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Chapter 1: What is the main topic discussed in this episode?
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Chapter 2: Why is there skepticism about AI's return on investment?
They don't know. They have no idea.
No, and this is the deep structural problem because they were brought in through the side door of bundled pricing and now that's becoming unbundled. And of course, that also is reflected in...
What we're told, and I think the Wall Street Journal and others have written about this, and it'll be interesting, we see the final S1s for some of the upcoming IPOs, that there is this attempt to try and even mask it in the financial filings where you get into this phenomenon of what we used to call earnings before bad stuff. And so what they're trying to do is hide the costs.
of training the models and saying that's not actually an operating cost, that's a capital cost, and we shouldn't have to show that as a function of what actually the margins are on producing tokens. And that is, of course, a cheat, right? Because if that's true, then you should be able to capitalize these things and expense them for a long period of time.
And we know full well that these are actually operating costs because they tell us that every 18 months we're launching a new major model. These things are not capitalized. These are operating expenses that should be treated accordingly with respect to the actual cost of token production.
So there's a multifaceted game going on here, both in terms of how it's being presented to users, but also in terms of how they're trying to sell it in the context of the upcoming S1s for the Anthropic and OpenAI IPO filings.
Well, what's really funny as well about the idea of capitalizing training costs is they're never going away. Because it's not just pre-training, shoving the stuff in the models. They have to constantly tweak them.
That's right. Which from a classic, my years ago accounting, whenever you have a regular predictable cost that you have to incur to continue operating your business, that is no longer a capital cost. That's an expensable item that should be expended.
So you get into, as I said, like back in the dark days of dot-com and even the telecom boom, you get into this problem of earnings before bad stuff where they want to exclude all of the things that make the numbers look bad. And then, of course, on the other side, you have this run rate problem where we continually hear about what the run rates are at these companies.
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Chapter 3: How does the GitHub Copilot pricing model impact users?
No one. We have three, four years in and everyone's like, oh, we'll do ASICs. No, we won't. That didn't work. We're like two or three generations of Tranium, Inferentia, TPUs, still not profitable.
Still not profitable. Right. We know.
We would know.
I was just looking at some data yesterday with respect to how small language models are increasingly closing the gap with large language models, which is causing training cycles on large language models to have to accelerate, become more expensive, throw more compute at it, more reinforcement learning. the costs are particularly not declining.
They're actually increasing sharply at the frontier because they're essentially being chased like the rabbits and like Wile E. Coyote and the Roadrunner. And so they're being chased into this very costly corner as a result. And that's a classic commoditization problem. If you go back to the 1900s,
I don't know, late 19th century, a very similar thing happened in railroads as people were racing desperately to try and find a way to build a corner and control themselves so that they could compete with all of these other upstart railroads. And of course, all that really happened was CapEx exploded, margins went to shit, and multiple railroads failed.
And we led to the crash of what, 1873, 1893, and arguably... was a cause in the Great Depression. So you're playing out this exact same game because you're sitting in this high capex world that's increasingly funded by debt and built on top of this duration mismatch with token prices being now exposed and raw in front of people.
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Chapter 4: What are the implications of token pricing in AI?
And that was the last time I saw him.
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I understand the game. As somebody who creates shows, I'll even say this. At the end of the day, when people are at home, they want entertainment.
to hear this and more listen to reality with the king on the iheart radio app apple podcast or wherever you get your podcast and the other thing is as well is people just literally in my piece today people make this point about oh it's like the dot-com bubble in the we will we will simply just we'll reuse these things in the future like we'll just pick these up and it's
I hear this from very smart people, people who are not like beguiled by the AI industry, but it's like, okay, let's talk about what happened to the dot-com bubble. So when it exploded, you had those, some microsystems, the ultra, whatever it was, I forget.
43 50 grand a server but that one server could run an entire company you could run everything on it database databases uh messy crms they had like did they have on-prem lotus notes anyway um you had those things but you could run that and it you could probably run that in a garage you might need to use the washing machines plug but you could do it yeah
And those things were 50 grand, so you'd probably get them at, what, 20, 30 large. Okay, great. What happens when the AI bubble bursts? You can't just plug in an AI GPU. A B200 GPU is about 50 grand. It requires about, I think... I looked this up very recently.
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