The Learning Loop and LLMs: Why AI Cannot Replace Understanding in Software Development

The Learning Loop and LLMs

TLDR: Despite the productivity promises of LLMs, software development remains fundamentally a learning activity. AI can accelerate setup and boilerplate generation, but it cannot replace the hands-on experimentation and contextual understanding that transform fragmented knowledge into durable expertise. The tools that generate code fastest often create the steepest maintenance cliffs.

Summary:

Martin Fowler's latest article strikes at the heart of a growing tension in software development: the collision between AI-generated code velocity and the essential role of learning through doing. The core argument is deceptively simple yet profound—software development has never fit the assembly line model where experts design and workers execute, and LLMs are dangerously reintroducing this flawed metaphor.

The assembly line works for physical engineering because physics is stable and construction patterns are repeatable. Software resists this model because design emerges through implementation, not before it. You often need to write code to understand what the right design should be. The feedback from running code becomes your primary guide, and this feedback loop requires human interpretation, not just execution. Agile methodologies recognized this two decades ago by emphasizing iterations, pair programming, and continuous integration—all mechanisms that honor the learning-through-doing nature of software work.

Fowler's personal experience building a distributed systems framework reveals the trap clearly. LLMs excelled at brainstorming, naming conventions, and generating boilerplate code. But just as often, they produced code that was subtly misaligned with deeper architectural intent—correct syntax, wrong semantics. He found himself discarding large sections and starting fresh, eventually learning to use LLMs as brainstorming partners rather than autonomous builders. This is the crucial insight: the tool's output quality matters less than what you learn while generating that output.

The learning loop itself breaks down into three non-negotiable phases. First, observe and understand—you build a mental map by reading documentation or studying existing code. Second, experiment and try—you move from passive observation to active participation, changing code, breaking it, seeing what happens. Third, recall and apply—the critical moment where you face a new challenge and must actively retrieve what you learned before, adapting it to a different context. This retrieval and application is what transforms fragmented information into durable skill. AI can generate perfect solutions instantly, but it cannot give you the experience gained from struggling to create it yourself.

This explains why high-level code reuse has remained elusive beyond technical libraries and frameworks. Reuse works for data structures and web clients because these solve well-defined, universal problems. Beyond that layer, most software challenges are embedded in unique business contexts that must be learned and internalized. Low-code platforms and starter kits provide initial velocity but bundle context—countless design decisions and tradeoffs—into black boxes. You get functionality without learning, leaving you with zero internalized knowledge when requirements inevitably deviate from what the readymade solution provides.

LLMs amplify this dynamic massively. The claims of double-digit productivity increases ignore what happens after the initial code generation. True expertise is built by applying knowledge to build deep context. Any tool that offers readymade solutions without this journey presents a hidden danger—what Fowler calls the Maintenance Cliff. The code appears perfect at lightning speed, but when you need to modify, extend, or debug it, you lack the mental model to reason about changes confidently. The time supposedly saved in the first few days gets consumed in understanding opaque generated code later.

Yet LLMs do have a genuine superpower: they provide a natural language interface to the many specialized languages of software development. Build files, performance tools, SVG graphics, configuration DSLs—each has its own syntax and constraints. LLMs let you describe intent in plain English and receive working code in the appropriate notation instantly. This lowers entry barriers and removes friction, making exploration smoother. But this fluency in translation is not the same as learning. The specialized notations embody decades of engineering wisdom. Learning them is what enables you to reason about change.

For architects and teams, this has profound implications. The tools that maximize short-term velocity—AI-generated code, low-code platforms, starter kits—often minimize long-term adaptability unless paired with deliberate learning investment. Teams need practices that ensure developers internalize the systems they're building, even when AI assists generation. This means code reviews that explain why, not just what. It means pair programming where the navigator understands the AI-generated code before the driver proceeds. It means retrospectives that ask not just "did we ship?" but "what did we learn?"

The article's warning about performance graphs at saturation is particularly apt. Systems show a knee point where latency increases exponentially and throughput drops sharply. Teams using AI without learning hit a similar knee—initial velocity is high, but the moment requirements deviate slightly, productivity collapses because no one understands the generated code deeply enough to modify it confidently. What seems like a small change becomes a time-consuming investigation into opaque abstractions.

Key takeaways:

• Software development is fundamentally a learning activity—design emerges through implementation, not before it
• The learning loop (observe, experiment, recall-and-apply) cannot be automated away; AI can generate code but not the understanding gained from creating it yourself
• LLMs excel at setup, boilerplate, and natural-language interfaces to specialized syntaxes, but struggle with contextual alignment to deeper architectural intent
• High-level code reuse fails beyond technical libraries because most software problems are embedded in unique business contexts requiring internalized learning
• The Maintenance Cliff: tools that maximize initial velocity without learning investment create black boxes that collapse productivity when requirements inevitably change
• Teams must pair AI-assisted generation with deliberate practices (code reviews, pairing, retrospectives) that ensure developers internalize what's being built

Tradeoffs:

• LLMs provide initial velocity and remove setup friction but sacrifice the deep understanding gained from manual implementation that enables confident modification later
• Low-code platforms and starter kits deliver fast time-to-first-feature but create black boxes that exponentially increase effort when requirements deviate from provided abstractions
• Natural language interfaces to specialized syntaxes lower entry barriers but bypass learning the design constraints and tradeoffs embedded in those notations
• AI-generated boilerplate accelerates starting new projects but without hands-on creation, developers lack the mental models needed to debug or extend generated code effectively

Link: The Learning Loop and LLMs