Replaces the simplified single-subscriber market with a full competitive simulation:
shared TAM with softmax market shares across 4 segments, multi-tier consumer
subscriptions (Free/Plus/Pro/Team) and API tiers (Free/PAYG/Scale/Enterprise),
enterprise sales pipeline (Lead→Qualification→POC→Negotiation→Active→Renewal)
with SLA tracking, developer ecosystem flywheel, technology obsolescence pressure,
seasonal demand cycles, and two new product lines (Code Assistant, AI Agents Platform).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
Replace flat DataCenter/Rack model with Cluster > Campus > Data Center > Racks
hierarchy. Individual rack entities eliminated in favor of statistical batch
simulation using deployment cohorts. Adds tiered network topology (ToR/agg/core)
with proportional outage model, DC retrofitting, bulk operations, and drill-down
UI navigation with breadcrumbs. First cluster and campus are free to preserve
early game flow. Rebalances starting economy ($600K), funding rounds, and
cohort scaling for hypercluster-scale gameplay.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
Leaderboard page with category tabs and score submission, shareable
company stats card with clipboard copy, dynamic regulation system
(compliance costs scale with capability and era, regulatory standing
tracks safety research), 6 geopolitical events (export controls, energy
crisis, natural disaster, AI safety summit, immigration policy, data
sovereignty), safety-capability tradeoff (safety score affects benchmark,
low safety triggers incidents with reputation damage), and enhanced
event consequence handling for regulation and talent types.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
josh