Artificial Intelligence (AI) has taken center stage in investment discussions since OpenAI released ChatGPT to the public in November 2022. Viral posts showcasing eerily human-like responses from a chatbot drew comparisons to blockbuster films like The Terminator and The Matrix. The initial media buzz has since transformed into billions of dollars of investment in companies developing and supporting AI, fueled by the potential for reduced human error, enhanced data processing, and greater workforce productivity.
So, how does the implementation of AI translate into an investment thesis? Broadly, companies can be divided into three categories: adopters, enablers, and disrupted companies.
Adopters are companies implementing AI directly into the development and delivery of their products and services. Many large technology companies fall into this category, using AI for customer service, speech-to-text applications, software development, document summarization, and personalized recommendations. If implemented effectively, AI can reduce operating costs, increase employee productivity, and improve the products and services offered to customers. Over time, these benefits could translate into higher profit margins and greater earnings.
Enablers are akin to selling shovels during the Gold Rush. Rather than searching for gold themselves, these companies provide the infrastructure and tools necessary for others to pursue it. Semiconductor manufacturers, data-center operators and builders, electrical utilities, and companies supplying the equipment and materials required to build AI infrastructure may all benefit from increased demand for computing power. For investors, these companies offer another way to participate in AI’s growth without relying entirely on the success of a single AI application.
Disrupted companies, meanwhile, include companies whose business models face disruption from AI but have been slower to adapt. Chegg provides a notable example. The online education company built its business around providing students with subscription-based homework assistance. The emergence of AI-powered tools capable of answering many of the same questions created a significant competitive challenge, and Chegg’s market capitalization subsequently declined dramatically.
However, identifying companies that may benefit from AI is only the first step. A promising technology does not necessarily make every related investment attractive. Investors must also consider the risks associated with AI. Expectations for future growth can become embedded in stock prices, creating the possibility that even companies experiencing strong AI-driven growth could produce disappointing investment returns if that growth falls short of expectations. Rapid technological development also creates the risk of obsolescence, while intense competition could make it difficult for companies to maintain pricing power or profit margins. Significant capital requirements for data centers, semiconductors, and energy infrastructure add another layer of risk, particularly if investment in AI infrastructure eventually exceeds sustainable demand. More recently, whistleblowers have raised concerns about AI safeguards, potentially inviting greater regulatory scrutiny.
Ultimately, AI may prove to be one of the most transformative technologies of our time, but technological transformation and investment returns are not synonymous. For long-term investors, the more important question may not be simply “How much will AI grow?” but rather “How much of that growth is already reflected in the price of the investments I own?” Maintaining appropriate diversification and evaluating both the opportunities and risks associated with AI can help investors participate in its potential without allowing a single technological trend to disproportionately influence their portfolio.
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