Kevin Boudreau (@KevinJBoudreau) header
Kevin Boudreau (@KevinJBoudreau) profile picture

Professor @Northeastern | @NBERpubs | strategy, innovation, entrepreneurship, platforms, science | prev @Qualcomm @TheEconomist @DeloitteStrategy @Startups

USA, Canada
Joined December 2010
Photo by Kevin Boudreau (@KevinJBoudreau)Photo by Kevin Boudreau (@KevinJBoudreau)Photo by Kevin Boudreau (@KevinJBoudreau)Photo by Kevin Boudreau (@KevinJBoudreau)Photo by Kevin Boudreau (@KevinJBoudreau)Photo by Kevin Boudreau (@KevinJBoudreau)
Rideau River, Canada
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A very interesting, and IMO mechanistically plausible, argument. It the sort of argument scholars of AI in science *could* be measuring/testing but currently aren't
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Kevin Boudreau retweeted
Seeing this study being widely re-shared with the take away that now AI >= human in peer review, in every dimension. People need to tone it down a little... This is primarily GPT evaluating Claude's comments. To me, the more useful signal is the 3 past editors of unnamed journals rating human vs Claude reviews, but this is only related to just 10 papers, it's not clear the editors have specific expertise in the paper topics or methods, and it reads as though the editors looked at the reviews but not the actual papers. I know we will need AI in the loop in some fashion to manage the paper deluge. I also realize that AI is already widely used in peer review and it's better to step in to manage / govern that process than let people do whatever they want. In my experience, AI certainly surfaces useful / good comments, particularly on methods, but many of the comments can be pedantic. Many models also still have terrible judgment about the other aspects that determine a study's value, e.g., whether the question is important. We're not yet at the point of ditching the human. Note: I'm pushing back here on the interpretations people are sharing when circulating this work, not the discussion in the paper itself.
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Randomized Experiments in the Science of Science: Lessons from Peer Review and the Evaluation of New Knowledge, @nberpubs chapter papers.ssrn.com/sol3/papers.… An analytic synthesis of all journal-published RCTs in #ScientificEvaluation and #PeerReview.
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Kevin Boudreau retweeted
Great exercise and apparently the answer is ‘no.’ Which is interesting given the magnitude of investments taking place. nber.org/papers/w34243
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Still the most undervalued AI company
Infrastructure is capital-intensive, and we’re building a lot of it. Time to get back to work.
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Kevin Boudreau retweeted
In retrospect it is surprising that OpenAI released o1-preview. As soon as they showed off reasoning, everyone copied it immediately. And if they had held off releasing a reasoning/planning model until o3 (& called that GPT-5) it would have been a startling leap in AI abilities.
Replying to @polynoamial @OpenAI
o1 is trained with RL to “think” before responding via a private chain of thought. The longer it thinks, the better it does on reasoning tasks. This opens up a new dimension for scaling. We’re no longer bottlenecked by pretraining. We can now scale inference compute too.
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🚨New WP🚨 I used to think AI would replace workers. Studying a database with the near universe of commercial AI (including GenAI + factory, and office tools) I found the opposite: AI increases employment by giving low-skilled workers expert knowledge.🧵
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💥 I’m starting something new inside OpenAI! It’s called OpenAI for Science, and the goal is to build the next great scientific instrument: an AI-powered platform that accelerates scientific discovery.
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Bana, S., Boudreau, K. (2025) — AI-ENABLED JOB MARKETS & MARKET PARTICIPATION: A FIELD EXPERIMENT ON HOW AI SHAPES JOBSEEKERS’ EXPECTATIONS OF COMPETITION Field experiment showing that use and disclosure of AI reduces jobseeker participation by about 25%. lnkd.in/dsERsfy3
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Best analogy for @karpathy Vibe Coding. Is watching @justinbieber conduct an orchestra. With no sheet music, no conducting training—just vibes, hums and vision. And it's the future of how creators and designers will work. We will think in ''concept level'', orchestrating AI to bring ideas to life. @antonosika on the @20vcFund Listening, iterating, and flowing with it. Bieber is like master Vibe-Designer here. It's hypnotic and awesome to watch this tbh. 😎🥰 This is what 'text-to-x' paradigm will do for behavior. And why the @lovable of this era are creating. (sourced from his IG LIVE)
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Kevin Boudreau retweeted
AI labs have managed to grab a significant piece of the profit generated by SaaS companies. Interesting piece by @mims: wsj.com/tech/ai/ai-costs-exp…
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Call for papers: HEC Paris AI & Entrepreneurship Workshop (#3) Topics: Entrepreneurship & AI, and implications of AI for orgs & markets, entrepreneurship, … When: Dec 13 Where: HEC Paris Keynotes: @EmmaBrunskill @lugaricano Deadline: Sept 22 Info: sites.google.com/view/hecpar…
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Is AI already impacting the job market? A new paper from me, @erikbryn, and @RuyuChen at @DigEconLab digs into data from ADP. We find some of the ***first large-scale evidence of employment declines for entry-level workers in AI-exposed jobs.*** A thread on our paper:
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Kevin Boudreau, Lars Bo Jeppesen, Milan Miric "ARTIFICIAL INTELLIGENCE AS A PLATFORM TECHNOLOGY: STRATEGIC IMPLICATIONS OF COMPETING ON TOP OF AN AI PLATFORM". forthcoming chapter in HANDBOOK ON AI & STRATEGY eds. Felipe Csaszar and Nan Jia papers.ssrn.com/sol3/papers.…
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Kevin Boudreau retweeted
Assessing the potential and pitfalls of using Natural Language Processing in social science research on innovation, from @a_bergeaud, Adam B. Jaffe, and @DimitrisPapan20 nber.org/papers/w33821
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Kevin Boudreau retweeted
Between 50 to 60 percent of a scientist's research output is attributable to the institution where they work, and two thirds of this effect is driven by the presence of star researchers, from @amitabhchandra2 and Connie Xu nber.org/papers/w33996
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Does professor quality matter for training the next generation of researchers? Or will good PhD students do good research either way? Quality matters
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