If you read one Matrix, read this one

Dr. Ryan Ries here. Today, I want to discuss something that's been bothering me about the direction AI is taking.

The Memory Problem

Simon Willison, a respected AI researcher, recently shared a discovery about ChatGPT's new memory feature. When he asked ChatGPT to put his dog in a pelican costume, the AI unexpectedly added a "Half Moon Bay" sign to the image. 

You might be thinking, “Why does that matter?”

It matters because ChatGPT had been quietly building a detailed dossier about him from all their previous conversations.

The AI "remembered" from previous chats that he lived in Half Moon Bay and decided to helpfully add local context (without being asked).

When Simon dug deeper, he discovered ChatGPT had compiled an extraordinarily detailed profile including his technical interests, his fondness for pelicans (!), his cooking experiments, even metadata about his device usage and conversation patterns. The AI knew he was "an avid birdwatcher with a particular fondness for pelicans" and that he "frequently cross-validates information, particularly in research-heavy topics."

Simon realized that he had no control over what was being remembered or how it influenced future interactions.

Side note: if you’re curious what AI remembers about you, here’s the prompt Simon shared in his article on how he figured out what all it remembered:

Why This Matters for Your Organization

This memory situation goes beyond privacy (though that's important). This is about something much more fundamental: memory corruption in AI systems.

When you're building AI agents or implementing AI workflows, memory is a core component that directly affects learning and decision-making. Bad memory leads to corrupted learning.

If your AI agent remembers irrelevant or misleading information from past interactions, that "knowledge" gets baked into future decisions. 

In Simon’s case, his pelican obsession started influencing his serious technical requests. In a business context, this could mean:

• Customer service agents who apply personal quirks to professional interactions
• Decision-making systems that factor in irrelevant historical context
• AI assistants that develop biases based on casual conversations mixed with critical business data

The Agent Memory Challenge

As we build more sophisticated AI agents, memory becomes even more critical. Agents aren't just responding to single prompts or working in a silo. 

They're maintaining context across multiple interactions, learning from experience, and making decisions based on accumulated knowledge.

But here's what we don't know yet: How does corrupted memory affect agent performance over time?

If your agent learns the wrong lessons from bad memory, those errors compound. Unlike humans, who can consciously separate casual conversation from a professional context, current AI systems struggle with this distinction.

Designing Better Memory Systems

So, what's the solution? We need to think deliberately about memory architecture from the ground up:

1. Scoped Memory Systems

Instead of one giant memory bank, design a compartmentalized memory that serves specific functions. Keep casual interactions separate from business-critical data.

2. Memory Quality Control

Implement systems to evaluate what's worth remembering. Not every interaction needs to become part of the permanent record.

3. User Control

Give users explicit control over what gets remembered and how it influences future interactions. Simon's frustration wasn't just about the pelican costume – it was about losing control of the context.

4. Learning Boundaries

Establish clear boundaries between different types of learning. Personal preferences shouldn't influence technical analysis, and casual experimentation shouldn't corrupt professional workflows.

My Thoughts

I'm excited about AI agents and their potential, but I'm also concerned about rushing into complex memory systems without thinking through the implications. 

When you're designing your next AI system, ask yourself:

• What should this system remember?
• How will that memory influence future decisions?
• What control do users have over their memory profile?
• How do you prevent memory corruption from degrading performance?

Let me know if you want to chat more about this. I enjoy hearing your experiences and talking through your challenges with AI. 

Until next time,
Ryan

Now, time for our AI-generated image and the prompt I used to create it.

Create a picture of a muppet in the matrix. He is deciding between the red pill or the blue pill.