Matthew Chalmers

@matthewchalmers.bsky.social

Computer scientist into Ubicomp, HCI, theory and (a long time ago) data visualisation. Also kind of keen on mountain things, fine food things, and fine food in the mountains.

Someone had just emailed me on a Friday night to ask if I have any thoughts to share on AI in digital govt and I think my answer is that we should focus on making basic services work for everyone and stop worrying about AI

I can see how the team at Anthropic might be getting off on feeling all Renaissance Man in making this kind of funding investment, but - without wishing to sound very unimaginative and old-school here - probably the last thing we need is for tech companies to enclose/colonise humanities research.

The reason I am particularly incensed by this is because Google's biggest "avoided emissions" claim (and they're all flimsy, but this is the worst) is from Google Earth specifically, where Google take 100% of the emissions reductions credit for any wind/solar project ketanjoshi.co/2026/07/01/g...

Google’s total claim for emissions reductions “enabled” by their products is a whopping 41 million megatonnes of CO2-e, more than the company’s entire footprint by a good margin. Most of this comes from the extremely absurd claim that Google Earth (!) enables the siting of clean energy projects – and so Google claims credit for the entire emissions reductions of all those projects for the full year of 2025:



I mean. Come on. There are so many satellite imagery programs out there. They could do the same thing for Google Docs or Gmail and get similarly huge numbers. This is such weak logic, and it’s the bulk of this headline number.Google’s total claim for emissions reductions “enabled” by their products is a whopping 41 million megatonnes of CO2-e, more than the company’s entire footprint by a good margin. Most of this comes from the extremely absurd claim that Google Earth (!) enables the siting of clean energy projects – and so Google claims credit for the entire emissions reductions of all those projects for the full year of 2025:

I am assuming literally zero people will read this footnote, so just in case you were interested in an incomplete list of people who literally cannot stop themselves from doing the thing that they have repeatedly raised public objections to.

Some of the AI company CEOs who have recently signed open letters expressing concern about the direction of AI development include: Demis Hassabis, CEO, Google DeepMind, January 2015, July 28, 2015, July 2018, and May 30, 2023; Mustafa Suleyman, co-founder, DeepMind, later CEO, Inflection AI and Microsoft AI, January 2015, August 2017, July 2018, and May 30, 2023; Shane Legg, co-founder, Google DeepMind, January 2015, July 2018, and May 30, 2023; Elon Musk, later founder and CEO, xAI, July 28, 2015, August 2017, July 2018, and March 22, 2023; Oren Etzioni, CEO, Allen Institute for AI, July 28, 2015; Sam Altman, CEO, OpenAI, May 30, 2023; Dario Amodei, CEO, Anthropic, May 30, 2023 and July 28, 2026; Emad Mostaque, CEO, Stability AI, March 22, 2023 and May 30, 2023; Adam D'Angelo, CEO, Quora, May 30, 2023; Connor Leahy, CEO, Conjecture, March 22, 2023.

Data centres are the horrific hyper-stimulated version of something once relatively harmless (like a golden retriever with rabies), but humanoid robots have always been and will always be a dangerous, expensive and resource-sucking hypey scam, and I will never stop enjoying when they fail

Just think of all the pedagogical innovations and research on best practices for teaching over the decades that have gotten zero support and funding for implementation, but AI has no proven pedagogical value and is literally sucking up all the funding available in higher ed. These are facts.

The anatomy of an AI-supported academic article is becoming more apparent. I desk reviewed ~70 manuscripts this week for our journal. Reckon 20ish had significant AI assistance. On top of hundreds of others recently I'm starting to see patterns. Are other editors are seeing the same or similar? 1/

This paper keeps showing up on my feed. I'm just a guy who studies science but I don't think the message here is that LLMs are crazy powerful. We already know that LLMs aggregate opinions. And we know social scientists don't tend to test hypotheses thought to be 50-50 to go in either direction.

Large language models can predict the results of social science experiments - Nature

Large language models can be used to estimate the results of social science experiments about as accurately as a group of human forecasters—even for experiments published after the...

nature.com