Discovering that Enterprise customers tolerate higher prices compared to retails consumers is discovering demand elasticities not PMF. Am I missig something?
Sales people using it a lot to scout prospects and understand a person's seniority in an organisation, to target better and prepare a strategy to pitch higher up the chain.
Geopolitics and industrial policty aside, I think it's important to check how stable and reliable these chips are. I wouldn't count on them being on par with "western" ones. Correct me if I am wrong here.
My two cents that this is part of the learning curve. With collective experience this type of work will be more understood, shared and explored. It is intense in the beginning because we are still discovering how to work with it. I think the other part being that this is a non-deterministic tool which does increase some cognitive load.
The former IMF chief Kenneth Rogoff has been talking about this and appeared on NYT Ezra Klein's podcast that I highly recommend[0]. He also talks about China and the role of the dollar at the end with Dwarkesh Patel[1]. A lot of the discussion I see here is adressed by him.
What constitutes real "thinking" or "reasoning" is beside the point. What matters is what results we getting.
And the challenge is rethinking how we do work, connecting all the data sources for agents to run and perform work over the various sources that we perform work. That will take ages. Not to mention having the controls in place to make that the "thinking" was correct in the end.
You have to understand that these large corpos move like whales, and the money you quoted is a rounding error. I´ve seen a company department burn cash it was asigned on purpose so it wouldn´t go back to finance (indicating that the department isnt using all their money and something is wrong).
Nobody with interest in politics thinks it's about drugs. It's a pretext and a way to gain legitimacy to exert force over foreign nation with some legitimacy that would otherwise clearly go against international law.
There is some circular financing going on, but AI accelerationists think this will be offset by demand, value, and adoption in businesses. Hence these deals are warranted for the incoming demand.
>> "Turns out the major bottleneck is not intelligence, but rather providing the correct context."
But this has more or less always been the case for LLMs. The challenge becomes context capure. Which in my opinion is the real challenge with LLM adoption.
Without the right contex, some tasks just cannot be reliably completed.
Sure, I could it put it less definitively, but realistically, what else can it be? The transformer won't change much and all of the models, at the core use it. It's a closely guarded secret because it's easy to replicate.
Is this not an incredibly expensive/unsustainable method? I agree with the sentiment that MoE is the way to go as the newer models will probably see diminishing returns. But the compute for a single prompt will suddenly increase 7-15 fold?