While there's a popular narrative about a US manufacturing resurgence, the massive capital spending on AI contradicts it. By consuming a huge portion of available capital and accounting for half of GDP growth, the AI boom drives up the cost of capital for all non-AI sectors, making it harder for manufacturing and other startups to get funded.

While aggregate gross investment numbers look strong due to the AI boom, this hides weakness in classic cyclical sectors like residential investment, construction, and industrial equipment. This divergence creates opportunities for trades like long tech/short energy, which capitalizes on the two-speed economy.

Unlike previous years dominated by a single theme, 2026 will require a more nuanced approach. Performance will be driven by a range of factors including country-specific fiscal dynamics, the end of rate-cutting cycles, election outcomes, and beneficiaries of AI capex. Investors must move from a single macro view to a multi-factor differentiation strategy.

If AI is truly transformational, its greatest long-term value will accrue to non-tech companies that adopt it to improve productivity. Historical tech cycles show that after an initial boom, the producers of a new technology are eventually outperformed by its adopters across the wider economy.

For 2026, AI's primary economic effect is fueling demand through massive investment in infrastructure like data centers. The widely expected productivity gains that would lower inflation (the supply-side effect) won't materialize for a few years, creating a short-term inflationary pressure from heightened business spending.

The dominant market driver will transition from macro risks like tariffs and policy uncertainty to micro, asset-specific stories. The key focus will be on company-level analysis of AI capital expenditure plans and their impact on earnings.

The tangible economic effect of the AI boom is currently concentrated in physical capital investment, such as data centers and software, rather than widespread changes in labor productivity or employment. A potential market correction would thus directly threaten this investment-led growth.

China’s economic strategy prioritizes technology and manufacturing competitiveness, assuming this will create a virtuous cycle of profits, jobs, and consumption. The key risk is that automated, high-tech manufacturing may not generate enough jobs to significantly boost household income, causing consumer spending to lag behind industrial growth.

The long-term health of U.S. fiscal policy appears heavily dependent on a future surge in corporate capital expenditures. This spending is expected to fuel a growth burst specifically in the manufacturing and AI sectors, driven by the strategic imperative to outcompete China.