For the first time, OpenAI also spelled out its AGI definition: “highly autonomous systems that outperform humans at most economically valuable work.”

He spoke like Michelangelo, as though AGI already existed within the marble he was carving. All he had to do was chip away until it revealed itself.

He was sheepish to admit that he also read books for fun—science fiction epics like The Three-Body Problem trilogy by Chinese writer Liu Cixin and Isaac Asimov’s Foundation series.

It’s a common saying among researchers that what is considered AI today will no longer be AI tomorrow.

“Digital” technologies do not just exist digitally. The “cloud” does not in fact take the ethereal form its name invokes. To train and serve up AI models requires tangible, physical data centers.

By 2030, at the current pace of growth, data centers are projected to use 8 percent of the country’s power, compared with 3 percent in 2022; AI computing globally could use more energy than all of India, the world’s third-largest electricity consumer.

There are indeed many AI technologies, as cataloged by the initiative turned nonprofit Climate Change AI, that can accelerate sustainability, but rarely are they ever generative AI technologies. “What you need for climate are supervised learning models or anomaly detection models or even statistical time series models,” says Luccioni, who is also a founding member of the initiative. All of these models are previous generations of AI technologies—primarily machine learning tools—that are small and energy efficient, and in some cases could even run on a powerful laptop.

They found that producing one thousand pieces of text from generative models used as much energy on average as what it would take to charge the standard smartphone nearly four times. Generating one thousand images used on average as much energy as 242 full smartphone charges; in other words, every AI-generated image could consume enough energy to charge a smartphone by roughly 25 percent.