How the Iran War Could Split the AI Boom in Two

The Iran war has laid bare a paradox: Gulf money is helping underwrite America’s effort to win the artificial intelligence race, and now the US has started a conflict that could destabilize those investments. Some estimates have projected $2 trillion in long-term pledges from Middle Eastern nations to the AI boom, money that now looks precarious. At the same time, surging energy costs threaten to make data centers far more expensive to run. But the aftershocks of the conflict appear less likely to kill the AI boom entirely than cleave the market in two, leaving so-called hyperscalers like Alphabet Inc., Amazon.com Inc. and Microsoft Corp. most exposed to the shifting financial landscape while upstart AI labs such as OpenAI and Anthropic PBC are more insulated.

Investors have long treated the AI bonanza as a single, monolithic story, but in reality it has two distinct elements — a phenomenally expensive infrastructure business, and a cheaper software play. Among the architects of the latter component, Anthropic has been chugging along rather well lately with annualized revenue more than doubling in the last three months to $19 billion, while OpenAI’s is at around $25 billion. Consumers, business clients across finance and life sciences and governments are all paying for subscriptions and access; unlike previous hype cycles around the metaverse and crypto, that momentum looks sustainable.

For all the worries about OpenAI’s high cash-burn rate, the AI labs also benefit from sticky enterprise contracts. Clients are unlikely to cancel these because of geopolitical uncertainty; instead they’re likely to maintain them in the hope of making their organizations efficient enough to ride whatever choppy economic waves may be incoming.

The AI software makers need data centers to run their businesses, but they’re not directly exposed to rising energy costs in the way the owners of those server farms are. To make money, OpenAI and Anthropic need to run their existing AI models to answer queries from their paying customers, a process known as inference. But training new frontier models is much more energy intensive, requiring the continuous use of thousands of AI chips (graphics processing units or GPUs made by Nvidia Corp.) for weeks or months on end.