Leveraging the AI Revolution: An investor’s perspective
I have recently suggested for the AI bubble to become a bona fide boom, the fad will need to be replaced by revenue and profits. In this blog post, we dive a little deeper into the business and revenue models companies propose to employ to leverage AI for capital gain and what it might mean for investors.
There is no question AI revolution sentiment is underway. With an increasing number of individuals and businesses learning how AI can improve productivity, big tech is positioned squarely to benefit. Provided of course, they can charge a fee that more than compensates for the capital expenditure that may yet serve only to maintain their relative competitive positions.
Banks and retail first to gain from AI
As large cloud and software providers accelerate their AI offerings, a report by McKinsey indicates that banks and retail will be the first sectors to reap substantial gains from generative AI.
This shift is significant, and for investors, it offers new avenues for profit, growth, and diversification. We believe you should pay attention to companies that are utilising AI in innovative ways, particularly within the industries most likely to benefit.
The potential financial implications of AI for these businesses are potentially monumental. McKinsey estimates that generative AI could deliver total value between $2.6 trillion to $4.4 trillion a year, a sum larger than the GDP of Germany.
For example, full implementation in retail and packaged consumer goods companies could result in $660 billion a year in productivity gains, representing a 44 per cent boost to profits. One wonders whether that incorporates lower labour costs, which implies more unemployed people now unable to pay for their former employer’s products.
Banks, known for their higher profit margins, may not experience as significant a profit boost (projected at nine to 15 per cent), but could gain up to $340 billion annually. One example would be JPMorgan Chase, which uses AI to identify fraudulent transactions, helping to save the company billions of dollars each year. Likewise, Amazon’s AI-powered recommendation system is a key driver of its retail success, contributing to a significant portion of their sales.
It’s worth noting that McKinsey’s estimates are based on an extensive level of AI adoption, and the actual timelines for these gains remain uncertain.
In the business world, McKinsey predicts 75 per cent of productivity gains from generative AI will be concentrated in four key areas: customer operations, marketing and sales, software engineering, and R&D. As an investor, companies with durable involvement in these areas are on our radar.
Notably, reliable investment opportunities will only exist if AI providers produce the tools to convert theoretical AI productivity boosts into reality. Meanwhile, high-paying and highly educated work functions are most likely to be impacted by AI, which could mean significant gains for firms in tech, finance, and other knowledge-based industries.
Tech giant’s AI offerings
Tech giants Oracle and Salesforce are worth keeping tabs on. Oracle and Salesforce recently announced their latest offerings in AI. Oracle launched an end-to-end platform for generative AI services, while Salesforce introduced its Cloud AI offering, both targeting regulated industries with an emphasis on privacy and security. Oracle’s Digital Assistant uses AI to understand contextual and conversational nuances, while Salesforce’s Einstein AI offers predictive analytics to help companies make more informed decisions. Their focus on privacy and security features is an attempt to give them a competitive edge, especially in highly regulated sectors like healthcare and banking, making them potentially attractive investment opportunities.
The risks can’t be ignored
However, AI investment’s inherent risks and uncertainties cannot be ignored. While AI offers investors potential profits, implementation timelines are unpredictable, and firms must navigate the complexities of data security and regulatory requirements, especially when selling more lucrative enterprise-level solutions. Also, the success of AI largely depends on strategic applications chosen by management teams.
Furthermore, as much as generative AI promises ground-breaking advancements, non-generative AI—the foundational machine-learning technology—could yield more immediate dividends. The obvious example is Nvidia, the (current) leading manufacturer of graphics processing units (GPUs) essential for machine learning. Their solid recent growth and their strategic position selling ‘picks and shovels’ to the AI boom make them a potentially compelling investment opportunity.
And while Microsoft is backing and integrating OpenAI’s large language models, there are many competitors emerging. It is certainly the case that OpenAI is no longer the only game in town, especially for software developers looking for alternative foundation models to capitalise on a trillion-dollar market for artificial intelligence.
Take AI storytelling start-up Tome as an example. Tome helps users build slides faster. Tome was originally built on GPT-3, but after reaching three million users, its founders have added a text model from Anthropic. It also plans to migrate from DALL-E, OpenAI’s photo generation model, to an alternative and open-source model called Stable Diffusion, which Stability AI authors.
AI developers will discover benefits from reducing their reliance on a single model if they have not already. Greater reliability, lower costs and the specialisation of different models will influence buyers, and investors will follow.
Which companies should investors watch?
For investors, it’s not just individual companies that hold appeal. Exchange Traded Funds (ETFs) focused on AI, like the Global X Robotics & Artificial Intelligence ETF (BOTZ) or the ARK Autonomous Technology & Robotics ETF (ARKQ), offer diversified exposure to this sector. But I am reluctant to support thematic ETFs as most investors lose money in them, and they tend to underperform the broad indices.
However, as with any investment, it’s crucial to consider the risks and uncertainties. The potential impact of AI is massive, but the timeline remains uncertain, and companies must overcome significant challenges, including managing data security and meeting regulatory requirements. Additionally, while AI holds tremendous promise, its impact will depend on human oversight and strategic thinking.
Investors should also be aware that while generative AI’s potential is enormous, non-generative AI — the basic machine-learning and analytics technology that has been evolving for the past 15 years — offers potentially greater dividends. Thus, a balanced approach to investing in AI might include a mix of both.
Generative AI is indeed poised to drive substantial efficiencies across various business sectors. However, like any promising technology, it could also lead to market volatility, so monitoring technology developments and AI regulations is crucial.
Remember, fads breed bubbles, and bubbles can burst very, very quickly.
While it might be tempting to go all-in on AI, remember the importance of diversification. Our managers will continue to prioritise business quality and management in their portfolios, and by balancing investments across sectors, we believe they can benefit from the growth potential of AI while mitigating risks.
Investing, for example, in a blend of established players as well as newer, innovative companies makes sense at this early stage of a technology’s emergence. Limiting risk by setting aside smaller allocations is also prudent.
AI, particularly generative AI, appears set to revolutionise many sectors of the economy. If AI is as important as the microprocessors or personal computers have been in terms of its transformative effect on human life, it will also create new winners and losers. For investors, understanding the potential and the risks of AI is prudent. But while AI presents an attractive opportunity and breeds the Fear of Missing Out, it should remain part of a larger, diversified investment strategy.