Do AI coding tools help with imposter syndrome or make it worse?

Imposter syndrome. That old chestnut. Persistent feelings of self-doubt, nervous comparisons to peers, a deeply rooted fear of being exposed as inadequate and inferior. Not long ago I wrote about imposter syndrome among software developers and how our perennial fascination with this concept can mask the more pressing challenges developers face at work: unrealistic expectations for upskilling with new tools, languages, and frameworks; pressure to integrate AI into workflows whether or not it makes sense; return-to-office mandates; dysfunctional teams and communication breakdowns.

Now there’s a whole other dimension to imposter syndrome. We’re talking, of course, about AI. Tools like Copilot, Cursor, Claude Code, Codex, Vertex, and Windsurf are time savers and learning accelerants. Seen that way, they might assuage the symptoms of imposter syndrome. After all, with the help of AI coding tools you can learn (sort of) how to code (sort of) anything (sort of).

But in reality, these tools are a double-edged sword when it comes to imposter syndrome: capable of doing battle for you or against you.

The opportunity for continuous learning draws many people into software development—a feature, not a bug, as the aphorism goes. But sometimes the fulfilling journey of continuous learning turns into unhealthy pressure from peers or managers to add new skills to your repertoire faster than you can realistically master them. By speeding and smoothing the learning curve, AI coding tools can ease feelings of imposter syndrome for devs experiencing that kind of pressure.

By now it’s clear that overreliance on AI creates downstream problems for engineering organizations and, potentially, the entire industry. Sure, AI coding tools can make you more productive. They can make you feel more confident. They can also produce developers who don’t understand the context behind the code they’ve written or how to debug it. Overreliance on AI coding tools can ding your code quality, erode users’ trust in your products or platform, and create a mirage of momentum that collapses when you take a closer look.

AI coding tools are also a new source of anxiety for developers prone to imposter syndrome (which is to say, developers). That anxiety can often be boiled down to one question, whether in an interview or when you tackle a new project or when you chat with other devs: Are you a real coder, or are you using AI?

We don’t think it’s an either/or situation, for the record. The answer to, “Are you a real coder, or are you using AI?” is probably, “Yes.”

Let’s break down some of the ways AI coding tools can both help dispel feelings of self-doubt and open the door to those fears. We’ll also offer some ideas for balancing the individual and organizational benefits of AI tools against their negative impact on a community already prone to feelings of self-doubt.

In plenty of ways, AI coding tools can alleviate feelings of self-doubt in developers, from CS students and junior devs to more seasoned coders and architects. These tools can keep imposter syndrome at bay by:

• Lowering the barrier to entry. One of the oft-touted benefits of AI tools is that they help junior devs write boilerplate code and make it easier for them to explore unfamiliar languages. For junior and aspiring developers still learning how to get their arms around a project, AI tools help break down large, overwhelming problems into manageable steps. Tools like Copilot make “Blank page syndrome,” where you’re paralyzed by the scale of the challenge and how and where to get started, more manageable.
• Providing instant feedback, pair programmer style. With AI coding tools like Copilot, coders get real-time suggestions, explanations, or code snippets. These tools are built to function like silent pair programmers: highly useful for self-teaching or independent work. For instance, the AI might suggest a regex pattern or proactively correct a syntax error, saving you time and confusion.
• Encouraging experimentation (and eliminating some embarrassment). Developers can feel safer trying new approaches when an AI backs them up with ideas. Plus, an AI is not going to judge you for asking what you fear is a stupid question or one that will reveal the depth of your ignorance.
• Future-proofing your skills. There’s a lot you can learn from AI models and plenty you can learn about how to optimize their use—and both avenues will help future-proof your skill set. Savvy developers are always looking to leverage the newest generation of coding tools to expand and deepen their knowledge; the same is true for AI.

Of course, it’s not all sunshine and rainbows. Here are some ways AI coding tools can deepen feelings of imposter syndrome:

• The illusion of expertise. Developers may begin to rely too heavily on AI-generated answers without deeply understanding the code. Asked to explain or debug the code “they” wrote, they’re lost. AI coding tools can create an illusory sense of forward momentum that falls apart under scrutiny.
• Unfair comparisons. The perception that “everyone is coding faster and better now with AI” is everywhere, from marketing materials to water-cooler chatter. Developers feel like they must keep up or risk being obsolete; even if they’re not sure how to use an AI solution or how it would deliver value, they’re under pressure from themselves and often their managers to integrate these tools. And as anyone who’s ever worked with a GenAI model can testify, AI tends to encourage quantity over quality. It’s not hard to imagine how that would impact overall code quality. Speaking of which…
• Missing pieces. Quality and content gaps are inevitable when teams over-rely on AI coding tools. AI may offer code that looks right syntactically but is insecure, inefficient, or subtly wrong. Junior devs capable of performing at a relatively high level with AI tools as a crutch may lack the experience to catch these issues. For example, an AI might generate a database query that fails to handle edge cases or performance implications. If you don’t have the human experience to spot and correct the issue, you may be setting yourself up for major problems downstream.

By now, it’s clear that finding the right balance with AI coding tools is tough, whether you’re looking through the lens of imposter syndrome and developer experience, code quality, or velocity. Here are some concepts to keep in mind as you negotiate AI’s double-edged potential:

• AI is a thought partner, not a crutch. AI coding tools are not all-seeing oracles; they’re more like brainstorming buddies. Bounce ideas off them; ask them to generate new approaches for you. But don’t accept their answers without question. Before you follow a suggestion, make sure you understand how it’s supposed to work. Ask follow-up questions to clarify and challenge the AI, rather than blindly copy-pasting.
• Build in learning time. Pair programming, mentoring, and open code reviews help reinforce learning beyond AI. Challenge yourself or your junior developers to regularly write code without AI assistance. Taking the training wheels off builds independent problem-solving skills. Managers can lead intentional code walk-throughs where the team dissects and analyzes AI suggestions line by line. AI feedback loops (e.g., “why did you suggest this?”) can serve as teaching moments for the AI and for human developers.
• Acknowledge AI’s limitations and model healthy use. Tech leads and senior engineers should demonstrate thoughtful, critical engagement with AI tools. Managers should encourage team-wide discussions about code quality, explainability, and misuse, even as they communicate that turning to AI tools for help doesn’t diminish a developer’s expertise or the value of their contributions. Don’t shy away from acknowledging when AI tools get it wrong; these, too, are learning opportunities. Crucially, leadership should avoid measuring dev performance based on AI-inflated velocity. Better areas to focus on are code clarity, collaboration, maintainability, and growth mindset.

Coding with AI can be a surprisingly emotionally complex experience: it can empower developers or erode their confidence, depending on how it’s used and how leadership models its use for the organization. AI coding tools can encourage developers’ innate curiosity, help them understand new platforms and languages, and act as a springboard for their professional development. But in an industry already plagued by imposter syndrome, it’s critical that managers think about how to incorporate AI coding tools that will build up devs’ confidence and skill sets, not tear them down.