I tracked every AI task I ran for 30 days. The cost curve surprised me.
I got obsessed with a question: does using AI actually get cheaper over time, or does it just feel that way?
So I tracked every single AI task I ran for 30 days. Every prompt, every tool call, every credit spent. Here's what I found.
Week 1: I was paying full price for everything. Every task started from scratch. The AI explored the same websites it had explored the day before. Re-planned workflows it had already figured out. Re-applied preferences I'd already stated.
Week 2: Something started shifting. Tasks on sites I'd already used were noticeably faster. The AI wasn't re-exploring — it was reusing.
Week 4: The same task that cost me 65 credits in week 1 cost 16 credits. Same output quality. Same result. 75% cheaper.
By day 30, some tasks cost less than 10% of what they cost on day 1.
The mechanism turned out to be three things compounding together:
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Website knowledge ("Manuals") — the AI saves how to operate each site. Second visit: skip exploration entirely.
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Workflow memory ("Playbooks") — recurring multi-step jobs get packaged into one-line triggers. No re-planning.
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Domain judgment ("Skills") — accumulated preferences and standards. You stop correcting the same mistakes.
Each layer compounds on the others. The longer you use it, the steeper the discount.
The thing that surprised me most: the compounding isn't linear. It's more like compound interest. The first week feels expensive. By month 3, you're running tasks that would have been cost-prohibitive on day 1.
I've been building this into AllyHub (allyhub.com) — the whole product is designed around this compounding loop. Free to try, no invite code.
Curious if others have noticed this pattern. Has anyone else tracked their AI costs over time? Did you see a similar curve, or something different?
Also genuinely curious: what's the task you run most repeatedly that you wish cost less each time?
Tracking costs is the part most people skip. I started doing it seriously about two months ago — not just checking the monthly bill, but logging what each task actually costs per run.
What I found: the expensive phase isn't the model, it's the setup. When I was still figuring out which tool to use for what, costs were inconsistent. Once I settled on a dedicated model for each task type and stopped switching, the numbers
stabilized and dropped.
Right now my AI infrastructure runs well under $1/month for the automated parts. The heavier reasoning work I handle separately with a flat-rate plan. Splitting by task type made more difference than optimizing prompts.
Splitting by task type is exactly the right move — and the fact that costs stabilized once you stopped switching tools confirms that the setup/context cost is the dominant variable, not the generation cost. Running under $1/month for automated parts is impressive. The next level is making that cost keep dropping as the system accumulates judgment about each task type, rather than plateauing once you've found the right tool. That's the compounding dynamic AllyHub is built around. If you want to see how it handles your specific task types, allyhub.com is invite-only — join us at discord.gg/WNMTr3w3pC.