There was a time when hardware was everything. The processor, the storage, the power—it was all about the physical components. But over time, hardware became a commodity, and the real intelligence moved to software—the brain that made everything work.
I believe LLMs (Large Language Models) are heading in the same direction.
Today, LLMs are impressive, but above-average performance is no longer enough—everyone is doing that. Soon, just like hardware in smart buildings, LLMs will be necessary but not the differentiator. The real power will lie not in the model itself, but in how we use and move our data between models.
Imagine This: Seamless LLM Portability
What if, instead of training a new LLM from scratch every time you switch, you could export all your training, memory, and preferences from one model and load it into another?
- Think of it like exporting browser bookmarks from Chrome to Edge—simple, quick, and frictionless.
- Or how smart building automation isn’t about having the best sensors, but the best software to control them.
If someone solves this seamless transfer problem, it would be a gamechanger. Suddenly, I as the user would have full control over which LLM I want to use. It would be a free market of AI models, where I decide which one serves me best—not the other way around.
But here’s the challenge: Why would companies allow this?
The Moat Problem: Will AI Stay Closed or Go Open?
Right now, the biggest LLM companies—OpenAI, Anthropic, DeepSeek—are building moats, just like Apple did with its App Store and music ecosystem. They don’t want you to leave. They don’t want you to take your trained data elsewhere.
Why? Because data is the real moat. If I allow my users to transfer their AI memories and training to a competitor, I lose my stickiness. I lose my power. I lose my users.
So, will AI follow the Apple model (closed ecosystem) or the Android model (open, interchangeable, user-centric)?
Right now, it’s looking more like Apple—companies are keeping their data walled off, because why would they give up control?
But here’s the twist—eventually, they may not have a choice.
A Future Where AI Models Become Interchangeable?
Once the market gets saturated, and there are too many competing LLMs, the biggest differentiator will no longer be the models themselves. It will be how well they work together.
At that point, the pivot to interoperability will be inevitable. Some LLM company—or maybe an entirely new startup—could rise up as the AI platform that enables seamless training across all models.
- Think of what Android did to Apple. Instead of one locked system, they enabled an open ecosystem where users had freedom of choice.
- Think of what API-first companies did to traditional software. They created layers that made everything compatible, and suddenly, closed ecosystems lost their advantage.
Could this happen with AI? Maybe not yet—but in a few years, as competition increases, it might be the only way forward.
What About AI Agents? Are They Moving Towards Openness or Isolation?
I’ll admit—I don’t know enough about AI agents to say for sure. But I do wonder:
- Are they being developed to connect models and enable interoperability?
- Or are they reinforcing more isolation, making it even harder to move from one LLM to another?
POE (Quora’s AI marketplace) kind of does this by offering multiple models under one roof. But as far as I know, it doesn’t allow seamless data exchange between them—it just acts as a layer function that gives memory to different LLMs.
So, are we headed towards more walled gardens or an open AI economy?
Dreaming of the Wild Possibilities
Maybe I’m naïve. Maybe a smart AI engineer would think I’m nuts for even suggesting this.
But if we don’t imagine new possibilities, we will never disrupt.
The future is always built by those who dare to think differently. And if seamless, user-controlled AI memory becomes reality, hopefully, you’re the one who builds it before your competition does.
What do you think—are we moving towards LLM portability, or will the walled gardens stay? 🚀
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