When key problems in science are revisited from the computational viewpoint, occasionally unexpected progress results. There is a reason for this: Implicit algorithmic processes are present in the great objects of scientific inquiry—the cell, the brain, the market—as well as in the models developed by scientists over the centuries for studying them. This unexpected power of computational ideas, sometimes called “the algorithmic lens,” has manifested itself in these past few decades in virtually all sciences: natural, life, or social, for example, in statistical physics through the study of phase transitions in terms of the convergence of Markov chain-Monte Carlo algorithms, and in quantum mechanics through quantum computing. This talk will focus on three other instances. Almost a decade ago, ideas and methodologies from computational complexity revealed a subtle conceptual flaw in the solution concept of Nash equilibrium, which lies at the foundations of modern economic thought. In the study of evolution, a new understanding of century-old questions has been achieved through surprisingly algorithmic ideas. Finally, current work in theoretical neuroscience suggests that the algorithmic point of view may be useful in the central scientific question of our era, namely understanding how behavior and cognition emerge from the structure and activity of neurons and synapses.