Avishai Abrahami
Analyst · Citizens
All right. Those are 3 different questions. The first one is Base1, will it improve in parallel with open source model. So I want to be saying, first of all, the answer is yes, of course. However, I don't think that, that is the most significant part of the improvement, right? For most of the applications that you want to build today, Kimi K3, GLM 5.2, 5.4, I think, and Claude, or 5 -- Opus 5, or all the latest codecs, good enough for most applications, right? Because most of us don't do those complicated things that require that extra few percent in model intelligence, right? So for most of the applications, the improvements that we need to make on Base1 are to build those applications better, okay? And it's a different thing. It's not about how we make it more intelligence in Hungarian poetry, right? Which is one of the things that on any other of those things on molecular biology, right, all those things that those models are trying to push the envelope. A lot of what we need to do is how we make applications that people prompt. And a lot of the time, those are not professional developers, they don't prompt them in a professional way, right? They're locking a lot of the definitions. They don't put all the information that you need. How do you make those base make Base1 better at solving those specific problems. And so yes, we will benefit from continuation of development in the open source environment, absolutely. But the vast majority of the value will come from what we do and not from improvement in the open source models. How does Base1 improved impact conversion and retention. I believe that was your second question. Well, obviously, if you come to Wix to BASE44 and you prompt something and you get a bad application, you're going to convert a lot going to a lot less than if you went to BASE44, the same prompt and get a better application. Even if you don't get a perfect application, but in a place that you can now feel confident that if you start working and continuously to prompt, you get to what you want, that's already a dramatic improvement in your chances to convert. And we see that. We measure that. We have now a year of consistent measurements on that. We know that, that's the case. So by improving Base1, I'm confident we will continuously improve BASE44 conversion. which is why I'm so excited about it. The other side of it, of course, is exactly the same for retention. What is the competitive moat for best one and open source broadly based on tier models? That's a very long question. I think that is beyond the scope of this conversation, I'll just say my two cents. Currently, everybody is using exactly the same algorithm, okay? Yes, we do some engineering modification on top of it, and -- but that's pretty much it. So the moat is not huge, okay, for frontier models. However, they've proven in the past that they have the ability to innovate even within the same algorithm, which is attention-based transformers, right? That's the algorithm and create new things. So if they continue to do that, they'll have some kind of a moat. There is another side to that is that there is a level where it doesn't matter or legal action or government action will start preventing the evolution and release of new models. We see it with [indiscernible], right, on security, cybersecurity issues. So I think that between -- and the fact that for a lot of the clients, the current model are already intelligent enough. So if you're a commercial model, a continuous commercial model, but for the task that your customers need that extra intelligence that you've added are not significant, okay? It's very hard to justify prices. In other words, it's a very interesting conversation. I think beyond the conversation of our discussion, but I think it's extremely important for the Western world to have really strong frontier models in the Western world. And I really hope that those guys at OpenAI and Anthropic and Google will continue to innovate and drive fantastic products.