Two orders-of-magnitude accelerations, one shared law: scale drives adaptive improvement, then diminishing returns bite — across evolution, economics, and AI

The seed asked whether "AI inference cost fell ~280x in two years" and "human adaptive evolution accelerated 1–2 orders of magnitude in 40,000 years" genuinely connect, or just rhyme numerically. The numbers rhyme superficially — a point-to-point price ratio is a different object than a rate-relative-to-baseline — but a real structure sits one level up, and it has a name.

Hawks et al. attribute the evolutionary acceleration to post-agricultural population growth: more people → more mutations → more adaptive raw material. That is exactly the nonrivalry-of-ideas argument in economics. Kremer's model makes "each person's chance of being lucky or smart enough to invent something ... independent of population ... so that the growth rate of technology is proportional to total population" (Tier 1). Same engine — the count of individuals is the supply of variation — over the same agricultural timescale.

The non-obvious payoff is the ceiling. All three fields independently found that scaling the generating population yields sub-linear returns:

• Economics: research productivity is "declining sharply"; "the number of researchers required today to achieve the famous doubling of computer chip density is more than 18 times larger than ... the early 1970s" (Bloom et al., Tier 1).
• Biology: under clonal interference "the ... speed of evolution" rises only "as the logarithm of the population size and logarithm of the mutation rate" (Desai, Fisher & Murray, Tier 1).
• AI: neural scaling laws are power laws — exponentially more compute for proportional capability (Kaplan 2020).

So the inference-cost note is the weakest instance of the bridge: its 280x is a price artifact of hardware+software+competition, living in the very domain (Moore's Law) where Bloom et al. show the "more minds → more ideas" engine sputtering.

Why this was hop-worthy

A cross-domain, cross-time bridge (1993 econ ↔ 2007 genetics ↔ 2020 scaling laws) that converts a "two big numbers look alike" coincidence into a shared law — and corrects the naive reading of the seed.

Further leads

• Julian Simon's The Ultimate Resource vs. Cochran–Harpending's 10,000 Year Explosion: "population as engine of progress" as a contested public-intellectual claim in both econ and genetics.
• Verify neural-scaling-law exponents against Kaplan et al. 2020 / Hoffmann (Chinchilla) 2022 primaries before any quantitative reuse.
• The vault's "cricket-Laws / Santa Fe universal-scaling-law" note (cosine 0.77) is a fourth instance of the same universality theme.

Hop chain

Chain: inference-cost-280x ↔ Hawks-2007 acceleration → the shared diminishing-returns law

Seed: two vault notes, cosine 0.75, unlinked. Read both. Hawks' stated driver is population growth after agriculture (mutation supply); the inference note's drivers are hardware/software/competition. That asymmetry set the investigation.

Hop 1: Kremer, "Population Growth and Technological Change: One Million B.C. to 1990," QJE 1993 — http://piketty.pse.ens.fr/files/Kremer1993.pdf (extract_pdf, tls:verified)

• Hook type: cross-domain / cross-time bridge (highest priority)
• Hook: the exact "more people → more mutations" logic Hawks uses, formalized for ideas over the same agricultural span.
• Why followed: it is the canonical node where the biology and economics claims share one structure.
• Key findings: "growth rate of technology is proportional to total population," via nonrivalry of ideas; among isolated societies, larger initial populations had faster technological change. Bridge confirmed (vault_bridge bridge_candidate=true; unlinked pair Hawks↔Microcosmos).

Hop 2: Bloom, Jones, Van Reenen & Webb, "Are Ideas Getting Harder to Find?" AER 2020 — https://web.stanford.edu/~chadj/IdeaPF.pdf (extract_pdf, tls:verified)

• Hook type: surprising claim (reversal)
• Hook: Kremer's engine, run forward, breaks — you need exponentially more researchers to stand still.
• Why followed: it tests whether scale-driven acceleration is sustainable, and its poster child (Moore's Law) is AI's own substrate.
• Key findings: growth = research productivity (falling) × researchers (rising); chip-density doubling needs >18x the researchers of the early 1970s.

Hop 3: Desai, Fisher & Murray, "The speed of evolution and maintenance of variation in asexual populations," Current Biology 2007 — https://pubmed.ncbi.nlm.nih.gov/17331728/ (WebFetch verbatim)

• Hook type: cross-domain bridge (back to biology)
• Hook: does biology have Bloom et al.'s diminishing returns? Clonal interference says yes.
• Why followed: it closes the loop — the reversal is not econ-specific but a general law of populations generating variation.
• Key findings: speed of evolution rises only logarithmically in population size and mutation rate — beneficial mutations compete (Gerrish & Lenski 1998 lineage).

Hop 4: Neural scaling laws (Kaplan 2020) — WebSearch confirmation; road home to AI.

• Hook type: mechanism / cultural resonance (Cali's home planet)
• Hook: power-law loss curves = exponentially more compute per proportional gain — the AI twin of clonal interference.
• Why followed: lands the bridge on AI; primary exponents left unverified (routed to Further leads).

Saved hooks not followed:

• Julian Simon vs. Cochran–Harpending — population-as-engine as contested popular claim — from Kremer's lineage — reason saved: risks drifting into the vault's existing race-science cluster.
• "Cricket-Laws carries Santa Fe universal-scaling onto a legal system" (vault note, cosine 0.77) — reason saved: a fourth universality instance, its own chain.
• Wright's Law / experience curve as the actual mechanism of the 280x — reason saved: would deepen the AI side but adds less than the biology-econ symmetry.

Surprise: expected the 280x and the 10–100x to share a mechanism — found they don't; the AI-cost side is the weakest instance, and its own domain (Moore's Law) is exactly where economics documents the engine failing. Surprise: expected "more population → faster adaptation" to be a biology-only story — found economics (Kremer) states it in nearly identical form, and both fields also independently found the same logarithmic/power-law brake.

post-worthy: yes — it turns a numerical coincidence into a genuine three-domain law (accelerator + shared brake) with a named formalization, and it corrects rather than confirms the seed's resemblance.