AI Is Creating Better Coders and Fewer Senior Developers

The struggle was part of the education

Before AI, solving an unfamiliar technical problem usually required a certain amount of productive struggle. You searched for answers, read documentation, compared conflicting recommendations, tested assumptions, broke things, and eventually found a direction that worked.

The obvious lesson was how to solve that particular problem. The more important lesson was that almost every meaningful technical decision is situational.

There is rarely one universally correct architecture, framework, database design, integration pattern, or deployment strategy. There are different approaches with different consequences. The right decision depends on the product, the team, the timeline, the scale, the existing system, and the risks the business can tolerate.

Hunting for solutions forced developers to encounter those alternatives. We had to determine not only whether something could work, but why one approach made more sense here than another. Over time, that process developed technical judgment.

AI can now collapse hours or days of that process into minutes. That is an extraordinary productivity gain. It also removes much of the friction that once forced developers to think.

When an AI produces a polished answer, working code, and a confident explanation, it becomes very easy to mistake receiving a solution for understanding one.

Producing code is not the same as engineering

An entry-level developer can now ask AI to implement patterns they could not independently explain. They can generate an authentication system, introduce a message queue, restructure an application around domain-driven design, or deploy cloud infrastructure without understanding the tradeoffs involved.

The feature may work. The pull request may look sophisticated. The developer may appear far more capable than someone at the same career stage would have appeared five years ago.

But visible output is no longer reliable evidence of underlying ability.

That creates a dangerous form of overconfidence. Some developers already carry senior titles without having developed senior-level judgment. AI will make that problem more common because it allows people to produce the artifacts of expertise without necessarily possessing the expertise behind them.

Seniority has never been about how quickly someone can type code. It is the ability to recognize that several solutions are possible, understand what each one costs, anticipate where each one can fail, and choose the approach that fits the situation.

It also includes knowing when a technically elegant answer is the wrong product decision.

AI can recommend. It cannot accept accountability. The engineer—and eventually the product leader—must still own the consequences.

Using AI well requires the knowledge it appears to replace

I recently built a personal project with the explicit goal of allowing AI to do most of the heavy lifting. This was not an experiment in avoiding AI. It was an experiment in leaning into it.

The result reinforced both my enthusiasm and my concern.

Throughout the project, I made more than 50 course corrections. Some were minor adjustments. Others required challenging major decisions about the direction of the application. I identified pitfalls in approaches the AI proposed, questioned assumptions it treated as settled, and redirected it toward solutions that better fit the actual product.

AI dramatically increased my output, but only because I could evaluate what it produced.

Had I accepted its first answer each time, the project would still have generated a great deal of code. It might even have looked impressive during an early demonstration. That does not mean it would have been built well.

The paradox is that the better AI becomes at writing software, the easier it is to overlook the expertise required to supervise it. The technology appears to replace technical knowledge while actually making sound technical judgment more consequential.

You cannot effectively challenge a decision if you do not know that alternatives exist. You cannot identify a tradeoff you have never learned to see. You cannot recognize an architectural problem merely because the code compiles and the application appears to work.

AI does not eliminate the need for senior developers. It changes where their value is expressed.

We are disrupting the path to seniority

The traditional development path was imperfect, but it contained an informal apprenticeship. Junior developers handled smaller problems, received feedback, watched systems fail, inherited earlier decisions, and gradually learned why experienced engineers asked certain questions.

Now we are placing an extremely capable abstraction layer between junior developers and many of those learning experiences.

If we do nothing differently, we risk creating developers who are excellent at directing AI but weak at evaluating it. They will know how to request an implementation without knowing how to judge its architecture. They will move quickly until the product encounters a problem that cannot be solved through another prompt.

This is not an argument for making junior developers work inefficiently or withholding AI from them. Artificial difficulty is not education. The answer is not to recreate the past or celebrate how long technical work used to take.

The answer is to change what we expect junior developers to do with AI-generated solutions.

They should be required to understand why an approach was selected, identify credible alternatives, explain the tradeoffs, and formulate their own opinion. “The AI recommended it” cannot become an acceptable architectural justification.

Engineering leaders must actively cultivate that habit. Ask the junior developer why the solution belongs in this system. Ask what else they considered. Ask what assumption would cause the recommendation to change. Ask what will become difficult if the product grows tenfold or moves in a different direction.

The goal is not to see whether they can defend the AI’s answer. It is to discover whether they have made the decision their own.

Technical judgment will become more valuable, not less

Developers who formed genuine technical judgment before AI—and who now know how to pair that judgment with AI—are likely to become disproportionately valuable.

Not simply because they are “pre-AI developers.” Years in the industry do not automatically produce good judgment, and resistance to new tools is not expertise. The advantage belongs to people who endured the reasoning process, learned to see multiple paths, and can now use AI to execute at a much greater speed without surrendering responsibility for the direction.

That combination will be difficult to reproduce if companies treat AI output as a substitute for developer development.

This matters beyond engineering. Judgment is one of the defining requirements of product leadership. A product leader must connect technical choices to business consequences, distinguish short-term speed from long-term leverage, and know when a reasonable answer is wrong for this product, this team, or this moment.

Companies do not ultimately need leaders who can generate the most code. They need leaders who can decide what should be built, determine how it should be built, and recognize when the apparent answer is leading the product in the wrong direction.

AI will continue to become more capable. The question is whether the people using it will continue to develop the judgment necessary to lead it.

If we allow AI to make every decision before junior developers learn how decisions are made, we will gain faster programmers today—and create a shortage of genuine technical leaders tomorrow.