Comment by xyzelement | Hacker News Reader

Comment by xyzelement | Hacker News Reader

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// The need for ongoing friction in long-term skill formation.

The subtitle of the story tells it all.

There are some people who seek out friction. Think about an athlete or a hardcore nerd.

The best engineers are ones who were fascinated with computers and learning as kids and persued it at every opportunity. Found their own friction in other words.

For those kinds of people, friction-seeking is the constant and what LLMs did is moved the point of where the friction occurs.

For example - the best engineers I've worked with didn't necessarily have lots of experience coding in assembly because that kind of friction was no longer necessary. But they could solve hard problems (and if a problem really required assembly they could go learn it)

What I think will be hit much harder by AI is the low tier engineer. Someone who was never truly curious and committed to it, for whom it was just a job. For example a typical offshore ticket pusher kind of person. That kind of person never went out to find friction and that's the kind of thing that's never going to fly again - if I want mediocre or average, the LLMs are sufficient

I think this expresses what I've been trying to form in my mind.

I've been trying to map the LLM advancements and the current state of software development onto prior technological improvements. History is littered with similar cases where the abstraction layer ends up getting lifted, and people struggle with getting accustomed to working at that higher abstraction level.

For the people that fall in love with a single abstraction layer or don't have an interest in learning new paradigms, when their known pattern is abstracted away, they're condemned to being left behind, either unwilling or unable to adapt.

I don't think any industry is free from this, any person in any industry/profession over a period of 20 years or more has likely had to undergo massive adjustments as technology changed their field.

We're not unique, but that doesn't stop it from feeling so jarring when it happens to us

> History is littered with similar cases where the abstraction layer ends up getting lifted, and people struggle with getting accustomed to working at that higher abstraction level.

But there's no abstraction layer that ends up getting lifted. When I use a library like SDL or a standard like POSIX, I don't tend to look at the underlying implementation. Instead I work with the high level concepts that they come up with. There's no such things with AI tooling. The most similar is when fully vibing software and everyone knows the quality of the result.

I've learned something at every abstraction layer in computing from electronics (hardware), theory of computation (software) to high level programming languages with their paradigms. Same with several domains embodied by libraries. LLM tooling is more like shamanic ritual than engineering.

> There's no such things with AI tooling. The most similar is when fully vibing software and everyone knows the quality of the result.

This is the same “but the ai can’t even do fingers” argument all over again, and it will age just a well.

The whole vibe coding thing only really started taking hold with the release of opus 4.5. That wasn’t even a year ago. you absolutely can be quality software without looking at the code. The only thing up for debate in my mind is how big that software can get before you need to look under the hood.

But that’s dictated by current model capabilities. And I don’t see any reason to believe that they will suddenly stop improving on this front.

>and everyone knows the quality of the result.

Yeah, a bespoke program that does exactly what I need it to do, at a speed that I had forgotten was possible on computers, with customization that is an exact fit to me, at a cost that is smaller than a rounding error.

I get that LLMs struggle with the old paradigm of a single piece of software meant to serve every conceivable use case of every conceivable user, but I kinda hope that paradigm dies.

Exactly this. You still have to understand the abstraction layer the LLM is working at in order to be able to form an opinion about the quality of the code. (If you DON’T care about the quality of the code, there’s always been ways to get an extra 1000% short-term speed improvement by just not giving a shit.)

AI isn’t “a code abstraction layer” any more than Jira is, or the engineer themselves is.

I think "the engineer themselves" is much closer to the abstraction layer I'm referring to.

Imagine for a moment that, rather than using a compiler to translate c++ into assembly, that it instead has to be done by a person on the team. If that were the case, the resulting assembly code would certainly have to be reviewed and assessed before it was accepted.

Why?

Because people can also be leaky, non-deterministic abstraction layers. The only reason that the output of a compiler isn't regularly reviewed (in 99% of cases) is because it's extraordinarily reliable and consistently correct, or correct enough for most cases.

It's not terribly dissimilar from delegating engineering tasks to other engineers. When I ask someone else to develop one component of a larger application, I'm not telling them exactly what lines to write, I'm giving them some kind of structure and they're filling in the rest. The communication to them is above the implementation layer.

Agents are increasingly letting us work at that same boundary. Just because the current state of LLMs requires engineering knowledge to review the result doesn't mean an abstraction hasn't occurred. It speaks more to the current quality of that abstraction than the absence of one.

I think that if LLMs were able to achieve the same consistency as a compiler, most people wouldn't ever bother to check the underlying code it produced. I also think it would be difficult to not acknowledge that LLMs have gotten better at converting natural language into functioning software. The abstraction is certainly not perfect, but it is clearly improving.

With AI you learn contributor, project, and product management.

I think coding experts who are only experts at writing code will also be hit pretty hard. Every problem given to them is solved by writing code, and only code, their way and only their way. I think people like that are having a very hard time relinquishing control regardless of the quality and correctness of the LLM output. If it didn't come from them, or conform to their conventions and style, then it's wrong. These are the people that seem to be terrific developers but then when asked to be a team lead and bring a group of developers to their level completely fail.

Curiosity is really key, and I find that LLMs diminish curiosity in people that might otherwise have it because the allure of the answer is often a lot stronger than the allure of the friction. I guess that's just the distilling of the industry over time, but that could certainly lead to a shortage of expertise.

I don't see this at all. If anything they are curiosity amplifiers. But perhaps this mostly applies to the kind of people I spend most of my time with.

Who else is going to talk about a topic like how Searle would approach differentiating ownership from privilege in RBAC?

If that is representative of your personal experience, then I'm saddened to hear it. I believe your article is an argument from incredulity [1]. You may not personally know anyone who thrives in learning from and applying knowledge with AI. But being unaware that it is even possible is not evidence that expertise will inevitably collapse.

[1]: https://en.wikipedia.org/wiki/Argument_from_incredulity

This has largely been my experience. On the one hand I now have AI writing 99% of my code. On the other hand I'm not dealing with a whole new class of problems that come out of that.

I mean if you think about it, that sounds like a miserable existence to begin with. I would not rule out that some people will use this.. uh.. disruption, to start pursuing something that actually brings them joy.

Of course, bills need to be paid and it's not all that simple. But at least there might be such a silver lining.

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