Two stocks are drawing attention as Nvidia’s post-earnings momentum cools, with analysts pointing to upside before Aug. 26

Nvidia has had a tough stretch in the market’s latest reaction cycle, with shares described as having dropped after six earnings reports since 2024. The weakness has become a benchmark for how investors are weighing AI demand, supply-chain execution, and expectations for next steps in data-center growth.

In market commentary carried by Yahoo Finance, two different stocks were singled out as quietly outperforming Nvidia, suggesting that investors may be reallocating attention away from the chip designer and toward parts of the AI stack that are perceived as having steadier near-term fundamentals. The same commentary also referenced TSMC as a stock that has “absorbed every hit better” than Nvidia, indicating that at least some market participants are more confident about what happens upstream in advanced-chip production.

The roundup also included Ciena in the comparison, framing the debate as not just about semiconductor demand, but about how the networking layer and data-transport equipment are being valued in the current cycle. Ciena is often associated with optical networking and network infrastructure used in high-bandwidth environments, which can make it a proxy for how much investment is flowing from data centers to the systems that move data.

The key market point in the commentary is the expectation of upside ahead of a specific date. The report said analysts were looking for roughly 27% upside before Aug. 26, and it framed the relative outperformance as implying about 30% higher valuation potential for the two referenced names compared with where Nvidia was trading.

By contrast, the commentary’s portrayal of Nvidia’s post-earnings pattern implies that each earnings cycle may be resetting expectations in a way that has left investors less comfortable with the stock’s near-term trajectory. While Nvidia remains the central brand in AI accelerators, the market reaction described here points to the sensitivity of the stock to guidance indicates, demand read-through, and the pace of deployment.

Sector context matters because the AI market is not a single-product story. It is a chain that runs from advanced chips to manufacturing yields and capacity, then into the systems and networks that support training and inference workloads at scale. When investors feel that one part of the chain is de-risked relative to others, they can rotate capital quickly, even if end demand for AI remains intact.

The report does not provide the full underlying assumptions behind the upside estimates, nor does it detail how those targets are calculated, whether they rely on revenue growth, margin expansion, order visibility, or multiple expansion. It also does not disclose whether the two outperformers are being favored for company-specific reasons, or simply because they are less exposed to the specific expectations embedded in Nvidia’s earnings.

What to watch next is whether Nvidia’s subsequent earnings and guidance change the market narrative around AI spending durability and incremental product ramps, and whether the two outperformers keep translating optimism into results. If Nvidia continues to lag after earnings, the comparisons may become more frequent, but if it reasserts clearer visibility, investors could narrow the gap quickly.