Description du poste
Discussion with Claude on AI and global warming: Claude pointed out that climate science isn't so much the main bottleneck in making forward progress, and so we talked about negotiations and using AI to find points of agreement.
I read an article in Bloomberg today about modeling the climate, and took a look also at another article on using AI to improve modeling (unfortunately, there's a paywall with Bloomberg, but these brief comments can be read independently, and I can still link to the chat with Claude, Opus 5.5, medium).
COP31 Climate Talks: Global Warming Defies Efforts to Predict Its Effects by Eric Roston, Laura Millan, Julia Janicki. November 10, 2026. Bloomberg.
AI ‘Scientists’ Help Human Ones Answer Urgent Climate Questions By Eric Roston March 13, 2026. Bloomberg.
Claude verified what I had heard earlier that the hyperscalers aren't going to meet the targets that they had set earlier.
But it's also the case that emissions from data centers are projected to be only a small portion of overall total emissions - now at 0.5% of CO2 emissions, and projected to be 1% of CO2 emissions by 2030.
I asked Claude if the argument is that the net benefit to science outweighs the cost in terms of emissions, and Claude agreed with that, but made another point that I thought was interesting:
The benefits and the costs come from different places. Climate research uses a tiny share of AI computing. The emissions growth comes from the commercial buildout: chatbots, ads, enterprise tools. The article justifies AI for science. It doesn’t show that the whole buildout pays for itself. It’s like defending a whole new airport because one of its flights carries medical supplies.
And Claude also pointed out, as mentioned in the summary for this post, that the issue isn't necessarily even so much climate science:
Better understanding doesn’t cut emissions by itself. We’ve known roughly what’s needed to hit Paris targets for years. The bottleneck has been political and economic, not