i've been going on and on about this, but basically, AI flips electricity on its head. previously, nearly half the cost of electricity to the consumer was allocated towards distributing it where (and when) you want it. AI data centers want upwards of hundreds of megawatts of power in one place, most of the time. (and for training they don't care about latency) The most constrained thing isn't even the energy itself -- there's a surplus during the solar peak -- it's often the sheer availability & the interconnection. It now can take multiple years to get a grid interconnect to draw power of this magnitude, if the location can even handle it. The capital cost of the power infrastructure is just a tiny fraction (like 3%!) of the capex of the compute, and even just the depreciation of the compute exceeds the cost of even premium power. Hence, AI hyperscalers and those that aspire to be in their class are traveling to where the power is, are building where power has been (and there's legacy transmission to support it, like old nuclear plants) and are getting into the business of actually building powerplants and reactors. Utilities and transmission and distribution companies, interconnection queues, all are used to react much slower -- over many years -- unlike the top technology companies, now vying to compete at the highest levels of AI performance. Since ~99% of energy technologies previously died withering while waiting for utilities to consider them bankable, this represents an extremely fertile, attractive new state of play if you're bringing a new energy technology to market (💁🏻‍♀️🔆). Whereas before you'd have to brave what was once called a "green valley of death", now you have teracap companies like microsoft bidding on: - Conventional, large nuclear fission - Small modular nuclear reactors - Engineered Geothermal - Fusion! - Stirling Engines 😬 - Who knows what else - Maybe you, Anon! Even Tesla is standing up gas generator arrays to burn fossil fuels for their AI power supply. Shit is getting real