Year Experts Expect to Declare AGI

Optimists often emphasize the long timeline—“2100 is a long way away!”—as well as the pace of recent progress: “Given the rapid progress in foundation-model capabilities, the robustness of scaling laws observed through 2025, and the scale of capital and talent commitments across frontier labs, a commercially available system meeting the stated AGI definition...is more likely than not to arrive before 2100.” Pessimists tend to view recent trends as misleading: “LLMs give the illusion of reasoning but fundamentally fall short,” writes one, while another notes that “AI models still hallucinate,” and still others express skepticism that “the current direction of AI (based on neural networks and probabilistic answers) will reach the level of AGI,” arguing instead that different architectures will be necessary, “which might or might not lead to other methods being discovered that will work.”

Pessimists tend to emphasize the need for human trust built on human relationships, and that these factors are intertwined with performance: “Al can't be integrated and trusted like an employee not simply because the tasks are too complex but also because it fundamentally does not fit into how humans organize teams and companies.”; “Psychotherapy, K-8 education, clergy, social work [are jobs] where the human relationship is constitutive of professional performance, not incidental to it.” Optimists tend to not engage with this argument. One writes that most “professional work can be decomposed, instrumented, benchmarked, and delegated,” and this can potentially be done “before Al systems fully solve world modeling or situated judgment.”

Many pessimists point to the difficulty of satisfying the requirement that AGI “can outperform the 90th percentile professional human employee in every primarily non-physical occupation.” One writes, “Every single non-physical occupation? That seems to invite a lot of edge cases.” Another echoes that sentiment, speculating that the panel would be unlikely to “think that AIs are capable enough for jobs across all sectors.” A third argues that “jobs change in response to technologies…That suggests that AI may never have AGI by this definition, because jobs will keep evolving away from AI's capabilities.” Optimists tend to assume a less restrictive interpretation of the question language will be applied. One writes that “the resolution mechanism—a 50%+ vote of a LEAP-style expert panel—will in practice not test every long-tail occupation. Panelists will judge based on representative coverage of major economically significant occupations.” Others focus on less restrictive elements of the question: “Replacing 90% of non-physical stuff that humans do seems like a very low bar.”

Several optimists point to rapidly falling compute costs as increasing the likelihood that the resolution criteria will be met. One writes, “Between 2024 and 2026, the inference cost per token [plummeted]... The economic threshold is the easiest of the four dimensions to cross.” Pessimists express skepticism: “In some cases, the human cost of completing the job may be significantly lower than the cost of developing an AGI agent for that process. If this turns out to be the case, many non-physical roles may never receive AGI agents.”

Many early-resolution respondents not only extrapolate from rapid capability gains as they did when predicting the likelihood of AGI occurring, but also argue a feedback loop from “rapid recursive self-improvement” could lead to an intelligence explosion happening soon. One writes: “The path to superhuman coder is only a log-linear extrapolation away. AGI is probably a close follower.” Late-resolution respondents tend to point again to the need for architectural breakthroughs for AGI to occur, and emphasize that these will take time to materialize: “We still need some other 50 years to reach commercial AGI. First, we need to overcome the transformer-based architectures as we may be reaching their top performance. The transformer phase will still last some other 10-12 years. Then, a paradigm change is to come... and we will need at least 25 years for this new way of computing to be functional + 10 years more of commercial development.”