{
  "disclaimer": "Goldirham Insight: educational research, not financial advice. Scores are editorial opinions, not forecasts. Simulated prices are illustrations, not market data.",
  "rows": [
    {
      "symbol": "NVDA",
      "name": "NVIDIA",
      "theme": "The platform every AI model runs on",
      "upside": 8,
      "safety": 7.5,
      "ai_exposure": 10,
      "review_status": "unreviewed",
      "reviewed_by": null,
      "reviewed_at": null,
      "review_sources": null,
      "thesis": "GPUs, CUDA, networking and software — the full-stack toll road for AI compute.",
      "why_it_could_work": "CUDA software moat locks in developers and frameworks | Owns compute AND networking (NVLink, Spectrum, InfiniBand) | Annual product cadence keeps competitors a generation behind | ~80%+ share of AI training accelerators",
      "key_risks": "Customer concentration — a few hyperscalers drive most revenue | Custom silicon (in-house ASICs) chips away at the long-term moat | Any AI-capex 'digestion' phase hits the multiple hard",
      "cycle_stage": "Cycle leader; debate is about durability of data-centre capex, not direction."
    }
  ]
}