Software Scaled in the Cloud. AI Scales on the Grid.
AI is changing more than software. It’s changing what software depends on.
On Wednesday morning, July 22, 2026, more than three gigawatts of electricity demand disappeared from the largest U.S. power grid in a matter of seconds.
A transmission line in Northern Virginia went out of service, prompting numerous data centers to disconnect from the grid and transfer to backup power. The sudden shift removed roughly 3% of PJM’s total demand at the time and created a voltage disturbance that was detected from Washington, D.C., to Chicago.
PJM said the event didn’t affect overall grid reliability, and Dominion Energy restored normal operating conditions within minutes. But the incident offered a glimpse of what happens when concentrated computing demand becomes large enough to behave like grid infrastructure.
Most software companies could grow for years without asking a utility whether enough power would be available for the next product release.
The servers existed. The data centers existed. The cloud kept most of that machinery out of view. Product teams could add users, enter new markets, and ship new features without waiting for a substation or transmission study.
The more I worked through the reporting for this edition, the more one idea kept resurfacing.
AI isn’t just changing software anymore. It’s changing what software depends on.
A proposed data-center campus requiring 500 megawatts to 1 gigawatt is not just a larger software deployment. At that scale, compute depends on generation, transmission, substations, cooling, land, equipment, utility agreements, and permits.
Models can improve in a matter of months. Building the infrastructure underneath them doesn’t.
That’s the part I don’t think the market has fully absorbed yet. The timelines are completely different, and those timelines are starting to shape where AI can actually be built.
None of this makes chips or models less important. But they now depend on a physical system that moves at a completely different speed.
That changed the question for me.
The debate is no longer whether AI needs more electricity. Everyone accepts that.
The harder question is who secured the right power, in the right market, before everyone else realized how scarce it was becoming.
In the full edition, I walk through why the software playbook no longer explains what’s happening.
We look at how grid timelines are becoming part of AI strategy, why the biggest technology companies are moving upstream into energy and infrastructure, and why announced capacity often has very little to do with capacity that can actually be delivered.
We also look at how the largest technology companies are moving upstream into energy and infrastructure, how power is redrawing the map of AI development, and why announced capacity is not the same as capacity that can be delivered.
If AI really is becoming infrastructure, then investors may have to start evaluating it very differently.
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