Making a Billion Intelligent Machines

✍ Marc Andreessen🕐 2026-07-22📦 12.5 KB 🟢 已读 𝕏 文章列表

文章介绍了Applied Intuition公司如何通过提供仿真、数据和操作系统工具，从自动驾驶领域的非主流选择发展为物理AI应用平台，并成功拓展到国防、建筑等多个行业。

物理AI 自动驾驶仿真技术 Applied Intuition 操作系统国防工业应用技术平台人工智能转型

# Making a Billion Intelligent Machines **作者**: Marc Andreessen **日期**: 2026-07-21T15:03:42.000Z **来源**: [https://x.com/pmarca/status/2079583099135156603](https://x.com/pmarca/status/2079583099135156603) --- ![Image](/doc/3275/image/img_29e001734ae2.jpg) This week, @AppliedInt is launching Dana, an agentic platform for developing physical AI applications. Applied Intuition began by building the tools engineers needed to develop autonomous systems, then the OS that underpins them all, and finally moved into the intelligence running on the machines themselves. As co-founder and CEO Qasar Younis describes the company today, the destination was always much bigger than tooling for self-driving vehicles. The mission is intelligence on a billion machines. # The unfashionable layer In 2017, the autonomy industry shared a collective prediction: one of a small number of vertically-integrated companies would solve self-driving cars, operate the vehicles, and capture the entire market. As a result, capital overwhelmingly flowed into full-stack robotaxi programs. Each program hired its own engineers, assembled its own fleet, and rebuilt roughly the same internal development infrastructure. Engineers who moved between major autonomy programs encountered a funny ritual. They left behind the tools they had used to develop and test autonomous systems, arrived at the next company, and began building those tools again. The industry had plenty of conviction about autonomy but very little shared best-practices for producing it. Applied Intuition proposed to make those best-practices a company. This was a minority view, and it wasn’t a conventional way to participate in an increasingly fashionable market. The large Level 4 autonomy developers did not think they needed an outside tooling supplier. Applied Intuition tried to sell to them anyway. Prominent self-driving programs like Cruise said no. Maybe it was an understandable position at the time: they weren’t going to wait for a startup like Applied Intuition to build tools that their own engineers already intended to build internally. Many of the era’s prominent full-stack programs no longer exist in the form they did then. Cruise, for example, was acquired by General Motors and subsequently sunsetted after a safety incident. Meanwhile, Applied Intuition has outlasted (and outperformed) the majority of them. Applied Intuition’s founding deck contained a slide called “Ideas We Believe To Be True.” One of those ideas was that any sole autonomous vehicle maker would remain a single-digit share of the global car market because the structure of the automobile industry tends toward distribution across many manufacturers. If one vertically-integrated company took the vehicle market, the rational strategy was to be (and invest in) that company. But if it wasn’t a winner-take-all market, then the other ninety-odd percent of cars would acquire intelligence through the existing automotive industry. Those manufacturers could not each reproduce every layer of the modern software stack. An independent supplier would have to provide common infrastructure. Qasar and Applied Intuition’s CTO and other co-founder Peter Ludwig were wagering on diffusion over concentration. The autonomous future would arrive not only via new entrants replacing incumbents, but because incumbents could become technically different companies. Applied Intuition would give them the means to do it. Qasar and Peter brought together experience from General Motors, Google, and Y Combinator with technical depth and an unusual tolerance for the slow, exacting work of building safety-critical systems. After the large Level 4 companies initially declined to become customers, Applied Intuition sold into smaller Bay Area autonomy teams, including Voyage and its cohort. The products they developed followed the demands of their customers’ work: first a simulator for planning, then a simulator for perception, and finally infrastructure for managing data and executing millions of these simulations. Deterministic simulation that correlates with physical reality can sound like an unglamorous product category. In practice, it gave the industry a shared touchstone for determining whether an autonomous system worked, without needing to put a vehicle on the road and testing in prod. A simulator turns a road event into a repeatable test, which can then be implemented in reality. Market structure has since reflected Applied’s “Ideas We Believe…” slide in favor of the monolithic manufacturer thesis. This really wasn’t that obvious in advance, but in retrospect it makes sense. The automobile is not a pure software product. It is a regulated, capital-intensive physical system that’s sold through entrenched distribution networks. Software can reorganize where value accrues and which capabilities matter. But it cannot, by itself, flatten that industrial complexity into a single manufacturer. # On a clear day you can see General Motors Around 2018 and 2019, General Motors issued a formal request for development tooling. Twenty-eight companies bid, including NVIDIA and Ansys. Applied Intuition was still a small startup and GM had procurement procedures and myriad options all vying to be chosen. Applied’s products performed better against the specification, and that’s why their pitch won. The GM award converted a wedge among Silicon Valley autonomy startups into legitimacy with traditional manufacturers. The playbook became repeatable: begin with technically aggressive smaller companies, then use the resulting product and evidence to serve incumbents operating at industrial scale. Roughly eighteen months after founding, Applied Intuition entered the defense industry. The company hired people who understood the field from the inside, and then applied the automotive pattern of simulation, data, integration, and validation to defense systems. Construction and mining followed, then commercial trucking. A generic software company often enters a new vertical by changing some of the nouns in its sales deck. Applied entered by hiring teams native to each domain and rebuilding the product around the constraints of the customer, procurement system, and safety cases. Meanwhile, the product moved up a technical ladder. The lower layer consisted of simulation and data infrastructure: generating scenarios, collecting machine data, reproducing events, and evaluating behavior. Above it came operating systems for physical machines: scheduling, middleware, memory management, communications, and functional safety. These were operating systems, responsible for the machine rather than a collection of applications displayed on it. Above that came the intelligence itself: world models and planning systems deployed across machines operating on land, in the air, and at sea. By 2024 and 2025, the result could be measured externally. Applied Intuition raised a round at a $15 billion valuation. By 2025, eighteen of the twenty leading non-Chinese automakers were customers. Level 4 trucks using Applied Intuition technology were operating without drivers in Japan. The company had deployments with the U.S. Army and an operating system aboard U.S. Navy warships, along with production work in mining, construction, agriculture, and trucking. And in what is an anomaly for many Silicon Valley startups, Applied Intuition has largely preserved the primary capital raised from investors (close to $1 billion) while nearly doubling revenue at scale for multiple years in a row. By then the original problem had changed. In 2017, the industry wondered when autonomous intelligence would become capable enough. By the middle of the 2020s, thanks to the increasing sophistication of transformer models, model capability was arriving faster than large organizations could deploy it. This is why tool companies can become platform companies. A tool begins by solving a bounded task. If it succeeds, it becomes the common interface through which many tasks are performed. It accumulates integrations, test cases, workflows, and organizational memory, and eventually it stops being an accessory to the production system and becomes the environment in which production occurs. The web browser offers a historical corollary. It didn’t “create” the underlying internet, but it did make the net usable by the vast majority of people who weren’t nerdy hobbyists, and trillions of dollars of economic activity moved toward what that access allowed them to build and do. Applied Intuition’s progression from simulator to data infrastructure to operating system followed the work its customers were already doing. As more of that work moved into Applied’s products, the software became part of how those companies built machines rather than a tool used for one stage of development. # Dana Today Applied Intuition launches Dana. For nine years, Applied Intuition has built the technology used to develop intelligent machines. Dana puts an agentic interface over that accumulated system. An engineer can start with a requirement, connect it to the relevant code, run the change through simulation and defined evaluations, move it onto a test bench or hardware-in-the-loop system, and eventually stage it for a physical machine. Much of that work previously required engineers to pass results manually between specialized tools. Dana can coordinate the path while retaining a record of how the system changed and why. This arrives as the technical method behind autonomy is changing. For most of the self-driving industry’s history, advancements came in the form of imitation learning: gather enough recorded human driving and train the model to copy it. The frontier has moved toward end-to-end reinforcement learning in a closed development loop. The system encounters a problem, the team finds or generates more examples of that situation, the model trains again, and the same scenario is rerun to see whether its behavior improved. Applied already has the simulation, synthetic data, evaluation, and deployment systems required to run that loop. Dana gives agents a role in operating it. The physical economy has historically remained resistant to software for reasons that have seemed intractable. These industries have long hardware cycles and lots of physical world considerations. These are mundane engineering problems until the machine weighs several tons and is moving near people, and someone gets hurt. At that point, every link between a spec and a deployment decision has to survive scrutiny. Autonomous machines face an unusual burden of proof. A manufacturer may believe that a model performs well and still be unable to ship it until regulators can understand how that conclusion was reached. Dana was built around that. Its usefulness depends as much on traceability and evaluation as on the speed with which it can generate code or run a workflow. The economics are changing at the same time. Intelligence is moving down a steep price curve. The autonomy layer itself will trend toward abundance and, in many settings, toward a negligible marginal price. One robot maker can sell only the machines it manufactures. Dana can be used to upgrade equipment already expected to remain in service for another twenty years, and to develop new machines whose shape is no longer determined by the need to fit a human driver inside them. In other words, Dana is one way we can get a billion intelligent machines. For years, autonomous systems were limited by the models themselves. That constraint has eased. The remaining work sits inside the manufacturers and operators trying to deploy them, in the form of adapting models to real hardware and supporting the system once it is operating far from a research lab. The market is ready to use AI in the physical world, but most companies cannot afford to assemble a thousand-person autonomy organization to do it. Applied Intuition spent nine years learning how intelligence survives contact with hardware and the physical world. Dana puts that knowledge in the hands of anyone trying to make a machine move on its own. ## 相关链接 - [Marc Andreessen](https://x.com/pmarca) - [@pmarca](https://x.com/pmarca) - [169K](https://x.com/pmarca/status/2079583099135156603/analytics) - [@AppliedInt](https://x.com/@AppliedInt) - [Upgrade to Premium](https://x.com/i/premium_sign_up) - [11:03 PM · Jul 21, 2026](https://x.com/pmarca/status/2079583099135156603) - [169.8K Views](https://x.com/pmarca/status/2079583099135156603/analytics) - [View quotes](https://x.com/pmarca/status/2079583099135156603/quotes) --- *导出时间: 2026/7/22 09:51:44* --- ## 中文翻译 # 制造十亿台智能机器 **作者**: Marc Andreessen **日期**: 2026-07-21T15:03:42.000Z **来源**: [https://x.com/pmarca/status/2079583099135156603](https://x.com/pmarca/status/2079583099135156603) --- ![Image](/doc/3275/image/img_29e001734ae2.jpg) 本周，@AppliedInt 正在发布 Dana，这是一个用于开发物理 AI 应用的智能体平台。Applied Intuition 最初致力于构建工程师开发自动驾驶系统所需的工具，随后是支撑这些系统的操作系统，最终扩展到运行在机器本身的智能层。正如联合创始人兼首席执行官 Qasar Younis 今天对公司的描述，他们的目标远不止是为自动驾驶汽车提供工具。其使命是为十亿台机器赋予智能。 # 不起眼的层级 2017 年，自动驾驶行业达成了一个共同的预测：少数几家垂直整合的公司将解决自动驾驶汽车问题，运营车辆，并占据整个市场。结果，资本大量涌向全栈自动驾驶出租车项目。每个项目都招聘自己的工程师，组建自己的车队，并重建大致相同的内部开发基础设施。在主要自动驾驶项目之间流动的工程师们遇到一种奇怪的现象。他们留下了开发和测试自动驾驶系统所使用的工具，来到下一家公司，然后开始再次构建这些工具。该行业对自动驾驶充满信心，但在如何实现方面却几乎没有共享的最佳实践。 Applied Intuition 提议将这些最佳实践转化为一家公司。这是一种少数派观点，而且不是参与一个日益流行的市场的常规方式。大型 L4 级自动驾驶开发商并不认为他们需要外部工具供应商。Applied Intuition 还是尝试向他们推销。像 Cruise 这样著名的自动驾驶项目拒绝了他们。这在当时或许是可以理解的：他们不打算等待像 Applied Intuition 这样的初创公司来构建那些他们自己的工程师本来就打算在内部构建的工具。那个时代许多著名的全栈项目已不复存在，或者不再以当年的形式存在。例如，Cruise 被通用汽车收购，随后在发生安全事故后被关停。与此同时，Applied Intuition 的寿命（和业绩）已经超过了它们中的大多数。 Applied Intuition 的融资演示文稿中有一张名为“我们确信为真的观点”的幻灯片。其中一种观点是，任何单一的自动驾驶汽车制造商在全球汽车市场中都将只占个位数份额，因为汽车行业的结构倾向于在众多制造商之间分布。如果一家垂直整合的公司占据了车辆市场，理性的策略就是成为（并投资于）那家公司。但如果这不是一个赢家通吃的市场，那么其余 90% 多的汽车将通过现有的汽车行业获得智能。这些制造商不可能各自复制现代软件堆栈的每一层。必须有一家独立的供应商来提供通用基础设施。 Qasar 和 Applied Intuition 的首席技术官兼另一位联合创始人 Peter Ludwig 将赌注押在了扩散而非集中上。自动驾驶未来的到来，不仅将通过取代现有企业的新进入者，也将因为现有企业能够转型为技术型公司。Applied Intuition 将为他们提供实现这一目标的手段。 Qasar 和 Peter 汇聚了来自通用汽车、谷歌和 Y Combinator 的经验，兼具技术深度和对构建安全关键系统这一缓慢而严苛工作的非凡耐性。在大型 L4 公司最初拒绝成为客户后，Applied Intuition 向较小的湾区自动驾驶团队销售产品，包括 Voyage 及其同类公司。他们开发的产品紧随客户工作的需求：首先是用于规划的模拟器，然后是用于感知的模拟器，最后是用于管理数据和执行数百万次这些模拟的基础设施。与物理现实相关的确定性模拟听起来像是一个并不光鲜的产品类别。在实践中，它为行业提供了一个共同的基准，用于确定自动驾驶系统是否有效，而无需将车辆上路并在生产环境中进行测试。模拟器将道路事件转化为可重复的测试，然后可以在现实中实施。市场结构随后印证了 Applied 的“我们确信为真的观点……”这张幻灯片，支持了制造商多元化的论点。这在事先并不是那么显而易见，但事后看来是合乎情理的。汽车不是纯粹的软件产品。它是一个受监管的、资本密集型的物理系统，通过根深蒂固的分销网络销售。软件可以重新组织价值积累的位置和哪些能力至关重要。但它本身无法将这种工业复杂性压缩为单一制造商。 # 晴空万里可见通用汽车大约在 2018 年和 2019 年，通用汽车发出了开发工具的正式招标。28 家公司竞标，包括 NVIDIA 和 Ansys。Applied Intuition 当时还是一家小型初创公司，而通用汽车有采购程序和无数的选择都在竞争中标。Applied 的产品在规格对比下表现更好，这就是他们的推介方案获胜的原因。通用汽车的奖项将 Applied Intuition 从硅谷自动驾驶初创公司中的一个楔子，转变为在传统制造商那里的合法供应商。这套打法变得可复制：首先从技术上激进的小公司开始，然后利用由此产生的产品和证据服务于以工业规模运营的现有企业。在成立大约十八个月后，Applied Intuition 进入了国防工业。公司聘请了从内部了解该领域的人员，然后将汽车的模拟、数据、集成和验证模式应用于国防系统。随后是建筑和采矿，然后是商业卡车运输。一家通用的软件公司进入一个新的垂直领域，通常只需要在销售演示文稿中更改一些名词。Applied Intuition 的进入方式是聘请每个领域的原生团队，并根据客户、采购系统和安全案例的约束重新构建产品。与此同时，产品在技术阶梯上也不断向上攀升。底层由模拟和数据基础设施组成：生成场景、收集机器数据、重现事件和评估行为。其上是物理机器的操作系统：调度、中间件、内存管理、通信和功能安全。这些是负责机器本身的操作系统，而不是在上面显示的一组应用程序。再往上是智能本身：部署在陆地、空中和海上机器上的世界模型和规划系统。到 2024 年和 2025 年，结果可以从外部衡量。Applied Intuition 以 150 亿美元的估值进行了一轮融资。到 2025 年，二十家领先的非中国汽车制造商中有十八家成为了客户。使用 Applied Intuition 技术的 L4 级卡车正在日本无人驾驶运营。该公司与美国陆军有合作项目，在美国海军军舰上部署了操作系统，并在采矿、建筑、农业和卡车运输领域开展了生产工作。而且，对于许多硅谷初创公司来说这是一个反常现象，Applied Intuition 在规模上连续多年营收几乎翻倍的同时，很大程度上保留了从投资者那里筹集的主要资金（接近 10 亿美元）。到那时，最初的问题已经发生了变化。2017 年，行业想知道自动驾驶智能何时能变得足够强大。到 2020 年代中期，得益于 transformer 模型日益成熟，模型能力的到来速度快过了大型组织部署它的速度。这就是为什么工具公司可以成为平台公司。工具始于解决一个有限的任务。如果它成功了，它就成为执行许多任务的通用接口。它积累了集成、测试用例、工作流程和组织记忆，最终它不再是生产系统的附属品，而是变成了生产发生的环境。网络浏览器提供了一个历史类比。它并没有“创造”底层的互联网，但它确实让互联网对大多数不是极客爱好者的人变得可用，数万亿美元的经济活动转向了这种访问允许他们构建和做的事情。Applied Intuition 从模拟器到数据基础设施再到操作系统的进程，遵循了其客户已经在做的工作。随着更多工作转移到 Applied 的产品中，软件成为了这些公司制造机器方式的一部分，而不仅仅是用于开发某个阶段的工具。 # Dana 今天，Applied Intuition 发布 Dana。九年来，Applied Intuition 一直致力于构建用于开发智能机器的技术。Dana 在这个积累的系统之上放置了一个智能体界面。工程师可以从一个需求开始，将其连接到相关代码，通过模拟和定义的评估运行更改，将其转移到测试台或硬件在环系统，并最终为物理机器进行部署。此前，这些工作中有许多需要工程师在专门的工具之间手动传递结果。Dana 可以协调这一路径，同时记录系统如何变化以及为什么变化。 Dana 的推出正值自动驾驶背后的技术方法发生变革之际。在自动驾驶行业的大部分历史中，进步的形式是模仿学习：收集足够的人类驾驶记录并训练模型来复制它。前沿已经转向封闭开发循环中的端到端强化学习。系统遇到问题，团队找到或生成更多该情况的示例，模型再次训练，然后重新运行相同的场景以查看其行为是否有所改善。Applied 已经拥有运行该循环所需的模拟、合成数据、评估和部署系统。Dana 赋予了智能体在操作该循环中的角色。由于某些看似棘手的原因，实体经济一直对软件具有抵抗力。这些行业具有漫长的硬件周期和大量的物理世界考量。这些都是平淡的工程问题，直到机器重达数吨并在人群附近移动，并且有人受伤。在那一刻，从规范到部署决定之间的每一个环节都必须经得起审查。自动驾驶机器面临着不同寻常的举证责任。制造商可能认为模型表现良好，但在监管机构能够理解如何得出该结论之前，仍然无法发货。Dana 就是围绕这一点构建的。它的有用性不仅取决于它可以生成代码或运行工作流程的速度，同样也取决于可追溯性和评估。与此同时，经济情况也在发生变化。智能正处于一条陡峭的价格下降曲线上。自动驾驶层本身将趋向于丰富，并且在许多环境中，趋向于可以忽略不计的边际价格。一家机器人制造商只能销售其制造的机器。Dana 可用于升级预计将继续服役二十年的设备，并开发那些不再受限于需要容纳人类驾驶员的形状的新机器。换句话说，Dana 是我们获得十亿台智能机器的途径之一。多年来，自动驾驶系统一直受到模型本身的限制。这一限制已经放宽。剩下的工作存在于制造商和运营商内部，他们试图部署这些系统，工作形式包括将模型适配到真实硬件以及在系统远离研究实验室运行后对其进行支持。市场已准备好在物理世界中使用 AI，但大多数公司无法组建一个一千人的自动驾驶组织来实现这一目标。 Applied Intuition 花了九年时间学习智能如何在接触硬件和物理世界后生存下来。Dana 将这些知识交到了任何试图让机器自主移动的人手中。 ## 相关链接 - [Marc Andreessen](https://x.com/pmarca) - [@pmarca](https://x.com/pmarca) - [169K](https://x.com/pmarca/status/2079583099135156603/analytics) - [@AppliedInt](https://x.com/@AppliedInt) - [升级到 Premium](https://x.com/i/premium_sign_up) - [11:03 PM · Jul 21, 2026](https://x.com/pmarca/status/2079583099135156603) - [169.8K Views](https://x.com/pmarca/status/2079583099135156603/analytics) - [查看引用](https://x.com/pmarca/status/2079583099135156603/quotes) --- *导出时间: 2026/7/22 09:51:44*

🔗 相关文档