> This is pretty typical for silicon valley though, we always burn investor money to corner the market and then tech usually catches up.
Yes though the cost breakdown has traditionally been large upfront development costs and low to moderate running costs. This time around the running costs are astronomical and Moore's law ain't what it used to be.
It seems like a repeat of Uber's play but at 10x scale. Lose 10s of billions of dollars scaling a product that loses you money on every sale in the hopes that it will position you well for the massive disrupter of [self-driving | AGI]. Uber's play didn't shake out so now they are very very slowly digging out of a 30bn hole. I guess its just a question of how big the AI hole gets before they either make AGI or give up and start shoveling.
While there is no reasonable explanation for why Uber can't just turn a profit (and it looks like they have for the last few years), deep-learning models have very hard physical constraints on how much they cost.
I would argue that physical cars driven by people have much harder constraints on cost than LLMs which can see huge cost savings (for the same performance model) as hardware improves. I agree that they aren't perfect parallels but in principle there's nothing stopping AI companies from massively cutting R&D and raising prices until marginal revenue is positive, it just would mean accepting not getting "take over the world" level profitability or getting run out of town by someone willing to keep burning money.
Yes though the cost breakdown has traditionally been large upfront development costs and low to moderate running costs. This time around the running costs are astronomical and Moore's law ain't what it used to be.