1. Can you give a brief overview of your strategy in terms of what you are trying to achieve for investors, your investment process and the make-up of the investment team?
The pace of innovation and rate of adoption of artificial intelligence (AI) is rapidly accelerating. It is no longer an emerging technology and has reached an inflection point. Investors in the 50-80 stock Polar Capital Artificial Intelligence Fund will have exposure to both the enablers and the beneficiaries of this rapidly accelerating technology as the investment landscape is rewritten.
The Fund's managers are Xuesong Zhao, Ben Rogoff and Nick Evans; Xuesong is responsible for portfolio construction, with Ben and Nick contributing to risk management. They are all part of the Polar Capital Technology team of nine dedicated investment professionals, one of the largest and most experienced technology teams in Europe.
The team assess the anticipated impacts of AI across all sectors, looking to identify scope for disruptive entrants, for companies that are already well positioned as AI winners or with the right assets and strategy to become AI beneficiaries. Avoiding potential AI losers is also a core part of the investment approach, very much a bottom-up approach that aims to capture the winners from significant, structural changes they see taking place in many industries. This provides the opportunity to invest in companies that can deliver above-peer-group profit growth, as well as benefit from multiple rerating with more sustainable growth ahead.
Investors should expect a highly active, all-cap, multi-sector investment approach in a global equity mandate from an investment team aiming to outperform its benchmark, the MSCI All Country World Index Net TR Index.
2. How are you currently positioning your portfolio?
The inflection point reached in AI development and adoption means that the team are seeing increasing investment opportunities across all sectors as they build on an already strong track record of identifying the leading innovators around AI.
The Fund has three sub-themes that the team uses to communicate and capture the disruptive change that AI is driving in all sectors:
Applications are those companies using AI in either internal or external-facing roles to drive incremental profit growth. With the advent of newer generative AI technologies, they are seeing the pace of innovative applications accelerate significantly.
Industry datasets are those companies that are very well positioned with respect to growing AI usage through their ownership of proprietary data, a key differentiating factor with wider availability of the core foundation models that power generative AI.
Enabling technologies are those that underpin or contribute to ongoing advancements and the deployment of AI and are some of the earlier-stage beneficiaries through infrastructure buildout and capex spending.
3. Can you identify a couple of key investment opportunities for your fund you are playing at the moment in the portfolio? This could be at a stock, sector or thematic level.
The rollout of generative AI-powered tools and assistants into existing platforms is one of the most interesting and easily scalable new applications we are seeing. Microsoft, for example, are attracting much attention for their AI-powered ‘Copilot' technology that is being layered onto existing products such as Office, and we are seeing similar approaches elsewhere. Such tools are helping to dramatically improve data extraction and validation from datasets in financial services, while the legal industry is using this technology to better identify and summarise existing precedent before writing the first drafts of briefs for review. The development of natural language interfaces (such as those used with ChatGPT) has been key in broadening the accessibility of this technology and we expect to see more applications continually emerge.
We are also seeing AI, and generative AI in particular, transform the robotics industry. Both Microsoft and Google (Alphabet) have focused on this area, allowing users to program robotics much more intuitively with natural language commands that are then transformed to code. Combined with AI-improved machine vision, this is now allowing robots to carry out more generalised tasks, where the task or query has not been explicitly coded but the robot is able to refer to a wide base of knowledge to accomplish an objective. One very interesting confluence of these technologies is in robotic surgery, where the combination of improved robotics, proprietary data and AI analytics, and natural language models is allowing for real-time feedback on, and improvement of, surgical procedures to improve clinical outcomes.
