A Different Level of Consensus

Another day another troubling story of AI spending and debt. What's notable about this post from Jared Bernstein and Ryan Cummings is how remarkably close expert opinions are when it comes to the size of the hole.

In an analysis released last month, we estimated that the big-spending hyperscalers need $13-$18 trillion in revenues over the next 10 years to justify their AI-infrastructure investments.This is, by definition, speculative work, requiring judgment calls on technical aspects of the analysis, such as discount rates, depreciation schedules, taxes, and revenue paths. So, we were happy to see that several other analysts and researchers have, independently, been working on the same problem. Callum Williams of the Economist, whose produced his own excellent analysis on the topic, put together a comparison of these different estimates, including ours.
Turns out we’re pikers! But what’s striking is how close each of the estimates are: firms will need between $2.4-3.0 trillion in annual incremental AI revenues by 2030 to justify their big spending; this is about 5-6 times more than they are currently making under the most general possible assumptions.They may clear that hurdle. Eventually. But as one of us (Ryan) told :“Can all these people eventually turn this into something profitable? I think they will…I’m not sure who’s going to do that, but I do think there will be trillions of dollars of profits that are up for grabs in the future, but just not in this super-accelerated timeline that is required to justify the investments.”...It’s thus revealing when pieces of information about the finances of these two firms become public, as with Anthropic last week, and OpenAI, whose financials were leaked in June. Here again, the absence of profitability—in these cases, steep operating losses—is not encouraging and underscores our overarching thesis that the sector is in the throes of a financial bubble.AI optimists argue that many firms that went on to be successful—Amazon, Uber, and Tesla—lost money for years before starting to make it. But what the leaked financials reveal to us is that both the speed and scale of the losses this time are dramatically different. The chart below shows that last year, Anthropic and OpenAI lost more than $1.76 and $1.60, respectively, for each dollar of revenue they brought in. This was more than double Uber’s in 2016 and more than five times Amazon’s in 1999.The extent of the losses so far is also historically notable. The next figure shows how long it took different companies to move from (cumulative) losses to gains. OpenAI and Anthropic, at least so far and based on what we can see, are in a class by themselves, with OpenAI having lost roughly 75 percent in one year of what Uber lost over the course of eight.