The Humble Thought Peddler

Here in the U.S. we just concluded our Labor Day holiday, and I spent it thinking about labor. 

More specifically, I was intrigued by the difference between the labor required to design something versus actually building that thing.

For example, in almost every engineering discipline, an engineer cannot his or herself physically build the entirety of their design. One person alone usually cannot build a house or an automobile or a bridge. I’m obviously not including outliers like small rural shacks which can be envisioned and built by the same person. I’m talking about things that bring lasting value to a significant number of people.

In contrast, software engineering stands out as one of the only, if not the only engineering discipline where one person could conceivably build out their entire vision, no matter how large.

In fact, it’s no secret that Facebook founder Mark Zuckerberg coded almost the entire site himself. [1] Bear in mind that Facebook is web software that’s roughly equivalent to a large urban agglomeration in the physical world. To think that one man could singlehandedly build a city of that size would be absurd. But in the realm of software, one individual could build a proverbial palace of software with nothing more than their own intellect and rapid-fire typing.

This amazes me, but it also leads to deep questions about the long-term sustainability of our labor market.

The evidence has been mounting lately about the structural nature of post-recession U.S. unemployment. [2] As the Federal Reserve contemplates QE3 (“Operation Twist”) and Treasury officials scratch their heads over possible fiscal or monetary fixes, it’s become clear that deeper underlying conditions are keeping more and more Americans jobless.

[3]

The labor efficiency of software engineers and related high-technology workers is likely a culprit in the structural unemployment problem. As more highly utilized products and services are digitized, more things are built by fewer people. This isn’t even about robots and automation, but the changing nature of what we’re building and how it’s built.

This could be the end of the theory: software is less labor-intensive than previous economic output and therefore labor demand will continue to drop as software demand rises.

But stopping here would be naive. The digital revolution has just begun. We are only now beginning to build out the “application layer” of the technology stack upon all the infrastructure that was built out in the 90s and 00s. [4] This means that while overall demand for labor may not increase, demand for software engineering and related fields will continue to skyrocket as a larger chunk of economic output is dominated by software.

This makes our economics problem an education problem: making sure enough of the labor force is trained in software engineering, computer science and related fields. There should be a twofold focus on both technical retraining for adult workers and strong reform of K-12 programs that emphasize these disciplines. The former received a creative proposal by Jason Calacanis a month ago entitled “A New New Deal” that’s worth taking a little seriously in times like these. [5]

Hopefully by Labor Day 2012, policymakers have realized this and allow me to enjoy the holiday without such intellectual exercises. We can only hope (or learn to code).

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[1] – Most of the early product, at least, was coded solely by Zuckerberg. Shortly after launching, other top programmers like Adam D’Angelo joined the effort and it scaled up from there.

[2] – A recent report on investing blog Seeking Alpha contemplates “Quantitive Easing 3″ in light of structural unemployment concerns. It quotes an FOMC member as stating that, “the current high level of long-term unemployment, which as of July accounts for about 44% of all the unemployed, might signal a mismatch between the skills of the unemployed and the jobs currently available.”

[3] – Structural unemployment chart from Felix Salmon’s warning in June.

[4] – See Carlota Perez’s book Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages