The sigmoids won't save you

Original Article ↗ Hacker News Discussion ↗

Overall reaction to the piece

  • Some praise the essay as a clear, entertaining explanation that early exponential segments don’t reveal sigmoid parameters and that people are bad at calling plateaus.
  • Others criticize it as long-winded, rehashing a trivial point (“exponentials often become sigmoids and we can’t time it”), or as motivated by the author’s pre‑existing AGI views.
  • A few note the value of non‑experts who synthesize, explain, and speculate accessibly, while others see this as “slop” or intellectual gatekeeping.

Exponentials, sigmoids, and Lindy’s Law

  • Many accept that most real-world exponentials eventually hit constraints and look sigmoid, but stress this doesn’t help predict when.
  • Several point out “stacked sigmoids”: each technology wave saturates, then a new one starts, which can approximate an overall exponential until innovation slows.
  • Some think invoking Lindy’s Law for AI capability growth is clever; others see it as an overextension of a heuristic that only applies under specific assumptions (e.g., Pareto-like processes, “non‑perishable” phenomena).
  • There’s concern about “laundering ignorance into precise math” versus the usefulness of outside‑view heuristics when information is scarce.

Measuring AI progress: benchmarks and “intelligence”

  • Debate over the METR “time horizon” graph: some see clear exponential progress; others question definitions, methodology, and whether it really implies “double capability.”
  • Several argue these benchmarks mostly capture task completion and coherence, not “big-I Intelligence.”
  • One line of critique differentiates:
    • Reasoning performance on tasks (improving, benchmarkable).
    • Human-like recursive intelligence (self-reflection, internal loops), where some claim little visible progress.
  • Others push back that models already do in-context introspection and that alternative architectures (RNNs, SSMs, memory nets) could change the picture.

Limits, hardware, and stacked improvements

  • Some think we’re nearing limits of the transformer paradigm, data, and compute (Moore’s law slowdown, fabs, electricity), implying a coming plateau.
  • Others expect major hardware advances (ASICs, analog/photonic compute, memristors) and better algorithms/RL/synthetic data to extend growth.
  • There is disagreement on whether recent progress shows diminishing returns in real-world quality, or acceleration (e.g., coding/maths automation).

AGI timelines and risk framing

  • Views range from “AGI/ASI with full labor automation by ~2040–2050” to skepticism that current LLMs can ever match human-like intelligence.
  • Some argue “AI doom” is speculative and overconfident; others stress that even a modest probability of catastrophic risk justifies serious mitigation and public warning.
  • Several note that claims like “things must plateau” or “exponential to AGI” have little predictive power without concrete mechanisms or constraints.