{"id":"thesis-ai-infrastructure","category":"thesis","title":"AI Infrastructure Supercycle","summary":"Hyperscaler capex, semiconductor demand, power and cooling, edge inference buildout.","trigger":{"always":false,"sectors":[],"tickers":["NVDA","AMD","MSFT","AMZN","GOOGL","META","INTC","AVGO","SMCI","ARM","AMAT","LRCX"],"regimes":["trending_bull","range_bound"]},"ttl_hours":336,"content":{"thesis":"AI infrastructure represents a multi-year capex supercycle. Hyperscalers have committed $300B+ combined in 2026 AI infrastructure spend. This flows through: GPUs → networking → power infrastructure → cooling → edge silicon.","primary_beneficiaries":{"NVDA":"80%+ market share in AI training GPUs — pricing power intact through 2027","AMD":"MI300X gaining traction in inference — credible alternative to NVDA at 70% cost","AVGO":"Custom ASICs for hyperscalers — massive TAM from GOOGL TPU, META MTIA, AMZN Trainium"},"second_order_plays":{"AMAT":"CVD and etch equipment demand driven by advanced packaging for AI chips","LRCX":"Etch equipment exposure to HBM memory ramp for GPU stacks"},"risks":["Capex cycle peak — hyperscaler ROI on AI spend still unproven at scale","Export control expansion to additional countries","Power availability constraining datacenter builds in key markets"],"time_horizon":"2-4 years","conviction":"high"}}