They can “profit” (benefit in product development) from it.
They just can't profit (return gains to investors) much from it, because that requires a moat rather than a market free for all that devolves into price competition and drives market clearing price down to cost to produce.
All that cloak and dagger stuff comes at a cost, so it's only worth paying if you think you can maintain your lead while continuing to pay it. If the open source community is able to move faster because they are more focused on results than you are, you might as well drop the charade and run with them.
It's not clear that that's what will happen here, but it's at least plausible.
DeepSeek did something legitimately innovative with their addition of Group Relative Policy Optimization. Other firms are certainly free to innovate as well.
They just didn't.
Worse for the proprietary labs is how much they've trumpeted safety regulations. They can't just release a model without extensive safety testing, or else their entire regulatory push falls apart. DeepSeek can just post a new model to Hugging Face whenever they feel like it — most of their Tiananmen-style filtering isn't at the model level, it's done manually at their API layer. Ditto for anyone running finetunes. In fact, circumventing filtering is one of the most common reasons to run a finetune... A week after R1's release, there are already uncensored versions of the Llama and Qwen distills published on HF. The open source ecosystem publishes faster.
With massively expensive training runs, you could imagine a world where model development remained very centralized and thus the few big labs would easily fend off open-source competition: after all, who would give away the results of their $100MM investment? Pray that Zuck continues? But if the training runs are cheap... Well, there are lots of players who might be interested in cutting out the legs from the centralized big labs. High Flyer — the quant firm that owns DeepSeek — no longer is dependent on OpenAI for any future trading projects that use LLMs, for the cost of $6MM... Not to mention being immune from any future U.S. export controls around access to LLMs. That seems very worthwhile!
As LeCun says: DeepSeek benefitted from Llama, and the next version of Llama will likely benefit from DeepSeek (i.e. massively reduced training costs). As a result, there's incentive for both companies to continue to publish their results and techniques, and that's bad news for the proprietary labs who need the LLMs themselves to be profitable and not just the application of LLMs to be profitable... Because the open models will continue eating their margins away, at least for large-scale deployments by competent tech companies (i.e. like Linux on servers).
They kinda did: https://en.wikipedia.org/wiki/Azure_Linux