AI has rapidly reshaped what everyday coding looks like, and “vibe-coding” has become one of the defining ideas of this new era. The term, coined to describe writing code with heavy assistance from AI tools, has quickly moved from niche jargon to mainstream developer vocabulary. As AI becomes more deeply embedded in software development, many engineers are asking themselves a simple question: are they ahead, behind, or just keeping pace with this shift?
How developers feel about vibe-coding
In a recent survey of 167 software engineers, respondents were asked where they stand when it comes to vibe-coding with AI. The largest group – 75 engineers, or 46.9% – said they feel they are “keeping up.” They see themselves as neither early adopters nor laggards, but as professionals actively adapting to new tools while still finding their footing in a fast-changing landscape.
Beyond this middle group, 30 engineers reported feeling ahead of the curve. These developers tend to embrace AI coding tools as a core part of their workflow, experimenting aggressively and building confidence in steering AI systems. On the other side, 27 engineers said they feel behind, reflecting anxiety about the pace of change, the learning curve of new tools, and the fear of losing relevance as AI capabilities grow.
The engineers opting out of AI coding tools
Not every developer is on board with vibe-coding. In the same survey, 28 engineers – about 17.5% of respondents – said they are choosing not to use AI code editing tools at all. Many in this group feel the tools are not yet advanced enough to justify the overhead of learning and integration, or that they introduce more friction than value in their current workflows.
Interestingly, while their perspectives add an important counterpoint to the general enthusiasm around AI, these opt-out engineers were not willing to speak on the record. Their hesitation underscores how charged and sensitive the conversation around AI, skills, and career security has become in the software industry.
Helpful companion or career risk?
Follow-up conversations with a smaller group of engineers reveal a nuanced picture of how AI code editors are perceived in practice. All of them acknowledged that AI tools are useful in some capacity, but the way they rely on them varies dramatically. Some treat AI as a powerful one-off assistant for specific tasks, while others view it as a daily “lifesaver” that accelerates everything from boilerplate generation to debugging.
One common theme is that the value of vibe-coding depends heavily on the developer’s underlying skills. Engineers who can read, reason about, and critique code tend to see AI as an amplifier of their abilities. They use AI to move faster while still maintaining control and judgment over what ultimately gets shipped. Others warn that over-reliance on AI, without strong fundamentals, can put careers at risk, especially for those who treat AI prompts as a replacement for understanding.
The productivity question: faster, or just different?
Productivity remains one of the most debated aspects of vibe-coding. Some engineers report that AI tools have doubled or even tripled their output, especially by reducing context switching and automating repetitive coding tasks. For them, AI has turned long coding sessions into faster, more iterative cycles guided by high-level intent.
However, other developers point out that time saved on writing can be offset by time spent reviewing, prompting, and correcting AI-generated code. Studies and anecdotal experience alike suggest that while AI may speed up initial code creation, it can also increase the volume of code to review and introduce subtle bugs that are harder to detect. For many engineers, the job now looks less like writing every line by hand and more like continuously inspecting, shaping, and verifying AI output.
Redefining what it means to be a developer
As vibe-coding becomes more common, software engineers are rethinking what their role actually is. Experienced developers often emphasize that senior work revolves around architecture, design, and problem framing, with AI taking on more of the lower-level implementation. From that perspective, AI does not replace seasoned engineers so much as it changes where they focus their expertise.
At the same time, some worry about the long-term impact on critical thinking and foundational skills. If new developers skip the deep practice of hand-written code, they may struggle to debug complex systems or maintain large codebases when AI falls short. Many engineers now believe that the most resilient careers will belong to those who combine strong core skills with the ability to effectively direct, audit, and collaborate with AI.
In this evolving landscape, vibe-coding is less a passing trend and more a sign of a broader transition. Software engineers are learning to balance intuition and automation, speed and scrutiny, delegation and ownership- and their answers to whether they feel ahead, behind, or keeping pace are shaping the future of the profession.
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