Brett Harrison

Introducing the American Innovation Exchange, the first U.S. derivatives exchange designed for trading the AI economy. Trade futures and options on compute, metals, energy, and other critical instruments in the AI supply chain. Coming soon from Architect.

Drinks on Architect, Luxor, and CMT for @Supercomputing SC26 on 11/17! Come hang out and share ideas on inference and compute trading. luma.com/ComputeCocktailsChi…

It’s compute day at FIA Chicago. Index providers are up first at 1:30pm, exchanges offering compute futures to follow at 2:20pm. Compute Desk, Silicon Data, NativX, Ornn, Architect, CME, ICE, and Nodal all in one place. Expect new alpha to be dropped.

A story about how Jane Street unwittingly reinvented datacenters in 2012, involving office server rooms, natural disasters, market maker obligations, recovery sites, colocation facilities, and old limestone telco buildings in lower Manhattan. When I joined Jane Street from college in 2010, all of the firm’s proprietary trading systems ran out of a 8’X12’ server room on the 33rd floor of 1 New York Plaza, on the southern tip of Manhattan. Jane Street’s ETF market-making, options vol pricing, intercountry ADR arbitrage, and pan-equity stat-arb operated from this small deployment. The room itself was an engineering feat: the Dell server arrays were too heavy for the 1960s building’s floors to support, so the racks were affixed to the ceiling with concrete. By 2012 Jane Street was not yet a member of most major US exchanges, even NYSE, and had virtually no presence in colocation facilities. Nevertheless, it was a ~$2B company experiencing rapid growth, a designated liquidity provider in most listed ETFs, a significant percentage of US and international equity volume, and a critical operator in capital markets. In October 2012, Hurricane Sandy devastated lower Manhattan. 1 New York Plaza, Jane Street’s home, flooded to street level and the basement power generators were fully submerged. The resulting gas leaks made the office building hazardous to enter, and power and water were weeks away from being restored. Unprecedentedly, all major US exchanges closed for two business days after the storm but would be back up by day three. Jane Street was in danger of failing its lead-market-maker obligations if it didn’t relocate its business in time. Jane Street had recently set up a partial office in a central New Jersey office park to comply with FINRA/SEC regulatory obligations for business continuity planning. A few engineers and I showed up to the empty site during the hurricane to begin mass-redeploying the firm’s software and infrastructure for the next critical 48 hours. A truckload of Dell PowerEdge R610s arrived the next morning, some salvaged from 1 New York Plaza’s server room, carried down 33 floors in pitch black by Jane Street’s IT team. For the next few months, everyone at the company from Rob Granieri to new-hires rode rented regional travel buses from Manhattan to NJ to keep Jane Street alive and growing. When we all returned to Jane Street’s repaired 1 New York Plaza office in January 2013, the technology team realized something that’s obvious now: hardware doesn’t need to be colocated with people. The NJ disaster site had accidentally become Jane Street’s first firm-wide datacenter. The site afforded significantly more space, cheaper electricity, ground-floor weight support, and abundant cooling. This decision, to leave Jane Street’s servers in post-hurricane locations, would have profound effects on the firm’s infrastructure footprint and growth. In 2013 it became a top priority for the technology and infrastructure teams to move all latency-sensitive trading into colocation facilities in order to cross-connect directly to exchange matching engine switches. And the search began for a new primary datacenter to house the firm’s research cluster and non-latency-sensitive applications. In 2015 Jane Street found its new electronic home: lower Manhattan, limestone and 32-story windows, originally owned by the New York Telephone Company. It had retina-scan security and neon-colored liquid cooling and everything else we know today. They joined other major financial firms and software companies who had operated there for years. In modeling disaster scenarios, experts tend to underrate the technological redundancies that already exist, or can be willed into rapid deployment, to ensure the US financial system remains secure.

Superintelligence markets will begin in Chicago due to DRW, Jump, CME, Architect, and hundreds of CFTC-regulated industry partners working to financialize compute and inference in the US. If you’re at FIA come visit Architect’s office, trade B300s, and play our CTO in 301.

In agentic trading, exchanges without MCP will be invisible. It was an honor to join the CFTC’s Frontier Forum on super intelligence. A big question is how exchanges/brokers enable agents to trade derivatives while ensuring appropriate risk management. Exchange protocols and MCP:

Liquid Inference is serving 10B tokens/day and growing 200% week over week. Most of the traffic is agents and long chats. Across 470k charged jobs, 88.7% of input tokens were cache reads. Here’s how our matching algorithm prices offers with cache hit rate included: In multi-turn and agentic workloads, the biggest cost and latency wins come from prefix/KV cache reuse. Each turn resends the full conversation, so if a provider still has that prefix in its cache it can bill those tokens at its cache-read rate, often a small fraction of the normal input rate. Naive load balancing or pure price auctions scatter a session across different providers or replicas, forcing full prefill on every turn and destroying the savings. Our matching algorithm ranks offers on the expected charge inclusive of cache hit rate rather than the full list price: 1. We measure how much of each workspace’s input repeats from one turn to the next (7-day window, at least 50 jobs). 2. We multiply that share by the measured cache hit rate of each deployment. 3. We price those tokens at the offer’s cache-read rate and subtract the expected saving from the offer’s ranking charge. 4. The deployment that served the previous turn keeps a 5% margin, so a cheaper offer has to win by more than 5% to pull a warm session away. In other words, for the right price we will abandon the cache and choose the cheaper alternative. The credit only changes the order of the offers. A wrong estimate cannot make a job cost more than its price cap. Every receipt shows the cache share we expected, the share that was realized, and the credit in dollars. That’s how we continue to improve on cache hit rate and provide the best economics for our clients’ sessions.

Fifty providers are now in the Liquid Inference network, up 22 in a week, over half the size of competitors’ networks in two weeks since launch. Every frontier open-weight LLM is quoted, and we actively benchmark all model+provider pairs to ensure model weight fidelity.

Free tokens will do for intelligence what zero commissions did for stock and options trading.

I’m honored to speak at the @CFTC’s first Frontier Forum in DC next week. Looking forward to discussing the role of SI in US futures markets. Thank you to Chairman Selig and the Innovation Task Force.

More Liquid Inference integrations launched over the weekend: - @Deepseek_ai V4.1-Flash-1017 model support and Deepseek Harness integration - @Openclaw plugin for agent backend - @ComfyUI plugin for image and video generation and editing - Embedding models We ship every day.

Liquid Inference now supports images and video! Video inference costs aren’t optimized to the same level as text, and a fair auction brings iterative AI content creation in line. Access via our API or check out the compare tool in our web playground.

Stats from Liquid Inference’s first few days live: • Cheaper token prices than 98% of OpenRouter traffic • ~2B tokens served • 24 verified providers onboarded to the network • 88% cache hit-rate • 26 harnesses/IDEs supported Buckle up, many new features launching this week.