Software development is changing faster than most imagined. What was once the guarded realm of skilled programmers is now being cracked open by AI tools that can write, refactor, and even review code. This shift, often described as “vibe coding“, is reshaping how software is built, with large language models now capable of spinning up entire applications from a few prompts.

These developments have sparked excitement, uncertainty, and debate across the software community.

In just a year, development teams have moved from skepticism to cautious integration, and now, in some cases, full-scale adoption, of AI in their workflows. Conversations on social platforms revolve around AI agents, automated pull request (PR) reviews, and whether it’s still necessary for humans to comb through every line of code.

There’s growing concern about what happens when AI is both author and reviewer.

What does this mean for code quality? Who’s responsible when something breaks? And where do experienced engineers fit in a workflow that’s increasingly handled by machines? These questions are becoming central to how software gets built and shipped today.

Why It Matters: AI is in the driver’s seat for many parts of software development. While this boosts efficiency, it also forces a rethinking of team structure, accountability, and the long-term sustainability of technical expertise. Understanding where and how to use AI wisely could define the next era of productive and reliable software development.

• AI Is Now Part of the Development Pipeline, End to End: AI has graduated from simply assisting with code suggestions to owning large parts of the development lifecycle. Developers can now prompt AI to generate full applications, test logic, and even conduct initial code reviews. Tools like CodeRabbit, Cursor, and Windsurf are becoming embedded in everyday workflows, offering automated pull request summaries, style corrections, and error detection. This automation can significantly speed up development cycles, but it also creates a feedback loop where code is written and reviewed by the same category of tool, raising critical questions about oversight, accountability, and trust.

• Vibe Coding Has Accelerated Adoption, But Not Without Risks: The practice of “vibe coding” has gone from novelty to mainstream discussion. Entire projects, from mobile apps to multiplayer games, are being prototyped with this approach. However, as seen in numerous developer forums and case studies, this convenience often comes at a cost: unpredictable outputs, brittle functionality, or subtle security flaws. The growing tension lies in the gap between what AI can build and what it understands. Without clear architectural design and human guidance, vibe-coded software can become difficult to scale or secure over time.

• Traditional Code Review Is Being Reimagined: Code review has long been a cornerstone of quality assurance, with senior developers painstakingly checking for errors, enforcing team standards, and mentoring through feedback. Yet this process is often slow, inconsistent, and overly focused on low-level issues like formatting or syntax. AI now offers an alternative by automating routine checks and surfacing potential problems before human reviewers even step in. This reduces review fatigue and also shortens development timelines. Still, these AI tools can’t fully replace the nuanced understanding of experienced engineers, especially when evaluating trade-offs or design decisions with long-term impact.

• Senior Developers Are Evolving, Not Disappearing: As lower level tasks are handed off to AI, the role of senior developers may be evolving into something more strategic. Rather than policing code for typos and tabs, they are becoming system architects, mentors, and tooling orchestrators. Their time is increasingly spent selecting and configuring AI tools, guiding younger developers on how to interpret AI suggestions, and aligning engineering outcomes with business goals. They also play a key role in ensuring that AI doesn’t introduce unintended consequences, technical, ethical, or financial, into the codebase. Far from becoming obsolete, these developers are being repositioned to drive higher-order impact.

• AI Alone Can’t Understand Context or Intent: Despite rapid improvements, AI remains context-blind. It doesn’t know your last planning session ruled out a particular design pattern. It doesn’t understand why a workaround exists in the codebase or how customer feedback influenced an architectural decision. This lack of team-specific awareness limits its ability to fully own the review process. More importantly, AI cannot be held accountable when things go wrong. For these reasons, experienced developers are still essential, not just as fail-safes but as curators of quality and consistency. When AI suggestions fall short or misalign with goals, it’s up to humans to catch the gap and steer the work back on course.

Go Deeper → Vibe Coding Is Coming for Engineering Jobs – WIRED

AI and Vibe Coding Are Radically Impacting Senior Devs in Code Review – TheNewStack