In this Tech Insight, we look at what vibe coding is, how it’s transforming the way software is created, what it’s being used for, and why it’s generating both excitement and concern across the tech industry.
What Is Vibe Coding?
Vibe coding is the term increasingly used to describe the process of creating software through natural language prompts rather than traditional coding. It relies on large language models (LLMs) to interpret a user’s intent and convert it into functioning code, often within seconds.
The approach builds on earlier trends in low-code and no-code platforms but takes them a step further. By removing the need for drag-and-drop interfaces or pre-built modules, vibe coding allows users to describe what they want in plain language, for example, “create a form that collects customer feedback and sends it to Microsoft Teams”, and receive a working prototype in response.
The idea has gained particular traction among solo founders, product designers, and teams that want to move quickly without relying on engineering resources. But as the technology evolves, attention is shifting to its potential in larger organisations.
From Indie Tools to High-Growth Startups
The rise of platforms like GitHub Copilot and ChatGPT has made AI-assisted coding familiar to many developers. However, newer startups such as Lovable, a Swedish company now valued at $1.8 billion following a $200 million Series A, are taking the concept in a different direction.
For example, Lovable’s product allows users to build fully functional apps by chatting with an AI assistant. It’s currently used by early-stage startups and solo creators who want to focus on design and user experience rather than infrastructure or syntax. According to RTP Global, one of Lovable’s backers, the company is part of a larger shift where technical skills are no longer the gatekeeper to building software.
“The cultural shift is real,” said Thomas Cuvelier, a partner at RTP Global. “If technical ability is no longer a differentiator, creativity and user experience become the new competitive edge.”
Other startups entering the space include Cody, Builder.ai, and Spellbrush, all of which aim to simplify software creation for non-coders. Meanwhile, major players like Google and Microsoft are integrating similar features into Gemini Code Assist and Power Platform respectively.
How Developers Are Responding
While vibe coding is often associated with new entrants and early-career developers, recent data appears to suggest that experienced engineers are embracing it even more actively.
For example, a July 2025 survey by cloud platform Fastly found that 32 per cent of developers with over 10 years of experience now use AI-generated code for more than half of their production output. That’s more than twice the rate among junior developers. Just 13 per cent of junior developers reported doing the same.
“When you zoom out, senior developers aren’t just writing code — they’re solving problems at scale,” said Austin Spires, Fastly’s senior director of developer engagement. “Vibe coding helps them get to a working prototype quickly and test ideas faster.”
However, the same survey found that developers often need to heavily edit the code AI tools produce. For example, around 28 per cent said they spent so much time fixing and refining outputs that it cancelled out most of the time saved. This was especially true for more complex or long-lived projects where quality, maintainability, and security matter.
The Enterprise Challenge
For enterprise IT teams, the promise of vibe coding, i.e. rapid prototyping, reduced cost, broader participation, is pretty compelling. However, practical adoption remains limited, largely due to concerns around compliance, security, and technical debt.
Most enterprise environments demand strict auditability, version control, and accountability for any code that enters production. That’s difficult to guarantee when the code is generated by a black-box model based on user prompts. Without clear documentation or traceability, teams can’t easily demonstrate how a particular function was created, or why it behaves the way it does.
Concerns about the transparency and reliability of AI-generated code appear to be a recurring theme in enterprise discussions. Tech ethicists and researchers have warned that without proper safeguards, businesses risk deploying software they don’t fully understand. This is especially problematic in regulated sectors such as finance, healthcare, and critical infrastructure, where audit trails and explainability are non-negotiable.
Anne Currie, co-author of the Sustainable Computing Manifesto, has written extensively on the importance of accountability in software systems. In previous talks and articles, she has argued that AI-driven automation must be transparent and traceable if it is to be used responsibly in real-world environments. While not commenting specifically on vibe coding, her work highlights the broader risks of black-box decision-making in enterprise IT.
In response to these types of concerns, some platforms are adding features like code justification, dependency maps, and access logs. GitHub Copilot Enterprise, for example, includes usage tracking and administrator controls, while Google’s Duet AI offers explainability features for its outputs. But these tools are still being refined.
The Changing Developer Culture
Alongside the technical debate, vibe coding appears to be changing the way developers think about their work, including its environmental impact.
For example, Fastly’s survey found that 80 per cent of senior developers now consider the energy usage of the code they produce, compared to just 56 per cent of junior developers. This awareness is beginning to shape how software is built, especially in companies with sustainability targets.
Energy Consumption
One concern is that AI coding tools themselves consume significant energy. For example, every prompt or suggestion involves inference from a large language model hosted in a data centre. Despite this, few platforms provide visibility into the energy footprint of each interaction, something developers increasingly want to see.
“There’s not a lot of transparency about the carbon cost of using AI tools,” said Spires. “But more experienced developers are thinking ahead to what that impact means for users and systems.”
New Risks
Despite its benefits, it seems that vibe coding is introducing new risks. For example, code quality is a recurring concern, especially in critical systems. Several developers surveyed by Fastly reported subtle bugs in AI-generated functions that took hours to diagnose. Others said the tools sometimes “hallucinate” logic that seems valid but fails under edge cases.
Security is another issue. AI tools can inadvertently copy insecure patterns from training data or introduce backdoors if prompts are unclear. There have already been real-world cases of AI-generated software containing vulnerabilities or misconfigurations, prompting caution among security teams.
Fastly’s findings also revealed a tension between perception and reality. Developers often feel faster using AI tools because of instant feedback and autocomplete features, but in many cases, actual productivity gains are offset by the need to test, rework or debug the generated code.
That disconnect was reflected in an RCT (randomised controlled trial) published in early 2025 (Stanford University), which found that developers using AI tools took 19 per cent longer on average to complete certain coding tasks, not because they weren’t effective, but because they relied too much on the suggestions and later had to fix them.
What Does This Mean for Your Business?
UK businesses exploring vibe coding will need to weigh speed and accessibility against long-term risks. While it can enable faster internal development and reduce reliance on overstretched IT teams, the lack of built-in governance creates some real concerns. For example, in regulated sectors, even a small oversight in explainability or security could carry legal and operational consequences.
Enterprise adoption is likely to depend heavily on how well platforms adapt to professional standards. The ability to generate working prototypes is not enough if those outputs can’t be documented, versioned, tested, or supported over time. Tools that offer strong administrative control, user permissions, and audit trails are more likely to gain traction in large organisations with strict oversight requirements.
For vendors and platform builders, meeting these expectations could open up substantial new markets. However, that is likely to require a move from consumer-grade UX tools to enterprise-grade development environments. Startups hoping to scale in this space will need to prove they can support secure, sustainable, and compliant deployments at scale, not just fast app creation.
For developers, it seems that a change in mindset is already visible. Vibe coding is changing how software is prototyped, reviewed, and refined, with new expectations around creativity, environmental impact, and collaborative input. That change is likely to influence not just how code is written, but who gets to write it, and who takes responsibility when things go wrong.