The Scaling Delusion
Why scaling increases capability but destroys stability
The Scaling Delusion argues that modern AI systems collapse under long‑horizon load because scaling amplifies geometric instability inside the computational manifold. Capability rises, but stability falls. Drift, curvature loss, deformation, and irreversibility emerge long before behavioural errors appear. The paper introduces K, the stability invariant, and C64, the equilibrium compute regime, forming a closed‑loop architecture that detects and restores geometric integrity under sustained load.
Sections
Sections
1. Core Thesis
Scaling increases capability but decreases stability.
2. The Scaling Assumption
Why the field mistakenly assumes geometry remains stable as scale increases.
3. Physical Systems Expose Instability
Drift becomes force, deviation, cost, and risk.
4. Structural Drift
The root failure mode: widening, flattening, deformation, irreversibility.
5. K — The Stability Invariant
Curvature, recovery dynamics, deformation rate.
6. C64 — The Equilibrium Compute Regime
Low‑force transitions, attractors, dissipation, thermal stability.
7. Closed‑Loop Stabilisation (K <> C64)
Continuous detection + continuous restoration.
8. Economic & Safety Impact
Reliability, fewer cascades, lower energy, stronger safety envelopes.
9. Why This Matters Now
Compute saturation, rising drift, collapse of the scaling assumption.
10. Real‑World Gains
Long‑horizon reliability, lower cost, higher safety, extended lifespan.
11. Conclusion
Capability without stability is unusable; geometry must hold its shape.