Nathan Lambert@natolambert · XNathan Lambert 批评 AI 领域过度关注持续学习与样本效率，认为这如同专注于弥补弱点而非最大化优势。人类大脑虽是存在性证明，但未必是 AI 最佳路径。前沿实验室实际加速推进现有开发树，对进步有利，但对安全与地缘政治影响复杂。他引用 @dwarkesh_sp 的观点：数据是进步主要驱动力，开源与后来者可通过从公开 API 蒸馏数据快速追赶前沿，而超参数、训练技巧等难以复制。他认为未来已来，AGI 研究应拥抱未知、规模化资源，而非等待不确定的科学突破。
I feel like the obsession with continual learning / sample efficiency leads the field in the wrong direction. It's the bad career strategy of focusing on addressing your weaknesses instead of maximizing your strengths.
Yes， there is an existence proof in the human brain， but it doesn't by any means guarantee that that'll be the most interesting AI. It may require $100T of R&D on chips and AI methods to get that unlock.
On the other side of things， it's obvious that the coming models are extremely transformative and built on technologies that we already have. There's great reason to focus on just maximizing this. In reality， this is what the frontier labs are doing. They're going as fast as possible down the current development tree. This is good for progress and mixed for safety/geopolitics.