AGI为什么不会到来？这位研究员把AI的“物理极限”讲透了

Group 1 - The article discusses the skepticism surrounding the realization of Artificial General Intelligence (AGI), emphasizing that current optimism in the market may be misplaced due to physical constraints on computation [1][4]. - Tim Dettmers argues that computation is fundamentally bound by physical laws, meaning that advancements in intelligence are limited by energy, bandwidth, storage, manufacturing, and cost [3][4]. - Dettmers identifies several key judgments regarding AGI: the success of Transformer models is not coincidental but rather an optimal engineering choice under current physical constraints, and further improvements yield diminishing returns [4][6]. Group 2 - The article highlights that discussions about AGI often overlook the physical realities of computation, leading to misconceptions about the potential for unlimited scaling of intelligence [5][9]. - It is noted that as systems mature, linear improvements require exponentially increasing resource investments, which can lead to diminishing returns [10][16]. - The article points out that the performance gains from GPUs, which have historically driven AI advancements, are nearing their physical and engineering limits, suggesting a shift in focus is necessary [18][22]. Group 3 - Dettmers suggests that the current trajectory of AI development may be approaching a stagnation point, particularly with the introduction of Gemini 3, which could signal a limit to the effectiveness of scaling [33][36]. - The cost structure of scaling has changed, with past linear costs now becoming exponential, indicating that further scaling may not be sustainable without new breakthroughs [35][36]. - The article emphasizes that true AGI must encompass the ability to perform economically meaningful tasks in the real world, which is heavily constrained by physical limitations [49][50]. Group 4 - The discussion includes the notion that the concept of "superintelligence" may be flawed, as it assumes unlimited capacity for self-improvement, which is not feasible given the physical constraints of resources [56][58]. - The article argues that the future of AI will be shaped by economic viability and practical applications rather than the pursuit of an idealized AGI [59][60].