2010s

The 2010s were the decade biology became computational. CRISPR gave us editing; machine learning gave us pattern recognition at scale. The citations from this period share a common thread: the assumption that with enough data, any system becomes predictable. Business absorbed this same faith—hence the explosion of 'data-driven decision making.' The breakthroughs were transformative. CRISPR-Cas9 (Doudna and Charpentier, 2012) made gene editing accessible to any competent lab. The microbiome revolution revealed that humans are ecosystems, not individuals—our bodies contain more bacterial cells than human cells, and those bacteria influence everything from immunity to mood. Machine learning moved from academic curiosity to commercial deployment: deep learning recognized images, translated languages, and beat humans at games that seemed to require intuition. The blind spots were significant. Both biology and business underestimated the irreducibility of complex systems. Machine learning could recognize patterns but couldn't explain them; it could predict but couldn't understand. The microbiome turned out to be far more complex than early enthusiasm suggested—probiotics mostly don't work, and personalized medicine based on gut bacteria remains elusive. The assumption that more data always yields better predictions proved false for systems with genuine uncertainty. Business absorption was enthusiastic and often superficial. 'Big data' became mandatory vocabulary. Companies hired data scientists faster than they could define what they wanted from them. A/B testing became religion, even for decisions that couldn't meaningfully be tested. The deeper insight—that biological systems are robust precisely because they don't optimize for single metrics—was largely ignored in favor of KPI dashboards. The legacy of 2010s citations is mixed. Kahneman's Thinking, Fast and Slow (2011) correctly identified how systematic biases shape decisions. Harari's Sapiens (2014) popularized big-history thinking. The citations that aged best are those that pushed back against data triumphalism: work on complexity, ecological resilience, and the limits of prediction. Read 2010s citations with awareness that their optimism about data-driven solutions often proved premature.