Software Evolution Theory in the Age of AI

In many businesses today, most decision-making, execution, verification, and improvement take place on software systems. Customer touchpoints, pricing and contractual changes, supply and inventory adjustments, log collection and analysis, and internal operational workflows are all deeply dependent on software. This is no longer a stage where IT has merely been introduced; the operation of the business itself is tied to the state of its software, and the ability to update software has become equivalent to the ability to update the business.

This situation is not limited to specific industries. Across sectors and company sizes, businesses that operate with a certain level of speed and complexity can no longer function without software at their core. As external conditions change more rapidly and the frequency of decision–execution cycles increases, the ability to change itself becomes a competitive factor. When shifts in customer value, service conditions, operational constraints, regulatory requirements, and cost structures overlap, a business that cannot update its software cannot translate decisions into action, cannot make corrections, and ultimately comes to a halt.

In this environment, many cases are observed where software updates become a bottleneck for business decisions and policy changes. Decisions may be made, but the structural changes required to execute them cannot be completed in time, narrowing the range of initiatives that can realistically be tested.

The longer software updates take, the greater the distance between decision and execution becomes. During that delay, environmental conditions continue to change. As a result, more decisions remain unexecuted, and the operational range of the business gradually contracts.

When we look at software that has been used for an extended period, it is rare to find systems that remain in their original state. Features are added, configurations change, operations are adjusted, and the software evolves into a form quite different from its initial design. It is uncommon for early specifications or design documents to fully match the implementation and operational reality years later. This does not mean that the original design was meaningless; rather, it reflects the observation that the conditions assumed at the outset are difficult to preserve over long periods of operation.

As software remains in use, tasks and decisions that were not originally anticipated become part of everyday operations. User behavior changes, the volume and meaning of data evolve, and relationships with surrounding systems shift. Additional processing, reorganization, replacements, and workarounds accumulate. What initially appears as a small exception eventually becomes the norm, and those norms push outward on the internal structure. Over time, a design that was once straightforward becomes more complex as it absorbs real-world demands.

It is also uncommon for the same people to remain responsible throughout the system’s lifetime. Developers and operators change, organizational structures evolve, and roles are reassigned. Even when documentation remains, the contextual assumptions behind past decisions are not fully shared. What is lost is not information volume, but the set of conditions under which earlier decisions made sense. When those assumptions fade, the same text no longer leads to the same conclusions. Changes become more cautious, local workarounds increase, and overall consistency gradually deteriorates.

These changes do not arise from specific failures or exceptional circumstances. Similar patterns are observed repeatedly across different organizations, industries, and technical domains. What they share is that software is used over long periods while surrounding conditions continue to change. Although the nature of those changes differs by context, the fact that change persists is common.

Small differences in assumptions accumulate over time. Adjustments that could once be absorbed through routine operations eventually require structural reconsideration. At that point, the weight and scope of change increase. As the impact range grows, verification costs rise, rollback becomes more difficult, and decision-making slows. When decisions slow, businesses can no longer test what they want to try. This state is not one of low quality, but of inhibited learning—and the faster the environment changes, the more damaging this becomes.

Many development efforts have traditionally followed a model in which designs are finalized as much as possible before implementation begins. This approach has been effective for building consensus, enabling division of labor, and managing projects at scale. In environments where implementation costs are high and experimentation is expensive, solidifying designs early was a practical choice, and design served to reduce complexity upfront.

However, this approach has inherent time-structure constraints. From the moment a design is completed, the conditions it assumes begin to change. The longer the gap between design completion and implementation, the greater the divergence between assumptions and reality. When conditions change rapidly, this divergence can become significant by the time the system is finished. What shifts is often not a minor specification detail, but fundamental priorities, operational constraints, or the meaning of data.

This does not imply that the design was incorrect. In many cases, it was the best possible decision at the time. The problem arises when the fact that assumptions will move over time is not accounted for. If adjustment after completion is not built in, the system becomes difficult to update the moment it is finished. When completion is treated as the endpoint, subsequent changes are handled as exceptions, accumulating as afterthoughts. Over time, updates pile up as local fixes, the structure hardens, and the business’s learning speed declines.

This development approach emerged for clear reasons. High implementation costs and heavy experimentation burdens made early planning essential. The ability to assess conditions, organize dependencies, and define a complete system upfront played a critical role in such environments. Consensus-building, risk front-loading, and structured division of labor were practical necessities.

As conditions change, the position of value changes as well. Past judgments, failures, and adjustments do not become invalid. Instead, they are referenced and applied differently. Experience gained from design reviews is no longer used to perfectly predict the future, but to recognize where systems are likely to break under change. Operational lessons inform which foundations should remain fixed and which areas should remain flexible. Past experience is not discarded; it is reused.

As this reuse becomes possible, the value of experience often increases rather than decreases. In fast-changing environments, incorrect judgments amplify quickly. Lower experimentation costs mean more attempts—including wrong ones. As a result, the quality of prioritization and directional judgment has a greater influence on outcomes.

In recent years, clear changes have emerged in development conditions. The cost of implementation and experimentation has decreased, and the time required to turn hypotheses into testable forms has shortened. This shift is driven in part by the widespread adoption of AI-based software that directly supports code generation and modification. These tools reduce the initial cost of validating implementations and make it practical to try, discard, and restructure designs.

What matters here is not whether AI is adopted, but that conditions have changed. When conditions change, the structures that function effectively under them also change.

Importantly, this is not a matter of pitting AI-driven development against human-driven development. What is occurring is the convergence of human judgment—such as prioritization, structural decisions, and contextual understanding—with AI-assisted code generation and modification. Humans decide what to try and where to change; AI reduces the cost of implementing those decisions. Through this cooperation, experimentation and learning at speeds previously impractical have become feasible.

As a result, development that continuously updates software in step with business change has become a realistic option for the first time.

Under these conditions, structures that allow post-hoc adjustment are more manageable than those that attempt to fix everything upfront. As scale grows and requirements evolve, the ability to revisit and modify structure becomes a prerequisite. This does not mean abandoning design. It means narrowing the fixed foundation, clearly defining what should remain flexible, and maintaining the ability to reorganize structure incrementally with clear priorities. Foundational design becomes more important, not less.

As systems scale, infrastructure is inevitably replaced. Configurations that once sufficed require redundancy, partitioning, distribution, observability, and recovery mechanisms. Ongoing operations bring demands for reorganization and feature expansion. In real environments, upgrades, downgrades, rollbacks, staged migrations, parallel operation, and partial replacements are routine activities—not exceptional incidents. Structures that cannot move back and forth increase risk and cost with every change, eventually halting updates altogether.

For this reason, software structures must support reversibility and replaceability. When boundaries are unclear and systems grow in a single direction, changes propagate widely, validation becomes coarse, and rollback is difficult. Clearly defined boundaries and modular replacement units allow learning to continue through change.

These decisions cannot be left to individual ingenuity alone. Determining what remains fixed, what stays flexible, and which changes are acceptable must be treated as shared assumptions. This requires more than tool choices or coding standards; it requires common tactical understanding. Where such shared judgment is absent, updates become person-dependent, speed declines, and learning stops.

Each time conditions shift, new constraints are added to both software and business. While past designs and implementations may no longer apply directly, this does not invalidate the experience behind them.

Judgments formed through previous change—understanding where systems break, where bottlenecks arise, and how far changes propagate—continue to be used when conditions change again. Even as form changes, these judgments resurface when deciding what to try next and where to intervene.

In modern development environments, the combination of human situational judgment and AI-assisted implementation allows such experience to be applied at much shorter intervals. Accumulated knowledge remains embedded in judgment quality and flows directly into subsequent implementations and validations.

As a result, systems are not rebuilt from scratch at every change, nor are past forms rigidly preserved. Instead, experience is reused as conditions shift, and software evolves accordingly.

Change will continue. New technologies and constraints will appear. But accumulated experience will not be lost. As the speed and frequency with which experience can be reused increase, its value becomes more directly and consistently reflected in outcomes.