Part 10/14:
A key insight from industry reactions, particularly from technology analyst Chris Camilo, revolves around the relentless pace of hardware upgrades. Unlike traditional capital equipment, whose value depreciates over a decade or more, AI chips improve rapidly—often becoming outdated within a year or two.
Camilo explains that companies buy high-performance GPUs and ASICs, such as Nvidia's H100 chips, invest months to optimize their algorithms, and then face the challenge of upgrading to newer, more efficient chips like Blackwell or upcoming models—almost immediately after deployment if they wish to stay competitive. This creates a "treadmill" where the overall effective capital cost skyrockets, and companies must continually accept lower returns on each hardware cycle.