AI challenges traditional monetary policy logic. Historically, lower interest rates spur capital investment that creates jobs. However, if lower rates now incentivize investment in job-reducing AI, the Fed's primary tool for boosting employment may become less effective or even have ambiguous effects, a new dynamic policymakers must understand.

Eclipse Ventures founder Lior Susan shares a quote from Sam Altman that flips a long-held venture assumption on its head. The massive compute and talent costs for foundational AI models mean that software—specifically AI—has become more capital-intensive than traditional hardware businesses, altering investment theses.

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.

For current AI valuations to be realized, AI must deliver unprecedented efficiency, likely causing mass job displacement. This would disrupt the consumer economy that supports these companies, creating a fundamental contradiction where the condition for success undermines the system itself.

The most immediate systemic risk from AI may not be mass unemployment but an unsustainable financial market bubble. Sky-high valuations of AI-related companies pose a more significant short-term threat to economic stability than the still-developing impact of AI on the job market.

The global shift away from centralized manufacturing (deglobalization) requires redundant investment in infrastructure like semiconductor fabs in multiple countries. Simultaneously, the AI revolution demands enormous capital for data centers and chips. This dual surge in investment demand is a powerful structural force pushing the neutral rate of interest higher.

The current AI investment boom is focused on massive infrastructure build-outs. A counterintuitive threat to this trade is not that AI fails, but that it becomes more compute-efficient. This would reduce infrastructure demand, deflating the hardware bubble even as AI proves economically valuable.

A theory suggests Sam Altman's massive, multi-trillion dollar spending commitments are a strategic play to incentivize a massive overbuild of AI infrastructure. By driving supply far beyond current demand, OpenAI could create a 'glut,' crashing the price of compute and securing a long-term strategic advantage as the primary consumer.

A theory suggests Sam Altman's $1.4T in spending commitments may be a strategic move to trigger a massive overbuild of AI infrastructure. This would create a future "compute glut," driving down prices and ultimately benefiting OpenAI as a primary consumer of that capacity.