The economy is down, but AI is hot. Where do we go from here?

Oh man, it’s brutal out there. One by one, the world’s richest tech companies have announced massive layoffs. Just last week, Alphabet announced it was laying off 12,000 people. There have been bruising rounds of layoffs at Amazon, Meta, Microsoft, and Twitter, too, affecting not only individual AI researchers but entire AI teams.

It was heartbreaking to read over the weekend about how some Googlers in the US found out about the company’s abrupt cull. Dan Russell, a research scientist who has worked on Google Search for over 17 years, wrote how he had gone to the office to finish off some work at 4 a.m., only to find out his entry badge didn’t work.

Economists predict the US economy may enter a recession this year amid a highly uncertain global economic outlook. Big tech companies have started to feel the squeeze.

In fact, Big Tech is counting on AI to give it an edge.

AI research has swung violently in and out of fashion since the field was established in the late 1950s. There have been two AI winters: one in the 1970s and the other in the late 1980s to early 1990s. AI research has previously fallen victim to hype cycles of exaggerated expectations that it subsequently failed to live up to, says Peter Stone, a computer science professor at the University of Texas at Austin, who used to work on AI at AT&T Bell Labs (now known as Nokia Bell Labs) until 2002.

For decades, Bell Labs was considered the hot spot for innovation, and its researchers won several Nobel Prizes and Turing Awards, including Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. The lab’s resources were cut as management started pushing for more immediate returns based on incremental technological changes, and ultimately it failed to capitalize on the internet revolution of the early 2000s, Jon Gertner writes in his book The Idea Factory: Bell Labs and the Great Age of American Innovation.

The previous downturns happened after the hottest AI techniques of the day failed to show progress and were unreliable and difficult to run, says Stone. Government agencies in the US and the UK that had provided funding for AI research soon realized that this approach was a dead end and cut off funding.

Today, AI research is having its “main character” moment. There may be an economic downturn, but AI research is still exciting. “We are still continuing to see regular rollouts of systems which are pushing back the frontiers of what AI can do,” says Michael Wooldridge, a computer science professor at the University of Oxford and author of the book A Brief History of AI.

This is a far cry from the field’s reputation in the 1990s, when Wooldridge was finishing his PhD. AI was still seen as a weird, fringe pursuit; the wider tech sector viewed it in a similar way to how established medicine views homeopathy, he says.

Today’s AI research boom has been fueled by neural networks, which saw a big breakthrough in the 1980s and work by simulating the patterns of the human brain. Back then, the technology hit a wall because the computers of the day weren’t powerful enough to run the software. Today we have lots of data and extremely powerful computers, which makes the technique viable.

New breakthroughs, such as the chatbot ChatGPT and the text-to-image model Stable Diffusion, seem to come every few months. Technologies like ChatGPT are not fully explored yet, and both industry and academia are still working out how they can be useful, says Stone.

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