"You can see it as a sort of super coding agent," says Pushmeet Kohli, a vice president at Google DeepMind who leads its AI for Science teams. "It doesn't just propose a piece of code or an edit, it actually produces a result that maybe nobody was aware of." In particular, AlphaEvolve came up with a way to improve the software Google uses to allocate jobs to its many millions of servers around the world. Google DeepMind claims the company has been using this new software across all of its data centers for more than a year, freeing up 0.7% of Google's total computing resources. That might not sound like much, but at Google's scale it's huge.

An algorithm used trillions of times a day around the world could run up to 70 per cent faster, thanks to an artificial intelligence created by UK-based firm DeepMind. It has found an improved way for computers to sort data that has been overlooked by human programmers for decades. "We honestly didn't expect to achieve anything better: it's a very short program, these types of programs have been studied for decades," says Daniel Mankowitz at DeepMind. Known as sorting algorithms, they are one of the workhorses of computation, used to organise data by alphabetising words or ranking numbers from smallest to largest. Many different sorting algorithms exist, but innovations are limited as they have been highly optimised over the decades.

In the field of computer science, there is perhaps no more fundamental task than to sort. Bubble, heap, merge--take your pick. The methods for reordering data inside a computer have been theorized to death, served as practice exercises for millions of novices, and been optimized for decades by expert developers. Type a sort() function in any programming language, and it's code you can rely on. But last year, an AI system developed by engineers at Google's Deepmind improved on great by just enough to matter. The system, which Deepmind calls AlphaDev, was tasked with coming up with a new way to sort short sequences in numbers in C, the popular coding language.

"Moore's Law is coming to an end, where chips are approaching their fundamental physical limits," says Daniel Mankowitz, a research scientist at Google DeepMind. "We need to find new and innovative ways of optimizing computing." "It's an interesting new approach," says Peter Sanders, who studies the design and implementation of efficient algorithms at the Karlsruhe Institute of Technology in Germany and who was not involved in the work. "Sorting is still one of the most widely used subroutines in computing," he says. DeepMind published its results in Nature today.