I noticed an interesting trend in the market. Companies that recently spent tokens lavishly, as if it were water from a tap, are now sitting over their calculations with a calculator in hand. The era of free use has officially ended.

Two years ago, everything was simple. Large investors paid the bills, we wrote long prompts, threw entire PDF documents into the model, and no one cared. Now? Each token is real money. Not conditional units, but actual cash.

Really, what has changed? First, the cost of computational power has skyrocketed. The fight for NVIDIA H100 chips has turned into a geopolitical conflict. Second, when the daily volume of API requests exceeds millions, a modest “1K Tokens” suddenly becomes a money pump. A token is now equal to real currency.

I understand that many didn’t realize where the money was going. Look at the bill — shock. But the problem isn’t in the prices themselves, but in how we spend. The solution lies in three things: semantic caching, prompt compression, and model routing. This is no longer luxury; it’s a necessity.

Semantic caching is the simplest way to save. A user asks “How to reset a password?” hundreds of times a day. Why run GPT-4 every time? First, compute it, cache the result, and subsequent requests return from cache. The delay shifts from seconds to milliseconds, costs almost to zero.

Prompt compression is already surgery. Algorithms analyze which words are critically important and which are redundant. You can compress a text from 1k tokens to 300, preserving the meaning. I allow machines to communicate in their own language — the result is the same, but the cost is reduced by 70%.

Model routing is an architectural task. Not everything requires GPT-4o. Simple data retrieval? Route it to a cheap Llama 3 8B or Claude 3 Haiku. Complex logical reasoning? Then yes, use a powerful model. Like in a company: the reception doesn’t pass questions directly to the CEO.

I’ve watched how leading teams do this. OpenClaw on mobile devices almost fully controls tokens. Instead of free generation, it forces the model to fill in a JSON schema. It looks restrictive, but it actually saves traffic. Hermes Agent takes a different approach — dynamic memory. It stores the last 3–5 conversations, summarizes older ones with a lightweight model, and saves them in a vector database. This isn’t reckless; it’s surgical context management.

Now, the most important thing — a change in mindset. Previously, tokens were seen as a consumable good. You saw a discount — you threw it in the cart. Blindly connected LLMs to everything, even to have AI create cafeteria menus. The bill at the end of the month was a shock.

Now, you need to switch to an investment mindset. Each token is an investment. Ask yourself: what did this give me? Did the ticket closure rate increase? Did bug fixing time decrease? Or is it just entertainment? If a rule-based function costs 10 cents, and an LLM requires a dollar per token but increases conversion by 2%, then cut it without hesitation.

We move from “big and comprehensive” solutions to “small and refined” precise hits. When a business asks, “Can AI read 100,000 reports?” I ask, “Will the revenue cover several million tokens?” Let’s count. Save money. Think of tokens as a grocery store owner.

It sounds far from technological, more like agriculture. But this is precisely the stage of AI industry maturity. The era of unlimited subsidies is over. Only those who understand architecture, know how to optimize on mobile devices, and look at token numbers with cold calculation will remain. When the tide recedes, it will be clear who is swimming naked. This time, it will be companies that haven’t learned to economize. Those who extract every drop as if it were gold will survive.