The hidden tech quietly cushioning the world’s biggest oil crisis

The closure of the Strait of Hormuz has sent shockwaves through the global economy, halting an estimated 20–30% of the world’s oil trade almost overnight. Now, even following Iran’s ceasefire after Pakistan-led backchannel talks between the U.S. and Iran, the strait remains at a virtual standstill, with only a few ships passing through. 

Oil prices are climbing, supply chains are faltering, and economies worldwide are feeling the strain of this geopolitical chokehold: gas prices in the U.S. jumped 7.5% on the day following the start of the conflict, freight costs to northern Europe and the Mediterranean spiked by 28% and 30%, respectively, while shortages have emerged across Asia.

“It’s clear to me if this crisis lasts more than three or four months, it becomes a systemic problem for the world,” Patrick Pouyanne, Chief Executive Officer of global energy firm TotalEnergies SE said at the late March CERAWeek conference in Houston.

Dan Jørgensen, European Commissioner for Energy and Housing, similarly told Financial Times: “This will be a long crisis…energy prices will be higher for a very long time.” 

Amid the turmoil spilling westward, a quiet but potent force is stepping into the limelight. From delayed shipments to skyrocketing gas prices at the pump, consumers and companies are feeling the strain, but artificial intelligence (AI) is working behind the scenes to soften the blow.

Taking centre stage, predictive AI logistics and analytics are helping systems endure the crisis, while modelling how AI can convert limited energy into disposable efficiency for industries, economies and populations.

AI and new energy elasticity

AI technologies can radically reshape the consequences of oil shocks by optimising how energy is used across industries, cities and entire economies; from traditional fossil-fuel hubs and petro-states to emerging renewable power grids and electro-states. 

As global technology leaders, the U.S. and European Union are putting their AI capabilities to the test: not just as innovative tools, but as buffers against crisis.

The extent to which AI technologies – particularly those developed and deployed by advanced economies – can soften the economic and operational impact of oil shocks by improving energy efficiency, enabling more responsive demand management and strengthening supply chain resilience, is increasingly evident. In conjunction with existing energy policies, AI complements and enhances measures. 

AI has emerged as a critical tool in managing disruptions: optimizing freight routes, predicting energy demand, and balancing loads across grids. However, the tool poses a double-edged sword: in optimizing logistics and energy use, it still requires substantial energy inputs, often sourced from fossil fuels. 

In a prolonged crisis, AI’s effectiveness becomes conditional: systems trained to optimize consumption may become constraints if the energy required to run them competes with other critical infrastructure during prolonged supply strain. 

When facing shorter-term energy shocks, however, AI-driven scenario planning in logistics is critical. In absorbing the shocks of short-term volatile fuel markets, predictive analytics and dynamic routing aren’t just efficiency tools; they’re survival mechanisms for supply chains.

Smarter forecasting, stronger logistics 

Google, via Deepmind, has demonstrated energy consumption cuts in data centers and how load balancing safeguards against energy shocks. When scaled to national grids, these innovations support the development of AI-driven smart grids that can balance electricity demand in real time, reducing waste and preventing overload during supply shocks.

This is particularly important for the logistics sector. AI-enhanced grids can prioritise energy allocation, ensuring that critical infrastructure, including freight hubs, ports and distribution centers, remain operational.

AI enhances the reliability of renewable energy by improving forecasting and grid integration. Companies such as Florida-based NextEra Energy and Danish firm Vestas use AI to predict energy output from wind, solar, and other renewable sources, allowing energy systems to compensate for reduced fossil fuel availability.

AI also supports the shift to alternative fuels in logistics, including electric and hydrogen fleets. Electric delivery vehicles can be optimised for battery life and charging schedules, while hydrogen-powered transport can be coordinated to match supply and demand. 

DHL in Germany, for example, uses AI to optimize electric vehicle fleet charging to match solar energy peaks. In these operations, AI helps determine when and where these cleaner fuels are most cost-effective to reduce emissions – even when non-reliable energy markets are fraught. 

Meanwhile, the technology also enables better demand-side management, where consumption is shifted away from peak periods through pricing signals and automated systems, reducing strain without requiring drastic behavioral changes from the population.

Reimagining freight and delivery

Fuel shocks hit transport first, making efficiency critical. AI helps mitigate this through route optimisation, predictive analytics and operational coordination. 

Bulgarian firm Transmetrics uses predictive analytics to streamline freight movement, optimize delivery windows and routes based on traffic and fuel costs, reduce empty or underutilized freight journeys, and improve warehouse efficiency and inventory distribution while cutting fuel consumption across supply chains.    

These improvements significantly reduce fuel consumption across supply chains, meaning even marginal efficiency gains, when applied at scale, can offset the demand shock caused by rising oil prices. 

AI technology facilitates rerouting shipments away from disputed regions, adjusting delivery schedules to avoid peak fuel costs and coordinating multimodal transport to defend economies from the shock.

AI in ports and critical infrastructure 

Beyond trucks and warehouses, AI is also reshaping the critical gateways for global trade by altering port operations. 

The technology can predict congestion and optimise docking schedules, dynamically allocate fuel or electricity for equipment to minimise energy spikes and avoid costly delays. 

Rotterdam’s port authority, for instance, piloted AI systems and saw a 15% reduction in energy spikes and operational inefficiencies during peak traffic periods – freight kept moving even as fuel prices surged. 

While U.S. logistics benefit from large-scale intermodal optimisation, Europe faces additional cross-border regulations. AI can forecast delays at borders, optimise freight routing across Member States and dynamically adjust shipment sizes to reduce fuel consumption.

“When fuel moves 30% in weeks and capacity flips from loose to tight in days, humans can no longer guess their way to a profitable quote,” said Asparuh Koev, CEO at Transmetrics. 

“In both the U.S. and EU, the winners are moving toward variable capacity models. If you aren’t simulating what-if scenarios for a 20% volume drop on a specific lane, you are flying blind.” 

Preparing for future energy shocks

AI enables governments and stakeholders to anticipate and respond to disruptions. Namely, the U.S. Department of Energy and the European Commission use advanced modelling tools to forecast energy demand, simulate crisis scenarios and guide policy inputs such as strategic reserve releases or coordinated consumption reductions.

These predictive models are becoming essential for managing geopolitical consequences – including this quarter’s Iran-fuelled energy shocks. By enhancing efficiency, enabling real-time demand response and strengthening supply chain resilience, AI has softened the economic impact of recent disruptions – acts as a mitigating layer on top of physical energy systems rather than a substitute. 

From smarter grids to smarter deliveries, it is proving to be indispensable in supporting economies weathering fuel shocks, keeping goods on-the-go, shelves stocked and ensuring the lights stay on. 

Limits and the AI energy paradox 

Enhancing adaptability and predictive capacity in the face of energy shocks is shared priority. Alexandr Wang, CEO of data infrastructure firm Scale AI, highlighted this tension before the U.S. House Energy and Commerce Committee, arguing that the AI paradox means that the technology both consumes and optimises energy:

“AI can help optimize grid performance and energy systems, even as it drives increased demand [but] we are facing a mismatch between the pace of AI advancement and the speed of infrastructure development,” Wang said. 

The European Parliament has noted similar tensions, framing the issue as a dual sustainability and infrastructure challenge.

Businesses are investing in resilience, not just efficiency. Governments are supporting stable energy grids and supply chain resilience via AI-driven simulations, while industry stakeholders seek to integrate AI with energy realities. Meanwhile, Transmetrics, NextEra, Vestas and Google – via Deepmind – demonstrate AI’s potential to soften the impact of global fuel shocks.

Disclosure: This article mentions clients of an Espacio portfolio company.