Achieving human-level power efficiency and capability in general-purpose robotics will take a very long time; the gap is far larger than in LLMs because robotic systems require reliability across hardware, software, and physics at every layer.
Yunzhu Li explains that every working robot is a complete system requiring careful integration of hardware, software, and physical details, and while progress is faster than expected, human-level capability is a distant goal — a sentiment Fei-Fei Li reinforces by noting even LLMs don't match the human brain's 30-watt efficiency. ✦ AI generated
Yunzhu Li · a16z Podcast · 2026-07-28 · original ↗
starts at this moment · 33:22
“Do you believe we'll ever be able to build robots that have the power efficiency of a human being when it comes to menial tasks? How far away are we from this?”
I think it's going to take a very long time. If you really think about robots in the real environment, it will always be a system. Every working robot in the real environment is a system. You need to be very mindful and thoughtful about how the system comes together: the hardware, the software, the brain, even the details of the friction coefficients of your fingers. There's a lot of things you have to consider to make these things a reality. The state-of-the-art is always moving faster than I expected, so what I'm focusing on is very different from when I started my PhD. But we also have to be calibrated about our predictions. To achieve human level efficiency and capabilities, it will take longer.
verbatim transcript · starts at 33:22
33:22to take a very long time. So if you really think about like a robots in the real environment in the end it it will always be a system. So every working robot in the real environment is a system work. It's you need to be very mindful and thoughtful about how the system are coming together. the hardware, the software, the brain, even like to the details of for example
33:41what's the friction coefficients of your of your fingers. So there's a lot of things you have to consider to make these things uh a reality and it will take iterations. But what I am excited about is that I have always been at the state of the arts of robot learning and also trying to push the state of the art forward. Yeah. >> But the state-ofthe-arts always moving
34:00faster than I expected. So what's I'm focusing on and trying to investigate right now is very different from for them when I started my PhD. So this is a speak to how fast the whole ecosystem has been evolving and all the moving pieces started coming together or building this robotic systems but we also have to be like calibrated about our predictions. So we will see a lot of
34:24like a progress but to achieve for example human level efficiency and capabilities it will take uh longer. Martin, the hardest thing in today's AI is to have the right measured optimism, >> right? >> That's right. It's totally true. Yeah. >> I mean, even LLM does not have human brain efficiency. Human brain operates on 30 watts. >> Yeah, that's true. >> That's so we are far from that. So,
34:49>> but that but I mean performance to power it may be close, right? in narrow task like software >> like generating an image or or software engineering than it is right. Yeah, I think so. I don't think we're anywhere close when it comes to robotics. Does this change how you think about your um like strategically the level of ambition that your team can go after? I mean does
35:12it is it changed that or is it still very much in line with what you expected to do when you started? >> It definitely changed the trajectories in a very profound manners. So we see a lot of unlock in be able to do this whole process do the modeling of the environments yeah much more efficient and much more scalable manners especially in partner together with word labaps and I also want to add to f if