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7 hours ago
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Why zero-dependency design, and AI-native code generation are reshaping how we build software.
For decades, software engineering has marched to the rhythm of reuse. We were taught to avoid reinventing the wheel, to leverage the power of open source, and to break down systems into isolated services that scale independently. Reuse was more than a design philosophy , it was an economic necessity.
But something profound has changed.
Today, large language models can generate code from scratch, given only a narrative prompt. They don’t just complete boilerplate. They understand the intent, structure, and domain. They write, refactor, and even architect. And as this capability matures, we face a startling realization:
In the age of AI-generated software, reuse may no longer matter, at least not in the manner we have historically leveraged.
More than that: some reuse may be a hindrance.
Code reuse was born out of constraints. Humans have limited bandwidth, limited memory, and limited time. Software libraries arose as a way to encode and distribute reusable knowledge. Dependencies became foundational: they saved time, reduced bugs, and offered trust in hardened solutions.
The microservices boom extended this idea to the system level. Services became independently deployable, independently own-able units of functionality , optimized for team boundaries, not system elegance.
In short, reuse was a way to scale human effort, not machine understanding.
Large language models like OpenAI’s Codex can now synthesize complex logic in real-time, grounded in a natural language description. They can:
- Write full-featured applications from scratch
- Understand and refactor legacy systems
- Generate tests, documentation, even deployment logic
But most importantly, LLMs can work without relying on third-party libraries.
They don’t need to remember the quirks of a package API. They don’t struggle with adapting a general-purpose library to a specific use case. They just write what you ask for, optimized for your context.
When the cost of generation approaches zero, reuse becomes at least economically speaking irrelevant, insofar as code creation is concerned.
The actor model offers an elegant middle ground between concurrency and cohesion. In systems like Apache Pekko, each actor encapsulates its own state and behavior, interacting with others via asynchronous messages.
This pattern brings:
- Scalable concurrency without shared state
- Fault tolerance through supervision hierarchies
- Location transparency, enabling future distribution
From an LLM’s perspective, this is ideal. Actor systems form a single, coherent, traceable codebase. The LLM can see the full picture: who talks to whom, when, and why. It can reason over the entire scope of a solution space.
In effect, the actor-based monolith provides you the best of both worlds: a distributed highly scalable system and a single comprehension in terms of design and code base.
While this is not the only pattern that might gain favor in an AI driven development world, it presents the case that we must revisit the trusted patterns of the past. Architectures in the future need to be optimized not around hundreds of cooperating human teams, but instead optimize around an entire new set of constraints, driven by an agentic SDLC.
The dependency economy, one of open source’s greatest successes, becomes a liability in an LLM-native environment.
Because dependencies abstract behavior into black boxes. They introduce:
- Versioning complexity
- Indirection in debugging
- Security vulnerabilities
- External configuration mismatches
- Reasoning Opaqueness.
When code is generated on demand, these issues vanish. Instead of adapting your problem to someone else’s abstraction, the LLM adapts the solution to your domain.
This is not about NIH syndrome, it’s about total system transparency in a world where code is written by machines and reviewed by humans. While lines of code within a solution might grow, it is the LLM that takes on the burden of creating them. At the same time solution bloat decreases and as such the solution can be optimized to be far more resource efficient.
There are still domains where reuse is not just helpful, but essential:
- Cryptography
- Compression and Encoding
- Math and Science Libraries
- Regulated Domains
In these cases, LLMs can still play a valuable role, wrapping, adapting, and testing the library usage, but not necessarily replacing it. Reuse here isn’t about convenience, it’s about assurance.
As LLMs become embedded in the software lifecycle, we’re seeing the emergence of a new paradigm:
Zero-Dependency Architectures
These systems are:
- Self-contained
- LLM-native
- Composable
- Transparent
They are easier to optimize, scale, and reason about. Cold start times vanish. Cloud bills drop. The efficiency gains aren’t just technical, they’re economic.
Global development teams were built around maximizing productivity across time zones.
But LLMs change the game:
- A single engineer with an LLM can do a week’s work or more in a day.
- Real-time collaboration replaces global handoffs.
- Coordination overhead becomes a cost, not a benefit.
The bottleneck is no longer coding , it’s describing intent with clarity.
Imagine a solution space where your business expert is sitting down with their solution engineer and iterating on the requirements dozens if not hundreds of times a day. In this model a sprint isn’t a mini-waterfall, but a daily set of iterations that continuous clarify requirements and instantaneously build the result. This model is clearly facilitated by proximity and time zone.
It is the cumulative set of requirements constantly being refined and iterated upon that are the key element of reuse. These requirements will be compositional. They will consist of highly reusable non-functional requirements that will drive construction and delivery. They will consist of functional requirements that differentiate one solution from the next.
The art of composing these requirement efficiently and effectively will determine velocity. The ability to reuse requirements in a compositional manner so that they do not have to be recreated for each use case will resolve to consistency and quality. The ability to create them in a manner in which LLM’s can transform them into solutions will ultimately determine success.
We are at the beginning of a software renaissance.
The tools we’ve built to scale human limitations are being replaced by tools that scale cognition itself.
In this world, reuse fades. Distribution fades. Complexity fades.
What remains is intent, clarity, and execution at the speed of thought.
The future belongs to those who embrace simplicity, precision, and storytelling as the new foundations of software architecture.
And it starts with letting go of the idea that someone else has already built what you need.
Because now, you can build it yourself faster, cheaper, and better than ever before.
