Nvidia Is Officially the Largest Stock in the World. Is the Artificial Intelligence (AI) Giant Still Cheap?

Nvidia is being framed as attractively valued at 23.5x forward earnings versus 22x for the S&P 500, despite expectations for another major growth leg in 2027. The article cites $1 trillion-plus in 2027 AI hyperscaler data-center capex and 41% expected revenue growth next year, implying the market has not priced in Nvidia's future earnings power. The piece is bullish on NVDA, though the stock may be more of a valuation-and-expectations call than a new catalyst-driven move.

Analysis

The market is treating NVDA like a mature megacap when the demand curve still looks like a pre-cycle infrastructure buildout. If hyperscaler capex really re-accelerates into 2027, the key second-order effect is not just more GPU shipments; it is tighter long-duration supply, richer mix from networking/optics, and more pricing power on the ecosystem as customers prioritize time-to-compute over cost efficiency. That makes the earnings bridge into 2027 potentially more levered than the headline revenue growth suggests.

The bigger signal is what this implies for the rest of the AI stack. If capex is still compounding at that pace, the next leg of winners is likely to shift from pure compute to adjacent bottleneck providers: high-speed interconnect, memory, and power/thermal infrastructure. Conversely, custom silicon efforts at the hyperscalers become more of a negotiation tool than a true substitution threat in the next 12-24 months, because the opportunity cost of deployment delays remains enormous.

The market is underpricing duration risk around the AI trade, but the asymmetry is that NVDA’s multiple can compress even while fundamentals remain strong if investors decide 2027 growth is already fully discounted. The setup is therefore less about whether the business is good and more about whether the stock can continue to outrun elevated expectations. Any sign of capex digestion, deployment slippage, or gross margin pressure from mix normalization would hit the name hard because the valuation cushion is thin.

Contrarian take: the consensus is probably still too focused on absolute capex rather than returns on that capex. If hyperscalers start demanding evidence of monetization, spend could shift from broad buildouts to more selective deployment, which would still support leaders but likely cap the multiple expansion thesis. In that regime, the better risk/reward may be owning the enabling bottlenecks and hedging NVDA beta rather than chasing the core name outright.

Key Decisions for Investors

• Maintain a tactical long NVDA into any post-earnings or post-guidance pullback, but size it as a momentum/estimate-revision trade rather than a long-duration compounder; target a 1-2 quarter horizon and cut if the market stops rewarding 2027 visibility.
• Pair trade: long NVDA / short a basket of AI capex proxies with weaker operating leverage if hyperscaler spending looks crowded; the idea is to own the category leader while fading names that need perfect execution to monetize the same spend cycle.
• Build a long basket in AI bottlenecks adjacent to NVDA — specifically networking and memory beneficiaries — for a 6-18 month horizon; these tend to capture the second-order margin expansion when compute demand stays tight.
• Use out-of-the-money NVDA put spreads as a hedge against multiple compression over the next 3-6 months; risk/reward improves if the stock rallies into earnings while expectations keep rising faster than fundamentals.
• For more conservative exposure, prefer a basket approach over a single-name overweight: own NVDA plus selective suppliers, then reduce NVDA concentration if forward guidance implies the 2027 growth bridge is already fully embedded in consensus.