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For most professionally operated sites in 2026, an AI data center offers the stronger profit opportunity per constrained megawatt—but only when GPU capacity is contracted, utilization is high and power is dependable. Bitcoin mining can still win when electricity is exceptionally cheap, stranded or interruptible, because ASICs are faster to deploy, easier to curtail and simpler to restart.
There is no universal revenue-per-megawatt winner. Bitcoin income moves with BTC price, network hashrate, the block reward and fleet efficiency. AI income depends on GPU-hour pricing, customer contracts, utilization, cooling, networking, financing and downtime. The right comparison is a site-specific, all-in model rather than a headline revenue figure.
What each business is actually selling
Bitcoin mining sells hashing capacity
A miner operates application-specific integrated circuits (ASICs) that repeatedly hash block-header data. The Bitcoin network pays the successful operator with the block subsidy and transaction fees, but an individual facility usually evaluates its expected output through hashprice: the revenue earned by one petahash per second (PH/s) of hashing capacity per day.
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Hashprice is a market snapshot, not a guaranteed tariff. It falls when more machines compete for the same block rewards, when the BTC price declines or after a subsidy halving. The April 2024 halving reduced the subsidy to 3.125 BTC per block; the next halving is expected in 2028. IREN’s FY2026 annual report describes Bitcoin-mining revenue as principally affected by Bitcoin’s market price, global network hashrate and the block reward. Electricity price and availability, machine efficiency, uptime and curtailment determine whether that gross revenue becomes profit.
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AI data centers sell accelerated computing
An AI facility sells access to GPUs and the surrounding platform: servers, high-speed networking, storage, orchestration, security and technical support. Customers may pay by GPU-hour, reserve a committed cluster under a contract, or purchase a managed training and inference service. Revenue therefore depends on the number of usable GPUs, the realized price, billable utilization and the customer’s willingness and ability to keep paying.
AI demand can be steadier than mining revenue when capacity is under contract, but the business is operationally harder. A GPU hall needs dense electrical distribution, liquid or advanced air cooling, low-latency fabric, high availability and software operations. Idle GPUs still incur depreciation, financing and facility costs, so an apparently high hourly rate is not enough.
Bitcoin mining and AI data centers compared
| Axis | Bitcoin mining | AI data center |
|---|---|---|
| Revenue unit | Hashprice per PH/s per day, plus any transaction-fee contribution | GPU-hour, reserved cluster or contracted AI service |
| Revenue volatility | Directly exposed to BTC price, global difficulty/hashrate and the four-year halving cycle | Usually tied to customer contracts and utilization; spot GPU demand can still change quickly |
| Power profile | Large, relatively uniform and often interruptible load; curtailment can be used as an operating tool | High-density load with tighter voltage, cooling and uptime requirements; interruptions can breach service commitments |
| Hardware cycle and residual value | ASICs are purpose-built and can lose value rapidly when newer, more efficient generations arrive | GPUs can serve several workloads, but value depends on memory, interconnect, software support and the pace of new accelerator releases |
| Cooling and networking | Air cooling is common; networking is comparatively simple | High-capacity cooling, high-speed fabric, storage and cluster management are core production systems |
| Capex and financing | ASIC purchase, containers/buildings, transformers and electrical work; shorter deployment can reduce carrying time | Servers plus specialized power, cooling, networking, controls and commissioning; larger capital stack and longer payback risk |
| Site and grid lead time | Can use remote, curtailed or otherwise difficult-to-monetize power if interconnection and permits allow | Requires firm capacity, equipment reservations, permits and often new transformers or substations |
| Downside and restart flexibility | Machines can be switched off during expensive hours and restarted when economics improve | Contracts, cluster scheduling and customer data make frequent interruption impractical |
| Regulatory and permitting exposure | Energy-market rules, noise, land use, environmental limits and local restrictions on mining loads | Data-center zoning, water or refrigerant rules, grid approvals, cybersecurity, data handling and service-level obligations |
The 2026 figures that set the economics
The following figures are useful anchors, but they are not interchangeable company-wide benchmarks. Some are market observations, some are reported accounting measures and one is an illustrative AI assumption.
| Measure | Reported figure | How to interpret it |
|---|---|---|
| Monthly Bitcoin hashprice | US$27.7/PH/s/day in June 2026 | CoinShares called this an all-time low, showing how little gross revenue a unit of hashing capacity can produce in a weak market. |
| Q1 2026 hashprice | US$36–US$38/PH/s/day | CoinShares’ quarterly range illustrates that even a few months can materially change mining revenue. |
| Efficiency stress point | About US$0.06/kWh or higher for fleets less efficient than S19 XP at roughly US$30/PH/day | CoinShares estimated those fleets were losing money and represented 15–20% of the global fleet; the result is an operating snapshot, not a permanent cutoff. |
| MARA purchased energy cost | US$38,956 per bitcoin in 2025 versus US$29,084 in 2024 at its owned sites | MARA’s reported increase shows how power costs can rise even before considering depreciation, financing or corporate overhead. |
| Ionic Digital energy share | Energy costs were 51.1% of cryptocurrency-mining revenue at owned and leased sites in 2025 | This is a company-reported ratio; hosting contracts, hedges, curtailment credits and accounting choices differ by operator. |
| Ionic Digital production cost | US$93,330 average cost to earn one bitcoin, including depreciation, in Q1 2026 | Including depreciation gives a fuller replacement-cost view than electricity alone, but it remains specific to that fleet and period. |
| Illustrative AI pricing | US$2.00–US$2.50 per GPU-hour at US$0.045/kWh | IREN presented this as an assumption for an AI Cloud model, not a guaranteed market rate or realized margin. |
| Data-center electricity growth | Global data-center demand rose 17% in 2025; AI-focused demand rose 50% | IEA reported both growth rates in 2026, indicating strong demand but not automatic profitability for every facility. |
| Large-technology-company capex | More than US$400 billion in 2025, with a further 75% increase expected in 2026 | IEA’s figure signals investment momentum; it does not specify what portion reaches an individual colocation site. |
| Projected data-center consumption | 485 TWh in 2025 to 950 TWh in 2030 | IEA’s base case is a forecast, not a guarantee that every planned project will connect or operate at full utilization. |
| Miner-to-AI revenue example | One operator reported AI Cloud revenue of US$70.5 million versus mining revenue of US$66.7 million in Q2 2026 | CoinShares’ example shows that diversification can overtake mining revenue, but it is not an industry average. |
| Aggregate AI/HPC contracts | More than US$70 billion of deals signed by miners in 2025 and early 2026 | CoinShares reported the aggregate; contract duration, counterparties, capex obligations and profitability vary widely. |
How to model Bitcoin-mining profit
Start with the machine fleet, not the facility’s nameplate megawatts. For each ASIC, record its hashrate in TH/s, energy draw in kW, purchase price, useful life and expected uptime. Convert the fleet to PH/s and multiply by the current hashprice:
Daily gross mining revenue = fleet hashrate (PH/s) × hashprice (US$/PH/s/day)
Then subtract purchased electricity, demand charges, hosting or land costs, pool fees, repairs, labor, insurance, taxes, financing and the economic cost of replacing obsolete machines. If the site receives curtailment payments or sells power back to the grid, model those credits separately rather than treating them as mining revenue.
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Electricity often dominates the result. Riot has stated that power is overwhelmingly the largest marginal input cost in Bitcoin mining. The CoinShares threshold for sub-S19-XP-efficiency fleets at roughly US$0.06/kWh or more demonstrates why a small power-price change can turn positive hashprice into a loss. A newer, more efficient ASIC may remain profitable at a price where an older unit must be shut down.
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How to model AI data-center profit
AI revenue begins with sellable capacity rather than total installed GPU count. Separate commissioned GPUs, available GPUs, contracted GPUs and actually billable GPU-hours. A basic model is:
Monthly AI revenue = sellable GPUs × hours in month × utilization × realized US$/GPU-hour
For a reserved-cluster contract, replace the hourly assumption with the contracted monthly fee and test the customer’s minimum commitment, payment terms, renewal probability and concentration. For on-demand capacity, use a lower utilization case and include sales, support and scheduling overhead.
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AI facilities also need a power-usage-effectiveness assumption. A one-megawatt utility connection is not one megawatt of GPU boards: cooling, conversion losses, networking and resilience consume part of the envelope. Use measured or vendor-guaranteed load for the exact server configuration and model degraded operation during maintenance or partial failure.
Why “revenue per megawatt” has no universal answer
Two sites with the same electrical capacity can have radically different results. A mining site may run nearly all of its available power through ASICs but curtail during high-price hours. An AI site may have a higher theoretical revenue rate yet earn less if GPUs wait for interconnection, commissioning or customers.
To compare one megawatt, define the same period and boundaries:
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- Set the electrical boundary. Decide whether the megawatt is utility input, usable IT load or average delivered power after cooling and conversion losses.
- Use actual hardware specifications. For mining, enter ASIC joules per terahash and fleet mix. For AI, enter GPU board power, server configuration, networking and cooling overhead.
- Apply realistic uptime. Account for maintenance, curtailment, grid outages, failed components and customer scheduling.
- Use realized prices. Apply a hashprice range for mining and signed-contract or observed GPU-hour rates for AI; distinguish guaranteed minimums from optimistic spot prices.
- Subtract all-in costs. Include energy, demand charges, labor, repairs, depreciation, financing, insurance, taxes, land, connectivity and compliance.
- Stress the downside. Test a BTC drawdown and hashprice decline against an AI customer cancellation, lower utilization, delayed energization or GPU price compression.
Three practical sensitivity cases
Case 1: Stranded or highly flexible power
Bitcoin mining is often the better first use when power is remote, curtailed, seasonal or difficult to sell to a firm customer. ASIC containers can be deployed with comparatively simple networking, and the operator can turn them off when the marginal value of electricity rises. This flexibility has real option value that a contracted AI cluster cannot easily replicate.
The case only works if the all-in energy price and machine efficiency leave room below the prevailing hashprice. Include transmission, generators, fuel, repairs and the cost of moving or replacing ASICs; “free” or stranded electricity is not free if it requires major interconnection or dedicated generation.
Case 2: Firm industrial power and current hashprice
This is the most fragile middle case. At ordinary industrial tariffs, a miner may show positive gross revenue while losing money after depreciation and financing. The June 2026 hashprice snapshot of US$27.7/PH/s/day and CoinShares’ Q1 range of US$36–US$38/PH/s/day show how quickly the revenue line can move.
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Run the model with the exact fleet’s efficiency and a shutdown rule. If the site cannot curtail, renegotiate power or survive several weak months, an apparent mining margin is not a durable investment return.
Case 3: Contracted GPUs with high utilization
AI generally has the stronger expected revenue density when a customer has committed to capacity, the grid can provide reliable power and the operator can keep the cluster busy. Contracted revenue can be less sensitive to BTC-style market swings, while the customer relationship may support financing for the infrastructure.
Do not confuse demand headlines with contracted utilization. Verify delivery dates, minimum take-or-pay obligations, credit quality, termination rights, service-level penalties and who pays for GPU refreshes. A delayed transformer or missing networking fabric can push revenue months beyond the financing model.
Should a Bitcoin miner convert its power to GPUs?
Conversion is not a binary choice. A hybrid design can preserve interruptible mining while a portion of the site is upgraded for firm AI load, but shared electrical and cooling systems must be engineered for the stricter requirement.
- Audit the existing power position. Document interconnection rights, hourly prices, demand charges, curtailment rules, transformer capacity, outage history and the percentage of power that is genuinely firm.
- Calculate the mining shutdown price. For each ASIC class, find the electricity price at which expected hashprice no longer covers electricity and variable operating costs. Add a separate replacement-cost test including depreciation.
- Obtain a real AI demand commitment. Require a signed term sheet or contract specifying GPU type, quantity, start date, utilization or minimum payment, support scope, security requirements and remedies for delay.
- Check the physical conversion. Confirm floor loading, busway design, rack density, cooling method, water availability, fire protection, fiber routes and spare electrical capacity. Existing mining buildings may not meet AI requirements without major reconstruction.
- Model financing and delivery risk. Price GPUs, servers, switches, storage, cooling and commissioning separately. Include deposits, lead times, warranty limits, interest during construction and a contingency for grid equipment.
- Stage the investment. Start with a small, separately metered AI block or a co-location partnership. Expand only after the first cluster demonstrates contracted utilization, measured power use and reliable operations.
- Preserve an exit path. Keep enough electrical flexibility to curtail mining or sell power if the AI customer leaves. Avoid equipment leases whose payments continue after a contract termination unless the residual-value risk is explicit.
Infrastructure differences that change profitability
Power quality and interruption tolerance
ASICs can usually be stopped and restarted without losing a customer workload, although repeated cycling still affects operations and revenue. AI training jobs, distributed storage and inference services need coordinated shutdowns, resilient networking and documented recovery procedures. A site with cheap but unstable power may be excellent for interruptible mining and unsuitable for an AI service-level agreement.
Cooling and water
Mining deployments commonly use air cooling with a relatively uniform heat profile. AI racks can exceed the density that existing air systems were designed for, making direct-to-chip liquid cooling, rear-door heat exchangers or other specialized designs necessary. Water rights, treatment, leak detection and heat rejection can add both capital cost and permitting time.
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Networking and operations
Mining needs reliable connectivity for pool communication and monitoring. AI clusters need low-latency, high-bandwidth fabrics, storage throughput, scheduler integration, tenant isolation, observability and specialized support. Underbuilding the network can leave expensive GPUs idle even when the electrical system is ready.
Hardware cycles and residual value
ASIC value is tightly linked to joules per terahash and the current hashprice. New generations can make older machines uneconomic quickly. GPUs are more versatile, but resale value still depends on memory capacity, interconnects, firmware, warranty and whether customers want that generation. Both businesses should use conservative residual values rather than assuming equipment can be sold at book value.
Grid, permitting and financing lead time
The IEA reported that AI-server power density increased elevenfold from 2020 to 2025 and could rise another fourfold by 2027. Transformers, switchgear, chips, cooling equipment, storage, permits and financing can therefore become the schedule’s critical path. Strong AI demand does not create revenue until the facility is energized, commissioned and connected to paying workloads.
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- Comparing gross revenue with net mining profit. Hashprice is before electricity, hosting, depreciation and financing. Put both businesses on the same all-in basis.
- Using a single BTC or hashprice forecast. Build downside, base and upside paths and include the 2028 halving in long-duration plans.
- Treating a GPU-hour quote as guaranteed utilization. Separate list price, contracted minimum, scheduled hours and collected cash.
- Ignoring the power boundary. State whether figures use utility input, facility load or IT load and include cooling and conversion losses.
- Assuming an existing mine is AI-ready. Inspect cooling, rack density, fiber, fire protection, water, controls and service procedures before valuing conversion.
- Overlooking downtime and customer penalties. AI contracts can impose service-level credits; mining can simply stop, but every stopped hour loses hashprice revenue.
- Mixing company accounting measures. MARA’s purchased energy cost, Ionic Digital’s energy share and its depreciation-inclusive cost are different measures. They are evidence of cost pressure, not a league table.
- Counting announced deals as present revenue. CoinShares’ more than US$70 billion of miner AI/HPC deals includes contracts with different timing, terms and obligations.
- Assuming demand growth guarantees margin. The IEA’s electricity and capex growth figures describe market expansion, while local competition, chip supply and power pricing determine a particular site’s return.
A decision worksheet for owners and investors
Before choosing a workload, put these inputs in one model and lock the definitions:
- Available and firm megawatts by hour, delivered electricity price, demand charges and curtailment revenue.
- Mining fleet PH/s, joules per terahash, uptime, pool fee, ASIC purchase cost, depreciation life and replacement schedule.
- AI GPU count, GPU and server power, facility overhead, cooling design, utilization, realized GPU-hour price and contract minimums.
- Interconnection, transformer, construction, networking, storage, labor, insurance, taxes, financing and maintenance costs.
- BTC price, network hashrate, block subsidy, transaction-fee assumption and halving dates for mining scenarios.
- AI customer credit, contract duration, renewal, termination, service-level obligations and payment timing.
- Downside cases for power outages, delayed equipment, lower utilization, hardware failure and weaker market pricing.
Evaluate both net operating cash flow and return on invested capital. A workload with higher revenue per megawatt can still be inferior if it requires twice the capital, takes two years longer to energize or carries non-cancellable debt.
Bottom line
AI data centers are generally the better profit opportunity per constrained megawatt when GPU demand is contracted, utilization is demonstrably high, power is reliable and the operator can fund specialized infrastructure. Bitcoin mining remains compelling for exceptionally cheap, stranded or interruptible electricity and for owners who value rapid deployment and the ability to shut down when economics deteriorate.
The defensible decision is conditional: build both models with the same power boundary and all-in costs, stress the downside, verify contracts and schedule risk, and choose the workload that fits the site’s physical and financial constraints—not the one with the most impressive headline revenue.
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