The Real AI Bubble Is in Data Centers No One Can Power Up - HackerNoon Article

Oct 27, 2025·

Dhyey Mavani

· 2 min read

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Abstract

In this widely read HackerNoon essay, Dhyey Mavani argues that the real bubble in AI isn’t in algorithms or startups—but in the global race to build data centers that can’t get enough electricity. Drawing parallels to the early-2000s fiber glut, he details how speculative investments in GPU clusters, substations, and cooling systems risk outpacing grid capacity, supply chains, and actual monetizable demand. The piece analyzes transformer shortages, renewable-energy PPAs dominated by hyperscalers, water-use constraints, and rapid silicon obsolescence. Mavani warns that overbuilding infrastructure without disciplined financing or realistic utilization models could leave a trail of stranded megawatts and depreciated hardware. Instead, he advocates treating compute as a project-financed asset—underwriting utilization, aligning PPAs with real load profiles, and engineering for modularity and efficiency. The takeaway is that AI infrastructure will normalize like the internet after 2001, rewarding sustainable operators and punishing speculative builders who confuse capacity with business value.

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The Real AI Bubble Is in Data Centers No One Can Power Up - HackerNoon Article

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In “The Real AI Bubble Is in Data Centers No One Can Power Up,”
software engineer and AI researcher Dhyey Mavani warns that the exuberance around generative-AI infrastructure echoes the telecom fiber boom of the early 2000s.

While GPUs and AI-specific data centers seem like a sure bet, the real bottlenecks—**electric-power availability, grid interconnect queues, water usage, and silicon turnover—**are colliding with investor impatience and limited expertise. Lead times for transformers now exceed two years, hyperscalers are locking up renewable energy contracts, and rapid efficiency gains could make today’s “AI parks” obsolete before they break even.

Mavani’s central message: the bubble isn’t in AI models, it’s in the assumption that every new megawatt equals profit. To avoid a crash, builders and investors must:

Underwrite utilization, not capacity.
Align PPAs with real load shapes and interconnect timelines.
Design for modular upgrades as GPU architectures evolve.
Plan for water, environmental, and regulatory limits early.
Demand verifiable delivery data from suppliers and utilities.

“The internet didn’t die in 2001; it became normal,” Mavani writes.
“The AI infrastructure boom will go the same way—today’s exuberance will be tomorrow’s baseline. But normalization always arrives with a bill.”

This analytical piece positions the AI infrastructure build-out as both a technological revolution and a cautionary financial cycle—urging readers to replace hype with hard engineering discipline.

Last updated on Oct 27, 2025