Hey, I’m Matt and I’m back in Milan after spending a couple weeks in the States. If you’re new here, I’m a former Bloomberg News reporter, and you’re reading AI Street, where I report on how Wall Street uses AI.
Stripe, which provides the infrastructure for internet payments, agreed to buy OpenRouter, which provides infrastructure for using AI models.
OpenRouter evaluates AI requests and routes them to models based on price, speed, reliability and the complexity of the task.
I’ve heard repeatedly from financial firms that compute expenses are exploding as AI moves from isolated experiments into production. One hedge fund was spending about $24 million a year on LLMs. About one-third of that spending was wasteful.
A financial firm might use a smaller model to classify documents, a frontier model for complex research and a specialized system for extracting numbers. Prices and capabilities also change quickly. Without a routing layer, each application has to make those decisions itself—or default to an expensive model for everything.
I previously called the person overseeing this expense a “token master”: someone responsible for allocating compute budgets across departments and deciding which tasks warrant the most capable models.
OpenRouter tackles one part of the problem by routing each request to the model offering the best balance of capability, cost, speed and reliability.
Bridgewater Leaders Want to Tax AI Tokens
Hedge funders rarely call on lawmakers for more regulation. But Bridgewater’s Greg Jensen and Nir Bar Dea did just that in a New York Times opinion essay last Friday, urging a new tax on AI use and sweeping new regulations on the technology.
Bridgewater’s internal analysis estimates that AI could displace 18% of current U.S. jobs within five years. Jensen and Bar Dea warned that concentrating the benefits among companies and investors could provoke a political backlash.
The two proposed a tax on companies’ use of AI tokens, which they described as the closest equivalent to wages for machine labor. They argued that the current tax system makes replacing workers with AI comparatively attractive. That revenue could lower income taxes on workers and help the government acquire shares in leading AI companies.
They also called for strict safety standards covering models in development, publicly released systems and their use after deployment. The executives acknowledged that Bridgewater would be disproportionately affected by the policies they recommend.
Also in Bridgewater news:
Bridgewater Co-CIOs Back AI Startup for Stockpickers
Bridgewater co-CIOs Greg Jensen and Karen Karniol-Tambour have backed Multiplier, an AI startup founded by one of the hedge fund’s former investors.
The pair participated in a $6 million seed round led by Lux Capital. Y Combinator, Fortress Chairman Pete Briger and several of Multiplier’s hedge fund clients also invested.
Multiplier, previously called WithAI, was founded by former Bridgewater investment associate Ian McInnis, who worked under Jensen on large language model projects. Its coding agents filter news, make charts, monitor stocks and update investment memos. The platform connects those agents to a fund’s structured data, documents and internal research process.
Multiplier hosts the system on client infrastructure and interviews employees to teach the agents how each firm works. The company says it is live with six hedge funds and that users spend more than four hours a day on the platform.
CFTC Seeks Input on AI Compute Futures
The Commodity Futures Trading Commission is seeking public comment on proposed futures contracts tied to the computing power used to develop and run AI systems.
CME Group, Intercontinental Exchange and Architect Financial Technologies have announced plans to offer the contracts, subject to regulatory approval. They could allow AI developers and data-center operators to hedge changes in the cost and availability of compute, much as companies use energy derivatives to manage fuel costs.
Computing costs vary by chip, location, electricity supply and other infrastructure. The CFTC is asking whether the market needs common standards, including price indexes for settling contracts.
Compute is increasingly being treated as a scarce commodity as AI companies spend heavily on chips and data centers. A futures market could help them lock in prices, but only if the contracts reflect the capacity they actually use.
Google Wins Auction for Spirit Data to Improve AI
In simplified terms, AI scaling laws say more data + more computing power = better results from AI models.
The more computing power angle is well covered in the media. In May, I wrote:
Global tech companies have announced about $740 billion in AI-related spending for 2026, according to Morgan Stanley. That’s about what it cost to build the entire U.S. interstate highway system (in today’s dollars), which took 30+ years.
But the more data storyline is less covered. As we’ve written (and many others have covered) before pretty much all the publicly available data has been hoovered up. So much so that frontier AI labs are hiring experts, like ex-bankers and analysts, to create new data.
There are still vast stores of private data. Getting access is a slog. Labs need to establish who owns it, secure permission to release it, clear privacy and confidentiality checks, agree how it can be used and protected, and settle on a price.
That scarcity is creating a market for data left behind by failed companies. From Bloomberg Law:
Google Aims to Boost AI With Purchase of Spirit Airlines Data
Here’s the short of it:
Google won the bankruptcy auction with a $10 million bid for Spirit Airlines’ deidentified corporate data, including 100 million emails, 500 million Teams messages, billions of pricing and transaction records, and 30 million lines of code. Google says the cache will help improve its products and AI models. Mercor is the backup buyer after bidding $7.5 million.
TLDR
Corporate data that once had little value outside the company that created it is becoming an asset AI developers will bid for. Google’s $10 million bid for Spirit’s archive shows how far they will go to get it—and why investors should scrutinize the data behind an AI tool as closely as the model itself.
ROUNDUP
What Else I’m Reading
Hudson River Trading Posts Record $11.4 Billion Trading Revenue | BBG
Jane Street Suffers Loss of About $15 Billion Following Troubles at Situational Awareness | WSJ
Etched raises $700 million at $21 billion valuation | Quartz
SpaceX Pitches Grok to Hedge Funds | Hedge Fund Alert
AI Is Starting to Define Bank Internships as Much as Long Hours | BBG
Optiver to take majority stake in power and gas trader Northpool | PR
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