The immense and quickly advancing computing requirements of AI models could lead to the industry discarding the e-waste equivalent of more than 10 billion iPhones per year by 2030, researchers project.

In a paper published in the journal Nature, researchers from Cambridge University and the Chinese Academy of Sciences take a shot at predicting just how much e-waste this growing industry could produce. Their aim is not to limit adoption of the technology, which they emphasize at the outset is promising and likely inevitable, but to better prepare the world for the tangible results of its rapid expansion.

Energy costs, they explain, have been looked at closely, as they are already in play.

However, the physical materials involved in their life cycle, and the waste stream of obsolete electronic equipment … have received less attention.

It's necessarily a hand-wavy business, projecting the secondary consequences of a notoriously fast-moving and unpredictable industry. But someone has to at least try, right? The point is not to get it right within a percentage, but within an order of magnitude. Are we talking about tens of thousands of tons of e-waste, hundreds of thousands, or millions? According to the researchers, it's probably toward the high end of that range.

The researchers modeled a few scenarios of low, medium, and high growth, along with what kinds of computing resources would be needed to support those, and how long they would last. Their basic finding is that waste would increase by as much as a thousandfold over 2023:

"Our results indicate potential for rapid growth of e-waste from 2.6 thousand tons (kt) [per year] in 2023 to around 0.4–2.5 million tons (Mt) [per year] in 2030," they write.

Now admittedly, using 2023 as a starting metric is maybe a little misleading: Because so much of the computing infrastructure was deployed over the last two years, the 2.6 kiloton figure doesn't include them as waste. That lowers the starting figure considerably.

But in another sense, the metric is quite real and accurate: These are, after all, the approximate e-waste amounts before and after the generative AI boom. We will see a sharp uptick in the waste figures when this first large infrastructure reaches end of life over the next couple years.