Year Experts Expect Rapid AI Progress

Fast-progress respondents typically cite the 2100 timeline, arguing 74 years provides ample time for massive, compounding technological leaps: “74 years is enough time for multiple further cycles of AI winter and summer, if necessary.”; “2100 is far out with these exponential model improvements, so moderate progress is baked in and rapid likely.”; “These are all things likely to be surpassed in the next few years, let alone 74.” Slow-progress respondents tend to point to the potential bottlenecks discussed below (robotics, architectural sufficiency, the popping of an AI investment bubble, and societal backlash) as reasons to remain skeptical. “Most breakthrough technologies eventually encounter diminishing returns,” writes one, and another that “the current pace of AI progress is in many ways a product of a specific convergence of available training data, transformer architecture scaling, and an unprecedented influx of capital. I expect at least one of these constraints to bind meaningfully before [2100].”

This is the most cited reason slow-progress respondents give for why they think progress will not be deemed rapid. As one writes: “The robotics component of the rapid scenario is a pretty high bar. Seeing that I have significant uncertainty that the coffee test will ever be achieved by 2100, adding all these autonomous robotic capabilities makes it all that much unlikely.” Another adds, “The whole ‘fix any plumbing issue in any arbitrary home anywhere in the world’ seems too high a barrier on a physical, not intelligence level.” Fast-progress respondents tend to argue that breakthroughs in digital cognition will inevitably translate to the physical world, and they also cite recent manufacturing and scaling successes. One writes: “Waymo robotaxies are already reported to have 13x fewer serious crashes than humans through December of last year. There are significant advancements in humanoid robots, with Figure scaling 24x in production in less than four months and Optimus, Unitree, and 1X all advancing.”

Most fast-progress respondents project current pace of gains forward, implying that the incremental improvements to the current models will be sufficient: “This year has seen rapid progress, so unless we see a collapse in scaling laws, I’m fairly confident we’ll get to this level of response by 2100.”; “AI capabilities are rising e,g. METR benchmarks, Frontiermath… If the rate of progress increases, it can likely pass as rapid progress in the next 10 years.” Slow-progress respondents tend to view that rate of progress as unlikely to continue: “To achieve rapid progress, AI models need to be able to surpass human performance on nearly every task. This will require significant technological breakthroughs, and should not be achievable using methods that we have right now.”; “More of, and faster, versions of the current AI will not give us ‘Reliable, Rapid progress’. So to increase the percentages this question depends on different techniques emerging.”

Several slow-progress respondents point to human resistance to AI deployment as a potential obstacle. One writes that “regulatory barriers could prevent the scenario from being realised,” and another that “the rapid progress seems too good to be true, humans will not let it happen…legislators or politicians will not let it happen.” Fast-progress respondents, when they engage with this factor, tend to treat backlash as a small tail risk against an otherwise overwhelming default. As one writes: “Hard to imagine we don’t have these capabilities. Only blockers are butlerian jihad, pandemic, nuclear war, etc…”

Fast-progress respondents generally view capital and geopolitical competition as a durable engine of progress: “There are large incentives both in the private sector (profit and productivity increases) and public sector (geostrategic advantage) that make it likely that there will be a continued focus on AI progress…” writes one. Some slow-progress respondents instead view the current funding surge as likely to be transient. One writes: “I believe the AI bubble is reasonably likely to burst within the next 5-10 years, at which point I think we will be disabused of notions of rapid progress.”

Fast-progress respondents often view Pulitzer/Grammy-caliber work from AI as achievable, with one writing, “I anticipate Al crossing this threshold within the next decade i.e., being indistinguishable in a blind test from world leading human literary and musical art.” Slow-progress respondents often emphasize the sociocultural element of these thresholds, with one noting, “The quality of creative output is socially defined, not by some objective metric, and I do not believe AI artists to have a lively social following.”

Fast-progress respondents often argue that, conditional on resolution, the event will happen early given current capability momentum and the recent hype cycles: “If we see rapid progress, we’ll see it sooner rather than later."; “This question is related to the current concept of AI advancement…expecting higher intensity efforts will come upfront in the 20s, 30s and 40s, with efforts likely diverted elsewhere in the later years of this century.”; “By 2030 most of the current CAPEX data center build out will be complete, providing inexpensive inference across a continent. Improvements in inference engines will also support robotaxis and humanoid robots with appropriate computing power for autonomous behavior.” Slow-progress respondents typically point toward a mid- to late-century resolution date, citing the likelihood of slower than anticipated change on a variety of fronts. One writes: “Due to the amount of societal change required, if this happens at all, I would expect it to be towards the end of the time frame.” Another: “Slower timelines here than for the AGI questions because this definition involves a lot of progress on interaction with the physical world.” A third focuses on the LEAP panel: “The delay comes from the fact that this is not a technical threshold. It’s a social consensus problem. The panel includes people from domains that move slowly, people who are cautious by training, and people who will require overwhelming evidence before they label something as rapid.”