AI coding tools can speed up task completion but may undermine genuine skill development
AI is accelerating productivity across many technical roles, software development in particular. We’ve created tools that can generate error-free code in seconds, tools that don’t sleep, don’t get distracted, and don’t second-guess. It’s not science fiction. These systems are reshaping engineering workflows in real time. But here’s the downside: when developers outsource problem-solving to machines, they risk skipping the hard, but necessary, process of learning how systems really work.
A controlled study by AI safety company Anthropic put this to the test. Fifty-two junior developers undertook a new coding challenge, learning to use an asynchronous Python library called Trio. Some used AI; others didn’t. Those who used the AI assistant scored 17 percentage points lower on conceptual questions, even after just completing tasks related to those exact concepts.
So the tradeoff is clear. Speed goes up. Understanding drops.
Which raises a big question for leadership: Are we building engineering teams that can actually engineer, or teams who just babysit AI? If your people can’t debug or explain what the underlying code is doing, they’re not solving problems, they’re just forwarding answers from a machine.
The tools are powerful. That’s not in question. But how we use them will determine whether we build true technical depth or just scale average output. And average won’t cut it in high-stakes environments, especially where safety, security, or mission-critical systems are involved.
The method and approach to AI usage significantly affect developers’ learning outcomes and skill retention
AI won’t make you smarter by doing the thinking for you. It’s only valuable if it forces you to think better yourself.
That insight shows up in Anthropic’s findings. Developers who used AI without letting it think for them actually did just fine. They asked it to explain concepts. They cross-checked its suggestions. They treated it as a collaborator, not a shortcut. Those users scored in the top half of the test group, achieving 65% or better on comprehension assessments.
But the developers who fully delegated the task to AI, delegators, iterative debuggers, passive implementers, scored under 40%. In simple terms, they got the job done fast, but learned almost nothing from it.
From a leadership standpoint, this matters. You can’t afford a generational talent pipeline of engineers who move fast today but can’t build anything new tomorrow. AI can’t innovate. It can optimize and autocomplete. Innovation still depends on people being able to think from first principles, challenge assumptions, and create original solutions, not just recreate patterns the model has seen before.
The key isn’t whether your teams use AI. The key is how. If they’re using it to test thoughts, sharpen knowledge, and pursue deeper fluency, they grow. If they don’t… they won’t.
As Wyatt Mayham of Northwest AI Consulting put it: “AI coding assistants are not a shortcut to competence.” He’s right. They are tools, but only as effective as the discipline and intellectual effort applied by the user. If your team offloads all the thinking, they give away the one thing that makes them valuable: their creative edge.
Maintaining cognitive engagement and independent problem-solving is essential for true mastery
Mastery doesn’t happen by hitting “run” and watching clean code execute. It happens when something breaks, when results are unexpected, and the developer has to figure out why.
This isn’t theoretical. Anthropic’s study found that the group who wrote code without AI ran into more errors. But those errors triggered deeper learning. The developers had to troubleshoot, reason through logic gaps, and reinforce their understanding of the Trio Python library. That’s how durable knowledge is formed. It requires effort. It’s uncomfortable, but necessary.
Now contrast that with the AI-assisted group. They made fewer mistakes, completed their tasks more efficiently, but retained less understanding of what they just did. When quizzed immediately after the assignment, many couldn’t explain the key concepts they had just applied using AI-generated code.
For executives, this is a strategic concern. When cognitive friction is eliminated, so is growth. The path of least resistance rarely leads to strong long-term capability. Developers who never debug become engineers who can’t explain failures. That’s a risk multiplier if you’re building mission-critical platforms or scaling new products fast. Getting stuck, and figuring it out, isn’t wasted time. It’s where compound learning takes place.
Teams don’t need to avoid AI. But they need to stay mentally engaged while using it. That’s where real skill compounds. Productivity without understanding is short-term thinking. Competence built through effort is what keeps innovation resilient.
Developers must intentionally use AI as a tool for learning rather than a substitute for personal analytical effort
AI doesn’t remove the need for human reasoning. If anything, it makes disciplined reasoning more important.
Developers who treat AI as an answer machine will get results, but quickly degrade their own capability. Developers who use AI to deepen inquiry, by asking why a solution works, prompting for conceptual explanations, and manually verifying outputs, walk away smarter. The key difference is active engagement versus passive delegation.
Wyatt Mayham, from Northwest AI Consulting, nailed it when he said, “Use it to understand the ‘why’ behind the code, not just the ‘what.’” That’s the mode top engineers operate in. They don’t offload their thinking, they augment it. They remain decision-makers, not just prompt engineers.
Leadership should promote that mindset. Disciplined use of AI isn’t just a style choice, it directly affects skill retention, independent thinking, and innovation capacity. If unchecked, heavy AI reliance can reduce developers to implementation roles, where understanding is shallow and interventions are reactive.
To avoid that, smart organizations will implement usage guidelines focused on learning, not just acceleration. Encourage developers to refactor AI-generated code. Require self-review, contextual questioning, and verification by design. These habits prevent cognitive atrophy and turn AI into a force multiplier instead of a crutch.
High output doesn’t mean high competence. The developers who thrive in this AI-integrated environment will be the ones who stay in the loop, thinking, questioning, and always sharpening their edge.
Organizations should deploy AI tools in a way that sustains continuous learning and quality output
Speed means nothing if retained knowledge drops off. Organizations pushing AI into development workflows at scale need to understand this tradeoff clearly. Boosting output is easy with today’s AI coding assistants. But preserving engineering capability, judgement, autonomy, and technical precision, that takes intent.
Anthropic’s research makes it clear: developers who use AI passively may complete tasks faster but retain significantly less understanding. That weakens long-term team performance. If developers begin relying too heavily on AI, their ability to independently troubleshoot, architect, or improve systems declines. This has downstream effects on product quality, security, and scalability.
Companies have to take ownership of how AI tools get integrated into daily workflows. This isn’t about limiting the use of AI, it’s about deploying it intelligently. Large model providers like Anthropic and OpenAI already offer structured learning environments, Claude Code Learning, Explanatory Mode, ChatGPT Study Mode. These tools exist to give teams a space where they can ask questions, explore concepts, and challenge AI’s output. They build knowledge, not just velocity.
Executives who want durable technical teams will need to set the guardrails. That includes implementing training protocols that enforce verification of AI-generated code, encouraging engineers to prompt not only for solutions but for the reasoning behind them, and backing workflows that require human insight and accountability at every level of the product build cycle.
This is a long-term investment. But the upside is clear. Teams that use AI to work faster and learn deeper will outpace those who use it only to move fast. As deployment of generative tools accelerates, the differentiator won’t be access, it will be how thoughtfully the organization trains its people to remain in control of the technology.
Key takeaways for leaders
- AI boosts speed but erodes skill depth: Developers who rely heavily on AI assistants complete tasks faster but score significantly lower on comprehension, especially in debugging and design. Leaders should balance speed with learning to avoid weakening core engineering capabilities.
- How developers use AI matters more than if they use it: Teams that use AI for exploration and explanation retain more knowledge than those who delegate tasks entirely. Executives should promote usage patterns that encourage conceptual engagement, not passive implementation.
- Cognitive effort drives long-term expertise: Developers who debug and resolve issues independently build deeper mastery than those who avoid error entirely through AI. Leadership should create workflows where mental engagement remains a core expectation, even when AI is involved.
- AI must support thought: Developers who use AI to understand the why behind code maintain skills better than those who just accept the what. Organizations should encourage disciplined AI use, prompting for reasoning.
- AI rollout needs intentional guardrails: Without structured guidance, widespread AI use risks degrading institutional knowledge and developer autonomy. Leaders should integrate tools like explanatory modes, mandate verification, and track learning quality alongside productivity.
