The AI Demand Bubble with Ed Elson
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That's right, we are back with Prof G Markets' Ed Elson, and we're here to talk about the naughty little buggers in the Magnificent Seven, primarily the big four, but really the big three, the Amazons, the Googles, and the Microsofts of the world. Ed, thank you for joining me again.
Thank you so much for having me. I'm very, very excited to be here.
Yeah, so tell me, they just reported earnings, and as you well know from the conversation we just had, I got some points to make about it, but why don't you run me through how they did on their earnings?
Absolutely. So let's just run through what we learned on Microsoft. Microsoft's end, we saw pretty good growth, $90 billion in revenue, up 18%. Amazon, similar story, $200 billion in revenue, up 18%. 20% Meta's revenue increased 28%. I mean, on its surface, this was pretty good. Let me just look at the numbers, even the net income, which exploded for many of these companies. But of course, that's not really what matters here. What really matters is how is the AI trade actually working? working out. There are two main questions that we really want to understand when we look at these big tech companies. The first question is, how much revenue did you generate from your AI business specifically? That's what we really want to know the answer to.
And then the second question is, how much of that revenue came from two relatively unstable companies financially, specifically OpenAI and Anthropic? Those are the two questions I want to know the answer to.
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Chapters
6 chapters
1
What is the main topic discussed in this episode?
0:00–11:13
2
What do Ed Elson and Ed Zitron say about the latest tech earnings results?
11:13–19:55
3
How much of Microsoft, Google and Amazon’s reported earnings are driven by unrealized AI equity gains?
19:55–27:55
4
Why do PE multiples look deceptively cheap after AI-related valuation adjustments?
27:55–42:32
5
How does accounting and GAAP treatment explain Microsoft’s different OpenAI reporting?
42:32–50:39
6
Why are AI annualized run-rates (ARR) misleading for measuring true AI revenue?
50:39–1:06:48