# Stop Debating Which LLM to Use, That's Not Where the Lock-in Happens
*Published: 2026-04-11*
*Tags: ai, insights*
*Source: https://chrislema.com/stop-debating-which-llm-to-use*
---I hear developers debating which coding harness is better than the rest. It changes every week. And if you're working in a corporate setting, companies are trying to establish an AI policy, which drives them to the question of which solution is the right one to standardize on.

That's a mistake. First, the LLMs are getting easier and easier to swap. But there's a bigger second issue.

Nobody's talking about the thing that's actually hard to move.

Your memory.

## The Question Nobody's Asking

Think about what happens when your organization starts working with an LLM. You don't just type prompts and get answers. Over time, you build up something far more valuable than any model.

You write instructions. You encode expertise. You create workflows that reflect how your team actually thinks and operates. You feed it your processes, your frameworks, your institutional knowledge, the stuff that took you years to develop.

That's not the LLM. That's you. That's your organization's intelligence, externalized into a system.

And here's the thing most people miss: if all of that knowledge lives inside one vendor's infrastructure, you haven't adopted AI. You've adopted a landlord.

## The Vendor Playbook You're Not Seeing

I don't think most vendors are doing this maliciously. But the incentive structure is clear.

Every vendor benefits when your expertise, your memory, your encoded knowledge becomes deeply embedded in their platform. Not because they're trapping you on purpose. Because the deeper you go, the harder it is to leave. That's just how platforms work.

I've seen this pattern before. I lived through it in the WordPress ecosystem. I've watched it play out in SaaS over and over. The product itself is never the lock-in. The data is the lock-in. The integrations are the lock-in. The institutional knowledge that accumulates inside the system, that's what makes switching painful.

And right now, with AI, the same pattern is forming. Except this time, the data isn't just your customer records or your content. It's your organizational expertise. Your judgment. Your decision-making frameworks. The stuff that makes your company your company.

That's a much bigger thing to hand over to a single vendor.

## What Portable Knowledge Actually Looks Like

The other day I wanted to create a new version of our enterprise application with some new features. I went to Claude Code to build it. Three days later, the whole thing was live, working, with the new features deployed.

That application represents more than two years of work. So how did it get rebuilt in three days?

Because all our core learning, all our core data, all our algorithms were saved to external files that I could load up quickly and easily. They weren't trapped in Claude's memory. They were mine.

If that knowledge had been locked inside a single vendor's system, I'd still be rebuilding. Or worse, I'd have no choice but to stay, regardless of whether a better tool came along.

This is the difference between owning your expertise and renting access to it.

## Freedom to Switch Is the Strategy

This is why I recommend looking at external memory solutions like [RedPlanetHQ's CORE](https://www.getcore.me/), or [Mem0](https://mem0.ai/). Not because they're perfect. Because they're portable.

If I switch from Claude Code to Codex, or any other coding solution, I can integrate a third-party memory solution, and nothing slows down. The knowledge travels with me. The LLM is just the engine. My memory is the cargo, and I decide which truck carries it.

This is also why open source is so critical right now. It's easy to let the LLM provider "offer" to keep your institutional memory. It feels convenient. It feels like a feature. But the result is that you're trapped.

And it's too early to get trapped.

We're in the first real year of serious AI adoption. The models will change. The pricing will change. The capabilities will shift in ways none of us can predict. The one thing you can control is whether your organizational knowledge moves with you when you need it to.

## The Habit That Changes Everything

The good news is that solutions are showing up, cloud-based options that can also be self-hosted. But the tooling isn't even the most important part.

The most important part is being explicit about your knowledge capture.

Every time I end a session with Claude Code or Codex, I ask: "What have you learned about how I like to build products?" And I ask it to give me the answer in a format that any LLM can use.

The result is a set of files I save. Portable. Vendor-independent. Mine.

That habit, asking the LLM to externalize what it's learned in a portable format, is worth more than any tool you'll buy this year. Because it forces you to think about your knowledge as an asset you own, not a byproduct of someone else's platform.

## The Real Decision

Here's what most people get wrong about AI adoption: they think the big decision is which model to use.

It's not.

The big decision is where your organizational intelligence lives. Inside a vendor's walls, or in a system you control.

Every other AI decision flows from that one. Get it right, and you can change everything else whenever you want. Get it wrong, and two years from now you'll discover that you've built your entire AI operation on rented land.

And the rent is going up.