Pizu, Emilio Saldaña

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🚨 SHOCKING: AI can now generate a full research paper for $15, and I honestly had to sit with that number for a second because it changes the whole economics of publishing. A new 65-page paper called “AI for Auto-Research” breaks down how far this has already gone. These systems are being tested across almost every part of the research process: coming up with ideas, searching papers, writing code, running experiments, making charts, drafting manuscripts, simulating peer review, writing rebuttals, and turning papers into slides, posters, videos, project pages, and social posts. The wildest examples are buried in the paper. The AI Scientist generated complete research papers at roughly $15 per paper. FARS ran for 228 hours, used 11.4 billion tokens, and produced 100 papers, which works out to one paper every 2.3 hours. ARIS reportedly ran more than 20 GPU experiments overnight, removed weak claims, and improved a draft score from 5.0 to 7.5 through review and revision loops. That sounds insane on the surface, but the scary part is what happens after the paper exists. A paper can now have a clean title, a polished abstract, organized sections, good-looking figures, citations, experiments, and a confident conclusion, while the actual science underneath may still be fragile. The code may run while testing the wrong thing. The idea may sound original until someone tries to implement it. The review may sound intelligent while missing the hidden flaw. The rebuttal may promise revisions that never actually make it into the final work. This is where research gets weird. The cost of producing a paper is collapsing, but the cost of trusting a paper is about to rise. A serious reader will have to inspect more than the PDF. They will have to ask where the idea came from, which papers were used, whether the code matched the method, whether the experiments were actually run, whether the claims followed from the evidence, and whether the final paper preserved the original trail of proof. The paper makes one point that feels obvious once you see it: AI is useful when the task is structured, grounded, and easy to check. It becomes risky when the task depends on taste, judgment, novelty, responsibility, and knowing which result actually matters. That is probably the real future of AI research. Faster writing will become cheap. Better verification will become the edge. Because once the internet gets flooded with research-looking papers, the valuable person will be the one who can tell which ones actually deserve to exist. Paper: AI for Auto-Research: Roadmap & User Guide on arxiv