Yann Lecun published the most heretical AI paper of the year.
He opens by arguing Magnus Carlsen isn't good at chess and only gets more unhinged from there.
The Turing Award winner and his co-authors dropped a paper demanding the AI industry abandon its biggest obsession, AGI.
Right now, everyone from Silicon Valley CEOs to politicians assumes AGI is the ultimate goal. A machine that can do everything a human can do.
LeCun argues that this entire concept is a biological illusion.
Humans do not possess "general" intelligence. We are highly specialized biological machines, tuned by evolution simply to survive in the physical world.
We only think our intelligence is "general" because we are completely blind to the millions of cognitive tasks we are incapable of comprehending.
Which brings us to the chess argument.
Magnus Carlsen is the greatest human chess player in history. But compared to a modern computer? He is fundamentally terrible.
Our belief that Carlsen is "good" at chess is pure human-centric bias. He isn't objectively good. He's just better than the rest of us, who are biologically awful at it.
LeCun says we need to stop building AI to mimic human generality.
Instead, he proposes a new North Star: SAI.
Superhuman Adaptable Intelligence.
Instead of trying to build a machine that mimics our flawed, biologically-limited brains, we need to embrace extreme specialization.
SAI is about the speed of adaptation.
It is an intelligence that can learn to exceed humans at any specific, economically important task.
More importantly, it is designed to fill the vast skill gaps where humans are fundamentally incapable.
Things like managing global energy grids in real-time. Or predicting complex molecular structures.
The entire AI industry is obsessed with building a digital reflection in our own image.
LeCun's paper is a brutal wake-up call.
Particles don’t fall; they follow the shortest paths through curved spacetime.
The geodesic equation from general relativity, showing how a particle’s position x(τ) obeys the motion governed by the Christoffel symbol Γ^α_βγ, which encodes the twists in the spacetime metric g_μν.
It is essential for real-world tech like GPS, where satellites must correct for gravitational time dilation and orbital curvature to deliver accurate positioning on Earth.
Smaller may be better when it comes to transistors.
But what if the next leap in computing is embedding geometries that compute through resonance itself?
Enter quartz crystals.
They’re already extraordinary:
stable oscillators,
coherent frequency regulators,
piezoelectric transducers.
But viewed another way, they are structured resonant manifolds.
Traditional silicon computing pushes electrons through etched pathways.
Crystalline computing could eventually work more through:
wave interference,
phase coupling,
standing resonances,
topological information flow.
Less “current moving through wires.”
More geometry constraining the possible states information can occupy.
Which is interesting because the same structures keep reappearing everywhere:
helices,
lattices,
toroids,
braids,
crystal symmetries.
Biology uses them.
Plasma uses them.
Photonics uses them.
Even AI latent spaces increasingly resemble them.
At a certain point it becomes difficult to ignore: geometry may not just carry computation.
The geometry itself is the computation.