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Links to the Research:
MIT Technology Review write up: https://www.technologyreview.com/2026...
Full Study: https://arxiv.org/pdf/2607.27191
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Princeton researchers just ran a first-of-its-kind test of the AI industry's boldest promise: that AI will soon improve itself with almost no human oversight. They gave frontier AI agents six days, $3,000 in compute, and every resource they asked for — and both papers were unambiguously rejected. But the machine didn't fail the way you'd think. It aced every task. What it couldn't do was the job. In this video: the tasks vs. jobs distinction, the five failure modes, and the Turing Award-winning math that explains exactly which parts of your work AI can take — and which it can't touch.
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Links
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https://www.brendandell.com/freelance...
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(Per request: Extended for an Additional 50)
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0:00 The AI Self-Improvement Myth
2:06 The Princeton Research Experiment
5:02 Tasks Versus Jobs: The Critical Gap
8:48 Five Reasons Why AI Research Failed
12:20 The Ladder of Causation Explained
16:42 The Future of Work and Human Demand
Brendan Dell examines a recent Princeton study investigating whether AI agents can conduct open-ended research. The analysis explores the fundamental architectural limitations of large language models and distinguishes between task-based execution and professional judgment, providing a framework to help workers understand the future of their roles in an increasingly automated landscape.
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