The Tower Keeps Rising

> But it’s not the biblical story. At Babel, the loss of common language stops construction whereas in AI-assisted engineering, construction can continue after shared understanding has already collapsed. The lack of an immediate failure is what makes it curious and a bit disorienting. The tower does not fall, and so we do not notice what was lost. It just keeps rising.

I don't know whether the author thinks this is a good or a bad thing, but in my eyes it's clearly a bad thing. Intelligence is knowing that a tomato is a fruit, wisdom is knowing not to put it in a fruit salad. AI is the the ultimate form of intelligence with zero wisdom. Actually, it's not even intelligence, it's an illusion of intelligence. If there is no human who can understand what the AI is doing it's time to stop and accept that we do not have the wisdom to contain what we are building.

Since GPT 5.2, AI has been writing code much better than I can. In 5.6, it beat Tourlist in competitive coding. After seeing that, I only practice about an hour a day just to keep the feel of coding.

Honestly, rather than pointless debates about whether human coding is bad or AI coding is bad, I just think it's good to build tools that help me understand the world. I don't really care whether it's hand-coded or bad code.

Because most of my career has been spent as an on-site programmer. Staying at factories, visiting public institutions, deploying services for financial companies. My career is short, but I was lucky enough to work in various places.

When AI first came out, I thought I still wrote better code. But after the GPT 5 series, I've completely switched to AI and I'm now thinking about how to avoid errors and maintain larger codebases.

In the world I work in, it's common to see functions with 10,000 lines. Many people don't consider coupling or cohesion. So these days, instead of focusing on programming syntax, I'm studying programming theory and thinking about how to handle code when it becomes massive and turns into a black box through vibe coding. And I think this approach is right, because I believe I need to get used to using AI, so I keep coding with it.

But due to my cognitive limits, I've restricted myself to C# and TypeScript, which I'm comfortable with. C++ has too much to memorize and is hard to keep up with. In my region, there are very few C++ jobs, and those that exist are either extremely high-paying or garbage-tier jobs. There's nothing in between. So I stick with C# and TypeScript.

In practice, when building large programs, I often just set external configuration values I don't fully understand and code based on heuristics. I don't know the internals of Kafka, RabbitMQ, or PostgreSQL. I just know how to use them. And yet they work fine.

I feel the same way about AI code. Even if AI code is messy, if it runs, I use it. When bugs appear or performance is off, I just plug in debuggers or print statements and fix the necessary parts, like working with legacy code. Programming is so complex that if you try to understand everything, you can only design very small parts. Do the people who wrote Linux understand the entire codebase? They trust people they can rely on.

I've also reached an internal agreement to trust AI code. To support that, I'm spending time on creating rules for how to get good code from AI. Things like adding gates or CI, and seeing if that improves the code.

The problem is, I know this means no one will want to use other people's work or collaborate. The middle layer will disappear. There will be only highly admired projects or personal projects. In the past, even mid-sized projects had humans helping each other. But now mid-sized projects barely need human help. So I think projects will become increasingly polarized and become a zero-sum game.

Brooks divided complexity into two types in The Mythical Man-Month: Essential Complexity and Accidental Complexity. Personally, I think AI has greatly reduced Accidental Complexity. However, the essential difficulty, the problem of modeling, still needs to be done by humans. Because AI has no physical embodiment, it's inherently hard for it to understand domains the way humans do. Learning about something is different from experiencing it.

So I've decided to believe that vibe coding is also a valid approach. Supporters talk about compilers being deterministic, but LLMs are not deterministic. Critics say AI only produces garbage code, but I've seen that with high-quality prompts, the output becomes much better. Math PhDs say AI is good at things like theorem proving, and most of coding is similar to theorem proving.

It's not about good or bad. I've decided to believe it's just another approach. Yes, this is just a religion. My religion.

No matter how much people say vibe coding is bad, those who use it well do use it well. And there's no reason to criticize those who don't use it. I've just decided to treat this programming approach as a religion. Arguing about what's right or wrong is pointless anyway. Everyone has different values based on their environment, and convincing others is a waste of time.

People in open source communities might feel like AI code is destroying their communities. The code they used to communicate with, and the time they spent on it.

But for someone like me, who's been in delivery and on-site work, it feels like an escape hatch. It freed me from the hell of dealing with difficult people. So I've decided to rationalize it to myself: AI coding is just one way of doing things

I just think new methodologies will emerge. Instead of dividing code by functions or methods, people will think about how to divide things at a larger scale.

I'm just living to adapt to this era. I have nothing to lose anyway. I'm just waiting for the new era.

I mean that at the bottom of the Tetris board, the lines need to vanish so that the Tetris board keeps moving downward and doesn't grow unbounded.

I had a discussion with folks at work about what information is worth retaining in the face of AI doing everything for us. A lot of what we have in our heads to qualify as "domain experts" is pretty esoteric. How to invoke command line tools, gotchas because library A uses one convention over library B, AWS vs GCP; so much is specific to a tool rather than a method. There are also a lot of entrenched tools that are effectively unfixable due to the risk of breaking changes, so you have to shrug and accept + learn that's how it works.

Catch-22 is it's still important to know the fundamentals so you know what to ask for, but if you don't know the esoterica, the model is eventually going to make an assumption and screw things up. And the models don't have much taste either in prose, or in coding/comment style.