AI ROI Mirage: How to Measure AI Coding ROI Post-Usage-Based Billing

Engineering leaders today face a critical challenge: how to measure AI coding ROI effectively. The common wisdom that AI tools simply make developers "faster" is giving way to a more complex reality. With major shifts to usage-based billing and a growing "productivity paradox," relying on superficial metrics is a fast track to wasted spend and a skeptical leadership team. We need to move beyond simple output and focus on what truly matters: code-level business impact.

The air in developer communities is thick with frustration, and for good reason.

The Looming Cost Crisis: Usage-Based Billing Ignites Debate

For years, many AI coding tools like GitHub Copilot offered flat-rate subscriptions, providing a predictable line item in the budget. That era is ending. Starting June 1, 2026, GitHub Copilot is transitioning all plans to a token-based AI Credits model. While code completions and "Next Edit" suggestions remain unlimited, features like Copilot Chat, CLI, and cloud agents will now consume credits.

This change has ignited significant debate. GitHub's official announcement garnered "more than 400 comments and nearly 900 downvotes" from developers grappling with the implications. The core concern: agentic coding sessions, which involve planning, researching, and executing tasks, are likely to drive up costs significantly. A fixed monthly fee no longer guarantees predictable spend when powerful AI features now deplete a credit pool.

This isn't an isolated incident. Tools like Cursor, Windsurf, and Kiro already operate on credit-based or token-based systems. For example, Cursor’s paid plans include a credit pool that depletes when using frontier models like Claude Sonnet or GPT-4. Anthropic itself is splitting Claude subscription billing into two pools starting June 15, 2026: one for first-party tools and another for third-party agent/SDK usage, with new monthly "Agent SDK credits" for the latter.

The message is clear: the era of predictable, flat-rate AI licensing is over. Without granular visibility into how and where tokens are consumed, engineering leaders are flying blind.

The Productivity Paradox: Feeling Fast, Going Slow

Developers overwhelmingly embrace AI tools. A 2025 Stack Overflow survey indicated that 84% of developers were using or planning to use AI coding tools. And many feel faster. Reports suggest developers can feel 20-24% faster, or even 55% faster on specific tasks with GitHub Copilot.

However, this perceived speed often doesn't align with reality. A July 2025 METR study, while later acknowledged to have methodological flaws, found that experienced developers using AI tools actually took 19% longer to complete complex tasks. This "productivity paradox" is a hot topic in developer communities. On Reddit, an /r/ExperiencedDevs thread from July 2025 discussing the METR study highlighted the "massive disconnect between perception and reality".

This isn't merely an academic discussion; it's a critical disconnect that fuels frustration and makes justifying AI spend incredibly difficult. We've observed that "AI is not a universal speed multiplier. It is a task-shape multiplier.". AI excels at boilerplate, tests, and documentation, but can hinder productivity when the bottleneck is architecture or complex codebase context.

The Hidden Cost: AI-Generated Technical Debt

The pursuit of speed through AI also introduces a silent killer of ROI: technical debt. Studies show that AI, while fast, can lead to:

• Increased Code Churn: GitClear data from 2024-2025 revealed code churn rising from a 3.3% baseline in 2021 to 5.7-7.1%. Generating more code faster only exacerbates this if that code doesn't "survive". Elite teams, in contrast, maintain code turnover ratios below 1.3x compared to human-only baselines.
• Higher Code Duplication: AI can generate code that works but is poorly understood, easily broken, and often duplicated. Code duplication is up 4x with AI.
• Reduced Refactoring: Refactoring, crucial for codebase health, dropped from 25% to under 10% of changed lines between 2021-2024.
• Decreased Delivery Stability: The Google 2024 DORA report noted a 7.2% decrease in delivery stability.

These issues highlight that simply producing "more code" doesn't equate to "more value." It can lead to "AI legacy code" – code that works, but quickly becomes a maintenance burden. The "overhead" of managing AI itself, including quality control, bias checks, and prompt refinement, consumes developer time and erodes perceived gains.

Our Take: Beyond Velocity — Measure Outcome and Quality

The solution is not to abandon AI coding tools. It's to stop scoring AI by consumption or raw output and start scoring it by results. This requires a fundamental shift in how to measure AI coding ROI.

Traditional metrics like lines of code (LOC), pull requests per week, or commit counts are unreliable and actively misleading in an AI-assisted workflow. A developer using Copilot or Cursor can generate 3-5x more lines per session, but this raw volume says nothing about whether that code survives in production. As a team, we believe it’s time to adopt a data-driven, holistic approach that prioritizes value and sustainable impact.

Here are the metrics that engineering leaders should focus on to prove AI value to leadership:

1. AI Code Share & Survival Rate:

   • What it is: The percentage of code that is AI-generated and how long it remains in the codebase without significant rework or deletion.
   • Why it matters: This is arguably the most telling metric. High velocity with low code survival is "fast waste." A pivotal study observed scenarios where 40% of AI-generated code was deleted within 14 days due to refactoring and rework.
   • Target: A healthy code turnover rate for AI-assisted code is below 12% at 30 days. Industry average AI-assisted code share is 15-25%, with top-quartile teams at 40-60%.
   • Actionable Insight: If AI-generated code churns at more than 1.5x the rate of human-written code, your AI code share is too high for your current review processes.

2. AI vs. Human PR Cycle Time & Rework Ratio:

   • What it is: Compare the lead time for changes, change failure rate, and time to restore for AI-touched pull requests versus human-only pull requests. Also track the ratio of AI-generated code that needs significant rework.
   • Why it matters: AI can shift bottlenecks downstream to code review. Faster generation doesn't mean faster delivery if review times balloon or if the generated code introduces bugs that increase change failure rates.
   • Actionable Insight: Focus on whether AI improves delivery outcomes without harming stability, rather than just increasing activity.

3. Cost per Merged Pull Request (Post-Usage-Based Billing):

   • What it is: Divide the total AI spend (including token and usage-based costs, not just seat licenses) by the number of pull requests that actually merge and ship to production.
   • Why it matters: This metric directly ties AI costs to tangible output. Code that never merges is pure cost. With token-based billing, understanding the true cost of each successful feature delivery is paramount.
   • Actionable Insight: This ratio exposes teams whose token bills are climbing while their merged output is flat.

4. Longitudinal AI Incident Rates:

   • What it is: Track incidents, vulnerabilities, and missing error handling directly attributable to AI-generated code over time.
   • Why it matters: AI speeds up greenfield projects but can introduce technical debt and vulnerabilities in complex enterprise code. This metric reveals the hidden quality cost.
   • Actionable Insight: This helps measure the long-term impact on system stability and maintenance burden.

The Verdict: Optimize for Value, Not Just Velocity

The era of uncritically celebrating "AI developer productivity" based on simplistic metrics is over. Engineering leaders must adopt a systematic measurement approach that analyzes actual code, not just workflow metadata. Elite teams see healthy ROI on AI coding tools, averaging 2.5-3.5x, and reaching 4-6x for top-performing teams. But this ROI is only realized when the cost denominator includes actual token and usage-based costs, not just flat seat licenses.

This means:

• Integrating AI usage data with your existing development analytics.
• Tracking the full lifecycle of AI-generated code, from prompt to production.
• Focusing on quality and stability alongside speed.

Companies like Booking.com have achieved a 16% increase in throughput and saved 150,000 developer hours by focusing on strategic, measured AI investment. This was done by tracking utilization, impact, and cost metrics.

We don't guess which code is AI-generated. We know — because we're in the loop when it happens. Tools like CostLens connect GitHub activity to AI usage automatically — giving you the ROI report your CFO is asking for. Try it free at costlens.dev.