For more than two years, no headlines have dominated Wall Street more than those dealing with the rise of artificial intelligence (AI). Empowering software and systems with the capacity to reason and act on their own gives this technology a seemingly limitless long-term ceiling.
While the adoption rate and utility of AI solutions remains fluid, analysts at PwC see this game-changing technology completely altering the global growth trajectory by 2030. Come the turn of the decade, PwC's Sizing the Prize report estimates a $15.7 trillion positive impact on gross domestic product, all thanks to AI.
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A $15.7 trillion pie is big enough to accommodate a long list of winners. However, no company has been a more direct beneficiary of the AI revolution than graphics processing unit (GPU) giant Nvidia (NVDA +2.79%). Nvidia grew from a $360 billion business at the end of 2022 to one that came within a stone's throw of reaching a $4 trillion market cap on an intra-day basis just over two years later.
Although there's no denying Nvidia has been an AI success story, there's a real possibility this leading business could soon become a victim of its own success.
Nvidia's ascent has been nothing short of textbook
Before connecting the dots of how Nvidia's perfect operating expansion can potentially unravel on itself, it's important to understand the background of how it became the foundation of the AI revolution.
At the heart of Nvidia's success is its Hopper (H100) and next-generation Blackwell GPUs. The former effectively became the standard in AI-accelerated data centers over the previous two years. Meanwhile, the recently rolled-out Blackwell GPU architecture meaningfully improves computational capabilities for generative AI solutions and quantum computing, all while being considerably more energy-efficient than its predecessor chip.
To build on this point, no other chipmakers have come particularly close, as of now, to challenging the all-around functionality of Nvidia's hardware. While others might offer narrow competitive advantages in certain tasks, no other GPUs have truly challenged the Hopper or Blackwell on the basis of computing performance within AI-accelerated data centers.
NASDAQ: NVDA
Key Data Points
Adding fuel to the fire, Nvidia has been perfectly positioned to benefit from AI-GPU scarcity. Despite the best efforts of world-leading chip fabrication company Taiwan Semiconductor Manufacturing to rapidly expand its chip-on-wafer-on-substrate capacity (CoWoS) -- CoWoS is essential for packaging the high-bandwidth memory necessary for high-compute data centers -- demand for AI-GPUs has persistently overwhelmed their supply. As a result, Nvidia has been commanding a 100% (or greater) price premium for its AI-GPUs, relative to its competition.
Nvidia's CUDA software platform ties things into a nice bow. This toolkit, which is used by developers to maximize the performance of their GPUs, as well as to build large language models, has been pivotal in keeping clients within Nvidia's ecosystem of products and services.
Nvidia can be the victim of its own success
Although the overwhelming consensus among Wall Street analysts is that this artificial intelligence darling is headed higher -- the average analyst price target of $171 points to 50% upside -- two viable paths exist where Nvidia becomes a victim of its own success, and its share price suffers for it.
To begin with, Nvidia's AI-GPUs are considered superior, in terms of their computing capabilities. While short-term demand continues to overwhelm supply for AI-GPUs, the computing superiority of Hopper and Blackwell might dramatically slow down any future upgrade/replacement cycles.
Think of it this way: When Apple first came out with the iPhone, consumers and businesses jumped at the chance to upgrade to this revolutionary device. Over time, next-generation versions of the iPhone have featured upgrades, but they've been far less dramatic or impressive, compared to what the original iPhone did for consumers and businesses in 2007. With Nvidia's hardware being pricier and faster than its peers, the impetus for companies to upgrade their AI-data center hardware may be pushed out for years. That's not good news for a company that Wall Street expects to sustain an eye-popping growth rate.
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The second potential problem for Nvidia is that Wall Street might punish it for simply ceding some of its monopoly like market share in AI-data centers in the coming quarters.
Based on an analysis from semiconductor firm TechInsights, Nvidia accounted for an astounding 98% of the GPUs shipped to enterprise data centers in 2022 and 2023. Even with the likes of Advanced Micro Devices ramping up production of its Instinct series AI-accelerating chips, Nvidia is still likely to have commanded a near-monopoly like share of the "brains" of AI data centers last year.
The issue for Nvidia is that many of its top customers by net sales, which primarily includes members of the "Magnificent Seven," are internally developing AI-GPUs and AI solutions of their own that can be used in their data centers. Even though these internally developed chips aren't a threat to the computing speed of Hopper or Blackwell, and there are no plans for them to be sold externally, they could easily cause Nvidia to lose out on future data center real estate. These chips are both cheaper and more readily available than Nvidia's hardware.
If future upgrade cycles become more drawn out and/or Wall Street's AI darling loses precious data center real estate to its own top customers, what's arguably its biggest competitive advantage -- AI-GPU scarcity -- will disappear. This means less in the way of pricing power and weaker future margins.
Nvidia's historic ascent has left no room for error. If the company's growth rate were to slow, or even if its gross margin continues to decline from a peak of 78.4%, it could quickly become a victim of its own success.
