Reimagining AI development for a planet in crisis

The emergence of prosocial AI presents a critical opportunity to address climate challenges while reshaping how businesses deploy artificial intelligence. As Earth Day 2025 arrives with its theme “Our Power, Our Planet,” the dichotomy of AI becomes clear: powerful environmental problem-solving tools paired with substantial energy consumption and resource demands. This tension requires businesses to move beyond viewing AI solely through profit and efficiency lenses, and instead consciously develop systems that serve both planetary and human wellbeing.

The big picture: Prosocial AI—technology developed to benefit humans and planet rather than merely maximize profits—represents a transformative approach to addressing environmental challenges.

• At its core, prosocial AI aligns with a holistic view of wellbeing that considers environmental impact, social dimensions, and long-term planetary health.
• This approach runs counter to traditional AI development, which often prioritizes optimization and efficiency at the expense of environmental footprints and ethical considerations.

Key examples: Various organizations have already begun implementing prosocial AI solutions across the environmental spectrum.

• IBM‘s GreenHorizon utilizes AI forecasting tools to predict air pollution levels in Beijing up to 72 hours in advance, enabling preventative action.
• Microsoft‘s AI for Earth initiative provides technological infrastructure and grants to projects focusing on biodiversity conservation, climate adaptation, and sustainable agriculture.
• Earth Genome’s “Green Infrastructure Support Tool” helps organizations weigh nature-based versus traditional infrastructure solutions, optimizing for both cost and ecological impact.

Business integration framework: The article proposes EARTH—an acronym representing a framework for businesses to implement prosocial AI practices.

• E (Ethical Guidelines): Develop comprehensive ethical boundaries to ensure AI systems are designed with human and planetary wellbeing as priority considerations.
• A (Assessment Mechanisms): Implement robust assessment tools to evaluate the environmental impact of AI development, from data centers to algorithmic design.
• R (Resource Optimization): Focus on creating energy-efficient models that maintain performance while reducing computational demands.
• T (Transparency Practices): Publicly share environmental metrics, enabling stakeholders to make informed decisions and hold companies accountable.
• H (Holistic Evaluation): Adopt metrics that measure success beyond financial performance to include environmental and social impact dimensions.

Why this matters: The exponential growth of AI systems has created an urgent need to address their environmental footprint while leveraging their problem-solving capabilities.

• Computing infrastructure required for advanced AI training can produce carbon emissions equivalent to the lifetime emissions of five cars.
• Without conscious intervention, AI development could significantly accelerate energy consumption at precisely the moment when climate scientists warn of critical planetary boundaries.

The road ahead: Developing prosocial AI requires fundamental shifts in how businesses conceptualize and measure success.

• Organizations must move beyond traditional ROI calculations to incorporate metrics that value environmental stewardship and social impact.
• Industry collaboration will be essential to establish standards and best practices for sustainable AI development.
• Regulatory frameworks will likely evolve to require greater transparency around AI’s environmental impacts, making proactive adoption of prosocial AI principles strategically advantageous.