How AI Changed the Economics of Architecture
SkyWalking GraalVM Distro: A case study in turning runnable PoCs into a repeatable migration pipeline.
The most important lesson from this project is not that AI can generate a large amount of code. It is that AI changes the economics of architecture. When runnable PoCs become cheap to build, compare, discard, and rebuild, architects can push further toward the design they actually want instead of stopping early at a compromise they can afford to implement.
That shift matters a lot in mature open source systems. Apache SkyWalking OAP has long been a powerful and production-proven observability backend, but it also carries all the realities of a large Java platform: runtime bytecode generation, reflection-heavy initialization, classpath scanning, SPI-based module wiring, and dynamic DSL execution that are friendly to extensibility but hostile to GraalVM native image.
SkyWalking GraalVM Distro is the result of treating that challenge as a design-system problem instead of a one-off porting exercise. The goal was not only to make OAP run as a native binary, but to turn GraalVM migration itself into a repeatable automation pipeline that can stay aligned with upstream evolution.
For the full technical design, benchmark data, and getting-started guide, see the companion post: SkyWalking GraalVM Distro: Design and Benchmarks.
From Paused Idea to Runnable System
This journey actually began years ago. Shortly after this repository was created, yswdqz spent several months exploring the transition. The project proved much harder in practice than the individual GraalVM limitations sounded on paper, and the work eventually paused for years.
That pause is important. The missing ingredient was not ideas. Mature maintainers usually have more ideas than time. The real constraint was implementation economics. Even when the architect can see several promising directions, limited developer resources force an earlier trade-off: choose the path that is cheapest to implement, not necessarily the path that is cleanest, most reusable, or most future-proof.
This is a very common reality, not an exceptional one. In open source communities, much of the work depends on volunteers or limited company sponsorship. In commercial products, the pressure is different but the constraint is still real: roadmap commitments, staffing limits, and delivery deadlines keep engineering resources tight. In both worlds, good ideas are often abandoned not because they are wrong, but because they are too expensive to validate and implement thoroughly.
There is another constraint that matters just as much: the architect is usually also a very senior engineer, not a full-time implementation machine. That means limited personal coding energy, fragmented time, and a constant need to explain ideas to other senior engineers before the code exists. Traditionally, that explanation happens through diagrams, documents, and conversations. It is slow, lossy, and unpredictable. We all know some version of the Telephone Game: even simple words are easy to misunderstand, and by the time the misunderstanding becomes visible, a lot of time has already passed.
What changed in late 2025 was that AI engineering made multiple runnable ideas affordable. Instead of picking an early compromise because implementation capacity was scarce, we could switch repeatedly between designs, validate them with code, discard weak directions quickly, and keep iterating until the architecture became solid, practical, and efficient enough to hold.
That design freedom was critical. GraalVM documentation gives clear guidance on isolated limitations, but a mature OSS platform hits them as a connected system. Fixing only one dynamic mechanism is not enough. To make native image practical, we had to turn whole categories of runtime behavior into build-time artifacts and automated metadata generation.
There was also a very concrete mountain in front of us in the early history of this distro. In the first several commits of the repository, upstream SkyWalking still relied heavily on Groovy for LAL, MAL, and Hierarchy scripts. In theory, that was just one more unsupported runtime-heavy component. In practice, Groovy was the biggest obstacle in the whole path. It represented not only script execution, but a whole dynamic model that was deeply convenient on the JVM side and deeply unfriendly to native image.
To bridge the gap, we re-architected the core engines of OAP around an AOT-first model. Earlier experiments had to confront Groovy-era runtime behavior directly and explore alternative script-compilation approaches to get around it. The finalized direction went further: align with the upstream compiler pipeline, move dynamic generation to build time, and add automation so the migration stays controllable as upstream keeps moving. Concretely, that meant turning OAL, MAL, LAL, and Hierarchy generation into build-time precompiler outputs instead of leaving them as startup-time dynamic behavior.
AI Speed Changed the Design Loop
The scale of this transformation was not only about coding faster. AI changed the loop between idea, prototype, validation, and redesign. We could build runnable PoCs for different approaches, throw away weak ones quickly, and preserve the promising abstractions until they formed a coherent migration system.
That does not reduce the role of human architecture. It raises the value of it. Human judgment was still required to decide what should become build-time, what should stay configurable, where to introduce same-FQCN replacements, how to keep upstream sync controllable, and which abstractions were worth preserving. But AI speed made it realistic to pursue those better designs instead of settling for a simpler compromise too early.
This is the real change in the economics of architecture. In the past, an architect might already know the cleaner direction, but limited engineering capacity often forced that vision back toward a cheaper compromise. Now the architect can return much closer to being a fast developer again: building code, shaping high-abstraction interfaces, and using design patterns to prove the vision directly in the real world.
That changes communication as much as implementation. In open source, we often say, talk is cheap, show me the code. With AI engineering, showing the code becomes much more straightforward. The design no longer depends so heavily on a slow top-down translation from idea to documents to interpretation to implementation. The code can appear earlier, and it can run earlier.
Other senior engineers benefit from this too. They do not need to reconstruct the whole design only from diagrams, meetings, or long explanations. They can review the actual abstraction, see the behavior in code, run it, challenge it, and refine it from something concrete. That makes architectural collaboration faster, clearer, and less lossy.
This is also where I think the current AI discussion is often noisy. Many projects are fun, surprising, and worth exploring, but advanced engineering work is not improved merely by attaching an agent to a codebase. The important question is not which demo looks most magical. The important question is which engineering capabilities are actually being accelerated without losing the discipline of software development itself.
For architects and senior engineers, the capabilities that mattered most here were:
- Fast comparative prototyping: Building several runnable approaches in code instead of defending one idea with slides and documents.
- Large-scale code comprehension: Reading across many modules quickly enough to keep the whole system in view.
- Systematic refactoring: Converting reflection-heavy or runtime-dynamic paths into designs that fit AOT constraints.
- Automation construction: When a migration step must be repeated every upstream sync, doing it manually once is already expensive. Doing it manually again next time is even more expensive. AI made it practical to invest in generators, inventories, consistency checks, and drift detectors that turn repeated manual work into repeatable automation.
- Review at breadth: Checking edge cases, compatibility boundaries, and repeatability across a large surface area.
Those capabilities were visible in the resulting design. Same-FQCN replacements created a controlled boundary for GraalVM-specific behavior. Reflection metadata was generated from build outputs instead of maintained as a hand-written guess list. Inventories and drift detectors turned upstream sync from a vague maintenance risk into an explicit engineering workflow.
For junior engineers, I think the lesson is equally important. AI does not remove the need to learn architecture, invariants, interfaces, testing, or maintenance. It makes those skills more valuable, because they determine whether accelerated implementation produces a durable system or just more code faster. The leverage comes from engineering judgment, not from novelty.
Claude Code and Gemini AI acted as engineering accelerators throughout this process. In the GraalVM Distro specifically, they helped us:
- Explore migration strategies as running code: Instead of debating which approach might work, we built and compared multiple real prototypes, discarded the weak ones, and kept what held up.
- Refactor reflection-heavy and dynamic code paths: Replace runtime-hostile patterns with AOT-friendly alternatives across the codebase.
- Make upstream sync sustainable: Every time the distro pulls from upstream SkyWalking, metadata scanning, config regeneration, and recompilation must happen again. AI helped build the pipeline so that each sync is a controlled, largely automated process rather than a fresh manual effort that grows longer each time.
- Review logic and edge cases at scale: Especially in places where feature parity mattered more than raw implementation speed.
The result was not just a large rewrite. It was a repeatable system: precompilers, manifest-driven loading, reflection-config generation, replacement boundaries, and drift detectors that make upstream migration reviewable and automatable.
For the broader methodology behind this style of development, see Agentic Vibe Coding in a Mature OSS Project. This post is the next step in that story: not only enhancing an active mature codebase, but reviving a paused effort and making it actually runnable.
What Actually Changed
The most important outcome of this project is not a benchmark table. The benchmark results belong to the distro itself, and they matter because they prove the system is real. But for this post, the deeper result is methodological: AI engineering changed how architecture could be explored, validated, and refined.
Instead of treating architecture as a mostly document-driven activity followed by a long and expensive implementation phase, we were able to move much faster between idea, prototype, comparison, and redesign. That made it realistic to pursue higher-abstraction solutions, preserve cleaner boundaries, and build the automation needed to keep the migration maintainable over time.
The technical evidence for that work is the SkyWalking GraalVM Distro itself: not only a runnable system, but a migration pipeline expressed as precompilers, generated reflection metadata, controlled replacement boundaries, and drift checks. The benchmark data matter because they prove the system works in practice, but the architectural result is that the migration became a repeatable system rather than a one-time port. For detailed benchmark methodology, per-pod data, and the full technical design, see SkyWalking GraalVM Distro: Design and Benchmarks.
The project is hosted at apache/skywalking-graalvm-distro. We invite the community to test it, report issues, and help move it toward production readiness.
For me, the deeper takeaway is broader than this distro. AI engineering does not make architecture less important. It makes architecture more worth pursuing. When implementation speed rises enough, we can afford to test more ideas in code, keep the good abstractions, and build systems that would previously have been judged too expensive to finish well.
For senior engineers, that means the bottleneck shifts away from raw typing speed and toward taste, system judgment, and the ability to define stable boundaries. For junior engineers, it means the path forward is not to chase every exciting AI workflow, but to become stronger at the fundamentals that let acceleration compound: understanding requirements, reading unfamiliar systems, questioning assumptions, and recognizing what must remain correct as everything around it changes. AI changed the economics of architecture because it lowered the cost of validating better designs without lowering the bar for engineering judgment.