The Learning Loop at the Heart of Software

Software development has never fit the assembly-line model. In mature engineering disciplines, a few experts design a system and less specialized workers execute the plan. That separation works because the underlying physics are stable and the construction patterns repeatable. Software is different. Design emerges through implementation; we often write code before we fully understand the right design, and the feedback from that code guides us. There is no useful division between "designers" and "implementers" because the people writing the code are the ones discovering what the system should be. Constant interaction between developers, product owners, users, and other stakeholders is essential, and every participant brings their own insights.

This is a lesson Agile practices established more than two decades ago, and it risks being forgotten as LLMs tempt us back toward the idea that code can be generated in isolation once the design structure is thought through. That view ignores what development actually is.

LLMs as Brainstorming Partners, Not Builders

I recently built a framework for distributed systems based on the patterns in my book, experimenting heavily with LLMs along the way. They proved useful for brainstorming, naming, and generating boilerplate. But just as often, they produced code that was subtly wrong or misaligned with the deeper intent, and I had to discard large sections and start over. Eventually, I learned to treat them as brainstorming partners rather than autonomous builders. The experience clarified something fundamental: writing software is an act of learning, and no LLM agent can remove the need for that learning.

One place where LLMs genuinely help is the setup phase. Before meaningful work begins, we face the first hurdle of getting tools running—installing dependencies, choosing the right compiler or interpreter, resolving version mismatches, and wiring up runtime libraries. The "Hello, World" program is legendary precisely because it marks the moment imagination meets execution; that first successful output closes the loop and signals that the system is ready. LLMs are excellent at lowering this threshold, drafting build files, finding correct flags, suggesting dependency versions, and producing bootstrap snippets that get experimentation started. But once that boilerplate compiles, the real work begins.

The Learning Cycle Cannot Be Automated

Continuous learning is the engine of our work, regardless of the tools available. Building deep, lasting knowledge follows a hands-on pattern with three repeating steps:

  1. Observe and Understand. You take in new information by watching a tutorial, reading documentation, or studying existing code, building a basic mental map of how something should work.
  2. Experiment and Try. You move from passive observation to active participation, writing the code yourself, changing it, breaking it, and seeing what happens. This is where abstract ideas become concrete.
  3. Recall and Apply. When you face a new challenge, you actively retrieve what you learned and apply it in a different context. This act of recall transforms fragmented information into durable skill.

An AI can generate a perfect solution in seconds, but it cannot grant the experience gained from struggling through the creation process. The small failures and "aha!" moments are features of learning, not bugs to be automated away.

Why Reuse and Speed Hit Their Limits

This learning imperative explains one of software's persistent challenges: the limited success of high-level code reuse. Reuse works well for technical libraries and frameworks—data structures, web clients—that solve well-defined, universal problems. Beyond that level, it falters because most software challenges are embedded in unique business contexts that must be learned and internalized by the people maintaining them.

It also explains what I call the illusion of speed in starter kits and low-code platforms. These give powerful initial velocity for standard use cases, but the readymade components are compressed bundles of context—countless design decisions, trade-offs, and lessons hidden inside. Using them delivers functionality without the accompanying learning, leaving you with little internalized knowledge of machinery you've adopted. The moment a requirement deviates from what the readymade solution provides, the initial speedup evaporates. The developer faces a black box, and what seems like a small change becomes a time-consuming black hole, consuming all the time supposedly saved in the first few days.

LLMs amplify this dynamic. We're swamped with claims of double-digit productivity gains and cost reductions, but without acknowledging the nature of learning, these metrics are a trap. By offering seemingly perfect code at lightning speed, LLMs become the ultimate version of the maintenance cliff—a tempting shortcut that bypasses the essential learning required to build robust, maintainable systems over the long term.

Translation Without Understanding

There is a real reason for the excitement around LLMs. Software development uses a profusion of specialized languages: Gradle and Maven syntax for build files, structured outputs from Linux performance tools like vmstat and iostat, XML-based markup for SVG graphics, and general-purpose languages such as Python, Java, and JavaScript, not to mention the countless APIs, DSLs, and configuration files. LLMs excel as translators between human intent and these dialects. You can describe what you want in plain English—"create an SVG of two curves," "write a Gradle build file for multiple modules," "explain cpu usage from this vmstat output"—and instantly receive appropriately formatted code.

This capability is tremendous. It lowers the entry barrier, removes friction, and accelerates getting started. But fluency in translation is not the same as learning. These specialized notations embody decades of engineering wisdom; learning them is what lets us reason about change, modifying and evolving systems with confidence. LLMs make exploration smoother, but the maturity comes from deeper understanding.

The real capability—our ability to respond to change—comes not from speed of code production but from depth of understanding. Tools will keep getting smarter; the nature of the learning loop stays the same. If we forget that, we will always find ourselves at the maintenance cliff.

Why the Learning Loop Matters for LLM Adoption

Large language models change the economics of building software, but only in contexts where the core development loop—write, test, observe, revise—remains intact. The critical difference from earlier tooling is that the model participates in that loop as both an author and an artifact. This makes the quality of feedback signals the single largest factor in whether LLM-assisted work succeeds.

A model that writes code with weak specification or thin tests produces output that looks plausible but whose correctness is unverified. That output enters the codebase, and subsequent edits—human or model—operate on weaker foundations. The opposite also holds: when tests, type definitions, and interfaces describe a tight contract, the model's output is constrained in the right ways.

The design implication is that teams working with LLMs should not reduce test coverage. Instead, they should strengthen the contract-level details that help the model generate correct, integrateable code on the first attempt. The evaluation step is not a backstop anymore; it is a primary driver of code quality.

Engineering for the Loop

The described arrangement rescues a genuine advantage: improved model support for test-driven development. Rather than acting as purely an autocomplete on steroids, an LLM operating inside a fast cycle—where the compiler, failing test, or code reviewer yields tight feedback—lets each iteration converge quickly.

That is only true if engineers invest time in the surrounding assembly. A key insight from the article's argument is that the same principles that enable conventional engineering apply to LLM-assisted work. The "clean architecture" boundaries—interface stability, dependency freedom, restricted data ownership—are precisely what let a model work independently on one slice without breaking its neighbors.

What Breaks the Loop

    Model hallucinations are not the primary risk; the bigger risk is code that tempts the human reviewer to accept comfortingly fluent but incorrect output. This happens when the model's context is too broad, or when tests are too coarse to catch a semantic slip.
  • Long feedback cycles undermine the loop's benefits. If the model asks for too much context and evaluates too slowly, the machine isn't just busy—the human loses the thread of why a particular change was made. Developers disengage, and errors slip into code that then propagates through the knowledge base.
  • Accumulated clutter in context windows—irrelevant API details, unused imports, older patterns—wastes the model's ability to form a correct answer. Treating the model's input as a curated workbench, not an everything-dump, is a deliberate design act.

Testing is the Win

The same style of narrow contract that makes tests reliable also makes an LLM efficient. Each test is effectively a mini pair programmer, flashing a real signal when an edit goes sideways. The human remains in the loop at the moment of failure. That moment is the raw material for improvement: it shows exactly where the interface holds or cracks.

That is how to judge the promise of LLM-based development. Teams discover whether test coverage + model assistance outpaces test coverage alone, in observable engineering output—not as a headline claim about productivity. This comparison manages expectations.

The Competitive Loop and the Ghost Engineer

The described pattern surfaces during a broader industry shift in software economics. From 2022 onward, the technology wave driving accessible LLMs was itself enabled by previous code—lauded, but fundamentally correct and conventional. That clean pool of code created the quiet infrastructure, but its creators rarely receive acknowledgement.

This imbalance explains an equally undisclosed risk: the shadow engineer. Data and models from a thousand jurisdictions, where software is the scaffolding for surveillance economies, rest heavily on anonymous code authors whose code underlies their monitoring layers. The tension is not an abstract ethics debate. Those contributors see AI credit redistributed toward a politically favored few.

Two guarded outcomes result. In some contexts, the economic gate to a chip shortage closes loudly. In others, a knowledge embargo silently moves forward—the idea that open solutions to AI-era coordination are exchanged for gated royalties grows. Either route compounds the shadow problem, elevating code with a private guardian over clean, shared code.

That asymmetry matters to architecture. If a knowledge tax is quietly applied to the exact artifacts—interfaces, contracts, open models—needed to build internal capacity with LLMs, the teams who cannot pay that tax will find themselves downstream of others. While prominent firms are fighting hardware scrap, the unpaid toll is likely the more lasting division.

The current threat is not that bad code runs faster. It is that good code gets lost or monopolized. The loop measures correctly only for version control repositories and reviewers, not for unseen orchestration overhead. Build systems record lines and passes.

The overlooked pile-up—shims in a single service, metadata in local conventions outside the VCS, hardcoded config with its own undocumented restarts—stays invisible in the metrics. Yet this is exactly the waste type that LLM generation risks expanding daily, precisely because generating new code is less frictional than finding and constraining an orphaned function. Each clean API is a check against that disorder. Each dependency mess is a growth chamber for mistakes and complexity.