No breakthrough innovation has captured investors’ attention and capital more than the evolution of artificial intelligence (AI). Providing software and systems with the tools to make split-second decisions without human oversight is a multi-trillion-dollar opportunity.
Among the laundry list of publicly traded companies benefiting from the artificial intelligence revolution is the lynchpin of the graphics processing unit (GPU), Nvidia (NASDAQ: NVDA). Nvidia has added approximately $4 trillion in market value since early 2023, driven by its GPU dominance in AI-accelerated data centers.
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But it’s not looking in the rearview mirror that has Wall Street and investors excited. CEO Jensen Huang’s recent comments raise eyebrows. The only problem is that Huang’s trillion-dollar prediction may not tell the full story.
At Nvidia’s annual GPU Technology Conference (GTC) in mid-March, the company’s billionaire boss outlined a sales forecast for its two most advanced GPUs, Blackwell and Vera Rubin (the latter of which will debut later this year), that included the “t” word… trillion. said Huang,
I’m here to tell you that where I stand now – a few months after GTC DC, a year after the last GTC – where I stand now, I see at least $1 trillion through 2027.
The combined turnovers of Blackwell and Vera Rubin would be overwhelming at baseline. It would cement Nvidia’s AI hardware as the undisputed top choice of companies in enterprise data centers.
Furthermore, this $1 trillion estimate demonstrates the often overlooked importance of the CUDA software platform. This is the toolkit that developers use to maximize the computing capabilities of their Nvidia GPUs, including building and training large language models.
But while this $1 trillion Blackwell-Rubin sales forecast likely has investors seeing dollar signs, it may only tell half the story.
It’s inevitable that Nvidia will face increasing GPU competition. However, the biggest competitive risk may come from within.
Many of the company’s largest customers by net revenue are currently developing GPUs and solutions for use in their data centers. While these internally developed chips are a number of rungs below Nvidia’s hardware, they have the advantage of being significantly cheaper and more readily available.
