Macro Markets and Machines_The Economic and Market Transformation Driven by AI_GWM report - Flipbook - Page 46
Macro, Markets, and Machines
November 2025
Safeguarding the revenue base is essential, but it must not come at the cost of innovation. Economic theory
supports lighter early taxation on high-risk, complementary investments, particularly where private capital is
scarce. In advanced economies, private investment plummeted by 25% in the years following the global
financial crisis, with limited recovery since, according to the IMF. This persistent weakness reflects subdued
demand, financial constraints, and ongoing uncertainty. Outside the United States, thin capital markets and
structurally low investment levels make the balance between fostering innovation and safeguarding revenues
especially delicate.
Another central tension lies in preserving fiscal capacity without exacerbating the labour tax wedge. In many
advanced economies, labour force participation is projected to stagnate or decline through 2030, driven by
aging populations. Heavier taxation on labour risks deepening disincentives to work, accelerating participation
declines and shifting activity toward less-taxed, capital-intensive domains. This would compound fiscal
pressures and underscores the need for a more balanced tax mix – one that shifts gradually toward locationtethered, rent-like returns, ideally under coordinated international rules, to sustain equity, efficiency, and
fiscal cohesion.
The fiscal upside of AI is real but conditional. A rising tide growth path, such as those illustrated in U.S.
Congressional Budget Office scenarios, could lift revenues with broad-based output gains, but that trajectory is
far from guaranteed. Aside from growth dividends, realizing fiscal gains also depends on policy alignment and
institutional safeguards. AI-enabled improvements in program integrity and tax collection require robust privacy
protections, auditability, and human-in-the-loop governance. Moreover, fiscal design must align who pays with
who benefits. With costs often local and rents mobile, coordination across levels of government will be essential
to ensure fairness and avoid base erosion.
Section 5.3: Services, Society, and Satisfaction
Welfare, not output alone, must be the benchmark for economic success. The economic test is whether AI
improves lived outcomes, not just aggregate output. Household purchasing power – through wages, hours, and
participation, not to mention cost of living – remains central, but so too are the quality, accessibility, and fairness
of public services. Harmful uses, such as fraud, disinformation, and unsafe systems, can also erode welfare even
as income grows.
AI will prove its public value in frontline services. Sectors like health, education, justice, and income support are
where citizens could experience AI benefits most tangibly and where trust will be earned or lost. Well-governed
deployment can reduce wait times, improve service targeting, and raise throughput per worker. Poorly governed
deployment risks entrenching bias, error, and exclusion. Critically, quality-adjusted gains, such as improved
health outcomes or more equitable access, often escape conventional productivity metrics, so governments
must assess user experience and equity alongside cost and speed.
Public institutions can also be a launchpad for breakthrough innovation. Advances in areas like precision
medicine, diagnostics, and educational technologies often emerge from publicly funded initiatives, underscoring
the dual role of government as both service provider and innovation catalyst. Indeed, the acceleration in medical
device approvals by the U.S. Food and Drug Administration reflects the rapid innovation in healthcare
technology and the growing importance of regulatory frameworks (Exhibit 40). Partnerships with academic
institutions are critical not only for preparing the future workforce, but also for driving foundational research and
bridging the gap between discovery and deployment.
Scotia Wealth Management
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