Antoine Vernet

@antoinevernet.com

Associate Professor in Management at UCL. Networks and Organizational Design. Mostly found reading. Very occasionally on Youtube: www.youtube.com/@antoinevernet

I find arguments that AI can't do judgement or creativity or taste to be especially obviously false in the time of agents. Any long task requires lots of taste, judgement & creativity. I am much more sympathetic to arguments about the quality or diversity of the AI's taste, judgement, or creativity

Systems that do not have regulatory barriers or gatekeepers to AI are rapidly getting overwhelmed / transformed: mathematics, paper writing, code, etc. For everything else, it'll be slower but it makes sense to start planning now!

7 months ago I read about gas town and today I read the new thing. what worries me now is either he’s gotten less insane or I’ve gotten more insane.

annoying codex thing last week they just dropped luna prices by 80% but you can't actually make luna subagents from sol or terra fr fr Luna is marked as "V1" and Sol & Terra are marked as supporting "V2" subagent API, and there's a Codex check preventing you from mixing them

I'm so happy to announce version 2.0.0 of my #Rstats package WeightIt is out on CRAN! New features: censoring weights, multilevel propensity scores, improved weights for continuous treatments, bias-reduced ordinal and multinomial models, M-estimation in subgroups Check out the website below!

Weighting for Covariate Balance in Observational Studies

Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality ...

ngreifer.github.io

"I hate data centers" is such a weird take. It's like "I hate injection molding facilities". I just don't have that strong of feelings about light industrial buildings.

Alibaba’s Qwen3.8-Max is out, a frontier model they say is competitive with Anthropic’s Fable. It’s a 2.4T parameter model with a 1M context window and will open source the weights next week. Following so shortly after the release of Kimi K3, it’s hard to claim China is months behind the US in AI.

Qwen Studio

Qwen Studio offers comprehensive functionality spanning chatbot, image and video understanding, image generation, document processing, web search integration, tool utilization, and artifacts.

qwen.ai

This. Absolutely this! The bar for what counts as a "publishable paper" has risen considerably, and will keep rising. I was joking to a coauthor this week that, David Card's displeasure notwithstanding, the average WP was going to get longer. We will become a book field--I'm not joking.

Clément Canonne@ccanonne.github.io · 4d ago

🧵To juniors: - Move away from LPUs, at all costs. (Nobody likes to think of their own paper as LPU: what I mean here is "a paper just good enough to be published in a fairly good conf even a year ago") These publications are most likely going to die, or not being valued any more, as flood-prone 2/

Using LLMs should allow us to think bigger, harder to grasp, difficult to execute, longer to develop, ideas. That idea that’s been in you brain since forever but you hadn’t had the time because you had to retool and what not. Trust yourself. Go for that shit. Now is the fucking time.

Clément Canonne@ccanonne.github.io · 4d ago

"We're all worried," as what it means to do research (in my field, Theoretical CS) seems to be shifting, and shifting fast. What to do? Senior researchers must lead by example, knowing that not everything will pan out. What I'm suggesting below may not work everywhere, but here's my own advice: 1/

Cruelty is the foundation of those 'policies'. Making us feel threatened is the purpose. We have no physical proof of our status, we are at the mercy of a glitch. We are aliens, and we must be made aware of it. Reform's 'plan' to strip us of our Leave to remain is just a darker shade of grey.

Lisa O’Carroll@lisaocarroll.bsky.social · 3d ago

Exclusive: Some EU citizens in the UK have been contacted by the Home Office to say they were granted settlement status after Brexit "in error". If you know anyone this happened to contact me on lisa.ocarroll@theguardian.com www.theguardian.com/politics/202...

yes. if I say 'this is my code' about AI gen stuff, that's just shorthand for 'this exists because of my decisions and i take responsibility for it' - and sensible people understand that. this ego thrashing about authorship is undignified and irrelevant. get over yourself.

atticus goldfinch@atticusgf.bsky.social · 3d ago

Every time I see a comment like the screenshot, I feel like I know _exactly_ what kind of dev said this, and would bet money they aren't particularly loved by coworkers, bosses, or clients.

🧵To seniors: - move away from bean-counting (easy, right?). E.g., when evaluating CVs or grants or applicants, ask for the top 3 pubs, with a paragraph explaining why these are important. Hiring on a CV of 50 pubs was always sort of meaningless, now it's become meaningless-er (!) and gameable. 2/

I have read this Response to my Comment and have written a Rejoinder, showing three things: 1. The multiverse is many correlated tests. 2. The randomization inference is wrong. 3. The trichotomous moderator reaffirms my point. I also enumerate 7 misleading claims, now in print in the APSR.

A Rejoinder to ‘Still Instrumentally Inclusive’ (Turnbull-Dugarte & López Ortega)
Daniel de KadtCornell University, Department of Governmentdekadt@cornell.edu
2026-08-01
Abstract Turnbull-Dugarte and López Ortega’s “Still Instrumentally Inclusive” (the Response) answers my replication (the Comment) of their 2024 American Political Science Review paper (the Article) with, among other things, a multiverse of 2,970 analyses based on eighteen different weighting schemes, randomization inference, and a trichotomous moderator. As I show in this rejoinder, each is undermined by bona fide errors in their code, their writing, and their interpretation. First, the multiverse is just many correlated variants of the same test. This is in large part due to the weighting schemes which suffer multiple problems (some due to code errors, some intrinsic), and are largely correlated variations on one weighting scheme. Second, due to code errors the randomization inference compares a weighted statistic against an unweighted null and, in the two heterogeneity tests, permutes the interaction column rather than treatment assignment column. Every permutation p-value the Response reports is therefore computed against the wrong reference distribution; when they are recomputed correctly, two of the five it reports as significant are no longer so, and they are the two the Response emphasizes. Third, the trichotomous moderator is erroneously described as terciles, but it is in fact based on non-tercile cutpoints. The key subgroup effect reported in the Response thus rests on comparing just thirty high-weight control respondents against thirty-one high-weight treated respondents, and ends up reaffirming my original point that the heterogeneity in study 2 is with respect to weights, and not immigration sentiment as their theory predicts. I close by documenting seven claims in the Response that are untrue or misleading.
Cambridge University Press Political Science & IR@cambup-polsci.cambridge.org · last wk.

#OpenAccess from @apsrjournal.bsky.social - Still Instrumentally Inclusive - https://cup.org/4bhrhsp "Do individuals in Western democracies shift their views on LGBT+ inclusion when exposed to opposition from Muslim out-groups?" - @turnbulldugarte.com & @bertous.bsky.social #FirstView

Banner with the hashtag #OpenAccess on a green background and the text "American Political Science Review" on a blue background below.

There's lots of stuff LLMs can't do, but if you want to disbelieve all the recent math, you have to believe a bunch of Fields Medalists with long track records are just straightforwardly lying about the thing they care most about. Here's a recent blog by Gowers: gowers.wordpress.com/2026/05/08/a...

A recent experience with ChatGPT 5.5 Pro

We are all having to keep revising upwards our assessments of the mathematical capabilities of large language models. I have just made a fairly large revision as a result of ChatGPT 5.5 Pro, to whi…

gowers.wordpress.com

OpenAI announces 10 discoveries from their next model. Observations:: 1) AI is getting very good at math 2) Two years ago LLMs failed at basic math 3) This cost less than $2000 in current API fees 4) OpenAI is focusing on announcing benefits, not just risks, of new models openai.com/index/ten-ad...

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