NVIDIA is today the most valuable company in the world, but things might not have turned out the same way had it not bet big on AI.
Dylan Patel, founder and lead analyst at SemiAnalysis, a semiconductor industry tracking company, recently shared a fascinating behind-the-scenes story about one of NVIDIA’s most crucial decisions. His account reveals just how close the chip giant came to potentially missing the AI revolution entirely—and how a last-minute gamble may have saved its dominance in what would become the most important computing market of the decade.
The story centers around NVIDIA’s Volta architecture, launched in 2017 as the company’s first chip to feature specialized tensor cores designed specifically for AI workloads. But according to Patel, these game-changing tensor cores were almost never included at all.
“There’s a story about how Volta, which was the first NVIDIA chip with tensor cores,” Patel explained. “They saw all the AI stuff on the prior generation P100, Pascal, and they decided we should go all in on AI. And they added the tensor cores to Volta only a handful of months before they sent it to the fab. Like they said, screw it, let’s change it.”
The implications of this decision were enormous. “It’s like if they hadn’t done that, maybe someone else would’ve taken the AI chip market, right?” Patel noted. This wasn’t just a minor tweak—it was a fundamental architectural change made at the eleventh hour that would define NVIDIA’s future.
But this kind of rapid pivoting isn’t unusual for NVIDIA, according to Patel. “There’s all these times where they just… those are major changes, but there’s often minor things that you have to tweak, right? Number formats or some architectural detail. NVIDIA’s just so fast.”
To understand the significance of this decision, it’s important to know the context. The Pascal architecture, which powered chips like the P100 released in 2016, was already showing promise for AI workloads despite being primarily designed for high-performance computing. The P100 featured NVIDIA’s first implementation of FP16 (half-precision) floating-point arithmetic, which proved surprisingly effective for deep learning tasks.
Volta, released the following year with the V100 as its flagship product, took this much further. The addition of tensor cores—specialized processing units optimized for the matrix multiplication operations at the heart of neural networks—gave Volta chips up to 12x the performance of Pascal for AI training tasks. This wasn’t just an incremental improvement; it was a quantum leap that established NVIDIA’s dominance in the AI chip market.
The broader implications of Patel’s revelation are striking. In an industry where development cycles typically span years and architectural decisions are locked in well before fabrication, NVIDIA’s willingness to make such dramatic changes at the last minute demonstrates both exceptional technical agility and remarkable strategic vision. Had the company not made this bet, competitors like Google with its TPUs, or even traditional rivals like AMD, might have seized the opportunity to dominate what would become a hundreds-of-billions-dollar market. Today, as AI drives unprecedented demand for specialized computing power, that split-second decision to add tensor cores may have been worth hundreds of billions in market capitalization.