AI revenue does not capture the value of its compute

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 1:10:46

   Right. So Anthropic is generating about a billion dollars of revenue per couple of gigawatts of energy. That's the current thing. So that's gigawatts per year. per year. So at this level, you're talking quite a bit, like that sounds huge, but take New York City. You've got a bunch of human beings doing a whole bunch of stuff. They're using 5.5 gigawatts of electricity, but they're generating 1.2 trillion in GDP. So when you run the math, human civilization is delivering like 200 billion of economic value per gigawatt, right? So there's a a human metropolis is therefore like something like 30 times more energy efficient at creating value than the world's most advanced ai currently now it may scale very quickly to that level walk me through that math again there i gotta blow holes in this yeah go for it it can't be right

2. 1:11:44

   because that's why i want like i can have a genius level ai working for about a buck a day um yeah and and it's equal like i can spend i'm looking at i'm looking at my revenue not not so So let's take Anthropoc, right? Okay, they've gotten to about $100 billion in revenue, right, with a couple of gigawatts of power, okay? So if you run the math, I ran a quick set of math on it. An AI cluster roughly yields about $8 billion in revenue per gigawatt.

3. 1:12:18

   But if you take New York City as a cluster of human activity operating to generate economic output, you have five and a half gigawatts of energy, but that's generating 1.2 trillion. gigawatts. I mean, it's 1.2 trillion of GDP, right? And I may be comparing apples and oranges. It may not be totally fair. And it may be with the various beginnings of the AI scaling. Right. When you're talking about power, you're talking about power, like here's the current power run rate. Power is just a continuous variable. And you're saying this city right not including its construction, not including feeding the people, not including the cost of educating the people to get them to where they were, not including any of those costs, just the amount of electricity it's using in this next 10 minutes, say, is higher, is lower by what you, what did you say about a factor of 100?

4. 1:13:13

   Yeah, something like that. You're making a good point. You're saying all the things that got you here, getting everybody educated, clothed, fed, infrastructure, that's essentially retraining for this analogy. And I'm discounting that value. That's fair. Okay. Yeah. And then when you used Anthropic, you're saying Anthropic as a company. So here's its revenue. Here's its power consumption. but a lot of that power consumption is not inference right now du jour you know so you so on the anthropic side you used all of the pre-training for example is baked into that number on the human side all of the education of the human being is not baked into the number so that's yeah it's definitely on both sides of the ledger i think we should nuance that run it run that it'd be an interesting talk it would be initially benchmark to track where does

5. 1:14:05

   AI generate economic output that starts to merge with the economic output of a city fully operating. I don't think the numbers are that far off, surprisingly. So as we're talking, did an analysis. My analysis has, but obviously this is based on trying to triangulate from a number of public sources, Anthropik's total compute footprint by the end of this year at, call it four gigawatts-ish, So perhaps Salim even higher than what you're estimating and trying to reach materially higher than that potentially by 2027 in the 16 to 18 gigawatt range in 2027, 2028.

6. 1:14:48

   So that is the equivalent of several large nuclear power plants that could certainly power an American city. But I also think from an economic output, apples and oranges, because I think we're already to the right of AIs or SIs being so much more economically efficient than human workers per watt anyway. Well, also, the other thing is I mentioned on the last pod that Cam, outside here, had 5,000. He had half a billion tokens on Friday, which is about the equivalent of, I swagged at it, about 5,000 workers working for him on that day.

7. 1:15:28

   And very, you know, brilliant workers working for him on that day. And it cost about $5,000, so about a buck per worker per day. If those were actual human beings here in the office, they would have eaten more potato chips than that alone. Like, it easily would have cost more than that than just potato chips. So, like, well, where's the math wrong? Well, we're counting the productivity of those people as a buck a day using that math.

8. 1:15:51

   Like, what did we pay Anthropic? But that has nothing to do with the value that we got out the other side of that AI that day. That's orders of magnitude higher than what we paid Anthropik, assuming we manage them well, which I'm still checking on. But the value you can get out of it is so much higher than Anthropik's collections, you know. And I did that just to kind of tease out some fun conversation.

9. 1:16:13

   But I also want to pay homage to a company generating close to a revenue run rate of $100 billion a year in such a short time. I mean, it's just so incredible to see.