Making a Billion Intelligent Machines

Applied Intuition is launching Dana, an agentic platform for developing physical AI applications

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This week, Applied Intuition 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.

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