How Companies Are Building Apps Just by Talking to AI

A new way of making software is moving from experimental projects into real-world use at large companies. These tools let people build working applications by describing what they want, rather than writing code by hand. Instead of typing out instructions in a programming language, users have a conversation with an AI that writes the code for them.

Several companies are backing this approach with serious money. Supernova raised $9.2 million in funding to develop what it calls a "vibe coding" platform—software that helps teams at big companies build applications faster according to the company. The platform is already used by brands like Air France, KLM, Kraft Heinz, and Paramount. My Instant AI launched something similar called CHARM, expanding its AI platform. These moves suggest that major companies see value in this way of working.

Big Employers Are Jumping In

Companies aren't just trying these tools out—they're investing heavily. Cognizant, a large consulting firm, held a vibe coding event involving more than 53,000 of its employees across 40 countries and made it into a world record. That kind of scale signals that the company sees this as a serious change to how work gets done.

Investment firms are also betting on the space. Tesonet, an investment company, put money into six AI startups focused on this approach, including Lovable, a popular tool for building apps through conversation.

The Tools Behind the Work

These platforms rely on underlying services that handle the technical details. Supabase, a company that provides the "backend" (the invisible machinery that apps need to run), now serves 5 million developers worldwide, often paired with AI coding tools. Developers using vibe coding platforms can focus on describing their ideas while these backend services handle storage, security, and other technical requirements.

Think of it like this: if building an app the traditional way is like constructing a house from scratch (you need to know about foundations, framing, electrical work), using vibe coding with backend services is more like renting and customizing a prefab home. You describe what you want, and the AI handles the technical plumbing.

How the Technology Actually Works

Vibe coding platforms use AI systems trained on thousands of real code examples, documentation, and programming best practices. When you describe what you want to build, the AI generates actual working code based on that training. The key feature is the back-and-forth: you can refine your description, the AI updates the code, and you keep going until it's right.

When companies deploy these tools in production—meaning they actually use the apps in their business—they add extra safeguards. Code review processes check quality. Security scans catch vulnerabilities. The apps connect to monitoring tools so teams know if anything goes wrong.

Learning from the Past

This isn't the first time technology promised to let non-programmers build software. In the late 1990s, tools like Dreamweaver and FrontPage said they'd let anyone make web pages without writing code. They did succeed in bringing more people into web development, but eventually proved limited for complex projects. Most teams ended up going back to writing code by hand.

The current tools have genuine advantages over those earlier attempts. Today's AI is much more capable. Cloud services are far more mature and easier to use. That suggests vibe coding might avoid the limitations that stopped visual development tools from taking over entirely. The practical difference is that modern AI can now write code that handles errors properly, keeps data secure, and runs fast enough for real business use.

What This Might Change for Teams

Vibe coding could shift how development teams spend their time. Instead of days or weeks writing code, teams might spend more time figuring out what problem they're actually trying to solve and how users will experience the solution. Implementation details move into the background.

This opens up possibilities. Companies could test new ideas much faster. They could build the small internal tools that currently aren't worth the effort to develop. They could move teams away from purely technical work toward more strategy and design.

That said, production reality remains unchanged. Apps still need proper testing, monitoring, and maintenance once they're live. The speed of building doesn't eliminate the need for software engineering discipline—it just happens at a different stage of the process.

What Comes Next

The amount of money flowing into these tools and the scale at which companies are testing them suggests this is moving past the experimental phase. Organizations that get good at describing their needs to AI—and thinking about problems in that conversational way—may find they can build and update software faster than their competitors.

The approach works best for specific scenarios: rapid prototyping, building internal company tools, and connecting different existing systems. These are exactly the kinds of projects companies are desperate to move faster on.