The Impact Layer for AI-assisted engineering
The token bill arrived.
The proof of value did not.
You're spending $200-$2,000 per engineer per month on AI coding tools. You know what you spent. You don't know what survived in your codebase.
Claude
Cursor
Codex
Copilot
Antigravity
OpenCode
AI Spend Reality · $8,000 tracked
82% of your AI coding spend was absorbed by bugs, rework and refactoring. Only $1,400 survived review and shipped.
Survival Index
the four numbers your bill never came withAI Spend
AI spend, by day
Hover a day to see spend — and how much of it leaked.
Tool Yield
Which tools earned their seat
The problem
Every AI tool tracks what it generates. None of them track what survives.
Your team runs:
Each ships its own analytics: lines generated, acceptance rate, tokens consumed. None of them tell you whether the code is still in your repo at day 30, day 60, or day 90.
Only a fraction reaches production.
Much of what the agent writes is reviewed, rewritten, or deleted before it ships. You paid for all of it.
The rest goes to cleanup.
Bug-fixing, refactoring, friction on AI-written code. A large share of the spend goes straight back into fixing what AI produced.
Was it worth it? Nobody knows.
The invoice shows the spend. Nothing on it tells you what survived, so the one question leadership asks has no answer.
Statement
#ENG-2026-05
Billing period
May 2026
- Claude CodeTeam plan · 22 seats$2,750
- CursorBusiness · 22 seats$880
- CodexPro · agentic usage$2,460
- CopilotBusiness · 22 seats$834
- AntigravityStandard · 18 seats$410
- OpenCodeAPI usage · 14 devs$836
The spend, you can see. The leak, you can't. That's what Codelitics measures.
Why now
The AI coding ROI question just became public.
The conversation moved from adoption charts to budget, durability and proof. Recent reporting and founder discourse are all asking the same question: what did the token spend actually buy?
The VergeMicrosoft started canceling Claude Code licenses after adoption ran ahead of the plan.
FortuneUber burned through its 2026 AI budget in four months. Now its COO is asking whether it was worth it.
TechCrunchTokenmaxxing turned the industry argument from usage to measured productivity.
BloombergEuropean banks are confronting the uncomfortable economics of their Claude AI dependence.
XThe token-burn conversation has moved from finance meetings into the builder timeline.
METRDevelopers believed AI made them faster while controlled data showed the opposite.
Token usage is now material enough for finance teams and platform leads to care.
More generated code does not automatically mean more production value that survives review.
Teams need defensible, workflow-level evidence of what AI spend delivers, the kind finance and the board will accept.
Token Control Tower
Four numbers your bill never came with.
What actually stayed
The fraction of AI-generated code still in your repo at 7, 30, and 90 days. Not lines accepted, but lines that survived review, QA, and your team's judgment.
The hidden time cost
Time from AI-authored commit to merge, plus fixup-commit density within 14 days. The J-curve cost measured in observed hours, not guessed inputs.
What the spend bought
Token spend plus subscriptions, divided by AI lines surviving at 30 days. The number your finance team can put next to a marginal hire and compare directly.
Which tools earned their seat
Survival multiplied by volume, broken down per tool. The first cross-tool view in the category, across every AI coding tool your team already runs.
How it works
One Survival Index.
Three steps to get there.
Install Codelitics
Install the Codelitics agent across your team and connect your repositories. It runs alongside your AI coding tools and in CI, so survival is measured from real activity.
Plug in your tools
Connect the AI coding tools you already use. Cursor, Copilot, Claude Code and more. Multi-tool from day one, no migration required.
Get your Survival Index
A defensible number for leadership, a per-tool breakdown for engineering, and a baseline you can track over time.
Trust & security
Full visibility. Built for security review.
Codelitics measures AI work from real activity across the tools and repositories you connect. The questions your security team asks first:
Runs where AI work happens
A Codelitics agent installs with your team's AI tools and connects to your repos and CI. Survival comes from real activity, not surveys or self-reports.
You control the scope
Codelitics reads only the repositories and AI tools you connect it to, and you decide what's in scope from day one.
Tool, workflow, and team level
See where AI spend turns into shipped work across tools, repos, and teams. Granular enough to act on, defensible enough to take to the board.
Numbers you can defend
Every figure is exportable and traceable to how it was calculated, so finance and security can verify it instead of taking it on faith.
FAQ
The AI coding ROI questions leadership asks first.
Private beta
See what your AI spend actually ships.
We install on one repo and show you exactly how much of your AI code actually survived.
18 engineering leaders are already measuring what survives
- Your data stays in your repo
- One install on one repo
- Leave anytime, keep everything