NVIDIA's Market Value Grows from $1 Trillion to $5.2 Trillion in Three Years, Jensen Huang's AI Narrative Realized
Three years ago, when NVIDIA (NVDA) first surpassed a market value of $1 trillion, the market widely considered the valuation too high, with strong skepticism.
At that time, only Jensen Huang clearly articulated the logic of computing power demand in the AI era, while Dell's CEO and others present remained somewhat skeptical; now, NVIDIA's market value has exceeded $5.2 trillion, growing more than fivefold in three years.
Jensen Huang continues to promote the construction of AI training and inference infrastructure, with global capital reallocating around the GPU and CUDA ecosystem, putting pressure on traditional tech giants and skeptics, making NVIDIA the biggest beneficiary of AI capital concentration.
Source: Public Information
ABAB AI Insight
Jensen Huang shifted the company's strategy from gaming GPUs to data center AI accelerators as early as 2022-2023, launching the H100/H200/B200 series and securing long-term supply agreements with leading labs like OpenAI and Meta, while traditional hardware partners like Dell still viewed NVIDIA through a PC/server lens.
On the capital front, NVIDIA achieved explosive cash flow growth through high-margin GPU sales and CUDA software barriers, continuously repurchasing stock and increasing R&D investment, forming a dual compounding effect of "selling shovels + platform lock-in," raising its market value from $1 trillion to $5.2 trillion in three years, becoming one of the largest weighted stocks in the U.S. market.
Similar to Cisco's dominance in the internet infrastructure wave of the 1990s and Apple's iPhone ecosystem in the 2000s, NVIDIA is currently in the mid-to-late stage of the AI computing power's transition from experimental to full commercialization, with CUDA having become the de facto standard for AI infrastructure.
Structural judgment: This essentially represents a transfer of pricing power. Jensen Huang, through a clear AI narrative and technological lock-in, has transformed GPUs from general-purpose hardware into a scarce infrastructure for the AI era, driven by the exponential growth of training and inference computing power demand far exceeding supply, pushing capital and ecosystems from traditional computing towards NVIDIA-led AI-specific platforms.
ABAB News · Cognitive Law
Those who tell a coherent story ultimately leave skeptics behind.
When others think it's expensive, it often means the new paradigm has just begun.
The real moat is not today's valuation, but the narrative that others will still be chasing three years from now.