Remarks by Tiff Macklem
Governor of the Bank of Canada
National Bureau of Economic Research,
Economics of Artificial Intelligence Conference
September 20, 2024
Toronto, Ontario
Artificial intelligence, the economy and
central banking
Introduction
Around the world, digitalization is pulling economies in new directions. Artificial
intelligence (AI)-and particularly its offspring, generative AI (GenAI)-are accelerating
this pull. It's still early days, but AI is already disrupting existing industries and creating
new ones.
How AI will affect the global economy-and indeed humanity-is on all our minds. It's
easy to find enthusiasts who say AI will take us to a land of plenty. But it's also easy to
find doomsayers who say we are headed for a bleak world with a few big winners and
many more have-nots.
Be wary of anyone who claims to know where AI will take us. There is too much
uncertainty to be confident. We don't know how quickly AI will continue to advance. And
we don't know the timing and extent of its economic and social impacts.
But that doesn't mean we can't gain insights into what could happen. The past is still a
useful starting point. The laws of economics still matter. People will still respond to
incentives-and that includes prices. As central bankers, we care a lot about prices. It's
in our mandates to keep price inflation low and stable.
So what does the latest wave of AI, and more broadly, ongoing digitalization, mean for
what we do at the Bank of Canada? There are two broad elements. First, we need to
better understand how AI will affect workers, consumers, the economy and inflation. And
second, we need to use AI ourselves to best deliver on our mandate for Canadians.
In my remarks today, I will focus mostly on how AI could impact the economy through
productivity, employment and price-setting behaviour. Then I will discuss some of the
implications for monetary policy. Finally, I will say a few words about how we are using
AI at the Bank of Canada.
Productivity
How we apply our monetary policy tools depends on our assessment of the broad price
pressures in the economy and where we think inflation is headed. A key input is our
estimate of the economy's maximum non-inflationary growth rate. We call this potential.
Potential growth depends on the supply of labour and the productivity of that labour. AI
is expected to mainly impact productivity. When labour productivity is rising, the
economy can grow more quickly without causing inflation.
So the questions we need to tackle seem simple enough: How and when will AI affect
labour productivity?
AI has all the hallmarks of a general-purpose technology, or GPT-meaning a
technology that can have significant and wide-ranging effects on an entire economy. But
how large and how wide-ranging are hard to predict. And forecasting the timing is no
easier.
We know from history that it takes years for a GPT to diffuse through the economy. We
also know that the first applications are typically less transformative than the new
businesses and new business models that eventually emerge. This all suggests that we
won't see the full effects of this wave of AI anytime soon.
Nevertheless, we are seeing encouraging early results from firm-level studies on AI
adoption. In one study, German companies that used AI were found to be more
productive.1 In another, consultants at a global firm were, on average, able to produce
better work and do that work faster when they used AI.2 A third study found AI helped
make call centre employees more efficient by sharing best practices.3 And some of the
biggest gains can be seen in coding-where GenAI has halved the time needed to
complete some coding tasks.4
Still, these studies can't tell us how AI will affect the broader economy. To understand
that we need to consider the productivity effect of AI across all major sectors.
One estimate is that AI could automate 25% of all work tasks in the United States and
1
D. Czarnitzki, G. P. Fernández and C. Rammer, "Artificial intelligence and firm-level productivity," Journal
of Economic Behavior & Organization no.211 (2023): 188-205.
2
F. Dell'Acqua, E. McFowland III, E. Mollick, H. Lifshitz-Assaf, K. C. Kellogg, S. Rajendran, L. Krayer,
F. Candelon and K. R. Lakhani, "Navigating the Jagged Technological Frontier: Field Experimental
Evidence of the Effects of AI on Knowledge Worker Productivity and Quality," Harvard Business School
Working Paper No. 24-013 (September 2023).
3
E. Brynjolfsson, D. Li and L. R. Raymond, "Generative AI at work." National Bureau of Economic
Research Working Paper No. 31161 (April 2023).
4
S. Peng, E. Kalliamvakou, P. Cihon and M. Demirer, "The Impact of AI on Developer Productivity:
arXiv:2302.06590
Evidence from GitHub Copilot," (February 2023).
boost total factor productivity (TFP) by 9% over the next decade.5 A similar sustained
improvement in TFP in Canada would raise the average income per-person by roughly
$4,000 a year.
This productivity boost is not just from automating tasks. As workers in lower
productivity jobs are replaced by AI, they are freed up to fill other, more productive jobs
in the economy. And new products and services emerge. These latter two effects
provide most of the positive impact on productivity.6
However, more pessimistic estimates suggest that AI might have only a modest impact
on productivity.7 These assessments take the view that fewer tasks can be effectively
automated. AI could also create negative outcomes, such as amplifying internet
addiction and enabling malicious actors. These adverse effects could substantially
decrease the net positive impact of AI.
As we look at the effects of AI on the broader economy, we also need to ask if this
technology will be transformative enough to significantly boost productivity growth. Or
will AI simply be the latest innovation in a chain of innovations-like the invention of the
sewing machine or the evolution of telecommunications-to keep productivity rising at
its historical pace. This matters a lot because productivity growth plays a key role in
determining how fast the economy can expand without sparking inflation.
So what is actually happening? The infrastructure needed to make AI broadly available
is being built quickly. AI platforms are sprouting up, and it is getting easier for
businesses-large and small-to access the technology. But we are still looking for the
new products and services-and the new business models-that will transform
efficiency and productivity.
Prices and inflation
AI adoption-and its full effect on productivity-will play out over many years. In the
long run, we can expect AI to boost productivity. Higher productivity allows for higher
wages and more spending without pushing up inflation.
But what about in the short run? Already, strong investment in AI technologies is
boosting demand in the economy. The run-up in equity prices is supporting
consumption, as is the hiring boom for workers with AI and related skills. Electricity
demand is also surging as new data centres are built to accommodate the enormous
computing requirements of AI.
This all suggests that, in the short run, AI could boost demand more than it adds to
supply through faster productivity growth. And if that happens, AI adoption may add to
inflationary pressures in the near term.
Labour markets
One of the biggest questions is whether AI will be something that helps people do their
jobs better or something that simply replaces jobs? There is a lot of talk, and some good
research, on this issue.
Studies suggest that a significant share of all job-related tasks could be done by AI in
the next few decades. Over the very long run, machines could be doing a lot of the
tasks that people do now.
Does this mean that we are doomed to a surge in long-term unemployment?
Economic history offers us many lessons on the effects of technological change. Over
the past 200 years the displacement effects, or the jobs lost due to changes in
technology, have been outweighed by the countervailing effects, or the increases in
labour demand arising from these innovations. This includes jobs that are created
because of increased capital investment, the advent of new goods and services, and
greater demand for tasks that can't be automated.
To put it another way, from the mechanization of agriculture to the emergence of the
assembly line to the introduction of computing and the internet, technological change
has ultimately been a net positive for overall employment.
But some argue that this time is different. In an AI-dominated world, the displacement
effects could be bigger and the countervailing effects more muted. AI could shrink the
number of non-automated tasks so much that there isn't enough work left for the
displaced workers. And if many of the new goods and services are created by AI itself,
the labour market may not benefit from increased demand.
In addition, in past change cycles the technology was diffused over a prolonged period,
so the labour force had time to adjust. But this time adoption may happen much faster,
creating more disruption and a loss of livelihoods that will be difficult to replace.
So far, we don't have much evidence that labour is being displaced by AI at rates that
would lead to declines in total employment. If anything, digitalization-and the
commercialization of AI-have likely been net job creators in Canada.
Employment in Computer systems design and related services, which is a proxy for
digitalization, has risen 48% since the end of 2019, compared with a 6% increase in
employment for the rest of the economy. This recent growth builds on an existing
trend-while overall employment is up 17% over the past decade, employment in the
digitalization-related segment has more than doubled.
But we know there are probably more profound effects to come. As AI becomes more
established in the economy and its impacts more transformative, it could end up
destroying more jobs than it creates. And the people who lose their work to automation
may struggle to find new opportunities. This is a concern for us all. Understanding and
shaping the labour market impacts will be increasingly important as AI continues to
advance and diffuse through our economies.
Price-setting behaviour
In addition to productivity and the labour market, AI may also affect how businesses set
prices. There is already evidence that digitally intensive firms change prices more
frequently than less digitally intensive firms.8
For us central bankers, this means the Phillips curve might be steeper than previously
thought.9 When combined with a more shock-prone world, this suggests inflation could
be more volatile than it was in the 25 years before the pandemic.
AI could also affect the level of competition in the economy, although its impact is
ambiguous. Initially, AI-intensive start-ups could seize market share by undercutting
incumbents. This would increase competition and lower prices. However, AI could also
result in markets dominated by a handful of companies with monopoly power. In this
scenario, AI would ultimately lead to less competition and higher prices.
The monopoly effect is easy to imagine, with several superstar firms already dominating
their sectors. Fortunately, we have competition authorities to deal with undue market
power. But authorities will need to keep pace.
All this means central banks need to be closely attuned to how AI is affecting inflation,
both indirectly through overall demand and supply and directly through price-setting
behaviour.
AI and central banks
Navigating uncertainty
We know that AI could have profound effects on the economy. But we also know that
the timing, magnitude and even direction of these effects is uncertain. So how do
monetary policy-makers manage this uncertainty?
When you enter a dark room, you don't go charging in. You cautiously feel your way
around. And you try to find the light switch. That is what we are doing. What we central
bankers need is more light. This means better information, along with research and
analysis on how technology is diffused. Its impact on businesses and on workers. And
its effect on the overall economy and inflation.
To this end, I encourage academics to work with businesses and policy-makers to better
understand and predict the impacts of AI. And I applaud the National Bureau of
Economic Research and my former colleagues at the University of Toronto for this
conference series, which is putting a spotlight on the economics of AI.
We can also use scenarios to better understand the potential effects of AI and manage
risks so that we can get more of the good with less of the bad. For example, in a recent
speech, Gita Gopinath, Deputy Managing Director of the International Monetary Fund,
explored how the adoption of AI could worsen the next recession for workers.10 She
noted that in previous downturns, most automation-related job losses happened in the
first year of the recession and those jobs never came back. Widespread adoption of AI
could make this problem worse because more jobs could be ripe for automation.
AI adoption could also lead to financial stability issues. Banks and financial institutions
are investing in AI to improve customer service, to enhance compliance and risk
management, and to better assess credit and liquidity risk. In principle, these
investments should improve efficiency and stability.
But there are pitfalls. Operational risks could become concentrated in a few third-party
service providers and an event at one of them could quickly spread through the financial
system. The predictive ability of AI can deteriorate unexpectedly, suffer from
hallucinations or be biased and discriminatory. And AI makes everything move faster,
which could amplify severe market runs and herding behaviour in times of market
volatility.11
Scenarios help us ask tough questions about how monetary and financial policies
should respond to the risks we are facing. They also help us be proactive in protecting
our economies and financial systems.
Using AI at central banks
Finally, let me say a few words about the use of AI at central banks. AI is not just for
start-ups and tech giants. Many central banks are already using AI as they strive to
deliver on their mandates. At the Bank of Canada, we use AI to:
• forecast inflation, economic activity and demand for bank notes
• track sentiment in key sectors of the economy
clean and verify regulatory data
•
• improve efficiency and de-risk our operations
And we've only just begun to explore this technology. With very large and highly
disaggregated data sets now available, there is huge potential to use AI to understand
how consumers and businesses are behaving and how companies are setting their
prices.
But to take full advantage of the potential of AI, we will need to invest in data and
computing power. We also need to ensure staff have the skills to make the most of that
investment. And we will need to leverage outside expertise. This is why we're so excited
to be part of the new Toronto Innovation Centre run by the Bank for International
Settlements. The Innovation Centre is designed to encourage collaboration among
academics, the private sector and central banks to explore the latest technologies.
As we deploy AI, we also need to consider the ethical implications. It is not always easy
to strike a balance between moving forward and fostering innovation and taking the time
and care to be responsible and secure. At the Bank of Canada, we have principles that
guide our use of AI. For example, we need to be transparent when we've used AI for a
task, and we need to have safeguards in place to ensure we are appropriately skeptical
when we use it to generate content or analysis.
Conclusion
It's time for me to wrap up.
The recent rapid advances in AI, and GenAI in particular, have the potential to transform
economies around the world. However, a lot of uncertainty remains.
We all need more light. We need academics and businesses to work together to
examine how technology is diffused through the economy. And we need to better
understand how AI will affect productivity, employment, price-setting behaviour and
inflation. This work will take time. In the interim, we should use increasingly informed
scenarios to help manage our uncertainty.
We are still in the early days of what could be a transformative technological shift. We
don't know yet what will come, but we can shape the future path. For central banks, that
means maintaining price and financial stability in the face of disruptive technological
change. And it means leveraging AI to do our jobs better.
Thank you.