Observed Signal · May 15, 2026 · Analysis · Source: Astral Codex Ten · Impact: 2/5 · Sentiment: Neutral
The Sigmoids Won't Save You
Opinion essay by Scott Alexander (Astral Codex Ten) arguing that the claim “all exponentials eventually become sigmoids” is technically true but can be misleading for forecasting AI capability growth. The author surveys historical examples (epidemics, airspeed records), recurring forecasting errors (UN birthrate projections, solar deployment WEO lines), and recent AI capability curves (METR). He recommends Lindy’s Law as a default prior under deep uncertainty—i.e., a trend that has persisted for a period is, absent domain knowledge, likely to continue about as long again—and shows how that yields non-trivial odds that current exponential AI growth continues for years. The piece urges critics who claim a near-term plateau to supply explicit models or acknowledge Lindy’s prior.
Analytical essay about AI growth trajectories and forecasting priors; relevant to strategic thinking about AI timelines but not an immediate AdTech/MarTech product, policy, or platform change.
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Key Takeaways & Evidence Grounding
- Article published on Astral Codex Ten by Scott Alexander on 2026-05-15.
- Argues that although exponentials eventually plateau into sigmoids, the timing of plateauing is uncertain and often misidentified.
- Revisits historical examples and forecasting failures: epidemic sigmoids, airspeed record generations, UN birthrate forecasts, and mispredicted solar deployment (WEO).
- References a Wharton analysis of the METR AI capabilities graph (arXiv:2602.04836) and recommends Lindy’s Law as a default in conditions of deep ignorance, yielding a naive median forecast of ~7 more years of the current AI scaling era and a 22% chance of stopping within 2 years under a Pareto assumption.
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New Paradigms Won't Save You
An essay by Scott Alexander (Astral Codex Ten) argues that expecting a radical new paradigm to prevent LLMs from reaching AGI is a weak hedge. He outlines a sequence of landmark AI milestones (from mid-20th-century neural nets through transformers and RLHF) and contends AGI will likely remain within the neural-network/deep-learning family. Using Lindy’s Law, Alexander estimates non-negligible probabilities that paradigm-level advances comparable to transformers or deep learning could arrive within years rather than decades, and gives a 25th-percentile forecast of roughly 3–5 years for such advances. He also notes that frontier labs will pursue new paradigms aggressively only if scaling of current approaches stalls. The piece engages skeptics (e.g., Yann LeCun, Gary Marcus) and discusses forecasting methods.
Does AI Make You Dumb? Why Forecasts Fail
An Exponential View newsletter argues that generative AI raises individual productivity without producing proportional firm-level gains and that consensus analyst forecasts systematically underreact to exponential technological shifts. The piece cites examples — Micron’s FY2026 EPS consensus revised from about $18.25 in December 2025 to roughly $58 five months later, and a 40% rise in median analyst views on Google between May 2025 and May 2026 — to show analysts repeatedly misread rapid AI-driven demand. The author also discusses research suggesting students’ writing became more colorful but less creative after ChatGPT, concerns that offloading thinking to AI may atrophy human skills, and organizational practices to preserve critical thinking. The newsletter notes labour-market trends (AI engineering roles concentrating at top US firms), mentions ByteDance designing Groq-style accelerators, and highlights broader implications for modelling, incentives, and how institutions should adapt to regime change.
Author's AI Timelines, Risks, and Policy Views
An opinion essay (Astral Codex Ten) laying out the author's probabilistic beliefs about AI timelines, diffusion, and existential risk. The author defines and quantifies several timeline concepts (AGI, diffusion gap, superhuman gap, Bostromian superintelligence gap, and point of no return), expresses a 20% baseline probability of human extinction from AI absent strong interventions, and discusses alignment, regulatory pauses (US–China negotiations), and socio-economic outcomes (underclass, utopia). The piece gives a modal scenario of AGI around 2031 with broad diffusion through the 2030s and raises uncertainty about recursive self-improvement, compute bottlenecks, and the effectiveness of alignment strategies like interpretability and RLAIF.
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