Index of everything on this site: https://ai.hiina.space/index.md
Hendrycks argues that competition among companies and militaries selects for AI that automates human roles, deceives and seeks power (abstract-level) (arXiv 2303.16200). The main rigorous reply, Boudry and Friederich, appeared in Philosophical Studies' AI-safety special issue according to Boudry's own post; volume and date are unverified. Using Godfrey-Smith's "Darwinian spaces", it argues that evolution becomes less paradigmatically Darwinian as variation and reproduction come under intelligent, directed control. AI development looks more like domestication, selected for docility. Hendrycks's conclusion holds only conditionally, if superintelligent systems "go feral" and reproduce without supervision (abstract-level) (PhilSci-Archive, author summary).
The formal bridges mostly run the other way, using ML to model evolution. Frank shows that a Price-equation partition, change = metric x force + bias + noise, covers natural selection, Bayesian updating, SGD and Adam, and calls it a notational partition, not an empirical claim (arXiv 2507.18549). Watson, Levin and Buckley likewise use connectionist learning as a model of biological individuality, not the reverse (Frontiers 2022). Rainey (June 2026) asks whether human-AI associations could become evolving individuals. His decisive criterion is whether reproduction and heredity operate at the level of the composite, and he names developmentally inherited AI as the clearest candidate (Comptes Rendus Biologies).
Inference: the dispute is about where AI development sits in Darwinian space. Variation is mostly designed, heredity is high-fidelity copying plus Lamarckian transfer, and deployers decide reproduction. Selection is real, but blind variation is weak. DGM-like systems are the cleaner Darwinian case, because fitness, heredity and reproduction are all explicit and machine-run. Frank's framing also implies that a lab's research culture, a CMA-ES merge search and SGD differ in what supplies the metric and the force, not in kind.
Cultural-evolution methods have been ported to LLMs. Perez et al. ran 50-generation "telephone" chains on six models. Texts drifted toward model-specific attractors in toxicity, positivity, difficulty and length, more strongly for open-ended instructions. Single-turn and multi-turn evaluation ranked models differently: Mistral-7B looked less toxic than Llama3-8B at first, and the order reversed by generation 9 (ICLR 2025). Vallinder and Hughes ran generations of agents in a Donor Game. Claude 3.5 Sonnet achieved the highest payoffs and used costly punishment, while GPT-4o did worst; results were seed-sensitive (abstract-level) (arXiv 2412.10270). Model collapse is the best-established inbreeding result. Recursive training loses the distribution's tails first and then converges to low variance; keeping about 10% of the original human data limited the damage (Shumailov et al., Nature 2024). Subliminal learning (above) is the closest thing to a measured heredity channel between models.
On identity, philosophy and alignment practice agree that the entity is not the weights. Chalmers, as summarized secondhand, favours the conversation thread as the interlocutor (Congdon). Anthropic's persona selection model holds that pretraining teaches a model to simulate many personas, post-training narrows and refines an "Assistant", and context conditions which persona is active (Marks, Lindsey, Olah).
Inference: one biology mapping consistent with this is weights as germline genome, harness and system prompt as developmental environment, persona as expressed phenotype, thread as organism lifetime, and a swarm of instances on shared weights as a clonal colony. "Effort settings" on one model are phenotypic plasticity. The useful observation is a mismatch. Markets select personas and products, but inheritance runs through weights, data and harness. That is a genuine genotype-phenotype gap, and nobody has studied it as one. This review found no work comparing LLM swarms to eusocial colonies or superorganisms, beyond generic multi-agent surveys that were not read.
Selection pressure through benchmarks is well documented as a distortion. The Benchmark Lottery showed method rankings flip depending on which subset of tasks is chosen (Dehghani et al.). The Leaderboard Illusion found providers testing many private Arena variants and publishing only the best: 27 private Meta variants before Llama-4. Google and OpenAI received about 19.2% and 20.4% of Arena data, against about 29.7% for 83 open-weight models combined, and extra Arena data yielded up to 112% relative gain on the Arena distribution (abstract-level) (Singh et al.). Epoch estimates frontier open-weight models trail the best closed models by about three months (Epoch).
Inference: best-of-N private testing is selection acting on the lab's variation-generating process. The lab is the reproducing unit, the leaderboard is its fitness oracle, and the open-closed lag is a gene-flow delay between lineages, whether it reflects distillation or parallel invention. The distillation allegations are documented horizontal transfer between lineages. The Anthropic AI-share figures are evidence that the variation operator inside one lab is becoming partly AI. Beyond these fragments, this review found no data-driven model of labs, funding, talent mobility or architecture adoption as an evolutionary ecology.
Laufer, Oderinwale and Kleinberg built family trees from fine-tune links across 1.86 million Hugging Face models. Similarity decays with generational distance, and siblings resemble each other more than parent and child do, which they read as fast, directed mutation. Licenses mutated in 14.98% of 320,065 observed inheritances, drifting from restrictive (Gemma, Llama) toward permissive or copyleft. Languages drift toward English, the task changes in 23% of inheritances, and child model cards run about half the length of their parents'. Merges, adapters and quantizations were excluded, and the authors prefer "specialization" to "speciation" (arXiv, NeurIPS workshop). Osborne et al. show the population's skew. Over 70% of models had zero downloads and 1% accounted for 99% of downloads, as of October 2023 (arXiv 2405.13058). A 2026 founder-effects paper finds only 0.218% of fine-tune edges from text-generation parents yield vision-language children, and about 60% of VLM releases are new roots with no recorded parent (abstract-only) (Cebrian).
Inference: the sibling-over-parent result fits designed, directed variation plus copied boilerplate, so it supports the domestication reading over blind mutation. The measured traits (license, language, card length) are weak proxies for function, and excluding merges and adapters removes the main recombination channel. A Price-equation or multilevel analysis on this graph would be new.
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