Nobel laureate Yang Chen-Ning, whom I personally think was one of the greatest Chinese ever lived, famously said in 1980, when asked about his opinion on the next ten years of high-energy physics: “In the next ten years, the most important discovery in high-energy physics is that ‘the party’s over.’ ”
He was right, of course. The symmetry-breaking euphoria of the ’70s gave way to the long winter of null results. The accelerators grew, the budgets swelled, but the surprises stopped coming. It was as if nature herself had pulled down the blinds and whispered, enough for now.
But perhaps the party did not end—it merely changed venue. While physicists mourned the silence of their colliders, another kind of machinery began humming quietly in the background. Not cyclotrons or bubble chambers this time, but GPUs. Not hunting for particles, but for patterns.
Today, AI researchers train networks deeper than any potential well, and tune loss landscapes as delicate as Feynman’s integrals. They, too, speak of symmetry, invariance, duality—only now these symmetries live not in spacetime but in data. The tensor, once a tool for fields and curvatures, now describes thoughts and pixels.
Some old physicists scoff: “You’ve mistaken computation for comprehension.” Yet one suspects that if nature had a sense of humor, she would find this fitting—that intelligence itself became the next frontier of physics. After all, what is learning but the renormalization of information? What is consciousness but the universe performing gradient descent on itself?
So perhaps the party was never over—it simply moved from the laboratories of matter to the laboratories of mind. And if we listen closely to the soft whirr of cooling fans in a data center, we might just hear the echo of the same music that once played at CERN: the sound of curiosity, endlessly trying to model the infinite.