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How Artificial Intelligence, Tech, and Productivity Impact Portfolios

June 16, 2023

The world has been abuzz over artificial intelligence and its potential benefits and threats. These range from practical benefits, such as better tools for knowledge workers and students, to philosophical considerations, such as sentience and civilization. There are also questions around the economy and markets, especially for technology-related sectors. Given the potential promises and pitfalls associated with AI, what can long-term investors do to maintain perspective and stay properly invested?

Tech Stocks Have Benefited From Falling Interest Rates And Enthusiasm For AI

The two decades since the internet bubble have witnessed numerous hype cycles surrounding new technologies. In just the past few years, these have included the so-called metaverse, virtual reality, blockchain, self-driving cars, private space exploration, and many more. Each of these has been accompanied by narratives on how they will transform society. Computer scientist Roy Amara famously said that people tend to overestimate the impact of technology in the short run and underestimate its effects in the long run. Although many new technologies do eventually play an important role in business and everyday life, investors can often get ahead of themselves in the meantime.

When it comes to AI, the term naturally ignites the imagination. However, even if the promises are vast, today's generative AI and large language models are the culmination of statistical and computer science techniques. Just as with any other new development, investors should strive to maintain levelheaded views on how new technologies can benefit companies and individuals.

For example, while products such as OpenAI's ChatGPT, Google's Bard, and others have only recently burst onto the scene, the methods underlying these tools have been decades in the making. The latest cutting-edge AI models, known as transformers, were described by Google researchers in 2017. Previous state-of-the-art techniques, such as recurring neural networks (RNNs) and long short-term memory units (LSTMs), were invented in the 1980s and 1990s. The exponential growth in computing power, especially the wide availability of graphics processing units (GPUs), and perhaps more important, an abundance of natural language data, is what have allowed the field to leap from academic research to practical application in short order.

From an economic perspective, the promise of any new technology is its ability to boost productivity. Whether it's new machines, software, or just improved processes, technology is what allows us to accomplish more with less. One simple way to think about the economy is that growth occurs when there are more workers (labor), more machines (capital), or improved technology (e.g., better trained workers and/or better machines). Productivity, or the ability for the same number of workers to produce more, is what improves quality of life generation after generation.

Productivity Is The Key To Sustainable Economic Growth

New technologies also often lead to societal questions surrounding "creative destruction," a term coined by economist Joseph Schumpeter to describe the cycle of innovation and replacement. This is especially true when new technologies disrupt established methods, ideas, and businesses, creating a source of resistance as jobs are lost and existing skills become redundant. At the same time, technological progress has created countless new industries, benefiting workers with the proper skills and training, as well as the consumers of these new products and services. Whether this progress is positive or negative is a classic debate that is revived each time a seemingly transformational technology disrupts the status quo.

Regardless of one's views on AI and technology, it's undeniable that productivity growth has slowed in recent decades. The average year-over-year productivity growth rate since 1948 is 2.1%, but only 1.5% over the last few years. Prior to the pandemic, one of the biggest macroeconomic concerns cited by many economists was known as "secular stagnation," or the idea that the economy would grow at a slower pace due to poor demographic trends, aging infrastructure, and slowing productivity. This isn't just a concern in the U.S.; many parts of the world, including Japan and Europe, have aging populations and declining productivity as well.

While the differences in growth rates may seem small, they have big implications when compounded over years and decades. If the economy grows at a steady 3% annual rate, it can double in size every 23 years. In contrast, a growth rate of 2% requires 35 years while 1% growth takes nearly 70 years. Clearly, small differences in growth can have huge differences on economic outcomes. AI could potentially give long-run productivity a much-needed boost, which is important for maintaining the quality-of-life improvements that we have grown to expect over the past century.

Market Returns Have Been Concentrated In Tech-Related Sectors This Year

From a market perspective, enthusiasm for AI has boosted tech-related sectors and benefited diversified investors. While the S&P 500 has gained 12% this year, the information technology and communication services sectors have climbed 35% and 33%, respectively. Macroeconomic factors such as yesterday's Fed pause, improving inflation, and steady economic growth have boosted these sectors as well. These returns have more than offset the poor performances of sectors such as energy and financials, and have overshadowed problems in the banking and commercial real estate industries.

One concern with this dynamic is that a small number of stocks have generated most of the returns this year. This is often referred to as "narrow market leadership" or "limited market breadth." Indeed, the largest 50 stocks in the S&P 500 have generated an outsized proportion of the returns this year, far outpacing a broader equal-weighted index. This has been the trend over the past decade as mega caps have played an ever-growing role in market performance. This is due in part to the economies of scale of large technology companies, which have pushed many market caps to the trillion-dollar level and beyond.

While there is a debate around whether this is good or bad for markets, the reality is that this is not something investors can control. What they can control is their level of exposure to these sectors. Those that have appropriate portfolio exposures have benefited from these trends, just as they benefited from strong energy returns last year, while also staying prudent as valuations rise to higher and higher levels. These dynamics are further evidence that it is difficult to predict what will outperform in any given year, and thus it remains important for investors to be vigilant about their allocations.

The bottom line? Investors should take interest in new technologies such as artificial intelligence but also maintain a level head. Greater productivity growth and strong sector returns are a good sign, but allocating incrementally will serve most growth investors best in the long run.

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