Artificial intelligence is revolutionizing software development, making it easier for less experienced developers to write code, generate solutions, and build applications faster than ever before. But as AI lowers the barrier to entry, it creates an unexpected challenge—an increasing burden on tech leads who must navigate a landscape filled with AI-assisted code that often lacks structure, scalability, and maintainability.
The Rise of AI-Assisted Development
Tools like GitHub Copilot, ChatGPT, and other AI-powered coding assistants have significantly boosted developer productivity. Junior and mid-level developers can now produce complex code snippets, automate repetitive tasks, and solve problems they previously struggled with. While this sounds like a win for the industry, the reality is more nuanced.
AI-generated code is often syntactically correct but semantically flawed. It may work in isolation but lack architectural integrity when integrated into a broader system. AI lacks the human intuition necessary to understand business logic, future scalability, and team-specific best practices. This means that while development velocity increases, so does the risk of accumulating technical debt.
The Burden on Tech Leads
Tech leads, already responsible for guiding teams, making architectural decisions, and ensuring high code quality, now face an additional challenge: reviewing and correcting AI-generated code. Here’s why AI can be a curse for tech leads:
1. Increased Review Workload
AI accelerates code production, but not always in the right direction. More code means more pull requests, more code reviews, and more debugging. Tech leads spend significant time analyzing whether AI-assisted code is functionally correct and adheres to best practices.
For example, a junior developer might use AI to generate an API endpoint for handling user authentication. The AI might produce working code but fail to implement essential security measures such as rate limiting, input validation, or proper session management. The tech lead must then review, refactor, and educate the team on why these aspects are critical.
2. Poor Architectural Decisions
Junior and even mid-level developers often rely on AI to solve immediate problems without considering the long-term architectural impact. AI doesn’t inherently enforce good design patterns, leading to monolithic structures, poorly optimized queries, or brittle integrations that tech leads must later refactor.
Imagine a situation where AI generates multiple SQL queries inside a loop instead of optimizing them into a single batch query. While the code functions correctly, it causes unnecessary database load, which can lead to performance bottlenecks in production. A tech lead must identify and correct such inefficiencies before they escalate.
3. False Confidence in AI Solutions
Developers using AI often assume the generated code is correct because it “works.” However, AI does not understand the nuances of business requirements, security concerns, or industry-specific regulations. This creates hidden bugs and vulnerabilities that tech leads must catch before they become production issues.
For instance, an AI-generated function might handle user passwords but store them in plaintext instead of hashing them properly. If a less-experienced developer deploys this code, it could lead to a serious security breach. A tech lead must not only fix the issue but also implement processes to prevent similar mistakes in the future.
4. AI-Driven Technical Debt
Without proper oversight, AI can rapidly generate unmaintainable code that accumulates as technical debt. Tech leads must dedicate additional time to reworking solutions, refactoring poor implementations, and ensuring code consistency across the codebase.
Consider a scenario where an AI tool generates dozens of functions across different files with inconsistent naming conventions and redundant logic. While the code technically works, it becomes difficult to navigate, update, or debug. A tech lead must step in to unify the code, remove duplication, and enforce better organizational practices.
The Future: More Tech Leads, Not Fewer
Paradoxically, as AI tools become more advanced, the need for strong technical leadership increases. More tech leads will be required to:
- Establish clear coding guidelines and best practices for AI-assisted development.
- Educate teams on AI’s limitations and how to critically assess its output.
- Enforce architectural principles to prevent the system from collapsing under its own complexity.
- Prioritize code quality and long-term maintainability over short-term gains.
- Introduce automated testing and linting rules to catch common AI-generated mistakes before they reach production.
How Tech Leads Can Adapt
To thrive in this AI-assisted environment, tech leads need to adjust their approach to leadership and mentorship. Some strategies include:
1. Establish AI Review Processes
Tech leads should implement specific review processes for AI-generated code, ensuring that every piece of code passes through a structured evaluation. Code review checklists should include:
- Security best practices (e.g., input validation, authentication measures).
- Performance optimizations (e.g., avoiding redundant queries or inefficient loops).
- Maintainability and readability (e.g., clear variable names, well-structured functions).
2. Promote AI Literacy Among Developers
Instead of discouraging AI use, tech leads should train their teams to use AI responsibly. This means educating developers on when to trust AI-generated code, how to refine prompts to get better results, and how to critically evaluate AI suggestions.
3. Use AI to Help, Not Replace, Thoughtful Engineering
Tech leads can leverage AI themselves—not just to generate code, but to automate tedious tasks like refactoring, documentation generation, and code linting. By integrating AI into CI/CD pipelines, tech leads can ensure AI-generated code is continuously checked for quality issues before deployment.
AI is undoubtedly a powerful tool for accelerating development, but it is not a substitute for experience, intuition, and thoughtful engineering. While junior developers benefit from AI assistance, tech leads bear the responsibility of ensuring that AI-generated code aligns with best practices and long-term business objectives. As AI continues to shape the industry, the role of tech leads will become even more critical—not just in fixing what has been done, but in guiding teams to use AI responsibly and effectively.
In the end, AI doesn’t replace the need for great engineering—it amplifies the need for strong technical leadership. The companies that recognize this will be the ones that successfully integrate AI into their development processes without sacrificing code quality, security, or scalability.