This exemplifies NVIDIA’s vision. While everyone believed NVIDIA’s main competitor was AMD after its acquisition of ATI, NVIDIA opened a new path beyond gaming and design: scientific computing.
In June 2007, CUDA was released.
With NVIDIA’s consistent efforts, CUDA has become the de facto standard in scientific computing.
It wasn’t until August 2009 that a potential competitor, OpenCL, emerged.
OpenCL 1.0 was released on August 28, 2009, alongside Mac OS X Snow Leopard, and AMD released its Stream SDK supporting OpenCL.
Later, as AMD struggled against Intel and couldn’t match NVIDIA in gaming graphics cards, its financial stability was shaky, and it lacked the funds to support OpenCL. OpenCL itself was a loose alliance and heavily fragmented.
As a result, OpenCL was far less user-friendly than CUDA.
When neural network algorithms gained popularity, NVIDIA heavily supported them, and major AI frameworks prioritized CUDA, creating a positive feedback loop.
After that, NVIDIA expanded further into fields closely related to AI, such as autonomous driving and robotics, launching the Drive series of chips for cars and the Jetson series for industrial robots.
All of this was initiated with CUDA as the software entry point. In the end, CUDA became what it is today—a deep and wide moat.
So, rather than saying CUDA is the moat, it would be more accurate to say that NVIDIA has built a moat across scientific computing, autonomous driving, artificial intelligence, and robotics, with CUDA as one part of this moat, or the directly accessible portion of it.
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