In early 2026, climate stress in the Mekong Delta highlighted the fragility of regional rice production and the wider exposure of Southeast Asian food systems to environmental disruption. Subsequent market scrutiny has raised concerns that financially sophisticated actors such as hedge funds and large commodity trading firms that may be able to anticipate food system shocks faster than vulnerable states can respond by creating an information advantage in agricultural markets. This mismatch points to a threatening shift in international security through the advent of Climate Intelligence Asymmetry. With the artificial intelligence models supported by the state, which have never been as precise in their granularity in predicting weather patterns, environmental prediction has ceased to be a global common good and has become a highly secretive strategic tool. Climate Intelligence Asymmetry, which is defined as the divergence in access to advanced AI-driven environmental predictions, thus redefines geopolitical vulnerabilities.
For decades, the World Meteorological Organization (WMO) has facilitated a data sharing regime built on the free and unrestricted exchange of core Earth system information, supporting disaster preparedness and agricultural planning. This balance relied on the conventional physics-based supercomputing that created comparatively homogeneous capacities in the developed countries. Rapid advances in Neural Earth System Models (NESMs), advanced AI systems trained on large climate datasets, have begun to erode that parity. In contrast to conventional models, NESMs need enormous computational resources to train, and can only be developed in states with strong tech industries, mostly the US and China. Over 2024–2026, US National Oceanic and Atmospheric Administration (NOAA) investments exceeded $2 billion in NESM development, per congressional reports, mirroring China’s state-backed programmes.
Intelligence agencies and defence departments have begun to classify their most advanced NESMs, considering hyper-accurate, long-term climate prediction as a key national security benefit. This classification creates an intelligence, dividing states who have NESM capability, and those that do not. These classified NESMs strengthen long-range forecasting, but their strategic value lies less in perfect prediction than in widening the gap between states with advanced computation and those dependent on open data products.
The predominant short-term risk of NESM asymmetry is in the geo-economic domain. Improved climate intelligence can create a material edge in commodity trading by revealing supply risks earlier than open markets. State supported financial institutions can privately stock up on alternative foods and cut short certain agricultural markets by predicting regional crop failures. If used strategically, that information advantage can amplify price volatility and deepen the exposure of import dependent states before they secure emergency stocks. As a result, food security becomes subordinated to the computational dominance of foreign actors.
Climate intelligence asymmetry implicates a new dimension of grey-zone geopolitical coercion. Advanced foresight into natural disasters may also create incentives to use humanitarian assistance as a tool of political influence. In case a great power predicts a devastating typhoon to hit a strategically important yet defenceless island country, it can prepare naval and humanitarian resources in the region of the disaster several weeks beforehand. Upon the occurrence of the disaster, the forecasting power comes in with its immediate power to provide rapid humanitarian help to gain geopolitical concessions, including basing rights or alignment of United Nations voting patterns. This dynamic transforms disaster diplomacy from a reactive humanitarian need into a pre-calculated and predatory strategic action.
The rationale behind the classification of these models must be acknowledged. Defence ministries argue that open access to highly capable environmental forecasting systems creates genuine dual-use risks. High-resolution NESMs could be used by adversarial forces to optimise the use of biological agents, plan naval manoeuvres to avoid being spotted by satellites when there is a cloud cover, or to attack critical infrastructure when it is most susceptible to extreme weather conditions. Protecting domestic populations and military logistics is a legitimate requirement to protect some predictive capabilities. This perspective, articulated in 2023 SIPRI assessments, prioritises immediate defence needs over collective resilience. Yet, historical precedents like the 2010s open-source weather data during Typhoon Haiyan demonstrate that shared forecasting mitigated dual-use risks while enhancing global stability, suggesting tiered access as a viable alternative.
Nevertheless, limiting access to climate intelligence has systemic secondary risks which exceed the advantages of localised security. Asymmetric commodity trading creates macroeconomic instability, disrupting the global supply chains which advanced economies also depend on. Moreover, this intelligence hoarding deprives climate-vulnerable states of the ability to plan ahead and ensure infrastructure resilience. This deprivation increases levels of climate-related mass migration, which risks spilling over, as evidenced by 2025 migration patterns from the Sahel into the borders of the same countries that are stashing the predictive technology.
To reduce the risks of algorithmic climate hoarding, it is necessary to leave the old models of meteorological collaboration behind and consider predictive climate models as critical and dual-use infrastructure. To regulate this new area, policymakers need to put in place specific enforceable rules.
First, governments should establish a multilateral climate computing consortium with shared access rules, pooled infrastructure, and a mandate to produce open forecasting products for vulnerable states. Through the combination of allied computing capabilities and investment, middle powers will be able to create high-fidelity NESMs without reliance on individual superpowers, making sure that important predictive information is accessible to vulnerable areas.
Second, financial regulators should require large commodity traders and state linked funds to disclose material futures positions in staple food markets within a fixed reporting window. Sovereign wealth funds should report large-scale futures contracts in key food commodities which will help to avoid the silent market cornering that occurs after classified climate intelligence.
Climate forecasting with the use of artificial intelligence is one of the most important technological advances of the decade. Unchecked, it separates the international system into a hierarchy of the forewarned and the ignorant. The future of security in a world of increasing climate instability is not hoarding, but rather building resilient and open systems that do not allow the weaponisation of the environment itself.
Further Reading
CRS (2026). National Oceanic and Atmospheric Administration (NOAA) FY2026 Budget Request and Appropriations
Elmundo America (2026). Mekong Delta, the granary of Asia shaken by a distant war that paralyzes its harvest
Nature (2024). Probabilistic weather forecasting with machine learning
Nature Communications (2025). Early warning of complex climate risk with integrated AI systems