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>

apps/web/src/components/game/CompanyStatsCard.tsx

@@ -13,10 +13,8 @@ export function CompanyStatsCard({ onClose }: { onClose: () => void }) {
   const totalRevenue = useGameStore((s) => s.economy.totalRevenue);
   const valuation = useGameStore((s) => s.economy.funding.valuation);
   const subscribers = useGameStore((s) => s.market.consumers.totalSubscribers);
-  const models = useGameStore((s) => s.models.trainedModels.length);
-  const bestModel = useGameStore((s) =>
-    s.models.trainedModels.reduce((best, m) => Math.max(best, m.benchmarkScore), 0),
-  );
+  const models = useGameStore((s) => s.models.baseModels.length);
+  const bestModel = useGameStore((s) => s.models.bestDeployedModelScore);
   const reputation = useGameStore((s) => s.reputation.score);
   const achievements = useGameStore((s) => s.achievements.unlocked.length);
   const dataCenters = useGameStore((s) => s.infrastructure.totalDataCenterCount);