Learning from NVIDIA's AI Demand Creation: AI Work Strategies for Anticipating Waves of Change
Hello. I am Sanaichi, a truth-seeking researcher. Today, I will delve into the history of NVIDIA, which continues to fundamentally support the movements of technology and money around the world, and the core of CUDA, which became the decisive driving force behind its breakthrough. By unraveling the strategy behind why the company was able to continue massive upfront investments long before the AI era began in earnest, let's explore work strategies that allow us to anticipate the waves of change.
The background to NVIDIA identifying the GPU as indispensable for the development of AI was not mere coincidence, but rather the alignment of the structural characteristics of the hardware the company had built over many years with its strategic software investments.
☆ What were GPUs originally?
They are processors developed to render 3D graphics for things like computer games. Because they perform color and shading calculations simultaneously for the millions of pixels that make up a screen, GPUs had a structure that maximized 'parallel computing power to process vast amounts of data simultaneously.' This nature of processing large amounts of data at once was exactly what matched the structure of the massive matrix calculations required later to train AI, especially deep learning models.
However, GPUs were not made for AI from the beginning. The decisive turning point was the CUDA software development platform announced by NVIDIA in 2006. The management at the time made a huge upfront investment to evolve the GPU not just as a graphics board for games, but as a general-purpose processor that accelerates all kinds of scientific and technical calculations. Long before the full-scale AI boom arrived, they steadily built an environment where the parallel computing power of GPUs could be drawn out programmably.
☆ What is NVIDIA's CUDA?
CUDA is the name of the integrated device architecture and software development platform for GPUs announced by NVIDIA in 2006. It is known as the world's first platform designed to allow GPUs, which were originally specialized for graphics rendering, to be utilized for general-purpose computational processing as well.
While traditional CPUs had a specialty in performing complex processing at high speeds with a small number of cores, GPUs are equipped with thousands of small cores and excel at processing simple calculations in parallel in large quantities.
With the advent of CUDA, developers were able to use familiar programming languages like C and C++ to directly tap into the intense parallel computing power of GPUs. This technological innovation became the decisive infrastructure that supported the explosive evolution of deep learning and AI later on.
Massive matrix calculations are essential for AI training, and long before the market realized that necessity, NVIDIA had been continuing to invest in the future from both hardware and software perspectives.
☆ From the perspective of business and work strategies
This was an infrastructure-first investment strategy that looked ahead to a future paradigm shift. When deep learning achieved overwhelming results in the field of image recognition in 2012 and researchers around the world needed hardware capable of processing vast amounts of data at high speed, NVIDIA was the only company that already had a development environment and hardware capable of practical use.
In other words, rather than the company simply realizing that GPUs were needed for AI development, they hypothesized that their strength in parallel processing would resolve the bottlenecks of all future calculations and completed the platform before the market realized the need for it, which has led to today's massive actual demand and creation of demand.
Disclaimer
This article is intended for informational purposes and does not solicit the buying or selling of specific stocks. Please make final investment decisions based on your own responsibility and judgment.
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