For decades, learning to code meant mastering syntax, understanding libraries, and writing programs line by line. But over the past few years, that landscape has changed rapidly.
I learned to code the way many did: by searching Google, reading documentation, and piecing together answers from Stack Overflow. When something broke, I was usually guided in the right direction, but I still had to find and fix the issue myself.
When ChatGPT arrived, that dynamic shifted. Instead of just pointing me toward a solution, it began correcting my code directly, explaining what was wrong and suggesting fixes. Soon after, tools like Codeium (now Windsurf) brought AI into the code editor itself. I found myself completing loops and scaffolding logic with AI assistance before I had finished typing.
Today, with tools like Claude Code, much of the code I work with is written by the model. My role as a software engineer has changed. I spend less time writing individual lines and more time giving instructions, describing intent, reviewing what’s generated, and deciding what fits the system I’m building.
Software is still being built, often faster than ever. But writing code is no longer the primary measure of progress.
This raises a deeper question: what does the work of a software engineer look like now and what will it look like tomorrow?
When Working Code No Longer Signals Understanding
If the way we produce code has changed this dramatically, it forces a deeper question: what should learning to code optimize for this moment of transition?
AI hasn’t removed the need for software engineers. But it has significantly reduced the effort required to produce working code. Tasks that once demanded hours, like scaffolding, refactoring, and boilerplate, can now be generated in moments. Often, the output is immediately usable.
This creates a subtle shift. When producing code becomes easy, correctness alone is no longer a reliable signal of student understanding. A program can work even if the person who assembled it cannot fully explain why it works.
I see this gap often. In one online discussion, a developer described building an application using an AI-driven builder tool. The product worked, but they acknowledged they weren’t sure how the system functioned beneath the surface.
If working code no longer reliably signals comprehension, education must optimize for something else: the ability to reason about systems – how components interact, where constraints live, and how changes ripple through a codebase.
This is not a failure of the developer. It reflects how learning historically relied on friction. Writing code line by line once forced engagement with details that now arrive pre-assembled.
If working code no longer reliably signals comprehension, education must optimize for something else: the ability to reason about systems – how components interact, where constraints live, and how changes ripple through a codebase.
For junior developers, this shift is especially important. As AI accelerates output, leveling up now depends less on writing more code and more on understanding existing systems along with their architectures and embedded trade-offs.
The risk isn’t that future engineers won’t know how to code, but that software will be assembled faster than it is understood, allowing hidden assumptions and fragile decisions to accumulate over time.
Can Curriculum Introduce System Complexity Earlier?
One important nuance is often missed: AI doesn’t just generate new code. It can now read, analyze, and explain existing codebases.
Until recently, this wasn’t possible at scale. Large systems were difficult to navigate without time, mentorship, or prolonged exposure. As a result, internships became the primary place where students encountered real system complexity.
AI changes that.
Modern models can help learners orient themselves inside unfamiliar systems, trace execution paths, and surface architectural patterns. What once required weeks of onboarding can now begin with guided exploration.
This does not diminish the value of internships. Industry environments teach lessons classrooms cannot, such as team dynamics, responsibility, organizational constraints, and the pressure of production. Those experiences remain irreplaceable.
AI doesn’t just generate new code. It can now read, analyze, and explain existing codebases. Until recently, this wasn’t possible at scale. Large systems were difficult to navigate without time, mentorship, or prolonged exposure. As a result, internships became the primary place where students encountered real system complexity. AI changes that.
But if AI can make complex systems legible earlier, internships need not be the first place students encounter such complexity.
One way I’ve been exploring this question is through Revibe Codes, a small educational project focused on helping learners study real, production-grade open-source codebases. The emphasis is on examining architecture, data flow, trade-offs, and system evolution rather than on feature implementation. In practice, smaller but complete systems are especially valuable here. Educational codebases such as Andrej Karpathy’s nanochat (which I’ve analyzed as part of this exploration) demonstrate that meaningful architectural ideas can be studied without industrial scale.
This approach does not replace fundamentals or hands-on coding. It complements them. The goal is not to accelerate students prematurely into industry roles, but to ensure that when they arrive, complexity is not entirely new.
Can Engineering Judgment Be Designed For?
Engineering judgment has traditionally been treated as something that emerges slowly with experience. You write enough code, encounter enough failures, and eventually develop an intuition for what works.
AI complicates this assumption.
In industry discussions, a recurring concern has emerged: junior developers produce technically correct changes with AI, but struggle to explain why those changes were made or what their implications are. The issue is rarely the code itself. It is the absence of a mental model and the understanding of alternatives, assumptions, and system-level impact.
Much of engineering judgment once formed as a side effect of friction. Debugging unfamiliar systems and tracing failures forced engineers to reason deeply about decisions. As AI removes this friction, judgment can no longer be left to chance.
To a significant extent, it can be designed for.
Judgment develops through exposure to real systems and real decisions: seeing how abstractions are chosen, how coupling is managed, when simplicity matters more than flexibility, and how security or failure modes influence design. Open-source software contains enormous latent knowledge, like architectural trade-offs, compromises, and mistakes, if learners are guided to look for them.
As engineers increasingly work with AI systems by creating agents, setting instructions, reviewing outputs, and intervening when assumptions break, the nature of judgment becomes more explicit. The challenge is no longer producing code, but deciding what to ask for, what to accept, and what to reject.
There are limits. Operational realities such as scaling under real load, incident response, and organizational constraints are still best learned in industry. But many foundations of judgment can be introduced earlier: reasoning about boundaries, trade-offs, and failure before responsibility arrives.
As engineers increasingly work with AI systems by creating agents, setting instructions, reviewing outputs, and intervening when assumptions break, the nature of judgment becomes more explicit. The challenge is no longer producing code, but deciding what to ask for, what to accept, and what to reject.
This shift raises a central educational question: How can learners be taught to exercise judgment in AI-mediated work? And to what extent does education need to adapt to make that possible?
One implication is that judgment cannot be treated as something that simply emerges with time or experience. It must be practiced deliberately. Learners need repeated opportunities to evaluate alternatives, question assumptions, assess risk, and explain why one approach is preferable to another. This is especially true in contexts where AI systems can generate plausible solutions quickly. These skills are developed less through producing correct outputs, and more through activities that emphasize critique, comparison, and reflection.
Importantly, this does not require schools to abandon existing curricula. Rather, it suggests a shift in emphasis: from evaluating students primarily on what they produce, to also evaluating how they reason about decisions, how they justify architectural choices, anticipate failure modes, and respond when automated tools produce incomplete or misleading results. In an environment where code can increasingly be generated, education’s distinctive role may lie in helping learners develop the capacity to decide responsibly.
AI may write more of the code. Humans still decide what should exist and where responsibility lies.
What Education Can Change, Now
Use Open-Source Projects to Introduce System Complexity
Open-source projects already occupy a unique place in computing education. Students are encouraged to explore them, learn from them, and increasingly, to contribute to them. However, meaningful engagement with open-source systems requires more than the ability to write correct code. It demands the ability to navigate unfamiliar architectures, understand data and control flow across modules, reason about trade-offs, and anticipate the consequences of change. These are precisely the forms of system complexity that students often encounter abruptly in internships or industry roles, but rarely experience in a structured, guided way during formal education. In this process, students can also make use of modern AI-assisted development tools, as they would be expected to do in real-world engineering environments, allowing the focus to shift from producing code to understanding systems.
In some cases, this engagement can be deepened further by involving the original maintainers or creators of the project through guest lectures or structured interactions. Hearing directly from the people who made the architectural decisions – why certain trade-offs were accepted, where shortcuts were taken, and which constraints mattered most – adds a layer of context that static code analysis alone cannot provide. These conversations help learners see software not as a collection of files, but as a sequence of human decisions made under real-world pressures.
Software engineers of tomorrow may write less code than ever, but they will own more of it than ever before. The responsibility, then, falls on education and industry – and on the systems they shape – to ensure students are prepared for that shift.
Complement Analysis with Structured Discussion and Peer Reasoning
Analysis-first work is most effective when paired with opportunities for students to articulate and defend their understanding. Many programs already include seminars, project reviews, or discussion-based components; these can be intentionally oriented around system reasoning rather than implementation details. For example, students might present how they understood a system’s architecture, debate alternative design choices, or discuss the downstream impact of a proposed change. In an era where AI tools can rapidly generate code, these discussion-oriented settings create space for human judgment, disagreement, and collaborative reasoning – skills that are difficult to outsource to automation and central to real-world engineering practice.
Conclusion
The central risk facing software engineering education is not that students will stop learning how to code. Code and fundamentals will continue to matter. The deeper risk is that, as AI makes software easier to produce, understanding becomes optional rather than expected.
Internships will remain essential, but they need not be the first place students encounter system complexity. If AI can make real systems legible earlier, education has an opportunity to introduce complexity deliberately, before students are responsible for it in production settings.
Engineering judgment was once shaped implicitly through friction. As that friction fades, judgment must be cultivated intentionally through exposure to real systems, guided analysis, and structured opportunities to reason about decisions, not just outputs.
Software engineers of tomorrow may write less code than ever, but they will own more of it than ever before. The responsibility, then, falls on education and industry – and on the systems they shape – to ensure students are prepared for that shift.
