‘Revolution’ or ‘Delusion’? Despite AI Hype, Adoption Remains Slow
In March of 2023, as artificial intelligence barnstormed through the headlines, Goldman Sachs published a report on “the enormous economic potential of generative AI.” The writers explored the possibility of a “productivity boom,” comparable to those that followed seismic technological shifts like the mass adoption of personal computers.
Roughly 15 months later, Goldman Sachs published another paper on AI, this time with a sharply different tone. This one sported a blunt title — “Gen AI: Too Much Spend, Too Little Benefit?” — and it included harsh assessments from executives like Jim Covello, Goldman’s head of global equity research. “AI bulls seem to just trust that use cases will proliferate as the technology evolves,” Covello said. “But 18 months after the introduction of generative AI to the world, not one truly transformative — let alone cost-effective — application has been found.”
This skepticism has been echoed elsewhere. Daron Acemoglu, a prominent M.I.T. scholar, published a paper in May arguing that AI would lead to “much more modest productivity effects than most commentators and economists have claimed.” David Cahn, a partner at Sequoia Capital, warned in June that “we need to make sure not to believe in the delusion that has now spread from Silicon Valley to the rest of the country, and indeed the world. That delusion says that we’re all going to get rich quick.”
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“I’m worried that we’re getting this hype cycle going by measuring aspiration and calling it adoption,” says Kristina McElheran, an assistant professor of strategic management at the University of Toronto who recently published a paper examining businesses’ attempts to implement AI technology. “Use is harder than aspiration.”
The music industry is no exception. A recent survey of music producers conducted by Tracklib, a company that supplies artists with pre-cleared samples, found that 75% of producers said they’re not using AI to make music. Among the 25% who were playing around with the technology, the most common use cases were to help with highly technical and definitely unsexy processes: stem separation (73.9%) and mastering (45.5%). (“Currently, AI has shown the most promise in making existing processes — like coding — more efficient,” Covello noted in Goldman’s report.) Another multi-country survey published in May by the Reuters Institute found that just 3% of people have used AI for making audio.
At the moment, people use AI products “to do their homework or write their emails,” says Hanna Kahlert, a cultural trends analyst at MIDiA Research, which recently conducted its own survey about AI technology adoption. “But they aren’t interested in it as a creative solution.”
When it comes to assessing AI’s impact — and the speed with which it would remake every facet of society — some recalibration was probably inevitable. “Around the launch of ChatGPT, there was so much excitement and promise, especially because this is a technology that we talk about in pop culture and see in our movies and our TV shows,” says Manav Raj, an assistant professor of management at the University of Pennsylvania’s Wharton School, who studies firms’ responses to technological change. “It was really easy to start thinking about how it could be really transformative.”
“Some of that excitement might have been a little frothy,” he continues. “Even if this is a really important and big technology, it takes time for us to see the effects of these kinds of technological changes in markets.” This was famously true with the development of computers — in 1987, the economist Robert Solow joked, “You can see the computer age everywhere but in the productivity statistics,” a phenomenon later dubbed “the productivity paradox.”
It also takes time to settle the legal and regulatory framework governing AI technologies, which will presumably influence the magnitude of their effects as well. Earlier this year, the major labels sued two genAI music platforms, Suno and Udio, accusing them of copyright infringement on a mass scale; in recently filed court documents, the companies said their activities were lawful under the doctrine of fair use, and that the major labels were just trying to eliminate “a threat to their market share.” Similar suits against AI companies have also been filed in other creative industries.
When McElheran surveyed manufacturing firms, however, few cited regulatory uncertainty as a barrier to AI use. She points out that “they may have had bigger fish to fry, like no use case.” A U.S. Census Bureau survey of businesses published in March found that 84.2% of respondents hadn’t used AI in the previous two weeks, and 80.9% of the firms that weren’t planning to implement AI in the next six months believe it “is not applicable to this business.”
Tracklib’s survey found something similar to McElheran’s. Only around 10% of respondents said concern about copyright was a reason they wouldn’t use AI tools. Instead, Tracklib’s results indicated that producers’ most common objections to using AI were moral, not legal — explanations like, “I want my art to be my own.”
“Generative AI comes up against this wall where it’s so easy, it’s just a push of a button,” Kahlert says. “It’s a fun gimmick, but there’s no real investment on the part of the user, so there’s not much value that they actually place in the outcome.”
In contrast, MIDiA’s survey found that respondents were interested in AI tech that can help them modify tracks by adjusting tempo — a popular TikTok alteration that can be done without AI — and customizing song lyrics. This interest was especially pronounced among younger music fans: Over a third of 20-to-24-year-olds were intrigued by AI tools that could help them play with tempo, and around 20% of that age group liked the idea of being able to personalize song lyrics.
Antony Demekhin, co-founder of the AI music company Tuney, sees a market for “creative tools” that enable “making, editing, or remixing beats and songs without using a complicated DAW, while still giving users a feeling of ownership over the output.”
“Up until recently,” he adds, “the addressable market for those kinds of tools has been small because the number of producers that use professional production software has been limited, so early-stage tech investors don’t frequently back stuff like that.”
Demekhin launched Tuney in 2020, well before the general public was thinking about products like ChatGPT. In the wake of that platform’s explosion, “Investors started throwing money around,” he recalls. At the same time, “nobody knew what questions to ask. What is this trained on? Are you exposed to legal risk? How easy would it be for Meta to replicate this and then make it available on Instagram?”
Today, investors are far better informed, and conversations with them sound very different, Demekhin says. “Cooler heads are prevailing,” he continues. “Now there’s going to be a whole wave of companies that make more sense because people have figured out where these technologies can be useful — and where they can’t.”
