This article looks at humanoid robots: what tends to work, what usually fails, and how to start.
The Counterintuitive Truth
In humanoid robots, the best practitioners often do less, not more.
The lesson: the data tells a different story than your gut. It sounds simple. It's incredibly hard to execute.
What I've Learned From 121 Companies
A few patterns in humanoid robots come up again and again.
The founders who handle humanoid robots well understand that simplicity beats complexity every time.
The Framework That Actually Works
Here is a simple framework for evaluating humanoid robots. It's not complicated, but it requires discipline.
Step 1: the data tells a different story than your gut
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: your team matters more than your technology
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 humanoid robots right treat it as an ongoing process, not a one-time project.
Lessons From the Trenches
I want to share a few specific lessons I've picked up over the years. These aren't theoretical. They come from real companies, real failures, and real successes.
Lesson 1: The best time to start thinking about humanoid robots was yesterday. The second best time is now. Don't wait until you have the perfect plan.
Lesson 2: Hire for attitude, train for skill. The best people at humanoid robots are rarely the most technically gifted. They are the most curious and persistent.
Lesson 3: Your competitors are probably getting this wrong too. That's your opportunity. While everyone else is following the same playbook, you can zig when they zag.
This connects to broader themes around drone AI, humanoid robots, autonomous vehicles, Tesla Optimus that I've been thinking about a lot lately.
The Bottom Line
None of this is rocket science. But it does require intentionality, consistency, and a willingness to learn from mistakes.
If you take one thing from this article, let it be this: start now, start small, and iterate. The founders who win at humanoid robots aren't the ones with the best strategy on paper. They're the ones who execute, learn, and adapt faster than everyone else.
Companies that take humanoid robots seriously tend to pull ahead of the ones that don't.
If you're working on something interesting in this space, I'd love to hear about it. Drop me a line.
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 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.
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.