As AI extends further into the economy and markets, it becomes harder to find a viewpoint that sits mostly outside its influence. That’s why I read with interest recent research from Bridgewater Associates on where the hedge fund sees AI heading.

Written by co-CIO Greg Jensen and AIA Labs Chief Scientist Jas Sekhon, the article makes several noteworthy points:

“[The launch of Gemini 3] marks the first time since the launch of GPT-3.5 three years ago that OpenAI doesn’t have a leading model. ... Google is now the clear leader in the AI race.”

“Google’s possession of another AI ecosystem not reliant on Nvidia’s GPUs creates a significant risk to Nvidia’s ability to sustain such high market share and gross margins.”

“The next phase will come when a major business outside of the AI ecosystem realizes that its entire business model is about to collapse... At that point, every business will have to spend existentially to adopt AI technologies.”

"Given the immense potential of AI technologies, we believe companies will spend whatever it takes to keep up ... Stock market corrections, or modest increases in credit spread, don’t change the underlying reality of this dynamic.”

“Google cracked the challenge... Our rough approximation is it appears to have used at least 2-3 times more compute than GPT-4o and GPT-5... and possibly an order of magnitude more."

"We think the boost to the global economy in the next two years is underappreciated in most markets... it is these developments that are likely to be the central driver."

The whole article is worth reading, but one other point to highlight is that we’re still so early in this AI world.

“Current frontier AI models are still not easy to work with, and require technical skills, subject matter expertise, and meaningful work to get large gains out of them. But with the capability jump that Gemini 3 brings, the challenge of getting more productivity out of LLMs has become more surmountable, and the point of widespread adoption is getting closer.”

Irene Aldridge wrote the book on high-frequency trading—literally, the Wiley textbook that's become the industry standard.

Now she says that AI agents are the next step in trading evolution.

Her new paper, "Agentic Artificial Intelligence in Finance: A Comprehensive Survey," argues that we've crossed a threshold from AI that predicts to AI that acts. She published the new research with her students from Cornell University, where she's been a visiting professor for the last nine years. 

AI agents are autonomous systems with objectives, memory, and the capacity to adapt—agents that decide what to trade, execute, and learn from the outcome. This differs from “traditional” AI which has typically relied on human-written code.

The practical result, she argues, is systems that mirror how trading floors actually work: one agent maximizing profit, another minimizing risk, a third handling compliance. "Each agent has a different objective, but together they make the whole stronger because they control each other's behavior."

In a recent conversation, we dive into what this means for the future of markets:

This interview has been edited for clarity and length.

Irene Aldridge:

Now, instead of having multiple [human] traders, you have multiple automated agents performing trader functions. Some agents always seek to maximize profitability. But that's not enough. You need agents to control the risk as well. So you have compliance agents that work alongside the trader agents to contain risk, just like on a traditional trading floor!

The idea of cooperating or even adversarial agents is not unique to trading. Networks of adversarial agents, specifically, "Generative-Adversarial Networks (GANs)" are fundamental blocks of the modern Artificial Intelligence models like ChatGPT. We are expanding and adapting AI models to closely imitate traditional financial structures.

Right now, AI trading agents have an advantage: they're mostly trading against humans, and humans are predictable. You can assume that humans who are trading are profit-maximizing—they're always trading only to maximize profits.

But that's about to change. As more agents come online, they'll increasingly be trading against each other—and agents don't behave like humans.

Part of the problem is that agents are designed to occasionally act randomly. To avoid getting stuck in patterns, they'll "go off on a random tangent just to do something completely wild, just to try something new." Other agents observe those random decisions and take them as input—which generates noise throughout the system. Now you have to distinguish what's random and what's not random. And it's really hard.

And what if someone configured their agent to destabilize something? How do you actually identify them and separate them from the rest of the agents that are profit-maximizing?

That's a real challenge. I think that should be regulators' biggest concern.

Matt Robinson: Have you ever seen an experiment where they said, "Regenerate this image of the Rock," and they did it like a hundred times? By the end of it, it was complete gibberish.

Irene Aldridge: You have a random number generator that creates arbitrary stuff. And then you have these discriminators who critique whatever decisions were made by the random number generator and adapt them to make them better. That's really the essence of large language models or agentic AI. You have some innovation always filtering through the system, so it's not static—it allows it to respond to new things.

Agents continuously intake all environmental feedback and any data they can find. That allows them to be very adaptive. Traditional systematic traders had rules and checked if the environment matched those rules. Agents try to classify the world by breaking it down into certain states—using what's called a Markov Decision Process—creating a map of where you've been and where you are now, and determining what action is best for that particular state.

Advertise on AI Street to reach a highly engaged audience of decision-makers at firms including JPMorgan, Citadel, BlackRock, Skadden, McKinsey, and more. Sponsorships are reserved for companies in AI, markets, and finance. Email me ([email protected]) for more details.

73% of wealth and asset management executives believe AI is critical for the future of their businesses, according to a survey of 500 executives at investment industry firms worldwide by research firm ThoughtLab, per Financial Advisor.

Executives from banks including JPMorgan and Wells Fargo predict that AI adoption will boost operational productivity but lead to job cuts as financial institutions leverage the technology to accomplish more with fewer employees, per Reuters.

Banks including Morgan Stanley, Citi, and Capital One are retraining tens of thousands of engineers through courses, videos, and communication-focused AI education, per Business Insider.

BNY is integrating Google’s Gemini 3 into its Eliza platform to deploy agentic AI across complex financial workflows while enforcing strict oversight and safety controls as Wall Street accelerates its use of AI systems, according to Business Insider.

A Bloomberg Intelligence survey of 151 senior financial-services employees finds that early AI adoption will raise headcount and operating costs through at least 2027 as firms focus on capability building rather than near-term cost cuts.

OpenAI, Anthropic, Block, and others are joining a new Linux Foundation group to make sure AI agents can work together instead of becoming a tangle of closed-off, incompatible systems, according to a press release.

LSEG will integrate its data and news into ChatGPT and give credentialed users access to its market content through a phased rollout, according to Reuters.

I know we’re all bombarded with hot takes on how AI is changing everything, but the reality is we’re still very early on in AI & enterprise adoption.

The president said he would sign an order that would eliminate a patchwork of state laws that have emerged in recent years, according to the NYTimes.

FINRA has placed generative AI and cyber-enabled fraud high on its 2026 regulatory agenda, warning broker-dealers and RIAs that emerging technology and long‑standing compliance gaps are converging into higher risk for investors, according to Investment News.

AI regulation in the U.S. remains limited, and its development is likely to advance at the same gradual pace seen in enterprise adoption.

I joined Thomas Li, CEO of Daloopa, on the company’s InDaloop podcast to discuss the current state of AI in finance. One point that Thomas brought up that stuck with me is to not think about AI as what it produces or its output but to how it operates specifically by predicting the next token. And if you have a use case that doesn’t fit into this paradigm it’s not going to work. For example, an LLM will never be good at performing mathematical calculations.