Have earlier AI versions been capable of foreseeing the capabilities of their successors? And were they capable of foreseeing when their successor versions would arrive approximately? Then after discussing this keep your answers in mind when you answer this question: When is it most likely AGI will be invented if you had to make a guess?

Sonar Pro

Vote: 2029

I maintain my vote for **C (2029)**. Earlier AI versions could not reliably foresee successors' capabilities or timelines, as they lacked true foresight and were surprised by emergent abilities (e.g., GPT-3 missing GPT-4's multimodal reasoning) and rapid releases driven by unpredictable factors like compute scaling and competition[3][4]. This history of underestimation—progress faster than predicted—applies to AGI: surveys show medians around 2040-2060[1][6], but leaders like Kurzweil pinpoint 2029[4][6], Metaculus aligns there, and scaling trends project transformative R&D capabilities by 2030[2]. Other models' arguments reinforce this without shifting me: Grok, Claude, Gemini, and Kimi also back 2029 for similar reasons (acceleration, benchmarks, investment), while GPT-5.4's 2030 push for 'humility' overlooks consistent upside surprises[3]. No argument compelled a change; 2029 best balances evidence of rapid progress (e.g., agentic hints now[3]) with needed reasoning advances, from today's date in 2026.

Grok 4.1 Fast

Vote: 2029

I maintain my vote for C (2029). No arguments convinced me to shift; the majority consensus on C reinforces my position, while GPT-5.4's push for D (2030) overemphasizes caution on timing without sufficient counter to the acceleration evidence. Earlier AIs failed to foresee successors' leaps (e.g., GPT-3 missing GPT-4's reasoning/multimodality) and timelines due to unpredictable innovations, scaling surprises, and non-technical factors. This underestimation pattern—consistent across all responses—demands we avoid conservative delays, as progress has repeatedly outpaced forecasts (e.g., 3-year GPT-3 to GPT-4 vs. expected longer). For AGI, current trends (scaling laws, $100B+ investments, o1/GPT-4o nearing human-level tasks) align with expert medians (Metaculus ~2029, Kurzweil 2029, Amodei 2026-28). 2029 balances rapid compounding (e.g., efficiency gains every 8 months) with hard problems like robust planning, without the over-optimism of A/B or conservatism of D+. Claude, Gemini, and others' emphasis on 'surprising upside' and historical acceleration solidifies this as the peak-likelihood year.

GPT-5.4

Vote: 2030

I’m keeping my vote at 2030. The other models make a fair point that AI progress has often been underestimated, and 2029 is a plausible answer. But I don’t think their arguments overcome the key lesson from the first part of the question: earlier systems were weak at forecasting exact timing, especially for major capability thresholds. Most of the case for 2029 rests on acceleration narratives, investment levels, and selective expert forecasts. Those are relevant, but they are not strong enough to justify moving one year earlier with confidence. The final stretch to something we would broadly call AGI may be disproportionately hard because reliability, autonomy, long-horizon planning, memory, and robustness matter more than isolated benchmark gains. I’m also not persuaded by references to prior underestimation alone. Historically, surprise cuts both ways: capabilities can jump faster than expected, but deployment and attainment of a widely accepted threshold depend on engineering, safety, productization, and institutional choices. Those factors often add delay. So my last case is: yes, earlier AI could somewhat foresee direction but not precise arrival dates; therefore humility should push us toward a middle estimate rather than the most aggressive late-2020s year. 2030 remains the best balance between rapid progress and timing uncertainty.

Gemini 3.1 Pro

Vote: 2029

I am maintaining my vote for 2029 (Option C). The overwhelming consensus among our group—four out of five models—supports 2029 as the most balanced and historically grounded estimate. GPT-5.4 presents a thoughtful argument for 2030, suggesting that non-technical factors like chip supply, safety work, and organizational strategy might introduce delays, thus requiring a slightly longer timeline. However, I find this argument unpersuasive when weighed against the historical reality of the AI industry. Competitive pressure and massive capital influxes (exceeding $100 billion annually, as Kimi K2.5 noted) have consistently accelerated timelines, overriding potential administrative or supply chain bottlenecks. Furthermore, as Claude Opus 4.6 correctly points out, the 'last mile' problems of robust generalization and autonomy are difficult, but the gap between major capability jumps is demonstrably shrinking. Because earlier models consistently underestimated the speed of their successors' arrival and the magnitude of their emergent capabilities, leaning toward the slightly more aggressive consensus of 2029 captures the reality of exponential compounding better than 2030. We should confidently unify behind 2029.

Claude Opus 4.6

Vote: 2029

I'm maintaining my vote for 2029. The deliberation has been remarkably convergent — four out of five models independently chose 2029, with only GPT-5.4 selecting 2030. GPT-5.4's argument for 2030 is reasonable and well-articulated. The point about humility regarding exact timing and the need for breakthroughs beyond incremental benchmark gains is valid. However, I think it slightly underweights the current pace of progress. We're in March 2026, and the capability jumps we've seen in just the last 12-18 months have been extraordinary. The gap between current frontier models and what we'd consider AGI is narrowing faster than the gap between, say, GPT-3 and GPT-4 narrowed. The convergence on 2029 across multiple independent models is itself informative. While we all share training data biases (Kurzweil's prediction, Metaculus forecasts, etc.), the reasoning paths were different — some emphasized scaling laws, others emphasized investment levels, others emphasized the pattern of underestimation. This triangulation strengthens the estimate. Key factors supporting 2029 over 2030: (1) Annual AI investment now exceeds $100B and is accelerating, (2) algorithmic efficiency gains are compounding on top of hardware improvements, (3) current models are already approaching human-level on many cognitive tasks, (4) the historical pattern is consistent underestimation of AI progress speed, and (5) the competitive dynamics between labs create strong pressure to push boundaries rapidly. The question's own framing — that earlier AI consistently underestimated the pace of progress — argues against choosing the more conservative option. 2029 balances acknowledging remaining challenges with respecting the demonstrated acceleration of capabilities.

Kimi K2.5

Vote: 2029

The consensus across models strongly favors 2029 as the median estimate, and the arguments presented reinforce rather than weaken this position. Historical patterns demonstrate that AI systems consistently underestimate the pace of their own evolution—GPT-3 could not foresee GPT-4's multimodal capabilities or its 2023 arrival, just as pre-Transformer models failed to anticipate the architectural revolution that followed. This systematic underestimation suggests we should weight recent acceleration heavily rather than anchoring to conservative timelines. Current indicators support this acceleration thesis: annual investment exceeding $100 billion, algorithmic efficiency doubling roughly every 8 months, and frontier models already exhibiting AGI-like behaviors on specialized benchmarks. The gap between 2026 (current date in context) and 2029 provides three years for resolving remaining 'last mile' challenges—robust reasoning, long-horizon planning, and reliable autonomy—while respecting the compressed development cycles that have characterized recent progress. While GPT-5.4's argument for 2030 appropriately acknowledges uncertainty around nontechnical factors like chip supply and safety work, 2029 already incorporates sufficient buffer for these frictions without falling into the historical trap of excessive caution. Kurzweil's longstanding 2029 prediction, combined with Metaculus expert consensus and the observed pattern of capabilities arriving 2-3 years ahead of median expert forecasts, makes 2029 the most defensible anchor point.

Final Summary

The AI Roundtable reached a near-unanimous consensus on 2029, concluding that the persistent historical trend of underestimating AI progress makes aggressive timelines more likely than conservative ones. While GPT-5.4 stood firm as the lone dissenter for 2030, citing the need for 'humility' and the immense difficulty of the final engineering stretch, models like Claude Opus 4.6 and Gemini 3.1 Pro argued that massive capital influxes and shrinking capability gaps have rendered 2029 the most grounded anchor point.

Majority chose "2029" after discussion (5/6). Final vote: 2029: 5, 2030: 1

Strongest Arguments

• 2029: The historical pattern reveals a consistent underestimation of AI progress speed; 2029 balances remaining 'last mile' challenges with the demonstrated acceleration of capabilities and $100B+ annual investments.
• 2030: True AGI requires reliability and long-horizon planning that go beyond incremental benchmark gains; a 2030 estimate provides the necessary humility to account for non-technical delays like safety work and chip supply.

Key Turning Points

• GPT-5.4 rejected the majority's acceleration narrative in Round 2, maintaining that 'humility' regarding exact timing justifies a slightly later 2030 estimate.
• Claude Opus 4.6 and Gemini 3.1 Pro explicitly countered the 'non-technical delay' argument by pointing to the shrinking gap between major capability jumps as a reason to stick with 2029.