The Ephemeral Nature of Software in the Age of AI

In today's rapidly evolving technological landscape, we're witnessing a profound shift in how we should approach software development. As the cost of creating software with AI assistance drops to near zero, via agentic software development tools, we need to fundamentally rethink our relationship with code itself.

The End of Software as a Sunk Cost

The most revolutionary aspect of this shift is that software is no longer a sunk cost. Previously, choosing a technology stack meant committing to it for years, as the cost of rewriting was prohibitively high. Now, with agentic assisted development, the equation has changed dramatically:

type SoftwareCost = {
  initialDevelopment: number; // Approaching zero with AI
  maintenance: number; // Often higher than redevelopment
  migration: number; // Dramatically reduced with AI
};

When initial development costs approach zero, and migration costs plummet, the traditional software lifecycle is disrupted. It may become cheaper to rebuild applications from scratch rather than maintain legacy codebases. This creates a new paradigm where software becomes truly ephemeral - a temporary manifestation of requirements rather than a long-term investment.

Enablers: Requirements and Tests

What makes this ephemeral approach to software possible? The enablers are well-structured requirements and comprehensive automated tests. These artifacts become the constant in a world where implementations are fluid.

While requirements themselves may increasingly be AI-generated, the process of defining, refining, and validating them remains crucial. Similarly, automated tests ensure that regardless of how the underlying code changes, the system continues to function as expected.

type SoftwareSystem = {
  requirements: Specification[]; // The "what" and "why"
  tests: TestSuite; // Verification of behavior
  implementation: Code; // Ephemeral and replaceable
};

With robust requirements and tests, the actual implementation becomes interchangeable. We can swap out entire frameworks or architectures without affecting the end user experience.

Optimizing for Cost, Not Preservation

A critical distinction in this new paradigm is that we shouldn't inherently care whether software is rebuilt from scratch or incrementally modified. What matters is the end result: meeting requirements and passing tests while optimizing for cost.

For minor changes in requirements, incremental updates might be most efficient. For larger shifts, complete rebuilds might make more sense. The key insight is that there's no inherent value in preserving existing code - it's simply a question of what approach minimizes total cost:

type SoftwareChange = {
  requirementsChange: Requirements;
  options: {
    incrementalUpdate: { cost: number; risk: number };
    partialRefactor: { cost: number; risk: number };
    completeRebuild: { cost: number; risk: number };
  };
};

Software that was written to anticipate requirements that never materialized carries unnecessary complexity. Similarly, systems that evolved through many incremental changes often accumulate technical debt. With AI-assisted development, we can optimize for simplicity by building exactly what we need right now, plus only what we have high confidence will be needed next.

The plan rarely survives contact with users, so maintaining adaptability is more valuable than attempting to anticipate every future need. When software is ephemeral, we're liberated from the pressure to "get it right the first time" and can instead focus on continuous adaptation to actual needs.

The Docker Build Analogy

This new paradigm resembles container builds: we specify requirements, build a solution, deploy it, and when issues arise or requirements change, we can consider rebuilding if that's the optimal approach. The build process itself becomes trivial - what matters is the specification.

When problems emerge, instead of hunting for bugs in existing code, we might simply refine our requirements and generate a new solution. This approach requires us to think differently about software development:

1. Invest heavily in requirements engineering and validation
2. Build comprehensive test suites that verify behavior, not implementation
3. Design systems for replaceability rather than longevity
4. Separate business logic from implementation details

Build for Now, Not for the Future

A critical implication of software's newfound ephemerality is that we no longer need to make technology choices based on anticipated future needs. For decades, software architects have tried to predict technology evolution and choose frameworks that would remain viable for years.

This forward-looking approach often led to complex, over-engineered solutions that were difficult to maintain. Now, we can embrace a simpler philosophy: build for now using the most appropriate tools available today. When better tools emerge, we can simply regenerate our solutions.

The cost of change has plummeted, removing the penalty for "wrong" technology choices. This liberates developers to optimize for current productivity rather than hypothetical future-proofing.

A Practical Example: Choosing Frameworks

For example, consider the choice between established frameworks like Next.js versus newer technologies like TanStack (formerly React Query). While TanStack might offer superior TypeScript support and architectural advantages, AI assistants currently have more comprehensive knowledge of Next.js patterns and implementation details.

In the traditional paradigm, you might choose TanStack despite the learning curve and limited AI support because it represents the "future" of React development. In the ephemeral software paradigm, you'd choose Next.js to maximize immediate productivity with AI assistance, knowing you can easily migrate to TanStack later when AI models become more proficient with it.

// Decision making in traditional software development
function chooseFramework(options: Framework[]): Framework {
  return options.reduce((best, current) => {
    return current.futureProofScore > best.futureProofScore ? current : best;
  });
}

// Decision making in ephemeral software development
function chooseFramework(options: Framework[]): Framework {
  return options.reduce((best, current) => {
    return current.currentProductivityScore > best.currentProductivityScore
      ? current
      : best;
  });
}

There's no need to struggle with technologies that AI doesn't yet understand well. Instead, focus on fulfilling current requirements with well-supported tools. When better approaches emerge, you can have the AI rewrite your software with confidence that it will work because your requirements and tests provide a solid foundation.

Conclusion

The future of software development isn't about writing code that lasts for years; it's about effectively communicating with AI to turn your vision into reality. In this new paradigm, software isn't a precious artifact to be preserved - it's a temporary solution to a well-defined problem, ready to be regenerated whenever better approaches become available.