How IP can power the AI-driven cleantech revolution

July 2025 | SPOTLIGHT | INTELLECTUAL PROPERTY

Financier Worldwide Magazine

July 2025 Issue

The convergence of artificial intelligence (AI) and clean technology (cleantech) is rapidly transforming industries across the globe. Paradoxically, AI is both the main driver of a growing energy demand and also a key source of innovative cleantech solutions to meet this demand.

Within this dynamic landscape, intellectual property (IP) plays a pivotal role, providing a framework for companies to secure their innovations, attract crucial investment and establish a competitive edge.

The rapid pace of innovation at the nexus of AI and cleantech demands a proactive and strategic approach to IP management – one that enables companies to capitalise on emerging market opportunities while mitigating potential risks.

The AI-cleantech nexus

The nexus between AI and cleantech is twofold. On the one hand, it is well known that AI is energy intensive. One AI search query, such as with ChatGPT, consumes approximately 10 times the energy of a Google search.

Moreover, as AI models advance and become more prevalent, more energy is required to run, power and maintain them. Tech companies are turning to clean energy infrastructure and forming alliances to pursue cleantech projects to drive AI systems and meet data centres’ power demand. For example, recent reports indicate that Google has partnered with a utility in Nevada to purchase power generated from geothermal energy beneath the Earth’s surface, Amazon purchased a nuclear-powered data centre and Microsoft agreed to purchase nuclear power generated at Three Mile Island once it reopens.

On the other side of the spectrum, AI offers powerful solutions and enhancements to the cleantech sector. The global renewable energy market size was estimated at $1.14 trillion in 2023 and is projected to hit around $5.62 trillion by 2033. As this sector grows, AI is expected to play an increasingly important role by helping to make cleantech systems more advanced, efficient and adaptive.

AI’s capacity to process vast data sets and make predictions about energy supply and demand will facilitate efficient energy production and delivery. These predictions, powered by AI forecasting models, will improve the planning and management of cleantech projects. In industrial settings, AI optimises operations, reducing consumption and waste, and finds efficiencies, leading to cost and energy savings. AI is also proving useful in enhancing existing cleantech infrastructure and technologies.

For example, AI-enabled predictive maintenance can extend the lifespan of existing infrastructure (such as solar panels or wind turbines) by detecting potential issues before they lead to critical failures. AI is also aiding the integration of renewable energy into power grids through advanced grid management and grid enhancements.

By processing historical and real-time data, AI algorithms can address the intermittent availability of wind and solar energy, using weather analysing tools to predict energy demand and supply, and optimising the integration of battery and grid dispatch during periods of unavailability.

Furthermore, energy storage is essential to the deployment of renewable energy systems, and AI is being used to improve energy storage and battery performance.

Outlook for IP and cleantech

A recent International Energy Agency (IEA) report noted that while AI has the potential to drive the clean energy future, AI innovation is potentially underrepresented in the cleantech sector.

The report indicates that around 1 percent of energy-related patents reference the use of AI as part of the patented innovation, and only 2.3 percent of energy start-ups have an AI-related value proposition – which is lower than the 7 percent for life sciences and 4.3 percent for agriculture, and substantially lower than the AI-related share of all venture capital funding from 2020-24 at 15 percent.

However, several of the problems that the cleantech industry is facing are exactly the problems that AI is good at solving, such as the need to balance performance trade-offs for an optimal outcome and data analytics to improve efficiency and connections.

One contributing factor, identified by the IEA report, for a lower share of investment and fundraising related to AI and energy start-ups, is the nature of the energy industry. The energy industry has historically focused on reliability and safety, making it reluctant to adopt fast-paced, novel AI solutions.

This can lead to AI tools being transplanted into energy companies, rather than grown organically from within energy companies based on internal needs. As such, AI’s potential in the cleantech sector is largely being realised through incremental improvements to operations rather than through the emergence of entirely new, AI-driven product designs. Thus, there still exists substantial opportunities for AI to accelerate energy innovation.

Value of IP for cleantech AI assets

As AI continues to percolate through the cleantech industry – advancing some low-hanging fruit such as improvements to grid management and tackling larger challenges like discovering new battery material – IP remains a key differentiator for start-ups and established players alike.

According to a joint report by the IEA and the European Patent Office, cleantech companies with at least one patent or pending application attract a disproportionately large share of venture funding – offering investors greater confidence in the defensibility and uniqueness of the underlying technology.

Patents serve multiple strategic purposes: they protect core innovations, deter competitors and strengthen negotiating leverage in financing rounds, joint ventures and M&A. For investors, a well-structured IP portfolio signals that a company not only has technical merit but also understands how to protect and commercialise its innovation at scale.

But not all valuable innovations lend themselves to patenting. Trade secrets, proprietary datasets and software models often hold equal or greater value, particularly for companies using AI to improve grid optimisation, predict equipment failure or manage renewable energy storage.

These types of innovations are often difficult to reverse engineer and operate on the back-end of energy systems, making secrecy a practical and effective protection strategy. Trade secret law offers a flexible framework for protecting model architectures, training data and algorithmic refinements that may not meet the technical disclosure or subject matter requirements of patent law.

As a result, trade secret protection is increasingly being relied upon to safeguard AI-related innovations or datasets that might not meet the evolving standards of patent eligibility or are too valuable to disclose in exchange for limited patent protection.

Strategic licensing is another vital piece of the cleantech IP landscape, particularly where university-originated innovations or public-private collaborations are involved. Many tech transfer agreements still rely on traditional frameworks with field-of-use restrictions, royalty-based models and ambiguous terms around data ownership, which may not align with AI- and software as a service-driven business models. Cleantech AI companies should carefully negotiate these terms to ensure freedom to operate, especially where rights to modify or commercialise derived datasets are critical.

Finally, as large energy infrastructure players seek to integrate AI capabilities into their operations, cross-licensing arrangements have become a promising model. For example, an AI start-up may provide access to proprietary models in exchange for access to real-world operational data from a utility provider – creating mutual value without triggering traditional licensing costs.

These collaborative IP structures can accelerate commercialisation and deployment of cleantech AI, particularly in capital-intensive or regulated environments where exclusive control over all inputs and outputs is neither feasible nor efficient.

Conclusion

The intersection of AI and cleantech represents a fertile ground for innovation. IP is a critical enabler in this revolution, providing the necessary framework for companies to protect their novel technologies, attract essential investment and establish a sustainable competitive advantage.

As AI continues to drive advancements across the cleantech landscape, a proactive and strategic approach to IP management will be essential for companies seeking to capitalise on the immense opportunities.

Margaret Welsh and Michael Silliman are partners and Katherine Corry is an associate at Baker Botts LLP. Ms Welsh can be contacted on +1 (212) 408 2541 or by email: margaret.welsh@bakerbotts.com. Mr Silliman can be contacted on +1 (713) 229 1464 or by email: michael.silliman@bakerbotts.com. Ms Corry can be contacted on +1 (713) 229 6213 or by email: katherine.corry@bakerbotts.com.

Margaret Welsh, Michael Silliman and Katherine Corry

Baker Botts LLP