Last month I decided to track every single dollar I spent on AI development tools. Not just the subscription fees — the actual per-token, per-request costs across every tool in my workflow.
The results were... eye-opening.
My AI Tool Stack (Before the Experiment)
Here's what I was running:
- Claude Max ($200/month) — primary coding assistant
- Cursor Pro ($20/month) — IDE integration
- ChatGPT Plus ($20/month) — research and brainstorming
- Various API calls — for side projects and automation
Total fixed costs: $240/month. But that wasn't the whole story.
Week 1: The Uncomfortable Truth
I started logging every AI interaction. Model used, tokens consumed, what the task actually was. Within three days, I noticed something:
Over 60% of my Claude Opus requests were for tasks that Sonnet would've handled identically.
Things like:
- "Rename this variable across the file"
- "Add error handling to this function"
- "Write a unit test for this method"
I was burning premium tokens on grunt work. Not because I chose to — because I didn't have visibility into what was happening.
Week 2: The Focus Problem
Here's where it got interesting. I also started tracking when I was using AI tools versus when I was just... scrolling.
My workflow looked like this:
- Start a coding session
- Hit a problem, ask Claude
- While waiting, check Twitter "real quick"
- 30 minutes later, come back to a response I forgot about
- Repeat
The AI wasn't the bottleneck. My attention was.
I calculated that I was losing roughly 2 hours per day to "quick checks" on social media feeds during coding sessions. Not during breaks — during active work sessions.
Week 3: The Changes
I made two changes:
Change 1: Token-Level Cost Tracking
I started using TokenBar — a Mac menu bar app that shows real-time token costs across providers. $5, sits in your menu bar, shows you exactly what each request costs as you work.
The immediate effect: I started being intentional about model selection. Opus for architecture decisions and complex debugging. Sonnet for everything else. My effective cost per task dropped roughly 40%.
Change 2: Feed-Level Blocking
Instead of deleting apps (which never sticks), I started blocking just the algorithmic feed sections of social platforms while keeping DMs, notifications, and search. Monk Mode does this at $15 — blocks the infinite scroll while preserving the useful parts of each app.
The result: My "quick check" habit died because there was nothing to scroll. But I could still reply to DMs and check notifications.
Week 4: The Numbers
Here's the before/after:
| Metric | Before | After |
|---|---|---|
| Monthly AI spend | $240+ overflow | $240 flat |
| Tokens wasted on wrong model | ~60% | ~15% |
| Daily social media during work | ~2 hours | ~15 minutes |
| Productive coding hours/day | ~4 | ~6 |
| Features shipped/week | 2-3 | 5-6 |
The most surprising finding: cost awareness and focus improvement compounded each other. When I wasn't distracted, I wrote better prompts. Better prompts meant fewer iterations. Fewer iterations meant lower costs.
What I Learned
1. You Can't Optimize What You Can't See
Most developers have no idea what their AI usage actually costs per task. We know the subscription fee, but not the per-interaction breakdown. That's like knowing your rent but not tracking any other expense.
2. The Feed Is the Enemy, Not the App
Deleting Twitter or Instagram is nuclear. You lose the useful parts (DMs, communities, staying connected). The problem isn't the app — it's the algorithmic feed designed to keep you scrolling. Block the feed, keep the rest.
3. AI Productivity Gains Are Real — But Fragile
AI tools genuinely make you faster. But that speed advantage evaporates the moment you lose focus. A 3x coding speed boost means nothing if you're only coding 4 hours out of an 8-hour day because you're scrolling feeds between prompts.
4. The $5 + $15 That Changed Everything
This isn't about expensive solutions. TokenBar ($5) for cost visibility and Monk Mode ($15) for feed blocking. $20/month total. The ROI on those $20 has been multiple hundreds of dollars in reduced AI waste and reclaimed productive hours.
The Bigger Picture
We're in an era where AI tools are powerful enough to 10x your output — if you can maintain focus and use them intelligently. The bottleneck isn't the AI. It's us.
With Claude Code Review dropping today (dispatching teams of agents to review every PR), the amount of AI running in our workflows is only going up. More agents means more tokens. More tokens means more need for visibility into what you're spending and where.
Track your costs. Block your feeds. Ship more code.
What does your AI tool spending look like? Are you tracking it? Drop your numbers in the comments — I'm genuinely curious how other devs are managing this.
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