AI is a floor raiser, not a ceiling raiser

Of course AI increases inequality. It's automated ladder pulling technology.

To become good at something you have to work through the lower rungs and acquire skill. AI does all those lower level jobs, puts the people who need those jobs for experience on the street, and robs us of future experts.

The people who benefit the most are those who are already up on top of the ladder investing billions to make the ladder raise faster and faster.

Yeah, the graphs make some really big assumptions that don't seem to be backed up anywhere except AI maximalist head canon.

There's also a gap in addressing vibe coded "side projects" that get deployed online as a business. Is the code base super large and complex? No. Is AI capable of taking input from a novice and making something "good enough" in this space? Also no.

In a sense I agree. I don't necessarily think that it has to be the case, but I got that same feeling of that it was wearing a white lab coat to be a scientist. I think their honest attempt was to express the relationship of how they perceive things.

I think this could still be used as a valuable form of communication if you can clearly express the idea that this is representing a hypothesis rather than a measurement. The simplest would be to label the graphs as "hypothesis". but a subtle but easily identifiable visual change might be better.

Wavy lines for the axis spring to mind as an idea to express that. I would worry about the ability to express hypotheses about definitive events that happen when a value crosses an axis though, You'd probably want a straight line for that. Perhaps it would be sufficient to just have wavy lines at the ends of the axes beyond the point at which the plot appears.

Beyond that. I think the article presumes the flattening of the curve as mastery is achieved. I'm not sure that's a given, perhaps it seems that way because we evaluate proportional improvement, implicitly placing skill on a logarithmic scale.

I'd still consider the post from the author as being done in better faith than the economist links.

Id like to know what people think, and for them to say that honestly. If they have hard data, they show it and how it confirms their hypothesis. At the other end of the scale is gathering data and only exposing the measurements that imply a hypothesis that you are not brave enough to state explicitly.

The graphic has four studies that show increased inequality and six that show reduced inequality.

Yup. As a retired mathematician who craves the productivity of an obsessed 28 year old, I've been all in on AI in 2025. I'm now on Claude's $200/month Max plan in order to use Claude Code Opus 4 without restraint. I still hit limits, usually when I run parallel sessions to review a 57 file legacy code base.

For a time I refused to talk with anybody or read anything about AI, because it was all noise that didn't match my hard-earned experience. Recently HN has included some fascinating takes. This isn't one.

I have the opinion that neurodivergents are more successful using AI. This is so easily dismissed as hollow blather, but I have a precise theory backing this opinion.

AI is a giant association engine. Linear encoding (the "King - Man + Woman = Queen" thing) is linear algebra. I taught linear algebra for decades.

As I explained to my optometrist today, if you're trying to balance a plate (define a hyperplane) with three fingers, it works better if your fingers are farther apart.

My whole life people have rolled their eyes when I categorize a situation using analogies that are too far flung for their tolerances.

Now I spend most of my time coding with AI, and it responds very well to my "fingers farther apart" far reaching analogies for what I'm trying to focus on. It's an association engine based on linear algebra, and I have an astounding knack for describing subspaces.

AI is raising the ceiling, not the floor.

Thanks for the links. That should be obvious to anyone who believes that $70 billion datacenters (Meta) are needed and the investment will be amortized by subscriptions (in the case of Meta also by enhanced user surveillance).

The means of production are in a small oligopoly, the rest will be redundant or exploitable sharecroppers.

(All this under the assumption that "AI" works, which its proponents affirm in public at least.)

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> inequality

It's free for everyone with a phone or a laptop.

IMO the problem with this pets vs. livestock analogy is that it focuses on the code when the value is really in the writers head. Their understanding and mental model of the code is what matters. AI tools can help with managing the code, helping the writer build their models and express their thoughts, but it has zero impact on where the true value is located.

Great point, but just mentioning (nitpicking?) that I never heard about machines/containers referred to as "livestock", but rather in my milieu it's always "pets" vs "cattle". I now wonder if it's a geographical thing.

Thanks for pointing this out. I think this is an insightful analogy. We will likely manage generated code in the same way we manage large cloud computing complexes.

This probably does not apply to legacy code that has been in use for several years where the production deployment gives you a higher level of confidence (and a higher risk of regression errors with changes).

Have you blogged about your insights, the https://stillpointlab.com site is very sparse as is @stillpointlab

Oo, the "pets vs. livestock" analogy really works better than the "craftsmen vs. slop-slinger" arguments.

Because using an LLM doesn't mean you devalue well-crafted or understandable results. But it does indicate a significant shift in how you view the code itself. It is more about the emotional attachment to code vs. code as a means to an end.

LLMs playing chess isn't a big deal. You can train a model on chess games and it will play at a decent ELO and very rarely make illegal moves(i.e 99.8% legal move rate). There are a few such models around. I think post training messes with chess ability and Open ai et al just don't really care about that. But LLMs can play chess just fine.

[0] https://arxiv.org/pdf/2403.15498v2

[1] https://github.com/adamkarvonen/chess_gpt_eval

> people aren't aware of how wrong they can be, and the errors take effort and knowledge to notice.

I have friends who are highly educated professionals (PhDs, MDs) who just assume that AI\LLMs make no mistakes.

They were shocked that it's possible for hallucinations to occur. I wonder if there's a halo effect where the perfect grammar, structure, and confidence of LLM output causes some users to assume expertise?

It's super obvious even if you try and use something like agent mode for coding, it starts off well but drifts off more and more. I've even had it try and do totally irrelevant things like indent some code using various Claude models.

Yeah, the chess example is interesting. The best specialised AIs for chess are all clearly better than humans, but our best general AIs are barely able to play legal moves. The ceiling for AI is clearly much higher than current LLMs.

> you'd quickly find that it isn't able to track a series of moves for very long (usually 5-10 turns; the longest I've seen it last was 18)

In chess, previous moves are irrelevant, and LLM aren't good with filtering out irrelevant data [1]. For better performance, you should include only the relevant data in the context window: the current state of then board.

[1] https://news.ycombinator.com/item?id=44724238

Hogwash. If you can't figure out how to do something with project Y from npm try checking it out from Github with WebStorm and asking Junie how to do it -- often you get a good answer right away. If not you can ask questions that can help you understand the code base. Don't understand some data structure which is a maze of Map<String, Objects>(s) it will scan how it is used and give you draft documentation.

Sure you can't point it to a Jira ticket and get a PR but you certainly can use it as a pair programmer. I wouldn't say it is much faster than working alone but I end up writing more tests and arguing with it over error handling means I do a better job in the end.

They’re not. They’re good at many things and bad at many things. The more I use them the more I’m confused about which is which.

I think agents have a curve where they're kinda bad at bootstrapping a project, very good if used in a small-to-medium-sized existing project and then it goes downhill from there as size increases, slowly.

Something about a brand-new project often makes LLMs drop to "example grade" code, the kind you'd never put in production. (An example: claude implemented per-task file logging in my prototype project by pushing to an array of log lines, serializing the entire thing to JSON and rewriting the entire file, for every logged event)

Very concise, thank you for sharing this insight.

I read this as interloper. What's an extraloper?

How do you know it’s significantly better quality if you don’t know any of the “how”? The quality increase seems relative to the garbage you start with. I guess as long as you impress yourself with the result it doesn’t matter if it’s not actually higher quality.

I wouldn’t classify what you’re doing as “cheating”!

I don't find the "AI" answers all that accurate, and in some cases they are bordering on a liability even if way down below all the "AI" slop it says "AI responses may include mistakes".

>It's the opposite of finding an answer on reddit, insta, tvtropes.

Yeah it really is because I can tell when someone doesn't know the topic well on reddit, or other forums, but usually someone does and the answer is there. Unfortunately the "AI" was trained on all of this, and the "AI" is just as likely to spit out the wrong answer as the correct one. That is not an improvement on anything.

> wade through UI distraction like ads and social media

Oh, so you think "AI" is going to be free and clear forever? Enjoy it while it lasts, because these "AI" companies are in way over their heads, they are bleeding money like their aorta is a fire hose, and there will be plenty of ads and social whatever coming to brighten your day soon enough. The free ride won't go on forever - think of it as a "loss leader" to get you hooked.

"accurate"

This is in line with another quip about AI: You need to know more than the LLM in order to gain any benefit from it.

Above average people can also use it to get average results. Which can actually be useful. For many tasks and usecases, the good enough threshold can actually be quite low.

That explains why people here are against it, because everyone is above average I guess.

>Below average people can use AI to get average results.

But that would shift the average up.

I was reading some stuff by Michael A. Jackson (Problem Frames Approach) and T.S.E Maibaum (Mathematical Foundations on Software Engineering) because I also had the impression that too much talk around LLM-assisted programming focuses on program text and annotations / documentation. Thinkers like Donald Schön thought about tacit knowledge-in-action and approached this with design philosophy. when looking at LLM-assisted programming, I call this shaded context.

as you say, software engineering is not only constructing program texts, its not even only applied math or overly scientific. at least that is my stance. I suspect AI code editors have lots of said tacit knowledge baked in (via the black box itself or its engineers) but we would be better off thinking about this explicitly.

> When it comes to things I am not good at at, it has given me the illusion of getting 'up to speed' faster. Perhaps that's a personal ceiling raise?

Disagree. It's only the illusion of a personal ceiling raise.

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Example 1:

Alice has a simple basic text only blog. She wants to update the styles on his website, but wants to keep his previous posts.

She does research to learn how to update a page's styles to something more "modern". She updates the homepage, post page, about page. She doesn't know how to update the login page without breaking it because it uses different elements she hasn't seen before.

She does research to learn what the new form elements and on the way sees recommendations on how to build login systems. She builds some test pages to learn how to restyle forms and while she's at it, also learns how to build login systems.

She redesigns her login page.

Alice believes she has raised the ceiling what she can accomplish.

Alice is correct.

---

Example 2:

Bob has a simple basic text only blog. He wants to update the styles on his website, but wants to keep his previous posts.

He asks the LLM to help him update styles to something more "modern". He updates the homepage, post page, about page, and login page.

The login page doesn't work anymore.

Bob asks the LLM to fix it and after some back and forth it works again.

Bob believes she has raised the ceiling what he can accomplish.

Bob is incorrect. He has not increased his own knowledge or abilities.

A week later his posts are gone.

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There are only a few differences between both examples:

1. Alice does not use LLMs, but Bob does. 2. Alice knows how to redesign pages, but Bob does not. 3. Alice knows how login systems work, but Bob does not.

Bob simply asked the LLM to redesign the login page, and it did.

When the page broke, he checked that he was definitely using the right username and password but it still wasn't working. He asked the LLM to change the login page to always work with his username and password. The LLM produced a login form that now always accepted a hard coded username and password. The hardcoded check was taking place on the client where the username and password were now publicly viewable.

Bob didn't ask the LLM to make the form secure, he didn't even know that he had to ask. He didn't know what any of the footguns to avoid were because he didn't even know there were any footguns to avoid in the first place.

Both Alice and Bob started from the same place. They both lacked knowledge on how login systems should be built. That knowledge was known because it is documented somewhere, but it was unknown to them. It is a "known unknown".

When Alice learned how to style form elements, she also read links on how forms work which lead her to links on how login systems work. That knowledge for her went from an unknown known to a "known known" (knowledge that is known, that she now also knows).

When Bob asked the LLM to redesign his login page, at no point in time does the knowledge of how login systems work become a "known known" for him. And a week later some bored kid finds the page, right clicks on the form, clicks inspect and sees a username and password to log in with.