The Truth About the AI Chip War: Computing Power Cost is Key, Who Will Laugh Last?

Everyone is focused on the GPU chip competition, but industry insiders know - the real bottleneck is not in the chips, but in the Computing Power cost.

The situation is very clear: Nvidia's GPUs are now the leader, while AMD and Broadcom are helping companies create their own chips. However, as AI moves from training (one-time costs) to inference (ongoing costs), whoever can reduce energy consumption and costs will win.

The advantages of Alphabet are here: Over a decade of self-developed TPU (Tensor Processing Unit), now in its 7th generation, optimized specifically for its TensorFlow framework and Google Cloud. The key is—TPUs are much more energy-efficient than GPUs. With the explosive growth in inference demand, this energy consumption difference will bring significant cost advantages.

A more ruthless move: Alphabet does not sell TPU, but only offers it as part of Google Cloud. This way, users who want to use TPU must go to Google Cloud, allowing Alphabet to make money in two ways—selling Computing Power and using its self-developed chips to lower costs for running its own Gemini model.

Compared to OpenAI and Perplexity still burning money on GPUs, Alphabet's vertical integration (chips + cloud platform + large models + AI tools + fiber optic networks) has already created a super moat. Even Nvidia is anxious—after hearing that OpenAI is testing TPUs, Nvidia hurriedly invested in OpenAI to stem the losses.

Bottom Line: This is not a chip race; it's a competition to see whose system is the most cost-effective. From this perspective, Alphabet's position is quite stable.