Dark Fiber, Bright Future: Why the AI Infrastructure Boom May Need Its Bust

The companies that built the modern internet went bankrupt doing it.

In the late 1990s, firms such as WorldCom, Global Crossing, and Qwest spent hundreds of billions of dollars laying fiber-optic cable across continents and oceans. They financed most of it with debt. They justified it with a forecast that internet traffic was doubling every 100 days. In reality, traffic was doubling annually.

The consequences were brutal. Global Crossing filed for bankruptcy in 2002 with more than $12 billion in debt. WorldCom collapsed shortly afterward in what became the largest accounting fraud in U.S. history. By 2005, roughly 85% of all installed fiber remained dark, completely unused. Bandwidth prices had cratered by 90%.

From a financial perspective, it was a disaster.

From a technological perspective, it was the foundation of the modern internet.

The fiber networks built during that speculative boom later enabled cloud computing, streaming media, global SaaS platforms, and video communication at planetary scale. Infrastructure that bankrupted its builders ultimately powered the digital economy.

The companies building AI infrastructure today may follow the same path. That is not a cautionary tale. It is how technological revolutions actually work.

The Largest Infrastructure Buildout in the History of Software

Artificial intelligence has triggered a massive global investment cycle in computing infrastructure.

The world's largest technology companies are rapidly expanding data centers, purchasing specialized AI accelerators, and building high-capacity networks to support the training and operation of large models.

The numbers are extraordinary. The five largest U.S. hyperscalers (Amazon, Microsoft, Google, Meta, and Oracle) are projected to spend roughly $600 billion to $690 billion on infrastructure in 2026. That is a 36% increase over 2025, and roughly 75% of it targets AI compute directly. Capital intensity at some of these companies has reached 45% to 57% of revenue, ratios that resemble industrial utilities, not the asset-light software businesses they were five years ago.

The trend is unlikely to slow. Goldman Sachs estimates that cumulative hyperscaler capex from 2025 through 2027 will reach $1.15 trillion, more than double the $477 billion spent from 2022 through 2024. McKinsey projects that data-center investment associated with AI could reach $3.7 trillion to $7.9 trillion by 2030, depending on demand scenarios.

In 2025, tech capex as a percentage of GDP nearly matched the combined scale of the largest capital projects of the twentieth century: nationwide broadband, the interstate highway system, the Apollo program, and the Manhattan Project.

In other words, the AI revolution is not just a software story. It is an infrastructure story.

And infrastructure cycles rarely follow smooth economic curves.

The Revenue Gap

Despite rapid progress in generative AI tools, revenue remains strikingly small compared with the scale of infrastructure investment.

AI-related services generated roughly $25 billion in revenue in 2025. The hyperscalers spent more than $400 billion on infrastructure in the same period. That is a ratio of roughly 16 to 1.

Consulting firm Bain & Company has argued that the AI ecosystem may ultimately need to generate roughly $2 trillion in annual revenue to justify the infrastructure spending currently underway. Even the most aggressive forecasts for enterprise AI adoption fall well short of that figure, leaving a gap measured in hundreds of billions of dollars.

Meanwhile, the largest pure-play AI companies are burning cash at historic rates. OpenAI posted a $13.5 billion net loss in the first half of 2025. Internal projections show losses of $14 billion to $17 billion in 2026. The company expects cumulative cash burn of $218 billion between 2026 and 2029 before reaching profitability. Anthropic is on a more conservative path, projecting breakeven by 2028, but even disciplined players face enormous capital requirements.

The gap between infrastructure investment and realized revenue is striking.

But it is not unprecedented.

In fact, it is one of the most consistent patterns in technological revolutions.

The Installation Phase of a Technological Revolution

Economic historian Carlota Perez spent decades studying how major technologies transform economies. In her landmark work Technological Revolutions and Financial Capital, she found that industrial transformations tend to follow a recurring sequence.

First comes a breakthrough technology. Then comes a surge of financial speculation as investors race to build the infrastructure required to exploit it. Eventually, a correction occurs as overcapacity and unrealistic expectations collide with economic reality.

Perez describes this period as the installation phase of a technological revolution.

During this phase, capital floods into infrastructure projects. Railways in the nineteenth century, electrical grids in the early twentieth century, and fiber networks during the internet boom all followed this pattern.

Infrastructure is built faster than the economy can initially use it.

Speculative excess is common.

And financial collapse often follows.

But the infrastructure survives.

Once the financial bubble bursts and assets are repriced, a new phase begins. Perez calls this the deployment phase, when the technology spreads throughout the economy and productivity accelerates.

This is the phase where the real value of the infrastructure emerges.

Infrastructure Before Productivity

History offers many examples of this dynamic.

The economist Paul David demonstrated that electrification did not immediately increase industrial productivity because factories initially replaced steam engines with electric motors without redesigning production systems.¹⁴ Only after manufacturers reorganized factories around electric power did productivity surge.

Similarly, the internet required decades of infrastructure development before it produced global digital platforms, cloud computing, and the modern SaaS economy.

Technological revolutions often follow a pattern:

Infrastructure first. Transformation later.

Artificial intelligence appears to be following the same trajectory.

Large models and compute infrastructure are being built at extraordinary speed. But the organizational, economic, and societal transformations required to fully exploit them are still emerging.

The Risk Embedded in the Current Boom

None of this guarantees that the current AI investment cycle will produce financial winners.

Infrastructure booms are notorious for destroying capital even as they enable long-term technological progress.

During the telecom bubble, dozens of competing companies built fiber networks. Most of them went bankrupt. The infrastructure remained.

Today, a similar dynamic is unfolding in AI infrastructure. Hyperscalers are competing to build the largest compute platforms in the world, while specialized providers and "neoclouds" are financing massive GPU clusters with layers of private credit, securitized debt, and off-balance-sheet vehicles. In 2025 alone, Big Tech issued over $100 billion in bonds to fund AI capex. Google co-founder Larry Page has reportedly told colleagues he is willing to go bankrupt rather than lose the AI race.¹⁵

That statement reveals the Darwinian pressure driving these decisions. Companies are spending not because the ROI is clear, but because the cost of falling behind feels existential.

Some of these investments will succeed. Others will not. History suggests that the companies that ultimately capture the most value from a technological revolution are often not the ones that spent the most building the infrastructure.

Creative Destruction and the Economics of Innovation

The Austrian economist Joseph Schumpeter described economic development as a process of "creative destruction," in which waves of innovation disrupt existing firms while creating entirely new industries.

Infrastructure bubbles are often one of the mechanisms that make this process possible.

Speculative capital accelerates the construction of networks, factories, and systems that would otherwise take decades to build. When the bubble bursts, those assets are repriced and become accessible to a much wider range of innovators.

The telecom crash of the early 2000s offers a clear example. While investors lost billions, the fiber networks built during that period enabled the rise of global streaming services, cloud platforms, and digital collaboration tools.

The infrastructure outlived the balance sheets that financed it.

The same will likely be true of the AI infrastructure being built today.

What This Means for Technology Leaders

If you lead engineering teams, the question is not whether AI will transform your organization. It will. The question is how you position your teams and your architecture for a landscape where platform providers may not survive the cycle that created them.

Build skills, not dependencies. Every major AI provider is competing to lock you into their ecosystem. Invest in your team's ability to use AI tools effectively. But do not anchor your architecture to any single provider's survival. The infrastructure will outlast some of the companies that built it. Your abstractions and your people need to outlast them too.

Diversify your AI supply chain. If you are running production workloads on a single AI platform, you are carrying concentration risk that most enterprise leaders would never accept in any other part of the stack. Treat AI providers the way you treat cloud providers: with multi-vendor strategies, clean interfaces, and exit plans.

Measure outcomes, not activity. The pressure to "do something with AI" is intense right now. Resist the urge to optimize for visible AI adoption. Optimize for measurable business value. The organizations that thrive through technology transitions focus on solving real problems, not chasing installation-phase hype.

Lead with Empathy. As a servant leader, recognize that these cycles create immense pressure. Protect your teams from the "installation phase" burnout of chasing every hype cycle, and focus them on solving real business problems.

Prepare for the repricing. When the correction comes, and it will come, the cost of AI compute will drop dramatically. The organizations that have already built the skills and the organizational muscle to use AI effectively will be the ones positioned to move fastest during the deployment phase. That is where the real value gets created.

The Paradox of Infrastructure Bubbles

The paradox of technological revolutions is that they often require enormous financial excess in order to move quickly enough to transform the economy. Infrastructure bubbles look wasteful in quarterly earnings. Measured over decades, they are the price of progress.

The data centers and compute clusters being built today will likely outlive the balance sheets that financed them. Some companies will fail, but the infrastructure itself will power the next generation of innovation.

The future will be brighter than the balance sheets that built it.