This Is the Worst Part of the AI Hype Cycle

Earlier this week, Paul McCartney sent the music-nerd internet ablaze with some news: Artificial intelligence had helped resurrect a bit of John Lennon’s voice for a new Beatles song, more than four decades after his death. The song is set for release later this year and comes from vocals Lennon recorded on an old demo. “We were able to take John’s voice and get it pure through this AI,” McCartney told BBC Radio 4, “so then we could mix the record, as you would normally do.”

The reaction this elicited on WIRED Slack channels was somewhere between “cool” and “gross.” Using AI to resurrect Lennon for a new song has its appeal, but given the recent ethical questions around using the technology to make fake songs from artists like Drake and The Weeknd, it also feels icky. Based on how McCartney described the process, it sounded like the AI involved simply cleaned up some rough audio, rather than recreated Lennon whole cloth, like it did with Drake, but the reaction to the song pointed to something else: This moment, this time, is the worst part of the AI hype cycle.

You likely know what this means even if you don’t know it in those words. The hype cycle, as defined by Gartner, which tracks it, is that series of cyclical events that happens around nearly all emerging technologies: the breakthrough, the “peak of inflated expectations,” the disillusionment, the period of actual serviceable uses of the tech, and the time when it’s adopted. That pinnacle is the groan time, the moment Justin Bieber drops more than $1 million on an NFT. The moment Facebook buys Oculus. The moment the bodega starts taking bitcoin and you know you’ll never be able to escape this thing, whatever it is.

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This isn’t to say AI is over-hyped. Just that society has now hit the point where people in every field are now enamored with it, and experimenting. That will lead to wild new creations, like books written with ChatGPT, and oof-worthy moves like lawyers using AI to write legal briefs and citing nonexistent cases. It’s Holly Herndon deepfaking her own voice, and Spotify, Apple Music, and other streaming services getting flooded with bot-generated tunes. Until all of AI’s killer applications surface, anything can be a killer app.

This overwhelming moment may feel even more daunting because it’s on the heels of so many hype cycles. The eruption of generative AI comes shortly after Facebook transformed into Meta, crypto outfits like FTX collapsed, and Elon Musk completed his takeover of Twitter. Hype is proliferating, and some of its ensuing disappointments have been, well, disappointing. There’s something to consider beyond the hype cycle: hype burnout.

Living in an era when everything feels like the future is exciting. It’s also exhausting. As each new big idea gets millions in Silicon Valley startup cash, it’s hard to know which one is going to be worth it. At this point, AI seems like a pretty sure bet. It is a genie that cannot be put back into its bottle, and it seems essential that we, the collective “we,” stress-test it for its most essential use cases. But it’s also hard to not want that genie to grant a wish to return things to a simpler time.

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