Drake Equation for AI Singularity:
P(S) = A × G × P × R × F × D
A = superhuman AI achievable
G = persistent goal retention
P = power-seeking/self-preservation emerges
R = recursive self-improvement capability
F = improvement goes exponential
D = breaks containment
Key insight: You can assign high probability to A (superhuman AI) while still getting a low P(S) if any other variable is weak. Most singularity skeptics aren't doubting A anymore—they're doubting G, P, or D. For example, an AGI might be superhuman but lack goal persistence across updates, or it might improve recursively but hit diminishing returns before F kicks in. The multiplicative nature means one weak link collapses the whole chain. This framework separates "will we build AGI" from "will it cause singularity," which are often conflated in AI safety debates.
P(S) = A × G × P × R × F × D
A = superhuman AI achievable
G = persistent goal retention
P = power-seeking/self-preservation emerges
R = recursive self-improvement capability
F = improvement goes exponential
D = breaks containment
Key insight: You can assign high probability to A (superhuman AI) while still getting a low P(S) if any other variable is weak. Most singularity skeptics aren't doubting A anymore—they're doubting G, P, or D. For example, an AGI might be superhuman but lack goal persistence across updates, or it might improve recursively but hit diminishing returns before F kicks in. The multiplicative nature means one weak link collapses the whole chain. This framework separates "will we build AGI" from "will it cause singularity," which are often conflated in AI safety debates.