But what I'm more curious about is why you'd need to give shopping instructions to an AI in the first place.
2,939 karma · joined March 8, 2026
No promises on code quality, of course. Cheap things are cheap for a reason, right?
It's not work-related — feel free to reach out just because you want to get closer
Looking at this, I really feel that social norms have changed a lot. In each era, women's rights have shifted back and forth—sometimes equal to men's, sometimes lower. These things keep changing from generation to generation, and I'm curious how they'll change in future generations.
The truth is, "good code" is relative. It is determined by the specific domain and the composition of the team. Is incomprehensible FP (Functional Programming) code good? No, it isn't. A programmer must assess the team's capabilities and adapt accordingly. Good code is ultimately something that morphs based on the shape of the organization. Once defined this way, good code might share certain commonalities (like readability or a shared mental model), but its actual form varies wildly.
So, what is good code? That definition is missing. To be blunt, the Hacker News posts insisting that we must write "good code" are essentially a form of self-hypnosis.
Just look at paradigms. The mechanics of OOP have changed significantly, FP approaches have evolved, and DOD or DDD are fundamentally different from their early days. Whenever paradigms are discussed, someone claims, "That problem was solved in the past, and nowadays we do X," only for someone else to reply, "I don't think that's actually solved," leading to a fragmented breakdown in consensus. Ultimately, which knowledge remains as tacit knowledge is entirely dependent on the organization's capability.
You could argue that AI is terrible at simplifying code. However, I am skeptical that AI coding needs to be identical to human coding. When you actually code with AI, it often produces structures humans would call anti-patterns, including God Objects. Yet some of those structures can be faster or simpler for machines to navigate. There is no reason to assume that the optimal modularity for AI maintainers must be identical to the optimal modularity for human maintainers.
Of course, I am not denying that the rewards of good architecture are delayed, or that there comes a point where maintenance becomes impossible. But as the AI era ushers in an age of overproduction, software could become disposable, strictly personal, highly tailored to small niches, or ultimately, heavily polarized.
Realistically, programming domains fall into two major categories: "ship it and forget it" (one-offs) and continuous services. I agree with the OP's point that AI struggles to understand boundary delineations. But honestly, you can enforce those boundaries by injecting them into the spec. How those boundaries are drawn in the first place, however, is purely a matter of personal experience.
Personally, I define "good code" as code that allows the entity responsible for the software to achieve its purpose with a sufficiently low cost and error rate, factoring in the software's expected lifespan and future changes.
If you ask an AI to generate work based on this standard of what level of code is "adequate," you might get entirely different results. The biggest problem with discussions around AI is not just that ideological identities prevent proper evaluation (as seen in that article), but that the AI itself scales proportionally to its input. It is an incredibly difficult issue to judge because you don't know an individual's workflow or exactly how they are utilizing the tool.
I do think the value of reading code is important. However, much of what we are discussing in the AI era is actually rooted in the path dependency of how to become a good human senior developer.
Instead, the core focus of AI-driven development might shift toward defining broader abstractions: data semantics, invariant external contracts, and migration strategies.
Ultimately, I believe the paradigm shift of our era should lead us to ask: "How do we write code most economically in a system where AI is the primary maintainer?" The OP might think differently, but at least, that is where I stand.
This also follows from how current LLMs work. In practice, when I use them in domains I already understand, they can produce very high-quality results. But in domains I do not know well, the results can be poor, and the bigger problem is that I may not even be able to judge how poor they are.
So my conclusion is that AI will reduce the number of jobs, but it will not eliminate the need for people.
In education, the value of memorization may decline in the AI era. We may instead place more emphasis on domain modeling, problem framing, or the ability to choose and use tools effectively. But the more fundamental issue is that the IT industry may simply lose the capacity to employ as many people as it once did.
More precisely, I mean white-collar labor.
I think the deeper cause is a K-shaped economy in which the lower and middle classes become poorer. When ordinary consumers become poorer, one of the first things they tend to cut back on is discretionary spending, including spending on many kinds of IT services.
The core infrastructure layer is different. Large incumbents such as Microsoft and Google already dominate much of it, and they are likely to be more resilient. Search, video consumption, and a few other essential digital services will also remain strong. But many other IT services are, in practice, discretionary goods. Those companies may be hit much harder if consumers have less purchasing power.
People talk constantly about productivity these days, but we were already living in an age of overproduction before AI. AI is moving us from overproduction into an era of explosive production. The problem is that production can expand far faster than people’s ability to consume.
The cycle is supposed to be:
*products → revenue → employment*
But if the consumers who are supposed to support that revenue become poorer, the cycle weakens. Productivity alone cannot solve that.
I agree with the author that academics need to move beyond treating papers as the primary unit of achievement. Much of what the article argues is reasonable.
But there is another difficulty. Most academics built their reputations through papers. They use that reputation to obtain speaking opportunities, consulting work, grants, and other forms of income and status. Even if one person decides to move beyond the paper-centered system, it is difficult to change much unless the larger incentive structure changes as well.
My view is that IT workers have, in a sense, been working to reduce their own jobs since long before AI. The more infrastructure becomes centralized, the more peripheral and smaller companies are squeezed first. AI is simply another example of that process.
Until recently, people often said that highly skilled IT professionals were difficult to replace. AI changes that perception. Even when it does not fully replace knowledge workers, it can put significant downward pressure on the wage premium attached to specialized knowledge.
I do think AI will raise productivity. But companies will also reduce headcount accordingly. And if purchasing power becomes increasingly concentrated among a smaller group of people, product development itself may become more biased toward the preferences of those few consumers. That can create another negative feedback loop.
The claim that universities can simply choose important problems that are cheap to validate is also more difficult than it sounds.
If validation itself increasingly depends on AI, and universities cannot afford to own enough GPUs, then they remain dependent on large AI companies. That dependency will inevitably influence which research problems are practical to pursue.
Any research program is constrained by the institutions and funding sources that make the research possible. Always.
At the same time, I actually agree with the author that universities will become more important.
People often talk about “skill” as though it were some pure and independent quantity, but in my experience hiring rarely works that way. If one candidate is highly capable without a degree and another is equally capable with a degree, employers will usually prefer the credentialed candidate.
More broadly, people tend to hire those with whom they feel cultural familiarity and trust. University networks provide exactly that. Alumni often help other alumni, directly or indirectly.
So I think universities may increasingly become both social institutions and stronger elite-training clubs.
For someone like me, coming from a poorer country and without much money, there may not be many choices in that system anyway.
Still, I think the author’s argument is far too optimistic.
I use it mainly to check whether this code fits the rules I defined, just a yes or no. But I'm not sure if that's the right way to use it.
Fundamentally, a pattern is a reusable solution to a recurring problem. But what the author is describing here is simply an operational loop and a management strategy.
I think current agent methodologies are practically indistinguishable from human developer management theories. Isn't this just a feedback process? I would consider this an agentic workflow.
Also, people casually use the metaphor of a "factory" when churning things out, but a factory fundamentally operates on "orders"—it has a specific objective and a target production volume. The entire approach of just trying to dynamically respond to everything under this label feels somewhat contrived.
Furthermore, I always have this underlying question: why are we building software factories in the first place? Where are we manufacturing the people who will actually buy all this?
I've heard stories of people finding success by running "app factories" in the early 2010s when apps were scarce. But in the agent era, I believe we are already drowning in AI slop. We have to remember that back then, producer friction was high, and simply submitting an app was a difficult hurdle.
Now, AI handles most of the basics by default. Things that were once highly praised are now just the baseline. To create something actually worth selling today, you have to break the existing grammar entirely, build much more complex architectures, and offer deeper features. Given this new standard, I seriously question whether mass production is the right answer.
Human capability, when you think about it, is complex. Why is Newton praised as being so damn great? He established the law of universal gravitation, F=ma. Why is that such a big deal?
He distilled countless phenomena in an open system into a single mathematical formula.
What makes it great is that he found common state variables and relationships across entirely different phenomena like falling objects, planetary motion, collisions, and artillery trajectories.
But does F=ma hold true for the entire macroscopic world? No. There are various conditions and specific situations in motion, but within most scenarios and a certain range of approximation, it outputs values that are useful to humans.
Why is the Schrödinger equation so great? Because it turned the time evolution of quantum states into a calculable mathematical law.
Human thought is essentially creating a closed system by deciding what to cut out and what to keep from the infinite degrees of freedom in reality. Academia is what reinforces that closed system.
A great theory is great not because it perfectly replicates reality, but because it compresses the immense complexity of reality into a small, closed formal system while still managing to explain a multitude of phenomena.
In that process, it feels like human thought and progress are shifting into a different framework. What LLMs do well is primarily exploring within the ontology and representation space that humans have already built.
I think there are two broad categories of discovery: One is forming a new closed system, and the other is connecting fragmented knowledge within that closed system. I feel that the vast majority of research focuses on the latter.
What LLMs excel at is finding unvisited points within a given representation space. This is typically the process through which master's and PhD students connect dots, build their skills, and form their own mental models. But the logic behind criticizing LLMs seems to be that they eliminate the very work these graduate students need to do in order to grow.
However, looking at it from another angle, perhaps our current knowledge systems and classifications have reached a limit, suggesting that we might actually need a completely new classification and knowledge system.
What is the core principle of an LLM? It's predicting the probability of the next sequence.
Let's say you type the word "cat". Cat - is cute (90%), want to eat it (6%), furry (4%). Because "is cute" has the highest probability, the next sequence proceeds in that direction.
Within this framework, human knowledge and logic largely operate the same way. Once an initial logical proposition is established, we follow it up with whatever makes logical sense next. From that perspective, I think LLMs will actually do this better.
But what is it that LLMs cannot do right now? They cannot create that initial logical proposition. I believe they lack the ability to carve out a closed system from an open system.
Stacking logic step-by-step within a closed system—LLMs do this exceptionally well. But whether that constitutes true "intelligence" is a different matter.
I feel that being logical does not necessarily equate to having intelligence.
Humans preserve and create different mental models and knowledge systems within an open system. Just as your thoughts differ from mine, LLMs lack the ability to form these distinct mental models.
If so, within these limits, what humans must ultimately do is construct the logical frameworks that LLMs can then fill in. Perhaps a new kind of logic dedicated to designing these frameworks will become the next major trend.
Viewed from this perspective, I have no idea if we are in a mere technological transition or something else entirely. Or whether it is even correct to say humans are strictly necessary to build that framework. Maybe my learning is just lacking.
For me, reading is fine, but writing and speaking are hard. Especially speaking. What should I do about this?
Should I change my environment first, after all?
So it's really frustrating. how do you all relieve that frustration?
Personally, I think current LLMs have clear limits, so would engineering majors be at risk? That's a bit of a difficult point.
Because LLMs are good at writing code, but as complexity increasingly grows, if a person handles it, the number of lines one individual has to deal with will grow exponentially. To maintain that or find bugs, you'd need major-specific knowledge. After all, building a program is fundamentally about controlling complexity.
There's a slightly difficult point here. It's true that frontier AIs are good at coding, but once you start running agents at a large scale, token efficiency varies depending on whether a human intervenes or not. Ultimately, if you compare current token costs, isn't hiring a junior person cheaper? Is the US a different situation? Thinking about the API prices of GPT 6 or Fable makes me think that even more.
It's embarrassing to admit, but I tend to trust the research materials LLMs bring more than my colleagues or the programmer friends I once respected.