Nvidia Will Be the King of AI Semis and Software for Quite Some Time

Sector Director

Brian Colello

Revenue segments as reported by the company.

Gaming

Professional Visualization

Datacenter

Auto

OEM & IP

Revenue Segments

Gross Profit, Operating Income, and Net Income as a percentage of Revenue

Gross Margin

Operating Margin

Profit Margin

Margins

An operating expense is an expense a business incurs through its normal business operations. Often abbreviated as OPEX.

SG&A

Acquisition termination cost

Operating Expenses

Year over year growth in Revenue and Operating Expenses.

Revenue Y/Y

OpEx Y/Y

Growth

Cash & Short Term Investments

Shares outstanding refer to a company's stock currently held by all its shareholders, including share blocks held by institutional investors and restricted shares owned by the company's officers and insiders.

Outstanding Shares

Market Cap

Also known as Price/Sales Ratio. Calculated using Average market cap for the quarter / Annualized quarterly revenue.

Revenue Multiple

Nvidia has a wide economic moat, thanks to its clear leadership in graphics processing units, or GPUs, hardware and software tools needed to enable the exponentially growing market around artificial intelligence. In the long run, we expect tech titans to strive to find second-sources or in-house solutions to diversify away from Nvidia in AI, but most likely, these efforts will chip away at, but not supplant, Nvidia’s AI dominance.

Nvidia’s GPUs specifically handle parallel processing workloads, using many cores to efficiently process data at the same time. In contrast, central processing units, or CPUs, such as Intel’s processors for PCs and servers, or Apple’s processors for its Macs and iPhones, process the data of “0s and 1s” in a serial fashion. The wheelhouse of GPUs has been the gaming market, and Nvidia’s GPU graphics cards have long been considered best of breed. More recently, cryptocurrency miners found the need for parallel processing GPUs, leading to a boom-and-bust cycle for Nvidia. 

More important, parallel processing has emerged as a near-requirement to accelerate AI workloads. Nvidia took an early lead in AI GPU hardware, but more important, developed a proprietary software platform, Cuda, and these tools allow AI developers to build their models with Nvidia. We believe Nvidia not only has a hardware lead, but benefits from high customer switching costs around Cuda, making it unlikely for another GPU vendor to emerge as a leader in AI training. 

We think Nvidia’s prospects will be tied to the AI market, for better or worse, for quite some time. We expect leading cloud vendors to continue to invest in in-house semis (with Google and Amazon leading the way), while CPU titans AMD and Intel are working on GPUs and AI accelerators for the data center. However, we view Nvidia’s GPUs and Cuda as the industry leaders, and the firm’s massive valuation will hinge on whether, and for how long, the company can stay ahead of the rest of the pack.

Business Strategy & Outlook

We assign Nvidia a wide economic moat rating, thanks to intangible assets around its graphics processing units and, increasingly, switching costs around its proprietary software, such as its Cuda platform for AI tools, which enables developers to use Nvidia’s GPUs to build AI models.

Nvidia was an early leader and designer of GPUs, which were originally developed to offload graphic processing tasks on PCs and gaming consoles. Nvidia has emerged as the clear market share leader in discrete GPUs (over 80% share, per Mercury Research). We attribute Nvidia’s leadership to intangible assets associated with GPU design, as well as the associated software, frameworks, and tools required by developers to work with these GPUs. Recent introductions, such as ray-tracing technology and the use of AI tensor cores in gaming applications, are signs, in our view, that Nvidia has not lost its GPU leadership in any way. A quick scan of GPU pricing in both gaming and data center shows that Nvidia’s average selling prices can often be twice as high as those from its closest competitor, AMD. 

Meanwhile, we don’t foresee any emerging companies as becoming a third relevant player in the GPU market outside of Nvidia or AMD. Even Intel, the chip industry behemoth, has struggled for many years in trying to build a high-end GPU that would be adopted by gaming enthusiasts, and its next effort for a discrete GPU is slated to launch in 2025. We do see integrated GPU functionality within many of Intel’s PC processors, as well as portions of Apple’s and Qualcomm’s system-on-chip solutions in smartphones, but we perceive these integrated solutions as “good enough” for nongamers, but not on par with high end GPU needs.

GPUs perform parallel processing, in contrast to the serial processing performed by central processing units used to run the software and applications on PCs, smartphones, and many other types of devices. CPU examples include Intel’s and AMD’s PC and server processors, as well as Apple’s and Qualcomm’s smartphone processors. These CPUs conduct serial processing of 0s and 1s in a particular order in order to run an instruction set to run software and perform functions. In contrast, parallel processing does not need to run in a linear order. This is particularly useful, for example, when capturing an image. A GPU often has more cores than a CPU and performs simpler processing (such as capturing the data within a single image pixel) but throws many more cores at the image to catch all of the data quickly. If CPUs were to conduct this task, they would have to capture the pixels in order—one can envision the CPU starting at the top and working its way down the image, while the GPU takes a snapshot in full.

In our view, the nature of parallel processing in GPUs is at the heart of Nvidia’s dominance in its various end markets. PC graphics were the initial key application, allowing for more robust and immersive gaming over the past couple of decades. Cryptocurrency mining also involves many mathematical calculations that can run in parallel (in other words, each calculation is independent of the others), again effectively “mining” crypto faster than a CPU.

In the past decade, however, the parallel processing of GPUs was also found to more efficiently run the matrix multiplication algorithms needed to power AI models. AI development has two key phases. The first is AI training. Using an image recognition example, developers might feed 50,000 images into a model, labeling them as either a picture of a cat, or not a cat. The model will look for the various aspects of each image and determine the scores and weights that are most common within a “cat” image. This may be an image with whiskers (although mice have whiskers too) or one with pointy ears (although the ears of a fox are also pointy), but a combination of all these factors may lead the model to effectively recognize cats in future images.

The second AI phase is inference, where the AI model makes a decision on an image, based on its prior training. In the cat example, the user would feed an unlabeled image into the model, and the model provides an output of whether it recognizes a cat or not.

Similar techniques are used in large language models associated with generative AI. In these cases, LLMs are fed with massive amounts of data, which may come from the internet, research papers, databases, and so on Based on this data, the LLM determines scores and weights associated with language, and which words are associated with one another. 

As an overly simple example, a model might be asked to predict the word to come after “peanut butter and…” “Jelly” might be the next word when thinking about food categories, although “peanuts,” “honey,” or other foods could be matches. But if the model were to think of categories outside of food, words like “diet,” “allergies,” or others could be fit. This leads to scores (that is, how often is “jelly” the next food word, versus “honey,” and so on), but also the weighting of such dimensions (that is, how often is the next word a food word, versus a health word, versus a geographic location, versus many other types of categories). 

GPUs are best suited to make these many billions of calculations needed for LLMs to predict the next word in a query (GPT-3 was trained on 175 billion parameters, for example). More important, Nvidia made shrewd moves to build and expand the Cuda software platform, creating and hosting a variety of libraries, compilers, frameworks, and development tools that allowed AI professionals to build their models. Cuda is proprietary to Nvidia and only runs on Nvidia GPUs, and we believe this hardware plus software integration has created high customer switching costs in AI that contribute to Nvidia’s wide moat. 

Even if a chip competitor were to build a GPU on par with Nvidia, we surmise that the code and models built on Cuda to date would not be ported over to another GPU, giving Nvidia an incumbency advantage. It is conceivable that alternatives may emerge that might never touch Cuda or an Nvidia GPU, but Nvidia has virtually no competition in this arena in 2023, so any enterprise building an LLM but waiting for alternatives might be left in the dust.

In AI inference, we believe that a variety of chip solutions will be used over time to detect the patterns and provide the output associated with AI models. CPUs handle many inference workloads today, but GPUs will likely be part of the inference equation too. Meta Platforms even indicated that it is moving its inference workloads to GPUs rather than CPUs. In a bullish scenario for GPU vendors, it’s possible that GPUs might not only dominate AI training, but the vast majority of AI inference too.

Beyond Nvidia’s AI prowess today, which we believe is exceptionally strong, we think the company is making the proper moves to widen its moat even further. Nvidia’s software efforts with Cuda remain impressive, while Nvidia is also expanding into networking solutions, most notably with its acquisition of Mellanox. We don’t want to discount Nvidia’s know-how here either. Many AI models don’t run on solo GPUs, but rather a connected system of many GPUs running in tandem. Nvidia’s proprietary NVLink products do a good job of connecting Nvidia GPUs together to run these larger models. 

Nvidia is elbowing out even further with its DGX solutions, priced at well over six figures, which tie in up to 8 GPUs into integrated solutions. Nvidia is even offering DGX Cloud and has hyperscaler partners where Nvidia will run a portion of the customer’s data center to optimize AI workloads. In a best-case scenario, Nvidia might not only have the best GPUs on the market, but the best cloud infrastructure in AI, potentially becoming a cloud computing leader that enterprises might cherish even more than the world’s leading hyperscalers today, such as Amazon’s AWS or Microsoft’s Azure.

Looking at the competitive landscape, AMD is a well-capitalized chipmaker with GPU expertise, although we view the company as being in a position of weakness on the software front. Here, we think switching costs around Cuda will keep Nvidia ahead of AMD for the foreseeable future, although the key valuation question for both companies centers around how much business AMD can chip away from Nvidia in the years ahead.

Intel has not had much success in AI accelerators or GPUs but can’t be ruled out. Perhaps the biggest threat might be from in-house chip solutions from hyperscalers, such as Google’s tensor processing units, or TPUs, Amazon’s Trainium and Inferentia chips, or Microsoft’s and Meta Platform’s chips in development. There’s no guarantee that any of these chips will be superior to Nvidia’s GPUs across a wide range of applications, but it’s likely that each of these in-house chips might perform specific workloads better than a general AI GPU from Nvidia or others. 

However, we believe that cloud computing companies will have to offer their enterprise customers a full menu of GPUs and accelerators so that they can run AI workloads. Amazon and Google may use Trainium and TPUs to train their own AI models, respectively, but we think they may struggle to encourage a host of enterprise customers to optimize their AI models for these in-house semis. Doing so would prevent these enterprise customers from switching among cloud providers, and enterprises typically loathe to be locked in to a single vendor. Thus, neutral merchant GPU vendors will likely be demanded by enterprise customers, and again, we foresee Nvidia remaining at the head of the pack for quite some time.

Wide

Economic Moat

NVIDIA

NVIDIA Corp. engages in the design and manufacture of computer graphics processors, chipsets, and related multimedia software. It operates through the following segments: Graphics Processing Unit (GPU), Tegra Processor, and All Other. The GPU segment comprises of product brands, which aims specialized markets including GeForce for gamers; Quadro for designers; Tesla and DGX for AI data scientists and big data researchers; and GRID for cloud-based visual computing users. The Tegra Processor segment integrates an entire computer onto a single chip, and incorporates GPUs and multi-core CPUs to drive supercomputing for autonomous robots, drones, and cars, as well as for consoles and mobile gaming and entertainment devices. The All Other segment refers to the stock-based compensation expense, corporate infrastructure and support costs, acquisition-related costs, legal settlement costs, and other non-recurring charges. The company was founded by Jen Hsun Huang, Chris A. Malachowsky, and Curtis R. Priem in January 1993 and is headquartered in Santa Clara, CA.

Earnings data Q1 2024

Revenue

Cost of revenue

Gross profit

Operating expenses

Other

Operating income

EBITDA

Net income

Earnings per share

Total assets

Current assets

Cash & equivalents

Receivables

Inventory

Non-current assets

Net PPE

Intangibles

Total liabilities

Current liabilities

Short term debt

Income tax payable

Non-current liabilities

Long-term debt

Total equity

Stockholders equity

Minority interest

Total liabilities & equity

Total debt

Net debt

Book value per share

Cash from operations

Funds from operations

Change in working capital

Cash from investing

Capital expenditure

Purchase/sale of investments

Purchase/sale of business

Other sources

Cash from financing

Change in stock

Change in debt

Cash dividends paid

Cash from other sources

Net change in cash

Free cash flow

NVIDIA Corp. engages in the design and manufacture of computer graphics processors, chipsets, and related multimedia software. It operates through the following segments: Graphics Processing Unit (GPU), Tegra Processor, and All Other. The GPU segment consists of product brands, including GeForce for gamers, Quadro for designers, Tesla and DGX for AI data scientists and big data researchers, and GRID for cloud-based visual computing users. The Tegra Processor segment integrates an entire computer onto a single chip and incorporates GPUs and multi-core CPUs to drive supercomputing for autonomous robots, drones, and cars, as well as for consoles and mobile gaming and entertainment devices. The All Other segment refers to the stock-based compensation expense, corporate infrastructure and support costs, acquisition-related costs, legal settlement costs, and other non-recurring charges. The company was founded by Jen Hsun Huang, Chris A. Malachowsky, and Curtis R. Priem in April 1993 and is headquartered in Santa Clara, CA.

Celsius Holdings

Microsoft

Hypergrowth stocks offer substantial gains with notable annual revenue growth. These companies are often categorized as companies with fast growth rates, though profitability metrics can differ depending on the stock and its sector.

Build Your Fortune: 3 Hypergrowth Stocks for Long-Term Millionaire Status

Nvidia Corp. has dominated financial-news coverage for tech-oriented companies this year, and it is reasonable to question how long a company can continue a winning streak like the one it has been on.

Here's why Nvidia is a compelling stock when compared with the rest of the ‘Magnificent Seven'

Nvidia Corporation (NASDAQ:NVDA) plans to delay the launch of an artificial intelligence chip in China until the first quarter of 2024, according to reporting from Reuters on Friday.  The chip is the H20, the most powerful of three chips that the company developed to comply with US export restrictions with China in mind.

Nvidia delays launch of China-specific AI chip

The consensus price target hints at a 35.3% upside potential for Nvidia (NVDA). While empirical research shows that this sought-after metric is hardly effective, an upward trend in earnings estimate revisions could mean that the stock will witness an upside in the near term.

Wall Street Analysts See a 35.28% Upside in Nvidia (NVDA): Can the Stock Really Move This High?

Amazon

Palantir Technologies

Tesla

Cloud computing, artificial intelligence, and software should remain exciting growth opportunities. Some of the companies leading today are poised to lead the future.

5 Tech Stocks You Can Buy and Hold for the Next Decade

Nvidia stock was up after Wall Street analysts contended that shares were "quite cheap" compared to its semi peers.

Why Wall Street analysts and Jim Cramer think Nvidia has more room to run

Zacks.com users have recently been watching Nvidia (NVDA) quite a bit. Thus, it is worth knowing the facts that could determine the stock's prospects.

Is Trending Stock NVIDIA Corporation (NVDA) a Buy Now?

Nvidia is selling its AI chips almost as fast as it can produce them, and revenue and profits are soaring. The company's valuation is still reasonable for long-term investors.

Nvidia Earnings: 3 Reasons This Artificial Intelligence (AI) Stock Is Still a Screaming Buy

It is unlikely that NVIDIA NASDAQ: NVDA will lose its top spot in the AI world soon. The company has too big a lead and too much market share to fall by the wayside without some fundamental shift in the landscape.

Is Microsoft the NVIDIA Killer?

Nvidia stock has fallen back. A renewed focus on the chip maker's difficulties in exporting hardware to China has knocked it but it could benefit if investors take a broader view of its technology.

Nvidia Stock Faces a China Challenge. What Could Get It Rising Again.

Coinbase

Interactive Brokers

Block

Bitcoin hit a new high of $38,000, which is likely to help stocks like Coinbase Global, Inc. (COIN), Interactive Brokers Group, Inc. (IBKR), NVIDIA Corporation (NVDA) and Block Inc. (SQ).

4 Solid Stocks to Buy as Bitcoin Hits New High for 2023

Adobe

Advanced Micro Devices

Arista Networks

salesforce.com

Cognizant Technology Solutions

Palo Alto Networks

Snowflake

It's been a wild ride for generative AI platform ChatGPT and its financial backer Microsoft Corp. NASDAQ: MSFT. While those two companies, along with chip designer Nvidia Corp. NASDAQ: NVDA are among the companies most closely associated with the AI trend, plenty of other stocks are poised to rise as AI delivers on its potential.

AI revolution: 3 stocks set to soar as technology evolves

Use code "CYBERMONDAY" for 40% our premium trading community, Galaxy Trading! Ends Tuesday night.

Get Prepared For Another Week With Markets With Mando! - NVIDIA (NASDAQ:NVDA)

e.l.f. Beauty

Lamb Weston Hlds

Novo Nordisk

Lamb Weston (LW), e.l.f. Beauty (ELF), Novo Nordisk (NVO), and NVIDIA (NVDA) have been selected as the top picks with a high net income ratio.

4 Must-Buy Profitable Stocks Using Net Income Ratio

Apple

Broadcom

Alphabet

Johnson & Johnson

Eli Lilly

UnitedHealth Group

Exxon Mobil

For years now, a significant cohort of investors gravitated towards the allure of the S&P 500 index over individual stocks.

Investing $10K in S&P 500 buys this much of largest stocks

Avid Bioservices

PulteGroup

Royal Caribbean Cruises

Arcus Biosciences

Vir Biotechnology

Stocks driving that large-cap index performance include names you've become familiar with this year, such as Nvidia Corp. NASDAQ: NVDA, Tesla Inc. NASDAQ: TSLA and Meta Platforms Inc. NASDAQ: META, but also Royal Caribbean Cruises Ltd. NYSE: RCL and PulteGroup Inc. NYSE: PHM.

From laggards to leaders: Small caps on the rise

Nvidia has become the leading supplier of chips designed to power AI systems. Alphabet is preparing to debut a new AI model next year.

Better Buy: Nvidia vs. Alphabet

Nvidia's profits and revenue have increased at unheard-of rates. The company's performance has caused the stock's valuation to decrease dramatically.

Nvidia Just Tripled Its Revenue, but the Stock Is Down. Time to Buy?

Accenture Plc

Chegg

Capital One

Cornerstone Ondemand

Companies are actively hiring workers with generative AI skills.   Meta, Amazon, and Capital One are among the employers with the most job listings referencing genAI.

From Nvidia to Meta, these 9 companies are hiring the most generative AI talent

Artificial intelligence remains a major market force, propelling a handful of technology stocks to new heights. Not only is the technology behind AI stocks likely to cause major changes in society, but it also represents a huge investment opportunity.

Strong Buy Alert: 3 Stocks Leading the Artificial Intelligence Revolution

The tech-heavy Nasdaq has been on a tear recently, which has left investors hunting for the best Nasdaq stocks to add to their portfolios. But with a rally already in progress, some are questioning whether they've missed the boat.

3 Best Nasdaq Stocks to Buy in November

Recursion Pharmaceuticals

Nvidia will help Recursion build a massive supercomputer. This will be used its for AI-driven drug discovery efforts.

Recursion Pharmaceuticals Wants to 4X Its Supercomputing Power for AI With the Help of Nvidia. Here's Why It Matters.

AMC Entertainment

The Swiss National Bank sold most of its investment in AMC stock, and cut back in Apple, Nvidia, and Tesla investments in the third quarter.

Swiss Central Bank Slashed AMC Stake, Sells Apple, Nvidia, and Tesla Stock

Super Micro Computer

While there is hype in the AI market, there are several exceptional long-term investment opportunities. The AI market is growing rapidly, with North America and Europe leading the way, offering remarkable long-term sales and profitability growth potential for high-quality AI-tech firms. AI services and software are expected to have substantial growth, with global AI-related sales projected to reach over $2.5 trillion in the next decade.

6 AI Stocks I'm Thankful For

Central Garden & Pet Company

Enlight Renewable Energy Ltd

ReNew Energy Global PLC

After beating quarterly earnings estimates on Monday, here are three stocks to consider as these companies look poised to take market share in their respective industries.

News for NVIDIA Stock (NVDA)