The New Power Race
In the escalating global race for AI dominance, infrastructure—not algorithms—is emerging as the critical battleground. Kevin O’Leary, investor and Shark Tank personality, has sounded the alarm: the United States must urgently replicate the energy strategies of Bitcoin miners to stay competitive in artificial intelligence. His message is as direct as it is urgent—if America wants to lead in AI, it must first secure its grip on power.
The backdrop is a fierce geopolitical competition where nations like China are investing billions into vertically integrated AI complexes, complete with their own power generation. Meanwhile, the U.S. faces bottlenecks from strained grids, permitting delays, and fragmented energy planning. The solution, O’Leary argues, lies in learning from Bitcoin miners—operators who’ve already built energy-hardened, cooling-ready campuses in remote areas with abundant electricity. These miners have created infrastructure that AI builders now desperately need.
AI’s computational needs are skyrocketing. Training just one advanced large language model like GPT-4 can consume more than 1.3 gigawatt-hours of electricity—enough to power over 100 U.S. homes for a year. Multiply this by the explosion of AI use cases, and it’s clear that traditional data centers can’t keep up. Power-hungry, latency-sensitive workloads like model training, inference, and multimodal processing require facilities with massive, stable electricity inputs and efficient cooling.
This is exactly what Bitcoin miners already have. Over the past decade, mining firms have deployed gigawatts of energy, hardened their operations against market shocks, and optimized every watt for maximum hash output. That experience is now becoming a blueprint for AI scalability. The convergence of these two worlds isn’t hypothetical—it’s already happening.
Shared Infrastructure Needs: Power, Cooling & Proximity
AI and Bitcoin mining might seem like technological opposites, but at the infrastructure level, they require remarkably similar environments. High-density computing, whether it’s training a transformer or hashing SHA-256, consumes vast power and produces significant heat. This makes power provisioning and thermal management mission-critical.
Bitcoin mining sites are typically built near cheap, surplus electricity—hydro in Quebec, wind in West Texas, geothermal in Iceland. These locations are remote, but intentionally so. They offer low land costs, minimal zoning resistance, and grid stability. Now, those same characteristics are drawing AI firms looking to escape the bottlenecks of suburban data center hubs.
Many mining facilities operate at 100 megawatts or more and already have substation access, transmission lines, and fiber backhaul. These are essential prerequisites for modern AI training operations. Consider a single rack of NVIDIA H100 GPUs—it can draw over 130 kilowatts. Multiply that by thousands of racks and you approach mining-scale electricity loads.
Cooling is another crossover. Immersion and direct-to-chip cooling, pioneered in mining for overclocking ASICs, are now being adopted for high-performance AI clusters. Liquid cooling not only extends hardware lifespan but reduces operational overhead—critical in environments where heat is the enemy.
Geographic proximity to power generation is a bonus. Bitcoin miners have mastered grid orchestration, often acting as demand response nodes—powering down during grid peaks and ramping up when excess is available. That load flexibility is now highly desirable for AI firms, particularly those seeking renewable energy credits or zero-carbon branding.
O’Leary’s Blueprint: Mining Lessons for AI Builders
Kevin O’Leary isn’t just pointing to miners’ success out of admiration—he’s offering a strategic roadmap. The core of his argument is that Bitcoin miners have already figured out how to acquire cheap power, scale physical infrastructure rapidly, and manage compute efficiently—all under the pressure of hyper-competitive, volatile markets.
For years, miners have operated on razor-thin margins. They’ve built facilities in harsh climates, optimized for uptime, and responded to real-time power market signals. Their ability to co-locate near generation assets and form symbiotic relationships with utilities is now becoming essential in the AI arms race.
O’Leary also points to the financial discipline of miners. Many are publicly traded and must meet institutional-grade reporting standards, governance, and compliance. These capabilities make them ideal partners—or acquisition targets—for AI firms with fast-moving compute needs but no power infrastructure of their own.
Another key lesson is agility. Miners pivoted from home rigs to industrial-scale farms within a decade. Now, they’re pivoting again—this time into high-performance computing and AI. Hive Digital and Hut 8 are already rebranding themselves as HPC providers. O’Leary sees this as validation that mining infrastructure isn’t a relic—it’s a runway for AI’s next phase.
Case Studies: Miners Expanding into AI Data Centers
Real-world examples support O’Leary’s thesis. Hive Digital, originally a Bitcoin miner, now derives a growing percentage of its revenue from AI hosting and HPC workloads. The firm has retooled its sites in Canada with GPU clusters, leveraging its hydro-powered assets and immersion cooling to attract AI clients.
Core Scientific, another major mining player, was recently acquired by CoreWeave in a $9 billion all-stock deal. CoreWeave needed 1.3 gigawatts of power capacity for its AI expansion—and found it instantly by buying out a miner. That move not only saved them years of permitting and construction but positioned them to serve enterprise-scale AI clients at reduced cost.
Bit Digital, once a pure-play Bitcoin miner, has shifted part of its treasury into Ethereum and is developing dual-use infrastructure in Iceland, leveraging both geothermal and hydro sources. The strategy is clear: use mining cash flows to fund GPU procurement, AI platform partnerships, and hybrid data center offerings.
Marathon Digital and Iris Energy are experimenting with splitting workloads between mining and AI inference, allowing them to optimize utilization across different energy cost profiles. These hybrid models offer better economics than single-use deployments, particularly in volatile markets.
Integrator Playbook: How to Apply Miners’ Strategies
Integrators—firms seeking to combine AI and blockchain infrastructure—should adopt a focused strategy grounded in mining best practices.
Start by identifying power-dense, regulation-light regions. Look for sites with excess grid capacity, renewable access, and favorable tariffs. Partner with utilities willing to offer interruptible load contracts or time-of-use pricing.
Next, retrofit existing mining sites for AI readiness. This means upgrading cooling systems from air to liquid, enhancing physical and network security, and increasing rack density. Co-locate with AI training partners to share overhead and increase utilization.
Procure power through long-term PPAs or direct-from-generator deals. Use miners’ expertise in real-time energy markets to balance loads and reduce costs. Deploy containerized compute modules for modular scaling, and integrate with grid operators for demand-response incentives.
Invest in talent cross-training. Miners know energy and uptime. AI engineers know models and data. Cross-pollinate teams to build vertically integrated capabilities.
Finally, adopt a diversified monetization strategy. Run AI workloads during peak usage hours and switch to mining when prices are favorable. Monetize unused compute by offering inference APIs or GPU rentals.
Navigating Challenges: Policy, Environment & Volatility
Bitcoin mining has long attracted scrutiny for its power-intensive operations and environmental footprint. In the United States, states such as New York and Oregon have introduced regulations requiring emissions reporting and moratoria on new fossil-powered mining facilities. This emerging oversight now extends to AI data centers too. The European Union and Germany are crafting energy efficiency mandates for compute facilities. AI-integrated facilities must navigate a patchwork of evolving definitions of renewable eligibility, emissions thresholds, and energy sourcing disclosure.
Both crypto mining and AI operations place substantial demands on energy, water, land, and produce e-waste. Together, they could represent up to 3 percent of U.S. electricity usage by 2026. Communities have raised concerns about health issues from noise pollution and displacement from new crypto hubs. Without proactive mitigation, local resistance and legal pushback could derail projects. Regulatory incentives like carbon taxes or renewable energy credits demand upfront investment in sustainable systems.
Retrofitting mining sites to AI standards can cost 10 to 15 times more per megawatt than the original build-out. These upgrades require specialized technical talent. Rapid fluctuations in energy prices and unpredictable shifts in crypto and AI demand make financial modeling complex. A resilient financial plan is essential.
Handling sensitive AI workloads raises new challenges. Mining setups aren’t built for high-security environments. Integrators must invest in encryption, compliance, and robust cybersecurity protocols to protect proprietary data and ensure regulatory alignment.
Mining operations produce substantial electronic waste. Bitcoin ASICs may have lifespans of just a few years. AI hardware follows rapid upgrade cycles. Integrators must plan for sustainable disposal or refurbishment pathways.
Integrators also face volatility in demand and profitability. Bitcoin’s halving in 2024 cut rewards in half, while AI spikes from sudden model adoption or client churn can swing revenue forecasts. Flexible infrastructure and modular design help mitigate these risks.
Policymakers’ Role: Incentives for Co-location
Governments can accelerate AI–mining convergence by designing strategic policies. Pakistan is allocating 2,000 megawatts of underutilized grid capacity to AI data centers and Bitcoin miners. Paired with preferential electricity pricing and subsidies, this significantly lowers entry barriers.
Pakistan’s initiative also includes customs exemptions, tax holidays, and reduced income and sales taxes. U.S. states like Ohio and Texas offer similar benefits, including tax abatements tied to investment and job creation.
Special economic zones and regulatory sandboxes streamline permitting and testing. New Jersey is offering up to $500 million in tax credits to attract AI campuses. Grid upgrades and subsidized transmission corridors help bridge the gap between renewable generation and data demand.
Governments can condition incentives on clean energy usage, encouraging hybrid systems. Skill-development pipelines and job retraining, like Kentucky’s transition from coal to AI tech, further embed infrastructure into local economies.
Well-designed incentives include clawbacks, job quotas, and performance-based terms to ensure public assets deliver returns. This policy mix can transform AI-mining convergence into a scalable, sustainable national strategy.
Future Outlook: AI and Mining in Harmony
As AI demand surges, U.S. data-center power needs are projected to reach 45 gigawatts by 2030. Many regions are already overwhelmed with multi-year interconnection backlogs. Bitcoin mining sites provide a head start, with built infrastructure, power access, and cooling systems.
CoreWeave’s acquisition of Core Scientific unlocked 1.3 gigawatts of data center capacity. Such moves are being driven by economics—AI/HPC facilities command valuation multiples of 20 to 25 times EBITDA, far surpassing the 6 to 12 times typical of miners.
Technological evolution will accelerate this shift. AI servers are pushing rack densities to 250 kilowatts. Advanced cooling like immersion and two-phase systems will become standard. Sustainable energy integration—battery storage, modular nuclear, solar—will define future-ready facilities.
Policy is catching up. Both the U.S. and EU are backing AI “gigafactories” with multi-billion dollar programs. AI and mining infrastructure are aligning around power, scale, and resilience.
Tariffs on materials like copper could pose challenges. However, miners’ operational advantages, location strategy, and utility relationships make them prime nodes in the distributed AI future.
Seizing the Energy Advantage
As Kevin O’Leary framed it, the battle for AI supremacy is ultimately a battle for power. North America must embrace the miner’s approach—building energy sovereignty through co-location, on-site generation, and flexible load management.
Bitcoin miners have proven they can deploy gigawatts of power, manage intense cooling, and operate under fluctuating economics. This infrastructure is foundational for AI and HPC growth.
From CoreWeave’s $9 billion acquisition to Hive’s AI pivot, the market is clear: power-ready infrastructure is strategic gold. The energy advantage is not just technical—it’s operational. Co-located facilities offer agility, sustainability, and adaptability.
AI and blockchain integrators must act now. Acquire miner sites, negotiate long-term hosting, invest in renewables, and build resilient systems. In a world where compute is the new oil, mining is the drill.
Those bold enough to merge both fields will define the next decade of digital infrastructure. The future belongs to those who power it.
