Caroline Hyde

I think also intuitively this makes a lot of sense because autonomous vehicles clearly have a head start.

The technology has been around for nearly a decade now.

The production process is there.

It can leverage an existing automotive supply chain.

And I think more importantly, the AI models needed for autonomous vehicles can work with a much bigger real-world driving data set that's collected from millions of vehicles out there.

And that's a very different story if you compare it to where humanoid robots are at the moment.

And that's because when you think about humanoid robots, they really bridge the gap between the cognitive, the digital, and the physical world.

And in the physical world, the laws of mechanics and physics apply.

A humanoid robot needs precise instructions if it's going to function and perform properly in an unstructured world that is made for humans.

Take a simple example such as lifting a box.

This is a very simple task for us humans.

We have inbuilt dexterity.

We have inbuilt intelligence.

We know exactly what we have to do.

But a humanoid robot needs precise instructions.

It needs to know exactly how much force to apply, where to apply that force.

And that simple example can get really tricky if something changes.

If, for example, the box is actually heavier than expected,

If the surface is slippery, that means that the robot needs a new set of instructions.

And the challenge is that there is no dictionary out there.