Article URL: https://www.worksinprogress.news/p/ai-is-bottlenecked-by-the-grid Comments URL: https://news.ycombinator.com/item?id=48840620 Points: 25 # Comments: 28

Issue 24 of Works in Progress has now arrived with subscribers. Sign up here to receive this issue, plus another every two months – straight to your door. One of the most expensive projects in history is under construction in Abilene, Texas. This joint venture, Stargate, is the flagship of a bigger project by the same name led by OpenAI and Softbank, and is expected to cost well over $40 billion for a high-performance computing campus that will train new generations of AI models. Stargate is just one major project in one of the biggest investment booms in history, driven by the belief that increasingly powerful AI models can deliver explosive economic growth. But it will require enormous amounts of electricity to work: Stargate is expected to draw 1.2 gigawatts, as much as 313,000 median American family homes, at peak load. A report by EpochAI and an energy research institute projected that total AI computing power would reach 100 gigawatts worldwide in 2030 if the 2025 growth rate stays steady. And data centers aren’t the only energy-hungry element of the AI revolution. The biggest battery manufacturing plants in the US draw energy at a rate of 115 megawatts, and the first phase of TSMC’s Arizona semiconductor plant will draw 200 megawatts. The primary bottleneck to this growth is the availability of electricity. But this doesn’t mean there is an energy shortage. Instead, the constraint is connecting the flood of new data centers and the plants to power them to the electric grid. Before any new piece of infrastructure can be connected, grid operators must study how it will change power flows around the grid and determine whether upgrades to the system are required. That process is significantly backlogged. Though the median power plant in 2005 waited less than 20 months for interconnection, this had jumped to 55 months by 2023. The interconnection process wasn’t created for today’s world. Grids use an inflexible first-come, first-served queue that leaves some of the most valuable projects stuck behind less important ones. They also evaluate according to rigid conditions that don’t reward plants for being willing to cover their own power needs for short periods. To prepare for the AI age, grid processes need to change. Estimates vary for how much power will be needed by the data centers and chip manufacturers of the future, but the heads of every major AI company agree that they will need more than they are currently able to get. Jensen Huang, CEO of Nvidia, has said that ‘every data center in the future will be power-limited’. Mark Zuckerberg said Meta ‘would build … bigger [AI training] clusters … if we could get the energy to do it’. And OpenAI CEO Sam Altman told Congress that ‘the abundance of [AI] will be limited by the abundance of energy’. Regardless of how the data center boom plays out, there is a long-term shift towards electrification across the economy. Electricity can be converted into work instantly, unlike fuels, and with little energy loss. Electricity creates motion directly, while fuels must first be combusted in an engine. This is why electric vehicles can cost half as much to fuel even though electricity is more expensive than gasoline. The simplicity of electric motors also means that electric vehicles have half the lifetime maintenance costs of gas-powered ones. Electricity also transmits information. Transistors switch on or off depending on the voltage applied to their gates, which allows circuits to perform logical operations. Radios, screens, and computers cannot run on gasoline alone.