Nvidia's AI Dominance: Riding the Wave of Innovation and Market Challenges

All About Nvidia Chips, AI Hype and What Lies Ahead

(Bloomberg) -- The blistering stock rally that's made Nvidia Corp. one of the world's three most valuable companies is based on a number of assumptions. One is that artificial intelligence applications made possible by its semiconductors will become features of the modern economy. Another is that Nvidia, and the patchwork of subcontractors upon which it relies, can meet the exploding demand for its equipment without a hitch. The excitement surrounding artificial intelligence has ebbed and flowed, but Nvidia remains the preeminent picks-and-shovels seller in an AI gold rush. Revenue is still soaring, and the order book for the company's Hopper chip lineup and its successor -- Blackwell -- is bulging. However, its continued success hinges on whether Microsoft Corp., Google and other tech firms will find enough commercial uses for AI to turn a profit on their massive investments in Nvidia chips. Meanwhile, antitrust authorities have zeroed in on Nvidia's market dominance and are looking into whether it has been making it harder for customers to switch to other suppliers, Bloomberg has reported. Nvidia shares suffered their worst one-day decline in four weeks on Aug. 29 after it issued an underwhelming sales forecast. The company also confirmed earlier reports that Blackwell designs had required changes to make them easier to manufacture. The stock has since bounced back from its August low. And the company's revenue will more than double this year, according to estimates, repeating a similar surge in 2023. Barring a currently unforeseen share collapse, Nvidia will end the year as the world's most valuable chipmaker, by a wide margin. Here's what's driving the company's spectacular growth, and more on the challenges that lie ahead. What are Nvidia's most popular AI chips? The current moneymaker is the Hopper H100, whose name is a nod to computer science pioneer Grace Hopper. It's a beefier version of a graphics processing unit that normally lives in personal computers and helps video gamers get the most realistic visual experience. But it's being replaced at the top of the lineup with the Blackwell range (named for mathematician David Blackwell). Both Hopper and Blackwell include technology that turns clusters of Nvidia chips into single units that can process vast volumes of data and make computations at high speeds. That makes them a perfect fit for the power-intensive task of training the neural networks that underpin the latest generation of AI products. The company, founded in 1993, pioneered this market with investments dating back almost two decades, when it bet that the ability to do work in parallel would one day make its chips valuable in applications outside of gaming. The Santa Clara, California-based company will sell the Blackwell in a variety of options, including as part of the GB200 superchip, which combines two Blackwell GPUs, or graphics processing units, with one Grace CPU, a general-purpose central processing unit. Why are Nvidia's AI chips so special? So-called generative AI platforms learn tasks such as translating text, summarizing reports and synthesizing images by ingesting vast quantities of preexisting material. The more they see, the better they become at things like recognizing human speech or writing job cover letters. They develop through trial and error, making billions of attempts to achieve proficiency and sucking up huge amounts of computing power along the way. Blackwell delivers 2.5 times Hopper's performance in training AI, according to Nvidia. The new design has so many transistors -- the tiny switches that give semiconductors their ability to process information -- that it's too big for conventional production techniques. It's actually two chips married to each other through a connection that ensures they act seamlessly as one, the company said. For customers racing to train their AI platforms to perform new tasks, the performance edge offered by the Hopper and Blackwell chips is critical. The components are seen as so key to developing AI that the US government has restricted their sale to China. How did Nvidia become a leader in AI? The Santa Clara, California-based company was already the king of graphics chips, the bits of a computer that generate the images you see on the screen. The most powerful of those are built with thousands of processing cores that perform multiple simultaneous threads of computation, modeling complex 3D renderings like shadows and reflections. Nvidia's engineers realized in the early 2000s that they could retool these graphics accelerators for other applications. AI researchers, meanwhile, discovered that their work could finally be made practical by using this type of chip. What are Nvidia's competitors doing? Nvidia now controls about 90% of the market for data center GPUs, according to market research firm IDC. Dominant cloud computing providers such as Amazon.com Inc.'s AWS, Alphabet Inc.'s Google Cloud and Microsoft's Azure are trying to develop their own chips, as are Nvidia rivals Advanced Micro Devices Inc. and Intel Corp. Those efforts have done little to erode Nvidia's dominance for now. AMD is forecasting as much as $4.5 billion in AI accelerator-related sales for this year. That's a remarkable growth spurt from next to nothing in 2023, but still pales in comparison with the more than $100 billion Nvidia will make in data center sales this year, according to analyst estimates. How does Nvidia stay ahead of its competitors? Nvidia has updated its offerings, including software to support the hardware, at a pace that no other firm has yet been able to match. The company has also devised various cluster systems that help its customers buy H100s in bulk and deploy them quickly. Chips like Intel's Xeon processors are capable of more complex data crunching, but they have fewer cores and are slower at working through the mountains of information typically used to train AI software. The once-dominant provider of data center components has struggled so far to offer accelerators that customers are prepared to choose over Nvidia fare. How is AI chip demand holding up? Nvidia Chief Executive Officer Jensen Huang has said customers are frustrated they can't get enough chips. "The demand on it is so great, and everyone wants to be first and everyone wants to be most," he said at a Goldman Sachs Group Inc. technology conference in San Francisco on Sept. 11. "We probably have more emotional customers today. Deservedly so. It's tense. We're trying to do the best we can." Demand for current products is strong and orders for the new Blackwell range are flowing in as supply improves, Huang said. Asked whether the massive AI spending is providing customers with a return on investment, he said companies have no choice other than to embrace "accelerated computing." Why is Nvidia being investigated? Nvidia's growing dominance has become a concern for industry regulators. The US Justice Department sent subpoenas to Nvidia and other companies as it seeks evidence that the chipmaker violated antitrust laws, escalating an existing investigation, Bloomberg reported on Sept. 3. Nvidia subsequently denied that it had a subpoena, but the DOJ often sends requests for information in the form of what's known as a civil investigative demand, which is commonly referred to as a subpoena. The Department of Justice has sent such a request seeking information about Nvidia's acquisition of RunAI and aspects of its chip business, according to one person with direct knowledge of the matter. Nvidia has said its dominance of the AI accelerator market comes from the superiority of its products and that customers are free to choose. How do AMD and Intel compare with Nvidia in AI chips? AMD, the second-largest maker of computer graphics chips, unveiled a version of its Instinct line last year aimed at the market that Nvidia's products dominate. At the Computex show in Taiwan in early June, AMD CEO Lisa Su announced that an updated version of its MI300 AI processor would go on sale in the fourth quarter and outlined that further products will follow in 2025 and 2026, showing her company's commitment to this business area. AMD and Intel, which is also designing chips geared for AI workloads, have said that their latest products compare favorably to the H100 and even its H200 successor in some scenarios. But none of Nvidia's rivals has yet accounted for the leap forward that the company says Blackwell will deliver. Nvidia's advantage isn't just in the performance of its hardware. The company invented something called CUDA, a language for its graphics chips that allows them to be programmed for the type of work that underpins AI applications. Widespread use of that software tool has helped keep the industry tied to Nvidia's hardware. What is Nvidia planning on releasing next? The most anticipated release is the Blackwell line, and Nvidia has said it expects to get "a lot" of revenue from the new product series this year. However, the company has hit engineering snags in its development that will slow the release of some products. Meanwhile, demand for the H series hardware continues to grow. Huang has acted as an ambassador for the technology and sought to entice governments, as well as private enterprise, to buy early or risk being left behind by those who embrace AI. Nvidia also knows that once customers choose its technology for their generative AI projects, it will have a much easier time selling them upgrades than competitors. --With assistance from Jane Lanhee Lee and Vlad Savov.

Will Artificial Intelligence (AI) Colossus Nvidia Plummet 98%? | The Motley Fool

One Wall Street pundit is forecasting a wipeout for the leader of the artificial intelligence (AI) revolution. For the better part of two years, no trend has excited investors more on Wall Street than the rise of artificial intelligence (AI). The lure of AI is twofold. First, there's the ability for AI-driven software and systems to learn over time without human intervention. The capacity for software and systems to become more proficient at their tasks, or perhaps even learn new jobs/skill sets, gives this technology utility in almost every sector and industry around the globe. The other reason investors can't seem to get enough of AI stocks is the eye-popping addressable market attached to this game-changing technology. According to the researchers at PwC, AI is forecast to add $15.7 trillion to the global economy by 2030, with these gains coming from a combination of consumption-side benefits and productivity improvements. With a seemingly limitless ceiling for AI and an addressable market that dwarfs pretty much every other next-big-thing innovation for three decades, it's not a surprise to see investors flock to AI colossus Nvidia (NVDA -1.95%). Just be mindful that not everyone on Wall Street expects Nvidia to maintain its lofty valuation. When 2022 came to a close, Nvidia had a $360 billion market cap and was an important, but not critical, company in the tech sector. Less than 18 months later, it briefly became the largest publicly traded company in the world, with a peak market cap of $3.46 trillion. Nvidia's historic gains are a reflection of the otherworldly demand for its H100 graphics processing unit (GPU) used in high-compute data centers. Based on estimates from the analysts at TechInsights, Nvidia has accounted for a roughly 98% share of the GPUs shipped to data centers in 2022 and 2023. In other words, its hardware is being relied on as the undisputed preferred choice for training large language models (LLMs) and running generative AI solutions. In addition to its hardware being in high demand, Nvidia's CUDA software platform is doing its part to keep enterprises loyal to its ecosystem of products and services. CUDA is the toolkit developers use to build LLMs and maximize the computing capacity of their Nvidia GPUs. There's also plenty of excitement surrounding future innovation. Nvidia's next-generation Blackwell chip, which will be far more energy-efficient than its predecessor, is set for its debut in the coming months. CEO Jensen Huang also teased the release of his company's Rubin GPU architecture in 2026, which will run on a new processor, known as Vera. Investing in innovation makes it likely that Nvidia will maintain its No. 1 spot in terms of AI-GPU computing capacity. In an interview with Fox News Digital in June, Dent highlighted that the mammoth increase in money supply has set the U.S. economy and stock market up for disaster. He referred to the current market as the "bubble of all bubbles," with Nvidia expected to be a victim of an upcoming crash. Dent pegged Nvidia's potential decline at 98%. Dent further opined, This bubble has been going for 14 years. Instead of most bubbles [going] five to six, it's been stretched higher, longer. So you'd have to expect a bigger crash than we got in 2008 to '09. As an Nvidia bear myself, I don't expect it to come anywhere close to a 98% downdraft. Although we have witnessed a couple of next-big-thing market leaders eventually plummet by 98%, including Canopy Growth following the hype in cannabis stocks, and 3D Systems, which has lost just shy of 98% of its value after 3D-printing buzz failed to materialize, a 98% peak-to-trough loss for a market leader is rare. Furthermore, Nvidia had other established sales channels in place prior to its AI revenue windfall. It's a leading provider of GPUs used in gaming and cryptocurrency mining, and should enjoy steady gains from its virtualization software segment. While these now-ancillary operations won't offset substantial weakness if the AI bubble were to burst, they'd almost certainly keep Nvidia from losing 98% of its value. But even though a 98% decline isn't in the cards, a sizable drop that could extend beyond 75% is quite possible. Although Harry Dent's call for a 98% plunge in Nvidia may be out in left field, he did hit the nail right on the head when discussing history. While history doesn't repeat to a "t" on Wall Street, it does have a tendency to rhyme. Since the advent of the internet roughly three decades ago, every highly touted innovation, technology, and trend has worked its way through an early stage bubble-bursting event. Though the timing of when the music stops will differ in every instance, the end result is always the same -- market leaders getting pummeled. Without fail, investors overestimate the utility and uptake of game-changing innovations and technologies. While artificial intelligence appears to have a bright long-term future, the fact that most businesses lack a well-defined game plan to profit in the near-term from their AI investments is a pretty clear indication that investor expectations are vastly outpacing real-world utility. In other words, it points to the formation of an AI bubble. The vast majority of market-leading businesses for game-changing innovations over the last 30 years have shed 75% or more of their value on a peak-to-trough basis. There are other potential concerns, too. For instance, insiders have been selling stock for 45 months without a single insider purchase. Though there are benign reasons for selling stock, such as for tax purposes, there's only one reason to purchase shares on the open market -- you think they'll increase in value. No insider has bought shares of Nvidia on the open market since Chief Financial Office Colette Kress in December 2020. It's also logical to expect Nvidia's monopoly like AI-GPU market share to come under pressure in the quarters that lie ahead. The interesting thing is that internal competition might be even more worrisome than the AI-GPUs its rivals are bringing to market. Nvidia's four largest customers by net sales are all internally developing AI-GPUs for use in their data centers. Even with Nvidia's chips having superior computing capacity, these four customers will likely be incented to utilize their own hardware over Nvidia's. This is a recipe for fewer orders in the future.

Nvidia: Make Hay While The Sun Shines (NASDAQ:NVDA)

We're initiating on NVDA with a Sell Rating and a $70/share Price Target. Nvidia Corporation (NASDAQ:NVDA) appears invincible. The company is everywhere, from its GPUs powering Generative AI applications associated with Open AI and that of the cloud hyperscalers, to combining with Dell Technologies (DELL) to build an AI factory for Elon Musk's xAI. And why not. NVDA currently provides the fastest and most powerful GPUs in the world, its AI platform is end-to-end fully sufficient to render any corporation AI ready, with little friction, providing the hardware, software, and networking, required for AI. The competition appears floundering in the face of NVDA's scorched earth policy towards them. However, it is not a zero-sum game. If one gazes more closely at NVDA, there are chinks in the company's armor the competition can exploit to get its foot in the door. NVDA's Achilles Heel is its premium pricing policy, its attitude that stacking compute power is the solution for everything AI, and that hogging CUDA, its proprietary GPU programming software will handicap sales of the competition's AI accelerators enough to ensure NVDA's industry dominance into perpetuity. However, the winds of change are blowing in. Advanced Micro Device's (AMD) AI processors are improving exponentially, and Intel Corporation's (INTC) new chips, although comparable in computing power to NVDA's H100 (not B100), are priced at 1/3 that of NVDA's GPUs. In addition, a consortium of developers from Intel to Qualcomm (QCOM) to Alphabet (GOOG) (GOOGL), Amazon (AMZN), and Meta Platforms (META), are quietly preparing open-source code that will optimize performance of GPUs of any company, run on any hardware. Further, AMZN, GOOG, and Microsoft (MSFT) are developing custom silicon to power their internal AI workloads, and to support AI inference activity performed by their cloud computing clients. Overall, once the challengers efforts gather momentum, NVDA's bound to lose pricing power and market share within the AI semiconductor industry. Nevertheless, given the presupposition of AI thought leaders that the demand for AI processing power will likely only be limited by price, with the market having the ability to absorb large quantities of compute power, all major chip companies are likely to prosper for an elongated period into the future. The rising tide will lift all boats, all companies will benefit greatly, including NVDA. We're initiating on NVDA with a Sell Rating and $70/share Price Target. This is based on our 10 year Discounted Cash Flow ("DCF") model, adjusted for lower revenues and earnings growth over the out years, as NVDA's first mover advantage fades in the face of the competition's strategic onslaught. We're cognizant that we will possibly encounter pushback from investors on our Price Target and Sell Rating on NVDA. In that regard, it is notable that our valuation surmises significant growth over the coming years, which tapers substantially over the out years. In addition, it is important to note that our model inputs for NVDA are highly aggressive, compared to that we attributed to INTC (which we initiated on two weeks ago). Specifically, we deployed straight lined 10 year: revenue growth/year, operating cash flows as percent of revenues/year, and capital expenditures as percent of revenues/year, of 8%, 30%, and 22% for INTC, versus 30%, 35%, and 5% for NVDA. Cumulatively, although NVDA's stock price might jump every time the company reports earnings over the coming quarters, with some downside risk, a substantial decline in the price of the company's shares is imminent. Make hay while the sun shines. NVDA was founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem, in Sunnyvale, California. The firm's headquarters are located in Santa Clara, California. NVDA has data centers, and research and development and sales and administrative facilities in the U.S., shine, Israel, and Taiwan, among others. The company derives its revenues from two segments: data center and networking, comprised of sales associated with its data centers and networking paraphernalia, and graphics, which includes revenues from NVDA's gaming GPUs. During FY24, the data center and networking, and graphics categories accounted for 78% and 22% of the firm's total revenues. There appear to be two primary concerns surrounding the NVDA story. The predominant issue is whether the runaway growth the company is witnessing is sustainable. The secondary element drawing investor attention is related to NVDA's long-term financial performance. We analyze both concerns below. Runaway Growth Likely To Taper As Industry Matures NVDA's competitive advantage is built on the back of several factors. These include the prowess of its GPUs to process trillions of parameters and tokens of data at high speeds, its networking abilities which lash together tens of thousands of its GPUs to provide supercomputing powers, and its CUDA programming platform. Developers have built software libraries using CUDA to simplify AI tasks of all genres. CUDA is proprietary to NVDA and not open sourced. Therefore, GPUs of alternate companies cannot be easily optimized using CUDA. GPU programmers over the years (CUDA was launched in 2006) have developed programs on CUDA that perform numerous AI tasks. Consequently, when an enterprise purchases a NVDA GPU, their programmers don't have to program the hardware for associated tasks, as the programs already exist. In that regard, NVDA's GPUs are like Apple Inc's (AAPL) iPhone and CUDA is similar to iOS, upon which developers have built millions of applications, greatly enhancing the customer appeal of the iPhone. AMD's GPUs can be optimized using the firm's ROCm platform (however the number of programs available is relatively small) and INTC's SYCL platform translates CUDA, so INTC's AI accelerators can avail of CUDA's assets. Nevertheless, regarding the training of large language models, or LLMs, AMD's and INTC's semiconductors have not gained much market traction. Therefore, the firms along with large corporations, including GOOG, QCOM, MSFT, and Open AI, are collaborating to deliver the industry from CUDA's dominance. ROCm, PyTorch, and Trinton are platforms under development to achieve the objective. Further, stacking compute power, appears to be NVDA's strategy to overwhelm customers and the competition. However, AI mimics humans, and humans have a multitude of capabilities, which they utilize to accomplish different tasks. Over time, the realization will dawn across the AI industry that stacking processing power is not the solution for making the most of AI. To limit costs and optimize performance, a combination of CPUs, GPUs, and AI accelerators with varied computing powers, will be required. To illustrate, LLM training necessitates significantly more processing and speed, requiring supercomputers and Petaflops of compute power, to ensure the processing of trillions of parameters and tokens of data, at breakneck speed. However, an AI inference task, that is the output a LLM spits out when prompted following training, requires substantially reduced computing power than during training. In the context of inference workloads, INTC's Gaudi 2 and Gaudi 3 AI accelerators, with processing power comparable with NVDA's H100 GPU, and lower power requirements, appear more suitable. Moreover, prices associated with Gaudi 2 and Gaudi 3 systems, are between 1/3 and 2/3 that of NVDA's GPU systems. In that respect, it is noteworthy that AI inference is projected to ultimately account for 80% of AI revenues. Further, with an eye on the future and sensing an opportunity to reduce reliance on expensive NVDA processors, AMZN, MSFT, and GOOG are designing and producing their own: inference chips to fulfil AI computing demands of their customers, and custom silicon to support their own internal workloads. Given that NVDA generated 45% of its 2Q25 total revenues from sales of its GPUs to these cloud hyperscalers, with large Internet companies and multinational corporations accounting for another 50% of total revenues, the development will ultimately shrink the addressable market for NVDA's products. In addition, once this small group of companies NVDA derives 95% of its revenues from have accumulated sufficient computing power generating hardware, their demand for NVDA's offerings will decline dramatically. Overall, although processing power requirements for artificial general intelligence (AGI) will pick up some slack in NVDA's revenues, long-term market dynamics suggest that the company is bound to lose market share and pricing power, over time. NVDA reported solid financial results for FY24. On a year-over-year basis, revenues expanded 126% to $60.9 billion. The outperformance was driven by strong sales associated with the data center and networking category, which advanced 215% to $47 billion, and the graphics segment, which escalated 14% to $13.5 billion. Compared to FY23, operating income expanded by 681% to $33 billion. In addition, over the same period, net income increased by 584% to $30 billion, and earnings per share came in at $11.93 versus $1.74. Gross margins advanced 38% to 54%, and net margins expanded 327% to 49%. Data center and networking operating income escalated 530% to $32 billion. Graphics operating income, which accounted for 18% of total operating income, increased 681% to $5.8 billion. NVDA's blowout financial performance was fueled by the advent of Chat-GPT, which reflected in a hyper focus on the training of LLMs. NVDA's GPUs, with the ability to process trillions of parameters and tokens of data at speed, are highly proficient at LLM training. What ensued was a scramble for the product among large corporations and government institutions across the world. The runaway volume and premium pricing associated with NVDA's GPUs ensured that margins were extremely high, reflecting in significant growth in the company's earnings and free cash flows for FY24. We expect NVDA to repeat if not better the financial performance it experienced in FY24 over the upcoming few quarters, as enterprises focus on training LLMs on proprietary data, thereby requiring huge amounts of processing power. However, Moore's Law states that the number of transistors on an integrated circuit will double every couple of years, while the cost of the chip will be halved over the same period. Therefore, the premium pricing that NVDA's chips command due to the novelty of its technology, is likely to be damaged over time. As described above, comparable semiconductors, a programming language to rival CUDA, GPU pricing wars, declining demand for AI hardware, and a shift in focus towards AI inference from AI training are likely to dampen sales growth of NVDA's AI processors. This is despite AGI's substantial compute requirements. Lower sales volume and reduced pricing will reflect on NVDA's revenue growth rates, over the back end of the decade. Consequently, the firm's margins, earnings, and free cash flows are likely to suffer over the out years. Incorporating the above described qualitative narrative into our 10-year Discounted Cash Flow model, we arrive at a Price Target of $70/share for NVDA. Our valuation assumes a normalized 10-year revenue growth rate of 30%. In addition, we derive our net income for 10-years using a net profit margin of 42% (vs. the 49% net profit margin experienced in FY24). Based on our analysis of NVDA's historic financial reports, we model normalized 10-year operating cash flows as 35% of revenues/year, and straight lined 10-year capital expenditure as 5% of revenues/year. Furthermore, we deploy a perpetual growth rate of 3% and weighted average cost of capital of 10% to reach our terminal value and present value of free cash flow figures. We utilize the current diluted outstanding share count of 24,848 million to arrive at our Price Target for NVDA. AI Monetization Efforts Disappoint. Based on Sequoia Capital's estimates, corporations purchased $50 billion worth of NVDA's AI processors during 2023. In contrast, over the same period, AI processors derived revenues came in at less than $5 billion. Most large corporations are stacking upon processing power, hoping that the investment will pay off in the future. Open AI generated $2 billion in Generative AI revenues in 2023, the cloud hyperscalers are monetizing AI by providing AI tools and services to enterprises seeking to automate tasks related to their businesses. However, use cases for AI are still evolving. The path for AI derived riches appears unclear, except for semiconductor companies. Undoubtedly, the world will be transformed by AI. However, similar to the internet era, visibility into where the bulk of AI related profits will ultimately land is weak. In case AI fatigue unfolds among the client base of the cloud hyperscalers, a pullback in demand for NVDA's chips might follow. In addition, an economic recession might reflect in a decline in spending on AI hardware. Nevertheless, the risk that these scenarios might occur is minimal, in our assessment. Overall, it appears that the world has bought into the AI story, hook-line-and-slinker. Wherever, the chips might settle regarding future AI profits, the semiconductor companies' fortunes appear secure. NVDA and Open AI spearheaded the advent of Generative AI on Main Street. After all, Open AI's Chat-GPT and GPT-4 LLMs are powered by NVDA's GPUs. Large technology companies, harboring the fear of losing out, are stacking layer upon layer of computing power, by purchasing as many NVDA GPUs they can lay their hands on. NVDA, aware of its enviable position, is flexing its muscles, to dominate the industry as long as it can, by aggressive marketing. Undoubtedly, NVDA has strengths, and it can surprise with the staying power of an AMZN, AAPL, or MSFT. However, for now, investors should consider the reality we've outlined in our analysis, for they risk placing overly enthusiastic long-term bets on NVDA. Focusing on technology underdogs with solid game plans, as alternative investments, is worth considering.

All About Nvidia Chips, AI Hype and What Lies Ahead

The blistering stock rally that's made Nvidia Corp. one of the world's three most valuable companies is based on a number of assumptions. One is that artificial intelligence applications made possible by its semiconductors will become features of the modern economy. Another is that Nvidia, and the patchwork of subcontractors upon which it relies, can meet the exploding demand for its equipment without a hitch. The excitement surrounding AI has ebbed and flowed, but Nvidia remains the preeminent picks-and-shovels seller in an artificial intelligence gold rush. Revenue is still soaring, and the order book for the company's Hopper chip lineup and its successor -- Blackwell -- is bulging.