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This study examines over 5,000 machine learning scientists' career trajectories during the deep learning revolution initiated by AlexNet in 2012. The research reveals that persistence in traditional methods, while associated with higher productivity, became detrimental to scientific impact after the paradigm shift. Previously successful scientists and those in established teams adapted more slowly to deep learning approaches, demonstrating that accumulated expertise can become a liability during major technological transitions.
Why it matters
The findings challenge the assumption that persistence is always beneficial in science and highlight how paradigm shifts can create opportunities by devaluing incumbent expertise. This has implications for career strategy in rapidly evolving fields, research funding decisions, and understanding innovation dynamics in scientific communities.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextual nature, particularly how persistence can become a liability during paradigm shifts. We focus on the deep learning revolution catalyzed by AlexNet in 2012. Analyzing the 20-year career trajectories of more than 5,000 scientists active in top machine learning venues during the preceding decade, we examine how their research focus and output evolved. We first uncover a dynamic period in which leading venues increasingly prioritized cutting-edge deep learning developments, displacing traditional statistical learning methods. Scientists responded to these changes in markedly different ways: those who were previously successful or affiliated with established teams adapted more slowly. Such persistence is positively associated with productivity but, after 2012, negatively associated with scientific impact. Most researchers, and the largest share of the field’s output, cluster in a band of moderate persistence, pointing to a trade-off between output and impact, as well as to institutional frictions that make larger departures costly. These conclusions are robust to alternative identification strategies and to competing explanations such as topic popularity premiums and survivorship bias. Taken together, our macro- and micro-level findings suggest that, in this case, a paradigm shift creates an opportunity structure by devaluing the very expertise that conferred incumbents’ advantage in the first place.
Source: Persistence Paradox in Dynamic Science: Evidence from the Deep Learning Revolution