We are looking for a few great scientists! Come join our team!

The AI, Interaction, and Learning team at Microsoft Research is looking for interns! If you're working on your PhD in machine learning, computer science, statistics, economics, computational social science or related fields apply: jobs.careers.microsoft.com/g…

The AI, Interaction, and Learning team at Microsoft Research is looking for interns! If you're working on your PhD in computer science, statistics, economics, computational social science or related fields apply: Research Intern MSR AI Interaction and Learning | Microsoft Careers

big fan of this work from the microsoft team! what would be great next: * more from more labs on the where/when/who of AI use * experiments to validate how AI use translates to productivity gains * policy proposals for using data from labs as leading indicators of impact

New paper from my group at @MSFTResearch! 📄arxiv.org/abs/2507.07935 Promises about how AI will change work are cheap. What does the actual data say? We measured which work activities people use AI for, how successful they are, and which jobs do those tasks. 🧵1/8

I had a great time doing this interview recently with @gr3tchen and @ssuri for the @MSFTResearch podcast. We talk about Sid's and my now years-long collaboration/bromance, as well as our recent work on prompt engineering. Please give it a listen! microsoft.com/en-us/research…

Many people believe that AI advances will dramatically increase inequality. In a paper with two Nobel laureates, Daron Acemoglu and Simon Johnson, plus 30 multidisciplinary experts, we argue that it’s more complex than a simple “rich-get-richer” story. For example, we coined the term “inverse skill bias” to describe an emerging pattern: generative AI seems to benefit low-skilled workers more than high-skilled ones. We also suggest generative AI may reduce racial and gender bias in healthcare and education. However, some inequalities could indeed worsen. For example, companies with access to more data may gain an anticompetitive advantage, exerting market power over smaller firms. Additionally, companies may be incentivized to automate work rather than invest in enhancing and complementing human capabilities. Gender bias in career achievement may also worsen, as preliminary evidence shows that men are using chatbots more than women, leading to an increase in productivity among men but not women. We argue institutions will play a critical role in sharing AI’s benefits equitably. Unfortunately, current regulations fall short of addressing inequalities and fostering shared prosperity. Our paper ends with six policy suggestions we believe can help reduce socioeconomic inequality: 1) Create a more balanced tax structure, equating marginal taxes on hiring, training, and AI investments. 2) Engage workers and civil society in AI shifts, and establish data unions for control over data. 3) Boost support for research into human-complementary AI tools to enhance productivity and skillsets. 4) Train professionals, especially in healthcare and education, in AI use, including ethical aspects. 5) Invest in tools to counter AI-generated misinformation and in education on misinformation. 6) Embed AI expertise in government for sector-wide decision support. Read the full paper here: academic.oup.com/pnasnexus/a… Thank you to an amazing list of coauthors, without whom this work wouldn’t have been possible: @AustinLentsch @DAcemogluMIT @SelinAkgun9 Aisel Akhmedova @EBilancini @JFBonnefon @BehSnaps @lu_butera @Karen_Douglas @JimACEverett Gerd Gigerenzer @chrisgreenhow @Laparoscopes @PCASOLab @jholtlunstad @jetten_j @baselinescene @werkunz @longoni_chiara Pete Lunn @simone_natale Stefanie Paluch @iyadrahwan Neil Selwyn @viveksinghmed @ssuri Jennifer Sutcliffe @JoePTomlinson @Sander_vdLinden @PaulvanLange @FriederikeWall @jayvanbavel Riccardo Viale

🚨 A new working paper that tackles the big question: "Will prompt engineering still matter in the future?" 🤔 bit.ly/3YdExbt This is joint work w/ @eamanjahani, @BenSManning, @joe_z_zhang, @ocolluphid, @malsobay, @CNicolaides and @ssuri. More details below 👇 1/

🚨 Announcing a new working paper measuring the causal impact of generative AI on business performance in emerging markets! osf.io/preprints/osf/hdjpk This is joint work w/ @nickgotis (who led the project) @RowanPClarke @solenedelecourt @orgRem. More on our results below 👇 1/

We are visualizing our data all wrong. Typically, academic graphs show statistical confidence intervals or standard errors. This paper shows that everyone from doctors to tenured faculty misread these graphs, assuming effects are larger. Show variability! jakehofman.com/pdfs/illusion…

I bet the Trojan horse really ruined the horse dating scene. If I were a horse dating other horses, I would constantly be like, "prove to me that you aren't a small militia."

Are you recent college graduate wishing to gain research experience prior to pursuing a PhD in fields related to computational social science: apply now to MSR-NYC's predoc microsoft.com/en-us/research… work on awesome projects w/ some of the best in the field cc/ @dggoldst @jakehofman