Frontier Advisors’ Equities Team members, Cat Goh and Simone Gavin recently travelled to the US meeting with managers across global, emerging markets and small cap equities. This included visiting five cities and conducting 25 meetings, discussing both rated and not rated strategies across the style spectrum (growth, value, core) and different approaches (quantitative, fundamental).
As part of this trip, we asked managers a number of questions related to AI. We wanted to better understand the industries and stocks that are benefiting from AI and how this may evolve, particularly in any areas that may be less obvious. We also tested the case for US exceptionalism and discussed the potential ‘losers’ of AI.
While not the focus of this research paper, we also questioned managers on their use of AI in internal processes and the results were interesting for both fundamental and quantitative approaches. We believe there is a potential edge where managers can harness the opportunity, whether through more efficient research from a fundamental perspective or with the ability to more efficiently process large data sets from a quantitative approach. To some extent, AI could level the divide between fundamental and quantitative managers, helping fundamental managers to increase breadth through researching more stocks, and helping quants to put structure around unstructured datasets to quantify some of the more qualitative parts of investing (e.g. management quality).
In addition, we have and continue to observe a competitive advantage from firms with long-term internal data sets as well as firms willing to spend money on technology. While separate from AI, these are likely to be continual requirements to leverage AI and compound these advantages even further.
What is AI and how has it evolved?
The field of artificial intelligence (AI) dates back to the 1950s when the groundwork for symbolic AI was laid. Early efforts focussed on developing programs capable of theorem proving and general problem solving, and this was when natural language processing was born.
In the 1990s, statistical machine learning leapt forward when the focus shifted from a knowledge-driven approach to a data-driven approach, where algorithms learnt from data without explicit programming. Further advancements were then achieved in the 2010s with the availability of massive datasets and the advent of powerful computing resources, particularly graphics processing units (GPUs), which propelled deep learning models. Frontier has evidenced many managers, particularly quant/hedge fund types, using such technology for some time.
Now, AI is discussed in our everyday conversations and some terms are often used interchangeably. Mostly people are using ChatGPT or Gemini but AI has extended to a number of new areas. Generative AI (GenAI) includes techniques such as diffusion models (simulate the process of adding noise to data) and large language models (LLMs), which allow the creation and manipulation of content. We have entered a new era of rapid increases in GenAI capabilities, driven by greater LLM creativity. The next frontier of GenAI is agentic AI, allowing these systems to act autonomously and perform workflows with minimal human intervention. The future then promises physical AI, which lets autonomous systems like cameras, robots, and self-driving cars perceive, understand, reason and perform complex actions in the physical world.
Beyond this, companies are also racing to develop artificial general intelligence (AGI), a theoretical type of AI that would possess the ability to perform any intellectual task that a human can. A monumental prize could be waiting for the winner of this race, further incentivising investment in AI.
This paper digs into the opportunities for investors to participate in the continued evolution of AI and what is widely believed will be a transformation of many industries that will at least match the impact of the internet.
