Hey, it’s Matt. You’re reading AI Street, where I report on how Wall Street uses AI.

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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This Week in AI Street

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.

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

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