Overhaul model system with multi-stage training, variants, benchmarks, and eval

Replace the single-stage training + flat capability score with a realistic AI
development pipeline: pre-training with Chinchilla scaling laws, SFT with
specializations, alignment with safety/capability tradeoffs (RLHF/DPO/Constitutional),
model families with distillation/fine-tuning/quantization variants, named benchmark
suite with compute-costing eval jobs, and segment-specific market quality.

Phases 1-6 of the model rework plan: new types, engine rewrite, save migration,
training events/risk system, concurrent training, variant creation, benchmark
evaluation with leaderboard, and market integration.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

packages/game-engine/src/systems/fundingSystem.ts

@@ -35,9 +35,6 @@ export function canRaiseFunding(state: GameState): { canRaise: boolean; nextRoun
 export function computeValuation(state: GameState): number {
   const revenueMultiple = state.economy.revenuePerTick * 86400 * 365;
   const subscriberValue = state.market.consumers.totalSubscribers * 500;
-  const capabilityValue = Math.pow(
-    Math.max(...state.models.trainedModels.map(m => m.benchmarkScore), 0),
-    2,
-  ) * 1000;
+  const capabilityValue = Math.pow(state.models.bestDeployedModelScore, 2) * 1000;
   return Math.max(100_000, revenueMultiple * 10 + subscriberValue + capabilityValue);
 }