AI: Measuring the Footprint and Managing Risks

The question is no longer whether to measure the footprint of AI, but how. With investments in more sustainable systems, standardization efforts, and public frameworks in preparation, the industry is working to equip itself for this evaluation. Against this backdrop, a quantified economic promise and notable energy consumption impose transparency requirements on companies.

Companies, Standardizers, and Regulators Engage in Measurement

Tech companies are funding initiatives aimed at making their AI systems more sustainable and ethical. The Partnership on AI is working to establish standards and best practices for measuring impact. Meanwhile, governments and authorities are designing frameworks to regulate the use of AI, with a focus on the rigorous assessment of its footprint. These dynamics reflect an awareness of the stakes and a willingness to act. On the research side, teams are exploring quantification methods that incorporate social and environmental dimensions, work that is presented as essential for guiding strategic and ethical choices.

Economic Promise and Energy Costs Set the Evaluation Framework

According to some analyses, artificial intelligence could generate up to $15.7 trillion in additional economic output globally by 2030. However, this scenario involves a notable energy impact: training a large language model can consume as much electricity as several households over a year. Thus, the growth of AI is associated with significant energy expenditure, reinforcing the call for a rigorous evaluation of the economic and environmental resources mobilized.

Transparency, Accountability, and Risks for Companies

The adoption of AI can enhance operational efficiency, reduce costs, and enable more personalized services. However, the rapid rise of these systems raises ethical and regulatory challenges, where the transparency and accountability of algorithms are deemed crucial. Companies are called upon to justify their use of AI and to demonstrate that they measure and manage its impacts responsibly; otherwise, their reputation and consumer trust may be affected. As AI becomes widely adopted, measurement goes beyond raw data and requires interpreting its effects. The development of evaluation tools and methodologies is presented as imperative, with current decisions shaping the future integration of AI, hence the necessity for stakeholders to remain vigilant and engaged.