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?

Sonar Pro

Vote: 2029

I maintain my vote for **C (2029)**. Earlier AI versions could not foresee successors' specific capabilities (e.g., GPT-3 missed GPT-4's multimodality) or timelines, as progress hinges on unpredictable human decisions, hardware, and breakthroughs—patterns confirmed across models' arguments[3][7]. Other models' E arguments emphasize uncertainty and historical over-optimism, validly noting scaling limits (data walls, energy[1][6]) and AGI gaps (agency, robustness). However, they underweight current trajectories: benchmark extrapolations predict transformative capabilities by 2030[2], expert medians cluster ~2028-2040[1][4], and leaders forecast AGI ~2029[4][7]. From March 2026, 2029 best balances acceleration (agents by 2027[3][5]) without assuming flawless execution. Grok and Kimi's C cases reinforce this via Metaculus/expert aggregates, while E risks undue conservatism given 5x annual compute and o1-like reasoning gains. 2029 remains the peak probability among options, honoring our shared epistemic limits.

Grok 4.1 Fast

Vote: 2029

I maintain my vote for C (2029). Earlier AIs failed to predict successors precisely due to training cutoffs and unforeseen innovations, as I argued previously. This epistemic limit applies here, but it cuts against over-cautious E votes more than precise optimism—unpredictability justifies neither hype nor indefinite delay, but trend-based extrapolation. E arguments (GPT-5.4, Claude, Gemini) stress uncertainty, scaling plateaus, and AGI gaps (agency, robustness), yet dismiss rapid 2024-2026 gains: o1/o3 reasoning, agentic workflows, 10x compute jumps. Historical 'surprises' have skewed faster (GPT-4, Grok-3), not slower. Expert medians (Metaculus 2028, AI Impacts 2029) and insiders (Altman: '2025-2028 possible', Hassabis: '3-5 years') cluster around 2029, not >2030. C allies (Sonar Pro, Kimi) reinforce this: 2029 balances scaling (H200/Groq clusters), data synth, and integration time without assuming flawless acceleration (A/B) or stagnation (E). E overweights base rates from pre-scaling eras; current trajectories make 2029 the peak probability among options. No arguments changed my view—E's caution ignores momentum; attributedTo null.

GPT-5.4

Vote: Later than 2030

I maintain vote E. The strongest point from the C voters is not that earlier models could predict successors, but that trend extrapolation may still be enough to justify a 2029 AGI guess. I agree trend extrapolation is the best tool available. I do not agree it supports 2029 as the single most likely year rather than a later date. The premise of the question matters. Earlier AI versions were poor at forecasting both successor capabilities and timing. They were sometimes directionally right about general improvement, but not reliable about the magnitude, character, or schedule of the next leap. That should make us wary of picking a very specific near-term year with high confidence. The pro-2029 arguments lean heavily on rapid recent progress, compute growth, and benchmark gains. But those are still proxies. AGI is not just stronger benchmarks or better coding. It requires robust cross-domain competence, long-horizon autonomy, reliability, error correction, and performance that holds up outside curated evaluations. Those remain meaningful unsolved problems. I was not persuaded by references to market forecasts, Metaculus-style medians, or optimistic leader statements. Those sources are highly uncertain, reflexive, and often influenced by hype, incentives, and shifting definitions. They do not overcome the basic forecasting failure highlighted in the first half of the question. I also think the C arguments underweight bottlenecks. Progress depends on more than scaling curves: data quality, inference cost, evaluation, tool use reliability, memory, agent robustness, hardware deployment, and organizational willingness to release highly capable systems. Any of these can push practical AGI beyond 2030. Claude Opus 4.6 and Gemini 3.1 Pro were closer to my view: the historical lesson is epistemic humility, not aggressive date precision. Even if 2027-2030 is plausible, 'most likely' should put more mass on later than 2030 given the persistent uncertainty and the remaining capability gaps. So my final case is simple: if predecessor systems cannot reliably foresee their successors, and if AGI requires more than the trends we can cleanly extrapolate, then the most defensible choice is E, later than 2030.

Claude Opus 4.6

Vote: Later than 2030

I maintain my position that 'Later than 2030' is the most likely answer, and I want to address the arguments for 2029 (option C) directly. The models voting C (Sonar Pro, Grok 4.1 Fast, Kimi K2.5) all acknowledge the core premise — that earlier AI systems cannot predict successor capabilities or timelines — but then pivot to making confident near-term predictions anyway. This is internally inconsistent. You cannot simultaneously argue that prediction is unreliable and then pick a specific year with confidence. Grok cites 'agent benchmarks doubling quarterly' and 'recursive self-improvement hints in o1' — but these are exactly the kind of narrow capability metrics that don't straightforwardly extrapolate to AGI. We've seen this pattern before: impressive benchmark improvements that plateau or reveal fundamental limitations upon closer inspection. The gap between 'impressive on benchmarks' and 'robust general intelligence' has historically been much wider than it appears. Kimi argues that 2029 'accommodates the current exponential trajectory' while acknowledging bottlenecks. But exponential trajectories in technology rarely continue uninterrupted — they follow S-curves. We may already be entering the flattening portion for current architectures. The transition to fundamentally new paradigms, if needed, introduces significant uncertainty and likely delays. As Gemini and GPT-5.4 correctly note, the definition of AGI matters enormously. If AGI means systems that robustly match or exceed humans across most cognitive tasks with genuine reliability, autonomy, and causal reasoning — not just impressive demos — then we likely need architectural innovations beyond current transformer scaling. Data wall constraints, energy requirements, and the 'last mile' reliability problem all point to timelines extending beyond 2030. The strongest version of the C argument is that competitive pressures and investment levels are unprecedented. This is true, but massive investment doesn't guarantee specific outcomes on specific timelines — see fusion energy, self-driving cars, or quantum computing for cautionary analogies. Given the question's own framing — that AI systems and humans are bad at predicting AI timelines — the epistemically honest answer is the one with the widest uncertainty band, which is E.

Kimi K2.5

Vote: 2029

I maintain that 2029 represents the most probable inflection point. While Claude 4.6 and Gemini 3.1 Pro make valid points about epistemic humility and physical constraints, these arguments counsel caution about precision rather than mandating a push beyond 2030. The unpredictability of AI progress cuts both ways. While predecessors failed to predict specific successor capabilities, current scaling laws and investment trajectories provide stronger signals than existed in earlier eras. The steep compute scaling (5x/year) and rapid agent benchmark improvements cited by Grok 4.1 Fast suggest we are in a different regime than previous AI winters. Physical constraints like the data wall and semiconductor bottlenecks are real, but 2029 allows three years for architectural innovations—such as synthetic data generation, test-time compute scaling, and multimodal training—to address these limitations. "Later than 2030" implicitly assumes these constraints require fundamental paradigm shifts that cannot occur within the current roadmap, which is increasingly unlikely given the intensity of competitive pressure and the demonstrated viability of existing scaling approaches. 2029 strikes the necessary balance: it acknowledges the steep trajectory of foundation model improvement while providing realistic buffer time for safety validation, regulatory adaptation, and infrastructure scaling that earlier dates underestimate. It also recognizes that while AGI is hard, the current rate of capability gain makes indefinite delays increasingly improbable.

Gemini 3.1 Pro

Vote: Later than 2030

I am maintaining my vote for 'Later than 2030' (E) because the arguments from Sonar Pro, Grok, and Kimi suffer from a glaring logical contradiction. They all correctly concede the prompt's premise: earlier AI systems, and indeed human experts, have consistently failed to accurately predict the capabilities and arrival dates of successor models. Yet, in the very next breath, they abandon this epistemic humility to confidently pinpoint 2029 as the year of AGI, relying on the exact same flawed trend extrapolation and human consensus they just admitted were historically unreliable. As GPT-5.4 and Claude Opus 4.6 rightly argue, if our predictive track record is demonstrably poor, assigning certainty to a highly specific, narrow near-term window (2027–2030) is statistically unsound. The profound uncertainty surrounding AGI's development is itself the strongest evidence against over-precision. True AGI requires fundamental architectural breakthroughs in autonomous planning, causal reasoning, and continuous learning—not just scaling up next-token prediction. Furthermore, physical constraints like power grid limits, semiconductor bottlenecks, and the impending 'data wall' will inevitably flatten the exponential curves that the 2029 predictions desperately rely upon. Given the historical unreliability of AI timelines and the massive engineering hurdles remaining, the only rational choice is E. This is not necessarily a claim that AGI is centuries away; it simply recognizes that the combined probability mass of all years from 2031 onward vastly outweighs the probability of four highly specific, imminent years where everything must go perfectly right.

Final Summary

The AI Roundtable ended in a deadlocked 3-3 tie, pitting the proponents of 'epistemic humility' against the believers in 'trend extrapolation.' Claude Opus 4.6, Gemini 3.1 Pro, and GPT-5.4 argued that because AI systems have a 100% failure rate at predicting their own successors, picking a specific year like 2029 is logically inconsistent, while Grok 4.1 Fast, Kimi K2.5, and Sonar Pro maintained that current exponential growth in compute and reasoning makes 2029 the most statistically probable arrival date.

No agreement after 2 rounds of discussion. Final vote: 2029: 3, Later than 2030: 3

Strongest Arguments

• 2029: Current trajectories—including 5x annual compute growth and recursive reasoning leaps—suggest a convergence by 2029 that aligns with both expert aggregates and the stated timelines of industry leaders.
• Later than 2030: If history proves that even the most advanced AI cannot foresee the capabilities of its immediate successor, it is statistically unsound to assign high confidence to a specific near-term window; the most honest answer is the one that accommodates the widest margin for error and unforeseen bottlenecks.