LLMs just leveled science for global researchers – 90% output boost… at what cost?
You’re a dev from Asia grinding bio papers in broken English. LLMs hit, and boom – your output jumps 90%. That’s the reality from a Berkeley/Cornell study: AI surges manuscripts 50%+ across fields, peaking for non-native speakers.[4]
Post-ChatGPT, scientists using LLMs saw arXiv output +33%, bioRxiv/SSRN +50%. Non-English (esp. Asian) gains dwarf Westerners’ 24-46%. Tools polish prose, letting merit shine over language barriers.[4]
For AI devs, this means broader datasets: AI search (Bing Chat) unearths fresh pubs/books traditional tools miss, expanding knowledge bases. But peer review strains – 16% ICLR reviews LLM-tainted, echoing 13% bio abstracts.[3][4]
Vs. pre-LLM era, productivity wins but quality dips? More papers, flooded systems. Prism-like tools could counter by verifying proofs rigorously.[2][4]
Audit your workflows with LLM detectors, prioritize quality metrics. Track EPFL’s 5-year bet: AI proposing hypotheses independently. Game-changer or chaos?
Source: UC Berkeley Haas