There’s a fatal flaw at the heart of the AI revolution… With AI continuing to explode in popularity, tech companies are all scrambling to build hundreds of new mega data centers to keep up with the demand for training and using new AI models. But that’s where the problem is. It doesn’t matter how many new data centers we build, we won’t be able to keep up with the insatiable demand… We can build thousands of them — even millions. But we simply can’t power them. That’s because operating and developing new artificial intelligence models requires unparalleled energy demands… energy demands we cannot currently meet. As MIT Technology Review exposed, “training a single AI model can emit as much carbon as five cars in their lifetimes.” That’s not operating… just development. And right now, thousands upon thousands of AI models are being trained every single day. That’s why, by 2027, AI servers are predicted to consume as much as 134 terawatt hours annually. In other words, in less than two years, AI will have the same annual energy consumption as countries like Argentina, the Netherlands, and Sweden. And by 2030, AI alone could demand 25% of the grid. This explosion in energy requirements is simply not sustainable. As Mark Zuckerberg said recently, he’d be building out many more of these colossal data centers if they “could get the energy to do it.” The fact is, AI adoption is only at maybe 1% of where we’ll be in the next few years. For AI to penetrate just 10% or 20% of the market, we’ll need unprecedented amounts of energy. Elon Musk predicts that we soon won’t have “enough electricity…” and that by 2045 the power demand in the U.S. will have tripled from current levels. Sam Altman says “an energy breakthrough is necessary for future artificial intelligence.” Or as the Washington Post bluntly puts it: “Amid explosive [AI] demand, America is running out of power.” These guys know, as I do, that unless the insatiable energy demands of artificial intelligence are met, the industry will never truly go mainstream. The release of China’s DeepSeek technology has led some to believe that AI will require less computing power to train AI models… resulting in demand for electricity falling. This is very wrong. Strange as it sounds, the fact that DeepSeek has proven that we need less energy to train AI means EVEN MORE energy will ultimately be consumed. This strange phenomenon is called Jevons Paradox. 
Put simply, the increasing efficiency of a resource will – paradoxically - lead to increased consumption of that resource. It makes sense. Just think of it like this... If AI models become cheaper to train because they use less energy, on an individual basis... Then, naturally, it becomes more attractive to train and develop even more AI models. So, instead of power consumption dropping… it is going to accelerate at a mind-blowing rate. 
And artificial intelligence is just one of the new power-intensive technologies erupting right now… Throw in the rollout of EVs, Bitcoin and cryptocurrencies, robotics, and quantum computers and the problems are even greater than what I’ve shared. Fortunately, there is a solution. One that has been called “the only solution”... One that could meet America’s erupting AI energy needs for decades to come… I call it “The AI Keystone”. And, I believe, it’s the number one way to capitalize on the trillion-dollar artificial intelligence market. Because just a tiny little bit of The AI Keystone — no larger than the tip of your finger — produces the same amount of energy as: 
All while producing zero emissions. William Becker of the U.S. Department of Energy has gone on record saying, “[The AI Keystone] could save civilization.” And right now, there’s only one company that’s allowed to build it. Go here now to get the name of the company, along with my full investigation into why it’s the most important company to the future of AI. Porter |
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