Futurism is always extreme

The piece opens with an AI‑generated illustration of William Gibson’s *Neuromancer* rendered in Paul Klee’s style, created by OpenAI’s DALL‑E 2 in June 2022. It then builds a philosophical argument: when you formalize the world in a model—whether qualitative, like “humans are smarter than machines,” or quantitative, with precise timelines—the only attractors are a heaven‑like singularity (von Neumann probes, Matrioshka brains, uploaded minds living at millions of subjective years per second) or a hell‑like extinction. By framing the “ultimate” state as a normal form where no further rewrite rules apply, the author claims the model stabilizes only at these poles, dismissing any moderate future. This matters because it reframes AI foresight from a spectrum of possibilities into a binary that can shape public discourse, funding priorities, and policy narratives around existential risk.

The argument taps into a growing trend among AI think‑tanks, futurists, and media outlets that treat superintelligence forecasts as deterministic. Organizations such as OpenAI, DeepMind, and Anthropic regularly publish roadmaps and alignment research, yet most still acknowledge uncertainty in timelines and pathways. By contrast, the sidebar’s “crank‑until‑stable” metaphor echoes the more alarmist corners of the field—think the “AI doomsday” narratives popularized by figures like Elon Musk or the “Omega Point” vision of Ray Kurzweil. This binary framing competes with more nuanced approaches that model incremental advances, hybrid human‑AI systems, and sector‑specific impacts, highlighting a split in how the tech community communicates long‑term risk.

If the binary view gains traction, it could skew investment toward either hyper‑aggressive safety measures or, conversely, fatalistic disengagement. Policymakers might over‑react to worst‑case scenarios, imposing heavy regulation that stifles beneficial research, or under‑react by assuming an inevitable singularity will solve problems. Watch for the emergence of new think‑tank reports, academic papers, or corporate roadmaps that either adopt this extremal language or explicitly reject it in favor of probabilistic, multi‑scenario forecasting. The discourse will likely influence AI alignment funding, regulatory hearings, and public perception of AI’s trajectory.