157 To understand the evolution of AI hardware strategy, we’ll look at how control over chip design is shifting from traditional vendors to the platforms that rely on them. For years, NVIDIA has been at the center of the AI hardware stack. Its GPUs (graphics processing units) became the default engine for training and inference, prized for their ability to handle highly parallel computations at scale. Its proprietary technology – and unparalleled scale – has led to industry leadership. This reliance – combined with outsized sudden demand – has created constraints. Despite NVIDIA’s rapid – and impressive – scale-up, demand for NVIDIA GPUs has outpaced supply amid industry fervor for accelerated computing. Hyperscalers and cloud providers are moving to improve their supply chains to manage long lead times. That shift is accelerating the rise of custom silicon – especially ASICs, or application-specific integrated circuits. Unlike GPUs, which are designed to support a wide range of workloads, ASICs are purpose-built to handle specific computational tasks with maximum efficiency. In AI, that means optimized silicon for matrix multiplication, token generation, and inference acceleration. Google’s TPU (Tensor Processing Unit) and Amazon’s Trainium chips are now core components of their AI stacks. Amazon claims its Trainium2 chips offer 30-40% better price-performance than standard GPU instances, unlocking more affordable inference at scale. These aren't side projects – they’re foundational bets on performance, economics, and architectural control… AI-Related Monetization = Very Robust Ramps