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For most professionally operated sites in 2026, an AI data center offers the higher profit opportunity per constrained megawatt—but only when GPU capacity is contracted, highly utilized and backed by reliable power. Bitcoin mining can still win with exceptionally cheap, stranded or interruptible electricity because ASICs are faster to deploy, easier to curtail and simpler to restart. The right answer depends on a site’s all-in power cost, hardware efficiency, cooling, financing, uptime, utilization and customer contracts—not on a universal revenue-per-megawatt figure.
What actually determines the winner?
Bitcoin mining and AI data centers monetize the same basic input—electricity converted into computing work—but they sell that work in very different markets. A miner earns a variable amount of bitcoin based on hashprice. An AI facility sells GPU capacity through hourly usage, reserved capacity or longer contracts. That difference makes mining revenue transparent and continuously repriced, while AI revenue can be steadier but depends on finding and retaining customers.
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There is no authoritative, standardized industry figure for revenue per megawatt that applies to every site. Company results also differ in accounting treatment, depreciation, power hedges, curtailment credits, utilization and contract structure. Treat the figures below as market observations, reported company results or clearly labeled illustrations—not as a promise that any particular facility will earn the same amount.
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IREN’s FY2026 annual report says Bitcoin-mining revenue is affected principally by the market price of Bitcoin, the global network hashrate and the block reward. Electricity availability and cost, fleet efficiency and operating uptime determine how much of that revenue remains as margin.
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The April 2024 halving reduced the block reward to 3.125 BTC. The next halving is expected in 2028, although the exact date depends on block production. A halving cuts the new-coin reward approximately in half unless a higher bitcoin price, lower network competition or better efficiency offsets it.
The most useful short-term market measure is hashprice: the bitcoin revenue available per unit of hashing power, normally quoted in dollars per petahash per second per day (US$/PH/s/day). CoinShares reported a monthly average hashprice of US$27.7/PH/s/day in June 2026, an all-time low in its series. Its Q1 2026 report placed hashprice around US$36–38/PH/s/day. A miner’s realized result will differ with uptime, pool fees, transaction-fee income, curtailment and the efficiency of its machines.
AI data-center revenue equation
AI facilities generally monetize GPU-hours or contracted AI services. The customer may pay for on-demand usage, a reserved slice of a cluster or a multi-year capacity commitment. Revenue therefore depends on the hourly rate, the number of usable GPUs, utilization, service-level performance and the proportion of capacity covered by contracts.
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Side-by-side economics
| Decision axis | Bitcoin mining | AI data center |
|---|---|---|
| Revenue unit | Hashprice in US$/PH/s/day, plus bitcoin and transaction-fee exposure | GPU-hour, reserved GPU capacity or contracted AI service |
| Price and utilization risk | BTC price, global hashrate, difficulty, block reward and pool performance reset the economics continuously | Customer contracts can stabilize rates, but unused GPUs generate little or no revenue |
| Power profile | Large, comparatively uniform and often interruptible load; operators can curtail during expensive periods | High-density load with a strong preference for continuous, high-quality power and tight uptime commitments |
| Hardware cycle | ASICs are purpose-built and lose competitiveness as newer, more efficient models enter the network | GPUs and networking equipment also age quickly, but can have broader resale or redeployment options |
| Cooling and networking | Air cooling is common; immersion or other advanced cooling can improve density and noise management | High rack densities often require liquid cooling, high-capacity heat rejection and low-latency cluster networking |
| Capital and financing | ASIC purchase, containers or buildings, transformers, switchgear, cooling, networking and working capital | GPU servers, high-speed fabric, storage, liquid cooling, power distribution, software operations and larger commissioning budgets |
| Site and grid lead time | Can start at an existing powered site and scale in modules, subject to interconnection and equipment availability | Transformers, interconnection studies, permitting, cooling design and GPU supply can delay revenue substantially |
| Downside and restart flexibility | Very flexible: shut down or curtail machines when hashprice falls or power prices rise | Less flexible once customer contracts and uptime obligations are signed; frequent interruption can breach service commitments |
| Regulatory exposure | Energy policy, grid rules, digital-asset taxation and local mining restrictions | Data-center zoning, construction and water rules, grid approvals, data handling, export controls and customer compliance requirements |
Why Bitcoin mining margins are under pressure
Hashprice can fall faster than a machine’s costs
CoinShares estimated that miners operating below S19 XP-class efficiency at US$0.06/kWh or higher were losing money at roughly US$30/PH/day. It estimated that this affected about 15–20% of the global fleet. Because June 2026’s average hashprice was only US$27.7/PH/s/day, an inefficient fleet can move from marginally profitable to loss-making without any change in its facility.
Difficulty also adjusts as miners add or remove machines. When the network attracts more hashpower, each machine receives a smaller share of block rewards. A higher bitcoin price can improve revenue, but it can also attract additional competition and push difficulty upward.
Electricity is usually the dominant marginal cost
Riot Platforms stated in a Q2 2026 filing that “Power is overwhelmingly the largest marginal input cost in mining bitcoin.” The operating data from other miners illustrates why. Ionic Digital reported that energy costs equaled 51.1% of cryptocurrency-mining revenue at its owned and leased sites in 2025. It reported an average cost to earn one bitcoin, including depreciation, of US$93,330 in Q1 2026.
MARA reported purchased energy costs of US$38,956 per bitcoin in 2025, up from US$29,084 in 2024 at its owned sites. Those figures are company-specific and do not include every possible cost in the same way as another operator. They nevertheless show how a change in power pricing can overwhelm small efficiency gains.
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What a one-megawatt mining model must include
Start with the site’s actual metered load, not the nameplate rating. One megawatt running continuously consumes 24 megawatt-hours per day. Multiply the measured megawatts by 24, the applicable electricity rate, demand charges, taxes, hosting fees and any curtailment-related credits. Then model the machines’ total hash rate, joules per terahash, pool fee, uptime and repair reserve.
For a fleet measured in PH/s, a simplified gross-revenue model is:
daily mining revenue = deployed PH/s × hashprice (US$/PH/s/day)
Subtract electricity and all operating costs before calling the remainder profit. If the site receives a power credit for curtailing, model that credit as a separate, conditional cash flow rather than silently reducing the electricity rate.
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Why AI demand is growing faster
The International Energy Agency reported that global data-center electricity demand grew 17% in 2025, while electricity demand from AI-focused data centers grew 50%. Five major technology companies spent more than US$400 billion in capital expenditure in 2025, and the IEA expected a further 75% increase in 2026. Its base case projects total data-center electricity consumption rising from 485 TWh in 2025 to 950 TWh in 2030.
That growth creates a larger addressable market for GPU capacity than the market for a single digital asset. Public miners are responding: CoinShares reported that one operator’s AI Cloud revenue reached US$70.5 million in Q2 2026, overtaking its US$66.7 million of mining revenue. CoinShares also said AI/HPC deals signed by miners during 2025 and early 2026 exceeded US$70 billion in aggregate. These deals indicate strong demand, but announced contract value is not the same as recognized profit or free cash flow.
AI revenue can be more stable—but only with utilization
A GPU rack that is idle still consumes capital, occupies floor space and ages. A customer contract, reservation or minimum-commitment clause can protect revenue during slow demand periods, but it also imposes uptime, support and delivery obligations. Without contracted demand, an AI facility is exposed to utilization risk just as a miner is exposed to hashprice risk.
Power density changes the engineering problem
IEA analysis found that AI-server power density increased 11 times from 2020 to 2025 and may increase another fourfold by 2027. A building designed for conventional servers may lack the busway capacity, liquid-cooling loops, heat rejection, backup generation and network fabric required by modern AI clusters. Retrofitting can cost more and take longer than the original power-availability estimate suggests.
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When Bitcoin mining is the better use of power
- Electricity is exceptionally cheap, stranded or interruptible. A site with curtailed renewable output, excess generation or a flexible industrial tariff can mine during low-cost hours and shut down when prices rise.
- Power is available before an AI interconnection is ready. ASIC containers can monetize an energized site while transformers, permits and liquid-cooling systems for AI are being procured.
- The operator values rapid downside protection. Mining machines can be switched off without breaking a customer service-level agreement. The hardware can be restarted when hashprice improves.
- The fleet is near the efficiency frontier. Newer ASICs reduce joules per terahash and improve survival at low hashprice; an old fleet can lose money even when a newer one remains viable.
- The operator has no bankable GPU customer. Buying GPUs first and searching for utilization later reverses the normal risk order.
When AI data centers are the better use of power
- GPU capacity is contracted or supported by credible reservations. The contract should specify pricing, minimum utilization or commitment, payment security, uptime, power pass-through and termination rights.
- The site can deliver continuous, high-quality power. AI customers generally value predictable availability more than the ability to curtail for a few cheap hours.
- The operator can finance specialized infrastructure. GPUs are only one line item; liquid cooling, high-speed networking, storage, software, commissioning and support staff are essential.
- Utilization is high enough to cover idle capacity. A higher theoretical GPU-hour rate does not help if a large share of the cluster is waiting for workloads.
- The site has a credible expansion path. AI customers may need additional racks, power and network capacity on a schedule that mining can avoid but a data-center business must meet.
Three practical profitability scenarios
Scenario 1: low-cost power and a strong bitcoin market
Assume an operator has unusually cheap power, a current-generation ASIC fleet and the ability to curtail. Bitcoin mining can produce attractive cash margins because the operator’s break-even hashprice is low and fixed infrastructure is already paid for. The risk is that higher BTC prices attract more network hashpower, difficulty rises and the advantage narrows. This scenario favors mining when no firm AI customer exists.
Scenario 2: ordinary industrial power and weak hashprice
At around US$0.06/kWh or more, an inefficient fleet can lose money near US$30/PH/day, according to CoinShares. June 2026’s US$27.7/PH/s/day monthly average would be especially challenging for that fleet. In this case, curtailment, power hedges, newer ASICs or an alternative use of the site become urgent—not optional optimization projects.
Scenario 3: contracted GPUs and high utilization
Suppose the operator has a bankable customer, reliable power and an infrastructure plan that can sustain the required rack density. The IREN illustrative assumption of US$2.00–US$2.50 per GPU-hour at US$0.045/kWh provides a starting point for a model. The operator must then subtract GPU depreciation, financing, cooling, networking, storage, software, staffing, maintenance, insurance, taxes, downtime and customer-acquisition costs. This scenario generally offers higher expected revenue density and less direct exposure to BTC price, but it has greater execution risk and less freedom to shut down.
How to compare one megawatt without fooling yourself
- Measure the delivered load. Record interval data at the utility meter and at the IT load. Include transformer and cooling losses rather than using the contracted megawatt figure.
- Separate fixed and variable costs. List demand charges, leases, labor, insurance, taxes, network fees and financing separately from energy consumed.
- Build the mining case from hashprice. Use a low, base and high hashprice; include BTC price, difficulty, halving timing, pool fees, uptime, curtailment and machine replacement.
- Build the AI case from sellable GPU-hours. Start with installed GPUs, derate for maintenance and failures, apply realistic utilization and then apply the actual contract rate or a clearly labeled assumption.
- Model thermal and network limits. A megawatt of utility capacity does not equal a megawatt of GPU compute. Cooling pumps, chillers, fans and networking consume part of the budget.
- Stress financing. Test higher interest rates, delayed energization, hardware delivery slippage and a period with no customer revenue.
- Value flexibility explicitly. Give mining credit for curtailment revenue and rapid restart. Give AI credit for contracted minimums, but charge for uptime penalties and stranded capacity if a customer leaves.
- Compare cash flow, not headline revenue. Use after-power operating cash flow, then examine depreciation, principal repayment, taxes and replacement capex.
Should a Bitcoin miner convert its power to GPUs?
Conversion is a staged investment decision, not a simple hardware swap. ASIC halls may have the wrong floor loading, cooling, electrical distribution and network design for GPU clusters. A mine can preserve optionality by dedicating a portion of power to a pilot cluster while retaining interruptible ASIC capacity.
A sensible conversion checklist
- Obtain a signed or creditworthy GPU customer commitment before ordering a large cluster.
- Confirm that the utility and site permits allow the proposed continuous load and cooling system.
- Perform a rack-level electrical and thermal study, including worst-case ambient conditions.
- Validate fiber routes, switching capacity, storage throughput and interconnect latency.
- Price GPUs, servers, liquid-cooling equipment, heat rejection, software and support as one system.
- Set a utilization threshold at which the pilot expands; do not scale on optimistic demand forecasts alone.
- Retain a curtailment plan and enough electrical isolation to operate any remaining mining fleet safely.
Risks that can reverse the apparent advantage
Mining risks
- BTC price falls while difficulty remains high.
- The block reward falls at the next halving.
- Older ASICs become uneconomic before they are fully depreciated.
- Power prices, demand charges or grid restrictions rise.
- Repairs, heat, dust and downtime reduce realized hash rate.
- Local rules restrict mining loads or impose new environmental and grid requirements.
AI data-center risks
- GPU deliveries or transformers arrive after the customer’s contracted start date.
- Cooling or networking bottlenecks leave expensive GPUs idle.
- A customer cancels, delays workloads or negotiates lower rates.
- Power quality or downtime breaches service-level commitments.
- Rapid chip generations reduce resale value and raise replacement capex.
- Permitting, water availability, export controls, data handling or financing delays postpone revenue.
Bottom-line decision rule
Choose AI first when you have contracted demand, dependable power, high expected utilization and enough capital to build and operate specialized infrastructure. Choose mining first when power is unusually cheap or flexible, the ASIC fleet is efficient and the site’s main advantage is the ability to curtail or restart quickly. If neither condition is satisfied, preserve optionality: model a smaller GPU pilot, improve mining efficiency and wait for a bankable customer or clearer power economics rather than committing the entire megawatt to one volatile use.
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