AI is intriguing, and powerful. It has mesmerized the tech world with its ability to spring up apps, services, websites, and more via a simple prompt. But ask it to scale those same apps and services to handle thousands of concurrent transactions, and the limitations of natural language prompting quickly become obvious. Though "vibe coding" has democratized software creation, a growing chorus of industry veterans warns that we are conflating raw output speed with disciplined, educated engineering prowess done by real humans. AI-generated code introduces security vulnerabilities that require deep, senior-level architectural insight and experience to catch. The line separating a high-skilled engineer from a low-skilled prompt-engineer isn't the tool they use; it’s the repeatable, rigorous process that is followed.
Where do you draw the line between where "vibe" ends and "engineering" begins?
Jeff Jack, former PM lead at Tesla, says “I think the ‘vibe’ part comes up when you're just poking around and letting the AI do whatever it wants, with little guidance. You might make 20 requests of a thing before you get something that makes you go ‘Oh!’ or you might just get a bunch of jumbled code that doesn't work and is a mess. For me, the engineering part happens in how I organize the details.
“My background is in product design and development. I don't change a process that worked when I started. I start with a design and functional spec. If I'm extremely particular about what I want for a function or experience, I am extremely detailed in how I describe it. This is no different than how I would deliver material to a design team or a tech team. What has changed is that results are back in minutes, not days and weeks. I'm getting what I want with fewer revs, and I can test and verify code much more quickly.
Jacob Krell, Senior Director, Secure AI Solutions and Cybersecurity at Suze Labs, adds, “The line is not between AI and non-AI. The line is between process and improvisation. Engineering is the application of a repeatable process to a problem. When a developer moves from prompting and accepting output to building and following a structured workflow with review, documentation, and architectural intent, the work transforms from a low skilled endeavor into a high-skilled one. The tool does not determine whether the work is engineering. The discipline does.”
“The line is in infrastructure resilience,” says Systems Engineer & Full Stack Developer Caesar Engroba, Anyone can vibe code an app into existence with natural language, however when a platform needs to scale, handling thousands of concurrent requests, with resilience, compliance, and security in mind; this is where traditional engineering fundamentals are non-negotiable. Vibe coding can get you an MVP; engineering gets you to production.
Alex Kovalenko, Director of IT recruitment at Kovalenko IT Recruitment, tells Dice “As a developer-turned-IT-recruiter with 20 years of experience, I think vibe coding is a phase that will eventually go away or get seriously diminished in value.
Real coding is what goes down in the trenches at 2 a.m., not a kid talking to AI saying "Claude my dude, analyze this Excel file of my LinkedIn posts and create a viral hit for me." You need to get into the trenches and understand data, security, and how to scale, not just write Grade 5 English language prompts.
I've seen many tools come and go over 20 years, but at the end of the day, a consultant with deep knowledge and critical thinking will always win the race against so-called vibe coding specialists.
I remember back 10–15 years ago there were tools that kids would download software and try to hack websites with zero knowledge of how security or systems actually work, just blindly pressing buttons. That's exactly how I think vibe coding is going to play out.
Is "vibe coding"—the ability to describe and iterate on high-level logic through natural language—actually a new technical discipline, or is it simply a layer of abstraction that will eventually hit a ceiling where traditional computer science fundamentals remain the only way forward?
“What happens between missive requests and specific requests is significant,” notes Jack. “The missive will give AI the permission to use its ‘knowledge’ to give you a result that it ‘thinks’ is the result that you might want based on its interpretation of your request. It may or may not have some context for what you are requesting, so results can be wild. That's vibe coding.”
“It will hit a ceiling, and the term itself is part of the problem,” Krell tells Dice. “Andrej Karpathy coined ‘vibe coding’ in February 2025 to describe throwaway weekend projects where he stopped reading diffs entirely. In practice, the label is now being applied to all AI-assisted development, from hobby projects to production systems. That conflation obscures what is actually happening. The ceiling is not natural language. The ceiling is the absence of engineering discipline around natural language.”
Engroba adds “It's both. More so on the abstraction. As a new discipline, it requires mastering prompt engineering and understanding AI model limitations. The primary difference is that previous abstract tools were deterministic and were security auditable. With AI generated code, you are introducing non-deterministic elements into systems that handle potentially sensitive data.”
“Vibe coding is not a new discipline,” says Kovalenko. “Every few years we get a new tool that's supposed to replace developers or recruiters. It never happens. Does it make it easier to work with a specific set of data or knock out a small task? Sure. Build a simple tool? Maybe. But at the end of the day you still need someone who understands the systems, the people, and the bigger picture to make the final call. When it comes to complexity, security, and scale, vibe coding won't pull it off. That ceiling shows up fast, and there's no prompt that gets you through it.”
If vibe coding becomes a standard part of the workflow, what does "mastery" look like for a modern engineer? Does it shift from understanding memory management and syntax to mastering prompt engineering, architectural oversight, and "vibe" debugging?
“It depends on how AI matters to your work,” Jack says. “For roofing, plumbing, skilled trades, and other roles that AI cannot replace, there may be some tertiary benefit from AI. Someone will have to scrutinize that work to know how to construct a workflow that benefits from AI participation. The levels of mastery will be demonstrated by the quality of the work that is realized, and yes, enterprise-level AI businesses will and do exist. Claude is built with Claude, for instance.
“There are many efforts to solve the memory issue. One of my own projects SAOR.IO provides a shared context memory that is agnostic to the AI platform. My limitation is that I can't capture memory the way you and I do, continuously, and granularly. I've got the AI picking its own highlights and ‘I should note this’ moments. Along with my own ’save this to memory’ prompts. It's part of the current tedium and limitations, but then, that's what being able to engineer against a platform is about. You have to know the limitations to know how to move beyond them.”
Krell adds “Mastery looks like rapid acceleration and force multiplication of output. A modern engineer who has internalized AI-assisted workflows can commoditize the act of software development itself, building out repeatable processes that compound speed without sacrificing quality. The shift is from writing code to directing outcomes. Coding becomes about objectives, about what to build, far more than about how to build it.”
“Mastery is becoming a translator between business logic and system requirements,” says Engroba. “As AI-assisted coding reduces time spent writing code, we now use the time savings to focus on understanding the business at a fundamental level. More time is spent with the customer and less time writing code. The time savings from AI-assisted coding does not reduce technical complexity; it shifts our focus to high level technical decisions where deep system knowledge is irreplaceable.
Kovalenko adds “the best engineers and recruiters have always been the ones who knew how to use the tools available to them. If you can save hours using Claude Code to build a system, why wouldn't you? But you still need to think at the Senior level. Start with the final product, then work backwards and break it into small parts. That's Architect-level thinking. And that skill doesn't come from a prompt. Just telling Claude to do something doesn't make it right. The judgment of knowing what to build, why, and whether the output is actually correct and that still lives with the engineer.”
Can natural-language-driven development ever produce the rigorous, maintainable, and secure codebases required for enterprise-level applications, or is it strictly for rapid prototyping?
“Not alone,” Krell notes. “AI will tend to produce what it has been trained on, which means common patterns, popular frameworks, and average implementations. Enterprise systems require specificity. The developer must know the correct questions to ask, and that comes from deeper technical understanding that no amount of prompting can replace. Veracode's 2026 update found that 45% of AI code generation tasks introduced a known security flaw. Somebody has to know enough to catch those. That is not a prompting skill. That is an engineering skill.”
“We use AI extensively for rapid prototyping and feature development, but production infrastructure still requires rigorous testing, security audits, and performance optimization that no amount of natural language can replace,” says Engroba. “AI accelerates development but doesn't eliminate the need for enterprise grade practices.”
If AI handles the implementation "vibes," how do we train the next generation of engineers? Are we at risk of creating a skills gap where future senior engineers lack deep-level understanding?
“Fundamentals will always be fundamentals,” Jack adds. “When I began coding cobol and base were the standards. Root meant machine root. Working through different evolutions of platforms "coding" always follows a process. The basics will always be the basics, define the thing before you build the thing. What is the thing? What do you need to make it? Where will it live? Who will use it? How? Why? Etc.
“Technical knowledge is becoming less valuable as memorized syntax and more valuable as judgment,” says Krell. “The next wave of senior engineers will be defined by their ability to transform ideas into products. That has always been the core skill of an effective engineer. The engineer's job is no longer just to produce code. It is to know what should exist, what should not exist, and when the machine is wrong. The risk is real only if organizations eliminate the entry level roles where engineers develop that judgment in the first place.”
“This is my biggest concern,” Engroba says. “I am concerned we are creating engineers who can ship features fast but cannot debug or troubleshoot when systems fail. In our MSP business, when a client's VoIP system goes down, you need to understand the entire stack from network routing to codec negotiation. "Vibe debugging" doesn't fix a Kamailio memory leak.”
Looking at the history of software engineering (from assembly to high-level languages), is "vibe coding" just the next logical step in abstraction, or is it qualitatively different from "coding"?
“Vibe coding is not a different species from coding. It is coding at a higher abstraction layer, with a different interface and a higher penalty for weak review,” says Krell. “Every layer of abstraction in software history has compressed lower level decisions into higher level intent. Compilers condensed assembly. High level languages condensed memory management. Vibe coding condenses many conditional logic branches into normal human language and design instead of binary choice gates. The logic is identical. The interface is different.
“Unlike previous abstractions (assembly → high-level languages), vibe coding introduces non-deterministic elements,” says Engroba. “When someone writes C++ or Python code, the same input produces the same output. With AI-assisted development, there is an inherent variability that creates new classes of technical debt.”
From a hiring and management perspective, should companies prioritize "vibe coders" who can ship at 10x speed, or will the technical debt created by AI make traditional engineering more cost-effective in the long run?
“AI should not create more technical debt in the hands of a strong engineer,” Krell notes. “It should create less. The engineer has more bandwidth for architecture, documentation, testing, and review when the cognitive burden shifts from implementation detail to structural decisions. The debt does not come from the AI. It comes from the absence of process around it. Organizations that hire engineers who apply disciplined, repeatable process to AI assisted development will outpace those chasing raw output speed without oversight.”
Engroba adds “we are seeing over 10x in productivity gains for feature development but are also discovering bugs in production that traditional code review would not have caught. The long-term cost calculation is not entirely clear yet; however, the productivity gain can already be measured. Currently, we are hiring engineers who can ship fast while also being capable of diving deep when systems fail.”
Conclusion:
Tools will change, but core fundamentals of software development remain non-negotiable. Vibe coding offers undeniable hyper-acceleration for rapid prototyping and feature building but hits a ceiling when confronted with enterprise-level security, compliance, and infrastructure resilience. AI is a powerful force multiplier, but it lacks an intangible human element. If organizations eliminate entry-level roles where young engineers develop this vital troubleshooting judgment, they risk creating a skills gap. As the tech landscape settles, the ultimate winners will not be "vibe coding specialists" blindly pressing buttons, but the disciplined engineers who use AI as an efficient tool.
