Viewing enriched Twitter post
The mainstream view of AI for science says AI will rapidly accelerate science, and that we're on track to cure cancer, double the human lifespan, colonize space, and achieve a century of progress in the next decade. In a new AI Snake Oil essay, @random_walker and I argue that… https://t.co/p5OBUluVyg
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"text": "The mainstream view of AI for science says AI will rapidly accelerate science, and that we're on track to cure cancer, double the human lifespan, colonize space, and achieve a century of progress in the next decade. \n\nIn a new AI Snake Oil essay, @random_walker and I argue that AI's impact could be precisely the opposite: AI could slow rather than hasten science. \n\nThe production-progress paradox\n\nScientific papers have increased 500-fold since 1900, with funding and researchers growing exponentially. Yet genuine progress—measured by disruptive discoveries, new scientific terminology, Nobel-worthy breakthroughs, and research productivity—has remained constant or declined. Multiple metascience studies confirm this troubling disconnect between production and progress. \n\nAI could worsen this by making it even easier to chase productivity metrics while homogenizing research approaches.\n\nScience is not ready for software, let alone AI\n\nScientists are notoriously poor software engineers, lacking basic practices like testing and version control. Papers rarely share code, and when they do, it's often error-riddled. AI has already led to widespread errors across 600+ papers in 30 fields, with many COVID-19 diagnosis papers proving clinically useless. Science needs to catch up to 50 years of software engineering—fast.\n\nAI might prolong the reliance on flawed theories\n\nAI excels at prediction without understanding, like adding epicycles to the geocentric model (which improved predictive accuracy) rather than discovering heliocentrism. \n\nScientific progress requires theoretical advances, not just predictive accuracy. AI might trap fields in intellectual ruts by making flawed theories more useful without revealing their fundamental errors.\n\nHuman understanding remains essential\n\nScience isn't just about finding solutions—it's about building human understanding. AI risks short-circuiting this process, like using a forklift at the gym. Evidence shows AI-adopting papers focus on known problems rather than generating new ones.\n\nImplications for the future of science\n\nIndividual researchers should develop software skills and avoid using AI as a crutch. More importantly, institutions must invest in meta-science research, reform publish-or-perish incentives, and rethink AI tools to target actual bottlenecks like error detection rather than flashy discoveries. Evaluation should consider collective impacts, not just individual efficiency.\n\nFinal thoughts\n\nWhile we ourselves use AI enthusiastically in our workflows, we warn against conflating individual benefits with institutional impacts. Science lacks the market mechanisms that provide quality control in industry, making rapid AI adoption particularly risky. We're optimistic that scientific norms will eventually adapt, but expect a bumpy ride ahead.",
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