Enviromental Impact of Large Language Models

In this interview, Edoardo Madussi, Head of Business Development, Intelligencia AI discusses the potential impact of AI-driven drug discovery platforms like DeepSeek and Qwen, highlighting their democratizing potential while also acknowledging challenges related to data quality and validation. The conversation explores the potential disruptions to current R&D practices, including the acceleration of drug discovery and the optimization of manufacturing and supply chains.

The discussion also addresses the potential risks associated with relying heavily on open-access AI models, including data security, intellectual property concerns, and the potential for biases in underlying datasets. Finally, the interview touches upon the environmental impact of AI, emphasizing the energy consumption of large language models while acknowledging the potential for AI to improve efficiency and reduce the environmental footprint of drug development.

How does the environmental impact of large language models fit into the larger discussion of technology and sustainability within the pharma industry?
I picture a bit of a negative idea of the energy consumption here. There's a the positive side of the same coin that by using LLMs, or by using AI more broadly to try to improve the efficiency of the pharmaceutical throughput, there is potential upsides of gaining years in the pharmaceutical development, ultimately saving billions of dollars for the rollout of a clinical trial. If you think about the fact that currently on average, for every single clinical trial, we're spending $2.6 billion. The use of AI and the implementation of large language models may reduce this number significantly in the future, and therefore one would expect also an environmental positive impact on the on the resources that are being used within that specific clinical.