A step back in time: the old American oil strategy returns
The capture of Nicolás Maduro and Trump’s evocation of the Monroe Doctrine reopen an energy imaginary typical of the mid-20th century: large US oil companies aligned with Washington’s geopolitics. But the clash with reality is head-on. Venezuela has colossal reserves—300 billion barrels, 17% of the global total—but produces just over 1 million barrels per day, less than 1% of the crude oil on the market, hampered by degraded infrastructure, heavy crude oil and extreme geopolitical risk. The White House seems to assume that Exxon, Chevron or ConocoPhillips will respond out of patriotic reflex. However, the sector operates according to a different logic. The world is awash with oil, prices are hovering around post-Covid lows, and shareholders are demanding capital discipline, not hemispheric adventures. Reviving Venezuela would require annual investments of close to $12 billion for years, with break-even prices above $80 a barrel, far from the current market. The result is a strategic dissonance: Trump imagines an oil renaissance that clashes with supermajors fleeing sovereign risk. The initial stock market rise reflects collateral relief, not the dawn of a Venezuelan boom.
Next stop, Greenland. Donald Trump’s stubborn interest in Greenland brings the same nineteenth-century drive for oil to the rare earths of the Arctic. Under the guise of national security, the White House is displaying a range of pretensions, from purchase to military coercion, to strengthen its control over an autonomous island of 56,000 inhabitants, formally under Danish sovereignty. European confusion is understandable: Washington already has a military base in Greenland and extensive strategic prerogatives. What is new is the tone and the method. The Trump administration seems to be exploring a double-standard strategy. On the one hand, it is encouraging divisions between Nuuk and Copenhagen, flirting with local independence movements, and on the other, it is demanding a greater military presence, with or without Copenhagen’s consent. The rhetoric is reminiscent of the Monroe Doctrine applied to the Arctic, with Russia and China as the backdrop. Although direct annexation is unlikely, it would be a mistake to underestimate Washington. Trump seeks to alter the status quo in order to apply his transactional vision of alliances, make Europe uncomfortable and put pressure on NATO from within.
Geopolitical navigation chart:
- Greenland thus becomes a symbol of a more fragile Atlantic order (…) and of an increasingly less consensual American hegemony.
- Chevron and captive oil. The shipment of up to 50 million barrels of Venezuelan oil to the US is politically significant, but marginal in market terms. For Chevron, it is a matter of logistics and arbitrage, not a structural business.
- The crude oil market is not buying the narrative. The transfer of Venezuelan black gold eases logistical risks and reinforces the perception of oversupply. Prices are falling because the gesture is political, not a structural change in the global balance.
A more favourable economic climate than in 2025
Generated by an unusual combination of fiscal and monetary policies and deregulation measures that should lead to a prolonged stock market rally – especially in the US – and a less hospitable environment for sovereign bonds and traditional safe havens. US equities could significantly outperform their global peers, with estimates placing the S&P 500 on track for double-digit gains during the year, supported by robust corporate earnings, Fed rate cuts and massive investments in AI infrastructure that would act as catalysts for productivity and corporate margins. But this scenario contains asymmetric risks. Essentially, those linked to moderate global economic momentum with accommodative fiscal and monetary policies. Credit markets could experience fluctuations, with issuance linked to high technological capital expenditure and possible widening of spreads in investment grade segments, while commodities show differentiated biases. Thus, 2026 is shaping up to be a year of market transition towards narratives focused on capital intensity and earnings quality.
Global GDP of 3.2%. The multilateral and private consensus points to sustained expansion, albeit still below the pace seen before the Great Pandemic. The US economy is resilient, buoyed by consumption and a rebound in business capital. Inflationary moderation also features in this diagnosis, leaving room for central banks to gradually reduce rates in support of activity. Financial stability will depend once again on monetary strategies and their ability to balance risks and protect labour markets. In a context of aggregate demand that maintains household spending and hints at selective business investment. However, there is still uncertainty about how these benefits will be distributed among regions; Europe and some emerging economies face headwinds due to weaker internal and structural dynamics. Risks include geopolitical shocks, credit volatility, sharp adjustments in rate differentials, and socio-economic effects from disruptive technologies such as AI. This rebalancing of forces suggests that 2026 will not be a boom year, but it will consolidate more sustainable and less volatile growth.
Investment roadmap:
- Structural weakness of the dollar (…) which could lose up to another 10% by 2026 due to cheaper money in the US and its lower differential against other major economies. This depreciation reflects a change in the global monetary cycle.
- Gold as a market narrative. Its upward trend, rising well above traditional assets, cannot be explained solely by inflation or risk; rather, it encapsulates fears of technology bubbles and a possible reconfiguration of foreign exchange reserves.
- Private credit is expanding (…) as an alternative to bank financing in an environment of flexible loans and floating rates that protect against rate rises, making it a key segment for diversification.
AI will play a role in portfolio reconfiguration
Another breakthrough, that of AI agents—systems that not only generate analyses but also execute autonomous actions in complex workflows—is about to redefine how financial institutions approach critical, high-value, and highly complex tasks, such as corporate credit assessment. Unlike previous solutions, which automated isolated steps within fragmented processes, AI agents can coordinate end-to-end multitasking, from data ingestion and validation to the production of preliminary analyses and the identification of key risks, with a consistency and speed that eclipse traditional manual operations. This capability does not replace human judgement, but rather elevates the role of the financial expert, freeing them from repetitive tasks and allowing them to focus on strategic decisions and non-trivial scenarios that require contextual interpretation. The adoption of AI agents is not without its challenges. However, for the time being, data quality, integrated governance and regulatory oversight remain non-negotiable pillars.
While expectations for AI agents are enormous, their deployment requires a profound rethinking within investment firms. Autonomy, understood as the ability of systems to act without human supervision at every step, raises questions about transparency, control and accountability that did not exist with traditional AI tools. Integrating agents into sensitive processes such as credit risk review means ensuring that every automated decision is traceable, auditable and aligned with internal and regulatory standards, leading to the design of “critical agent” structures that monitor data quality and compliance criteria in real time. Other sectors, such as insurance and retail banking, are channelling AI agents. However, the transition from pilot strategies to full scale remains a challenge. So does the redesign of corporate data flows.
AI: hero or villain?
- The AI agent automates and coordinates the entire credit assessment cycle—from data to analysis—and operates in near real time.
- Autonomous systems improve the consistency and scalability of decisions, but require robust regulatory frameworks to ensure their oversight.
- Adopting AI agents at scale requires data quality, system interoperability, and an adapted corporate management culture; without these, the impact may be limited.
