The second law of thermodynamics states that the total entropy of a closed system tends to increase over time. Entropy is a measure of disorder or randomness. Ice melts, rooms get messy, machines break down, organisms age — all because disordered states are statistically more probable than ordered ones. This law gives time its direction: the past is low entropy, the future is high entropy.

The microscopic laws of physics are time-reversible — a video of colliding billiard balls played backwards looks physically plausible. But the macroscopic second law has a direction: entropy increases forward, not backward. This asymmetry explains the "arrow of time" — why we remember the past and not the future, why causes precede effects, why we can burn wood but not unburn it.

Entropy also appears in information theory: the more unpredictable a message, the higher its entropy. A completely random string of letters has maximum entropy (no compression possible). A repetitive message has low entropy (highly compressible). Claude Shannon showed thermodynamic and information entropy are mathematically equivalent — information is physical, and erasing information generates heat. This deep connection explains why computing has fundamental energy costs and why living brains consume so much metabolic energy.