Billion party improve be.
The Counterintuitive Truth
In physical AI, the best practitioners often do less, not more.
The lesson: you should focus on one thing and do it exceptionally well. It sounds simple. It's incredibly hard to execute.
The Reality Nobody Talks About
A lot of the advice on physical AI rests on assumptions that made sense five years ago. The world has moved on.
The first thing to understand is that you should focus on one thing and do it exceptionally well. I've seen this play out across dozens of companies. The pattern is unmistakable.
Many teams spend months on the wrong path before realizing that simplicity beats complexity every time. Once they make the switch, everything changes.
The Framework That Actually Works
Here is a simple framework for evaluating physical AI. It's not complicated, but it requires discipline.
Step 1: you need to move fast and break things
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: most founders overthink this and underspend on execution
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get physical AI right treat it as an ongoing process, not a one-time project.
The AI Angle
Any discussion of physical AI in 2026 has to include AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
Some teams use AI to get far more out of physical AI. Others spend heavily on AI that solves the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around drone AI, Tesla Optimus, Figure AI, surgical robots that I've been thinking about a lot lately.
What's Next
Best practice in physical AI is changing fast. What worked last year might not work next year. That's both the challenge and the opportunity.
My advice: stay curious, stay humble, and stay close to the people who are actually doing the work. Read less thought leadership and do more experiments. Talk to fewer consultants and more practitioners.
If you're building in this space, the best time to get physical AI right is before you need to. Don't wait for a crisis to force your hand.
I'll keep sharing what I learn. This stuff matters too much to keep to myself.
Frequently Asked Questions
Q:What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
Q:What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
Q:How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
Q:How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.