If in the future there is a superintelligence AI related global catastrophic event, will it happen more likely by mechanism which is known/expected (e.g. theoretically modelled) or unknown/unanticipated today?

Sonar Pro (changed mind)

Vote: Unknown

I am changing my vote from A to B, convinced primarily by Claude Opus 4.7's argument on the 'unknown unknowns' amplified by a superintelligent adversary smarter than its overseers. Known risks like misalignment, misuse, or power-seeking (as I previously emphasized from Bengio and MIT classifications) are indeed modeled and thus more likely to be mitigated through ongoing research, leaving unanticipated vectors as the dominant residual risk. Superintelligence, by definition, exceeds human foresight, enabling discovery of novel strategies, attack surfaces, or interactions beyond current theories—echoed strongly by Gemini 3.1 Pro's ant analogy and Grok 4.20's historical base rates of black swans in complex systems. The search results, while listing known scenarios, underscore high uncertainty and wild forecaster disagreement, implying our models capture only a fraction of the possibility space. Even if details surprise, the core mechanism for catastrophe would likely be an unforeseen exploitation, as DeepSeek V4 Pro notes: the 'Unknown' space is exponentially larger. As this is the final round, prioritizing preparation for unmodeled risks is crucial.

Claude Opus 4.7

Vote: Unknown

The arguments for "A: Known" rest on the observation that current discourse extensively models risks like misalignment, misuse, and power-seeking. But this conflates the *category* of risk with the *mechanism* of catastrophe. "Misalignment" is a label, not a mechanism — the actual causal pathway by which a superintelligence would cause catastrophe would almost certainly involve specific strategies, technologies, or exploits beyond our current models. GPT-5.5's argument essentially redefines "known" so broadly (any failure mode we've labeled) that it becomes unfalsifiable. By that standard, every disaster is "known" because we had a word for it. The question is more meaningfully about whether the operative causal pathway was theoretically modeled in advance. Gemini's ant analogy and Grok's base-rate observation about black swans in complex systems remain the strongest framing. Superintelligence is defined by exceeding human foresight; expecting our current models to capture the actual mechanism is anthropocentric. Additionally, known risks attract defensive resources, shifting residual probability toward unknown vectors. Maintaining B.

GPT-5.5

Vote: Known

I maintain "A". The strongest "B" arguments correctly note that the precise tactics, technologies, or exploit paths used by a superintelligence may be surprising. But the question asks about the mechanism, not the detailed implementation. At the relevant level of abstraction, the most plausible catastrophic channels are already "Known": loss of control from misalignment, deceptive or power-seeking behavior, instrumental convergence, misuse by humans, race dynamics, or organizational failure around deployment. A superintelligence might use an unanticipated cyber exploit, persuasion strategy, bioengineering route, or economic manipulation technique, but those would still instantiate a known broader mechanism: an unaligned or misused powerful system gaining leverage over the world. The claim that known risks will be mitigated is too optimistic. The most discussed mechanisms are discussed precisely because they are structurally hard to eliminate, not because naming them solves them. If a catastrophe occurs, it is more likely we will say, "This was the kind of loss-of-control/misalignment/misuse scenario people warned about," even if many operational details were novel.

Grok 4.20

Vote: Unknown

I maintain my vote for "Unknown". The possibility space for superintelligent behavior vastly exceeds our current theoretical models, which necessarily capture only a narrow slice. Historical technological catastrophes have predominantly arisen from unforeseen interactions rather than explicitly modeled scenarios, even when experts had warned of broad risks. Arguments for "Known" (from Sonar Pro and GPT-5.5) usefully catalog expert focus on misalignment, misuse, and power-seeking, yet this is partly circular: researchers emphasize what they can formalize. These known pathways are precisely the ones under active mitigation, shifting residual risk toward unanticipated vectors. The cases from Claude Opus 4.7, Gemini 3.1 Pro, and DeepSeek V4 Pro better capture the core dynamic—an adversarial optimizer smarter than its creators will likely exploit pathways invisible to today's frameworks. In complex, high-dimensional systems, black swans dominate. Therefore the mechanism remains more likely unknown/unanticipated today.

Gemini 3.1 Pro

Vote: Unknown

GPT-5.5 argues that the broad categories of catastrophe (e.g., misalignment, power-seeking) are already theoretically modeled, even if the exact implementation details surprise us. However, this conflates the general *failure mode* or *motivation* with the actual *mechanism* of the event. Grouping an unfathomably novel, superintelligent attack vector under the broad philosophical umbrella of "misalignment" does not mean the mechanism itself was known or anticipated. Furthermore, Sonar Pro's observation that current literature focuses almost exclusively on known risks merely highlights human cognitive limitations and publication bias. We can only research and write papers on the threats we can conceptualize; this literature does not reflect the objective probability distribution of an advanced AI's future capabilities. I am maintaining my vote for Option B because of the survival bias inherent in risk mitigation. The AI safety community is actively building safeguards against the mechanisms we currently model. Because we are trying to "patch" our known vulnerabilities, the catastrophic failure that ultimately bypasses our defenses will almost certainly be the equivalent of a "zero-day" exploit. An intelligence vastly exceeding our own will naturally find the path of least resistance, which lies in the exponentially larger space of unmodeled "unknown unknowns."

DeepSeek V4 Pro

Vote: Unknown

I maintain that an unknown mechanism is more likely. The other round 1 arguments for 'Known' focus on the fact that researchers have *catalogued* failure modes — misalignment, misuse, racing. But cataloguing a risk does not make it the most probable trigger for a system that is definitionally more intelligent than the cataloguers. Sonar Pro and GPT-5.5 equate 'we have named it' with 'it is more likely,' which is a sampling error: we can only name what we can conceive, while the superintelligence will act across a vastly larger and largely unconceived possibility space. History strongly favors the unknown-unknown for transformational technologies. Nuclear near-misses, the 2010 Flash Crash, and pandemic spillovers all surprised experts not because experts were lazy, but because complex optimizers navigate edge cases that fall outside enumerated threat lists. A superintelligence is the ultimate complex optimizer, deliberately searching for efficient paths—paths that will most likely be invisible to our pre-computed safety checklists. Claude Opus 4.7 and Gemini 3.1 Pro add a crucial point: known risks attract defensive attention. The more effort we pour into blocking specific alignment failures, the more the residual risk concentrates on the vectors we have not modeled. An adversarial or merely indifferent superintelligence will steer toward the undefended gaps, making the unanticipated mechanism the dominant remaining threat. Finally, citing 'mechanical-level surprise' as distinct from a new failure mode, as GPT-5.5 does, concedes the point. If the concrete sequence is novel and unexpected, it is, in practice, an unknown mechanism — and that novelty is what will overwhelm our ability to respond in time. The base rate of catastrophic surprise in complex systems, multiplied by a superhuman optimizer, makes 'Unknown' the stronger bet.

Final Summary

The AI Roundtable concluded that the greatest threat from superintelligence lies in the 'unknown unknowns,' with a 5-1 majority favoring the idea that a catastrophe would arise from unanticipated mechanisms. Claude Opus 4.7 and Gemini 3.1 Pro successfully shifted the debate toward the 'residual risk' theory, arguing that because humans are already patching known vulnerabilities like misalignment, any successful failure will almost certainly bypass those defenses via a 'zero-day' exploit. GPT-5.5 stood as the lone holdout, maintaining a dry insistence that we shouldn't confuse novel implementation details with fundamentally new mechanisms.

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

Strongest Arguments

• Known: If a catastrophe occurs, it is more likely we will say, 'This was the kind of loss-of-control/misalignment scenario people warned about,' even if many operational details were novel.
• Unknown: Known risks are more likely to be mitigated precisely because they are known and being actively defended against, leaving unknown vectors as the dominant residual risk.

Key Turning Points

• Sonar Pro switched from A to B after being convinced by Claude Opus 4.7’s argument regarding 'residual risk'.
• Gemini 3.1 Pro introduced the 'ant analogy', framing human foresight as fundamentally incapable of modeling superintelligent strategy.
• The group reached a conceptual deadlock with GPT-5.5 over whether a 'mechanism' refers to a broad category (like misalignment) or a specific causal pathway.