Is Nvidia Stock a Buy in September 2026?

Nvidia reported $96.2 billion in latest-quarter revenue, up 106% year over year, with hyperscaler revenue doubling to $48.7 billion and enterprise/AI-cloud revenue rising to $40.3 billion in Q2 2026. The early-August shipment of the Vera Rubin platform provides a potential new inference and agentic-AI growth driver, but likely contributed little to the reported quarter. At a roughly $5.4 trillion market capitalization and 29x P/E, the article argues that continued earnings growth—not multiple expansion—will be required, while customer-developed AI chips and uncertain infrastructure demand remain key risks.

Analysis

The key underwriting issue is no longer AI demand visibility but NVIDIA’s share of AI infrastructure economics. As systems shift toward inference and agentic workloads, NVDA can defend dollar content through networking, software and rack-scale integration—but hyperscalers have the strongest incentive to internalize the lowest-margin, most predictable inference workloads. That creates a mix risk: revenue can remain strong while incremental gross-margin and valuation support weaken.

Over the next 1-3 months, the stock likely trades on evidence that the Rubin ramp is additive rather than a customer digestion event. The relevant datapoints are hyperscaler capex guidance, lead times, NVLink/networking attach, and management commentary on inference utilization—not aggregate AI spending headlines. A miss in any one of these may have an outsized multiple effect because a premium valuation leaves little room for a pause in earnings-estimate revisions.

AMZN, GOOG, META and MSFT are not straightforward losers from custom silicon: successful deployment improves their AI-service gross margins and reduces supplier concentration. The second-order beneficiary is likely the cloud platform with the best ability to monetize lower-cost inference; the risk is that savings are competed away through price reductions, benefiting enterprise customers rather than shareholders. Consensus may be too binary on custom ASICs: they are a workload-specific margin tool, not a near-term replacement for NVIDIA in frontier training or rapidly changing model architectures.

Maintain structural AI exposure, but express it through relative value rather than unhedged NVDA beta. The thesis is falsified positively by sustained acceleration in NVDA data-center gross margin and forward estimates after Rubin availability; it is falsified negatively by a material capex reset from two or more hyperscalers, declining networking attach, or evidence of custom-silicon workloads displacing premium GPU clusters.

Key Decisions for Investors

• Do not add outright NVDA into strength ahead of the next earnings cycle; retain existing core exposure only with a 1-3 month review tied to forward revenue and gross-margin revisions. Add only after a post-results pullback if forward EPS estimates remain intact; the principal risk is multiple compression rather than an immediate revenue collapse.
• Initiate a 3-6 month relative-value basket: long MSFT and GOOG versus short a smaller notional NVDA position. This captures cloud/software monetization and potential inference-cost deflation while limiting broad AI-factor exposure; cover the short if NVDA demonstrates accelerating networking/software mix and renewed upward EPS revisions.
• Use NVDA put spreads dated 3-6 months out as event-risk protection rather than selling stock outright. Structure strikes around a 10-20% downside window, where a capex-guidance disappointment is most likely to re-rate the multiple; avoid expensive far-out-of-the-money puts unless implied volatility is unusually low.
• Create an earnings watchlist for AMZN, GOOG, META and MSFT: increase the relative-value trade only if aggregate capex remains elevated while management discloses higher custom-silicon utilization or lower inference unit costs. Without those disclosures, custom-chip displacement remains an unverified narrative rather than a trade signal.