Disposable Code: A New Reality for Software Development
Alex Worden
Technical engineering leader with a track record for building high performing teams and delivering innovative customer focused production quality software.
Once upon a time, code was precious—crafted by hand, carefully maintained, and treated as a long-term asset. Now, in the age of AI coding assistants, code is cheap. It can be generated, rewritten, and discarded at an unprecedented pace. This isn’t just a shift in development speed; it’s a fundamental change in how we think about software architecture, team dynamics, and business strategy.
The End of Framework Empires
For years, companies invested heavily in proprietary frameworks—Application Fabrics designed to make human engineers more efficient. But here’s the catch: AI models aren’t trained on your secret sauce. They thrive on open-source, widely adopted libraries, leaving in-house frameworks as obscure relics that AI struggles to navigate. Even open-source giants like Spring and React introduce friction when AI can generate a lightweight, purpose-built solution on demand.
The old-school mentality of committing to heavyweight frameworks is looking increasingly outdated. Instead, the new paradigm values disposable, modular, and AI-friendly architectures. Think "Composition over Inheritance" at a macro level—highly focused implementations, no unnecessary bloat, and a constant readiness for replacement.
How to Build for the Disposable Code Era
If code is cheap to generate, the challenge shifts from writing it to ensuring it’s reliable, maintainable, and easily replaced when something better comes along. This demands some adjustments:
Micro-Implementations Over Monoliths
Treat features as standalone, loosely coupled components. If an AI-generated replacement does the job better, swap it out without drama.
Automated Verification Pipelines
Just because AI can write code fast doesn’t mean it’s bug-free. CI/CD pipelines with robust automated testing are non-negotiable.
Iterative Replacement
Software isn’t built to last—it’s built to evolve. Test new AI-generated alternatives frequently and don’t be afraid to throw away yesterday’s code.
APIs, Not Frameworks
AI works best when it has clear, well-documented interfaces to interact with. APIs define clear boundaries for modular functionality, enabling rapid iteration without disrupting the larger system. By treating APIs as stable contracts, AI-generated components can evolve independently, following domain-driven design principles while ensuring flexibility and scalability.?
Lightweight Governance
Enable experimentation while keeping a firm grip on security and compliance. AI-generated code needs oversight, but too much red tape kills agility.
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Productivity in an AI-Powered World
For decades, companies have measured developer productivity with output-based metrics: pull requests, lines of code, commit frequency. In an AI-driven landscape, these become meaningless. When AI can generate and refactor large portions of an application in minutes, raw code production is no longer an indicator of impact.
Instead, the focus must shift to:
Customer Value Delivered
How much tangible value does a feature bring? Adoption rates, customer satisfaction, and engagement matter far more than commit counts.
Cycle Time from Idea to Deployment
The real metric of engineering efficiency: how fast can an idea move from conception to production?
System Stability and Performance
Speed is irrelevant if your AI-generated code is an unmaintainable mess. Measure uptime, response times, and overall system health.
Innovation Velocity
How quickly can teams validate and implement new ideas? AI allows for rapid iteration—embrace it.
Business Impact
Ultimately, software is a means to an end. Is it driving revenue, reducing costs, or providing a competitive edge? If not, why are you building it?
Conclusion
AI-generated code isn’t the future—it’s already here. The companies that thrive won’t be the ones fixated on outdated productivity metrics or clinging to massive frameworks. Instead, success will belong to those who embrace AI as an accelerator while maintaining a relentless focus on customer value, business impact, and the agility to iterate faster than the competition.
The real question isn’t how much code you’re writing—it’s how much your code matters. In a world where AI can write software faster than ever, the winners will remain to be those who focus on delivering real value, getting immediate feedback, and evolving quicker than their competitors.
Very well said! In today's fast faced world, the teams should focus more on impact from the delivered code and not the longevity and maintainability of the code.
Engineering Leader in FinTech, Payments, Fraud, AI
1 个月Love this Alex. This reminds me of when we transitioned to cloud and servers migrated from being pets to cattle. I think this is highly relevant to code as you point out. Ever since I started using Cursor, who cares about auto complete?! Just generate the whole feature, test cases, terraform and data persistence all at once. We are coming into an era where mainly machines consume data and information, as people we just enjoy the end results. I could even see this extend to the Web as a whole. Already my children do not Google webpages, but only read the AI summaries to their questions and move on. If that's where things are moving, we soon won't need pretty web pages easily navigated by people, the AI bots will do just fine with RSS feeds. But I wonder where the financial invectives will come from.