Traders Turn to AI to Process Iran War Market Impacts
Investment professionals managing portfolios through the Iran conflict are relying on AI tools to compress weeks of research into hours. Large-language models have become central to how traders at firms across Dubai, Sydney, London and São Paulo assess market ripple effects from a war that has disrupted energy supplies and killed at least 4,000 people.
Maxence Visseau, founder of Dubai-based macro trading firm Arkevium, cut his research time by roughly 80% using Anthropic's Claude to stress-test multiple scenarios in parallel and map potential impacts across asset classes. "I was up for almost 48 hours straight, monitoring the interceptions in the United Arab Emirates while simultaneously running scenarios and preparing for the market open," he said. "That's precisely the kind of moment where AI becomes indispensable."
Brent crude surged as much as 11% to $119 a barrel Thursday on concern over escalating attacks on Middle East energy facilities, before settling around $109. The volatility has made rapid information processing a practical necessity for traders positioning around potential supply disruptions.
Speed Gains Are Substantial
Jian Shi Cortesi, a fund manager at Zurich-based GAM Investment Management, now gets war updates in seconds that previously took 30 minutes of reading multiple sources. Researching individual companies takes a day or less instead of multiple days. "The speed has probably increased by five times," she said.
Anna Wu, a cross-asset strategist at Van Eck Associates in Sydney, used ChatGPT and Claude to track 100 years of war-driven oil breakouts and identify which asset classes outperformed in each case. She cross-referenced results with inflation and economic growth data to improve accuracy.
Gustavo Pessoa at São Paulo hedge fund Legacy Capital uses AI to understand ship types, analyze oil demand elasticity, and estimate barrel flows needed to stabilize markets after the virtual closure of the Strait of Hormuz. "We use it for everything," he said.
Accuracy Remains a Problem
The technology produces errors frequently enough that traders cannot treat outputs as reliable without verification. A Bank of England policymaker warned that AI adoption in trading may amplify market shocks and herd behavior.
Visseau said he treats AI results as a starting point. "It's an iterative process - I'll challenge the output, refine assumptions, introduce new data points," he said.
Michael Brown, senior research strategist at Pepperstone Group in London, said traders need deep subject knowledge to spot when AI generates spurious information. "Participants still need a deep understanding of the situation themselves in order to make that final trading call," he said.
Entry-Level Roles at Risk
The efficiency gains are reshaping hiring decisions. Cortesi said she could eliminate junior analysts entirely, noting that AI handles complex requests faster and more reliably than entry-level staff. "I can ask AI: use a Warren Buffett approach to give me the key points of this company, and the AI will do it right away," she said. "But if you ask a junior, the junior may not know what Warren Buffett approach is."
John Foo, founder of Valverde Investment Partners in Singapore, said AI remains complementary rather than a replacement for human judgment. "There is still an element of good judgment and experience that goes into decision making that AI at this stage - maybe the next 2-3 years - still cannot fathom," he said.
For traders managing positions through an active conflict, the time savings have become practical. Whether those gains persist as the technology matures depends on how accurately these tools can model complex geopolitical scenarios.
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