OpenAI CEO Sam Altman took to the stand in front of Congress, testifying on the future of AI regulation at Capitol Hill. During his hearing, he spelled out the truth about the limits of how far we can take AI: "The cost of AI will converge to the cost of energy."

AI is often framed as a purely digital phenomenon, operating seamlessly in the intangible realm of codes and algorithms. But behind every image generated, every response crafted, lies a significant and measurable energy cost. The technology we all use relies on minerals, chips, semiconductors, and data centers where your data is being churned and processed. Technology requires energy to power it, and the extraction of scarce minerals to build it. So, as we think about accelerating technology, what lies ahead is not just a computational challenge, but an infrastructural and ecological one.

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Despite AI's promise as a technology with unlimited and infinite potential, there is a very real limiting factor to its growth. In his testimony, Altman skipped the fluff: "Eventually, chips, network gear... will be made by robots, and we'll make that very efficient and cheaper and cheaper, but an electron is an electron." This is a fundamental economic principle that will shape AI's future. As AI manufacturing processes become increasingly automated and optimized, the variable costs of hardware production will steadily decline. What remains immutable is the physics of computation itself: the energy required to power these systems. In a mature AI economy, the marginal cost of intelligence will approach the marginal cost of electricity. This creates a direct relationship between energy innovation and AI capabilities; regions with abundant, reliable, and affordable energy will gain decisive advantages in computational power.

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