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AI can eventually help businesses produce more with the same resources, a change that could ease price pressure. But the investment boom needed to build and adopt AI can arrive first. That timing mismatch was the point of Bank of Canada Governor Tiff Macklem’s September 20, 2024 warning: AI could add to inflationary pressure if demand grew faster than the economy’s productive capacity.
It was a conditional risk, not a prediction that AI would permanently make everything more expensive. Bank of Canada analysis in 2026 continues to describe both possibilities: productivity gains that may lower costs over time, and a transition whose effects depend on how AI is adopted and how quickly its benefits spread.
What did Macklem say about AI and inflation?
Speaking at the National Bureau of Economic Research Economics of Artificial Intelligence Conference in Toronto on September 20, 2024, Macklem said AI could boost demand more than it adds to supply in the short run. If that happened, the imbalance could put upward pressure on prices. He was discussing an economic risk, not announcing a Bank of Canada interest-rate decision or forecasting that AI would inevitably drive inflation.
The distinction is between how much people and firms want to spend and how much the economy can produce. Investment and spending can rise quickly; productivity gains that expand output may take longer to reach businesses across the economy. Macklem’s remarks are available in the Bank of Canada’s speech summary and the full speech.
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How could AI add to price pressure?
AI is not only software. It requires computing infrastructure, electricity, equipment and workers. A rapid build-out can draw on resources that are limited in the near term, even if the technology eventually helps firms use resources more efficiently.
- Investment in infrastructure: Firms may spend on servers, chips, data centers, networks and AI services before those investments generate measurable productivity gains. That spending adds to demand for equipment and construction.
- Power and infrastructure demand: Data centers and computing workloads require electricity. Where power supply or grid capacity is constrained, added demand could put pressure on electricity and related infrastructure costs. That does not automatically mean higher prices for consumers nationwide.
- Competition for skilled workers: AI companies and businesses adopting the technology may compete for specialists, adding wage pressure in some occupations even as AI changes demand for other kinds of work.
- Investment and wealth effects: Strong valuations for AI-related businesses can encourage further investment. If rising asset values increase some households’ spending, that could add another demand channel.
- Faster price-setting: Digitally intensive businesses may be able to adjust prices more quickly using data and algorithmic tools. Faster changes do not necessarily mean prices rise; they can make prices more responsive to changing demand and costs.
Macklem’s speech and a contemporaneous Reuters account reproduced by Investing.com discussed the demand-versus-supply risk, including investment and electricity demand. They do not establish that any one channel has become a dominant cause of Canadian consumer-price inflation.
Why might AI reduce inflationary pressure later?
If AI helps workers and businesses produce more with the same inputs, it can raise productivity. Better forecasting, logistics, customer service and product development may also reduce operating costs. When the economy can produce more without running into the same constraints, its productive capacity—or potential output—increases.
Those gains could eventually support higher real wages and lower costs for goods or services. Whether consumers see lower prices depends in part on competition: a business may pass efficiency gains on to customers, or retain some as higher profits. The Bank of Canada’s May 2026 analysis of AI and productivity presents these as potential benefits, not guaranteed outcomes. Adoption may be uneven, and gains in a few industries do not necessarily translate into economy-wide productivity growth.
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The timing problem: investment can lead productivity
The economic tension is easiest to see as a sequence. Spending on the technology can be visible well before the resulting efficiency gains are broad enough to change the economy’s overall supply.
- Businesses commit to AI spending. They buy computing equipment, build or expand data centers, and hire workers to develop and deploy systems.
- Demand for inputs rises. Construction, electricity, equipment and specialized labor may become more sought-after, especially where supply is slow to adjust.
- AI use spreads unevenly. Some firms may quickly improve output or reduce costs; others may need time, new processes or complementary investment.
- Productivity may show up later. If adoption becomes widespread and effective, more output can be produced from available labor and capital.
- Prices and wages respond differently across sectors. Competition may pass efficiency gains to customers, while bottlenecks or strong demand can keep some costs elevated.
This is not a guaranteed path. AI could generate rapid productivity improvements, spread slowly, remain concentrated in a few sectors, or change work without producing a near-term increase in measured economy-wide productivity. The central question is whether AI expands supply before its investment and demand effects strain available resources.
What “inflationary pressure” does—and does not—mean
Inflation is the rate at which the overall price level rises over time. A higher price for a particular service, or a one-time jump in a price level, is not by itself the same as a persistent increase in the inflation rate. A temporary burst of demand or a sector-specific bottleneck can add upward pressure without starting a lasting inflation process.
Persistence depends on what happens next: whether demand continues to outstrip supply, whether wages and price-setting adjust, how inflation expectations evolve and how monetary policy responds. Macklem also raised the prospect that AI could contribute to greater inflation volatility in an economy already facing large shocks. He did not argue that AI alone would determine Canada’s future inflation path.
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How AI could affect workers, wages and spending
AI can augment workers by helping them perform tasks more efficiently, or automate tasks that people previously did. It can also create new tasks, products and industries. These effects can occur at the same time, but their consequences for jobs and spending differ.
- When AI complements workers, businesses may produce more per worker and demand may rise for people whose skills work alongside the technology.
- When AI replaces tasks, demand for workers doing those tasks may fall. If affected workers lose income or take time to move into other roles, household spending could weaken.
- Even without a decline in total employment, workers can face occupational change, wage pressure or a mismatch between where jobs are and where people live.
In September 2024, Macklem said there was then no evidence of AI-driven displacement on a scale that would reduce total employment; that was a statement about what was known at that time, not a current finding. A July 2026 Bank of Canada staff working paper models how the distinction between augmentation and automation can affect the inflation-employment trade-off. In its model, automation creates a larger decline in labor demand and can make that trade-off more difficult than augmentation. A staff paper is analytical research, not a Bank policy decision or an official forecast.
Why the Bank of Canada is watching the transition
For monetary policy, the important task is to distinguish a demand increase from a lasting expansion in the economy’s capacity to produce. The Bank needs to assess how AI is affecting investment, productivity, labor demand, price-setting and the accuracy of economic indicators. It also needs to consider whether concentrated investment or infrastructure constraints create risks that are not visible in broad inflation figures.
Monetary policy can influence overall demand, but it cannot directly build power infrastructure, resolve skills mismatches or remove a shortage of construction capacity. A sector-specific bottleneck can therefore matter to prices without being something interest rates can fix directly.
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The Bank itself has been experimenting with AI for tasks including inflation forecasting, economic analysis, sentiment tracking, data verification and operational efficiency. In the 2024 account, the adoption process was described as early. These uses illustrate how AI may change economic analysis as well as the broader economy; they are not evidence that the Bank has adopted a specific AI-driven rate policy.
Could AI become a general-purpose technology?
A general-purpose technology spreads across many industries and enables further innovations, as electricity, computers and the internet did. If AI reaches that breadth, it could change production, work and costs throughout the economy. If adoption remains concentrated, its effects may be important for particular companies or sectors without delivering comparable economy-wide productivity gains.
The Bank of Canada’s 2026 productivity analysis says AI has characteristics of a possible general-purpose technology while leaving its eventual diffusion and spillovers uncertain. Breadth matters for inflation: broad productivity gains can expand supply, while concentrated investment can raise demand in selected markets without a similar economy-wide lift in productive capacity.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What has changed since the 2024 warning?
Later Bank of Canada material develops the same central tension rather than reversing it. In February 2026, Governor Macklem described AI as one of several forces contributing to structural change in Canada. In May, Bank analysis discussed possible long-run productivity and disinflationary gains. In July, a staff working paper examined how inflation and employment outcomes can differ with the type and breadth of adoption and the monetary-policy response.
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Those publications make the 2024 warning more specific: the result depends on what firms automate or augment, how broadly AI spreads, whether productivity gains reach the wider economy and how policy responds. They do not establish that AI has already lowered Canadian consumer prices or that it will cause a particular change in interest rates. See the Bank’s February 2026 discussion of structural change, May 2026 remarks on productivity in the age of AI and July 2026 staff paper.
What this could mean for consumers and businesses
The effects are likely to vary by sector and over time. Some businesses may use AI to lower costs or improve output; others may face higher costs for power, equipment, construction or specialized workers. Consumers could see lower prices where efficiency gains are passed through, but a local infrastructure constraint could affect particular costs without moving national inflation substantially.
For workers and business owners, the useful distinction is between spending on AI and productivity from AI. A large investment announcement shows that resources are being committed; it does not by itself show that output per worker has risen. The broader economic effect will become clearer as adoption spreads and its consequences show up in production, wages, costs and prices.
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