---

type: claim
domain: ai-alignment
secondary_domains: [collective-intelligence, cultural-dynamics]
description: "Without AI, participants' ideas converged over time (β=-0.39, p=0.03); with AI exposure, diversity increased (β=0.53-0.57, p<0.03) — reframes the question from 'does AI reduce diversity?' to 'does AI disrupt natural human convergence?'"
confidence: experimental
source: "Theseus, from Doshi & Hauser (2025), 'How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas'"
created: 2026-03-11
depends_on:
  - "high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects"
  - "partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity"
related:
  - "task difficulty moderates AI idea adoption more than source disclosure with difficult problems generating AI reliance regardless of whether the source is labeled"
reweave_edges:
  - "task difficulty moderates AI idea adoption more than source disclosure with difficult problems generating AI reliance regardless of whether the source is labeled|related|2026-03-28"
---

# human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high-exposure conditions

The baseline assumption in AI-diversity debates is that human creativity is naturally diverse and AI threatens to collapse it. The Doshi-Hauser experiment inverts this. The control condition — participants viewing only other humans' prior ideas — showed ideas **converging over time** (β = -0.39, p = 0.03). Human social learning, when operating without external disruption, tends toward premature convergence on popular solutions.

AI exposure broke this convergence. Under high AI exposure, diversity increased over time (β = 0.53-0.57, p < 0.03). The AI ideas introduced variation that the human chain alone would not have generated.

This reframes the normative question entirely. The relevant comparison is not "AI vs. pristine human diversity" — it's "AI vs. the convergence that human copying produces." If human social learning already suppresses diversity through imitation dynamics, then AI exposure may represent a net improvement over the realistic counterfactual.

**Why this happens mechanically:** In the multiple-worlds design, ideas that spread early in the chain bias subsequent generations toward similar solutions. This is the well-documented rich-get-richer dynamic in cultural evolution — popular ideas attract more copies, which makes them more popular. AI examples, introduced from outside this social chain, are not subject to the same selection pressure and therefore inject independent variation.

This connects to [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]]: AI may function as an external diversity source analogous to weak ties in a partially connected network. The AI examples come from outside the local social chain, disrupting the convergence that full human-to-human connectivity would produce.

**Scope qualifier:** This convergence effect is measured within an experimental session using a constrained creativity task. The timescale of convergence in naturalistic, long-term creative communities may differ significantly. Cultural fields may have additional mechanisms (novelty norms, competitive differentiation) that resist convergence even without AI.

## Evidence
- Doshi & Hauser (2025), arXiv:2401.13481v3 — β = -0.39 for human-only convergence; β = 0.53-0.57 for AI-exposed diversity increase
- [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]] — the network science basis for why external variation disrupts convergence

---

Relevant Notes:
- [[high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects]] — the companion finding: not only does AI disrupt convergence, it does so without improving individual quality
- [[collective intelligence requires diversity as a structural precondition not a moral preference]] — if human social learning naturally converges, maintaining collective diversity requires active intervention — AI under some conditions provides this
- [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]] — AI as external diversity source parallels the function of partial network connectivity

Topics:
- [[domains/ai-alignment/_map]]
