The main point I’d disagree with here is that AI R&D ⇒ AI Robotics ⇒ Robotics-driven economy. I’ve spent a lot of time in industrial environments and the problems are never as simple as people think they are—and cannot be handwaved away like a software feature might be.
To be clear: I do think that AI Robotics is the future, but the aggressive timeline predictions that I’m seeing like AI 2030 etc. seem overly bullish because real-world adoption will almost certainly be slower than people in the research labs think. Even in the case where you somehow manage to get a large enough flywheel going that you essentially cut out everyone within the factor and have factories autonomously building themselves (very unlikely—the scope of problems within a factory is incredibly broad such that even generalized human intelligence is often not enough; you need domain experts for many basic tasks. The ‘basic’ task of sheet metal folding, for example, is brutally difficult as an ML problem.) it would take years in the extremely aggressive case to get these factories up and running, with permitting and raw resource/logistics timelines serving as major bottlenecks. And this is before we talk about the incumbent competitors for current demand.
I often see that researchers have a tendency to handwave away these ‘more basic’ problems within their research—which is all well and good, because research is about asking questions, and you’re best equipped to do that when you can remove a bunch of other confounding factors—but when you start talking about real world hyperscaling and real ‘rubber meets the road’ problems, the stack of your necessary dependencies grows exponentially and you cannot skip over steps like one might avoid a out-of-scope software feature or leaving a proof to the reader.
Granted, I’m not saying that robotics cannot succeed—but simply that the timeline is longer than I think people are expecting. Look at the timelines on industrial companies versus SaaS from the same era: SpaceX was founded in 2002, before Reddit, LinkedIn, Facebook, and is only just now reaching IPO, whereas the SaaS companies were household names nearly a decade earlier. Uber was founded in 2009 and took until 2023 to become profitable. In general, industrial timelines are slower because the real world works at a slower pace than software. If I founded the next big industrial robotics company today I would expect it to take till 2040 until it reached maturity—and we’re not yet at the point where I would say robotics has hit the inflection point for that.
The main point I’d disagree with here is that AI R&D ⇒ AI Robotics ⇒ Robotics-driven economy. I’ve spent a lot of time in industrial environments and the problems are never as simple as people think they are—and cannot be handwaved away like a software feature might be.
To be clear: I do think that AI Robotics is the future, but the aggressive timeline predictions that I’m seeing like AI 2030 etc. seem overly bullish because real-world adoption will almost certainly be slower than people in the research labs think. Even in the case where you somehow manage to get a large enough flywheel going that you essentially cut out everyone within the factor and have factories autonomously building themselves (very unlikely—the scope of problems within a factory is incredibly broad such that even generalized human intelligence is often not enough; you need domain experts for many basic tasks. The ‘basic’ task of sheet metal folding, for example, is brutally difficult as an ML problem.) it would take years in the extremely aggressive case to get these factories up and running, with permitting and raw resource/logistics timelines serving as major bottlenecks. And this is before we talk about the incumbent competitors for current demand.
I often see that researchers have a tendency to handwave away these ‘more basic’ problems within their research—which is all well and good, because research is about asking questions, and you’re best equipped to do that when you can remove a bunch of other confounding factors—but when you start talking about real world hyperscaling and real ‘rubber meets the road’ problems, the stack of your necessary dependencies grows exponentially and you cannot skip over steps like one might avoid a out-of-scope software feature or leaving a proof to the reader.
Granted, I’m not saying that robotics cannot succeed—but simply that the timeline is longer than I think people are expecting. Look at the timelines on industrial companies versus SaaS from the same era: SpaceX was founded in 2002, before Reddit, LinkedIn, Facebook, and is only just now reaching IPO, whereas the SaaS companies were household names nearly a decade earlier. Uber was founded in 2009 and took until 2023 to become profitable. In general, industrial timelines are slower because the real world works at a slower pace than software. If I founded the next big industrial robotics company today I would expect it to take till 2040 until it reached maturity—and we’re not yet at the point where I would say robotics has hit the inflection point for that.