Fit is the new moat: The Economics of Software Development in the AI Era
Software has always been shaped by economics. We’ve seen prices fall, ownership give way to subscriptions, and SaaS dominate the landscape. But with AI entering the scene, the rules are changing again. This time, the shift doesn’t just affect how we buy software: it transforms how we build it.
A Golden Age for Custom Software
We may be entering a new golden age of custom software development. For decades, the cost of software has been trending downward. First came the disruption of app stores like Apple’s App Store and Google Play, which broke the old model of boxed software sold for a one-time fee. Then came SaaS and subscription-based services, where costs follow usage rather than ownership. Today, commodification of software is advanced: we rarely buy a product outright, the access is the default.
My prediction is that the same economic shift is now happening in the production of code itself, thanks to large language models (LLMs) and generative AI.
When Code Becomes (Almost) Free
If code no longer has to be manually written, its unit cost plummets. Add to this the incredible speed at which AI can generate code, and the economics of building software systems change fundamentally.
Developers who work with tools like Model Context Protocol and AI agents describe the experience less as coding and more as directing: a driver guiding a team of tireless, all-directional horses. Yes, the wagon sometimes tips over, but the system can rebuild itself in moments. Bug fixing, patching, and root cause analysis become faster and less costly, because debugging can happen in seconds and new builds can be generated instantly. And of course, good guardrails and safety features must be in place when developing, so the action doesn’t become just ‘vibe coding’.
This is not just an efficiency gain. It is a complete redefinition of what it costs to build software.
Implications for Ready-Made vs. Tailored Solutions
Traditionally, custom IT systems carried a high upfront price tag but benefited from lower long-term costs of ownership. Off-the-shelf products, by contrast, shone in their standardized implementation models and larger talent pools, but came with ongoing fees, vendor dependency, and less flexibility in meeting unique requirements.
AI turns this equation on its head. With near-zero cost code generation, we can dramatically reduce the upfront costs of custom solutions while keeping their traditional advantages of fit-for-purpose design and adaptability.
The result is a double win:
Lower TCO than many standardized systems.
Closer alignment with user needs, making systems easier to adopt and cheaper to evolve.
In other words: everyone gets a tailor-made system, at a price once reserved for standardized products.
Conclusion: Custom Software Regains the Edge
Just as SaaS commodified access to applications, AI is commodifying the creation of code itself. Controlled studies already show AI assistants cutting coding time by more than 50%, while enterprise leaders confirm they are building more software with fewer people. Nearly 90% of developers now use AI tools in daily work, making these gains widespread. Using generative AI in development has become the new standard, according to the 2025 DORA report on the state of AI-assisted software development.
Meanwhile, new integration standards like the Model Context Protocol (MCP) make it cheaper to connect systems to real business workflows. With SaaS bloat and license creep becoming a visible operating expense, the balance tilts back: custom solutions can be faster to build, cheaper to maintain, and strategically more flexible than ready-made alternatives.
What once required large budgets, long timelines, and specialized teams can soon be delivered faster, cheaper, and more flexibly than ready-made alternatives. The trade-off between standardization and customization begins to collapse. Instead of adapting processes to generic software, organizations can demand systems built around their workflows without paying a premium.
For businesses, this means fewer compromises, happier users, and reduced vendor lock-in. Custom software is becoming not just viable, but strategically advantageous again.
Key Takeaways
Cost dynamics flip: AI-driven generation erases the historical price gap between custom and off-the-shelf solutions.
Strategic flexibility: Tailored systems evolve quickly, reducing upgrade pain and long-term costs. Atlassian’s 2025 developer report shows the real bottleneck is now coordination, not coding.
SaaS is becoming a visible cost burden: Subscription creep, vendor lock-in, and “SaaS bloat” are now being questioned. In contrast, AI-enabled custom systems offer tighter control over costs and architecture.
Competitive advantage: In a SaaS-saturated world, custom software becomes a differentiator.
The AI era doesn’t just accelerate software development, it rewrites its economics. Those who adapt early will gain the edge. Fit is the new moat: With AI handling the heavy lifting of code, the real differentiator becomes how well software matches your unique processes and needs.
TL;DR: Why Custom Software Is Coming Back
AI makes coding faster and cheaper: Studies show AI assistants can cut development time in half, while large enterprises like SAP already report producing more software with fewer people.
Adoption is mainstream: Nearly 90% of developers now use AI tools in their daily work, spreading these productivity gains across the industry.
Integration is getting easier: New standards like the Model Context Protocol (MCP) simplify connecting custom systems to real business workflows.
SaaS costs are rising: Subscription bloat and vendor lock-in are pushing organizations to reconsider their total cost of ownership.
Who’s Jani?
Jani works as a Managing Partner at Hidden Trail, his mission being turning quality from a cost into competitive advantage. This means working with teams, managers, product people, developers and testers to recognize where their product quality comes from and how to make it a tangible asset. Jani has 20+ years experience in software industry, during which he has worked with many teams in managerial, development and quality related roles. Jani firmly believes that quality means business!
Main competence areas:
Strategic Advising: Guiding organisations to invest and create products and services that are driven by quality, business success and sustainability.
Quality Engineering Practices: Enforcing robust quality engineering methodologies to ensure high standards in software development.
Endorsing Visibility: Increasing transparency of development processes for management and teams, fostering informed and balanced decision-making.
Vendor Management: Optimizing vendor relationships to align with quality objectives and project goals.
If you want to know more on these subjects or quality in general, please be in touch: jani@hiddentrail.com
