To What Extent Can Software Development Be Automated?

Why doesn't "the capacity to change fast" suffice?

We've all heard mantras about how "the most important thing is to adapt quickly" in the tech and startup world. Move fast. Break things. Keep iterating. But here's the twist: even more crucial than the ability to change is the capacity to preserve what's already great. If your AI assistant — or any tool, really — keeps rewriting core components of your software every time you request an update, you'll soon lose the gems you painstakingly refined in the previous iteration.

Think of it this way: if each update erodes the best parts of your codebase, your project's foundation becomes shakier over time. And that's exactly the risk with current LLM-powered tools. They can produce fantastic MVPs but struggle to save and protect your well-crafted features once you pivot to new functionality or expand your scope.

The DNA analogy: nature's lesson about the importance of permanency

To understand why preserving good code is so important, look no further than nature's most brilliant solution: DNA. DNA doesn't just change randomly every day. It locks in the information essential for survival over millions of years, preserving the species' best adaptations.

A pine tree's DNA, for example, has evolved to withstand harsh winters and varied conditions — from deadly forest fires to massive insect attacks. Sure, it mutates a bit, enough to allow adaptation, but the core remains strong and stable. It can stand both forest fires and massive insect attacks while still adapting to change.

That permanency is what keeps the species flourishing instead of reverting to square one every generation.

In software terms, we need a DNA-like system that stores and protects our best code. Current automatic systems don't do this. LLMs don't know how to do it. So as you keep adapting and iterating, you start losing these proven segments — much like forgetting a hard-earned lesson and making the same mistakes over and over again.

When LLM-aided development becomes a trap

So how does this play out in the real world? Let's say you use an LLM-powered tool to build an impressive proof-of-concept in one shot. You show it off to investors or your team, and everyone's excited.

Now comes the tough part: implementing new features, scaling the system to handle more users, and integrating complex business logic. Here's what often happens next:

• You prompt your AI tool again to add a feature.
• The AI starts rewriting or "improving" existing code but can't fully grasp which parts should stay as they are.
• Suddenly, a feature that once worked flawlessly becomes buggy or vanishes altogether.
• You scramble to fix the fallout, sometimes losing time and money to rebuild what you already had.

Sure, you could limit the AI's editing privileges to protect some of your code. But then you risk layering new features on top of a codebase that was never designed to accommodate them — leading to Frankenstein-like structures that are even harder to maintain in the long run.

In essence, you're stuck. You can generate small, not-production-eligible MVPs brilliantly. But the moment you need a full-blown, polished product with carefully curated, trustworthy features, the AI's lack of permanency and stability becomes a bottleneck.

Solution: human-AI co-creation

Imagine a system that identifies high-quality code and safeguards it — even as it integrates new functionality. It enables lightning-fast software development, even 25 times faster compared to traditional or even LLM-supported code generation. It builds tests, covers software specifications, runs code, debugs, and can be scaled almost without limit horizontally — meaning it's capable of handling huge enterprise applications. It protects the working code and improves the weak parts. It does everything needed for complete software development.

While it does most of the work, it doesn't do it alone. It does it with a human. Or humans.

We call it Automata. Our grand application built on the principle of preserving what works and surgically replacing what doesn't. It's complex, it's deeply collaborative, and it's forged from — I think I can say this almost without exaggerating — thousands of hours of refining an approach that merges human insight with AI's relentless speed and brute force.

How does it work? That's our secret sauce. We can't answer that. We can only say it was greatly inspired by the cormorants I studied for weeks in the Finnish archipelago — creatures that thrive by doing a lot of work, yet doing it while holding some undiscovered wisdom.