What the NASDAQ 100 is already pricing in

_{* Earnings per share}

_{Sources: Bloomberg Finance L.P., DWS Investment GmbH as of 8/2/23}

A more suitable comparison to current times may be the experience of the Web 2.0 era during the mid-2000s. AI certainly has the potential to improve productivity growth. Recent research from McKinsey estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across 16 global business functions.^[1]  Yet, getting there could take a while. “The view that the world gets from the media”, AI veteran Stuart Russell argues, “bears very little relation to what really happens in the world’s research labs”. In other words, in AI, as in other areas, “A good idea – a real breakthrough – will often go unnoticed and may only later be understood as having provided the basis for a substantial advance in AI”.^[2]

Of course, even AI tools already developed might bring revolutionary changes to the productivity of numerous professions – once firms learn how to make the most of them. Artificial intelligence, like steam engines or electricity, is a general-purpose technology that will take time to be adopted across industries.^[3]

Which brings us back to those dizzying NASDAQ price-earnings ratios, currently seen on Wall Street. Justifying those on a case-by-case basis, with detailed assumptions about products, competition, use cases and the like remains of utmost importance. Currently, there are only a few, very experienced companies developing and deploying AI models profitably in their product suite. However, the majority of companies is just getting started with AI and eventual, future AI winners might not even been founded. “As with any other product, not all market participants will develop profitable products” warns DWS AI portfolio manager Felix Armbrust, highlighting that “identifying AI winners and losers requires experienced investors that have a realistic view on the topic”.