The Spectrum of AI Adoption and Vibe Coding

As a slight riff on what I have written before on this topic, "Vibe coding" manifests differently across three distinct groups, each bringing unique perspectives and gaining different benefits. Below is a slightly clearer breakdown of each group.

Group 1: Complete Newcomers

• Background: Never programmed before, unfamiliar with software development
• AI Impact: Suddenly empowered to create products and generate revenue
• Vibe Coding Benefits: Gain entry into previously inaccessible domain; can prototype and build without traditional barriers
• Learning Curve: Steepest transformation, moving from zero capability to product creation
• Adaptation Advantage: Easiest to accept the process as they have no legacy background to compare with or shed

This demographic will drive significant demand for development services, forcing AI agents to make a critical strategic choice: focus on serving them or stick with traditional approaches. Those who choose to serve this market will need to fundamentally reimagine their offerings across every dimension.

Take UX as an example. Most AI coding services have built on VS Code and other established IDEs, starting from the assumption that code is central to the experience. But for this audience, code is secondary—it eventually matters, but their initial priorities are (in this order):

• Look and feel - how the end product appears and feels to users
• Functionality - what the product actually does
• Integrations - how it connects with other tools and services

This drives toward software production (deliberately avoiding the loaded term "development") that emphasizes visual, point-and-click interfaces with extensive guidance. The language used will increasingly diverge from traditional development terminology as the practice itself moves further from established processes.

If you believe that AI coding in its purest form represents the future—where code becomes increasingly abstracted away—then this group deserves the most attention. Their current needs perfectly align with that vision: they want to create software without needing to understand the underlying code.

Despite being largely overlooked in current AI coding discussions, which focus heavily on developers, this demographic represents the true growth engine of the AI-powered development landscape. The platforms that recognize and serve them effectively will capture the largest market opportunity.

Group 2: Software-Adjacent Professionals

• Background: Product managers, designers, stakeholders who understand software conceptually but don't code
• AI Impact: Bridge the gap between vision and execution
• Vibe Coding Benefits: Can directly implement ideas they previously needed to delegate; gain hands-on understanding of development constraints and possibilities
• Learning Curve: Moderate - leveraging existing software knowledge while gaining practical skills
• Unique Position: Very empowering for this group - those already in the industry have a lot to gain as they can do things they could not do before, and they have a framework (traditional software development) to communicate their ideas and expectations

Product managers and designers who adopt vibe coding find themselves in a uniquely complex position within their organizations. While they gain unprecedented ability to translate vision into working prototypes, they must carefully navigate relationships with developers who may view this shift with skepticism. Traditional team dynamics often rely on a clear division of labor—product people articulate requirements, designers create mockups, and developers build solutions. When product managers begin producing functional prototypes, this established workflow becomes blurred, potentially creating friction with development teams who are accustomed to being the sole technical implementers.

This new capability enables product managers to engage in what might be called "code preparation"—delivering prototypes that go far beyond traditional wireframes or specifications. Instead of handing developers a document describing desired functionality, they can present working demonstrations with specific interactions already implemented, particular libraries already selected, and technical approaches already validated. These prototypes carry tremendous specificity about user experience details that might otherwise be lost in translation between product vision and technical implementation. The acceleration this creates is significant, as ideas can move from concept to detailed implementation blueprint without the usual rounds of back-and-forth clarification.

However, this evolution raises complex questions about traditional developer roles, particularly for front-end specialists. When product managers arrive with prototypes that have already solved interaction challenges and made key technical decisions, they may inadvertently encroach on territory that developers consider central to their expertise. Front-end developers often take pride in translating design concepts into elegant code solutions, making architectural decisions about component structure, and optimizing user interactions. If product managers begin delivering solutions that pre-determine these choices, it could create tension around professional boundaries and reduce the creative problem-solving that many developers find most rewarding in their work.

The challenge for organizations becomes establishing new collaborative frameworks that harness the benefits of vibe coding while preserving the valuable expertise that developers bring to the process. Product managers must learn to position their prototypes as starting points for technical collaboration rather than finished specifications, while developers need to adapt to working with more technically sophisticated product requirements. Success likely requires explicit conversations about how roles evolve and where each team member's expertise adds the most value in this new dynamic, balancing the efficiency gains of detailed prototyping with the need to maintain collaborative relationships and respect professional domains.

Group 3: Experienced Developers

• Background: Professional programmers with deep technical knowledge
• AI Impact: Enhanced productivity and capability expansion, but also fundamental disruption of established workflows
• Vibe Coding Benefits: Advanced autocomplete, rapid prototyping, exploration of unfamiliar technologies
• Learning Curve: Paradoxically challenging - must surrender deeply ingrained practices as the software development process is presented to them significantly anew through vibe coding
• Unique Challenge: Despite having the deepest technical knowledge, they may face the steepest psychological adjustment as their expertise becomes both asset and potential barrier

The technology world has been caught off guard by an unexpected phenomenon: software developers, the very professionals who should theoretically embrace AI coding assistants most readily, are proving to be among the most reticent adopters. This resistance runs counter to the prevailing narrative that positions programming as AI's most obvious conquest, where productivity gains seem most measurable and immediate. Yet what we're witnessing in developer communities offers profound insights into how workplace AI adoption actually unfolds—not as a seamless efficiency upgrade, but as a complex negotiation between established expertise, professional responsibility, and technological promise.

Developers' caution stems from practical realities that management often underestimates. First, the timeline mismatch is stark: while executives envision rapid deployment and immediate productivity boosts, developers know that fundamentally altering one's workflow is measured in months, not weeks. They cannot simply flip a switch and operate at the speed expected by higher-level management. Second, and more critically, developers remain accountable for understanding every line of code that ships—a responsibility that becomes precarious when AI generates significant portions of that code. Heavy reliance on "vibe coding" risks creating a dangerous knowledge gap where developers lose the mental model of their own systems, becoming vulnerable to dependencies on AI agents that haven't yet earned their trust.

This trust deficit has concrete technical foundations. Current AI coding assistants operate with limited context windows, meaning they cannot grasp the entirety of complex codebases. In critical debugging situations, experienced developers can often diagnose and fix issues faster than agents who must "re-read" limited portions of code and reconstruct understanding from scratch. More subtly, AI assistants frequently don't comprehend the intent behind seemingly suboptimal code—what appears to be an improvement opportunity might actually be a carefully crafted solution addressing specific constraints or edge cases that breaking would destabilize the entire system.

This dynamic creates a predictable tension that's playing out across tech organizations: managers see massive economic promise in AI-driven productivity gains, while developers feel that management "doesn't get it." The developers aren't being obstinate or change-averse—they're being professionally responsible. Their measured approach to AI adoption, far from being a failure to embrace progress, actually models how expertise-heavy professions should navigate transformative tools: with careful evaluation, gradual integration, and healthy skepticism about claims that complex professional judgment can be easily automated.

What makes this particularly instructive for understanding broader workplace AI adoption is how it reveals that technical sophistication doesn't correlate with eager AI embrace. In fact, the deeper one's expertise, the more acutely one understands both the potential and the pitfalls. Developers' cautious approach offers a preview of similar dynamics that will emerge as AI tools proliferate across other knowledge-work domains where professional judgment, accountability, and years of accumulated expertise create complex considerations that resist simple automation narratives.