Karsten Wenzlaff, Advisor
August 26th, 2025
Karsten Wenzlaff is a Crowdfunding and Fintech Expert from Germany. He is Secretary-General of Digit[...]
Bank Of Canada Framework For Measuring The AI Economy
June 19, 2026 | NCFA Resource | Artificial Intelligence And Data
Why Rapid AI Growth May Be Missing From Traditional Economic Statistics
On June 18, 2026, the Bank of Canada published Measuring the AI Economy, a staff working paper by Anton Korinek and Patrick McKelvey. The paper examines whether traditional economic statistics can properly capture AI production, compute growth, model training, inference output, and the value being created inside the AI economy.
The paper argues that AI activity is difficult to see through standard GDP categories because it is spread across cloud computing, software, professional services, data centres, chips, electricity, and model development. That makes AI look smaller in official statistics than it may be in production capacity.
What It Does In Practice
The research builds a first macroeconomic estimate of US AI production from 2023 to 2025. It starts with compute as the core input, generated from AI chips, data centre capacity, and electricity. That compute is then split between inference and training.
Inference produces AI outputs used across the economy. Training creates model capital, which the authors treat as an intangible asset that improves future AI output.
The headline numbers are large. The authors estimate that nominal AI compute spending grew from $36.92B in 2023 to $90.46B in 2024 and $219.17B in 2025. That implies annual growth of about 145% in 2024 and 142.3% in 2025.
Physical compute output grew faster, rising about 211.9% in 2024 and 213.9% in 2025. After quality adjustments, the paper estimates AI production growth above 2,000% per year. Its early AI GDP framework estimates real AI GDP growth of about 2,600% in 2024 and 2,658% in 2025.
The authors are careful about the limits. These aren't official GDP statistics. The framework relies on strong assumptions, limited data, and uncertainty about how benchmark performance turns into economic value.
Who Gets Value
This resource is useful for fintech founders, AI companies, policy teams, investors, economists, regulators, data centre operators, infrastructure investors, and anyone tracking how AI affects productivity, capital allocation, labour markets, and public policy.
It is especially relevant for teams working on AI infrastructure, compute markets, AI governance, productivity measurement, model economics, and public sector digital strategy.
Strengths And Limits
The strength of the paper is its measurement lens. It doesn't treat AI as a single software category. It treats AI production as a system built from chips, power, data centres, inference, training, and model capital. That connects directly to the market question of pricing access to scarce AI capacity.
That's valuable for NCFA readers because compute is becoming an economic input, not just a technical resource. If compute markets, energy access, chip supply, and model efficiency determine AI output, then AI policy and AI competition cannot be separated from infrastructure.
The paper also gives policymakers a warning. If official statistics do not capture AI capacity early enough, fiscal planning, productivity analysis, tax policy, and monetary policy may be working with incomplete information. The same measurement issue shows up in central bank operations, where the Bank of Canada has already examined AI adoption in central banking.
The limit is uncertainty. The authors don't claim to replace GDP. They propose a measurement framework that can support future AI satellite accounts and better statistical infrastructure.
Key Resources
Bank Of Canada Measuring The AI Economy (primary Bank of Canada working paper)
AI Agents Enter Governed Financial Workflows (AI governance and operations)
AI Risk Taxonomy For Audits And Controls (AI risk classification)
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