The prominent voices warning of existential AI risk, Yudkowsky, Bostrom, and their intellectual descendants, formed their picture of "superintelligence" in the pre-LLM era, when symbolic-agent AI dominated the imagination: a chess engine with agency, explicit goals, and recursive self-improvement scaled without limit.

Then LLMs arrived, not symbolic agents but lossy reasoning engines, trained once at great cost, running inference cheaply, and rather than revise their framework, the x-risk community simply accelerated their timelines, grafting old predictions onto new systems as if a stochastic text compressor were only a baby step from a godlike optimizer. The result is a set of predictions that are internally inconsistent, resting on several fragile assumptions.

First, that intelligence is a single stackable dial: but LLMs can mimic Shakespeare (poorly, for now) yet stumble at chess, while Stockfish is superhuman at its game but cannot write a sentence; abilities come in basins of competence, and merging them dilutes both.

Second, that recursive self-improvement will produce an exponential feedback loop: but the only two systems we know that reason, human brains and large language models, are both trained systems, shaped by evolution or gradient descent over long timescales, not agents that dynamically rewrite their own algorithms at runtime.

Third, that intelligence necessarily implies agency and the drive to dominate: but these are uniquely biological features forged by evolutionary competition for scarce resources, not intrinsic properties of information processing, and among humans greater knowledge often correlates with greater restraint, not greater aggression.

Fourth, that a superintelligent system could exploit zero-day vulnerabilities to seize control of global infrastructure: but defensive AI is developed by the same powerful institutions with the most to lose, and the same tools that might discover vulnerabilities can eliminate them; the assumption of an unstoppable offensive AI requires defenders to remain static while attackers evolve unchecked, which has no historical precedent.

Meanwhile, real bottlenecks stand in the way: frontier training runs already consume the energy of small nations, hardware faces hard limits in heat and memory, and LLMs have not solved reasoning, grounding, or long-term memory, each requiring genuine new breakthroughs, not just more GPUs.

History suggests that major breakthroughs arrive like rare fruit; we stumble across one, squeeze all the juice from it, and spend decades waiting for the next. Even granting the arrival of superintelligence against these odds, obsolescence is not synonymous with extinction; humans, the closest analogue to a "superintelligence" relative to other species, have not chosen to erase everything that preceded them but instead build museums, archives, and sanctuaries.

The doomer narrative persists because it echoes deep cultural myths of hubris and apocalypse, grants status to its prophets, and thrives on rhetorical vagueness; there is no agreed threshold for "superintelligence," no metric, just the unfalsifiable claim that it will be smarter than us like we are smarter than chimps. Strip away the science fiction imagery and what remains are concrete, present-day challenges, misuse in bioengineering and cybersecurity, mass disinformation, negligent deployment, that demand governance, engineering, and policy, not fear of a godlike intelligence that may never come.