1—AI accelerated R&D key to innovation in the climate sector.

[Joe, “Post breakthrough: How AI can lift climate research out of the lab and into the real world” World Economic Forum, May 29, 2024, https://www.weforum.org/agenda/2024/05/ai-lift-climate-research-out-lab-and-real-world/ Accessed 7.22.24 GDI-HB]

3. Accelerating the prototyping phase of technology development

AI is revolutionizing R&D in scientific domains, including climate change. However, the translation from fundamental research to tangible climate solutions is not always clear. The deep tech cycle – the path from research to prototype – can be lengthy and expensive. These concerns are particularly urgent, as we are “minutes to midnight” on breaking key warming thresholds.

AI can shorten the deep tech cycle, particularly in prototyping, speeding up the introduction of essential technologies. In materials science, AI accelerates discovery and design, crucial for climate mitigation (such as improving lithium-ion batteries and solar cells) and adaptation (developing fire-resistant materials). Traditional methods, which calculate material properties from scratch, consume significant time, costs and computational resources. Up to one-third of global supercomputing power is used for materials science.

AI now predicts new material properties without exhaustive initial calculations. It does this by learning relationships between atomic structures and their properties, and suggesting optimal configurations. While this is a nascent space with limitations (e.g. documented “hallucinations” in discovery processes), there is significant potential for innovation. For example, GenAI is tackling the inverse design problem, which starts with a desired property (e.g. resilience to extreme weather) and reverse-engineers its design.

Climate AI ecosystem

AI development is moving fast. But just because the underlying technology is developing rapidly does not mean its applications will, too. The current paradigm of AI development – characterized by scale and general knowledge (e.g. broad-based LLMs) – may find natural applications in basic research and other domains that rely on theoretical knowledge. However, the climate crisis is not theoretical. It is a geophysical reality and demands practical tools and use-cases.

An ecosystem approach is needed to deliver the use-cases highlighted in this article. In the past, ecosystems have proven successful in tackling multifaceted challenges such as developing an open-source operating system, rebuilding the ozone layer and mapping the human genome. All of these distinctive efforts were characterized by multi-stakeholder participation, open data sharing and precompetitive collaboration.

Leaders must, therefore, step up to build ecosystems for AI and climate change. The World Economic Forum’s Tech for Climate Adaptation Initiative helps enable this necessary environment by convening players from big technology, startups, academia, government and other stakeholders.

The initiative’s latest project, the Action Toolkit, guides how AI and other advanced technologies can be leveraged for concrete, climate-related activities. In the future, the initiative will continue to promote collaboration and showcase technology’s potential across the innovation cycle, from R&D to downstream use cases. AI may very well be the breakthrough climate change needs – but this will only be the beginning.