J.D. (@Jadzo1_)

Goldman Sachs: LLM primer A week or so ago there was a lot of questions about the model layer economics when LLMs are without a doubt viewed more and more as a commodity+ reaching a level where being on the frontier of intelligence is no longer the swaying factor. Economics 101, in a market with many substitutes like restaurants, price undercutting becomes a crucial factor and just like EV's is where China wins. Now these talks have been pushed to the background as the Lag 7 has revived on the back of Zucks considerations of an AI cloud business+producing an AI chip (positive read through to SUMCO/ I sold to early 😞). Back to the initial topic, Goldman provides a few insights into the landscape: → China's top coding models (GLM5.2, Qwen3.7 Max) sit at ~$1 per 1M blended tokens while US SOTA runs $4-8 for the same rung of output → And they are selling it below cost. GS pegs the value for money agentic model at a -30% EBIT margin today and the coding model at -39%, cash rich balance sheets eating the loss until it flips to +14% and +22% by 2030 on their numbers → The reason they can serve that cheap is architecture, sub-8% of params activated per token across the board, DeepSeek V4 Pro firing 49B of 1.6T and GLM5.2 40B of 744B, fewer FLOPs and a structural floor under the price → The adoption already shows up on OpenRouter where China models are 5-16% of spend by task but 85% of agent tokens and 89% of code tokens, winning wherever duration and volume make cost per task the number that matters → And the blended token price rolled over with it, SDLLMTK peaked around 2.07 in early June and sits at 1.67 now This seems somewhat similar to the EV playbook to me, we the consumers should win/benefit from a price war but the return to equity shareholders is more ify.