I've been pondering a somewhat painful question lately: those AI services that once claimed to be "free trials," why are they all starting to charge now?

The logic behind this is quite simple—computing power has become more expensive. Not a small increase, but a comprehensive rise. NVIDIA's chip competition has escalated into a geopolitical-level game, and data center energy consumption is approaching the limits of the power grid. The era of using investors' money to subsidize us has officially come to an end.

I've looked at some company bills before. My goodness, those numbers could wake a CFO up in the middle of the night. One company’s monthly API calls exceeded ten million, and they realized they were doing the dumbest thing—using GPT-4 to help users reset passwords, throwing dozens of lengthy PDFs directly into the model to "find answers on its own," and those agents without proper circuit-breaking mechanisms frantically retrying when the API crashes.

These seem like engineering problems, but fundamentally, they are thinking problems.

I’ve found that truly successful teams are now doing three things. First is semantic caching—users ask "how to reset password" hundreds or thousands of times a day. Why call the large model every time? Directly match similar questions and return cached answers, consuming no tokens. Second is prompt compression—using algorithms to losslessly compress lengthy system prompts from 1,000 tokens down to 300, so that machines communicate in their own language. Third is model routing—assign simple tasks to cheap small models, and only use GPT-4 for complex reasoning.

Even more interesting are the approaches of cutting-edge frameworks. OpenClaw, to adapt to resource-constrained environments like mobile devices, controls token usage to an obsessive degree. It enforces models to output according to JSON Schema, preventing AI from "chatting" and only allowing it to "fill out forms." Hermes introduces a dynamic memory mechanism—retaining recent dialogue turns, and when exceeding limits, summarizing them into key points with lightweight models stored in a vector database. This isn’t trash disposal; it’s surgical memory management.

In plain terms, the industry’s mindset is shifting. The previous consumer mindset of "looks cool, just connect to LLM" must now turn into an investment mindset. Every token spent must be justified by ROI. What does this money bring to the business? If a traditional solution costs 0.1 yuan and can solve the problem, but accessing a large model costs 1 yuan and only improves conversion by 2%, then cut it. Without hesitation.

Recently, I told the business department "no." When they asked, "Can AI read all 100k research reports and give a summary?" I responded with a question: "This API cost of several hundred million tokens—can your business benefits cover it?"

Silence.

It doesn’t sound cool at all, like a traditional grocery store owner calculating procurement costs—very down-to-earth. But this is the inevitable path the AI industry must take. When the tide recedes, those who survive won’t be the ones holding the most expensive models, but those watching the rapidly jumping token numbers on their dashboards and remaining calm, confident that they are earning more than they are spending.

Only teams that treat every drop of tokens as gold can truly wear armor.