By generating prototypes that are based on different design models each end product can be assessed for specific criteria like code readability, reliability, or fault tolerance and then quickly be revised repeatedly to serve these ends better. No longer would the victory dance of vibe coding be simply "It ran!" or "Look how quickly I built it!".

AI tends to accept conventional wisdom. Because of this, it struggles with genuine critical thinking and cannot independently advance the state of the art.

AI systems are trained on vast bodies of human work and generate answers near the center of existing thought. A human might occasionally step back and question conventional wisdom, but AI systems do not do this on their own. They align with consensus rather than challenge it. As a result, they cannot independently push knowledge forward. Humans can innovate with help from AI, but AI still requires human direction.

You can prod AI systems to think critically, but they tend to revert to the mean. When a conversation moves away from consensus thinking, you can feel the system pulling back toward the safe middle.

As Apple’s “Think Different” campaign in the late 90s put it: the people crazy enough to think they can change the world are the ones who do—the misfits, the rebels, the troublemakers, the round pegs in square holes, the ones who see things differently. AI is none of that. AI is a conformist. That is its strength, and that is its weakness.

[1] https://www.modular.com/blog/the-claude-c-compiler-what-it-r...

Maybe the current allocation of technical talent is a market failure and disruption to coding could be a forcing function for reallocation.

I believe the same pattern is inevitable for these higher level abstractions and interfaces to generate computer instructions. The language use must ultimately conform to a rigid syntax, and produce a deterministic result, a.k.a. "code".

Source: https://www.youtube.com/watch?v=h5fmhYc4U-Y

First, I disagree that good code is required in any sense. We have decades of experience proving that bad code can be wildly successful.

Second, has the author not seen the METR plot? We went from: LLMs can write a function to agents can write working compilers in less than a year. Anyone who thinks AGI is far away deserves to be blindsided.

But of course writing code directly will always maintain the benefit of specificity. If you want to write instructions to a computer that are completely unambiguous, code will always be more useful than English. There are probably a lot of cases where you could write an instruction unambiguously in English, but it'd end up being much longer because English is much less precise than any coding language.

I think we'll see the same in photo and video editing as AI gets better at that. If I need to make a change to a photo, I'll be able to ask a computer, and it'll be able to do it. But if I need the change to be pixel-perfect, it'll be much more efficient to just do it in Photoshop than to describe the change in English.

But much like with photo editing, there'll be a lot of cases where you just don't need a high enough level of specificity to use a coding language. I build tools for myself using AI, and as long as they do what I expect them to do, they're fine. Code's probably not the best, but that just doesn't matter for my case.

(There are of course also issues of code quality, tech debt, etc., but I think that as AI gets better and better over the next few years, it'll be able to write reliable, secure, production-grade code better than humans anyway.)

I have not really found anything that shakes these people down to their core. Any argument or example is handwaved away by claims that better use of agents or advanced models will solve these “temporary” setbacks. How do you crack them? Especially upper management.

I remember being aghast at all the incomprehensible code and "do not modify" comments - and also at some of the devs who were like "isn't this great?".

I remember bailing out asap to another company where we wrote Java Swing and was so happy we could write UIs directly and a lot less code to understand. I'm feeling the same vibe these days with the "isn't it great?". Not really!

Valuable? Yep. World changing? Absolutely. The domain of people who haven't the slightest clue what they're doing? Not unless you enjoy lighting money on fire.

What you are seeing here is that many are attempting to take shortcuts to building production-grade maintainable software with AI and now realizing that they have built their software on terrible architecture only to throw it away, rewriting it with now no-one truly understanding the code or can explain it.

We have a term for that already and it is called "comprehension debt". [0]

With the rise of over-reliance of agents, you will see "engineers" unable to explain technical decisions and will admit to having zero knowledge of what the agent has done.

This is exactly happening to engineers at AWS with Kiro causing outages [1] and now requiring engineers to manually review AI changes [2] (which slows them down even with AI).

[0] https://addyosmani.com/blog/comprehension-debt/

[1] https://www.theguardian.com/technology/2026/feb/20/amazon-cl...

[2] https://www.ft.com/content/7cab4ec7-4712-4137-b602-119a44f77...

Electric Clojure: https://electric.hyperfiddle.net/fiddle/electric-tutorial.tw...

"In order to make machines significantly easier to use, it has been proposed (to try) to design machines that we could instruct in our native tongues. this would, admittedly, make the machines much more complicated, but, it was argued, by letting the machine carry a larger share of the burden, life would become easier for us. It sounds sensible provided you blame the obligation to use a formal symbolism as the source of your difficulties. But is the argument valid? I doubt."