Author: Zen, PANews
In today's rush of AI, GPUs have become the true "digital crude oil." However, the trading methods for this underlying resource that supports the trillion-dollar AI industry have remained in the "private negotiation" primitive era, prompting some companies to attempt to make this business open and transparent.
Recently, Ornn announced the completion of a $33 million seed round financing, led by a16z Crypto, with participation from Galaxy Ventures, Nordstar, and SV Angel, and follow-on investments from Vine Ventures, Crucible Capital, Link Ventures, and Box Group.
Founded in 2025 by MIT graduates Kush Bavaria and Wayne Nelms, this company has garnered significant early-stage investments from various venture capital firms, not only due to the founders' prestigious educational background but also because they have set their sights on a more fundamental link in the chain by introducing a trading market centered around computing power, riding the wave of the AI infrastructure boom.
Why the computing power market needs a new pricing system
Currently, GPU, data center, electricity, and network resources have shifted from engineering issues within tech companies to one of the core cost items of the entire AI industry chain.
However, unlike mature commodity markets for oil, natural gas, and electricity, the computing power market remains highly opaque. Prices are usually determined through private negotiations between buyers and sellers, long-term contracts are hard to exit, and there is a lack of unified price benchmarks across different GPU models, regions, datacenter conditions, and contract durations. Ornn aims to solve this problem as an emerging project.
Ordinary cloud computing rental platforms primarily sell GPU computing power usage duration directly to AI companies. In contrast, Ornn's core positioning is to transform the computing power usage rights of specific GPU models within a certain timeframe from privately negotiated, long-term locked-in contracts into products that can be priced, traded, financed, and hedged.
With the rapid development of the AI era, computing power has already become one of the world's most important commodities. However, the market mechanism surrounding computing power remains relatively "primitive." Traditional capital-intensive commodity markets usually gradually form price benchmarks, risk transfer tools, and investable assets, while the AI computing power market currently lacks these infrastructures. a16z Crypto believes that what Ornn is doing is moving computing power from “relying on private negotiations and individual contracts” to a truly operational market.
From price index to trading platform: establishing liquidity for GPU computing power resources
Addressing the "pricing transparency" issue is Ornn's starting point, and the key is how to solve it. Ornn's idea is to directly change the liquidity status of computing power by building a trading platform.
One of Ornn's core products is the price index. The company previously launched the Ornn Compute Price Index, abbreviated as OCPI, to track the market prices of GPU computing resources. According to Ornn’s official website and a16z Crypto’s official blog, OCPI is not simply about capturing publicly listed prices, but rather forming a price benchmark based on executed or cleared transactions, covering major GPU types such as H100, H200, B200, RTX 5090, and standardizing processing by dimensions such as hardware, region, and contract duration.
One of the biggest problems in the current computing power market is that the "listed price" may differ significantly from the actual transaction price. The prices obtained by large AI laboratories, cloud service providers, data center operators, and small and medium-sized enterprises vary, and contract terms are highly personalized. In this situation, if the market lacks a unified reference price, it becomes difficult for buyers to determine whether they are overpaying, for sellers to assess how future computing power resources should be priced, and for financial institutions to adequately evaluate the collateral value and future cash flow of GPU assets.
In April this year, Ornn announced that OCPI had interfaced with Bloomberg Terminal, further opening it up to institutional users. According to the company announcement, OCPI tracks GPU hourly leasing prices in the cloud and on-premises markets, covering Nvidia H100, A100, H200, B200, and RTX series GPUs. Ornn claims that more than 400 data center operators, investors, and AI companies are using its platform to track GPU prices.
Building upon the price index, Ornn is advancing its second-tier product: the GPU computing power usage rights trading platform Ornn Compute. Currently, a large amount of GPU computing power resources are locked in private transactions and long-term contracts, while Ornn Compute hopes to act as a bridge between buyers and sellers. Buyers can lock in dedicated GPU computing power resources of specific models, regions, and durations under one contract; when workloads change, they can also transfer or sublease the remaining usage rights to other users, turning originally idle resources back into income.
This is also where Ornn’s model differs from traditional cloud providers. Traditional cloud services are more about purchasing computing power on demand or signing long-term cloud resource contracts; Ornn seeks to give GPU computing power usage rights intrinsic secondary liquidity. For small to medium-sized AI startups, this means they can flexibly obtain short-term GPU resources without having to commit to overly long resource contracts from the start; for data centers and small to medium cloud service providers, it means they can standardize the sale of idle or future available GPU resources and obtain more predictable revenue.
ICE's entry signifies that computing power may move towards the derivatives market
From the price scales provided by OCPI to the liquidity support of the spot trading platform, the entry of another derivatives giant transforms it into financial infrastructure, paving the financial path for the AI economy.
In May of this year, the parent company of the New York Stock Exchange, Intercontinental Exchange (ICE), announced its plan to collaborate with Ornn to launch GPU computing power futures contracts based on OCPI. The ICE announcement indicates that these contracts will be denominated in US dollars and settled in cash, referencing the OCPI series indices, covering mainstream GPU types on the market, with the specifics of the launch still dependent on regulatory approval.
If the price index is the first step in the formation of the market, then futures and hedging tools are important markers of a mature commodity market. ICE’s involvement elevates Ornn's narrative from a computing power trading platform to further develop into computing power derivatives infrastructure.
For data center operators, a decline in GPU leasing prices can impact future revenues and financing capabilities; for AI companies, an increase in GPU prices can raise training and inference costs. Through OCPI-based futures contracts, theoretically, buyers and sellers can lock in future prices in advance and reduce operational volatility.
From a broader industrial perspective, the competition for AI infrastructure has entered a more refined stage, centered on how to transform high capital expenditure assets into financially measurable entities. Data centers need financing, lenders require valuations, AI companies need budget certainty, and investors also seek more direct exposure to AI infrastructure. In this process, GPUs can evolve beyond mere hardware to become assets that can produce cash flow, serve as collateral, form price curves, and be traded.
Additionally, Ornn has recently expanded its index business to AI Token costs, launching the Ornn Token Price Indices (OTPI) in June this year. The previously mentioned OCPI measures the input costs for the AI economy, namely the GPU time costs required for training and running models; while OTPI measures the output costs, used to evaluate the actual costs of tokens generated by major model developers such as Anthropic and OpenAI. The combination of the two indices can provide the market with a cost curve from computing power investment to AI consumption demand.
Overall, the rise of Ornn reflects the transition of the AI computing power market from resource competition to financialized pricing. As GPU, data center, and electricity investments become one of the most important cost items in the AI industry, the market's demand for transparent pricing, flexible trading, and risk management tools is also on the rise. What Ornn is betting on is precisely the financialization opportunities behind this change.
The emergence of Ornn is not only a financing story but also a reflection of the transformation of the underlying logic of the AI industry: computing power is undergoing a metamorphosis from 'heavy asset hardware' to 'financializable assets.'
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