It is easy to think you have physical AI figured out early on.
Find compare no organization history drug.
The Counterintuitive Truth
In physical AI, the best practitioners often do less, not more.
Trying to do everything at once is one of the most common ways to fail: even a team with strong technology and talent can spread itself too thin.
The lesson: simplicity beats complexity every time. 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 your team matters more than your technology. 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 the market doesn't care about your roadmap. Once they make the switch, everything changes.
What I've Learned From 71 Companies
A few patterns in physical AI come up again and again.
The founders who handle physical AI well understand that your team matters more than your technology.
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 warehouse robots, surgical robots, autonomous vehicles that I've been thinking about a lot lately.
Final Thoughts
There are no shortcuts in physical AI, but there are smarter paths.
The strongest teams treat physical AI as a competitive advantage, not a checkbox. They invest in it early, measure it obsessively, and never stop improving.
If you're just getting started with physical AI, don't be intimidated. Everyone starts somewhere. The key is to start with the right mindset and the right framework, and then execute like your company depends on it. Because it probably does.
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: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: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 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.