Carl Sagan famously said that if you wish to make an apple pie from scratch, you must first invent the universe. I have been thinking about that line for months, ever since I began writing the chapters on energy and compute in this book. Because Sagan wasn't being whimsical. He was being precise. The atoms in that apple pie — the carbon, oxygen, hydrogen, nitrogen — were forged in the cores of stars that exploded billions of years before our sun existed. Every particle in your body has a cosmic address. And every watt of electricity powering the datacenters racing to build artificial superintelligence has the same lineage. It all started with the Big Bang. It all ends, if we are not careful, with a civilisation that ran out of power before it ran out of ideas.
Energy is not a supporting character in the AI story. It is the protagonist. Without power, there are no datacenters. Without datacenters, there are no frontier models. Without frontier models, there is no AGI. Without AGI, the singularity remains a line on a graph rather than a lived reality. The nations that control energy will control intelligence. And the numbers, when you look at them honestly, are not encouraging for the United States.
In 2024, China added 546 gigawatts of new power capacity. The United States added 51 gigawatts. That is not a gap. That is a chasm. China added more power in a single year than the United States has built in the last five combined. And America's stated need — roughly one million gigawatts by 2030 to meet AI datacenter demand — is being addressed primarily through nuclear plants that produce between one and one-and-a-half gigawatts each, take a decade to permit and construct, and face fierce local opposition at every stage. The math does not work. The timeline does not work. And yet the conversation in Washington continues to unfold as if this is a problem that can be solved by the next committee. (I first ran these same two numbers a week earlier, in economic rather than cosmic terms, in Issue No. 2.)
China has no such committee. It has a plan, a grid, and the political will to execute both at a pace that democratic systems structurally cannot match. This is not a criticism of democracy. It is a description of physics meeting politics. And physics, as always, is indifferent to the outcome.
This issue is a two-country story — America and China. It leaves out the third: a country that generates roughly a fifth of the world's AI training data and owns about 3.5 percent of the compute to process it. I wrote the full version of that asymmetry, refinery metaphor and all, in The Refinery Problem.
A reader wrote to me this week and asked: "Ashok, do you actually believe America can lose this race?" My answer is that America is not losing the race. It is running a different race — one defined by innovation at the frontier, the best researchers, the most capable models. What it is losing is the infrastructure race. And without infrastructure, frontier innovation eventually stalls.
I have built companies from nothing. I know what happens when you have the idea but not the power supply. You stall. You improvise. And sometimes — if you are lucky — you find a different way. America needs to find that way. Quickly.
by 2030
The Nuclear Math Problem
- US target ~1,000,000 GW by 2030 for AI demand
- Nuclear output 1–1.5 GW per plant
- Plants needed ~667,000–1,000,000 new plants
- Build time 10–15 years per plant, minimum
- Ireland has already halted all new datacenter construction
- N. Virginia utilities warn demand now outpaces capacity
- Conclusion The grid cannot scale at the pace AI requires on current policy