AI’s productivity payoff is still decades away… as it ought to be

The technology media apparatus is obsessed with artificial intelligence, machine learning and neural networks. I would know, because most of what I write about falls within those fields; they hold the most promise for American technological progress of any technologies we’re experimenting with at the moment. AI will rewrite human history for the better if we get it right, which is why so many firms are dedicating so many resources to that end. But with all the coverage dedicated to the topic, and the myriad enterprise advances touted within that coverage, you’d think a productivity explosion would not only be in the cards, but we’d be underway by now!

You’d be forgiven for coming to that conclusion; the most revolutionary general-use technological advances always, always, always show up as massive productivity and economic output spikes… 25 years later. I would argue that’s exactly what’s happening now.

Historical precedents for productivity bumps

According to Edoardo Campanella, writing for Foreign Policy, “Among advanced economies, productivity growth is slower now than at any time in the past five decades. National GDPs and standards of living, meanwhile, have been relatively stagnant for years.” This is where the question about a lack of productivity increase comes from — since Deep Blue burst onto the scene 21 years ago, to Watson winning Jeopardy in 2011, to AlphaGo pounding the best Go players in the world, it looked as if AI had reached the level of transformational advance.

Previously, the six innovations that powered economic growth from 1870 to 1970 were “electricity, urban sanitation, chemicals, pharmaceuticals, the internal combustion engine, and modern communications technologies.” And to many observers, these advances were simply far more revolutionary than say, Alexa, which is why you’re not seeing a resulting spike in productivity. For example, once “electricity became widespread in the United States in the 20th century, labor productivity started growing at an annual rate of 4 percent—almost four times higher than the current rate,” according to Foreign Policy. We’re most certainly not seeing that with AI… yet.

Our results timetable is simply too truncated

“…yet” is the key word there, though. Because with each of those prior technologies, it took about a quarter century to come to fruition. “General-purpose technologies, as the economists Boyan Jovanovic and Peter Rousseau have written, are innovations that are pervasive, improve over time, and spawn further innovation,” Campanella observes. And because those types of technologies can be used so variably, it takes a long time for the adoption thereof to achieve critical mass. Campanella presents the case for how that came to be previously:

“It took more than two decades for electricity to surpass steam (in terms of share of total horsepower in manufacturing), for example, and almost four decades to become the undisputed source of power generation. That makes sense: To make use of electricity, governments had to invest in nationwide electric grids; entrepreneurs had to invent complimentary technologies like light bulbs, cables, and switches; bureaucrats had to agree on standards such as the voltage of the current and the shape of the plug; and ultimately, businesses had to create saleable products compatible with the new source of power.

“A similar process took place with modern information and communications technology. It took about two decades for such equipment to surpass more than 1 percent of all capital stock. Then, between 1991 and 2001, the share rose to 5 percent before jumping again to 8 percent in 2008, where it has roughly stabilized.

What does that mean for AI? Well, we’re only about a third of the way through the AI revolution, and governments, entrepreneurs, bureaucrats, and businesses haven’t coalesced around the attending technologies, standards, integrations, etc. that will allow AI to truly become core to the economic output of this country. It’s not that AI as a technology is less powerful than pharmaceuticals or electricity before it, but rather that we haven’t given it (and its stakeholders) enough time for the productivity and economic impact results to reveal themselves.

But trust me… it’s coming. And that’s where first movers can really set their businesses and services apart — that’s precisely why we do what we do here at ENO8. We can help companies surf the first wave of AI advancement to huge enterprise returns by building software that matters.