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Chamath criticizes long-horizon tasks in AI as ineffective, predicts a hype cycle leading to disillusionment, and suggests using symbolic spaces to guide embedded spaces for better AI performance.
Chamath revealed that his company found AI token costs double every 45 days while downstream productivity improves by at most 5%. He believes AI development is approaching a bottleneck and suggests companies reconsider their strategies or even consider exiting.
Chamath reveals the harsh reality of AI costs vs. returns: token costs double every 45 days, but downstream productivity gains are at most 5%. Large model capability improvement has hit an asymptote, and within the next 3-4 years, every company will face an ultimate reckoning between cost and benefit.
Chamath argues that companies should build their own AI intelligence using models like GLM to avoid leaking competitive advantage, emphasizing cost efficiency and control.