Age of AI: perspectives from Franklin Templeton
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Age of AI: perspectives from Franklin Templeton

26 Jun
7 Jun

Artificial intelligence (AI) is a key pillar of the digital transformation theme, which is driving significant disruption and spurring new growth. Grant Bowers, portfolio manager with Franklin Equity Group, offers a unique perspective of the challenges and benefits of this evolving and disruptive technology.

This article was originally published by Franklin Templeton on 17 May 2023. Please refer to the original article for additional disclaimers.

For decades, the idea of computers seemingly becoming sentient and engaging with humans was incorporated in comics, books, television and movies. 

These computers and animatronic figures played games, controlled spacecraft, and generally sought to ease the lives of those with whom they interacted. These fictional characters were largely based on technology that was developing concurrently with the rise of media. 

While to many of us they seemed like fanciful characters at the time, we believe these ideas are now entering the mainstream reality of our day-to-day lives, bringing new opportunities for growth and productivity across a wide range of industries. 

AI technology has been in development since the 1950s, but it wasn’t until the late 1990s that it started to become more widely used. Early forms of AI in the 2000s focused on business intelligence and machine learning and saw rapid enterprise adoption. Since 2017, AI adoption has more than doubled globally (Exhibit 1) as companies have embraced the potential that the technology unlocks. The increase in computing power and ability to analyse large data sets and build predictive models has been a tremendous driver of productivity gains not just for the technology sector, but for every industry around the globe. 

The next wave of AI has arrived and is driven by natural language models, like the recently released Generative Pre-Trained Transformer (i.e., ChatGPT). These models combine large amounts of data and computing power to string together words in a meaningful way. They understand words in context and have a vast vocabulary and information. They bring the promise of AI to act as an assistant for many human tasks closer, than ever before. 

What is ChatGPT?

In late 2022, OpenAI, an artificial intelligence company that was founded in 2015 by several technologists including Elon Musk and Sam Altman, released its latest version of their AI platform ChatGPT. 

The platform uses OpenAI’s GPT language technology and can understand and create human-like conversation. The platform quickly reached over one million users in one week and Microsoft CEO Satya Nadella called it “the biggest technological platform of the generation”.

ChatGPT is a natural language model developed by OpenAI, which is based on the GPT architecture. The model has been trained on a massive amount of text and data from the internet, books, and conversations. It can understand a wide variety of topics and can perform a wide range of natural language processing tasks such as text generation, text summarisation, question answering, and language translation. 

The model has been praised for its ability to predict what word should be next in a sentence and to generate human-like text, which makes it ideal for use in chatbots. ChatGPT is continuously learning, allowing for ongoing improvement in its ability to generate relevant content. 

An example of this evolution can be exemplified through automated customer service chatbots — currently, when customers interact with a chatbot on a website, the chatbot generally uses canned responses when customers are initiating a return, trying to change their cable services, trying to purchase airline tickets, etc. These chatbot experiences are stilted in nature and have limited information scope. 

ChatGPT and other related large language models (LLMs) advancements could improve the fluency of communication between humans and computers with a broad set of applications, like automatically providing transcripts of work meetings, creating schedules to increase productivity in workplaces and drafting emails. 

Economic impact and the AI investment universe

The potential market for AI is enormous, as the technology has the potential to disrupt a wide range of industries (Exhibit 3). PWC estimates that AI could contribute up to US$15.7 trillion to the global economy by 2030, more than the combined output of China and India. 

The following will likely drive the primary economic impact: 

  • Productivity gains from businesses automating their processes (including the use of robots and autonomous vehicles). 
  • Productivity gains from businesses augmenting their existing labour force with AI technologies. 
  • Increased consumer demand from the availability of personalised AI enhanced products or services.

What industries might be disrupted first?

Artificial and machine learning should be thought of as “enabling technologies” — in other words, technology that must be paired with something else to be useful. We believe that these models will be disruptive to a multitude of sectors, and we see massive opportunity in how sectors may incorporate AI for greater productivity potential. 

Companies are just starting to think about how to leverage this technology to foster improvements in their business models and customer engagement. Just as the launch of the iPhone in 2007 unleashed the last great technology shift to mobile, we expect a wave of productivity improvements to be seen across the entire market (Exhibit 4). 

Health Care 

The health care sector is one area that could see significant change from the new AI models.

  • Democratised health care access: Generative AI and its user-friendly interface has the ability to democratise access to health care for patients by analysing symptoms and providing personalised treatment plans. This can improve the quality of patient care and reduce health care costs. 
  • Improved patient care and drug development: AI can also improve the standard of patient care by being the primary point of review for standard tests such as X-rays, lab results, drug development and MRIs. A hypothesis-driven discovery method currently drives drug development, which is both a time- and money-extensive endeavour.

    However, AI could enable analysis of large swaths of data to expedite and enhance the research and development process. One company used AI to observe the progress of various cancers by following data from cancerous and non-cancerous cells created. Other companies are investigating how to integrate machine learning into their research processes by using predictive technology to determine how potential drugs could impact the body, and then filter out less-effective compounds before lab work begins.


Large language models could be very disruptive to several areas of the technology sector.

  • Internet search: Even though many people may use the same internet search provider, each person’s internet experience could be vastly different, due in part to AI learning of people’s tendencies and creating a tailored experience.

    Traditional online search is one of the areas the arrival of generative AI may transform. The current search model is advertising-driven; in other words, what shows up at the top of a user’s search may not always match exactly what that individual is looking for, because it’s paid content. Generative AI technology offers a more conversational model able to deliver search answers directly to the user, using a language model to retool how search engines rank and serve relevant information. 
  • Coding and software development: Recently released software programming copilot products such as Microsoft’s GitHub Copilot and Deep Mind’s AlphaCode leverage large language models (LLMs) to act as assistants or guides for software development — automating and improving code quality. We believe that there could be a transformation in the coding landscape, transforming a currently labour-intensive exercise to a more automated one. In the years ahead, we see these copilot products being integral to the coding and software development process.

Retail and customer service

Anticipating customer needs and streamlining the service pipeline.

  • Retailers are beginning to use AI to anticipate demand and, taking it a step further, to use deep learning to predict customers’ orders in advance. We believe this will drive more on-demand customisation for consumers. This AI-driven customisation should contribute to improvements in customer loyalty, which ultimately drives a virtuous circle of more demand. 
  • AI-driven chatbots are poised to disrupt the customer service industry. With the ability to provide instant responses to queries, chatbots can reduce wait times and improve overall service levels at lower cost. The travel, transportation and retail industries have been early adopters of AI technology and will embrace the new abilities GPT (and other LLMs) bring.

Automotive, transportation and education

Improving autonomous driving and new learning experiences.

  • Autonomous or semi-autonomous driving will be built on the backbone of AI. Currently we see many driver assistance or collision avoidance systems utilising AI. In the not-too-distant future, we expect to see autonomous ride sharing fleets in cities and large trucking companies using AI to manage transportation networks. 
  • The education sector could see disruption as large language models provide students with personalised learning experiences, answering questions or even writing papers for them. It also opens the door for teachers and students to rethink core skills instruction and development to identify and reinforce conceptual learning without over-reliance on these new tools. 

Investment opportunities in artificial intelligence

In our view, structural shifts in the technology landscape often create attractive investment opportunities for long-term investors who can position portfolios to take advantage of these shifts. 

AI represents a set of technologies, but many wonder how to invest in it. AI is under development at multiple large companies for a variety of purposes, including most large technology and technology-related companies, either as standalone products or as ways of improving existing products and business processes. 

Machine learning (ML) technology — a branch of artificial intelligence that enables computers to emulate how humans learn and adapt by using data and experience — is also under development at many companies, including semiconductor industry players situated at the core of ML/AI computing power. 

Additionally, as technology generally becomes more pervasive across industries, companies outside of the traditional information technology sector may have exposure to or are investing in AI. 

Read more: Artificial intelligence and technology investing — Q&A with Franklin Templeton

Long-term opportunities

The “Age of AI” is fast approaching, and we believe that these changes will make AI integral to everyday life, interwoven in how we interact with computers. 

However, like many technologies, we believe the adoption will be moderate at first and build over time. Technology will continue to improve. Investment will grow, and more applications will be built on the platforms. Enterprises and consumers will realise the benefits of the tools built based on this new technology. 

We are excited by the tremendous opportunities that AI, ChatGPT and other large language models bring and will continue to search for opportunities for our strategies to invest in the revolution.


Risk Warnings

Investment involves risk. Past performance is not an indicator nor a guarantee of future performance. The value of investments and the income from them can go down as well as up, and you may not get the full amount you invested. Rates of exchange may cause the value of investments to go up or down. 

This article is not intended to be relied upon as a forecast or research or investment advice, and should not form the basis of any investment or other decisions. The information contained herein is not intended, and should not be construed, as any legal, tax, regulatory, accounting or financial advice. If you would like investment, accounting, tax or legal advice, you should consult with your own professional advisors regarding your individual circumstances and needs.

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