In the bustling world of artificial intelligence startups, domain names have become more than mere web addresses—they’re status symbols and strategic assets. Dharmesh Shah, the co-founder of HubSpot and a prominent figure in tech entrepreneurship, recently shelled out $700,000 to secure a coveted .ai domain from the Caribbean island nation of Anguilla. This transaction, highlighted in a report by Business Insider, underscores the explosive demand for .ai suffixes amid the global AI frenzy, turning what was once an obscure country-code top-level domain into a multimillion-dollar revenue stream for a tiny territory.

Shah’s purchase isn’t an isolated case; it’s part of a broader trend where AI companies are vying for these domains to signal their focus on cutting-edge technology. Anguilla, with a population of just 15,000 and known more for its pristine beaches than its digital prowess, was assigned the .ai domain in the late 1980s by the Internet Assigned Numbers Authority. For decades, it languished in relative obscurity until the AI boom ignited interest, as companies like Stability.ai and Elon Musk’s x.ai snapped up addresses to align with the burgeoning field.

The Windfall from Digital Real Estate

The financial impact on Anguilla has been staggering. In 2024 alone, the island generated $39 million from .ai domain registrations and sales, representing nearly a quarter of its total government revenue, according to details shared in WebProNews. This influx has funded critical infrastructure projects, including hurricane-resilient buildings and improved public services, transforming what could have been a modest tourism-dependent economy into one buoyed by tech-driven windfalls.

High-profile deals like Shah’s highlight the premium pricing. While standard .ai registrations cost around $100 to $200 annually through registrars, premium or short domains command exorbitant sums in auctions or direct negotiations. The New York Times reported in a 2024 article that Anguilla earned $32 million the previous year from these domains, amounting to over 10% of its GDP, a figure that has only grown as AI investments surge globally.

Evolution of an Unlikely Tech Hub

The origins of this phenomenon trace back to the early days of the internet, when country codes were distributed without much foresight into future tech trends. As Fortune noted in 2023, early adopters like Character.ai recognized the branding potential, leading to a registration spike that could reach $30 million that year. Anguilla’s government, through its domain registry managed by a small team, has capitalized on this by streamlining sales and partnering with international brokers.

Yet, not everyone is optimistic about sustained growth. Shah himself, in comments to Business Insider, suggested that interest in .ai domains might taper off as the AI hype normalizes, comparing it to past tech bubbles. Still, for now, the domain has drawn interest from major players, with the BBC reporting in a recent piece that Anguilla’s .ai sales are funding everything from education to disaster preparedness, providing a buffer against economic vulnerabilities.

Broader Implications for Small Economies

This digital gold rush raises questions about equity in the global tech economy. Small nations like Anguilla, often overlooked in international trade, are finding innovative ways to leverage intangible assets. As detailed in The New York Times, the revenue stream has empowered local officials to invest in long-term resilience, such as upgrading power grids and water systems battered by climate events.

Industry insiders note that while .ai’s appeal stems from its brevity and relevance, competition from generic domains like .io or .tech could dilute its exclusivity. Nonetheless, Anguilla’s story illustrates how serendipitous assignments from the internet’s foundational era can yield outsized benefits today. For tech founders like Shah, securing a .ai domain isn’t just about branding—it’s a bet on AI’s enduring role in business innovation.

Sustaining the Momentum Amid Uncertainties

Looking ahead, Anguilla’s domain authority is exploring ways to maintain this revenue, including potential expansions into related digital services. Reports from BBC emphasize the island’s strategic positioning, with officials viewing .ai as a “gift from the digital gods” that has already amassed over $100 million cumulatively. Yet, as global AI regulations tighten and market saturation sets in, the challenge will be diversifying beyond this single asset.

For industry observers, Anguilla’s ascent serves as a case study in adaptive economics. Tech entrepreneurs continue to eye these domains for their cachet, but the real winners are the island’s residents, who benefit from an unexpected tech bonanza without ever coding a line. As Shah’s hefty payment demonstrates, in the AI era, even the smallest players can command big prices on the world stage.

Artificial intelligence may run on silicon chips, but its real fuel is electricity. After two decades of steady demand, AI and data centers are causing electricity consumption to soar, which will require utilities and tech giants to collaborate or confront each other. Either way, the aim is for the country to quickly upgrade its network to meet this AI-driven energy surge.

Companies like Meta, Oracle, and OpenAI are building large campuses that require reliable, continuous power. This expansion is testing the capacity of a grid designed for a slower digital economy and prompting utilities, regulators, and tech firms to reconsider their roles, partnerships, and investments.

“Utilities know how to capitalize the cost of building a transformer like the back of their hand, but can’t capitalize a cloud subscription,” Elizabeth Cook of the Association of Edison Illuminating Companies told the audience during a podcast in which we appeared together. That bias toward physical assets has historically limited investment in operations, data analytics, or predictive tools.

Utilities have traditionally been low-risk, capital-heavy institutions. They invest in tangible infrastructure —poles, wires, substations—where costs can be depreciated and returns are assured. The utility industry states that its members often choose to be the first to come in second, implying they let more agile companies lead.

Tech giants can move quickly on high-risk, high-reward projects. Kim Getgen, founder of InnovationForce that hosted the podcast, notes that one hyperscaler can outspend the entire energy sector many times over—by some estimates, as much as fortyfold.

AI mega-data centers like Stargate, backed by Oracle, SoftBank, and OpenAI, are projected to generate $30 billion in annual revenue by 2028. These firms can build data centers in 12–18 months, while new power plants and transmission lines take at least five years to construct and connect. Why’s that?

Utilities make money by persuading commissions to approve capital spending, but operational investments—like grid analytics—generate lower returns. Tech companies, on the other hand, operate in a free market, giving them more flexibility to quickly allocate capital to meet fast-growing demand.

AI’s Soaring Power Demand

According to the International Energy Agency, data center electricity demand worldwide will increase by 130% by 2030. The Department of Energy’s Lawrence Berkeley National Laboratory stated that data centers used about 4.4% of total U.S. electricity in 2023 and, depending on the growth of the rest of the economy, are projected to use between 6.7% and 12% of total U.S. electricity by 2028. It cautions that this depends heavily on AI adoption rates and efficiency gains.

The U.S. faces an unprecedented challenge in expanding its grid. Jeff Weiss, executive chairman of Distributed Sun, explained during a virtual press event hosted by the United States Energy Association: “We need to triple the grid. Everything we do today takes 10 years. We need to figure out how to do it in two.” In practice, that means tripling capacity—not literally rebuilding three new grids. One-third of that new capacity will be needed just to support data centers.

Existing generation, transmission, and workforce limits create bottlenecks. Supply chains for turbines, transformers, and other essential parts are insufficient for quick expansion. Regulatory permitting and interconnection procedures, designed for slower growth, cause further delays. High-powered transmission lines, which span multiple states, are extremely difficult to construct.

Despite having speed and capital advantages, tech giants cannot simply replace utilities. They must follow the same permitting, siting, and interconnection rules. So, it doesn’t matter if you are Google or the hometown utility. Meeting with stakeholders and complying with regulators is part of the process.

“You have to deal with the same federal laws, the same citing, the same public support, and the same supply chain,” says Tom Falcone, president of the Large Public Power Council, during the USEA event. “We deal with these issues in our social construct, in the laws and regulations that we have, and we all have to comply with them.”

However, the problem persists: data centers eager to run advanced AI models urgently need power. Utilities, limited by permitting timelines, supply chain issues, and workforce shortages, rarely meet that urgency. Derek Bentley, partner at Solomon Partners, highlighted the gap at the press event: “You can build a data center in 12 to 18 months. But a new power plant takes five years, plus years more to connect.”

Hybrid Solutions and Partnerships

This isn’t just inconvenient; it’s a fundamental mismatch between the 21st-century digital economy and a 20th-century grid. Progressive utilities are, therefore, working with data centers to develop hybrid solutions.

Indeed, some data centers are colocating with natural gas or nuclear facilities, sometimes combined with renewables and battery storage to enhance scale and reliability. These behind-the-meter setups—where power is generated on-site rather than solely from the grid—are becoming more common. These partnerships enable data centers to access power quickly while maintaining grid stability.

That relieves the burden on the central network, which lowers the risk of blackouts and congestion. Still, for those data centers connected to the main grid—in front of the meter—it results in higher revenues for utilities.

“Hyperscalers are more agile than many utilities, and they are more entrepreneurial and have the capital,” says Clinton Vince, head of the U.S. energy practice at the Denton law firm, during the USEA event. “I do think utilities have been working very well with hyperscalers, although the slower utilities will be disadvantaged tremendously.”

He highlights Meta and Entergy, which are partnering in Louisiana to build major infrastructure supporting Meta’s largest and newest data center, called Hyperion. It will be powered by both fossil fuels and renewable energy.

However, the main criticism is that the regulatory system encourages stagnation. Bud Albright, senior adviser at the National AI Association, pointed out during the press event that “The regulatory format is inadequate today to do the kind of build-out that we need, whether it’s behind the meter or in front of the meter.”

Beyond formal oversight, he adds that public opinion also plays a role. Communities wary of new data centers and transmission lines must understand the broader economic and technological benefits of these projects, from jobs to national competitiveness. Utilities and tech companies must prioritize education and outreach to demonstrate the benefits of their services.

Regulation and Public Perception

Rate design is also under review. Pacific Gas & Electric, for example, requires data centers to pay initial interconnection costs and recover them later as the facility earns revenue. This method ensures costs are shared fairly and stops residential customers from subsidizing commercial loads.

“Affordability is top of mind for us,” says Karen Omelas, director of large load program management for Pacific Gas & Electric. “But we see data centers as beneficial load.”

The energy mix is shifting. Solar and battery storage are expanding rapidly. In fact, storage is becoming a crucial tool to meet peak demand, helping utilities and hyperscalers manage load efficiently. Still, reliable, dispatchable power remains essential. Natural gas fulfills much of this need, while coal is dirtier, expensive, and increasingly irrelevant for electricity production.

All of this highlights a bigger truth: the ongoing transformation is a once-in-a-lifetime event. It’s not about who has the largest balance sheet or who is the most nimble. It’s about collaboration at scale. Utilities need to adopt new tools, rethink their operational models, and partner with the very tech companies they might have once seen as rivals. Meanwhile, tech firms must accept that building computers is one thing; powering them responsibly, reliably, and safely for the masses is another.

For policymakers, the challenge remains just as urgent. Innovation in regulation, faster permitting processes, and public education are essential to prevent congestion that could impede the digital economy. Without action, the infrastructure supporting AI—and the industries it drives—are at risk. And it won’t be because of a lack of creativity or ambition, but because of slow, outdated rules.

The AI revolution isn’t just about chips or advances in machine learning. It’s about wires and power plants. It’s also about the invisible links connecting millions of servers to millions of homes. AI’s ultimate limitation is less about computer intelligence and much more about how fast the grid can expand.

The global energy landscape is undergoing a dramatic transformation, and hydrogen is emerging as a cornerstone of the clean energy revolution. But producing, storing, and transporting hydrogen safely and efficiently is no small feat. Enter artificial intelligence (AI), a technological ally that is revolutionizing the way industries manage hydrogen operations. From optimizing production processes to enhancing storage safety and streamlining logistics, AI is redefining what’s possible in the hydrogen sector.

Rising Momentum for AI in Hydrogen

The AI in hydrogen operations market is gaining rapid traction worldwide. Climate concerns, the expansion of green hydrogen facilities, and significant research and development efforts by leading nations are fueling this growth. AI technologies such as machine learning, predictive analytics, digital twins, and automation are being integrated across hydrogen production, storage, and transportation to enhance operational efficiency, safety, and cost-effectiveness.

Europe currently dominates this market, holding 37.1% of the global share in 2024, thanks to stringent decarbonization policies and government-backed initiatives like the EU Hydrogen strategy and the European Green Deal. Meanwhile, Asia Pacific is poised to witness the fastest growth, driven by ambitious projects in countries like India, China, and Japan targeting carbon neutrality through hydrogen adoption.

Key Trends Shaping the Market

Safety & Reliability Through AI: Hydrogen is highly volatile, making safety a top priority. AI-powered monitoring systems can predict equipment failures and detect leaks in real time, minimizing downtime and financial losses. Hybrid models combining fluid dynamics and machine learning are increasingly used to predict leak behavior and prevent accidents.

Optimized Supply Chains: AI streamlines the complex logistics of hydrogen distribution. By analyzing data on transportation routes, geographical factors, and economic considerations, AI identifies the most efficient and cost-effective paths from production facilities to end-users.

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Driving Forces Behind Market Growth

High-Performance Electrolysers: AI optimizes electrolysers — essential for producing green hydrogen from renewable energy. By analyzing real-time sensor data such as temperature, current density, and pressure, AI dynamically adjusts operations for maximum efficiency. Companies like Honeywell are introducing AI-powered solutions like Protonium to make green hydrogen production scalable, cost-effective, and energy-efficient.

Increasing Hydrogen Production: AI’s predictive capabilities help maximize hydrogen output while minimizing waste and energy consumption. From detecting leaks to optimizing storage and transportation, AI opens doors to safer, more efficient, and cost-effective hydrogen operations globally.

Technology Spotlight

Machine Learning & Deep Learning: Dominating the market with a 28.5% share in 2024, ML algorithms analyze vast datasets to improve electrolysis efficiency, accelerate catalyst discovery, and reduce energy consumption.

Digital Twin Technology: Expected to grow at the fastest rate, digital twins simulate entire hydrogen plants, allowing operators to optimize operations and anticipate challenges before they arise.

Applications & Deployment

Hydrogen Production Optimization: AI-driven optimization remains the largest application segment, enhancing energy efficiency, reducing waste, and supporting sustainable hydrogen production.

Hydrogen Storage Management: The fastest-growing segment, AI ensures safe and efficient storage, a critical factor given hydrogen’s volatile nature.

Cloud & Hybrid Deployment: Cloud-based solutions dominate due to scalability and cost-efficiency, while hybrid deployments are gaining momentum for their flexibility, speed, and security.

End-Use Industries

The energy & power sector is the largest adopter of AI-powered hydrogen operations, leveraging AI for predictive maintenance, grid stability, and renewable energy integration. Meanwhile, transportation & mobility is the fastest-growing segment, where AI optimizes hydrogen refueling logistics, ensuring safety and cost-effectiveness.

Global Market Insights

Europe: Strong decarbonization policies and government incentives drive Europe’s leadership in AI-powered hydrogen operations.

Asia Pacific: Ambitious national programs in India, China, and Japan are accelerating growth, targeting millions of tons of green hydrogen production and carbon neutrality by 2030-2060.

Spotlight on Innovation

Recent developments highlight AI’s transformative impact. In July 2025, the world’s largest green hydrogen and ammonia facility was launched in China, fully powered and managed by AI-based renewable energy systems, producing 320,000 tons of green ammonia annually. Similarly, researchers at the University of Toronto leveraged AI to discover new alloys, enhancing the efficiency and affordability of hydrogen production.

Leading Players in the Market

Key companies driving innovation include IBM, Microsoft, Google, Amazon Web Services, Siemens Energy, Schneider Electric, Honeywell, ABB, Rockwell Automation, and Tata Consultancy Services, among others. These players are combining AI, digital twins, and predictive analytics to unlock the full potential of hydrogen as a clean energy source.

AI is no longer just a technological enhancement — it is a strategic necessity for the hydrogen economy. From safer operations to optimized production, AI enables hydrogen to emerge as a reliable, sustainable, and scalable energy solution for a decarbonized future. With global investments pouring in and technological innovations accelerating, the next decade promises a transformative journey for AI in hydrogen operations.
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