Measuring the ‘Claude Effect’: Provectus’ Metrics Framework for Dev Productivity ROI

Author:
Konstantin Makarychev, Director of Engineering at Provectus

Executive Summary

AI now produces a large share of new code, yet enterprise engineering productivity has barely moved because most teams use LLMs as faster autocomplete and measure the wrong outputs. That “speed illusion” increases code volume while shifting work into review, QA, and debugging, creating a Verification Tax that cancels gains. Provectus’ approach reframes Dev ROI around agentic workflows with Claude Code, where developers delegate cognitive work like analysis, design, refactoring, and test generation. To measure real impact, our Metrics Framework focuses on three signals: Cost per Merged Feature, Token Velocity , and Autonomous Execution Rate. The result is higher feature throughput with healthier systems, not more code.

Introduction: The 3.6% Reality Check

By 2026, the software engineering industry reached an important milestone: AI systems were generating approximately 29% of all new code globally – a nearly sixfold increase from just 5% in 2022. On paper, this looks like the promised industrial revolution of software engineering. Yet, in that same period, holistic organizational productivity metrics ticked up by a meager 3.6%.

This discrepancy, now known as the “AI Productivity Paradox,” has created a stalemate. Engineering leaders and CFOs were promised “10x developers,” but instead they received 10x more lines of code, inevitably followed by a massive bottleneck in QA and peer review.

At Provectus, we regularly observe this paradox when working with enterprise clients. The problem isn’t that artificial intelligence doesn’t work. The problem is that the industry is trying to measure the wrong revolution.

For the last few years, most companies have been trapped in the “Speed Illusion.” They treated Large Language Models (LLMs) purely as advanced autocomplete – a smart typewriter that helps developers type faster. Success was measured by acceptance rates and lines of code. This completely ignored the reality that typing speed has rarely been the primary bottleneck in modern software delivery. As a result, the industry accelerated output (code volume) without accelerating outcomes (delivered business features).

To capture real ROI, Provectus completely altered this approach. We shifted the focus from micro-efficiency (typing speed) to macro-efficiency (delegating cognitive load). To accurately measure this shift – the transition from using AI as an autocomplete to deploying fully agentic workflows – we developed the Provectus Metrics Framework.

The Efficiency Trap and the High Cost of “Vibe Coding”

To build a framework that actually works, we first need to understand the mechanics of the productivity paradox. Why does a 29% increase in code generation yield almost zero gain in real team velocity?

The answer lies in a hidden cost ignored by traditional lines-of-code metrics: The Verification Tax.

Industry data from 2025 paints a concerning picture of the “Copilot era.” According to Veracode reports, nearly 45% of AI-generated code contains vulnerabilities or architectural flaws if not rigorously reviewed. When developers use AI purely as a high-speed typewriter – a practice colloquially known as “vibe coding” – they are essentially borrowing time from their future selves. They write code in minutes, but then spend hours debugging, reviewing, and hunting down elusive bugs.

This phenomenon creates a dangerous “Seniority Gap” that standard DORA metrics fail to capture. Research from Fastly (2025) revealed a counterintuitive trend: Senior developers push 2.5x more AI-generated code to production than their Junior counterparts.

Why the disparity?

• Junior developers often use AI as a crutch. They generate code they don’t fully understand, leading to high rejection rates in code reviews and a spike in “Code Churn” (code that is rewritten or deleted shortly after being merged).
• Senior developers use AI as a lever. They have the expertise and context to validate the output instantly, thereby minimizing the “Verification Tax.”

From Assistant to Agent: Why We Bet on Claude Code

Understanding the difference between a crutch and a lever brings us to the core of the “Claude Effect.” To escape the efficiency trap, organizations must stop viewing AI as a monolithic “coding tool” and distinguish between two modes of operation:

• Assistant Mode (Micro-Efficiency): AI acts as an autocomplete on steroids. It generates boilerplate and helps recall syntax. This is where most companies are stuck today. It creates local speed but global gridlock due to bloated pull requests.
• Agent Mode (Macro-Efficiency): The developer delegates cognitive tasks, such as reasoning, architectural design, test generation, and refactoring strategies.

This is exactly why Provectus is moving away from the AI-assistant paradigm, architecting our development workflows around Claude Code. Thanks to its massive context window and exceptional reasoning capabilities, Claude Code doesn’t just autocomplete a function. It can analyze system logs, identify an obscure race condition, and propose a structural fix alongside regression tests.

The real ROI is hidden entirely within the agentic approach. But you cannot manage it if you are using outdated rulers to measure it.

The Provectus Framework: Measuring What Actually Matters

Our experience deploying Claude Code across dozens of enterprise projects has proven that using traditional metrics like DORA in isolation turns them into “vanity metrics.” AI tools make it trivially easy to game dashboards by generating frequent, verbose commits.

The Provectus framework divides productivity analytics into three fundamental pillars: Economic Efficiency, Delegation Depth, and System Health.

#1 Economic Efficiency: Cost per Merged Feature

For years, CFOs calculated ROI via “Cost per User” (e.g., the price of a seat license). In the AI era, this is a fundamental error because it treats AI as a static expense rather than a production multiplier. We are accelerating product development, not developer motor skills.
In an agentic workflow, the primary goal is to lower the marginal cost of delivering value.

• The Metric: (Total Team Payroll + AI Token Costs) / Delivered Features (or Story Points).
• The Signal: If your infrastructure costs (API/tokens) are climbing, but your Cost per Merged Feature is steadily dropping – that is a win. It means you are successfully substituting expensive manual toil with inexpensive compute, freeing up your engineers’ intellect for high-leverage architectural design.

#2 Delegation Depth: Token Velocity (Inference Intensity)

Measuring Daily Active Users (DAU) tells you nothing about the quality of tool usage. A developer using AI to fix a typo and a developer using AI to refactor a legacy module look exactly the same on a basic dashboard.

You cannot cheat the thermodynamics of Large Language Models: deep “thinking” requires energy (tokens).

• The Metric: Token consumption per active developer.
• The Signal: Do not fear high costs in this category. An engineer using autocomplete burns tokens linearly. In our practice, teams that have truly mastered Claude Code consume tokens exponentially. They load 50,000 tokens of repository context so the agent can run through multiple internal reasoning loops to output 100 tokens of flawless, stable code. High token consumption is the thermal signature of a true agentic workflow.

#3 System Health: Spec-Driven Development & Autonomous Loops

This is your primary safeguard against “Code Bloat.” As code generation accelerates, the bottleneck naturally shifts to the human reviewer. But asking one LLM to blindly review the code of another LLM is just playing hallucination roulette.

Real system health is achieved when agents stop being just writers and become testers.

To achieve this, Provectus implements spec-driven frameworks (powered by our internal tool, awos). In this paradigm, Claude Code operates within strict architectural specifications.

• The Metric: Autonomous Execution Rate.
• How It Works: Our Claude Code pipeline is a closed loop: Write Code -> Run Test -> Fail -> Self-Correct -> Pass -> Submit to Human. The agent burns thousands of tokens fixing its own syntax and logic errors inside an isolated environment before a human ever sees the pull request.
• The Signal (Review-to-Coding Ratio): If coding time drops but code review time skyrockets, your agents are generating technical debt. If review time drops alongside coding time (because Claude Code validated its own solution within a spec-driven framework), you are generating pure business value.

Conclusion: The Electrification Horizon

In the early 20th century, factories didn’t just bolt electric motors onto old steam-powered shafts; they tore down the old shop floors and redesigned the assembly lines for a new power source. Those who merely swapped power sources without changing their processes gained nothing. Those who changed the architecture of production won the era.

Today, we are in the midst of the transition from the “steam” to the “electric” era of software engineering.

The Provectus framework wasn’t built so executives could admire dashboards with green “AI ROI” percentages. Our ultimate goal is the moment the “AI” prefix disappears entirely because it is no longer necessary. We don’t say “computer-assisted software engineering” anymore; we just call it “engineering.”

Stop looking for 10x ROI in typing speed. Start measuring the depth of context delegation, the true cost of delivering a feature, and the ability of your Claude Code agents to autonomously validate their solutions.

The “electricity” is already here. It’s time to rewire the factory.

Ready to reimagine your engineering with Claude? Visit our Anthropic practice page and learn more about how Provectus can help!