Investor’s Guide to Artificial Intelligence

Key takeaways:

• In the first half of 2023, robust equity performance was largely attributed to AI-driven mega-cap tech;¹ there is more opportunity across the AI value chain and new sectors of the economy.
• Investors may consider expanding their focus to purer play AI firms with thematic ETFs and active strategies that focus on AI alongside digital disruption broadly. 
• Advisors may find it surprising that the typical moderate equity portfolio has less than 1.5% exposure to both pure-play AI and semiconductor companies.²
• Furthermore, advisors may leverage AI to enhance their practice efficiency and consider investment strategies that incorporate AI techniques to inform the investing process itself.

The evolution of the AI trade

AI has been the major theme in equity markets throughout 2023; the implications cut across the global economy as explored in detail by the BlackRock Investment Institute.

The AI theme’s contribution to strong equity market performance in the first half of 2023 was largely driven by the gains of mega-cap technology stocks. While AI has been well priced into the performance of these big names, it is critical for investors to identify “what’s next” across smaller, pure-play AI names, the broader AI value chain and the downstream businesses that will leverage AI most profitably.

Whether you view AI as being still in its infancy or already reshaping the world as we know it, there are two investment approaches to consider:

Market impact of AI

We are in the early stages of a multi-year secular theme with potential for value creation across the global economy — and the stock market has yet to fully reflect that.

The initial successes in Generative AI (GenAI) primarily centered around pioneering the introduction of Large Language Models (LLMs) to the market and facilitating their development. Since GenAI requires significant computational power, the AI revolution is driving enormous demand for semiconductors– especially graphics processing units (GPUs) – and other advanced computing infrastructure. As a result, investor focus on AI disruption has largely targeted mega-cap firms that develop or own the silicon and supercomputers required to train generative AI models.

So what comes next?

As the landscape evolves, the focus on AI will broaden out to computing infrastructure, “frontier” models, such as GPT-5, as well as “edge” AI that is narrower and can be run on local devices. The race to gain a competitive edge will widen the gap between winners and losers. As in the dot-com era, the initial beneficiaries identified by markets may not maintain an edge.

Consider the computing infrastructure needed for GenAI, which involves two key phases: training and inference. Training is how models learn from massive datasets (and demands significant amounts of GPUs). Inference is when models are used to generate outputs based on their training, and it relies on a wider variety of hardware and semis – such as central processing units (CPUs) – that have received less market attention. As GenAI adoption grows, we believe focus may shift to the infrastructure enabling inference. Read more about GenAI training versus inference here. 

The bottom-line is that the early AI beneficiaries in software and hardware are only the beginning; investors may now want to consider narrowing their focus on the range of firms poised to ride the next wave.

Impact beyond tech sector

We are only beginning to understand the potential applications of GenAI, but we expect it to be an enduring investment theme for years to come. Over the next 2 to 5 years, the use cases for GenAI are expected to multiply, creating opportunities and, in some cases, challenges across various sectors. This underscores the importance of identifying the winners and losers in this rapidly growing field.

Companies across a wide range of industries are testing how to use GenAI to improve productivity and magnify the power of general and proprietary data sets.

Read more about finding opportunities in healthcare, particularly in neuroscience, and AI.

Portfolio and pure-plays and practices – oh my!

The average moderate advisor portfolio is significantly underinvested in pure-play components of the AI value chain. BlackRock analyzed over 21,000 moderate equity portfolios to examine exposure across two key groups: robotics and AI, and semiconductors. We categorized the total level of exposure across mega-cap exposure (dilutive) and non mega-cap exposure (targeted).

Figure 1 below shows that almost the entirety of such exposure to these themes is coming from mega-cap firms with over $100B in market capitalization – not pure-play AI or semiconductor firms.

Figure 1: The average advisors' moderate equity portfolio exposure across robotics & AI, as well as semiconductors

It is also important to note that advisors are not excluded from the massive benefits of AI to improved business management and efficiency. In fact, leading wealth teams are already beginning to leverage AI in their practices.

Using AI to invest

Lastly, artificial intelligence is not only a structural theme to invest in – it may also be a tool for investing itself.

The BlackRock Systematic Team manages funds that make AI-informed investment decisions based on signals generated by proprietary transformer-based large language models, like ChatGPT, which have been trained on an extensive range of financial data inputs (encompassing everything from broker analyst reports and corporate earnings calls to regulatory filings and online news articles). 

Notably, they have been leveraging this approach for a significantly longer amount of time than ChatGPT has even been in existence. The first BlackRock Systematic investment signal incorporating natural language processing was integrated into portfolios as early as 2007.³ Read more about how the BlackRock Systematic Team integrates AI into their investing process.

Conclusion

Advisors seeking to harness the potential of AI may want to consider exploring explore ETFs and mutual funds to potentially capture more targeted investment opportunities within this dynamic field. At the same time, it is important to consider how to integrate these exposures, alongside the practical applications that AI offers across both the advisory business and within investing itself.

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