Generative artificial intelligence (AI) investing is taking the world by storm this year. With that, there are substantial, long-term investment implications.
The rapid expansion of generative AI benefits an array of individual stocks and exchange traded funds. That includes some that aren’t dedicated AI plays. That group of beneficiaries includes the Invesco QQQ Trust (QQQ) and the Invesco NASDAQ 100 ETF (QQQM). Both ETFs are linked to the Nasdaq-100 Index (NDX) – a benchmark chock full of large- and mega-cap companies emerging as AI leaders in the early innings of that game.
Emphasis on “early innings” because generative AI is just starting. This is paving the way for other, more sophisticated AI applications to emerge. That’s attractive to growth investors, but stock-picking to that effect is tricky, confirming the utility of QQQ and QQQM as practical AI access points, particularly for long-term investors.
QQQ, QQQM Could Get Language Learning Lift
Currently, generative AI can perform tasks such as composing prose, videos, and related media endeavors. However, some experts see generative AI as a floor, not a ceiling, to AI’s broader, longer-ranging uses.
In a recent interview with Lou D’Ambrosio, Head of Goldman Sachs’ Value Accelerator, AI researcher Dave Ferrucci highlighted the potential of language learning models as long-term drivers of the AI theme.
“People have been working on AI for decades, but what we’re seeing now is a watershed moment,” notes Ferrucci. “That’s because the ability to master language is one of the things closely associated with human intelligence. These large language models can do just that. We’re now seeing machines formulate coherent, fluent language like the best of us.”
While generative AI is capturing the bulk of AI-related headlines, language learning could have implications for assets such as QQQ and QQQM because this technology is likely to be data- and semiconductor-intensive. That could be good news for numerous QQQ and QQQM holdings.
Further cementing the status of QQQ and QQQM as credible language learning plays is the notion that as this technology is honed, more corporate customers will likely embrace it as an avenue for boosting efficiencies and trimming costs.
“The reporting role of many middle managers who act as go-betweens could be affected,” adds Ferrucci. “The value chain starts to shift because the decision-maker can get the synthesis, the summary, the aggregation, and the delivery of information from a machine and at a much lower cost. The company’s customers now have access to that knowledge much more readily and with less human expertise required.”
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