New Research Says: The Biggest Gains of AI Won't Go to AI Stocks. These 2 ETFs Could Be Better Buys.

A Contrarian Take on Where AI Profits Will Land

A new piece of research highlighted by The Motley Fool argues that the biggest financial winners from artificial intelligence may not be the companies building AI models or chips at all. Instead, the analysis suggests that the real value could accrue to businesses across unrelated industries and countries that successfully adopt AI to become more productive, cut costs, or unlock new revenue streams. The article points to two ETFs as potential vehicles for investors who want exposure to this broader, less obvious opportunity rather than concentrating bets on today's headline AI stocks like chipmakers and cloud providers.

Why This Matters Beyond the Portfolio

On the surface, this is a markets story, but it intersects meaningfully with the broader conversation around AI's trajectory — including safety, alignment, and red teaming. If capital increasingly flows toward companies deploying AI rather than the smaller set of labs building frontier models, the center of gravity for AI's real-world impact shifts. That has implications for oversight: the entities integrating AI into logistics, healthcare, manufacturing, or financial services may not have the same safety research infrastructure, red-teaming rigor, or alignment expertise as the foundation model developers themselves.

This diffusion pattern is a recurring theme in AI safety discussions. Much of the public focus on alignment and adversarial testing has centered on a handful of frontier labs — the OpenAIs, Anthropics, and Googles of the world — because they build the underlying models. But as AI capabilities get embedded into thousands of downstream applications by companies whose core competency isn't AI safety, the attack surface for misuse, unintended behavior, or inadequate testing arguably grows. Red teaming a foundation model is one challenge; ensuring every company fine-tuning or wrapping that model for a niche use case has done adequate safety diligence is a much harder, more distributed problem.

Reading the Investment Thesis Through a Risk Lens

The ETF-focused framing in this research is fundamentally about capturing productivity gains broadly rather than betting narrowly on AI infrastructure providers. For those tracking AI governance, this is a useful signal: it reinforces the expectation that AI's economic footprint will expand far faster than the industry's safety and alignment tooling can necessarily keep pace with. Regulators and researchers focused on red-teaming standards may need to think beyond frontier labs and consider how safety practices propagate — or fail to propagate — into the long tail of companies adopting AI commercially.

The Bigger Picture

While this piece is framed as investment advice, it's a reminder that AI's disruption is broadening beyond the companies most associated with building it. That broadening carries both economic upside and a more diffuse, harder-to-monitor safety landscape — a dynamic worth watching as adoption accelerates.