The Bell Curve Nobody Talks About in AI

Why I’m writing this

Over the past few months, I’ve been asked repeatedly what I think about AI and how large language models are reshaping our field. How do we approach them at Falcon? Are we “vibe-coding” now? Even though I’ve seen some hesitation from colleagues, I’m genuinely enthusiastic about using AI — with a few important caveats. And no, AI isn’t here to steal your job. Yes, the market is tight and AI plays a part, but the situation is far more nuanced. In this article, I want to share my perspective on the real challenges and benefits LLMs bring, and how this new wave of tools has managed to split the industry into distinct camps.

AI as Augmentation, Not Replacement

In today’s software-development world, I view AI as a powerful assistant — a tool for augmentation, not a replacement for human developers. AI handles the repetitive, low-value work: boilerplate writing, refactoring, scaffolding, test-stub generation — the 80% of tasks that drain time without contributing much creativity. That frees developers to focus on architecture, problem-solving, user needs, and innovation.

But even the most advanced models lack real understanding. They don’t invent abstractions or discover new patterns. They operate by recognizing statistical structures in existing code — nothing more, nothing less. And that limitation matters.

Let’s Clear This Up: LLMs Are Still Just Statistical Models

I didn’t think I’d need to spell this out, but the confusion keeps appearing: LLMs are just neural networks, and no matter how impressive they seem, they’re still statistical models. They don’t understand code the way a developer does; they learn patterns from massive datasets and predict what’s likely to come next. When they generate code, they’re not reasoning about architecture — they’re assembling the most statistically probable sequence of tokens.

And like every statistical system, they follow the oldest rule in the book: garbage in, garbage out. This is why training data quality matters so much. If most public code is average, outdated, or inconsistent, models naturally sink toward that middle. Neural networks don’t rise to the best examples; they gravitate toward the most common ones. And when that statistical center gets reproduced at scale, AI-generated code amplifies existing weaknesses rather than improving them.

Companies behind models like GPT or Copilot try to counter this by filtering insecure or low-quality repositories, running static-analysis tools, removing duplicates, and layering human feedback on top. Some add self-correction loops or retrieval systems to access more reliable external sources.

But let’s be honest: these measures only soften the problem — they don’t solve it. They raise the minimum quality, but they can’t escape the gravity of the training data. LLMs still default to the common, not the innovative.

Statistical Models In, Statistical Code Out

Because AI models learn from existing, community-wide code — public repositories, Q&A sites, countless snippets from developers of every skill level — they inevitably absorb whatever flaws, antipatterns, and outdated habits exist in that collective mass. And let’s be honest: most of the world’s code isn’t written by senior engineers at well-run companies. It’s written by developers of all levels, on side projects, experiments, school assignments, weekend ideas, and half-maintained libraries. Naturally, the distribution leans toward “good enough,” not exceptional.

If you imagine a graph with code quality on the x-axis and volume of training data on the y-axis, you end up with a bell curve skewed slightly left: a big mass of mediocre code, a smaller amount of genuinely bad code on the left tail, and a much thinner right tail containing the truly great, high-quality code. A statistical model trained on that distribution gravitates toward the center — the common patterns — not the best ones. And this is crucial: LLMs don’t know why a pattern exists; they only know it’s frequent. Frequent is not the same as correct, elegant, or secure.

Ask an LLM to write a simple REST API handler and you’ll often get the same familiar patterns: outdated error handling, missing input validation, or overly permissive defaults. Not because the model is “wrong”, but because that’s the statistical average of what it has seen. And here’s the part people keep missing: if most code in the wild cuts corners, the model will too — confidently.

On top of that, the situation risks getting worse over time. As more AI-generated code finds its way into public repositories, the next generation of models starts training on the outputs of the previous one. This feedback loop — often called “model collapse” — slowly erodes quality by reinforcing the very patterns we want to move past. Innovation usually comes from deliberately breaking patterns, but a statistical model has no incentive to deviate. Its entire objective is to conform.

In practice, this means AI-generated code often feels predictable, familiar, and safe — not because it’s optimal, but because it’s statistically average. Instead of pushing forward new ideas, new architectures, or new abstractions, LLMs risk amplifying the ecosystem’s existing weaknesses and cementing yesterday’s solutions as today’s defaults.

This Is Where AI Can Hurt You

AI-generated code doesn’t just risk being mediocre — it can quietly introduce security flaws you never intended. Studies already show that a worrying amount of AI-produced code includes vulnerabilities, outdated libraries, or dangerously permissive defaults. And because these snippets look clean and confident, they slip into codebases unnoticed. Worse, malicious actors are already experimenting with “poisoning” public code and documentation so LLMs will absorb and reproduce harmful patterns without realizing it. I won’t go deep into this here — it deserves its own article — but the short version is simple: if you’re not reviewing AI-generated code with real rigor, you’re taking on security debt you may never discover.

The Community Split: From “Vibe-Coding” to “AI-doom-sayers”

In the developer community, two broad camps have emerged. On one side are the “vibe-coders”: enthusiasts optimistic that AI will soon write most of the code; on the other side are skeptics or “doomsday” voices, warning of massive security flaws, stagnation, and erosion of craft.

The truth probably lies somewhere in the middle. AI tools — when used wisely — can dramatically boost productivity. But that requires discipline, awareness of the risks, and a willingness to critically review and refactor AI outputs.

“Trying AI once for a weird problem” or “pasting blindly what it gives you” doesn’t count as real use. Like any tool, AI needs practice, context, and good judgment to deliver value without causing harm.

My view – responsible use: Augment — but Always Review

AI’s greatest strength lies in automating the boring parts: scaffolding, testing, repetition, prototyping, brainstorming. Those are valuable gains.

But for architecture, core logic, security-critical paths, and genuine innovation — human developers remain indispensable. And with AI-generated code, rigorous code review, static and dynamic security analysis, and awareness of vulnerabilities must be part of the workflow.

In the end: AI should be a force multiplier, not a crutch. Used well, it can free human developers to do what machines cannot: think creatively, design thoughtfully, and build genuinely new software.

A future we still create

Even though I’d love nothing more than to “vibe-code” from a sunny beach while an AI handles the heavy lifting, we’re not there yet. AI can accelerate us, support us, and automate the boring work — but it still lacks the kind of reasoning and creativity real engineering relies on. One day the landscape might shift, and a new kind of system may emerge that truly understands problems instead of predicting patterns. But LLMs, built on statistical foundations, aren’t that system.

At Falcon we say, “The future is ours to create.” That still holds. AI can help us get there faster — but only if we stay in the driver’s seat.