The Rise of Vibe Coding and What It Means for Developer Creativity

Spend enough time writing code and you start to develop a sixth sense, not just for logic or syntax, but for rhythm. There’s a creative cadence to software development that, when you’re in it, feels a lot like flow. These days, that rhythm is changing.

We’re building artificial intelligence for healthcare at my company, Corti. But like many modern software companies, we now rely on AI ourselves to help build faster and better. One part of that is building quick prototypes, for which we usually throw away the code afterwards. We don’t have strict quality requirements for prototypes and we often tend to use AI models to do a lot of the coding for them. The term for this growing trend is “vibe coding,” and while it may sound like Silicon Valley jargon, it reflects something real: the way generative AI is transforming how developers create.

Done right, it’s a tool that removes the friction between a good idea and a working prototype. And I believe it’s about to unlock a new era of developer creativity.

From Blank Page to Prototype

The traditional process of building software is layered: product specifications, wireframes, technical architecture, development, QA. While that structure is important, it’s also slow and limited to professionals with the education and expertise to work on software projects. One of the biggest bottlenecks is getting from idea to a tangible version you can test and improve.

Imagine a nurse on a busy hospital floor who observes a way to optimize patient care. They have a brilliant idea for a digital tool to streamline communication or monitor vital signs. But traditionally, they might face hurdles: no development budget, limited coding skills, and long lead times.

Coding copilots are proliferating (MeshCube/Shutterstock)

Vibe coding changes that. Anyone from nurse to developer can describe what they want–“build a dashboard that lets nurses track patient vitals in real time”–and within minutes, an AI coding assistant can scaffold the initial structure. They can then share this prototype with colleagues to gather feedback and validate their idea, without needing to invest significant resources upfront.

This isn’t just theoretical. Our own engineers and product managers often use “vibe coding” to explore new ideas quickly. Instead of lengthy planning phases, someone can express an idea and use AI tools to put together a prototype. This accelerates innovation and allows for rapid experimentation. It’s democratizing creativity inside engineering and product teams.

The Role of the Developer Is Evolving, Not Disappearing

There’s a familiar fear that comes with automation: Will this replace us?

It’s a reasonable question. When AI can write functioning code, what happens to the developer?

I believe that their importance only grows. Tools like vibe coding make it easier to kickstart a project, but they don’t eliminate complexity. The grunt work may disappear, but not the craftsmanship. For example, when handling critical medical data and decisions, precision isn’t negotiable. While AI can generate the initial code structure, it’s the human who ensures the solution aligns with real-world needs and ethical considerations. Developers don’t just accept the code AI gives them–they improve it, challenge it, and adapt it to the unique edge cases of healthcare.

What we’re seeing is not job displacement, but job refocusing. Less time on scaffolding. More time on logic, ethics, security, performance, and the actual problem being solved.

Experimentation Fuels Innovation

How will vibe coding impact developers?

One of the biggest surprises in adopting vibe coding has been the shift in team culture. When it becomes easier to create a prototype, people are more likely to try out their ideas. We’ve seen team members, even those without deep technical backgrounds, quickly build and demonstrate full demos. Not every idea sticks, but when experimentation is cheap, teams can build more.

That’s not a small thing. In many engineering cultures, especially those focused on safety and scale, it’s easy to become risk-averse. But creativity requires experimentation. If developers have to justify every experiment with a business case and a roadmap, most creative ideas won’t make it off the whiteboard.

With vibe coding, the cost of trying something new has plummeted. And when creative confidence rises, innovation follows.

What Vibe Coding Can’t Do

None of this means AI is ready to replace the developer. Vibe coding is great at pattern replication. But it has no real understanding of human need, no judgment, no empathy. It won’t tell you whether your product actually solves a real-world problem–or whether it’s the right problem to solve in the first place.

In healthcare, where we are building tools to assist critical decision-making, human expertise is indispensable. “Vibe coding” is a tool that speeds up development, but it does not replace the need for deep domain knowledge, ethics, and responsible engineering.

That’s where society is headed: toward a future where the tools get faster and the decisions get harder. Developers won’t just be writing code–they’ll be shaping systems that interface with real human lives. And important stakeholders, like nurses, can contribute with their own ideas and even prototypes. That’s not less work. It’s more meaningful work.

A Creative Renaissance, Not a Disruption

It’s easy to look at vibe coding and see disruption. It should be seen as potential, especially for smaller teams and startups. Tech giants were built in an era when software was hard and capital was cheap. Today, we’re entering the inverse: software is getting easier to build, and capital is getting tighter.

In that world, the best ideas will win, not just the best-funded ones. The teams that embrace tools like vibe coding will be able to move faster, test more, and take bigger creative swings.

That’s the kind of future we’re building–one where the barrier to turning an idea into reality is lower than ever, and where engineers are seen not just as implementers, but as creative leaders.

About the author: Lars Maaløe is co-founder and CTO of Corti. Lars holds a MS and PhD in Machine Learning from the Technical University of Denmark. He was awarded PhD of the year by the department for Applied Mathematics and Computer Science and has published at top machine learning venues: ICML, NeurIPS etc. His primary research domain is in semi-supervised and unsupervised machine learning. In the past, Lars has worked with companies such as Issuu and Apple.