Today's market volatility can be overstated when set against previous episodes, but the drama of daily headlines can still be unsettling. This news service asked Livermore how AI helps quants navigate such conditions.

"In volatile periods the information flow accelerates and the value of being able to process it quickly and dispassionately rises. Systematic processes do not panic, and that is an advantage," he said. "The caveat is that models trained predominantly on calmer regimes can be at their least reliable precisely when conditions change. So, our approach is not to let the tools run harder in a crisis but to lean more heavily on the risk framework and on human oversight of what the signals are telling us and why."

What happens to returns?

"Judging the difference AI tools make to returns in any specific environment is difficult, and I would be cautious about any claim that puts a number on it. A tool's contribution cannot be separated cleanly from the signals it helped build, the risk framework around them, and market conditions themselves, and the period over which these tools have been in use is too short to draw conclusions," Livermore said.

There are various claims made about how well AI-powered investments fare. For example, in 2025, a report (aistreet.beehiiv.com, March 2025, Bloomberg) said Bridgewater Associates' $2 billion AI fund is, in the words of its CEO Nir Bar Dea, generating "unique alpha that is uncorrelated to what our humans do.” The article did not disclose specific figures. Run by co-CIO Greg Jensen, the fund combines Bridgewater's proprietary technology with plans to include models from OpenAI, Anthropic, and Perplexity. Bridgewater formed its Artificial Investment Associate (AIA) Labs division in 2023.

In another case, that of M&G (Lux) Global Maxima Fund, M&G was quoted saying in February 2025 that this portfolio, which blends AI stock-picking recommendations with human oversight, beat its benchmark, the MSCI ACWI Net Return Index, over the five years from inception. 

In general, however, it appears that while results from some cases appear promising, it may be premature to uncork the champagne just yet.

Kill or cure?
There is also the open question of whether AI is part of the volatility story as well as a way of handling it.

"I'd stress this is a hypothesis rather than something we can demonstrate. If many investors interpret the same event through similar tools, they may reach similar conclusions at similar times, which could in principle amplify market moves rather than dampen them. That concern would be greatest where users lack the expertise to evaluate the output and defer to it rather than to their own judgment. Whether any of today's volatility reflects this, we can't say. It is one more reason we view AI as a tool for testing hypotheses that can be defended on their own terms, rather than as a source of market views," Livermore said.

Looking ahead, Livermore predicted that "nearly every manager will describe itself as using AI, so the label will stop differentiating and process discipline will matter more."

He argued that easily replicated applications, such as asking an AI tool which stocks to buy, will see whatever edge they have decay quickly as they become commoditised; the durable advantages will sit in proprietary ideas, in how signals are combined, and in how the research function is organised.

"The research cycle continues to compress. That is a productivity gain, but it also raises the risk of explaining the past perfectly and predicting nothing, so the firms that benefit will be those that tighten their testing standards as throughput rises rather than relaxing them," he said. "I expect the winning model to be collaboration: humans generating and judging hypotheses, machines testing them at a scale that was not previously possible."