The Memory Problem Is Temporary—Sort Of
Today's AI assistants have amnesia. Each conversation starts fresh. Context doesn't persist.
This will change. OpenAI, Anthropic, Google—everyone is working on persistent memory. Soon, ChatGPT will remember your preferences. Claude will recall previous conversations.
But here's the question nobody's asking:
Whose memory will it be?
The Coming Memory Wars
As AI memory becomes standard, vendors will compete on it:
"ChatGPT remembers everything about your coding style"
"Claude learns your architecture preferences over time"
"Gemini builds a model of your entire codebase"
Sounds great. Until you want to switch.
Scenario 2027:
You've been using Claude for two years. It knows your patterns, your preferences, your project history. Then Gemini releases a breakthrough model that's 10x better for your use case.
But Gemini doesn't know anything about you. Your two years of accumulated context is locked in Claude.
What do you do?
Vendor Lock-In 2.0
We've seen this pattern before:
- Photo libraries locked in iCloud
- Documents locked in Google Drive
- Social graphs locked in Facebook
- Now: Development context locked in AI vendors
Each lock-in follows the same pattern:
- Offer a convenient service
- Accumulate user data
- Make switching expensive
- Extract rents from captive users
AI context is the new lock-in vector.
Why Portable Context Is Different
With photos, you can export JPEGs. With documents, you can download files. The format is standard.
But AI context isn't a file format. It's:
- Conversation history
- Learned preferences
- Project understanding
- Decision patterns
- Accumulated knowledge
There's no "export" button for understanding.
The Developer's Dilemma
For developers, this hits especially hard:
Your AI knows:
- Your codebase's patterns
- Your architectural preferences
- Decisions you've made
- Mistakes you've avoided
- Conventions you've established
Switching costs:
- Weeks of re-training new AI
- Lost institutional knowledge
- Repeated mistakes
- Productivity cliff
The more you invest in one AI's memory, the harder it becomes to leave.
Three Possible Futures
Future 1: Walled Gardens (Likely)
Each vendor builds proprietary memory:
- OpenAI memory only works in OpenAI products
- Anthropic memory only works in Claude
- Google memory only works in Gemini
Switching is painful. Users stay locked in. Innovation suffers because switching friction protects incumbents.
Future 2: Standards Emerge (Hopeful)
Industry agrees on context interchange formats:
- AI conversation export standards
- Preference portability protocols
- Context handoff specifications
Like email or HTTP—different providers, shared protocols. Users can switch freely.
Future 3: User-Owned Context (Ideal)
Context is stored and controlled by users:
- Your context lives on your machine/cloud
- Any AI can read it (with permission)
- You decide what to share, with whom
- Context is an asset you own
This is the vision worth fighting for.
Building for Portability Now
You don't have to wait for the industry to figure this out.
Principles of portable context:
- **Local-first:** Your context on your machine
- **Open format:** No proprietary encoding
- **Selective sharing:** You control what AI sees
- **Tool-agnostic:** Works with any LLM
- **Human-readable:** You can inspect and edit
When context is portable by default, lock-in becomes impossible.
The RCEP Approach
RCEP (Recoverable Context Exchange Protocol) is one approach to portable context:
Structure:
- Captured context in standard format
- Compression for efficiency
- Checksums for integrity
- Metadata for routing
Properties:
- Works with any LLM
- Stored locally
- User-controlled
- Version-trackable
This isn't the only approach, but it demonstrates the principles.
What Developers Should Do Now
1. Own your context
Don't let AI accumulate context you can't export. Periodically capture your conversation history in portable form.
2. Avoid deep vendor lock-in
Use AI features, but keep one eye on the exit. Can you export? Can you switch?
3. Build portability habits
Make snapshots. Use open formats. Keep your context tool-agnostic.
4. Support open standards
When tools offer portable context, use them. Demand export features from vendors who don't.
The Economic Argument
Portable context isn't just ethical—it's economically efficient.
With lock-in:
- Users stuck with suboptimal tools
- Vendors compete on switching costs, not quality
- Innovation slows (why improve if users can't leave?)
With portability:
- Users choose best tool for each task
- Vendors compete on actual value
- Innovation accelerates (must be better to win)
Portable context creates a better market.
For Tool Builders
If you're building AI tools, consider:
Export by default: Make context exportable from day one.
Standard formats: Use existing standards or help create them.
Interoperability: Can users bring context from other tools?
Local options: Can users keep context on their own machines?
Tools that respect user context ownership will earn trust—and users.
The Next 5 Years
Here's where we stand — and where we're headed:
2026 (now): MCP (Model Context Protocol) emerges as a cross-tool standard. RL4 ships MCP integration supporting Cursor, Claude Code, Codex, and Gemini CLI. Portable context is real.
2027: Lock-in complaints grow. MCP adoption accelerates as more tools support it.
2028: First formal interoperability standards proposed beyond MCP.
2029: Major vendor adopts open context format.
2030: Portable context becomes expected, not exceptional.
The developers who build portability habits now will navigate this transition smoothly.
Start Being Portable
Don't wait for the industry to solve portable AI memory. Take control of your AI context future today. Start by understanding context lossunderstanding context loss/cursor/blog/cursor-context-loss-killing-productivity and learning to switch LLMs seamlesslylearning to switch LLMs seamlessly/cursor/blog/switch-llm-without-losing-context.
**Try RL4 Snapshot**Try RL4 Snapshot/cursor/form — portable, local-first cross-platform AI context that works with any LLM. Your context, your control. True AI memory persistence.
The future of AI memory should be one you own.