Ornn's rise is not just a funding story, but also a microcosm of the underlying shift in the AI industry: computing power is undergoing a transformation from "capital-intensive hardware" to "financializable assets."Author and source: Zen, PANews
In today’s AI-driven era, GPUs have become the true "digital oil." Yet, this foundational resource underpinning the trillion-dollar AI industry is still traded in the wild west era of private negotiations—some companies are now attempting to bring transparency and openness to this market.
Recently, Ornn announced the completion of a $33 million seed round led by a16z Crypto, with participation from Galaxy Ventures, Nordstar, and SV Angel, and continued participation from Vine Ventures, Crucible Capital, Link Ventures, and Box Group.
The company, founded in 2025 by two Massachusetts Institute of Technology graduates, Kush Bavaria and Wayne Nelms, secured substantial early investments from multiple venture capital firms not only due to the founders’ prestigious academic backgrounds, but also because they capitalized on the rapid expansion of AI infrastructure by targeting an even more fundamental layer—the creation of a trading marketplace centered around computing power.
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Currently, GPU, data center, power, and network resources have evolved from internal engineering challenges within tech companies into one of the most critical cost components across the entire AI industry chain.
However, unlike mature commodity markets such as oil, natural gas, and electricity, the computing power market remains highly opaque. Prices are typically determined through private negotiations between buyers and sellers, long-term contracts are difficult to exit, and there is a lack of standardized price benchmarks across different GPU models, regions, data center conditions, and contract durations. Ornn is an emerging project aiming to address this very issue.
Traditional cloud computing power rental platforms primarily generate revenue by directly selling GPU computing time to AI companies. In contrast, Ornn’s core positioning is to transform the right to use specific GPU models over a defined period—from privately negotiated, long-term contracted resources—into a tradable commodity that can be priced, traded, financed, and hedged.
As the AI era rapidly advances, computing power has become one of the most important commodities globally. However, the market mechanisms surrounding computing power remain relatively "primitive." Traditional capital-intensive commodity markets typically evolve to develop price benchmarks, risk-transfer instruments, and investable assets—yet the AI computing power market currently lacks these foundational elements. a16z Crypto believes that what Ornn is doing is transforming computing power from a system of private negotiations and individual contracts into a truly functional market.
From Price Index to Trading Platform: Building Liquidity for GPU Computing Resources
Solving the issue of "pricing transparency" is where Ornn begins, and how to solve it is crucial; Ornn’s approach is to build a trading platform that directly transforms the current liquidity landscape of computing power.
One of Ornn's core products is a price index. Previously, the company launched the Ornn Compute Price Index (OCPI) to track the market prices of GPU computing resources. According to Ornn’s official website and the a16z Crypto official blog, OCPI is not simply scraping publicly listed prices, but rather derives its price benchmark from executed or settled transactions, covering major GPU types such as H100, H200, B200, and RTX 5090, and standardizing data across dimensions such as hardware, region, and contract duration.
One of the biggest issues in today’s computing power market is the significant discrepancy between listed prices and actual transaction prices. Large AI labs, cloud service providers, data center operators, and small-to-medium enterprises all receive different pricing, and contract terms are highly customized. In this environment, without a unified market reference price, buyers struggle to determine whether they are paying a premium, sellers find it difficult to set appropriate future pricing for computing resources, and financial institutions are unable to adequately assess the collateral value and future cash flows of GPU assets.
In April this year, Ornn announced that OCPI has been integrated into the Bloomberg Terminal, further opening access to institutional users. According to the company’s announcement, OCPI tracks GPU hour rental prices in both cloud and on-premises markets, covering Nvidia H100, A100, H200, B200, and RTX series GPUs. Ornn stated that over 400 data center operators, investors, and AI companies are using its platform to monitor GPU prices.
Above the price index, Ornn is developing its second-layer product: the GPU computing power trading platform, Ornn Compute. Currently, a large amount of GPU computing power is locked in private transactions and long-term contracts. Ornn Compute aims to serve as a bridge between buyers and sellers. Buyers can lock in dedicated GPU computing power of specific models, regions, and durations under a single contract; when workloads change, they can transfer or sublease the remaining computing power to other users, turning previously idle resources back into income.
This is precisely where Ornn’s model differs from traditional cloud providers. Traditional cloud services typically involve purchasing computing power on-demand or signing long-term contracts for cloud resources; Ornn, however, aims to make the right to use GPU computing power itself liquid on a secondary market. For small- and medium-sized AI startups, this means they can more flexibly access short-term GPU resources without being required to commit to lengthy resource contracts upfront. For data centers and smaller cloud providers, this means they can more standardize the sale of idle or future-available GPU computing power and achieve more predictable revenue streams.
ICE enters the scene, and computing power may move toward the derivatives market.
From the price benchmark provided by OCPI to liquidity support on spot trading platforms, the entry of another derivatives giant transforms it into financial infrastructure, laying the financial groundwork for the AI economy.
In May of this year, Intercontinental Exchange (ICE), the parent company of the New York Stock Exchange, announced plans to launch GPU computing power futures contracts based on the OCPI index in collaboration with Ornn. According to ICE’s announcement, these contracts will be denominated in U.S. dollars, settled in cash, and reference the OCPI index series, covering mainstream GPU types currently on the market; the exact launch date remains subject to regulatory approval.
If price indices are the first step in market formation, then futures and hedging tools are key indicators of a mature commodities market. ICE’s involvement elevates Ornn’s narrative from a compute power trading platform to a compute power derivatives infrastructure.
For data center operators, a decline in GPU rental prices may impact future revenue and financing capabilities; for AI companies, rising GPU prices increase training and inference costs. Through futures contracts based on OCPI, buyers and sellers can theoretically lock in future prices in advance, reducing operational volatility.
From a broader industry perspective, competition in AI infrastructure has entered a more refined phase, centered on making high-capital-expenditure assets financially measurable. Data centers require financing, lenders need valuations, AI companies seek budget certainty, and investors desire more direct exposure to AI infrastructure. In this process, GPUs can transcend their role as mere hardware and become assets that generate cash flow, serve as collateral, form price curves, and be traded.
In addition, Ornn recently expanded its index business to include AI token costs with the launch of the Ornn Token Price Indices (OTPI) in June this year. As mentioned above, the OCPI measures the input side of the AI economy—the cost of GPU time required to train and run models—while the OTPI measures the output side, capturing the actual cost of tokens generated by major model developers such as Anthropic and OpenAI. Together, these two indices provide the market with a comprehensive cost curve spanning from computational input to AI consumer demand.
Overall, Ornn’s rise reflects a shift in the AI computing market from resource competition toward financialized pricing. As GPUs, data centers, and electricity become among the most significant cost components in the AI industry, demand is growing for transparent pricing, flexible trading, and risk management tools. Ornn is betting on the financialization opportunities underlying this transformation.
Ornn's rise is not just a funding story, but also a microcosm of the underlying shift in the AI industry: computing power is undergoing a transformation from "capital-intensive hardware" to "financializable assets."