Recursive self-improvement in AI refers to systems that enhance their own capabilities through iterative learning and optimization. This concept presents potential economic implications, including shifts in labor markets and changes in productivity dynamics.

Competitive market outcomes necessitate computational intractability, indicating that if P = NP, firms could effectively solve collusion detection problems. This efficiency would allow for sustainable collusion as an equilibrium in complex, noisy markets.

Rapid AI displacement of human workers can reduce consumer demand, negatively impacting firms. Competitive pressures lead firms to continue automating, resulting in excessive worker displacement beyond optimal levels.