Thinking about investing in AI? Get to know the basics first

By Alek Sawchuk, CFA 24 August 2023 4 min read

In global earnings calls for the second quarter of 2023, artificial intelligence (AI) was mentioned at least 7,409 times. It’s no surprise given the fact that AI-related companies are seeing record profits this year. In fact, the S&P 500 US technology sector is currently one of the index’s best performers of the year, gaining nearly 37%, with leading AI chipmaker Nvidia returning over 220% year to date (as of August, 24, 2023).

While the excitement of AI can be intriguing, investors should consider that company valuations can be euphoric—and temporary. In this two-part series we will discuss the relevance of AI to investors and how they may already be gaining AI exposure in their portfolios, in addition to discussing the sector applications of AI.

The power of AI

It’s nearly impossible to avoid seeing AI in market news, with good reason. AI is an umbrella term for anything that allows a machine or computer to perform a task or cognitive functions that we would normally associate with human minds. The fact above about AI being mentioned at least 7,409 times in earnings calls? That data is an example of AI at work. Bloomberg’s AI-powered research platform uses natural language processing (NLP) to screen millions of company transcript texts, searching for keywords instantaneously. Manually screening these documents would have been an exceptionally laborious task, prone to human error. Bloomberg’s tool completed it in seconds. Deeper-learning models of AI are so sophisticated they have been known to pass legal and medical exams.  

IBM Global AI Adoption Index 

AI extends far beyond writing essays and passing exams, and has multiple applications in business sectors including finance, manufacturing, health care and others. IBM sheds further light on AI adoption growth. In partnership with Morning Consult, IBM conducts an annual survey of over 5,000 businesses worldwide, diversified across various geographies, company sizes, and business sectors. Some highlights from the 2022 report1 include:

  • Global AI adoption rate for 2022 increased to 35%, four points higher than the 2021 rate of 31%.
    • 35% reported they are currently using AI in their business, with 42% exploring using AI.
    • The increase was predominantly driven by easier AI product accessibility and the requirement for companies to better automate tasks and reduce costs.
    • Automation is helping companies address labor and skills shortages, especially for repetitive tasks.
    • Notable gap between larger and smaller company adoption. In 2022, larger companies were twice as likely to adopt AI.
    • China and India had the highest adoption rates by geography. 

Worldwide AI Adoption Rates 2022

Source: IBM, Morning Consult

Subsets and branches of AI

Here we cover a few common examples of AI. It is equally important to recognize these subsets and branches are not mutually exclusive. For example, you can have an AI product that blends together NLP and generative AI. The result could be AI that interprets text or spoken language (NLP component) and proceeds to produce text, images or sounds in response (generative AI component). Alexa and Siri are great examples of AI that utilize both NLP and generative AI subsets, in addition to deep learning which improves performance over time. 

Machine learning: Enables computers to learn from data and experience without being explicitly programmed. It can learn from data, the past, and identify patterns. 

Deep learning: A subset of machine learning consisting of layers of processing nodes or neurons (similar to the human brain network). Classified as deep due to its many layers, it requires high degrees of data and processing power. Over time, deep learning performs tasks and functions repeatedly, tweaking it for better outcomes.

Robotics: Creates robots to perform tasks utilizing AI systems. Think of your Roomba vacuum cleaner, which uses visual sensor analysis and algorithms to map out its cleaning area. This could even apply to the manufacturing industry with robotics used to produce vehicles in an  assembly line process.

Natural language processing (NLP): Translates text or spoken language and responds to commands. An example of this is taking a picture of text with your smartphone, copying and pasting the text into a message, and being able to translate it to different languages. 

Visual analysis: Identifying a person, place or thing by image or video. This includes facial recognition ID. Some may not realize the power of visual AI at their disposal right now. For example, if you take a picture of a dog with your smartphone, AI is actually able to look up the exact breed of the dog and match it to an online image bank. 

Generative AI: Generates text, images, sounds, animations and other types of data. ChatGPT is a popular example of this technology.


Understanding the basics of AI helps us understand its application and impact across various industries, and ultimately why that matters in the market. The next article will explore how and where investors may already have AI exposure in their portfolios and the opportunities and risks that come with holding AI-related stocks.

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