An emerging trend called “Vibe encoding” is changing the way software is built. Instead of working on writing the code itself, developers are now directing AI assistants (such as Copilot or Chatgpt) with plain instructions, AI generates frameworks. The barrier to entry is greatly reduced: only people with a roughly and minimal technical background can rotate the working prototype.
The capital market has noticed it. Over the past year, several AI tool startups have raised nine-digit rounds and reached a billion-dollar valuation. Swedish startups are cute Received $200 million in funding In July (about eight months to be launched), it is worth nearly $2 billion. Anysphere, the manufacturer of cursor, is approaching Valuation of $10 billion. Analysts predict that by 2031, the AI programming market may be WOrth $24 billion. Given the speed of adoption, it may get there faster.
The pitch is simple: if the prompt can replace the boilerplate, it makes the software cheaper, faster, and easier to access. Whether the market ends up reaching tens of billions of dollars has changed the way they operate, which is more than whether the market ends up reaching tens of thousands of dollars. It was a breakthrough moment for many, and software writing became as simple and straightforward as sending text messages. The most compelling hope is democratization: Anyone with ideas, regardless of technical expertise, can bring it to life.
Where the wheels fall off
Vibe encoding sounds great, but despite this, the risks it poses may slow down future innovations. Consider safety. In 2024, AI produces more than 256 billion lines of code. This year, that number may double. Such speed makes thorough code review difficult. Snippets that slide without careful supervision can contain serious vulnerabilities, from outdated encryption default settings to overly allowed CORS rules. In a healthcare or financial industry where data is highly sensitive, the consequences can be far-reaching.
Scalability is another challenge. AI can make working prototypes, but scaling them to real-world usage is another story. Without careful design choices, around state management, retry, backpressure or monitoring, these systems can become vulnerable, fragile and difficult to maintain. These are construction decisions that the model cannot make on its own.
Then there is the problem of hallucination. Anyone who has used AI encoding tools before has encountered it A non-existent library that references data Or configuration flags are renamed inconsistently in the same file. While small mistakes in small projects may not be significant, these mistakes erode continuity and undermine trust when scaling across larger mission-critical systems.
Productivity trade-offs
None of these issues should be mistaken for rejecting atmosphere coding. There is no denying that AI-powered tools can meaningfully increase productivity. But they also changed what programmers need to have: from line-by-line creation to coaching, shaping and reviewing what AI produces to ensure it works in the real world.
The future of software development is unlikely to be structured as a binary choice between humans and machines. The most resilient organizations will combine AI with intentional practices, including security audits, testing and architectural design, to ensure that the code survives outside the demonstration phase.
Currently, only a small part of the global population writes to software. If AI tools continue to lower barriers, that number may increase significantly. A bigger creator is an encouraging prospect, but it also expands the surface area of the error and increases the stakes for responsibility and supervision.
What will happen next
It is obvious that Vibe encoding should be the beginning of development, not the purpose. To get there, a new infrastructure is needed: advanced audit tools, security scanners, and a testing framework designed for AI-generated code only. In many ways, such safeguards and emerging industries that support systems will be as important as code generation tools themselves.
The dialogue must now be expanded. It is no longer enough to celebrate what AI can do. The focus should also be on how to use these tools responsibly. For developers, this means caution and review. For non-technical users, this means working with engineers who can provide judgment and discipline. Vibe encoding promises are real: faster software, lower barriers, wider engagement. But without careful design and accountability, it promises to collapse at its own pace.
