Good morning. And thank you for inviting me to give the keynote speech at this year’s spring policy conference. It’s great to see some familiar faces here.
Today I’m going to talk about a technology that is changing how we live and work: artificial intelligence (AI). I will consider how AI is affecting the economy today, as well as what could unfold in the future.
AI may seem like a relatively new trend, but the core technology has been percolating—and steadily improving—for about 75 years. What’s changed is that recent advances have made AI far more powerful and accessible. Based on its current and promised applications, AI represents a significant technological advance that has the potential to boost productivity and raise living standards.
As AI continues to improve and its adoption spreads, it could permanently change how the Canadian economy works. By lowering costs for businesses and improving efficiencies, AI could support higher wages, reduce prices for consumers and spur new investment.
There’s also the question of what AI will mean for jobs. When economists look back at past transformative innovations, they find that while the transition period may have been disruptive for workers, widespread adoption did not lead to net job losses. But some worry that this time will be different.
AI also has implications for financial markets and financial stability. Its rapid rise has sparked concerns about overinvestment and overvaluation in AI-focused equities. And AI may make sophisticated cyber attacks easier to carry out and therefore more likely to occur.
To put it simply, the Bank of Canada cares about AI because of its potential to significantly affect productivity, economic growth, employment and inflation. AI also has the potential to impact the financial system, creating both new efficiencies and new risks.
These developments shape our assessment of the economy, so they matter for our monetary policy decisions and the Bank’s work to foster a stable and efficient financial system.
Governor Tiff Macklem first talked about AI in a speech in September 2024. At that time, he noted both the enthusiasm for AI and the uncertainty around how it would unfold. A lot has happened in the 20 months since that speech.
In my remarks today, I’m going to give you an update on how AI adoption is progressing in Canada and the effects it is having—and could yet have—on jobs and productivity. But before I do this, I want to put the recent buzz around AI into context for you by comparing it with some past episodes of large-scale technological change.
Let’s dive in.
Understanding transformative technologies
AI is both a transformative innovation and a potential driver of structural change. To help put this into perspective, let me take a step back to explore the concept of technological change.
Technological change refers to the way new tools or methods reshape how work is done. This rewiring typically improves efficiency and productivity. It also often ushers in new products and services.
Technological change doesn’t happen overnight. It’s often a long process—starting with an idea, moving into research and development, and then into commercialization and adoption. Most of the time, these changes unfold gradually in the background. People don’t notice them because they tend to be incremental—small steps that add up over time—or change that is limited to one industry.
But occasionally, technological change accelerates and becomes broadly transformative. Technologies such as steam engines, electricity, the internal combustion engine, computing and the internet reshaped entire economies and societies. Economists call these transformative innovations general-purpose technologies, or GPTs.
Turbocharged technological change: General-purpose technologies
GPTs have clear characteristics that set them apart from other technologies.
One is that GPTs are built around a single technological core that can be traced over time. They start small with lots of room for improvement—but they end up being dramatically improved, widely used and applied in many ways across the whole economy.
For example, when computers emerged in the late 1930s, they were giant machines used for code-breaking and complex calculations. Computing has since spawned laptops, smartphones, streaming services and even wearables such as fitness watches that allow us to send text messages and monitor our sleep.
Another way GPTs differ from smaller-scale technologies is that they fundamentally change how businesses and institutions operate. As they advance, they often spur new laws and regulations.
And, importantly, they generate significant spillovers. These include investment in supporting infrastructure that fuels more research and development and broader adoption. This investment, in turn, spawns other complimentary innovations—some of which may end up being GPTs in their own right.
For example, computers began as a spillover of electricity, which was itself a spillover of the steam engine. Computers, in turn, have generated spillovers of their own across many industries and with many different uses. These include smaller ones, such as digital watches, to larger ones such as software ecosystems, the internet, robotics—and now AI.
Finally, GPTs are anything but quiet. Their effects on society are large enough to permeate popular culture and generate significant debate.
At the advent of computerization, there was a lot of public debate about what computers would mean for humanity—including fears that workers would be replaced by machines and hopes that machines would give humanity endless free time. Does that sound like anything you’ve heard lately?
So now we are at the crux of the matter. Is AI a GPT?
I think it’s fair to say that AI has many characteristics of a GPT—but not all of them, yet. This is to be expected, because while AI has been evolving for decades, we are still in the early days of adoption (Figure 1).
