The Problem Nobody Puts on the Roadmap

Most engineering teams using Java, Kotlin, Groovy, Scala, or Clojure don’t say:

“Our build system is the problem.”

Instead, they say:

• “CI is just slow.”
• “Gradle sometimes breaks after upgrades.”
• “SBT takes forever.”
• “Kotlin compilation is heavy.”
• “Classpath issues again?”
• “Why did this pass locally but fail in CI?”

But here’s the uncomfortable truth:

Your JVM build pipeline may be silently slowing down your entire organization.

And unlike a production outage, this problem doesn’t trigger alarms.

It just slowly erodes productivity.

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The Hidden Cost of Slow JVM Builds

Let’s do simple math.

If your:

• Java Spring Boot project takes 8–12 minutes in CI
• Kotlin project struggles with kapt or Gradle configuration time
• Scala monorepo takes 15–20 minutes due to SBT + Zinc
• Groovy/Gradle DSL has fragile plugin interactions
• Clojure project hits dependency resolution inconsistencies

And you deploy 8–10 times per day…

You’re burning hours of engineering time daily.

Multiply that by:

• 10 engineers
• 220 working days
• Fully loaded salaries

Slow JVM builds are not a nuisance.

They are a structural tax on innovation.

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Dependency Hell Is Still Real in 2025

Despite improvements in:

• Maven
• Gradle
• SBT
• Leiningen / deps.edn
• Kotlin DSL
• Spring Boot dependency management

We still see:

• Version conflicts
• Transitive dependency surprises
• Classpath mismatches
• Illegal reflective access warnings
• Annotation processor conflicts
• Plugin compatibility breakage
• “Works on my machine” builds

Even modern stacks like:

• Kotlin + Ktor
• Scala + Cats Effect or ZIO
• Groovy + Grails
• Clojure + Ring
• Java + Spring Boot 3

…are not immune.

The larger the monorepo.

The more microservices you have.

The more fragile your dependency graph becomes.

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Why This Happens on the JVM

The JVM ecosystem is powerful — but layered.

You’re not just compiling code.

You’re orchestrating:

• Transitive dependency graphs
• Annotation processors (kapt, Lombok, MapStruct)
• Classpath scanning
• Reflection-based frameworks
• Plugin chains
• Code generation
• Multi-module builds
• CI caching layers

Every extra layer increases:

• Build time
• Cognitive load
• Upgrade friction
• CI unpredictability

And once your feedback loop slows down…

Developers refactor less.

Teams postpone upgrades.

Technical debt compounds quietly.

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Kotlin, Scala, Groovy and Clojure Are Not Immune

Different language. Same friction.

Kotlin

• kapt slows builds
• K2 migration introduces instability
• Gradle Kotlin DSL increases configuration complexity

Scala

• Slow compile times
• Heavy type inference
• SBT performance quirks
• Cross-building between 2.x and 3.x

Groovy

• Dynamic resolution surprises
• Gradle plugin fragility
• Joint compilation edge cases

Clojure

• Tooling fragmentation (Lein vs deps.edn)
• Classpath conflicts
• Reflection warnings
• JVM startup overhead

The root issue isn’t the syntax.

It’s the build and runtime model surrounding the language.

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When CI Becomes a Cultural Problem

Slow CI pipelines don’t just cost minutes.

They change behavior:

• Developers batch commits
• Small pull requests disappear
• Riskier merges become normal
• Version upgrades become “events”
• Engineers accept friction as inevitable

Eventually, the organization adapts to the slowness.

That’s when velocity truly drops.

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What a Modern JVM Workflow Should Feel Like

A modern JVM development workflow should provide:

• Deterministic dependency resolution
• Reproducible builds
• Minimal classpath surprises
• Fast incremental compilation
• Lightweight runtime startup
• CI behavior identical to local builds
• Cloud-friendly packaging by default

No magic.

No opaque annotation processors.

No fragile plugin chains.

Just:

Install → Run → Test → Deploy → Repeat.

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Rethinking the JVM Development Model

Over the past two decades, the JVM ecosystem accumulated tools designed to manage complexity.

Ironically, that tooling often became the complexity.

That’s one of the motivations behind BoxLang (currently v1.10) — a modern language for the JVM designed to reduce friction in:

• Dependency management
• Build determinism
• Runtime footprint
• Cloud-native deployment

Without abandoning the JVM ecosystem.

BoxLang focuses on:

• Declarative, reproducible dependency handling
• Lightweight runtimes
• Reduced classpath scanning
• Predictable dependency resolution
• Fast local feedback loops

If you’re interested in how BoxLang approaches compiler performance and reproducible builds, this deep dive explains the architecture:

👉 https://www.ortussolutions.com/blog/under-the-hood-of-boxlang-17-our-fastest-compiler-yet

And if you want to see how the language continues evolving — including distributed locking and modern functional patterns — here’s what’s new in 1.10:

👉 https://www.ortussolutions.com/blog/boxlang-1100-functional-arrays-elegant-loops-distributed-locking

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🚀 Try BoxLang

If slow builds, fragile dependency graphs, or heavy JVM stacks are slowing your team down, the best way to evaluate a different approach is to try it.

BoxLang runs on the JVM.

It works alongside existing Java libraries.

You don’t need to abandon your ecosystem.

You can install and run it in minutes.

👉 Try BoxLang and experience a lighter, more deterministic JVM workflow.

Because sometimes the problem isn’t your team.

It’s your toolchain.