Why AI feels generic: Replit CEO on slop, toys, and the missing ingredient of taste

Right now in the AI world, there are a lot of percolating ideas and experimentation. But as far as Replit CEO Amjad Masad is concerned, they're just "toys": unreliable, marginally effective, and generic. 

“There's a lot of sameness out there,” Masad explains in a new VB Beyond the Pilot podcast. “Everything kind of looks the same, all the images, all the code, everything.”

This "slop," as it’s come to be known, is not only the result of lazy one-shot prompting, but a lack of individual flavor. 

“The way to overcome slop is for the platform to expend more effort and for the developers of the platform to imbue the agent with taste,” Masad says.

How Replit overcomes being generic

Replit tackles the slop problem through a mix of specialized prompting, classification features built into its design systems, and proprietary RAG techniques. The team also isn’t hesitant to use more tokens; this results in higher-quality inputs, Masad notes. 

Ongoing testing is also critical. After the first generation of an app, Masad’s team kicks the result off to a testing agent, which analyzes all its features, then reports back to a coding agent about what worked (and didn’t). “If you introduce testing in the loop, you can give the model feedback and have the model reflect on its work,” Masad says. 

Pitting models against one another is another of Replit's strategies: Testing agents may be built on one LLM, coding agents on another. This capitalizes on their different knowledge distributions. “That way the product you're giving to the customer is high effort and less sloppy,” Masad says. “You generate more variety.” 

Ultimately, he describes a “push and pull” between what the model can actually do and what teams need to build on top of it to add value. Also, “if you wanna move fast and you wanna ship things, you need to throw away a lot of code,” he says. 

Why vibe coding is the future 

There’s still a lot of frustration around AI because, Masad acknowledges, it isn’t living up to the intense hype. Chatbots are well-established but they offer a “marginal improvement” in workflows. 

Vibe coding is beginning to take off partly because it's the best way for companies to adopt AI in an impactful way, he notes. It can “make everyone in the enterprise the software engineer,” he says, allowing employees to solve problems and improve efficiency through automation, thus requiring less reliance on traditional SaaS tools. 

“I would say that the population of professional developers who studied computer science and trained as developers will shrink over time,” Masad says. On the flip side, the population of vibe coders who can solve problems with software and agents will grow “tremendously” over time. 

In the end, enterprises must fundamentally change how they think about software; traditional roadmaps are no longer relevant, Masad says. Because AI capabilities are evolving so dramatically, builders can only “roughly” estimate what things might look like months or even weeks into the future. 

Reflecting this reality, Replit’s team remains agile and isn’t hesitant to “drop everything” when a new model comes out to perform evals. “It'll ebb and flow,” Masad contends. “You need to be very zen about it and not have an ego about it.” 

Listen to the full podcast to hear about: 

• The “squishy” divide in AI intelligence that impedes specialization;

• The cathedral versus bazaar debate in open source — and why a “cathedral made of bazaars” may be the best path to collective innovation;

• How Replit “forks” the development environment to create isolated sandboxes for experimentation; 

• The importance of context compression; 

• What really defines AI agents: They don’t just retrieve information; they work autonomously, repeatedly, without human intervention.  

Subscribe to Beyond the Pilot on Apple Podcasts, Spotify and YouTube.