111 summarised stories about AI & Academia, each linking back to the original source. Browse all topics →
Anthropic 25
Claude is expanding its support for scientific research by launching Claude Science and expanding the AI for Science credits program. It is opening 10,000 free seats and offering premium seats with 5x usage limits for $15 per month under a Claude team plan for one year. Access will broaden over the next months to other scientific fields and more AI-for-Science credits will be available up to $50,000 per project.
404 Media 1 month ago 47
A Samsung and University of Warsaw preprint paper argues that large language models repeatedly generate the same nonexistent expert names, which then appear as co-authors across hundreds of AI-generated academic documents. It reports that Zenodo records include 1,655 ghost-authored entries claiming nonexistent journals with fabricated publication dates. This makes academic-record contamination easier to detect via name patterns, but the accuracy may drop as more AI-generated content is added and then retrains future models.
OpenAI 1 month ago 8
A randomized study assessed how students use ChatGPT alongside critical-thinking training on a real university assignment. It included more than 1,000 students. The results indicate changes in student thinking and performance tied to ChatGPT use plus the training, rather than a purely technical effect.
Allen Institute (AI2) 1 month ago 41
Ai2 partnered with the Paul G. Allen Research Center at the Providence Swedish Cancer Institute to run AutoDiscovery on cancer datasets. The collaboration analyzed The Cancer Genome Atlas (TCGA) and found a stronger immune signature in invasive lobular breast cancer, covering about 15% of breast cancer cases diagnosed in the US each year. AutoDiscovery is now being deployed in Providence’s own cloud to support hypothesis generation on protected research and clinical data, with findings validated across independent datasets and lab analyses.
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16 October 2026
AI finance took center stage: the IMF warns that the build-out’s most immediate market risk isn’t model capability, it’s leverage. In a review of 14 publicly disclosed deals since 2023, the fund found hyperscalers backing AI with about $650 billion in debt against roughly $300 billion in equity, while committing to spend $1.4 trillion going forward. The prescription is blunt—limit sector leverage and liquidity risk through sector-specific capital requirements and constraints on bank lending concentration—because when funding rests on borrowed money, sentiment swings turn into funding crunches.
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