“Will human replacement — the production by ourselves of ever better substitutes for ourselves — deliver an economic utopia with smart machines satisfying our every material need? Or will our self-induced redundancy leave us earning too little to purchase the products our smart machines can make?” So ask three Boston University economists, Seth Benzell, Laurence Kotlikoff, and Guillermo LaGarda, and Columbia’s Jeffrey Sachs. In an attempt to answer the question, the researchers turned to — what else? — a computer. They programmed a “bare-bones” model of the economy, featuring high-tech workers (who produce software) and low-tech workers (who produce services), and let the simulation run under different sets of variables.
The results were, as the economists put it in a new paper on the experiment, “disturbing.” The simulation suggests that “technological progress can be immiserating” and that even talented software programmers may face tough times in an ever more automated economy. The reason lies in the durability and reusability of software. Code is not used up; it accumulates. As the cost of deploying software for productive work (ie, the cost of automation) goes down, demand for new code spikes, bringing lots of new programmers into the labor market. The generous compensation provided to the programmers leads at first to higher savings and capital formation, fueling the boom. But “over time,” the model reveals, “as the stock of legacy code grows, the demand for new code, and thus for high-tech workers, falls.”
As a simple illustration, the authors point to the development of a robotic chess player. Once you have a robot that can outperform all human players, the incentive for programming new robotic players drops sharply. This is something we’ve already seen, as the authors point out: “Take Junior – the reigning World Computer Chess Champion. Junior can beat every current and, possibly, every future human on the planet. Consequently, his old code has largely put new chess programmers out of business.” Once any program reaches a superhuman level of productivity in a task, the incentive to invest in further, marginal gains falls.
The authors lay out the resulting economic dynamic:
The increase in [the code retention rate] initially raises the compensation of code-writing high-tech workers. This draws more high-tech workers into code-writing, thereby raising high-tech worker compensation … Things change over time. As more durable code comes on line, the marginal productivity of code falls, making new code writers increasingly redundant. Eventually the demand for code-writing high-tech workers is limited to those needed to cover the depreciation of legacy code, i.e., to retain, maintain, and update legacy code. The remaining high-tech workers find themselves working in the service sector [and pushing down wages in those occupations]. The upshot is that high-tech workers can end up potentially earning far less than in the [model’s] initial steady state.
As usable code stocks swell, the model indicates, we will at some point pass the cycle’s point of peak code — the moment of maximum demand for new code — and the prospects for employment in programming will begin to decline. Code boom will turn to code bust. (The bust will be even deeper, the economists found, if software is distributed as open source and hence made easier to share.) Even though high-tech workers “start out earning far more than low-tech workers,” they “end up earning far less.”
One thing the economists don’t seem to account for is the automation of programming itself, particularly the use of software to perform many of the tasks necessary to maintain, update, and redeploy legacy code. The automation of coding, which would be encouraged as programmers’ wages increase during the boom period, would likely deepen the bust even further.
Computer models of complex systems are always simplifications, of course, but this study serves to raise important and complicated questions about the long-run demand for programmers. It’s become popular to suggest that all kids should be taught to code as part of their education. That way, the theory goes, they’ll be assured of good jobs in an ever more computerized economy. This study calls into question that hopeful assumption. There can be a glut of coders just as there can be a glut of code.
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