When disaster strikes, AI won’t ask who deserves to be saved. It’ll ask: who paid for priority access?
There’s an old machine learning story: the U.S. military trained an algorithm to spot enemy tanks. It aced tests but failed in the real world. Why? Because it hadn’t learned to detect tanks — it learned to detect clouds. The tank photos were taken on cloudy days; the others, on sunny ones.
AI optimizes for data, not human outcomes. And when the stakes are high, that gap can be catastrophic.
AI is hailed as a climate savior: smarter disaster response, predictive crop yields, carbon-capture algorithms. But what if AI doesn’t learn to prevent disaster — what if it learns to profit from it?
Insurance companies use AI to assess climate risk. It helps price policies accurately: right out of reach for vulnerable communities.
AI optimized supply chains don’t make food systems resilient to drought — they make corporations resilient to supply shocks, often at the expense of farmers and frontline communities.
AI isn’t neutral. It amplifies the systems that benefit from chaos.
Hedge funds use climate models to bet on food prices, not prevent famine. Insurance firms deploy wildfire algorithms to adjust premiums, not protect communities. Governments fund AI for disaster response, then repurpose it for border control and refugee management.
AI doesn’t lack ethics. It has none. Algorithms don’t ask, “Should we?” They ask, “How efficiently can we?” . Optimization without oversight doesn’t ask, “Who needs help?” It asks, “Who can pay for it?”
The danger isn’t that AI will fail to solve climate change. Unless we’re careful, it will succeed on terms that serve capital over humanity
In a world optimized for profit, humanity isn’t the priority. It’s the variable most easily left out.
--
If you have any questions or thoughts, don't hesitate to reach out. You can find me as @viksit on Twitter.