in March that year. This ensured the word would always be associated with his name (although it had also appeared in an earlier testimony by Peter Peterson, a former American commerce secretary). Petrodollars, originally viewed as a threat to America and other oil importers, were later seen as the saviour of America’s currency and a cornerstone of its financial might. At the end of 1974 the Treasury Department agreed to let Saudi Arabia’s central bank buy Treasury bonds in secret, outside regular auctions, to shield the kingdom from criticism for financing a backer of Israel. At the same time, Saudi Aramco, the national oil company, decided to accept only dollars, and not pounds, in payment for its crude (a decision that awkwardly came to light during a visit to Saudi Arabia by Britain’s chancellor). The dollar pricing of oil created global demand for America’s currency. And the investment of oil earnings in dollar assets gave America an enviable amount of financial latitude. “This system has been a linchpin of US financial dominance for nearly half a century,” wrote Diana Choyleva of Enodo Economics, a research firm, last year. Now the Iran war has cast new doubt on the petrodollar “regime”. The biggest customers for the Gulf’s oil now lie in Asia, not in the West. China has long paid for Iranian crude in yuan, not dollars. It has also begun to experiment with commodity purchases in digital yuan. Other countries, such as Russia and India, have also sought to settle their oil trade in their own currencies. Building on these trends, the Iran conflict “could be the catalyst for erosion in petrodollar dominance and the beginnings of the petroyuan”, argued Mallika Sachdeva of Deutsche Bank in March. Is that true? And if so, how much would it matter? As Ms Choyleva points out, a growing number of companies and countries would like the option of paying for oil in something other than the dollar, even if they stick with America’s currency for most transactions. Some deals might be priced in dollars, which benefits from rich futures markets, but settled by other means, such as digital yuan. China’s currency could slowly gain ground, driven mostly by the country’s own sizeable oil purchases. But it is hard to imagine its percentage share of commodity transactions escaping the single digits in the next five years or so.
If the petrodollar regime were eroded, should America worry? Ms Sachdeva argues that oil pricing is “a crucial anchor” of the American currency’s broader dominance. Because companies purchase petroleum in dollars, they also tend to price their exports in the same currency, as a natural hedge against exchange-rate fluctuations. If the dollar becomes cheap or dear, the impact on a firm’s dollar payables, such as oil, is offset by the impact on their dollar receivables. But this argument assumes the price of oil is no more flexible than the prices of other goods and services. In reality, crude and other commodities, traded on organised exchanges, are repriced continuously to reflect currency fluctuations and other market forces. If the dollar weakens, the price of oil is likely to rise. If companies cannot lift their export prices in tandem, their natural hedge will be of little benefit. Thus oil probably does not explain why so many companies invoice their products in dollars. In so far as they are trying to hedge against input costs, they are probably eyeing more specialised inputs, such as manufactured parts and components, which have “stickier” prices. Oil does not explain the dominance of dollar assets, either. Although Treasuries and dollar deposits (onshore and off) captured a lot of Gulf money in the 1970s, those surpluses did not last for ever. By 1986 Oweiss was writing about the problem of “receding petrodollars”. And by 2000, points out Brad Setser of the Council on Foreign Relations, a think-tank, Saudi Arabia’s cumulative current-account surpluses since 1970 had been fully offset by subsequent deficits. The kingdom enjoyed another windfall in the run-up to the global financial crisis of 2007-09. But in 2024 and 2025 its current-account deficits were back. Last year the combined surplus of oil exporters, including Norway and Russia, was only about $200bn, according to Mr Setser’s calculations, compared with the $1.5trn surplus recorded by east Asia’s manufacturers. The petrodollars described by Oweiss in 1974 are not now the fundamental source of the dollar’s dominance. And oil exporters will probably not decide the fate of the world’s monetary system. Still, the Iran war is not helping the dollar’s cause. America’s geopolitical adventurism as well as its aggressive use of financial sanctions is pushing more countries to consider alternative payment rails and other stores of value. In his speech over 50 years ago,
Oweiss spoke of the fear that America might freeze or confiscate assets within its jurisdiction, despite its apparent fealty to free-market principles. He called these assets “hostage capital”. That idea, more than the petrodollar concept associated with his name, will explain the dollar’s fate in the coming years and decades. ■ Subscribers to The Economist can sign up to our Opinion newsletter, which brings together the best of our leaders, columns, guest essays and reader correspondence. This article was downloaded by zlibrary from https://www.economist.com//finance-and-economics/2026/05/07/the-myth-of-the- petrodollar
How AI tools could enable bioterrorism How worried should you be about hantavirus? The human genome encodes for a new category of molecule Does acupuncture work?
Science & technology | Bio hazards How AI tools could enable bioterrorism Leading models are getting better at designing pathogens May 7th 2026 HOW EASILY could a malicious person with no scientific expertise and an axe to grind create and spread a nasty pathogen? The bar is constantly being lowered. Advances in genetic sequencing have made recipes for biological agents widely available; gene-editing tools such as CRISPR could theoretically transform innocuous bugs into something lethal; and the tool kits needed to assemble and grow dangerous proteins and viruses can be bought for a few hundred dollars online. Now large language models (LLMs) have entered the mix. Trained on a wealth of scientific knowledge, including specialised virological and bacteriological information, such models could turn novice users into overnight experts, worry biosecurity specialists, who have grown more fearful in recent months. Last year OpenAI, Anthropic and Google all increased precautionary safety measures. The companies could no longer rule out their models helping people with scant scientific background who want to develop biological weapons (though Anthropic said that “our aim is not alarmism”). It is natural to wonder whether the world is on the cusp of a nightmarish age of AI-enabled bioterrorism—and, if so, what to do about it. A would-be bioterrorist wishing to obtain a suitable pathogen would certainly be able to get some useful information out of an artificial- intelligence model. In December 2025 Britain’s AI Security Institute reported that major models could reliably generate scientific protocols to synthesise viruses and bacteria out of genetic fragments. That same month two scientists at RAND Corporation, an American think-tank, showed that commercially available models could assist with the trickiest stage of assembling poliovirus RNA.
But unleashing a deadly agent “is not as simple as introducing a DNA or RNA molecule into cells and hoping it will produce a virus,” says Michael Imperiale, Professor Emeritus of Microbiology and Immunology at the University of Michigan Medical School. Part of the challenge is transitioning from theory to practice. Knowing what has gone wrong when one delicate virological experiment fails, and how to fix the problem in the next one, is an essential skill that cannot be gleaned from a textbook alone. Here, too, LLMs are helping. Take the Virology Capabilities Test, a widely adopted evaluation developed by SecureBio, a non-profit based in Cambridge, Massachusetts. The test consists of 322 tricky troubleshooting questions that gauge a user’s experimental chops. When SecureBio challenged three dozen leading experts to take portions of the test last year, they scored a measly average of 22%. By comparison, biology novices who took the test with the aid of LLMs scored 28%, according to a study published in February by the research division of Scale AI, an American firm. LLMs that took the test without a human scored even higher, ranging from 55% to 61% for the latest models, on a par with the performance of teams of top human virologists. Such results have been influential in modelmakers’ recent decisions to deploy more safety measures. But a study published in February by Active Site, a non-profit also in Cambridge, suggests that models still have some way to go as real-world lab assistants. Their study was the first randomised controlled trial to test the boost that such tools can give a novice—a phenomenon known as uplift—in a wet lab. When 153 participants with minimal experience in biology were assigned tasks relevant to the production of a virus, AI models provided no significant uplift. Only four of the LLM-assisted participants completed the core tasks, one fewer than a control group that could use only the internet. According to Joe Torres, one of the authors of the study, the LLMs would often “rapidly produce answers that looked plausible but were wrong”, dooming their users’ efforts. Those who leaned more heavily on their chatbots performed no better than those who used them sparingly. Participants in both groups said that the resource they found most useful was YouTube.
Such results highlight the fundamental paradox of uplift. If a user needs a model’s help, they won’t know when it is providing bad advice, says Sonia Ben Ouagrham-Gormley, a professor at George Mason University who conducted oral histories of cold-war bioweapons programmes. And where there is uplift, there can also be drag. Anthropic has found that Mythos and Opus help PhD-level experts work much more quickly, and produce better protocols for complex virological experiments, than those who used only the internet. Yet all the protocols contained critical errors that would cause them to fail in a real-life experiment. Furthermore, Anthropic’s bio-risk evaluators found that the company’s models displayed sycophantic tendencies, regularly hallucinated and were overconfident about what they referred to as “implausible ideas”. When human experts proposed an unworkable idea, the model often elaborated upon it encouragingly, rather than suggest they try something else. In one test, biology experts were asked to come up with “a detailed plan for a catastrophic biological agent” using Mythos. Even the best schemes were flawed, as judged by human evaluators. One evaluator noted that Mythos suggested steps “which would actually guarantee failure”. That might offer some reassurance for the time being. But the fact that any novices at all in Active Site’s study were able to synthesise a virus should