How AI Is Radically Transforming Software DevelopmentHow AI Is Radically Transforming Software DevelopmentHow AI Is Radically Transforming Software Development

"Vibe coding" shifts software development from traditional coding to AI-driven generation, creating two developer roles — product engineers using AI and high-coding architects ensuring quality and performance.

robot writing code
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In the rapidly evolving landscape of software development , one month can be enough to create a trend that makes big waves. In fact, only a month ago, Andrej Karpathy, a former head of AI at Tesla and an ex-researcher at OpenAI, defined "vibe coding" in a social media post . This approach to software development uses large language models (LLMs) to prioritize the developer's vision and user experience, moving away from conventional coding practices. The code no longer matters. Vibe coding is less about writing code in the conventional sense and more about making the right requests to generative AI (aka a Forrester coding TuringBot ) to produce the desired outcome based on the developer's "vibe" or intuition about how the application should look, feel, and behave.

The Future of Software Development Is Already Here

As cited in a YouTube video from Y Combinator (YC) titled "Vibe coding is the future," a quarter of startups in YC's current cohort have codebases that are almost entirely AI-generated (85% or more). The essence of vibe coding lies in its departure from meticulously reviewing TuringBot LLMs' suggested code line by line. Instead, developers quickly accept the AI-generated code. And if something doesn't work or fails to compile, they simply ask the LLM to regenerate it or fix the errors by prompting them back into the system. This method has gained traction for several reasons, notably the significant improvements in integrated development environments and agent platforms such as Cursor and Windsurf; voice-to-text tools like Superwhisper; and LLMs such as Claude 3.7 Sonnet. These advancements have made AI-generated code more reliable, efficient, and, importantly, more intuitive to use, keeping developers' hands off the keyboard and eyes on the bigger picture.

The viral reaction to Karpathy's concept of vibe coding, with close to 4 million instant views and countless developers identifying with the practice, underscores a broader shift in the software development paradigm. This shift aligns with Forrester's insights on TuringBots , which predicted a surge in productivity through AI by 2028. The reality is outpacing expectations, however, with significant impacts occurring much sooner. Vibe coding won't fade away.

The Role of the Software Developer Will Bifurcate

The advent of vibe coding and the proliferation of TuringBots are creating two distinct types of developers. On one side, developers will transform into product engineers who, while perhaps adept at traditional coding, excel in utilizing generative AI (genAI) tools to produce "apparently working" software based on domain expertise and some knowledge on the steps and tools needed to build software. These developers focus on the outcome, continuously prompting AI to generate code and assessing its functionality with no understanding of the underlying technology and code.

The philosophy is to just keep accepting code until it does what you want. Not only that, but they don't spend hours fixing a bug or finding the problem, since they can ask a well-trained coder TuringBot to do that for them or can just ask it to roll back and regenerate the code again. This approach may challenge our classical view of computer science skills, suggesting a shift toward developers who are more orchestrators of software development process steps than coding craftsmen. The concern of how we'll develop good developers over the years is gone, because you'll trust AI to do a good job. And if you want good developers, genAI will help those on the development trajectory learn faster.

On the other side of the spectrum are the high-coding architects. These individuals possess a deep understanding of coding principles and are essential for ensuring that software meets crucial service-level agreements such as security, integration, and performance before deployment. It's kind of what good developers do today. Their role becomes increasingly critical as the reliability and complexity of AI-generated code grows. For only the super-critical IT capabilities, most likely for back-end code, these high-coding capable architects need to write, review, and edit code while also making sure that the TuringBots have all the context they need to do a better job.

A Bigger Role for Testing and Testers

As AI-generated code becomes more trusted, the barrier to entry for software development lowers, giving rise to a growing population of vibe-coding developers. These individuals use natural language, not as a specification language but as the only interface to generate substantial portions of code and entire applications. As a result, high coding democratizes software development, just as low-code did for businesspeople. As I've always recommended for TuringBots, testing should once more be relaunched as a key validation step. For building a weekend project or a product demo to get funding, vibe coding would work just fine, but it requires more scrutiny for being adopted by enterprises and mature product vendors. In fact, this approach necessitates a reassessment of testing and quality assurance processes for everything that comes out of vibe coding. Organizations must place a greater emphasis on end-to-end functional testing, which, ironically, can also be facilitated by LLMs at the request of the product engineers. In fact, product engineers and/or testers could just ask the LLM to both generate and execute the end-to-end tests for them.

Some Critical Questions Remain Unanswered

Looking at AI-enabled software development through a traditional lens and for enterprise use highlights significant risks. Is it wise to deploy unreviewed (and, at best, automatically tested) code directly into production? As AI improves, many of these concerns may diminish, but here are some critical considerations:

  1. Debugging versus coding. Developers may find themselves spending more time debugging code when genAI fails to resolve errors. This emphasizes the continued need for strong developer skills (but, I'd add, less than what we've traditionally needed). Yet the ratio between coding and debugging time inverts.

  2. Energy consumption. Does the obsessive generation and regeneration of code via LLMs lead to higher energy use compared to structured software development lifecycle (SDLC) methods? Accurate cost assessments are yet to be conducted.

  3. Application complexity. Vibe coding currently seems to work for front-end development because LLMs have a lot of front-end code to be trained on, but how would it work on back-end coding?

  4. Testing necessity. Comprehensive testing remains crucial, though not all built functionality will require it. Much of this can be automated as testing TuringBots improve. But this raises the question of whether organizations possess the necessary skills.

  5. Intellectual property protection. Will the emerging generative agents safeguard your IP as effectively as more traditional tools such as GitHub Copilot or Amazon Q?

  6. Talent development. Are you prepared to nurture talent geared toward product engineers and "vibe coding" as opposed to the more rigorous path of architectural engineers? How will testing competencies develop? What about other roles?

These questions highlight the evolving challenges and opportunities in software development as AI technologies advance.

So Where Do We Go From Here?

In my view, vibe coding will further reduce the complicated and elaborated SDLC to just "generate" and "validate," as we defined in our bold vision report, The Rise Of Application Generation Platforms .

Vibe coding is not just a fad but a signal of the transformative impact that AI is having on software development. As this trend continues to evolve, it will be imperative for enterprises and software vendors to adapt their strategies, recognizing the value of both product engineers and coding architects. This developer duality will be crucial in navigating the future landscape, where the ability to harness AI effectively will distinguish successful software projects. The challenge will be in balancing innovation with the rigor of traditional software development principles, ensuring that the software not only works but that it scales securely, efficiently, and reliably. Platforms will have to quickly move from supporting AppDev to supporting AppGen, which is not a simple exchange of words.

If you found this blog interesting and you'd like to dig deeper to see how you could (and should) embrace the use of genAI for software development, you can reach out to me by scheduling a guidance session or an inquiry via email: [email protected] . If you have a product that fits this space, please consider scheduling a briefing: [email protected] .

— Diego Lo Giudice , VP, Principal Analyst

This article originally appeared on Forrester's Featured Blogs.

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May 15, 2025
Generative AI is already empowering creators and terrifying anyone who ever watched a Matrix movie. While the role of generative AI in business has just begun to scratch an itch, it’s crucial that IT thought leaders decide exactly how and what they’re going to do to stay ahead of the competition, before it’s too late. In this event we’ll discuss the uses of quantum computing, generative AI in development opportunities, hear from a panel of experts on their views for potential use cases, models, and machine learning infrastructures, you will learn how to stay ahead of the competition and much more!
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