AI earnings test: why today’s rally remains far from dotcom territory

Ahead of results from several of the world’s largest AI-related companies this week, Franklin Templeton Director of ETF investment strategy Marcus Weyerer argues that comparisons between the current rally and the late-1990s internet boom remain imperfect. While pockets of speculation undoubtedly exist, today’s AI leaders are supported by stronger earnings, more established business models and, in many cases, more reasonable valuations than the market leaders of the dotcom era.

The chart below compares the performance of the S&P 500 following its 2022 market low with the rally that began in 1994. At the equivalent point in each cycle (week 188) the S&P 500 had gained 108% from its 2022 low, compared with 127% during the late-1990s cycle. The current rally therefore remains meaningfully shorter in duration and smaller in magnitude than the dotcom-era advance at the same stage.

Source: Bloomberg, Global ETF Investment Strategy. Market low indexed to 100.

Marcus Weyerer, Director of ETF Investment Strategy at Franklin Templeton, commented:

“We understand the questions surrounding whether today’s AI rally resembles the late-1990s internet boom, but in our view the comparison is imperfect. Many of that era’s most hyped companies lacked profitability, cash flow and sustainable competitive advantages. By contrast, many of today’s leaders operate at multiple layers of the ecosystem – including chips, cloud infrastructure, software platforms and proprietary data – which may help preserve their pricing power and margins for considerably longer.”

“Infrastructure AI is also exceptionally capital-intensive, creating higher economic moats and barriers to entry than many earlier technology cycles. By comparable timelines, the current rally remains meaningfully shorter in duration and magnitude than where the late-1990s cycle stood at an equivalent stage.”

“Before AI can unlock widespread productivity gains, it requires enormous investment in semiconductors, memory, electricity, data centres and digital infrastructure. In other words, the path to abundance is, for now, defined by constraint. Investor attention has shifted from the hyperscalers building AI applications towards the picks-and-shovels businesses supplying the infrastructure that makes them possible. To us, this signals a maturing of the AI investment cycle rather than a fading of the theme.”

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