The sigmoids won't save you
Exponential improvements in AI capabilities are prompting arguments over whether progress is about to hit a hard limit or continue much longer before flattening into a “sigmoid” curve. Commenters debate how to interpret benchmarks like long-horizon task performance, whether current large language models reflect true intelligence or just better reasoning and tooling, and how hardware, data, and algorithmic advances might sustain or constrain future gains. Underlying this is a broader uncertainty over forecasting methods — including Lindy-style heuristics — and what they really tell us about timelines to human-level or transformative AI and associated risks.
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.