The New Economy’s Old Business Model is Dead

The titans of the new economy are different from their predecessors in one very important way: They aren’t job creators — at least not on a scale to match their dizzying growth in value. General Motors, at its peak in 1979, had some 618,000 employees in the United States and 853,000 worldwide. Facebook had just a few more than 25,000 employees in 2017, up from nearly 12,700 as recently as 2015. Google’s parent corporation, Alphabet, is the third-largest company in the world by market capitalization but has only about 75,000 employees.

But the exponential difference between technology companies’ revenues and their payrolls probably won’t last. The fact that they can have billions of users but only tens of thousands of employees is in part thanks to algorithms and machine learning, which have taken the place of many ordinary workers. It is also the result, however, of political decisions made back in the 1990s that freed these companies from regulation — and those political decisions probably won’t withstand increased scrutiny. As politicians and citizens get more worried about the behavior of technology giants, these companies are going to have to shoulder new regulatory burdens — and will then have no choice but to hire many more people to manage them. In other words, the new economy’s old business model might be about to come to an end.

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Algorithms are the propelling engine of online service companies. The business model of Silicon Valley companies is relatively straightforward. First, come up with a clever and compelling new service, which people might plausibly want. Then, invest in the technology to deliver that service, combining commodity hardware (leased server space and computing power) and purpose-written software with algorithms to manage business processes and user interactions. Then, mortgage your future to promote the service, in the hope that it goes viral and starts being used by millions of people.

Under this model, new businesses must find real money upfront to design the software, straighten out the algorithms, and get the service up and running

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