Macro Markets and Machines_The Economic and Market Transformation Driven by AI_GWM report - Flipbook - Page 42
Macro, Markets, and Machines
November 2025
Section 5: Regulating the Revolution – The Policy Response to AI
Rebekah Young | VP & Head of Inclusion and Resilience Economics, Scotiabank Economics
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Modernizing employment insurance, reskilling, and lifelong learning are essential to helping workers adapt to
AI-driven changes. Policies should focus on supporting transitions rather than protecting specific jobs, with
early warning systems for displacement risk.
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Boosting investment in both tangible and intangible assets, building reliable AI infrastructure, and supporting
human capital development will accelerate AI adoption and productivity. Policies should enable small and
medium-sized enterprises (SMEs) to adopt AI and encourage test-and-scale pathways for innovation.
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Fiscal policy must adapt to shifting value from labour to capital. Automatic stabilizers, modernized tax
administration, and a balanced tax mix are needed to maintain public revenues and fund necessary
complements, while avoiding excessive payroll levies and supporting work incentives.
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Governments should embed AI in their core mandates, invest in public-sector capability, and treat enabling
infrastructure as public goods. Building public trust requires safeguards for privacy, safety, and cybersecurity,
along with clear rules for data and IP rights.
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Promoting competition, having open access to essential AI inputs, and strengthening resilience in financial
markets are crucial. International coordination on standards, taxation, and risk management will help realize
gains and contain risks, ensuring benefits are widely shared.
As explained in previous sections, mapping broader macroeconomic implications for AI is a difficult task.
Wages, inflation, interest rates, and exchange rates may move in divergent directions across time and
geographies. Early investment phases of AI scale-up could be inflationary, while widespread productivity gains
may later exert disinflationary pressure. Wages could rise with augmentation or fall where displacement
outpaces reabsorption. Uneven sectoral productivity – especially between tradable and non-tradable sectors –
can distort relative wages, prices, and competitiveness. IMF analysis, for example, suggests a concentrated
impact in domestic sectors could dampen the traditional role of real exchange rate adjustment – reversing the
usual Balassa–Samuelson dynamic – and leaving laggards even further behind.
From a policy standpoint, the real test of AI’s economic impact is whether it enhances welfare. Households
can lose if participation or wages lag, even as GDP rises. In public services (health, education, and social benefits),
AI can lift wellbeing or, if poorly governed, entrench error, bias, and exclusion. Harmful uses – fraud,
disinformation, safety failures – also reduce welfare without showing up in prices. Net gains hinge on state
readiness and credible safeguards.
This is all unfolding in real time. Global economic activity is softening, slack is building in labour markets, and
investment remains weak against geopolitical risk. Inflation, interest rate, and investment dynamics cannot be
viewed in a vacuum. Recent “hiring hesitation” may reflect any number of these forces on top of AI-induced
uncertainty around labour needs. Elsewhere, strong – but concentrated – AI-backed capex and equity market
momentum are masking a parallel, broad-based weakening in global economic activity.
Forecasting AI’s macro path is fraught and point estimates risk false precision. The future remains largely
unwritten. It is premature to declare hope, hype, or hallucination. While AI holds transformative potential,
outcomes will hinge on choices made now. Policy frameworks spanning education, labour markets, investment,
competition, and data governance can steer outcomes toward the desirable ones, or at least hedge against the
concerning ones.
This uncertainty sharpens the need to track emerging pressure points.
Scotia Wealth Management
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