STAC

I’m in New York this week, and yesterday I went to the STAC Summit, a quant trading, AI, and infrastructure conference.

It was my first time there. A lot of the panels were quite technical, and I’m not going to pretend I’m a hardware expert, so I asked James Corcoran, STAC’s head of AI, for a main takeaway.

His answer: memory.

The technical panels kept coming back to the same problem: how to get more memory, how to use it more efficiently, and how compute providers are thinking about memory when they design chips and systems. One issue is the KV cache, which is basically the model’s working memory. As the context gets longer, the model has to store more of what it has already processed, and retrieve that information fast enough for the answer to be useful.

As Corcoran put it:

“Memory has become the new bottleneck.”

I think this is telling because it shows how quickly AI is becoming part of financial workflows, and how much pressure that is putting on the underlying infrastructure.

I’m still reviewing my notes, but one topic I’m going to come back to is how AI, and access to large-scale computing power, change the old trading tradeoff between being fastest and being smartest. If you’re holding a position for seconds rather than microseconds, the edge may come less from raw speed and more from how much data and compute you can throw at the decision.

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Citadel Alumni Raise $78M to Bring AI Agents to Wealth Management

Moment, a fintech founded by former Citadel Securities quant traders and researchers, raised $78 million this week to build out the data infrastructure it says finance needs before AI agents can work with client portfolios.

The company’s pitch is that investment management software has grown into a patchwork of separate systems: one platform for bonds, one for rebalancing, one for compliance, and so on. That setup has worked because people sit in the middle, moving data, checking restrictions and reconciling outputs.

AI agents can’t operate in that environment because there’s no unified data model or audit trail. Moment built what it calls an operating system: a single platform with unified data, access controls, compute engines, and a full audit trail, on which agents can operate.

If a firm’s data and infrastructure are a mess, agents will produce bad results, burn unnecessary tokens and keep getting stuck because they do not know what to do or where to go.

Ken Griffin Changes Tone on AI

Andrej Karpathy, a founding member of OpenAI who joined Anthropic this week, wrote in late December that AI had moved from a better coding assistant into something more like “a powerful alien tool.”

He was reacting to the sudden jump in coding agents. The same realization is now working its way through finance.

Citadel’s Ken Griffin, who dismissed AI as “garbage” at Davos in January, told a Stanford Business School audience this month that work once done by finance PhDs over weeks or months is now being handled by AI agents in hours or days.

“You could just see how this was going to have such a dramatic impact on society,” Griffin said.

OpenAI Tests ChatGPT Finance Tool With Plaid

After acquiring two AI personal finance startups since October, OpenAI announced a preview of a ChatGPT finance tool that lets users link bank, credit card, investment and loan accounts through Plaid, then uses balances, transactions, investments and liabilities to answer personal finance questions.

I think people will be a little apprehensive at first about connecting all their financial information to a product that hallucinates. But as AI gets better, that will become more normal. Eventually, AI is going to take on more and more financial advice.

ICE Joins the Race to Price Compute

Last week, we talked about the emerging market for “compute” with CME and Silicon Data teaming up to create a futures market for computing power.

This week, Intercontinental Exchange and Ornn, a compute pricing data provider, announced plans to launch GPU compute futures contracts based on Ornn’s index data.

Foot, Meet Mouth

From the WSJ:

Standard Chartered Chief Executive Bill Winters touched a nerve when he said his bank would slash thousands of jobs and replace “lower-value human capital” with artificial intelligence.

He walked back the comments on Wednesday in a memo to bank employees, who turned out to be valuable enough that he needed to assuage their feelings.

The bank said earlier this week it plans to cut more than 7,000 jobs over the next four years as it relies more on AI.

As longtime readers know, this narrative doesn’t hold up. AI is a useful scapegoat for companies facing other problems in their business. If AI is such a job killer, shouldn’t JPMorgan, which has a tech budget of ~$20 billion, be cutting back drastically on headcount? The answer is no. Headcount is flat.

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What Else I’m Reading

• AI in the Family Office Citi

• Google and Blackstone to Create New AI Cloud Company WSJ

• The Supply and Demand of AI Tokens: Dylan Patel Invest with the Best

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CALENDAR

Upcoming AI + Finance Conferences

• AI & RegTech for Financial Services & Insurance – May 20–21 • NYC

  Covers AI, regulatory technology, and compliance in finance and insurance.

• Women in Quantitative Finance - May 21 • NYC

  Quants discussing current work in asset pricing, trading, risk, and portfolio construction.

• NY Tech Week Fintech Track - June 1-7 • NYC
  Multiple relevant events: AI agents in finance, HSBC/a16z AI in enterprise fintech, AI for finance with Claude/Excel/MCP, AI agents in finance ops.

• The AI in Finance Conference - June 8 • University of Maryland
  Academic AI + finance conference on LLM measurement error, AI regulation, analyst research, return predictability, and market fragility.

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The AI rater draws on company fundamentals, market data, financial news and sentiment to produce analyst-style stock recommendations that are tested against future returns, according to the patent application, which was published in April and initially filed in February 2025. The system generates one of five outputs: Strong Buy, Moderate Buy, Hold, Moderate Sell, or Strong Sell.

A JPMorgan spokesperson said the application came from the bank’s AI research group and was filed to protect the underlying research rather than to commercialize AI-generated stock ratings.

This is one of the first examples I’ve come across of a major Wall Street firm describing an AI system that could generate analyst-style stock-rating predictions. It is a structured pipeline that compresses news, scores sentiment, packages fundamentals and return data, prompts an LLM to reason through future rating horizons, then grades the output against realized forward returns. It’s not asking ChatGPT what stocks to buy.

I don’t read this as evidence that JPMorgan is about to automate sell-side research. It’s more a framework for overworked analysts dealing with information overload.

From the application:

Traditional stock rating methods rely heavily on the expertise of financial analysts and face several challenges such as data overload, inconsistencies in filings, and delayed reactions to market events. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), presents opportunities to enhance the equity stock rating process.

JPM also notes that the models face limits around context windows, numerical and tabular data, training bottlenecks and the risk of inaccurate responses.

The bank’s CEO, Jamie Dimon, has long been bullish on AI. In his 2023 shareholder letter, Dimon compared the technology’s potential impact to the printing press, steam engine, electricity, computing and the internet.

PATENT DATA

AI Street Patent Review Tracker

I’ve reviewed the relevant finance and AI patent applications published so far in 2026, and I’m continuing to review new publications as they appear, with the help of AI, of course.

This database is a research aid for paid subscribers. It collects finance, trading, market-structure, banking, AI, and infrastructure-related patent applications that appear potentially relevant to Wall Street, fintech, exchanges, clearing, settlement, fraud detection, and institutional data systems.

So far, the filings include applications from:

• JPMorgan around stock-rating predictions and financial time-series analysis

• CME and ICE/NYSE around exchange resiliency, matching engines, risk controls, and clearing mechanics

• Bank of America around AI-driven data plumbing and application connectivity

• BlackRock, Schwab, Fidelity, Morgan Stanley, and others around portfolio analytics, wealth infrastructure, model governance, and institutional data systems

Paid subscribers can scroll down to the end of this post to download the tracker.

More Specifics on JPM’s AI Stock Rater

• Core task: Generate stock-rating predictions with an LLM.

• Rating scale: Strong sell, moderate sell, hold, moderate buy or strong buy.

• Prediction horizons: Over 1, 3, 6, 12 and 18 months.

• Basic idea: Build a structured dataset around a company, date and future horizon, then ask the LLM to reason through the information and produce an analyst-style rating.

What data goes into the system

• Company identifiers: Company name, ticker and relevant date.

• Market data: Historical returns, price data, volatility and other technical indicators.

• Fundamentals: Financial metrics such as earnings, revenue, return on assets and other company-level data.

• News: Company and sector news, including raw articles and summarized versions.

• Sentiment: Scores derived from news summaries, with negative, neutral or positive readings.

• Forward-return labels: Future stock-return data used later to train or evaluate the rating predictions.

How the news pipeline works

• Filtering: A pre-processing LLM removes articles that are not relevant to the company.

• Summarization: The same preprocessing step condenses the remaining articles into short company-specific summaries.

• Key-event extraction: The summaries are designed to preserve the important developments without overwhelming the prediction model.

• Sentiment scoring: The summarized news is converted into a sentiment score from -5 to +5.

• Purpose: The news pipeline turns a large, noisy set of articles into a compact signal the rating model can use.

ICYMI Interview

Back in December 2024, I spoke with one of JPM’s patent co-authors, Tucker Balch, who’s now back in academia at Emory, about where he sees the best AI and investing use cases.

One example that I still remember is using AI to expand data sources in other languages:

For instance, if you can listen to the news in Vietnam, translate it in real time, and identify relevant information for specific stocks, you greatly expand your data sources.

How prediction works

• Prompt construction: The LLM receives a prompt telling it to act as a financial analyst and predict stock ratings.

• Example answer: The prompt can include an input-output example so the model knows the expected format.

• Future dates: The model is asked to identify which future months correspond to each prediction horizon.

• Explanation: The model is asked to explain the reasoning behind its ratings.

• Final output: The model then produces ratings for the specified future horizons.

How hallucination checks work

• Date check: The system asks the LLM to calculate the future dates tied to each horizon.

• Verification logic: If the model gets the dates wrong, that is treated as a warning sign about the reliability of the rating.

• Explanation check: The model’s explanation is used to see whether the rating is actually supported by the input data.

• Chain-of-verification: The system uses these intermediate steps to catch cases where the model may be producing unsupported answers.

How training and fine-tuning work

• Prompt-label pairs: Training examples pair a prompt with a correct rating label.

• Ground-truth labels: The labels are based on future stock performance, not just analyst opinions.

• Forward-return quintiles: Future returns are divided into quintiles and mapped to rating categories.

• Loss function: The system computes cross-entropy loss between the model’s predicted rating and the ground-truth rating.

• LoRA fine-tuning: The model can be fine-tuned using low-rank adaptation, which updates smaller added matrices rather than retraining the full LLM.

• Validation: The system can split the data into training and validation sets to test whether fine-tuning improves performance.

How the system checks whether the prediction was right

• Forward returns: The system looks at how the stock actually performed after the prediction date.

• Peer comparison: The stock’s return is compared with other companies over the same period.

• Sector-relative return: The company’s return can be adjusted against sector performance.

• Rating match: If the stock’s future-return quintile matches the model’s rating category, the prediction is treated as correct.

• Error measurement: Mean absolute error is used to measure how far the predicted rating was from the ground-truth rating.

What the results show

• LLMs did better in shorter-term tests: The application says the LLM may perform better on short-term predictions, while analyst errors declined over longer horizons and were slightly better in the 18-month period.

• Fundamentals mattered most: The best-performing setups were the ones using fundamentals, especially fundamentals plus sentiment.

• News alone helped less: News summaries and sentiment by themselves did not outperform the fundamentals-based setup.

• Sentiment added only modestly: Fundamentals plus sentiment performed slightly better than fundamentals alone.

• News may skew positive: The results suggest that news-derived inputs may push the model toward more positive ratings.

• Short-term versus longer-term signals: News appears more useful for short-term predictions, while fundamentals appear more useful across the main 3-, 6- and 12-month horizons.

AI Street Patent Review Tracker

Paid subscribers can download the Excel file below, which uses AI-assisted review to identify 300 patent applications published this year that appear tied to AI in trading and investing.

Read more

NEWS

Compute Is Getting a Futures Market

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.

All this money is chasing “compute,” the computing capacity needed to run AI models.

The problem: What is a standard unit of compute?

It’s not a barrel of oil, or a megawatt-hour or even the number of GPUs you have.

Right now, there’s no agreed-upon definition of what a unit of compute is across different chips, different data centers and different workloads.

Eventually, there will be. Compute may seem amorphous, but there’s precedent here. NIST, the U.S. standards setter for technology, has helped set standards for cloud computing, cybersecurity and even time itself with atomic clocks. Last summer, the agency released an early blueprint for AI testing and evaluation standards. But this is a long standard-setting process.

And buyers need compute today, so they have to call around to get a sense of pricing. This opacity creates friction. (It also creates an environment for bad deals. Simeon Bochev, the former CEO of Compute Exchange, told me last fall that he knew of companies being overcharged for compute by as much as 40%.)

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Standardizing Compute

This week, the compute market got two new efforts to make pricing more transparent.

From Bloomberg:

US derivatives exchange CME Group Inc. and index provider Silicon Data are teaming up to create a futures market for computing power, a key source driving the AI boom.

The futures will help traders, financial firms, AI builders and cloud providers manage volatility and price swings, according to a statement Tuesday. Indexes from market-intelligence firm Silicon Data will help underpin the products. The project is still pending regulatory review.

Computing power, also known as compute, has been in high demand as AI companies use it to power their systems. BlackRock Inc. Chief Executive Officer Larry Fink said last week that a new asset class will likely be buying futures of compute given the shortage and high demand.

For more on Silicon Data, see my interview with CEO Carmen Li from July:

Separately, former FTX US president Brett Harrison said on LinkedIn his trading infrastructure startup, Architect, has launched compute futures on AX, its derivatives exchange. The contracts track Nvidia H100 and H200 rental prices using indices from Ornn, a compute pricing data provider.

And for all this talk of “compute,” prices on the secondary market have jumped almost 50% since the end of April for Nvidia’s H100 chip.

I asked Ornn co-founder Wayne Nelms what was driving the recent increase. He pointed to demand from large AI companies running models for customers, rather than training them from scratch.

The price increase made me think of two big predictions made by well-known investors.

First: Don Wilson, founder of DRW, told the WSJ in January 2025 that annual spending on compute would exceed annual spending on oil within 10 years. The prediction came just after DeepSeek released an efficient open-source model that triggered a selloff in AI-linked stocks.

Second: Bridgewater’s co-CIO Greg Jensen and former AIA Labs Chief Scientist Jas Sekhon (who’s now at Google DeepMind) wrote at the end of last year that corporate panic would drive AI spending higher.

AI spending “is currently being driven by a small number of leading AI players recognizing the incredibly transformative power of AI. The next phase will come when a major business outside of the AI ecosystem realizes that its entire business model is about to collapse due to pressure from an upstart competitor using AI (as occurred with Amazon disrupting Barnes & Noble).”

These are not mealy-mouthed predictions from random AI hype men. They come from serious investors. And for now, the market is moving in their direction.

Turning Trading Chats Into Market Data

Traders in OTC markets still negotiate prices in Bloomberg chats or Symphony.

Traders are making markets while jumping between conversations across banks and brokers, trying to keep track of executable prices as they move.

Institutional traders can receive hundreds of broker messages per hour and miss up to 80% of them, costing firms millions per trader per year in mispricing and missed opportunities.

This is sort of known in the industry. That is, there’s a certain amount of, let’s call it, slippage in the market given the friction in the way information moves.

The problem has been surfacing the relevant information. As I’ve written a lot around here, AI is well suited for this kind of problem: organizing messy information.

TwoWay Finance says it uses LLMs plus deterministic code to turn broker-trader chats into structured market data. The Paris-based firm announced a €1.5 million pre-seed round this week led by welovefounders. TwoWay runs locally, so banks do not have to worry about sensitive trader chats leaking to an outside model provider.

The company’s pitch is broader than one asset class. By leveraging AI, TwoWay is trying to become an intelligence layer for chat-driven OTC markets, organizing prices like exchange order books do.

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Goldman Quant Exec Says AI May Make Markets Less Efficient

AI may be creating new market inefficiencies by pushing investors toward the same trades, according to Osman Ali, global co-head of Quantitative Investment Strategies in Goldman Sachs Asset Management.

If you ask these models the same type of question, they are going to give you the same type of answer, which will cause investors to pile into the same type of securities, which will cause markets to move in a direction that becomes predictable in terms of its reversion.

Ali said on the on the bank’s podcast.

Ali said his team often uses smaller models and fine-tunes them for specific tasks, including sentiment analysis of Japanese corporate disclosures.

Another quote that stood out to me:

“More than 50% of what we think drives the stock’s return over the next 12 months is not the fundamentals of the business,” Ali said. “It is what the market thinks about it.”

More here:

• Goldman Sachs Quant Chief Says AI Could Make Markets Less Efficient Traders Magazine

ROUNDUP

What Else I’m Reading

• HRT Notches Record $6.4 Billion Quarterly Markets Haul BBG

• Hedge Funds Are Making a Killing in the ‘Golden Age’ of AI Hardware WSJ

• Top Wall Street dealers join bond trading platform LTX Finextra

• Musk’s xAI Races to Get Wall Street Firms to Use Grok Chatbot BBG

Back in New York Next Week!

I’ll be back in New York next week to attend STAC Summit on May 20. Sessions include discussions on memory bottlenecks in inference, AI research at BlackRock, extracting structured data from SEC filings with LLMs, and deploying models into trading systems and engineering workflows. Registration is free for end users. Come say hi!

I’ll also be at the Women in Quant Finance conference the next day, May 21.

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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

• STAC Summit - May 20 • NYC

  Trading and analytics infrastructure, applied AI for research and execution, scaled deployment, and benchmark-driven insights across quant workflows. ← I’m attending.

• AI & RegTech for Financial Services & Insurance – May 20–21 • NYC

  Covers AI, regulatory technology, and compliance in finance and insurance.

• Women in Quantitative Finance - May 21 • NYC

  Quants discussing current work in asset pricing, trading, risk, and portfolio construction. ← I’m attending.

Thanks for reading!

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NEWS

LLMs Make Poor Stock Pickers

Two stories this week looked at how ChatGPT struggles with stock picking. My question is: why would you expect it to be good in the first place? These are large language models, trained on text, not financial data.

That said, these stories are important. There’s a lot of confusion about what AI can actually do.

The WSJ’s Gunjan Banerji tested ChatGPT as a hypothetical adviser for a $1 million portfolio and found it could explain risks, but struggled with actual investment calls. It gave a reasonable long-term allocation, but made a basic arithmetic error, drifted into market timing and picked a trade-war stock basket that rose about 5.5%, trailing the S&P 500’s roughly 8% gain.

Banerji highlighted the annoying, sycophantic part of AI that’s an investing risk:

“At times, it felt like ChatGPT responded with what I wanted to hear.”

Bloomberg’s Justina Lee took the question one step further, looking at trading competitions that pit major LLMs against each other, a topic I wrote about in November.

In Nof1’s Alpha Arena, eight models including Claude, Gemini, ChatGPT and Grok traded US tech stocks with $10,000 each across four competitions. The results were mostly ugly: the overall portfolio lost about a third of its capital, and only six of 32 model results finished in profit.

“LLMs can’t really make money by themselves,” said Jay Azhang, founder of Nof1.

Ashwin Paranjape, founding AI lead at Samaya AI, told me that LLMs can gather financial data, but stock picking requires higher-order skills: judging materiality, forecasting metrics and connecting signals across industries.

“Beating the market relies on information and reasoning asymmetry,” he said. “Eventually picking stocks will look like: ‘My AI knowing what I know, beats your AI knowing what you know.’”

These models are getting better, and there are some early results suggesting multi-agent setups can perform better than a single model acting alone. But that still does not make a chatbot a trading system.

Training models directly on market data to find signals is different. But that is a much harder, more expensive problem than asking a $20 chatbot what stocks to buy.

A NOTE FROM OUR SPONSOR

When your agent reads Bloomberg or Reuters, is it finding an edge?

On Tesla’s Q1 earnings, Goldman held at $375, TD Cowen reiterated Buy at $490, JPMorgan stayed at $145. You and your competitors are reading the same call.

The dispersion across sell-side targets. The reasoning behind each one. The hedge buried in the fifth paragraph of an operator quote.

That’s where the analytical signal is. It lives in the paragraphs your agent isn’t getting.

Typical retrieval looks fine. The agent doesn’t know what it’s missing, and neither do you.

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AI Is Narrowing the Hedge Fund Tech Gap

Craig Whiting, a hedge fund tech headhunter, wrote about recent conversations he’s had with four different engineers across Wall Street. They said they are writing less code than they did a year ago and increasingly work alongside tools like Cursor and Claude Code to draft code, review output and catch bugs.

The bar at any firm worth working for is now: you write good prompts, you read AI output critically, you know when to override it.

Whiting also points out how many firms are getting value out of relatively straightforward use cases like cutting down email volume by structuring unstructured text, an unsexy topic that I’ve written about before.

He also mentioned how AI is making it easier for smaller shops to compete:

The new reality: the gap between a £2bn credit fund and a $60bn multi-strat is collapsing because the tooling is cheap and the workflow is portable. Cursor is cheap. Claude Code is cheap. The bottleneck is not budget anymore. It is engineering culture, leadership willingness, and how legacy your stack is.

Craig Whiting

What Four Hedge Fund Engineers Told Me About AI This Fortnight

The capability gap is closing. The work is changing. How you communicate your work is critically important…

Read more

18 days ago · 1 like · Craig Whiting

Anthropic’s Busy Week

Anthropic has had a busy week announcing three different initiatives across Wall Street:

Anthropic and FIS Target AML

Anthropic announced it’s embedding Claude inside bank compliance departments through a partnership with fintech giant FIS, starting with anti-money laundering. Today, investigators spend most of their time manually pulling records from disconnected systems before any analysis can begin — the agent handles that assembly automatically, then flags cases by risk level. FIS claims it compresses investigations from days to minutes; BMO and Amalgamated Bank are the first pilot customers, with broader availability planned for H2 2026.

Anthropic Forms $1.5B JV with Blackstone, Goldman for PE Portfolio Companies

Anthropic is forming a $1.5 billion joint venture with Blackstone, Hellman & Friedman, Goldman Sachs, and several other Wall Street firms to embed Claude inside mid-sized companies — particularly PE portfolio companies — that can't staff frontier AI deployments on their own, according to the WSJ. Anthropic, Blackstone, and H&F are each putting in roughly $300 million; Goldman $150 million; General Atlantic, Apollo, Leonard Green, GIC, and Sequoia rounding it out. OpenAI is reportedly building a rival structure.

Anthropic Releases Ten Pre-Built Finance Agents

Anthropic released ten pre-built agent templates for financial services work — pitchbooks, KYC screening, month-end close, earnings review — deployable as plugins or as autonomous scheduled jobs on the Claude Platform. Claude also now runs inside Excel, PowerPoint, and Word via Microsoft 365 add-ins, carrying context between applications. Eight new data connectors went live alongside a Moody’s MCP app covering more than 600 million companies; Claude’s broader finance connector ecosystem includes FactSet, S&P Capital IQ, and PitchBook.

Merger Arb Funds on Using AI

The FT has a story on how merger arbitrage hedge funds are using AI to get a quicker read on deal dynamics by reading dense legal documents.

Traditionally, reading through the complex, lengthy deal documents — which often stretch to over 100 pages — would take an investment professional over an hour. Even a quick review would take 15 to 20 minutes. But the use of AI has now reduced the process to seconds.

“We think of AI as a very fast, very thorough intern who is brilliant at analysing big datasets,” says Daniel Caplan, chief executive of London-based Sand Grove.

This is the kind of use case that makes sense to me. Asking ChatGPT or Claude: “Where are the most important disclosures I should focus on in this document?” An actual merger arb specialist would ask a more sophisticated question than this, but you get the idea.

ROUNDUP

What Else I’m Reading

• BMO Turns to AI and Quantum Computing to Predict Earthquakes BBG

• Former Citadel Chief Technology Officer Joining Motive Partners BBG

• Lloyds in tie-up with Google to build AI agents City AM

• Subquadratic claims 1,000x AI efficiency gain with SubQ model VentureBeat

• Bessent Warns of Threat of AI-Powered Bank Account Hacks PYMTS

Back in New York

I’ll be back in New York to attend STAC Summit on May 20. Sessions include discussions on memory bottlenecks in inference, AI research at BlackRock, extracting structured data from SEC filings with LLMs, and deploying models into trading systems and engineering workflows. Registration is free for end users. Come say hi!

I’ll be at the Women in Quant Finance conference the next day, May 21.

Share AI Street

This Week in AI Street

CALENDAR

Upcoming AI + Finance Conferences

• AI in Financial Services – May 14 • Chicago
  Practitioner-heavy conference on building, scaling, and governing AI in regulated financial institutions.

• STAC Summit – May 20 • NYC

  Trading and analytics infrastructure, applied AI for research and execution, scaled deployment, and benchmark-driven insights across quant workflows. ← I’m attending.

• AI & RegTech for Financial Services & Insurance – May 20–21 • NYC

  Covers AI, regulatory technology, and compliance in finance and insurance.

• Women in Quantitative Finance – May 21 • NYC

  Quants discussing current work in asset pricing, trading, risk, and portfolio construction. ← I’m attending.

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

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Kim Posnett, the co-head of investment banking at Goldman, argued in the FT last year that AI may turn overlooked corporate data into a newly valuable asset:

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.

But you don't need decades of data. Scale works. Revolut, the UK-based neobank with 70 million customers across 40 countries, has been collecting billions of data points. Its users generate a continuous stream of timestamped card transactions, peer-to-peer transfers, in-app navigation events, and communications.

Revolut researchers and Nvidia say they have used that stream of banking activity to train PRAGMA, a foundation model for financial event data. The model is designed to analyze a user’s event history of transactions, app activity, communications and profile data, allowing one underlying model to be adapted for tasks such as credit scoring, fraud detection and product recommendations.

A NOTE FROM OUR SPONSOR

When your agent reads Bloomberg or Reuters, is it finding an edge?

On Tesla’s Q1 earnings, Goldman held at $375, TD Cowen reiterated Buy at $490, JPMorgan stayed at $145. You and your competitors are reading the same call.

The dispersion across sell-side targets. The reasoning behind each one. The hedge buried in the fifth paragraph of an operator quote.

That’s where the analytical signal is. It lives in the paragraphs your agent isn’t getting.

Typical retrieval looks fine. The agent doesn’t know what it’s missing, and neither do you.

Seltz returns full context in hundreds of milliseconds, every result traceable to source. Built for workflows where deep research matters more than the headline.

If you’re running agents on financial news, Seltz will run an eval on your setup.

Email CEO Antonio Mallia at antonio@seltz.ai or ask me for an introduction.

What Revolut Did

PRAGMA was trained on 26 million anonymized user records from 111 countries, covering 24 billion events, according to a research paper posted to arXiv. The model is not a chatbot. It is designed to make predictions from banking histories, not generate text.

“Most ‘foundation model for finance’ discussions still default to text. But bank data is not text,” said Revolut’s head of AI Pavel Nesterov on LinkedIn.

His point is that financial activity has its own structure. Customers generate long sequences of transactions, app actions, communications, trading activity and profile changes. Turning all of that into text for a generic language model, Nesterov wrote, means “you lose too much structure and waste too many tokens.”

PRAGMA keeps the records closer to their original form. Each event is represented by what happened, the value attached to it and when it occurred. A card payment, for example, can include the transaction type, amount, currency, merchant category and time. The model then looks for patterns across long sequences of customer activity.

The authors say PRAGMA beat Revolut’s internal task-specific baselines across credit scoring, external fraud detection, product recommendation, communication engagement, recurrent-transaction detection and lifetime-value prediction.

Results

The biggest reported gains came in credit scoring and customer communications. Compared with Revolut’s existing models, PRAGMA improved one credit-scoring measure by 130% and one customer-communications measure by 79%. It also improved fraud recall by 65% and product-recommendation performance by 41%.

For Revolut, the operational goal is to reduce the need for separate models for every use case. Nesterov said the company is trying to move away from “a separate model stack for every narrow use case” and toward one shared model that can be adapted for tasks such as credit scoring, fraud detection and product recommendations.

PRAGMA fell short on one task: anti-money laundering, where it significantly underperformed Revolut's existing system. The authors say that is because money-laundering detection often depends on relationships among accounts, counterparties and transaction networks. PRAGMA analyzes one customer history at a time.

Revolut joins Netflix and Stripe, which both trained models on their own internal data. Netflix built a foundation model on hundreds of billions of user interactions that now underlies its personalization across search and recommendations. Stripe’s payments foundation model, trained on tens of billions of transactions, increased its detection rate for card-testing attacks from 59% to 97%.

That’s the fun part of AI for me. It reveals patterns that were always there but we didn’t have the computing power to see.

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I’ve found myself searching through AI Street archives to pull together what I’ve reported on how hedge funds and market makers use AI. So, I created a running tracker that puts it in one place, combining news stories, regulatory filings, and some of my own reporting.

(If you think I should do the same for private equity firms or sovereign wealth funds, let me know. There is, in fact, a human behind the text you’re reading.)

I think of hedge funds and market makers as using AI in two main ways. I’ve put them in the same broad category because, as the FT has written, the two are converging.

The first way is straightforward: using large language models from frontier labs for familiar tasks like summarizing documents and surfacing ideas, plus more sophisticated workflows such as Man Group’s use of AI to generate and test trading signals.

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GPU as Edge

The second way funds are using AI is training their own models on financial data. This requires orders of magnitude more computing power – on par with frontier labs like OpenAI and Anthropic – but as HRT’s Marc Khoury said at an industry conference last year, larger models trained on more data keep improving.

There’s a growing body of research that suggests that in each field, be it weather, payments, or market events, there may be an underlying “language” in domains like weather, payments and market microstructure that transformers are unusually good at idenifying.

In other words, if you take a massive amount of weather data, train a transformer model on it, you generally get better results than previous state-of-the-art models. I’m not suggesting that transformer are oracles, but I think it is important to highlight how fundamental a shift this is.

We’ve gone from a world where humans design models with top-down deductive logic. That approach is being surpassed by bottom-up, pattern-matching, inductive logic. And the power of these models shows no signs of abating.

Which raises the question: do the firms with the most compute and the most data win?

And as with all of these massive, billion-parameter models, no one knows how they really work on the inside. These models are grown, not built, as Anthropic CEO Dario Amodei is fond of saying.

This is a longer preamble than I anticipated, but I hope it gives you a sense of the landscape.

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Man Group

AI is helping the world’s largest listed hedge fund find new investment strategies.

Man Group’s quant equity unit uses an internal tool, called AlphaGPT, to generate, code and backtest trading ideas, mimicking how researchers develop new trading signals. AI in investing here looks less like a single model answering questions and more like a small organization. For trade ideas, AlphaGPT uses a workflow that proposes signals, writes code, runs backtests, and then sends the output into Man’s standard human review process.

The big picture: AI gives them scale to test more ideas. Man Group announced a partnership with Anthropic in February to use Claude and work with Anthropic engineers on AI applications across the firm, with alpha generation as the primary focus.

I spoke with Ziang Fang, Senior Portfolio Manager at Man Numeric, in December about how the system works in practice. The core problem AlphaGPT is solving: there’s been an explosion in data availability, and no one can realistically go through thousands of alternative datasets, many of which are unstructured. The system processes that volume and proposes hypotheses. Humans validate them.

“If you didn’t know whether a signal came from AI or a human, you probably couldn’t tell. The main difference is formatting. The AI output is more consistent,” Fang told me.

“The system has produced signals that meet our standards and pass the same evaluation thresholds required for human-generated research,” Fang wrote in November.

“Along the way we ran into a lot of issues — hallucination, lookahead bias, multiple testing, and many other things,” Fang said. The firm uses prompt controls, validation checks, and human review to limit errors and prevent p-hacking.

Hudson River Trading

Hudson River Trading is building foundation-style models trained on decades of global market data, applying techniques similar to those used in frontier language models for automated trading.

The firm is training these models on more than two decades of data spanning equities, futures, and cryptocurrencies, totaling over 100 terabytes. That translates into “something like trillions of tokens, in the same realm as what you train frontier language models on,” said Marc Khoury, an algorithm developer at HRT, speaking at an academic conference last summer.

HRT’s goal is to model markets as sequences of interactions. Much of the predictive signal lies in how order book events evolve over time, especially during fast-moving conditions. “As I increase the model size, the model continues to improve,” Khoury said — the same scaling pattern seen in large language models.

HRT is responsible for about 10% of all US equity volume. HRT’s AI Labs team, HAIL, says deep learning is core to the firm’s trading, and that HRT has spent more than a decade integrating AI research and infrastructure into its trading strategies. HRT is a proprietary trading firm and doesn’t file an ADV, so there’s nothing to cross-reference on the regulatory side.

And to my earlier aside about whether the firms with the most compute and data outperform their competitors — here’s HRT’s data center inside a mountain:

Bridgewater

In March 2025, Bridgewater CEO Nir Bar Dea said its $2 billion AI fund is generating “unique alpha uncorrelated to what our humans do” at a Bloomberg conference. The fund is delivering returns “comparable” to the firm’s human-led strategies, Bar Dea said. No specific figures were disclosed.

The fund is run by Co-CIO Greg Jensen and uses Bridgewater’s proprietary technology with models from OpenAI, Anthropic, and Perplexity. Bridgewater formed its Artificial Investment Associate (AIA) Labs division in 2023. The AIA serves as the primary decision-maker in the fund, while human professionals oversee risk management, data acquisition, and trade execution.

In its ADV, Bridgewater describes AI and machine learning as examples of “new sources of alpha” the firm is developing. It’s a description of Bridgewater’s strategy and capabilities, not a disclosure of realized fund performance.. It’s a description of what Bridgewater is building toward, not what it’s delivered. Bar Dea’s claim at Bloomberg is the stronger statement, and even that came without figures.

Jane Street

Jane Street says deep learning is “the future of quantitative trading.” The firm, which reported record trading revenue in 2025, builds neural-network models that drive its trading strategies, along with the infrastructure needed for training and inference.

Jane Street says it has tens of thousands of high-end GPUs, more than 1 exabyte of current storage, and about $400 billion in daily filled dollars. It trades on more than 200 electronic exchanges and venues, making it one of the world’s largest market makers.

Last month, Jane Street committed about $6 billion to use CoreWeave’s AI cloud platform and made a separate $1 billion equity investment in the company. The agreement gives Jane Street access to next-generation compute across multiple facilities, including Nvidia’s Vera Rubin technology.

CoreWeave said Jane Street has relied on its infrastructure since 2024 to train and scale proprietary models. Jane Street’s head of quant research, Craig Falls, said CoreWeave provides the GPU infrastructure and technical support needed for the firm’s machine-learning and research workloads.

Bloomberg reported that Jane Street generated $39.6 billion in trading revenue in 2025, surpassing major Wall Street banks with only about 3,500 employees. That doesn’t prove AI or compute caused the trading haul. But it does show why access to GPUs, storage, and low-latency model infrastructure has become strategic.

More firms detailed below, including Citadel, XTX, AQR, Viking and Millennium.

Read more

NEWS

AI As Portfolio Manager

Up until the last week or so, I hadn’t heard of any institutional money managers letting AI take the wheel in terms of making trading decisions. (And to be clear here, I’m talking about generative AI. Systematic models have been making trading decisions for years, but these follow rules written by a human.)

Instacart co-founder, Apoorva Mehta, has raised $100 million in seed equity finance for his fund Abundance that essentially uses AI to replace fundamental portfolio managers, according to Bloomberg. The Palo Alto-based fund uses AI to scour the internet for trade ideas, conduct research, pick stocks, size bets, and execute trades.

People “can only track so many opportunities at once, process them only so deeply, make only so many high-quality decisions,” Mehta said. “Even for the exceptional investor, the process is locked inside their mind. AI changes that entirely.”

This also lines up with something Andy Legg, a recruiter for Riviera Partners, mentioned in this week’s AI Street interview:

The most advanced AI in trading capability I’m aware of is a particular hedge fund where the AI is the trader. The researchers and engineers are feeding the reasoning engine of this AI daily to make better trading decisions, but the AI is making the trading decisions. This business is doing very well thus far.

These are the closest examples I’ve heard of a Minority-Report-type hedge fund. I’m sure there will be more.

Rogo Raises $160M at $2B Valuation

Rogo raised $160 million in Series D funding this week at a $2 billion valuation, up from $750 million three months ago. Total funding is now north of $300 million, making the firm the best-financed AI-for-Wall-Street startup.

This is the first time I’ve seen a finance AI company spell out specific user numbers in a press release. More than 35,000 financial professionals at over 250 institutions use the platform daily, according to Rogo. Bloomberg reported the client list includes JPMorgan, Bank of America, Wells Fargo, Lazard, Moelis, Nomura, and Singapore’s sovereign wealth fund GIC.

Rogo’s platform handles work that would otherwise fall to junior analysts: building slide decks, running comparables, producing research summaries, and organizing deal-related data. Its workforce is roughly split between engineers and former finance professionals, whom the company calls “forward deployed bankers,” who help client firms get more out of the tool. The company expects to employ close to 300 people by year-end, according to Bloomberg.

Rogo is competing against the makers of the models it relies on. Anthropic has embedded engineers at Goldman Sachs and dedicated sales teams for investment banking and retail banking, and is pitching directly to the same institutions.

Bloomberg CTO Outlines AI Strategy

We’ve talked about how you can’t spin up a couple of AI models to recreate the Terminal. Unlike other media companies, including Dow Jones, that struck licensing deals with OpenAI, Bloomberg never did.

Fortune interviewed Bloomberg CTO, Shawn Edwards, on the Terminal’s ASKB chatbot, a natural language interface for financial data.

It touches on a couple of themes we’ve been discussing: AI compressing data workflows, and why one-size-doesn’t-fit-all for AI models.

Data ingestion that used to take four-and-a-half months now takes two days, he says. That’s freed up the large teams once dedicated to data entry and cleaning, many of whom have been redeployed onto building internal evaluations.

…

Next, cost discipline is fundamental. And that means workflows need to be multi-model. AskB uses a mix of commercial frontier models and open-weight ones, as well as its own internal models, routing queries to the cheapest model that can handle a given task with the kind of reliability and performance that workflow demands, Edwards says.

I may be dating myself by how often I highlight how AI can structure unstructured data. But if you’ve ever spent hours, or days even, staring at an Excel spreadsheet, toggling back-and-forth among data sources, you understand how much of an improvement AI is to your workflow.

Edwards also spoke to Wired on how the company sees this ASKB evolving. Emphasis mine:

Do you expect ASKB to become the primary method for interfacing with the Terminal, or is it more like an extension?

This will be the new Terminal. This will be the primary way most interactions are happening—definitely where they are starting.

I don’t think GUIs [graphical user interfaces] are going away. I should be able to pick up my mouse and do something. But by and large, people will start their analysis and workflows through ASKB.

AI in Financial Services in Early Stages: Survey

A new report from Cambridge’s Centre for Alternative Finance, produced in partnership with the BIS, IMF, World Economic Forum, IDB, CGAP, and Arab Monetary Fund, finds the financial services industry has broadly adopted AI but has yet to see transformational impact at scale. Some key numbers:

• 81% of firms say they’ve adopted AI, but only 14% say it’s transformational, suggesting most use cases haven’t changed how firms compete.

• 53% of firms are spending less than $100,000 a year on AI, pointing to small-scale deployments rather than deep system buildouts.

• 63% of firms build on external foundation models, meaning core AI capabilities are increasingly shared rather than differentiated.

• 79% use AI for process automation and 75% for software engineering, showing deployment is concentrated in internal functions rather than revenue generation.

Who they surveyed: 203 fintechs, 149 traditional financial institutions, 146 AI vendors, and 130 central banks and financial regulators across 151 jurisdictions.

One of the more striking numbers is how little these companies are actually spending: 81% of firms say they’ve adopted AI, yet 53% are spending less than $100,000 on it. (Survey responses are self-reported.) Companies spending above that threshold are more likely to report higher ROI, suggesting the ceiling on returns is partly a function of how much firms are willing to commit.

Turning Bond Chatter into Trades

This is fun. Trumid, a fixed income trading platform, introduces an AI tool that turns client conversations into populated trade tickets. The product has processed over $56 billion in traded volume since launch in December 2025, per the company.

I think the next generation will look back on this era of office work, hear the clicking and typing, and react the same way we do now when we hear a fax machine.

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ROUNDUP

What Else I’m Reading

• The Billionaire Math Geek Who Turned AI Into a Money-Printing Machine WSJ

• Kalshi Completes First Block Trade, Backed by Jump Trading BBG

• CFTC’s AI will review U.S. crypto registration applications CoinDesk

• This bank CEO let his AI clone handle an earnings call CNBC

• Jane Street’s $40 billion trading haul tops rivals, sources say Reuters

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Back in New York

I’ll be back in New York to attend STAC Summit on May 20. Sessions include discussions on memory bottlenecks in inference, AI research at BlackRock, extracting structured data from SEC filings with LLMs, and deploying models into trading systems and engineering workflows. Registration is free for end users. Come say hi!

I’ll be at the Women in Quant Finance conference the next day, May 21.

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INTERVIEW

Andy Legg has recruited for Citadel, Point72, AQR Capital Management and Two Sigma, helping drive Wall Street’s shift toward quant strategies and PhD-led teams.

Now, he’s hiring for the next phase: AI.

In a recent interview, Andy, now a Director at Riviera Partners, discusses how hiring became “PhD-centric” after the financial crisis, how the high cost of computing power is limiting who can build AI systems, and how at least one hedge fund is letting those systems make trading decisions.

PhD Hiring Took Hold After the Financial Crisis

“I started in quant recruiting in 2009, just after the crash. The majority of recruiting I’ve done ever since has been PhD-centric, and it has entirely changed the landscape of Wall Street.”

A Hedge Fund Using AI as Trader

“The most advanced AI in trading capability I’m aware of is a particular hedge fund where the AI is the trader. The researchers and engineers are feeding the reasoning engine of this AI daily to make better trading decisions, but the AI is making the trading decisions.”

Private Equity Is Running Into a Data Problem

“They [Private Equity firms] are realizing the quality of their data in terms of validation, alternative data, and structured versus unstructured data is not where it needs to be to run AI, let alone prediction or recommendation systems.”

The Seven-Figure Talent War

“Candidates in the R&D spectrum who have a CS/Stats/ML/AI PhD under a renowned professor, published relevant papers at leading conferences... can command north of seven figure total comp packages at a pretty young age.

The “Vibe Coding” Interview

“An increasing number of funds are changing their approach to acknowledge those that can use prompt engineering or vibe coding effectively. The onus is increasing now on asking what AI tools you use and how AI-savvy you are.”

This interview has been edited for clarity and length.

Matt: I feel like the image of Wall Street is still the “Masters of the Universe” type, but it’s become way more PhD-driven now.

Andy: It’s funny when you talk to people removed from finance; they think of the New York Stock Exchange and people being there Eddie Murphy-style, like in Trading Places or Wall Street, thinking everyone is still wearing suits and ties. It’s anything but that these days. Everything is algorithm-driven and automated. The difference now is the compute… very few AI startups-mid size tech firms can afford to train their own models because they haven’t got the money to pay NVIDIA for the compute.

I started in quant recruiting in 2009, just after the crash. The majority of recruiting I’ve done ever since has been PhD-centric, and it has entirely changed the landscape of Wall Street.

Matt: It seems like it’s going to be more and more that way. The OTC markets have basically been the barrier, but now if you can structure all this data, it changes things.

Andy: This evolution is no longer limited to the upper echelons of finance. We are seeing that now in private equity. The data drive we witnessed in quant maybe ten years ago—where big data providers started popping up—is now increasingly happening in PE because they want to do AI to benefit from operational efficiency. They are realizing the quality of their data in terms of validation, alternative data, and structured versus unstructured data is not where it needs to be to run AI, let alone prediction or recommendation systems.

Matt: AI has been around for a while, but LLMs have made it “AI” in the way people talk about it now. When did it start to shift recruiting-wise?

Andy: Machine Learning has been around for more than 40 years. The difference now is the compute and the scale at which it can be performed.

I placed my first machine learning research engineer at a hedge fund in 2013. If you look at some of the more illustrious quant hedge funds— Renaissance Technologies, D.E. Shaw, TGS —they were leveraging ML in the late 90s and early 2000s. G-Research is another where their ML quant group was started in the early 2010s and seeking to incorporate ML into their trading strategies.

If you watch the show Billions, which is primarily based on Point72, they do a good job of parodying elements of the market adoption and certain innovations. One of the more renowned data science to market intelligence stories of the 2010’s is about a lumber strike in Canada. The fund in question flew drones and used satellite imagery to figure out when the strike was going to end and how much lumber was piling up. These insights informed their trading strategies and its rumored they made millions of dollars in profit from this in a matter of days. Now in the 2020s, most quant funds are combining the insights data science has provided them with AI, whether gen-AI and/or agentic AI to find greater opportunities to beat the markets.

The most advanced AI in trading capability I’m aware of is a particular hedge fund where the AI is the trader. The researchers and engineers are feeding the reasoning engine of this AI daily to make better trading decisions, but the AI is making the trading decisions. This business is doing very well thus far. News broke recently that Instacart co-founder Apoorva Mehta is launching a new hedge fund, Abundance, where AI agents will work as the portfolio managers.

Matt: So, it’s truly autonomous trading?

Read more

NEWS

BlackRock Builds Platform to “Vibe Code” Agents

Nish Ajitsaria, BlackRock’s head of Aladdin Product Engineering, told the WSJ that AI will become the default mode for most processes, including research, coding, and operations. Humans will shift into smaller, cross-functional teams that oversee the work rather than do it themselves.

From the WSJ:

The new platform, RockAI, will become the go-to interface for all the AI agents built inside BlackRock. Its natural language interface lets users pick the AI models, enter the relevant context and connect to the necessary databases for, say, an agent designed to research the top two real-estate investment trusts in the U.S.

All the safety and security guardrails are already built in, meaning users can spin up new agents in minutes without actually writing any code, said Pavan Pemmaraju, a senior lead in software engineering on Ajitsaria’s team. And those agents will be ready to scale across the business.

The platform was rolled out to its 5,000 in-house developers last Friday, but BlackRock said the goal is to later roll out the tool firmwide, so that even those in nontechnical roles, dubbed “citizen developers,” can start vibe-coding agents that can replace large chunks of their own busywork—and share them with colleagues.

Takeaway

As someone who tried and failed to learn Python like ~10 years ago, it’s pretty cool that AI is going to let us noobs spin up assistants for ourselves. Before AI, engineering resources were finite at every company, but now you don’t really have that constraint. I imagine other firms on Wall Street and beyond will let non-developers develop tools for themselves.

COMPUTE

Jane Street Invests $1 Billion in CoreWeave

From Bloomberg:

Jane Street Group, a trading firm, has taken an additional $1 billion stake in AI cloud services provider CoreWeave Inc. and plans to spend about $6 billion on the company’s technology offerings.

… The agreement will give Jane Street access to Nvidia Corp.’s Vera Rubin chips — set to come out this year — running in multiple CoreWeave data centers.

I’ll have more to say about hedge funds and market makers securing access to computing power as I’m working on a bigger piece on this trend. It’s clear that to compete in these markets, you need computing power, and lots of it. Just as speed and co-location helped determine the HFT winners and losers in the 2010s, access to compute will be this decade’s differentiating factor.

Related:

• Citadel Securities Sees AI Costs Alongside Trading Benefits BBG

ADOPTION

JPMorgan’s AI Tool Anticipates Client Cash Needs

JPMorgan disclosed Smart Cash, an AI tool that moves money between checking and higher-yielding brokerage products based on predicted cash flow. The eventual goal is for AI to anticipate bills and handle budgeting without manual input. JPM flagged the deposit implication: if the tool works, it shifts idle cash out of checking and into higher-yield products, putting pressure on deposits.

CEO Jamie Dimon on efficiency gains more broadly: "Bad idea to think you're going to deploy AI and improve your efficiency ratio because everyone else is doing it too."

Citi Rolls Out AI Agent for Wealth Clients

Citigroup’s wealth unit introduced “Citi Sky,” an always-on AI agent built with Google Cloud and Google DeepMind, designed to deliver real-time market insights, prompt actions, and interact with clients alongside human advisors.

BNY: 40% of Code is AI Generated

BNY says it has more than 140 “digital employees” in production, each made up of multiple agents with roughly two dozen skills and assigned to specific workflows.

Also this week: BNY invested in Domyn, an Italian startup building AI tools for banks and asset managers. Former BlackRock executive Stefano Pasquali runs Domyn’s financial AI division.

Microsoft Acquires Fintool

Microsoft acquired Fintool. Dozens of financial AI platforms exist. Fintool is/ was the only one I know of that was benchmarked by VALS AI, a third-party evaluation platform.

Ray Dalio Creates AI Version of Himself

I mean sure, why not:

AI Job Losses Are Hard to Find

One theme I wrote about at the start of the year was that AI doesn’t take your job. So far, this prediction is holding up. You would think by now, 3.5 years into this AI boom, that there would be pretty solid evidence that AI is leading to job losses, or at very least, some evidence of job losses. But there really hasn’t been.

• AI won’t trigger mass layoffs yet, Fed study says The Street

• AI and Jobs: Limited Disruption So Far Morgan Stanley

• AI was going to thin junior banker ranks, instead it raised the bar AFR $

ROUNDUP

What Else I’m Reading

• The remarkable renaissance of high-frequency traders FT

• Three Reasons AI Is Now More Reliable Than Ever WSJ

• Quant pioneer Martin Lueck warns against handing over trading to AI FT

• AI Pioneers Back Startup Building Models to Predict Events BBG

• Trump backs AI safeguards in banking system, acknowledges risks Reuters

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This Week in AI Street

FYI

I updated last week’s research piece with more specifics on money managers using AI, according to ADV data:

Back in New York

I’ll be back in New York to attend STAC Summit on May 20. Sessions include discussions on memory bottlenecks in inference, AI research at BlackRock, extracting structured data from SEC filings with LLMs, and deploying models into trading systems and engineering workflows.

Registration is free for end users. Come say hi!

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• STAC Summit - May 20 • NYC

  Trading and analytics infrastructure, applied AI for research and execution, scaled deployment, and benchmark-driven insights across quant workflows.

• AI & RegTech for Financial Services & Insurance – May 20–21 • NYC

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• Women in Quantitative Finance - May 21 • NYC

  Quants discussing current work in asset pricing, trading, risk, and portfolio construction.

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Most research on AI and financial news asks whether a headline is positive, negative, or neutral. This paper, the Market’s Mirror, asks something different: how do different investors interpret the same headline, and where do those interpretations diverge? The answer, it turns out, is demographic.

The results surprised even the authors.

"What surprised us most was that political differences generate the most disagreement in our data, but they're relatively weak at predicting actual trading volume,” Marina Niessner, one of the paper’s coauthors and a professor at Indiana University, said in an email. “One explanation is that people voice strong political opinions about firm news without backing them with capital, which echoes earlier evidence that beliefs and portfolios can diverge."

When a company announces a $19 million CEO pay package, a high-income liberal investor reads a signal of corporate strength. A low-income woman over 55 reads a reason to sell — “it’s unfair to regular folks who can barely make ends meet,” in one example from the paper.

The problem with existing disagreement measures is frequency and specificity. Well-known investor expectation surveys run every six weeks at best. Analyst forecast dispersion covers only firms with analyst coverage. Social media skews toward a narrow demographic slice. None can tell you, for a specific headline about a specific company on a specific day, which investor groups disagree and why.

Here’s what they did:

• Persona construction. They crossed six demographic attributes drawn from FINRA’s 2021 National Financial Capability Study — age (18–34, 35–54, 55+), gender, race, income (<$50K, $50–100K, >$100K), education, and political orientation (conservative, independent, liberal) — producing 216 distinct investor profiles. Each profile seeds Llama 3.1 8B, an open-source language model run locally. The prompt instructs the model to adopt the persona’s worldview, then asks: given this headline about this company, would you buy, hold, or sell?

• Scale. 5.5 million RavenPack headlines for S&P 500 firms from January 2010 through April 2025, covering 1,070 firms. Each headline paired with each of the 216 personas gives 1.188 billion total elicitations. Each prompt took approximately 1.2 seconds on four Nvidia Tesla V100 GPUs. Using the larger 70B version of the same model would have taken roughly 14× longer — a computational cost difference of approximately 219,000 GPU-days. The 8B choice was driven by both cost and practicality.

• Measuring disagreement. For each headline, disagreement is the weighted standard deviation of buy/hold/sell responses across all 216 personas, with weights matching the actual demographic distribution of U.S. retail investors from the FINRA survey. They also computed dimension-specific disagreement by isolating variation along each of the six demographic axes separately.

• Validation against human benchmarks. Two out-of-sample checks, both using surveys published after the model's December 2023 training cutoff. In the first, personas were asked to rank ten corporate actions by moral wrongfulness — the same task given to a representative sample of U.S. adults in a separate study. They agreed: layoffs and CEO pay increases most objectionable, share buybacks least. In the second, demographics alone — no individual survey history — reproduced known behavioral biases with 64.87% accuracy, versus 59.16% for random guessing.

• Trading tests. They tested whether days with more disagreement among personas led to more trading, after accounting for the usual suspects — recent returns, volatility, media sentiment, and social media disagreement. They ran the same test on data from before and after the model's training cutoff to check that the results weren't an artifact of what the model had already seen.

Here’s what they found:

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NEWS

Anthropic’s Mythos Triggers Bank Cyber Risk Talks

Anthropic’s new model, Mythos, is being treated as a potential systemic risk to the financial system.

Anthropic says Mythos identified and exploited vulnerabilities in real-world software, including bugs that had remained unpatched for 10 to 20+ years, with one example dating back 27 years. Treasury Secretary Scott Bessent and Fed Chair Jerome Powell convened Wall Street executives to assess whether the model could increase cyber risk across financial institutions.

From Bloomberg:

The previously unreported meeting, arranged on short notice, is another sign that regulators consider the possibility of a new breed of cyber attacks as one of the biggest risks facing the financial industry. All the banks summoned to the meeting are classified as systemically important by top regulators, meaning their stability is a priority for the global financial system.

Beyond the immediate cyber concern, the episode is another sign that model capabilities are still improving.

A year to 18 months ago, there were concerns that LLMs were approaching the limits of scaling, with diminishing returns from additional data and compute.

Models have continued to improve, with gains increasingly coming from post-training and system design rather than larger pre-training runs.

The same architecture is being adapted to other data types. Netflix and Stripe have both reported improvements after training models on internal datasets.

We’re still early in figuring out how far this architecture can go. Transformers’ power showed up unexpectedly. Much of the work since has involved adapting them to new kinds of data and seeing where performance continues to improve.

Firms like HRT are training models on market data for trading. If the trajectory of LLMs is any guide, training models on market data still has room to run.

OpenAI Buys Second AI Finance Startup

OpenAI has acquired two AI personal finance startups in recent months — Roi in October and Hiro this week. Both products were built around financial decision-making: Hiro focused on modeling “what-if” scenarios, while Roi aggregated user data and adapted outputs to individual preferences.

The company has been contracting Wall Street bankers to improve model performance in finance. The work aligns with areas where models remain inconsistent, including numerical accuracy, multi-step reasoning, and handling structured financial data.

The moves point to a more direct push into financial services, particularly around financial decision-making.

Last summer, MIT professor Andrew Lo said AI could be managing money as a fiduciary in five years.

Citi Cuts Tech Contractors

Citi said in its Q1 earnings presentation that it has reduced technology contractors “as a result of productivity,” alongside broader adoption of AI tools, according to eFinancialCareers. The bank also disclosed that advanced and agentic systems are now used by more than 10,000 engineers, and that AI has been applied to tasks such as remapping decades-old legacy code in a matter of days.

This echoes what Goldman’s Marco Argenti said a few weeks ago on Bloomberg’s Odd Lots podcast. The bank has terminated some third-party software contracts, with engineers now able to build smaller applications in-house in days rather than months.

TIFIN Builds AI Platform for Multi-Agent Workflows

TIFIN, a wealth technology firm, said its AI platform is built around agents coordinating across workflows.

The platform includes an agent library spanning operations, investments, and client engagement. Firms can assign agents to roles including advisors and support staff, and coordinate them across workflows.

TIFIN said the platform has more than 10 enterprise clients but did not name them. The company was founded in 2018 and previously built 55ip, a portfolio management software platform it sold to JPMorgan.

The company’s approach suggests a shift from standalone AI tools to systems that coordinate work across the firm.

ROUNDUP

What Else I’m Reading

• Inside the AI Index: 12 Takeaways from the 2026 Report Stanford

• Vanguard gives advisors an AI portfolio analyst Investment News

• We’re Using So Much AI That Computing Firepower Is Running Out WSJ

• Lloyds Banking Group deploys AI boardroom bot FinTech Futures

• Meta creating AI version of Zuckerberg so staff can talk to the boss Guardian

Back in New York

I’ll be back in New York to attend STAC Summit on May 20. Sessions include discussions on memory bottlenecks in inference, AI research at BlackRock, extracting structured data from SEC filings with LLMs, and deploying models into trading systems and engineering workflows.

Registration is free for end users. Come say hi!

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As I’ve said before, AI excels at organizing tedious, repetitive material that no one would review by hand. Form ADV brochures fit that description. They’re long, dense, and there are thousands of them.

So I ran them through a local model.

I downloaded roughly 16,000 Form ADV filings filed in March, the annual disclosures registered investment advisers file with the SEC describing how they run their businesses. Of those, roughly 15,000 were standalone Part 2A brochures, covering about 12,600 advisory firms across hedge funds, private equity, venture, real estate, and traditional RIAs.

What the Filings Show

I scanned every filing for AI-related keywords. About 5,800, just under 40%, mentioned AI at all. In most cases, that meant boilerplate risk disclosures warning about potential impacts on portfolio companies or financial markets.

More than 1,200 firms described using AI in their operations. Fewer than 320 named a specific product. Of those, more than 450 firms disclosed a formal internal AI policy, and 88 said AI-related costs were being charged directly to clients or fund investors.

The detailed disclosures are concentrated at the top. The largest managers — those with more than $100 billion under management — were meaningfully more likely to describe specific AI use cases and governance frameworks than smaller firms. Building an AI governance framework requires lawyers, compliance staff, and engineers working in concert. Most smaller firms don’t have that infrastructure. Explicit references to AI driving investment decisions are less common, and usually more carefully qualified.

Where firms do describe AI in concrete terms, it’s mostly operational. Concentric Capital Strategies, with $3.1 billion in regulatory assets under management, discloses the use of LLMs, such as ChatGPT, within its investment research and business processes.

Caveat: Form ADV filings capture what firms consider material enough to disclose. Routine or limited AI use may not appear at all.

A Closer Look at the Largest Managers

I selected 100 of the world’s largest and most recognizable money managers — firms spanning traditional asset management, hedge funds, private equity, and venture capital — and scanned their 2026 ADV filings with Gemma.

Seventy-five of the 100 disclosed some form of AI use. Twenty-four named a formal governance policy. Thirteen disclosed that AI-related costs may be passed to investors.

The following is for paid subscribers and examines the firms that named governance policies and what those policies actually say, the private equity pattern of charging AI infrastructure costs to fund investors, and three large managers that went from no AI disclosure in 2025 to named frameworks in 2026.

The Governance Tier

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Funds Are Feeding Internal Research Into AI Systems

We’ve talked a few times about how AI is good at structuring unstructured data, or taking disparate documents and turning them into a searchable format.

Much of the data in the corporate world is unstructured, sitting in PDFs, siloed in different departments and scattered across systems. Estimates suggest that about 80% falls into this category.

Before LLMs, connecting data across sources was too tedious and too expensive. Now, the economics have changed.

Kim Posnett, global co-head of investment banking at Goldman Sachs, argued last year in an FT piece that AI is turning data itself into a market, with proprietary datasets becoming more valuable as public data gets exhausted.

News came out this week that BlackRock and Balyasny are ingesting their own internal research, notes, and communications as the next source of differentiation. Business Insider captured this shift well:

The Three-Person Fund Built Around AI

Since AI can pull together scattered pieces of information, you don’t necessarily need dozens of analysts.

I’ve previously written about OffDeal, which is using AI to handle deal sourcing, outreach, and execution with a much smaller team. The company says it automates much of the manual work typically handled by junior bankers, allowing a lean team to run multiple transactions at once.

That same approach is now being applied at Epicenter Capital, a three-person fund run by former Coatue Management investor Rahul Kishore and backed by the Laffont brothers. The firm centers around an internal AI system, Eve, that is plugged into the investment process.

From Bloomberg News:

With Eve at their side, Kishore and his two colleagues hope to outdo other funds that employ hundreds of people. Their fiendishly elusive goal: to invest in 10 companies that generate a 10-times return in 10 years, the people said.

The tasks Eve works on are not glamorous but are well-suited for a digital employee.

Eve also scours the disclosures of more than 13,000 companies; listens to podcasts; scrutinizes social media posts; summarizes the news; and, each morning, generates a podcast for Kishore to listen to while he drives to work.

“Eve allows us to consume 10x more information in 10x less time. It helps us identify new potential investment ideas and accelerates our research,” Kishore wrote in an investor letter seen by Bloomberg. Eve’s “repository of memories allows us to better evaluate company performance as well as our investment process.”

Crypto Entrepreneur Sells AI-Generated Research

Crypto entrepreneur Anthony Pompliano is launching ProCap Insights, an AI-generated research service under his company ProCap Financial. The product uses AI agents to scan markets and produce reports on individual stocks, macro trends, and themes. The company says it can generate hundreds of reports a day. A subscription runs $2,500 a year.

The build was fast and cheap — two weeks, one employee, a few thousand dollars. That’s the core argument: AI makes research production dramatically less expensive.

From the Journal:

“We think that AI agents are very good at finding undiscovered or uncovered insights in financial markets, but they also are very good at producing large amounts of well-written research faster and cheaper than humans.”

It’s certainly faster for producing equity research. Where it gets harder is verifying the output. As we’ve covered previously, even the best models struggle with long inputs, a problem called “lost in the middle.” It’s genuinely hard to distinguish what is right and wrong in AI-generated content unless you verify all the outputs yourself, which defeats the purpose of using AI to speed things up.

ROUNDUP

What Else I’m Reading

• The Age of the Engineer Is Here. The Numbers Prove It. Craig Whiting

• MIT study challenges AI job apocalypse narrative Axios

• Big AI spenders are reaping large productivity gains American Banker

• Creative LLM use cases Flat Circle

• Citigroup says AI helps speed account openings and systems upgrades Reuters

• Electronic trading giant Optiver is building out a new AI Lab eFinancialCareers

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Thanks for reading!

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Companies are using smaller, specialized systems because they’re easier to test, control, and generally, are more consistent.

Some examples:

• Capital One replaced a single LLM with a multi-agent system for call summaries, where agents interpret, reason, cross-check, and document the interaction before finalizing it.

• Daloopa built dozens of narrow models, each trained on structured financial data and focused on one task. “Our models have an IQ of 250 on one task and 2 on something else,” Thomas Li, CEO and co-founder of Daloopa, told me in September.

• At BlackRock, researchers broke stock screening into specialized agents—fundamentals, sentiment, and valuation—that debate and cross-check each other before reaching a final decision.

A new paper from Andrew Ang, a former BlackRock executive, Nazym Azimbayev, a sovereign wealth fund CIO, and Andrey Kim PhD, a Deutsche Bank quant, takes the BlackRock debate architecture further.

The paper, the Self-Driving Portfolio: Agentic Architecture for Institutional Asset Management, asks if autonomous driving is here, why not autonomous investing?

Their answer is a 50-agent pipeline that runs the process and documents each step of its reasoning.

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They view running a strategic asset allocation process at an institutional level as a bandwidth problem as much as an analytical one. A CIO can supervise maybe 10 to 15 investment departments. A research team can realistically cover 20 to 30 asset classes before the process bottlenecks and an investment committee meets quarterly.

To speed this process up, they built a 50 agent pipeline that produced a documented strategic asset allocation with capital market assumptions, portfolio construction, peer review, and a board memo.

Here’s what they built

The pipeline is organized around the Investment Policy Statement (IPS), which governs the whole system the way it would govern human portfolio managers. Every agent reads it; the chief risk officer agent checks compliance for every portfolio candidate; the final output must satisfy it.

To illustrate how the pipeline works in practice, the authors ran it in March 2026 against the following mandate: 18 liquid asset classes (6 equity, 8 fixed income, 4 alternatives), a target real return of CPI +3–4%, a volatility band of 8–12%, a maximum drawdown of −25%, and a tracking error ceiling of 6% relative to a 60/40 benchmark.

• Macro agent: Classifies the current economic regime — expansion, late-cycle, recession, or recovery — using macro data, market indicators, and web searches for real-time readings. Output flows downstream to every other agent.

• Asset class agents: Agents run in parallel, one per asset class. For equity classes, each estimates expected returns using six different methods, then blends them into a seventh composite. An LLM-as-judge step reads all seven alongside the current macro regime and valuations, and selects a final estimate with explicit weights and a written rationale.

• Portfolio construction agents: 20 agents each build a portfolio using a different method, ranging from simple rules of thumb to more sophisticated approaches. A 21st researcher agent scans the academic literature and proposes methods not yet in the pipeline. A separate adversarial diversifier, one of the original 20, deliberately constructs the portfolio most different from the consensus of all the others.

• Strategy review: Each agent reviews two others — one using a similar approach, one using a different one — and all reviews are released simultaneously. Agents then vote, ranking their top five and flagging a bottom pick. Votes are combined with a performance score, and the final shortlist must include methods from at least three of the four broad categories.

• CIO agent: Combines the top candidates using seven different aggregation methods and selects the one best suited to the current environment. Produces a board memo written for non-technical stakeholders.

• Meta-agent: After each rebalancing cycle, compares past forecasts against realized returns, identifies systematic weaknesses, and updates both the code and instructions governing the other agents. All changes are logged.

  ---

Here’s what they found

• The macro agent classified the current environment as late-cycle with stagflationary risk.

• When each asset class agent settled on its return forecast, the pattern was consistent: the more expensive the market, the more the agent discounted historical estimates. US Growth stocks had their forecast cut 2.0 percentage points below the composite; US Large Cap was cut 1.1 points; Emerging Markets were barely adjusted. The agents weren’t pessimistic across the board — they were specifically skeptical of backward-looking estimates for the assets where current prices already implied low future returns.

• The same reasoning surfaced in the portfolio construction vote. In a late-cycle environment where return forecasts are uncertain, the agents collectively favored methods that lean on historical volatility and correlation data rather than return predictions. Maximum Diversification — a method that spreads risk across assets without relying heavily on return forecasts — ranked first. The portfolio that was deliberately constructed to be as different as possible from all the others came last, which was expected: its value is in the final blending step, not as a standalone recommendation.

• The final portfolio came out modestly underweight stocks (44.9% vs. 60% in a standard balanced portfolio), roughly in line on bonds (41.7%), with an 8.1% cash position. Over a backtest from 1996 to 2026, it produced nearly the same return profile as a 60/40 portfolio — but with a peak-to-trough loss of 25.6% versus 34.3%.

To be sure, this is a proof of concept, not an investment strategy. One run producing a sensible-looking portfolio doesn't tell you much given the short-time horizon.

I asked the paper’s authors about the results and received email responses from Azimbayev, who is also CEO of Altbridge, which describes itself as an AI-native hedge fund.

Read more

One pattern shows up again and again: many investors tried AI a year ago or so, had a bad experience, and wrote it off because it got facts wrong, misstated figures, or invented citations.

Yet they keep hearing about AI in finance. That creates a different kind of pressure: not wanting to be left behind, without a clear sense of what has changed.

In this interview, he explains why product overload is slowing adoption, why “skills files” matter more than model training, and how to structure workflows so outputs can be trusted.

He also points to areas like fixed income, where these approaches may matter more than people expect.

The conversation also covers something that doesn’t get enough attention in finance: how bias shows up in ways that aren’t obvious. Not just demographic bias. Positional bias (the same information, presented in a different order, can produce different outputs), framing effects (the same odds stated two ways lead to different decisions), and the fact that models reflect the biases in the data they’re trained on.

We cover:

• Why many investors are still anchored to early AI failures

• Why comparing models is the wrong approach

• How “skill files” and workflows actually drive results

• Where early ROI is showing up (including fixed income)

• How bias shows up in model outputs

This interview has been edited for clarity and length.

Matt: You talk to a lot of investment professionals just getting started with AI. What’s your sense of adoption among investors?

There’s a large group that tried ChatGPT when it first went mainstream, had a bad experience — it made something up, got a calculation wrong — and wrote it off. That’s a reasonable response based on what it was at the time. The problem is anchoring. They’ve frozen their view of the technology at that moment, and the tools are genuinely different now. I tell them: forget everything you knew about this 18 months ago. You have to be experimenting again.

The other group has FOMO, but the anxiety from not knowing where to start is actually keeping them from doing anything. My advice to both groups is the same: treat it like a new employee. Give it a task. Check the output. See what it can do.

From Models to Workflows

Matt: We are seeing a total product overload right now. It isn’t like comparing phones based on pixel counts; it is very difficult to compare these AI models side-by-side. How should people navigate this?

Brian: It is very hard to compare them, and getting all of them at once can be overwhelming. I recommend trying to understand what you can do with the “Frontier” models and Claude’s skills—as well as the skills OpenAI is developing—and what can be achieved with connectors. For example, Notion already acts as an agnostic transcript writer that can connect to Claude. Many investment professionals are not yet aware of the tools that are used ubiquitously in the computer science realm.

Matt: I’ve had Claude skills on my radar for a few months, but I’m still trying to get my arms around them. How are you using them currently?

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NEWS

Goldman CIO on AI Inside the Bank

Goldman Sachs CIO Marco Argenti said AI has made it cheap enough to build smaller applications in-house, with the firm already terminating some third-party software contracts.

Marco Argenti, speaking on Bloomberg’s Odd Lots podcast, said the buy-versus-build calculation has shifted. Engineers can now build working applications in days rather than months, he said.

Other notes from the podcast:

• Goldman’s internal AI assistant is deployed to 47,000 employees logging more than a million prompts a month.

• On token costs, Argenti expects per-unit prices to fall but total consumption to rise faster. Goldman built a model gateway that routes queries to the cheapest model that can handle them. “Total token cost is going to be a major item of cost in any organization,” he said. “It’s to be compared to the cost of people.”

Agents as a Service

Citi Ventures says AI-native startups are beginning to convert work historically done by humans in functions like IT, sales, legal, and HR into software-driven workflows, charging for completed tasks rather than software licenses and putting pressure on traditional per-seat SaaS pricing.

The note points to the growing importance of specialized models, particularly large tabular models designed for structured financial data like fraud detection and credit risk, where traditional language models are less effective.

Related

Retail Broker Rolls Out AI agents that Place Trades

Public, a retail brokerage, is rolling out AI agents that can execute trades on behalf of customers, automating tactics like protective hedging, cash sweeps into higher-yield assets, and stop-loss orders based on predefined rules.

Users write out a strategy, refine it through follow-up prompts, and review a step-by-step workflow before the agent goes live. The system logs every action and operates within fixed instructions. “It can only do what you tell it to do,” co-CEO Jannick Malling told the WSJ.

Public joins Robinhood and eToro in pushing AI beyond research and into execution. Brokerages are now starting to let those systems convert user-defined strategies into live trades.

FDIC Moves to Ease AI Model Risk Rules for Banks

The FDIC is revisiting its model risk management guidance, with an agency official telling Congress it has been applied too broadly and imposed unnecessary burdens, particularly on smaller banks, without a meaningful reduction in risk. The agency is working with the Fed and OCC on a more tailored, risk-based approach that accounts for a bank’s size, complexity, and the materiality of each model.

The shift does not change safety expectations but could reduce supervisory friction for lower-risk AI applications such as internal copilots and summarization tools, while models used in credit and risk management remain tightly governed. The official also said the FDIC expects to roll out GenAI tools to its own staff by mid-year.

Top Bridgewater Scientist to Join Google DeepMind

Jasjeet Sekhon, former head of AI at Bridgewater Associates, is set to join Google DeepMind as chief strategy officer, a move that reflects how quantitative investing and AI research are converging: both train models on large datasets to identify patterns and make predictions.

Perplexity Runs AI Agent Stock Pitch Contest

Perplexity is running a stock pitch competition for college students, offering $17,500 in prizes for investment theses built using its Computer product, a general-purpose AI agent that it says executes multi-step workflows autonomously.

The judges are Philippe Laffont of Coatue, Dan Loeb of Third Point, and Ken Hao of Silver Lake. Submissions are due April 3.

What Else I’m Reading

• AI Companies Shatter Fund-Raising Records, as Boom Accelerates NYTimes

• Bank of America’s wealth management firms roll out AI tool Banking Dive

• Starling Bank Launches UK’s First Agentic AI Money Manager Fintech Times

• QuantumStreet AI launches long-short global equity strategy Hedgeweek

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Cornell’s new AI in Finance certificate began in this vacuum. Victoria Averbukh, Professor of Practice and Director of Cornell Financial Engineering Manhattan, spent two years talking to portfolio managers, traders, and strategists before designing it.

“People would say it was about not having a clear way to think about the systems, what the system is doing,” Averbukh said. “New terminology kept coming in.”

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The program — 30 sessions, 13 instructors — mixes Cornell faculty with practitioners from fintech, asset management, investment banking, and trading. The goal is judgment, not tool proficiency.

Kathryn Zhao, Head of Institutional API Product, OKX, says it reflects a shift already underway in hiring. Domain experience used to be the deciding factor. Now she screens for AI awareness.

“If someone understands how to work effectively with AI tools [...] they can onboard quickly and begin contributing almost immediately,” Zhao said.

In the conversation below, we discuss:

• Why applying AI in finance can’t be a direct translation from tech

• How the certificate balances academic foundations with practitioner insight

• What AI awareness means for hiring and talent development

• The biggest stumbling blocks for AI adoption in financial services

• Why chasing the pace of change is less useful than building understanding

The below conversation has been edited for clarity and length.

Matt: What was the genesis of this certificate? When did you decide to do this, and what was the catalyst?

Victoria: I kept hearing, across different finance sectors, that people were either using AI and finding it useful but not fully comfortable with whether they could trust it, or they didn’t even know where to start. That hesitation was very consistent—for portfolio managers, traders, execution people, strategists, research people, more quantitative people, less quantitative people. It didn’t really matter. People would say it was about not having a clear way to think about the systems, what the system is doing. New terminology kept coming in. We started with AI, then the word “agent” appeared. It just felt either overwhelming or there was a lack of trust.

My light bulb went on around 2024, about two years ago. I remember that Kathryn and I actually went to have coffee at Breads Bakery on the Upper East Side, and I said, “Kathryn, I have this thought.” And Kathryn said, “Yes!” She was one of my very early supporters. That coffee at Breads Bakery is what gave me confidence to go and investigate more.

Matt: What makes applying AI in finance different from applying it in tech?

Victoria: After speaking with Kathryn, I also spoke with Andrew Chin, Marcos López de Prado, and others. They were all very clear that education is needed, partly because of the hesitation we just talked about, but also because finance is not tech, and applying AI here requires respecting that difference.

Machine learning, big data technologies, and large language models were all built for something else, not for finance. Uber’s business model, for example, is built around offering a service powered by new technology. That is fundamentally different from what a bank or a hedge fund does. So applying AI to investing, to execution, to alpha generation, or even to forecasting market exposure cannot be a direct translation. The objectives are different, and the data is different. Financial data is non-stationary, often smaller, and rarely clean, so you cannot just take machine learning methods from tech and apply them directly.

Our industry is and will continue to adopt AI, but it has to be done carefully, with a real understanding of what works and what does not. Everyone I spoke with strongly supported the idea that training is needed specifically because of these differences, and that developing critical understanding, judgment, and a clear sense of potential ROI before adoption is essential.

Which is why the real question is not whether we use AI, but how we use it in a way that actually improves decision-making rather than just adding complexity.

Matt: Can you talk about the structure of the certificate and the role of practitioners in it?

Victoria: The full certificate is about 30 sessions with 13 instructors. The curriculum is deliberately structured to start from fundamentals — faculty from Johnson School and Engineering explain what the data is, what an LLM is, and work through use cases.

But because it’s so fast-changing, you really need practitioners to understand what needs to be done. Finance is an extremely regulated industry. I think that’s another thing that differentiates it. Even probably from healthcare.

The industry instructors are very carefully curated to give breadth of coverage — fintech, asset management, investment banking, and trading. This is not a certificate just for trading or fraud detection or financial advising. It’s for everybody. Ideally you have some experience on Wall Street, but also if you’re just starting out, it’s really for everybody.

Do you know the quote from Einstein? “If you can’t explain it simply, you don’t understand it well enough.” That was my guiding principle. I know that our Cornell faculty can take the complicated topics — transformers, LLMs, all of that — and make it intuitive. Developing that intuition is really the intention behind the certificate. It’s what enables you to make sound judgments about when, where, and how AI should be used, and when it shouldn’t.

Recent Interviews

Matt: Kathryn, where do you see this heading? How do you see AI impacting finance in the next couple of years?

Kathryn: Speaking from a practitioner’s perspective, my approach to hiring has fundamentally changed. A few years ago, I would evaluate candidates primarily based on their prior experience in the specific role or industry. Today, that is no longer the deciding factor, particularly for junior hires.

What I prioritize now is AI proficiency and AI awareness. If someone understands how to work effectively with AI tools (how to ask the right questions, interpret outputs critically, and apply insights to real business problems) they can onboard quickly and begin contributing almost immediately. With access to AI-generated materials and the ability to leverage AI as a day-to-day copilot, the learning curve is dramatically compressed.

In that sense, traditional domain experience is no longer a strict prerequisite. What matters more is a strong baseline understanding of the real world at a college-educated level, combined with the ability to operate fluently in an AI-enabled environment.

That is why I believe an AI in Finance certificate program is highly relevant. It prepares participants to become AI-aware and AI-capable without requiring them to be programmers. More importantly, the AI literacy and applied mindset the program builds will open a wide range of opportunities for participants in the years ahead.

Victoria: When you say the person needs to be AI-aware—does that mean the person can have zero finance knowledge, or do you mean they don’t need deep knowledge of Python and machine learning?

Kathryn: They don’t need to come in with deep expertise in Python or extensive financial industry knowledge. Those capabilities can be developed on the job. What matters most as a baseline is their ability to work effectively alongside tools like Claude: knowing how to frame the right questions, extract the right information, and translate insights into action.

Victoria: I agree with Kathryn, but maybe a notch below the enthusiasm. Here’s why: This certificate is not about the tools. It’s about understanding the lay of the land and developing intuition. Learning what Claude does can be done on YouTube. There are plenty of tutorials.

The AI awareness Kathryn mentioned, that’s what we bring in the certificate. Ideally, as an educator, I want participants to leave thinking: I know what questions to ask. I know how to bring judgment to that Claude-generated code. So maybe we’re fast-tracking people a little bit through the first nine months on the job once Kathryn hires them.

I also think finance is segmented. You can be an expert in energy, or equities, or fixed income, or mortgages. You can be a really great financial advisor, but you wouldn’t necessarily know how to construct a global allocation as a portfolio manager. At some point, applications of AI are going to become more tailored to all these different areas. It’s almost like you’re not going to go to a dentist if you need new glasses.

Ideally, if this certificate is successful and we offer it again and again, I certainly want to make sure that we have significant participation from practitioners, from industry. Engineers will be inventing new AI 2.0 and 3.0 and 10.5, but the industry participation will always be needed. Maybe we break it up or reshape it to focus on specific areas of finance, that’s also a possibility.

Matt: What is the biggest stumbling block right now for AI adoption in finance?

Victoria: I think it’s uncertainty. I think it’s leadership that is probably older and did not grow up with phones in their hands. There’s a certain inertia. Bridging the generational gap is going to be harder. I think CEOs are going to get younger.

Matt: How do people keep up? It feels like the terminology alone is a moving target.

Victoria: There’s no glossary out there. That glossary changes dynamically. That’s going to be part of the certificate. Once people finish, they’re going to know the terms and will be more comfortable and ready for a new iteration of terms. But ultimately, I think trying to chase the pace is impossible. Focus on understanding, not the hype.

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The transformer breakthrough began in text, but researchers are adapting the architecture to other kinds of sequential data. With enough data, transformer models can learn the patterns of “language” in that dataset in ways that traditional models missed.

For example, AlphaFold is a transformer-based system trained on protein data to predict how proteins fold into their 3D shapes from amino acid sequences, which determine how they function. It effectively solved the protein folding problem. (The work later contributed to a Nobel Prize in Chemistry awarded to its creators, including Demis Hassabis, who is not a chemist.)

As I’ve written before, no one knows exactly how these models work. They’re grown rather than built, as the CEO of Anthropic likes to say. We didn’t know how aspirin worked for like 70 years, but we knew it was effective.

This brings us to a new paper from JPMorgan researchers, who trained a transformer model on market data.

The market as a language

Every buy, sell, order submission, or cancellation leaves a trace: what happened, how much size was involved, how far from the market mid-price it was placed, and when it occurred. Multiply that across thousands of stocks and millions of events per day, and you get a massive stream of sequential data.

TradeFM is a 524-million-parameter model trained on 10.7 billion training tokens drawn from more than 9,000 U.S. equities, using data spanning 368 trading days from February 2024 to September 2025.

Instead of predicting the next word in a sentence, their model — called TradeFM — predicts the next event in a sequence: its timing, size, price depth, and direction.

Trading data is messy. Stocks trade at different prices. A $5 move on a $2 stock is massive. A $5 move on one that’s $500 isn’t news.

If you feed those raw numbers into a model, it can’t really compare one stock to another, so it struggles to learn general patterns.

So the researchers adjusted the data before training. They expressed price-related features in relative terms, compressed volumes so large and small trades are easier to compare, and measured time as the gap between events.

That puts different stocks on a common scale, so moves are comparable whether it’s a $2 stock or a $200 stock.

They then discretized each event’s features and combined timing, price depth, volume, side, and action type into a single composite token. The result was a vocabulary of 16,384 trade event tokens.

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What they found

The researchers tested the model inside a simulated exchange, where it predicts trades in a continuous loop. The resulting data reproduces core patterns seen in real markets, including clustered volatility and large price swings. Across 9 stocks, 3 liquidity tiers, and 9 months of held-out data, it matched those patterns 2 to 3 times more closely than a standard baseline known as a Compound Hawkes process.

What’s most interesting is that TradeFM’s behavior extends beyond the U.S. data it was trained on. JPMorgan tested the model, without any adjustments, on trading data from China and Japan, where market structure differs meaningfully. Japan uses batch auctions at the open. China imposes 10% daily price limits. Spreads in both markets are several times wider than in the U.S. Despite those differences, the model’s performance degraded only moderately. It had never seen these markets, yet it still captured their core dynamics.

The model appears to be learning structure that carries across markets.

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Arman Khaledian, PhD, a former quant at Millennium and now CEO of Zanista AI, said: “That’s not a toy result. It means the model is picking up something real about how markets work at a structural level.”

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NEWS

AI as Money Manager

From Investment News:

Era, a San Francisco startup founded by ex-Stripe employees, has registered with the SEC as an RIA that delivers investment advice through AI without client-facing human advisors.

Era’s Context financial data hub connects to AI agents such as Anthropic’s Claude, OpenAI’s ChatGPT and Google Gemini, among others. Clients can subscribe to predetermined portfolio mixes for their investments and access automated services such as spending analysis, savings round-ups, money transfers between accounts, budgeting, and portfolio rebalancing.

As a registered investment adviser, Era is held to the same fiduciary standard as other RIAs, meaning it must act in the client’s best interests, according to Era CEO Alex Norcliffe.

Rather than rely on raw LLM outputs, the firm says it engineered a system to deliver consistent, traceable advice across clients with similar profiles.

While those who are old enough to remember those thundering herd commercials aren’t likely to trust AI with their money, younger folks are. About 41% of Gen Z and millennials would allow an AI assistant to manage their investments, versus 14% of Baby Boomers, according to a World Economic Forum survey.

AI Joins the C-suite at HSBC

HSBC named David Rice as its first chief AI officer, tasked with scaling AI across the bank, including giving staff access to generative tools for internal workflows and customer interactions.

More Wall Street firms will elevate AI executives to the C-Suite. The technology is slowly evolving into a company’s “brain.”

DoorDash Turns Couriers Into AI and Robot Trainers

From Bloomberg:

DoorDash Inc. is paying delivery couriers in some markets to submit video clips and complete other digital tasks to help improve artificial intelligence and robotics models, following competitors that have found creative new uses for gig workers in the AI boom.

The latest models draw attention as they surpass prior state-of-the-art benchmarks, but their voracious appetite for data is often overlooked. As I wrote last week:

The latest models have hoovered up the whole internet and that’s still not enough. Last year, OpenAI was paying YouTubers as much as $4 for a minute of their unused footage.

Legacy firms are sitting on decades of data. Unlocking it is hard, but it’s an advantage. You can’t hire DoorDash drivers to mimic financial transactions.

Trump Pushes for Federal Control Over AI

The Trump administration is pushing Congress to preempt some state AI laws and create a lighter national framework, while still leaving states authority in areas such as fraud, consumer protection, zoning, and their own use of AI. The administration argues that a patchwork of state AI laws could create conflicting requirements for companies operating nationally. A federal standard would reduce some state-by-state compliance differences, though states would still retain authority in several areas.

So far, there have been few changes to financial regulatory rules. U.S. regulation sets broad standards and applies them to new technologies like AI rather than writing detailed, tech-specific rules.

Fed Expects Banks to Expand AI Use Beyond Low-Risk Work

A senior Federal Reserve official said banks are still using AI in limited, low-risk areas like summarization and coding, but expects adoption to expand into more material functions as use cases mature.

The Fed is not treating this as a new regulatory category. Existing expectations around model risk, data quality, and governance apply, with a continued emphasis on testing and human oversight. “Judgment and decisionmaking will remain with subject matter experts.”

Supervisors are also starting to use AI themselves, including to analyze earnings calls, filings, and other public data as part of bank monitoring.

WHAT ELSE I’M READING

• Mark Zuckerberg Is Building an AI Agent to Help Him Be CEO WSJ

• Goldman Sachs Puts AI at Core of New Strategy Banking Exchange

• VanEck CEO Details AI’s Impact on ETF Asset Management ETF Trends

• AI Has Already Changed Software Contracts: They’re Shorter BBG

• AI in banking ‘not a silver bullet’: Mike Mayo BankingDive

• AI boom risks widening wealth divide, says BlackRock’s Larry Fink Guardian

• Quilter signs deal with AI adviser tool Aveni for its network Citywire

• Norway wealth fund moves towards some AI-driven decisions but with humans in control Reuters

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Morgan Stanley’s ex-AI head: Start with the Work, Not the Model

Most firms are still stuck in pilots. The constraint is how work is structured, not model capability.

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AI spots when companies change which metrics they highlight

Firms that frequently swap metrics tend to underperform. LLMs improve detection by extracting full phrases instead of keywords.

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Thanks for reading! I’m always happy to receive comments, questions, and feedback.

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His advice: don’t start with technology. Identify work that can be automated.

Many companies are doing the opposite— buying tools first and figuring out where they fit later.

“We’re letting the vendor marketplace drive our strategy as opposed to asking the question: what do you want?”

McMillan, who recently launched McMillanAI, where he advises executives on AI strategy, says many organizations are still early in figuring out how to deploy AI at scale.

In practice, that means starting with tasks that take up a lot of time and are repeated across large teams—call centers, onboarding, compliance review. These are areas where AI can replace or augment work in a measurable way.

What breaks at scale isn’t the model. It’s everything around it: how data is structured, who has access, how systems are monitored, and how much autonomy they’re given.

Most firms haven’t solved that yet. They’re experimenting with tools, but haven’t redesigned how work actually gets done.

We cover:

• Identifying high-volume work AI can replace

• What breaks when you try to deploy AI at scale

• Why most firms are still stuck in pilot mode

• How to think about vendors vs building in-house

• Where agents are actually being used today (and where they aren’t)

This interview has been edited for clarity and length.

Matt: Why start McMillanAI now?

Jeff: There’s an enormous gap in the marketplace around education and awareness. The people that need to make the decisions — and by the way, I’ve probably spoken to no less than 30 CEOs in the last six weeks, CEOs of Fortune 500 companies — they want to do AI. They’re getting pressured to do AI. But we have a workforce that knows more about this technology than most senior people do in organizations. That’s a gap, and that’s an opportunity.

I don’t want to sound Pollyannaish about this because I’m not: this is a once-in-a-generation type of technology, and I really do believe that we have a choice as humanity. We have a choice about how we deploy this for the benefit of all of us as opposed to maybe a few. I’d like to be part of that dialogue.

Matt: Going back to those 30 conversations, what were the common threads?

Jeff: There’s an enormous amount of external pressure on them. There’s no CEO I talked to that says, “I don’t believe in AI.” That was maybe true three years ago — “Is this just the next crypto? Is it the next metaverse?” No one believes that now. Everyone believes there’s something going on here. So that’s number one.

Number two, there’s a tremendous desire to do something, but they don’t have the skills and the competencies to do this technology at an enterprise level. If you look at every major technical transformation, it takes eight to 10 years to fully play out. So, we’re very early in the process.

The problem with AI is it requires a different approach, and it’s not a technology problem.

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