Jon Gray outlines a tripartite market landscape shaped by AI. It includes clear AI winners, physical-world businesses like medical supplies that are largely immune, and a high-risk category of software and services companies whose moats are now uncertain. This framework guides investment toward clarity and away from ambiguity.

The current tech landscape is not a universally rising tide. While investor enthusiasm buoys AI-native companies, the disruptive threat of large language models is simultaneously depressing valuations and venture capital interest for traditional software companies whose business models are now at risk.

For the first time, the high-multiple software industry faces a potential existential threat from AI. Even the possibility of disruption is enough to compress valuations, causing massive dispersion where indices look calm but underlying sectors are experiencing extreme rotation.

A safer way to play the AI boom is to invest in companies selling the underlying compute infrastructure rather than the hyperscalers buying it. This strategy captures the upside of the secular trend while avoiding direct exposure to how the massive capital expenditure is funded, which may involve risky credit.

AI will primarily threaten purely cognitive jobs, but roles combining thought with physical dexterity—like master electricians or plumbers—will thrive. The AI-driven infrastructure boom is increasing demand and pushing their salaries above even those of some Silicon Valley engineers.

The historic rotation between asset-light (tech) and asset-heavy (commodities) industries is breaking down. AI requires massive physical infrastructure (data centers), turning 'bits' companies into 'atoms' companies and creating huge new demand for energy and materials.

As AI commoditizes software, the most defensible businesses are no longer asset-light SaaS models. Instead, companies with physical world operations, regulatory moats, and liability are safer investments. Their operational complexity, once a weakness, now serves as a formidable barrier against pure AI-driven disruption.

AI is rapidly automating knowledge work, making white-collar jobs precarious. In contrast, physical trades requiring dexterity and on-site problem-solving (e.g., plumbing, painting) are much harder to automate. This will increase the value and demand for skilled blue-collar professionals.

The 50-year supremacy of asset-light software may be an anomaly. If AI makes software creation nearly free, economic value will shift back to the historical mean: tangible assets like infrastructure, energy, and regulated, liability-bearing businesses that touch the physical world.