UBS, HSBC Invest in Financial AI Startups
Bank investments point to the growing value of the AI workflow layer. Plus, the financialization of compute and agentic AI at top quant firms.
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.
FUNDRAISING
Despite it being mid-August, lots of fundraising news this week:
UBS, HSBC Invest in Financial AI Startups
Over the past year and a half, banks have started buying stakes in the AI startups trying to automate their employees’ work.
This week, UBS backed Finster AI and HSBC invested in Model ML. FT Partners, which led Model ML’s $75 million Series A last year, is rolling out the platform firmwide. JPM invested in Rogo in April 2025, followed by Wells Fargo in January 2026.
These companies sit between the models and the bank. Their software connects to internal data, applies permissions and turns the output into something a banker can use: a presentation, financial model, research note or client briefing. Banks can swap the underlying model. Replacing the system wired into their data and workflows would be much harder.
Banks continue to build their own AI systems and sign deals with Google, OpenAI, Anthropic and other large technology providers.
TL;DR
The investments suggest banks are beginning to treat AI as an operating layer for financial work. The models underneath may be replaceable. The system that connects those models to the bank’s data, permissions and workflows may not be.
Quartr Raises $18M to Structure Company Data for AI
Quartr, a Stockholm-based provider of public-company research data, raised $18 million to expand its products and global coverage. Existing investor Altos Ventures led the round and became Quartr’s largest shareholder. The financing follows raises of $6 million in 2024 and $10 million last year.
The company collects earnings calls, transcripts, filings, presentations and other investor-relations material that remains scattered across corporate websites, conference lines and webcast providers. It links the audio, documents, speakers and company events in a common dataset, allowing analysts and AI systems to search across the material and trace results back to the original source.
Named API customers and partners include Perplexity, Yahoo Finance, Rogo, RavenPack and TradingView. The company says it now covers more than 15,000 public companies across over 65 markets and serves more than 800 financial institutions and technology companies.
ICYMI
The Financialization of “Compute”
Back in July 2025, I wrote:
New commodities often become financialized once volatility and market demand make risk hedging necessary.
That moment may be arriving for compute, with some well-known Wall Street traders believing compute demand will rival and then exceed the demand for oil.
Back in May, I highlighted this quote from DRW founder Don Wilson, who has a history of bringing new markets to the mainstream.
“The total dollars spent on compute will, over the next 10 years, exceed total dollars spent on oil.” DRW’s Don Wilson to the WSJ.
He invested in Silicon Data, a company that provides GPU pricing data and benchmarking services to hedge funds, banks and AI firms.
Just about a year later, Silicon Data raised $30.5 million in the initial closing of a Series A led by the Valor Atreides AI Fund. The round also drew backing from CME Group and a mix of trading firms, asset managers and technology investors. The New York-based company didn’t disclose its valuation.
CME plans to launch cash-settled futures tied to GPU rental costs, subject to regulatory approval, using Silicon Data’s benchmarks as the reference price.
Silicon Data publishes nine indices covering GPU rental prices and large-language-model costs, along with data on supply, utilization and pricing. The company says its platform has more than 1,000 registered users across semiconductor manufacturing, AI development and financial services.
Part of the new capital will fund SiliconMark, a system for measuring the performance of GPU infrastructure. Clusters built with the same chips can produce different results depending on their networking, configuration and the way their components are connected.
Silicon Data wants to standardize those performance differences, giving market participants a way to measure—and eventually hedge—the risk that a cluster delivers less computing power than expected. The company says the measurements could also support the physical delivery of computing capacity.
The remaining capital will go toward additional pricing benchmarks, institutional data products and infrastructure for derivatives, insurance and credit markets.
Silicon Data previously raised a $4.7 million seed round in March 2025.
AI at Two Sigma, DE Shaw and Susquehanna
AI leaders from some of Wall Street’s largest quantitative investment firms discussed how agentic AI is changing research, model evaluation, compute spending and hiring during a panel at Berkeley RDI’s Agentic AI Summit. The “Agentic AI in Capital Markets” speakers were:
Jeff Wecker, chief technology officer at Two Sigma
Jen Allum, senior vice president and co-head of generative AI at D.E. Shaw
Ali Nazari, head of deep learning research at Susquehanna International Group
Li Deng, chief AI officer at Vatic Investments and former chief AI officer at Citadel
“It’s becoming very clear, as we look at various metrics, that those who adopt sooner and faster are clearly becoming much more productive.”
—Jeff Wecker, Two Sigma, 09:21–09:31
“The hardest part didn’t disappear; it just moved. I found myself, instead of trying to generate ideas, trying to figure out which of the ideas it gave me were worth my time. These bottlenecks, these challenges, they don’t just disappear. They just move.”
— Ali Nazari, Susquehanna, 03:56–04:21
“Let lots of experiments run, be maximalist in the policy — build and buy and everything in between — and then see what catches fire and think about where we scale that, for whom and how.”
— Jen Allum, D.E. Shaw, 12:52–13:05
“We are on the eve of potentially an explosive use of compute demand… If behind those 1,800 employees there are a quarter of a million agents, all making their own compute demand, we really have to think about the implications and the return on investment.”
— Jeff Wecker, Two Sigma, 17:20–17:47
“If you want to bring that kind of system into the finance world, you are going to fail, because in financial markets, every time you put an order into the market, people react differently. It changes the environment much more than in the high-tech world.”
— Li Deng, Vatic Investments, 30:02–30:17
“Traditionally, we try something, we make a mistake, we learn from that mistake, and that’s how judgment is developed. Now that AI does most of the experimentation for us, the question is how that judgment is going to be developed.”
— Ali Nazari, Susquehanna, 20:42–21:01
Many trading firms have massively grown their personal GPU clusters which facilitate their machine learning strategies. Jane Street seems to have doubled its number of GPUs in the past year and now has tens of thousands of them. Quadrature, a smaller AI trading firm paying millions per head, had ~20,000 GPUs in November, equivalent to roughly 11 chips per employee. XTX Markets has one of the most impressive clusters; open job listings say the market maker has "25,000 GPUs with 650 petabytes of usable storage."
From How AI is changing careers in electronic trading and HFT
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What Else I’m Reading
How AI is changing careers in electronic trading and HFT | eFinancialCareers
Five Takeaways From Zuckerberg’s 6,500-Word Manifesto on AI | BBG
Ex-Nubank CTO raises $85m for AI-native wealth advisory platform | Finextra
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Prodigy Research: The Frontier AI Trading Research Lab | Y Combinator
Some US adults are using AI for financial guidance but few trust it: Gallup | AP
The new Wall Street career path | Business Insider
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