Abstract: Set against the backdrop of the 1939 debate between Alan Turing and Ludwig Wittgenstein on the nature of paradoxes, this article examines how the pursuit of logical consistency—a cornerstone of the classical scientific paradigm—has limited humanity’s ability to comprehend complex, real-world systems. It argues that data-driven deep learning and the resulting large-scale models represent a new class of tools for understanding and modeling complexity by transcending the constraints of strict consistency. The revolutionary significance of these tools extends beyond a mere transformation of scientific instrumentation; they hold the potential to be catalysts for a broader scientific revolution.