The Real Impact of AI in Software Engineering
Artificial Intelligence (AI) is stuffed into everything. From code generation to bug detection, from architectural decisions to cost modeling, AI is changing the way software is built, maintained, and understood. But with this transformation come new complexities, risks, and responsibilities.
AI as a Magnifying Lens
AI doesn’t just solve problems—it amplifies both strengths and weaknesses within a development process. In that sense, AI functions more like a magnifying lens than a silver bullet. If your software engineering team already practices clean code, sound design principles, and disciplined testing, AI tools can multiply their productivity. Conversely, if your codebase is messy, poorly documented, or inconsistent, AI may accelerate chaos rather than clarity.
For example, AI-assisted code generation tools can churn out boilerplate or scaffolding at high speed. But if the underlying architecture is flawed, these tools simply replicate poor patterns more quickly. In short, AI amplifies the underlying quality of your engineering discipline—it rewards the good and punishes the bad.
AI as Rocket Fuel
AI is not a magic fix for dysfunctional software teams—it’s an accelerant. If your engineering practices are disorganized, your documentation is sparse, or your team lacks clear coding standards, introducing AI into the mix will only make matters worse. Before leveraging AI tools, it's critical to first establish solid foundational practices.
This means having version control discipline, a well-defined CI/CD pipeline, rigorous code review processes, and a strong culture of testing. Clear documentation, consistent architecture, and modular design patterns all contribute to making AI integrations more effective and less risky. When these fundamentals are in place, AI can be safely used to automate repetitive tasks, suggest improvements, and boost developer productivity.
Without this groundwork, AI will simply amplify inconsistency and confusion—automating poor design choices, generating low-quality code, and increasing the difficulty of future maintenance. Like pouring gasoline on a smoldering fire, using AI without preparation will quickly escalate existing issues into unmanageable chaos.
In short - don’t plug in AI until your house is in order. Treat AI not as a replacement for sound engineering, but as a multiplier for teams that are already operating with discipline and clarity.
A nice analogy is that AI is like rocket fuel for development - if used correctly it can launch cargo into orbit - if used without good practices it can blow up your development.
Who Understands Your Codebase
Traditionally, human engineers held the tribal knowledge of a codebase. With AI, this dynamic is shifting. Large Language Models (LLMs) can now read, summarize, and even refactor codebases without prior context. In many cases, AI tools can "understand" code more comprehensively than any one developer can—especially in legacy systems with years of accumulated logic and few maintainers.
However, this raises critical questions - Who is truly accountable for understanding the code? If a developer relies on AI to generate patches or suggest features, does that developer still "own" the resulting logic? As we grow increasingly dependent on AI for comprehension, the risk grows that human developers will lose sight of the bigger picture—or worse not be able to understand the system to fix complex issues or hot fix bugs under pressure if a major issue occurs.
Software Accelerators and Intellectual Property
One of the results of AI mass adoption is there is little incentive to create your own seed projects, since having your own boilerplate counts for a little when you can generate it at a request with the latest version of a framework.
Another issue with intellectual property (IP) arises when developers use generative AI trained on public code repositories, it becomes difficult to track the provenance of the generated code. Did the AI pull patterns from open-source projects with restrictive licenses? Who owns the output?
Legal frameworks have not yet caught up with the pace of AI adoption. Software teams must tread carefully—especially in industries where IP is core to competitive advantage. The use of AI in code generation demands not only technical scrutiny but legal and ethical diligence as well.
Buy vs Build vs AI
The classic engineering decision—Buy vs Build — now has a third option, especially for smaller components - AI-generate. In the past, teams had to choose between purchasing a module/tool or building it in-house. Now, AI can often "fill the gap" by quickly generating a solution that is neither fully built nor entirely bought.
However, relying on AI-generated code brings hidden trade-offs. While it might be fast to produce a prototype using AI, maintaining that code, integrating it with existing systems, or ensuring long-term security and compliance can be difficult. Organizations must now evaluate not just cost and time, but also the sustainability of AI - generated solutions. "Buy vs Build vs AI" is no longer just a technical question — it’s a strategic one.
Dunning-Kruger and AI
The Dunning-Kruger effect—where individuals overestimate their knowledge or ability — is especially pronounced with AI. AI can make junior developers feel more confident than they should be, simply because it produces convincing, often functional output. But functional isn’t the same as correct, scalable, maintainable, secure nor understood.
The presence of AI risks flattening the learning curve in harmful ways. Developers may skip the deep understanding of data structures, design principles, or debugging practices because the AI appears to “handle it.” Long-term, this erodes expertise and creates fragile teams dependent on tools they don’t truly understand and even worse - maintaining a codebase they don't understand.
Maintenance Costs
AI promises to reduce software costs—but only in the short term. Automated code generation, bug fixing, and testing are appealing when deadlines loom. However, the long-term cost of maintaining AI-assisted code can be higher than expected. Generated code often lacks context, intention, or documentation. Even worse, it may introduce subtle bugs that only emerge in production, driving up maintenance and incident response costs.
Remember - it's just patterns based on someone else's imperfect code.
Moreover, AI tools themselves come at a cost - licensing (and vendor locking), integration, compute resources, and the overhead of validating their outputs. Organizations must reassess their total cost of ownership (TCO) when AI is in the loop—not just initial delivery speed.
The Mountains of Technical Debt
AI tools can help identify and even remediate technical debt—but they can also generate it. The speed at which AI can write code sometimes tempts developers to skip design, testing, or peer review. Over time, this creates brittle systems and architectural sprawl.
Just as with credit cards, the ease of borrowing (or in this case, generating code) can mask the long-term debt being incurred. Without deliberate governance, AI can leave behind mountains of untraceable logic, inconsistent patterns, and unexplained decisions. Managing this debt requires not just tooling, but discipline and human oversight.
AI is a powerful force multiplier in software engineering—but it's not a replacement for thoughtfulness, discipline, or human accountability. It magnifies good practices and accelerates outcomes, but it can also obscure ownership, erode expertise, and compound technical debt. The organizations that benefit most from AI in software development will be those that apply it with intention, enforce guardrails, and retain a strong foundation of human expertise. In the end, the real impact of AI is not in writing code faster—but in reshaping how we think about building, understanding, and sustaining software.