66 Finance and economics The EconomistJune 9th 2018
T
HE race to bring driverless cars to market is fierce and crowd-
ed. All the leading carmakers are in the field: on May 31st Soft-
Bank’s Vision Fund said that it would invest $2.25bn in the auton-
omous vehicle (AV) arm of General Motors. So are tech upstarts,
from Uber to Tesla to Waymo, Alphabet’s self-drive division and
the leader in driverless technology, which recently announced
plans to add 62,000 minivans to the fleet of cars that will make up
its autonomous ride-hailing service. Intense competition has
both benefits and costs, but will probably prove short-lived.
Thanks to powerful economies of scale, the roads may soon be
ruled by no more than a handful of firms.
The advantages of scale begin with data. Like humans, the
computers which power driverless cars improve with experi-
ence. The computers sitting inAVs are essentially in the business
of learning and improving on what a good human driver would
do, write Ajay Agrawal, Joshua Gans and Avi Goldfarb in their
new book, “Prediction Machines”. The more data they have, the
better they become at predicting whether that blur ahead is a pe-
destrian or sunlight reflecting off the road, and reacting accord-
ingly. And the more miles under anAVproject’s belt, the more un-
usual events—amoose in the road, say—the system faces.
Fortunately such lessons, once learned by computers, are not
forgotten, and can be drawn upon by every vehicle using the
same software. This, and the fact thatAVs never fall asleep at the
wheel or pull their eyes from the road to check their phone, sug-
gests that driverless cars should ultimately be far safer than the
human-driven sort, which contribute to the roughly 1.25m road
deaths each year worldwide, a bigger body-count than malaria.
But someAVsystems will be safer than others. Those that beat
competitors on safety and general reliability will attract more
drivers and corporate partners, allowing them to gather more
data still.
Regulators might further thin the field, by forcing firms with
poor safety records to curtail testing or by setting standards that
only the best can meet. Indeed, performance gaps could create
ethical quandaries for governments: should the safest firms be
forced to share their technology; should they be given exclusive
rights to the roads; should policymakers tolerate preventable
deaths in cars using inferior software? Such questions might notremain academic for long. Between December 2016 and Novem-
ber 2017 Waymo reported three collisions in 350,000 miles
(560,000km) of driving in California; GM, the nearest American
competitor, had 22 in 132,000. Neither has been involved in a fatal
accident, as Tesla and Uber have.
Scale will yield still other benefits. Though some people will
want their own driverless cars, the market is likely to favourAV-
based ride-hailing services. Driverless cars will not come cheap.
But cars used in ride-sharing services will cost less per mile than
personal vehicles, which spend much of their time sitting idle.
Maintenance and other costs should be lower for fleets of hail-
ableAVs, because centralised facilities ought to enjoy productivi-
ty advantages over distributed mechanics’ shops, and because in-
dividual owners are at an informational disadvantage to their
mechanics, which creates opportunities for overcharging.
Individual owners might nonetheless shell out for the conve-
nience of a car at their personal beck and call. Yet car-hailing ser-
vices, like bike-sharing businesses, become more useful as their
user-base grows. The more riders there are in an area, the more
vehicles it pays to operate, and the more likely a user is to find an
open ride nearby. If waiting times fall to almost nothing—as clev-
erAVs learn to roll up at the time you usually leave home or
work—the extra value of having your own car will fade.
Convenient, safeAVs, which allow riders to nap rather than
mind the wheel, should reduce the hassle of travelling by car.
That creates a potential snag: people may travel more, making
congestion worse. Ironically, though, scale could fix this too. Con-
gestion occurs because individual drivers do not take into ac-
count the inconvenience they cause to others. One way to solve
this is to force drivers to bearthose costs, bycharging them a fee.
But governments’ plans to introduce congestion tolling are un-
popular—and charging is consequently rarer than chronically
jammed highways.
But a ride-hailing service which grew to account for a substan-
tial share of traffic would face a different set of incentives. Con-
gestion costs imposed by one of its cars on another would be in-
ternal to that firm, which would have both the reason and the
ability to do something about it—by varying prices with demand,
perhaps, or by offering reduced rates to customers willing to
share a car. Other cars or services could attempt to free-ride on the
free-flowing traffic created by dominant firms. But concentrated
control over roadways could make the politics of road fees more
tractable: as the winning firms’ bargaining power rose, as fewer
middle-income households owned their own cars, and especial-
ly if, as Daniel Rauch and David Schleicher of Yale University sug-
gest,AVfirms join with governments to provide public transport
and mobility services.Waymo regulation
Assuming, that is, that governments do not take a much larger
role in the market. Historically, scale economies in transport,
from railways to public transit, pushed systems towards monop-
oly and eventual government interference. Economies of scale
will likewise thin the ranks of driverless contenders and create
pressure for government involvement. Driverless cars might not
be much faster than those controlled by humans. But the market
could go from cut-throat competition to oligopoly to state control
with extraordinary speed. 7Road hogs
Economies of scale will push the market for driverless vehicles towards monopolyFree exchange
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