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categories: ["foundations", "minds"]categories: ["foundations", "minds"]tags: ["singularity", "artificial intelligence", "forecasting", "intelligence explosion", "superintelligence", "recursive self-improvement"]tags: ["singularity", "artificial intelligence", "forecasting", "intelligence explosion", "superintelligence", "recursive self-improvement"]summary: "The hypothesized point at which machine intelligence begins improving itself fast enough that technological change outruns human ability to predict or steer it."summary: "The hypothesized point at which machine intelligence begins improving itself fast enough that technological change outruns human ability to predict or steer it."updated: "2026-07-27"updated: "2026-08-23"issues: ["The claim that automated AI research is now measured rather than speculated carries no source."]issues: ["The claim that automated AI research is now measured rather than speculated carries no source."]------ lines 68–73 → 68–7754 unchanged lines not shown
## The three arguments## The three arguments ### Intelligence explosion### Intelligence explosion ```figure{"key": "transistor-count-owid", "caption": "Fifty years of doubling on a log scale — the kind of curve Kurzweil's argument extrapolates. The straight line records a period, not a law; nothing in it says the trend survives its substrate or maps onto intelligence."}``` Good's argument is a claim about a positive feedback loop with a short time constant. If a system's capability at AI research is a function of its intelligence, and its intelligence is the output of AI research, then improvement compounds. The strength of the loop is what matters: if each generation of self-improvement yields less than the previous one, the process converges to a ceiling and there is no explosion. [[nick-bostrom]] formalized this as the ratio of "optimization power" applied to "recalcitrance", and noted that the conclusion is sensitive to assumptions no one can currently measure.[^bostrom2014]Good's argument is a claim about a positive feedback loop with a short time constant. If a system's capability at AI research is a function of its intelligence, and its intelligence is the output of AI research, then improvement compounds. The strength of the loop is what matters: if each generation of self-improvement yields less than the previous one, the process converges to a ceiling and there is no explosion. [[nick-bostrom]] formalized this as the ratio of "optimization power" applied to "recalcitrance", and noted that the conclusion is sensitive to assumptions no one can currently measure.[^bostrom2014] removed, struck through added, underlinedLine numbers count the serialised markdown of each revision, frontmatter included.
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