1 Reason to Buy Nvidia Stock Right Now

Nvidia CFO Colette Kress said AI infrastructure spending could reach $3 trillion to $4 trillion annually by the end of the decade, implying roughly 358% growth from the expected $765 billion this year to a $3.5 trillion midpoint in 2029. The article argues Nvidia is best positioned to capture that spend, citing strong demand for Blackwell and the upcoming Vera Rubin platform, while noting the stock trades at 23.8x forward earnings. Sell-side analysts expect revenue to grow 45.6% annually and adjusted EPS 48.8% annually from fiscal 2026 to fiscal 2029.

Analysis

The market is still underestimating how much of the AI capex cycle is effectively a toll road for NVDA. If the spending trajectory is even directionally right, the key second-order effect is not just higher unit shipments but sustained pricing power in the highest-value nodes of the stack, which keeps gross margin and EPS compounding even if unit growth normalizes. That matters because the stock no longer needs a heroic multiple expansion; a mid-20s forward P/E can re-rate modestly if earnings revisions keep moving up over the next 2-4 quarters.

The more interesting read-through is to the supply chain: accelerated Blackwell/Vera Rubin adoption should pull forward demand for advanced packaging, HBM memory, photolithography, and foundry capacity, creating bottlenecks that favor the most constrained upstream vendors rather than broad semis. The risk is that this thesis becomes self-limiting if hyperscalers and sovereign buyers begin staggered digestion in late 2026 to 2027, which would show up first in order timing, then in lead-time normalization, and only later in weaker guide. In other words, the near-term setup is still favorable, but the inflection risk is more about backlog quality than a collapse in AI demand.

The consensus is treating NVDA as a pure beneficiary of a secular megatrend, but the more subtle issue is concentration risk: as AI capex becomes a bigger share of tech budgets, any pause in cloud monetization or model ROI could trigger abrupt multiple compression across the entire AI basket. That said, the article’s implied skepticism is probably too cautious on timing; even if the final addressable market is overstated, the next 12-18 months likely still support upward estimate revisions because deployment remains constrained by supply, not demand. The asymmetric setup is therefore to own the few names that can convert capex into earnings fastest while fading the lower-quality AI exposures that need perfect adoption to justify valuation.

Key Decisions for Investors

• Long NVDA on 1-3 month pullbacks; target a 15-20% upside window into the next two earnings cycles, with risk defined by any guide that implies backlog digestion or margin normalization.
• Pair trade: long NVDA / short a basket of overextended AI software names with limited near-term monetization visibility; thesis is that hardware monetizes first and best over the next 6-12 months.
• Add selective exposure to upstream AI bottlenecks via a basket of HBM/advanced packaging/foundry proxies over 3-9 months; these are the cleaner second-order beneficiaries if capex stays hot.
• For traders, buy NVDA call spreads 3-6 months out to express upside with defined downside; prefer spreads over outright calls given event-driven volatility and rich implied vol.
• Avoid chasing broad AI thematic ETFs into strength; if hyperscaler capex comments soften over the next two quarters, beta-heavy baskets are the first to de-rate.