Three Laws of Machine Discovery: Why AGI Must Be Plural
机器发现的三定律：为什么 AGI 必须是复数的
Position paper, draft v0.1. Discovery-capable AGI must be plural: cognitive inertia, conjecture, and reaction.
Reflections
Reflections
Some thoughts, in the hope that one day they take root and grow into more rigorous inquiry.
Why Generative AI Helps Some People More Than Others
Fourteen empirical studies, and four dimensions for comparing them
Same class of tool. Sometimes it narrows a gap, sometimes it widens one, sometimes it slows down experienced maintainers. Why?
The Shape of Ability
Why AI makes some people faster and others slower
Several studies of people working with AI have produced strikingly different results. Read together, they suggest that rank may be only part of the story. Where a person gets stuck—and where a tool can actually help—may matter just as much.
Large Language Models Stratify How, Not What, They Tell Different Users
How identity, stated opinion, and prompt literacy shape an AI system’s epistemic posture
Do large language models give different people different realities? This pilot separates user identity, stated opinion, and prompt literacy across 28 benign bilingual questions and three model families. In the latest aggregate analysis, explicitly doubting a question’s premise increases the odds of framing override to 1.65, while identity does not follow the hypothesized low-to-high-status gradient. A large identity gap in answer completeness at low prompt literacy nearly disappears at the highest tier. Lower-status personas also receive fewer safety caveats, although the gap narrows markedly on high-stakes questions. The visible stratification is therefore better described as a difference in epistemic posture than factual position. Prompt literacy is both an equalizer and an emerging axis of information inequality.