The growing attention around vibe coding is no coincidence. Artificial intelligence is fundamentally reshaping how web and mobile applications are designed and built, dramatically reducing prototyping timelines and lowering barriers to entry for software development.
Describing a requirement in natural language and rapidly obtaining a working application is no longer an exception. It is becoming standard practice. This approach democratizes development and accelerates experimentation. Speed, however, does not automatically translate into value.
As projects scale, move into production, and integrate with complex ecosystems, design, architecture, and governance decisions continue to be, and increasingly are, the real differentiators.
AI and Vibe Coding: What Actually Changes
The term vibe-coding, popularized in 2025 by Andrej Karpathy, refers to a prompt-first approach to software development. The desired outcome is defined, AI generates the code, and the result is refined through successive iterations.
This shifts the starting point of digital initiatives. Projects no longer necessarily begin with a fully defined architecture, but with an AI-driven exploratory phase. It represents a meaningful paradigm shift and helps explain both the growth of application development platforms and the deeper integration of generative AI into development tools.
Beyond the initial enthusiasm, the real impact of this shift can be understood across three key dimensions.
Time and Cost: More Speed, Not Less Accountability
AI makes it possible to rapidly produce early software prototypes, even from high-level prompts. This accelerates the initial phases of projects and allows multiple solutions to be tested in parallel.
This does not mean development becomes cost-free. The work simply moves elsewhere. Without experienced guidance, teams risk producing fast but misaligned solutions that fail to address real needs, ultimately wasting time and resources. Value emerges when speed is paired with direction, turning AI acceleration into sustainable design choices.
Evolving Roles, Enduring Expertise
As AI becomes embedded in development processes, professionals with a strong understanding of business models and operational processes, alongside technical expertise, become even more critical. Their role is to steer development, define clear rules, and ensure solutions are reliable, secure, and accessible.
Development is no longer just about writing code. It is about orchestrating different competencies and constraints to deliver outcomes that work in real-world contexts.
Quality and Risk: The Real Area of Focus
Greater speed can also mean greater risk if adequate controls are missing. For this reason, vibe coding and automation must be governed through human oversight, testing, and robust quality and security standards, especially for mission-critical systems.
Experienced professionals in architecture, application ownership, QA, security, and accessibility play a decisive role here. They translate requirements and constraints into practical rules, define what is acceptable, identify what should be avoided, and ensure solutions are properly validated before release.
Digital Accessibility: From Compliance Obligation to Design Driver
The challenge is heightened by the mandatory digital accessibility requirements of the European Accessibility Act coming into force from June 28, 2025. These apply to a broad range of digital products and services, including websites, e-commerce platforms, mobile applications, web apps, and digital content.
For organizations, this is not merely about avoiding sanctions. The real challenge lies in integrating accessibility structurally, without slowing innovation or inflating costs. The principle is clear. Accessibility cannot be added later. It must be designed in from the outset.
In AI-assisted, rapid development environments, this becomes even more critical. Interfaces, components, workflows, and content must be compliant by design, or early speed will quickly turn into technical and organizational debt.
Think before you build
One principle remains constant throughout. The ability to build quickly is not the same as the ability to build well. AI accelerates execution, but it does not replace human judgment, product vision, or sound design decisions.
Experience still matters. Listening to client needs, interpreting requirements, and translating them into effective solutions before and after development remain essential. AI has no inherent design intent. It lacks context, does not understand which problems truly matter, and cannot distinguish relevance from noise.
The role of people remains essential in connecting technology to the real world. First through business needs, and then through user needs, to determine whether what has been built actually works and meets its original objectives.
Roles evolve and timelines shorten, but the underlying principle does not change. AI expands what is possible, not who is responsible. Today, as always, progress depends on those who can set direction, ask the right questions, and critically assess the results.
