AI and the Economy:

MIT Technology Review’s Melissa Heikkilä writes, “The economy is down, but AI is hot. Where do we go from here?” AI research, she writes, has gone through two winters and is currently experiencing a spring. This current spring is due to breakthroughs in neural networks, the availability of vast amounts of data and powerful computers.

Computer science professor Peter Stone states that previous downturns happened after the hottest AI techniques of the day failed to show progress and were unreliable and difficult to run. But today, despite the economic downturn, AI research is still productive. Each new rollout pushes back the frontiers of what AI can do. This is a far cry from the field’s reputation in the 1990s. Computer science professor Michael Wooldridge notes that back then, AI was still seen as a fringe pursuit. The tech sector viewed it in the same way established medicine todays views homeopathy.

Long-term goals vs. short-term returns

Heikkilä proposes that in the future, instead of another AI winter, there may be a drop in funding for longer-term research and more pressure to make money using AI technology in the short run. For example, ChatGPT has caused Google to declare a red alter, all-hands-on-deck situation for its core product, Search. It is reworking its flagship product with its own AI research. Successfully maneuvering the always precarious balance between longer-term research and monetizable short-term results may be what keeps our next AI spring from turning into another winter.