The AI Trap We’re Walking Into - by Unvoid
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The AI Trap We’re Walking Into<br>Cheap Tokens, Expensive Power
Unvoid<br>Jun 02, 2026
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We were promised that artificial intelligence would democratize knowledge work. That a kid with a laptop in a small town would have the same cognitive firepower as a corporation. For a brief, dizzying moment, that even felt true.<br>I’m not so sure anymore. Here’s the story I see unfolding, and why I think we’re about to repeat one of humanity’s oldest mistakes.<br>Thanks for reading Signal in the Noise! Subscribe for free to receive new posts and support my work.
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LLMs and agents are becoming a commodity
Two years ago, a capable language model felt like magic. Today it feels like electricity, something you plug into. The numbers are staggering: the cost of LLM inference for equivalent performance is dropping roughly 10x every year, faster than compute fell during the PC revolution or bandwidth during the dotcom boom. A capability that cost about $20 per million tokens in late 2022 now costs around $0.40, and the cheapest models matching early GPT-3 quality have fallen by a factor of 1,000 in three years. (a16z, Introl)<br>Open-weight models are closing in on the frontier too, trailing the best closed models by only around four months on key benchmarks. When the gap is that small, raw capability stops being scarce. (Epoch AI)<br>This is what commoditization looks like. And whenever a technology becomes a commodity, the interesting question stops being “Can you do it?” and becomes “Can you afford to do it at scale?”<br>Agentic work gets more expensive, not less
Here’s the counterintuitive part. The price per token keeps falling, and yet the cost of meaningful agentic work is climbing.<br>Why? Because agents don’t make one call. They read a task, get a response, then re-read everything before the next action, then re-read all of that plus the new response, building one expensive context snowball. A Stanford Digital Economy Lab study found that agentic tasks are “uniquely expensive, consuming 1000x more tokens than code reasoning and code chat,” with the cost driven mostly by input tokens. Worse, that usage is wildly unpredictable: runs on the same task can differ by up to 30x in total tokens, and burning more tokens doesn’t even guarantee a better answer. (Stanford Digital Economy Lab)<br>Reasoning models pour fuel on this. They “think” in hidden token sequences you still pay for, consuming five to twenty times more tokens per request than standard models. A query that takes 700 tokens normally can balloon to 3,700 once the model reasons internally. (Keito) At enterprise volumes, a support agent that looks cheap at 100 tokens per interaction can hit 2,000 to 5,000 once tool calls and multi-step reasoning kick in, producing “monthly token bills that dwarf even your infrastructure spend.” (DataRobot)<br>The unit price drops while total consumption explodes. For a hobbyist, that’s a rounding error. For a company running millions of autonomous workflows a day, it becomes a serious line item that scales with ambition. Researchers warn that without major system-level innovation, per-request costs could rise “by orders of magnitude,” making large-scale agent deployment “economically and environmentally prohibitive.” (arXiv)<br>The result: the more valuable the AI work, the more it costs to run.<br>Those with the budget buy the power
If agentic capability is metered, then capability becomes a function of capital . Whoever can pour the most money into compute gets faster and more thorough agents, more parallel experiments, the freshest frontier models the moment they ship, and the luxury of not thinking about cost at all.<br>This is a familiar pattern. Capital concentrates around whatever resource is scarce. Yesterday it was land, factories, and data. Tomorrow it’s inference budget. Researchers already warn that AI is poised to widen income inequality unless we deliberately steer it otherwise. (Brookings)<br>Those without it work by hand
Meanwhile, everyone else does what humans have always done when they can’t afford the machine: they work by hand. They label, moderate, correct, and annotate, filling the gaps the cheap models can’t.<br>We already have a name for an early version of this: the global, often invisible workforce that labels data and tunes models for a pittance. Investigations have documented Kenyan workers training AI systems for around $2 an hour under grueling conditions, churn-by-design contracts, and unpaid labor. (Brookings, TechCrunch) Kenyan data labelers have since organized into a Data Labelers Association to push back. (Computer Weekly)<br>As AI eats more white-collar work, this human-in-the-loop layer doesn’t disappear. It grows, and it slides down the value chain.<br>The handwork becomes the training fuel
Here’s the loop that makes the whole thing self-reinforcing, and genuinely uncomfortable.<br>Every correction, every label, every “the AI got it wrong, let me fix...