Yuchen Jin 谈新 AI 实验室：多数会失败，挑战 scaling laws 者才有机会
SWE-bench 上表现很强的 LMArena 团队创始人聊了点行业实话：抄 OpenAI 作业的新实验室大多活不下去，他押注的是那些挑战 Transformer 和下一词预测范式的团队，还点破了研究员创业的通病。
Yuchen Jin 在 X 上回应外界关于他批评新 AI 实验室的提问。他认为多数新实验室会失败属正常现象，但质疑照搬 OpenAI 和 Anthropic 打法、融资 10 亿美元再把 50% 花在 GPU 上的路线撑不起百亿估值。他更看好挑战 scaling laws、挑战 Transformer 架构和下一词预测范式的逆向者。他同时指出，优秀研究者未必是好创始人，研究到产品的转化是普遍短板。
People ask: “Are you dunking on neolabs?”
Most neolabs will fail. That’s normal. But I believe more AI breakthroughs are coming.
I’m skeptical of those following the same playbook as OpenAI and Anthropic. Raising $1B and spending 50% on GPUs doesn’t make you a $10B company.
I’m rooting for the contrarians. Challenge scaling laws. Challenge transformers. Challenge how models learn. Challenge the next-token prediction paradigm.
My bigger concern is that brilliant researchers don’t necessarily make great founders. In my experience, researchers often struggle to turn their research into great products / sustainable businesses.
