By Claire Sawyer, Associate Director of Content Development, CAIA Association
Life moves fast, and we get it—you can’t always find the time to tune in to every episode of the Capital Decanted podcast. That’s why we’ve distilled the key takeaways from our latest episode into this quick-read summary. Whether you’re curious about the topic or simply prefer to read, we’ve got the highlights for you. This time, we’re diving into Episode 3: Will Artificial Intelligence Replace the Investment Professional? Read on to discover the insights and perspectives you won’t want to miss!
In this episode, our hosts are joined by Martin Escobari, President of General Atlantic and Dave Morehead, CIO of Baylor University to unpack these emerging technologies and their influence on our industry.
So … Is it Time to Panic?
“It will touch all corners of the world. It will touch all industries. It will change most of the activities of businesses in ways that are, perhaps, yet to be fully understood but are going to be completely transformative.” - Martin Escobari
If you’d played a game of word association a few years ago, “artificial intelligence” would likely have conjured responses like Skynet, The Matrix, Ex Machina, and I, Robot. A suite of bleak, dystopian futures in which humanity was, in one way or another, outsmarted and outmatched by our own Promethean inventions. We opened the proverbial Pandora’s box of technology, naively believing that we knew what we were doing, and unknowingly started down the path of our eventual, hubris-fueled destruction.
If you played the game today, you may get some of the same responses, but you’d likely be met with some real-life examples too: Chat GPT, Siri and Alexa, customer service chatbots, self-driving cars (reliability aside), and more. Remarkable but relatively benign forms of technology, in the grand scheme of Orwellian possibilities. Yet our public consciousness surrounding AI, its largely dystopian impression on the zeitgeist, continues to fuel our perception and skews many of us towards a pessimistic and alarmist outlook on these technologies. At the same time, you have AI evangelists shouting from the rooftops that AI is the solution to virtually all of humanity’s problems. But there’s an argument to be made that many (perhaps most) of our fears and hopes are being driven by a largely fantastical conception of what AI can do, which may not correspond to its realistic potential. As is often the case, the truth may lie somewhere in the middle.
Where We’ve Been
“As we step back and look at AI, I think there’s been some commentary in the press about perhaps it being as impactful or more impactful than the internet, and we agree with that.” - Dave Morehead
Just as we can be prone to catastrophizing AI’s impact on humanity, we’ve been similarly conditioned to associate this type of technology with modernity, but AI’s origins go back farther than you might expect. When was the first time (that we’re aware of) that an AI tool was wielded to not only recognize patterns and anticipate future events, but to manipulate consumers and even destabilize politics? Take a guess.
You might, understandably, be thinking back to the U.S. Presidential election of 2016, or maybe further back – the early 2000s or even the 90s. But you’d still be off by a few decades. The first manifestation of AI used in this capacity actually dates back to the John F. Kennedy and Lyndon B. Johnson campaigns, with the development of an aptly named “people machine.” It captured both the exciting and concerning potential that AI presents: the ability to mimic (albeit imperfectly in its current state) and, in certain respects, outpace human intelligence. This brings us to perhaps the most important question in this conversation: what can, and can’t AI do?
Where We’re At
“I think the push-pull in AI use case and benefit to investors is that everyone sees the potential, but the use cases today don’t make full use of what that potential could be.” - Dave Morehead
In the episode, 3 broad categories of AI use cases within the industry are outlined:
Efficiency: exploiting AI’s vast computing power to fast-track some of the rudimentary, tedious processes that investment professionals currently perform manually. This might look like natural language processing and voice-recognition technologies being used to scrape, transcribe, and summarize things like earnings calls, reports, public records, and so on. Generative AI tools could potentially be used to go a step further and produce a first draft of investment memos and other documentation. These applications would save time, allowing professionals to focus on judgement and higher order thinking – what to make of the data vs. time spent gathering it in the first place.
Unstructured data insights: similarly, using AI to capture, sift, and organize alternative data from web scraping, social media, credit card purchases, and other less traditional data sources. Through natural language processing, AI could draw insights from this data, identifying relationships and patterns from otherwise unrelated sets of data that may not be discernable to the human eye. This type of data analysis could provide new insights into price trends, ESG factors, consumer behavior, and many other variables.
Recommendations: AI autonomously considering and analyzing data and beginning to make independent decisions. In other words, moving from the position of a human’s co-pilot to actually sitting in the captain’s seat.
As you might expect, “recommendations” is the most concerning with respect to feasibility, room for error, and threat to human job security. At the same time, the other list items offer an ability to optimize one’s time in such a way that higher-order activities take a front seat, and professionals have more of an opportunity to demonstrate their worth. In other words, as some AI capabilities are utilized, human potential is further unlocked, not overshadowed.
Where We’re Going
“This idea that computers are going to replace humans, I don’t think it’s correct. Pilots in airplanes have been using autopilot for 30 years – you still need a pilot. 80% of the time it’s the machine driving the plane, but you need the pilot for that critical 5 minutes.” - Martin Escobari
At the end of the day, there are many questions about AI still left unanswered. Our imaginations may run away from us, but we ultimately don’t know what hard limitations there may be on AI’s potential, and how close we may be to reaching some of them. The step-order improvements of something like Chat GPT have been pretty astounding to date, but who’s to say that will continue? Who’s to say that we can digitally replicate some of the complex neural processes that occur in the human brain, something about which we still understand surprisingly little? Not to mention AI’s propensity to hallucinate and, quite simply, get things wrong. As the saying goes: garbage in, garbage out – and the information landscape is a bit of a dumpster fire these days.
There’s no question that AI has some very compelling, potentially transformative use cases within the investment industry, but will it necessarily take your job? If accuracy, reliability, and higher-order thinking are part of what you do, then probably not. Or at least, not anytime soon, if some of Google AI’s highly dubious recipe recommendations are any indication (don’t put glue in your pizza, folks!).
To hear more about the evolution of AI within the industry and some of the fascinating ways our guests are exploring and using AI, listen to the full episode.
About the Contributor
Claire Sawyer is Associate Director of Content Development at CAIA Association. Prior to her current role, she served as Program Manager and Relationship Manager for the UniFi by CAIA™ learning platform. She holds the Sustainability and Climate Risk (SCR) certificate from GARP and is a Level 2 CAIA Candidate. She earned a BA in Legal Studies from UC Berkeley.
Learn more about CAIA Association and how to become part of a professional network that is shaping the future of investing, by visiting https://caia.org/