Is AI Helping or Hindering Sustainable Development?

AI-exposed companies – semiconductors, software, internet platforms and infrastructure – have driven the majority of the S&P 500’s gains in 2026 to date. While recent financial performance appears clear, far less so is AI’s net effect on sustainable development.

What are the SDGs?

The Sustainable Development Goals (SDGs) were established by the United Nations in 2015 to tackle the world’s most pressing environmental and social challenges. AI is emerging as one of the most powerful tools to help solve them – but it could just as easily set them back.

Pointed at the right problem, AI is already delivering for the SDGs:

• AI diagnostics and predictive analytics enable earlier disease detection and more personalised treatment.
• AI optimises energy systems and renewable efficiency, accelerating the energy transition and improving clean-energy economics.
• AI sharpens climate modelling, helping anticipate catastrophic events and improving how climate risk is priced.

Applied poorly, it can set them back:

• Training and running AI is energy- and water-intensive, and a fast-growing source of emissions.
• AI-driven automation may displace workers and widen inequality.
• AI models may reproduce existing societal biases at scale, disproportionately harming affected groups.

What are the implications across the investment industry?

We see three avenues where the intersection of AI and the SDGs is potentially the most interesting.

First, there is opportunity to capture both commercial return and impact in AI-enabled solutions. For example, in the education sector companies are using AI to personalise learning and break down language barriers that widen access to quality education. Where impact is built into the solution, it can scale with the business, advancing the goal and the return together.

Second, AI is a powerful lever for efficiency, helping companies do more with less. A growing body of evidence suggests that more resource-efficient companies – those with lower carbon, water and waste per unit of output – may outperform peers over the long term. Here, the same efficiency that supports returns can also ease pressure on the climate and resource systems these companies depend on.

Third, AI is making it easier to identify and assess sustainable investments, using greater computing power to streamline analysis and build the commercial and impact case. This helps resolve a historical constraint on SDG-aligned investing, where data limitations, inconsistent ESG ratings and time-intensive analysis have held capital back.

Help or hinder?

AI technology is neutral – what matters is how it is used. Building, powering and training models consumes energy, water and precious resources, and draws on intellectual property that often goes unrewarded. Used poorly, AI adds to the very problems it could solve; used well, it has the potential to build a more sustainable society.