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Posted February 18, 2026 at 10:30 am
This insight examines the early-February 2026 volatility in US tech and equities broadly to highlight the expected long-run effects of AI on the economy, specifically areas that the market views as likely to be disrupted by AI.
Early in February the AI narrative took yet another turn. The Russell 1000 index was down almost 2% and the Tech industry was down more than 5% by the fourth trading day of the month (Exhibit 1). However, the market aggregates did not tell the full story. What was more interesting was the individual stock names flashing up on the screen with particularly large price drops. With markets largely recovering by the fifth trading day, the week’s wobbles may be quickly forgotten, but market performance during this brief period may give insights into the expected long-run effects of AI on the economy.
As AI companies of various flavors have poured billions of dollars into building out resources needed for developing Large Language Models or Generative AI (GenAI), it has created a divide between AI-related companies that are being paid today—the picks and shovels businesses of the AI build-out—and those that will see a payoff in some vaguely described future scenario. However, there is another distinction that has important implications for the cross-section of equity returns: companies that will be disruptors and companies that are likely to be disrupted.
For the most part, AI disruption scenarios have been fairly speculative, with disruption optimists outlining rosy outcomes of AI adoption, and disruption pessimists, frequently in the target sector, enumerating reasons for why humans cannot be replaced by AI. In early February, investors weighed in. The immediate trigger for the market moves seems to have been the release of AI tools from Anthropic that would facilitate knowledge-based activity in various fields including legal, finance, sales, and marketing. It generated cascading market reactions that the new tools would disrupt the business models of existing firms.
February equity volatility gave a glimpse of how the market views the possibility of AI disruption in different industries, sectors and sub-sectors. [note1] It is preliminary and certainly not conclusive, and there were other idiosyncratic factors during the period such as earnings releases that impacted market performance; however, the broad pattern of returns is informative. Exhibit 2 shows the 3-day return for Russell 1000 and its 11 industries. While the index was down only 2.5%, it was dragged down by steeper losses in the Technology and Consumer Discretionary industries. These industry-level returns still mask nuances related to the impact within industries.
Looking at the more granular 43 sectors in the Russell 1000 adds detail. Exhibit 3 shows the worst 15 sector returns for the 3-day period and is packed with service sectors related to knowledge-based fields including finance, real estate, media and medical services, besides software.
Very roughly, GenAI synthesizes all the “thinking” on a topic that exists in the training data. If a person can gain skills from reading all the text in the training data, then the skill can be learned by GenAI and disrupted. One such early use of GenAI is for coding, which has allowed coders to generate relatively good code very quickly requiring fewer human hours. So, ironically, Software was perhaps the first sector that both disrupted and was disrupted by GenAI.
Financial services related to data analytics, providing wealth and tax planning are another example of a sector ripe for disruption by GenAI given the structured, sometimes formulaic, skills involved.
Finally, the most granular sub-sector level (Exhibit 4) shows these same general patterns. Radio and TV Broadcasters, Media Agencies, and Publishing (which includes advertising) within the Media Sector face potential competition from software that can automate and make more efficient scores of tasks. Electronic Entertainment (within Leisure Goods) that includes providers of gaming software was another sub-sector in the crosshairs.
Previous waves of technology have disrupted the structure of the economy usually by replacing physical labor (think steam engines, cotton gins, factory automation, etc.) and making production more efficient. Generative AI disruption will be different because it will disrupt knowledge-based skills, and the market is beginning to distinguish between the winners—those who adopt and adapt—and the losers. The AI tide, still rising, may no longer lift all boats. Crucially, the impact of AI disruption is diffused across industries and sectors. The resulting return dispersion is likely to provide differentiated opportunities for investors within the AI theme.
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Originally Posted February 17, 2026 – The US market’s take on AI disruption
[1] Three of the ICB’s granular levels of economic classification. | Back to Note 1
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