Doge

Here is a breakdown of that business model in English, focusing on how each layer feeds into the next:

The “Full-Stack” Flywheel: From Sand to Service

1. Hardware Layer: The TPU Advantage

Unlike many competitors who are at the mercy of GPU supply chains, Google has its own Tensor Processing Units (TPUs).

• The Logic: By designing its own chips, Google avoids the “NVIDIA tax.” This significantly lowers the cost of training and running massive models.
• The Scale: Their latest TPU v6 and v7 are custom-built specifically for the Transformer architecture, offering a performance-per-watt that is hard to beat for internal workloads.

2. Model Layer: Gemini (The Engine)

This is where the raw compute turns into intelligence.

• Multimodality: Gemini isn’t just a chatbot; it’s a foundation model that processes video, audio, and code natively.
• Optimization: Because Google owns the chips, they can optimize the Gemini 3 architecture to fit perfectly onto the TPU hardware, achieving efficiency that “off-the-shelf” hardware cannot match.

3. Cloud Layer: Google Cloud & Vertex AI

This is the monetization engine.

• AI-as-a-Service (AIaaS): Google sells access to these TPUs and Gemini models through Vertex AI.
• The “Lock-in”: Enterprises don’t just buy a model; they buy into an ecosystem where their data, their training, and their deployment all happen on one seamless platform.

Summary Table: Why This Matters

Layer	Component	Business Impact
Silicon	TPU (Custom AI Chips)	Cost Leadership: Drastically reduces CAPEX and OPEX.
Intelligence	Gemini (Foundation Models)	Product Differentiation: Powers Search, YouTube, and Workspace.
Distribution	Google Cloud / Vertex AI	Market Capture: Turns developers and enterprises into long-term subscribers.

The “Secret Sauce”: Google is essentially the only company that controls the entire stack. Microsoft has the software but relies heavily on OpenAI and NVIDIA; Meta has the models but lacks the Cloud infrastructure. Google owns the “mine,” the “refinery,” and the “jewelry store.”