Ever feel like your AI coding assistant is that intern who means well but breaks stuff when unsupervised?

AI in Programming: Stupid Colleague or Evolving Partner?

Over the past few years, the tech world has experienced an undeniable surge in AI-driven tools aimed at assisting with software development. From code generation to unit test automation and even AI agents capable of querying databases autonomously, we've seen a wide spectrum of performance and reliability. Yet, as with any tool in its early stages, the reception has been mixed—and sometimes, downright frustrating.

A recent post compared working with AI programmers to collaborating with an inexperienced and lazy teammate who "lied on their resume." It's a sentiment that resonates with many who have bumped into the limitations of AI when expecting polished, senior-developer-level output. The AI can be shallow on best practices, forgetful of nuance, and occasionally blissfully unaware of context. And while prompt engineering helps mitigate some of these gaps, it’s true that marketing hype often exceeds current capabilities.

Why Does AI Sometimes Feel "Stupid"?

1. Shallow Experience by Design: Most AI systems are trained on a wide range of code samples from public datasets (and the internet, with all its messiness). They're great at pattern matching and regurgitating common approaches but can lack the deep contextual understanding a senior engineer brings to a problem.

2. Prompt Sensitivity: AI's output is highly dependent on how well you frame the problem. If the request is too vague, overly complex, or nuanced, it may produce an incomplete or even incorrect solution—like a junior developer who's out of their depth.

3. The Data Problem: As one commenter astutely pointed out, sloppy data = sloppy AI. AI systems reflect the data they’re trained on. If training data contains poorly written code or lacks real-world edge cases, the AI will mirror those imperfections.

But There’s Another Side to This Coin

While it's fair to critique the shortcomings of AI programming tools today, there are engineers and teams achieving remarkable results. One technologist shared how they’ve deployed an AI agent that autonomously queries databases, interprets metadata, writes SQL, and provides actionable insights—all with no human writing a single query. That’s a clear sign of AI’s potential when scoped correctly and integrated thoughtfully.

It’s also worth acknowledging that AI systems tend to "think differently" than human engineers. They approach problems as probability engines, not with human intuition. Sometimes this leads to suboptimal results, but in other cases, it creates genuinely novel solutions.

Where Does That Leave Us?

• AI for Programming is Still Beta: The technology is improving, but for now, it’s best thought of as an eager intern or junior dev who benefits from clear instructions, regular code reviews, and careful oversight.

• Marketing Needs to Catch Up to Reality: There’s truth in the call to temper marketing claims. We shouldn’t dress AI as an "instant senior engineer" when it still struggles with basic design patterns and real-world complexity.

• Data Is King: Whether you're building AI-driven products or using AI tools, investment in better, cleaner, and more domain-specific data will always be the differentiator. We can't shortcut this truth, no matter how shiny the tech appears.

Moving Forward

AI won't replace experienced programmers in the near term, but it can augment them. Used strategically, AI can automate repetitive tasks, generate boilerplate code, and even serve as a creative partner for prototyping or exploring unfamiliar languages and frameworks.

Still, as engineers, leaders, and product builders, we have to be honest about its limitations, demand rigor in training, and ensure that AI tools remain transparent, accountable, and trustworthy.

The conversation is ongoing, and like any new tool, it will take a community of builders, skeptics, and innovators to shape what comes next.