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.

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 plan on 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 plan on 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

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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If you prefer to receive one weekly email with all AI Street content, uncheck Research and Interviews.

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CALENDAR

Upcoming AI + Finance Conferences

• Momentum AI New York – Apr. 27–28 • NYC
  Senior-leader forum on AI implementation across financial services, from operating models to governance and execution.

• 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.

• 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.

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

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!

Share AI Street

This Week in AI Street

CALENDAR

Upcoming AI + Finance Conferences

• Momentum AI New York – Apr. 27–28 • NYC
  Senior-leader forum on AI implementation across financial services, from operating models to governance and execution.

• 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.

• 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.

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Thanks for reading!

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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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NEWS

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.

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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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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.

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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.

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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.

Related Research

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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INTERVIEW

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.

RESEARCH

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

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.

Read more

The C-suite can choose what numbers to highlight — revenue per user, lifetime value, total addressable market, etc.

These selected metrics support the corporate narrative, but they’re not static. When a number is strong, it gets airtime. When it softens, it tends to quietly disappear from the script, replaced by whatever metric tells a better story that quarter.

Shifting corporate narratives happens so often it has a name: “moving targets.” This tracks the fraction of previously highlighted metrics that go missing from the next comparable earnings call. Research showed that firms with high metric turnover tend to underperform in subsequent months. The more a company reshuffles the numbers it talks about, the worse its stock tends to do.

The challenge is detection. Most approaches rely on keyword matching across transcripts, comparing terms quarter over quarter. You need to be able to link “revenue growth” to “top-line expansion.” Keyword search can’t distinguish “North America cloud revenue” from “revenue.”

At scale, this becomes difficult to track. Following the metrics that appear and disappear across thousands of earnings calls is not feasible to do consistently by hand. This is where LLMs fit: extracting and standardizing how companies describe performance over time. This is tedious work for humans and easy to scale with AI.

A group of researchers at MIT, BlackRock, and J.P. Morgan asked whether LLMs could close this detection gap.

Here’s what they did:

• Instead of scanning transcripts for predefined terms, they use an LLM to extract full phrases with context. Where keyword methods pull “revenue,” the model pulls “North America cloud revenue.” Where it grabs “dividends,” the model also captures “cash flow,” “share repurchases,” and “cash flow from operations.”

• They then compare metrics across quarters using semantic similarity rather than exact matches. Instead of forcing a binary match, they allow for an “ambiguous” range where similarity is scaled.

• They apply this across firms listed in the S&P 100 index from January 2010 to December 2024, yielding 5,615 firm-quarter observations across 64 quarters.

• To test it, they sort stocks by how much their metrics shift and compare returns, then run cross-sectional regressions with standard controls for size, valuation, and prior returns.

Here’s what they found:

Read more

• News Roundup: Nvidia announces NemoClaw, HSBC, Terminals, Grok

• Research: AI for Excel still can’t make it on Wall Street

• Interview: Kevin McPartland on the internet-scale impact of AI

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NEWS

Nvidia Says OpenClaw Is “the New Computer”

Nvidia this week introduced NemoClaw, a software toolkit designed to make OpenClaw usable inside enterprise environments. OpenClaw is an open-source framework for building AI agents that can plan, execute tasks, and coordinate subagents across tools and data sources.

“Every company in the world today needs to have an OpenClaw strategy,” said CEO Jensen Huang. “This is the new computer.”

OpenClaw has quickly become one of the most widely used agent frameworks, allowing developers to build systems that can plan tasks, execute them, and spin up subagents to handle specialized work with limited supervision.

The problem: OpenClaw is a security nightmare as Cisco puts it.

Nvidia hopes its NemoClaw is the answer. The operating system runs agents inside controlled environments, with restrictions on what they can access and how they behave. The system adds monitoring, policy enforcement, and guardrails around data and network activity.

Currently, AI tools are powerful for individuals, but they aren’t designed for coordinated use across teams, systems, and models.

Separately, Meta-acquired Manus launched a desktop application that enables its AI agent to operate locally on personal devices, allowing direct control over files and applications to rival the popular OpenClaw agent.

We’re starting to see a world where AI doesn’t live in a chatbot, but is part of the operating system. That shifts the enterprise question away from “which model is best” and toward something more practical: what specific tasks AI agents can reliably execute inside existing workflows.

You can start to imagine a TaskRabbit-style marketplace for agents, where firms deploy specialized systems on demand to handle discrete tasks.

HSBC is considering cutting up to 20,000 jobs (about 10% of its workforce) over the next three to five years as it uses AI to automate middle and back-office functions, particularly in non-client-facing roles, according to Bloomberg.

I’m skeptical that AI is the primary driver here. (The preliminary plan also includes attrition, restructuring, and potential business exits.)

If AI were already replacing labor at scale, you would expect to see more consistent signals across large banks. That’s not happening yet. JPMorgan, which spends roughly $20 billion a year on technology, reported headcount was essentially flat last month, according to CNBC.

As I’ve said many times around here, there’s currently no clear evidence that AI is dramatically reducing jobs. Maybe that changes, but right now it’s a convenient narrative for broader cost-cutting.

Finance Bros to Tech Bros: Don’t Mess With My Bloomberg Terminal

The rumors of the death of the Terminal are greatly exaggerated. Startups have been “coming for” the financial giant for like 40 years. The WSJ had a piece this week about how the VC crowd misunderstands the staying power of the ubiquitous black and orange screens on Wall Street.

I’m not saying this because I worked there for 10+ years, but because what we’ve seen in this current AI boom: proprietary data is becoming more valuable. Hell, there is so much demand for data, they’re creating synthetic data. 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.

Incumbents with decades of data have an advantage.

Musk’s xAI Hiring Credit Experts, Bankers to Teach Grok Finance

From Bloomberg:

Elon Musk’s artificial intelligence startup xAI is looking to hire bankers and private credit lenders to make its Grok chatbot better at finance strategy, joining rival AI firms in pushing software for investing professionals.

I still see commentary online that the people training AI are basically talking themselves out of a future job. That’s just not true. From October when news broke that OpenAI was hiring bankers, I wrote:

Reinforcement learning — I’m very much oversimplifying — is basically a giant game of hot and cold. The data helps tell the model when it’s getting “warmer” or “colder” to the right answer. It doesn’t explain why it’s the right answer.

So while AI companies like to market their models as “thinking,” they’re not really reasoning in the syllogistic sense. (All men are mortal; Socrates is a man; therefore Socrates is mortal). These models are mimicking thinking, not doing it.

AI is coming for investment banking tasks. The tech is starting to automate the tedious point-and-click white-collar work that has historically taken junior analysts 100+ hours a week.

ROUNDUP

What Else I’m Reading

• Markets misread AI’s impact on brokerages, MS says Investment News

• Beyond productivity: AI’s structural shift in active management Schroders

• Rogo Acquires AI Agent Company Offset PR

• Market data price increases slow for first time in five years: study The Trade

• Agentic AI Startup Lyzr Raises Funds at $250 Million Valuation BBG

• AI Nears ‘Inflection Point’ in Asset Management MarketsMedia

• Six skills for financial service professionals Anthropic

RESEARCH

AI Still Falls Short in Excel: Study

Even the best model wouldn’t make it on Wall Street

AI for Excel has improved dramatically over the last six months, but it still wouldn’t make it as a junior analyst on Wall Street.

The three leading AI providers, OpenAI, Anthropic and Google’s Gemini, have all released updated capabilities for Excel and are reporting higher accuracy on benchmarks.

For example, OpenAI said this month that performance on its internal investment banking benchmark jumped to 87.3% (GPT-5.4 Thinking) from 43.7% (GPT-5).

While these are material improvements, AI still can’t be relied on without supervision. “Mostly right” is not good enough.

A recent benchmark on AI in Excel points to the same issue: performance drops sharply as tasks get more complex.

FinSheet-Bench tests how models handle real-world private equity workbooks with messy layouts, multiple funds, and non-standard formatting. Across 10 models from OpenAI, Google, and Anthropic, the best result came from Gemini 3.1 Pro at 82.4% accuracy, followed closely by GPT-5.2 with reasoning and Claude Opus 4.6 with thinking, both around 80%.

On simple lookups, top models exceed 90% accuracy.

The gap widens further on large, realistic files. On the most complex workbook tested, with 152 companies across eight funds, average accuracy was worse than a coin flip.

Read the Full Research Report

INTERVIEW

The Limits of AI in Trading

Kevin McPartland has spent more than 20 years studying how technology changes market structure.

He expects AI to have an internet-scale impact on markets:

𝘐 𝘢𝘮 𝘢 𝘣𝘦𝘭𝘪𝘦𝘷𝘦𝘳 𝘵𝘩𝘢𝘵 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘪𝘯 𝘴𝘰𝘮𝘦 𝘸𝘢𝘺𝘴 𝘭𝘪𝘬𝘦 𝘵𝘩𝘦 𝘧𝘪𝘳𝘴𝘵 𝘥𝘰𝘵-𝘤𝘰𝘮 𝘣𝘰𝘰𝘮. 𝘚𝘶𝘳𝘦, 𝘪𝘵 𝘸𝘪𝘭𝘭 𝘤𝘩𝘢𝘯𝘨𝘦 𝘫𝘰𝘣𝘴 𝘢𝘯𝘥 𝘵𝘩𝘦𝘳𝘦 𝘸𝘪𝘭𝘭 𝘣𝘦 𝘫𝘰𝘣 𝘭𝘰𝘴𝘴𝘦𝘴, 𝘸𝘩𝘪𝘤𝘩 𝘯𝘰𝘣𝘰𝘥𝘺 𝘦𝘷𝘦𝘳 𝘸𝘢𝘯𝘵𝘴. 𝘉𝘶𝘵 𝘪𝘯 𝘵𝘩𝘦 𝘭𝘰𝘯𝘨 𝘳𝘶𝘯, 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘢 𝘵𝘰𝘰𝘭 𝘢𝘯𝘥 𝘢𝘯 𝘢𝘪𝘥 𝘵𝘰 𝘩𝘦𝘭𝘱 𝘱𝘦𝘰𝘱𝘭𝘦 𝘥𝘰 𝘵𝘩𝘦𝘪𝘳 𝘫𝘰𝘣𝘴 𝘣𝘦𝘵𝘵𝘦𝘳 𝘢𝘯𝘥 𝘵𝘰 𝘤𝘳𝘦𝘢𝘵𝘦 𝘯𝘦𝘸 𝘫𝘰𝘣𝘴 𝘸𝘦 𝘥𝘰𝘯’𝘵 𝘬𝘯𝘰𝘸 𝘢𝘣𝘰𝘶𝘵 𝘺𝘦𝘵. 𝘐 𝘳𝘦𝘢𝘭𝘭𝘺 𝘵𝘳𝘶𝘭𝘺 𝘧𝘦𝘦𝘭 𝘭𝘪𝘬𝘦 𝘵𝘩𝘢𝘵’𝘴 𝘸𝘩𝘦𝘳𝘦 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘨𝘰𝘪𝘯𝘨.

He leads market structure and technology research at Crisil Coalition Greenwich, where he tracks how banks, asset managers, and trading firms deploy new systems. He previously worked at BlackRock and TABB Group.

But AI adoption on trading desks has yet to scale.

A recent report from the firm shows the most common use cases among bond traders are still data analysis and document review, not execution or decision-making.

Trading desks face clear regulatory and reputational risk, where firms need to explain and defend decisions to clients and regulators. As McPartland puts it, you can’t tell regulators: “Well, the AI did it.”

In this interview, McPartland explains where AI is being deployed today, what’s holding back trading applications, and why coding and developer productivity may be the most important near-term use case.

This interview has been edited for clarity and length.

Why AI Adoption in Trading Is Moving Slowly

Matt: I was checking your reports on AI, and you guys are focusing on how it’s in the back office. That seems like it’s the story across the street. When do you think it goes beyond that to more trading applications?

Kevin: I think we’re starting to get there. I was at FIA Boca earlier this week and there was definitely a lot of talk about AI. I think the industry is excited and interested but really trying to be cautious. There’s a reputational risk issue, a regulatory issue. You don’t want to do the wrong thing for your clients from the sell-side perspective. If there’s an issue and regulators come to you and ask what happened, you can’t just say, “Well, the AI did it, I’m not sure.” That’s not a good answer. So I think that’s leaving people cautious.

We actually just got back a study of bond traders and we asked them where they saw the opportunity in AI. Not surprisingly, data analysis was number one, document review number two. So it still really is about poring through data and unstructured data to help digest it, find insights, find patterns. I think that’s still the biggest use case now.

My two cents — I think where really a lot of the impact will be in the short, medium, and long term is on the coding side. Everything from making the most sophisticated quant developers even more efficient than they already are, to letting business users prototype what they want in a way they never could before, and then handing it off to IT. I just think the possibilities are absolutely huge in that regard.

Read the Complete Interview

ICYMI

CALENDAR

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• Future Alpha – Mar. 31–Apr. 1• NYC
  Cross-asset investing summit focused on data-driven strategies, systematic investing, and tech stacks.

• AI in Finance Summit NY – Apr. 15–16 • NYC

  The latest developments and applications of AI in the financial industry.

• Momentum AI New York – Apr. 27–28 • NYC
  Senior-leader forum on AI implementation across financial services, from operating models to governance and execution.

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

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

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

What’s your favorite story this week?

Hit reply with the number of your favorite story. Thanks for reading!

1. News analysis

2. AI Still Falls Short in Excel

3. Interview with Kevin McPartland

The three leading AI providers, OpenAI, Anthropic and Google’s Gemini, have all released updated capabilities for Excel and are reporting higher accuracy on benchmarks.

For example, OpenAI said this month that performance on its internal investment banking benchmark jumped to 87.3% (GPT-5.4 Thinking) from 43.7% (GPT-5).

While these are material improvements, AI still can’t be relied on without supervision. “Mostly right” is not good enough.

A recent benchmark on AI in Excel points to the same issue: performance drops sharply as tasks get more complex.

FinSheet-Bench tests how models handle real-world private equity workbooks with messy layouts, multiple funds, and non-standard formatting. Across 10 models from OpenAI, Google, and Anthropic, the best result came from Gemini 3.1 Pro at 82.4% accuracy, followed closely by GPT-5.2 with reasoning and Claude Opus 4.6 with thinking, both around 80%.

On simple lookups, top models exceed 90% accuracy.

The gap widens further on large, realistic files. On the most complex workbook tested, with 152 companies across eight funds, average accuracy was worse than a coin flip.

One reason: models don’t actually “see” the spreadsheet. They operate on a text-serialized version that strips out layout, formatting, and visual structure.

So this:

Becomes this

Spokespeople for OpenAI, Anthropic and Google didn’t respond to requests for comment on FinSheet-Bench’s results.

What I’d like to see from model providers, rather than the latest numbers from internal benchmarks, is basic operating data: real error rates, how often outputs need to be corrected, and what level of reliability users should expect.

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ICYMI Research

The problems of AI in Excel echo the broader challenges of AI, namely that if the model has to traverse thousands of pages or dozens of tabs in Excel, accuracy plummets.

Over the last few weeks here, we’ve covered how there’s more of a premium on reliable models rather than the most powerful.

Read more

He expects AI to have an internet-scale impact on markets:

𝘐 𝘢𝘮 𝘢 𝘣𝘦𝘭𝘪𝘦𝘷𝘦𝘳 𝘵𝘩𝘢𝘵 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘪𝘯 𝘴𝘰𝘮𝘦 𝘸𝘢𝘺𝘴 𝘭𝘪𝘬𝘦 𝘵𝘩𝘦 𝘧𝘪𝘳𝘴𝘵 𝘥𝘰𝘵-𝘤𝘰𝘮 𝘣𝘰𝘰𝘮. 𝘚𝘶𝘳𝘦, 𝘪𝘵 𝘸𝘪𝘭𝘭 𝘤𝘩𝘢𝘯𝘨𝘦 𝘫𝘰𝘣𝘴 𝘢𝘯𝘥 𝘵𝘩𝘦𝘳𝘦 𝘸𝘪𝘭𝘭 𝘣𝘦 𝘫𝘰𝘣 𝘭𝘰𝘴𝘴𝘦𝘴, 𝘸𝘩𝘪𝘤𝘩 𝘯𝘰𝘣𝘰𝘥𝘺 𝘦𝘷𝘦𝘳 𝘸𝘢𝘯𝘵𝘴. 𝘉𝘶𝘵 𝘪𝘯 𝘵𝘩𝘦 𝘭𝘰𝘯𝘨 𝘳𝘶𝘯, 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘢 𝘵𝘰𝘰𝘭 𝘢𝘯𝘥 𝘢𝘯 𝘢𝘪𝘥 𝘵𝘰 𝘩𝘦𝘭𝘱 𝘱𝘦𝘰𝘱𝘭𝘦 𝘥𝘰 𝘵𝘩𝘦𝘪𝘳 𝘫𝘰𝘣𝘴 𝘣𝘦𝘵𝘵𝘦𝘳 𝘢𝘯𝘥 𝘵𝘰 𝘤𝘳𝘦𝘢𝘵𝘦 𝘯𝘦𝘸 𝘫𝘰𝘣𝘴 𝘸𝘦 𝘥𝘰𝘯’𝘵 𝘬𝘯𝘰𝘸 𝘢𝘣𝘰𝘶𝘵 𝘺𝘦𝘵. 𝘐 𝘳𝘦𝘢𝘭𝘭𝘺 𝘵𝘳𝘶𝘭𝘺 𝘧𝘦𝘦𝘭 𝘭𝘪𝘬𝘦 𝘵𝘩𝘢𝘵’𝘴 𝘸𝘩𝘦𝘳𝘦 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘨𝘰𝘪𝘯𝘨.

He leads market structure and technology research at Crisil Coalition Greenwich, where he tracks how banks, asset managers, and trading firms deploy new systems. He previously worked at BlackRock and TABB Group.

But AI adoption on trading desks has yet to scale.

A recent report from the firm shows the most common use cases among bond traders are still data analysis and document review, not execution or decision-making.

Trading desks face clear regulatory and reputational risk, where firms need to explain and defend decisions to clients and regulators. As McPartland puts it, you can’t tell regulators: “Well, the AI did it.”

In this interview, McPartland explains where AI is being deployed today, what’s holding back trading applications, and why coding and developer productivity may be the most important near-term use case.

This interview has been edited for clarity and length.

Manage Email Preferences

If you’d like to receive fewer emails, you can manage your subscription and turn specific sections on or off.

Manage How Often You Receive AI Street

Why AI Adoption in Trading Is Moving Slowly

Matt: I was checking your reports on AI, and you guys are focusing on how it’s in the back office. That seems like it’s the story across the street. When do you think it goes beyond that to more trading applications?

Kevin: I think we’re starting to get there. I was at FIA Boca earlier this week and there was definitely a lot of talk about AI. I think the industry is excited and interested but really trying to be cautious. There’s a reputational risk issue, a regulatory issue. You don’t want to do the wrong thing for your clients from the sell-side perspective. If there’s an issue and regulators come to you and ask what happened, you can’t just say, “Well, the AI did it, I’m not sure.” That’s not a good answer. So I think that’s leaving people cautious.

We actually just got back a study of bond traders and we asked them where they saw the opportunity in AI. Not surprisingly, data analysis was number one, document review number two. So it still really is about pouring through data and unstructured data to help digest it, find insights, find patterns. I think that’s still the biggest use case now.

My two cents — I think where really a lot of the impact will be in the short, medium, and long term is on the coding side. Everything from making the most sophisticated quant developers even more efficient than they already are, to letting business users prototype what they want in a way they never could before, and then handing it off to IT. I just think the possibilities are absolutely huge in that regard.

ICYMI

AI Regulation and Governance Are Still Catching Up

Matt: What’s your sense of what needs to happen on the regulatory and standardization side? It’s such a new technology — there are no best practices yet.

Kevin: It does need to happen, although it’s hard to put a finger on it, because almost by definition it’s not something that is structured. You could ask different LLMs or even the same LLM the same question and it might give you a different answer. So by definition, it’s not structured. But yes, maybe it’s just continuing to learn what the potential risks and pitfalls are. How do you look out for them? How do you catch them? How do you prevent them?

Of course the models themselves will continue to get better, which should limit some of those things, but it could create new ones as well. Just saying “no, it’s not safe, we can’t use it” — that’s not the answer either. This is here to stay. It’s going to have a big impact on the market. All that work is required, and I think we’re already starting to see more working groups and roundtables and people working through it, talking to their peers, trying to understand what are the best practices. What are you doing? What are you doing? So we can all sort of try to figure out the most effective way forward, because there is just a lot of opportunity.

Coding May Be the Most Underestimated AI Use Case

Matt: What do you think is underappreciated or not talked about enough in this space?

Kevin: The coding agents are talked about broadly — Claude Code and OpenClaw, that’s all over the news. But for capital markets and trading specifically, I don’t feel like it’s talked about very much. What does that look like? How is it used on a trading desk? Is it used on a trading desk yet? Are there rules there?

Matt: I spoke to Man Group. They’ve developed something called AlphaGPT. I think the hedge funds have a little more flexibility — they’re regulated, but they’re not tens of thousands of people usually. Some of the quants are doing this, but I think the technology has moved faster than the humans in terms of how they can actually put this out in a responsible way.

Kevin: Yeah. To me it all feels inevitable. It’s just figuring out how to test it and how to do it safely.

AI’s Impact on Finance May Look Like the Dot-Com Era

Matt: Is that opinion shared broadly? A year or two ago, AI in finance was not really considered as impactful as some other areas. Is the industry stance now that this is going to have a big impact?

Kevin: This industry doesn’t ever all agree on anything. I am a believer that this is in some ways like the first dot-com boom. Sure, it will change jobs and there will be job losses, which nobody ever wants. But in the long run, this is a tool and an aid to help people do their jobs better and to create new jobs we don’t know about yet. I really truly feel like that’s where this is going. Not about large-scale job loss, but people in the seats being able to do things they never knew how to, never had time for, or just never could before.

Matt: High frequency trading has gotten so fast that it’s approaching the speed of light, so you can’t really top that. You have to find other ways to make money.

Kevin: That’s right. It’s not just about speed. In equities, maybe it is, but that’s why there’s only a few dominant firms left doing it at scale. Somebody said to us a year or two ago, it’s not about being faster anymore — it’s about being smarter.

• News:

  • Balyasny Taps OpenAI

  • Manulife Adds Infrastructure for AI Agents,

  • Wall Street is Worried About AI Job Losses

• Research: AI Replicates Human Investor Biases

• Interview: AI Saved this Money Manager $1M

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NEWS

Balyasny Details AI Platform

Balyasny, the $32 billion hedge fund, recently announced its partnership with OpenAI and how it is using the technology inside its investment research workflow.

The case study on OpenAI’s website is unusually detailed, given how tight-lipped hedge funds are about their research stack.

One of the first points Balyasny highlights is how much effort went into evaluating the models before deploying them. That’s something I’ve been covering the last few weeks: how do you know if model outputs are accurate? There are surprisingly limited ways to test them with any off-the-shelf benchmarks.

Another notable point: Balyasny appears to be one of the large hedge funds working with OpenAI rather than Anthropic. Firms including Man Group and Goldman Sachs have leaned toward Anthropic in recent partnerships.

There are also some interesting specifics about how they’re using the technology, which I detail here:

• Merger arbitrage agent: Continuously monitors filings, press releases, and regulatory developments and automatically updates the probability that a deal will close.

• Central bank speech analyst: Parses speeches and policy signals to generate macro scenarios, cutting analysis time from roughly two days to about 30 minutes.

• Deep research agent: Synthesizes tens of thousands of documents including filings, broker research, earnings transcripts, and expert calls into structured research outputs.

Takeaway

Balyasny becomes the latest hedge fund to outline how they’re using AI in its investment process. The firm uses OpenAI and agents to analyze filings, broker research, and other financial documents. The whole case study is worth reading.

Manulife Adds Infrastructure for AI Agents

Manulife said this week it is building an internal platform to run AI agents across its insurance and investment businesses, tapping distributed systems company Akka to provide the infrastructure.

Akka’s software is used to run applications that must stay online even when parts of the system fail, such as payments networks, airline reservation systems, and trading platforms.

“AI is the epitome of the ultimate distributed system because now what you have are lots of agentic services that are all gonna cooperate with each other and start making these sort of complex decisioning systems,” Tyler Jewell, CEO of Akka, told me.

Manulife said the enterprise AI platform is currently in beta testing and is designed to support high-volume, business-critical AI applications across the firm. The system already includes other AI infrastructure components.

Akka records detailed logs of how agents interact and the decisions they make, creating an audit trail for governance and regulatory oversight.

“The platform is foundational to our strategy,” said Ben Schwartz of Manulife’s Global AI Platforms team.

Manulife expects AI to generate more than $1 billion in enterprise value by 2027, with roughly one-fifth coming from efficiency gains.

Takeaway

Deploying AI agents requires coordinating many systems at once, pushing AI adoption toward the same distributed infrastructure used in trading and payments platforms.

Wall Street is Worried About AI Job Losses

Lots of people worry about AI taking their jobs. But every week there’s news of some professional using AI at work and then losing their job.

An assistant U.S. attorney, who submitted a legal briefing with made-up case law, resigned before getting chewed out by the judge.

(In fact, the AI problem in the legal field is so widespread that there’s a database tracking more than 1,000 court decisions involving fake citations.)

This story was making the rounds earlier this week after Anthropic came out with this report that says computer programmers, customer service reps and financial analysts are the most exposed to AI disruption.

The Anthropic report doesn’t go into detail about what specific financial analyst skills are most at risk, but it does point out that there’s currently no evidence that this is having a meaningful impact on employment rates outside of young professionals.

So far, the narrative about AI replacing analysts is running ahead of the evidence.

Takeaway

From my conversations, AI is creating more work for analysts since they can cover more companies.

ROUNDUP

What Else I’m Reading

• Hedge fund run by ex-OpenAI researcher bets on power and crypto miners HedgeWeek

• Citigroup raises AI capex and revenue forecasts amid rapid enterprise adoption Reuters

• Why the AI Boom Will Make Phones, Cars and Electronics More Expensive BBG

• Roundtable debate: Can the market electronify block trades? The Desk

REEARCH

AI Replicates Human Investor Biases

A study of 48 models found framing and sunk-cost effects distort AI investment decisions.

AI hallucinations get a lot of attention. But another risk is bias.

The way you write a prompt can steer a model toward different answers, even when the underlying question is exactly the same. I wrote in November:

If you asked a (human) financial analyst whether Microsoft or Apple is the better investment, the answer wouldn’t depend on whether you said Microsoft or Apple or Apple or Microsoft. For LLMs, that word order matters, according to new research.

This risk is harder to detect since an answer isn’t necessarily wrong, the model just chooses to highlight a different point.

Individual investors and, I suspect, some institutional ones as well, are likely falling for this risk by asking AI for research ideas and stock-picking guidance.

More and more retail investors are relying on AI tools, and almost three quarters of millennials do so, according to an October eToro survey.

To investigate how widespread this issue is, researchers at Auburn University and the University of Tulsa evaluated 48 large language models across investment-style decision tasks.

They presented identical financial scenarios twice, changing only how the information was framed, such as wording risk as a gain versus a loss, adding a prestigious source, or mentioning prior spending. Many of the same biases have long been documented in human investors. The difference is that AI systems can reproduce them consistently and at scale.

What they did

• Showed each model the same scenario twice: once neutral, once with a subtle wording or context change

• Tested 11 well-known investor errors, including framing, anchoring, herding, narrative appeal, and sunk costs

• Ran 25 scenario pairs per error across all 48 models

• Evaluated mitigation methods such as debiasing instructions and prompt rewriting

Results

• Framing alone moved ratings by 1.62 points on a 10-point scale, enough to flip decisions around common thresholds

• Narrative cues dominated fundamentals: describing founders as fitting a familiar archetype raised ratings by 65% or when attributing the analysis to a Nobel Laureate.

Full Analysis + researcher commentary

INTERVIEW

AI Saved this Money Manager $1M

Ben McMillan says LLMs have saved his investment firm more than $1 million in operating costs.

The CIO and founder of IDX Advisors says AI has helped cut legal bills, replace outsourced developers, and automate proprietary workflows over the three years since ChatGPT launched in November 2022.

His team comes from a quant and coding background, which made it easier to start experimenting.

The firm, a systematic asset manager focused on risk-managed digital asset strategies, began testing large language models shortly after ChatGPT’s release. One of the first use cases they built was a way for AI to read PDFs, something models couldn’t do three years ago.

What started as a tool for reviewing documents has now grown into a broader internal system for coding, compliance and CRM automation. The firm now runs multiple models on the same task and has them critique each other’s output before a human reviews the results. The same approach has allowed the team to replace an offshore development group and build internal tools that would previously have required outside vendors.

I’d like to think I’m pretty current with the new AI tools by trying them myself, but Ben is ahead of me with OpenClaw, which he describes this way:

In our chat, Ben explains how the firm structures these AI workflows, the tools it relies on, and where he sees the biggest opportunities for AI in financial services.

He walks through how IDX built an AI-powered paralegal workflow, replaced an offshore development team with coding models, and created internal agents that automatically research and enrich potential clients.

He also explains why he believes persistent systems like OpenClaw could become a core layer of AI infrastructure inside small firms.

One theme that comes up repeatedly is that AI handles much of the grunt work while Ben and his team review and validate the results.

This interview has been edited for clarity and length.

Matt: How did you get started with AI?

Ben: I’ll give you a quick overview from day one of everything we did that was material. I’ll caveat it by saying that myself and the other founders come from a quant hedge fund background. We were coming into this already with some software development capability. We were doing our own APIs and things like that. We had machine learning models predicting Bitcoin prices. There was at least a modicum of technical experience in-house.

When ChatGPT first came out, like everybody else, we thought it was an interesting chatbot that could write poetry or create raps. But instantaneously we started just throwing things at it. A lot of people forget—it wasn’t that long ago—but the original ChatGPT couldn’t read PDFs. So the very first thing we built was a simple PDF reader. That was something we had experience with, because you have to vectorize the data. There’s OCR and all that. We did that specifically for legal. Compliance is expensive, and we’re a small business—a 10-person team with revenue under $3M, which is not low, but we need to save money where we can. Using ChatGPT to basically run our own paralegal department instantaneously cut our legal bills. I did the math: we easily saved a million dollars in legal bills since the launch of LLMs, which is material.

Matt: What were you doing previously, and how did you implement this?

Ben: Previously we had different lawyers for different things: a compliance lawyer, corporate attorneys, and JV lawyers. Everything was a back-and-forth. These are expensive Wall Street lawyers. A perfect example is new LP docs. That should be pretty “control C, control V”—a lot of that is templated. Why are we paying $75,000 for another set of LP docs?

I’ll zoom out and make a meta comment. Yes, AI is going to be disruptive—this is the new industrial revolution. But it’s also going to be hugely democratizing for small businesses. It has been tough to compete, irrespective of industry, as a small business in America for really the last 10 years. This is going to disintermediate massively in favor of small businesses.

Legal is a perfect example. We had a busy year in 2024, and what we did (regarding using LLMs in-house)—we always use a red team, blue team approach. That is the quickest way to dramatically increase the quality of the LLM output.

Read the Full Interview

CALENDAR

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What’s your favorite story this week?

Do me a favor and hit reply with the number of your favorite story from today:

1. Balyasny details AI research platform

2. Manulife taps Akka for AI infrastructure

3. Wall Street is worried about AI job losses

4. AI replicates investor biases

5. Interview with IDX’s Ben McMillan

The way you write a prompt can steer a model toward different answers, even when the underlying question is exactly the same. I wrote in November:

If you asked a (human) financial analyst whether Microsoft or Apple is the better investment, the answer wouldn’t depend on whether you said Microsoft or Apple or Apple or Microsoft. For LLMs, that word order matters, according to new research.

This risk is harder to detect since an answer isn’t necessarily wrong, the model just chooses to highlight a different point.

Individual investors and, I suspect, some institutional ones as well, are likely falling for this risk by asking AI for research ideas and stock-picking guidance.

More and more retail investors are relying on AI tools, and almost three quarters of millennials do so, according to an October eToro survey.

To investigate how widespread this issue is, researchers at Auburn University and the University of Tulsa evaluated 48 large language models across investment-style decision tasks.

They presented identical financial scenarios twice, changing only how the information was framed, such as wording risk as a gain versus a loss, adding a prestigious source, or mentioning prior spending. Many of the same biases have long been documented in human investors. The difference is that AI systems can reproduce them consistently and at scale.

What they did

• Showed each model the same scenario twice: once neutral, once with a subtle wording or context change

• Tested 11 well-known investor errors, including framing, anchoring, herding, narrative appeal, and sunk costs

• Ran 25 scenario pairs per error across all 48 models

• Evaluated mitigation methods such as debiasing instructions and prompt rewriting

Results

• Framing alone moved ratings by 1.62 points on a 10-point scale, enough to flip decisions around common thresholds

• Narrative cues dominated fundamentals: describing founders as fitting a familiar archetype raised ratings by 65% or when attributing the analysis to a Nobel Laureate.

Read more