The Iran war has laid bare a paradox: Gulf money is helping underwrite the US’ effort to win the artificial intelligence (AI) race, and now the US has started a conflict that could destabilize those investments. Some estimates have projected US$2 trillion in long-term pledges from Middle Eastern nations to the AI boom, money that now looks precarious. Meanwhile, surging energy costs threaten to make data centers far more expensive to run. However, the aftershocks of the conflict appear less likely to kill the AI boom entirely than cleave the market in two, leaving hyperscalers such as 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 chugged along rather well with annualized revenue more than doubling in the last three months to US$19 billion, while OpenAI revenue is at around US$25 billion. Consumers, business clients across finance and life sciences and governments all pay 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 are likely to maintain them in the hope of making their organizations efficient enough to ride whatever choppy economic waves might be incoming.
The AI software makers need data centers to run their businesses, but they are 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. However, training new frontier models is more energy intensive, requiring the continuous use of thousands of AI chips (graphics processing units made by Nvidia Corp) for weeks or months on end.
Daily costs of inference add up over time, especially for a company such as OpenAI, which claims 900 million weekly users. Yet the energy load is much lower, more distributed and easier to manage than training the next generation of models, something the labs can afford to delay while they focus on urging businesses to plug existing tech into their workflows.
Hyperscalers such as Amazon, Google, Microsoft, Meta Platforms Inc and Oracle Corp are more at risk given how much their US$1.15 trillion buildout relies on cheap, reliable energy, especially natural gas. It is the dominant single energy source for US data centers, providing about 40 percent of their power, the International Energy Agency said — a problem when the Iran war is driving up prices.
The chip supply chain is similarly exposed. Taiwan Semiconductor Manufacturing Co makes nearly all the high-end chips designed by Nvidia, but Taiwan also depends on the Middle East for about a third of its fuel, and the nation gets most of its helium from Qatar. The gas is critical in semiconductor manufacturing thanks to its unique ability to cool and protect silicon wafers during production. Helium production at QatarEnergy’s Ras Laffan Industrial City was crimped last week following an Iranian drone attack; the broader implication could be a months-long wait for chip output to recover.
That leaves Nvidia perhaps the most exposed of all. The world’s most valuable public company, with a market cap exceeding US$4 trillion, derives most of its revenue from selling chips to hyperscalers. Anything that slows down the buildout of vast new server farms would hurt its order book.
While Alphabet and Amazon have recurring cloud subscriptions to act as a financial cushion, Nvidia does not have any such revenue stream. It just sells chips, which face the double whammy of being harder to manufacture in Taiwan in addition to a question mark over mega-deals with the Middle East.
In November last year, the US government approved Nvidia’s sale of 70,000 of its most advanced chips to the United Arab Emirates and Saudi Arabia, a deal that now looks more uncertain. Energy and cash from the Gulf have helped fuel the AI boom.
However strong the revenue growth is for its applications, the outlook for the underlying infrastructure looks ever more fragile the longer the war carries on.
Parmy Olson is a Bloomberg Opinion columnist covering technology. A former reporter for the Wall Street Journal and Forbes, she is author of Supremacy: AI, ChatGPT and the Race That Will Change the World. This column reflects the personal views of the author and does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.
Taiwan’s higher education system is facing an existential crisis. As the demographic drop-off continues to empty classrooms, universities across the island are locked in a desperate battle for survival, international student recruitment and crucial Ministry of Education funding. To win this battle, institutions have turned to what seems like an objective measure of quality: global university rankings. Unfortunately, this chase is a costly illusion, and taxpayers are footing the bill. In the past few years, the goalposts have shifted from pure research output to “sustainability” and “societal impact,” largely driven by commercial metrics such as the UK-based Times Higher Education (THE) Impact
History might remember 2026, not 2022, as the year artificial intelligence (AI) truly changed everything. ChatGPT’s launch was a product moment. What is happening now is an anthropological moment: AI is no longer merely answering questions. It is now taking initiative and learning from others to get things done, behaving less like software and more like a colleague. The economic consequence is the rise of the one-person company — a structure anticipated in the 2024 book The Choices Amid Great Changes, which I coauthored. The real target of AI is not labor. It is hierarchy. When AI sharply reduces the cost
The inter-Korean relationship, long defined by national division, offers the clearest mirror within East Asia for cross-strait relations. Yet even there, reunification language is breaking down. The South Korean government disclosed on Wednesday last week that North Korea’s constitutional revision in March had deleted references to reunification and added a territorial clause defining its border with South Korea. South Korea is also seriously debating whether national reunification with North Korea is still necessary. On April 27, South Korean President Lee Jae-myung marked the eighth anniversary of the Panmunjom Declaration, the 2018 inter-Korean agreement in which the two Koreas pledged to
I wrote this before US President Donald Trump embarked on his uneventful state visit to China on Thursday. So, I shall confine my observations to the joint US-Philippine military exercise of April 20 through May 8, known collectively as “Balikatan 2026.” This year’s Balikatan was notable for its “firsts.” First, it was conducted primarily with Taiwan in mind, not the Philippines or even the South China Sea. It also showed that in the Pacific, America’s alliance network is still robust. Allies are enthusiastic about America’s renewed leadership in the region. Nine decades ago, in 1936, America had neither military strength