War information overload prompts Wall Street traders to turn to AI: directly saving 80% of research time

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The information overload and oil market volatility triggered by the Iran war are pushing AI deeper into trading workflows. Maxence Visseau says large models have reduced research time by about 80%, and investors are using ChatGPT, Claude, and others for second-level war summaries, century-long historical retrospectives, and scenario assessments for the Strait of Hormuz.

The Iran war has pulled the market back to the pricing framework of "geopolitics and energy," compressing trading decision windows and making research chains more easily drowned out by noise. Several traders told Bloomberg that the value of AI at such moments is not about "prediction," but in "compressing time, expanding search, and parallel scenario simulation."

Arkevium founder Maxence Visseau says he places large models at the core of his investment process, reducing research time by about 80%, and he can simultaneously run multiple scenarios, compare historical precedents, and map potential cross-asset ripple effects.

AI is now indispensable in trading

In the first few days of the Middle East conflict, Arkevium founder Maxence Visseau placed AI at the center of his investment workflow. He used Anthropic's Claude model to conduct parallel stress tests, compare historical precedents, and map out potential chain reactions across asset classes.

"I was almost awake for 48 hours straight, monitoring UAE's interception actions while running scenario simulations and preparing for the market open," said Visseau, based in Dubai, specializing in macro trading strategies. "It is exactly at such moments that AI becomes indispensable."

Using large language models, Visseau reduced research time by about 80%. Nick Twidale, chief market analyst at AT Global Markets in Sydney, pointed out: "We are witnessing history—this is the first major conflict where AI is used in combat, and also the first time traders depend on AI in an unprecedented way to map out the course of war."

Using AI to review 100 years of history

Using AI tools such as OpenAI's ChatGPT, Google's Gemini, and China's DeepSeek, one of the most notable advantages is the tremendous improvement in time management.

Jian Shi Cortesi, a fund manager at GAM Investment Management in Zurich, said that previously she might spend half an hour reading news from different sources, but now she can get a summary of the latest developments in the war in just a few seconds. The time needed to gather information on specific companies has also shrunk from days to a day or less. "It used to be like digging a hole with a shovel; now you're digging with a large excavator," Cortesi said. "The speed may have improved five-fold."

In addition, AI can almost instantly mine historical data, providing insights and context for forecasting future trends. Anna Wu, cross-asset strategist at Van Eck Associates Corp. in Sydney, used ChatGPT and Claude to review oil price surges triggered by wars over the past 100 years, and to identify which asset classes outperformed in each event. To enhance the usefulness of the answers, she asked AI to cross-check with other data points such as median inflation and global economic growth.

On more specific energy and shipping issues, Gustavo Pessoa, partner at Legacy Capital Gestora de Recursos Ltda. in São Paulo, said that as it becomes more critical for investment to assess the "virtual shutdown" of the Strait of Hormuz, AI makes some previously hard-to-access information instantly available.

"We use it for almost everything, from understanding ship types, to analyzing the elasticity of oil price to demand, even to estimating how many barrels of oil are needed to stabilize shipping flows."

"Erroneous outputs" still exist

Several respondents simultaneously cautioned that AI has shown errors in various fields, from game development to news content presentation. Bloomberg reported that a Bank of England policymaker warned that using AI in trading may amplify market shocks and reinforce herd behavior.

Michael Brown, senior research strategist at Pepperstone Group in London, pointed out that to use AI well, the prerequisite is still for participants to have a deep understanding of the situation itself; it is not a "silver bullet."

"Participants still need to truly understand the situation themselves to make the final trading decisions and to judge whether AI models have given information that we all know sometimes can be false."

Junior analysts face threats

As the efficiency of AI tools improves, entry-level research analyst positions may be at risk. Cortesi bluntly said she now no longer needs junior analysts, calling AI her "best" research assistant.

"I can ask AI: give me the key points of this company using Warren Buffett's method, and it will do so immediately," she said. "But if you ask a junior employee, he may not know what Warren Buffett's method is. So for these more complex requests, AI absolutely does better—and much faster."

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