How badly does AI harm the environment? We now have some answers to that question, as both Google and Mistral have published their own self-assessments of the environmental impact of an AI query. In July, Mistral, which publishes its own AI models, published a self-evaluation of the environmental impact of training and querying its model in terms of the amount of carbon dioxide (CO2) produced, the amount of water consumed, and the amount of material consumed. Google took a slightly different approach, publishing the amount of power and water a Gemini query consumes, as well as how much CO2 it produces. Of course, there are caveats: Each report was self-generated, and not performed by an outside auditor.

PaI methods manage to find trainable subnetworks that outperform random pruning, their performance in terms of both accuracy and computational reduction is far from satisfactory compared to post-training pruning and the understanding of PaI is missing.

Nate Soares doesn't set aside money for his 401(k). "I just don't expect the world to be around," he told me earlier this summer from his office at the Machine Intelligence Research Institute, where he is the president. A few weeks earlier, I'd heard a similar rationale from Dan Hendrycks, the director of the Center for AI Safety. By the time he could tap into any retirement funds, Hendrycks anticipates a world in which "everything is fully automated," he told me. That is, "if we're around."