AI has the potential to fundamentally change our world, and when it comes to climate, the right AI solutions can make a huge difference. But instead of focusing on large-scale, general AI systems that try to solve everything at once, we should think the opposite – small, specific AI solutions that can be scaled and used by many.
The real power lies in the exponential effect: individual, smart tools that can be spread and help more people make the right choices in their everyday lives. By combining AI with existing resources, such as legacy technology, crowdsourcing and local knowledge, we can create innovations that truly contribute to a sustainable future.
Repurposed technology – AI that protects the rainforest
One of the most exciting AI solutions for the climate is based on something as simple as used mobile phones. Organizations like Rainforest Connection have shown that old phones can be repurposed to monitor forest areas and prevent illegal logging.
Here’s how it works:
- The phones are placed high in trees, powered by solar cells and act as listening stations.
- AI analyzes the sound in real time to identify chainsaws, vehicles, or other sounds that indicate deforestation.
- When the AI detects suspicious sounds, an alert is sent to forest rangers or local communities who can intervene immediately.
This type of AI is simple but powerful. It specializes in a single task – listening for signs of deforestation – and does it better than a human monitor. By spreading these devices across large areas of forest, we can create a global safety net that stops logging before it happens.
AI and climate monitoring – smart sensors instead of satellites
Many climate problems, such as deforestation, water scarcity, and air pollution, are currently monitored primarily with satellite images and big data systems. But what if we instead built networks of small, local AI devices that work together?
Examples of decentralized climate AI:
- Cheap AI-powered sensors in rivers that monitor water quality in real time and warn of pollution.
- AI models that analyze soil quality and help small farmers optimize their crops without overusing water or fertilizers.
- Small, autonomous drones that can detect forest fires before they spread.
All of these solutions have something in common: they are simple, inexpensive, and can be used by local communities without requiring advanced technical knowledge.
AI for sustainable cities and energy optimization
AI can also help create more sustainable cities by analyzing data and optimizing energy use, traffic flows, and resource management.
Examples of AI solutions for urban development:
- AI for climate-adapted urban planning – By analyzing weather data, traffic patterns, and energy consumption, AI can help design sustainable cities with greener transportation solutions and energy-efficient buildings.
- AI-powered microgrids for renewable energy – Smart algorithms can optimize the flow of energy in local power grids by predicting demand and maximizing the use of solar and wind energy, reducing dependence on fossil fuels.
AI that reduces food waste and promotes circular economy
Another important use case for AI is to reduce resource consumption and food waste, which is a crucial factor in reducing climate impact.
Examples of AI solutions for resource efficiency:
- AI that reduces food waste – By analyzing consumer behavior, AI can help stores and restaurants optimize their inventory and reduce food waste, which both saves resources and reduces climate impact.
- AI in the circular economy – Automated systems can identify materials in waste and streamline recycling processes, allowing more resources to be reused instead of being wasted.
The future may be small-scale collaborative AI – not an omniscient superintelligence
We often talk about AI as if it were a single, central intelligence that will solve all our problems for us. But if we really want to use AI to address climate change, we need to think the other way around.
From central AI to decentralized AI:
- Instead of creating an omniscient climate AI, build thousands of small AI models that solve specific problems.
- Instead of collecting huge amounts of data into a single supermodel, let AI devices analyze the data locally and share results.
- Instead of focusing on maximum computing power, make AI so simple and cheap that it can run on an old phone or a Raspberry Pi.
The real power of AI for climate lies not in making it bigger – but in making it smarter, more accessible and more adapted to local needs.
AI as a toolbox for climate
The climate issue is one of humanity’s greatest challenges, and AI can be part of the solution – but only if we use it right.
By focusing on small-scale AI solutions that can be spread and interact, we can create an exponential effect where millions of small actions make a difference. It’s about giving people the tools to do the right thing rather then trying to solve everything from a central point.
The climate AI of the future may be simple, scalable and accessible and help us act, not just analyze.
