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Comment by dgellow | Hacker News Reader

Those points were true at the time and most are still true now. But they aren’t predictions.

- it’s correct there isn’t much fresh data anymore

- it’s correct that compute is scarce, that was 100% the case and a huge issue at the beginning of the year, it is better now but still scarce, and hardware is now way, way more expensive

- it’s correct the finances don’t make sense

But there is no way to know when a bubble pop, because it’s a psychological phenomenon across an extremely complicated distributed system (ie the stock and bonds markets)

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The amount of irrationality I see in the economy with AI makes me more convinced that the wall street bankrollers know very well they're throwing money into a pit, but it's a pit they're gambling will turn into some world hunger ending AI (that will somehow also keep them making money off of scarcity)

never mind that theres no guarantee we'll get that mythical AI. Never mind that the societal reformations would also impact their revenue numbers.

I’m pretty convinced Wall Street is clueless and relies mostly on vibes. It’s the same people who thought all SaaS would become unnecessary after seeing a Claude code demo. Though eventually they will have to ask for the ROI, and that’s where the whole thing will calm down

The new hardware (TPU v8 and VR) are more expensive but they are significantly cheaper per flop. e.g. many multiples more performance for only 2x the price.

If I have some ML workload to run I can buy $x of Blackwell chips or I can buy significantly less $ worth of Vera Rubin chips to get the same performance. That's the key thing to keep in mind when you're talking about financials.

I was thinking the same thing in terms of running out of data a few months ago. But aren't most gains in the past year+ due to reinforcement learning in some form? Which doesn't need "fresh data" per se, as the model effectively creates the data as it goes. As long as engineers can come up with proper environments, tasks/goals, rewards, and actions, I don't really see data being a limit to model improvement in an agentic sense. Maybe as a knowledge base

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