alt Hacker NewsThis framing clarifies something people get wrong about humanoid robots. The competition isn't "humanoid vs. better robot" — it's "humanoid vs. hiring another person."
And that reframes the economics entirely. You don't need the robot to be better than a human at any given task. You need the total cost of ownership to be lower than a salary, benefits, turnover, and training. That's a much easier bar to clear once the AI catches up to the body.
The interesting question is whether the AI problem gets solved generally (one model that can do everything) or whether we end up with task-specific AI in a general-purpose body — basically the robot arm paradigm wearing a humanoid suit.
Em-dashes aside, I favor "one model that can do everything" in principle because scaling laws and distillation exist, and in practice because "one model that you can point at any problem" is a massive operational advantage.
If you can get 5 specialist models that can use the same robot body, you can also get 1 generalist model with more capacity and fold the specialists into it. If you have the in-house training that made those specialists, apply them to the generalist instead, the way we give general purpose AIs coding-specific training. If you don't, take the specialists as is and distill from them.
If you do it right, transfer learning might even give you a model that generalizes better and beats the specialists at their own game. Because your "special" tasks have partial subtask overlap that you got stronger training for, and contributed to diversity of environments. Robotics AI is training data starved as a rule.
Same kind of lesson we learned with LLM specialists - invest into a specialist model and watch the next gen generalists with better data and training crush it.