Don't Be So Sure the Big Tech Breakthroughs Are Behind Us
Vox tech writer Timothy B. Lee used to be one of the most ardent techno-optimists. But he’s had a bit of a conversion, of late, and is now on the side of those who think tech progress is slowing. Maybe it was the economist Robert Gordon who convinced him, or maybe years of observing the tech world changed his mind. In any case, Lee now broadly suggests that the inventions of tomorrow won’t be as world-changing as those of yesteryear. The idea that tech will remake our lives, he writes:
has fallen flat in recent years, and I think it’s going to continue failing in the years to come. There are a number of industries — with health care and education being the most important — where there’s an inherent limit on how much value information technology can add. Because in these industries, the main thing you’re buying is relationships to other human beings, and those can’t be automated.
Lee illustrates his argument with a chart of prices for various goods and services in the U.S. economy during the past four decades. As the chart below shows, manufactured goods have mostly fallen in price, while college and health care have soared. He reasons that these are difficult industries for technology to disrupt, since they rely so much on human-to-human interaction.
It’s a compelling argument, but I see a number of ways it could potentially be wrong. There’s a case to be made for continued techno-optimism.
First, Lee’s chart only includes final goods -- the things that consumers buy. But there are a vast number of other goods that also use huge amounts of time and resources to create. Economists call these intermediate goods. That category includes parts and components, but also all the back-office services that make the business world run -- accounting, payroll, legal services, human resources and the rest. It also includes finance, which is a huge cost both to businesses and to investors.
Technology that makes these things cheaper will make the business world more efficient, just like cheaper steel makes manufacturing cars more efficient. And it’s here, in the realm of white-collar work, where I believe the technologies bow under development have the potential to create huge productivity gains.
A lot of effort right now is being poured into machine learning and artificial intelligence, thanks in part to technical advances in the field, and also thanks to the availability of large amounts of data to train machines. In a recent interview with Lee, venture capitalist Marc Andreessen explained why he thinks machine learning is the next transformative technology.
Essentially, machine learning allows machines to do your thinking for you. One of the earliest applications was recognizing addresses on envelopes -- instead of armies of humans sitting there doing the reading, the process could be accomplished with just one or two humans managing the machine readers. That’s a big productivity improvement.
It isn't hard to imagine fancier versions of that technology taking over many of the tasks we now spend our time and energy on. Machines will evaluate business proposals for banks and other lenders. Machines will scan contractors and take bids. Machines will seek out targets for mergers and acquisitions. Machines will write most of the text of legal briefs. A machine might even write my columns someday. In fact, many of the things that white-collar workers now spend hours on every day will be managed by machines. That will free up enormous amounts of time -- machines don’t have to go to meetings or read e-mails.
Technology is fundamentally about saving labor, and most of the labor in the typical white-collar work-day consists of thinking. Just as factory tools and vehicles saved physical labor in the Industrial Revolution, smart machines will save more and more mental labor in the Information Revolution. And since machine learning is still in its infancy, at least in terms of applications, it’s a good bet that this part of the Information Revolution isn’t over.
Now, one might look at Lee’s graph and say “OK, fine and good, but which of the consumer goods on this graph will get cheaper as a result of all this automation? If the things we want don’t fall in price, who cares?”
There are two answers to that. The first is that Lee’s graph only includes the things that people consumed in the 1980s. But as technology frees humans from the work necessary to produce the old things, humans will spend their time creating new things. We just don’t yet know what most of those things will be. Forty years ago, video games barely existed -- now they’re a major consumption item. Who knows what we’ll desire four decades from now?
Of course, there’s also a second possibility -- the possibility that many humans might become redundant. If machine learning makes most of us obsolete, we will have to alter the structures of society to redistribute the massive abundance created, in order to make everyone’s leisure time as pleasant as possible. This is the rise-of-the-robots scenario that lots of people are worried about, but it doesn’t have to be a scary thing, if society changes accordingly.
But whichever future occurs, it seems likely that the world of white-collar work is due for some much-needed disruption. That makes me a little more optimistic than Lee.
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