ok, I think this overlooks two important things:

1. what we currently perceive as a lack of capability in a particular domain often comes down to the model simply not having received targeted RL training for that domain (yet). Look at Opus 5.5 in motion graphics: after its first round of specialized RL, it was already producing work that surpassed the average professional designers(and it's using vanilla javascript vs human with After Effects, let that sink in). It doesn't need to beat the top 1% of designers to fundamentally disrupt the profession. Most professionals aren't the top 1%.

2 people rarely hold agents to the same standards and workflows they apply to human professionals. A human designer gets to iterate and refine until the work meets expectations. Yet ppl often judge AI based on oneshot or fewshot attempts (these are just "wishes") and treat whatever comes out as the ceiling of its capabilities. Why shouldn't an agent be allowed to iterate against the same acceptance criteria until the result is good enough?

that's before considering domain-specific skills& loops, which can dramatically improve performance in specialized tasks.

The relevant comparison isn't an experienced professional vs LLM given a prompt five mins ago,it's a professional vs agentic system w access to the same project context(including tools and feedback, and opportunities to iterate)