Applied Intuition: A Billion Intelligent Machines

**SPEAKER_1** (0:03)
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**SPEAKER_2** (0:52)
Today, we are breaking down Applied Intuition. Our guests are co-founders Qasar Younis and Peter Ludwig, who started the company in 2017 with a mission to make a billion machines intelligent. The simplest way to understand Applied Intuition is that it builds the brains for machines and the tools other companies use to build those brains. If a manufacturer wants its tractor, truck, or mining vehicle to drive itself, it can buy the intelligence from Applied Intuition or use its platform to develop its own.
Just as Nvidia sells chips into everyone else's machines, Applied Intuition sells intelligence into everyone else's machines across automotive, defense, mining, agriculture, and robotics without building any single machine itself. We discuss why the most important companies of the next 25 years will all be physical AI companies, Dana, their new agentic platform for developing and deploying these systems, and how the company raised a billion dollars without spending any of it. Please enjoy this breakdown of Applied Intuition.
I know a lot of the story we're going to tell today is going to be about a single business, Applied Intuition, but it's also really a story of the physical AI market and how far autonomous technology has come. And you too see this across as many industries as about anyone. Maybe just describe the state of the physical AI market, how the whole landscape feels to you now in 2026, and maybe some of the important key hash marks on the timeline since when you started the company in 2017

**Qasar Younis** (2:14)
In our case, in Applied Intuition's case, our mission is to make a billion machines intelligent. One simple way of that you could think of is self-driving cars.
Those are intelligent machines, but it's one example like Instagram is an app on the phone. There's many also other apps. So physical AI is this intersection of AI and hardware typically, but in the real world.
Humanoids falls into this as well as a category. The particular technical challenges of physical AI are quite different from digital AI, which is like your LLMs and your information retrieval systems like chatbots, stuff like that, because you have the constraints of the real world, you have the safety criticality of the physical world. Often when we're talking about moving machines, they're moving in a time and space with humans and suddenly that becomes something where you have to really think about safety. The real time nature of the problem. So if you ask the chatbot, tell me about Peter Ludwig, it can take 20 seconds to process that information. But when you're flying down the highway, a humanoid is making a decision, there's very hard real time constraints there. And then probably an under reported aspect of physical AI is the dollars. You need to put this on machines and on silicon that is affordable within the use case that you're talking about. You don't just throw endless compute at processing something. You have to do it in a compute envelope, not only a time envelope, but also like a cost envelope.

**Peter Ludwig** (3:47)
It's useful also to think about the separation of digital AI and physical AI. It's a digital AI, typically thinking about what you're using on your desktop or your mobile phone, or there's a screen that's showing you the result of the AI.
And then where that crosses into physical AI is when anything is in the real world actually moving. And this is where the real impact on the economy will happen. When you talk about all of the industries that fundamentally have moving things. So think about anything from industrial companies, to manufacturing, use cases in health care and energy. There's so many different fields where in order to get the benefits of AI, you actually have to impact these physical systems.

**Qasar Younis** (4:23)
Just to add on to that, by putting self-driving and intelligence on machines, we're really making some of the worst jobs on the planet easier. In digital AI, there's a lot of teeth-mashing and hand-wringing about what's gonna happen to accountants and maybe even podcast hosts.