A multi-model future is inevitable because using multiple models together produces more creative ideas than any single model, and companies are always incentivized to experiment with the ecosystem's new models to improve productivity and reduce costs.
Alex Atallah argues that consolidation on one model makes no sense — creativity is non-verifiable and using models trained on different data yields more novel ideas. Even when companies build proprietary models, the ecosystem constantly generates new data and models that incentivize cross-model experimentation. ✦ AI generated
Alex Atallah · 20VC · 2026-08-10 · original ↗
starts at this moment · 10:55
“In a world of every company having specialized models that's really tuned to them and their preferences, is that good for an open router business or not?”
I think our mission from the very beginning has been to increase neurodiversity in AI for the whole ecosystem. And we really believe that like a multi-model future is inevitable. And when you start, let's say like let's say there's one model that like, you know, hypothetically, let's say you're right. Let's say there's one model that fulfills all of your desires, um either within your company or like as a consumer. Um and every you know more and more people start using that model. And then um someone decides, you know what? I'm going to like create a neurodivergent model. I'm going to create a model that's like a little bit different that like talks a little differently, that has ideas that the first model like could never have come up with cuz it's like completely different data um that's being used to train it. Then it kind of creates inevitable demand to use both models. Like creativity is not a um it's not verifiable. There's there's like you can't really put an easy number on on creative ideas. And when you use two models together, um you're more likely to get creative ideas than if you just use one. It's it's just a fact if if that other model was trained in different way on a different data set, like or has like made a big update. So, um consolidation on one just seems like it just doesn't make any sense to me.
verbatim transcript · starts at 10:55
10:55>> Oh, definitely. I mean, our goal >> Why because you'd stick on one model which is yours, proprietary, trained on yours, and not be open to the diaspora of models that is available. >> I No, I disagree. I I think our mission from the very beginning has been to increase neurodiversity in AI for the whole ecosystem. And we really believe that like a multi-model future is inevitable.
11:22And when you start, let's say like let's say there's one model that like, you know, hypothetically, let's say you're right. Let's say there's one model that fulfills all of your desires, um either within your company or like as a consumer. Um and every you know more and more people start using that model. And then um someone decides, you know what? I'm going to like create a neurodivergent
11:45model. I'm going to create a model that's like a little bit different that like talks a little differently, that has ideas that the first model like could never have come up with cuz it's like completely different data um that's being used to train it. >> [snorts] >> Then it kind of creates inevitable demand to use both models. Like creativity is not a um it's not verifiable. There's there's like you can't really
12:11put an easy number on on creative ideas. And when you use two models together, um you're more likely to get creative ideas than if you just use one. It's it's just a fact if if that other model was trained in different way on a different data set, like or has like made a big update. So, um consolidation on one just seems like it just doesn't make any sense to me.
12:35>> Totally get you. So, you will have companies which have like a core workflow or their core which is their own specialized model, and then they'll use a plethora of other models and they'll use OpenRouter for those other model selection. >> Yes. And and I think that when companies make like to get back to your question, when they make their own you know their own model trained on their own data,
12:56um like you are the [clears throat] ecosystem around you is all doing the same thing. You have to like play out the game theory for these things a little bit. Like if everybody is doing this as well and create and all the model apps are creating new models constantly using new data that they've acquired, that they've bought from other companies, that's all like potentially data that's valuable to you. What is in
- ·Creativity is non-verifiable — you can't put a number on novel ideas
- ·Different training data yields ideas one model could never produce
- ·Two models together beat one for creative output
- ·Companies always incentivized to try new ecosystem models
- ·Neurodivergent models trained on different data create inevitable demand
- ·New data and models constantly emerge in the ecosystem
- ·Cross-model experimentation drives productivity gains and cost reduction