The bet that “makes sense” is that quality of Claude 3.6 Sonnet, GPT-4o and DeepSeek-V3 is the best that we’re going to get in the next 2-3 years, and DeepSeek-V3 gets it much cheaper (less active parameters, smaller margins from open weights), also “suggesting” that quality is compute-insensitive in a large range, so there is no benefit from more compute per token.
But if quality instead improves soon (including by training DeepSeek-V3 architecture on GPT-4o compute), and that improvement either makes it necessary to use more compute per token, or motivates using inference for more tokens even with models that have the same active parameter count (as in Jevons paradox), that argument doesn’t work. Also, the ceiling of quality at the possible scaling slowdown point depends on efficiency of training (compute multiplier) applied to the largest training system that the AI economics will support (maybe 5-15 GW without almost-AGI), and improved efficiency of DeepSeek-V3 raises that ceiling.
The bet that “makes sense” is that quality of Claude 3.6 Sonnet, GPT-4o and DeepSeek-V3 is the best that we’re going to get in the next 2-3 years, and DeepSeek-V3 gets it much cheaper (less active parameters, smaller margins from open weights), also “suggesting” that quality is compute-insensitive in a large range, so there is no benefit from more compute per token.
But if quality instead improves soon (including by training DeepSeek-V3 architecture on GPT-4o compute), and that improvement either makes it necessary to use more compute per token, or motivates using inference for more tokens even with models that have the same active parameter count (as in Jevons paradox), that argument doesn’t work. Also, the ceiling of quality at the possible scaling slowdown point depends on efficiency of training (compute multiplier) applied to the largest training system that the AI economics will support (maybe 5-15 GW without almost-AGI), and improved efficiency of DeepSeek-V3 raises that ceiling.