Earlier quoted context omitted.
According to Dario, each model line has generally been profitable: i.e. $200MM to train a model that makes $1B in profit over its lifetime. But, since each model has been more and more expensive to train, they keep needing to raise more money to train the next generation of model, and the company balance sheet looks negative: i.e. they spent more this year than last (since the training cost for model N+1 is higher),…
> if I give you $15B, you will probably make a lot more than $15B with it "probably" is the key word here, this feels like a ponzi scheme to me. What happens when the next model isn't a big enough jump over the last one to repay the investment? It seems like this already happened with GPT-5. They've hit a wall, so how can they be confident enough to invest ever more money into this?
If model training has truly turned out to be profitable at the end of each cycle, then this company is going to make money hand over fist, and investing money to out compete the competition is the right thing to do.
Most mega corps started out wildly unprofitable due to investing into the core business... until they aren't. It's almost as if people forget the days of Facebook being seen as continually unprofitable. This is how basically all huge tech companies you know today started.