4 Key AI Terms Nvidia Investors Should Understand for Smarter Investments

Nvidia has been one of the stock market’s standout performers in 2024, with its stock price surging by over 150% despite some recent market fluctuations. The company’s leading role in providing the essential chips that fuel artificial intelligence (AI) technologies used by tech giants like Apple, Amazon, and Microsoft has driven its growth during the generative AI boom.

With AI rapidly reshaping industries and Nvidia dominating the chipmaking space, investors are eyeing opportunities in this sector. Nvidia’s influence has been so significant that over a third of the S&P 500's market cap gains in the first half of 2024 can be attributed to the company alone. However, this concentration of gains presents potential risks for investors who prefer a diversified portfolio. The brief stock downturn earlier this month, which saw Nvidia's shares drop by up to 10%, served as a reminder of the inherent volatility in the tech sector.

For those interested in navigating the AI-driven market, understanding key AI technologies and terminologies is essential. Here are four important AI concepts that Nvidia investors should grasp to make informed investment decisions.

Inference: AI’s Decision-Making Process

Inference is the process through which an AI model, like ChatGPT, generates responses based on the data it has learned during training. Essentially, it's the "thinking" stage of AI, where algorithms analyze and produce outcomes from massive datasets. The performance of an AI system’s inference is crucial to its overall effectiveness and directly impacts user experiences. This process relies heavily on the technology stack beneath it, including the high-performance chips that power these AI models. Nvidia’s GPUs are pivotal in enabling rapid and accurate inferences, making the company's products vital to the AI ecosystem.

Compute: The Power Behind AI's Success

Compute power refers to the processing capability that drives AI systems. Think of it as the engine that fuels an AI model’s performance, much like horsepower propels a car. The greater the compute power, the faster and more efficiently AI systems can process data and generate results. In AI development, compute power is vital for both training models and for executing inferences. Memory, storage, and processing power all contribute to an AI system’s compute capability. As chipmakers like Nvidia continuously develop new generations of chips with improved compute power, their ability to meet the growing demands of AI developers and enterprises strengthens their market position.

GPUs: The AI Workhorse

Graphics processing units, or GPUs, are specialized chips designed to handle complex computations, especially those required for AI tasks. Originally developed for rendering graphics, GPUs have proven to be ideal for the parallel processing needs of AI, making them the backbone of modern AI computations. Nvidia has dominated the GPU market, commanding over 80% of the market share. The company has significantly enhanced AI performance over the years, improving AI inference capabilities by as much as 1,000 times over the past decade. Nvidia’s cutting-edge GPUs are now a cornerstone of AI development, providing the raw computational power needed for tasks ranging from machine learning to deep learning.

Hyperscalers: The Giants Driving AI Adoption

Hyperscalers are the large technology companies that can quickly scale up AI applications due to their vast infrastructure. These companies, including Microsoft, Google, Meta, and Amazon, are not only consumers of AI technologies but also direct competitors in the AI space. Hyperscalers play a dual role in the AI market: they utilize massive amounts of AI chips to power their products and services, and they drive the development of AI through their own innovations. Companies like Microsoft with its AI-powered Copilot, Google with its Gemini AI system, and Meta with its Llama project are at the forefront of AI advancements. As these hyperscalers continue to push the boundaries of AI, their demand for Nvidia’s high-performance GPUs remains strong, further boosting Nvidia’s growth potential.

Why Investors Should Pay Attention to AI Trends

The AI sector is rapidly evolving, and Nvidia is positioned at the heart of this transformation. However, the concentration of market gains in a few key players, like Nvidia, can lead to volatility. Investors looking to benefit from AI's growth should diversify their portfolios and stay informed about the latest technological trends and advancements in the AI space. Understanding core AI concepts such as inference, compute, GPUs, and hyperscalers will help investors make more informed decisions in this ever-changing market.

Nvidia's pivotal role in AI development, coupled with its strong market position, makes it an essential company for those investing in AI. However, the broader AI ecosystem offers numerous opportunities for diversification, allowing investors to explore a range of options beyond Nvidia while still capitalizing on the AI boom.

The Future of AI Investment

As AI continues to advance and reshape industries, Nvidia’s role as a chip provider is expected to grow. Yet, the potential for diversification in AI investment remains vast. Emerging AI companies, alternative chipmakers, and advancements in software and AI applications are areas that investors should keep an eye on. By understanding the key technologies driving AI, such as inference, compute, and the role of hyperscalers, investors can better navigate the future of AI and make strategic investment choices that align with the growing demand for artificial intelligence solutions.

Also Read: Nvidia AI Chip Delay Could Impact Microsoft, Google, Meta