To be clear, We don’t really know the architecture or model size of claude or chatgpt. If opus is a 1 trillion parameter dense model, then yes of course it’s going to be way more expensive to run than deepseak which is an 862 billion parameter MoE model.
American models have focused on being the best regardless of cost where chinese model makers were forced to focus on efficiency because of lack of access to chips. Which will probably give them an advantage as the free investor money runs out for the american companies.
The constant training is simply how AI works and will continue to work chinese or american. That cost will never go away. Anytime you need your model to learn new skills or gain new knowledge, it needs to be trained.
The best advancements in usability of AI haven’t all come from training larger models. Tool usage is super useful and doesn’t require new training for new tools, etc.
Deepseek for example doesn’t have a monthly release schedule, they’ve only released one version so far this year.
Plus for new knowledge, there’s web search. It’s no longer strictly true that AI output is restricted to information available before the cutoff date.
At this point you only really need to train a new model if you’re trying out architectural changes, you don’t need to crank out constant updates.
To be clear, We don’t really know the architecture or model size of claude or chatgpt. If opus is a 1 trillion parameter dense model, then yes of course it’s going to be way more expensive to run than deepseak which is an 862 billion parameter MoE model. American models have focused on being the best regardless of cost where chinese model makers were forced to focus on efficiency because of lack of access to chips. Which will probably give them an advantage as the free investor money runs out for the american companies.
The constant training is simply how AI works and will continue to work chinese or american. That cost will never go away. Anytime you need your model to learn new skills or gain new knowledge, it needs to be trained.
The best advancements in usability of AI haven’t all come from training larger models. Tool usage is super useful and doesn’t require new training for new tools, etc.
Deepseek for example doesn’t have a monthly release schedule, they’ve only released one version so far this year.
Plus for new knowledge, there’s web search. It’s no longer strictly true that AI output is restricted to information available before the cutoff date.
At this point you only really need to train a new model if you’re trying out architectural changes, you don’t need to crank out constant updates.