The Coming Growth of Custom Software
For most of the software lifecycle, developing an application required coordinated teams, including product managers, engineers, and designers, along with extended development cycles before production. The associated costs in time, talent, and capital functioned as a constraint, limiting development to solutions addressing sufficiently large markets or critical problems. Other use cases were typically managed through spreadsheets, manual workflows, or general-purpose software with partial fit.
AI is reducing these constraints.
This shift has been described as “vibe coding,” a term introduced by Andrej Karpathy in early 2025 to define workflows where developers express intent in natural language and AI generates executable code. What initially emerged as an experimental capability has become widely adopted. By early 2026, more than 80% of developers report using or planning to use AI coding tools (GitHub Octoverse 2025), with approximately 41% of new code estimated to be AI-generated. The supporting tooling ecosystem (including Cursor, Codex, Claude Code, Replit, and Lovable) raised $9.4 billion in equity funding between 2022 and 2025, indicating sustained investor interest.
The more significant change is not limited to increased developer productivity, but rather the reduction in barriers to software creation. Adoption of AI coding tools among non-technical users increased by more than 500% year-over-year through early 2026. Individuals who previously required engineering teams to validate ideas can now develop functional prototypes within hours, while operational roles can build internal tools to replace manual workflows.
These dynamics affect the structure of the software market. Industry perspectives, including those from Andreessen Horowitz, indicate that AI is reducing the cost and complexity of software development, enabling the creation of applications that were previously not economically viable (a16z). This includes both highly individualized tools and enterprise applications tailored to specific workflows, edge cases, and internal processes not addressed by horizontal SaaS platforms. Historically, such needs were managed through manual processes or rigid automation solutions, whereas AI-assisted development enables more targeted systems.
The economic relationship is direct. As the cost of building custom applications declines significantly, the number of viable development use cases increases proportionally. Use cases that were previously too narrow, internal, or low-priority to justify investment become feasible, enabling broader realization of previously constrained enterprise software demand.
This shift does not eliminate the role of SaaS platforms. Horizontal solutions that control core workflows and benefit from switching costs maintain structural advantages. However, the balance between purchasing and building software shifts toward in-house development, as organizations gain viable alternatives to generalized software that may not fully align with their operational requirements.
For technology services firms, this dynamic expands the addressable market. Increased custom software development does not remove the need for services but shifts demand toward higher-value activities. System architecture, integration with existing infrastructure, data governance, and ongoing maintenance remain in required capabilities. The gap between prototype and production-grade systems persists, positioning services firms within that transition. While AI reduces lower-value tasks, it increases demand for expertise in design, architecture, and integration.
A comparable transition occurred with cloud computing, which did not reduce demand for IT services but redefined service requirements and expanded the accessible customer base. AI-assisted development follows a similar trajectory, lowering barriers to software creation while increasing the need for specialized support in more complex implementations.
The coming years are likely to see a significant increase in custom software, driven by faster development cycles, lower costs, and broader participation in software creation. Organizations with capabilities in AI-enabled development, combined with strengths in architecture and system integration, are positioned to support this shift.
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