When Git Meets AI: Rethinking Version Control for Machine‑Speed Development

For six decades, the amount of code a developer could produce was limited by how fast a person could type. That constraint has vanished. A single engineer with a laptop can now generate more code in an afternoon than an entire team could in a week a few years ago. Large language models (LLMs) can produce thousands of lines of code in seconds, fundamentally altering the assumptions that underpin our development infrastructure.

The Old Assumptions of Git

Git was conceived for the Linux kernel workflow, where code was written slowly, commits represented logical units of human reasoning, and history served to explain decisions. Its architecture—a content‑addressable filesystem with a linear DAG of commits, a single serialized staging area, and a lock‑file to serialize writes—was perfectly suited to human‑paced, sequential work. The index file became a global mutex; SHA‑1 hashing and sequential tree building were acceptable overheads when commits were infrequent.

The New Reality: AI‑Generated Code at Scale

GitHub reports that nearly 50 % of code in repositories with Copilot enabled is AI‑generated, a figure expected to approach 80 % in the next 3‑5 years. When tools like Cursor and Claude Code enter the mix, the proportion may be even higher. The bottleneck has shifted from typing to understanding and validating code. Agents can iterate rapidly—generating, testing, and refining code dozens of times in a minute—creating a churn that Git was never designed to handle.

"An agent might touch the same file 50 times in a minute while iterating." – Source: evis.dev

The context that drives an agent’s decisions is lost with each new session. A fresh prompt cannot remember why a particular approach was rejected, which edge cases were discovered, or what constraints were implicitly learned. The result is code that works but whose rationale is unknowable, making debugging and maintenance a nightmare.

Why Git Needs to Become a Coordination Layer

Version control can no longer remain a simple storage layer. It must become a system that records not only what changed, but also who changed it (human or agent), why (prompt, context), and how it relates to the rest of the codebase.

Performance Bottlenecks

  • SHA‑1 hashing for every blob
  • Sequential tree construction
  • Index lock contention
  • Pack‑file delta compression

These factors make Git commits of 10 k files take ~25 seconds on typical hardware—acceptable for humans but prohibitive for agents that need sub‑second commits to preserve exploratory state.

Micro‑Commits and Exploration

Agents think in iterations rather than logical commits. Each iteration—modify, test, adjust—should be checkpointed. A VCS designed for AI must support micro‑commits that record exploratory states without blocking generation loops.

Semantic Intent and Provenance

Git’s text‑based diffs are insufficient when agents rewrite entire functions. Storing vector embeddings of semantic changes alongside diffs would allow queries by intent (e.g., "Show me commits that fixed memory leaks in the blob storage module."). Provenance metadata—agent identity, model, prompt, accessed files, previous iterations—provides a complete dataset for debugging, statistical analysis, and seamless hand‑offs between agents.

Emerging Workflows for an AI‑Native Development Stack

  1. Semantic Diffs – Capture intent rather than line changes.
  2. Agent‑Specific Workspaces – Automatic branch isolation per agent.
  3. AI‑Mediated Conflict Resolution – Use semantic understanding to merge.
  4. Quality Gates – Enforce tests, type checks, and performance benchmarks before merge.
  5. Performance Snapshotting – Versioned benchmarks and resource profiles.

These workflows transform the VCS into a true coordination layer, enabling multiple agents to collaborate efficiently while preserving human‑readable history and full provenance.

The Opportunity

Git has served the software industry well for twenty years, but it was engineered for a different era. The current wave of AI‑generated code—fast, iterative, and parallel—demands a new infrastructure that can keep pace with machine‑speed development. Building a VCS that is fast enough for micro‑commits, semantically aware, and rich in provenance is not just an incremental upgrade; it is a fundamental shift in how we build, review, and maintain code.

Source: https://www.evis.dev/posts/vcs