MIT Tech Review Investigation: The Mixed Reality of AI Coding Tools

A recent investigation by MIT Technology Review, based on interviews with over 30 developers, tech executives, and researchers, reveals a complex picture of AI-powered programming tools in practice. While tech leaders claim that AI generates up to a quarter of their code, the real-world impact is far more nuanced.

AI tools like GitHub Copilot are now used at least weekly by 65% of developers, excelling at tasks such as generating boilerplate code, writing tests, and debugging. However, their effectiveness varies widely depending on the project and organizational workflow. Limitations include LLMs’ constrained context windows, which hinder their ability to manage large codebases, and a tendency to produce inconsistent or “hallucinated” code that compromises long-term maintainability.

Studies indicate that while AI can increase code output, it may also reduce code quality. GitClear data shows a rise in copy-pasted code and a decline in code refactoring, accelerating technical debt. Some developers report that AI tools actually slow them down, contradicting perceived productivity gains.

Despite these challenges, AI programming is evolving rapidly. Autonomous coding agents can now tackle multi-step tasks, and innovations like formal verification (“vericoding”) could enable bug-free code generation. Experts suggest that developers will increasingly shift from writing code to designing systems and orchestrating AI agents.

Yet concerns remain: junior developers may over-rely on AI, senior reviewers face mounting code volumes, and the very nature of programming as a creative profession is being redefined. As the technology iterates at breakneck speed, the industry must balance innovation with sustainable software practices. https://www.technologyreview.com/2025...