When Nvidia (NVDA) reports its must read earnings report on Wednesday evening, it will be confronting this frustrating reality.

Strong earnings reports haven’t been too kind to Nvidia’s stock of late!

Nvidia shares have fallen in response to earnings in six of the past eight quarters including the last four, per Yahoo Finance AlphaSpace analysis.

The reality is that the market is positioned for the company to post something great and for CEO Jensen Huang to sound super bullish on the earnings call.

The market also knows there is minimal downside risk to Nvidia’s growing investment portfolio, given the rising valuations (see Anthropic (ANTH.PVT), for example) being afforded to most privately held names in artificial intelligence.

“Because many of the debates around AI infrastructure spend/return on investment and credit risk are out of NVDA’s hands, we think the numbers are more important than the narrative and coming out of this call, we expect investors to gain greater confidence in a path to $15+ EPS in 2027E and $20 in 2028E – numbers that should keep the stock grinding higher,” UBS analyst Tim Arcuri said in a note.

Factor in Nvidia’s stock outperforming the S&P 500 (^GSPC) by five percentage points over the past month, per Yahoo Finance AlphaSpace data, and it suffices to say expectations on the Street are running hot into earnings.

And that hasn’t turned out too well the last few quarters.

So a rerating in the chip king’s stock — where an even better valuation could be unlocked due to increased investor interest — won’t be easy. It can be done, though, HSBC analyst Frank Lee wrote in a note ahead of the results

“The next major re-rating for Nvidia will be driven by a new narrative as earnings and the product roadmap have become less meaningful narratives for re-rating,” Lee said.

Lee contended that this new narrative could be the company’s “positioning itself as the world’s largest contributor to open-source AI.”

“According to Nvidia, the aggregate of open-source models now represents the second most popular category by token generation,” Lee said. “This is critical as open-source small language models (SLMs) are emerging as the preferred engine for agentic AI and on-device applications.”

“A boost in small language models presents significant earnings upside by lowering the barrier to entry for enterprise inference, expanding the total addressable market (TAM) for Nvidia’s infrastructure beyond just a few frontier labs to millions of individual developers and sovereign nations,” he added.