NVDA has dropped 15% from its peak, and market sentiment shifted from extreme optimism to growing skepticism in less than three weeks. Looking back at the data, the forward P/E ratio for the technology sector in the S&P 500 reached 28x in June this year—historically not extreme—but the issue isn’t there. The problem is that expectations raced ahead too fast. The rally in the AI sector from last year through the first half of this year essentially priced in one assumption: that compute demand would grow exponentially, and every company tied to AI would benefit. This assumption holds true on the demand side—major cloud providers all raised their capital expenditure guidance for this year, with Microsoft, Google, and Amazon alone committing over $200 billion. But on the supply side, cracks are beginning to appear. First, GPUs are no longer the only bottleneck. NVIDIA’s delivery lead times have dropped from 40 weeks last year to under 12 weeks, and B100 production is even slightly ahead of schedule. When supply is no longer scarce, pricing power begins to erode. Second, the path toward custom chips is widening. Google’s TPU, Amazon’s Trainium, and Microsoft’s newly announced Maia—each is building its own backup plan. This trend doesn’t mean NVIDIA is doomed, but it signals a shift in the competitive landscape of the compute market—and markets always lag in reacting to such shifts. Now consider the application layer—that’s where the real caution lies. Infrastructure has seen $200 billion in investment, yet the revenue generated on the application side is still less than one-tenth of that. Copilot is growing steadily but not explosively; ChatGPT Enterprise is still climbing; most AI startups still have ARR in the tens of millions of dollars. It’s not that applications lack potential—it’s that there’s an 18- to 24-month lag between infrastructure investment and monetization. The market is now being forced to confront this gap. This correction isn’t about the AI story being disproven—it’s about the market finally beginning to distinguish between short-term reality and long-term narrative. From my own experience with such situations: don’t make decisions on the day panic hits. On the first day of a sharp drop, all analysis is emotion-driven. The real signals emerge in the second week: capital flows. Last week, one data point stood out: while net outflows from technology sector ETFs accelerated, semiconductor ETFs saw significantly smaller outflows than software ETFs. This suggests institutions aren’t fleeing the entire tech sector—they’re rotating within it, shifting from companies with no visible earnings to those with tangible results. For individual investors, at this point, chasing gains or trying to catch a falling knife are both the worst moves. The rationale for chasing has vanished; the rationale for bottom-fishing hasn’t emerged yet. The only worthwhile action is observation: which companies showed the smallest declines and rebounded fastest during this pullback? Those are the names the market will reward with premiums in the next phase. It’s not a complex methodology—just let the market tell you the answer.