The consequences of AI-based technologies for jobs

AuthorPeter Cappelli, University of Pennsylvania
Recent discussion f‌irst in the business press and
then in related public policy communities has
considered the notion that industrial countries
are on the verge of important changes that
stem from information technology (IT) broadly,
including notions of artif‌icial intelligence (AI),
and its implications for how work is performed.
The size and pervasiveness of these discussions
merits a serious look at the ideas behind them
and the fundamental question they ask: is
there something happening already or about
to happen in information technology that will
change in a fundamental way businesses and
organisations, jobs, and outcomes like pay and
unemployment? I consider these issues below.
1. The nature of the discussion
Before considering the arguments and
assertions about the implications of evolving
IT, it is worth thinking through the context in
which those stories take place. Followers of the
media are well aware that there is a bias toward
reporting stories that represent something new,
especially something new and dramatic. That
includes claims about developments that will
This contribution follows the recent public
debate on the changes across industrial
countries that stem from information
technology, including notions of artif‌icial
intelligence and its implications for how work
is performed. While acknowledging the size
and pervasiveness of these discussions, the
article discusses the core arguments related
to the impact of information technology on the
way businesses and organisations operate,
how these changes could translate to the
labour market, and other potential outcomes
such as lower wages or unemployment.
The argument begins with an introduction
to the two ways in which people tend
to anticipate future developments. This
either happens through estimates based
on prior experience (commonly known as
forecasting) or through a belief in a real
uncertainty of future developments and
reliance on other kinds of evidence besides
traditional forecasts. The article maps the
projected impact of technological uptake
on the labour markets and reviews the
empirical evidence. It touches upon many
of the above-discussed trends, such as
skill-biased technological change or routine-
biased technological change, and their
implications for skills demand. Applying an
historic perspective, the article argues that
predictions based on the past may be less
relevant in the current context. Although
new equipment and practices could
eliminate certain jobs, on balance they do
not necessarily destroy jobs because their
overall ef‌fects on improving productivity and
overall wealth create jobs elsewhere.
To understand why assumptions claiming
that the future is like the past are not correct
and extrapolations from prior experiences
are unlikely to be accurate predictors of the
future, read this chapter.
happen, even if there is little or no evidence of
them yet. We may notice these stories especially
when they relate to health, e.g. epidemiological
studies showing that some particular food group
is associated with either remarkably better or
worse life outcomes. It is extremely dif‌f‌icult to
run a story that says, for example, ‘still nothing
new in ef‌fective weight loss’. A f‌irst question
to ask is whether the apparent magnitude of
the stories of technological change ref‌lects
a change in the nature of the media and public
discourse rather than ref‌lecting something
about the merits of the arguments themselves.
There have been changes in the media that might
help create the impression that particular stories
are more important than would have been the
case in the past, such as the fact that there are
now many more outlets for stories, including
social media, where surprising or frightening
accounts are repeated and reinforced over and
over. There is also considerable expansion of
organisations focused on public policy, especially
those businesses which advocate ideas that
are important and support those that attract
attention. Hosting discussions, producing reports,
commenting on media stories are standard
practices for such organisations. Every major
consulting company now produces reports and
markets their views on policy-related stories,
including technology and workplace topics.
The fact that there is a great deal of discussion
about IT certainly suggests that it is a topic
worth investigating, although it is not prima
facie evidence that the arguments which
provoke that discussion are correct. The truth
is typically more boring than the speculations.
2. Anticipating the future
Assessing the merits of arguments about
the potential ef‌fects of IT in the workplace or
elsewhere should begin with thoughts about
epistemology: what is it that we know, and
how can we know it? Specif‌ically, how can
we distinguish reasonable belief from mere
opinion? What constitutes knowledge is always
a pertinent question, but it is especially important
in this context because of the unique nature of
the claims being made. They are claims about
the future rather than the present, although
they may well be informed by the present.
There are at least two quite dif‌ferent types of
claims about the future that are made in the
social sciences. The f‌irst concerns probabilities
and risk: we have very little idea about, for
example, whether my house will burn down but,
based on prior experience of houses like mine,
we can estimate with considerable accuracy
what the odds of that are.
Forecasts move us from predictions about
common events and about individual units in
a population to anticipating events that have
not happened before. They go a step further
than identifying average experiences in the
past to extrapolate from the past. To predict,
for instance, the unemployment rate in a year’s
time, they look back to previous unemployment
rates and to variables that determined them
or at least were associated with them. If
the model using those variables explained
a reasonable amount of the variation in
previous unemployment rates then we will
try to use it to extrapolate into the future.
We do so by assuming that the structure of
the model remains the same going forward
or, in practical terms, that the coef‌f‌icients of
regression-related models in the future will be
the same as they are in the model. Assuming
we have more recent values for the variables
in the model, we apply them to that model and

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