If an obscure 1980s paradox is any guide, AI may be about to hit a huge tipping point

There’s an old joke among economists that goes like this: “You can see the computer age everywhere but in the productivity statistics.”

I didn’t say it was a funny joke. But when labor economist Robert Solow originally wrote those words in 1987, they were certainly true.

Personal computers, corporate mainframes, and the first vestiges of the modern internet were all anyone could talk about. 

Yet productivity wasn’t budging. These whizzy technologies, in short, weren’t earning anyone any money. The phenomenon became known as Solow’s Paradox.

Of course, we all know how that story ended. By the mid-1990s, productivity was on a tear, and tech was making lots of people fabulously wealthy. And (despite a subsequent crash and recovery), tech is now the linchpin of the modern economy.

Today, AI is following a similar path. And new data suggests that a similarly massive productivity–and wealth–tipping point may be just around the corner.

Old paradoxes

Since generative AI surged into mainstream usage with the launch of ChatGPT in 2022, it has largely followed the same path that computers did in their infancy.

The world can’t stop talking about LLMs and AGI. Yet as late as last year, even the buzziest of AI companies earned shockingly little.

OpenAI, for example, had annualized revenue of around $20 billion as of the end of last year. For comparison, the pest control industry is about the same size, and the pizza industry is about two times bigger.

The chasm between excitement and actual economic impact shows up in bigger datasets, too. A massive study published in February asked 6,000 business leaders how AI was impacting their operations.

The answer? Not at all.

While 63% of business leaders say they’ve adopted AI, 90% found it had no impact on their firm’s employment or productivity.

Official stats tell largely the same story. A study from the Federal Reserve Bank of Saint Louis found that generative AI led to a 5.4% improvement in worker productivity–hardly the massive, workforce-wide gains baked into AI companies’ insane valuations.

Solow’s old paradox, it would seem, is back.

Real impact

New data suggests, though, that that may be changing.

It’s still early days. But a slew of new earnings reports and recent studies hint that AI may finally be starting to find its economic groove.

Alphabet (Google’s parent company)’s Q1 earnings provide the strongest evidence for a coming AI productivity boost. The company says that AI increased its core Search revenue by 19% and boosted Google Cloud revenue by 63%.

Even more tellingly, Alphabet said that AI enterprise tech was driving the majority of Google Cloud’s gains, and that AI-driven revenue from big clients was up 800% in the last year.

Likewise, Microsoft is seeing huge revenue from AI adoption start to pour in. In its latest earnings report, the company said its AI business was earning revenue at an annual run rate of $37 billion. Again, enterprise adoption drove much of those gains.

Salesforce, ServiceNow and Databricks–three comparatively smaller AI companies–also said that enterprise AI is starting to earn them serious money.

Taking a broader perspective, Deloitte looked across multiple industries last year, and found that generative AI is finally starting to show real impacts.

Most companies that have adopted AI are seeing ROI from it, Deloitte says, and almost a quarter of companies are seeing gains of 30% or more.

Generative AI, in short, is fast becoming something that companies use as part of their core business–not something they begrudgingly adopt to avoid seeming like Luddites.

Hockeystick time?

So what happens next? If the original Solow’s Paradox is any guide, the answer is: “quite a lot.”

Even by the early 1990s–years after Solow coined his paradox–computers and the Internet still hadn’t impacted productivity much. 

Then, all of a sudden, productivity growth exploded.

By the late 1990s and early 2000s, productivity growth had roughly doubled, with computer tech driving most of that gain. 

The hockeystick-like growth of both productivity and the valuations of big tech firms (again, once the dust of the dot-com bust had settled) remade the economy. Looking back years later, the New York Fed called it a “productivity revival.”

In 1987, computers seemed like a bust. Today, it’s impossible to imagine a world without them.

Despite its slow start, AI may yet cause the same hockeystick-like growth, and defy today’s gloomy predictions.

Again, the past may be instructive; most economists now believe that computers began driving real growth only when companies learned how to use them properly, building the kinds of infrastructure and processes that let them squeeze real value from the tech.

The enterprise AI revenue growth reported by Alphabet, Microsoft, and the like suggest AI may be in a similar moment of real adoption. 

Initially blindsided by generative AI–then dazzled by it–big companies now seem to be settling down to the tough, expensive, fruitful process of figuring out how to actually put it to use.

That will take time. But when the first Solow’s Paradox showed up in the stats, its ultimate resolution radically changed the economy and the world. It could well be about to happen again.

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