This AI Stock Is Crushing Nvidia in 2026. It's Still a Buy After Soaring 240% This Year, According to Wall Street.

Nvidia remains the dominant AI infrastructure supplier, with nearly 90% share in AI accelerators, 53% annual adjusted earnings growth expected through fiscal 2028, and a 45x P/E viewed as reasonable. DigitalOcean reported Q1 revenue up 22% to $258 million, raised 2026 revenue growth guidance to 26% and 2027 growth to over 50%, and unveiled its AI-Native Cloud for inference and agentic workloads. The article is constructive on both names, though it favors Nvidia more on valuation and competitive durability while calling DigitalOcean expensive after a 240% YTD rally.

Analysis

The market is separating infrastructure winners from platform wrappers. NVDA remains the toll collector because every AI workload still has to pass through its silicon, networking, and software stack, but the next leg of upside is likely to come from utilization rather than just unit growth as inference shifts mix toward lower-latency, higher-throughput deployments. That makes the real competitive threat less about a near-term share grab and more about custom accelerators gradually compressing pricing power over 12-24 months.

DOCN’s move is more interesting as a second-order beneficiary of AI democratization than as a pure AI winner. If enterprises and SMBs adopt inference through simplified workflows, the incremental spend is likely to be more elastic and stickier, but only if DigitalOcean can prove it can monetize capacity without permanently sacrificing margin. The market is implicitly paying for a future where AI demand more than offsets rising capex intensity; that works only if customer acquisition and retention improve fast enough to keep compute from becoming a low-return balance-sheet burden.

The contrarian read is that the crowd is over-penalizing NVDA on sustainability fears while over-earning DOCN on narrative momentum. NVDA’s valuation is defensible if earnings growth stays above high-30s over the next 2 years, but DOCN at a premium multiple faces a harder bar because its guidance revision is capital-intensive and execution-dependent. In other words, the probability-weighted upside is skewed toward the incumbent with pricing power, while the smaller name has more room to disappoint if AI demand normalizes or enterprise adoption slows.

Catalyst timing matters: NVDA’s next re-rating likely comes on proof that Vera Rubin drives a step-function in inference economics and preserves gross margin; DOCN needs two consecutive quarters of AI-led growth without an ugly margin reset. If hyperscalers or custom-chip players win more inference workloads than expected, the pain will show up first in DOCN-like platforms that depend on simplicity as a differentiator, not in NVDA’s core share immediately. That argues for expressing exposure with defined risk rather than chasing either name outright at current levels.

Key Decisions for Investors

• Long NVDA vs short DOCN in a 3-6 month pair trade: own the incumbent with durable pricing power while fading the higher-multiple narrative name most exposed to capex drag and execution risk.
• Use NVDA call spreads into the Vera Rubin catalyst: structure for upside tied to inference-performance validation, with defined premium outlay and limited downside if sentiment cools.
• Avoid adding fresh DOCN equity at current levels; if long-term interested, wait for a 15-20% pullback or for two earnings prints confirming AI revenue mix without margin deterioration.
• If seeking AI infrastructure exposure beyond mega-caps, consider a basket approach weighted to NVDA over smaller cloud enablers; the risk/reward is better when the market is still unsure on spending durability.
• Set a stop on DOCN if forward growth guidance decelerates or capex rises faster than revenue for another quarter; that would signal the market has fully priced the AI story before the economics are proven.