Current Themes 2026 07

As we pass the mid-year mark, it is perhaps useful to take stock of current themes that occupy the mind of the markets.

Fiscal sustainability

Global public debt reached just under 94% of GDP in 2025 and is now projected to hit 100% by 2029, according to the IMF. The accumulation is driven primarily by the world’s major economies, with mounting spending pressures across social needs, defence, and strategic autonomy compounding rising interest burdens.

In the US, the CBO projects a federal deficit of 5.8% of GDP, well above the 50-year historical average of 3.8%. The One Big Beautiful Bill Act made matters worse. Higher average interest rates on government debt, partly driven by policy changes, push debt further still and stresses debt service.

China’s general government debt crossed 100% of GDP in 2026 on IMF projections (107%, counting on-balance-sheet local government financing vehicles), with debt projected to reach 127% by 2031, the second-largest rise after the US. Fiscal policy should remain expansionary until the economy reflates durably, but over the medium term, ensuring sustainability will require significant fiscal consolidation, reforms, and a restructuring of LGFV debt to tackle the local government debt overhang.

India is the relative bright spot among major EMs. Growth expanded 7.8% in the first quarter of FY2025/26. Fiscal consolidation has advanced and the current account deficit has been contained. State-level finances remain weak, however. Consolidated state debt stood at 28.2% of GSDP in FY2022/23, up from 22.2% in FY2012/13. The growing interest payment burden adds to already-high non-discretionary expenditure including salaries and pensions, explaining high-debt states’ difficulties in consolidating their deficits.

Europe: The aggregate deficit ratio in the euro area is expected to increase to 3.2% of GDP in 2025 and 3.3% in 2026, partly as rising defence spending pushes up government expenditure. Public debt is forecast to rise to 88.8% of GDP in 2025 and 89.8% in 2026.

Germany’s 2026 federal budget envisages expenditure of €524.5 billion, with defence spending rising to €83 billion plus an additional €25.5 billion from the off-budget Bundeswehr Special Fund. Public debt is projected to reach 80.25% of GDP by 2029, and Germany risks being placed under an excessive deficit procedure in 2026 and 2027. France remains the euro area’s most acute sovereign risk. A compromise budget should allow the deficit to reach 5% of GDP in 2026, compared with 5.4% in 2025, pushing public debt to 118% of GDP.

UK: The OBR projects public sector net borrowing to fall from 5.2% of GDP in 2024/25 to 4.3% this year and then to 1.6% by 2030/31. Public sector net debt is expected to be broadly stable and settle at around 95% of GDP in the early 2030s. The public debt position remains fundamentally unsustainable, and a serious medium-term plan to bring debt down as a share of the economy will be needed.

Brazil is the most immediate concern in the emerging market universe. The consolidated public sector ran a primary deficit of nearly R$25 billion in the first five months of 2026, reversing a surplus of around R$69 billion in the same period of 2025, a YOY swing of roughly R$94 billion. Gross general government debt reached 81.1% of GDP, the highest in five years.

Argentina: Milei’s reform agenda of reducing public spending and subsidies, partially liberalising the exchange rate, and introducing the RIGI framework for large investments has helped put Argentina back on the map for international investors.

US and Brazil occupy the two most concerning positions, the US by sheer systemic weight and trajectory, Brazil by the combination of high interest rates, political cycle dynamics, and a structural spending-revenue mismatch. China’s official numbers are manageable, but the augmented balance sheet is not. The EU is muddling through with a new framework that is already bending under political pressure. India is the cleanest picture: high growth providing the denominator effect, a credible anchor, and consolidation progress,  though state-level debt deserves watching. The UK is stable but structurally boxed in with almost no headroom. Across Latin America, the election-year Brazil risk is the dominant variable for the region’s sovereign credit story.

These conditions are not robust against significantly rising interest rates thus making inflation a key factor in fiscal sustainability. Interest rates will be influenced by fiscal policy, confidence, and inflation. There is not much room for debt monetization to tame interest rates as that would only serve as an accelerant to inflation. Central banks face a narrow policy window, keeping inflation and thus rate expectations under control, while avoiding directly elevating debt service for sovereigns. Inflation can come from other sources, such as industrial policy and geopolitics, as we have seen. The impact of AI on productivity and thus inflation is yet another factor.

To do:

• Monitor the trajectory of interest rates and exchange rates. The importance of interest rates on funding and valuations will be significant. Maintain a short duration posture in the credit book, favouring floating rate product.
• Given the relative tensions and constraints in US fiscal policy, maintain an underweight position in USD.
• Be cautious long duration assets such as core or core plus real estate and infrastructure.

AI

The productivity case for AI is real but unevenly distributed. Task-level evidence is consistent: the BIS notes that task-level studies consistently show productivity gains of 20% to 50% in time savings.  But there is a well-documented gap between task-level gains and total factor productivity or TFP the “productivity paradox” that attended computing, the internet, and every prior general-purpose technology.

The labour market implications are asymmetric. AI automates cognitive, white-collar tasks. Roles involving routine cognitive work such as coding, translation, basic analysis face the highest displacement risk. A “reasoning wage premium” is emerging that benefits workers who can orchestrate models, while a “growth without hiring” trend sees companies expanding output without proportional headcount increases. Physical jobs remain largely unaffected: automating fine motor skills is harder than automating high-level reasoning. The conditions under which a rapid AI productivity boom can coexist with, and indeed cause, a macroeconomic contraction are real and underappreciated. If productivity gains accrue to capital owners while labour income stagnates, the consumption multiplier is lower than historical cycles, and fiscal stabilisers face greater strain.

The scale of the AI investment cycle is unprecedented. Wall Street estimates total AI capex could exceed US$1 trillion in 2027. The five largest hyperscalers are on pace to spend more than US$1 trillion on AI-related capex across 2025 and 2026 combined, a sum that is already outpacing earnings and free cash flow, forcing some to issue debt to cover the gap. Somewhat concerning is the circular nature of the AI ecosystem’s financing. Hyperscalers take equity stakes in AI labs, which in turn commit to multi-year purchases of chips or computing power from those same hyperscalers. This creates a closed, recursive financing loop.

AI intersects with monetary policy in two ways. First, the productivity question directly affects the neutral rate of interest. If AI delivers a Solow-style productivity boost, the neutral rate rises because the return on capital rises. Central banks would need to tighten more. Second, the AI capex boom is itself inflationary in the near term. Massive investment in data centres, power infrastructure, and chips puts upward pressure on prices in those specific markets. Power demand from data centres is already a material constraint in the US, Europe, and parts of Asia.

AI creates a potential fiscal dividend, higher growth, broader tax base, but only if the productivity gains are large, fast, and distributed broadly enough to raise labour income and corporate profits in taxable forms. This is not guaranteed.

The bull case is essentially the post-war US analogy: a genuine general-purpose technology that takes time to diffuse but ultimately raises TFP broadly, lifts real growth sustainably above neutral rates, and makes current debt loads manageable.

The bear case is the dot-com analogy. Returns disappoint relative to the capex committed, the circular financing loop unwinds, equity valuations crash, financial conditions tighten, and at a moment when sovereign balance sheets have no room for fiscal expansion.

The stagflation case: AI delivers real productivity gains, but they accrue to capital rather than labour, suppressing aggregate demand while the AI capex boom sustains inflationary pressure in factor markets. Central banks face rising prices and weak demand simultaneously.

To do:

• Underweight the hyperscalers and their ecosystem, particularly those within their circular financing loops.
• Just avoid the mania.
• Seek zero cost, levered, net short trade expressions referencing these companies.

Energy

Global data centre electricity consumption was approximately 415 TWh in 2024, representing about 1.5% of global electricity use, growing at 12% per year over the last five years. The IEA’s base case has global electricity consumption for data centres is projected to reach around 945 TWh by 2030, representing just under 3% of total global electricity consumption. That is roughly equivalent to the entire current electricity consumption of Japan. In the IEA’s “Lift-Off” scenario, demand exceeds 1,700 TWh by 2035. In the US specifically, data centres currently consume the equivalent of 4.4% of total US electricity, projected to rise to between 6.7% and 12% by 2028. A single AI task can consume up to 1,000 times more electricity than a traditional web search, and AI-focused facilities now require 80 MW of power, more than double the 32 MW standard data centres consume.

The energy shortage is not primarily a problem of insufficient generation capacity in aggregate. It is a problem of the wrong capacity, in the wrong places, connected through infrastructure with lead times that cannot match the pace of AI deployment. The bottlenecks are a) transmission and interconnection, b) ageing grid architecture, c) geographic concentration, and d) hardware supply chains (can’t make enough equipment quickly enough.)

Constrained by slow grid connections, data centre developers in the United States are pushing forward projects with onsite natural gas-based power generation. Total power generation for renewables is projected to grow 22% per year until 2030, meeting nearly half the anticipated growth of data centre electricity demand. However, renewable intermittency conflicts with data centre 24/7 uptime requirements. The first commercial SMR-powered data centres will come online by 2030 at the earliest, with 22 GW of SMR projects in global development. Capital expenditure of just five hyperscaler technology companies is now larger than global investment in oil and natural gas production. The problem is not in generation or storage but in evacuation and distribution.

To do:

• Grid infrastructure is the most direct and least speculative investment thesis. High-voltage substations, transformers, cables, and transmission expansion face structural demand that is independent of which AI model wins or whether productivity gains materialise. The constraint is physical, the demand is contractual, and the regulatory moats are durable.

Climate

Global investment in climate mitigation reached a record $2.3 trillion in 2025, up 8% from 2024. The largest drivers were electrified transport ($893 billion), renewable energy ($690 billion), and grid investment ($483 billion). Against the need, it is less than half of what is required. To keep the world on a net-zero pathway, investment in climate mitigation must rise to between $6.2 trillion and $9.5 trillion per year by 2030, leaving an annual gap of $4.5-7.8 trillion compared to 2023 flows of $1.7 trillion. The geographic mismatch is severe. Since 2015, renewable energy investment in emerging markets excluding China has nearly tripled to $140 billion in 2024, but developing economies’ share of global clean energy spending has averaged just 18% over the past decade. In contrast, developed economies and China together capture 82% of total funding.

UNEP’s 2025 Adaptation Gap Report finds that adaptation finance needs in developing countries by 2035 are over $310 billion per year, 12 times as much as current international public adaptation finance flows. UNEP estimates the private sector’s realistic contribution at $50 billion per year toward national public adaptation priorities, ten times greater than current private flows, but still only one-seventh of total need.

The mitigation/adaptation asymmetry in private capital flows is not accidental. It reflects fundamentally different return structures.

Mitigation investments such as solar, wind, EVs, grid storage, generate revenue through energy or transport services. They have clear offtake mechanisms, liquid secondary markets, bankable cash flows, and increasingly competitive economics against fossil fuel alternatives. The private sector has learned how to price, structure, and exit these assets.

Adaptation is structurally harder because most of its value is in avoided loss rather than generated revenue. The beneficiary and the payer are often different parties, creating a public-goods problem. Many adaptation investments deliver broad public benefits but do not generate clear or predictable financial returns, making them less attractive to investors.

Opportunities:

Physical losses are becoming significant insurance events. Insurers are re-pricing or withdrawing coverage from high-risk geographies. Closing the insurance protection gap is one of the most specific short-term priorities from the COP30 with the insurance industry directed to work with climate-vulnerable developing countries to reduce the financial protection gap. Taxonomy and measurement infrastructure is improving. COP30 launched a set of common principles to make the world’s more than 50 sustainable taxonomies interoperable, with the potential to cut barriers to sustainable finance and reduce transaction costs for cross-border adaptation investments. Blended finance is scaling into adaptation. Southeast Asia faces particularly acute exposure with Vietnam, the Philippines, Indonesia, Thailand and Cambodia among the most climate-vulnerable economies globally.

To do:

Direct origination towards

1. adaptation,
2. insurance,
3. blended finance and
4. vulnerable regions such as Southeast Asia.

Private credit

Private credit has reached an inflection point, not in crisis, but entering its first genuine “test” as a major asset class.  The market has grown to an estimated US$1.5–2 trillion in assets and, at its current size and scope, has not been tested during a severe economic downturn. Signs of stress are emerging.

Headline default rates understate true stress. While commonly cited default rates in private credit often remain around 2–3%, more comprehensive measures indicate higher levels of distress, circa 5.8% for the trailing 12 months through January 2026. The widespread use of PIK interest is a signal that cash flows are under stress.

The growing use of private ratings, sometimes from lesser-known providers, to facilitate investment by rating-reliant investors such as insurers warrants monitoring.

Interconnections between private credit funds and banks, insurers, and private equity firms are deepening, raising potential vulnerabilities.

Semi-liquid vehicles for the wealth channel now command almost a third of the $1 trillion US direct lending market. Liquidity issues stem from the growing popularity of funds offering redemption options to investors, which may heighten the procyclicality of private credit.

Over the past decade, major private equity firms have either acquired life insurers outright or built their own. The structure creates a three-layered conflict: the PE firm originates assets, the affiliated insurer provides the permanent capital to fund them, and captive offshore reinsurers reduce the regulatory capital burden. Each layer individually passes regulatory scrutiny; the compound effect is a largely opaque self-dealing ecosystem where policyholders’ retirement assets are effectively funding the PE firms’ own credit books. The system has not yet been tested through a genuine credit cycle at this scale.

To do:

Tighten fund due diligence standards to account for potentially directly or indirectly conflicted GPs.