The year 1995 was heralded as the beginning of the
“New Economy.” Digital communication was set to upend markets and change everything. But economists by and large
didn’t buy into the hype. It wasn’t that we didn’t recognize that something
changed. It was that we recognized that the old economics lens remained useful
for looking at the changes taking place.
The economics of the “New Economy”
could be described at a high level: Digital technology would cause a reduction
in the cost of search and communication. This would lead to more search, more
communication, and more activities that go together with search and
communication. That’s essentially what happened.
Today we are seeing
similar hype about machine intelligence. But once again, as economists, we
believe some simple rules apply. Technological revolutions tend to involve some
important activity becoming cheap, like the cost of communication or finding
information. Machine intelligence is, in its essence, a prediction technology,
so the economic shift will center around a drop in the cost of prediction.
The first effect of
machine intelligence will be to lower the cost of goods and services that rely
on prediction. This matters because prediction is an input to a host of
activities including transportation, agriculture, healthcare, energy
manufacturing, and retail.
When the cost of any
input falls so precipitously, there are two other well-established economic
implications. First, we will start using prediction to perform tasks where we
previously didn’t. Second, the value of other things that complement prediction
will rise.
Lots of tasks will be reframed as prediction problems
As machine intelligence
lowers the cost of prediction, we will begin to use it as an input for things
for which we never previously did. As a historical example, consider
semiconductors, an area of technological advance that caused a significant drop
in the cost of a different input: arithmetic. With semiconductors we could
calculate cheaply, so activities for which arithmetic was a key input, such as
data analysis and accounting, became much cheaper.
However, we also started
using the newly cheap arithmetic to solve problems that were not historically
arithmetic problems. An example is photography. We shifted from a
film-oriented, chemistry-based approach to a digital-oriented, arithmetic-based
approach. Other new applications for cheap arithmetic include communications,
music, and drug discovery.
The same goes for machine intelligence and prediction.
As the cost of prediction falls, not only will activities that were
historically prediction-oriented become cheaper — like inventory management and
demand forecasting — but we will also use prediction to tackle other problems
for which prediction was not historically an input.
Consider navigation.
Until recently, autonomous driving was limited to highly controlled
environments such as warehouses and factories where programmers could
anticipate the range of scenarios a vehicle may encounter, and could program
if-then-else-type decision algorithms accordingly (e.g., “If an object
approaches the vehicle, then slowdown”). It was inconceivable to put an
autonomous vehicle on a city street because the number of possible scenarios in
such an uncontrolled environment would require programming an almost infinite
number of if-then-else statements.
Inconceivable, that is,
until recently. Once prediction became cheap, innovators reframed driving as a
prediction problem. Rather than programing endless if-then-else statements,
they instead simply asked the AI to predict: “What would a human driver do?”
They outfitted vehicles with a variety of sensors – cameras, lidar, radar, etc.
– and then collected millions of miles of human driving data. By linking the
incoming environmental data from sensors on the outside of the car to the
driving decisions made by the human inside the car (steering, braking,
accelerating), the AI learned to predict how humans would react to each second
of incoming data about their environment. Thus, prediction is now a major
component of the solution to a problem that was previously not considered a
prediction problem.
Judgment will become more valuable
When the cost of a
foundational input plummets, it often affects the value of other inputs. The
value goes up for complements and down for substitutes. In the case of
photography, the value of the hardware and software components associated with
digital cameras went up as the cost of arithmetic dropped because demand
increased – we wanted more of them. These components were complements to
arithmetic; they were used together. In contrast, the value of
film-related chemicals fell – we wanted less of them.
All human activities can
be described by five high-level components: data, prediction, judgment, action,
and outcomes. For example, a visit to the doctor in response to pain leads to:
1) x-rays, blood tests, monitoring (data), 2) diagnosis of the problem, such as
“if we administer treatment A, then we predict outcome X, but if we administer
treatment B, then we predict outcome Y” (prediction), 3) weighing options:
“given your age, lifestyle, and family status, I think you might be best with
treatment A; let’s discuss how you feel about the risks and side effects”
(judgment); 4) administering treatment A (action), and 5) full recovery with
minor side effects (outcome).
As machine intelligence
improves, the value of human prediction skills will decrease because machine
prediction will provide a cheaper and better substitute for human prediction,
just as machines did for arithmetic. However, this does not spell doom for
human jobs, as many experts suggest. That’s because the value of human judgment
skills will increase. Using the language of economics, judgment is a complement
to prediction and therefore when the cost of prediction falls demand for
judgment rises. We’ll want more human judgment.
For example, when
prediction is cheap, diagnosis will be more frequent and convenient, and thus
we’ll detect many more early-stage, treatable conditions. This will mean more
decisions will be made about medical treatment, which means greater demand for
the application of ethics, and for emotional support, which are provided by humans.
The line between judgment and prediction isn’t clear cut – some judgment tasks
will even be reframed as a series of predictions. Yet, overall the value of
prediction-related human skills will fall, and the value of judgment-related
skills will rise.
Interpreting the rise of
machine intelligence as a drop in the cost of prediction doesn’t offer an
answer to every specific question of how the technology will play out. But it
yields two key implications: 1) an expanded role of prediction as an input to
more goods and services, and 2) a change in the value of other inputs, driven
by the extent to which they are complements to or substitutes for prediction.
These changes are coming. The speed and extent to which managers should invest
in judgment-related capabilities will depend on the how fast the changes
arrive.
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