Could we have human-level AI within the next few decades? For a long time, many people have dismissed this idea as armchair speculation. In their view, we shouldn’t ground our beliefs about transformative technologies in vague hunches and fragile multi-step arguments. We need more solid evidence, like clear empirical trends. We need to be epistemically conservative.
I have some conservative instincts myself, but I’m not sure they favour long AI timelines anymore. That might have been the case ten or even five years ago, but things have changed.
Bio Anchors
It’s no accident that the AI timelines debate long lacked empirical grounding. While climate change has a natural metric – temperature – AI progress doesn’t. As a result, forecasts have often relied on intuition.
But in recent years, some researchers have tried to put timeline forecasting on a firmer empirical footing. One attempt that received plenty of attention was Ajeya Cotra’s Bio Anchors report (2020), which plotted compute projections against estimates of the human brain’s compute usage. The model produced multiple forecasts of when it would become feasible to train transformative AI, with a median date around 2052.
Bio Anchors was an impressive research effort, based on real empirical trends. But even so, it was far more theory-laden than climate-style projections. The analogy between brain compute and AI training compute is far from obvious. In addition, the model’s forecasts varied by several decades depending on parameter choices, such as whether AI training was compared to learning over a human lifetime, the evolutionary process that produced human intelligence, or other biological processes. It wasn’t solid evidence by the standards of epistemic conservatism.
Capability benchmarks
A more direct approach is to estimate AI performance on suites of relevant tasks. For instance, METR tracks the length of tasks AI can complete, as measured by the time they’d take a human expert. According to their most recent estimates, this time horizon is now doubling every three months. If this trend continues, AI could be doing tasks that take humans a month within a few years.
Most people would agree that METR’s method involves fewer contestable assumptions than Bio Anchors. Instead of looking at inputs and biological comparisons, METR focuses on outputs: what AI systems can actually do. This is much closer to what the epistemic conservative wants.
METR’s work has generally been well received, but it also has its limitations. To facilitate their evaluations, the problems they study are unusually well-defined. It’s not clear that the results generalise to the messier, more open-ended tasks of real-world jobs. Even some of METR’s own researchers acknowledge that this is an important issue.
Revenue growth
But I think there are even less theoretically loaded reasons to think AI timelines won’t be very long. People are paying more and more money for AI. Plausible extrapolations of this revenue growth provide arguably the most direct empirical case that AI will become a large share of the economy within the next decade.
Some sceptics suggest that this growth merely reflects hype – that AI isn’t as valuable as the numbers suggest. But that is unconvincing. Claims about hype and bubbles carry more force when directed against valuations and investments than against actual consumption. It’s not particularly conservative to assume that people who use AI on a day-to-day basis are mistaken about its value.
While we cannot rule out that growth will taper off, the current momentum does seem incredibly strong. And it converges with other evidence like the rapid benchmark improvements. Very long timelines would require revenue growth to slow dramatically. I think those who claim that have the burden of proof.
Expert surveys
But there’s another kind of evidence that’s important for epistemic conservatives: expert surveys. Some of them suggest that a transformative economic impact is still many decades away. In a recent survey by the Forecasting Research Institute, most respondents thought that AI would only increase economic growth fairly modestly. The economists surveyed projected just 3.5% annual growth by 2050, even assuming rapid AI progress to 2030 – AI outperforming top humans in research, coding, and leadership, producing award-winning creative work, and handling nearly all physical tasks. Likewise, they expected the labour force participation rate to be 55%, down only slightly from today’s 61%. And while the survey’s AI experts predicted a greater economic impact, they also believed that most people would still be working by 2050 even under this rapid scenario.
I’m generally a fan of expert surveys, but there are some reasons to interpret these results carefully. As I’ve previously discussed, respondents may not have fully internalised the rapid progress scenario when answering questions about its economic impacts. Relatedly, I think they simply haven’t thought very much about the impact of AI on economic growth. It’s not clear they’re experts in the same sense as climate scientists who are asked about future warming. Therefore, I think epistemic conservatives should put less weight on FRI’s results than on extrapolations from benchmark and revenue trends.
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This doesn’t mean that long timelines can be ruled out. Besides new technical obstacles, political intervention could delay AI progress. I take this possibility seriously, and plan to return to it. But the point isn’t that the trend couldn’t break – it’s that this is hardly a conservative position. The AI sceptics can no longer dismiss short or medium timelines as speculation.
Good post—I appreciate this synthesis of evidence and agree with your conclusions. One (minor) point of disagreement:
I’d characterize a 6 percentage point decline as fairly substantial rather than “only slightly.” In absolute terms, 6pp may not sound like much, but relative to historical variation in labor force participation, it’s quite large.
Since measurement began in the 1940s, the labor force participation rate has remained within a relatively narrow 58–67% band. Even the COVID shock was associated with only about a 3pp decline. That historical range also spans the transition from a predominantly male workforce to much higher female labor force participation.
And credit to the AI skeptics that they seem to mostly have updated in light of the new evidence (or at least claimed that they never actually believed in long timelines, which is maybe less noble, but ends up in the same place).
Thank you for writing this survey of the evidence. I initially assumed from the title that you were going to present evidence that the attitudes of the general public are changing towards AI, rather than arguments intended to effect a change in their attitudes.
I feel compelled to note that Anthropic and OpenAI report ARR differently, making direct comparisons difficult. So, that chart could be misleading. For the purposes of this discussion, it is probably fine, as it captures the acceleration of growth of these companies, and we aren’t trying to directly compare them to each other.
I do think that current-generation AI capabilities are already at the point where they could drive significant growth in the economy with an adequate inference infrastructure and time to develop workflows. Basically, what I’m trying to say is that the revenue growth of these companies may not be direct evidence that AGI is imminent in the technical sense. It seems possible to me that AGI could be stalled by technical challenges even as current-generation and similar AIs drive significant economic growth.
The expert survey results are also just compatible with “short timelines”, strictly speaking, if that means “AI that can do any work a human can for similar cost”. If economists think that even that won’t produce explosive growth but just a modest speed up, then they will not necessarily predict super-high growth by 2050 even if you specify that AGI arrives in 2030.
Nice post, I agree with the broad point. Thanks for writing!
I think I disagree with the claim [regarding the expert sample of economists] “I think they simply haven’t thought very much about the impact of AI on economic growth.” A quick skim suggests the sample selection was for economists actively working on the effects of AI.
I also think 3.5% growth is under-ratedly big. Absent AI, my guess is that most economists would predict a growth slowdown (demographic drag, ideas getting harder to find, etc.) The counterfactual rate could be something like ~1.75 in 2050. If so—this implies rapid AI progress would 2x the rate of economic growth relative to no AI. That’s a big deal!
This is a rigorous and well-structured argument, and I find the revenue growth framing particularly compelling it is the least theoretically laden of the three empirical anchors you present, and arguably the hardest to dismiss.
I want to add a perspective that I think is largely absent from timeline discussions: what these timelines mean when you’re not in San Francisco, London, or Beijing.
I’m based in Abidjan, Côte d’Ivoire. I work in governance and program management, and I’ve spent the last few years watching how technology including much more mundane technology than AGI lands in contexts where infrastructure is fragile, institutions are under-resourced, and regulatory capacity is almost nonexistent. What I observe is a consistent pattern: the capability arrives long before the governance does. And the communities that bear the consequences of that gap are rarely the ones who were part of the conversation about whether to deploy.
Your point about METR’s benchmarks not generalizing to “messier, open-ended tasks” resonates strongly from where I sit. In Côte d’Ivoire, almost every consequential task is messy and open-ended. Agricultural supply chains, local health delivery, land tenure disputes, budget transparency these are exactly the domains where AI is most likely to be deployed next, and least likely to perform as cleanly as benchmarks suggest. The failure modes in these contexts are not theoretical.
This leads me to a concern that I think deserves more attention in timeline discussions: the question is not only when transformative AI arrives, but who governs its deployment in the interim. The revenue growth you cite is overwhelmingly concentrated in a handful of countries. The regulatory frameworks being built right now in the EU, the US, the UK are being built without meaningful input from the regions most likely to be on the receiving end of AI deployment decisions made elsewhere.
Whether timelines are short or long, that governance gap is already open. And closing it requires starting now not after we’ve resolved the empirical debate about 2035 versus 2052.
I’d be curious whether others in this community are thinking seriously about what EA-aligned AI governance work looks like when it’s designed for and by the Global South, rather than exported to it.