AI is becoming increasingly important in the advertising world, and AI Max for Search from Google is a perfect example. Its core concept is simple: you provide some basic information, and the AI finds the most relevant users, automating your ad campaigns. For advertisers, this sounds very convenient.

But a question quickly arises: how exactly does this powerful AI work? Its internal decision-making process is invisible to us. We know what we put in and what comes out, but everything in between is like a “black box.”

If you have already followed the previous guide and successfully placed Google AdSense ads on your website, congratulations — you’ve taken the first step toward earning! However, many new site owners find that traffic comes, but revenue is not ideal. At this point, improving AdSense revenue becomes crucial.

Understanding AdSense Revenue Mechanism

Before optimizing revenue, it’s essential to understand how AdSense makes money. Simply put, your website traffic and ad interactions determine revenue, which mainly comes from ad impressions (RPM) and ad clicks (CPC).

Have you ever thought that your website or YouTube channel could be more than just a hobby platform and actually generate a steady, ongoing income? For beginner content creators, figuring out how to monetize traffic is often the top concern.

Among many options, Google AdSense is arguably the most common and beginner-friendly tool. It allows you to earn revenue by displaying ads while you focus on creating content, without requiring complex operations.

You may have experienced this: when chatting with AI, it can help you write poems, generate code, or even explain complex concepts like a teacher — its intelligence can be astonishing. But when you ask it for your company’s latest sales data, or to book a meeting room, it suddenly seems to “forget” everything and cannot respond. Why does this happen?

The reason is that most AI models do not directly access your proprietary information; they mostly rely on general knowledge learned from training data. To enable AI to truly “get things done,” it requires two key components: Agents and Knowledge Bases. The agent serves as AI’s “action brain,” capable of understanding instructions, invoking tools, and executing tasks; the knowledge base acts as AI’s “memory repository,” storing enterprise data, documents, and rules so the AI can access and utilize up-to-date information.

We actually interact with AI every day. Opening ChatGPT to draft a copy? That’s AI. Using your phone to take a photo of a dish and get calorie information? That’s also AI. Translation apps, speech-to-text, automatic recognition in your photo library—all rely on AI.

They may look similar, but the underlying mechanisms are completely different.

• General-Purpose Model: Can handle a wide range of tasks. ChatGPT is an example. In academia, you might also hear the term Foundation Model, while in practical use, people often refer to Large Language Model (LLM).
• Inference Model: Specialized for a specific type of task, such as image recognition, translation, or speech recognition. Sometimes called Deployment Model or Task-Specific Model, emphasizing optimization for specific tasks.

Why differentiate? Because it determines how you should use them:

B2B and B2C marketing both require effectiveness measurement, but their focus differs. B2C marketing typically targets individual consumers, with shorter decision chains and higher purchase frequency. Therefore, B2C metrics often emphasize immediate conversions, user engagement, and customer lifetime value. B2B marketing, on the other hand, targets business clients, with more complex purchasing decisions, longer cycles, and multiple decision-makers. As a result, B2B marketing focuses more on lead quality, sales conversion rates, and the contribution of marketing to the entire sales funnel.