In 1965, Gordon Moore predicted that computer chips would keep doubling in power every two years while getting cheaper. He was right. That’s how we went from room‑sized machines to smartphones in our pockets.
Logic says infrastructure should have shrunk too. Instead, we now see mega‑data centers sprawling across the globe: Microsoft’s 600‑acre campus in Arizona, Google’s 23 giant facilities, Meta’s $800 million site in Illinois, and Amazon’s 125+ centers worldwide. These aren’t just buildings — they’re small cities, consuming as much electricity as entire countries.
AI broke the equation.
Moore’s Law vs. AI’s Appetite Modern chips are incredibly efficient. But artificial intelligence demands far more than efficiency — it demands scale.
Training today’s frontier models costs …
In 1965, Gordon Moore predicted that computer chips would keep doubling in power every two years while getting cheaper. He was right. That’s how we went from room‑sized machines to smartphones in our pockets.
Logic says infrastructure should have shrunk too. Instead, we now see mega‑data centers sprawling across the globe: Microsoft’s 600‑acre campus in Arizona, Google’s 23 giant facilities, Meta’s $800 million site in Illinois, and Amazon’s 125+ centers worldwide. These aren’t just buildings — they’re small cities, consuming as much electricity as entire countries.
AI broke the equation.
Moore’s Law vs. AI’s Appetite Modern chips are incredibly efficient. But artificial intelligence demands far more than efficiency — it demands scale.
Training today’s frontier models costs tens or even hundreds of millions of dollars and uses enough electricity to power thousands of homes. Running them daily consumes energy on the scale of entire towns.
Moore’s Law promised we’d need fewer machines over time. AI flipped the script: bigger models demand exponentially more machines, housed in ever‑larger facilities.
The Scale of the Build‑Out The AI market has exploded from $25 billion in 2013 to over $200 billion today, with projections of $400 billion by 2030.
Data centers already consume more electricity than Argentina, and by 2030 could use nearly 1 in 10 watts of global power.
Some facilities use billions of gallons of water a year for cooling, often in drought‑prone regions.
This isn’t just growth. It’s a reshaping of global infrastructure.
Four Walls Closing In Physics: Chips are nearing atomic limits. Shrinking them further may take decades.
Monopoly: Only a handful of tech giants can afford the billions needed to train frontier AI. Startups are locked out.
Environment: Carbon emissions, water use, and energy strain are mounting. “Carbon neutral” claims often mask the reality.
Pushback: Communities from Ireland to Singapore are blocking new data centers over grid strain, water use, and minimal local benefits.
We’ve Seen This Movie Before In the 1990s, telecom companies spent over $100 billion laying fiber‑optic cables, betting on endless internet growth. When the dot‑com bubble burst, much of that fiber sat unused, and companies went bankrupt.
Today’s AI boom shows similar signs: sky‑high valuations, massive infrastructure spending, and every company rushing to add “AI” to its products. If the hype slows, data centers could sit half‑empty, GPUs sold for pennies, and billions written off.
Three Possible Futures
AI Delivers (30%)
Real productivity gains, new breakthroughs, and energy solutions. 1.
Bubble Pops (40%)
Growth slows, facilities underused, valuations collapse. 1.
Hard Stop (30%)
Energy caps, water limits, or public resistance force a halt.
Bottom Line Moore’s Law promised efficiency. AI demands scale at any cost.
But energy is strained, water is scarce, carbon targets are breaking, and five companies dominate the field. We’re building as if exponential growth will last forever. History says it won’t.
The question isn’t whether we can build larger data centers.
It’s what happens when we realize we shouldn’t have.
Are we building the future — or repeating history?