Mike D’Aurizio and Christophe Defert

To date, an influx of capital and talent has led to the emergence of key hardware and infrastructure to support the climate transition: photovoltaic solar cells, lithium-ion batteries, drones, robots, satellites, and even bioengineered molecules.

As production of this hardware increased, costs fell – which further boosted production, which further reduced costs, and so on. Today, the unit economics of these technologies are so attractive that they’re displacing incumbent technologies across power generation, transportation, manufacturing, and other industries. Now the biggest barrier to progress is deployment and optimization: making these proven technologies work more efficiently and effectively at increasing scales.

Advances in artificial intelligence are giving entrepreneurs powerful new tools to address these bottlenecks. This is ushering in a new ‘intelligence era’ of climate technology – and creating some exciting new business opportunities.

Foundation of the intelligence era

The climate intelligence era is shaped by the convergence of three simultaneous global forces:

1 –  The emergence of clear hardware winners

The core technologies behind the clean industrial transition, from photovoltaic solar cells to lithium-ion batteries, follow Wright’s Law: the cost per unit of production falls at a consistent, predictable rate as cumulative production increases. For example, the cost of photovoltaic cells falls 20% with every doubling of cumulative production. As a result, since 1975, the cost of photovoltaic cells has fallen more than 99%¹.

Similarly, the cost of lithium-ion batteries, drones, robots, satellites, and even DNA sequencing have fallen so much that they’ve now crossed commercial viability thresholds, triggering ever-greater deployment waves. Given the time for new hardware to reach mass adoption and commercial viability, newer technologies like perovskite tandem cells struggle to compete today. Despite their many benefits, decades of accumulated production have given their predecessors a cost advantage that will take a long time to overcome, at least within a venture investment time horizon.

2 –  An acute deployment bottleneck

Although these technologies have crossed commercial viability thresholds, system-level constraints are slowing their deployment at scale – from years-long grid interconnection queues to skilled labour shortages, to increasing grid volatility and safety risks.

This is a costly problem. In recent years, landmark government initiatives including the Inflation Reduction Act in the United States and the European Green Deal have mobilized trillions of dollars towards the clean industrial transition. But the deployment bottlenecks are stranding these capital flows and adding years to project timelines. Worse still, these technical challenges are compounding as production increases. When renewables were first added to the grid, volatility was manageable; at 20-40% penetration, it is not.

The faster the transition moves, the more acute – and the more costly – the bottleneck becomes.

3 – AI reaches a critical productivity threshold

The last few years have seen a step-change in AI capability – driven by foundation models, lower compute costs and AI-native software development. This is enabling entrepreneurs to create powerful new tools to address the deployment bottlenecks at scale.

AI can now be used to compress solar engineering timelines from weeks to hours, accelerating project development. It can be used to detect early warning signs of thermal runaway in individual battery cells, enabling intervention before a single failure can cascade through an array of millions of cells. It can be used to downscale drone and satellite imagery, unlocking new applications in infrastructure inspection, crop health monitoring, and catastrophe modelling. And it can be used to shorten the design-build-test-learn cycles of biological process development, facilitating the commercialization of industrial biotechnology.

These are not speculative capabilities. They are proven solutions, being developed today by AI-native companies at the forefront of the transition – leveraging foundation models and machine learning practices to produce software-delivered, cloud-hosted solutions that run on existing compute. The opportunity is when companies pair these AI capabilities with their deep domain expertise and distribution built around customer needs, solving their pain points and accelerating the flywheel of adoption.

This combination forms a durable competitive moat.

A transformative opportunity

After two decades of hardware and software investment and progress, the next ten years can be transformative for the clean industrial transition. In this new climate intelligence era, even as new hardware technologies continue to be developed, value will be created by engineering AI-driven solutions that enable this hardware to operate better than ever, accelerating the transition.

Investing in the climate intelligence era is interdisciplinary. It requires expertise in hardware cost curves, deployment bottlenecks, AI capability trajectories, and evolving regulatory frameworks. It requires traditional venture capital expertise like identifying trends through the noise, building relationships with entrepreneurs, and helping founders scale their commercial and operational teams. And it requires repeatably identifying companies with AI-native solutions, strong founder-market fit, and outcomes-oriented distribution that will separate them from the pack in the years that follow.

Getting this right will build great companies; it will ensure that investing in better outcomes for people and the planet is a source of superior financial return.

Christophe and Mike lead Bridges Climate Transition Partners. In this series, they’ll be sharing their thoughts on how investing in the intelligence era drives alpha.

¹ IRENA (2025), Nemet (2009), Farmer and Lafond (2016), – with major processing by Our World in Data