OpenAI research scientist says AI agents still can’t figure out what’s worth researching

Your AI assistant can write code, summarize papers, and synthesize data across dozens of sources. What it can’t do is look at a messy, open-ended research landscape and decide which problem actually matters. That gap has a name: research taste. And according to one of OpenAI’s own scientists, today’s models are pretty bad at it. Noam Brown, a research scientist at OpenAI, recently stated that current AI models are “poor” at research taste, the intuitive ability to identify which research directions are promising and which are dead ends. He expects improvements within one to two model generations, but for now, the limitation is stark enough that Brown is highlighting it publicly. The automated intern that still needs a boss OpenAI has made real progress on the execution side of AI research. The company confirmed it achieved its milestone for building an “automated research intern” by September 2026, with agents performing 3.1 times the human labor days on multi-day research tasks. That sounds impressive until you hear the rest. Over 50% of those tasks still require human intervention. The cost structure tells a revealing story too. The median researcher at OpenAI spends over $600 pe...