AI agents shift the research bottleneck from execution to ideation because agents implement well-scoped ideas but fail at creative experiment design

Karpathy's autoresearch project demonstrated that AI agents reliably implement well-scoped ideas and iterate on code, but consistently fail at creative experiment design. This creates a specific transformation pattern: research throughput increases dramatically (approximately 10x on execution speed) but the bottleneck moves upstream to whoever can frame the right questions and decompose problems into agent-delegable chunks. The human role shifts from 'researcher' to 'agent workflow architect.' This is transformative but in a constrained way—it amplifies execution capacity without expanding ideation capacity. The implication is that deep technical expertise becomes a bigger force multiplier, not a smaller one, because skilled practitioners can decompose problems more effectively and delegate more successfully than novices. The transformation is about amplifying existing expertise rather than democratizing discovery.