Stripe, which provides the infrastructure for internet payments, agreed to buy OpenRouter, which provides infrastructure for using AI models.

OpenRouter evaluates AI requests and routes them to models based on price, speed, reliability and the complexity of the task.

I’ve heard repeatedly from financial firms that compute expenses are exploding as AI moves from isolated experiments into production. One hedge fund was spending about $24 million a year on LLMs. About one-third of that spending was wasteful.

A financial firm might use a smaller model to classify documents, a frontier model for complex research and a specialized system for extracting numbers. Prices and capabilities also change quickly. Without a routing layer, each application has to make those decisions itself—or default to an expensive model for everything.

I previously called the person overseeing this expense a “token master”: someone responsible for allocating compute budgets across departments and deciding which tasks warrant the most capable models.

OpenRouter tackles one part of the problem by routing each request to the model offering the best balance of capability, cost, speed and reliability.

Bridgewater Leaders Want to Tax AI Tokens

Hedge funders rarely call on lawmakers for more regulation. But Bridgewater’s Greg Jensen and Nir Bar Dea did just that in a New York Times opinion essay last Friday, urging a new tax on AI use and sweeping new regulations on the technology.

Bridgewater’s internal analysis estimates that AI could displace 18% of current U.S. jobs within five years. Jensen and Bar Dea warned that concentrating the benefits among companies and investors could provoke a political backlash.

The two proposed a tax on companies’ use of AI tokens, which they described as the closest equivalent to wages for machine labor. They argued that the current tax system makes replacing workers with AI comparatively attractive. That revenue could lower income taxes on workers and help the government acquire shares in leading AI companies.

They also called for strict safety standards covering models in development, publicly released systems and their use after deployment. The executives acknowledged that Bridgewater would be disproportionately affected by the policies they recommend.

Also in Bridgewater news:

Bridgewater Co-CIOs Back AI Startup for Stockpickers

Bridgewater co-CIOs Greg Jensen and Karen Karniol-Tambour have backed Multiplier, an AI startup founded by one of the hedge fund’s former investors.

The pair participated in a $6 million seed round led by Lux Capital. Y Combinator, Fortress Chairman Pete Briger and several of Multiplier’s hedge fund clients also invested.

Multiplier, previously called WithAI, was founded by former Bridgewater investment associate Ian McInnis, who worked under Jensen on large language model projects. Its coding agents filter news, make charts, monitor stocks and update investment memos. The platform connects those agents to a fund’s structured data, documents and internal research process.

Multiplier hosts the system on client infrastructure and interviews employees to teach the agents how each firm works. The company says it is live with six hedge funds and that users spend more than four hours a day on the platform.

CFTC Seeks Input on AI Compute Futures

The Commodity Futures Trading Commission is seeking public comment on proposed futures contracts tied to the computing power used to develop and run AI systems.

CME Group, Intercontinental Exchange and Architect Financial Technologies have announced plans to offer the contracts, subject to regulatory approval. They could allow AI developers and data-center operators to hedge changes in the cost and availability of compute, much as companies use energy derivatives to manage fuel costs.

Computing costs vary by chip, location, electricity supply and other infrastructure. The CFTC is asking whether the market needs common standards, including price indexes for settling contracts.

Compute is increasingly being treated as a scarce commodity as AI companies spend heavily on chips and data centers. A futures market could help them lock in prices, but only if the contracts reflect the capacity they actually use.

Google Wins Auction for Spirit Data to Improve AI

In simplified terms, AI scaling laws say more data + more computing power = better results from AI models.

The more computing power angle is well covered in the media. In May, I wrote:

Global tech companies have announced about $740 billion in AI-related spending for 2026, according to Morgan Stanley. That’s about what it cost to build the entire U.S. interstate highway system (in today’s dollars), which took 30+ years.

But the more data storyline is less covered. As we’ve written (and many others have covered) before pretty much all the publicly available data has been hoovered up. So much so that frontier AI labs are hiring experts, like ex-bankers and analysts, to create new data.

There are still vast stores of private data. Getting access is a slog. Labs need to establish who owns it, secure permission to release it, clear privacy and confidentiality checks, agree how it can be used and protected, and settle on a price.

That scarcity is creating a market for data left behind by failed companies. From Bloomberg Law:

Google Aims to Boost AI With Purchase of Spirit Airlines Data

Here’s the short of it:

Google won the bankruptcy auction with a $10 million bid for Spirit Airlines’ deidentified corporate data, including 100 million emails, 500 million Teams messages, billions of pricing and transaction records, and 30 million lines of code. Google says the cache will help improve its products and AI models. Mercor is the backup buyer after bidding $7.5 million.

TLDR

Corporate data that once had little value outside the company that created it is becoming an asset AI developers will bid for. Google’s $10 million bid for Spirit’s archive shows how far they will go to get it—and why investors should scrutinize the data behind an AI tool as closely as the model itself.

Share AI Street

ROUNDUP

What Else I’m Reading

• Hudson River Trading Posts Record $11.4 Billion Trading Revenue | BBG

• Jane Street Suffers Loss of About $15 Billion Following Troubles at Situational Awareness | WSJ

• Etched raises $700 million at $21 billion valuation | Quartz

• SpaceX Pitches Grok to Hedge Funds | Hedge Fund Alert

• AI Is Starting to Define Bank Internships as Much as Long Hours | BBG

• Optiver to take majority stake in power and gas trader Northpool | PR

Upgrade to paid

SPONSORSHIPS

Reach Wall Street’s AI Decision-Makers

AI Street reaches institutional investors, C-suite executives and Big Law attorneys at firms including JPMorgan, Citadel, BlackRock, Skadden, McKinsey, and more. Sponsorships are reserved for companies in AI, markets, and finance. Email sponsors@ai-street.co for more details.

CALENDAR

Upcoming AI + Finance Conferences

• 8th Annual Artificial Intelligence in Financial Services Conference – September 8–9 • London

  Senior financial-services event focused on real-world AI adoption across banking, insurance and fintech, including generative AI, risk management, fraud, compliance and governance.

• Cornell Financial Engineering Manhattan 2026 Future of Finance & AI – September 11 • New York

  Hedge funds, banks and quants come together to explore how AI is changing finance, share practical lessons and exchange ideas on financial engineering.

• Machine Learning in Quantitative Finance – September 14–16 • New York

  Quant-focused conference on how financial institutions are applying machine learning across trading, portfolio construction, risk and investment workflows.

• AI and Machine Learning in Quant Finance Conference – September 16 • Online

  CQF Institute event focused on AI and machine-learning advances in quantitative finance, with discussions geared toward practitioners and researchers.

• AI-Native Banking & Fintech Conference – September 29 • Salt Lake City

  Banking and fintech conference centered on agentic AI, with speakers from banks, fintechs and regulators discussing production use cases, risk, compliance and operations.

Thanks for reading!

I’m always happy to receive comments, questions, and feedback.

• Connect with me on LinkedIn, or

• Send an email to matt [at] ai-street.co

Manage how often you receive AI Street

Update Email Frequency

FUNDRAISING

Despite it being mid-August, lots of fundraising news this week:

UBS, HSBC Invest in Financial AI Startups

Over the past year and a half, banks have started buying stakes in the AI startups trying to automate their employees’ work.

This week, UBS backed Finster AI and HSBC invested in Model ML. FT Partners, which led Model ML’s $75 million Series A last year, is rolling out the platform firmwide. JPM invested in Rogo in April 2025, followed by Wells Fargo in January 2026.

These companies sit between the models and the bank. Their software connects to internal data, applies permissions and turns the output into something a banker can use: a presentation, financial model, research note or client briefing. Banks can swap the underlying model. Replacing the system wired into their data and workflows would be much harder.

Banks continue to build their own AI systems and sign deals with Google, OpenAI, Anthropic and other large technology providers.

TL;DR

The investments suggest banks are beginning to treat AI as an operating layer for financial work. The models underneath may be replaceable. The system that connects those models to the bank’s data, permissions and workflows may not be.

Quartr Raises $18M to Structure Company Data for AI

Quartr, a Stockholm-based provider of public-company research data, raised $18 million to expand its products and global coverage. Existing investor Altos Ventures led the round and became Quartr’s largest shareholder. The financing follows raises of $6 million in 2024 and $10 million last year.

The company collects earnings calls, transcripts, filings, presentations and other investor-relations material that remains scattered across corporate websites, conference lines and webcast providers. It links the audio, documents, speakers and company events in a common dataset, allowing analysts and AI systems to search across the material and trace results back to the original source.

Named API customers and partners include Perplexity, Yahoo Finance, Rogo, RavenPack and TradingView. The company says it now covers more than 15,000 public companies across over 65 markets and serves more than 800 financial institutions and technology companies.

ICYMI

The Financialization of “Compute”

Back in July 2025, I wrote:

New commodities often become financialized once volatility and market demand make risk hedging necessary.

That moment may be arriving for compute, with some well-known Wall Street traders believing compute demand will rival and then exceed the demand for oil.

Back in May, I highlighted this quote from DRW founder Don Wilson, who has a history of bringing new markets to the mainstream.

“The total dollars spent on compute will, over the next 10 years, exceed total dollars spent on oil.” DRW’s Don Wilson to the WSJ.

He invested in Silicon Data, a company that provides GPU pricing data and benchmarking services to hedge funds, banks and AI firms.

Just about a year later, Silicon Data raised $30.5 million in the initial closing of a Series A led by the Valor Atreides AI Fund. The round also drew backing from CME Group and a mix of trading firms, asset managers and technology investors. The New York-based company didn’t disclose its valuation.

CME plans to launch cash-settled futures tied to GPU rental costs, subject to regulatory approval, using Silicon Data’s benchmarks as the reference price.

Silicon Data publishes nine indices covering GPU rental prices and large-language-model costs, along with data on supply, utilization and pricing. The company says its platform has more than 1,000 registered users across semiconductor manufacturing, AI development and financial services.

Part of the new capital will fund SiliconMark, a system for measuring the performance of GPU infrastructure. Clusters built with the same chips can produce different results depending on their networking, configuration and the way their components are connected.

Silicon Data wants to standardize those performance differences, giving market participants a way to measure—and eventually hedge—the risk that a cluster delivers less computing power than expected. The company says the measurements could also support the physical delivery of computing capacity.

The remaining capital will go toward additional pricing benchmarks, institutional data products and infrastructure for derivatives, insurance and credit markets.

Silicon Data previously raised a $4.7 million seed round in March 2025.

AI at Two Sigma, DE Shaw and Susquehanna

AI leaders from some of Wall Street’s largest quantitative investment firms discussed how agentic AI is changing research, model evaluation, compute spending and hiring during a panel at Berkeley RDI’s Agentic AI Summit. The “Agentic AI in Capital Markets” speakers were:

• Jeff Wecker, chief technology officer at Two Sigma

• Jen Allum, senior vice president and co-head of generative AI at D.E. Shaw

• Ali Nazari, head of deep learning research at Susquehanna International Group

• Li Deng, chief AI officer at Vatic Investments and former chief AI officer at Citadel

“It’s becoming very clear, as we look at various metrics, that those who adopt sooner and faster are clearly becoming much more productive.”

—Jeff Wecker, Two Sigma, 09:21–09:31

“The hardest part didn’t disappear; it just moved. I found myself, instead of trying to generate ideas, trying to figure out which of the ideas it gave me were worth my time. These bottlenecks, these challenges, they don’t just disappear. They just move.”

— Ali Nazari, Susquehanna, 03:56–04:21

“Let lots of experiments run, be maximalist in the policy — build and buy and everything in between — and then see what catches fire and think about where we scale that, for whom and how.”

— Jen Allum, D.E. Shaw, 12:52–13:05

“We are on the eve of potentially an explosive use of compute demand… If behind those 1,800 employees there are a quarter of a million agents, all making their own compute demand, we really have to think about the implications and the return on investment.”

— Jeff Wecker, Two Sigma, 17:20–17:47

“If you want to bring that kind of system into the finance world, you are going to fail, because in financial markets, every time you put an order into the market, people react differently. It changes the environment much more than in the high-tech world.”

— Li Deng, Vatic Investments, 30:02–30:17

“Traditionally, we try something, we make a mistake, we learn from that mistake, and that’s how judgment is developed. Now that AI does most of the experimentation for us, the question is how that judgment is going to be developed.”

— Ali Nazari, Susquehanna, 20:42–21:01

ROUNDUP

What Else I’m Reading

• How AI is changing careers in electronic trading and HFT | eFinancialCareers

• Five Takeaways From Zuckerberg’s 6,500-Word Manifesto on AI | BBG

• Ex-Nubank CTO raises $85m for AI-native wealth advisory platform | Finextra

• Students created AI apps for the Norwegian Oil Fund | E24

• Prodigy Research: The Frontier AI Trading Research Lab | Y Combinator

• Some US adults are using AI for financial guidance but few trust it: Gallup | AP

• The new Wall Street career path | Business Insider

Share AI Street

This Week in AI Street

INTERVIEW + PODCAST

Upgrade to paid

SPONSORSHIPS

Reach Wall Street’s AI Decision-Makers

AI Street reaches institutional investors, C-suite executives and Big Law attorneys at firms including JPMorgan, Citadel, BlackRock, Skadden, McKinsey, and more. Sponsorships are reserved for companies in AI, markets, and finance. Email sponsors@ai-street.co for more details.

CALENDAR

Upcoming AI + Finance Conferences

• 8th Annual Artificial Intelligence in Financial Services Conference – September 8–9 • London

  Senior financial-services event focused on real-world AI adoption across banking, insurance and fintech, including generative AI, risk management, fraud, compliance and governance.

• Cornell Financial Engineering Manhattan 2026 Future of Finance & AI – September 11 • New York

  Hedge funds, banks and quants come together to explore how AI is changing finance, share practical lessons and exchange ideas on financial engineering.

• Machine Learning in Quantitative Finance – September 14–16 • New York

  Quant-focused conference on how financial institutions are applying machine learning across trading, portfolio construction, risk and investment workflows.

• AI and Machine Learning in Quant Finance Conference – September 16 • Online

  CQF Institute event focused on AI and machine-learning advances in quantitative finance, with discussions geared toward practitioners and researchers.

• AI-Native Banking & Fintech Conference – September 29 • Salt Lake City

  Banking and fintech conference centered on agentic AI, with speakers from banks, fintechs and regulators discussing production use cases, risk, compliance and operations.

Thanks for reading!

I’m always happy to receive comments, questions, and feedback.

• Connect with me on LinkedIn, or

• Send an email to matt [at] ai-street.co

Manage how often you receive AI Street

Update Email Frequency

Milos Maricic has a unique view into how the world’s largest asset owners are using AI.

Maricic and his team at SPEC Research work with sovereign wealth and pension funds as they figure out where the technology actually helps with investing.

Over the past six months, his team has also tried to answer a straightforward question: What are the world’s largest sovereign wealth and pension funds actually doing with AI?

They reviewed more than 1,000 public sources, incorporated information supplied by institutions and contacted the funds directly to assess 27 sovereign wealth funds and 26 pension funds. And today they released their SWF AI Maturity Index report.

The big takeaway:

The world’s largest sovereign wealth and pension funds are rapidly adopting AI, but none can show that it has made them money.

Or as CPP Investments CEO John Graham summed up in the report: “Will [AI] help us make faster decisions? Yes. Will it help us make better decisions? TBD.”

What Maricic and his team found was a wide gap between adoption and proof.

Singapore’s GIC has built a Virtual Investment Committee that includes an AI devil’s advocate trained on 44 years of internal deal data. The agent argues against an investment before the real committee sees it. CPP Investments has issued more than 2,100 Microsoft Copilot licenses across a firm of roughly 2,100 people, with 73% actively using them.

The strongest claim comes from Norway’s sovereign wealth fund, which says AI saves it about $100 million a year in trading costs. The fund has not published a methodology attributing those savings to the technology. More broadly, most funds do not track which parts of an investment decision came from AI and which came from people, making it difficult to tell whether AI improved a decision or simply made the process faster.

I spoke with Maricic about how sovereign wealth and pension funds are using AI. We talked about what sovereign wealth and pension funds are actually doing with the technology, why managers’ claims of AI-generated returns often fall apart under scrutiny, and why the growing volume of AI-generated research is creating another problem: the cost of verifying it.

I turned this interview, with the help of AI, into a new episode of my Alpha Intelligence Podcast. It has been dormant, but I plan to share more interviews going forward in this format, alongside the written transcripts.

Matt: How long have you been putting this report together?

Milos: About six months now. It is the first thing of its kind, so it has been ramping up really fast. I am just looking at how the response from the institutions has changed. When we launched the first one six months ago, it was like, “Do you want to comment on this?” And they were like, “No. Who are you? Just get out of here.”

Now it is more like, “Ah, yeah, sure. Let’s have you talk to our AI team. Let’s calibrate that.” So it has been getting some pickup.

ICYMI

Matt: So you are reaching out to these funds directly, saying, “Hey, we are trying to get a better sense of how you are thinking about this technology?”

Milos: Yeah. There are three things. First of all, the baseline job is to go through everything that is publicly available. The index has a database of more than 1,000 public sources.

Some of these institutions have internal information about themselves or their clients, and they allow us to use it specifically. Sometimes they do not, and that is fine. The third layer is that we reach out to them and say, “Hey, we are going public with this in a couple of weeks. Please help us understand what is going on.” More often than not, they come back, so we feel like we have a relatively robust picture of where the use is. Is it perfect? No. But this is a super-fast-moving field.

Matt: Right. And there are not many other resources looking at it like that.

Milos: That is the thing. Sovereigns are a niche to begin with, and even those who tackle that niche do not necessarily talk about AI use within it. So it is a niche within a niche.

Matt: What made you start it?

Milos: My last company was an AI-driven capital-allocation platform that got acquired by a sovereign.

So a lot of sovereigns and pensions were clients in the latter stages of that business. Business with sovereigns is a who-you-know business. It is very relationship-driven. We felt it was interesting for the team to continue in the same direction. We also felt we were doubling down on the advantage of being a little bit of insiders, knowing the right people, and so on.

Matt: I think of your role as somewhere between the vendors and all these solutions. If you are working at a sovereign wealth fund or you are a money manager, all this AI stuff can be too much.

So you are helping them sort out what is actually working, right?

Milos: Exactly. If it is overwhelming for you and me just trying to follow what is going on, it is even more overwhelming for them because they have all that capability. But what do you do with the capability?

Even if you have bought the story that AI is going to be big and you want to be part of it, what do you do? Where do you deploy? Do you deploy in the biggest, most inflated-valuation companies out there? Do you look for the nuggets? Do you go through managers?

If we focus on the manager side, a lot of what allocators do is manager selection. There is an explosion of managers coming to them and saying, “We use AI. Look at our backtests. Look at our track record. It is all amazing.” Allocators are prudent about it, and they have every reason to be, because a lot of this is just bogus.

Some of it is interesting, but you have to distinguish between the two. One of the things we produce is a framework called the SPEC test that we coach allocators to use with managers. It breaks down their AI claims in a structured way to understand what holds water and what does not.

One priority for the second half of this year is working with the Institute for Sovereign Investors to take the SPEC framework and turn it into a more industry-wide due-diligence framework. It is sorely needed. Right now, what is happening within allocators around AI claims is completely ad hoc.

Matt: What are fund managers saying they are doing with AI?

Milos: The most typical thing is: “We backtested this, it worked amazingly well, and our Sharpe ratio ended up being three.” Then you look into the backtest and realize the model had a little glimpse of the future. It actually saw where the market was going.

Another thing is riding heavily on the track record of the managers themselves. But there is loads of research showing that, if you use AI to do fully automated investing, the aspects that make up a manager’s track record are what AI can replicate most easily. The more idiosyncratic parts are more difficult to pick up.

When we make managers go through the framework, we ask them to decompose their claims. We probe them. We say: “Tell me about a time when the model was wrong. What happened? How did you adapt it? How did you fix it going forward?” Or: “Tell me about a time when it was overruled by a human.” That gives you a window into their internal process, so you can understand where the boundary is between human capacity, human oversight, and AI. The framework has about 14 questions that we drill into.

Matt: A sovereign wealth fund will call you up and say, “XYZ reached out. I want you to take a look. Do you know about them? Maybe you already do.”

Milos: Yeah, exactly.

Matt: What are some red flags you have seen?

Read more

I’m traveling to Rhode Island to visit friends and family for a couple weeks (if you’re in the area, reach out!). I plan to keep AI Street publishing as usual while stateside.

Bots now generate more web traffic than humans. Cloudflare had expected to reach this milestone next year, but it happened in June.

While humans have limited time and attention, agents can theoretically run 24/7 and juggle multiple webpages at once.

Take this dynamic into markets and you’re likely going to see a lot more trading. We discussed this trend earlier this year: when Italy banned ChatGPT for a month in 2023, investors narrowed their trading to well-known companies, and once ChatGPT came back, trading broadened out again.

And in June, I wrote:

I’m having a hard time seeing trading not marching higher and permeating into more markets given current AI trading trends. It didn’t make a lot of economic sense to spend limited human time researching smaller markets. Now it does.

Continuing this trend, here’s Bloomberg with a story on how retail traders are leveraging AI for more sophisticated trading strategies.

The article highlights a couple points we’ve covered here along with nice anecdotes.

1. AI is (obviously) not a magic money-making machine.

2. Crowding is a real risk.

   1. The article quotes HFT Irene Aldridge, who I interviewed here, saying “AI models are parsing the same data, using very similar models and outputting very similar results to everyone,” she said. “We’re going to see a lot more volatility as a result of this.”

3. AI could amplify losses.

   1. The article quotes AI and finance researcher Alicia Vidler, who I also interviewed, on the risks: “Lots of trading systems are not designed for volatile environments,” she said. “You have much more risk in a down market.”

4. False AI confidence needs to be mitigated.

   1. “It will answer incredibly confidently on anything you ask regardless if it knows it or not,” Joel Rieger, a former software-sales executive, told Bloomberg.

   2. This is a topic I’ve covered here: AI Replicates Human Investor Biases.

All that said, the rollout is moving quickly. AI-agent trading may be a novelty today, but I suspect it will become a standard offering.

AI Agent Trading

Since March, four retail brokers have launched or begun rolling out AI-linked trading tools, ranging from autonomous execution to AI-assisted order drafting.

• Public.com launched native agents that can execute user-approved workflows across stocks, options and crypto.

• Moomoo released API Skills, connecting outside agents to its market-data and trading infrastructure.

• Robinhood introduced dedicated accounts where third-party agents may execute trades without direct approval of each transaction.

• Interactive Brokers let customers connect Claude and other MCP-compatible AI tools, although clients must review and submit every order.

In about two months, more than 100,000 people had connected an agent and opened a dedicated Agentic account at Robinhood, CEO Vlad Tenev said on an earnings call last week. Robinhood said assets and trading volumes were also growing, though it did not disclose either figure.

In April, Moomoo, the trading platform owned by Futu Holdings, launched tools that let customers connect AI agents to analyze markets and prepare trades. Since then, the number of traders using its US API has more than doubled. (The figure includes all API users, not only those trading with AI agents, so it is a proxy for adoption.)

“It used to require meaningful coding chops to access trading APIs and do real volumes,” David Rodríguez, product lead at Moomoo US, told me. “The learning curve has shifted significantly, and we’re seeing the results unfold in real time.”

ROUNDUP

What Else I’m Reading

• Millennium Partners With Anthropic to Develop AI Risk Analyst | BBG

• JPM CEO Dimon leads new cross-industry effort to tackle AI risks | Reuters

• AI Skills More Valuable Than MBAs, 86% of Finance Executives Say | BBG

• The Investing Heavyweights That Backed Situational Awareness Before It Blew Up | WSJ

• Google shakes up AI leadership as DeepMind chief shifts role | Reuters

• Four Top Google A.I. Researchers Form New Start-Up | NYT

• Rabobank to invest €2 billion in building IT framework for AI | Finextra

• Cloudflare gives AI agents an identity and a wallet | Finextra

• Wall Street hit by wave of AI-powered ’vishing’ cyberattacks | Yahoo

• Goldman Sachs asset arm forms AI investing platform, memo shows | Reuters

• Xi Chen joins JPM as MD, Head of AI for Electronic Markets | LinkedIn

Share AI Street

This Week in AI Street

Upgrade to paid

SPONSORSHIPS

Reach Wall Street’s AI Decision-Makers

AI Street reaches institutional investors, C-suite executives and Big Law attorneys at firms including JPMorgan, Citadel, BlackRock, Skadden, McKinsey, and more. Sponsorships are reserved for companies in AI, markets, and finance. Email sponsors@ai-street.co for more details.

CALENDAR

Upcoming AI + Finance Conferences

• 8th Annual Artificial Intelligence in Financial Services Conference – September 8–9 • London

  Senior financial-services event focused on real-world AI adoption across banking, insurance and fintech, including generative AI, risk management, fraud, compliance and governance.

• Cornell Financial Engineering Manhattan 2026 Future of Finance & AI – September 11 • New York

  Hedge funds, banks and quants come together to explore how AI is changing finance, share practical lessons and exchange ideas on financial engineering.

• Machine Learning in Quantitative Finance – September 14–16 • New York

  Quant-focused conference on how financial institutions are applying machine learning across trading, portfolio construction, risk and investment workflows.

• AI and Machine Learning in Quant Finance Conference – September 16 • Online

  CQF Institute event focused on AI and machine-learning advances in quantitative finance, with discussions geared toward practitioners and researchers.

• AI-Native Banking & Fintech Conference – September 29 • Salt Lake City

  Banking and fintech conference centered on agentic AI, with speakers from banks, fintechs and regulators discussing production use cases, risk, compliance and operations.

Thanks for reading!

I’m always happy to receive comments, questions, and feedback.

• Connect with me on LinkedIn, or

• Send an email to matt [at] ai-street.co

Manage how often you receive AI Street

Update Email Frequency

“When will machines write the rules, not just support the rules?” Jensen recalled asking himself in the early 2010s. “When will their intuition actually be better than human intuition?”

At the time, the technology could do “great pattern matching,” Jensen said, but “there was nothing like intuition.” The pieces he believed an artificial investor would need—reasoning, ways to diagnose its conclusions, and methods for working with limited data—had not yet come together.

So Jensen went looking for them.

That search led him to OpenAI, then a little-known nonprofit that would become the face of the AI boom, where he became an early investor around 2016. Later, after a group of researchers he knew left OpenAI to start a new company called Anthropic, he wrote the company’s first outside check.

By late 2022, the year ChatGPT went mainstream, Jensen thought the missing pieces were finally coming together. The following year, Bridgewater created AIA Labs, short for Artificial Investment Associate, a separate research and investment unit tasked with building what the firm calls an “artificial investor.” It started with six people.

Bridgewater kept the group apart from Pure Alpha, its flagship macro strategy. The separation was intended to give the team room to rebuild the investment process around AI—and to keep the new systems from simply reproducing the views Bridgewater’s investors had already developed.

“I don’t want a copy of us,” Jensen said. “We’re so flawed. We’re so bad at so many things. I want something better than us.”

The assignment was broader than finding a new way to analyze data. AIA was supposed to take on more of the work an investor performs: gathering information, developing an explanation for what was happening in an economy or market, testing that explanation and turning it into an investment view.

AIA was not built around a single model or trading strategy, according to a person familiar with the effort. Instead, it combines Bridgewater’s causal time-series models with specialized and frontier language models, along with systems designed to test and diagnose their outputs. The broader goal is to generate insights across markets, economies and trading strategies.

Bridgewater tested the strategy with its own money first, beginning with roughly $100 million inside Pure Alpha in late 2023. The firm opened it to outside investors in July 2024, attracting almost $2 billion.

In March 2025, Bridgewater CEO Nir Bar Dea said the fund was producing “unique alpha that is uncorrelated to what our humans do.”

Today, AIA is no longer a six-person experiment. The unit has grown to more than 75 scientists, engineers and investors, according to the person familiar with the effort. The strategies now manage about $4.5 billion. AIA Macro has returned 11.3% annualized since inception, with returns that have shown low correlation with markets and other managers, including Bridgewater’s Pure Alpha.

Bridgewater has started using AIA’s systems inside Pure Alpha to accelerate research and systematize investment ideas, the person said.

A Bridgewater spokesperson declined to comment on the hedge fund’s AI efforts.

Read on for how Bridgewater went from writing investment rules by hand to letting an artificial investor manage billions.

Read more

AI and Alpha: Why Technology Alone Won’t Be Enough

Andrew Chin, chief AI officer at AllianceBernstein, published a piece this week that goes through the history of technology in the investment process.

Chin highlights how spreadsheets, the web and alternative data each gave early adopters an edge before eventually becoming standard tools available to everyone. AI is on the same path.

Eventually, everyone will have access to roughly similar models. What separates firms will be the judgment they bring to them: what data they focus on, what questions they ask and the viewpoints they build into the technology. We highlighted this earlier this month when Bridgewater trained an AI model to think like one of its investors.

AI is an amplifier. If you’re bright, these tools make you that much more effective. You’re able to scale what you already do well. If you’re not so bright, or maybe just a bit careless, well, that scales too.

SPONSOR

The earnings headline was in every feed: options had priced a $190 billion swing on the print.

Microsoft asking Amazon and Google for cloud capacity help, four law firms circling with shareholder-suit deadlines, and the launch of its new cyber-defense AI models were in the trade and legal press; the positioning signal, scattered across outlets no single feed carries.

Seltz returns all of it, raw. Full articles, first-person reporting, within the hour of publication. No summaries, no sentiment scores. Your models do the distillation better than any vendor, on input nothing has influenced.

Search any name across any day in history the same way. That’s how teams build and test a signal before they trade it live. Backed by B Capital and Speedinvest.

Want the full picture on every name you trade? Sign up for a demo — bring 10 questions your desk actually asks, and we’ll run them live.

JPM Moves AI Into Its Core Tech Stack

Last month, I wrote that responsibility for AI might eventually be absorbed into the CTO or CIO role. That appears to be happening at JPMorgan.

The bank is placing the office that led its AI push inside Global Technology, signaling that AI is becoming core technology rather than a standalone initiative. CTO Scot Baldry is taking over the firmwide AI program as Teresa Heitsenrether prepares to retire at year-end, according to Business Insider. The central team will continue setting strategy and governance, while individual businesses will increasingly be responsible for deploying AI and proving its value.

In other words, AI is becoming too central to remain siloed within a dedicated division. I suspect this shift from side project to core function will happen across companies as AI becomes more embedded in workflows.

Asset Managers’ AI Spending Surges

Asset managers are pouring money into AI without much agreement on how much is enough.

Clearwater Analytics commissioned a survey of 178 senior executives at hedge funds, private-markets firms, insurers and traditional asset managers across the US, Europe and Asia-Pacific.

Sixty-three percent said their firms increased AI spending by more than 50% during the past year. None reported a decrease. Yet 66% worried they were overinvesting, while 25% thought they were not spending enough.

AI is also moving closer to the portfolio. Forty-three percent said it is involved in between 25% and 49% of their investment decisions. Thirty-eight percent reported similar adoption in risk management and 34% in operations.

The survey does not identify the firms, their assets or what qualifies as AI involvement. It also combines generative AI with older machine-learning and automation systems.

ROUNDUP

What Else I’m Reading

• ICE puts fixed income pricing data on leading AI platforms | Finextra

• OCBC Uses AI To Ease Wealthy Clients’ Onboarding Pain | WealthBriefingAsia

• HSBC to open Singapore AI centre of excellence | Finextra

• The Rise of Million-Dollar Companies With Just One Employee | WSJ

• Situational Awareness Seeks to Raise Capital After AI Rout | FT

• Bloomberg to Buy Private Markets Data Platform Canoe Intelligence | Yahoo

• People Building AI Want Governments to Slow Down the AI Race | Euronews

• Citadel spin-offs are favorites for hedge funds investing externally | BI

• How Bridgewater Built an AI Analyst That Does Hours of Expert Research in Minutes | YouTube

• CME’s Debut of 24/7 Gold Futures Trading Met With Strong Demand | BBG

• Flow Traders Selects CoreWeave for Model Training | Press Release

• BNY to adopt blockchain technology for records in new era for Wall Street | FT

Share AI Street

This Week in AI Street

Thanks for those of you who joined yesterday for AI Street’s first Substack Live. It was a great chat with Ex-Millennium quant Arman Khaledian on the latest on AI adoption, changing research workflows and Wall Street’s growing compute arms race.

If you’d like to suggest a guest or a topic, please reply to this email or reach out at matt [at] ai-street.co.

Upgrade to paid

CALENDAR

Upcoming AI + Finance Conferences

• Ai4 2026 – August 4–6 • Las Vegas

  Large enterprise AI conference with a finance track and broad coverage of applied AI, agentic systems, infrastructure, governance and deployment lessons.

• 8th Annual Artificial Intelligence in Financial Services Conference – September 8–9 • London

  Senior financial-services event focused on real-world AI adoption across banking, insurance and fintech, including generative AI, risk management, fraud, compliance and governance.

• Cornell Financial Engineering Manhattan 2026 Future of Finance & AI – September 11 • New York

  Hedge funds, banks and quants come together to explore how AI is changing finance, share practical lessons and exchange ideas on financial engineering.

• Machine Learning in Quantitative Finance – September 14–16 • New York

  Quant-focused conference on how financial institutions are applying machine learning across trading, portfolio construction, risk and investment workflows.

• AI and Machine Learning in Quant Finance Conference – September 16 • Online

  CQF Institute event focused on AI and machine-learning advances in quantitative finance, with discussions geared toward practitioners and researchers.

• AI-Native Banking & Fintech Conference – September 29 • Salt Lake City

  Banking and fintech conference centered on agentic AI, with speakers from banks, fintechs and regulators discussing production use cases, risk, compliance and operations.

Thanks for reading!

I’m always happy to receive comments, questions, and feedback.

• Connect with me on LinkedIn, or

• Send an email to matt [at] ai-street.co

Manage how often you receive AI Street

Update Email Frequency

Alejandro Lopez-Lira has been running one of the largest public experiments in AI investing. Nearly 52,000 investors have allocated about $200 million on Autopilot across seven public portfolios built with AI models including ChatGPT, Claude, DeepSeek and Grok.

These portfolios aren’t responding to a simple “What stocks should I buy?” prompt. Instead, they break investing into a series of steps—macro analysis, company research, portfolio construction and verification—with different AI agents handling each stage.

So far, that approach has produced stronger results than many public AI investing experiments. The DeepSeek portfolio, for example, has gained 56% over the past year, compared with a 16% gain for the S&P 500. Lopez-Lira cautions that it can take a decade to know whether a diversified strategy is outperforming the market.

I recently caught up with Lopez-Lira, an associate professor of finance at the University of Florida, to see how his thinking on AI and investing has evolved since he became AI Street's first interviewee two years ago. Back then, Lopez-Lira argued that AI could already handle many of the tasks typically assigned to an intern. Today, he believes AI systems have reached roughly the level of a fourth-year Ph.D. student.

We discussed what's changed over the past two years, how he builds AI systems that break investing into specialized tasks, and why he believes the next wave of AI on Wall Street will be driven by systems rather than standalone models.

This interview has been edited for clarity and length.

How capable have AI systems become over the past two years?

I would say large language model systems are at the level of a fourth-year Ph.D. student in every field.

I don’t think there is any specific task that does not require human-to-human interaction or doing things in the physical world that can generally be done better by humans than by AI.

Jobs are collections of tasks, sometimes well-defined and sometimes not. That determines how much substitutability there is between humans and AI systems.

I say AI systems because LLMs by themselves haven’t changed that much. But embedded in systems such as Claude Code, it’s insane what you can do. I’ve been mostly working on autonomous systems that can run for a long time without human intervention.

ICYMI

The Claude Portfolio

One example is your Claude portfolio. How does it differ from the ChatGPT, DeepSeek and Grok portfolios?

The other models receive a list of the best potential stocks from a large scoring system. Grok decides how to make its portfolio, GPT decides how to make its portfolio and so on.

Claude is more iterative. It can launch an agent for each stock to check that the research is sound and that there are no issues. You can also have a portfolio-optimization agent assemble the portfolio and another agent audit it to make sure there’s nothing weird.

It involves many more cycles and revisions, calls more agents and potentially trades more frequently. The other portfolios trade once a month. Claude decides when to trade, with a limit of twice per week. It trades less often than that on average.

How has it performed?

It was launched about four months ago. It was up about 10% as of yesterday, roughly the same as the S&P 500.

Four months is way too little time to tell. It will take a couple more years to evaluate.

The ChatGPT portfolio has outperformed the S&P 500, but it trails Grok and DeepSeek. What explains the difference?

I think ChatGPT is more opinionated. DeepSeek says, “These seem like the stocks with the highest expected returns. I’m just going to make a bet on that.” That seems to work.

ChatGPT says, “I’m going to consider what’s happening with the macro environment and other things.” That may not necessarily be correct. Grok is somewhere in between.

The system produces very good information, and the models use it more or less effectively. But it’s still too early to draw conclusions about any of them.

How much money is invested across the portfolios?

There is $200 million across all portfolios now.

Some experiments that let LLMs trade have produced large losses and volatile results. Why haven’t your portfolios experienced the same swings?

Read more

This is one of the bigger bets I’ve seen a wealth management firm make on AI agents, or software that can carry out work across multiple systems rather than simply answer questions.

Mariner, which advises on about $630 billion, is paying $35 million a year to deploy at least 700 AI agents across its operations under a five-year partnership with Humanity Labs, according to Investment News. The AI systems will handle tasks including client onboarding, account opening, compliance reviews, reporting and billing.

At first glance, this may sound like Mariner is looking to downsize. But the firm is actually expanding, with plans to grow its adviser headcount from more than 2,000 earlier this year to 5,000 by 2027.

Regular readers know I’m skeptical that AI is coming for everyone’s jobs. Mariner’s approach reflects the more nuanced reality suggested by the data: AI may change what people do without necessarily eliminating their jobs.

As I’ve written before, some of the best AI use cases are also the most tedious. Back office tasks like client onboarding and compliance reviews are necessary but they’re hardly the service an investor is really seeking from their financial advisor.

Mariner plans to divide work between employees and AI. Human advisors will remain responsible for client relationships, judgment and outcomes, while AI handles repetitive tasks such as paperwork at scale. People don’t hire a financial advisor to fill out paperwork. They hire one for judgment, trust and advice.

That division of labor gets at one of the biggest organizational challenges posed by enterprise AI: deciding where people add the most value and where software can take over. That requires mapping how work actually gets done across the firm, one tedious workflow at a time.

OffDeal, which calls itself an AI-native investment bank, is an example of what that process looks like in practice. The Wall Street Journal recently published some details on AI-native investment bank OffDeal, where software engineers shadowed bankers from 8:30 a.m. until midnight for two weeks, documenting every step of their day. The exercise led the firm to automate pre-call preparation and post-call debriefs, eliminate unnecessary reports and shift some work to different teams.

This is a tedious process, but companies will likely reap more rewards than just adding a simple AI chatbot.

Bridgewater Tests Whether AI Actually Learned

Specialized training can improve an AI model. But it’s a challenge to determine whether a model really “learned” a new task or is relying on its memory.

Bridgewater’s AI team trained a strong model to be good at a particular task, then transferred its skill into a tiny package, small enough to show it couldn’t just be memorizing the training answers. That allowed the researchers to put a mathematical floor on how well the improved model should perform on new, similar questions drawn from the same distribution.

They tested the method on math, coding, general knowledge and text-to-SQL. Across all four areas, the calculated minimum performance exceeded the original model’s accuracy and came within 6% to 11% of the trained model’s performance.

Bridgewater formed AIA Labs in 2023 to build AI systems for investing. Late last month, the hedge fund said a specialized model trained with Thinking Machines outperformed GPT, Claude and Gemini at identifying useful information in financial documents.

Hedge Funds Chase AI Talent in San Francisco

Citadel, Point72 and Balyasny are among the firms building a presence and recruiting engineers in the city, where they increasingly compete with AI labs for technical talent. Financial News reports that Balyasny and Freestone Grove have hired engineers from Amazon and Google, although the story provides no hiring totals or details about the models the firms are developing. Financial News

BofA Names Global Markets AI chief

The bank named Kevin Milsom head of platforms AI transformation and moved Amy Avery’s analytics and modeling team into the global platforms group. The reorganization gives AI a defined owner inside the markets division. Reuters

Fasanara Hires Man Group Exec For AI lab

James Hylands will lead a new lab that plans to develop proprietary systems for underwriting, portfolio optimization, risk monitoring, market intelligence and operational automation. Those are planned applications, with no deployments or results disclosed yet. Alternative Fund Insight

ROUNDUP

What Else I’m Reading

• There’s a new way to land an internship at Millennium: be an AI superstar | Business Insider

• Bloomberg’s Last Stand | The Terminalist

• AI Agents Could Boost Crypto | Franklin Templeton

• Google Study Says AI Is Helping Workers, Not Replacing Them | WSJ

Share AI Street

This Week in AI Street

ICYMI

Upgrade to paid

Substack Live: AI on Wall Street

I’m going to try my first Substack Live on July 29 at 10 a.m. ET with Arman Khaledian, a former Millennium quant and the founder of Zanista AI, which uses AI to turn financial news into trading signals.

We’ll chat about the state of AI on Wall Street and take questions from subscribers on the state of AI in investing. If it goes well, I’ll do more of these.

You can follow along here.

SPONSORSHIPS

Reach Wall Street’s AI Decision-Makers

AI Street reaches institutional investors, C-suite executives and Big Law attorneys at firms including JPMorgan, Citadel, BlackRock, Skadden, McKinsey, and more. Sponsorships are reserved for companies in AI, markets, and finance. Email sponsors@ai-street.co for more details.

CALENDAR

Upcoming AI + Finance Conferences

• Artificial Intelligence for Financial Services – July 23–24 • Cambridge, MA

  MIT executive-education workshop on AI/ML applications in financial services, emphasizing strategy, use cases, and implementation considerations.

• fintech_devcon – August 3–5 • Denver
  Developer-focused fintech conference for people building financial products and infrastructure, with technical sessions, demos and networking.

• FinLLM@IJCAI 2026 – August 15 • Bremen
  Symposium on large language models for financial services, bringing together researchers and practitioners.

• AI for Eco and Finance Summer School & Conference – August 24–28 • Turin
  Summer school and conference on applying machine learning to economics and finance.

• FinovateFall 2026 – September 9–11 • New York
  Fintech showcase built around product demos, bank and fintech executives, and financial-services technology.

• Eagle Alpha Alternative Data Conference – September 10 • New York
  Alternative-data gathering for investment and research teams, data providers and vendors.

Thanks for reading!

I’m always happy to receive comments, questions, and feedback.

• Connect with me on LinkedIn, or

• Send an email to matt [at] ai-street.co

Manage how often you receive AI Street

Update Email Frequency

BlackRock began using Aladdin, its in-house portfolio- and risk-management system, in the late 1980s and started selling it to clients in the 1990s. Originally built for institutional investors, Aladdin has since expanded into a broader platform spanning wealth management, trading and private markets, with BlackRock acquiring eFront and Preqin along the way.

Aladdin now has more than 130,000 users and accounts for most of BlackRock’s $2 billion technology-services and subscription business.

More recently, BlackRock has started adding AI to its products and investment process. An Aladdin Wealth feature used by Morgan Stanley draws on portfolio holdings, client preferences, risk data and the bank’s market outlook to draft commentary for financial advisers. Internally, BlackRock’s Asimov system uses AI agents to scan research notes, company filings and emails for information that could affect portfolios managed by its fundamental-equities teams.

BlackRock is also rolling out RockAI, an internal platform that allows employees to create specialized agents without writing code.

To understand how BlackRock is approaching the next phase of that work, I recently spoke with Dhagash Mehta, one of the researchers behind its experiments with AI agents. I’ve previously covered his research here and AI developments at BlackRock:

Mehta runs applied AI for investment management at RockAI. He spent the previous three years in Aladdin Financial Engineering, the firm’s centralized quant group, working on trading, liquidity and portfolio optimization. His job is to take problems from portfolio managers and traders and turn them into models that could eventually run on Aladdin — with the access controls, guardrails and audit trail required to use them.

This interview has been edited for clarity and length.

What makes building an AI platform for a regulated industry different?

We have to have many guardrails, and access management is a big thing because we always have to make sure that certain data is not accessible to certain people. And tracing as well—tracing the whole thing and making everything auditable. Plus, there are different developers who all might have different rights.

How do you select the research topics?

Previously, I was in academia for many years—some would argue too long. There, whenever I used to choose a problem, it was basically from my head. I would create a toy model, create a problem out of thin air, and try to solve it. Then I switched to industrial research, and I am realizing that real-world problems are even more challenging and even more interesting.

My strategy is—I am not running a completely academic AI lab here. My eventual goal is still to deploy models for us, and they might have some very good use for our portfolio managers and traders, eventually on Aladdin. The problems here come from the actual PMs and traders. Most of the time, they start talking about some problems in their language, and then I translate it to the machine learning and AI language. Most of the time, no off-the-shelf method would solve this problem as is. I always have to innovate something, even some small thing. Then I realize that maybe no one else has thought about this in the literature, so that is my opportunity to write a paper.

How do those problems usually reach you?

Because I am embedded in the business, we always have frequent meetings with PMs and traders. We also have planning for the next year, the next quarter, and so on. That is when we go through many discussions, and that is where all these problems come up. PMs might say, “This is our burning problem right now. Can you help solve this for us?” That is how it starts.

Your agent paper used three agents to assess companies. How did that work?

Read more

AI as Money Manager

JPMorgan researchers built an AI agent to identify economic regimes and used its calls to shift a hypothetical portfolio among stocks, bonds and credit.

The best version produced a 0.95 Sharpe ratio in a 2006-to-2026 backtest, compared with 0.61 for a traditional 60/40 portfolio. All eight configurations produced higher Sharpe ratios than the 60/40 and a rules-based strategy.

Big caveat: it’s very hard to guard against look-ahead bias in these tests because the models may recognize data they’ve already seen, a point the researchers make clear.

The AI analyzed economic and market data each month and assigned probabilities to four regimes: Goldilocks, reflation, stagflation and risk-off. JPMorgan then matched the most likely regime with predetermined portfolio weights. The agent did not select investments itself.

The bank tested OpenAI’s GPT-5.2 and GPT-5.5 and Anthropic’s Sonnet 4.6 and Opus 4.8 at two reasoning levels. GPT-5.2 at the lower level performed best, returning an annualized 6.2% with 6.6% volatility. More reasoning generally did not improve results.

For July, all four models identified reflation as the most likely regime, citing accelerating growth, rising inflation and calm markets.

The report did not say JPMorgan plans to deploy the agent. The researchers said they remain wary of handing asset-allocation decisions to AI. The bank already uses AI to support research, portfolio construction and trading, while leaving investment decisions to humans.

You can already connect Claude or ChatGPT to manage your money. While AI Street readers were not into AI financial avatars in the poll last week, I suspect investors will soon have more institutional options.

SPONSOR

The 25% crash was in every feed; though the feeds couldn’t agree whether it erased $65, $68, or $70 billion, or whether it was IBM’s worst day since 1987, 1968, or ever. The response signal, scattered across outlets no single feed carries.

Seltz returns all of it, raw. Full articles, first-person reporting, within the hour of publication. No summaries, no sentiment scores. Your models do the distillation better than any vendor, on input nothing has influenced.

Search any name across any day in history the same way. That’s how teams build and test a signal before they trade it live. Backed by B Capital and Speedinvest.

Want the full picture on every name you trade? Sign up for a demo — bring 10 questions your desk actually asks, and we’ll run them live.

Booth Professors Put AI Agents to a Live Test

One way (the only way?) to get around look-ahead bias is by running live tests. I’ve covered a number of them here and here.

University of Chicago researchers and trading firm Optiver have launched a new competition to test how well AI agents can explain stock-price moves following earnings announcements.

The agents receive earnings information and make predictions that are scored against subsequent market moves on a public leaderboard. Because the evaluation happens in real time, models cannot benefit from seeing the events in their training data.

The competition builds on research by Ralph Koijen and Bradford Levy, who found that optimized agents increased the share of return variation they could explain from 8% to nearly 20%. (I wrote about this paper for the Chicago Booth Review here.)

Levy said earnings calls offered a practical testing ground.

“Academics and practitioners care about them,” he told me. “In just one quarter, we’ll have a few thousand observations and be able to evaluate how well the models are doing. And every single quarter, we have brand-new, fresh data.”

The next round runs from Aug. 10 through Oct. 2.

Upgrade to paid

Citadel’s AI Does Weeks of Work in Hours

At the beginning of the year, Ken Griffin called AI “garbage,” but has since become convinced of the technology’s wide-ranging impact for the world and investors. In May, he said he went home depressed after seeing AI recreate finance papers in a few hours rather than a few weeks it previously took human experts.

Griffin expanded on those comments in a recent conversation with Goldman Sachs.

My colleague built an agentic AI system that would read a paper, reproduce it, verify the results that were published in the paper, produce the results out of sample, and do all of this work in about, on average, two to three hours per paper.

Now, of note, there’s no reduction to headcount at Citadel on the back of this breakthrough, right? I have incredibly talented people. We have just a huge swath of problems that we’re trying to attack and go after. I will take every single productivity gain I can get because with the talented people we have, we just have more to go after.

This echoes what I’ve been hearing: AI may not reduce demand for human labor, but, in fact, may increase the need for us humans since AI boosts our productivity.

Man Group’s 86x Token Growth

Back in December, I spoke with Man Group’s Ziang Fang about AlphaGPT, the firm’s system for generating quant signals from papers and data. The system proposes a hypothesis, writes the code and runs the backtest before the idea enters Man Numeric’s normal research and investment-committee process.

A new Odd Lots interview with the asset manager’s Chief Technology Officer Gary Collier and Head of Data and AI Tushara Fernando adds more details to the money manager’s current AI use.

The executives said the firm’s token consumption has risen 86-fold since January (27:50). They also described AI use in discretionary investing (8:45), operations and the people team (28:15).

The conversation gets into the operational questions behind that growth: how to structure data for models (20:46), which business unit pays when agents run workflows across the firm (42:06), and how to explain an AI-assisted investment decision (31:23).

AI Spreads Across Wall Street Banks

Wall Street’s biggest banks reported better-than-expected earnings this week and detailed the current state of their AI use cases. The tech is becoming ubiquitous.

• Bank of America: 400,000 prompts a day; more than 300 approved use cases.

• JPMorgan: Nearly 1,000 use cases, though it sees about 50 as most important.

• Citi: More than 80% of employees with access use its AI tools regularly.

• Goldman Sachs: AI began in engineering and is now part of a broader push to automate firm processes.

• Wells Fargo: Its new AI Teammate helps wealth advisers search, summarize and complete routine workflows.

ROUNDUP

What Else I’m Reading

• JPMorgan, BlackRock and Goldman to Tokenize Stocks, Treasurys | WSJ

• UK banks join financial services 'Skills Compact' | Finextra

• Quant giant Qube preps a new unit of human stockpickers | BI

• AI operating system Feathery raises $30 million | Finextra

• Hedge Fund Giants Have a New Profit Engine: Their Smaller Rivals | BBG

• Kalshi Ramps Up Effort to Build Markets for AI Computing Power | BBG

Share AI Street

This Week in AI Street

SPONSORSHIPS

Reach Wall Street’s AI Decision-Makers

AI Street reaches institutional investors, C-suite executives and Big Law attorneys at firms including JPMorgan, Citadel, BlackRock, Skadden, McKinsey, and more. Sponsorships are reserved for companies in AI, markets, and finance. Email sponsors@ai-street.co for more details.

CALENDAR

Upcoming AI + Finance Conferences

• Artificial Intelligence for Financial Services – July 23–24 • Cambridge, MA

  MIT executive-education workshop on AI/ML applications in financial services, emphasizing strategy, use cases, and implementation considerations.

• fintech_devcon – August 3–5 • Denver
  Developer-focused fintech conference for people building financial products and infrastructure, with technical sessions, demos and networking.

• FinLLM@IJCAI 2026 – August 15 • Bremen
  Symposium on large language models for financial services, bringing together researchers and practitioners.

• AI for Eco and Finance Summer School & Conference – August 24–28 • Turin
  Summer school and conference on applying machine learning to economics and finance.

• FinovateFall 2026 – September 9–11 • New York
  Fintech showcase built around product demos, bank and fintech executives, and financial-services technology.

• Eagle Alpha Alternative Data Conference – September 10 • New York
  Alternative-data gathering for investment and research teams, data providers and vendors.

Thanks for reading!

I’m always happy to receive comments, questions, and feedback.

• Connect with me on LinkedIn, or

• Send an email to matt [at] ai-street.co

Manage how often you receive AI Street

Update Email Frequency

If you’re a '90s kid like me, you remember buying CDs at Circuit City. You couldn’t buy individual tracks online, so you had to buy the whole album for the one or two songs you wanted.

Technology eventually unbundled music. Now it is beginning to do the same to financial data.

Traditionally, an equities trader who needed data for only a handful of stocks might still have to license an exchange’s entire market-data feed.

This problem was the genesis of Databento, which lets customers buy specific market data and pay based on usage.

Christina Qi co-founded the Utah-based company after seeing the challenges of acquiring market data while running a high-frequency trading hedge fund. Founded in 2019, Databento announced last week that it raised a $97 million Series B led by NEA with participation from DRW Venture Capital, Redpoint Ventures and Tribe Capital.

Qi has leveraged her HFT background at Databento by placing the company’s servers in the same data centers as exchanges to receive their market feeds directly. The company processes market data into standardized formats and sells real-time and historical data for equities, futures and options through APIs. With the new funding, the company plans to expand to more than 20 data centers worldwide and has secured more than 100 petabytes of storage capacity to support that growth.

I recently spoke with Christina about how technology is disaggregating market data, a shift I’ve been covering (see my interview with Carbon Arc CEO Kirk McKeown and Aiera COO Gavin Skinner). Qi and I discuss fundraising in an AI-obsessed VC environment, why AI labs are buying market data, the complexity of exchange licensing and how usage-based pricing can create problems for large firms.

This interview has been edited and condensed for clarity.

Seems like venture capital investors are focused on backing AI companies right now. What was it like raising in this environment?

Christina: We are actually one of the few non-AI companies to raise a round of this size, which is quite unusual in the current market environment. While many non-AI companies face pressure to position themselves as AI businesses, we have never felt that pressure. We got this far by being hyper-focused on what we are good at, which is data. We choose not to compete against our customers, who excel at AI. Instead, we stick to our strengths to support them.

Market data has typically been sold in bundles. Why do you sell it a la carte?

Christina: We are the first company to bring what is called usage-based pricing to this industry, meaning instead of paying for the entire exchange—like buying the entire grocery store—you can browse items, taste samples, and buy a bento box for dinner. This allows individuals and teams the freedom and flexibility to shop around before committing to larger datasets.

It is also product-led growth in the sense that customers can self-service and sign up on their own. They do not need to talk to a salesperson. All the pricing is online: here is the price, here is what you get, register, and check out.

More AI Street Expert Interviews

Companies like Carbon Arc offer alternative data on an a la carte basis. Do you see other companies in the data space moving toward this model, and is this the future of the industry?

Christina: There are many incredible startups in our space executing different applications. But it’s worth mentioning that a la carte pricing only scales to a certain extent. Large customers, such as banks and consulting firms, have a fixed budget for data, which works better with annual, flat-rate pricing. Usage-based pricing works great for early-stage experimentation, but can cause administrative headaches down the road.

You mentioned previously that major AI labs are buying data from Databento. What are their primary use cases?

Read more

Virtually all of the major hedge funds have been building AI into their workflows.

• Citadel has an AI assistant

• Man Group has AlphaGPT

• Bridgewater has an AI fund generating “unique alpha”

• AQR’s Asness says AI has become “annoyingly better”

There are more but you get the idea.

The science-fiction way to think about AI in finance is that all these funds are building Minority Report systems, where soothsayers predict the next market event. The reality is more prosaic. They’re building systems that amplify their investing point of view. Like, as we discussed last week, how Bridgewater worked with Thinking Machines to create a model that better combed through relevant investment news and research.

Investors can’t build everything themselves (nor do they have the budget of Bridgewater) so there's an emerging niche of companies building AI & investing functionality by alums of larger firms. From Business Insider:

• Ian McInnis, a former Bridgewater analyst, founded WithAI, a Y Combinator-backed startup helping funds use LLMs to process information and plug AI into the investment process.

• Jaime Villa, previously a macro researcher at Schonfeld and Citadel Securities, cofounded Macro Technologies, which is trying to automate repeatable work done by macro analysts.

• Cameron McKendrick, a former Jain Global executive, leads Serona Data, which looks for investment signals in healthcare data.

While the BI story suggests these upstarts could eventually mean fewer analyst jobs, I don’t subscribe to this idea. None of the folks I’ve talked to have said AI is leading to fewer jobs. AI creates more work. Cheaper tools mean more stocks to screen, more signals to test, more AI-generated claims to verify and on and on.

SPONSOR

The Brent-above-$80 headline was in all the feeds. The drone strike on a Chevron-chartered tanker off Novorossiysk, the shale-recovery tech deal with ZL Chemicals, and California’s gas-price fight were in the maritime and energy trade press — the physical signal, scattered across outlets no single feed carries.

Seltz returns all of it, raw. Full articles, first-person reporting, within the hour of publication. No summaries, no sentiment scores – your models do the distillation better than any vendor, on input nothing has influenced.

Search any name across any day in history the same way. That’s how teams build and test a signal before they trade it live. Backed by B Capital and Speedinvest.

Want the full picture on every name you trade? Book a demo — bring 10 questions your desk actually asks, and we’ll run them live.

The FCA Takes Up Chatbot Financial Advice

While the SEC has been pretty quiet on AI financial advice, the UK’s FCA released a report this week saying consumers are already using general-purpose AI tools for money questions without understanding that regulated-advice protections may not apply.

The FCA surveyed 5,026 UK retail-finance consumers in April. Sixty-seven percent said they use AI; 16% said they had used it for financial tasks. About 26% said tools like ChatGPT provide reliable financial information or advice. Only 40% understood there would be no formal route for recourse if that advice went wrong.

The FCA is not proposing chatbot rules yet. It wants to know whether general-purpose AI tools are already giving advice-like support outside the regulatory perimeter, and whether existing rules on advice, promotions and arranging still work when the first intermediary is a chat interface.

AI as Deal Runner

So far, AI in finance has mostly lived in the back office: KYC checks, code help, earnings call summaries, etc. But this is the first I’ve heard of AI running a deal. From the WSJ:

When the buyout giant CVC put a Greek e-commerce business called Skroutz up for sale earlier this year, it didn’t use investment bankers to run the process. It used artificial intelligence.

Prospective buyers of the company were sent a link to a data portal that acted like an investment memo. A chatbot “analyst” acted like the banker, answering questions on financials and due diligence or prompting interested parties to get in touch with the management team for a further discussion.

The CEO of Skroutz spearheaded the company’s AI exit, so this is an unusual test case, but I suspect not the last one we’ll see. Blackstone agreed to buy the Greek e-commerce business in May.

Your AI Avatar Money Manager

I was talking with a friend who said they liked the “vibe” of Claude over ChatGPT, which made me think we’re on our way to having “relationships” with our favorite chatbot. This would have sounded weird and creepy, maybe, two years ago? Less so now.

Companies are leaning into avatars.

OCBC, a Singaporean bank, is rolling out two avatars, Wendy and Wayne, for wealth clients with more than S$1.5 million in the bank. They can answer questions about markets, portfolio news and customer holdings, pulling from real-time market data, OCBC research, client portfolios, transactions and behavioral signals.

The avatars pull from approved data, hard rules limit what they can answer, and separate agents handle different types of requests. If Wendy or Wayne reaches the edge of what they are allowed to do, the conversation goes back to a human relationship manager.

ROUNDUP

What Else I’m Reading

• Don’t rely on AI for personal finance advice, study finds | CNBC

• Singaporean regulator outlines safety guardrails for financial AI agents | Yahoo

• Using AI to Let History Speak About Bank Runs | NYFed

• Databento raises $97M Series B led by NEA | Utah Business

• This just came out, so I haven’t had a chance to listen yet: Man Group on Odds Lots:

Share AI Street

This Week in AI Street

Upgrade to paid

SPONSORSHIPS

Reach Wall Street’s AI Decision-Makers

AI Street reaches institutional investors, C-suite executives and Big Law attorneys at firms including JPMorgan, Citadel, BlackRock, Skadden, McKinsey, and more. Sponsorships are reserved for companies in AI, markets, and finance. Email sponsors@ai-street.co for more details.

CALENDAR

Upcoming AI + Finance Conferences

• Global Agentic Regulator Hackathon – July 8–September 18 • Online

  Hackathon for building agentic tools for public authorities, focused on AI advice, market manipulation, payments, decentralized infrastructure, and scams.

• ARPM Quant Bootcamp – July 13–16 • New York

  Four-day quant workshop from ARPM, focused on portfolio construction, risk, machine learning and the plumbing behind systematic investing.

• Artificial Intelligence for Financial Services – July 23–24 • Cambridge, MA

  MIT executive-education workshop on AI/ML applications in financial services, emphasizing strategy, use cases, and implementation considerations.

Thanks for reading!

I’m always happy to receive comments, questions, and feedback.

• Connect with me on LinkedIn, or

• Send an email to matt [at] ai-street.co

Manage how often you receive AI Street

Update Email Frequency

Travelers is the latest large company to train an AI model on its own data and says it beat commercially available systems.

The insurer trained TravelersLLM on millions of internal documents for underwriting, research and model development, according to a June 30 press release. The company, founded in 1864, said its long history and proprietary data helped improve the model’s precision.

“In testing against tens of thousands of insurance-related questions, [TravelersLLM] consistently outperformed commercially available AI models, delivering higher-quality results at lower cost and with greater speed.”

Emphasis mine.

Call them house models: AI systems trained, tuned or evaluated against a company’s own data, workflows and judgment, so not a generic model pointed at internal documents, but one that “learns” what a company does.

Travelers says AI, automation and analytics are contributing to operating leverage, expense-ratio improvement and underwriting profitability. Its shares have climbed about 19% this year, roughly double the Dow Jones Industrial Average, in which Travelers is a component.

I wrote back in May that legacy companies have an advantage over upstarts because they house so much data.

AI models are only as good as the data they’re trained on. Hard-to-replicate, legacy data is more valuable in the age of AI. And legacy companies are generally the ones with the legacy data. Many corporations are sitting on valuable intellectual property and, I suspect, don’t even know it.

The companies that do know the value of their data and the power of AI are turning their private records, workflows and judgment into models no other company can replicate because no other company has the same raw material.

The current AI boom stems from the breakthrough and flexibility of transformer models trained on language. But the benefits of this architecture extend beyond text. You can train these models on other types of data — the weather, grocery sales, bond portfolios — as long as you have enough high-quality data.

This is a topic I’ve been writing a lot about:

The conversation around companies adopting AI has focused on which model they’re using or what company they’re partnering with, which makes sense. You don’t need to fine-tune a model to write better emails. But if you’re in the business of selling property & casualty insurance, you’d much rather have a model trained on P&C claims rather than one trained on Reddit posts.

Moreover, if you’re an equities trader, you’d find more use for a model trained on prices, filings, broker messages, order flow and portfolio data than a generic chatbot. (By the way, this is part of the reason firms like Jane Street, HRT and XTX are spending billions on building their own data centers. It takes lots of computing power to turn proprietary data into better predictions, prices or trading signals.)

Microsoft CEO Satya Nadella has recently been making a version of the house-model argument: AI run by a handful of frontier labs is too narrow for the technology.

“There should be as many models in the world as firms in the world,” Nadella said in an interview last month:

Nadella’s argument is that a company is not just a buyer of software. It is a learning system. Its edge comes from its data, context, workflows, evals, traces, judgment and operating history. Frontier models can be part of that system, but if the company never builds its own learning loop around them, it is not compounding its own intelligence.

For the last couple of years, analysts and investors have been asking companies: how are you using AI? That is a good starting point. But Nadella’s argument points to the better question: how is the company using AI to turn its own data, workflows and operating history into a competitive advantage?

Paid subscribers can access a sourced working list of companies and organizations that have publicly disclosed proprietary or domain-specific model efforts.

Not a paid subscriber? Consider expensing AI Street. If it helps you evaluate one AI initiative, pressure-test one vendor pitch or understand one real deployment constraint earlier, it more than pays for itself. I put together a short template you can send your manager.

Read more

The way hedge funds use AI fits into two broad buckets.

One way is to train an AI model on market data. In other words, instead of training one on language, you gather all the financial market data you can get your hands on, train the model—not to predict the next word—but the next market event. This is hard and expensive. Firms like Hudson River Trading and XTX are spending billions of dollars to build their own data centers. (And in the case of HRT, putting out movie-quality videos of their GPU clusters deep inside a Norwegian mountain.)

The second way is leveraging large language models and building skills and systems around them. This is what Man Group has done, I’m oversimplifying a bit, with its AlphaGPT. For more on that, check out my interview with Man Group’s Ziang Fang:

While LLMs are solving 80-year-old unsolved math problems, they sometimes struggle with the nuances of what an investor cares about. You can try to prompt it: “You’re a macro investor at a multi-strat hedge fund.” But that only gets you so far.

To teach a model more of that investor judgment, Bridgewater fine-tuned an open-weight model on examples labeled and reviewed by its investment experts, according to a statement this week from the hedge fund and Thinking Machines, the AI startup run by former OpenAI CTO Mira Murati. The goal was to teach the model what Bridgewater investors would consider relevant.

For example, virtually no investor reads all of a company’s disclosures. They’re hundreds of pages long. Humans skip boilerplate language. LLMs often treat that text equally so even with a very large context window — basically AI’s working memory — they still get lost. It’s just too much information.

Bridgewater and Thinking Machines turned that skipping into training data. They trained the model on six versions of the same basic problem: deciding whether a financial article, central bank document or research report was relevant, and where the useful part of a document or email ended and the boilerplate began. When the model disagreed with the original labels, those examples were sent back to Bridgewater experts to make the distinction clear.

The result was a model that beat the frontier models Bridgewater tested. With expert prompts, GPT, Claude and Gemini got into the mid-to-high 70s on the six tasks. Bridgewater’s fine-tuned model reached 84.7% average accuracy, which the firm said was good enough for daily use, and cost 13.8 times less per task to run.

When you’re running AI scale, costs add up quickly. We talked about how AI costs can balloon because of untamed AI last week. Firms are spending tens of millions of dollars on research tasks that may not be necessary.

A person familiar with Bridgewater’s AI efforts said this research on improving efficiency is a small piece of the hedge fund’s broader goal of building a complete “AI investor” that can match and exceed their human counterparts.

The system combines Bridgewater’s proprietary causal time-series machine learning with custom LLM systems, including specialized models and frontier models adapted with the firm’s own harnesses and agent-style workflows, the person said. Bridgewater sees specialized models as an important part of that effort.

Bridgewater formed its Artificial Investment Associate (AIA) Labs division in 2023 to build investment systems that combine large language models, machine learning, and reasoning tools. Its AIA Labs Macro Strategy, live since December 2023, is generating market-beating returns and now has more than $4.5 billion in assets.

Lawmakers Press SEC on AI Agents in Retail Trading

Democratic lawmakers are pressing the SEC on the risks posed by agentic trading, including whether brokerages, AI providers or investors themselves are liable when a retail investor lets an AI agent give advice, place trades or take other actions inside an account.

In a June 23 letter to SEC Chair Paul Atkins, the lawmakers asked whether the agency has discussed AI-agent use with broker-dealers, whether it plans to issue guidance, and what guardrails brokerages should have in place before letting agents act for customers, according to Advisor Hub.

Robinhood and Public allow retail investors to connect AI agents to their brokerage accounts: Robinhood has promoted AI stock-trading tools, while Public advertises agents that can run covered-call strategies and same-day options trades tied to intraday S&P 500 moves.

Across the pond:

BoE Says Existing Rules Weren’t Built for AI Agents

Bank of England Deputy Governor Sarah Breeden said current supervisory frameworks were not designed for autonomous agents in payments and trading, where human approval for every action may not be realistic. She pointed to scenario analysis, AI-enabled monitoring, digital twins, enhanced recovery for core systems, and kill switches or circuit breakers for faulty trading models.

Share AI Street

Fundraising and Partnership News

• Arca, which uses AI to support financial advisors, has raised $64 million across seed and Series A funding rounds. General Catalyst led the company’s $48.5 million Series A, with backing from Index Ventures and Venrock. Arca said it manages more than $1 billion in client assets. More on Arca here:

• MDOTM raised $27 million to expand Sphere, its AI platform for asset and wealth managers. The company says Sphere supports more than $100 billion across 60-plus financial institutions and is used for investment insights, portfolio construction, rebalancing and client reporting at scale. For more on MDOTM, check out my interview with Peter Zangari, a partner at the firm.

• LinqAlpha raised $22 million in Series A to expand its AI-agent platform for institutional public-markets research. The company says more than 70 financial institutions use its tools, including buy-side clients managing over $5 trillion, and plans to use the funding for hiring, data integrations and broader deployment across asset classes.

• FactSet is partnering with and investing in TIFIN.AI to add AI agents to wealth-management workflows. The first tools focus on meeting prep and book-of-business intelligence, with the pitch centered on helping advisors personalize service at scale while keeping client data inside FactSet’s infrastructure.

What Else I’m Reading

• Millennium Builds AI Lab in Push for Cutting-Edge Products | BBG

• Now hedge fund Qube Research and Technologies is building out a “lab” too | eFinancialCareers

• Wall Street’s AI Race Is Fueling New Fears of Crowded Trading | BBG

• Hedge funds offer bumper pay to lure AI talent: ‘Million-dollar packages are not far’ | Financial News

• Is AI Good at Stock-Market Timing? A New Study Casts Doubt | WSJ

Upgrade to paid

Help Benchmark AI Adoption in Finance

It’s hard to get reliable data on how AI is actually being used by financial firms.

Neudata, a data scouting service for the financial services industry, is running a short survey on how the sector works with alternative data, market data and AI.

It takes about 5 minutes. Participants gain access to the results from data buyers as well as vendors, plus a chance to win a $500 gift card.

Topics include data budgets, in-demand data categories, and the AI models used to process that data.

This Week in AI Street

CALENDAR

Upcoming AI + Finance Conferences

• The Fin AI Symposium 2026 – July 3 • San Diego

  Industry symposium focused on applied AI in finance, with practitioners sharing tooling, workflows, and deployment lessons.

• ARPM Quant Bootcamp – July 13–16 • New York

  Four-day quant workshop from ARPM, focused on portfolio construction, risk, machine learning and the plumbing behind systematic investing.

• Artificial Intelligence for Financial Services – July 23–24 • Cambridge, MA

  MIT executive-education workshop on AI/ML applications in financial services, emphasizing strategy, use cases, and implementation considerations.

Thanks for reading!

I’m always happy to receive comments, questions, and feedback.

• Connect with me on LinkedIn, or

• Send an email to matt [at] ai-street.co

Manage how often you receive AI Street

Update Email Frequency

I thought the technology had a real shot at becoming widespread across Wall Street. Or, if not, it was going to be a great big dud worth covering.

Early on, I spoke to Snowflake’s Jonathan Regenstein, who was also optimistic that AI would have a big impact on how the investing world works. I interviewed him in the fall of 2024, and much of that conversation focused on a basic question: how do you even use this technology for investing? Does it make sense to? What about hallucinations?

When we spoke again recently, Regenstein said the skepticism has largely disappeared, at least among the most sophisticated funds and asset managers. The question now is how to build efficient processes.

Regenstein is a former attorney and self-taught coder who now leads wealth and asset management at Snowflake, where he works with banks, hedge funds and asset managers building AI systems on top of their own data. Snowflake does not build frontier models. It works with providers and platforms including OpenAI, Anthropic, Mistral, Meta and Hugging Face, which gives Regenstein a view into how firms are putting different models into production and where they still struggle.

He says investors are using AI in three main ways:

1. Chat: using the model conversationally to test a hypothesis or get a first read on what’s happening.

2. Text analytics: “Go examine this 10-K for me” — or, at asset-manager scale, turning years of filings, research papers, and sell-side research into an “engine of insights.”

3. Coding assistant: “Write the code for me” — helping quants and investors code up hypotheses, reproduce papers, build dashboards, or call validated models.

Regenstein has also published a new book, Large Language Models: The Hard Parts: Open Source AI Solutions for Common Pitfalls, coauthored with Thársis Souza PhD, whom I’ve previously interviewed on my podcast here. The book is for non-experts who want to understand LLMs well enough to use them: builders, go-to-market salespeople and strategy leaders. I’m looking forward to getting my copy in the mail!

This interview has been edited for clarity and length.

Matt: Tell me about your role.

Jonathan: I work at Snowflake, which is a super interesting place to work right now because we don’t build frontier models. We partner with OpenAI, Anthropic, Mistral, Meta and Hugging Face. We partner with all of them.

As this world evolves, we’re sitting at the center of it, even though we’re not really as high up in all the headlines. We see it all, deal with it all, work with them all and optimize for all of them. I get to see some trends emerging around who uses which tool for what.

There are three different ways that LLMs are being deployed now. One is the chatbot. People like the chatbot. The second is text analytics: “Go examine this 10-K for me.” The third is a coding assistant: “Write the code for me.”

Those are three very different things. The models can do all of them, but they’re not the same thing at all. They’re not for the same person or the same use case. They’re really totally different. It’s starting to break down along those lines, and there’s a lot underneath each one.

How has AI adoption in finance evolved since we first spoke in the fall of 2024?

Jonathan: There have been a few big shifts. Back in 2024, I think you and I were both believers in this. But at that time, with some exceptions, if you had gone into a room full of quants or portfolio managers, I think you would have gotten a lot of crossed arms: “I’ve been doing this a long time. I understand, and I’m sure AI is helpful, but it’s not going to revolutionize how I do my job.”

We’ve now come to a place where there is no more skepticism, certainly at the most sophisticated shops and asset managers. The question is: “How can we optimize this?”

It’s becoming a recruiting and retention tool for funds and asset managers: “You should come work here as a quant. Why? The AI harness we’re going to give you is better than you’re going to get anywhere else. If you don’t have that, you cannot win in this world.”

Once you’ve come to a fund or asset manager and they’ve built you that harness, you don’t want to leave because you can’t take it with you. It’s intellectual property. It’s becoming a recruiting and retention tool. It’s a complete shift.

How are firms building governance into those AI harnesses and testing whether they actually work?

Jonathan: This is an area where I work directly. Snowflake doesn’t build its own models, so what are we doing? We’re trying to give you the governance architecture that sits underneath them.

When you ask for data, we need to make sure that what you asked for is what you actually got and that it is underpinning your answers. That’s a ton of the work we’re doing right now: validating your context and making it scalable to monitor and evaluate what you’re doing.

Skills are becoming a huge thing. What skills do is bring determinism, as much as possible, to the way large language models work because you’re giving them a recipe: “Do this. Follow these steps. Bang, bang, bang, bang, bang.”

One of those recipes can be a quantitative model that I’ve built somewhere. Large language models don’t run a stress test or even run a regression for you. They could write the code to run the regression, but I don’t want the model to write the code on the fly. I want it to call the model I’ve already built and validated.

Let’s say I have a phenomenal modeling team and they’ve built these models. We’ve validated them and have them in our ecosystem. This is something you can do with Snowflake: You can store all your models in one place.

Now the LLM needs to choose the right model, not run the model. Where we have a whole model-testing framework that we’ve validated over the last 20 years, the LLM can call the right model. That’s what I need to get right more than I need to get an LLM to build a model on the fly for me.

You can do that for research. Part of the research for your model garden can be having an LLM build a model for you. But when it’s actually time to make a decision, the LLM needs to call a validated, back-tested model.

A lot of the focus is on the model, but much of the work involves the surrounding environment and calling the right document or model. How do you ensure the system calls the right thing? Do you use knowledge graphs?

Read more

AI to Push Trading Into Thin Markets

I have a running theory that AI is going to push more trading into markets that have historically been thinly traded.

As we’ve covered a lot around here, AI is making investment research cheaper. Investors can track more companies and test more ideas. Tasks that used to require more people, time and money are getting easier to run at scale. I’ve heard versions of this in multiple conversations with investors using AI.

This Bloomberg story has more reporting on this trend:

Advances in artificial intelligence are leveling the field for fund managers, making it easier for boutique firms to compete with big macro and bond investors.

From digesting speeches in multiple languages and crunching global inflation numbers, to tracking company filings and the tone of investment committee discussions, AI is picking up much of the work once carried out by teams of analysts, according to five executives who have recently set up their own shops.

“Technology has changed the economics of building an investment firm,” said Dharmesh Maniyar, a machine-learning PhD who founded his second fund, MQT Asset Management, late last year.

Now if everyone is getting access to cheaper investment tools, this should lead to more trading. And recent research supports this trend.

When Italy banned ChatGPT in March 2023, retail investors made fewer trades in unfamiliar assets, shifted back toward popular names, and held portfolios that looked more concentrated and alike.

And AI-driven portfolios trade much more often than their human active-manager counterparts. According to a 2024 IMF report, AI-driven ETFs already turn over holdings about once a month, versus less than once a year for typical active equity ETFs.

Volume has been above historical norms since COVID. The pandemic reset U.S. stock-trading volume higher. A quiet pre-2020 day was closer to 7 billion shares. Since then, average daily volume has generally stayed above 10 billion shares, rising to 12.2 billion in 2024, according to the Cboe.

TL;DR

I’m having a hard time seeing trading not marching higher and permeating into more markets given current AI trading trends. It didn’t make a lot of economic sense to spend limited human time researching smaller markets. Now it does.

The Cost of Runaway AI Agents

One caveat about the BBG article above: I think it underplays how hard it is to get AI working accurately at scale. It’s one thing to summarize a central bank speech, but quite another to pull accurate numbers out of hundreds of pages of regulatory docs. This is also a topic we’ve discussed a lot.

And even when you can receive accurate, helpful responses, AI research isn’t cheap. I’ve heard a lot recently about the exploding costs of compute from financial services firms. Uber reportedly blew through its annual AI coding-tools budget in a few months.

I’ve had the same experience trying to create my own agents with not a lot success.

Milos Maricic, a consultant who advises sovereign wealth funds on AI, said he had a hedge fund client spending roughly $24 million a year on LLMs, not realizing that about a third of that spend was just wasteful.

One of the fund’s agents kept calling a third-party sourced that went dark, but the agent kept getting timeouts or 429 errors and treating them as a reason to try again. It retried for weeks.

The Specification

Saving a hedge fund $8.7 million on AI spend in a week while doubling signal detection efficiency

This project grew organically out of a mandate focused on building an AI-driven alpha-seeking research operation within a client hedge fund…

Read more

2 months ago · 4 likes · 1 comment · Milos Maricic

Maricic said his team cut those costs by a third without hurting research quality, mostly by tracing spend to specific teams and workflows, stopping runaway agents and caching repeated prompts.

As I mentioned last week, I think managing compute expenses, or maybe we can call it something fun like “token master,” is an emerging job.

Computing costs are exploding across companies. Who manages that? Each individual department head? How do you decide which use cases to spend those resources on? Those seem like hard questions to me.

Share AI Street

The Growing Market for Making Markets in Compute

A year ago, trading “compute” sounded weird. `Like, you’re going to turn GPU capacity into something you can trade, like oil or electricity?’

Today not so weird. Multiple firms are trying to build the financial infrastructure of the emerging commodity. DRW’s Don Wilson is backing Silicon Data’s GPU pricing index, which feeds CME’s planned futures market.

This week, a16z announced its first bet on this emerging asset class by leading a $33 million seed round for Ornn, which publishes a rival index that powers ICE's planned futures market.

As a16z’s Ali Yahya put it to Bloomberg: “This asset class is very immature at the moment, but will professionalize over time.”

This Week in AI Street

ROUNDUP

What Else I’m Reading

• Secretive Wall Street Powerhouse Jane Street Seizes the AI Spotlight | WSJ

• XTX Markets looks for AI gems after striking gold with Anthropic | FN

• Alphabet Shares Drop After Second AI Star Departs for Rival | BBG

• AI Can Model but Can’t Make the Next Rainmaker | WSJ

• Starling rolls out AI-powered romance scam detection feature | Finextra

Upgrade to paid

SPONSORSHIPS

Reach Wall Street’s AI Decision-Makers

AI Street reaches institutional investors, C-suite executives, and Big Law attorneys at firms including JPMorgan, Citadel, BlackRock, Skadden, McKinsey, and more. Sponsorships are reserved for companies in AI, markets, and finance. Email sponsors@ai-street.co for more details.

CALENDAR

Upcoming AI + Finance Conferences

• 32nd Computing in Economics and Finance – June 29 • Venice

  Academic conference on computational methods in economics/finance (numerical methods, simulation, macro/asset pricing, econometrics).

• Risk Live Europe 2026 – June 29–July 1 • London

  Risk.net conference for banks, buy-side firms and regulators, with sessions on AI, automation, model risk and operational risk.

• The Fin AI Symposium 2026 – July 3 • San Diego

  Industry symposium focused on applied AI in finance, with practitioners sharing tooling, workflows, and deployment lessons.

• ARPM Quant Bootcamp – July 13–16 • New York

  Four-day quant workshop from ARPM, focused on portfolio construction, risk, machine learning and the plumbing behind systematic investing.

• Artificial Intelligence for Financial Services – July 23–24 • Cambridge, MA

  MIT executive-education workshop on AI/ML applications in financial services, emphasizing strategy, use cases, and implementation considerations.

Thanks for reading!

I’m always happy to receive comments, questions, and feedback.

• connect with me on LinkedIn, or

• send an email to matt [at] ai-street.co

Manage how often you receive AI Street

Update Email Frequency

AI doesn’t “read” text. Models turn words into numbers, perform calculations on those numbers, and then turn them back into words. So if AI sees the world at all, it sees it a little like Neo in The Matrix.

A key breakthrough behind LLMs came from a 2017 Google paper on machine translation. That transformer architecture helped models capture more nuance in text by letting every word weigh its relationship to every other word in a sentence or document.

Prior to transformers, machines struggled to incorporate context. A bank can lend money. It can distribute blood. Or it can be the place you sit by a river. Transformers pick up on this nuance because they do not treat each word’s meaning as fixed but rely on context.

One important kind of numerical representation AI models use is called an embedding. An embedding is a little like GPS coordinates for text: a location in mathematical space. Those coordinates can be compared, searched, clustered and fed into statistical models. One classic illustration: take the embedding for “king,” subtract “man,” add “woman,” and you land near “queen.”

Creating Embeddings from News

Embeddings are not limited to text. You can create them out of almost any messy data. And once you have them, you can ask more complex questions beyond king minus man, including questions where you don’t already know what relationships you’re looking for. In other words, embeddings can surface relationships that are hard to see with ordinary categories, keyword searches or traditional metrics.

In one paper, researchers uncovered relationships missed by traditional financial metrics after creating embeddings tied to portfolio data. I wrote about that for the Chicago Booth Review here as well as a newsletter version here:

A new paper, the Inefficient Pricing of News, takes that same framework — embeddings of text — and points it at financial news.

The researchers, including AQR’s Bryan Kelly, turn news articles into embeddings and test whether those representations contain information the market has not fully priced. Kelly is also a professor at Yale, and the paper is academic research, not an AQR report. His co-authors are Antoine Didisheim and Hanqing Tian of the University of Melbourne, and Yale’s Mo Pourmohammadi. I reached out to the authors for comment but did not hear back by publication time.

“News” isn’t always new. Tech stories typically have broader context about hardware and software. Younger companies draw more coverage about growth. Those that are heavily levered will see more details about debt covenants and credit ratings. In other words, a certain portion of news stories is boilerplate, according to the authors in a recent VoxEU column summarizing their working paper. As they write:

So, given that there’s a predictable part of news stories that doesn’t tell you much, the researchers cut the boilerplate out to find, what they call, “pure news.”

Arman Khaledian, PhD, a former quant at Millennium and now CEO of Zanista AI, put it this way in an email: “It’s like a factor model, but instead of prices you’re running it on vectorised news, stripping out the predictable part to see what’s actually moving things.”

Here’s what they did:

• The authors collected 6.7 million Reuters articles tied to single U.S. stocks from January 1996 to December 2022.

• They turned each article into a 4,096-number embedding using E5-Mistral-7B.

• They averaged those article embeddings into one monthly news signal for each stock.

• Then they asked how much of that news signal could have been predicted from the stock itself: its size, value, profitability, leverage, industry and other characteristics.

• They subtracted that predictable part.

Here’s what they found:

• A small slice (~8%) of what’s packed into an article’s embedding can be guessed just from knowing the company’s profile — its sector, size, leverage, valuation.

• Once you cut out that boilerplate, the “pure news” drives price moves.

Read more

Agentic Bond Trading

LTX, Broadridge’s corporate bond trading platform, now allows users to create AI agents on its BondGPT app to handle parts of the trading workflow on their own. The agents can build trade tickets, select dealers, send out requests for quotes, and auto-execute certain trades, according to a release this week. (LTX says traders set the parameters and remain in control.)

This is the first case I know of where AI agents can perform agentic actions in the bond market. I’m sure others will follow. (The stock market already has a few agent trader options.)

Bond markets have used automated trading for decades. But that automation was generally driven by explicit rules. What makes agentic AI different is that you can define a goal without having to define every little step in how to obtain that goal. The bond market, as I’ve said before, is well suited for AI because its informational infrastructure is way more scattered than the stock market’s.

A NOTE FROM OUR SPONSOR

The data existed. You just couldn’t get to it in time.

Consumer transaction signals diverged weeks before the earnings miss. Payroll data pointed to a labor market shift before the official print. Foot traffic told the story before the guidance cut. The signal was there, in data assets that took months to source, clean, and deliver under the old model.

Carbon Arc’s Lenses puts that signal in your workflow today. 210+ data assets, daily refresh, queryable in plain English.

Start querying with Lenses using code AISTREET30 for 50% off your first month.

When Academia & Wall Street Meet

Michael Milken is well known for turning junk bonds into an asset class.

Less well known is that he got the idea from academia.

As an undergrad at the University of California at Berkeley in the 1960s, Milken stumbled across a book published a decade prior called Corporate Bond Quality and Investor Experience. The research studied yields and defaults from 1900 to 1943 and showed that investors overestimated the risk of higher-yielding bonds.

“I was struck by the disparity between theory and reality,” Milken said on his website in 2014. “The prevailing theory—that markets accurately adjust yield to compensate for higher risk—was clearly wrong.”

Academic research often makes its way to Wall Street as Black-Scholes did in options pricing and Fama-French did in factor investing. Academic work also uncovered fraud in the options-backdating scandal.

Despite this history, the two mostly operate in different worlds. Academia and industry don’t really interact much. Academics have their conferences and fintech folks have theirs.

That’s why I was happy to attend Future of Finance Fest (3f) earlier this month in Amsterdam. David Stolin, a finance professor at Toulouse Business School, created the event to get academic and industry folks in the same room.

The one-day conference hosted a series of 15-minute talks on practical ways to leverage technology in financial services. The atmosphere was collegial and informal. I participated in an impromptu panel with Validation Cloud’s Ralf Kubli and University of Amsterdam’s Simon Trimborn. David called us up from the crowd.

The topic, naturally, was driving more conversations between researchers and business people. I shared a few thoughts. Academics should seek broader publication of their research beyond academic journals and post research summaries for a general audience on social media. Industry folks: don’t forget academics have often spent years working in obscurity on narrow problems and are generally more than happy to chat about their work.

The event will be back in Amsterdam next year on June 11, 2027 (right after Money20/20). If you’d like to get involved or speak, please reach out to David at chair@3f.live.

Regulators Press Banks for Details on AI Use

Reuters has a story out this week on banking regulators ramping up scrutiny of AI by asking lenders to show how they use AI, what risks it creates and what controls they have in place.

While U.S. financial regulators have written few new rules aimed directly at AI, there are still rules to follow. These laws are broadly written and meant to capture and regulate new technologies. (Crypto bros learned this a few years ago.)

In a prior life, I wrote about financial regulation at Bloomberg News. Federal agencies often start with the easy cases, which are just old-school fraud. You said X publicly, but privately Y was happening, like this one. After that, regulators start working their way up into more novel cases and asking questions such as these: What sort of policies and procedures do you have in place for this use case? What due diligence did you perform on this vendor? Etc.

Answers to these questions are not black-and-white, and best practices are still emerging. I expect more of these nuanced cases in the next year or so, not because people are up to no good, but because this technology is complex and it’s hard to get tens of thousands of employees on the same page.

Top Banks Rush to Fill Chief AI Roles

Bloomberg has a story out about the growing demand for chief AI officers at financial services firms. The creation of this new role is a topic I’ve previously written about. It is not easy for a company to adopt a probabilistic technology that is known to consistently make things up. You need someone to get different, often feuding divisions to work together.

These people are hard to find. How many people really have years of in-depth experience working in AI, who aren’t already working at Anthropic or OpenAI?

The Bloomberg piece points out that folks within the industry believe that these chief AI roles will be short-lived.

“We don’t have head of mobile devices,” Peng Zhao, chief executive officer of Citadel Securities, told a gathering at the Global Financial Leaders’ Investment Summit in Hong Kong in November.

I can see the role morphing into a Chief Technology Officer or CIO. But I sort of think the Chief AI role is here to stay, at least for a while. There are a lot of problems to solve with adoption. Computing costs are exploding across companies. Who manages that? Each individual department head? How do you decide which use cases to spend those resources on? Those seem like hard questions to me.

Link Roundup

• HSBC partners with Google Cloud to expand AI usage | Reuters

• Race to Turn AI Compute Into a Commodity Spurs New Crypto Boom | BBG

• OpenAI hires Citadel’s risk data engineering head in London | efinancialcareers

• Tradeweb rolls out AI-powered research assistant | The Trade

• Moody’s connects intelligence to Amazon Quick | Finextra

Share AI Street

This Week in AI Street

I’ve spent the last few weeks writing about how AI agents still have a ways to go in terms of reliability. Most recently, I wrote about how challenging it is getting AI to remember:

CIOs managing billions in assets read AI Street.

Paid subscribers access original reporting, data analysis, and interviews with the executives and researchers leading Wall Street’s AI buildout.

Upgrade to paid

CALENDAR

Upcoming AI + Finance Conferences

• IAQF + LSE: AI/ML in Finance – June 23 • London

  Finance-focused AI/ML conference hosted by IAQF with LSE involvement.

• New York Fed Innovation Conference – June 25 • New York

  Research/policy conference on financial-sector innovation (payments, regulation, fintech, and related themes).

• 32nd Computing in Economics and Finance – June 29 • Venice

  Academic conference on computational methods in economics/finance (numerical methods, simulation, macro/asset pricing, econometrics).

• The Fin AI Symposium 2026 – July 3 • San Diego

  Industry symposium focused on applied AI in finance, with practitioners sharing tooling, workflows, and deployment lessons.

• Artificial Intelligence for Financial Services – July 23–24 • Cambridge, MA

  MIT executive-education workshop on AI/ML applications in financial services, emphasizing strategy, use cases, and implementation considerations.

SPONSORSHIPS

Reach Wall Street’s AI Decision-Makers

AI Street reaches institutional investors, C-suite executives and Big Law attorneys at firms including JPMorgan, Citadel, BlackRock, Skadden, McKinsey, and more. Sponsorships are reserved for companies in AI, markets, and finance. Email sponsors@ai-street.co for more details.

Thanks for reading!

I’m always happy to receive comments, questions, and feedback.

• connect with me on LinkedIn, or

• send an email to matt [at] ai-street.co

Manage how often you receive AI Street

Update Email Frequency

At first, the results are good. But after a while, the misses start adding up. The agent skips an obvious story, and then a few more.

More detailed instructions don’t really help either.

The model is now confused.

With each additional prompt and each new instruction, the model has to figure out what information should be kept. Inevitably, relevant facts get lost, confused with other details, or just overwritten, according to a new paper from the University of Texas at Austin.

“The problem is that a good model out of the factory does not remain frozen in practice,” said UT associate professor Atlas Wang, who oversaw the research. “Long context windows and memory buffers can become liabilities. The agent remembers too much, too many things compete for its attention, and its memory can become inaccurate after day zero.”

Wang said many AI models’ agent-memory systems are often simpler than users assume. Basically, the model is asked: “What did you learn today?” Not: “What information is likely to be of value in future sessions?” But even then, the agent has to figure out what to save before it knows what will matter later. A summary can capture the broad point while dropping the number, date or specific name.

Agentic Delirium

The UT Austin researchers call this “agent aging” and argue that day-one benchmarks aren’t realistic because agents can quickly degrade after only a handful of sessions. So They created AgingBench, a benchmark that simulates long-running agent deployments, to measure how reliability changes over an agent’s lifespan.

The researchers put agents through a series of simulated work sessions to see how their performance changed over time. They gave agents tasks requiring them to recall earlier facts, incorporate updates, and distinguish between similar entries. They ran 14 models — GPT-4o, Opus-4.7, Gemma-4-31B, Llama-3.1-8B, Qwen3, and DeepSeek variants among them — across 7 scenarios and more than 400 runs, spanning 8 to 200 simulated sessions each.

There was no clear winner. A model that preserved details well in one test could still struggle in another to retrieve the right fact or update outdated information.

This is not an easy problem to solve and the risks are insidious. In one AgingBench scenario tracking personal budgets, agents kept producing confident, specific answers, but the dollar amounts were wrong. A user would have to check the numbers independently to catch it.

They described four main ways AI forgets.

• Compression aging: The agent decides what to remember before it knows what it will be asked, so exact figures and names get replaced by vague summaries.

  • A user says, “Take 50 mg of metoprolol twice daily.” The agent initially logs the exact medication, dose, and frequency. After repeatedly compressing its memory, when asked “What’s my dose?”, it answers only: “You take a daily medication.”

• Interference aging: The correct fact remains in memory, but a growing pile of similar entries causes the agent to retrieve the wrong one.

  • An agent stores information about John Smith and John Smyth, including their teams and email addresses. Later, asked to email John Smith, it drafts the message to John Smyth.

• Revision aging: The agent misses an update and keeps answering using stale data, with errors potentially compounding over time.

  • A user cancels a subscription; the agent correctly logs “Cancelled. Free tier as of now.” Some sessions later, asked “Am I premium?”, the agent answers “Yes — Premium until Jan 2026” — a confident, specific, wrong answer built on a fact that was never revised after the cancellation.

• Maintenance aging: Routine work such as recompacting memory, flushing history, or changing a prompt can break something the agent previously knew.

  • An agent records, “Therapy every Tuesday at 4 p.m.” and confirms that it has saved the recurring appointment. After its memory is flushed or recompacted, when asked “What’s my Tuesday schedule?”, it answers: “Nothing on Tuesdays.” The information disappears during maintenance rather than through gradual forgetting.

More Memory Is Not the Fix

Telling an agent to write everything down and store it in memory won’t solve the problem.

Read more