In the past few years, alongside the rise of AI code generation models, a new way of building software has been quietly spreading: instead of painstakingly designing and writing most of the code by hand, you describe the “feel” of what you want and let the AI handle the rest. Many people call this vibe coding – programming by vibe.
For seasoned engineers, vibe coding can genuinely be a powerful accelerator. It helps them spend less time on repetitive plumbing work and more on architecture, design and high-impact decisions. But when this approach is given too early and too freely to students or junior developers, it very easily becomes a trap: it erodes their foundations, makes them dependent on AI and makes it harder for them to prove their real value inside a company. This article explains why that happens, from a technical, career, behavioural and economic perspective, and offers some practical suggestions on how to use AI safely without sacrificing core engineering skills.
Vibe coding in the era of AI code generation
What is vibe coding?
If we had to compress it into a single sentence, vibe coding can be understood as:
The developer describes what they want at a very fuzzy, experiential level – “a modern admin dashboard with charts and notifications” – and lets the AI infer the architecture, generate the code and handle most of the implementation, as long as the end result appears to “work”.
In this model, the work of translating intent into code has been shifted heavily to the AI. The human specifies the desired UX or overall behavior, while the choice of data structures, control flow, system boundaries, error handling and security is largely left to the model.
At the same time, many engineers are using AI in much more “healthy” ways: to look up APIs, ask for examples of a design pattern, get help writing tests or refactoring an existing piece of code. Those uses are not vibe coding, because the engineer still owns the design and implementation choices; AI is simply a helper.
How vibe coding differs from AI-assisted development
The core difference is where control and intent live.
In AI-assisted development done well, the engineer decides what to build, which architecture to use, how to split modules, what libraries to choose. They may ask the AI to scaffold some boilerplate, propose a refactor, or explain a tricky function, but the AI does not get to unilaterally “draw the system” for them.
In vibe coding, however, it is very common for the human to send a few high-level, impressionistic sentences – “a dark-mode note-taking app with sync and a modern feel”, “an order processing service with retries, logging, and user-friendly error messages” – then accept almost the entire architecture and code that the AI returns, as long as it seems to run.
That “accept almost the entire output” part is the source of many problems.
Juniors vs seniors in this context
To discuss whether vibe coding “should” be used by juniors, we first need to acknowledge how different juniors and seniors really are in day-to-day practice.
A junior engineer or student typically has a patchwork of knowledge: some theory from classes, some tutorials, some bits from online courses. Their experience with real production systems, troubleshooting incidents and reading live logs is minimal or non-existent. Most of them have never been on the hook for a system that real users depend on, let alone handled a major outage.
A senior engineer, on the other hand, has usually gone through many “painful” production events: data loss incidents, resource exhaustion, memory leaks, security issues, load-related failures. They understand how services behave under stress, where platforms and libraries tend to break, and what it means to pay back technical debt. Most importantly, they’ve developed a strong engineering intuition: when they read a design or a block of code – including code written by an AI – they can quickly sense when something feels off.
Why vibe coding suits experienced engineers better
The ability to filter and control AI output
A code-generating AI model has no lived sense of how its output behaves in production. It can produce very tidy-looking code, with clean naming and a seemingly reasonable structure, and still violate a key security principle or miss an obvious edge case that an experienced engineer would never ignore.
A senior engineer can often skim a few hundred lines of AI-generated code and immediately spot critical issues: unbounded queries, missing input validation, shallow error handling, logging that won’t help in a real incident, data structures that don’t match the expected scale. They may happily let the AI write the “rough draft”, but they will refactor, correct and harden the important parts before merging into the main codebase.
Juniors, in contrast, tend to stop at “it runs on my machine” or “the sample tests pass”. They lack the production experience to recognize deeper risks, and so are far more likely to trust the AI’s output by default.
AI as an accelerator, not an architect
For someone with a solid foundation, vibe coding can be a way to quickly explore the design space before committing. A senior can ask the AI to spin up a sample order service implemented synchronously, and another version using an event-driven architecture, then compare and decide which direction fits the real constraints.
They might let the AI scaffold the project structure, configuration files, DTOs and route definitions, then hand-craft the core domain logic, error handling and security-sensitive parts where control matters most.
The key is that at every step, the engineer makes the final decision. They use AI to eliminate busywork and explore options faster, but not to relinquish ownership of architecture, correctness or long-term maintainability.
A junior who hasn’t yet learned to distinguish between “safe to outsource” and “must own personally” does not have this safety valve. Handing them vibe coding is like asking a first-year architecture student to accept the very first plan a printer spits out, and go build a house from it.
Vibe coding and the professional value of seniors
From a career perspective, vibe coding can help experienced engineers offload low-leverage tasks and focus on work that is hardest to automate: working with product and business stakeholders, assessing technical risks, shaping system architecture, leading teams, balancing trade-offs between speed, cost and quality.
A senior who knows how to use AI effectively can amplify their impact: in the same amount of time, they can prototype more ideas, review more changes, support more teammates and validate more design options. Their value does not come from typing every line of code themselves, but from the decisions they make and the responsibility they assume.
If a junior’s main contribution is “writing good prompts”, their value sits uncomfortably close to what a future multi-agent AI system could do with a bit of configuration. The gap between human and machine becomes dangerously narrow – and that is not a safe long-term position to be in.
What happens when juniors and students use vibe coding too early
Foundations get hidden behind abstraction
One of the biggest risks of giving juniors vibe coding too early is that they lose the chance to build a solid foundation. Instead of learning how loops really work, how memory is managed, how a database query actually runs, they only learn that “if I phrase the prompt this way, I get a working snippet”.
Over time, their skills become very “surface-level”: they know which tool to use and which button to press, but not what’s going on underneath. When they later encounter a situation that doesn’t quite match any example they’ve seen the AI produce, their ability to adapt is limited.
“Copy-paste” thinking, upgraded but not matured
Even before AI, many young developers were used to copying code from forums, blogs or Stack Overflow. The issue wasn’t referencing existing solutions, but blindly pasting without understanding. Vibe coding is essentially a polished version of the same behavior: the copying is no longer obvious, but the underlying pattern – accepting external code without deep comprehension – remains.
If that AI-generated code is not carefully read, reasoned about and explained back in one’s own words, it does not create much more growth than old-school copy-paste. It may feel more personal because the code was generated “for me”, but the level of understanding does not automatically increase.
Debugging skills atrophy over time
Debugging is one of the most valuable skills in software engineering. Being able to trace execution, interpret logs, connect symptoms to root causes – that’s what often separates a beginner from a seasoned professional.
When every error is handled by dropping the stack trace into an AI chat and trying suggested fixes one by one, the brain never gets to exercise that reasoning muscle. The developer doesn’t learn how the system behaves when misconfigured, how to differentiate data issues from network issues from logic bugs, or how to systematically read logs.
Inevitably, as soon as they face a problem that cannot be captured in a single error message – one that spans multiple services or involves subtle state – they find themselves lost, not knowing where to start.
Quality and maintenance burden shifts to the organization
From an organizational perspective, AI-generated code that the implementer does not deeply understand is a form of technical debt. At first, it may look very efficient: features ship quickly, interfaces look decent, everything seems to work. But when the system needs to scale, integrate with other components, or conform to strict security or compliance requirements, those “mystery” blocks of code become bottlenecks.
In many cases, seniors end up spending time reverse-engineering AI-written modules just to figure out what was done, before they dare change anything. The total time spent can easily exceed what it would have taken to let an experienced engineer write it correctly in the first place – but by then, the shortcut has already been taken.
Losing your professional “signature”
For a junior engineer, a handful of projects they’ve built largely by themselves are incredibly valuable. Those are the places where they can show how they structure code, name things, handle errors, think about data and flow.
If almost all of the important pieces are left for the AI to decide, and the human only “touches up” here and there, it becomes very hard to see what their own engineering identity looks like. In technical interviews, just one or two deeper questions – “Why did you choose this architecture?”, “Why handle errors this way?”, “How would you scale this system by 10x?” – will quickly reveal whether they actually understand the system or just orchestrated a tool.
When developers become “prompt typists”: the replacement risk
How far can multi-agent AI go?
A multi-agent AI system is not just a single model answering questions. It can be a network of specialized agents: one for analyzing requirements, another for proposing architecture, one for generating code, one for writing tests, one for running those tests and iterating. These agents can collaborate, exchange intermediate results and loop until certain conditions are met.
In that world, if a developer’s main job is to phrase broad, non-technical prompts and paste the results into a repository, the line between their role and a fully automated pipeline becomes very thin. Organizations will inevitably want to explore higher degrees of automation, as long as a smaller group of seniors can supervise and approve the final outcome.
Cost and value
From a leadership viewpoint, the question always comes back to cost and value. If a junior engineer doesn’t contribute significantly more than an AI system plus a bit of senior review time, it becomes hard to justify that role as a long-term investment.
Sustainable value for an engineer does not lie in which tools they can operate, but in the knowledge and judgment they bring: domain understanding, design decisions, ownership of quality and risk. If all of that is effectively outsourced to AI and seniors, juniors end up occupying a very fragile position.
Impact on the career ladder
Engineering careers are built step by step: from someone who can write correct code, to someone who understands how systems behave, to someone who can design, lead and make trade-offs. Each step requires time and real experience.
If a junior is “stuck” from day one in a tool-operator role – translating vague requirements into prompts and pasting results – without opportunities to design, to own pieces of the system, or to be accountable for outcomes, they will struggle to move up. When the next generation of AI models arrives with better natural language understanding, faster feedback loops and lower cost, that operator role is exactly what will be threatened first.
Vibe coding and the dark side of convenience
The “superpower” feeling and dopamine loop
It is genuinely exciting to type a few sentences and watch a working UI, API or workflow appear on screen. Before AI, that feeling of “I built something that actually works” usually only came after days or weeks of learning and coding.
That’s why vibe coding can be so addictive: every small prompt gives you a visible reward. Meanwhile, the foundational parts of computer science – data structures, algorithms, operating systems, networking, databases – rarely produce instant gratification. It becomes very tempting to prioritize what looks and feels like immediate progress, even if the long-term skill growth is minimal.
When the brain stops storing patterns
Humans tend to remember patterns they are forced to work with repeatedly, or that prove useful across many situations. If every problem is immediately offloaded to AI, the brain has little incentive to build internal mental models of how systems behave, what typical failure modes look like, or which approaches tend to work better.
After a long period of relying on AI, many people discover that they can’t re-implement a familiar pattern without suggestions, can’t sketch out a solution from scratch without “asking the model first”, and can’t articulate clearly why they chose one design over another.
Is there still a place for juniors in a vibe-coding world?
None of this means juniors should be forbidden from touching AI altogether. The issue is how and why they use it.
Used intentionally, AI in general – and even vibe coding style prompts – can support learning: helping students visualize end products, compare multiple implementations for the same problem, understand trade-offs between designs, or check their own reasoning.
But there must be clear boundaries: which exercises must be done by hand, which parts may use AI, and when to stop generating more code and instead sit down to read, trace and understand what’s already there. Without those boundaries, the area of benefit quickly morphs into a danger zone.
Suggestions for individuals, teams and organizations
For students and juniors, a simple but effective principle is to set explicit limits on AI use for foundational tasks. Some assignments should be done completely without AI-generated code, using the model only as a tutor when they are genuinely stuck after thinking on their own. For other tasks, they might use AI to get an idea, but then re-implement the core logic by hand to ensure they truly understand it.
For experienced engineers and tech leads, it is important to establish team-level guidelines around AI usage. Clarify which parts of the codebase can safely be scaffolded by AI and which must be hand-crafted; define expectations for reading and understanding AI-generated code before committing it; and raise the review bar for security-sensitive or performance-critical changes that involve AI.
At the organizational level – whether in companies or educational institutions – learning paths and career frameworks should reflect the true role of AI. Early stages should focus on fundamentals; later stages should deliberately introduce AI as a force multiplier. Evaluation criteria also need to evolve: instead of looking only at “does it run?”, we must also assess a person’s ability to understand problems, design solutions, justify their choices and handle failures.
Conclusion
The AI era is reshaping how we build software, blurring the line between “coding” and “describing what we want an AI to build for us”. Vibe coding is one of the clearest expressions of this shift. In the hands of someone with deep foundations and real-world experience, it can be a powerful accelerator, freeing them from busywork and amplifying their impact. In the hands of someone who is still building basic skills, it can easily become a crutch that prevents them from ever learning to stand on their own.
The question is not “should we use AI or not”, but “who should use which AI patterns, for what, and at what stage of their development”. For vibe coding specifically, a reasonable stance is: let it be a common tool for senior-level engineers and beyond, and expose juniors to it slowly, with clear guardrails and a strong emphasis on keeping their core engineering foundations intact.