Long read: An AI Addendum

Last week’s announcements of investments in OpenAI by Nvidia and AMD in exchange for buying their chips seemed to bring a sense of Déjà vu. Here’s Harris Kupperman back with his rational approach to showing the impossible economics of the AI boom. This is a follow up to his viral post from about a month ago titled ‘Global crossing reborn…”, referring to the fibre network company which went bankrupt in the dotcom bust. We recommend reading the original post before getting down to this one.

In this post, the author talks about how following his post, he received responses from industry insiders who had similar doubts about the AI economics stacking up. Some of the responses provided more data points which makes any sensible RoI on the AI GPU spend that much harder. His new math suggests the industry needs to realise a revenue of a trillion dollars versus the $15-20bn today for this to make any economic sense. There were criticisms too of his post. But one criticism was that why should it make economic sense when it is a strategic matter likening it to the rail rood boom of the 19th century – the government will backstop this much like they did with the rail roads:

“Think back to the building of the transcontinental railroad. For strategic reasons, the government wanted to connect the coasts, and realized that subsidies would be needed. The government tried all sorts of tricks to accomplish their goal. There were land grants and cheap loans. Laws were ignored, and founders were allowed to siphon funds. The unity of purpose superseded all else, and the nation was better off for having accomplished the goal of completing the transcontinental railroads. It’s obvious that the government wants the US to win in AI, and maybe the government really does sit there, fully prepared to ensure that American AI dominates globally. Then again, the 1800s had multiple financial panics caused by the railroads going bankrupt. Much like AI, the economics simply didn’t justify building multiple transcontinental railroads, especially when the railroads then competed transport rates below operating costs.”

He says it was odd to hear the critics bring up the railroad analogy given not only did the rail roads go bust, they also caused global financial panics: “Think about the railroads in the second half of the 19^th  century. When they failed, it wasn’t just the collapsing bonds held by investors, often pledged as collateral for other loans, that pulled down the economy. It was also the inability of the railroads to raise additional capital to order new steel and railroad ties, along with the thousands who got laid off at railroad yards and locomotive manufacturing plants. Then, there were huge multiplier effects to all of this. Railroads became dominant, not just to the financial system, but to the real economy as well. They became so important, that they would become responsible for periodic and deep economic depressions every time they experienced funding stresses.”

He then shows how much like rail roads back then, AI today is driving a significant part of the economic growth: “How big is AI to our economy?? Let’s use some abstract math that is likely to be rather imprecise, yet directionally credible. From a starting point, let’s work with my $400 billion capex estimate for 2025. Now add in some unallocated corporate expense, some R&D, some spending on new electricity generation capacity, plus some other expenses, and the AI buildout likely represents approximately 1.5% of expected 2025 US GDP of approximately $30 trillion. Now, nothing economic happens in a vacuum. Every dollar spent has a multiplier downstream in other industries to support this spend. Is it irrational to expect that with a bit of a multiplier, AI capex is clocking in closer to 2% of GDP?? Which is a good chunk of total expected US economic growth this year. Actually, without the AI buildout, the US economy would probably be teetering on the cusp of a recession”

So, if funding stops for AI, we might see bigger economic and financial crises as unlike railroads whose utility lasts a long time, the AI technology infrastructure becomes obsolete fairly quickly resulting in massive capital destruction.

But what will stop the funding? The OpenAI deals with Nvidia and AMD might suggest that it has already stopped in a way: “If you think back to the fiber-optic bubble of 2000, the warning signs were obvious. When the capex spending started to outstrip the desire of investors to fund it, the vendors started to act in irrational ways in order to hit Wall Street targets. Lucent and Nortel started lending their customers money to buy networking equipment, they took equity stakes in their customers, so that they could purchase more equipment, and they even bought capacity on their customers’ fiber-optic networks so that their customers could show revenue growth, and hit Wall Street targets. All of this was done in the hope that their customers could raise more capital to keep buying networking equipment. As you can imagine, when you’re the vendor, the customer and the investor in a company, there’s a strong incentive to artificially inflate the numbers by signing preferable contracts that use very large numbers, and then round-trip the capital. With extreme pressure to hit targets, especially as the funding cut off, it should be no surprise that this led to endemic fraud at both Lucent and Nortel, ultimately leading to their collapse.”  

He ends with: “Like many things in finance, the outcome remains obvious, it’s the timing that is the hard part…”

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