AI is a floor raiser, not a ceiling raiser

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.)

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.

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.

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

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.

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.

You are correct. Its not hard to see why, (AI imposes cost interference), but there are a lot of bots that keep promoting slop, and moderation doesn't seem to be doing anything about it.

I'm tired of seeing a significant percentage of the article posts in the top 300 being slop.

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.

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

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?

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

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

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

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.

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)

But that would shift the average up.

>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.

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.

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.

---

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.

It seems with computers we often think and reason far less than without. Everything required thought previously, now we can just copy and paste out word docs for everything. PowerPoints are how key decisions are communicated in most professional settings.

Before modern computers and especially the internet we also had more time for deep thinking and reasoning. The sublimity of deep thought in older books amazes me and it feels like modern authors are just slightly less deep on average.

So then LLMs are in my view an incremental change rather than a stepwise change with respect to its effects on human cognitive.

In some ways LLMs allow us to return a bit to more humanistic deep thinking. Instead of spending hours looking up minutia on Google, StackOverflow, etc now we can ask our favorite LLM instead. It gives us responses with far less noise.

Unlike with textbooks we can have dialogues and have it take different perspectives. Whereas textbooks only gave you that authors perspective.

Of course, it’s up to individuals to use it well and as a tool to sharpen thinking rather than replace it.

I can watch a video about the subject, when I want to go deeper, I go to LLMs, throw a bunch of questions at it, because thanks to the videos I now know what to ask. Then the AI responses tell me what I need to understand deeper, so I pick a book that addresses those subjects. Then as I read the book and I don’t understand something, or I have some questions that I want the answer for immediately, I consult ChatGPT (or any other tool I want to try). At different points in the journey, I find something I could build myself to deepen my understanding. I google open source implementations, read them, ask LLMs again, I watch summary videos, and work my way through the problem.

LLMs serve as a “much better StackOverflow / Google”.

GPT-1 June 2018

GPT-2 February 2019

GPT-3 November 2021

GPT-4 March 2023

Claude tells me this is the rough improvement of each:

GPT-1 to 2: 5-10x

GPT-2 to 3: 10-20x

GPT 3 to 4: 2-4x

Now it's been 2.5 years since 4.

Are you expecting 5 to be 2-4x better, or 10-20x better?

More recently, it seems like that's not the case. Larger models sometimes even hallucinate more [0]. I think the entire sector is suffering from a Dunning Kruger effect -- making an LLM is difficult, and they managed to get something incredible working in a much shorter timeframe than anyone really expected back in the early 2010s. But that led to overconfidence and hype, and I think there will be a much longer tail in terms of future improvements than the industry would like to admit.

Even the more advanced reasoning models will struggle to play a valid game of chess, much less win one, despite having plenty of chess games in their training data [1]. I think that, combined with the trouble of hallucinations, hints at where the limitations of the technology really are.

Hopefully LLMs will scare society into planning how to handle mass automation of thinking and logic, before a more powerful technology that can really do it arrives.

[0]: https://techcrunch.com/2025/04/18/openais-new-reasoning-ai-m...

[1]: https://dev.to/maximsaplin/can-llms-play-chess-ive-tested-13...

Even in nature this is clear. Humans are a great example: cooked food predates homo sapiens and it is largely considered to be a pre-requisite for having human level intelligence because of the enormous energy demands of our brains. And nature has given us wildly more efficient brains in almost every possible way. The human brain runs on about 20 watts of power, my RTX uses 450 watts at full capacity.

The idea of "runaway" super intelligence has baked in some very extreme assumptions about the nature of thermodynamics and intelligence, that are largely just hand waved away.

On top of that, AI hasn't changed in a notable way for me personally in a year. The difference between 2022 and 2023 was wild, between 2023 and 2024 changed some of my workflows, 2024 to today largely is just more options around which tooling I used and how these tools can be combined, but nothing really at a fundamental level feels improved for me.

Nothing is free, and I for one prefer a subscription model, if only as a change from the ad model.

I am sure we will see the worst of all worlds, but for now, for this moment in history, subscription is better than ads.

Let’s also never have ads in GenAi tools. The kind of invasive intent level influence these things can achieve, will make our current situation look like a paradise

Also part of the workflow of using AI is accepting that your initial prompts might not get the right answer. It's important to scan the answer like you did and use intuition to know 'this isn't right', then try again. Just like we learned how to type in good search queries, we'll also learn how to write good prompts. Sands will shift frequently at first, with prompt strategies that worked well yesterday requiring a different strategy tomorrow, but eventually it will stabilize like search query strategies did.