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Comment by MangoCoffee | Hacker News Reader

its hyperscalers and neoclouds data center build out that ate those trillions.

a lot of those money is the commitment from US AI labs that's not yet spend until the data center is roll out.

you are conflating.

Maybe I'm conflating. I could be dead wrong about all this. But I'm trying to zoom out and look at the big picture. So, I'm letting the details sort of blend away and I'm just asking myself: "Is such a large financial sink in our economy, at the macro level, really going to offer the value that the investment warrants?". So, the particulars of who exactly is spending what where I guess doesn't concern me as much. What I'm thinking about is American companies, American people, American dollars going to something that seems like an iffy value proposition.

Now, if you tell me American businesses are turning cheap Chinese tokens into goods and services of high value, for Americans and people all over the world, then obviously I would have a different outlook on what's happening here.

You are conflating. The build out is almost entirely for running inference, not training. Open weight models will make that inference build out bet more likely to work as it adds diversity to the suppliers, all the data centers are not beholden to just OAI and Anthropic to buy their compute - they can host their own models (even Chinese ones, in America) if that becomes a popular option.

Zooming out, I think that build out still makes sense. There will be losers in it, but think about how ridiculous the amount of compute we're using has increased from 4 years ago. If I had to guess 60-70% of developers and engineers everywhere in the world are now burning thousands in compute individually every month, whereas it was previously approximately 0.

I see. So maybe the situation isn’t as bad as I thought. I was thinking if you took all the money and resources spent on building the biggest models, across the two big labs, the hyperscalers, the dozens of neo labs, and you reallocated that money to building value on cheaper Chinese models that would be a much better proposition than doing whatever it takes to be head of Chinese model performance by a few percentage points for a few months. And I wasn’t just referring to the cost of training one model, I was considering the total cost of maintaining an entire ecosystem with the ability to do this.

But what I’m hearing is this is a negligible cost compared to inference buildouts. Fair enough.

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