They’re calling this out because Anthropic is afraid of dirt cheap, “good enough” open weights models undercutting them. Probably very afraid now that even Nvidia is on that boat, with huge Nemotron models.
The real battle isn’t pro AI vs anti AI. It’s closed weights answers-as-a-premium-service vs open weights, hackable tools. It’s Huggingface vs OpenAI. It’s akin to Lemmy vs Reddit.
Why would anyone use Anthropic once people figure out LLMs are configurable tools, not “AGI,” and efficient ones cost like 2 orders of magnitude less to run?
So they want to squash open research. Because businesses are asking about costs now, they don’t realize they can just host assistants on-prem or through dirt cheap competing providers, but they’re starting to figure it out.
Not just them. GLM, Qwen, Kimi, Stepfun, Baidu’s models. Z-Image. Small finetuners, Huawei’s prototype. There’s even a Chinese fast food chain that trains a ridiculously good audio/text mixed model (Longcat).
I actually thought the recent Deepseek preview was a little underwhelming and “deep fried” compared to competition, though maybe it’s just underbaked. And the architecture is interesting.
Gemma is great, too, if Google would actually unrestrain it and give it Gemini’s architecture.
Europe is struggling though. Mistral (and everyone else) basically can’t do anything because the EU left regulation ambiguous; however strictly they regulate AI (and it should be pretty strict), anything is better than “we have no idea if we’ll get litigated, the law is clear as mud and might change?” They have at least one communal training project too, but everything I’ve seen is weirdly dated, architecture wise, like they’re living two years in the past.
They’re calling this out because Anthropic is afraid of dirt cheap, “good enough” open weights models undercutting them. Probably very afraid now that even Nvidia is on that boat, with huge Nemotron models.
The real battle isn’t pro AI vs anti AI. It’s closed weights answers-as-a-premium-service vs open weights, hackable tools. It’s Huggingface vs OpenAI. It’s akin to Lemmy vs Reddit.
Why would anyone use Anthropic once people figure out LLMs are configurable tools, not “AGI,” and efficient ones cost like 2 orders of magnitude less to run?
So they want to squash open research. Because businesses are asking about costs now, they don’t realize they can just host assistants on-prem or through dirt cheap competing providers, but they’re starting to figure it out.
Deepseek is going to eat their lunch. They are not that far behind and cost pennies in comparison.
Not just them. GLM, Qwen, Kimi, Stepfun, Baidu’s models. Z-Image. Small finetuners, Huawei’s prototype. There’s even a Chinese fast food chain that trains a ridiculously good audio/text mixed model (Longcat).
I actually thought the recent Deepseek preview was a little underwhelming and “deep fried” compared to competition, though maybe it’s just underbaked. And the architecture is interesting.
Gemma is great, too, if Google would actually unrestrain it and give it Gemini’s architecture.
Europe is struggling though. Mistral (and everyone else) basically can’t do anything because the EU left regulation ambiguous; however strictly they regulate AI (and it should be pretty strict), anything is better than “we have no idea if we’ll get litigated, the law is clear as mud and might change?” They have at least one communal training project too, but everything I’ve seen is weirdly dated, architecture wise, like they’re living two years in the past.