We Need Git for Identity in the AI Era

Software has had versioned, portable, auditable state for decades. Code can be branched, reviewed, rolled back, and collaborated on. Identity — the context that defines how AI acts on your behalf — has none of that. It's time to fix it.

The Analogy That Actually Holds

Before Git, code was fragile. You'd make a change, something would break, and you'd have no reliable way to understand what changed, when, or why. Teams would overwrite each other's work. There was no way to experiment safely without risking everything.

Git didn't just solve version control. It changed how people thought about building software. It made branching cheap, experimentation safe, and collaboration structured. It introduced a mental model — commit, branch, merge, diff, rollback — that became foundational to how the industry works.

We're at the same inflection point with identity in the AI era. Except instead of code going wrong, it's AI agents acting on your behalf — and getting it wrong in ways that are much harder to audit.

The Identity Fragmentation Problem

Right now, when an AI system acts on your behalf, it does so without a stable, versioned representation of who you are. Your identity exists as:

• A system prompt you pasted into one tool three months ago
• A "Custom Instructions" field in another tool you set up and forgot
• An implicit style that emerged from thousands of prompts with no structure
• A brand guidelines document that nobody reads — including the AI

Each AI tool has its own version of you. None of them agree. None of them are tracked. And when something goes wrong — when an AI sends something in your name that doesn't sound like you, or makes a decision that violates your values — you have no diff to look at, no history to audit, no rollback to perform.

What "Git for Identity" Actually Looks Like

Imagine your identity as a repository. Instead of files and code, it contains:

Now imagine you can version this. Every time you update your values, change how you communicate, or shift your priorities — it's a commit. You have a log. You can see what changed.

You can branch it. Running a client project where you need a more formal tone? Branch. Exploring a new career direction? Branch. Testing a different framing for a specific audience? Branch and merge what works.

You can diff it. How did I describe my core constraints six months ago versus today? What shifted? That's not just useful for AI — it's useful for self-understanding.

And you can deploy it. Push your twin to any AI system that supports the interface. Your context travels with you, not trapped in one tool's proprietary format.

Why This Matters More Than It Looks

The immediate value is consistency. AI tools produce outputs that actually sound like you. Agents make decisions aligned with your constraints. Delegated work stays on-brand.

But the deeper value is accountability. When AI is acting on your behalf at scale — writing emails, answering customer questions, making operational decisions — you need to know exactly what identity it was operating from. You need the equivalent of a commit hash: a precise, immutable reference to the context that was active at the time.

Without versioned identity, AI delegation is trust without auditability. You're handing off your representation to a system with no record of what you told it to be.

The Infrastructure Gap

This isn't a product feature. It's infrastructure. The same way Git is not a product — it's the underlying system that products are built on — versioned AI identity needs to be a foundational layer.

It needs to be portable (works across models and tools), structured (machine-readable, not just prose), versioned (tracked changes over time), and permissioned (controlled exposure based on context).

Nobody has built this yet. The pieces exist — MCP defines how AI systems consume structured context, version control principles are well understood — but they haven't been assembled into a coherent identity layer.