Globally, manufacturing and industrial processes account for almost a fifth of global carbon emissions, and consume about half of the world’s energy [1]. So decarbonising manufacturing is an essential part of the transition to a more sustainable economy.

A key element of this is reducing the inefficiencies that persist in so many manufacturing processes around the world.  Issues like overuse of resources, system down-time and excess waste are costly for manufacturers and damaging to the planet. But they are not inevitable. They are just symptoms of systems and processes that have historically operated without adequate data and feedback loops – and if operators can’t measure it, they can’t optimise it.

So decarbonising manufacturing does not have to mean regulating industries out of existence. It means making manufacturing more intelligent: improving processes and resource efficiency in ways that are better for the planet and the bottom line.

Thanks to the rapid advances and cost declines in sensors, optics, robotics, and edge computing, we now have an opportunity to do that in ways that simply were not feasible a decade ago.

Manufacturing in the intelligence era

The global market for AI-driven industrial equipment and systems efficiency could be worth $300bn by 2028, according to BCG [2]). And we’re already starting to see evidence of this on the factory floor, thanks largely to the rise of physical AI – robots and machines that can dynamically interact with and learn from their environments.

AI implementations are delivering energy savings of 10-20% at manufacturing facilities, while companies deploying AI to predict faults are reporting reductions in unplanned downtime of up to 50% [3]. If scaled across the global manufacturing sector, gains of this order potentially represent hundreds of billions of dollars in avoided costs – and a substantial reduction in carbon emissions.

Predictive maintenance is arguably the most mature application in intelligent manufacturing. By analysing sensor data from production equipment, AI platforms can identify failure modes weeks before a breakdown occurs, allowing operators to schedule maintenance during planned downtime – rather than scrambling to recover from unplanned stoppages. For capital-intensive industries (such as chemicals, metals, semiconductors etc.), the economic benefits of this are compelling, even before the environmental benefits are considered.

Servo loop control in industrial robotics is another example of how physical AI can improve manufacturing outcomes.

Legacy industrial robots (e.g. as seen in goods manufacturing) are ‘blind’ to their environments, and follow pre-programmed servo loops (instructions that dictate how the motors and robotic arm move through physical space). These robots are generally poor at responding to changing conditions or positions of objects moving along a factory line, causing breakdowns, delays, and waste.

Computer vision systems, leveraging edge computers, can connect directly with different robotic platforms and steer them to increase precision, reduce costly breakdowns, and improve manufacturing efficiency.

In the longer term, the more transformative opportunity of the intelligence era lies not just in optimising existing industrial process – but in reinventing them completely.

Take data centres for example, which are generating increasing amounts of electronic waste as data servers reach end-of-life faster. There are now robotics companies tackling the difficult problem of how to disassemble these millions of devices globally, to recover their valuable parts and materials for reuse in new data servers.

Where intelligence meets impact

For impact-driven investors, this investment theme is particularly compelling because there’s such a strong alignment between commercial returns and measurable environmental impact. The companies best positioned to win are those that help manufacturers reduce waste, use fewer inputs, and extend the life of materials – which also helps to reduce emissions, cut pollution, and use fewer natural resources.

Two external forces are accelerating this alignment. The first is regulatory pressure: the EU’s Corporate Sustainability Reporting Directive, evolving supply chain due diligence legislation, and growing pressure from institutional investors are forcing manufacturers to measure and report their environmental footprint in ways they never have before. This creates clear incentives for manufacturers to optimise and improve their processes – and again, the intelligence era is giving them the tools they need to do that.

The second tailwind is geopolitical. Supply chain disruption and the accelerating fragmentation of global trade is driving a wave of industrial reshoring and nearshoring across North America and Europe. These new factories are being designed and built on digital infrastructure from the outset, so they are far more receptive to AI-enabled optimisation than the legacy plants they are replacing.

Companies that help these facilities run more intelligently – and can prove it quantitatively, with real operational data – clearly have a strong commercial opportunity. And in a sector responsible for nearly a quarter of global emissions, making manufacturing smarter is not a niche sustainability play. It is one of the most important levers available to accelerate the transition to a genuinely sustainable economy.

This is the fourth in our series on the intelligence era of climate technology. Next: why intelligence is critical to Europe’s energy security and competitiveness.

[1]: https://www.iea.org/data-and-statistics/data-product/greenhouse-gas-emissions-from-energy

[2]: BCG, 2026: https://www.bcg.com/publications/2026/the-capital-opportunity-in-ai-enabled-sustainability

[3]: McKinsey Global Institute, The Next Normal in Manufacturing, 2024; Deloitte, 2024 Manufacturing Industry Outlook

In January 2025, the devastating wildfires that tore through Los Angeles destroyed almost 12,000 homes and killed 31 people [1] (while potentially contributing to another 400 deaths [2]). Over 175,000 people were forced to evacuate their homes, and 70% of those displaced had still not returned a full year later [3]. This was the costliest wildfire disaster in US history, with total economic losses estimated to be at least $53bn [4].

Unfortunately, this event was not just a random outlier. It was more the starkest illustration yet of a growing trend: natural catastrophes resulting from rising global temperatures.

The past 11 years have been the 11 warmest in the 176-year observational record. For the first time ever, the three-year average temperature for 2023-2025 was 1.5°C above pre-industrial levels [5] – the supposed limit enshrined in the Paris Agreement. In 2025, natural catastrophes caused $224bn in economic losses, while insured losses exceeded $100bn for the sixth consecutive year [6]. The effects of climate change are no longer hypothetical; they are already happening, with tangible social, environmental and economic costs.

Much of the debate around climate has focused on mitigation: reducing emissions to prevent further warming. This is essential work (and continues to account for most of our own investment activity). But at the same time, we also need to address the consequences of our failure to act faster: the global warming already baked into the climate system, which will intensify for decades.

Investing in adaptation – building the capacity to withstand, respond to, and recover from climate impacts – is not just a necessity in its own right. It’s also a precondition for the transition more broadly, because communities and overwhelmed by climate disruption will struggle to transform in the way decarbonisation requires. Thankfully, the rapid advances in artificial intelligence and data science are now giving us new tools to improve adaptation and resilience.

The intelligence opportunity

Adaptation is, at least in part, an information and risk management problem.

For most of history, climate risk assessment has relied on imperfect historical data: flood maps drawn from past events, actuarial tables calibrated to previous loss patterns. That approach is increasingly inadequate in a world where the climate is moving beyond the realm of past human experience.

AI and data science are enabling a more forward-looking, granular, and dynamic approach to risk management. This can potentially unlock a substantial economic opportunity: BCG estimates the total market for AI-powered climate risk modelling could be worth $75bn per annum by 2030 [7].

Insurance is one sector where the case for investing in adaptation is compelling. Every dollar invested in adaptation today potentially generates more than ten dollars in benefits over the next decade [8]. Of the $417bn in natural disaster losses in 2024, only $154bn was insured – leaving 63% unprotected [9]. And critically, uninsured losses fall disproportionately on the communities with the fewest resources to absorb them.

Delos Insurance, a US-based company we backed at Series A in 2024, shows how better climate intelligence can create scalable market solutions. Delos uses AI and remote sensing data, combined with proprietary wildfire science, to score catastrophic wildfire risk at the individual property level – a granularity that traditional actuarial models simply cannot achieve. They can also update their wildfire models dynamically to respond to changing physical risks. This AI-driven approach to measuring wildfire risk allows Delos to offer home insurance to households in wildfire-exposed markets that incumbent insurers have abandoned, while also providing policyholders with personalised guidance on reducing their specific risks. The impact is tangible: millions of California families who had lost access to affordable insurance can now be served affordably, with intelligently priced coverage that can be the difference between financial resilience and ruin.

The same logic applies across a range of adaptation applications. AI-powered early warning systems are extending the accuracy and lead time of extreme weather predictions, enabling pre-emptive action rather than reactive response. In agriculture, for instance, precision forecasting helps farmers make better planting, irrigation, and harvesting decisions, reducing both yield losses and water use. Applied to infrastructure, predictive intelligence allows operators to reroute power flows ahead of a storm, position maintenance crews before equipment fails, and manage water resources before a drought becomes critical.

What unites these applications is a shift from managing climate risk retrospectively – through disaster relief and recovery – to managing it proactively, through better data and earlier decisions. The economic rationale for this is clear and compelling.

Where intelligence meets impact

For investors like us, what’s particularly attractive about adaptation and resilience as an investment theme is the clear convergence between financial return and social value. The businesses best placed to succeed are those that help customers – whether that’s uninsured homeowners, farmers, infrastructure operators, or city planners – to survive and thrive in a more volatile climate.

There are also positive regulatory tailwinds. Physical climate risk disclosure requirements are tightening across both the EU and the US, creating demand for the data and analytical tools that make credible disclosure possible. Companies that cannot quantify their climate exposure will increasingly face both regulatory and investor pressure to do so. But it’s still early days: the tools to manage climate risk are still being built, the datasets required to power them are still being assembled.

For impact-driven investors, this combination – a large and growing addressable market, clear commercial logic, an early-stage competitive landscape, and measurably better outcomes for underserved populations – is compelling. We must not falter in our attempts to reduce current and future emissions. But thanks to the intelligence era, we also have an opportunity to improve our adaptation and resilience to climate change that is already happening. Getting this right will be an essential part of accelerating the transition to a more sustainable future.

[1] https://www.nature.com/articles/s43017-026-00793-z

[2] https://www.bu.edu/sph/news/articles/2025/death-count-for-2025-la-county-wildfires-likely-hundreds-higher-than-official-records-show/

[3] https://www.calfund.org/news-and-events/a-year-after-la-wildfires-most-survivors-are-still-displaced-and-struggling-financially-department-of-angels-survey-finds/

[4] https://www.munichre.com/rmp/en/the-re-brief/risk-adaptation/what-if-the-next-california-scale-wildfire-happens-in-the-midwest.html

[5] https://wmo.int/news/media-centre/wmo-confirms-2025-was-one-of-warmest-years-record

[6] https://www.munichre.com/en/company/media-relations/media-information-and-corporate-news/media-information/2026/natural-disaster-figures-2025.html

[7] https://www.bcg.com/publications/2026/the-capital-opportunity-in-ai-enabled-sustainability

[8] https://www.wri.org/news/release-wri-study-finds-climate-adaptation-investments-yield-massive-returns

[9] https://www.ajg.com/gallagherre/-/media/files/gallagher/gallagherre/news-and-insights/2025/natural-catastrophe-and-climate-report-2025.pdf

Every day, wind turbines around the world stop spinning and solar PV plants are turned off – not because of a lack of wind or sun, but because the grid cannot absorb the energy these generators produce.

This ‘curtailment’ issue is an expensive problem for the renewables sector. According to a 2025 report, €7.2bn worth of clean energy was generated across seven European countries in 2024 that was never paid for, lost to grid capacity constraints[1]. To take just one example: in Germany, solar curtailment roughly doubled in 2024, then again in 2025[2]. And it’s not just a European issue: in 2024, the state of California alone lost 3.4m MWh of renewable electricity, 29% more than the year before[3].

After twenty-five years of investment in renewable generation and storage hardware, costs have fallen to the point where solar and wind are now the cheapest forms of new electricity in most markets. Electric vehicles are reaching mass-market price points. Battery storage is scaling rapidly (108 GW of new battery storage capacity was deployed worldwide in 2025, 40% more than in 2024[4]). The hardware era has, in many respects, succeeded.

But this has created a new problem: how does a grid designed for predictable, centralised power generation adapt to managing millions of distributed, intermittent sources in real time?

Clearly investment in grid infrastructure is urgently required (Goldman Sachs estimates that approximately $720bn in grid upgrades will be needed by 2030[5]). But this is only part of the answer. To solve this incredibly complex coordination challenge, we also need to make our infrastructure more intelligent.

The intelligence opportunity

Fortunately, AI-enabled software is beginning to address this gap in ways that were simply not possible even five years ago.

For instance, demand forecasting platforms can now predict electricity consumption patterns with a precision that fundamentally changes how utilities and energy traders operate. Grid optimisation tools can dynamically balance supply and demand across distributed energy resources like rooftop solar, battery storage, EV charging and industrial loads. AI-based predictive maintenance systems can identify faults in transformers and transmission infrastructure before they cause outages; the IEA estimates that this could reduce outage durations by 30–50%[6]. And remote sensors, autonomous drones, and AI-based management can unlock additional transmission capacity on existing lines, without a single new cable being laid. The same IEA report suggests that AI-based grid management tools could unlock up to 175 GW of additional transmission capacity – equivalent to about 70% of the entire installed wind capacity of the European Union.

This clearly represents a substantial market opportunity. The global AI-in-energy market is projected to grow from around $9bn in 2025 to nearly $59bn by 2030, a compound annual growth rate of almost 37%[7] .

Demand forecasting, the application that most directly addresses the curtailment issue, is the fastest-growing segment within this. And the companies best-placed to capture this opportunity share a common characteristic: they were built by founders who deeply understand the operational reality of energy markets, as well as the technology.

Take Amperon, a US-based company we backed at Series A in 2022. Its founder spent over a decade trading power at Tenaska, EDF, and E.ON before building an AI-powered electricity demand forecasting platform that delivers three times greater accuracy than the industry standard. That domain expertise is exactly why utilities and energy traders trust Amperon with consequential decisions, enabling the business to increase revenue by more than 4x between 2022 and 2025.

Battery storage is another area where the economic logic is clear. AI-driven analytics – such as those developed by Accure, a German company we backed at Series A – can enhance battery performance by more than 20% and extend operational life by 25% or more. That is a material improvement in the economics of storage projects that are often valued over 15–20-year horizons, and a meaningful reduction in the materials and manufacturing cost incurred when replacing battery assets prematurely.

This opportunity is so timely and compelling for investors because it reflects the convergence of two significant forces. The first is the sheer volume of data now being generated by energy infrastructure – via smart meters, grid sensors, EV charging networks, weather stations, satellite imagery. And the second is the exponential increase in computational capability that enables us to process and act on this data.

Where intelligence meets impact

It’s also important to remember that the environmental impact of solving (or failing to solve) the grid intelligence problem is enormous.

Curtailment is not just a financial inconvenience for renewable energy developers. Every megawatt-hour of wind or solar power that is wasted because the grid cannot absorb it is a megawatt-hour that must be replaced by something else – usually gas peakers. More accurate forecasting means grid operators can integrate higher shares of intermittent renewables with confidence, use gas peakers less frequently, and move faster towards a cleaner generation mix.

That’s why the IEA projects that the widespread adoption of existing AI applications in energy and industry could deliver 1,400 million tonnes of CO₂ reductions by 2035 – equivalent to roughly 5% of global energy-related emissions in that year, and four times more than the emissions generated by AI data centres themselves. A 2025 study led by Nicholas Stern and co-authors at the LSE[8] puts the potential higher still: between 3.2 and 5.4 gigatonnes of CO₂e annually by 2035, driven largely by improvements in power systems, transport, and industrial efficiency. These are not purely speculative numbers, based on technologies yet to be invented: they are based on AI applications that exist today, applied to existing infrastructure. The constraint is deployment, not invention.

The pressure to solve the clean energy integration challenge is ramping up, particularly in Europe: the REPowerEU programme is targeting 42.5%-45%[9] renewable electricity penetration by 2030. By backing technology businesses that can make the grid more intelligent and efficient, investors have a huge opportunity to accelerate this transition and unlock economic value – while also helping to build a cleaner, more sustainable world.

This is the second in our series on the intelligence era of climate technology. Next: adaptation and resilience.

[1] https://beyondfossilfuels.org/wp-content/uploads/2025/05/REPORT_FINAL.pdf

[2] Federal Network Agency (BNetzA) via Clean Energy Wire: https://www.cleanenergywire.org/news/solar-power-curtailment-rise-germany-grid-expansion-lags-behind

[3] US Energy Information Administration: https://www.eia.gov/todayinenergy/detail.php?id=65364

[4] IEA: https://www.iea.org/reports/global-energy-review-2026/technology-battery-storage

[5] Goldman Sachs: https://www.goldmansachs.com/insights/articles/ai-to-drive-165-increase-in-data-center-power-demand-by-2030

[6] IEA, Energy and AI, April 2025

[7] MarketsandMarkets, 2025

[8] Stern, N. et al., Green and Intelligent: The Role of AI in the Climate Transition, npj Climate Action (Nature), June 2025

[9] https://energy.ec.europa.eu/strategy/repowereu-phase-out-russian-energy-imports/repowereu-4-years_en

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