Nghịch lý kinh tế AI: mô hình tốt hơn nhưng chi phí vẫn tăng vọt

Much has been written regarding the questionable economics of the AI space, but most of the discussion revolves around high-level concepts like market shares, datacenter investments, and power expenditure. The general expectation of technology is that it gets cheaper as it improves, but the AI space has a rather peculiar problem: Usage costs are actually soaring even as models get better, as detailed in a write-up at VentureBeat . With all the advancements in models over the last two years, having a bot that answers questions of simple-to-moderate difficulty is now old news, as they all do that with reasonable accuracy. Agentic workloads are where the real potential is at, letting bots loose on multi-stage, repeatable tasks that would previously take a human days, if not weeks, to perform. Grant a bot access to your billing system, some Excel spreadsheets, and your CRM, and ask it something like "who are my profitable customers by category and what are their trends" becomes child's play. While it's trivial for you to ask that question and get a fairly accurate answer back in a couple minutes, behind the scenes there is a lot of processing going on, and far more than you'd expect. AI computing time is measured in tokens — a short question and answer might take somewhere between 200 to 2,000 tokens, and one that requires the models to do some internet research might be around 1,000 to 4,000. An agentic task, though, can easily spend millions of tokens on a seemingly innocuous request. How? Token amplification, a recently coined term. In a simplified manner, because a model has no memory or cognition, every time you ask it another question in a conversation, it will re-load and process the entire exchange — everything you wrote, everything the bot replied with, and every file you uploaded. That means that additional questions in a long conversation progressively get more costly. Each question in a conversation might only need 500 tokens by itself, but reprocessing all the previous information adds up, so the second one might spend 600, and so on and so forth. The conversation as a whole uses up the cumulative number of all individual interactions, and as an added penalty: Response speed also tends to get slower as chats drag on. The aforementioned task of generating a report will have to be run in stages, say three for looking up Excel sheets, four for the CRM, perhaps a half-dozen web searches for contextual information about products, and a good dozen intermediary processing and calculation steps. Each step tacks on potentially several thousands of tokens, and by the end of it, you may be looking at millions of tokens cumulatively spent for all the steps combined. Costs, then, can spiral out of control very quickly — and that's assuming a best-case scenario where all of the data is easy to interpret, the model won't need any retries, and you're using a moderately powerful model rather than something like Claude Opus. In financial terms, this means that one innocent question might spend 280,000 tokens and cost $1, with ballpark estimates at current prices. That may not sound like much, but it was just one task. If you have this scheduled to run every 15 minutes for a dashboard, then suddenly this costs $96 per day , or $2,880 in a month. And that task example was fairly simple; a tricky multi-stage report might need millions of tokens, turning that $1 into, say, $10, or a grand total of $28,800. For the one report, for a limited number of users, in one department of a corporation. That's the key reason why Anthropic, Microsoft, OpenAI, and almost every other player moved their major offerings to usage-based billing back in April, and severely limited token spends in fixed-rate plans. This generated many sticker shock events , as developers around the globe found out how much their vibe coding actually cost and were left in a daze. In a large company using many AI agents, such costs can become prohibitive and oftentimes costlier…