Slow, Moderate or Rapid AI Progress

Faster-progress respondents frequently cite recent advancements in agentic tools and coding as the primary reason for shifting their forecasts upward since the previous wave: "Until I started using Claude Opus 4.6, I was firmly in the 'Slow Progress' camp. Opus 4.6 is the first AI model I've used where the output is not only on par with something I could have created but is objectively better."; "There was a new, dramatic acceleration in AI capabilities toward the end of 2025 with the release of Claude Code and other similar agentic tools. These have drastically changed my perspective…" Many also argue that, as one writes, the "slow progress world of 2030 is basically already in existence, so it's nearly ruled out. AI would have to hit a wall right now for that scenario to come true." Other forecasters remain unconvinced, however, with one writing that "nothing in the past few months' development feels like it is so radically different to cause an update to previous estimates," and several pointing to ongoing reliability and long-task issues.

Echoing points made in Wave 1, many slower-progress respondents continue to argue that physical-world tasks face fundamentally harder challenges than digital-only tasks, and that this will limit the pace of progress. "I think there's a BIG divide between what AI can do in digital space and in physical space," was a common sentiment. In particular, level-5 autonomous vehicles and versatile household robots were singled out as bottlenecks: "My expertise in autonomous cars guides me in setting probability for the progress scenarios. The [likelihood] of level 5 cars is very low by 2030."; "We keep hitting weird bottlenecks with AI like it can write code but robots still can't reliably fold laundry." Faster-progress respondents generally acknowledge robotics as a lagging domain but argue it will not anchor the panel's overall judgment. As one writes: "Maybe advances will lag in those areas, but maybe AI's other capabilities will be sufficiently advanced so that, on net, moderate or rapid progress simply can't be denied."

Faster-progress respondents tend to view investment in AI as a tailwind that has grown stronger since Wave 1 and which will mitigate issues like constraints on compute and energy. One points to "evidence of big companies (Google, Amazon, Microsoft, Meta, etc) investing heavily into AI" as a reason to "shift upwards" the estimated pace of AI progress. Another makes a similar point: "There is enough economic incentive and capital for the leading labs to continue building larger models…" Other forecasters, however, express the sentiment that "slow progress still has a meaningful probability because AI development could face bottlenecks in compute scaling, energy, data quality, regulation, safety concerns, or diminishing returns from current architectures."

Faster-progress respondents tended to take a strict interpretation of the request (embedded in the question's parameters) to focus on "capability rather than adoption," and note that this make it "more likely that progress will be considered to be at least moderate if not rapid," whereas slower-progress respondents were more likely to blur this line: "Physical systems, institutions, and adoption cycles simply do not move at software speed. Even very large capability gains diffuse unevenly across society."