Lessons from the AI boom and climate bust

This week, a Chinese startup called DeepSeek astonished the tech community by unveiling an artificial intelligence model that rivals some of Silicon Valley’s most advanced systems for a fraction of the usual cost. At the same time, homeowners in California woke up to an unsettling truth: more insurers are refusing to cover properties in areas threatened by climate-driven disasters.

At first glance, these stories look unrelated. One is about the leapfrogging capabilities of a new AI contender, while the other is a financial blow to a region grappling with wildfires and floods. Yet they share an unbreakable link. Both AI and insurance hinge on data and predictive modeling, and both are feeling the shockwaves of a world that can no longer rely on historical patterns in the face of escalating climate change.

Training large AI models devours enormous amounts of energy, a fact that has gone from industry footnote to urgent topic.

According to one study, training a single advanced AI system can emit as much carbon as a car does over its entire lifetime. This hunger for power challenges electricity grids and threatens to deepen dependence on fossil fuels if cleaner alternatives are not prioritized. Yet DeepSeek’s arrival adds an unexpected glimmer of hope. Unlike other massive AI models, DeepSeek claims to have drastically reduced the energy required for training, suggesting that it is possible to push innovation forward without fueling the climate crisis. By championing efficiency and a smaller carbon footprint, DeepSeek raises an important question: how do we ensure that AI remains a boon to climate solutions rather than a hidden contributor to environmental damage?

On the opposite side of the globe, a homeowner in the Sierra foothills finds herself scrambling for coverage when her insurer refuses to renew her policy. Wildfires, once seasonal, now arrive with a ferocity that defies historical risk models, forcing insurance companies to pull back. This is not just a local crisis. When insurance markets collapse in one region, property values nosedive, mortgage markets wobble, and global financial systems feel the tremors. New solutions, such as parametric insurance that pays out based on event triggers rather than lengthy claims processes, are gaining traction. Yet the fundamental issue remains: climate change is accelerating so fast that old risk algorithms cannot keep up.

From these two examples, one lesson emerges. Innovations in AI and financial models, however impressive, must be built with a realistic view of our changing climate. For Oman and other nations, the urgency to balance technological growth with environmental readiness grows stronger by the day. Investment in renewable-powered data centers, more efficient computing architectures, and climate-resilient economic policies is not optional. If powerful economies like the United States can be blindsided by climate risks, then emerging markets must be even more vigilant, designing systems that can endure an era of uncertainty.

Real resilience comes from weaving climate considerations into every plan and policy. AI can empower us to predict and adapt to threats, but it can also be a voracious consumer of energy that speeds up the crisis if it fails to evolve responsibly. Insurance can offer a buffer against catastrophe, but if its structures remain locked in outdated assumptions, entire financial ecosystems could collapse. The future will be shaped not by who races ahead fastest, but by who adapts most effectively. That adaptability, more than raw innovation or economic might, will define our path in a world that grows hotter and more unpredictable every year.