Written by Robert Hwang
ChatGPT created massive hype around artificial intelligence (AI), specifically generative AI. The popularization of chatbots has attracted investors to suppliers of AI development. At the top is Nvidia, a tech company whose products are relied on by companies developing AI. This boosted Nvidia’s share price by an incredible 88% year-to-date. This article provides context to the recent AI hype, explores Nvidia’s role in it, and considers whether it’s a good time to buy.
To understand the recent developments, one must understand AI. AI is “the capability of a computer program… to think and learn and take actions without being explicitly encoded with commands,” and generative AI is a subfield where systems like chatbots generate texts, images, videos, and numbers (“Generative AI”). This is the subfield currently receiving the most attention. It develops from machine learning, which was defined by AI pioneer Arthur Samuel as the process “that gives computers the ability to learn without explicitly being programmed” (Brown). According to an MIT article, it starts with inputting data—text, numbers, photos—for the AI system. Programmers let it train—process data for various patterns to understand it—and attempt to identify images or write competent sentences based on its training until it’s correct. Eventually, it understands the content and can describe, predict, and suggest (Copeland). This is how AI becomes the convenient tool being discussed today.
Generative AI includes chatbots, which are large language models (LLM). LLMs use machine learning to learn relationships between words, numbers, and images (Lee). This eventually gives chatbots the diverse functions users love.
Recognition arose when ChatGPT was released in November 2022 (Sundar). It garnered global attention, causing significant developments. For example, in February Microsoft announced it would use ChatGPT’s model for a Microsoft Bing chatbot, and Google announced its own chatbot called Bard (Pichai; Vanian). The hype also led to realizations of the widespread potential of AI. It could innovate online applications, including stylized avatars and deep fakes, as well as other fields, including drug design, self-driving, and chip design (Flatow; Wiles). This diverse utility drove AI’s popularity.
So, where does Nvidia fit in this?
Nvidia designs chips to manufacture graphic processing units (GPUs), which then go into their supercomputers, but each are sold separately as well. Its chips, both traditional and AI variants, are extremely advanced. They give GPUs their power, which are known for innovating 3D gaming graphics with their efficient parallel processing- breaking down large data into smaller, simultaneously processed parts (“Parallel Computing”). That power can also be used for AI development, so Nvidia’s DGX supercomputers are highly demanded (“Accelerating AI with GPUs;” David). In March, Nvidia also released DGX Cloud, providing the DGX supercomuter’s functions as a software (Hardawar; McPhee). This also means it has cost-effective monthly subscriptions and saves companies storage space.
Nvidia has become a top supplier to a growing customer base. Unsurprisingly, this demand has attracted investors to boost Nvidia’s share price, but the 88% increase begs the question: is now the time to buy?
To preface, most investors agree Nvidia will significantly grow in future years. However, this doesn’t necessitate that it’s a good time to buy, and there’s uncertainty around whether prices have far exceeded fair value.
The primary reason to buy Nvidia is that there’s strong evidence – ChatGPT, Bard, Bing’s chatbot – that the future will be LLM and AI-dependent. And, since Nvidia’s tech makes it a key supplier to companies wanting to create and implement AI into their operations, Nvidia could profit immensely. Simply put, companies need Nvidia; therefore, it’s logical to buy a key supplier of a growing industry.
Industry numbers support Nvidia’s rise. Precedence Research says the global AI market was estimated at $119.78B in 2022 and is expected to hit $1.59T by 2030 with an incredible compounded annual growth rate of 38.1%. Nvidia can utilize much of this growth because it operates in Europe, Asia, and the US (Ladha). Furthermore, Jon Peddie Research found it has an 88% market share in the discrete GPU- the type that Nvidia manufactures- market, meaning it will experience more growth than competitors from industry growth (Pope et al.).
Nvidia’s new DGX Cloud is a huge new revenue stream because it fulfills AI needs, and monthly subscriptions eliminate depreciation costs and provide on-demand access, whereas the physical DGX supercomputers depreciate and are a massive expense. This cost reduction is especially useful because aggregate demand is down, so Nvidia’s customers will prioritize cutting expenses.
Nvidia also has customers in other AI fields. For instance, Nvidia supplies the tech for Amazon’s AI warehouse robots’ training (Tarasov). It also supplies self-driving car development for automakers like BYD, which recently announced it will expand its use of Nvidia’s hardware (TipRanks Staff). Analyst Vijay Rakesh estimates this to bring in $14B (Tipranks.com Staff). As AI rapidly grows into new markets, Nvidia’s customer base and revenue will expand as well.
Nvidia’s return on capital invested (ROIC) reflects earnings divided by the funds used, and an ROIC of >2% is considered good (“What is ROIC?”). According to gurufocus.com, Nvidia boasts a 22.72% ROIC (Jan. 2023), and compared to competitors AMD’s 0.14% in December 2022 and Intel’s -1.62% in December 2022, it is incredibly high. This is a great competitive edge because Nvidia would have to use much less capital to earn a number of earnings compared to competitors.
From its late 2021 peak to its late 2022 low, Nvidia’s stock dropped by >65% (FactSet). This resulted from a bear market, one that is declining due to low investor sentiment. Also, considering the current banking crisis, high inflation, high interest rates, and a cooling labor market, aggregate demand will fall. This suggests an impending bear market, which could drop Nvidia’s pricey stock.
It’s also arguable that Nvidia is already slowing down. Year-over-year numbers of Nvidia’s revenue, revenue growth rate, and profit are all low or decreasing, so most stock growth is from temporary AI hype that will slow down eventually (Pope et al.). Furthermore, revenue growth is down 20% from four quarters ago while inventory has grown 40%, suggesting Nvidia is producing at a surplus (Pope). To sell inventory later, it will likely be discounted and hurt revenue and profit margins. Overall, it’s possible that the hype is covering the reality of a slowdown, and investors may switch focuses to this.
Furthermore, these signs of a slowdown are important when considering Nvidia’s price to earnings ratio (P/E). A high P/E could indicate that a company is incorrectly overvalued or that investors are willing to pay more now and expect earnings to catch up to the price growth. If Nvidia’s case is the latter, signs of a slowdown are detrimental to its stock because they hurt Nvidia’s earnings, and if investors’ expectations of growing earnings aren’t met, they could pull out.
Another consideration is that Nvidia’s customers are increasingly designing their own AI chips, an important portion of revenue. Google, Tesla, and Apple have already begun, and as more companies invest money now to reduce dependence on Nvidia, it could lose some giant buyers and experience revenue decreases from that stream (Tarasov).
Overall, the risk and uncertainty associated with Nvidia’s extremely expensive shares is very high. Nvidia’s growth potential seems to already be excessively priced in, and while it may increase in the short term, it is unsustainable in the long run. Instead, it seems more logical to wait because Nvidia is an excellent supplier to a rising industry, but there are strong suggestions of an impending dropoff from both the company and the whole economy. Buying after this dropoff would make Nvidia much less risky and gains would still be huge.
Works Cited
“Artificial Intelligence (AI) Market Size, Growth, Report 2022-2030” www.precedenceresearch.com/artificial-intelligence-market. Accessed 19 Apr. 2023.
Brown, Sara. “Machine Learning, Explained.” MIT Sloan, 21 Apr. 2021, https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained.
Buxbaum, Shoshannah, and Ira Flatow. “Chatgpt and beyond: What’s behind the Ai Boom?” Science Friday, 10 Feb. 2023, https://www.sciencefriday.com/segments/chatgpt-microsoft-google-ai-future/.
Copeland, Michael. “What’s the Difference between Deep Learning Training and Inference?” NVIDIA Blog, 22 Aug. 2016, https://blogs.nvidia.com/blog/2016/08/22/difference-deep-learning-training-inference-ai/.
David, Shimon Ben. “GPU for Ai.” WEKA, 12 Feb. 2021, https://www.weka.io/blog/ai-ml/gpu-for-ai/.
“Deep Learning and AI Solutions from Nvidia.” NVIDIA, https://www.nvidia.com/en-gb/deep-learning-ai/products/solutions/.
FactSet, https://my.apps.factset.com/navigator/company-security/income-statement/NVDA-US.
“Generative AI.” BCG Global, https://www.bcg.com/x/artificial-intelligence/generative-ai.
Hardawar, Devindra. “Nvidia’s Big AI Moment Is Here.” Engadget, 21 Mar. 2023, https://www.engadget.com/nvidia-gtc-ai-dgx-cloud-161517378.html.
Howley, Daniel. “Nvidia CEO: ‘We’re Seeing an Acceleration in Demand… Because of Generative AI’.” Yahoo! Finance, Yahoo!, 24 Mar. 2023, https://finance.yahoo.com/news/nvidia-ceo-were-seeing-an-acceleration-in-demand -because-of-generative-ai-143741028.html.
Ladha, Vaishali. “NVIDIA’s Revenue Breakdown by Region (Bill to Location)” 15 Jan. 2020, businessquant.com/nvidia-revenue-by-region-bill-to-location. Accessed 19 Apr. 2023.
Lee, Angie. “What Are Large Language Models Used for and Why Are They Important?” NVIDIA Blog, 26 Jan. 2023, https://blogs.nvidia.com/blog/2023/01/26/what-are-large-language-models-used-for/.
Lee, Jane. “AMD Closes Record Chip Industry Deal with Estimated $50 Billion Purchase of Xilinx.” Reuters, 14 Feb. 2022, https://www.reuters.com/technology/amd-closes-biggest-chip-acquisition-with-498 -bln-purchase-xilinx-2022-02-14/.
McPhee, Shannon. “Nvidia Launches DGX Cloud, Giving Every Enterprise Instant Access to AI Supercomputer from a Browser.” NVIDIA Newsroom, 21 Mar. 2023, https://nvidianews.nvidia.com/news/nvidia-launches-dgx-cloud-giving-every-enterprise -instant-access-to-ai-supercomputer-from-a-browser.
“NVIDIA ROIC .” NVDA (NVIDIA) ROIC %, Jan. 2023, https://www.gurufocus.com/term/ROIC/NVDA/ROIC-/NVIDIA.
Pichai, Sundar. “An Important next Step on Our Ai Journey.” Google, The Keyword, 6 Feb. 2023, https://blog.google/technology/ai/bard-google-ai-search-updates/.
Pope, Justin, et al. “April Fool! We’re Not Buying the Rally in These 3 Growth Stocks.” The Motley Fool, 1 Apr. 2023, https://www.fool.com/investing/2023/04/01/april-fool-not-buying-rally- in-growth-stocks/.
Pope, Justin. “Nvidia Stock: Is Trouble on the Horizon?” The Motley Fool, 1 Apr. 2023, https://www.fool.com/investing/2023/04/01/nvidia-stock-is-trouble-on-the-horizon/ .
Staff, Tipranks.com. “Top Analysts Are Bullish on These Five Long-Term Picks.” CNBC, 2 Apr. 2023, https://www.cnbc.com/2023/04/02/top-analysts-say-buy-stocks-like-nvidia-nike.html.
Sundar, Sindhu. “If You Still Aren’t Sure What Chatgpt Is, This Is Your Guide to the Viral Chatbot That Everyone Is Talking About.” Business Insider, 1 Mar. 2023, https://www.businessinsider.com/everything-you-need-to-know-about-chat-gpt-2023-1.
Tarasov, Katie. “Nvidia CEO Jensen Huang’s Big Bet on A.I. Is Paying off as His Core Technology Powers Chatgpt.” CNBC, 7 Mar. 2023, https://www.cnbc.com/2023/03/07/nvidia-grew-from-gaming-to-ai-giant-and-now- powering-chatgpt.html.
Vanian, Jonathan. “Microsoft Adds OpenAI Technology to Word and Excel.” CNBC, 16 Mar. 2023, https://www.cnbc.com/2023/03/16/microsoft-to-improve-office-365-with-chatgpt -like-generative-ai-tech-.html#:~:text=In%20February%2C%20Microsoft%20debuted %20a,than%20the%20GPT%2D3%20technology.
“What Is Parallel Computing? Definition and Faqs.” What Is Parallel Computing? Definition and FAQs | HEAVY.AI, https://www.heavy.ai/technical-glossary/parallel-computing.
“What is Return on Invested Capital (ROIC)?” Robinhood, learn.robinhood.com/articles/3jB0ufT6VhxpsJthBMuY1o/what-is-return-on- invested-capital-roic/. Accessed 19 Apr. 2023.
Wiles, Jackie. “Beyond Chatgpt: The Future of Generative AI for Enterprises.” Gartner, 26 Jan. 2023, https://www.gartner.com/en/articles/beyond-chatgpt-the-future-of- generative-ai-for-enterprises.