Co-evolving with Physical AI

Physical AI is showing up in more conversations, but as an emerging field, it's often approached with assumptions and sometimes misunderstandings.

Here, John shares what physical AI actually is and why the correct definition matters.

When I think of physical AI, I think of intelligence that understands the physics of the world.

It means understands force, causality, consequences.

We all are familiar with large language models.

You know, LLM-based models process text or information.

If I type, "Pour me a glass of red wine," it's basically a string of words, right? But physical AI understands the weight of the glass, its contents, the friction it is...

that is needed to move it safely.

So it translates my text into action.

Basically, physical AI sees the world through sensors, understands context, reasons about what is happening, and then translates that into action.

Physical AI is very multidisciplinary.

It is one of those few fields where you need software engineers, hardware engineers, designers, strategists, and it spans the entire innovation funnel, all the way from opportunity definition, ROI framing, through product definition, early concepting, to down selecting those concepts and prototyping it to validate whether it works and is the kind of product that we want to build, and then finally building it and scaling it for impact and scale.

So yeah, our teams are involved in every stage.

And in my role, I'm really fortunate to be able to get, like, a front-row seat talking to clients who are ambitious, who want to impact the life of their consumers and users, as well as create new business value.

So understanding what their aspirations and ambitions are and translating that into technical requirements and design requirements and bringing it to reality is kind of what is part of my role.

This is where AI stops being something we look at and starts becoming something we live with.

For John, a lot changes when AI leaves the screen and begins to operate in the physical world.

It means understanding that there are constraints like gravity and, as I mentioned, things like friction and distance, time, human unpredictability.

It also means understanding affordances.

Now, I'm using a design language here, but what objects allow, what spaces constrain, and what do humans expect? The difference between, as I explained earlier, AI that knows what a door is versus physical AI is knowing how to open the door.

So it translates intent and text into action.

It really is like making sense of the 3D world, perceiving it, reasoning about it, and then taking autonomous action.

Sensors is a very broad term, uh, for multi-mo-modal perception.

So it can be voice, it can be recognizing voice or gesture or biomarkers like temperature and all of those things.

So it's multimodal and, you know, computer vision, camera-based systems as well, but they also fall under the definition of sensors.