Many tech leaders have said that AI will soon become too cheap to meter, but there might be a lower bound to how cheap it can be.
Sam Altman, CEO of OpenAI, the company behind ChatGPT, believes that the ultimate limiting factor on AI cost won’t be silicon or software, but rather the fundamental energy required to power these increasingly sophisticated systems. His perspective offers a compelling insight into the long-term economic and strategic implications of the AI revolution, especially as the demand for AI continues to surge, putting immense pressure on existing infrastructure and energy resources.
Altman articulated this vision with characteristic directness to US lawmakers. “I think it’s hard to overstate how important energy is to the future here,” he said.
“Eventually, chips, network gear, that will be made by robots and we’ll make that very efficient, and we’ll make that cheaper and cheaper. But an electron is an electron. Eventually the cost of intelligence, the cost of AI, will converge to the cost of energy. And the event and if it’ll be how much you can have the abundance of it will be limited by the abundance of energy.”
“So, in terms of long-term strategic investments for the US to make, I can’t think of anything more important than energy. You know, chips and all the other infrastructure, all but but energy is where this I think this ends up,” Altman said.
Altman’s assertion is particularly relevant given the recent explosion in AI development and deployment. The training and operation of large language models (LLMs) like GPT-4 and others are notoriously energy-intensive. As these models become more complex and are integrated into more aspects of our lives, the demand for energy will only increase. This could put a strain on existing power grids and potentially limit the scalability of AI applications.
This convergence of AI cost with energy cost suggests a future where the availability and price of energy become critical factors in determining who can afford to participate in the AI revolution. Nations and companies with access to abundant, cheap energy sources will likely have a significant competitive advantage. This underscores the importance of investing in renewable energy sources, nuclear fusion research, and other technologies that can provide clean, affordable, and reliable power to fuel the AI-driven future. The abundance of AI might not just be limited by Moore’s Law, but by the laws of thermodynamics.