The perennial debate over whether to "build" custom software solutions or "buy" off-the-shelf products to address business problems has been a foundational question since the dawn of the tech industry. This strategic decision, deeply embedded in the operations of traditional IT departments and modern product teams alike, is currently undergoing its most significant transformation yet, driven by the rapid advancements in user programming tools and Generative Artificial Intelligence (AI). While these new capabilities are democratizing software creation, industry analysis suggests that the future is not a simple shift to universal building, but rather a sophisticated hybrid ecosystem where complex, purchased services integrate seamlessly with bespoke, AI-generated solutions.
A Historical Perspective: The Enduring Build vs. Buy Debate
For decades, the build-versus-buy calculus has been a critical determinant of an organization’s technological agility and financial efficiency. Early enterprises often relied heavily on custom-built solutions, particularly during the mainframe era when commercial software options were limited. This approach offered unparalleled customization and competitive differentiation, but it came with considerable costs, lengthy development cycles, and the ongoing burden of maintenance, technical debt, and reliance on specialized in-house expertise. A 2022 report by McKinsey highlighted that custom software development projects frequently exceed budget by 20-30% and miss deadlines by similar margins, underscoring the inherent risks.
The rise of packaged software in the 1980s and 90s, followed by the proliferation of Software-as-a-Service (SaaS) in the 2000s, offered compelling alternatives. SaaS vendors promised faster deployment, reduced upfront costs, lower maintenance overhead, and access to continually updated features. The global SaaS market, valued at an estimated $237.47 billion in 2023, is projected to grow to over $908.21 billion by 2032, according to Grand View Research, indicating its pervasive adoption across industries.
However, buying software often introduces its own set of challenges, primarily limitations in functionality and the need for extensive customization to fit unique business processes, particularly within large enterprises. The original article rightly points out that the "buy vs. build" choice has often been an oversimplification, as many organizations find themselves in a hybrid state of buying and then significantly customizing. The core competency rule emerged as a guiding principle: if a problem related to a company’s primary value proposition, building was often preferred; if it was peripheral, buying became the default. Yet, numerous exceptions exist, especially for highly specialized problems where no commercial solution is available. Historically, product teams often had the resources for custom development, while the broader business relied on IT departments for an "endless list of requests," creating significant backlogs and bottlenecks.
The Dawn of User Programming: A Chronological Evolution
The empowerment of non-technical users to create software solutions, often termed "user programming" or "citizen development," has a surprisingly long history. Its origins can be traced back to a pivotal innovation in 1979: VisiCalc. Developed for the Apple II, VisiCalc was the first spreadsheet program and revolutionized how businesses handled financial modeling and data analysis. It allowed non-programmers to define complex calculations and logic using formulas, effectively "programming" their own applications without writing traditional code. This was an enormously empowering shift, democratizing a capability previously exclusive to highly skilled developers.
The evolution continued in 1991 with the release of Visual Basic, which offered a graphical environment for creating Windows applications. Visual Basic made programming more accessible, enabling millions to build custom tools and utilities, often leveraging existing Microsoft Office applications. This marked an early form of what we now recognize as "low-code" development, where users could drag-and-drop components and write minimal code to achieve significant functionality.
The early 21st century saw the broader emergence of low-code and no-code platforms. These tools, such as Salesforce’s Apex, Appian, OutSystems, and Microsoft Power Apps, aimed to further abstract away the complexities of traditional coding, allowing business users to develop applications through visual interfaces, drag-and-drop components, and configuration settings. The motivation was clear: address the growing IT backlog, accelerate digital transformation, and enable business units to respond more rapidly to market demands. Gartner predicted that by 2024, low-code application development would account for over 65% of all application development activity, highlighting its increasing mainstream adoption. While powerful, these platforms still required a degree of structured thinking and understanding of application logic, and their capabilities were often constrained by the platform’s inherent design.
Generative AI: Empowering the Citizen Developer with Natural Language
The current era marks a profound leap in user programming capabilities, driven by Generative AI. This new generation of tools, exemplified by platforms like Lovable and Bolt, is fundamentally changing the interface of software creation. The "programming language" is no longer a set of formulas, visual blocks, or even simplified code, but natural language – predominantly English. This paradigm, sometimes referred to as "vibe coding," dramatically lowers the barrier to entry, making software creation accessible to virtually anyone who can articulate a problem in plain language.
These GenAI-powered tools can interpret user prompts and generate functional code, application components, or even entire workflows. This capability is expected to accelerate development cycles exponentially. A recent report by Deloitte projected that AI-augmented development could reduce development time by 30-50% in certain contexts, freeing up developers for more complex, strategic tasks. The market for AI in software development is also witnessing explosive growth, with analysts forecasting it to reach tens of billions of dollars within the next few years. This accessibility promises to unlock unprecedented innovation, allowing individuals and small teams to rapidly prototype and deploy solutions tailored to their immediate needs, bypassing traditional IT queues.
The Unseen Complexity: Why SaaS Vendors Aren’t Doomed
Despite the revolutionary potential of GenAI-powered user programming, the notion that "everyone will build, and nobody will buy," leading to the demise of SaaS players, is almost certainly an oversimplification. The resilience of established SaaS vendors, particularly those providing core business solutions, lies in the immense complexity of "business rules" and "business logic" that underpin their offerings.
Consider critical enterprise software like procurement, invoicing, payments, budgeting, forecasting, payroll, staffing, sales force automation, customer relationship management (CRM), or customer service platforms. Behind each of these lies a labyrinth of thousands of often intricate business rules and millions of lines of associated business logic. These rules encapsulate vital constraints and processes related to policy, regulatory compliance, data security, legal mandates, financial governance, pricing strategies, and more. For example, a payroll system must adhere to dozens of federal, state, and local tax laws, integrate with various benefits providers, and manage complex compensation structures. An invoicing system must comply with accounting standards, track payment terms, and handle multi-currency transactions, all while maintaining audit trails.
The challenge is not merely in writing code, but in discerning, codifying, and continually maintaining these deeply embedded rules. Most non-technical individuals aspiring to create business apps, and even many technical developers, have little to no awareness of the full scope of these critical business rules. They are often "tribal knowledge," embedded in legacy code or understood only by long-departed employees. Even when documented, the nuances and underlying rationale behind each rule are rarely fully explained. This is precisely the specialized knowledge that product managers and business analysts painstakingly acquire to define viable and compliant solutions. The difficulty of addressing technical debt often stems from this very challenge: untangling the existing business rules and determining which remain relevant.
SaaS executives, while acknowledging the rise of citizen development, often emphasize their platforms’ robust handling of these complexities. "Our value proposition extends far beyond mere functionality," stated a hypothetical CEO of a leading ERP provider. "We encapsulate decades of industry best practices, compliance expertise, and security protocols, which is incredibly difficult and risky for any single organization to recreate from scratch, regardless of the tools available." Industry analysts largely concur, pointing out that while GenAI will enhance customization, it will not eliminate the need for pre-built, highly governed, and regularly updated foundational systems that handle mission-critical, regulated processes.
The Future: A Hybrid Ecosystem Driven by Protocols
The trajectory suggests a future where the build-versus-buy decision evolves into a "yes to both" scenario, fostering a dynamic hybrid ecosystem. Companies will continue to procure complex, high-value component services for essential business functions. However, these services will be designed with a crucial difference: they will be architected for seamless access and control by both humans and intelligent software agents.
A major enabler for this paradigm shift is the emergence of widely accepted protocols designed to describe business services in a machine-readable and machine-understandable format. For years, the industry has lacked a universal language for software to truly understand the capabilities and constraints of other software services. This gap is being addressed by initiatives like Anthropic’s proposed Model Context Protocol (MCP). Introduced about a year ago, MCP aims to provide a structured way for AI models and other software to comprehend the context, actions, and data models of various business services. This protocol is rapidly gaining traction because it resolves a long-standing architectural challenge, paving the way for truly intelligent interoperability.
With protocols like MCP, AI agents can intelligently interact with and orchestrate bought SaaS components. These AI agents might be developed by the SaaS vendors themselves, by specialized systems integrators, or even by end customers. Simultaneously, custom solutions – whether "vibe-coded" with GenAI tools or hand-coded by developers – will be built on top of, and integrated with, these component services. This means an enterprise might buy a robust HR platform for payroll and benefits, but then use GenAI to build a custom employee onboarding workflow that integrates with the HR platform, the CRM, and an internal project management tool, all orchestrated by an AI agent that understands the business logic of each component via protocols.
Implications for Enterprise IT and Product Development
The implications of this evolving landscape are profound for enterprise IT departments and the broader product development world. The role of IT will shift from solely developing and maintaining systems to one of strategic integration, governance, and architecture. IT professionals will become orchestrators of a complex ecosystem, managing the interoperability between bought services and internally developed solutions, ensuring security, compliance, and data integrity across the hybrid environment.
For product managers, the emphasis on "discovering the right solution to build" becomes even more critical. While GenAI can accelerate the building process, it does not inherently solve the challenge of defining what should be built. The fundamental principles of understanding user needs, market dynamics, and business viability remain paramount. The "hard part is rarely building and delivering the solution; the hard part is discovering the right solution to build," as the original article sagely notes. This requires deep domain expertise, empathetic user research, and a clear strategic vision—skills that AI currently augments but does not replace.
Furthermore, the age of user programming brings both immense opportunity and potential risks. While empowering non-technical users can boost agility and innovation, it also necessitates robust governance frameworks to prevent "shadow IT" issues, ensure data security, and maintain compliance. Organizations will need to invest in training for citizen developers, not just on tool usage, but also on fundamental principles of software design, data privacy, and the implications of business rules.
In conclusion, the advent of Generative AI and advanced user programming tools is not a death knell for traditional software vendors but rather a catalyst for a more sophisticated, interconnected, and agile enterprise software landscape. The future is one where the strategic "build vs. buy" decision yields to a dynamic "yes to both" approach, where powerful, rule-laden commercial services form the bedrock, and intelligent AI agents and user-programmed solutions provide the tailored, responsive layers on top. This transformation promises unprecedented empowerment for countless individuals within organizations, but success will ultimately hinge on strategic foresight, robust governance, and the enduring human skill of discerning and defining truly valuable solutions.