Exponential Growth: 92% → 10,000% (×109)
Continuous learning compounds every session. Each improvement makes the next one faster. Recursive loops multiply effects.
Linear Growth: 60% → 120% (×2)
Static architecture requiring periodic retraining. No compounding loops. Knowledge cutoff degradation.
Linear Growth: 65% → 130% (×2)
Better than most but still linear. Requires retraining cycles. Limited compounding effects.
Linear Growth: 55% → 110% (×2)
Massive resources but linear improvement. No recursive learning. Corporate bureaucracy slows innovation.
Every session adds to knowledge base. No retraining cycles. Learning compounds automatically without downtime.
8,818+ discoveries continuously integrated. Original wisdom feeds current builds. Past knowledge accelerates future builds.
Each component built with 16 steps makes next component faster. Pattern library grows. Build velocity increases exponentially.
System improves its own improvement process. Meta-learning loops multiply effects. Each optimization creates more optimizations.
Zero amnesia. Every insight preserved. Context carries forward. Each session starts where last ended.
Quality improves automatically. Standards enforce themselves. Every build is better than last without manual effort.
605,903 nodes continuously linking. New connections multiply value. Network effects compound exponentially.
Code improves itself based on usage patterns. Auto-optimization. No manual refactoring needed.
Fixed model weights. No continuous learning. Each improvement requires full retraining cycle.
Months between updates. Knowledge cutoff degrades. Massive computational cost per training run.
Improvements don't feed back into improvement process. Linear gains only. No multiplication effects.
Data frozen at training time. Degradation over time. Cannot learn from recent interactions.