Where is technology taking the economy?
We are
creating an intelligence that is external to humans and housed in the virtual
economy. This is bringing us into a new economic era—a distributive one—where
different rules apply.
A year ago in Oslo Airport I checked in to an SAS flight. One airline kiosk
issued a boarding pass, another punched out a luggage tag, then a computer
screen showed me how to attach it and another where I should set the luggage on
a conveyor. I encountered no single human being. The incident wasn’t important
but it left me feeling oddly that I was out of human care, that something in
our world had shifted.
That shift of course
has been going on for a long time. It’s been driven by a succession of
technologies—the Internet, the cloud, big data, robotics, machine learning, and
now artificial intelligence—together powerful enough that economists agree we
are in the midst of a digital economic revolution. But there is less agreement
on how exactly the new technologies are changing the economy and whether the
changes are deep. Robert Gordon of Northwestern University tells us the
computer revolution “reached its climax in the dot-com era of the 1990s.”
Future progress in technology, he says, will be slower.
So in what way exactly
are the new technologies changing the economy? Is the revolution they are
causing indeed slowing—or is it persistent and deep? And if so how will it
change the character of the economy?
I argued a few years
back that the digital technologies have created a second economy, a
virtual and autonomous one, and this is certainly true. But I now believe the
main feature of this autonomous economy is not merely that it deepens the
physical one. It’s that it is steadily providing an external intelligence in
business—one not housed internally in human workers but externally in the
virtual economy’s algorithms and machines. Business and engineering and
financial processes can now draw on huge “libraries” of intelligent functions
and these greatly boost their activities—and bit by bit render human activities
obsolete.
I will argue this is
causing the economy to enter a new and different era. The economy has arrived
at a point where it produces enough in principle for everyone, but where the
means of access to these services and products, jobs, is steadily tightening.
So this new period we are entering is not so much about production anymore—how
much is produced; it is about distribution—how people get a share in what is
produced. Everything from trade policies to government projects to commercial
regulations will in the future be evaluated by distribution. Politics will
change, free-market beliefs will change, social structures will change.
We are still at the
start of this shift, but it will be deep and will unfold indefinitely in the
future.
The third morphing
How did we get to where
we are now? About every 20 years or so the digital revolution morphs and brings
us something qualitatively different. Each morphing issues from a set of
particular new technologies, and each causes characteristic changes in the
economy.
The first morphing, in
the 1970s and ’80s, brought us integrated circuits—tiny processors and memory
on microchips that miniaturized and greatly speeded calculation. Engineers
could use computer-aided design programs, managers could track inventories in
real time, and geologists could discern strata and calculate the chance of oil.
The economy for the first time had serious computational assistance. Modern
fast personal computation had arrived.
The second morphing, in
the 1990s and 2000s, brought us the connection of digital processes. Computers
got linked together into local and global networks via telephonic or
fiber-optic or satellite transmission. The Internet became a commercial entity,
web services emerged, and the cloud provided shared computing resources.
Everything suddenly was in conversation with everything else.
It’s here that the
virtual economy of interconnected machines, software, and processes emerges,
where physical actions now could be executed digitally. And it’s also here that
the age-old importance of geographical locality fades. An architecture firm in
Seattle could concern itself with the overall design of a new high-rise and
have less expensive workers in Budapest take care of the detailing, in an
interactive way. Retailers in the United States could monitor manufacturers in
China and track suppliers in real time. Offshoring took off, production
concentrated where it was cheapest—Mexico, Ireland, China—and previously
thriving home local economies began to wither. Modern globalization had arrived
and it was very much the result of connecting computers.
The third morphing—the
one we are in now—began roughly in the 2010s, and it has brought us something
that at first looks insignificant: cheap and ubiquitous sensors. We have radar
and lidar sensors, gyroscopic sensors, magnetic sensors, blood-chemistry
sensors, pressure, temperature, flow, and moisture sensors, by the dozens and
hundreds all meshed together into wireless networks to inform us of the
presence of objects or chemicals, or of a system’s current status or position,
or changes in its external conditions.
These sensors brought
us data—oceans of data—and all that data invited us to make sense of it. If we
could collect images of humans, we could use these to recognize their faces. If
we could “see” objects such as roads and pedestrians, we could use this to automatically
drive cars.
As a result, in the
last ten years or more, what became prominent was the development of methods,
intelligent algorithms, for recognizing things and doing something with the
result. And so we got computer vision, the ability for machines to recognize
objects; and we got natural-language processing, the ability to talk to a
computer as we would to another human being. We got digital language
translation, face recognition, voice recognition, inductive inference, and
digital assistants.
What came as a surprise
was that these intelligent algorithms were not designed from symbolic logic,
with rules and grammar and getting all the exceptions correct. Instead they
were put together by using masses of data to form associations: This
complicated pixel pattern means “cat,” that one means “face”—Jennifer Aniston’s
face. This set of Jeopardy! quiz words points to “Julius
Caesar,” that one points to “Andrew Jackson.” This silent sequence of moving
lips means these particular spoken words. Intelligent algorithms are not genius
deductions, they are associations made possible by clever statistical methods
using masses of data.
Of course the clever
statistical techniques took huge amounts of engineering and several years to
get right. They were domain specific, an algorithm that could lip read could
not recognize faces. And they worked in business too: this customer profile
means “issue a $1.2 million mortgage”; that one means “don’t act.”
Computers, and this was
the second surprise, could suddenly do what we thought only humans could
do—association.
The coming of external intelligence
It would be easy to see
associative intelligence as just another improvement in digital technology, and
some economists do. But I believe it’s more than that. “Intelligence” in this
context doesn’t mean conscious thought or deductive reasoning or
“understanding.” It means the ability to make appropriate associations, or in
an action domain to sense a situation and act appropriately. This fits with
biological basics, where intelligence is about recognizing and sensing and
using this to act appropriately. A jellyfish uses a network of chemical sensors
to detect edible material drifting near it, and these trigger a network of
motor neurons to cause the jellyfish to close automatically around the material
for digestion.
Thus when intelligent
algorithms help a fighter jet avoid a midair collision, they are sensing the
situation, computing possible responses, selecting one, and taking appropriate
avoidance action.
There doesn’t need to be
a controller at the center of such intelligence; appropriate action can emerge
as the property of the whole system. Driverless traffic when it arrives will
have autonomous cars traveling on special lanes, in conversation with each
other, with special road markers, and with signaling lights. These in turn will
be in conversation with approaching traffic and with the needs of other parts
of the traffic system. Intelligence here—appropriate collective action—emerges
from the ongoing conversation of all these items. This sort of intelligence is
self-organizing, conversational, ever-adjusting, and dynamic. It is also
largely autonomous. These conversations and their outcomes will take place with
little or no human awareness or intervention.
The interesting thing
here isn’t the form intelligence takes. It’s that intelligence is no longer
housed internally in the brains of human workers but has moved outward into the
virtual economy, into the conversation among intelligent algorithms. It has
become external. The physical economy demands or queries; the virtual economy
checks and converses and computes externally and then reports back to the
physical economy—which then responds appropriately. The virtual economy is not
just an Internet of Things, it is a source of intelligent action—intelligence
external to human workers.
This shift from
internal to external intelligence is important. When the printing revolution
arrived in the 15th and 16th centuries it took information housed internally in
manuscripts in monasteries and made it available publicly. Information suddenly
became external: it ceased to be the property of the church and now could be
accessed, pondered, shared, and built upon by lay readers, singly or in unison.
The result was an explosion of knowledge, of past texts, theological ideas, and
astronomical theories. Scholars agree these greatly accelerated the
Renaissance, the Reformation, and the coming of science. Printing, argues
commentator Douglas Robertson, created our modern world.
Now we have a second
shift from internal to external, that of intelligence, and because intelligence
is not just information but something more powerful—the use of
information—there’s no reason to think this shift will be less powerful than
the first one. We don’t yet know its consequences, but there is no upper limit
to intelligence and thus to the new structures it will bring in the future.
How this changes business
To come back to our
current time, how is this externalization of human thinking and judgment
changing business? And what new opportunities is it bringing?
Some companies can
apply the new intelligence capabilities like face recognition or voice
verification to automate current products, services, and value chains. And
there is plenty of that.
More radical change comes
when companies stitch together pieces of external intelligence and create new
business models with them. Recently I visited a fintech (financial technology)
company in China, which had developed a phone app for borrowing money on the
fly while shopping. The app senses your voice and passes it to online
algorithms for identity recognition; other algorithms fan out and query your
bank accounts, credit history, and social-media profile; further intelligent
algorithms weigh all these and a suitable credit offer appears on your phone.
All within seconds. This isn’t quite the adoption of external intelligence; it
is the combining of sense-making algorithms, data-lookup algorithms, and
natural-language algorithms to fulfill a task once done by humans.
In doing this,
businesses can reach into and use a “library” or toolbox of already-created
virtual structures as Lego pieces to build new organizational models. One such
structure is the blockchain, a digital system for executing and recording
financial transactions; another is Bitcoin, a shared digital international
currency for trading. These are not software or automated functions or smart
machinery. Think of them as externally available building blocks constructed
from the basic elements of intelligent algorithms and data.
The result, whether in
retail banking, transport, healthcare, or the military, is that industries
aren’t just becoming automated with machines replacing humans. They are using
the new intelligent building blocks to re-architect the way they do things. In
doing so, they will cease to exist in their current form.
Businesses can use the new opportunities in other ways. Some large tech companies can
directly create externally intelligent systems such as autonomous air-traffic
control or advanced medical diagnostics. Others can build proprietary databases
and extract intelligent behavior from them. But the advantages of being large
or early in the market are limited. The components of external intelligence
can’t easily be owned, they tend to slide into the public domain. And data
can’t easily be owned either, it can be garnered from nonproprietary sources.
So we will see both
large tech companies and shared, free, autonomous resources in the future. And
if past technology revolutions are indicative, we will see entirely new
industries spring up we hadn’t even thought of.
Reaching the ‘Keynes point’
Of course there’s a
much-discussed downside to all this. The autonomous economy is steadily
digesting the physical economy and the jobs it provides. It’s now a commonplace
that we no longer have travel agents or typists or paralegals in anything like
the numbers before; even high-end skilled jobs such as radiologists are being
replaced by algorithms that can often do the job better.
Economists don’t
disagree about jobs vanishing, they argue over whether these will be replaced
by new jobs. Economic history tells us they will. The automobile may have wiped
out blacksmiths, but it created new jobs in car manufacturing and highway
construction. Freed labor resources, history tells us, always find a
replacement outlet and the digital economy will not be different.
I am not convinced.
Erik Brynjolfsson and
Andrew McAfee of the Massachusetts Institute of Technology point out that when
automotive transport arrived, a whole group of workers—horses—were displaced,
never to be employed again. They lost their jobs and vanished from the economy.
I would add another
historical precedent. Offshoring in the last few decades has eaten up physical
jobs and whole industries, jobs that were not replaced. The current transfer of
jobs from the physical to the virtual economy is a different sort of
offshoring, not to a foreign country but to a virtual one. If we follow recent
history we can’t assume these jobs will be replaced either.
In actual fact, many
displaced people become unemployed; others are forced into low-paying or
part-time jobs, or into work in the gig economy. Technological unemployment has
many forms.
The term “technological
unemployment” is from John Maynard Keynes’s 1930 lecture, “Economic
possibilities for our grandchildren,” where he predicted that in the future,
around 2030, the production problem would be solved and there would be enough
for everyone, but machines (robots, he thought) would cause “technological
unemployment.” There would be plenty to go around, but the means of getting a
share in it, jobs, might be scarce.
We are not quite at
2030, but I believe we have reached the “Keynes point,” where indeed enough is
produced by the economy, both physical and virtual, for all of us. (If total US
household income of $8.495 trillion were shared by America’s 116 million
households, each would earn $73,000, enough for a decent middle-class life.)
And we have reached a point where technological unemployment is becoming a
reality.
The problem in this new
phase we’ve entered is not quite jobs, it is access to what’s produced. Jobs
have been the main means of access for only 200 or 300 years. Before that, farm
labor, small craft workshops, voluntary piecework, or inherited wealth provided
access. Now access needs to change again.
However this happens,
we have entered a different phase for the economy, a new era where production
matters less and what matters more is access to that
production: distribution, in other words—who gets what and how they get it.
We have entered the
distributive era.
The realities of the distributive era
A new era brings new
rules and realities, so what will be the economic and social realities of this
new era where distribution is paramount?
1. The criteria for
assessing policies will change. The old production-based economy prized anything
that helped economic growth. In the distributive economy, where jobs or access
to goods are the overwhelming criteria, economic growth looks desirable as long
as it creates jobs. Already, unpopular activities such as fracking are
justified on this criterion.
The criteria for
measuring the economy will also change. GDP and productivity apply best to the
physical economy and do not count virtual advances properly.
2. Free-market
philosophy will be more difficult to support in the new atmosphere. It is based on the popular
notion that unregulated market behavior leads to economic growth. I’ve some
sympathy with this. Actual economic theory has two propositions. If a
market—the airline market, say—is made free and operates according to a host of
small-print economic conditions, it will operate so that no resources are
wasted. That’s efficiency. Second, there will be winners and losers, so if we
want to make everyone better off, the winners (big-hub
airlines, in this case) need to compensate the losers: small airlines and
people who live in remote places. That’s distribution, and overall everyone is
better off.
In practice, whether
with international trade agreements or deregulation or freeing up markets, the
efficiency part holds at best sort of; often unregulated behavior leads to
concentration as companies that get ahead lock in their advantage. And in
practice, in the United States and Britain, those who lose have rarely been
compensated. In earlier times they could find different jobs, but now that has
become problematic. In the distributive era free-market efficiency will no
longer be justifiable if it creates whole classes of people who lose.
3. The new era will not
be an economic one but a political one. We’ve seen the harsh beginnings of this in the
United States and Europe. Workers who have steadily lost access to the economy
as digital processes replace them have a sense of things falling apart, and a
quiet anger about immigration, inequality, and arrogant elites.
I’d like to think the
political upheaval is temporary, but there’s a fundamental reason it’s not.
Production, the pursuit of more goods, is an economic and engineering problem;
distribution, ensuring that people have access to what’s produced, is a
political problem. So until we’ve resolved access we’re in for a lengthy period
of experimentation, with revamped political ideas and populist parties
promising better access to the economy.
This doesn’t mean that
old-fashioned socialism will swing into fashion. When things settle I’d expect
new political parties that offer some version of a Scandinavian solution:
capitalist-guided production and government-guided attention to who gets what.
Europe will find this path easier because a loose socialism is part of its
tradition. The United States will find it more difficult; it has never prized
distribution over efficiency.
Whether we manage a
reasonable path forward in this new distributive era depends on how access to
the economy’s output will be provided. One advantage is that virtual services
are essentially free. Email costs next to nothing. What we will need is access
to the remaining physical goods and personal services that aren’t digitized.
For this we will still
have jobs, especially those like kindergarten teaching or social work that
require human empathy. But jobs will be fewer, and work weeks shorter, and many
jobs will be shared. We will almost certainly have a basic income. And we will
see a great increase in paid voluntary activities like looking after the
elderly or mentoring young people.
We will also need to
settle a number of social questions: How will we find meaning in a society
where jobs, a huge source of meaning, are scarce? How will we deal with privacy
in a society where authorities and corporations can mine into our lives and
finances, recognize our faces wherever we go, or track our political beliefs?
And do we really want external intelligence “helping” us at every turn:
learning how we think, adjusting to our actions, chauffeuring our cars,
correcting us, and maybe even “nurturing” us? This ought to be fine, but it’s
like having an army of autonomous Jeeveses who altogether know too much about
us, who can anticipate our needs in advance and fulfill them, and whom we
become dependent upon.
All these challenges
will require adjustments. But we can take consolation that we have been in such
a place before. In 1850s Britain, the industrial revolution brought massive
increases in production, but these were accompanied by unspeakable social
conditions, rightly called Dickensian. Children were working 12-hour shifts,
people were huddled into tenements, tuberculosis was rife, and labor laws were
scarce. In due time safety laws were passed, children and workers were protected,
proper housing was put up, sanitation became available, and a middle class
emerged. We did adjust, though it took 30 to 50 years—or arguably a century or
more. The changes didn’t issue directly from the governments of the time, they
came from people, from the ideas of social reformers, doctors and nurses,
lawyers and suffragists, and indignant politicians. Our new era won’t be
different in this. The needed adjustments will be large and will take decades.
But we will make them, we always do.
By W. Brian Arthur McKinsey
Quarterly October 2017
https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/where-is-technology-taking-the-economy?cid=other-eml-alt-mkq-mck-oth-1710&hlkid=1899966e42b542c3883237c0926ca703&hctky=1627601&hdpid=8f2c9ddf-cb14-445c-a905-35bb42206f36
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