Is Nvidia Still the Best AI Stock to Buy in 2026, or Is This Challenger?

Nvidia remains the author’s preferred AI stock, citing $215 billion in latest full-year revenue, up 65% year over year, and a 1,300% share gain over five years. Broadcom is presented as a strong but non-direct competitor, with AI revenue up more than 100% to $8.4 billion and a 2027 target of over $100 billion in AI chip revenue. The article argues Nvidia’s valuation has become especially attractive versus forward earnings, while noting Vera Rubin availability is still on track for later this year.

Analysis

The key market implication is not that AVGO is “rising to NVDA’s level,” but that AI capex is fragmenting into two monetization regimes: generalized training/inference silicon versus custom ASICs plus networking. That usually expands the total addressable market for both, but it also changes bargaining power with hyperscalers: as custom silicon adoption rises, the largest buyers gain leverage on unit economics and can pressure gross margin mix across the ecosystem. The secondary winners are the pick-and-shovel vendors around packaging, advanced substrates, optics, and high-speed interconnect, because custom deployments are more architecture-specific and require more adjacent infrastructure per dollar of compute deployed.

The near-term catalyst path is better for NVDA, while the medium-term multiple case may be better for AVGO. NVDA has a cleaner earnings setup because product cadence plus supply tightness can keep revisions positive over the next 1-2 quarters; that typically matters more than narrative when the stock is already large and liquid. AVGO’s upside is more dependent on design-win visibility converting into 2027 revenue, which is a longer-duration story and more exposed to hyperscaler budget scrutiny if AI monetization evidence lags capex growth.

The contrarian risk is that the market is underestimating how much of today’s AI spend is still pre-revenue infrastructure, which makes any slowdown in cloud monetization a valuation shock rather than a growth shock. If hyperscalers push out orders, AVGO can de-rate faster than NVDA because the market is paying for a longer-dated custom-chip annuity that is harder to verify quarter by quarter. Conversely, if custom ASIC share expands meaningfully, NVDA’s premium could compress without any demand problem, simply because the market starts capitalizing AI growth at a lower multiple for more commoditized training workloads.

Key Decisions for Investors

• Long NVDA into the next earnings window / product cycle as the cleaner near-term revision story; use pullbacks to build, but trim if post-print guide stops inflecting upward.
• Pair trade: long NVDA / short AVGO over 3-6 months if the market is overpaying for 2027 custom-chip visibility; the trade works if NVDA retains earnings momentum while AVGO’s growth becomes more back-end loaded.
• Long AVGO only on a 10-15% retracement or after confirmation that hyperscaler AI capex is still accelerating; otherwise the stock is vulnerable to a multiple reset because expectations are more distant.
• Overweight beneficiaries of AI infrastructure adjacency—ANET, AMAT, and optics/interconnect names—because the second-order spend intensity rises when both general-purpose and custom AI architectures scale simultaneously.
• Hedge crowded AI beta with a small NVDA put spread into major tech capex commentary if you think sentiment has outrun immediate monetization; the risk/reward is favorable because a capex pause would hit the whole complex, not just one name.