GLM 5.2 and the coming AI margin collapse (part 1)

This is a two part series focusing on what I believe is perhaps the least understood upcoming shift in AI economics. If you've enjoyed this and want to be notified about the second post, please feel free to sign up for my newsletter. The real DeepSeek moment is upon us What feels like decades ago, markets recoiled at DeepSeek's R1 model. The theory being that given the underlying V3 model reportedly cost under $6m to train, the market therefore thought the huge investment in capex for model training was over, and thus the stock price of Nvidia et al collapsed overnight. Of course, this was a hugely poor read of where the costs actually lie in AI. Training - while no doubt capex intensive - is a fixed, up-front cost. You spend hundreds of millions to train a model, then you are "done".[1] Inference, on the other hand, scales with your demand. It has genuine marginal costs. I've written about this at length over the past year or so. Again, the mainstream understanding of this - that the…