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/pages/CompetitorsPage.tsx

@@ -22,9 +22,7 @@ const ARCHETYPE_COLORS: Record<string, string> = {
 export function CompetitorsPage() {
   const rivals = useGameStore((s) => s.competitors.rivals);
   const industryBenchmark = useGameStore((s) => s.competitors.industryBenchmark);
-  const playerBest = useGameStore((s) =>
-    s.models.trainedModels.reduce((best, m) => Math.max(best, m.benchmarkScore), 0),
-  );
+  const playerBest = useGameStore((s) => s.models.bestDeployedModelScore);
   const era = useGameStore((s) => s.meta.currentEra);
   const money = useGameStore((s) => s.economy.money);
   const acquireCompetitor = useGameStore((s) => s.acquireCompetitor);