What is AI’s role in financing biodiversity conservation?
Artificial intelligence (AI) has great potential to support the financing of biodiversity conservation. Concerns related to AI’s negative impacts on climate and the environment should be acknowledged. However, AI can assist in the analysis of biodiversity data, reducing investment risks in the process. Furthermore, surging valuations of AI companies that rely on the natural world for inputs could encourage countries to include biodiversity data in their accounting frameworks, making the contribution of biodiversity more visible and promoting the need for conservation.
The context: why do we need to scale up biodiversity conservation finance?
Biodiversity is in decline, particularly in areas exposed to human activity. More than half of the world’s GDP (US$44 trillion) is moderately or highly dependent on nature and ecosystem services — nature’s contributions to people — and as a result is exposed to nature loss. Ecosystem services include the provision of food, energy and medicines, plus materials fundamental for wellbeing and maintaining culture; their degradation threatens our ability to maintain a good quality of life and economic prosperity.
Today, governments provide about 82% of nature conservation funding, the total of which falls far short of what is required. It has been calculated that by 2050, annual investments in nature must quadruple (to US$737 billion) if they are to meet the nature-related targets set out in the Rio Conventions. Past biodiversity targets were missed and current ones are also unlikely to be attained unless investment is ramped up.
Private sector finance, philanthropic grants and technology could help. Technologies that enable nature-based solutions — ‘nature tech’ — are already growing rapidly. AI in particular offers a range of opportunities. For example, it can assist in analysing biodiversity data, thereby reducing investment risks. Biodiversity also provides an essential input factor for training many AI models, a detail which can be drawn on to compel AI companies to fund conservation efforts and ensure future products can be developed based on previously unexploited biodiversity data.
How is AI already being used to analyse biodiversity data — and how can this impact financing?
Conservationists and policymakers face a flood of complex data from satellites, sensors and fieldwork. AI enables them to analyse it more effectively and efficiently. For example, AI already plays a key role in biodiversity monitoring by enabling richer assessments that combine multiple metrics from a variety of data sources, like acoustic recordings, species distribution modelling, environmental DNA, or camera trap footage, in ways that were previously considered ‘inconceivable’. AI can quantify some of the trade-offs between the different objectives of conservation projects. Consequently, AI can enable faster and more targeted responses by, for example, detecting ecosystem degradation or identifying potential poachers. These advancements contribute to making conservation efforts more efficient and targeted, ensuring that funding delivers greater impact per dollar spent. Further, biodiversity data assessments supported by AI that are easier to comprehend, more accurate and thus reduce investment uncertainties can enable increased financial flows.
AI companies could pay directly for biodiversity conservation
A decision adopted in 2024 at the 16th meeting of the Biodiversity COP, the UN’s biodiversity conference, proposes that companies that directly or indirectly benefit from the use of digital sequence information (DSI) and meet specific economic thresholds make contributions to a new fund. Called the Cali Fund, this will channel financing to biodiversity protection projects. DSI is the digital data of an organism’s genomic characteristics, such as its DNA and RNA. Although contributions to the fund are not mandatory, the decision could lead to companies operating in this area, like Google DeepMind (which has developed an AI system called AlphaFold that uses DSI), contributing 1% of their profits or 0.1% of their revenue to actively conserving biodiversity through the Cali Fund.
The Cali Fund is the first fund of its kind to rely on the private sector as the principal contributor. Fund payments will be distributed to eligible Parties to the Convention on Biological Diversity to support the implementation of the Kunming-Montreal Global Biodiversity Framework to halt and reverse loss of biodiversity by 2030. At least 50% of its resources will be allocated to the self-identified needs of indigenous peoples and local communities, including women and youth, who are the stewards of the vast majority of the world’s most critical ecosystems and the custodians of biodiversity. However, although some companies have indicated an intention to pay into the fund in the future, as of early August 2025 no contributions had been received.
Surging AI valuations could speed up inclusion of biodiversity data in accounting frameworks
The digital economy continues to evolve and gain importance and AI companies have been raising significant capital based on eye-catching valuation figures. Access to data is a cornerstone of this AI-driven economic growth, with the market capitalisation of the ‘Magnificent Seven AI wonder stocks’ (seven especially high-performing, dominating stocks) valued at around US$12 trillion. AI startups combining machine learning and gene synthesis, such as LabGenius, Curve Therapeutics and Healx, closed notable funding rounds in 2024. In 2025, Isomorphic Labs, an AI drug-discovery platform, raised US$600 million and was valued at US$3.6 billion. Such synthetic or digital biology companies rely on biodiversity as a source of genetic data that they can use in AI models to develop products such as pharmaceutical drugs and therefore have an interest in supporting biodiversity conservation.
As national accounts focus on ‘values’ where a transaction price can be directly assigned, such as the market price, for many resources this focuses only on their value once they have been harvested or extracted from the environment. However, AI companies that rely on biodiversity data benefit from the protection of biodiversity, rather than its exploitation. This could lead to improved inclusion of the values of biodiversity data in ‘natural capital accounts’, that measure the contributions of natural resources to economic development. Furthermore, making the economic contributions of biodiversity data more visible can help governments prioritise conservation in budgetary and policy decisions. Progress on accounting practices at the national level can, in turn, influence the way companies account for the value of nature and make financial decisions. It can also improve transparency around companies’ dependence on biodiversity data, which is information that investors need for understanding and managing risks associated with potential biodiversity collapse. Providing this information should ultimately help channel more funding towards biodiversity protection across industry sectors as the dependence becomes more visible.
AI-accelerated product development based on biodiversity information could spur investments to protect it
The use of AI has grown significantly, particularly in product and service development. AI can aid the crucial transition away from fossil fuel-based products by accelerating the discovery of new genomes, enzymes and other synthetic biological assets used to develop commercial products. In agriculture for example, alternatives to nitrogen fertiliser like bio-based fertiliser are gaining importance. Overall, this creates investment opportunities across multiple sectors, as AI-driven innovation can unlock new commercial growth while increasing incentives to conserve biodiversity as a valuable input to future products.
While AI models rely heavily on data such as DSI on genetic resources for product development, access to the underlying physical protein structures of organisms remains essential for many companies that use this to create products. For companies with long-term innovation strategies that depend on AI and biodiversity, it is therefore in their business interests to support biodiversity conservation. Protecting biodiversity is crucial not only to preserve existing data sources and product inputs but also to enable future discoveries that could provide data or other inputs.
While ‘free-rider’ issues persist, where companies benefit from the conservation support others provide, there are international agreements that provide a legal foundation to address parts of this challenge. For example, the Nagoya Protocol sets out that companies accessing physical genetic resources need prior informed consent or to negotiate mutually agreed terms with the source country’s government to ensure fair compensation for the biodiversity the country conserves.
This Explainer was written by Lea Reitmeier, and was reviewed and edited by Sarah King and Georgina Kyriacou. The author thanks Sylvan Lutz, Jasmine Kindness, Gustavo Pinilla, Giles Atkinson, Franka Huhn, Elena Almeida and Laudine Goumet for their comments on earlier drafts. Lea Reitmeier authored this commentary when employed as a Policy Fellow at the Grantham Research Institute.
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