Base44’s CEO Maor Shlomo says his company trained and released its own large language model, Base 1, to stop vibe-coded products from producing the same UI everyone else is shipping. His core argument is blunt: when teams rely on general frontier models, “everybody feels like they're getting the same UI when they're coding with the general models.” In other words, the problem is not whether AI can make a site. It is whether it makes your site look like it came from a conveyor belt.

Base 1 is already in the workflow. Before users prompt the platform to build an app, they can select Base 1 from a selection of AI models, alongside models from Claude (Opus 4.8), Fable (5), and OpenAI (GPT-5.5). Shlomo tells Business Insider he hopes Base 1 will create interfaces that are “uniquely different” each time, though he also says it is “not yet there.” That “not yet” matters, because it frames Base44’s pitch as an ongoing quality arms race, not a one-time feature drop.

If you are a product leader or investor looking at this space, the more interesting part is why Base44 thinks it can outperform frontier models in a narrow area. Shlomo’s explanation is that frontier models must be generically good at everything, from poetry to coding. That broad training goal means they are pressured to land on safe, broadly appealing patterns. Base44’s wager is the opposite strategy: take a model and “squeeze its ability to be really, really good at one use case,” which Shlomo argues gives it a shot at producing UI that does not read like everybody else’s.

The startup is also explicit about what “better” looks like. Base44 wants to help users ditch the cookie-cutter look of AI-coded websites and apps, a critique that has been repeated by UI/UX observers as these tools spread. Shlomo points to the telltale generic look that many vibe-coded products have, and that matches a broader design conversation in the market. Paul Bakaus, CEO of AI design startup Impeccable, previously told Andreessen Horowitz in a June interview that common signs of AI-coded products include beige or tinted backgrounds and sans-serif fonts. Bakaus described it as an “algorithmic Uniqlo or Ikea” look, meaning not necessarily ugly, but not uniquely yours.

So what does Base44 do differently once it has Base 1? It plans to run reinforcement learning on the new model. Shlomo says this involves prompting Base 1 to keep generating designs that look new and unique. Conceptually, that is the key lever for the “same UI” problem. If a model is evaluated only on whether it can generate working code and acceptable layouts, it can converge on the most common solutions. If a model is pushed to differentiate, you can start steering it toward novelty and away from default aesthetics.

For context, Base44 is not some scrappy standalone experiment anymore. The San Francisco-based vibe coding startup was acquired by Wix last June for $80 million, and it is now part of Wix’s website-building arsenal. That acquisition changes the incentives. Wix has a large team of designers, which generates a lot of data for its model to train on, and that data advantage can accelerate improvement loops. In other words, Base44’s “we built our own model” strategy is not just about taste. It is about building a distribution-ready system inside a platform that already serves millions of websites, with internal feedback and training material.

At the same time, Base44 is moving into a crowded battlefield. Startups like Lovable, Replit, and Cursor compete with Base44, each aiming to make coding and building feel faster and more guided. In that world, model choice becomes a product decision, not just an engineering detail. Base44’s UI model selection menu, where Base 1 sits next to Claude’s Opus 4.8, Fable 5, and OpenAI’s GPT-5.5, is effectively saying: “Even if you can prompt a frontier model, you might still prefer ours for distinct output.” It is a bet on perceived quality and differentiation as the next switching cost.

There is also a second-order implication for boards and exec teams watching AI product adoption. When customers start to recognize the “default AI look,” they stop caring solely about how quickly something can be generated and start caring about ownership of the result. Base44’s framing makes that explicit: it believes generating unique designs will set it apart from competitors. If it succeeds, the competition will likely respond by investing in narrower training, better evaluation metrics, and tighter design constraints. If it does not, the market lesson is harsher: scale without specialization may still produce sameness.

Right now, Shlomo’s own caveat that Base 1 is “not yet there” tells you how early this quality race is. But the direction is clear. Base44 is trying to turn model training into a competitive moat for UX distinctiveness, leveraging Wix’s $80 million acquisition runway and internal designer data, then iterating with reinforcement learning. For decision-makers, the question is whether your roadmap should treat “unique output” as a measurable KPI, and whether your model strategy should prioritize specialization over generality in the experiences users actually pay for.