Platform pull remains strong as infrastructure buyers standardize on integrated, rack-scale deployments and attach more software and services. The main swing factors are export policy and how fast custom silicon absorbs inference growth.

Nvidia remains the default AI-factory platform; the non-linear upside is expanding monetization from GPUs into rack-scale systems, fabrics, and software/marketplace control planes that turn power, security, and utilization into billable product surfaces through 2030.

Pivotal to “compute supremacy”: it sells the picks-and-shovels for AI cognition at scale and compounds advantage via ecosystem trust, deployment tooling, and platform switching costs.

The company’s edge is increasingly “time-to-usable-compute,” not just chip specs: it standardizes deployment (rack-scale blueprints + ops tooling), raises utilization, and lowers integration risk for buyers. As inference scales into every product and industry, buyers value reliability, security, and predictable throughput—areas where full-stack integration and ecosystem lock-in matter. Competition (custom silicon, AMD, open software) should compress multiples from peak, but durable platform pull plus growing software/ops attach can keep a premium valuation through 2030.

The big risks are (1) multiple compression from already-elevated expectations, (2) hyperscaler bargaining power and custom-accelerator substitution—especially in inference, (3) export-policy volatility (China rules, fees/tariffs, enforcement), and (4) physical bottlenecks (power, cooling, packaging/memory) that can turn demand into delayed revenue.

Core thesis intact; incremental update is that inference-first positioning is becoming more explicit via deals, while policy remains the dominant exogenous variable.

Improved near-term visibility from recent results/guidance and clearer inference strategy modestly strengthened the medium-term upside case versus the prior period.

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