Why Vibe Coding Breaks in the Real World
The hype peaked in early 2025. By mid-year, traffic to major vibe coding platforms had dropped over 50 percent. People tried, hit the wall, and moved on.
The wall is predictable. A generated app looks complete to anyone who is not an engineer. The interface renders. Buttons work. Forms submit data. It feels done.
Under the surface, the foundations are missing. Authentication that lives on the client side. APIs with no rate limiting or error handling. No logging, no tests, no deployment automation. Code that was produced quickly but structured poorly, making future changes expensive.
These are not edge cases. They are the baseline requirements of any system that serves real users.
But the technical gaps are only half the story. The workflow is fundamentally broken.
AI-generated code still needs to follow engineering process. It needs to live on a branch. It needs to go through code review. It needs to be merged deliberately, not pushed straight to production from a chat window. The tools that skip these steps are not accelerating development. They are creating liability.
Software that lasts is built on fundamentals. Any tool that bypasses them is building on sand.
Where the Value Actually Lives
Every major AI coding tool launched in the past two years has made the same bet: help people create new software from nothing. A blank canvas. A fresh repository. An empty prompt. The entire category is optimized for the moment before a product exists.
But that moment is a tiny fraction of a product's life. The vast majority of software work happens after launch. New features on existing architectures. Bug fixes across interconnected systems. Design updates that need to respect years of accumulated logic. Performance improvements on codebases that serve real traffic.
This is the 1 to 100. It is where most engineering time goes, where most business value is created, and where most teams are struggling to move faster. Yet the entire vibe coding market is fighting over the 0 to 1.
Code Is Free. Software Is Still Expensive.
Something fundamental shifted, AI can generate working code in seconds now. The raw material of software is no longer scarce.
But companies are still bottlenecked. Product teams still wait weeks to see their ideas in production, and backlogs compound. Engineering capacity is still the constraint for every business that runs on software.
The paradox is obvious once you see it: code became free, but software stayed expensive, because getting the right people aligned, moving fast, and deploying with confidence is still broken.
That is the $465 billion problem sitting inside the SaaS market today, and the tools claiming to solve it are solving the wrong half of it.
The Real Problem Nobody Is Solving
And there is another dimension to the bottleneck that the market keeps ignoring. Every company with an existing product has it. Non-technical team members like PMs, designers, and marketers generate ideas constantly. But they have no way to act on them. They write tickets. They wait. The backlog compounds. Changes that could ship in a day sit untouched for months.
Meanwhile, a new generation of AI coding tools promised to unlock software creation for everyone. Lovable, v0, Bolt.new, Replit. They delivered on that promise, but only for greenfield projects. Only for prototypes. Only for the 0 to 1.
These tools are genuinely useful for exploration and validation. Describe what you want, and AI generates a working interface in minutes. That speed matters when the goal is to learn fast.
But here is what none of them can do: work on your existing product.
Most software value lives in codebases that already exist. Products with real users, real revenue, and real complexity. Different stacks, different configurations, different deployment pipelines. You cannot spin up a generic environment and expect it to work against a production system.
That is the hard problem. And the market is ignoring it.
Background Agents Are Broken
Background agents sound good in theory. Assign a task, let it run, come back to a finished pull request. The problem is what happens next.
If you cannot QA a change, the pull request is useless. If you do not have a preview, you cannot QA.
Review apps were supposed to solve this. In practice, they do not. The back and forth with a Vercel preview is painful. Worse, depending on your stack, you only get a frontend preview. No backend. No database. No real environment. You can only test surface-level UI changes.
A New Product Category
Look at what a modern engineering team actually runs. GitHub for version control. Vercel for deployment. Sentry for error tracking. PostHog for product analytics. Linear for project management. Claude Code and Codex for code generation. CodeRabbit for code review.
Each tool owns a clear step in the lifecycle. Source control. Deployment. Observability. Analytics. Planning. Generation. Review. The stack is mature, it works, and every layer has a winner emerging.
But every single one of those tools was built for developers. The PM who wrote the spec cannot use them. The designer who created the interface cannot use them. The marketer who needs a landing page update cannot use them. They all end up in the same place: writing a ticket in Linear and waiting.
That is not a tooling gap. It is an entire product category that does not exist yet.
None of these tools let a non-technical contributor push a change to an existing product. Not a prototype. Not a throwaway app. The production codebase your team ships from every day, through the same branches, the same reviews, and the same engineering standards.
LLMs Will Be a Commodity
The raw material of software is commoditizing in front of us. Every major lab is converging on the same capabilities. The gap between the best closed model and the best open one shrinks every quarter. And the moment research hits a plateau (and plateaus are normal in AI, we have seen many summers and winters), a new era begins. Optimization, distillation, smaller models running on cheaper hardware. Token generation becomes background noise in the cost structure of a product.
When that happens, value does not stay at the model layer. It moves up, captured by whoever bet on the right product direction and the right user experience. Cursor did not win because it had a better LLM. It won because the interaction felt native to how developers already work. The model was a commodity. The UX was not.
For the next generation of products, stop pretending the model is the moat. Assume the LLM layer is a commodity. Assume anyone can access frontier capabilities. Then ask the harder question: what does the product look like on top of that?
Autoregressive transformers trained on next-token prediction are hallucination machines. Different input, different output. No amount of scale has eliminated them, and no credible research path suggests they will disappear soon. If you are building on the assumption that models will one day be 100 percent reliable, you are building on a timeline that does not exist.
That is why the workflow matters more than the model. That is why code still needs to live on a branch, go through review, and ship through the same process engineers already trust. Because the technology underneath is unreliable by design, and the only thing that makes it safe in production is a human in the loop at the right moment.
QA Is the Ultimate Bottleneck
Code generation is now essentially free. That pushes the constraint to the next step: code review. For most teams today, review is already the chokepoint.
But that will not last. Tools like CodeRabbit are already surprisingly good at automated review. AI will get there. Maybe not 100 percent coverage, but a reliable 80 percent. Good enough to stop being the constraint.
So what remains? QA.
Among all the AI capabilities being developed, browser and computer use is by far the most broken. Automated testing that actually works like a human tester is still years away from reliable.
And even when automated web testing improves, there is a deeper truth: nobody will merge pull requests that were both generated and tested entirely by agents. Not for production systems. Not when real users are on the other side.
Quality requires a human in the loop. The question is where that human adds the most value. Writing code is no longer it. Reviewing code is fading. Testing and validating that changes actually work. That is where human judgment will remain essential.
The Vision: We Will ALL Be Builders
Developers and designers are still working in silos. Not because the technology is not good enough. Because the tools are built that way. Every tool is designed for one or the other. Figma for designers. Cursor for developers. Nobody is building for product teams that want to contribute to the same codebase.
That is the gap we are filling.
We are not replacing developers. We are not skipping pull requests. Code is still the source of truth. Engineers still review everything. But now designers, PMs, marketers, and business teams can contribute too. Same codebase. Same workflow. No silos.
We will ALL be builders. Builders with different skill sets, but at the end of the day, all shipping on the same codebase. The company that unlocks collaboration for builders on existing codebases will win. We intend to be that company.
That is the future we are building.
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