The "90% Reality": Why Google’s Hardware Strategy Is Winning the Hidden War for Inference
For the last two years, the stock market has been obsessed with one phase of the AI lifecycle: Training. This focus made $NVIDIA(NVDA)$ the most important company on earth, as every tech giant scrambled to buy H100s to "teach" their models. But a structural shift is underway that the market is just beginning to price in. According to new industry data, the AI sector has flipped. Inference—the act of actually using a live model to generate a response—now accounts for 80% to 90% of all AI workloads in mature environments. The era of "training" is giving way to the era of "operating." In this new economic reality, the winner isn't necessarily the one with the most powerful general-purpose chip, but the one with the most efficient specializ
For two years, the market narrative was simple: "Google is late." If you listened to that noise, you missed the fundamental shift taking place in the technology landscape. With the rollout of its latest models, the "catch-up" narrative is fading. $Alphabet(GOOG)$ hasn't just closed the gap; it has revealed a structural economic advantage that suggests it might actually be the long-term winner. Here is why the "Empire" might have just won the AI arms race. 1. Evading the "Nvidia Tax" While OpenAI and Microsoft are paying a massive premium for Nvidia ($NVDA) H100s, Google runs on its own Tensor Processing Units (TPUs). The Math: Google trains models for significantly less cost than competitors who rely on third-party chips. The Edge: In a price war,
I am using a portion of my profit this year for my trip to Austria. I look forward to another good year next year to fund my Scotland trip. [Happy] [Happy]