AI's rise depends on infrastructure: chips, power, data centers, and software. Data center power demand is set to 30× by 2035, pushing systems to their limits.
The Power Equation
AI doesn't run on algorithms alone — it runs on electricity. And the power demands of modern AI workloads are staggering. A single large-scale training run can consume as much electricity as a small city uses in a month. Inference at scale requires continuous power delivery measured in megawatts.
The 30× Challenge
Current projections suggest that data center power demand will increase 30× by 2035. That's not a typo — thirty times current levels. To put that in perspective, the U.S. would need to add the equivalent of dozens of large power plants dedicated solely to computing.
The Infrastructure Stack
Meeting this demand requires innovation across every layer of the stack:
Power generation: New nuclear, solar, and wind capacity dedicated to data centers. Some companies are exploring small modular reactors co-located with computing facilities.
Cooling: Traditional air cooling can't keep up with GPU density. Liquid cooling and immersion cooling are becoming mandatory, adding complexity and cost.
Chips: More efficient architectures that deliver more compute per watt. But even the most efficient chips can't overcome the fundamental scale of demand.
Software: The most overlooked lever. Software that maximizes the useful work extracted from every watt of power consumed can effectively multiply the capacity of existing infrastructure.
Why Software Efficiency Is the Key
Building new power plants takes years. Deploying more efficient software takes weeks. When a compute efficiency layer can double the useful output of an existing data center, it's equivalent to building a second data center — but without the years of construction, the billions in capital expenditure, or the environmental impact.
The infrastructure arms race will be won not by those who build the most, but by those who waste the least.
