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/economySystem.ts

@@ -22,7 +22,7 @@ export function processEconomy(
   const talentExpenses = state.talent.totalSalaryPerTick;
   const dataExpenses = state.data.partnerships.reduce((sum, p) => sum + p.costPerTick, 0);
 
-  const bestCapability = state.models.trainedModels.reduce((best, m) => Math.max(best, m.benchmarkScore), 0);
+  const bestCapability = state.models.bestDeployedModelScore;
   const eraIdx = ['startup', 'scaleup', 'bigtech', 'agi'].indexOf(state.meta.currentEra);
   const complianceCost = bestCapability > 30 ? bestCapability * REGULATION_COMPLIANCE_PER_CAPABILITY * (1 + eraIdx * 0.5) / 100 : 0;