The current AI market is characterized by a structural deficit where demand for compute far exceeds the industry's ability to build data centers and manufacture components. This creates significant backlogs ($25B) and suggests sustained growth rather than a speculative bubble.
The AI hardware ecosystem is severely constrained by key chokepoints, including HBM memory (only three suppliers with 80%+ gross margins), advanced packaging, and cutting-edge fabrication capacity. These limitations drive up costs and create significant backlogs for GPU providers.
There is a strong strategic imperative for the U.S. to control the most advanced semiconductor technology and onshore its fabrication. The argument is that providing cutting-edge chips to China poses a national security risk, and that existing supply chain chokepoints can effectively prevent China from catching up independently.
The primary barriers to enterprise AI adoption are not technical (like data cleanliness) but organizational, specifically risk-averse legal and security departments. These groups, operating in a 'no credit, only blame' environment, default to 'no' when faced with novel technology that lacks legal precedent.
Building the next generation of multi-gigawatt data centers is a monumental challenge, frequently delayed by local municipal opposition and permitting issues. The industry has historically done a poor job of community engagement, but must now shift to becoming a 'good neighbor' by self-funding infrastructure upgrades and being transparent.
Keep pulling the thread on Andrew Feldman.