What’s the hardest part of being a product manager in embodied AI?

Originally published on Substack.

Someone once asked me what embodied AI product managers struggle with most every day.

After thinking about it, I realized it’s probably not requirements, roadmaps, or even technology.

In an early-stage startup—especially one fueled by venture capital—the question that keeps coming back is surprisingly simple:

Will what I’m doing today increase the company’s valuation?

It sounds a little cynical, but that’s often the reality.

Sometimes a team spends weeks refining a feature. The engineering is solid, the product works, and everyone feels good about the result. Yet nothing really changes for the company.

On the other hand, some initiatives may not look impressive from a pure product perspective, but they can completely change how investors view the business.

Landing a flagship customer. Deploying the first meaningful batch of robots. Proving that proprietary data can be collected at scale. Discovering a use case that can be replicated across thousands of sites.

None of these things necessarily make the robot smarter overnight. But they can make the company’s future look dramatically different.

That’s why many embodied AI product managers aren’t really doing traditional product management.

They spend much of their time deciding where limited resources should go:

What is worth building?

What can become a defensible advantage?

What moves the company one step closer to the next stage?

Because in this industry, technology changes constantly. Models change. Hardware changes. Markets change.

The only thing that cannot stop is the company’s ability to prove that its future is becoming more valuable.

And more often than not, the product manager is standing at the front of that process.