Hey, I’m Matt. I’m a former Bloomberg News reporter, and you’re reading AI Street, where I report on how Wall Street uses AI. Every Thursday, I cover the week’s biggest AI-in-finance stories, plus original reporting and analysis.
Financial firms are starting to tie pay and hiring to AI fluency. But some don’t have anyone inside who can tell who’s actually fluent. Which, honestly, is a pretty good indication that we’re all still figuring this whole AI thing out.
The survey:
25% of HR professionals in U.S. finance said their organizations had tied raises, promotions and bonuses to AI proficiency, according to recruiting firm Selby Jennings.
30% said employees must disclose their AI use in formal performance reviews.
31% said their biggest obstacle to hiring for AI roles was that no one inside the firm could confidently evaluate candidates.
Half said the share of underqualified candidates they interview has increased over the past year.
Firms are trying to test for it: 42% said their organizations had added more technical assessments, while 27% said they use AI-conducted interviews.
“For decades, leaders on Wall Street wanted talent with specific backgrounds, degrees, and experience… AI has broken that framework, and most firms haven’t built a new one yet.”
Ben Hodzic, Managing Director, Selby Jennings
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Bloomberg Rolls Out AI Chat to All Clients
Terminal users can now add their own info into the ASKB interface, which the company had been rolling out in limited fashion early this year, according to the WSJ. More than a third of subscribers currently use it.
Full Disclosure: I used to work there.
ASKB can build repeatable workflows. Bloomberg is adding an AI assistant to BQuant to help users create investment models and dashboards in Python.
The system uses multiple models and dozens of agents. Bloomberg says it combines proprietary and open-weight models with its data, analytics, news and sell-side research, and provides citations. The article gives no independent accuracy results.
“ASKB is not just another function. Absolutely not. It’s a synthesis of all of our content. A synthesis of all of our analytics. It’s reaching into all the existing systems.”
Bloomberg Chief Technology Officer Shawn Edwards in the WSJ
Singapore Sets AI Guidelines for Financial Firms
The financial world recently received more clarity on what regulating AI looks like in practice, at least from one regulator.
The Monetary Authority of Singapore issued its final AI risk-management guidelines on October 7, which take effect in stages starting next year and cover AI from traditional machine learning to generative AI and agents.
Firms are expected to assign board and management oversight, keep an inventory of AI uses, assess their risks, and apply controls including testing, human oversight and monitoring.
Gary Ang, who previously led AI supervision at MAS where he helped develop the guidelines and now runs an AI risk practice Quaintitative, told me in an email that he views the guidelines as having “the broadest coverage in the global financial sector right now.”
Separately, the Financial Stability Board, an international body that coordinates financial regulators, published 12 recommended practices for AI use today. They cover much of the same ground as Singapore’s guidelines, but are not binding on firms or regulators.
Ang said some other jurisdictions are likely to follow Singapore’s lead, either with standalone guidance or by adding AI requirements to existing technology and model-risk rules.
TL;DR
We’re still in the early days of AI adoption and even earlier days in regulating the tech at financial firms, but more are likely to follow Singapore’s lead.
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AI-Run Funds Attract Modest Assets
Right now most of the AI and investing options are human plus AI rather than AI alone.
Reuters has a story on the handful of ETFs and funds being run by AI. The funds cited launched earlier this year and have not gathered much in assets.
This feels like where driverless cars were in the early 2010s. Back then, during training, humans sat in the driver’s seat in case things went sideways. But now, we’ve gotten comfortable with robot rides. They’re all over San Francisco. Humans have learned to trust the results.
That said, back in the unproven, training days, I wouldn’t have been eager to jump into one.
Few people will throw their money at an unproven money manager. But, and this is a big but, if AI does prove itself with solid returns at low cost, AI funds should proliferate like Waymos have.
“Until now you’ve had two choices: be a passive investor and own an index, or be an active investor” with a human picking stocks, said Tal Schwartz, founder and CEO of Ai Funds. “I want to offer a third option: where it’s AI that is doing it all for you.”
If you’d risk your life by taking a driverless car, why wouldn’t you risk your money with an AI money manager?
TL;DR
There’s a handful of small AI-driven investments cropping up, but it’ll take years and solid returns for robot active money managers to take hold.
ROUNDUP
What Else I’m Reading
Jump Trading Hires Startup Founders to Lead Compute Desk | BBG
How a trillion-dollar hedge fund borrowing spree became Wall Street’s cash cow | FT
Thomson Reuters CTO: Why Build Your Own AI Model When Frontier Models Already Exist? | The Data Exchange Podcast
Chatham scales its capital markets expertise with OpenAI | OpenAI
SEC Approves Cboe’s KPI Prediction-Market Bets Via Robinhood | BBG
Balyasny Asset Management brings Google Gemini into AI research platforms | The Trade
The Former NFL Lineman Who Solved a Decades-Old Math Problem | WSJ
Jamie Dimon says JPMorgan Chase is putting up gates to protect against ‘rogue agents’ | Business Insider
Two Sigma, the $80 billion quant fund, is raising the bar for interns after a very productive summer | Business Insider
St. Louis Fed Connects FRED Data to AI Assistants | Federal Reserve
This Week in AI Street
Telling AI to Act Like Different Investors Barely Changes Its Forecasts: Study
If you tell ChatGPT “Take on the persona of Warren Buffett,” you know you’re not getting the real thing. But you might think the instruction helps get 𝘴𝘰𝘮𝘦 of his investing judgment to come through.
New research is calling that assumption into question.
In a University of Chicago study, researchers asked an AI model the same prediction market questions dozens of times, changing the investor persona it was instructed to take on. They varied traits including age, income, politics and tolerance for financial risk, testing 54 profiles for each event on a given day.
The different personas mostly agreed with one another.
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Recently added events in BOLD.
AI in Finance Summit Fall — October 21–22 • Hoboken, NJ
Financial-services AI summit covering agentic systems, governance, fraud, forecasting, trading, data infrastructure and the challenges of scaling AI into production.Evident AI Symposium — October 22 • New York
Curated gathering of senior banking executives, AI leaders and policymakers focused on turning financial institutions into AI-first organizations.Open Source in Finance Forum New York — November 4–5 • New York
FINOS conference for financial-services technologists featuring keynotes, panels and technical sessions on open-source infrastructure and collaboration.FinTech Founder & Investor Evening — November 4 • Frankfurt
Invitation-only evening hosted by Finteda, Schalast and Fincite, bringing together founders and investors to discuss capital, regulation and scaling fintech companies in Europe.Columbia MAFN & AI Conference — November 5 • New York
Rebellion Research-hosted Columbia conference on AI and machine learning in portfolio management, with speakers from AQR, Two Sigma, and Citadel.Asset & Wealth Management Summit — November 10–11 • Boston, MA
Nicsa summit on alts and AI transforming asset/wealth management, with speakers from Anthropic, OpenAI, Morgan Stanley, KKR and Microsoft.21st SFI Annual Meeting — November 12 • Zurich
Swiss Finance Institute's annual meeting on AI's impact on finance, with keynotes from UBS, ETH AI Center and Oxford on AI's risks and opportunities in banking and trading.ICAIF '26 — November 14–17 • Milan
7th ACM International Conference on AI in Finance, bringing together researchers and practitioners on generative AI, trading, risk, fraud detection and responsible AI, hosted at Bocconi University.
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