The AI revolution isn't just about software. For the first time in years, venture capital is flowing into hardware like specialized semis and even into energy generation, because power is the core bottleneck for all AI progress.

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.

AI's ability to generate software at near-zero marginal cost is erasing the scarcity premium that propelled software stocks for over a decade. This realization is causing a massive capital rotation out of software ETFs and into tangible, scarce assets like metals and commodities.

As AI commoditizes software, hardware is re-emerging as a key defensibility layer for startups. A decade ago, VCs avoided hardware, but now a physical device tied to a software subscription creates powerful stickiness and justifies high valuations, representing a major shift in investment strategy.

While model performance gains headlines, the true strategic priority and bottleneck for AI leaders is the 'main quest' of securing compute. This involves raising massive capital and striking huge deals for chips and infrastructure. The primary competitive vector has shifted to a capital war for capacity.

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.

AI's ability to replace traditional software is causing software company stocks to decline. Simultaneously, the massive computational power AI requires is driving a historic surge in chip manufacturer stocks, creating an inverse market relationship.

Cost savings from AI-driven productivity are not just boosting profits or going to shareholders. Companies are redirecting that capital to buy their own GPUs and TPUs, vertically integrating their tech stacks. This trend represents a major capital rotation from software and headcount into owning the underlying hardware infrastructure.

The vast majority of spending and market capitalization in AI today is in the infrastructure layer—compute (NVIDIA), foundation models (OpenAI), and data services. The entire application layer's revenue combined is a rounding error in comparison, highlighting a massive, though likely temporary, imbalance in where value is currently being captured.