Gradient descent (when applied to train AIs) allows much more fine-grained optimization than evolution, for these reasons:

• Evolution by natural selection acts on the genome, which can only crudely affect behavior and only very indirectly affect values, whereas gradient descent acts on the weights which much more directly affect the AI's behavior and maybe can affect values
• Evolution can only select between two alleles in a discrete way, whereas gradient descent operates over a continuous space
• Evolution has a minimum feedback loop of one organism generation, whereas RL has a much shorter minimum feedback loop length of one episode
• Evolution can only combine information from different individuals inefficiently through sex, whereas we can combine gradients from many episodes to produce one AI that's learned strategies from all episodes
• We can adapt our alignment RL methods, data, hyperparameters, and objectives as we observe problems in the wild
• We can do adversarial training against other AIs, but ancestral humans didn't have to contend with animals whose goal was to trick them into not reproducing by any means necessary; the closest was animals that try to kill us. (Our fear of death is therefore much more robust than our desire to maximize reproductive fitness)
• On current models, we can observe the chain of thought (although the amount we can train against it while maintaining faithfulness is limited)
• We can potentially do interpretability (if that ever works out)

It's unclear the degree to which these will solve inner alignment problems or cause AI goals to be more robust than animal goals to distributional shift, but we're in much better shape than evolution was.