After two years of daily ChatGPT use, I recently started experimenting with Claude, Anthropic’s competing AI assistant.

Related: Microsofts see a  ‘protopian’ AI future

Claude is four to five times slower generating responses. But something emerged that matters more than speed: I discovered I had no idea what ChatGPT actually knows about me.

This isn’t a theoretical concern. Millions of professionals now use AI assistants for everything from drafting client emails to strategic analysis. These systems are rapidly becoming cognitive infrastructure for knowledge work. Yet most users have never considered a basic question: what does my AI remember about me, and who controls that knowledge?

The answer depends entirely on which system you’re using, and the difference reveals a fundamental split in how the AI industry is approaching personalization.

2 ways to remember

ChatGPT’s personalization works through what researchers call emergent learning. My thousands of prompts over two years created statistical patterns the model leverages. It knows my communication style, anticipates my workflows, adapts to my professional context. The system clearly remembers things about me. But I can’t see what it knows. I can’t audit the information. I can’t correct errors or remove outdated details.

The knowledge exists in what’s effectively a black box. OpenAI hasn’t fully disclosed how its personalization mechanisms work. Users experience the benefits but have no transparency into what information is being stored or how it’s being used.

Claude takes a different approach. The system maintains explicit, structured memory that users can view and edit. At the start of every conversation, Claude loads a text block of information about me: my work context, current projects, communication preferences, standing instructions for different types of tasks. I can see exactly what’s recorded. More importantly, I can modify it directly.

I can update Claude’s memory rather than hoping the system eventually figures it out through repeated prompting. If the AI misunderstands my workflow or makes incorrect assumptions, I have direct access to fix the record.

This transparency costs something. Claude’s approach requires more computational resources per user: nightly analysis of conversations, structured storage, loading context into every interaction. That overhead shows up in slower response times. But Anthropic made a deliberate choice to spend those resources on interpretability and user control.

The governance gap

The architectural difference matters because AI adoption is outpacing governance. Gartner projects that by 2026, more than 80 percent of enterprises will have used generative AI in production, up from less than 5 percent in 2023.

Most organizations lack policies around what employees can share with AI assistants. Few have considered what happens when these systems accumulate detailed knowledge about proprietary workflows, client relationships, strategic priorities. The systems work well enough that adoption happens first, questions about data sovereignty and control come later.

Individual users face the same dynamic. We integrate AI into critical workflows without fully understanding what’s happening under the hood. The brittleness gets masked by good enough performance. Both approaches work fine until they don’t.

Opaque personalization creates specific risks. When an AI makes decisions based on patterns users can’t see, there’s no way to correct course except through trial and error. You’re modifying your behavior to shape an invisible model, adapting your prompting to work around assumptions you can’t audit.

For professionals handling sensitive client information or working in regulated industries, this opacity compounds. What exactly has the AI learned about your clients? Your negotiating strategies? Your company’s competitive positioning? You’re trusting emergent patterns you have no visibility into.

Code-embedded corporate truths

The split between transparent and opaque personalization reflects deeper differences in how AI companies approach user agency.

In 2015, OpenAI launched as a nonprofit committed to keeping AI research open and transparent. By 2023, it had become one of the most secretive companies in the industry, as reported by Fortune Magazine, among others. The trajectory from proclaimed openness to aggressive secrecy represented a choice: make it work, make it scale, make it indispensable. Interpretability becomes negotiable.

Anthropic positions transparency as core to its AI safety mission. The ability to audit what an AI knows isn’t ancillary, it’s central to building systems where users maintain meaningful control. That philosophy costs something in processing overhead and response speed, but it’s a deliberate tradeoff.

Neither approach is inherently wrong. ChatGPT’s emergent learning creates genuinely fluid adaptation. Claude’s structured memory provides control at the expense of some spontaneity. Users will reasonably prefer one based on their priorities.

But as these systems become essential infrastructure rather than experimental tools, the transparency question gains weight. We’ve seen this pattern before in technology adoption: tools appear, they work well enough to spread, infrastructure gets built before anyone thinks through implications. By the time hard questions about agency and control surface, the architecture is locked in.

What comes next

The current moment won’t last. Right now, users can choose between systems with different transparency models. Competition creates options. But as AI assistants consolidate into a handful of dominant platforms, the architectural choices being made now will compound.

If opaque personalization becomes the standard because it scales better and performs faster, we’ll have normalized black box knowledge about millions of professionals. If transparent memory becomes standard, we’ll have accepted slower processing as the price of user control.

For business and technology leaders making decisions about AI adoption, the personalization question deserves attention alongside more obvious concerns about accuracy, security and compliance. What does the system know about your organization? Who can see that knowledge? Can you audit and modify what’s been learned?

These aren’t theoretical questions. They’re infrastructure decisions that will shape how cognitive tools function for years to come.

I’m still working out my optimal split between ChatGPT and Claude for different workflows. But the exercise clarified something important: I have more agency with the system that lets me see its memory than with the one that keeps its knowledge of me hidden, even when the hidden system performs better in some contexts.

In an adoption cycle moving this fast, that agency matters. It’s going to matter more.

I’ll keep watch, and keep reporting.

Pulitzer Prize-winning business journalist Byron V. Acohido is dedicated to fostering public awareness about how to make the Internet as private and secure as it ought to be.

(Editor’s note: I used Claude and ChatGPT to aggregate research from multiple sources, compile relevant citations, and generate initial section drafts. All interviews, analysis, fact-checking, and final writing are my own work. AI tools accelerate research and drafting, allowing deeper reporting and faster delivery without compromising editorial integrity.)

The post MY TAKE: Transparent vs. opaque — edit Claude’s personalized memory, or trust ChatGPT’s blindly? first appeared on The Last Watchdog.

*** This is a Security Bloggers Network syndicated blog from The Last Watchdog authored by bacohido. Read the original post at: https://www.lastwatchdog.com/my-take-transparent-vs-opaque-edit-claudes-personalized-memory-or-trust-chatgpts-blindly/