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One megawatt is not a universal unit of useful computing. A Bitcoin mining site and an AI data center can draw the same electrical capacity while producing entirely different outputs, using different equipment, requiring different cooling and networking, and tolerating different interruptions. A sound comparison therefore combines power (MW), energy (MWh), workload output, facility overhead, operating schedule, flexibility and site constraints.
Use the framework below to decide how much capacity each workload needs, what a shared site can realistically support, and which questions must be answered before considering a conversion from mining to AI.
Start with the five quantities that make a comparison meaningful
Before comparing facilities, separate the terms that are often collapsed into “power.”
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Capacity is the maximum rate at which a site can draw electricity at a given moment. It is set by the utility interconnection, transformers, switchgear, distribution equipment and operating limits. A 20 MW interconnection does not mean the site will consume 20 MW every hour; it means that is the available ceiling, subject to contractual and engineering restrictions.
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Energy consumption (MWh or TWh)
Energy is capacity multiplied by time. A load that runs continuously at 10 MW uses 10 MWh per hour and 87,600 MWh (87.6 GWh) in a 365-day year. If it operates at a 70% average load, annual use is about 61,320 MWh. State the operating hours or average utilization whenever you quote an energy figure.
Workload output
Bitcoin mining output is normally described with hash rate, such as terahashes per second, and an efficiency figure for the ASIC fleet. AI output must name the task and metric: training progress, completed training tokens, inference requests per second, generated tokens per second, latency or another service-level measure. Hash rate and AI throughput are not interchangeable.
Facility load versus IT load
The electricity consumed by servers or ASICs is only part of a data center’s demand. Cooling, power conversion, storage, networking, lighting, controls and other infrastructure also draw power. The International Energy Agency says servers average around 60% of electricity demand in modern data centers, with the share varying by data-center type. Compare total facility MW and MWh, not only the nameplate rating of the computing hardware.
Reliability and flexibility
Record how quickly a load can be reduced, how long it can stay offline, how often curtailment is allowed, and what contracts or service commitments restrict it. A workload that can be interrupted economically is not automatically equivalent to one that must maintain a customer-facing service.
What the available scale figures actually say
Published estimates provide context, but they are not a substitute for a site interconnection study or utility data.
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| Measure | Reported figure | How to interpret it |
|---|---|---|
| Global data-center electricity use | 485 TWh in 2025, projected to roughly 950 TWh in 2030 | International Energy Agency global outlook published in 2026. It covers all data-center activity, not AI alone; AI-focused centers are expected to grow faster than the total. |
| Global Bitcoin-mining electricity use | 67–240 TWh for 2023, with a 120 TWh point estimate | Cambridge estimate reported by the U.S. Energy Information Administration in 2024. The wide range reflects uncertainty in identifying mining activity and facility data. It is a 2023 estimate, not a current consumption reading. |
| Conventional data-center scale | About 10–25 MW | An illustrative range described by the IEA in 2025, not a design recommendation. |
| Hyperscale AI-focused data-center scale | 100 MW or more | An illustrative IEA description of large AI-oriented facilities, not a requirement for every AI deployment. |
| Large mining-facility equipment count | 10,000–20,000 mining units at facilities identified by EIA; some of the largest reported as many as 100,000 | EIA reporting from 2024 illustrates physical scale. It is not a current census of all facilities. |
| ERCOT flexible-load enrollment | Up to 1,530 MW | EIA reported this 2024 total for large industrial consumers in the program. Cryptocurrency miners were major participants, but the entire 1,530 MW must not be attributed to miners. |
How Bitcoin mining uses capacity
Specialized ASIC computation
Bitcoin mining uses application-specific integrated circuits (ASICs) to perform the hashing algorithm used to compete for block rewards. An ASIC cannot be repurposed as a general AI server. The relevant inventory includes each miner’s hash rate, watts consumed, efficiency, firmware settings, operating temperature and uptime.
Fleet-level power calculation
For a mining fleet, begin with the electrical draw of one unit and multiply by the number of units. Add the facility’s cooling and electrical overhead to obtain total site demand. A credible comparison should report both the ASIC-only figure and the measured or engineered facility figure, with the measurement period and operating conditions.
Output depends on network conditions
Hash rate is a physical capability; mined bitcoin is an economic outcome influenced by network difficulty, block rewards, transaction fees, bitcoin price, electricity cost and pool arrangements. Consequently, a MW-to-revenue claim is time-specific and cannot be inferred from capacity alone.
Why mining can be interruptible
EIA reports that miners have participated in demand-response programs and may reduce or stop consumption when wholesale prices rise. This makes mining potentially interruptible, but not universally flexible: an operator’s contracts, hardware, cooling limits, power-market rules and financial strategy determine what can actually be curtailed.
How AI workloads use capacity
Servers and accelerators
AI centers run conventional servers together with specialized accelerators. Training jobs may use many machines in parallel for long periods; inference systems may need predictable latency and continuous availability. Storage, data movement and high-speed networking can be material parts of the facility design even when the accelerator nameplate dominates the IT load.
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Training and inference are different loads
- Training: large, coordinated jobs can run for hours or weeks. Interruptions may waste progress or extend completion time, although some workloads support checkpointing and rescheduling.
- Inference: production services are governed by demand, latency and availability targets. Batch inference may be shiftable; interactive services generally have tighter response requirements.
- Development and evaluation: smaller or sporadic jobs can leave headroom that a scheduler can use for other work.
Do not assign one universal AI load profile. Flexibility must be documented for the particular model, scheduler, customer agreement and service-level objective.
AI output must be defined before comparison
Specify the model, precision, accelerator configuration, batch size, sequence length and metric being measured. “AI capacity” could mean training tokens per day, images classified per second or an inference latency target. Without that definition, comparing AI with a mining hash rate is only a comparison of electrical infrastructure.
Compare the whole facility, not just the compute racks
Use the following inventory for both workloads.
- Grid connection: contracted import capacity, voltage level, interconnection queue status, firm versus non-firm service and any demand charges.
- Electrical distribution: utility service, transformers, switchgear, busways, rack distribution, protection settings and generator or battery arrangements.
- Cooling: heat load at the rack and room level, cooling technology, water availability, temperature limits and the ability to reject heat during curtailment or restart.
- Compute footprint: rack count, floor loading, rack power density, equipment dimensions, replacement cycles and spare-parts logistics.
- Networking: carrier diversity, fiber routes, switching capacity, east-west bandwidth and latency to data sources or users.
- Storage and data handling: local storage, backup, data-ingest bandwidth and retention requirements.
- Operations: monitoring, physical security, staffing, maintenance windows and fire-suppression systems.
- Compliance and contracts: land-use permissions, environmental rules, utility tariffs, customer service commitments and demand-response obligations.
Servers averaging about 60% of modern data-center electricity demand does not mean 40% is “wasted.” The remainder supports the conditions that allow the computing equipment to operate safely and reliably, and the percentage changes by facility type.
A step-by-step method for an apples-to-apples comparison
- Define the boundary. Decide whether the comparison covers the utility meter, the data hall, or only the computing equipment. Use the same boundary for mining and AI.
- Collect interval data. Obtain at least hourly—and preferably five-minute—meter readings for a representative operating period. Record ambient conditions, curtailments, outages and equipment that was offline.
- Separate capacity from energy. List firm and maximum MW separately from average MW, peak MW and annual MWh. Include the hours represented by each number.
- Describe the workload. For mining, record ASIC model, unit count, hash rate, efficiency and uptime. For AI, record accelerator and server configuration, model, job type, utilization and the selected throughput or latency metric.
- Measure overhead. Attribute cooling, power conversion, networking, storage and other facility loads. If a value is estimated, label the assumptions rather than presenting it as a measured result.
- Normalize output. Report hash rate per facility MW for mining or the chosen AI throughput per facility MW. This is a workload-specific indicator, not a claim that one output unit is economically equivalent to another.
- Map the operating schedule. Show baseload, ramp rates, minimum run times, restart time, planned maintenance and curtailment windows.
- Apply site constraints. Test the result against the interconnection limit, transformer and cooling capacities, local power prices, reliability requirements and program rules.
- Use dated economics. Attach electricity prices, hardware quotes, financing assumptions, network conditions and service prices to a date and geography. The available evidence does not establish a universal capital cost, conversion cost or revenue-per-MW figure.
How to reason about a possible mining-to-AI conversion
A mining building is not automatically an AI data center. Conversion feasibility depends on the actual site, not on the former workload’s MW rating.
Questions about power and interconnection
- What is the utility’s contracted import capacity, and how much is firm?
- Are transformers, switchgear, protection systems and busways sized for the proposed AI rack density?
- Does the tariff permit the intended load profile, and are there curtailment or power-quality requirements?
Questions about cooling and the building
- Can the heat-rejection system handle the target AI equipment continuously?
- Are floors, racks, doors and clearances suitable for the new servers?
- Is water available if the proposed cooling design needs it, and are discharge or environmental permits required?
Questions about connectivity and operations
- Can the site obtain diverse, high-capacity fiber connections?
- Does the building support the storage, switching, security and monitoring expected by the AI service?
- Can staff maintain the equipment and meet the required uptime and response commitments?
Questions about the target AI workload
- Is the project for batch training, interactive inference, or a mix?
- What accelerator models, rack power, networking topology and software stack are required?
- What service-level objectives limit interruption or load shifting?
The cited industry sources do not quantify the cost, schedule or probability of converting a particular mining site. Those answers require a facility survey, electrical and mechanical engineering, utility confirmation, equipment quotations and commercial diligence.
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Flexibility and demand response: compare commitments, not labels
Mining is often described as flexible because an operator may shut down ASICs when prices spike. The EIA’s reporting supports describing this as a documented possibility, not a guarantee. Check the actual curtailment contract, notice period, duration, frequency limits, restart behavior and compensation.
AI flexibility varies by workload. A checkpointed training run may be delayed; an inference service with strict latency and availability targets may not be. Even a flexible AI scheduler can be constrained by data-transfer time, checkpoint storage, customer commitments or a need to keep a minimum fleet online.
For either workload, create a dispatch curve showing normal demand, the minimum safe operating level, the ramp rate, the maximum curtailment duration and the energy required to restart. That curve is more useful to a utility than a generic statement that a facility is “flexible.”
Common comparison mistakes and how to avoid them
- Treating 1 MW as 1 MW of useful work. Correct it by naming the workload output and measuring facility-level overhead.
- Quoting the 2023 mining estimate as current. Identify it as the EIA-reported Cambridge range of 67–240 TWh, with a 120 TWh point estimate, and keep the year attached.
- Using a global total to size a local project. Global IEA and EIA figures provide context only; use the site’s utility bills, interconnection study and operating data for design.
- Counting the entire ERCOT 1,530 MW program as mining. EIA says it covers large industrial consumers, with miners as major participants; do not assign the full total to cryptocurrency.
- Comparing ASIC nameplate power with AI rack power. Include cooling, distribution, networking, storage and operating reserves on both sides.
- Assuming AI is always inflexible. Classify the specific training or inference service and its contractual requirements.
- Assuming a mining building is conversion-ready. Verify cooling, rack density, fiber, electrical equipment, permits, staffing and uptime requirements before making a business case.
- Claiming which workload earns more per MW. Revenue depends on volatile market prices, network conditions, utilization, hardware costs, electricity tariffs and service contracts. No universal comparison is established by the cited evidence.
A practical decision checklist
Before approving a new build, expansion or conversion, require a written answer to each item:
- What are the firm, peak and average MW values at the utility meter?
- How many annual MWh are expected, under which operating schedule?
- What exact output metric will be delivered, and how will it be measured?
- Which equipment models and quantities produce that output?
- What percentage of facility demand is computing, cooling, power conversion, networking and storage?
- What are the ramp, curtailment, restart and minimum-load characteristics?
- Which reliability, latency and availability commitments apply?
- Are interconnection, tariff, water, environmental, land-use and network requirements satisfied?
- Which figures are measured, modeled, projected or dependent on a market assumption?
- What dated quotations and sensitivity cases support the capital and operating economics?
Bottom line for capacity planning
Compare Bitcoin mining and AI by pairing electrical capacity and energy with a clearly defined workload output, complete facility overhead, operating schedule and site constraints. Bitcoin mining uses specialized ASICs and may be interruptible under particular market and contractual conditions. AI uses servers and accelerators whose flexibility depends on whether the job is training, inference or another service. The same MW allocation can therefore produce very different technical and economic results.
Use global figures only to understand the scale of the sectors: the IEA projects total data-center electricity use to rise from 485 TWh in 2025 to about 950 TWh in 2030, while EIA’s 2024 reporting places estimated 2023 Bitcoin-mining use between 67 and 240 TWh. For an individual facility, the decisive evidence is the utility connection, measured interval load, equipment specification, cooling and networking design, and a workload-specific output test.
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