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A lot of new technology goes through an initial “what is this good for” stage, but when it comes to generative AI, some folks are getting impatient for an answer.
Driving the news: People are struggling to find practical use cases for generative AI. And it’s not just nay-sayers reacting to hype: Engineers, technology historians, and execs from Amazon and Google have questioned its utility and ability to live up to promises.
- Skepticism is inevitable when most people associate AI with deepfakes, error-prone customer service bots, and really bizarre spam content.
- Creatives and artists are some of the most obvious generative AI users, but they are also some of the most resistant due to concerns about AI taking their jobs or being trained on their work without permission.
Why it matters: If generative AI is a letdown and demand dips, that’s a lot of wasted money, and the market will react. That might remind some of the dotcom bubble, with investment flowing into technology that appears to have great potential despite a lot of current uses feeling pretty frivolous.
- Based on the reaction to Meta’s first-quarter earnings, investors don’t seem eager to wait years for AI to pay off.
Yes, but: Generative AI is useful for some things, and those are likely to stick around after the fervour dies down or the markets correct, like how search and e-commerce propelled Google and Amazon through the dotcom bust. Think of the tedious parts of work like coding, transcribing, or designers resizing ads.
- And unlike the dotcom bubble, many AI companies aren’t startups burning through cash. The Googles and Microsofts of the world have diversified revenue streams and can afford to be more patient.
Zoom out: Whether or not people see a use for AI in their day-to-day lives, enterprise clients are the ones generating revenue. They might also need some convincing — especially in Canada — but if AI helps them save on costs, they could overlook its practical shortcomings.