。您可以通过马克的公告[此处]了解更多关于埃里克的信息。 他们共同探讨了： - 为什么传统风投需要革新 - a16z如何通过平台模式而非合伙人模式实现规模化 - 重塑当今风险投资的“杠铃策略” - 为什么即使在人工智能时代，风险投资仍然是一门人类手艺 我们非常高兴欢迎埃里克加入团队——希望您喜欢这次对a16z结构、理念和未来的内部解读。 涵盖主题： 00:00 - 开场 / 欢迎埃里克·托伦伯格加入a16z 00:26 - 为什么传统风险投资存在缺陷 03:05 - 马克谈发现风投及其传奇人物 05:12 - 挺过互联网泡沫破裂和天使投资崩溃 07:05 - 帮助创始人融资 / 修复风投关系 08:47 - a16z战略：构建支持平台 12:07 - 首支基金的成功案例：Skype、Instagram、Slack、Okta 12:50 - 打造一个“主宰世界的巨兽” 15:00 - “寿司船”风投问题 18:07 - 以不同方式对待有限合伙人 21:40 - 马克和本的工作关系 23:30 - 为社交媒体时代更新a16z的媒体策略 27:20 - 去中心化媒体环境的历史 30:36 - 企业品牌的衰落与直接面向受众 36:06 - 公司命名 40:13 - 构建a16z的“电影宇宙”人才库 42:16 - 创建联邦模式 51:02 - 决定营销公司 53:26 - 招募普通合伙人 56:33 - 向全栈公司演进 01:03:53 - 杠铃理论：中型风投的消亡 01:11:50 - 为什么风险投资应保持资金过剩 01:19:50 - a16z何时知道自己能跻身顶级行列 01:25:58 - 风险投资是艺术，而非科学 本期节目提及的书籍： - 《公众的反叛》作者：马丁·古里 https://bit.ly/4jDloba - 《臭名昭著的写手》作者：埃里克·伯恩斯 https://bit.ly/4lSkaKy 收听我们！ Apple: https://bit.ly/3SdsfNt Spotify: https://spoti.fi/3SclPOr 资源： 马克的X账号：https://x.com/pmarca 马克的Substack：https://pmarca.substack.com/ 本的X账号：https://x.com/bhorowitz 埃里克的X账号：https://x.com/eriktorenberg 埃里克的Substack：https://eriktorenberg.substack.com/ 保持关注： 在X上找到我们：https://x.com/a16z 在LinkedIn上找到我们：https://www.linkedin.com/company/a16z 此信息仅供一般教育目的，并非购买、持有或出售任何投资或金融产品的建议。Turpentine是a16z Holdings, L.L.C.的收购对象，并非银行、投资顾问或经纪交易商。本播客中提及的个人和公司并非认可AH Capital或其任何关联公司（包括但不限于a16z Perennial Management L.P.）。本播客中提及、引用或描述的任何投资或投资组合公司并不代表a16z的所有投资，且无法保证这些投资将盈利，或未来进行的其他投资将具有类似特征或结果。a16z的投资清单可在https://a16z.com/investment-list/获取。所有投资均涉及风险，包括可能损失本金。过往业绩并不保证未来结果，所呈现的观点不能视为未来业绩的指标。在做出涉及法律、税务或会计影响的决策前，您应咨询适当的专业人士。信息来源于出版日期被认为可靠的来源，但a16z不保证其准确性。

America’s defense, energy, manufacturing, and robotics booms are increasingly becoming one story: the rebuilding of the country’s industrial base. This year’s AD50 highlights the companies building across that stack, from critical materials and factories to the systems they ultimately power. a16z’s Erin Price-Wright and Connor Love join Elena Burger to unpack what has changed. They discuss how defense demand can help finance manufacturing capacity that ultimately serves much larger commercial markets, why AI is changing the economics of American factories, and how a new generation of founders is applying lessons from SpaceX, Tesla, Anduril, and Palantir to mining, energy, manufacturing, and defense. They also make the case that these companies are no longer simply important to America. With private capital increasingly willing to fund physical infrastructure and production before government demand is guaranteed, they can become some of the most valuable companies in the world. Timestamps: 0:00 Intro 0:49 No Defense Base Without an Industrial Base 5:09 How DJI Went From Drones to Robots 8:38 The Elon Playbook: Vertical Integration 10:16 Mariana Minerals Bought a Copper Mine 13:32 Why AI Still Can't Turn a Wrench 17:46 Building It Before the Government Asks 22:07 When Hardware Became the Most Valuable Game 24:47 Steel-Toed Boots and Giant American Flags 28:39 How Defense Unlocks the Robot Wave 32:05 The Rise of the Manufacturing Middle 37:43 Spinning Out of Anduril and SpaceX 41:16 America's Real Edge: Its Capital Markets 44:23 The US vs China Power Chart Resources: Explore the AD50 : https://www.a16z.news/p/ad-50-2026-production-is-deterrence Follow Erin Price-Wright: https://x.com/espricewright Follow Connor Love on X: https://x.com/ConnorLoveCA Follow Elena Burger on X: https://x.com/VirtualElena Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

Travis Kalanick, co-founder of Uber, on what changes by a founder's third or fourth company. Before Uber, Travis left UCLA in 1998 to help launch the file-sharing startup Scour, then founded Red Swoosh, which he sold to Akamai in 2007. His current company, Atoms, builds specialized robotics and automation for food, mining, and transport. Watch the full episode on The a16z Show.

Mark Cuban thinks AI could transform healthcare, but it won’t fix opaque prices and broken incentives on its own. His starting point for employers: run every benefits contract through AI and ask where you’re getting ripped off. Mark joins a16z General Partner Julie Yoo and Erik Torenberg to discuss what he’s learned building Cost Plus Drugs, why trust is its core product, and how greater transparency could put pressure on healthcare costs. They also explore where AI can help, what personalized medicine could look like, how the role of doctors may change, and why Mark believes founders should use AI to learn, not as an excuse to stop thinking. Timestamps: 0:00 Intro 0:50 Cost Plus Drugs: 111 Meds to 4,000 SKUs 6:25 $20M Mobile Factories That Make Any Drug 9:00 Direct Contracts That Bypass the Middlemen 11:30 How UnitedHealth Really Makes Money 15:25 Why AI Can't Save You After You Sign 19:45 "Where Are They Screwing Me Over?": Cuban's AI Prompt 25:50 Why Tech Alone Won't Fix Healthcare 31:40 The Biggest AI Win Is for Patients 35:30 The End of One-Size-Fits-All Drugs 39:23 Why Doctors Won't Be Replaced 47:36 If Mark Advised the President 53:06 His Advice to the AI Industry Resources: Follow Mark Cuban on X: https://x.com/mcuban Follow Julie Yoo: https://x.com/julesyoo Mark Cuban Cost Plus Drug Company: https://costplusdrugs.com/ Cost Plus Wellness: https://costpluswellness.com/ Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

What should school look like if AI can help students learn dramatically faster? Alpha School principal Joe Liemandt argues the answer isn’t to fill the extra time with more academics, but to redesign school around the skills traditional classrooms struggle to teach: agency, teamwork, entrepreneurship, communication, and doing hard things. Joe joins Erik Torenberg and Horowitz Andreessen Academy co-founder Gagan Biyani to discuss Alpha’s model, where students spend two hours a day on core academics and the rest developing real-world skills through projects. They explore what that approach can teach higher education, why motivation may matter more than curriculum, and how HAA is applying similar principles to college-age students by putting building, relationships, and real-world experience at the center of the learning experience. They also discuss what parents need to believe before choosing a new educational model, why Joe expects AI to drive an explosion in entrepreneurship, and why the most important question for students may not be which field is safest from AI, but what they care enough about to build. Timestamps: 0:00 Intro 0:40 How Alpha Teaches Kids 10x Faster 6:44 Why Kids Love School More Than Vacation 10:40 The 85% Rule That Keeps Kids Engaged 15:14 Why This Summer's Interns Are Terrified 23:01 Why Ivy League Parents Are Switching 27:21 You Just Need Someone to Go First 35:37 "My Son's a Moron": Joe's Dropout Story 42:12 What You'd Study If You Were 18 Today 48:31 Founders School: Earn $1M or Get Tuition Back Resources: Learn more about Horowitz Andreessen Academy: https://www.theacademysf.com/ Learn more about Alpha School: https://alpha.school/joe-liemandt-and-the-future-of-education/ Follow Joe Liemandt on X: https://x.com/jliemandt Follow Gagan Biyani: on X https://x.com/gaganbiyani Follow Erik Torenberg on X: https://x.com/eriktorenberg Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

DoorDash was originally called PaloAltoDelivery.com and grew out of a business school project Xu started at Stanford in 2013. He says the V1 took 45 minutes. Before that, he worked at McKinsey, eBay, and Square.

AI agents are approaching a point where they can use computers faster than expert humans, turning the user interface itself into a universal way for AI to interact with software. OpenAI’s Ari Weinstein and James Sun argue that computer use is now entering its scaling moment, as better models, faster execution, and stronger safety controls make agents practical for more consumer and enterprise work. Ari and James join a16z’s Seema Amble and Fabrizio Serafini to explain how computer-using agents are already navigating legacy ERPs, reconciling financial data, handling procurement, and operating software without APIs or custom integrations. They also discuss why the harness around the model can matter as much as model intelligence, how enterprises can set deterministic guardrails around agent behavior, and why computer use could simultaneously extend the life of legacy software and accelerate the arrival of new AI-native applications. Looking ahead, they make the case for a more proactive and fluid relationship with computers, where agents work in the background, collaborate with users in real time, and increasingly adapt to us rather than requiring us to learn how to use them. Timestamps: 00:00 Intro 00:51 What GPT-6 Astra unlocks for consumers and enterprises 05:43 How the OpenAI team uses dots 08:05 Approaching superhuman: speed, cost and control 11:24 Agents on the open web and the ad problem 13:51 Which tasks to hand over to an agent 17:21 Getting enterprises to trust computer use 20:04 The best enterprise use cases: boring is opportunity 25:36 Will agents run legacy software or replace it? 32:38 Advice for founders building on computer use 37:31 What’s next for computer use Resources: Follow Seema Amble on X: https://x.com/seema_amble Follow Fabrizio Serafini on X: https://x.com/fabserafini Follow Ari Weinstein on X: https://x.com/AriX Follow James Sun on X: https://x.com/JamesZmSun Related a16z research from Seema Amble and Fabrizio Serafini: Can Agents Use a Computer Yet? We’ve Got the Data Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

Amazon expects to spend roughly $220 billion in CapEx in 2026, yet CEO Andy Jassy says the company is still leaving meaningful AI demand on the table. He believes the buildout could ultimately help make AWS a $1 trillion annual run-rate business. Andy joins a16z co-founder Ben Horowitz and Erik Torenberg to discuss why AI demand is growing so quickly, how Amazon decides how much infrastructure to build, and why lower inference costs could unlock broader enterprise adoption. They also discuss how AI is reshaping Amazon itself, from smaller teams building products faster to reimagining Alexa, and why Andy sees cybersecurity, human judgment, and critical thinking as increasingly important in the AI era. Timestamps: 00:00 Intro 00:47 Five years as CEO: from pandemic overbuild to the AI boom 09:38 How much AI CapEx is too much? 15:44 Is the backlash against data centers fair? 20:56 AI demand as a barbell, and the capacity crunch 28:12 Annapurna Labs and Amazon’s custom chips 33:36 Why Amazon invested in Anthropic and OpenAI 37:14 Smaller teams, faster security: rebuilding orgs for AI 46:29 Rebuilding Alexa and reinventing existing products 53:06 Advice for an 18-year-old 56:36 How Amazon avoids spreading itself too thin Resources: Follow Andy Jassy on X: https://x.com/ajassy Follow Ben Horowitz on X: https://x.com/bhorowitz Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

a16z is expanding a16z crypto into a16z Frontier, a broader mandate to back founders building at the edges of what's possible. Chris Dixon joins Marc Andreessen, Ben Horowitz, and Erik Torenberg to explain why founders should increasingly ask not “is this too ambitious?” but “is this ambitious enough?” — and how AI could pull decades of technological progress forward across hardware, robotics, entertainment, science, and entirely new forms of media. They also explore the coming internet of AI agents, why open source matters as AI becomes more powerful, and why some of the most important companies of the next decade may combine technologies and business models that previously belonged in separate categories. Timestamps: 00:00 Intro 00:49 Announcing a16z Frontier 15:15 Is being too early still a risk? 18:18 The widest gap between early adopters and everyone else 22:06 Will AI favor incumbents or startups? 28:33 AI social networks and agents online 30:08 Beyond “product manager brain”: games, film and hardware 38:22 Is AI centralizing? The case for open source 46:37 The internet’s broken covenant 49:12 What comes after the iPhone 53:59 What to study if you’re 18 today Resources: Follow Chris Dixon on X: https://x.com/cdixon Follow Marc Andreessen on X: https://x.com/pmarca Follow Ben Horowitz on X: https://x.com/bhorowitz Learn more about a16z Frontier: a16zfrontier.com a16zfrontier.substack.com x.com/a16zfrontier Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

Instagram started as Burbn, a location check-in app that Kevin Systrom and Mike Krieger moved on from. This is Systrom on what they did differently the second time. Source: Foundation 16, Kevin Rose

More than 300 million people use ChatGPT for health and wellness questions each week. OpenAI’s Karan Singhal thinks that’s only the beginning: the longer-term goal is AI that can help people navigate their health proactively, while making trustworthy medical intelligence dramatically more accessible. Karan joins a16z Partner Jay Rughani to explain why OpenAI trains healthcare expertise directly into its general-purpose frontier models, how it works with hundreds of medical experts to evaluate safety and disagreement, and why the company sees AI as a complement to doctors rather than a replacement for medical care. They also discuss integrations with Epic, AI copilots for clinicians, and what happens as personal agents begin navigating appointments, health records, and other fragmented parts of the healthcare system. Looking further ahead, Karan makes the case that the cost of medical intelligence could continue to fall dramatically, allowing AI to act as a safety net for patients and clinicians, surface insights from years of health data, and potentially accelerate the discovery and validation of new treatments. Timestamps: 00:00 Intro 01:00 The story of Nathan Labenz's son 07:57 A doctor in your pocket 14:20 Teaching AI who it's talking to 19:44 Medical consensus and its 5–17 year lag 28:03 The mental health problem 30:59 Information vs actual care 38:21 A "move 37" for health 43:39 Driving down the cost of care 50:27 Procedural care and what's next Resources: Follow Karan Singhal on X: https://x.com/thekaransinghal Follow Jay Rughani on X: https://x.com/JayRughani Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

Jean-Denis Greze was CTO of Plaid. His next startup, an AI tax product for small businesses, made nearly $1M in its first year and was on track for $2.5M to $3M. He shut it down anyway. The reason: in the age of AI, it was never going to be venture scale. Incumbents could use the same technology to automate tax prep for their existing customers faster than a startup could win market share. Tax is also sold once a year, so he had about a one-month window to pivot before the door closed. The pivot became Town, an AI assistant that works from your email and calendar. It found product-market fit fast enough that Ben Horowitz messaged a16z partner Alex Rampell to say it “changed my life,” without knowing whose company it was. In this episode, Alex and Jean-Denis cover: - Why a profitable business can still be the wrong business to build - How to know when to kill something that’s working - Why Town competes with OpenAI and Anthropic, the companies it buys its tokens from - Why the “forward deployed engineer” model gets worse as AI gets better - How a $1/month AI routine can save a restaurant thousands - What agents mean for Amazon, advertising and the future of shopping Timestamps: 00:00 Intro 00:56 Leaving Plaid for the SMB tax bet 06:57 Walking away from a working business 10:28 How Town was born 16:16 The iPhone moment for AI at work 23:22 Town as a token-allocation machine 29:10 Horizontal vs vertical 34:03 Why Town over OpenAI and Anthropic 44:32 Why AI is underhyped outside the Valley 52:33 Neutral agents and the broken ad covenant Resources: Follow Alex Rampell on X: https://x.com/arampell Follow Jean-Denis Greze: https://x.com/jgreze Learn more about Town: https://town.com Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

Mark Zuckerberg, founder and CEO of Meta, on why staying stagnant is the riskiest choice. Source: How to Build the Future, Y Combinator, 2016.

AI can write production code in minutes. But can it debug and operate that code when something goes wrong? Grepr CEO Jad Naous argues that running real-world systems is a fundamentally harder challenge, with unpredictable workloads, failures, and countless possible states. Jad joins a16z General Partner Martin Casado to discuss how coding agents have transformed engineering inside his 10-person team, including projects that once took six months and now take roughly 10 days. They also explore whether AI can troubleshoot production systems by detecting changes, forming hypotheses, and running experiments, and why the best engineers may be gaining the most leverage from AI. Timestamps: 00:00 Intro 00:54 From Stanford to founding Grepr 07:23 Why observability got so expensive 10:15 How AI is changing engineering 18:25 Jad's AI stack and workflow 22:55 A six-month project in ten days 24:55 Can AI reason about operations? 30:10 How Grepr catches incidents early 42:16 Why security is the harder problem 53:33 What comes after code Resources: Follow Jad Naous on X: https://x.com/jadtnaous Follow Martin Casado on X: https://x.com/martin_casado Learn more about Grepr: https://www.grepr.ai/ Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

Adam Neumann, co-founder of WeWork and Flow, on thinking big while staying grounded in the work. Source: The Diary of a CEO with Steven Bartlett, 2026.

What happens when AI agents start spending your money? VGS' Terry Angelos argues that agentic commerce will need new payment infrastructure that gives AI the ability to transact without sacrificing the protections consumers expect. Terry joins a16z General Partner Alex Rampell to discuss the emerging wallet wars, how agentic tokens could enable secure purchases, and what happens when an AI buys the wrong thing. They also explore why merchants may resist agentic commerce, how AI could change credit card rewards and subscriptions, and who controls the customer relationship when agents make purchasing decisions on our behalf. Timestamps: 00:00 Intro 00:56 What a payment credential actually is 09:48 When agentic commerce changed the game 13:43 The wallet wars 19:42 Stripe as the AOL of agentic commerce 23:51 The new agentic token standard 29:45 The three checks behind every transaction 35:43 Why Amazon blocked Muse 39:47 Over-hyped vs under-hyped 47:09 Where VGS goes next Resources: Follow Terry Angelos on X: https://x.com/terryangelos Follow Alex Rampell on X: https://x.com/arampell Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

a16z Partner Lisha Li sits down with AI researcher and Trillium founder Nathan Lambert to ask how durable the gap really is between frontier AI labs and open models. Nathan argues that OpenAI and Anthropic have structural advantages in compute, capital, and the ability to pioneer new training paradigms, but open-model labs, particularly in China, have proven remarkably capable of following close behind. They unpack how much of that comes from distillation versus technical talent, why compute remains a major constraint, and how data, RL environments, and post-training are increasingly shaping model progress. They also discuss Nathan’s new nonprofit research lab, Trillium, and its plans to open up parts of AI research that are increasingly difficult to study from the outside. That includes releasing RL environments and documenting how AI is actually being used to automate AI research, with the goal of demystifying recursive self-improvement and giving researchers and policymakers a clearer picture of what frontier systems can and cannot do. Timestamps: 00:00 - Intro 00:58 - Compute, data, and talent 08:48 Compute-starved, and the distillation debate 14:57 Is distillation just competition? 19:24 Open and closed on different timescales 25:00 Frontier progress is really about data 31:29 What Trillium will build 38:05 Demystifying RSI 44:32 Can open source pre-train? 49:32 Harnesses and agent swarms Resources: Learn more about Trillium: https://trilliumlabs.org/ Follow Nathan Lambert on X: https://x.com/natolambert Nathan Lambert’s Newsletter: https://www.interconnects.ai/ Follow Lisha Li on X: https://x.com/lishali88 Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

In 2017, Mark Zuckerberg returned to Harvard, the university he left in 2004 to build Facebook, to deliver the commencement address. This is his case against the single eureka moment. Source: Harvard University

a16z General Partner Martin Casado sits down with MakerMaker.AI co-founders Dhaval Adjodah and Owen He to unpack recursive self-improvement, or RSI, and separate the increasingly popular term from what it would actually mean for an AI system to improve itself. Dhaval and Owen distinguish true RSI from simply using AI to automate pieces of AI development. Their goal is a closed loop where the system being improved is also doing the improving, across components including models, harnesses, and eventually more of the AI stack. They explain why closing that loop still doesn’t guarantee runaway “takeoff,” and why measuring real progress requires accounting for both capability gains and the resources required to achieve them. They also share what they’re seeing in their own research, including early signs that self-improving systems can increase performance while improving efficiency. And they dig into the practical challenges of proving those gains are real, from variance and reward hacking to the enormous difficulty of distinguishing genuine improvement from noise. Timestamps: 00:00 - Intro 00:58 - What actually counts as RSI 05:10 - Capability as a numerator and denominator 06:58 - Closed loop vs takeoff 08:32 - Humans as agents in the system 13:39 - Why most RSI papers don't scale 17:41 - Why they chose inference 24:00 - "It's not a belief, it's happening" 25:42 - The Viceroy of Variance 29:39 - The garden hose company in Wisconsin Resources: Follow Dhaval Adjodah on LinkedIn: https://www.linkedin.com/in/rand0m Follow Owen He on LinkedIn:https://www.linkedin.com/in/owen-he-b7b064279/ Follow Martin Casado on X: https://x.com/martin_casado Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.

a16z's Raghu Raghuram and Guido Appenzeller sit down with Playground Global General Partner and former Intel CEO Pat Gelsinger to discuss the next wave of semiconductor innovation and the physical constraints shaping the AI buildout. Drawing on his experience designing Intel's 386 and 486 processors, Pat explains how AI could transform chip design, but also why faster design alone won't solve the industry's biggest problems. They examine the bottlenecks in manufacturing, memory bandwidth, advanced packaging, and power, and why today's explosion of specialized AI chips may eventually consolidate around a smaller number of architectures. They also discuss the potential for new memory technologies, the shift from copper to optical networking, and why energy capacity could become a major constraint on AI growth. Finally, they revisit Pat's VMware years to ask what virtualization might look like when infrastructure is built for agents rather than humans. Timestamps: 00:00 - Intro 01:00 - From tech school to Intel at 18 05:44 - The 486 and the birth of modern EDA 07:23 - How AI changes chip design 09:18 - Why silicon still takes nine months 14:49 - Will 100 AI chips converge to a few? 22:08 - Why HBM is a hideous memory 25:15 - How tall can chips get? 42:49 - Energy capacity equals economic capacity 48:42 - A VMware for agents Resources: Follow Pat Gelsinger on X: https://x.com/PGelsinger Follow Raghu Raghuram on X: https://x.com/RaghuRaghuram Follow Guido Appenzeller: https://x.com/appenz Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show 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.