Are we thinking too small about generative AI?

Perhaps the most promising area for AI to date has been software development, where it seems to be having a sustained impact. Even here, though, only a subset of experienced developers are seeing significant productivity gains, and the impact is nowhere near covering the $1 trillion in AI investments that Goldman Sachs expects during the next few years. As Covello continues, “Replacing low-wage jobs [like creating content marketing assets] with tremendously costly technology is basically the polar opposite of the prior technology transitions” we’ve seen over the past few decades, including the advent of the Internet.

We’re far too cavalier, he notes, in assuming that AI infrastructure costs will fall far enough, fast enough, to make it a worthwhile replacement for many tasks today (assuming it’s capable of doing so, which is by no means guaranteed). Speaking of the dropping cost of servers that helped spark the dot-com boom, Covello points out, “People point to the enormous cost decline in servers within a few years of their inception in the late 1990s, but the number of $64,000 Sun Microsystems servers required to power the internet technology transition in the late 1990s pales in comparison to the number of expensive chips required to power the AI transition today.” Nor does that factor in the associated energy and other costs that combine to make AI particularly pricey.

All of this leads Covello to conclude, “Eighteen months after the introduction of generative AI to the world, not one truly transformative—let alone cost-effective—application has been found.” A damning indictment. MIT professor Daron Acemoglu argues that this will persist for the foreseeable future, because just 23% of the tasks that AI can reasonably replicate will be cost-effective to automate over the next decade.