Authors: Caleb Shack, Alana Levin
Compiled by: Jiahuan, ChainCatcher
At Variant, we are passionate about exploring emerging markets. New asset classes, financial products, asset issuance, expanded market access, and novel ways of participation are deeply embedded in our founding DNA.
Lately, we've been thinking about markets built around computing power.
Access to computing power is a vast and growing domain, one that is arguably ripe for further financialization.
However, the supply-demand dynamics of compute are highly complex, opaque, and evolving. Many questions remain unanswered regarding market timing, structure, and even what exactly the traded asset is.
In the spirit of debate and discussion around these questions, we want to share an emerging analytical framework as a window into thinking about compute markets.
The birth of new futures markets typically requires five prerequisites:
- Fragmented supply
- Persistent price volatility
- Some form of physical settlement infrastructure
- Standardized, tradable units
- Lack of alternatives for price discovery or hedging
Our framework examines the current landscape of compute markets through these five lenses. We borrow historical analogies to explain the importance of each and to predict when the market might reach a tipping point.
Summary of Key Points
A quick glance at this framework reveals that today's compute market lacks the maturity needed to sustain a robust futures market.
(Nonetheless, the space is vibrant, and many startups are actively working to change this; if you're working on this, please reach out!)
Here are our current ratings for the compute futures market across the five dimensions:
- Fragmented Supply: 🔴 Supply is highly monopolized by hyperscale cloud providers
- Price Volatility: 🟢 GPU prices are highly volatile
- Physical Settlement Infrastructure: 🟢 Physical settlement infrastructure exists at the OTC broker level
- Standardization: 🔴 Compute lacks standardized, tradable units
- Lack of Alternatives: 🟡 Vertically integrated suppliers can hedge internally; other participants are forced to be long only
1. Fragmented Supply (Compute Score: 🔴)
Futures markets are mechanisms for price discovery.
Under monopolistic supply, price discovery becomes unnecessary, as prices are set by a few large suppliers, eliminating any pricing uncertainty.
Throughout history, this has been a common pattern.
Oil futures only flourished after the supply-side cartels (like the "Seven Sisters," the seven transnational giants that dominated global oil in the mid-20th century) weakened.
Electricity markets formed only after government deregulation broke monopoly pricing and allowed independent producers into the market. Fragmented supply propelled futures markets as crucial venues for price discovery.
Looking at today's compute dynamics, the supply side appears relatively concentrated.
Four cloud giants (e.g., AWS, Azure, GCP, Oracle) control roughly 78% of self-built critical IT power capacity globally and about 69% of the H100 supply (according to the original article's calculation, assuming 12.4 million H100s in Q4 2025).
We infer they similarly dominate the supply of global compute hours. Supply is not fragmented.
Nevertheless, we ponder what might change this dynamic.
New cloud providers are emerging. New chip architectures create opportunities for other suppliers to gain market share.
Some long-term contracted capacity with major labs may end up underutilized, meaning those labs might eventually become compute suppliers or sellers in the market.
Thus, while we are uncertain about future concentration levels, our current assessment is: the market's supply side will trend towards being more fragmented than it is now.
2. Price Volatility (Compute Score: 🟢)
The Ornn H100 Index on Bloomberg Terminal
Another prerequisite for a futures market is a highly volatile underlying asset.
Without significant price uncertainty, hedgers lack the incentive to guard against volatility.
Volatility also attracts speculators, who profit from large price swings. If markets are stable or predictable, speculators will look elsewhere.
We saw this in the 1950s oil market.
When the Soviet Union posted prices below the "Seven Sisters" listed prices due to an oil glut, the "Seven Sisters" cut prices in the Middle East region without notifying the producing countries.
The resulting shock waves led to the nationalization of Middle Eastern oil, the formation of OPEC, and heightened global oil price uncertainty. Oil volatility then triggered electricity market volatility in the 1970s.
Compute pricing has been and will continue to be volatile.
The rate at which new supply comes to market is uncertain. New chips or data center architectures could improve token efficiency for specific tasks. Demand continues to surge, expanding in unpredictable ways.
We are very confident this prerequisite is already met today.
3. Physical Settlement Infrastructure (Compute Score: 🟢)
For a market to function efficiently, buyers must be confident they can receive and consume the underlying instrument at the stipulated date and time.
This requires infrastructure: mechanisms for aggregating supply, ensuring reliable delivery, clearing trades, handling collateral, and managing settlement. These tasks typically fall to intermediaries or brokers.
In electricity markets, these functions are performed by Independent System Operators (ISOs), which act as neutral third-party, quasi-governmental entities.
There's no exact equivalent in today's compute market, but our hypothesis is that compute brokers or OTC desks are starting to (and increasingly will) take on many of these functions.
Today, brokers are building indices and data aggregation tools around compute purchase and lease agreements to anchor market prices.
Ornn and Silicon Data have begun publishing pricing data for data center-grade GPUs.
The broker community is also forming consensus around contract agreements, akin to how SAFE agreements standardized early-stage financing terms. These tools refine the underlying physical settlement infrastructure—previously, much of this coordination happened in group chats.
We give physical settlement infrastructure a green score because it lays the groundwork for price discovery.
But compared to a mature spot market, it's far from perfect. These purchases occur at the infrastructure layer, and not all market participants have the right to resell publicly after purchase. We are closely watching developments toward new market creation at this layer.
4. Standardization (Compute Score: 🔴)
A major challenge for new commodities is often the uniqueness and non-fungibility of their units.
Too many variables can fragment liquidity across numerous markets or create basis risk too high for most hedging and delivery needs.
For example, crude oil is measured by density and sulfur content, which varies by origin.
NYMEX found product-market fit with its WTI index (light, sweet crude) because it locked in a standard that serves the global upstream market, even used downstream (e.g., by airlines) for hedging.
Electricity is standardized by region, accounting for supply-demand fluctuations that vary due to temperature, population density, etc.
The compute market lacks the level of standardization needed for general hedging purposes.
The challenge: one H100 instance is not always equal to another.
Factors like region (and local power input), rack configuration (i.e., hardware and networking components), and term (i.e., contract duration) exacerbate the differentiation in GPU instance pricing.
However, we see early signs of standardization, especially when demand comes from long-tail (i.e., non-frontier lab) inference.
Unlike training, inference workloads require far fewer nuances and can run on distributed, rather than co-located, setups.
Standardization could emerge if inference supply fragments across many suppliers, for example, as open-source weight models gain share.
5. Lack of Alternatives (Compute Score: 🟡)
This is a subtle but often overlooked point in market formation.
Futures markets are built to serve hedgers. If alternatives exist with sufficient liquidity and negligible basis risk, the alternative contract will find no takers.
A textbook example is the lack of adoption for jet fuel futures—WTI and other upstream indices already adequately served the need.
In the electricity domain, temperature-based futures failed because market participants found it more efficient to hedge the outcome of price fluctuations (electricity) than its cause (temperature).
Today, model providers hedge compute risk via long-term lease agreements or joint ventures, often structured as "take-or-pay," exchanging spot price exposure for counterparty risk.
Hyperscale cloud providers typically own the GPUs they deploy outright.
On the other hand, long-tail suppliers, lacking both the contractual leverage for favorable lease terms and the capital to build their own vertically integrated infrastructure, bear the brunt of spot market volatility.
From a market perspective, alternatives don't exist; however, players controlling supply can hedge internally via vertical integration.
Overall Assessment
Looking at the combined scorecard, it may be too early for compute to support a robust futures market.
The market has volatility to attract speculators and early settlement infrastructure to support trades, but it lacks the supply fragmentation and standardization needed for genuine price discovery at scale.
Most trading occurs OTC.
Brokers are building price feeds, Ornn and Silicon Data are publishing indices, and group chat trades are being formalized into contract templates.
This isn't meaningless, but it's not yet a formed market like WTI or PJM. Volumes are too small, contracts too bespoke, and supply too concentrated for existing infrastructure to clear at scale.
The right way to interpret this framework is as a diagnostic tool, not a final verdict. It tells us what's missing, not what's impossible.
Open Questions
The market will evolve in ways we cannot yet be certain of.
We have many open questions and some preliminary hypotheses. These hypotheses are tentative, needing further validation or refutation. Below, we articulate the strongest arguments for them.
▍ In the next 1-2 years, will the market supply side become more fragmented or more concentrated?
We expect moderate fragmentation.
New cloud providers are bringing new capacity online faster than any other category.
As power becomes the core constraint, new regions are coming online, favoring operators who can build capacity near cheap power (not near existing hyperscale footprints).
Fortune 2000 companies are even backing small-scale data centers. Expansion in this area seems inevitable.
However, standard business models rely on large, long-term contracts with reliable counterparties (like hyperscalers and frontier labs).
Cloud brokerage service providers like Hyperbolic and SF Compute are bucking this trend, offering capacity by the hour.
These serve the long-tail compute needs of AI-native startups, application-layer companies running inference on open-source weights, and research labs without frontier-level budgets.
We believe adoption of open-source weights, in particular, will lead to further fragmentation of compute capacity—as supply "de-verticalizes" from frontier labs and hyperscalers.
▍ How will standardization unfold?
Index providers are setting standards around per-GPU-instance-hour costs.
These data sources represent rough estimates, not precise prices.
Instance prices vary due to many factors, including region, rack configuration, and term, making standardized pricing difficult.
Differentiation in rack configuration is particularly pronounced, a result of data centers being tailored for custom workloads and hyperscalers optimizing for ecosystem lock-in rather than market uniformity.
Standards emerge when there is unified market demand.
The WTI standard gained adoption because it served a wide range of downstream refined products like gasoline, diesel, and jet fuel.
Today, compute demand is driven by AI training and inference workloads.
Training infrastructure is bespoke, optimized for computationally intensive, long-running tasks in large, centralized facilities, making underlying compute instances nearly non-fungible.
On the other hand, inference infrastructure requires simpler hardware specs and less power; it's optimized for latency, meaning infrastructure is distributed across regions rather than co-located.
Inference is homogeneous and is expected to account for over 65% of AI compute demand by 2029. We suspect optimization around the infrastructure layer serving this market will lead to convergence in compute requirements across suppliers.
If differences at the chip-instance level persist, another pathway to standardization could be hardware-level benchmarking.
NVIDIA created the MLPerf benchmark for scoring inference and training performance across various model architectures.
Under this vision, GPU instances would be traded based on the quality and efficiency of their output, not their hardware specs.
▍ What could prevent standards from emerging in the next 1-2 years?
We think "walled gardens" and bespoke workloads will stifle attempts at standardization.
Over the next 1-2 years, hyperscalers and frontier labs will work hard to maintain their dominance in AI infrastructure and model provision.
If the two don't decouple, they will maintain hardware based on their own needs, which differ across companies. Adoption of new chip architectures will further fragment hardware specs, making standard-setting difficult.
▍ How will open-source weights gain meaningful adoption?
This is the simplest path to compute market formation.
The two core bottlenecks these markets face today are supply-side concentration and lack of standardization.
Widespread adoption of open-source weights democratizes the ability to run inference.
This, in turn, creates incentives for independent operator formation and drives infrastructure optimization tailored for these specific models.
We saw the same story in Bitcoin mining: open-source software spawned numerous miners and drove standardization around hardware configurations.
So far, open-source weights have lagged closed-source models in performance.
But if trends hold, open-source weights will soon reach the performance thresholds we see in closed-source models today.
Enterprises are already widely embedding closed-source models into their systems and seeing significant productivity gains. In three months, a model delivering similar productivity gains might cost a fraction of today's price.
However, most enterprises might still gravitate toward the best-performing model.
We think there will come a day when frontier closed-source models become too expensive for the tasks they perform, and enterprises will optimize intelligence allocation across different models.
Remember, frontier labs currently provide inference at a loss; they must eventually raise prices to sustain operations. That's when open-source weights will have their moment.
▍ What will be the final unit of trade?
Compute roughly breaks down into three layers: Chip, Chip-Instance-Hour, Token.
Chip Layer—Supply is highly concentrated.
ASML monopolizes the lithography machines used by TSMC, TSMC monopolizes the chip fabs used by NVIDIA, and NVIDIA monopolizes frontier chip design.
Moreover, chips are only useful when plugged into power with high uptime. This leads us to believe individual, deliverable chips will not be the final unit of trade.
Chip-Instance-Hour Layer—Refers to the period a chip is actually usable.
This is arguably the most valuable state for a chip and the core layer discussed in this article.
At this layer, compute as a commodity behaves similarly to electricity, as long as there is sufficient demand for the compute resource.
We envision compute being traded similarly to electricity and other utilities: standardized in regional contracts (compute as a function of electricity) with spot and futures markets layered on top for hedging. This is possible in the "chip-instance-hour" format.
Token Layer—The downstream output of compute instances, could also become the final unit of trade.
If tokens are the primary driver of compute instances, then token markets would offer the demand side a way to hedge costs and allow the supply side to lock in revenue.
The supply side could hedge costs via ongoing long-term contracts or vertical integration and remain concentrated.
However, tokens are not uniform across models. Each model has its own text tokenization standard and produces distinct outputs, making them not fully interchangeable across use cases. Nevertheless, we are watching this space closely.
