Agility Robotics co-founder Jonathan Hurst explains that this time is different because large language models have made perception 'all but solved' — robots now understand their environment and can identify objects generically, whereas previously every object had to be programmed in a narrow way. ✦ AI generated
Professor Jonathan Hurst · All-In Podcast · 2026-07-29 · original ↗
starts at this moment · 49:08
“explain to the audience why this time is different and why you believe this time we're going to see robotics and humanoid robotics specifically deployed at a scale...”
It is very easy to make a robot that looks like a person. And that's why we've seen humanoid for 100 years in one. It's very hard to make a robot that can do useful things in human spaces. And we're starting to see that today and that's the difference. Because of large language models, a lot of things have now become free. When these robots look at a table here and you say, 'What's on the table?' It knows that's a phone. It knows this is paper, tea, water. It probably knows how many ounces are in each. If it were sitting here 3 or 4 years ago, it wouldn't actually know what was in the world. You would have to program it in a very narrow way. Perception was incredibly difficult. And the fact that perception is all but solved at this point is a really, really huge inflection point.
verbatim transcript · starts at 49:08
49:08>> yeah well I would say generally it is very easy to make a robot that looks like a person >> and that's why we've seen humanoid for 100 years in one. It's very hard to make a robot that can do useful things in human spaces. >> And we're starting to see that today and that's the difference. So even if it doesn't look exactly like a human, but
49:25maybe a little bit humanoid, but it's doing useful work, >> that's where the impact matters. >> And because of large language models, >> a lot of things have now become free. When these robots look at a table here, >> Yeah. >> and you say, "What's on the table?" It knows that's a phone. It knows this is paper, tea, water. It probably knows how many ounces are in each.
49:47>> Yeah. >> If it were sitting here 3 or 4 years ago, >> it wouldn't actually know >> what was in the world. You would have to program it in a very narrow way. Yeah. >> Yeah. Perception was incredibly difficult. And the fact that perception is all but solved at this point is a really, really huge inflection point. I mean, you know, I said, yes, robots doing useful things, but also people can
50:07now see the future of generality. AI is really enabling that much more broad um you know context awareness for these robots so people can see that this is going to be useful gen generally doing many useful things very soon. >> So there's perception the robot has to understand the world. >> Yep. But then there always seemed to be this blocker with getting the robot out of a very confined narrow task like you
50:32know in a factory >> and I my perception is it was the communication and the training level. Maybe we can unpack that a bit because my understanding was previously you basically had to hardcode the robot if you were going to make a cup of coffee. We have a company I invested in Cafe X and it is a robotic arm. >> Mhm. makes a cup of coffee perfectly