Why Investors Are Completely Wrong About Nvidia — and What the Correct Thesis Actually Looks Like

When you’re making an investment, there is a specific type of mistake that doesn’t feel like a mistake. It feels like caution. It feels like pattern recognition and hard-won discipline. And then, about eighteen months later, you’re watching a stock you passed on sitting at three times the price you declined to pay, and the caution starts to look like something else entirely.

Nvidia sits in that uncomfortable space right now. Trading at a forward price-to-earnings multiple of roughly 21 — a number that would have seemed laughably cheap for this company at almost any point in the past three years — it has drifted out of the spotlight at precisely the wrong moment. Investor attention has migrated toward AI memory chips, model efficiency debates, and the broader question of what happens when hyperscalers start building their own silicon. These are real concerns. They’re just not the whole story, and there’s a sense that the market is treating partial worries as terminal ones.

The pricing power argument is the one that tends to get glossed over, and it probably shouldn’t. Jensen Huang made a comment a while back that has aged remarkably well: that Nvidia’s GPUs are so capable that even when a competitor’s chips are free, they still aren’t cheap enough. That sounds like corporate bravado until you look at what Meta, Microsoft, and Amazon have actually been spending. These are not companies that overpay out of habit. They buy Nvidia because the performance gap between its hardware and everything else — reinforced by the CUDA software ecosystem that developers have been building on for nearly two decades — makes switching costs genuinely prohibitive. It’s possible that gap narrows over time. So far, it hasn’t.

During COVID, the cryptocurrency mining boom, and the AI surge, an investor who worked at Intel before it became one of the most painful stock stories of the decade recounted seeing Nvidia raise prices without losing a significant portion of the market. Such a pricing move caused internal panic and protracted discussion at Intel. It was practically standard at Nvidia across the valley. Although it’s more difficult to measure than a P/E ratio, the cultural and structural distinction between a company with pricing power and one that just wishes it did may be more significant in the long run.

The Nvidia bear thesis is not illogical. There is market saturation; the GPU market is becoming less of a seller’s market and more of a balanced one, with big buyers having more negotiating power than they did two years ago. There are legitimate concerns about how much raw compute the next generation of AI will actually need in light of the release of more effective large language models, such as DeepSeek.

The stock price has taken these developments into account. A forward multiple of 21 on a company projecting $8.30 in earnings per share, with analyst targets ranging from $222 to $270 and technical setups suggesting potential toward $300, looks less like a warning sign and more like a company that was marked down for issues that were already apparent.

The narrative cycle surrounding Nvidia has a tendency to rhyme, which is difficult to ignore. Analysts declare the AI trade completed, the stock is deemed overvalued, a correction occurs, and then the earnings are released. As that cycle continues, the more intriguing question is whether the market has once again mistaken a pause for a conclusion rather than whether Nvidia faces actual competition, which it does. The ecosystem remains intact, demand is growing, and the ostensibly forgotten company continues to be the one that all customers continue to purchase.