China's AI labs are splitting into two camps:
1. Innovation-first players pushing boundaries
2. Distillation-focused teams optimizing existing models
The gap is widening. Labs chasing pure innovation are burning cash on compute and talent. Meanwhile, distillation shops are quietly shipping profitable products by making existing models smaller, faster, cheaper.
Here's the alpha: distillation isn't just cost-cutting—it's a moat. When you can run GPT-4 level reasoning on a phone, you own distribution.
Watch which Chinese AI companies are filing distillation patents vs model architecture papers. That tells you who's building for exits vs who's building for IPOs.
The real question: can innovation labs pivot to distillation fast enough before they run out of runway? Or will the distillers acquire the innovators' IP for pennies?
This dynamic mirrors crypto L1s vs L2s. Pure innovation (L1s) vs practical scaling (L2s). We know how that played out.
1. Innovation-first players pushing boundaries
2. Distillation-focused teams optimizing existing models
The gap is widening. Labs chasing pure innovation are burning cash on compute and talent. Meanwhile, distillation shops are quietly shipping profitable products by making existing models smaller, faster, cheaper.
Here's the alpha: distillation isn't just cost-cutting—it's a moat. When you can run GPT-4 level reasoning on a phone, you own distribution.
Watch which Chinese AI companies are filing distillation patents vs model architecture papers. That tells you who's building for exits vs who's building for IPOs.
The real question: can innovation labs pivot to distillation fast enough before they run out of runway? Or will the distillers acquire the innovators' IP for pennies?
This dynamic mirrors crypto L1s vs L2s. Pure innovation (L1s) vs practical scaling (L2s). We know how that played out.