Will AI Replace Mid-Level Engineers?
The Truth Behind the Hype and the Future of Software Development
Mark Zuckerberg recently claimed AI will replace mid-level engineers by 2025.
As someone actively building AI developer tools and studying their real-world applications, I believe this view misunderstands both the current state of AI and the role mid-level engineers play in software development.
Here’s where Meta is getting it wrong.
The Current Reality
AI coding assistants have made impressive strides. They generate boilerplate code, explain functions, and assist with routine programming tasks. But there’s a world of difference between writing isolated snippets and understanding complex systems.
In real-world testing, even the most advanced AI tools falter when handling interconnected systems. They produce syntactically perfect code while missing critical dependencies, leading to subtle yet costly runtime errors.
The rise of AI-driven development tools has proven valuable, especially for indie developers and small projects. Companies like Lovable and Bolt (both surpassing $4M ARR) are thriving by enabling non-developers to create software. That’s a powerful shift—but it’s not the same as maintaining Meta’s colossal codebase or engineering at scale.
Where AI Falls Short
After developing AI tools and surveying 87 engineers on their experiences, I’ve identified three fundamental gaps separating AI from mid-level engineers:
1. Context Awareness
Mid-level engineers develop a comprehensive mental model of a system’s architecture. They understand service boundaries, data flows, and dependencies. AI, on the other hand, struggles with large codebases. It lacks the persistent context necessary to assess system-wide impacts.
2. Memory & Historical Insight
Engineers retain crucial historical knowledge about the codebase—why certain decisions were made, which optimizations failed, and the edge cases driving current implementations.
AI, however, treats every interaction as a blank slate. It doesn’t remember past discussions, PR debates, or previous failures.
3. Systems Thinking
Software engineers excel at recognizing cross-system interactions. They anticipate performance bottlenecks, predict cascading failures, and identify hidden dependencies—skills that today’s AI tools lack.
The Road to Smarter AI Development Tools
For AI to truly match mid-level engineers, several breakthroughs must happen:
1. Advanced Code Understanding
We need AI that maintains persistent models of entire codebases, recognizing architectural patterns, coding conventions, and dependencies. While research in program analysis and semantic code understanding is advancing, the context problem remains unsolved.
2. Temporal Codebase Analysis
Future AI tools must understand how code evolves over time. That means analyzing Git history, PR discussions, and past architectural decisions to develop a deeper understanding of why things are built the way they are.
3. Multi-Agent Development Systems
Software engineering is too complex for a single AI agent. A multi-agent system could provide:
A Planning Agent – Breaking down high-level requirements into technical tasks, maintaining system graphs, and assessing technical debt.
Implementation Agents – Specialized for frontend, backend, and infrastructure, ensuring best practices and consistency.
A Testing Agent – Leveraging property-based testing and historical bug patterns to generate robust test suites.
A Review Agent – Checking for architectural consistency, security vulnerabilities, and performance issues across the entire system.
Could all of this be built by 2025?
Maybe. The building blocks—LLMs, graph databases, retrieval systems—exist. But software development isn’t just about writing code. It’s about thinking critically about code, and AI isn’t there yet.
The Evolution of Software Engineering
Rather than replacing engineers, AI is reshaping their roles. We’re seeing the rise of two specialized tracks:
Traditional Software Engineers
Engineers will focus more on high-level system design, complex problem-solving, and architectural decision-making. AI will handle the mundane parts—boilerplate code, debugging, and basic feature implementation—freeing engineers to work on what truly matters.
AI Engineering Specialists
A new breed of engineers will emerge, responsible for integrating AI into development workflows. Their work will involve:
Designing prompt architectures for different coding scenarios
Creating AI training sets from proprietary codebases
Developing tools for AI-human collaboration
Optimizing AI performance and resource allocation
Both paths demand strong engineering fundamentals but cater to different specializations. Traditional engineers will need to understand AI’s capabilities and limitations, while AI specialists will focus on enhancing AI-driven development environments.
A New Era of Software Development
The future of engineering isn’t about AI replacing developers—it’s about AI transforming how software is built. Imagine a world where:
Engineers spend 80% of their time on creative problem-solving instead of routine tasks.
Development cycles accelerate dramatically while maintaining high quality.
AI-powered tools catch subtle bugs before they reach production.
Teams focus on innovation while AI handles repetitive implementation details.
Engineers who integrate AI effectively already report spending more time on system design and less on tedious coding tasks. AI isn’t making them obsolete—it’s making them more powerful.
The key isn’t to resist AI’s rise. It’s to shape its evolution. The companies and engineers who master AI-assisted development will be the ones leading the next era of software engineering.