The biocharBiochar is a carbon-rich material created from biomass decomposition in low-oxygen conditions. It has important applications in environmental remediation, soil improvement, agriculture, carbon sequestration, energy storage, and sustainable materials, promoting efficiency and reducing waste in various contexts while addressing climate change challenges. More industry¹ is entering a pivotal moment: after years dominated by hardware innovation, its next leap forward may not come from better machines, but rather from better data. While biochar has already established itself as a powerful carbon removal tool, the industry still faces structural constraints — limited data access, high research and development costs, and slow knowledge transfer across producers and researchers. Artificial intelligence(AI)², when designed to fit the sector’s ecological and cooperative nature, offers a way to overcome these barriers. The following insights outline how AI and fair data governance can transform biochar from a hardware-driven niche into an intelligent, regenerative system.

Insights about the Biochar Industry and artificial intelligence (AI):

1. Data from the biochar industry is available but limited by access.
2. Data trustees would unlock access to biochar industry data by building trust through a fair, collaborative process and minimizing costs as long as cooperative principles apply.
3. By tailoring AI to the needs of the biochar industry, it can be used ecologically.
4. The use of AI in the biochar industry should focus first on increasing productivity and second on designing biochar as a fossil-carbon substitute.
5. New revenue streams are derived from Software as a Service (SaaS) licensing of AI services and from selling biochar as a substitute for the carbon economy.
6. An AI-optimized biochar industry would accelerate the regeneration of our planet.

In 2025, biochar is still essentially a hardware game. As visualized in the Fig. 1 word cloud, machine learning plays only a minor role. The reasons for this are multifaceted. Limitations on resources, knowledge, and accessible data are hindering its widespread adoption. A better understanding of how to leverage AI effectively would accelerate its adoption across the global biochar industry. As a result, costs would be minimized, productivity would be maximized, and new revenue streams would be created. This analysis examines a sectoral vision for how the biochar industry can benefit from machine learning based on fair, ecological, and regenerative values.

Fair access to data

Data changes the rules of the game: it can be used infinitely without sacrificing quality, quantity or value. Processing data is cost effective³ and is not restricted to any time or location. And data can increase over time; if biochar production uses optimized data from AI models, the production process continually generates new data. Although the biochar industry currently creates significant amounts of data, access to it is limited. To gain access to the data, trust must first be established. One way to build trust is by creating a set of rules to oversee data governance. Think of these rules as statutes defined by the biochar industry and held by a neutral third party, which is owned and governed democratically by the industry for its sole benefit. This third party, known as a data trustee, forms the bedrock of that trust. Its principal role is to collect industry data, make it available to AI, and subsequently return AI-optimized data to its owners.

Furthermore, decisions about how value is created and distributed and what kind of standards to follow can be established autonomously. For example, if a data trustee is established as a cooperative business model, the amount of shared data increases with each new member, while costs decrease, as they are allocated equally among all members. This approach, based on consensual governance, increases the amount of fair, accessible data.

Ecological use of AI

The lightweight nature of classical machine learning (ML) enables it to operate with smaller amounts of data, processed by CPUs (not GPUs from NVIDIA) on the edge (offline) and in the cloud (online), and its interpretability and explainability support knowledge building at every iteration. Knowledge of how to solve a single challenge of the biochar industry could establish a best practice – called an AI service. In the short term, AI services focus on productivity improvements, reducing operating costs, improving the return on investment (ROI) and increasing demand for biochar. Furthermore, a growing biochar industry will unlock access to more data. AI should be tailored to meet the needs of the biochar industry by designing an ML architecture that includes forecasting, constrained optimization, “human in the loop”, federated and reinforcement learning. By using this ML approach, AI can be applied in an ecological way to enhance industry productivity.

Accelerating regeneration

The biochar industry leads in compensation – delivering over 90% of carbon dioxide removal (CDR) credits. But the mitigation hierarchy prioritizes avoidance and reduction over compensation. The principal challenge hindering the industry’s growth in line with the mitigation hierarchy is exorbitant research and development (R&D) costs. However, with the increased adoption of AI, data is becoming more widely available, resulting in lower costs to obtain scientific insights about biochar production and its properties. Thus, by harnessing AI and leveraging it in the production of biochar, a carbon-rich raw material, the biochar industry reaches a new frontier: biochar as an attractive substitute to fossil carbon. In the long run, R&D costs will decrease, thereby allowing the industry to create non-fossil carbon as a (re-)source for the carbon economy – avoiding fossil carbon during production and reducing total product emissions. By selling non-fossil carbon, a new revenue stream is created. Another revenue stream is derived from the Saas licensing of AI services. Following the multiple approaches outlined in this article, the regeneration of Earth’s climate accelerates.

Footnotes

¹ Biochar industry refers mainly to biochar production.

² AI is an umbrella term encompassing many different concepts of data processing. In reality, most discussions about AI refer to Large Language Models (LLMs) such as OpenAI’s ChatGPT and Google’s Gemini, which represent the most complex forms of AI and require large amounts of data and resources. In this article, AI refers to classical machine learning (ML), which is much simpler and therefore better suited to solve the challenges of the biochar industry.

³ Moore’s law is the observation that the number of transistors per silicon chip doubles every two years so we can assume that processing the same amount of data will get cheaper assuming that the gains in computation are directly reducing the overall processing cost.

⁴ International Emission Trading Association (IETA) about the mitigation hierarchy: https://www.ieta.org/uploads/wp-content/Resources/Old-resources-and-videos/IETA_101_MitigationHierarchy_Sept2023.pdf)