AI raises everything - the ceiling is just being more productive. Productivity comes from adequacy and potency of tools. We got a hell of a strong tool in our hands, therefore, the more adequate the usage, the higher the leverage.
Only for the people already affluent enough to afford the ever-more expensive subscriptions. Those most in need of a floor-raising don’t have the disposable income to take a bet on AI.
Since agents are good only at greenfield projects, the logical conclusion is that existing codebases have to be prepared such that new features are (opinionated) greenfield projects - let all the wiring dangle out of the wall so the intern just has to plug in the appliance. All the rest has to be done by humans, or the intern will rip open the wall to hang a picture.
You'll find many people lack the willpower and confidence to even get on the floor though. If it weren't for that they'd already know a programming language and be selling something.
People should be worried because right now AI is on an exponential growth trajectory and no-one knows when it will level off into an s-curve. AI is starting to get close to good enough. If it becomes twice as good in seven months then what?
There are some things that you still can't do with LLMs. For example, if you tried to learn chess by having the LLM play against you, 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) before it starts making illegal choices. It also generally accepts invalid moves from your side, so you'll never be corrected if you're wrong about how to use a certain piece.
Because it can't actually model these complex problems, it really requires awareness from the user regarding what questions should and shouldn't be asked. An LLM can probably tell you how a knight moves, or how to respond to the London System. It probably can't play a full game of chess with you, and will virtually never be able to advise you on the best move given the state of the board. It probably can give you information about big companies that are well-covered in its training data. It probably can't give you good information about most sub-$1b public companies. But, if you ask, it will give a confident answer.
They're a minefield for most people and use cases, because people aren't aware of how wrong they can be, and the errors take effort and knowledge to notice. It's like walking on a glacier and hoping your next step doesn't plunge through the snow and into a deep, hidden crevasse.
I was thinking about this sentiment on my long car drive today.
it feels like when you need to paint walls in your house. If you've never done it before you'll probably reach for tape to make sure you don't ruin the ceiling and floors. the tape is a tool for amateur wall painters to get decent results somewhat efficiently compared to if they didn't. If your an actual good wall painter, tape only slows you down. You'll go faster without the "help".
This mirrors insights from Andrew Ng's recent AI startup talk [1].
I recall he mentions in this video that the new advice they are giving to founders is to throw away prototypes when they pivot instead of building onto a core foundation. This is because of the effects described in the article.
He also gives some provisional numbers (see the section "Rapid Prototyping and Engineering" and slides ~10:30) where he suggests prototype development sees a 10x boost compared to a 30-50% improvement for existing production codebases.
This feels vaguely analogous to the switch from "pets" to "livestock" when the industry switched from VMs to containers. Except, the new view is that your codebase is more like livestock and less like a pet. If true (and no doubt this will be a contentious topic to programmers who are excellent "pet" owners) then there may be some advantage in this new coding agent world to getting in on the ground floor and adopting practices that make LLMs productive.
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
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.
> This means cheaters will plateau at whatever level the AI can provide
From my experience, the skill of using AI effectively is of treating the AI with a "growth mindset" rather than a "fixed" one. What I do is that I roleplay as the AI's manager, giving it a task, and as long as I know enough to tell whether its output is "good enough", I can lend it some of my metagcognition via prompting to get it to continue working through obstacles until I'm happy with the result.
There are diminishing returns of course, but I found that I can get significantly better quality output than what it gave me initially without having to learn the "how" of the skill myself (i.e. I'm still "cheating"), and only focusing my learning on the boundary of what is hard about the task. By doing this, I feel that over time I become a better manager in that domain, without having to spend the amount of effort to become a practitioner myself.
The greatest use of LLMs is the ability to get accurate answers to queries in a normalized format without having to wade through UI distraction like ads and social media.
It's the opposite of finding an answer on reddit, insta, tvtropes.
I can't wait for the first distraction free OS that is a thinking and imagination helper and not a consumption device where I have to block urls on my router so my kids don't get sucked into a skinners box.
I love being able to get answers from documentation and work questions without having to wade through some arbitrary UI bs a designer has implemented in adhoc fashion.
The blog post has a bunch of charts, which gives it a veneer of objectivity and rigor, but in reality it's just all vibes and conjecture. Meanwhile recent empirical studies actually point in the opposite direction, showing that AI use increases inequality, not decrease it.
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.
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.
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.
Definitely. I think it's worse than that too. I have a feeling it's going to expose some people higher up that ladder who really shouldn't be. So it won't just be junior people who struggle but also "senior" people as well. I think that only deepens the inequality.
I'm honestly tired of all the misinformation about AI being posted.
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.
Oh man i love this take. It's how I've been selling what I do when I speak with a specific segment of my audience: "My goal isn't to make the best realtors better, it's to make the worst realtors acceptable".
And my client is often the brokerage, they just want their agents to produce commissions so they make a cut. They know their top producers probably wont get much from what I offer, but we all see that their worst performers could easily double their business.
I wonder: the graphs treat learning with and without AI as two different paths. But obviously people can switch between learning methods or abandon one of them.
Then again, I wonder how many people go from learning about a topic using LLMs to then leaving them behind to continue the old school way. I think the early spoils of LLM usage could poison your motivation to engage with the topic on your own later on.
I'd argue that AI reduces the distance between the floor and the ceiling, only both the floor and ceiling move -- the floor moves up, the ceiling downwards.
Just using AI makes the floor move up, while over-reliance on it (a very personal metric) pushes the ceiling downwards.
Unlike the telephone (telephones excited a certain class of people into believing that world-wide enlightenment was on their doorstep), LLMs don't just reduce reliance on visual tells and mannerisms, they reduce reliance on thinking itself. And that's a very dangerous slope to go down on. What will happen to the next generation when their parents supply substandard socially-computed results of their mental work (aka language)? Culture will decay and societal norms will veer towards anti-civilizational trends. And that's exactly what we're witnessing these days. The things that were commonplace are now rare and sometimes mythic.
Everyone has the same number of hours and days and years. Some people master some difficult, arcane field while others while it away in front of the television. LLMs make it easier for the television-watchers to experience "entertainment nirvana" while enticing the smart, hard-workers to give up their toil and engage "just a little" rest, which due to the insidious nature of AI-based entertainment, meshes more readily with their more receptive minds.
59 comments
[ 4.2 ms ] story [ 73.2 ms ] threadBelow average people can use AI to get average results.
Because it can't actually model these complex problems, it really requires awareness from the user regarding what questions should and shouldn't be asked. An LLM can probably tell you how a knight moves, or how to respond to the London System. It probably can't play a full game of chess with you, and will virtually never be able to advise you on the best move given the state of the board. It probably can give you information about big companies that are well-covered in its training data. It probably can't give you good information about most sub-$1b public companies. But, if you ask, it will give a confident answer.
They're a minefield for most people and use cases, because people aren't aware of how wrong they can be, and the errors take effort and knowledge to notice. It's like walking on a glacier and hoping your next step doesn't plunge through the snow and into a deep, hidden crevasse.
it feels like when you need to paint walls in your house. If you've never done it before you'll probably reach for tape to make sure you don't ruin the ceiling and floors. the tape is a tool for amateur wall painters to get decent results somewhat efficiently compared to if they didn't. If your an actual good wall painter, tape only slows you down. You'll go faster without the "help".
I recall he mentions in this video that the new advice they are giving to founders is to throw away prototypes when they pivot instead of building onto a core foundation. This is because of the effects described in the article.
He also gives some provisional numbers (see the section "Rapid Prototyping and Engineering" and slides ~10:30) where he suggests prototype development sees a 10x boost compared to a 30-50% improvement for existing production codebases.
This feels vaguely analogous to the switch from "pets" to "livestock" when the industry switched from VMs to containers. Except, the new view is that your codebase is more like livestock and less like a pet. If true (and no doubt this will be a contentious topic to programmers who are excellent "pet" owners) then there may be some advantage in this new coding agent world to getting in on the ground floor and adopting practices that make LLMs productive.
1. https://www.youtube.com/watch?v=RNJCfif1dPY
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
It is a false confidence generator
> This means cheaters will plateau at whatever level the AI can provide
From my experience, the skill of using AI effectively is of treating the AI with a "growth mindset" rather than a "fixed" one. What I do is that I roleplay as the AI's manager, giving it a task, and as long as I know enough to tell whether its output is "good enough", I can lend it some of my metagcognition via prompting to get it to continue working through obstacles until I'm happy with the result.
There are diminishing returns of course, but I found that I can get significantly better quality output than what it gave me initially without having to learn the "how" of the skill myself (i.e. I'm still "cheating"), and only focusing my learning on the boundary of what is hard about the task. By doing this, I feel that over time I become a better manager in that domain, without having to spend the amount of effort to become a practitioner myself.
It's the opposite of finding an answer on reddit, insta, tvtropes.
I can't wait for the first distraction free OS that is a thinking and imagination helper and not a consumption device where I have to block urls on my router so my kids don't get sucked into a skinners box.
I love being able to get answers from documentation and work questions without having to wade through some arbitrary UI bs a designer has implemented in adhoc fashion.
https://www.economist.com/content-assets/images/20250215_FNC...
https://www.economist.com/finance-and-economics/2025/02/13/h...
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.
And my client is often the brokerage, they just want their agents to produce commissions so they make a cut. They know their top producers probably wont get much from what I offer, but we all see that their worst performers could easily double their business.
I wonder: the graphs treat learning with and without AI as two different paths. But obviously people can switch between learning methods or abandon one of them.
Then again, I wonder how many people go from learning about a topic using LLMs to then leaving them behind to continue the old school way. I think the early spoils of LLM usage could poison your motivation to engage with the topic on your own later on.
Unlike the telephone (telephones excited a certain class of people into believing that world-wide enlightenment was on their doorstep), LLMs don't just reduce reliance on visual tells and mannerisms, they reduce reliance on thinking itself. And that's a very dangerous slope to go down on. What will happen to the next generation when their parents supply substandard socially-computed results of their mental work (aka language)? Culture will decay and societal norms will veer towards anti-civilizational trends. And that's exactly what we're witnessing these days. The things that were commonplace are now rare and sometimes mythic.
Everyone has the same number of hours and days and years. Some people master some difficult, arcane field while others while it away in front of the television. LLMs make it easier for the television-watchers to experience "entertainment nirvana" while enticing the smart, hard-workers to give up their toil and engage "just a little" rest, which due to the insidious nature of AI-based entertainment, meshes more readily with their more receptive minds.