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I’m sure AI already has some value with the amount of time it saves researchers and developers, and this is economic value because I’m sure these groups would pay above inference costs to keep using it. My understanding is that the vast majority of big AI companies’ massive debt is from training, and even the current inference costs may be profitable, but if they’re not and most customers had to they’d pay more.
I'm not convinced LLMs are a net productivity gain for anyone, let alone with inference costs, and LLMs will require constant training or they'll eventually drift woefully out of date. In two years, I expect it to be as clear to everyone that OpenAI and Anthropic aren't worth anywhere near their current valuations let alone a trillion dollars, and we enter into a third major AI winter.
If LLMs have a future, it's going to be in cheap, open-source models. Even then, as they stand, they appear to be a net distraction rather than value add, atrophying professional skills, and reducing quality standards wherever they're implemented, leading to a lot of technical debt, which will have to eventually be repaid. Maybe in 20-30 years, we'll have an abundance of chips and memory, then we'll be able to continue the scaling experiment (which I believe any future successful more generalistic AI will need scale similar to current LLMs rather than some scientific, algorithmic breakthrough).
I program and would be surprised if LLMs haven't increased my productivity. Although maybe they atrophy skills, I'm still working myself at the high level, the LLMs help with boilerplate and bug fixes. They also answer questions that are too complex for a search engine, faster than I could on my own (and provide sources so I know they're accurate).
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Yes, I think this is correct. Hence my prediction of a R&D slow-down once the investor money runs out.
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