No-code platforms changed the game. They allowed non-developers to build apps, automate workflows, and bring digital ideas to life without ever touching a line of code. But as generative AI continues to reshape how software is made, the no-code space finds itself at a crossroads.

Large language models (LLMs) and the rise of vibe coding, where builders use natural language to describe what they want and watch it come to life, are pushing the boundaries of what was previously possible. In this new environment, no-code tools can’t afford to remain static. They need to evolve, or risk becoming the tools people experiment with before moving on to something more powerful.

Why no-code was a breakthrough

The early promise of no-code was clear: empower more people to build software. Whether it was entrepreneurs launching MVPs, operations teams automating internal workflows, or designers creating fully functional front-ends, no-code platforms like Bubble, Webflow, and others gave users a new level of creative autonomy.

This shift was about more than convenience—it was about distribution. Engineering talent is scarce and expensive. No-code allowed teams to move faster, prototype more often, and iterate without bottlenecking on internal development resources.

But there were trade-offs. Most no-code tools abstract away complexity at the cost of flexibility. Once your app grows beyond a certain threshold—when performance, security, or integration demands increase—teams often find themselves constrained. Many no-code users ultimately rebuild their applications with custom code or migrate to more developer-friendly platforms.

This is where AI enters the picture.

Vibe coding and AI are reshaping expectations

The emergence of LLMs and tools that support vibe coding have introduced a new way of building that feels intuitive, dynamic, and fast. Instead of dragging components onto a canvas, builders now type what they want: “Create a CRM with customer records, contact history, and follow-up reminders.” With the right system, the AI does the rest.

This approach is more than a UX upgrade. It marks a shift in how we translate human intent into software logic. And in some cases, it bypasses the constraints of both traditional coding and rigid no-code interfaces. When done right, AI app builders can generate full-stack applications with data models, action flows, and security rules in place, all from a structured conversation.

But it’s still early. AI app builders face real challenges: maintaining context across iterations, capturing nuanced requirements, and building safely and securely. They don’t replace everything yet, but they do show us where things are heading.

And that has implications for the entire no-code space.

What no-code platforms need to do next

For no-code platforms to stay relevant in this new landscape, they’ll need to do more than defend what they’ve already built. They’ll need to adapt.

First, no-code tools should explore how to integrate structured AI support into their platforms. Prompting is powerful, but prompting alone isn’t enough. The most effective AI tools will combine natural language with purpose-built interfaces for setting conditions, defining workflows, and handling edge cases.

Second, platforms need to reimagine their approach to complexity. Instead of hiding it behind abstraction, they should look for ways to manage it transparently by giving users better ways to define requirements, structure data, and maintain logic across updates. This is where AI can shine if guided by the right frameworks.

Finally, and most importantly, no-code founders and teams must avoid the temptation to punch down. Innovation doesn’t happen in silos. It happens when people build on top of each other’s ideas, test the limits, and share what they’ve learned. Public squabbles over traffic metrics or definitions of “production-ready” distract from the real opportunity: making software development more accessible and more powerful for everyone.

At Sutro, we’re building our AI app builder with this future in mind. We believe that the next wave of software creation tools must prioritize structured intent, not just interaction. That means capturing what users actually want to build and using LLMs to generate applications that are scalable, secure, and flexible from day one.

The no-code movement broke down the walls around software development. AI has the potential to build something even more inclusive and dynamic on top of that foundation. But only if we adapt and if we’re willing to work together.