Why Is Nvidia Stock So Cheap? This Is the Only Plausible Answer

Nvidia is up just 4% year to date, lagging the S&P 500's 8% gain and the Nasdaq Composite's 9% rise, while the stock remains down 17% from its May peak. Despite that underperformance, first-quarter revenue jumped 85% to $81.6 billion and adjusted net income rose 139% to $45.5 billion, with Wall Street projecting EPS to nearly double this year and reach $15.76 by fiscal 2029. The article argues the stock is being held back by uncertainty over an AI bubble and cyclical demand concerns, even as analysts may be underestimating Nvidia's long-term growth.

Analysis

The market is treating NVDA less like a growth compounder and more like a cyclical semiconductor with a very long duration. That discount is rational if capex rolls over, but it may be overstating how quickly AI infrastructure spend can decelerate: hyperscalers, sovereign AI buyers, and enterprise inference deployments are still in the early innings of load growth, so the install base can keep compounding even if training intensity normalizes. The bigger second-order effect is that any near-term congestion in memory and networking supply shifts incremental budget toward the vendors with the tightest bottlenecks, which supports peers, but it does not automatically impair NVDA if customers are buying systems to solve throughput constraints rather than single-chip shortages.

Consensus appears to be underpricing the asymmetry in earnings revisions. If forward estimates are still lagging actual demand, the next catalyst is not just another beat — it is another upward reset in the out-years, which can re-rate the stock even if the headline multiple stays unchanged. The risk is that the trade becomes hostage to sentiment around an AI bubble rather than fundamentals, so NVDA can underperform for weeks or months despite strong execution; in that regime, price action will likely be driven by capex commentary from hyperscalers, not by reported gross margins.

The most interesting contrarian is that the market is chasing the wrong beneficiaries of the AI buildout. Memory and CPUs may capture more incremental attention because their supply-demand inflections are easier to see, but NVDA remains the toll collector on overall AI deployment intensity; if inference expands broadly, it should still monetize the workload even when unit growth migrates across the stack. The unresolved question is not whether the AI boom ends, but whether the current equity market is already pricing an orderly normalization that never arrives.

Key Decisions for Investors

• Buy NVDA on 3-6 month weakness, but only on pullbacks toward post-earnings support; target a 15-25% upside reset if forward EPS estimates continue to rise, with downside limited by already-de-rated sentiment.
• Pair trade: long NVDA / short SNDK or MU over the next 1-3 months if memory shortages persist; the long leg benefits from system-level AI spend, while the short leg is more exposed to cyclical mean reversion once scarcity sentiment peaks.
• For traders skeptical of a near-term AI bubble repricing, use NVDA call spreads 6-9 months out instead of outright stock; this captures upside from another estimate revision cycle while limiting drawdown if the multiple compresses first.
• Avoid chasing INTC/AMD purely on flow momentum unless you have a 6-12 month horizon; those names are more exposed to inference narrative rotation, and the risk/reward worsens if AI capex broadens back toward accelerators rather than CPUs.
• Use NVDA as a hedge against softening AI capex names elsewhere in the basket: if the market starts separating winners from bottlenecks, NVDA should reassert as the highest-quality earnings compounder among AI semis.