AI Can Empower the Financial Industry

 

“The release of Chinese artificial-intelligence company DeepSeek's R1 model in January 2025 renewed debate over the technological rivalry between the U.S. and China. Less widely noted is that DeepSeek began as a spinoff from a quantitative trading firm's application of early AI techniques to financial markets. That connection highlights the immense promise of AI applications to the financial industry. It should remind us that in this domain, the U.S. has an unparalleled competitive advantage because of the depth of its capital markets, the talent that trades in those markets, and the technology they run on. So how do we use that advantage to drive the next wave of AI developments?

 

As in other industries, AI is already streamlining antiquated and inefficient workflows in finance, for example through tools that extract terms from dense financial agreements and develop code. But those gains, though useful, are incremental. The more consequential applications of AI in finance involve improving the core functions of markets themselves: pricing assets, measuring risk, detecting shifts in the economy, and managing volatility. These are areas where AI's ability to detect subtle patterns in large volumes of data can be transformative.

 

Consider how the Black-Scholes pricing model, an innovative mathematical formula introduced in 1973, spawned a whole new market for derivatives and made them widely accessible. AI is likewise a set of mathematical tools that can expand what markets do. A good illustration comes from some of the new trading markets that have recently gained prominence, such as prediction markets and cryptocurrencies, where pricing is complex, supply and demand fluctuate rapidly and continuously, and uncertainty is pervasive. Modern AI will be necessary to supply the algorithms that offer fair prices to participants in these markets, making them more liquid, stable, accessible and safe for everyone.

 

To make the most of AI in financial markets, the U.S. needs to invest not only in the data and technology that power these tools, but also in a regulatory system that doesn't hold back responsible innovation. One major hurdle is the expectation that we must fully interpret every decision an AI makes -- for example, why it predicts that a stock will go up or down. As these systems become more advanced, their internal logic becomes harder to trace. They often operate as "black boxes" -- we can see the results but not the precise reasoning behind them. If we insist on perfect transparency, we risk missing out on useful tools that can improve how markets function.

 

A more practical principle is controllability. This entails the ability to monitor models effectively, set explicit boundaries on their behavior, and intervene when necessary. Rather than requiring complete interpretability, regulators should focus on whether these models can be kept within safe limits and prevented from causing harmful outcomes. This approach allows innovation to continue while protecting the stability of markets.

 

Yet even with strong infrastructure and sound regulation, significant challenges remain. Financial markets aren't static environments governed by fixed rules. They are nonstationary adversarial systems where behavior changes constantly in response to political shocks, technological shifts and unpredictable human decisions. Current AI models often struggle to respond to such volatility.

 

Therefore, while applying AI to finance is important, less appreciated is how finance can, in turn, help advance AI. These nonstationary environments offer rare opportunities for rigorously testing AI, combining strong financial incentives with daily feedback from price movements and a clear objective: maximizing profit within given constraints. When the financial world undergoes sudden regime changes, as it regularly does, it remains unclear how models will adapt in real time. Imagine building a chatbot whose users suddenly begin speaking a completely new language; the system would need to continue functioning while gradually learning the new rules and structure. This kind of disruption is routine in financial markets as expectations change, risk is repriced, and secular trends emerge. Existing AI models struggle with the adaptive, long-term planning it demands.

 

Future solutions to artificial general intelligence will likely require solving this adaptive challenge. Humans spend their days navigating new surprises, shifting goals and evolving strategies. AGI will need to do the same. For AI researchers working on these fundamental problems, finance offers a testing ground where the solutions being developed to handle nonstationary markets may prove to be the linchpin for achieving the next level of machine intelligence.

 

This is one reason the U.S. must continue to attract and cultivate world-class mathematical talent. Quantitative finance has long been a magnet for mathematicians precisely because the field poses some of the most intricate mathematical problems used anywhere in industry. Mathematicians uncover patterns in the markets, finding ways to make them more efficient and revealing new patterns for others to explore. All this contributes to a virtuous circle of innovation and efficiency. Preserving this ecosystem through academic investment and open pathways for global talent is essential if the U.S. is to remain the world's center for financial innovation.

 

Finance remains one of the few environments where real-world data arrives continuously, consequences are immediate, and the incentive to compete and innovate is unending. If the U.S. chooses to take advantage of its dominance in capital markets through investment, thoughtful regulation and sustained support for the people who drive these breakthroughs, it can ensure that Wall Street pioneers the next generation of AI systems.

 

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Mr. Schmidt is chairman of Relativity Space and a former CEO of Google. Mr. Tsementzis is founder and CEO of hLevel, an AI startup, and a former head of Applied AI at Goldman Sachs.” [1]

 

1. AI Can Empower the Financial Industry. Schmidt, Eric; Tsementzis, Dimitris.  Wall Street Journal, Eastern edition; New York, N.Y.. 19 Nov 2025: A17.