Are software engineers losing skills? Rise of ‘vibe coders’ vs programmers

The landscape of software engineering is rapidly evolving as artificial intelligence tools become deeply embedded in daily workflows. As someone who has spent years building large-scale systems, I have observed a striking transformation in how developers approach their work.

The proliferation of advanced AI code completion tools has given rise to a new archetype: the “vibe coder.” These engineers can quickly generate functional code using AI suggestions or online templates, often without a thorough understanding of the underlying systems.

In contrast, traditional programmers have long prized foundational knowledge, architectural thinking, and methodical debugging. This shift is more than cultural; it is fundamentally changing the way we build, maintain, and secure complex systems while also having a profound impact on the psychology and behavior of software engineers.

Not long ago, engineering was synonymous with patience and curiosity. Debugging required a willingness to trace logs, understand nuanced system behavior, and ask difficult questions.

Today, many developers expect immediate answers. When faced with a bug, the instinct is often to consult an AI tool or copy a code snippet from the internet. If the fix doesn’t work instantly, frustration sets in, and the drive to investigate deeper fades.

This is not a question of intelligence — modern engineers are often quick learners. However, with powerful tools at their fingertips, it becomes easy to bypass foundational thinking.

Developers can assemble working software rapidly, but when systems break or need to scale, they may lack the intuition to diagnose or resolve the issue. The mindset has shifted from “I’ll figure it out” to “If the AI doesn’t help, I’m stuck.”

Vibe coders vs True programmers

The distinction between vibe coders and true programmers is becoming increasingly pronounced. Vibe coders excel at delivering quick, functional solutions by leveraging AI or pre-existing templates.

They are adept at producing code that passes initial tests or meets immediate requirements but often overlook critical questions about scalability, security, and resilience.

For example, a vibe coder might use AI to implement retry logic for an API call but fail to consider how this logic will behave under heavy load or in the face of intermittent failures.

The result is software that works in controlled environments but falters under real-world conditions.

True programmers, on the other hand, approach development with a systematic mindset. They see each piece of code as part of a larger, interconnected whole. Before implementing a solution, they consider how it will perform under stress, whether it gracefully handles network delays, and if it adheres to memory and security best practices.

When faced with errors, true programmers systematically investigate, relying on documentation, metrics, and a deep understanding of architecture. While a vibe coder may respond to a cryptic error by trying a series of AI-suggested fixes, a true programmer isolates the issue, simplifies the problem, and methodically works toward a resolution.

This disciplined approach not only produces more robust systems but also cultivates a culture of continuous learning and improvement.

The behavioral and psychological effects of AI overdependence

The integration of AI into software engineering workflows has introduced a complex set of psychological and behavioral challenges. One of the most significant impacts is the phenomenon of cognitive offloading, where individuals increasingly delegate memory, reasoning, and problem-solving tasks to technology.

While this can free up mental resources for higher-level thinking, excessive reliance on AI can erode critical thinking skills and reduce the inclination to engage in deep, reflective analysis.

Engineers who habitually turn to AI for solutions may become passive consumers of information rather than active problem solvers.

This shift also brings about a subtle but pervasive sense of impatience. The expectation of instant answers can undermine perseverance and diminish the satisfaction that comes from overcoming complex challenges.

When AI-generated solutions fail, engineers may experience helplessness and frustration, feeling ill-equipped to troubleshoot issues independently. Over time, this can lead to a decline in confidence and a sense of diminished ownership over one’s work.

The fear of job displacement is another psychological stressor, as some engineers worry that AI tools might eventually render their skills obsolete. This anxiety can reduce job satisfaction and hinder professional growth.

Furthermore, the collaborative nature of software development is at risk. AI tools, while powerful, can inadvertently erode the human connections that form when teams work together to solve difficult problems.

The loss of collective problem-solving and shared learning experiences can impact morale and reduce the sense of community within engineering teams.

Real-world Impact in cloud and distributed systems

In the context of cloud and distributed systems, the consequences of overreliance on AI-generated code are particularly acute. These environments demand robust, scalable, and secure solutions, yet the use of AI tools often introduces new risks.

AI-generated code can lack the necessary context about business logic, compliance requirements, and security best practices. This can result in software that is difficult to maintain, riddled with technical debt, and vulnerable to security breaches.

The rapid proliferation of AI-generated “glue code” can make systems opaque, complicating debugging and increasing the risk of outages.

Another challenge is the dilution of accountability. When code is produced by AI, it can be difficult to determine who is responsible for its quality and security. Developers may feel less ownership over AI-generated code, leading to less rigorous reviews and a higher likelihood of vulnerabilities slipping into production.

The speed at which AI can generate and deploy code may also outpace the ability of security and compliance teams to conduct thorough assessments, further increasing risk.

Ultimately, while AI can accelerate development, it cannot substitute for human judgment, especially when it comes to understanding complex system interactions and ensuring long-term maintainability.

The CARE framework: Balancing speed and substance

To harness AI’s speed without sacrificing engineering rigor and critical thinking, I propose the CARE Framework—a hybrid approach that blends foundational skills with AI-powered productivity.

Core Skills Preservation: The foundation of the CARE Framework is a deliberate commitment to maintaining and sharpening essential software engineering skills. This means engineers regularly practice architectural design, manual debugging, and security analysis, even in an era of advanced AI assistance.

For example, teams might hold weekly sessions where they solve problems without AI or review the underlying principles behind commonly used design patterns.

By keeping these core competencies strong, engineers are better equipped to understand, critique, and improve upon AI-generated code, rather than accepting it at face value.

AI-Augmented Workflows: Rather than viewing AI as a crutch, the CARE Framework positions it as a collaborative coding companion. AI tools are strategically integrated into workflows to handle repetitive tasks, suggest alternatives, and generate documentation or test cases.

However, every AI contribution is subject to human oversight and contextual review. For instance, while AI might draft an API endpoint or generate basic unit tests, engineers are responsible for ensuring these outputs align with system architecture and business requirements.

This hybrid approach leverages AI for speed but always within guardrails that preserve engineering intent.

Rigorous Validation: To prevent the pitfalls of “vibe coding,” the framework enforces a robust validation process for all AI-assisted work.

This includes enhanced static analysis, increased test coverage, and mandatory manual reviews for critical code paths—especially those related to security, performance, or system reliability.

Evolutionary Learning: Finally, the CARE Framework embeds a culture of continuous learning and adaptation. Teams regularly review incidents where AI-generated code contributed to failures, extracting lessons to refine both their engineering practices and their use of AI tools.

Periodic “AI lockouts,” where teams work without AI assistance, help ensure that foundational skills remain sharp and that engineers retain ownership of their craft.

Conclusion

The rise of vibe coders signals both opportunity and risk for the future of software engineering. AI can supercharge productivity, but overdependence threatens the very skills that underpin system reliability and innovation.

By adopting a hybrid framework that values both speed and substance, engineering teams can ensure they not only deliver quickly but also build systems that last and foster a culture of continuous growth.