There’s no denying that that generative artificial intelligence (AI) is captivating the technology sector and broader financial markets this year. Just look at semiconductor giant Nvidia (NASDAQ: NVDA), which is up 169.10% year-to-date — a staggering ascent for a company with market capitalization of nearly $950 billion.
Given the myriad applications of generative AI and its wide sector- and industry-level reach, broad investment strategies could benefit market participants. Those include exchange traded funds such as the Invesco QQQ Trust (QQQ) and the Invesco NASDAQ 100 ETF (QQQM).
QQQ and QQQM are pertinent AI plays because the foundation of AI, generative and otherwise, is technology, including computers, chips, and more. Those concepts are represented in significant fashion in the two Invesco ETFs.
“GenAI carries the promise to revolutionize the ways humans and computers work together,” according to TD Cowen research. “While these breakthroughs have much more potential to replace worker functions than with past AI, we expect adoption to take place in the form of human augmentation and copilots, where computers work with humans. This will lead to a major step-function in productivity gains and another new architectural computing cycle for software.”
QQQ, QQQM Relevant for Long-Term AI Investors
As has been widely discussed this year, while AI itself isn’t a new concept, its recently revealed applications are still in the early innings. One way of looking at the scenario is that spending and investment tied to AI is just scratching the surface, and a valid long-term investment thesis could be afoot.
“Our initial framework for potential productivity gains suggests $832B to $1.7T of US labor productivity gains,” added TD Cowen. “This will result in 8-16% of total annual US labor costs as the technology scales across most US industries in the coming years. We estimate companies creating GenAI technology could capture 20-30% of these savings, resulting in a GenAI tech spending TAM increase of $166B to $500B.”
Furthering the status of QQQ and QQQM as credible avenues for AI exposure is the point that massive technology investment is necessary to advance AI. Not only do the ETFs allocate almost half their rosters to tech stocks, many of those companies have the cash necessary to make significant AI investments across computing capacity, chips, and software.
“Today’s leading CPUs can generate only ~1% of the large language model tokens per second versus modern accelerators. This leads to our view that LLMs will lead to a paradigm shift in inference platforms to require acceleration over past CPU-based inference deployments,” concluded TD Cowen.
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