Diego Pacheco Tech blog

Burning tokens is a double-edged sword. Powerful weapons can either give a lot of leverage or drag you down. It's easy to think that you are not shooting yourself in the foot with unlimited tokens. The thing is, you won't be shooting yourself in the foot; you will be expending a lot of company money and perhaps with lots of amplified waste. But only if you do it wrong or don't know what you are doing, and that's the very tricky part. Big Tech and big companies already have unsustainable token usage, like Uber . The list goes on and on: Microsoft , Walmart , Meta , JPMorgan , AT&T , and many others. For some, tokenmaxxing is over. Is it? Really depends on the cost of inference. Jev is showing that a classifier can be powerful and reliable, at least in the sense of low cost and speed, not saying anything about accuracy, which is not the score. The problem is not using tokens; that is the symptom. The problem is a lack of training; there is a huge AI enablement GAP ...

More tokens are not more intelligence. Still, it is easy to get hooked on token usage. Is it learning, experimentation, or just FOMO? Subscription models trained us to extract as much as possible from a fixed monthly payment. But when more usage creates more code to review, why do we need to max everything out? Are we getting more from AI? Or are we repeating an old consumption habit with a new resource? I was reading and watching Gergely Orosz’s conversation with Dex Horthy, “Context engineering with Dex Horthy” , and it made me think about fast loops, slow loops, token harder, and token smarter. Faster loops do not necessarily accelerate value delivery. Unless the lead time decreases and the value increases, we may be trapped in local optimization.