INTERVIEW

Five Minutes with Silicon Data’s Carmen Li

When I first heard about trading “compute,” it struck me as odd. Why would we need market infrastructure for GPU chips the same way we do for oil?

Computing capacity, powered by GPU chips that run AI models, is reusable, unlike oil or wheat. But the more I thought about it, the more it started to make sense. 

The closest analogy is the freight market. Like a cargo ship that hauls different loads on different routes, the same compute can train different models.

The freight parallel goes deeper than just reusability. When freight markets matured in the 1990s, they became fully financialized—traders could buy and sell exposure to shipping rates, hedge price swings, and trade Baltic Dry Index futures.

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:

Wilson is betting on that future.

He invested in Silicon Data, a company that provides GPU pricing data and benchmarking services to hedge funds, banks, and AI firms.

I spoke with Silicon Data’s founder, Carmen Li, who’s just as bullish. After stints at Bloomberg, Citi, and DRW Trading, she launched the company in April 2024 to bring financial infrastructure to compute. The company raised $4.7 million in May, which I covered here.

Full disclosure: We both worked at Bloomberg in 2022 but never crossed paths. This interview has been edited for clarity and length.

What does Silicon Data do?

"Think of me as the Bloomberg for GPU pricing. You cannot buy compute from Silicon Data, just like you cannot buy actual stocks from Bloomberg. We're different from the spot exchanges—you can actually get physical compute from them. They do spot, we do the data layer. We're working with futures exchanges to launch products based on our indexes."

What problem are you solving in the GPU market?

"Even if you're a sophisticated user—say, a PM or machine learning engineer at a hedge fund—it's not your job to config and double-check GPUs. It's almost like being a great driver—it's not your job to fix the engine.

So let's say you're looking for GPU clusters and I tell you, 'Hey, I have 20 nodes in New Jersey, all H100s with the same configuration and Linux environment. You can run your workflow right away with good latency.' You pay—10 days, maybe a month—and it's expensive. Very expensive. You then discover it's 20 nodes with a different setup than promised, or some Linux environments are inconsistent. So it takes a lot of time to synchronize.

There's no insurance, no guarantee, no standardization. It's mind-blowing. If you and me buy a t-shirt on Amazon, we can return it. But GPUs are freaking expensive and there's no insurance policy, no guarantees, nothing."

How do you solve that?

"One of our benchmarking services helps clients verify everything before they start their workload. Think of it like Carfax for GPUs. A third party verifies everything the provider promised—the chip UIDs, connectivities, latencies, performance within expected distributions. If performance is 20% below spec, you can negotiate a lower price. There's a price for everything—it doesn't mean it's worthless, but you need transparency."

How are hedge funds actually using this pricing data?

"They're using our 4 million pricing points globally as leading indicators—for earnings next quarter, for supply-demand shifts in manufacturing cycles, for benchmarking against hyperscalers. For banks financing GPU clusters, they need to mark risk to market every day. They're not going to take your word that you're renting GPUs for $9 per hour—they'll look at public indexes to verify it's actually $3.20."

Are infrastructure providers passing GPU cost savings to customers?

"No. Their costs are going down as GPU prices fall, but they're not reducing token prices. Their margins are supposedly increasing. DeepSeek is experimenting—they charge lower prices during off-peak hours. But most providers aren't doing dynamic pricing yet, even though it makes sense."

Read the full interview here 

ADDENDUM

Future “Five Minutes with” Interviews

Above is the first “Five Minutes with” interview I’ve done this year. I had paused the written Q&As after launching the podcast, thinking they might be duplicative. But I’ve had a number of organic conversations where I’ve thought, this would make a great interview—including what started as an intro chat with Carmen. Thanks to AI recording and transcription tools, I was able to quickly share it with you. I plan to do more like this in the future. ICYMI: Here’s a list of the interviews I did last year covering many evergreen topics:

• Fitch’s Jayeeta Putatunda on causal AI reducing hallucinations

• Former JPM Exec Tucker Balch on scaling investment analysis with AI

• USC's Matthew Shaffer on using ChatGPT to estimate “core earnings”

• Moody’s Sergio Gago on scaling AI at the enterprise level

• Ravenpack | Bigdata.com’s Aakarsh Ramchandani on AI and NLPs

• PhD candidate Alex Kim on signals with executive tone in earnings calls

• MDOTM’s Peter Zangari, on AI for portfolio management

• Arta’s Chirag Yagnik on AI-powered wealth management

• Finster’s Sid Jayakumar on AI agents for Wall Street

• Sov.ai's Derek Snow on AI for fundamental investors

• Bain’s Richard Lichtenstein on AI adoption in private equity

• Snowflake’s Jonathan Regenstein on AI building novel datasets

• Skadden’s Dan Michael on the SEC’s AI stance

• Stardog’s Matt Lucas on hallucination-free AI

• Celent’s Monica Summerville on AI Adoption in capital markets

• Aveni's Joseph Twigg on building a finance LLM

• Persado’s Assaf Baciu on tailored AI marketing at banks

• Professor Alejandro Lopez- Lira on AI-driven stock predictions

OVERSIGHT

Trump Scraps Biden-Era AI Rules, Mum on Policy 

Trump just signed an executive order on AI, but there’s not much to it. The order revokes Biden’s 2023 AI framework and calls for systems to be built “free from ideological bias,” yet it doesn’t spell out how that’s supposed to happen. Instead, it tells agencies to review past policies and suspend anything that doesn’t align with the new vision—without defining clear standards or replacements.

There’s a 180-day window to come up with an “AI action plan,” and Trump has tapped VC David Sacks as his new Special Advisor for AI and Crypto, according to the Associated Press.

Takeaway: In the U.S., there’s very little actual rule-making going on when it comes to AI and there’s no clear regulatory framework in sight.

Fed’s Cook Says AI Could Alter Path of Rates

Federal Reserve Governor Lisa Cook said last week that AI is moving fast enough to reshape the U.S. economy and could eventually affect how the Fed sets interest rates.

Speaking at the NBER Summer Institute in Cambridge, Cook called AI a “general-purpose technology” like electricity or the internet, with the potential to lift productivity, alter labor markets, and change inflation dynamics. “Babies born today will ask what life was like before LLMs,” she said. ← This is not the kind of quote you expect from a Fed Governor.

Why it matters: AI is increasingly on the Fed’s radar—not just as a workplace tool, but as a macroeconomic variable. Cook’s remarks suggest LLMs and related tech could eventually shift the Fed’s estimates of full employment, productivity, and neutral interest rates.

MIT’s Lo Sees AI Ready to Run Money in Five Years

The idea of having an LLM run your money seems odd now, but it may not in a few years. From Bloomberg:

• Within five years, [Lo] predicts, large language models will have the technical capability to make real investment decisions on behalf of clients.

Younger folks, those under 30, are much more comfortable letting AI make investment decisions for them. There are already some AI adjacent products on the market, like ChatGPT Portfolio.

Takeaway: Forget Merrill Lynch’s running bull, some investors may be running their money with AI in a few years.

WALL STREET

Altman Predicts AI Will Reshape Banking, Lending Within Decade

OpenAI CEO Sam Altman predicted that AI will transform financial services within a decade, fundamentally changing how institutions move money, provide advice and assess risk. See ~27:00 in this YouTube clip.

Elsewhere in Altman & Wall Street news:

• OpenAI’s Altman didn’t expect banks to take to AI so soon (Banking Dive)

• OpenAI’s Sam Altman warns of AI voice fraud crisis in banking (Associated Press)

AI & LABOR MARKET

The AI Layoff Panic Isn’t Backed by Data, Says Goldman

While it feels there’s a new headline every day about how AI is leading to mass firings, there's virtually no data backing this up. 

That’s from a recent Goldman Sachs report. AI adoption among U.S. companies is growing rapidly (from 7.4% to 9.2% in Q2 2025) and delivering significant productivity gains of 23-29% where deployed, but it’s not leading to mass firings, according to the bank’s AI Adoption Tracker covered in Fortune.

Takeaway: Anecdotal evidence is driving the AI-is-taking-everyone’s-jobs narrative. Maybe that changes at some point but right now there’s no data to back that up.

WHAT ELSE I’M READING

• JPMorgan Adds Private Firms to Research Starting With OpenAI (Bloomberg)

• TSMC’s Taiwan Stock Value Surpasses $1 Trillion (Bloomberg)

• AI Comes Up with Bizarre Physics Experiments. But They Work. (Quanta Magazine)

• AI-Powered Law Firms Test the Bounds of Legal Work by Robots (Bloomberg Law)

• Vanguard's CEO Looks to Innovate Without Selling Out (ThinkAdvisor)

• AI Search Is Growing More Quickly Than Expected (WSJ)

• Interactive Brokers Partners with Reflexivity for AI Investment Tool (Trading View)

• Netflix uses AI effects for first time to cut costs (BBC)

• S&P Global Eyes Partnerships to Integrate Its Data Into AI Tools (Bloomberg)

• PE-backed Datasite buys Blueflame AI for dealmaking tech (Axios)

DIRECTORY

AI Tool Directory

I’ve put together a database of 70+ AI and investing tools categorized by use case, where you can search, for example, by due diligence or AI agents or earnings data. Each company card has a short description and a link to the platform.

I did my best to add all relevant companies, but I know there are probably more out there. If you know of any that should be included, please reach out: [email protected].

To access this (free) database, please recommend AI Street to someone you think will get value from reading AI Street. (Please don’t send this to a burner email; I can see where the referrals go 🙂) Share the link below and after they join, you’ll receive the database by email.