We would like to highlight the valuable contributions of the following team members for putting this piece together: Will Oswald, Prabhnoor Singh Jolly, Ritika Mankar, and Mark Tan.

Major macro forces are complex and interconnected

Three major macro forces are shaping the investment environment: a changing world order, AI, and fiscal risks.

• Changing world order: Geopolitical tensions are elevated. Governments are prioritising national security and resilience, adopting a more mercantilist approach to trade. These dynamics are contributing to the emergence of a more activist state with greater appetite for fiscal spending. This, along with a focus on the ‘security of everything’, points to a more inflationary world.
• AI: AI is a transformative theme with the potential for tremendous value creation. Progress will not be linear because spending and adoption follow different trajectories and run into different constraints. Its impact on productivity may be significant, but the cone of outcomes is wide. That uncertainty spans the scale of the impact, how value is distributed, and who ultimately benefits.
• Fiscal risks: Fiscal sustainability is emerging as a defining macro constraint and a source of market volatility. Governments are under pressure to spend, not just today, but with ageing populations, increasingly in the future as well. Fiscal space varies a great deal from country to country, and financial repression is a real possibility. With fiscal strain evident globally and bond markets more closely linked across countries, the risk is more systemic. Investors may extrapolate outcomes across all fiscally vulnerable countries, which raises the odds of global contagion.

These forces reinforce one another (Exhibit 1). A changing world order raises defence and industrial spending needs, adding to fiscal strain. AI capex and security spending compete for the same capital, pushing rates higher. Tighter financing in turn feeds back into geopolitics, as capital becomes a strategic resource.

Key market focus areas within the three forces

Markets are already pricing in elements of all three forces.

A changing world order

Governments are spending on building resilience and the ‘security of everything’. The impact is higher growth, higher inflation, and higher rates, all of which are transmitted into markets through multiple conduits:

• Geopolitical conflicts and commodity market shocks: Conflicts in the Middle East and Ukraine have made energy prices volatile, as critical shipping corridors and gas pipelines are compromised.
• Rising demand for capital and debt market strain: Capital requirements to fund AI capex, together with government spending in highly leveraged economies and inflation shocks, are creating volatility in debt markets.
• Multipolarity and equity market shocks: The rise of multipolarity is likely to bring about higher equity market volatility.

The second point is where the three forces meet. In debt markets, security-linked spending, AI, and fiscal pressures all draw on the same savings pool.

AI

While AI’s economic contribution is coming into sharper focus, the speed at which the landscape is shifting makes it harder for investors to pinpoint durable value. Three dynamics stand out:

• Rapid model innovation: AI capabilities are advancing quickly. Models can now work through multi-step reasoning, retain context over extended interactions, use external tools independently, and coordinate complex workflows. Emerging standards such as the Model Context Protocol are also making it simpler for systems to connect with tools and with each other. Early breakthroughs are, however, an unreliable guide to long-term winners. Competition is fierce among researchers and across nations. Distinct ecosystems are taking shape around each country’s access to compute, advanced chips, and energy, often with government support for local champions. The central question for investors is which layers of the stack will keep pricing power as the frontier evolves.
• Heavy infrastructure spending: Surging compute needs have pushed hyperscaler capex to record highs, nearing 2% of US GDP1. Efficiency gains from better design and custom silicon will help. Still, supply chain and power bottlenecks are likely to persist, creating opportunities to finance critical infrastructure, but also making the path ahead less predictable.
• Uneven enterprise adoption: Hurdles in security, compliance, and data governance mean adoption will vary widely, separating leaders from laggards. New business models are emerging to close the gap. Investors must distinguish companies that can turn AI adoption into a compounding advantage from those exposed to disruption.

Fiscal risks

Rates face upward pressure owing to growing deficits and AI-driven investment competing with governments for capital. US yields are backed by strong growth while parts of the global economy face higher rates in less resilient environments, so separating good drivers from bad ones is key to asset allocation.

• Political challenges to addressing fiscal risks: Today’s fiscal challenges stem largely from periods of fiscal expansion not being followed by compensatory tightening phases. Going forward, higher rates and ageing could compound this problem for developed economies.
• Competition for capital: Rising government deficits are colliding with a surge in private AI capex, intensifying competition for finite capital. With hyperscalers’ net debt issuance over the past year exceeding that of any single European country2, the magnitude of the problem is no longer trivial.
• Structural increase in cross-market correlations: As pension and insurance savings pools shrink, price-sensitive buyers are filling the gap, pushing up cross-market correlations.
• Distinguishing between good and bad drivers of rates: The balance between good drivers, like stronger growth, and bad ones, like fiscal sustainability concerns, will largely determine winners and losers in an asset class where micro-markets are increasingly correlated.

Assessing the key debates

The post-Cold War order was built on efficiency. It is now giving way to multipolarity, where security matters more than efficiency and resilience matters more than costs, leading to structurally higher inflation. With AI, the challenge is identifying lasting value in a fast-moving landscape. In terms of fiscal risks, countries face debt stabilisation and rollover challenges. Moreover, fiscal space depends on more than just the headline debt ratio.

Changing world order: Unlikely to be ephemeral

Uncertainty is what markets see now, but the forces behind it are structural rather than temporary. Three primary drivers are at work:

• Gradual shift to multipolarity: The world will keep moving towards a multipolar reality. The pace of this change, however, will be incremental.
• Security over efficiency: As nations prioritise the ‘security of everything’, resilience and self-sufficiency will take precedence over supply chain efficiency.
• Defence becomes a priority: As conflicts grow more frequent, countries around the world will be compelled to spend more on defence.

The implication for macro is direct. Resilience costs more than efficiency, translating into higher structural inflation, more public spending, and a larger call on savings.

AI: Understanding the landscape and identifying long-term winners

GIC uses two frameworks to navigate the AI landscape. One shows where to look across the AI value chain, and the other sets out what to look for in individual companies.

Where to look

The first framework divides the investment universe into three categories:

• Enablers: Firms that build the core infrastructure behind AI, from computing hardware to high-speed networks and energy systems.
• Monetisers: Firms that create AI-powered products and services.
• Adopters: Firms that build AI into their existing operations to improve processes, raise productivity, and drive growth.

We apply this framework to both public and private markets, since each category has leaders in both. Among enablers, for instance, listed semiconductor firms have driven the hardware layer, while private companies are leading the development of large language models. Knowing where to look is useful, but it is not enough to identify lasting value.

What to look for

The second framework takes a bottom-up view of which companies are best placed to succeed in each category.

• Moat: We look for business models with structural advantages that can endure as AI reshapes industries. These include proprietary data, strong governance over critical workflows where errors are costly, and products and services that are hard for AI to displace.
• Management: Companies need leaders who combine vision with the ability to execute. Good leaders tie AI to real business outcomes instead of simply rolling out tools. They attract and keep talent, use AI to strengthen core capabilities rather than only to cut costs, and reskill their workforce to build organisational resilience.
• Momentum: Early wins produce better data, which improves AI performance, so the cycle feeds itself. Companies that make use of this momentum are likely to gain a competitive edge.

Fiscal risks: When debt stabilisation slips out of a country’s control

Fiscal strength is not about the headline debt ratio alone. It also depends on the primary balance (governments’ total revenue minus non-interest expenditures), on how the debt is structured and who holds it, and on longer-term pressures from ageing populations. Strengthening public finances requires either laying the foundations for sustainable growth or reducing deficits.

After the Global Financial Crisis, interest rates sat comfortably below growth rates, and healthy growth met the return government spending needed to break even. Today, higher rates are changing the dynamic. Fiscal prudence is warranted but politically unpalatable. The IMF estimates that fiscal tightening since COVID-19 has been the lowest since 1990 (Exhibit 3).

Even when countries have tightened, they have generally done so only under pressure from higher yields. Given debt ratios globally and the larger scale of fiscal adjustment required today, markets could increasingly price in fiscal risks.

The burden of adjustment will grow as populations age, with both healthcare and social security costs rising. The impact of ageing demographics goes beyond government balance sheets.

For decades, price-insensitive insurers and pension funds in developed economies have been steady buyers of long-dated bonds, but they are now buying less. Demand is shifting to more price-sensitive global investors, making it more likely that stress in one bond market spreads to another.

Macro and portfolio implications

The odds of a higher-growth, higher-inflation environment have risen

Greater spending, including by governments, is increasing the probability of a higher-growth, higher-inflation environment. In the US, AI and IT investment already contributes significantly to real GDP growth, alongside a consumption pattern that leans heavily on higher-income households. The outlook nonetheless remains highly uncertain, so we employ scenarios and signposts to assess which macro environment may be emerging (Exhibit 4).

A higher resting rate, but the drivers matter

The forces behind this environment, AI capex, and fiscal spending, are also competing for capital. Hyperscalers are increasingly funding capex with debt rather than operating cash flows alone. As a result, AI and fiscal forces meet in the bond market, adding to upside risks for rates.

Rates are likely to settle higher, and the underlying drivers matter as much as the level itself. Good and bad drivers carry different implications for investors (Exhibit 5). If higher rates reflect higher nominal growth from productive AI capex, equities may thrive. If they reflect fiscal risk and rising term premia, the dollar may weaken and gold may benefit.

Yields are currently rising mainly due to higher nominal growth, which is broadly supportive for equities and the dollar. The risk is that the same rise in yields comes to reflect a fiscal premium and competition for capital instead. The cross-asset response would then be very different.

Granularity across time horizons, sectors, and countries

These forces call for granularity across time horizons, sectors, and countries, especially for AI, where the key issues shift over time.

In the near term, the focus is on financing the AI flywheel and managing the physical constraints on capex. Over the medium term, the issues are the gap between AI capex and productivity, and the widening divide between strong adopters and those being disrupted. Over the longer term, the key debate is the extent to which AI will generate a disinflationary productivity impulse, which could be positive for risk assets, with benefits unevenly distributed.

Conclusion

Overall, the macroeconomic outlook is constructive on the longer-term productivity potential of AI, but a higher resting level for rates in a more volatile, more inflationary world merits caution. This is why we stress the core investment principles of diversification, agility, and granularity, which are key to building a resilient portfolio.