RESEARCH

AI Startup Says It Cuts Out the Noise in Financial News 

Staying on top of news sounds straightforward, but in the fragmented media landscape we’re in — LinkedIn, podcasts, Substack, legacy news outlets, etc — the news flow is overwhelming.

And the problem isn’t just volume. You have to wade through a lot of repetitive stories and superficial takes, making it genuinely hard to find out what’s “new.” At scale, this wastes time and computing power.

A startup called Zanista AI says it has an answer. Its system pulls from more than 500,000 global sources and, by design, filters out duplicate and low-value stories before you start searching. The company claims that this makes financial news search up to 60 times faster.

The approach, described in a recent research paper, applies a method known as PCA-RAG—short for Principal Component Analysis for Retrieval-Augmented Generation. In practice, it works by compressing “embeddings,” the mathematical representations that AI models use to capture meaning in text. Instead of storing 3,072 dimensions, Zanista trims each down to roughly 110. That step shrinks the index size by nearly 30-fold, making search both leaner and faster.

“News is scattered—LinkedIn posts, Substack writeups, niche research sites,” Arman Khaledian, Zanista's CEO and former quant at Millennium, told me. “We needed a way to index all of it, structure it, and make it searchable in real time.”

He points to Transformer-based embeddings as the breakthrough that made this possible. These models can embed meaning across fragmented sources in a way that older techniques could not. “You couldn’t have built this five years ago,” he said.

Zanista is commercializing this process by selling data packages to hedge funds. A small U.S. hedge fund has purchased a Russell 3000-linked news feed, and major U.S. funds are testing it as well, according to Khaledian.

Takeaway:

AI may finally be able to help answer the simple but elusive question: what’s the most important news I should know about my investments?

SPONSORSHIPS

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NYC

AI Street Meetup - Sept 18 NYC

As previously mentioned, in two weeks, I’m in New York to attend Cornell’s Future of Finance & AI conference.

I’m putting together an AI Street drinks meetup on Sept 18 at 7:00. I’m co-hosting this one with Jordan Hauer of Amass Insights, so it should be a nice mix of the data + AI community.

If you’d like to attend, please add your email to this google form. Hope to see you then!

INTERVIEW

AI’s Biggest Cost Lacks Standard Pricing 

Five Minutes With Simeon Bochev, CEO of Compute Exchange

By now, you’ve heard how expensive AI is to train and run. McKinsey estimates the boom could require nearly $7 trillion in data center investment by 2030.

Numbers that big are hard to wrap your head around.

Even harder to grasp: buyers of computing power have almost nothing to benchmark against. There’s no Expedia for compute the way there is for flights. It’s hard to know if what you’re paying is “fair” or “market rate.” It’s similar to calling around plumbers for quotes to fix a busted pipe.

And how do you keep track of how much compute you’re using? Does that match your invoice?

Today, there’s very little standardization.

That gap is what Simeon Bochev is betting on. His startup, Compute Exchange, is building a marketplace to treat compute more like a commodity. I spoke with him about how this market could evolve, why he teamed up with DRW’s Don Wilson, and where he thinks compute belongs on the spectrum between oil, electricity, and currency.

As AI costs balloon and more companies scramble for scarce GPU resources, opaque pricing lets bad actors exploit uninformed buyers. The market is ripe for the kind of transparency common in today’s commodity exchanges.

This interview has been edited for clarity and length.

How did you connect with Don Wilson?

I was invited to a dinner hosted by Don to talk about the commoditization of compute. The CEOs of most of the neo clouds were there.

The reason my CEO wasn't there is because he's in Vermont and I was his leadership person in the office, in the headquarters in San Jose. And so I went and Don and I really hit it off. And we really talked about how there are these structural issues in the market.

I described one of them, which is pricing transparency, right between Amazon and Apple. But the issues go well beyond that, and I don't want to go into a monologue, so I'll skip the issues for now. But we identified a lot of structural issues on both the supply and the demand side.

And so we said maybe a market can solve that. And so I ended up being Don's point of contact within Lambda about building this market. And as we went down that path more and more, I'm like, actually, why don't we do this together? And so, with the blessing of Stephen (Balaban, CEO) at Lambda, we started Compute Exchange in April of 2024.

And now, more than a year and a half later, we launched the market in February, we've had over 80,000 GPUs in supply in about five months. To go from zero to 80,000 GPUs, I challenge anyone else to find someone that's grown that fast. And it's all in GPUs that we don't own. We don't take balance sheet risk on. We open them up to the market.

I see. So, you're the auction? You are the place to source GPUs.

We are the market. Our goal is to be the place where all compute in the world transacts.

So, think about how oil transacts largely in one place, whether you're buying a barrel to speculate as a market maker or you're a country buying a billion for, or maybe not a billion, but a million for strategic oil reserve. They all transact in an exchange. That's what we aim to be.

Read the full interview here 

HEADLINES

Cutting-Edge AI Was Supposed to Get Cheaper. It’s More Expensive Than Ever.

With models doing more ‘thinking,’ the small companies that buy AI from the giants to create apps and services are feeling the pinch. (WSJ) ← Maybe it’s my years covering financial crime, but I suspect the high cost comes partly from a lack of transparency.

Ex-BofA quant finds that agentic LLM traders don't like to trade

When integrating large language models (LLMs) into an agentic trading system, "it proved difficult for LLM agents to make a decision to buy or sell, even when prompted to do so." (eFinancialCareers) ← The research was done by Alicia Vidler, who we had on the podcast earlier this year to discuss agentic AI.

ExodusPoint Hires Point72’s Head of Generative AI

ExodusPoint Capital has appointed Sridhar Nimmagadda as its new Head of AI Transformation, marking another high-profile move in the hedge fund industry’s push to embed generative AI into trading and operations, according to a report by eFinancial Careers. (Hedgeweek)

Citi rolls out a pair of AI-powered banking platforms

The financial firm’s data, analytics and innovation team deployed two client-communication assistants for its wealth advisory division Monday. (CIO DIVE)

Bessent: An AI boom Is Coming, and the Fed Should Take Note

The administration's enthusiasm for AI is crossing over its desire for lower interest rates. (Axios)

CALENDAR

Upcoming AI + Finance Conferences

• AI in Financial Services (Arena) – Sept 9–10, 2025 • London

  Focused on AI strategy, implementation, and ROI in finance.

• Cornell Financial Engineering Manhattan 2025 Future of Finance & AI Conference – Sept 19, 2025 • New York (I’ll be attending)

  A one-day forum on AI, quantitative finance, and hedge-fund strategies, attracting leading quants and industry practitioners.

• Bloomberg-Columbia ML in Finance Conf – Sept 25, 2025 • New York

  Academic–industry event hosted by Columbia University and Bloomberg, focused on ML applications in finance including asset pricing, market forecasting, and LLM risk.

• GAIIM Conference 2025 – Sept 30, 2025 • New York

  Forum on practical applications of AI in investing, featuring tools for research, valuation, and portfolio workflows.

• AIFin Workshop at ECAI 2025 – October 26, 2025 • Bologna, Italy

  One-day academic workshop on AI/ML in finance, covering trading, risk, fraud, NLP, and regulation.

• AI in Finance 2025 – October 27–30, 2025 • Montréal

  Academic event covering ML in empirical asset pricing and risk.

• ACM ICAIF 2025 – November 15–18, 2025 • Singapore

  Top-tier academic/industry conference on AI in finance and trading.

• AI for Finance – November 24–26, 2025 • Paris

  Artefact’s AI for Finance summit, focused on generative AI, future of finance, digital sovereignty, and regulation 

• NeurIPS Workshop: Generative AI in Finance – Dec. 6/7 • San Diego One-day academic workshop at NeurIPS focused on generative AI applications in finance, organized by ML researchers.

WHAT ELSE I’M READING

• Anthropic Completes New Funding Round at $183 Billion Value (Bloomberg)

• Hedge Fund Magnetar Ties Half Its Assets to AI Data Center Firm (FT)

• Google ML Performance Engineer Joins Citadel Securities (eFinancialCareers)

• AI Agents Will Become Biggest Stablecoin User, Says Novogratz (Bloomberg)

• ChatGPT Puts Pressure on AI Tools for Lawyers (Bloomberg Law)

• Benchmark Bets Big on Exa, Which Wants to Be Google for the AI Era (Bloomberg)

• AI Chatbots Can Be Just as Gullible as Humans, Researchers Find (Bloomberg)

• Rogo Acquires Subset to Build a Spreadsheet Agent for Investment Bankers (Press Release)