I noticed an interesting trend— the era of cheap tokens has officially ended. Earlier, when large companies subsidized APIs, we all lived like kings. We dumped thousands of words into prompts, forcing GPT-4 to do absurd little tasks like “capitalize the first letter.” Why? Because it was cheap. But the wind has changed direction.

Now, the bills for computing power have become a reality. NVIDIA H100 is a geopolitical conflict, not just commercial competition. Each API call costs real money. A token is no longer just a unit—it’s truly like gold.

The thing is that most teams don’t understand where the money is really leaking. People look at the bill at the end of the month and are shocked. The losses are hidden in the least obvious places. You politely communicate with the model—hi, thanks, please. But every word, every space is a token you pay for. The prompt system accumulates, repeats in every session, and you pay for what you already paid for yesterday.

RAG often turns into a disaster. Ideally, you’d extract three relevant sentences. In practice, the user asks, and the system dumps ten PDF documents—10,000 words each—into the model. The developer thinks: let it figure it out. This isn’t laziness; it’s a crime against computing power. Irrelevant contextual information doesn’t just throw off the attention mechanism—it leads to astronomical token consumption.

Uncontrolled agents are already an extreme. When AI gets caught in a cycle of mistakes, it spins in there endlessly, wasting expensive output tokens. Without a proper emergency-stop mechanism, it can drain your credit card overnight.

But there is a solution. Semantic caching is the simplest way. User requests are often basically the same. Instead of calling GPT-4 every time, you check similarity against the cache. If someone has already asked something similar, you use the ready-made answer. Zero tokens spent. Delays from seconds shrink to milliseconds.

Prompt compression is the second layer. Algorithms based on information entropy analyze which words are critical and which are unnecessary. You can compress a text from a thousand tokens down to three hundred while preserving the meaning. Give machines the ability to communicate in machine language—what seems clumsy to people is completely understandable to models.

Model routing is the biggest challenge for architects. Don’t put all tasks on the most expensive model. For simple format transformation or translation, route to cheaper APIs or to locally deployed small models. Costs nearly disappear. For complex logical reasoning, then use powerful tools. Like a well-run company: the reception doesn’t pass requests straight to the CEO.

Here’s where it gets truly interesting—look at OpenClaw and Hermes. These are agents that understand the reality of limited resources. OpenClaw almost obsessively controls tokens. Instead of a free flow of text—forced output in JSON Schema. AI doesn’t “talk”; it fills out forms. At first glance, it’s about parsing convenience, but in reality it’s surgical traffic economy.

Hermes from Nous Research demonstrates precise instruction execution. Getting it right the first time is the biggest savings. In multi-step interactions, they don’t keep the entire history. Working memory—the last 3–5 messages. When the window overflows, a lightweight background model summarizes several key sentences and stores them in a vector database. The old dialogue is deleted, but the knowledge remains. This isn’t garbage removal—it’s surgical memory pruning.

Now the key point: this isn’t a technical problem; it’s a shift in mindset. Previously, we treated tokens as consumers in a supermarket. See a discount—throw it in the cart. Companies blindly connected LLMs to everything, even cafeteria menus. Now you need to switch to an investment mindset. Every token is an investment. The question is: what did it bring me? Did the ticket-closure rate go up? Did the bug-fixing time go down?

If a rule-based function costs 10 cents, and a large model costs $1 per token—but increases conversion by only 2%, then cut it out. Without hesitation. Stop chasing big, all-encompassing AI solutions. Look for small, refined, precise hits. When a business asks, “Can I read 100,000 reports and provide a summary?” ask back: “Will your revenue cover several million tokens via API?”

Count it. Save it. Count tokens like a product-store owner. It doesn’t sound very cyberpunk—more like very agricultural. But it’s a necessary step on the path to AI maturity. The era of unlimited free use is over. Now, only those who understand architecture, routing, and can make the most of every drop of computing power will win. When the tide goes out, you can see who’s swimming naked. This time, the tide of cheap tokens is receding. Only those who forge every last drop like gold will get real armor.