AI and Cryptocurrency: Bitcoin Miners Drive Data Center Boom

introduction

Artificial intelligence has turned computing capacity into one of the world’s most scarce resources. Training large language models (LLMs), supporting generative AI, and running high-performance computing (HPC) workloads require facilities that provide high-density rack power, low-latency networking, and industrial-scale cooling.

Traditional data centers are not built for this type of demand. The U.S. Department of Energy says it takes two to four years to bring a new site online in the U.S. due to grid interconnection and permitting delays. As AI adoption accelerates, hyperscalers cannot afford to wait.

This urgency explains why Bitcoin miners are in the spotlight. Many miners already manage assets such as land, substations, and high-capacity grid connections that fit the needs of AI/HPC data centers. Not every mining site is suitable, but those with the right characteristics can transform into one of the fastest-growing infrastructure markets in decades.

AI Data Center Opportunity

AI and HPC workloads are creating demand that far exceeds historical trends.

• Goldman Sachs Research projects that U.S. data center capacity will more than double to 45 GW by 2030. This represents approximately 8% of the total power generation in the United States.

• JP Morgan predicts hyperscaler AI spending will reach $370 billion per year by 2038, up from $163 billion in 2024.

• According to Pitchbook, AI/ML startups have raised $680 billion since 2016, with $120 billion expected to be invested in 2024 alone.

Rack Density Explosion

The average rack density in 2020 was 8.4 kW per rack, with 30 kW or more considered high performance (Uptime Institute). Currently, high-end clusters such as the NVIDIA GB200 NVL72 require 132 kW per rack.

Why Bitcoin Miners Have the Edge

1. Performance at scale – Many miners already operate contracted sites of hundreds of megawatts. Some control gigawatt-scale pipelines that few data center companies can match.

2. Land and cooling potential – Mining facilities are usually located on large tracts of land near water sources, allowing for advanced liquid cooling.

3. Dark fiber access – Strategic sites are connected to a backbone fiber network that is essential for AI workloads that require ultra-low latency.

4. Permission and Jean reed equipment – Miners often pre-purchase substations and transformers. These lead times now extend by years, creating a barrier to entry.

5. Operational experience – Mining workloads vary, but the industry has built deep expertise in operating power-intensive facilities at scale.

case study

• CoreWeave and Core Scientific – In 2025, CoreWeave acquired Core Scientific, transforming the former Bitcoin miner into an AI data center leader.
• Crypto Mining – Google participated in the financing, signing a 10-year contract with Fluidstack for 168 MW of capacity.
• Hive Digital – Hive has positioned itself as a dual-play company, operating both ASIC mining rigs and GPU farms.
• Bit Farm – Explores ways to transform Canadian hydropower sites into AI-enabled facilities.

A challenge for miners

• Networking – AI requires fast fabrics like InfiniBand. The miner has a simple Ethernet setup.
• Cooling – ASIC farms cannot handle 132 kW racks. Liquid-to-chip cooling and redundancy are required.
• capital – GPUs are expensive and procurement is limited. Financial partners are essential.
• Talent – Running an AI cluster requires ML operations expertise, workload schedulers, and software stacks that miners do not yet possess.
• Regulatory environment – Mining permits do not always extend to AI. New approval may be required.

financial aspect

The economic incentives are powerful.

• Valuation Re-Rating – Public miners with 6-12x EV/EBITDA valuations could approach the 20-25x multiples of traditional operators like Digital Realty and Equinix if they switch to AI hosting.

• Predictable Cash Flow – AI data centers sign multi-year lease agreements with high-credit tenants. This smoothes returns compared to Bitcoin’s price-linked volatility.
• Access to Capital – Institutions that avoid cryptocurrency exposure, such as pension funds and insurance companies, actively fund AI/HPC projects. This significantly expands the funding pool.

strategic outlook

• Hybrid operation – Until demand specializes, miners are expected to run both Bitcoin rigs and GPU clusters.
• Energy load balancing – Mining can compensate for the surge in demand for AI training by consuming power during idle periods.

• Geographical changes – Bitcoin mining growth could accelerate in energy-rich emerging markets as U.S. sites transition to AI.
• Hardware convergence – ASICs can evolve into rack-friendly formats, blurring the lines with GPUs and facilitating dual-purpose designs.

conclusion

The AI ​​boom has created one of the strongest tailwinds in infrastructure history. Existing data centers cannot scale fast enough to meet demand. Long seen as a pure cryptocurrency role, Bitcoin miners now have assets that hyperscalers urgently need: abundant power, land, and pre-approved infrastructure.

Not all miners qualify. Sites without chilled water, fiber optics or approvals can be missed. But for those who do, there are transformative opportunities: predictable cash flow, access to institutional financing, and higher business valuations.

The lines between “cryptocurrency mining companies” and “data center operators” are already blurring. Bitcoin miners could emerge as one of the most valuable infrastructure players over the next decade as AI reshapes global computing needs.