Irving Wladawsky-Berger turns his attention in a recent Wall Street Journal article to the well-known productivity paradox. As Wladawsky-Berger summarizes,

Despite the relentless advances of digital technologies, productivity growth has been declining over the past decade. Investment and interest rates have remained low, and income has continued to stagnate for the majority of workers in the US and other developed economies. The world seems to be stuck in a period of slow growth and no one is quite sure what’s causing this apparent contradiction.

Wladawsky-Berger notes that leading economists have suggested various reasons for this paradoxical lack of productivity. Larry Summers has offered up secular stagnation, where companies fall short in investing (consider the cash reserves of Apple and Google, who can’t seem to find good returns for investment). Meanwhile, consumers buy less than they might, both of which slow growth. Robert Gordon has argued that a global decline in innovation and productivity over the past few decades is a return to an intrinsically lower rate, as was the norm prior to 1900. Others have noted that the world’s population growth has slowed in recent decades, and that could act as a brake on productivity. I’ve written recently (in The Hidden Economics of Ideas) about research by economist Nicholas Bloom and his colleagues that seems to indicate that the costs of finding new ideas are rising:

Across a broad range of case studies at various levels of (dis)aggregation, we find that ideas — and in particular the exponential growth they imply — are getting harder and harder to find.

The primary cause of the rising costs of new ideas is that in industries where we have seen steady innovation — such as in semiconductors — the number of researchers required to maintain that innovation has been growing steadily. As I wrote,

The researchers believe they have answered the question: can a constant level of research effort generate constant exponential growth in the entire economy or in specific economic niches? The answer seems to be ‘no’. Turned around, constant exponential growth requires a growing number of researchers.

It appears, according to Bloom et al that we need to double the number of researchers every 13 years just to keep the economy growing at the current rate.

There may be additional negative factors contributing to the lack of productivity, or, turning it around, there may be positive factors that could increase productivity if we could only harness them. Some technology observers — like Erik Brynjolfsson, Daniel Rock, and Chad Syverson in AI and the Modern Productivity Paradox — have suggested that there is just such a bottleneck in the adoption of the transformative technologies that make up the fourth industrial revolution: the internet, AI, ubiquitous mobility, big data, IoT.

In recent research, Brynjolfsson has returned to this question, this time joined by Seth Benzell, and created a sweeping macroeconomic model to dig into that economic bottleneck. In their Digital Abundance and Scarce Genius, the pair of researchers model the question: what possible factors can be leading to this decrease in productivity, even as digital technologies exist that can scale non-linearly and should therefore be ushering in an expansion of digital capital?

Their answer is the scarcity of an X factor — or, specifically, a G factor, where G represents the scarcity of genius.