Big Data. Big Tech. Big Science. Big Medicine. Big Money Billionaires. Right now, it seems to be all big all the time with more bigness on the way. In fact, it’s arguable we’ve reached the era of “Peak Big”—and people are tired of just how gargantuan everything has become. Consider just a small slice of the ample evidence.
Airbus recently announced it was stopping production of the A380 , its mammoth jumbo jet. With the exception of a few niche long haul routes, the plane was just too big to succeed. It required an enormous logistics operation and lacked the flexibility airlines need to compete in the modern world.
Michael J. Joyner, M.D. is a physiologist and anesthesiologist at the Mayo Clinic. David Epstein provided additional assistance in drafting this essay.
Last month, New York residents rejected Big Tech with such fervor that Amazon backed out of its plan to locate a new headquarters in Queens. Meanwhile, Amazon’s $0 corporate income tax bill was revealed amid increasing calls for halting jobs-related political bidding wars and corporate welfare .
People are talking openly about banning billionaires and higher marginal tax rates on the super wealthy. There was the rollout of Howard Schultz as an independent presidential candidate , which went approximately as well as the rollout of New Coke. And when tech titan Michael Dell rhetorically asked a crowd at Davos what country had ever succeeded with a 70 percent tax bracket and a historian pointed out that the United States did during the 1950s, resulting in high growth, the entire exchange went viral .
On the Big Medicine front, huge corporate electronic health records are hated by practitioners , and there are fundamental concerns about their accuracy . There is also evidence that the corporatization of medicine is stifling both patient care and innovation . As Big Pharma gets bigger, its capacity for innovation keeps managing to shrink . (This decline in innovation has been called Eroom’s Law, a reverse homage to Moore’s Law, which describes the exponential rise of computing power.) A recent study on the issue of big vs. small science has shown that disruptive innovation is more likely to come from small science . My colleague Nigel Paneth and I have presented evidence that the massive investment in Big Science genomics has had essentially no measurable impact on human health .
And then there are Big Weapons and Big War. For centuries, guerilla fighters have adopted so-called asymmetric tactics and repeatedly frustrated, slowed and even defeated the biggest armies who are outfitted with the most cutting-edge weapons. A recent example came during the early 2000s: retired Marine Corps General Paul Van Riper showed that a swarm of small boats can disable a high tech aircraft carrier, a low-tech workaround that could take down modern networked warfare. In the 1960s, efforts by Robert McNamara to conduct the Vietnam war using an early version of big data failed. As the U.S. struggles to extricate itself from our “longest war”, it bears asking, why are the limits of Big Weapons and Big War repeatedly missed?
In each of the examples above, going “Big” results either in underperformance, or it comes under political or regulatory threat. That is a long way away from the death of Big, but there are already hints as to how it could end.
The market may intervene, as appears to be the case with the A380.
The political process might also take care of it. The current political mess and gridlock doesn't need to last forever. If and when the political class and plutocrats get concerned enough about the pitchforks coming out , maybe Big Tech will be broken up or regulated and taxes on the rich will rise. (Democratic presidential candidate Elizabeth Warren just made this part of her platform.) In the 19th century, cities like Paris were re-imagined and re-engineered for reasons that included concerns about unrest by the “lower” classes. Bismarck was no liberal, but he instituted social insurance to stave off more radical and disruptive ideas.
The issues with Big Medicine and Big Science will likely need to be solved via changes in policy. Eventually, computers used in medicine will have to be interoperable and do more than just enhance billing and coding. The way research grants are distributed might also change, and there are calls for at least a partial lottery system as a way to overcome scientific group think.
Big Weapons and Big War appear to be persistent problems that have recycled as generals and political leaders try to fight the latest war. Perhaps it can only change via a shift in leadership culture . In fact, the need for a change in leadership culture also underpins many of the other areas where, I argue, we have reached “Peak Big”.
The paradox inherent to the potential solutions I propose is that at least some rely on what could fairly be called “Big Government.” However, we should never forget the lone innovator. Such innovators now have access to plenty of technology at low cost and for many kinds of innovation the barriers are as low as they have ever been. Innovators can upend things rapidly and the diffusion of innovation is faster than ever. In the middle 1960s a high school kid named Dick Fosbury invented a new way to high jump that became dominant within a decade. Less well known is that at about the same time a 13-year-old Canadian girl named Debbie Brill independently came up with the exact same solution. Fosbury and Brill invented a new way to high jump with no IT resources, no committees, and no advanced biomechanics advice. In fact, their innovation ran contrary to widespread advice. They showed the power of “Small.”
Who knows how “Big” will end, but the ideas outlined here suggest that we are reaching “Peak Big.” No one can know what will happen on the other side of the peak, but eventually we might all in fact be going over the bar backward.
WIRED Opinion publishes pieces written by outside contributors and represents a wide range of viewpoints. Read more opinions here. Submit an op-ed at [email protected]
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Earlier this month, researchers from the Oxford Internet Institute published a study in the journal Nature Human Behavior that plainly illustrates how that happened: The gigantic surveys underlying many tech-use studies can be interpreted in such a variety of ways that two different researchers looking at the exact same data set can—and have!—reached opposite conclusions about the association between screen time and well-being.