History seldom repeats verbatim; it rhymes. We remember the headlines of past bubbles, wars, and revolutions, yet we forget the cultural and institutional scaffolding that made those events possible. That vanishing context is why the past refuses to serve as a neat spreadsheet for prediction. Each generation adopts new tools and fresh certainties while quietly discarding the memories that once imposed caution. What looks unprecedented is often an old pattern dressed in contemporary fashion.
The dream of perfect foresight—Pierre-Simon Laplace’s idea that, with enough information, an omniscient observer could compute every future state—collapses in the face of this shifting backdrop. Human systems evolve faster than their data. Values shift, incentives mutate, technologies create feedback loops no historian has ever seen. Add more data and the picture only grows noisier; yesterday’s lessons turn into today’s blind spots. To navigate such terrain, we must practice unlearning: the willful shedding of outdated maps so we can sense new contours as they emerge.
Large Language Models magnify both the power and the weakness of Laplacian reasoning. They absorb oceans of text and reproduce yesterday’s patterns with dazzling precision. Yet their architecture is built for memory, not forgetting. Once an association is etched into billions of parameters, the model cannot tell when its supporting context has expired or when the world has pivoted to dynamics no corpus ever recorded. The system presumes continuity in a world that delights in rupture.
Long-term forces expose this blind spot. Generational commodity waves, century-spanning geopolitical shifts, even the hundred-year buildup that precedes a major earthquake—these unfold on time scales too slow, and with measurements too sparse, to feed modern algorithms. They are imprecise in timing and magnitude, yet accurate in shaping the fortunes of a living generation. Their signal resides in folk memory, myth, and half-remembered cautionary tales, not in high-frequency numerical trails.
Herein lies a crucial insight: intelligence has a mirror image, a memory antithesis, where progress depends on forgetting the short term in order to grasp the deep pulse of the long term. Current LLMs cannot perform this inversion. They optimize the next token—precision over breadth—while true foresight toggles between pixel-level detail and a wide-angle awareness of slow, fuzzy cycles.
This tension reflects the very logic of complexity. Complex systems thrive on a balance of order and surprise; if every ripple could be forecast, nature would lose its generative power. Order without noise is a contradiction in terms. For the same reason, a fully conquered, perfectly predictable world—a genuine Artificial General Intelligence in command of every contingency—is a pipe dream. To “solve” nature is to extinguish the novelty that makes it worth solving in the first place.
Progress, then, is not a straight line of accumulating facts but a spiral of learning and purposeful forgetting. We must know when to retire once-reliable models, when to distrust precision, and when to let the hazier accuracy of long cycles inform our decisions. Until machines can forget with discernment, humans must remain the custodians of context—listening for the quiet rhyme beneath the noisy data, and respecting the mysteries that continue to elude prediction.
Mukul Pal