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I Pay $1,800/Year for AI Tools. Here's the Exact ROI.

I added up my AI tool expenses for the year. $1,800. Did I save more than that? Honestly — I didn't know. So I sat down and calculated it.

This isn't an "AI changes everything" take. It's a table with hours, a rate, and a total. Including the rows where ROI is negative.

What I actually pay and for what

My stack as of October 2025:

  • Claude Pro — $20/month. Primary tool: code, review, refactoring, specs.
  • Cursor Pro — $20/month. AI-native IDE. Using it for 8 months.
  • GitHub Copilot — $19/month. Bought it a year ago, disconnected in July.

Current spend: $40/month = $480/year.

But the annual total is higher. There was also:

  • Two SEO automation tools — $25/month each, cancelled after 2 months. +$100.
  • GitHub Copilot for 9 months = $171.
  • Claude API directly for my automation system (13 cron jobs in production) — $47 for the year.
  • Various one-month trials — ~$80.

Total for 12 months: $1,878. Call it $1,800.

The question: what did that buy me?

How I measure "saved time" without lying to myself

Simple method. I take a task I did with AI — I time it. Then I recall (or actually try) how long the same task would take without AI. The difference is "saved time."

My billing rate on client work: $80/hour.

I only count what I actually bill. Time spent exploring or testing an idea doesn't count — that's skill investment, not tool ROI.

Where AI actually pays off

Tests for Bitrix PHP components. This is what I've written about before. A test for a PHP class working with iblock — used to take 2.5–3 hours (understand the contract, write mocks, run it). With Claude: 40–50 minutes. On one project over a quarter, I wrote ~35 tests. Saved: ~75 hours. In money: $6,000.

Refactoring legacy PHP for headless API. You feed Claude an old Bitrix controller and explain the expected output — you get 70–80% of the work done. The remaining 20% is business logic AI doesn't know. A 3-hour task becomes 1 hour. Over the year I ran through 4 major modules across two client projects. Conservatively: 30–40 hours saved. In money: $2,400–3,200.

Documentation and specs. I write a spec before asking AI to write code. The spec-writing process itself got 4x faster with Claude. 1 hour → 15 minutes. Over the year: 20–25 hours. In money: $1,600–2,000.

Code review assistance. Not production debugging (more on that below). Reviewing unfamiliar code or a junior's PR. Saved: 15–20 hours. In money: $1,200–1,600.

Total from winning tasks: $11,200–12,800.

Where AI slows me down or creates debt

The honest part.

Production debugging. I run 28k SKUs in Bitrix, headless Next.js, Elasticsearch. When TTFB spikes at 11pm, AI doesn't help. The issue is always specific: OPcache misconfiguration, PHP-FPM pool exhaustion, Redis session locks. Real incidents — I've written about each separately. AI doesn't know the system's history. It generates plausible hypotheses that still need verification. Debug takes the same time, plus I now have to reject 3–5 AI responses first.

Architecture decisions. I let Claude choose the structure for a new service twice. Both times I got a "technically correct" answer that ignored our team's capacity, Bitrix's legacy constraints, and the fact that clients always change their minds in six months. Once I caught it in 3 hours. The second time I didn't catch it quickly — we spent a day and a half cleaning it up.

GitHub Copilot inline. Disconnected in July. It interfered with thinking. Line-level autocomplete isn't about productivity — it's about having a constant "suggestion" in front of you. You start accepting the first option without thinking. Over 9 months I noticed: when it's off, I work slower but I understand what I'm doing. That's worth more than the time saved.

Vibe-coding trap. There are weeks when it feels like AI's writing everything. Then a code review or staging test hits, and part of the generated code turns out to be confident nonsense. AI writes fast and sounds convincing. Review each block as you go, not once at the end.

What I cancelled and why

GitHub Copilot — cancelled in July. Reason above: line-level autocomplete hurt focus.

Two SEO tools — cancelled after 2 months. I tried automating a content pipeline via third-party services. Ended up building my own automation on Claude API directly — cheaper and more precise. Same principle as with the tech stack: the boring, predictable tool wins over the flashy one.

Rule: if a tool doesn't save money within 60 days, cut it. Not "I need to adjust." Not "give it time." If it doesn't pay, it goes.

The calculation

| Category | Hours saved | Value ($80/hr) | |---|---|---| | Tests (Bitrix PHP) | ~75 | $6,000 | | Refactoring for headless | ~35 | $2,800 | | Specs and documentation | ~22 | $1,760 | | Code review | ~17 | $1,360 | | Total | ~149 | $11,920 |

Spent on AI tools: $1,878.

ROI: 6.3x.

That's before accounting for the time lost on bad-ROI tasks. Add roughly 15–20% for "AI work that needed undoing" — you get ROI around 5x–5.5x.

For reference: I expected 3x before I sat down to calculate. Came out better.

Should a studio owner pay for this?

Yes — if you're paying for a specific tool for specific tasks. No — if you're paying because everyone else is and you're not tracking it.

The key insight after a year: AI tools aren't a budget line called "AI." They're a paid assistant for predictable tasks. High-uncertainty work — debugging production, architecture, unfamiliar systems — isn't their zone. ROI there is negative.

I calculate AI agent ROI for client projects the same way: task first, tool second. Never the other way around.

$1,800/year is two days of work at my rate. I spend it once and get 149 hours back. That's a calculation I can make.

*Task numbers are real. I rounded down when uncertain.*