While cultural intelligence has indeed evolved rapidly, the genetic architecture supporting it operates through complex stochastic development and co-evolutionary dynamics that simple statistical models miss. The most promising genetic enhancements likely target meta-parameters governing learning capabilities rather than direct IQ-associated variants.
Longer:
You make a good point about human intelligence potentially being out of evolutionary equilibrium. The rapid advancement of human capabilities certainly suggests beneficial genetic variants might still be working their way through the population.
I’d also suggest this creates an even more interesting picture when combined with developmental stochasticity—the inherent randomness in how neural systems form even with identical genetic inputs (see other comment response to Yair for more detail). This stochasticity means genetic variants don’t deterministically produce intelligence outcomes but rather influence probabilistic developmental processes.
What complicates the picture further is that intelligence emerges through co-evolution between our genes and our cultural tools. Following Heyes’ cognitive gadgets theory, genetic factors don’t directly produce intelligence but rather interact with cultural infrastructure to shape learning processes. This suggests the most valuable genetic variants might not directly enhance raw processing power but instead improve how effectively our brains interface with cultural tools—essentially helping our brains better leverage the extraordinary cultural inheritance (language among other things) we already possess.
Rather than simply accumulating variants statistically associated with IQ, effective enhancement might target meta-parameters governing learning capabilities—the mechanisms that allow our brains to adapt to and leverage our rapidly evolving cultural environment. This isn’t an argument against genetic enhancement, but for more sophisticated approaches that respect how intelligence actually emerges.
(Workshopped this with my different AI tools a bit and I now have a paper outline saved on this if you want more of the specific modelling frame lol)
TL;DR:
While cultural intelligence has indeed evolved rapidly, the genetic architecture supporting it operates through complex stochastic development and co-evolutionary dynamics that simple statistical models miss. The most promising genetic enhancements likely target meta-parameters governing learning capabilities rather than direct IQ-associated variants.
Longer:
You make a good point about human intelligence potentially being out of evolutionary equilibrium. The rapid advancement of human capabilities certainly suggests beneficial genetic variants might still be working their way through the population.
I’d also suggest this creates an even more interesting picture when combined with developmental stochasticity—the inherent randomness in how neural systems form even with identical genetic inputs (see other comment response to Yair for more detail). This stochasticity means genetic variants don’t deterministically produce intelligence outcomes but rather influence probabilistic developmental processes.
What complicates the picture further is that intelligence emerges through co-evolution between our genes and our cultural tools. Following Heyes’ cognitive gadgets theory, genetic factors don’t directly produce intelligence but rather interact with cultural infrastructure to shape learning processes. This suggests the most valuable genetic variants might not directly enhance raw processing power but instead improve how effectively our brains interface with cultural tools—essentially helping our brains better leverage the extraordinary cultural inheritance (language among other things) we already possess.
Rather than simply accumulating variants statistically associated with IQ, effective enhancement might target meta-parameters governing learning capabilities—the mechanisms that allow our brains to adapt to and leverage our rapidly evolving cultural environment. This isn’t an argument against genetic enhancement, but for more sophisticated approaches that respect how intelligence actually emerges.
(Workshopped this with my different AI tools a bit and I now have a paper outline saved on this if you want more of the specific modelling frame lol)