Open Source After Dependencies: Rethinking Freedom in an LLM-Native World

For most of my career, "open source" meant freedom.

Freedom to read code. Freedom to change it. Freedom to build on top of other people's work.

Somewhere along the way, that freedom quietly turned into a different kind of dependency.

Today, a trivial product relies on hundreds—sometimes thousands—of indirect dependencies. Each one represents decisions I didn't make, tradeoffs I didn't evaluate, and maintenance costs I silently accepted. Bundlers, frameworks, UI kits, and "best practices" don't just help us move faster; they shape our products in ways that are hard to see and even harder to undo.

This article is an attempt to explore a different future—one where LLMs don't replace open source, but force us to reconsider what open source is actually for.

The Dependency Problem Isn't Technical, It's Cognitive

We often frame dependency sprawl as:

a security issue
a maintenance issue
a supply-chain issue

But at its core, it's a cognitive load problem.

To make a "simple" decision—say, upgrading a router or HTTP client—I'm expected to:

understand a large API surface
know the ecosystem politics
assess maintenance health
trust transitive dependencies I've never seen

This doesn't scale. Not for individuals, not for teams.

And worse: it subtly discourages thinking. The safest path becomes "use what everyone else uses," even when it's not a great fit.

LLMs Change the Economics of Repetition

LLMs are bad at true invention. They don't have taste, intuition, or intent. They remix what already exists.

But here's the thing: most of what we do in software is repetition.

parsing
routing
formatting
state machines
protocol glue
UI primitives

We keep re-solving the same problems, just with slightly different constraints. Open source helped by letting us share solutions. LLMs change the game by making it cheap to re-generate them.

This suggests a shift:

from depending on code
to depending on intent

From Packages to Intent

Instead of saying:

"I use library X"

What if we said:

"I need an HTTP server optimized for low latency under moderate concurrency, with predictable memory usage."

That intent is stable. The implementation is not.

In an LLM-native model:

the intent is declared explicitly
code is generated and vendored into the repo
tests, benchmarks, and documentation are generated alongside it
regeneration is always possible

The code becomes owned, not borrowed.

This alone solves a large part of the dependency problem—but it raises a more uncomfortable question.

Where Does Creativity Go?

Open source isn't just about not reinventing the wheel. It's about inventing better wheels.

If LLMs mostly recombine known solutions, how do we:

discover new approaches?
avoid converging on "average" designs?
ensure we're choosing the best solution for our product, not just a plausible one?

This is where the story gets interesting.

Innovation Doesn't Come From Generation, It Comes From Selection

Most breakthroughs in engineering don't come from a single flash of genius. They come from:

exploring a design space
trying weird variations
measuring what actually works
keeping what survives pressure

LLMs are terrible at deciding what matters. They are very good at enumerating possibilities.

So instead of asking an LLM for the solution, we can ask it for many different ones.

Different architectures
Different tradeoffs
Different assumptions

Then we let reality—not popularity—choose.

Benchmarks
Failure modes
Operational pain

This turns code generation into an evolutionary process, where humans define the direction, and machines accelerate the search.

A Concrete Example: HTTP Servers

Consider something as "solved" as an HTTP server.

Today, the decision looks like:

pick the popular framework
accept its abstractions
inherit its performance profile
live with its roadmap

In an intent-driven, evolutionary model, the starting point is different:

intent:
  capability: http-server
  constraints:
    latency_p99_ms: <5
    concurrency: 20_000
    memory_mb: <50
    deployment: single-binary
  explore:
    variants: 5

An LLM generates multiple implementations:

epoll-based async
thread-per-core
io_uring experiment
minimal blocking design
aggressively inlined, zero-copy variant

Each ships with:

benchmarks
memory profiles
failure modes
a short explanation of tradeoffs

No single one is "the best". But together they define a Pareto frontier.

At that point, humans do what they're good at:

deciding what matters for this product
noticing weird behaviors
pushing new constraints ("What if startup time matters more than throughput?")

That's not automation replacing creativity. That's creativity being given better tools.

Open Source as a Map of Design Space

In this model, open source changes role.

Instead of publishing:

"Here's my HTTP server library"

You publish:

"Here's a new point in the HTTP server design space, optimized for X, breaking assumption Y."

The value shifts from:

maintaining code forever

to:

articulating new constraints
exposing new tradeoffs
naming patterns others hadn't noticed

Forking becomes cheap. Variants are expected. Popularity stops being the main signal.

That feels closer to the original spirit of open source than today's dependency monoculture.

A Note on "Open Source" Incentives (The Tailwind Example)

Recent events around Tailwind are instructive.

When Tailwind Labs laid off roughly 75% of its team after traffic shifted toward paid products, it surfaced something many of us intuitively felt already: the core value wasn't the open source code, it was the distribution channel.

This isn't a moral judgment. It's an incentive reality.

The moment an open source project's survival depends on:

brand gravity
ecosystem lock-in
paid extensions

…it stops being about shared ownership of solutions and starts being about controlled leverage.

An intent- and variant-driven model weakens this dynamic:

value lives in ideas and constraints, not traffic
no single implementation becomes the choke point
innovation is harder to monetize exclusively, but easier to share meaningfully

That's uncomfortable—but arguably healthier.

dissimilarity constraints
human-seeded mutations
explicit exploration budgets

Convergence is a system design choice, not an inevitability.

What Humans Still Do (And Always Will)

LLMs don't replace creativity because creativity doesn't live in code.

It lives in:

noticing pain
questioning defaults
redefining "what matters"
inventing new axes of optimization

Humans decide:

what constraints exist
what tradeoffs are acceptable
what "better" even means

Machines just help us explore the consequences faster.

This Isn't a Finished Answer

I don't think this replaces today's ecosystem overnight. I don't even think it should.

But I do think LLMs give us a chance to:

stop outsourcing thinking to dependencies
regain ownership of our codebases
make open source about ideas again, not artifacts

This article isn't a proposal. It's a question:

If we didn't have to depend on code anymore, what would we choose to share?

#opensource #llm #ai #softwaredevelopment #engineering

Open Source After Dependencies: Rethinking Freedom in an LLM-Native World

The Dependency Problem Isn't Technical, It's Cognitive

LLMs Change the Economics of Repetition

From Packages to Intent

Where Does Creativity Go?

Innovation Doesn't Come From Generation, It Comes From Selection

A Concrete Example: HTTP Servers

Open Source as a Map of Design Space

A Note on "Open Source" Incentives (The Tailwind Example)

Counterarguments (And Why They Matter)

"This will lead to mediocre, average solutions"

"This fragments the ecosystem"

"Most teams don't want to think about this"

"LLMs will converge to the same patterns anyway"

What Humans Still Do (And Always Will)

This Isn't a Finished Answer