The AI world loves labels. If you listen to enough podcasts or read some LinkedIn posts, you’ll hear people passionately debating closed models, open models, and, increasingly, something that might best be described as “Open-ish”.
Confused? Let’s use a restaurant analogy.
A “Closed” model is like dining at an exclusive restaurant where the chef refuses to share the recipe. The meal is fantastic, the service is polished, and you leave happy. But if you ask how the sauce was made, you’re politely shown the door. You can enjoy the food, but you can’t peek into the kitchen or start your own branch.
An “Open” model is the opposite. Imagine a generous chef who not only serves the meal but also hands you the recipe, lets you into the kitchen, and says, “Go ahead, improve it if you like.” You can tweak the ingredients, experiment with new flavours, or even open your own restaurant. That’s the spirit of open source: transparency, collaboration and the freedom to build on what already exists.
Then there’s the increasingly popular middle ground: “Open-ish”.
This is the restaurant that proudly displays its kitchen through a giant glass window. You can watch the chefs at work, perhaps even buy the recipe book, but you’re not allowed behind the counter. Or maybe you can use the recipe, but only if you’re not planning to open a competing restaurant. It feels open, and in many ways it is, but there are strings attached.
Many modern AI models live in this category. “Open-ish” is an informal umbrella term, whereas “open-weights” is a specific technical category. Their creators may release the model weights, allowing people to run them locally, but restrict commercial use. Others publish research papers but not the training data. Some open almost everything except the secret ingredient that made the dish famous in the first place.
The reason people say “Open-ish” is that many “open” AI models aren’t fully open in the traditional open-source sense.
For example, a company might:
- ✅ Release the model weights.
- ✅ Allow you to run the model locally.
- ❌ Keep the training data secret.
- ❌ Not release the full training pipeline.
- ❌ Restrict commercial use through the licence.
That’s an open-weights model, but many open-source advocates would argue it isn’t fully open. Hence the nickname, “Open-ish.”
Which is the Right Model?
None of these approaches is inherently right or wrong. Closed models often deliver polished products, invest heavily in safety and can fund expensive research.
Open models fuel innovation, education and an astonishing amount of community-driven progress.
Open-ish models try to strike a balance between encouraging adoption and protecting business interests.
Perhaps the real lesson is that “open” isn’t a simple on/off switch anymore. It’s more like a dimmer control with dozens of settings. The AI community sometimes argues as though a model is either completely open or completely closed, when the reality is much messier.
So the next time someone proudly announces that their model is “open,” it’s worth asking a gentle follow-up: Open in what way? The code? The weights? The data? The licence? The answer is often more interesting than the label itself.
And just as every chef guards “some” secret, even if it’s only where they buy the tomatoes, every AI model has its own definition of openness. The trick is knowing which doors are actually unlocked.