From four years to four days as developer builds software products using ChatGPT Pro

Four years of product development compressed into four days for $200. That’s the kind of productivity leap that sounds too good to be true—until you experience it firsthand with AI-powered coding assistance.

This isn’t theoretical speculation about artificial intelligence transforming work. It’s a detailed account of how ChatGPT Pro, OpenAI’s premium AI service, eliminated the biggest bottleneck in solo software development: context switching. For developers juggling multiple responsibilities, the ability to maintain coding momentum without lengthy ramp-up periods represents a fundamental shift in how technical projects can be approached.

The productivity paradox of part-time development

Context switching has long been the silent killer of developer productivity, particularly for professionals who code as a secondary responsibility. Unlike full-time programmers who can maintain continuous focus on codebases, part-time developers face a recurring challenge: every return to a project requires significant mental overhead to rebuild familiarity with the code structure, development environment, and project requirements.

This challenge becomes exponentially more complex when managing multiple software products. Over the past decade, maintaining a collection of WordPress plugins—software extensions that add functionality to WordPress websites—meant accepting a frustrating reality. Each development session required 2-4 days of uninterrupted time to achieve meaningful progress. With only 3-4 such time blocks available per year, feature development moved at a glacial pace.

The mathematics were stark: 10 plugin add-ons completed across 10 years, averaging one substantial enhancement annually. Each add-on typically generated $20-30 in revenue, barely covering the monthly operational costs of hosting, support systems, and licensing fees that accumulated regardless of development progress.

Enter ChatGPT Pro and the context switching solution

ChatGPT Pro, priced at $200 monthly, offers unlimited access to advanced AI coding assistance through integration with Visual Studio Code (VS Code), Microsoft’s popular code editor. Unlike the $20 ChatGPT Plus plan, which implements usage limits that can halt development mid-stream, the Pro tier promises uninterrupted access for typical development workflows.

The integration works by analyzing existing code patterns and generating new functionality based on natural language instructions. Rather than starting each coding session with extensive review of previous work, developers can immediately begin requesting specific features or modifications. The AI maintains context about the codebase and can suggest implementations that align with existing architecture.

This represents a fundamental shift from traditional development workflows. Instead of spending the first day of each rare coding window rebuilding mental models of complex systems, development can begin immediately with feature requests like “create a user interface for tracking website visitor analytics” or “implement IP address blocking functionality.”

The four-day sprint: transforming limitations into advantages

With a rare four-day window of unscheduled time and access to unlimited AI coding assistance, an ambitious experiment began: could an entire software product be built from conception to completion in a single day?

Day 1: Site analysis and security monitoring tool

The first project tackled a common need among website administrators: comprehensive visitor tracking and security monitoring. Traditional development would have allocated the entire four days just to prototype the user interface. Instead, working iteratively with AI assistance, a complete product emerged within hours.

The process involved breaking complex requirements into smaller, manageable prompts. Rather than providing comprehensive specifications upfront—an approach that had previously failed—each component was developed piece by piece. First, the user interface layout. Then, data storage structures. Finally, the business logic connecting visitor detection with security analysis.

By day’s end, a fully functional WordPress plugin existed that could track login attempts, detect automated bots, consolidate security events into searchable logs, and generate administrative reports with visual indicators for suspicious activity. All visitor data remained under local administrator control, addressing privacy concerns while providing enterprise-level analytics capabilities.

Day 2: AI scraping protection and content licensing

With one complete product finished ahead of schedule, the experiment continued. Could a second product be developed just as rapidly?

The second day focused on an increasingly relevant challenge: protecting website content from unauthorized AI training. The resulting plugin integrated Really Simple Licensing (RSL), a recently developed standard that allows website owners to declare how artificial intelligence systems may use their content.

Beyond voluntary compliance mechanisms, the plugin included active countermeasures. Using existing security infrastructure, it could return HTTP 403 “Forbidden” errors to AI crawlers that ignored licensing declarations. The tool provided website owners with both diplomatic and enforcement options for content protection.

The development followed the same iterative approach: user interface first, settings management second, enforcement logic last. By evening, another complete product existed.

Day 3: Advanced IP blocking and network security

The third day addressed network-level security through sophisticated IP address blocking. This tool needed to handle both individual problematic visitors and entire network ranges, supporting both IPv4 and the more complex IPv6 addressing systems.

The technical complexity was significant. IPv6 networks can contain numbers so large that the interface required exponential notation to display blocked address counts accurately. The system needed to detect real visitor IP addresses even when websites operate behind content delivery networks or proxy services.

Performance optimization was critical—security checks occur on every page load, so inefficient code could slow entire websites. The AI assistance proved particularly valuable here, suggesting caching strategies and lookup optimizations that maintained speed while providing comprehensive blocking capabilities.

Day 4: Guest access control and temporary sharing

The final day tackled a frequently requested feature: secure temporary access to private websites without creating permanent user accounts. This required generating cryptographically secure tokens, managing time-based expiration, and providing administrative oversight of active guest sessions.

The complexity lay in balancing security with usability. Guest links needed to be truly random and unguessable, but the system also required intuitive management interfaces for administrators. The final implementation provided both one-time-use and time-limited access options, with instant revocation capabilities.

A minor family emergency interrupted development mid-day, but the AI’s ability to quickly resume context allowed work to continue seamlessly once resolved.

The mathematics of accelerated development

The results speak to a fundamental transformation in development velocity. Four complete products in four days represents a 365x improvement over the previous pace of one product annually. While individual circumstances vary, the core constraint that was eliminated—context switching overhead—affects most developers who balance coding with other responsibilities.

The $200 monthly cost becomes economically viable when viewed against traditional development timelines. If a weekend of focused development can replace what previously required a full year of intermittent effort, the AI assistance pays for itself through compressed project timelines alone.

Beyond development: the marketing reality

Interestingly, product development now represents the smallest time investment in bringing software to market. Each completed plugin still requires comprehensive testing, demonstration videos, technical documentation, product pages, promotional graphics, user notifications, and marketing campaigns.

These activities, which can be completed in smaller time increments, will collectively require more time than the original development. This shift creates an unusual situation where promotional activities become the primary time constraint rather than technical implementation.

For developers with limited large time blocks but abundant small time windows, this represents an ideal productivity balance. Marketing tasks can be completed during brief availability gaps that would be insufficient for meaningful coding progress.

Broader implications for technical productivity

The experiment reveals how AI assistance can eliminate specific productivity bottlenecks that disproportionately affect part-time developers. The technology doesn’t replace human expertise—it required continuous guidance, debugging, and architectural decisions—but it removes the cognitive overhead of rebuilding project context.

This has particular relevance for businesses where technical staff divide attention between development and other responsibilities. Product managers who code occasionally, consultants building client tools, or executives maintaining technical skills could all benefit from reduced context switching friction.

The key insight extends beyond WordPress development. Any technical work that requires deep context and suffers from interrupted attention patterns could see similar productivity gains from AI assistance that maintains project continuity between sessions.

Economic considerations and scalability

The $200 monthly cost positions ChatGPT Pro as a productivity tool rather than a casual experiment. For professionals who can leverage the context switching advantages during focused development periods, the investment becomes economically rational.

However, the value proposition depends heavily on having projects that benefit from compressed development timelines and sufficient technical expertise to guide AI assistance effectively. The technology amplifies existing capabilities rather than replacing fundamental programming knowledge.

Organizations evaluating similar AI investments should consider both the direct productivity gains and the secondary effects of faster iteration cycles. When development becomes less constrained by scheduling overhead, experimental features and rapid prototyping become more feasible, potentially accelerating innovation beyond just implementation speed.

Looking ahead: AI as a productivity multiplier

This experiment demonstrates AI’s potential to remove specific workflow bottlenecks rather than replace human capabilities entirely. The most significant impact came not from the AI’s coding ability, but from its capacity to eliminate the productivity friction that made part-time development economically unviable.

As AI coding assistance continues improving, the primary beneficiaries may be professionals who previously found technical projects too time-intensive to pursue. By collapsing the context switching overhead that historically made intermittent development inefficient, AI tools can make technical skills more accessible to broader professional audiences.

The ultimate question isn’t whether AI can replace developers, but whether it can remove the barriers that prevent more professionals from incorporating technical development into their broader skill sets. Based on this four-day experiment, that transformation may already be underway.