Overhaul infrastructure: replace GPU model with rack-centric system

Replace flat GPU buying with a realistic data center + rack pipeline:
- 4 DC tiers (small/medium/large/mega) with construction time, dual
  capacity constraints (rack slots + power budget kW), and era/research
  gating
- 10 predefined rack SKUs from consumer GPUs through custom ASICs, each
  with unique FLOPS, power draw, cost, and pipeline timings
- 6-stage procurement pipeline (order → mfg → receive → install → test
  → production) with Kanban UI, talent-influenced speed bonuses
- Test failures (5-25% base rate) reduced by cooling, ops talent, and QA
  research; auto-repair with cost and re-test cycle
- Production failures at low per-tick rate, racks sent to repair pipeline
- Cooling and redundancy upgrades per DC (reduce failure rates)
- 4 new tech tree nodes (DC Engineering II/III/IV, Quality Assurance)
- Save version bump (1→2) with migration that resets old saves
- Updated economy system to account for rack repair costs
- Redesigned Infrastructure page with pipeline Kanban, capacity bars,
  rack ordering, and DC upgrade panels

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

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

@@ -10,12 +10,6 @@ const ERA_INDEX: Record<string, number> = { startup: 0, scaleup: 1, bigtech: 2,
 function getFieldValue(state: GameState, field: string): number {
   if (field === 'meta._eraIndex') return ERA_INDEX[state.meta.currentEra] ?? 0;
   if (field === 'meta._deployedModelCount') return state.models.trainedModels.filter(m => m.isDeployed).length;
-  if (field === 'infrastructure._totalGpuCount') {
-    return state.infrastructure.dataCenters.reduce(
-      (sum, dc) => sum + dc.gpus.reduce((s, g) => s + g.count, 0), 0,
-    );
-  }
-
   const parts = field.split('.');
   let current: unknown = state;
   for (const part of parts) {