The Moat or the Commons

> DeepSeek, Qwen, Kimi, GLM — running on the LangChain, vLLM, llama.cpp, and Ollama stack

"running on the LangChain" ??

EDIT: look, I think the general discussion is important, so I don't want to denounce the article. I, for one, am excited for better control, ownership, and accessibility of models. The ride labs take us on can be quite frustrating. Maybe there's even signal that the model progression is stalling (ie Opus 4.7). If that's true, then some of the notions made in the article are important to discuss. Ref https://x.com/ClementDelangue/status/2046622235104891138?s=2...

EDIT: this is not a complaint about the grammar. Look at my reply in the comments.

There was a bigger opportunity here to mention OpenCode, Pi, etc - open Harnesses that provide accessibility to the oss models, a platform others can build around, and something enterprises can adopt in reliable ways; for the most dominant use case of AI today.

I think it's good to note that there are scenarios where the trend of open models keeping up will not continue forever.

AI development speed is increasingly influenced by the quality of the model you are able to use internally. The frontier labs could easily pull ahead again if they increasingly withhold their best models (e.g. Claude Mythos) from the public entirely. They will benefit from increased R&D speed internally that cannot be matched by open labs.

Also, it seems plausible that frontier labs will eventually accumulate architecture-level improvements in their models that make them significantly more efficient than open LLMs. In that case, if the open labs cannot reverse engineer and replicate that, then their models will forever fall behind. On the other hand, any architectural innovations in the open model space can be used freely by closed frontier labs.

There's a counter-trend that favors open models however, which is that the design of model harnesses and multi-agent systems is more and more important to AI quality today relative to the intelligence of the model itself. This MIGHT mean that having a bunch of dumb, but cheap models in the right harness can actually compete very well against raw frontier intelligence in most practical tasks. (In other words, better harnesses makes models more efficient at improving their task completion by spending extra tokens.) This would give cheaper open models an advantage in any task where they're smart enough to complete at all, since a good multi-agent harness might mean they can do these tasks reliably and typically for cheaper than frontier models even if pushing a higher number of raw tokens.

Too annoying; didn’t read.

Rigghht. If you're trying to make a point, go ahead and be straighter about it.