Part of the problem is that models don't have a strong sense of taste, part of the problem is that the context in which projects exist is incompletely represented in the LLM context, and part of the problem is that LLMs tend to be myopic.

The lack of taste can be mitigated to some degree by improved training, though taste is not a stationary distribution in humans (see trends/fads/etc), we can at least better track the cutting edge. I think this area still has low hanging fruit but frontier labs are more concerned with being able to solve problems than the style of the solution right now (for evidence of this just look at the Opus 4.5 -> 4.8 arc).

The problem of incomplete context is partly a human problem and partly a harness/interconnectivity problem.

LLM Myopia is a harder problem to solve just by virtue training models on question/answer pairs. Countering this requires emphasizing RL on solution paths rather than just prompt/response, which is doable but harder.