Every company I talk to is worried about the same thing: which model to bet on. The big one everyone posted about last week, the cheaper one, the open-source one. It's the wrong worry. The models are converging, they get cheaper every quarter, and your competitor can rent the exact same one you can. Whatever edge a model gives you today, everyone has it by next year.
The thing your competitor can't rent is your data - and the system you build around it.
Models are a commodity. Your context isn't.
A model out of the box knows the internet. It doesn't know your customers, your contracts, your five years of support tickets, or the weird exception your ops team quietly handles every Thursday. That knowledge is the actual asset. The model is just the engine; your data and your process are the thing worth owning.
Which is why the most valuable AI systems aren't the ones with the fanciest model. They're the ones plugged into the messy, proprietary reality of how your business actually runs.
This changes what you should build
If the data is the moat, a few things follow:
- Don't hand your proprietary data to a tool that treats it as their training material or their lock-in. Own the system that sits on top of it.
- Spend less energy agonizing over model choice and more on getting clean, connected access to your own information.
- Design so you can swap the model underneath without rebuilding everything - because you will, more than once.
We build systems to be model-agnostic on purpose. The model is a part you can replace; your data, your workflow, and the software around them are the parts that compound.
Betting your strategy on a specific model is betting on the one piece guaranteed to change. Bet on the pieces that are yours.
The uncomfortable question
Ask any vendor pitching you AI: where does our data live, who else can see it, and can we leave with it? If the answer is fuzzy, you're not buying a capability - you're renting one and paying with the one asset you can't get back. Own the moat. Rent the engine.