Thanks for this important work. One thing I remain uncertain about is whether the scenario’s rapid physical and economic scaling rests on multiple unstated (mutually dependent) assumptions:
The AI infrastructure boom must avoid a major investment correction before AI systems generate enough real economic value to justify the expenditure. If returns arrive later than expected, overcapacity, defaults, or a broader financial shock could sharply reduce capex and produce something resembling an AI funding winter.
Compute expansion must not be bottlenecked for years by grid connections, power generation, HBM, advanced packaging, fab capacity, or fab equipment. You model some of these constraints, but I am not convinced that the physical buildout can proceed on the implied timescale.
Robotics must become capable of reliably constructing and operating power plants, fabs, mines, and robot factories without depending on unpredictable breakthroughs in embodied autonomy: long-horizon reliability, dexterity, continual adaptation, and generalization to novel real-world situations. This seems partly bootstrapping-dependent, because rapid robotic infrastructure growth is then used to justify the compute growth enabling further AI progress.
How sensitive is the scenario to these assumptions? In particular, what happens if there is a multi-year capex contraction, an energy or semiconductor plateau, or a substantial lag between cognitive automation and broadly capable robotics?
These things point me to worrying more about the economy than these loss of control scenarios. Maybe you can correct me on that.
It’s possible that there’ll be an AI funding winter in the next few years but we don’t think there will be. The AI companies really do seem to be close to automating AI R&D and more generally close to automating huge swathes of white-collar work; insofar as this is true the valuations and revenue of the AI companies will keep growing very fast instead of plateauing.
“Must not be bottlenecked” that’s a strange way of putting it—it IS bottlenecked by those things, but at least based on our understanding the bottlenecks don’t bite hard enough to prevent the growth rates we are talking about.
The robots thing is correlated with the AI R&D automation thing. If you buy that the AI companies are close to fully automating AI R&D, then you should also think they are probably close to having robots that can reliably construct and operate power plants, fabs, mines, etc.
At the time of writing, EpochAI predicts that Hyperscaler Capex will Exceed Cash Flow by Q3 2026. The total hyperscaler capex in Q1 2026 was $148.4B, while Anthropic’s annualized revenue in May was $47 billion, letting one estimate the revenue as $12.5B per Q2. I suspect that Anthropic’s part of hyperscalers’ spending is more in Q1 than the gain in Q2, preventing the capex’ further growth beyond the 23%/yr (and having efficiency fail to grow more than 1.26 times a year, causing the total result to multiply by ~1.6 times a year instead of ~2 times a year. The METR scaling laws relied on the old trend of compute scaling)
What metrics could one use to understand when AI R&D or huge swathes of white-collar work do end up automated? For example, if automating legal work requires a model with an ECI of at least 170 or the 80% METR horizon of at least 10 hrs, then one could rule out that happening in Q3 2026.
Additionally, we would have to rule out the collapse of the USA’s economy and Taiwanese chip industry caused by exogenous shocks like the Iran War causing a blockage of the Bab-el-Mandeb strait and the Taiwan War.
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.
Thanks for this important work. One thing I remain uncertain about is whether the scenario’s rapid physical and economic scaling rests on multiple unstated (mutually dependent) assumptions:
The AI infrastructure boom must avoid a major investment correction before AI systems generate enough real economic value to justify the expenditure. If returns arrive later than expected, overcapacity, defaults, or a broader financial shock could sharply reduce capex and produce something resembling an AI funding winter.
Compute expansion must not be bottlenecked for years by grid connections, power generation, HBM, advanced packaging, fab capacity, or fab equipment. You model some of these constraints, but I am not convinced that the physical buildout can proceed on the implied timescale.
Robotics must become capable of reliably constructing and operating power plants, fabs, mines, and robot factories without depending on unpredictable breakthroughs in embodied autonomy: long-horizon reliability, dexterity, continual adaptation, and generalization to novel real-world situations. This seems partly bootstrapping-dependent, because rapid robotic infrastructure growth is then used to justify the compute growth enabling further AI progress.
How sensitive is the scenario to these assumptions? In particular, what happens if there is a multi-year capex contraction, an energy or semiconductor plateau, or a substantial lag between cognitive automation and broadly capable robotics?
These things point me to worrying more about the economy than these loss of control scenarios. Maybe you can correct me on that.
It’s possible that there’ll be an AI funding winter in the next few years but we don’t think there will be. The AI companies really do seem to be close to automating AI R&D and more generally close to automating huge swathes of white-collar work; insofar as this is true the valuations and revenue of the AI companies will keep growing very fast instead of plateauing.
“Must not be bottlenecked” that’s a strange way of putting it—it IS bottlenecked by those things, but at least based on our understanding the bottlenecks don’t bite hard enough to prevent the growth rates we are talking about.
The robots thing is correlated with the AI R&D automation thing. If you buy that the AI companies are close to fully automating AI R&D, then you should also think they are probably close to having robots that can reliably construct and operate power plants, fabs, mines, etc.
@Daniel Kokotajlo, @Vladimir_Nesov I think that 1) would benefit from an extra exploration.
At the time of writing, EpochAI predicts that Hyperscaler Capex will Exceed Cash Flow by Q3 2026. The total hyperscaler capex in Q1 2026 was $148.4B, while Anthropic’s annualized revenue in May was $47 billion, letting one estimate the revenue as $12.5B per Q2. I suspect that Anthropic’s part of hyperscalers’ spending is more in Q1 than the gain in Q2, preventing the capex’ further growth beyond the 23%/yr (and having efficiency fail to grow more than 1.26 times a year, causing the total result to multiply by ~1.6 times a year instead of ~2 times a year. The METR scaling laws relied on the old trend of compute scaling)
What metrics could one use to understand when AI R&D or huge swathes of white-collar work do end up automated? For example, if automating legal work requires a model with an ECI of at least 170 or the 80% METR horizon of at least 10 hrs, then one could rule out that happening in Q3 2026.
Additionally, we would have to rule out the collapse of the USA’s economy and Taiwanese chip industry caused by exogenous shocks like the Iran War causing a blockage of the Bab-el-Mandeb strait and the Taiwan War.
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.