Enterprises still have big contracts with github, those companies are imposing tight spending limits now and if the open weight models enable those limits to last a bit longer that's probably quite popular.
Everything is currently pointing towards inference being the main cost driver for LLMs in the future. Test-time-compute requires huge amounts of tokens in inference and makes providing frontier models as services unprofitable.
Anyone not under some kind of export restrictions can scrounge together some GPUs to train a frontier model (hell, even DeepSeek which is under these restrictions could) but providing a service that can compete with OpenAI et al. will prove to be quite costly. 3x improvements in inference are therefore nothing to sneeze at IMO.
Interesting! So did you do any experiments on a relevant subset of the data to test whether LLM performance degrades by introducing a new, presumably unknown to the LLM, format?