Nvidia (NVDA) delivered another blockbuster earnings report, but investors appeared unimpressed as the stock struggled to rally.
Shares fell around 1.9% on Thursday morning, with the stock slipping to around $219.62 in early trading.
The muted reaction came despite Nvidia once again surpassing Wall Street expectations and forecasting even stronger growth ahead.
Nvidia reported fiscal first-quarter revenue of $81.6 billion, up 85% from a year earlier and ahead of analyst expectations for roughly $78.9 billion.
The company also projected second-quarter revenue growth of approximately 95%.
Still, with Nvidia already valued at roughly $5.3 trillion and shares having surged sharply in recent months, investors appeared reluctant to push the stock materially higher immediately following the results.
Analysts also pointed to growing concerns around long-term competition in artificial intelligence chips and custom computing infrastructure.
“Demand has gone parabolic”
Nvidia’s results were once again driven by explosive growth in its data-center business, which remains at the center of the global AI infrastructure buildout.
Chief executive Jensen Huang said demand for AI computing infrastructure continues to accelerate rapidly.
“Demand has gone parabolic,” Huang told analysts during the earnings call.
“The reason is simple: the era of agentic AI is here.”
Nvidia now forecasts that annual AI infrastructure spending could rise to between $3 trillion and $4 trillion by the end of the decade as AI systems become embedded across industries.
The company continues to benefit from enormous spending commitments from hyperscalers, enterprises, governments, and startups racing to secure advanced AI computing capacity.
China remains a major question
One of the biggest areas of investor focus remains China.
Nvidia’s AI chips have become a central issue in the broader technology rivalry between the United States and China.
Earlier this year, the Trump administration allowed Nvidia to resume limited sales of its H200 AI chips to Chinese customers under specific conditions.
The H200 had previously been restricted over concerns that the technology could strengthen China’s military and technology capabilities.
However, Nvidia said it is not currently assuming any revenue contribution from Chinese data-center chip sales in the current quarter.
Huang also acknowledged to CNBC that the company has “largely conceded” the Chinese market to Huawei as Beijing increasingly pushes domestic semiconductor suppliers.
Last week, Huang joined a group of US business leaders accompanying Donald Trump on an official trip to Beijing, though it remains unclear whether semiconductor policy was meaningfully discussed.
Wall Street still sees significant upside
Despite the stock’s muted reaction, major Wall Street firms largely raised price targets following the earnings report.
Baird raised its price target on Nvidia to $500 from $300 while maintaining an Outperform rating.
The firm said Nvidia continues gaining market share in AI inferencing and hyperscale computing and expects adoption of the upcoming Vera Rubin architecture to outpace Blackwell among frontier AI model companies.
Baird also highlighted Nvidia’s growing push into CPUs, estimating the company now has visibility into nearly $20 billion in CPU revenue this year.
Goldman Sachs raised its target to $285 from $250 and maintained a Buy rating.
Goldman said it sees a “clearer path” for Nvidia shares to outperform as hyperscaler AI spending becomes increasingly sustainable.
The bank also pointed to Nvidia’s ability to reduce AI token-generation costs by more than 70% annually, helping maintain demand growth even as AI deployment scales.
Meanwhile, Morgan Stanley lifted its price target to $288 from $285 and maintained an Overweight rating.
Analyst Joseph Moore said strong compute demand continues to outweigh concerns around custom AI chips and application-specific integrated circuits, or ASICs.
Morgan Stanley noted that even hyperscalers developing their own chips still remain major Nvidia customers due to ongoing shortages of advanced AI compute capacity.
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