NeurIPS 2026 just desk-rejected hundreds of papers because an AI detector said they were AI-written (https://t.co/P2IDyWWOTB).
I understand why they did it. Reviewer time is scarce, and a flood of slop submissions is a real threat to peer review.
But here's where it gets tricky. The Position Paper Track requires papers be "substantially human-written," so I ran one of my own through the same detector they used. It flagged 73% as AI-written — on pretty weak evidence (see screenshots).
The funny part? It's actually closer to 99%. I only hand-edited a few sentences. But the idea, the literature review, the positioning, the data analysis ... all human. The AI wrote the text, across many rounds of back-and-forth with me.
It's like coding: most code today is generated almost entirely by AI. Do we "reject" those systems too?
To me, whether the words were typed by a human matters less than the substance behind them.
What do you think?
PS. I ran this very post through the detector too. It came back 100% AI-generated — even though it took me almost 30 minutes to write, and I think it carries my thinking and is worth reading.
#AI #PeerReview #NeurIPS #AIDetection #AcademicResearch #MachineLearning #ResearchIntegrity #AIEthics #ScholarlyPublishing #AIWriting
Last September, I left @stripe after 8+ years to build something new with my friend Rob Cheung.
Today, we're launching @SubstrateLabs , the first inference API optimized for multi-step AI workloads.
Modal + @substack 📰🧡
Learn how Substack slashed developer time and complexity by moving their spam detection and transcription models to Modal 🚀
https://t.co/SinsdkXBXu
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