From IT Management to Weekend Innovation
As a manager at a large IT services company, I’ve had a front-row seat to the digital transformation reshaping businesses across industries. But it’s my weekend hobby that has provided the most profound insights into where software development is truly headed.
For the past year, I’ve been creating AI-generated software tools with Replit as my primary companion. What began as curiosity has evolved into a revealing journey that’s changing how I view my day job and the future of our entire industry.
The Perfect Collaborator
The most striking aspect of working with an AI coding assistant is its temperament. My Replit companion never loses its temper, never talks back, and always provides a response. After spending weekdays navigating the complexities of human teams—managing personalities, navigating disagreements, and addressing communication gaps—the consistency of the AI creates a refreshingly different dynamic.
This emotional equilibrium creates a surprisingly productive environment. There’s no need to consider the AI’s mood or worry about pushing too hard for revisions. Every question receives an attempt at an answer, every problem is approached with the same level of enthusiasm, and no request is deemed too trivial.
Is it perfect? No. The AI is sometimes wrong—a critical limitation. But even in its errors, it maintains a consistency that creates a reliable rhythm to development that human teams often struggle to match.
From Months to Weekends
What truly amazed me was the compression of development timelines. Projects that would take weeks or months with teams at my company could reach 90% production-ready status in a single weekend. This isn’t hyperbole—I’ve built inventory systems, customer portals, and data analysis tools, each in the span of a weekend.
The elimination of handover friction and coordination meetings drastically compresses timelines. At work, I watch as requirements move through planning meetings, development sprints, code reviews, and testing cycles—each stage involving different people, schedules, and communication overhead.
With Replit, these stages collapse. I can iterate on an idea, implement it, test it, refine it, and move to the next feature—all in a continuous conversation. The context-switching costs plummet, and the development momentum rarely breaks.
The Essential Blueprint
The revelation that surprised me most: a proper Software Requirements Specification (SRS) remains absolutely essential. In fact, the quality of the AI’s output directly correlates with the clarity of my specifications.
Early attempts with vague requirements led to disappointing results and endless revisions. The AI would build exactly what I asked for—revealing that my own understanding of what I wanted was often incomplete.
I began using ChatGPT to help craft these specifications, creating a pipeline where one AI helped me communicate effectively with another. This workflow not only improved the code output but also sharpened my own thinking. The SRS became the currency of development—invest time here, and the returns in implementation quality were substantial.
At work, I’ve observed teams spend weeks refining requirements documents that still contain ambiguities. My weekend projects have taught me that the ability to create precise, comprehensive specifications is becoming more valuable than the ability to implement them.
The Commodity Code Revolution
This experience has forced me to confront an uncomfortable question: which types of software developers will remain essential in this new paradigm?
Junior developers face the most immediate challenge. The tasks traditionally assigned to entry-level positions—basic feature implementation, simple bug fixes, standard component creation—are precisely what AI excels at handling. In my weekend projects, I’ve replaced what would typically require 2-3 junior developers.
Development is becoming a commodity at an alarming rate. The cost of building basic to intermediate solutions has plummeted, and with it, the premium previously commanded by developers with ordinary skill sets.
As a manager in a services company, this shift has profound implications for our business model. When I can create in a weekend what would typically bill at hundreds of consultant hours, the economics of software development are fundamentally changing.
The Idea Economy
We’re entering an idea economy where conceptualization and distribution become the limiting factors. Having a vision for what to build and understanding how to get it to users matters far more than implementation details.
In my weekend projects, the technical implementation—once the defining challenge—became almost an afterthought. The real challenges were defining exactly what should be built, designing intuitive user experiences, and determining how the tool would integrate into existing workflows.
For my company, this suggests a strategic pivot. Our value proposition can no longer center on implementation excellence alone. We must shift toward helping clients conceptualize solutions and integrate them into their business operations—areas where human expertise remains irreplaceable.
The New Developer Profile
For professionals in our industry, this reality demands adaptation. Technical skills alone no longer provide job security. From my vantage point as both a manager and weekend developer, I see several profiles emerging:
- AI Orchestrators: Professionals who excel at directing AI tools, verifying outputs, and integrating components
- Business-Technical Translators: Those who can bridge business requirements and technical specifications
- Experience Designers: Professionals focused on how humans interact with technology solutions
- Integration Specialists: Experts who connect new solutions with legacy systems and business processes
- Domain Specialists: Developers with deep understanding of specific industries or technical domains
The vast middle ground of implementation is being automated away faster than many in our industry are willing to acknowledge.
Development Economics Transformed
The economics are undeniable. Projects that once required teams of 5-10 developers can now be handled by 1-2 skilled professionals working with AI. Development costs drop by an order of magnitude, enabling more experimentation and rapid iteration.
For large IT service providers like my employer, this represents both threat and opportunity. Our traditional billing models based on developer hours are under pressure. Yet, the demand for digital solutions is higher than ever—creating openings for those who can adapt to this new paradigm.
The Two-Track Future
I believe we’re entering a period where software development follows two distinct paths:
- Commodity Development: Most business applications, websites, and standard tools will be built primarily by AI with human oversight, at a fraction of previous costs. My weekend projects fall squarely in this category.
- Premium Development: Mission-critical systems, highly optimized applications, and specialized domains will continue to require significant human expertise, commanding premium rates but representing a smaller segment of the market.
As I look to the future—both for my weekend hobby and my professional role—the key insight is that we’re no longer selling code but outcomes. The technical details of implementation become less important than the business value created.
Looking Forward
My dual perspective as both a corporate IT manager and weekend AI developer has convinced me that we’re witnessing a fundamental shift in our industry. The gap between what I can accomplish alone with AI tools versus what requires entire teams at work continues to narrow.
For IT service providers, this demands a strategic pivot. We must move up the value chain to areas where human judgment, creativity, and domain expertise remain differentiated. The “heavy lifting” of code production will increasingly be automated.
For individual professionals, continuous adaptation is essential. The skills that secured a comfortable career just five years ago may not be valuable in five years’ time. The ability to direct and enhance AI-generated solutions, rather than producing code from scratch, becomes the new core competency.
The future belongs not to those who can code, but to those who know what to build and why. Distribution—getting solutions into users’ hands effectively—becomes the ultimate limiting factor once creation is democratized.
What has been your experience with AI coding tools? How do you see them reshaping our industry in the coming years? I’d welcome your thoughts and experiences as we collectively navigate this profound transformation.
