Video: Tom Fanter discusses applications and considerations of artificial intelligence and machine learning in finance.
In recent months, markets have been buzzing about artificial intelligence and machine learning (AIML). Sparked by the November 2022 launch of ChatGPT, a generative AI transformer model designed to generate human-like text that amassed more than 100 million users in just two months, AIML technology has been at the forefront of financial media and investor attention since.
The excitement picked up steam this spring as U.S. equity sector funds that invest primarily in the tech sector saw inflows of $1.1 billion in the last week of May alone, according to Reuters. The fervor among investors over AI helped to drive a very uneven market rally in early summer, with more than 90% of the S&P rally driven by just a handful of tech stocks, including Nvidia, Apple, and Alphabet. According to a PwC report, it is estimated that AIML could contribute as much as $15 trillion to the global economy by 2030, signaling a potential longer-term impact for both investors and the economy.
AIML could contribute as much as $15 trillion to the global economy by 2030.
The opportunities of AIML are real and attractive. There is potential for significant financial gain as AIML adoption expands across the economy, leading to improved efficiency, automation, and transformative solutions, along with its broad range of uses and potential boost to worker productivity in certain sectors.
That said, the risks are numerous as well: ethical concerns, regulatory landscape changes and debates, and the highly competitive nature of the AIML market and the tech sector in general. In addition, AIML’s potentially disruptive impact on the labor market could lead to structural changes in the distribution of jobs and skills, which could exacerbate income inequality and unemployment, particularly in developing economies.
Adding even more complexity is that AIML exposure comes in many different flavors beyond core foundational AIML enablers. These include “pick and shovel” tech providers that run data centers enabling AIML workloads, data infrastructure tech firms, cybersecurity companies, application software firms, and companies with indirect AIML exposure in industries as diverse as the financial and travel sectors. All these areas have varying degrees of potential opportunity and downside risk from widespread adoption of AIML.
Individual companies’ strategies and capabilities will play an outsized role in this emerging landscape, necessitating thorough analysis before investing in any company in this space. As always, talk to your RMB advisor about your financial goals and level of risk tolerance before making any investment decisions.
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