Alphabet, Amazon, and Microsoft; these tech giants’ cloud services, Google Cloud, AWS, and Azure, respectively, are considered the driving force behind all current business computing, data, and mobile services. But back in the mid-2000s, they weren’t immediately seen as best bets on Wall Street. When Amazon launched AWS, analysts and investors were skeptical. They dismissed AWS as a distraction from Amazon’s core retail business. The Wall Street wizards did not understand the potential of cloud computing services. Many critics believed enterprises would never move their mission-critical workloads off-premises and into remote data centers.

As we all know, the naysayers were wrong, and cloud computing took off, redefining global business. It turbo-charged the economy, creating trillions in enterprise value while reducing IT costs, increasing application agility, and enabling new business models. In addition, the advent of cloud services lowered barriers to entry for startups and enabled rapid service scaling. Improving efficiency, collaboration, and innovation through scalable, pay-as-you-go access to computing resources was part of the formula for astounding success. The cloud pushed innovation to every corner of society, and those wise financiers misunderstood it. They could not see how this capital-intensive, long-horizon bet would ever pay off.

Now, we are at that moment again. This time with artificial intelligence.

Headlines appear every day saying that we’re in an “AI bubble.” But AI has gone beyond mere speculation as companies (hyperscalers) are in early-stage infrastructure buildout mode. Hyperscalers understand this momentum. They have seen this movie before with a different protagonist, and they know the story ends with transformation, not collapse. The need for transformative compute, power, and connectivity is the catalyst driving a new generation of data center buildouts. The applications, the productivity, and the tools are there. And unlike the early cloud era, sustainable AI-related revenue is a predictable balance sheet line item.

The Data

Consider these most recent quarterly earnings:

• Microsoft Q3 2025: Revenue: $70.1B, up 13%. Net income: $25.8B, up 18%. Intelligent Cloud grew 21% led by Azure, with 16 points of growth from AI services.
• Amazon Q3 2025: Revenue: $180.2B, up 13%. AWS grew 20% to $33B. Trainium2, its second-gen AI chip, is a multi-billion-dollar line. AWS added 3.8 GW of power capacity in 12 months due to high demand.

• Alphabet (Google Parent) Q3 2025: Revenue: $102.35B, up 16%. Cloud revenue grew 33% to $15.2B. Operating income: up nearly 85%, backed by $155B cloud backlog.
• Meta Q3 2025: Revenue: $51.2B, up 26%. Increased infrastructure spend focused on expanding AI compute capacity. (4)

These are not the signs of a bubble. These are the signatures of a platform shift, and the companies leading it are already realizing returns while businesses weave AI into operations.

Bubble or Bottleneck

However, let’s be clear about this analogy: AI is not simply the next chapter of the cloud. Instead, it builds on and accelerates the cloud’s original mission: making extraordinary computing capabilities accessible and scalable. While the cloud democratized computing, AI is now democratizing intelligence and autonomy. This evolution will transform how we work, secure systems, travel, heal, build, educate, and solve problems.

Just as there were cloud critics, we now have AI critics. They say that aggressive capital spending, rising energy demand, and grid strain are signs that the market is already overextended. The pundits are correct about the spending:

• Alphabet (Google) Q3 2025: ~US $24B on infrastructure oriented toward AI/data centers.
• Amazon (AWS) Q3 2025: ~US $34.2B, largely on infrastructure/AI-related efforts.
• Meta Q3 2025: US $19.4B directed at servers/data centers/network infrastructure for AI.
• Microsoft Q3 2025: Roughly US $34.9B, of which perhaps US $17-18B or more is directly AI/data-center infrastructure (based on “half” of capex).

However, the pundits’ underlying argument is predicated on the same misunderstandings seen in the run-up to the cloud era: it confuses infrastructure investment with excess spending. The challenge with AI is not too much capacity; it is not enough. Demand is already exceeding grid capacity, land availability, power transmission expansion, and specialized equipment supply.

Bubbles do not behave that way; they generate idle capacity. For example, consider the collapse of Global Crossing. The company created the first transcontinental internet backbone by laying 100,000 route-miles of undersea fiber linking 27 countries.

Unfortunately, Global Crossing did not survive the dot-com bubble burst (1990-2000) and filed for bankruptcy. However, Level 3, then CenturyLink (2017), and Lumen Technologies knew better than to listen to Wall Street and acquired Global Crossing’s cables. Today, Lumen has reported total 2024 revenue of $13.1 billion. Although they don’t specifically list submarine cable business revenue, it’s reasonable to infer that these cables are still generating in the low billion-dollar revenue figures—a nice perpetual paycheck for not listening to the penny pinchers.

The AI economy is moving the value chain down the same path of sustainable profitability. But first, we must address factors such as data center proximity to grid strength, access to substation expansion, transformer supply, water access, cooling capacity, and land for modern power-intensive compute loads.

Power, Land, and the New Workforce

The cloud era prioritized fiber; the AI era is prioritizing power. Transmission corridors, utility partnerships, renewable integration, cooling systems, and purpose-built digital land strategies are essential for AI expansion. And with all that comes the “pick and shovel” jobs building data centers, which Wall Street does not factor into the AI economy. You need to look no further than Caterpillar’s Q3 2025 sales and revenue of $16.1 billion, up 10 percent.

Often overlooked in the tech hype are the industrial, real estate, and power grid requirements for data center builds, which require skilled workers such as electricians, steelworkers, construction crews, civil engineers, equipment manufacturers, utility operators, grid modernizers, and renewable developers. And once they’re up and running, data centers need cloud and network architects, cybersecurity analysts, and AI professionals.

As AI scales, it will lift industrial landowners, renewable power developers, utilities, semiconductor manufacturers, equipment suppliers, telecom networks, and thousands of local trades and service ecosystems, just as it’s lifting Caterpillar. It will accelerate infrastructure revitalization and strengthen rural and suburban economies. It will create new industries, just like the cloud did with Software as a Service (SaaS), e-commerce logistics, digital banking, streaming media, and remote-work platforms.

Conclusion

We’ve seen Wall Street mislabel some of the most significant tech expansions, from the telecom-hotel buildout of the 1990s to the co-location wave, global fiber expansion, hyperscale cloud, and now, with AI. Just like all revolutionary ideas, skepticism tends to precede them, even though there’s an inevitability to them. But stay focused: infrastructure comes before revenue, and revenue tends to arrive sooner than predicted, which brings home the point that AI is not inflating; it is expanding.

Smartphones reshaped consumer behavior within a decade; AI will reshape the industry in less than half that time. This is not a bubble. It is an infrastructure super-cycle predicated on electricity, land, silicon, and ingenuity. Now is the time to act: those who build power-first digital infrastructure are not in the hype business; they’re laying the foundation for the next century of economic growth.

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About the Author

Ryne Friedman is an Associate at hi-tequity, where he leverages his commercial real estate expertise to guide strategic site selection and location analysis for data center development. A U.S. Coast Guard veteran and licensed Florida real estate professional, he previously supported national brands such as Dairy Queen, Crunch Fitness, Jimmy John’s, and 7-Eleven with market research and site acquisition. His background spans roles at SLC Commercial, Lambert Commercial Real Estate, DSA Encore, and DataCenterAndColocation. Ryne studied Business Administration and Management at Central Connecticut State University.

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