How Vibe Coding is Transforming Software Development

I’ve been watching a fascinating shift in software development lately. The rise of what’s being called “Vibe Coding” or “AI assisted coding” is rapidly changing how we build software. As AI researcher Andrej Karpathy quipped, “the hottest new programming language is English,” and that couldn’t be more accurate in today’s development landscape.

Today, I want to share my thoughts on this transformation and what it means for developers at all levels of experience.

Table of Content

The Traditional Software Development Landscape

If you’ve been in the software industry for any length of time, you’re probably familiar with the traditional roles and responsibilities in software development:

• Entry-level engineers: You work on small components that require understanding specific pieces of the codebase. You implement features according to specifications handed down from more senior developers.
• Mid-level engineers: You take on more responsibilities, building systems and ensuring that the core organization, structure, and architecture make sense. You not only design but also develop, test, and launch features with greater autonomy.
• Architects: You now look at the big picture. You oversee end-to-end systems, examining how various components interact, identifying bottlenecks and vulnerabilities, understanding technical debt, and providing guidance to junior developers. Architects make critical decisions about technical stacks such as what languages to use, which cloud provider to choose, which services/databases/tools to leverage, and fundamental architectural choices.

This hierarchical structure has served the industry well, creating a clear career progression path and ensuring that software is built with expertise at all levels. For decades, we’ve followed this bottom-up development approach, where engineers carefully plan and implement systems component by component, writing and reviewing each line of code with precision.

Enter Vibe Coding: Shifting from How to What

Vibe Coding represents a dramatic shift in this paradigm. The term, originally popularized by Karpathy in early 2025, refers to “a new kind of coding where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” Vibe Coding is about relying more and more on AI and prompting to generate code instead of writing it yourself.

To clarify, while the term ‘vibe coding’ may have been coined recently, but the concept of AI-assisted coding has been steadily gaining ground and traction over the last few years.

A New Development Paradigm

With AI-assisted Coding, the focus transforms from telling the computer how to do something to simply explaining what you want. In a sense, developers are becoming more like product managers, describing desired functionality and letting AI figure out the implementation details.

This represents a fundamental top-down, goal-driven approach to development:

• You start with the end in mind (the feature or behavior you want)
• Let the AI fill in the implementation details
• Review and refine the code in an iterative loop

This contrasts with our traditional bottom-up development, where an engineer carefully designs and builds the system component by component.

Everything I’ve seen so far indicates that it’s an amazing assistant, but definitely not at a place where you can blindly trust it. Is this approach scalable? Probably not yet. We might imagine a future where everything is fully automated end-to-end, where agents automatically resolve bugs, change architecture as new technologies become available, optimize code on the fly, and understand business rules to modify accordingly. But we’re not there yet!

Today’s AI-Assisted Coding Landscape

AI-Powered Development Tools

The market is now flooded with tools designed to enable this new style of development:

• GitHub Copilot: One of the most widely adopted AI coding assistants. A 2023 Stack Overflow survey found that 56% of developers using AI coding tools cited GitHub Copilot as one of their tools.
• AWS CodeWhisperer: Now known as Amazon Q, Amazon’s AI code assistant with an emphasis on cloud integration and built-in security scanning for vulnerabilities.
• Cursor: A specialized IDE with a built-in AI assistant that you converse with. Y Combinator’s CEO Garry Tan described it as “cutting edge for code gen” that allows small teams to achieve the output of much larger teams.
• Replit Ghostwriter: Part of Replit’s online development platform, which has gone so far as to create a “Vibe Coding 101 with Replit” course with Andrew Ng.

These tools excel at taking a first pass. They can create different files, packages, and modules. They also can implement a lot of functionality, but there’s a major caveat: you’re still responsible for making sure the code is right. Before pushing anything to production, you as a developer must verify the validity of what’s been generated, both in terms of functionality and software quality.

Challenges with AI-Generated Code

The complexity increases when you’re working with bespoke, in-house technologies. AI tools work great when you’re building with simple, publicly available, or open-source components from scratch, but integration becomes tricky when dealing with complex internal codebases and tools.

Let me share some of my deeper concerns about vibe coding:

• Hallucinations: AI models can produce code that looks completely legitimate but calls nonexistent functions or uses incorrect APIs. This is especially dangerous because the code appears correct at first glance. These hallucinations aren’t always obvious unless you have deep domain knowledge. Many developers might not recognize these errors, this creates a dangerous false confidence that everything works as intended.
• Architectural blindness: AI tools fundamentally cannot see the big picture the way experienced developers do. When building a system, developers think about how it will evolve over time, where bottlenecks might appear when scaling to millions of users, and how future features might integrate. These AI models, despite their impressive capabilities, operate mainly at the tactical “implement this feature” level without strategic understanding. They can generate solutions that work today but could create maintenance nightmares down the road.
• Integration issues: One of the biggest challenges is how AI struggles to understand company-specific systems and infrastructure. Many organizations have custom middleware, proprietary authentication frameworks, and internal libraries built over years. When asked to generate code that interfaces with these systems, AI assistants often make naive assumptions or default to generic implementations that simply won’t work in proprietary environments. The “last mile” of integration almost always requires significant human intervention, sometimes requiring substantial effort.
• Security vulnerabilities: This is perhaps the most concerning issue. A survey by Snyk found that over half of organizations encountered insecure code from AI assistants. These models are trained on massive codebases from the internet, including all the bad practices and security anti-patterns that exist there. AI-generated code can contain SQL injection vulnerabilities, improper authentication checks, and other serious security issues. In a world where many teams are pushing to deliver faster, these vulnerabilities could slip through code reviews, especially when reviewers assume the AI “probably got the security stuff right.”

Who Benefits Most from Vibe Coding?

The Expert Advantage

Interestingly, there’s a paradox in vibe or ai-assisted coding: it works best for those who arguably need it least – the architects and experts!

Why experts benefit more:

• They know how to co-pilot the AI effectively, ensuring the use of optimal architectural and tech stack choices
• They can break down problems into smaller, more effective prompts
• They recognize when the AI’s output needs refactoring
• They know how to prompt for edge cases and best practices

In my opinion, if you’re a mid-level engineer or architect, these tools serve as excellent assistants. You already know the ins and outs of coding; you know the architecture, what’s expected, and the next steps. You might brainstorm with these tools, but ultimately you’ll make the decisions and take responsibility for the code that’s developed.

When to Use Vibe Coding

The consensus among many senior technologists is that vibe coding is fantastic for:

• Rapid prototyping
• “Throwaway” projects and POCs
• Speeding up boilerplate code generation
• Creating demos and quick experiments

But it must be tempered with caution for production software. Karpathy himself noted vibe coding is “fine for throwaway weekend projects.”

Juniors and Non-Technical Users

But what about junior developers fresh out of university who haven’t yet built large systems and are still developing their software and architectural skills? What about non-technical people trying to write code?

I think these tools can serve as great initial assistants to speed things up, but especially if you’re aspiring to be a software engineer, you must use this opportunity to learn best practices. For production code, you need to:

• Spend time reading through the generated code
• Double-check for correctness and security issues
• Consult with architects and mentors to understand what’s being done
• Never blindly trust the output

The Critical Need for Team Adaptation

This is where it becomes absolutely critical for teams to adopt proper frameworks when implementing Vibe Coding and AI-assisted development.

Remarkable Productivity Gains

The productivity gains are significant and backed by data:

• Individual developers: Zack Katz, CEO of GiveWP, reported that “vibe coding has dramatically accelerated our product development process… I can generate a working prototype of new functionality in a single day – far faster than starting from scratch.”
• Startup efficiency: Y Combinator’s CEO Garry Tan noted that some startups are reaching $1-10 million in annual revenue with fewer than 10 employees thanks to AI-driven development. According to Tan, “you can just talk to the large language models and they will code entire apps… What would’ve once taken 50 or 100 engineers to build can now be accomplished by a team of 10, when they are fully vibe coders.”

Organizational Safeguards

For these powerful tools to be implemented effectively, teams must establish comprehensive frameworks tailored to different roles and concerns:

For junior developers:

• Teams must create progressive learning journeys that introduce juniors to AI coding tools while simultaneously teaching fundamental programming principles.
• Reviews of AI-generated code should go beyond correctness to discuss architectural choices, potential optimizations, and security considerations.

For senior engineers and architects:

• Senior engineers should treat AI as an assistant, not a replacement for their expertise. They should document architectural decisions and constraints in formats that can be incorporated into prompts, ensuring AI output aligns with established patterns and system design principles from the start.
• Senior engineers need clear guidelines on their accountability when leveraging AI tools. This includes establishing boundaries for what types of code can be generated with minimal oversight versus what requires rigorous review.

Final Thoughts

AI-assisted coding is here to stay—of that I’m certain! We should embrace it and start using it the right way. As these tools continue to improve in functionality, we’ll need to regularly reassess their role in software development.

As of 2025, I see the state of vibe coding is one of optimistic caution. Early adopters hail it as a revolution. At the same time, the software engineering community is learning how to adapt its processes to handle the quirks of AI-generated code.

Key Takeaways for Different Roles

• For junior developers: Use these tools to learn, not to bypass learning. The foundations of computer science remain crucial.
• For mid-level engineers and architects: Embrace them as assistants that free you to focus on higher-level thinking and innovation.
• For teams and organizations: Establish frameworks that balance productivity with quality and learning. Update security processes specifically for AI-generated code.
• For everyone involved in software: Remember that while the tools may be doing more of the typing, the responsibility for what gets built still rests with us humans.

Related Reading

• From Code to People: Choosing the Management Path: Navigate the transition from coding to management. Learn how engineering and research leaders can excel in people management while driving technical impact.
• Being Technical as an Engineering Manager: Evolving Beyond the Code: Explore how engineering and research managers can stay technical, drive innovation, lead teams effectively, and balance coding with strategic leadership.
• Staying Technical as a Leader: Practical Strategies: Learn practical strategies for engineering leaders to stay technical while balancing leadership responsibilities. Stay relevant, lead effectively!
• The New Technical Leadership: Embracing AI/ML as a Core Competency: Discover how tech leaders can transition to AI/ML, build foundational skills, grow with their teams, and embrace real-world applications for lasting impact.
• Land Your Dream AI/ML Job: A Practical Guide: Want to land your dream AI/ML job? This practical guide breaks down key strategies, from mastering technical skills to optimizing your resume and networking effectively, helping you stand out in the competitive AI job market.

About the article

Disclaimer: 

The views and opinions expressed in my articles are my own and do not represent those of my current or past employers, or any other affiliations.