Generative AI and Vibe Coding in Software Development

Generative AI isn’t just changing how we write code — it’s reshaping entire workflows and team dynamics. With new trends like vibe coding emerging, developers are moving beyond task-based coding to more fluid, collaborative, and AI-augmented ways of building software. In this article, we break down what generative AI really means for software development today, how companies like Switch are putting it into practice, where the gaps still lie, and how these shifts could redefine the future of engineering work.

These days, talking about Generative Artificial Intelligence (GAI) has become almost routine. For those of us who have spent years in software development, this “holy grail” has appeared in different forms over time: domain-specific languages, modeling tools that promise to auto-generate code, and predictions about paradigm shifts that would relegate developers to being designers, analysts, or project managers. While those visions haven’t fully materialized, we have seen constant evolution: new roles have emerged, and the development ecosystem has become increasingly sophisticated.
There’s no doubt that AI, especially generative AI, is already having a significant impact and will continue to raise essential questions about workflow redesign, role transformation, and new business models across the industry.

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What Is Generative AI in Programming?

In the context of software development, generative AI refers to systems that can autonomously create content, including source code, technical documentation, automated test scripts, usage examples, and more.
At Switch, we adopted GitHub Copilot at the end of 2024, though tools like Amazon CodeWhisperer, Tabnine, or OpenAI are also viable options. All of these reflect how generative capabilities are being incorporated into developer workflows.

These tools work by training on massive volumes of open-source code, documentation, and design patterns, enabling models to learn the “grammar” of programming languages, frameworks, and coding styles. As a result, developers can receive contextual suggestions as they code, auto-generate functions from natural language comments, or even translate algorithms across languages. Some of these capabilities are already part of our daily work, while others, such as generating code from natural language, are still evolving. Nonetheless, they meaningfully enhance development quality, simplify test data mocking, and support more sophisticated code review processes.

This shift is also giving rise to what’s known as “vibe coding” — a trend where developers and AI tools essentially ‘jam together’ on code, using natural language prompts and iterative feedback to co-create solutions. As WebProNews describes, “code vibing” allows teams to harness AI not just for output, but as a creative sounding board that accelerates ideation and problem-solving (WebProNews, 2025).

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Real Use Cases at Switch

We’ve already started implementing practical uses of generative AI across our projects. While not yet a fully standardized practice across the organization, we’re continuously testing different applications and refining our process as we go. Some highlights:

Autocompletion and Function Generation: IDEs powered by generative AI provide more intelligent and contextual suggestions than traditional autocomplete. For simple to moderately complex problems, it’s a significant productivity booster. For more complex logic, human oversight is still essential.

Unit and Integration Test Creation: AI helps us generate test skeletons based on existing functions or descriptions, boosting coverage, improving quality, and saving time.

Automatic Documentation: Developers often find documentation tedious. We utilize generative AI to automate comment generation and README creation, thereby reducing repetitive work and enhancing maintainability.

Refactoring and Bug Detection: Tech leads and devs alike use AI to clean up and standardize code, suggest improvements, catch bugs, and enhance security, keeping software robust and up to standard.

Test Data Generation: We generate realistic mock data to support functional, load, or performance testing — without exposing sensitive information.

Developer Onboarding: New hires often face a steep learning curve. AI helps juniors quickly understand code blocks and system logic, shortening onboarding time and boosting productivity.

Infrastructure as Code (IaC): We’re exploring how AI can support IaC by assisting with Terraform scripts and cloud configurations — accelerating delivery and reducing human error.

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Where Is Generative AI in Software Heading?

AI is rapidly becoming a natural layer throughout the software lifecycle. Some companies even speculate that intelligent agents might fully replace human-led models, though we’re skeptical that’ll happen anytime soon. Still, the integration is well underway.

One promising direction is conversational programming, where developers describe requirements in natural language and AI translates them into architecture, modules, or tests. But it’s going even further: as Forrester describes in Forbes, and as we mentioned earlier, the next evolution is “vibe coding”. This means AI won’t just respond to static prompts — it’ll understand the intent, context, and desired outcome behind what you’re building. It’s an ambient, goal-aware collaborator that senses what you’re trying to achieve — and helps you get there faster.

We foresee AI evolving from “autocompleters” into intelligent supervisors, capable of understanding a project’s full context, suggesting design choices, flagging technical debt, and maintaining overall quality.

Additionally, domain-specific models trained in industries like finance, retail, or logistics will offer compliance-ready, faster-delivery solutions tailored to each sector’s specific standards. We’re already prototyping these in key verticals at Switch.

AI’s footprint will also grow beyond development into areas such as continuous security, policy generation, vulnerability scanning, and real-time behavior monitoring to strengthen DevSecOps practices.

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Governance and Trust Will Be Key

As these tools mature, speed alone won’t be enough. The Forbes piece stresses that the real differentiator will be trust and governance: frameworks for audit trails, quality gates, security, and compliance need to evolve alongside code generation. We see this already, as AI’s role expands into continuous security, generating policies, scanning for vulnerabilities, and monitoring behavior in production, strengthening DevSecOps end to end.

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Final Thoughts

Is the “developer extinction event” coming? Not likely, at least not in the short or medium term. There are still too many puzzle pieces to fit together. Creativity, intuition, and vision won’t be replaced overnight.

What’s certain is that AI is becoming a powerful ally. It boosts productivity, automates tedious tasks, and frees developers to tackle more complex, high-value challenges. At Switch, we’re embracing the change — because we can’t afford to ignore it. 

The line between human and artificial input is blurring. It’s up to us to use this evolution strategically, for our clients and for ourselves.
The future is here. The table is set. Place your bets.

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Ready to see how Generative AI and new approaches like vibe coding could transform your software development? Let’s talk about how we can help you unlock faster, smarter, and more resilient ways to build. Contact our team to get started.

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