When Software Grows Faster Than Our Understanding

You give a coding agent a task. A little later, the feature works and the tests pass. The diff spans dozens of files, introduces new structures, and fixes a few things you never explicitly asked it to address.

The result looks good. A few trial runs reveal no obvious problems, so you move on to the next task.

But some questions remain unanswered. Why this implementation? Which behaviors follow from your requirements, and which reflect choices the agent made? When you change this part of the system again, what needs to survive?

The software has moved forward. Your understanding is still where you started.

1. Keeping our understanding intact as software changes

Software exists as code and running processes, but it also exists in the understanding of the people who shape it. What is it for? Why does it work this way? Which choices must hold, and which are open to change? That understanding is what lets us keep shaping our software over time.

It does not have to cover every implementation detail. You can be unfamiliar with individual functions and still know what the software should become, judge whether a change serves your intent, and intervene when it does not.

In conventional development, complexity and team turnover can pull understanding and implementation apart. Yet designing, coding, debugging, and revising also give us repeated opportunities to build and correct our understanding as we work.

Coding agents change the pace. They can skip many of the steps that once required our direct involvement and produce substantial implementations on their own. The software keeps changing. Our understanding may not change with it.

The feature comes first; understanding becomes a follow-up task. Read the changes. Ask why. Work out what else they affect. These tasks fill some of the time saved on coding. The agent can keep generating, and the backlog of things we need to understand can keep growing.

There is a harder adjustment, too: the software may be progressing exactly as requested while becoming less familiar to you. You spend the day assigning tasks, answering questions, and checking results. Every agent is making progress, but you still have to work out how those changes fit together. The sense you develop by building something yourself—knowing where it stands and how to change it next—does not arrive automatically when the tasks are done.

Your own work gradually becomes unfamiliar. You remember what you asked for, but find it harder to explain why the result is the way it is, whether earlier decisions still hold, or what the next change will affect. The continuity of knowing what you have built, why it works that way, and how to change it begins to break. Continuing to shape it becomes harder.

2. Better AI does not erase the right to decide

One natural response is to make models more reliable: better code, better error detection, more thorough verification. If a model becomes good enough, do people still need to understand and decide what their work should become?

Our right to direct our own work does not depend on AI continuing to make mistakes.

“AI can write the code, but people will still have to design the architecture.” This answer ties our role to a skill in which we currently hold an advantage. What if models become excellent architects, too? Retreating to verification or system understanding leads to the same question: once a model is good at that as well, do we lose our reason to participate?

Take the argument to its limit. Suppose AI reaches general intelligence. It understands complex systems, makes excellent technical choices, and checks implementations more thoroughly than we can. Should we then hand over every decision about what we create?

As long as someone wants to create according to their own intent and choose the direction of their work, that question remains.

The ability to make a better decision does not, by itself, confer the right to decide for someone else.

You may choose to delegate many judgments to AI while keeping a particular decision for yourself. You can accept advice, change your mind, or acknowledge that an earlier choice was wrong. What matters is that you make an informed choice, rather than discover after the fact that the software has already changed.

The right to direct your own work does not depend on being more capable than AI. Nor does it depend on how many people still want to exercise it. Even if almost everyone is happy to delegate everything, the person who is not still has the right to decide what their software should become.

As long as one person wants to remain the author of their work, we still need an answer to how their intent can remain in force.

3. What it takes to keep shaping your software

Authorship continues after the first idea becomes working software. You need to be able to understand important choices, change direction, reject results that conflict with your intent, and keep revising the work. The right to direct it needs to translate into practical abilities.

If all you can do is click “Accept” when the agent finishes, without knowing which important choices the result contains, your control is limited. The same is true if you can reject a result but cannot find a way to change it, or if a requirement you made explicit yesterday quietly stops holding in today's implementation. You may still be approving the work while losing the ability to shape it according to your intent.

Keeping that ability means being able to answer some concrete questions:

• Is it still clear what the software is meant to become?
• Can you see how the current implementation responds to your requirements?
• Can you distinguish your important decisions from choices the agent made?
• When changing direction, can you understand the likely effects and intervene?
• After the next implementation change, will the decisions you explicitly made still hold?

Consider a personal writing tool with an explicit requirement: keep its content on the local device. You leave file organization, storage implementation, and many internal details to the agent. Later, to make the tool easier to use across devices, the agent proposes cloud sync.

Whether content may leave the device touches a decision you have already made. That choice needs to be visible, and you need to understand what it changes before you can accept or reject it. A result can be more convenient and more reliable while departing from the original requirement.

Tests can help establish that the software behaves in a particular way. You still need to judge whether that behavior is acceptable and whether it respects the decisions you want to maintain.

The agent can implement sync, check that data transfers correctly, and fix bugs. But whether content should leave the device, what evidence is enough to accept that change, and who is responsible if something goes wrong all need explicit answers beyond the implementation itself. Your judgment needs to determine where the work goes and when it stops, rather than serve only as a signature after everything is done.

4. Can reading all the code preserve authorship?

Code review offers a direct answer. The agent writes the code; a person reads it and checks what it does.

The code can contain behavior the model's report never mentions, structures that affect maintenance, and problems you can judge only by examining the implementation closely.

But as generation gets faster, can reading every change remain a sustainable way to stay in control?

Even without reading line by line, your day can still be full: clarify vague requirements, notice where a result has drifted, decide what evidence would establish correctness, and guide the agent back. Every step demands experience and concentration. Reading almost no code and working intensely can both be true.

Reading code is also an opportunity to build understanding. Reviewing a colleague's changes helps a team learn what the system now does, why it was designed that way, and what to watch for in future changes. If we read less code, we need other ways to develop that understanding.

Yet when changes pile up and people can only skim them before approving, keeping the review process does not necessarily preserve understanding. Our limited attention needs to reach the places that call for judgment: consequential design trade-offs, changes with a wide impact, and results that remain uncertain.

Which choices do we need to keep understanding over time? Which implementation details can we investigate when needed? How do we avoid spending all our attention on details while missing the decisions that actually need us?

5. What if we replace code with long specifications?

Another answer is to move our work into natural language. Describe how the software should behave, then have an agent implement the specification. Or ask the agent to explain the code, turning the system into documentation that is easier to read.

Clear requirements reduce misunderstanding. Documentation preserves context. A good explanation makes unfamiliar code easier to approach. But natural language does not make the cost of reading disappear.

If software contains many behaviors and trade-offs, a document that describes them all may grow alongside the implementation. We can go from too much code to read to too much prose to read. Even if every sentence is easier to understand than the corresponding code, the total can still exceed our time and attention.

And if the agent keeps expanding the specification while we merely approve it, calling it a human-approved document does not establish that a person understood every decision it contains.

What the software does, what the agent says it does, and what we actually understand and choose to stand behind are three different things.

A complete record and a thorough explanation can still leave us unsure how to keep shaping the software. We need to be able to find what matters, understand how it holds in the current implementation, and see the choices available when it changes next.

Keeping authorship in human hands

As implementation becomes less expensive, more people can turn their ideas into software. They should also be able to understand and continue shaping what they create.

This freedom extends beyond software. In any creative work, the person creating it should be able to choose what to delegate to AI and what to decide for themselves.

Finite Ground's mission is to help people create according to their own intent and keep shaping their work as AI expands what is possible.

Noema brings that mission to software, keeping people's intent in force as the implementation continues to change.

If a day comes when AGI takes over every part of human life and everyone gives up their autonomy, this question will no longer apply. Finite Ground will no longer have a reason to exist.

As long as one person is unwilling to give up that autonomy, there is a reason to keep going.

AI can bring more ideas into the world. The right to decide what those ideas become still belongs to the people who choose to keep it.