Finance
Nvidia Reports Earnings in May. Here’s Why I’m Loading Up Before the Report.
Artificial intelligence (AI) has been the most prominent investment theme on Wall Street over the past few years. Nvidia (NASDAQ: NVDA) has benefited dramatically from this trend, with the stock up over 640% in the past three years.
But with its next earnings report on May 20, investors are concerned about whether the stock has already run too far or if there is still more upside left.
Here are some reasons why I am considering buying the stock before the numbers come out.
Nvidia’s next earnings report could show whether the AI infrastructure boom is still in its early innings.
Artificial intelligence (AI) has been the most prominent investment theme on Wall Street over the past few years. Nvidia (NVDA +1.73%) has benefited dramatically from this trend, with the stock up over 640% in the past three years.
But with its next earnings report on May 20, investors are concerned about whether the stock has already run too far or if there is still more upside left.
Here are some reasons why I am considering buying the stock before the numbers come out.
Image source: Getty Images.
Evolving AI demand
Nvidia has guided for revenue of about $78 billion, plus or minus 2%, for the first quarter of fiscal 2027 (ending April 26, 2026). That implies roughly 73% to 80% year-over-year growth, which is an exceptionally strong growth rate for a company of Nvidia’s size.
Nvidia
Today’s Change
(1.73%) $3.66
Current Price
$215.16
The company’s recent performance already reflects strong momentum. Nvidia’s revenue soared 73% year over year to $68.1 billion in the fourth quarter of fiscal 2026. The company’s data center business generated revenue of $62.3 billion, up 75% year over year.
While Nvidia has already positioned itself as an AI infrastructure company, the changing nature of AI demand suggests that the growth opportunity may be even larger than it appears today.
Management has highlighted that AI is evolving from content creation to reasoning and now toward agentic AI, where systems can independently perform tasks. Since these systems need to continuously think, read information, reason, and generate outputs, they require significantly more inference computing capacity. Subsequently, power-constrained data centers operate like “token factories,” continuously generating AI output, or tokens. Instead of focusing solely on chip costs, customers are increasingly evaluating how many tokens their systems can generate per unit of power.
Robust product cycle
Nvidia’s latest product cycle is focused on addressing these evolving AI workloads. The company’s Blackwell systems are already seeing strong demand. Previously, management had highlighted $500 billion worth of high-confidence demand and purchase orders tied to Blackwell and next-generation Rubin systems through 2026. However, recently, CEO Jensen Huang said that he expects to see at least $1 trillion of opportunity tied to these systems through 2027. Since that forecast excludes additional opportunities from stand-alone CPUs, storage, and recently licensed Groq inferencing (running AI models in a production environment) technology, the actual total addressable market could be even larger.
The Rubin system is expected to deliver performance improvements far beyond traditional chip upgrades, especially for more advanced AI tasks such as reasoning and agentic AI. With Nvidia delivering large performance gains by combining chips, networking, and software into complete systems, customers see improvement in the economics of their AI deployments. That is helping support Nvidia’s strong revenue growth and profit margins.
Nvidia is also becoming more aggressive about securing AI infrastructure capacity directly. The company plans to invest up to $2.1 billion in data center operator Iren as part of a partnership to deploy up to 5 gigawatts of AI infrastructure.
Nvidia is also investing in the AI infrastructure supply chain. The company is helping fund new factories for glassmaker Corning through a multibillion-dollar prepayment. Corning’s glass is used in fiber-optic cables required for networking infrastructure inside AI data centers.
Durable demand
While the top five hyperscalers account for nearly 60% of Nvidia’s business, the remaining 40% is from enterprises, sovereign AI projects, regional clouds, industrial applications, robotics, big systems, supercomputing systems, small servers, and edge computing. The diversified customer base makes Nvidia resilient to spending slowdowns from any single industry or group of customers.
AI monetization also appears to be improving faster than expected. Management highlighted that some AI-native companies are reportedly adding nearly $1 billion to $2 billion in revenue every week as AI adoption increases. That helps address one of the biggest concerns around AI spending: whether customers can eventually generate meaningful returns from these investments.
Management has also highlighted that inference is becoming critical because it directly drives customer revenue. As AI systems handle more reasoning, coding, search, and agentic workloads, companies need significantly more computing capacity to generate tokens and serve users efficiently.
These trends underline the durable, broader, and more commercial nature of AI demand.
Nvidia looks attractive before earnings
Nvidia is exposed to risks such as export restrictions in China, competitive pressures from chip designers and hyperscalers developing proprietary chips, reduced AI spending, and a high valuation. Despite these challenges, the company’s broader growth story remains intact.
Going into the May 20 earnings report, investor expectations are undoubtedly high. But Nvidia’s underlying demand drivers still appear strong, broad-based, and increasingly commercial.
If management’s long-term demand commentary proves correct, Nvidia’s share price can soar even higher in the next few years.