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.
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.
_carbyau_ · 5m ago
Whether ladder raising is benefitting people now or later or by how much - I don't know.
But I share your concerns that:
AI doing the lesser tasks of [whatever] ->
less(no?) humans will do those tasks ->
less(no?) experienced humans to further the state of the art ->
automation-but-stagnation.
But tragedy of the commons says I have to teach my kid to use AI!
devonbleak · 5h ago
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.
skhameneh · 4h ago
The later remarks are very strong assumptions underestimating the power AI tools offer.
AI tools are great at unblocking and helping their users explore beyond their own understanding. The tokens in are limited to the users' comprehension, but the tokens out are generated from a vast collection of greater comprehension.
For the novice, it's great at unblocking and expanding capabilities. "Good enough" results from novices are tangible. There is no doubt the volume of "good enough" is perceived as very low by many.
For large and complex codebases, unfortunately the effects of tech debt (read: objectively subpar practices) translate into context rot at development time. A properly architected and documented codebase that adheres to common well structured patterns can easily be broken down into small easily digestible contexts. i.e. a fragmented codebase does not scale well with LLMs, because the fragmentation is seeding the context for the model. The model reflects and acts as an amplifier to what it's fed.
DrewADesign · 2h ago
> For the novice, it's great at unblocking and expanding capabilities. "Good enough" results from novices are tangible. There is no doubt the volume of "good enough" is perceived as very low by many.
For personal tools or whatever, sure. And the tooling or infrastructure might get there for real projects eventually, but it’s not currently. The prospect of someone naively vibe coding a side business including a payment or authentication system or something that stores PII— all areas developers learn the dangers of through the wisdom gained only by experience— sends shivers down my spine. Even amateur coders trying that stuff try old fashioned way must read their code and the docs and info on the net and such and will likely get some sense of the danger. Yesterday I saw someone here recounting a disastrous data breach of their friend’s vibe coded side hustle.
The big problem I see here is people not knowing enough to realize that something functioning is almost never a sign that it is “good enough” for many things they might assume it is. Gaining the amount of base knowledge to evaluate things like form security nearly makes the idea of vibe coding useless for anything more than hobby or personal utility projects.
Lerc · 2h ago
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.
Syzygies · 1h ago
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. I'm trying to finish a better proof of the Poincare Conjecture, and if I succeed the lesson for grad students won't be "Huh. Second Ascent." Rather "This 69 year old gheezer did this by ignoring the noise and becoming AI bionic."
Calavar · 6h ago
The graphic has four studies that show increased inequality and six that show reduced inequality.
tripletao · 1h ago
> The graphic has four studies that show increased inequality
Three, since Toner-Rodgers 2024 currently seems to be a total fabrication.
Read my comment again. keyword here is "recent". The second link also expands on why it's relevant. It's best to read the whole article, but here's a paragraph that captures the argument:
>The shift in recent economic research supports his observation. Although early studies suggested that lower performers could benefit simply by copying AI outputs, newer studies look at more complex tasks, such as scientific research, running a business and investing money. In these contexts, high performers benefit far more than their lower-performing peers. In some cases, less productive workers see no improvement, or even lose ground.
jjk166 · 5h ago
All of the studies were done 2023-2024 and are not listed in order that they were conducted. The studies showing reduced equality all apply to uncommon tasks like material discovery and debate points, whereas the ones showing increased equality are broader and more commonly applicable, like writing, customer interaction, and coding.
gruez · 5h ago
>All of the studies were done 2023-2024 and are not listed in order that they were conducted
Right, the reason why I pointed out "recent" is that it's new evidence that people might not be aware of, given that there were also earlier studies showing AI had the opposite effect on inequality. The "recent" studies also had varied methodology compared to the earlier studies.
>The studies showing reduced equality all apply to uncommon tasks like material discovery and debate points
"Debating points" is uncommon? Maybe not everyone was in the high school debate club, but "debating points" is something that anyone in a leadership position does on a daily basis. You're also conveniently omitting "investment decisions" and "profits and revenue", which basically everyone is trying to optimize. You might be tempted to think "Coding efficiency" represents a high complexity task, but the abstract says the test involved "Recruited software developers were asked to implement an HTTP server in JavaScript as quickly as possible". The same is true of the task used in the "legal analysis" study, which involved drafting contracts or complaints. This seems exactly like the type of cookie cutter tasks that the article describes would become like cashiers and have their wages stagnate. Meanwhile the studies with negative results were far more realistic and measured actual results. Otis et al 2023 measured profits and revenue of actual Kenyan SMBs. Roldan-Mones measured debate performance as judged by humans.
jjk166 · 30m ago
> Right, the reason why I pointed out "recent" is that it's new evidence that people might not be aware of, given that there were also earlier studies showing AI had the opposite effect on inequality.
Okay, well the majority of this "recent" evidence agrees with the pre-existing evidence that inequality is reduced.
> "Debating points" is uncommon?
Yes. That is nobody's job. Maybe every now and then you might need to come up with some arguments to support a position, but that's not what you get paid to do day to day.
> You're also conveniently omitting "investment decisions" and "profits and revenue", which basically everyone is trying to optimize.
Very few people are making investment decisions as part of their day to day job. Hedge funds may experience increasing inequality, but that kinda seems on brand.
On the other hand "profits and revenue" is not a task.
> You might be tempted to think "Coding efficiency" represents a high complexity task, but the abstract says the test involved "Recruited software developers were asked to implement an HTTP server in JavaScript as quickly as possible". The same is true of the task used in the "legal analysis" study, which involved drafting contracts or complaints.
These sound like real tasks that a decent number of people have to do on a regular basis.
> Meanwhile the studies with negative results were far more realistic and measured actual results. Otis et al 2023 measured profits and revenue of actual Kenyan SMBs. Roldan-Mones measured debate performance as judged by humans.
These sound like niche activities that are not widely applicable.
bgwalter · 6h ago
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.)
throwmeaway222 · 2h ago
> inequality
It's free for everyone with a phone or a laptop.
stillpointlab · 7h ago
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
stillpointlab · 1h ago
I'm currently in build mode. In some sense, my project is the most over complicated blog engine in the history of personal blog engines. I'm literally working on integrating a markdown editor to the project.
Once I have the MVP working, I will be working on publishing as a means to dogfood the tool. So, check back soon!
skmurphy · 1h ago
Is there a mailing list I can sign up for to be notified.
The check back soon protocol reminds me of my youth.
stillpointlab · 1h ago
Mailing list is on the roadmap but doesn't exist just yet.
What you could do: sign in using one of the OAuth methods, go to the user page and then go to the feedback section. Let me know in a message your email and I'll ping you once the blog is setup.
Sorry it is primitive at this stage but I'm prioritizing MVP before marketing.
falcor84 · 7h ago
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.
bayindirh · 6h ago
Yeah, the CERN talk* [0] coined the term Pets vs. Cattle analogy, and it was way before VMs were cheap on bare metal. I think the word just evolved as the idea got rooted in the community.
We use the same analogy for the last 20 years or so. Provisioning 150 cattle servers take 15 minutes or so, and we can provision a pet in a couple of hours, at most.
*: Engine Yard post notes that Microsoft's Bill Baker used the term earlier, though CERN's date (2012) checks out with our effort timeline and how we got started.
@randybias @dberkholz
CERN's presentation of pets and cattle was derived from Randy's (and Bill Baker's previously).
HPsquared · 6h ago
Boxen? (Oxen)
bayindirh · 6h ago
AFAIK, Boxen is a permutation of Boxes, not Oxen.
mananaysiempre · 6h ago
There seems to be a pattern of humorous plurals in English where by analogy with ox ~ oxen you get -x ~ -xen: boxen, Unixen, VAXen.
Before you call this pattern silly, consider that the fairly normal plural “Unices” is by analogy with Latin plurals in -x = -c|s ~ -c|ēs, where I’ve expanded -x into -cs to make it clear that the Latin singular comprises a noun stem ending in -c- and a (nominative) singular ending -s, which does exist in Latin but is otherwise completely nonexistent in English. (This is extra funny for Unix < Unics < Multics.) Analogies are the order of the day in this language.
bayindirh · 6h ago
Yeah. After reading your comment, I thought "maybe the Xen hypervisor is named because of this phenomena". "xen" just means "many" in that context.
Also, probably because of approaching graybeard territory, Thinking about boxen of VAXen running UNIXen makes me feel warm and fuzzy. :D
lubujackson · 6h ago
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.
recursive · 6h ago
I don't think it's exactly emotional attachment. It's the likelihood that I'm going to get an escalated support ticket caused by this particular piece of slop/artisanally-crafted functionality.
stillpointlab · 5h ago
Not to slip too far into analogy, but that argument feels a bit like a horse-drawn carriage operator saying he can't wait to pick up all of the stranded car operators when their mechanical contraptions break down on the side of the road. But what happened instead was the creation of a brand new job: the mechanic.
I don't have a crystal ball and I can't predict the actual future. But I can see the list of potential futures and I can assign likelihoods to them. And among the potential futures is one where the need for humans to fix the problems created by poor AI coding agents dwindles as the industry completely reshapes itself.
recursive · 4h ago
Both can be true. There were probably a significant number of stranded motorists that were rescued by horse-powered conveyance. And eventually cars got more convenient and reliable.
I just wouldn't want to be responsible for servicing a guarantee about the reliability of early cars.
And I'll feel no sense of vindication if I do get that support case. I will probably just sigh and feel a little more tired.
stillpointlab · 4h ago
Yes, the whole point that it is true. But only for a short window.
So consider differing perspectives. Like a teenage kid that is hanging around the stables, listening to the veteran coachmen laugh about the new loud, smoky machines. Proudly declaring how they'll be the ones mopping up the mess, picking up the stragglers, cashing it in.
The career advice you give to the kid may be different than the advice you'd give to the coachman. That is the context of my post: Andrew Ng isn't giving you advice, he is giving advice to people at the AI school who hope to be the founders of tomorrow.
And you are probably mistaken if you think the solution to the problems that arise due to LLMs will result in those kids looking at the past. Just like the ultimate solution to car reliability wasn't a return to horses but rather the invention of mechanics, the solution to problems caused by AI may not be the return to some software engineering past that the old veterans still hold dear.
recursive · 15m ago
I don't know about what's economically viable, but I like writing code. It might go away or diminish as a viable profession, which might make me a little sad. There are still horse enthusiasts who do horse things for fun.
Things change, and that's ok. I guess I just got lucky so far that this thing I like doing just so happens to align with a valuable skill.
I'm not arguing for or against anything, but I'll miss it if it goes away.
LeftHandPath · 7h ago
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.
og_kalu · 5h ago
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.
Jeez, that arxiv paper invalidates my assumption that it can't model the game. Great read. Thank you for sharing.
Insane that the model actually does seem to internalize a representation of the state of the board -- rather than just hitting training data with similar move sequences.
...Makes me wish I could get back into a research lab. Been a while since I've stuck to reading a whole paper out of legitimate interest.
(Edit) At the same time, it's still worth noting the accuracy errors and the potential for illegal moves. That's still enough to prevent LLMs from being applied to problem domains with severe consequences, like banking, security, medicine, law, etc.
smiley1437 · 7h ago
> 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?
emporas · 3h ago
It is only in the last century or so, that statistical methods were invented and applied. It is possible for many people to be very competent at what they are doing and at the same time be totally ignorant of statistics.
There are lies, statistics and goddamn hallucinations.
bayindirh · 6h ago
Computers are always touted as deterministic machines. You can't argue with a compiler, or Excel's formula editor.
AI, in all its glory, is seen as an extension of that. A deterministic thing which is meticulously crafted to provide an undisputed truth, and it can't make mistakes because computers are deterministic machines.
The idea of LLMs being networks with weights plus some randomness is both a vague and too complicated abstraction for most people. Also, companies tend to say this part very quietly, so when people read the fine print, they get shocked.
viccis · 6h ago
> I wonder if there's a halo effect where the perfect grammar, structure, and confidence of LLM output causes some users to assume expertise?
I think it's just that LLMs are modeling generative probability distributions of sequences of tokens so well that what they actually are nearly infallible at is producing convincing results. Often times the correct result is the most convincing, but other times what seems most convincing to an LLM just happens to also be most convincing to a human regardless of correctness.
> In computer science, the ELIZA effect is a tendency to project human traits — such as experience, semantic comprehension or empathy — onto rudimentary computer programs having a textual interface. ELIZA was a symbolic AI chatbot developed in 1966 by Joseph Weizenbaum and imitating a psychotherapist. Many early users were convinced of ELIZA's intelligence and understanding, despite its basic text-processing approach and the explanations of its limitations.
jeremyjh · 22m ago
Its complete bullshit. There is no way anyone ever thought anything was going on in ELIZA. There were people amazed that "someone could program that" but they had no illusions about what it was, it was obvious after 3 responses.
throwawayoldie · 3m ago
Oh, I guess you would know because you were there.
throwawayoldie · 6h ago
My experience, speaking over a scale of decades, is that most people, even very smart and well-educated ones, don't know a damn thing about how computers work and aren't interested in learning. What we're seeing now is just one unfortunate consequence of that.
(To be fair, in many cases, I'm not terribly interested in learning the details of their field.)
yifanl · 5h ago
If I wasn't familiar with the latest in computer tech, I would also assume LLMs never make mistakes, after hearing such excited praise for them over the last 3 years.
rplnt · 6h ago
Have they never used it? Majority of the responses that I can verify are wrong. Sometimes outright nonse, sometimes believable. Be it general knowledge or something where deeper expertise is required.
jasonjayr · 6h ago
I worry that the way the models "Speak" to users, will cause users to drop their 'filters' about what to trust and not trust.
We are barely talking modern media literacy, and now we have machines that talk like 'trusted' face to face humans, and can be "tuned" to suggest specific products or use any specific tone the owner/operator of the system wants.
dsjoerg · 5h ago
> I have friends who are highly educated professionals (PhDs, MDs) who just assume that AI\LLMs make no mistakes.
Highly educated professionals in my experience are often very bad at applied epistemology -- they have no idea what they do and don't know.
nomel · 5h ago
> 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.
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.
poszlem · 6h ago
My favourite example is something that happens quite often even with Opus, where I ask it to change a piece of code, and it does. Then I ask it to write a test for that code, it dutifully writes one. Next, I tell it to run the test, and of course, the test fails. I ask it to fix the test, it tries, but the test fails again. We repeat this dance a couple of times, and then it seemingly forgets the original request entirely. It decides, "Oh, this test is failing because of that new code you added earlier. Let me fix that by removing the new code." Naturally, now the functionality is gone, so it confidently concludes, "Hey, since that feature isn't there anymore, let me remove the test too!"
DougBTX · 5h ago
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.
pharrington · 4h ago
Large Language Models aren't general AIs. Its in the name.
manmal · 7h ago
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.
PaulHoule · 7h ago
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.
falcor84 · 6h ago
> Sure you can't point it to a Jira ticket and get a PR
You absolutely can. This is exactly what SWE-Bench[0] measures, and I've been amazed at how quickly AIs have been climbing those ladders. I personally have been using Warp [1] a lot recently and in quite a lot of low-medium difficulty cases it can one-shot a decent PR. For most of my work I still find that I need to pair with it to get sufficiently good results (and that's why I still prefer it to something cloud-based like Codex [2], but otherwise it's quite good too), and I expect the situation to flip over the coming couple of years.
I've not used it for long enough yet for this to be a strong opinion, but so far I'd say that it is indeed a bit better than Claude Code, as per the results on Terminal Bench[0]. And on a side note, I quite like the fact that I can type shell commands and chat commands interchangeably into the same input and it just knows whether to run it or respond to it (accidentally forgetting the leading exclamation mark has been a recurring mistake for me in Claude Code).
What you describe is not using agents at all, which my comment was aimed at if you read the first sentence again.
PaulHoule · 4h ago
Julie is marketed as an “agent” and it definitely works harder than the Jetbrains AI assistant.
yoz-y · 7h ago
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.
spion · 6h ago
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)
godelski · 1h ago
I think a good way to see it is "AI is good for prototyping. AI is not good for engineering"
To clarify, I mean that the AI tools can help you get things done really fast but they lack both breadth and depth. You can move fast with them to generate proofs of concept (even around subproblems to large problems), but without breadth they lack the big picture context and without depth they lack the insights that any greybeard (master) has. On the other hand, the "engineering" side is so much more than "things work". It is about everything working in the right way, handling edge cases, being cognizant of context, creating failure modes, and all these other things. You could be the best programmer in the world, but that wouldn't mean you're even a good engineer (in real world these are coupled as skills learned simultaneously. You could be a perfect leetcoder and not helpful on an actual team, but these skills correlate).
The thing is, there will never be a magic button that a manager can press to engineer a product. The thing is, for a graybeard most of the time isn't spent around implementation, but design. The thing is, to get to mastery you need experience, and that experience requires understanding of nuanced things. Things that are non-obvious. There may be a magic button that allows an engineer to generate all the code for codebase, but that doesn't replace engineers. (I think this is also a problem in how we've been designing AI code generators. It's as if they're designed for management to magically generate features. The same thing they wish they could do with their engineers. But I think the better tool would be to focus on making a code generator that would generate based on an engineer's description.
I think Dijkstra's comments apply today just as much as they did then[0]
An interloper being someone who intrudes or meddles in a situation (inter "between or amid) + loper "to leap or run" - https://en.wiktionary.org/wiki/loper ), an extraloper would be someone who dances or leaps around the outside of a subject or meeting with similar annoyances.
saltcured · 2h ago
Are you sure the extraloper doesn't just run away on tangents?
burnt-resistor · 2h ago
They run on secants towards the outside.
saltcured · 1h ago
Wouldn't that be an exloper?
Edit: Geometrically, I agree an extraloper could run on secants (or radii) but they're not allowed to strike a chord.
But getting back to generative text, I feel that "tangent" is more appropriate ;-)
exasperaited · 6h ago
Opposite of "inter-" is "intra-".
Intraloper, weirdly enough, is a word in use.
jjk166 · 5h ago
"inter-" means between, "intra-" means within, "extra-" means outside. "intra-" and "inter" aren't quite synonyms but they definitely aren't opposites of eachother.
exasperaited · 4h ago
Inter- implies relationships between entities, intra- implies relationships within entities.
In any single sentence context they cannot refer to the same relationships, and that which they are not is precisely the domain of the other word: they are true antonyms.
jjk166 · 59m ago
> and that which they are not is precisely the domain of the other word
External relationships are a thing, which are in neither of those domains.
An intramural activity is not the same thing as an intermural activity, but the overwhelming majority of activities which are not intramural activities are not intermural activities either, most are extramural activities.
djeastm · 6h ago
So we have also have the word "extra", but oddly the word "exter" is left out.
I'm exter mad about that.
canadaduane · 6h ago
Very concise, thank you for sharing this insight.
falcor84 · 7h ago
I agree with most of TFA but not this:
> 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.
righthand · 5h ago
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.
razzmatazmania · 8s ago
I don't think "quality" has anything like a universal definition, and when people say that they probably mean an alignment with personal taste.
Does it solve the problem? As long as it isn't prohibitively costly in terms of time or resources, then the rest is really just taste. As a user I have no interest whatsoever if your code is "idiomatic" or "modular" or "functional". In other industries "quality" usually means free of defects, but software is unique in that we just expect products to be defective. Your surgeon operates on the wrong knee? The board could revoke the license, and you are getting a windfall settlement. A bridge design fails? Someone is getting sued or even prosecuted. SharePoint gets breached? Well, that's just one of those things, I guess. I'm not really bothered that AI is peeing in the pool that has been a sewer as long as I can remember. At least the AI doesn't bill at an attorney's rate to write a mess that barely works.
tailspin2019 · 5h ago
I wouldn’t classify what you’re doing as “cheating”!
andrenotgiant · 7h ago
This tracks for other areas of AI I am more familiar with.
Below average people can use AI to get average results.
pcrh · 6h ago
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.
hirvi74 · 4h ago
I am not certain that is entirely true.
I suppose it's all a matter of what one is using an LLM for, no?
GPT is great at citing sources for most of my requests -- even if not always prompted to do so. So, in a way, I kind of use LLMs as a search engine/Wikipedia hybrid (used to follow links on Wiki a lot too). I ask it what I want, ask for sources if none are provided, and just follow the sources to verify information. I just prefer the natural language interface over search engines. Plus, results are not cluttered with SEO ads and clickbait rubbish.
dvsfish · 1h ago
Hmm I don't feel like this should be taken as a tenet of AI. I feel a more relevant kernel would be less black and white.
Also I think what you're saying is a direct contradiction of the parent. Below average people can now get average results; in other words: The LLM will boost your capabilities (at least if you're already 'less' capable than average). This is a huge benefit if you are in that camp.
But for other cases too, all you need to know is where your knowledge ends, and that you can't just blindly accept what the AI responds with.
In fact, I find LLMs are often most useful precisely when you don’t know the answer. When you’re trying to fill in conceptual gaps and explore an idea.
Even say during code generation, where you might not fully grasp what’s produced, you can treat the model like pair programming and ask it follow-up questions and dig into what each part does. They're very good at converting "nebulous concept description" into "legitimate standard keyword" so that you can go and find out about said concept that you're unfamiliar with.
Realistically the only time I feel I know more than the LLM is when I am working on something that I am explicitly an expert in, and in which case often find that LLMs provide nuance lacking suggestions that don’t always add much. It takes a lot more filling in context in these situations for it to be beneficial (but still can be).
Take a random example of nifty bit of engineering: The powerline ethernet adapter. A curious person might encounter these and wonder how they work. I don't believe an understanding of this technology is very obvious to a layman. Start asking questions and you very quickly come to understand how it embeds bits in the very same signal that transmits power through your house without any interference between the two "types" of signal. It adds data to high frequencies on one end, and filters out the regular power transmitting frequencies at the other end so that the signal can be converted back into bits for use in the ethernet cable (for a super brief summary). But if want to really drill into each and every engineering concept, all I need to do is continue the conversation.
I personally find this loop to be unlike anything I've experienced as far as getting immediate access to an understanding and supplementary material for the exact thing Im wondering about.
jononor · 6h ago
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.
djeastm · 6h ago
>Below average people can use AI to get average results.
But that would shift the average up.
itsoktocry · 7h ago
That explains why people here are against it, because everyone is above average I guess.
falcor84 · 6h ago
I'm not against it. I wonder where in the distribution it puts me.
leptons · 4h ago
At the "Someone willing to waste their time with slop" end?
sabakhoj · 6h ago
In things that I am comparatively good at (e.g., coding), I can see that it helps 'raise the ceiling' as a result of allowing me to complete more of the low level tasks more effectively. But it is true as well that it hasn't raised my personal bar in capability, as far as I can measure.
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?
I think a lot of these upskilling utilities will come down to delivery format. If you use a chat that gives you answers, don't expect to get better at that topic. If you use a tool that forces you to come up with answers yourself and get personalized validation, you might find yourself leveling up.
spartanatreyu · 1h ago
> 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.
---
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.
fellowniusmonk · 7h ago
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.
leptons · 6h ago
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.
margalabargala · 6h ago
I agree with the whole first half, but I disagree that LLM usage is doomed to ad-filled shittyness. AI companies may be hemmoraging money, but that's because their product costs so much to run; it's not like they don't have revenue. The thing that will bring profitability isn't ads, it will be innovations that let current-gen-quality LLMs run at a fraction of the electricity and power cost.
Will some LLMs have ads? Sure, especially at a free tier. But I bet the option to pay $20/month for ad-free LLM usage will always be there.
leptons · 4h ago
Silicon will improve, but not fast enough to calm investors. And better silicon won't change the fact that the current zeitgeist is basically a word guessing game.
$20 month won't get you much, if you're paying above what it costs to run the "AI", and for what? Answers that are in the ballpark of suspicious and untrustworthy?
Maybe they just need to keep spending until all the people who can tell slop from actual knowledge are all dead and gone.
LtWorf · 5h ago
"accurate"
resters · 4h ago
AI is going to cause a regression to the most anodyne output across many industries. As humans who had to develop analytical skills, writing skills, etc., we struggle to imagine the undeveloped brains of those who come of age in the zero-intellectual-gravity world of AI. OpenAI's study mode is at best a fig leaf.
edit: this comment was posted tongue-in-cheek after my comment reflecting my actual opinion was downvoted with no rebuttals:
I would say the modern digital world itself has already had the bigger impact on human thinking, at least at work.
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.
michaelhoney · 3h ago
I think all of this is true, but the shape of the chart changes as AI gets better.
Think of how a similar chart for chess/go/starcraft-playing proficiency has changed over the years.
There will come a time when the hardest work is being done by AI. Will that be three years from now or thirty? We don't know yet, but it will come.
cropher · 6h ago
Really liked this article.
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.
serial_dev · 6h ago
I learn about different subjects mixing traditional resources and AI.
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”.
bloomca · 5h ago
I use a similar approach. I tried to experiment going into a topic with no knowledge and it kinda fumbles, I highly recommend to have an overview.
But once you know basics, LLMs are really good to deepen the knowledge, but using only them is quite challenging. But as a complementary tool I find them excellent.
resters · 5h ago
AI will be both a floor and a ceiling raiser, since there is a practical limit to how many domains one person or team can be expert in, and AI does/will have very strong levels of expertise/competency across a large number of domains and will thus offer significant level-ups in areas where cross-domain synthesis is crucial or where the limits of human working memory and pattern recognition make cross-domain synthesis unlikely to occur.
AI also enables much more efficient early stage idea validation, the point at which ideas/projects are the least anchored in established theory/technique. Thus AI will be a great aid in idea generation and early stage refinement, which is where most novel approaches stall or sit on a shelf as a hobby project because the progenitor doesn't have enough spare time to work through it.
guywithahat · 6h ago
Wouldn't it be both by this definition? It raises the bar for people who maybe have a lower IQ ("mastery"), but people who can us AI can then do more than ever before, raising the ceiling as well.
kruffalon · 6h ago
Wouldn't "more" in this house metaphor be like expanding the floor rather than raising the ceiling?
bfigares · 7h ago
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.
infecto · 7h ago
Surprised to see this downvoted. It feels true to me. Sure there are definitely novel areas where folks might not benefit but I can see a future where this tool becomes helpful for the vast majority of roles.
verelo · 6h ago
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.
Madmallard · 1h ago
I guess the AI glazing has infiltrated everywhere
bluefirebrand · 7h ago
AI is not a floor raiser
It is a false confidence generator
burnt-resistor · 2h ago
It seems most suitable as an autonomous political speech writer and used car salesman coach.
billyp-rva · 7h ago
Mixing this with a metaphor from earlier: giving a child a credit card is also a floor raiser.
buffzebra · 4h ago
Only the first two mastery-time graphs make sense.
TimPC · 7h ago
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?
mattnewport · 7h ago
What's the basis for your claim that it is on an exponential growth trajectory? That's not the way it feels to me as a fairly heavy user, it feels more like an asymptotic approach to expert human level performance where each new model gets a bit closer but is not yet reaching it, at least in areas where I am expert enough to judge. Improvements since the original ChatGPT don't feel exponential to me.
samsartor · 6h ago
This also tracks with my experience. Of course, technical progress never looks smooth through the steep part of the s-curve, more a sequence of jagged stair-steps (each their own little s-curve in miniature). We might only be at the top of a stair. But my feeling is that we're exhausting the form-factor of LLMs. If something new and impressive comes along it'll be shaped different and fill a different niche.
roadside_picnic · 6h ago
People don't consider that there are real physical/thermodynamic constraints on intelligence. It's easy to imagine some skynet scenario, but all evidence suggests that it takes significant increases in energy consumption to increase intelligence.
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.
LeftHandPath · 7h ago
I was worried about that a couple of years ago, when there was a lot of hope that deeper reasoning skills and hallucination avoidance would simply arrive as emergent properties of a large enough model.
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.
The RAG technique uses a smaller model and an external knowledge base that's queried based on the prompt. The technique allows small models to outperform far larger ones in terms of hallucinations, at the cost of performance. That is, to eliminate hallucinations, we should alter how the model works, not increase its scale: https://highlearningrate.substack.com/p/solving-hallucinatio....
Pruned models, with fewer parameters, generally have a lower hallucination risk: https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00695.... "Our analysis suggests that pruned models tend to generate summaries that have a greater lexical overlap with the source document, offering a possible explanation for the lower hallucination risk."
At the same time, all of this should be contrasted with the "Bitter Lesson" (https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson...). IMO, making a larger LLMs does indeed produce a generally superior LLM. It produces more trained responses to a wider set of inputs. However, it does not change that it's an LLM, so fundamental traits of LLMs - like hallucinations - remain.
nwienert · 7h ago
Let's look:
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?
esafak · 6h ago
How are you measuring this improvement factor? We have numerous benchmarks for LLMs and they are all saturating. We are rapidly approaching AGI by that measure, and headed towards ASI. They still won't be "human" but they will be able to do everything humans can, and more.
No comments yet
righthand · 5h ago
It’s definitely about wage stagnation.
burnt-resistor · 2h ago
Wage suppression. The capital ownership class knows exactly how to weaponize the hype as an excuse now. It's also curious how software engineers working on it are cheerfully building their potential future replacements and long-term insecurity.
m3kw9 · 2h ago
At the very least
erlend_sh · 7h ago
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.
pdntspa · 6h ago
It's very easy to sign up for an API account and pay per-call, or even nothing. Free offerings out there are great (Gemini, OpenRouter...) and a few are even suitable for agentic development.
LtWorf · 5h ago
And how long until they raise the prices?
pdntspa · 4h ago
API prices have moving in a downward direction, not upward.
intended · 7h ago
Either you are the item being sold or you are paying for the service.
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
quirkot · 6h ago
I'd never buy anything as overt as an advertisement in an AI tool. I just want to buy influence. Just coincidentally use my product as the example. Just suggest my preferred technology when asked a few % more often than my competitors. I'd never want someone to observe me pulling the strings
LtWorf · 5h ago
Normally even if you pay you're still the product anyway. See buying smartphones for example… you pay a lot but you're the product.
recroad · 7h ago
AI isn't a pit. AI is a ladder.
layer8 · 6h ago
A ladder that doesn't reach the ceiling and sometimes ends up in imaginary universes.
anthk · 7h ago
Yeah, like in Nethack, while being blind and stepping on a cockatrice.
42lux · 7h ago
AI is chairs.
furyofantares · 5h ago
I feel like nobody remembers that facebook ad (Facebook is chairs), but it's seared into my own memory.
precompute · 6h ago
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.
tayo42 · 7h ago
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".
msgodel · 7h ago
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.
bitwize · 7h ago
AI is a shovel capable of breaking through the bottom of the barrel.
lupire · 7h ago
OP doesn't understand that almost everything is neither at the floor or the ceiling.
manyaoman · 7h ago
AI is a wall raiser.
dcchambers · 2h ago
I mean it makes sense that if the AI is trained on human-created things it can never actually do better than that. Can't bust through the ceiling of what it was trained on. And at the same time, it AI gives that power to people that just aren't very smart or good at something.
givemeethekeys · 7h ago
AI is a floor destroyer not a ceiling destroyer. Hang on for dear life!! :P
https://www.economist.com/content-assets/images/20250215_FNC...
https://www.economist.com/finance-and-economics/2025/02/13/h...
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.
But I share your concerns that:
AI doing the lesser tasks of [whatever] ->
less(no?) humans will do those tasks ->
less(no?) experienced humans to further the state of the art ->
automation-but-stagnation.
But tragedy of the commons says I have to teach my kid to use AI!
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.
AI tools are great at unblocking and helping their users explore beyond their own understanding. The tokens in are limited to the users' comprehension, but the tokens out are generated from a vast collection of greater comprehension.
For the novice, it's great at unblocking and expanding capabilities. "Good enough" results from novices are tangible. There is no doubt the volume of "good enough" is perceived as very low by many.
For large and complex codebases, unfortunately the effects of tech debt (read: objectively subpar practices) translate into context rot at development time. A properly architected and documented codebase that adheres to common well structured patterns can easily be broken down into small easily digestible contexts. i.e. a fragmented codebase does not scale well with LLMs, because the fragmentation is seeding the context for the model. The model reflects and acts as an amplifier to what it's fed.
For personal tools or whatever, sure. And the tooling or infrastructure might get there for real projects eventually, but it’s not currently. The prospect of someone naively vibe coding a side business including a payment or authentication system or something that stores PII— all areas developers learn the dangers of through the wisdom gained only by experience— sends shivers down my spine. Even amateur coders trying that stuff try old fashioned way must read their code and the docs and info on the net and such and will likely get some sense of the danger. Yesterday I saw someone here recounting a disastrous data breach of their friend’s vibe coded side hustle.
The big problem I see here is people not knowing enough to realize that something functioning is almost never a sign that it is “good enough” for many things they might assume it is. Gaining the amount of base knowledge to evaluate things like form security nearly makes the idea of vibe coding useless for anything more than hobby or personal utility projects.
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.
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. I'm trying to finish a better proof of the Poincare Conjecture, and if I succeed the lesson for grad students won't be "Huh. Second Ascent." Rather "This 69 year old gheezer did this by ignoring the noise and becoming AI bionic."
Three, since Toner-Rodgers 2024 currently seems to be a total fabrication.
https://archive.is/Ql1lQ
>The shift in recent economic research supports his observation. Although early studies suggested that lower performers could benefit simply by copying AI outputs, newer studies look at more complex tasks, such as scientific research, running a business and investing money. In these contexts, high performers benefit far more than their lower-performing peers. In some cases, less productive workers see no improvement, or even lose ground.
Right, the reason why I pointed out "recent" is that it's new evidence that people might not be aware of, given that there were also earlier studies showing AI had the opposite effect on inequality. The "recent" studies also had varied methodology compared to the earlier studies.
>The studies showing reduced equality all apply to uncommon tasks like material discovery and debate points
"Debating points" is uncommon? Maybe not everyone was in the high school debate club, but "debating points" is something that anyone in a leadership position does on a daily basis. You're also conveniently omitting "investment decisions" and "profits and revenue", which basically everyone is trying to optimize. You might be tempted to think "Coding efficiency" represents a high complexity task, but the abstract says the test involved "Recruited software developers were asked to implement an HTTP server in JavaScript as quickly as possible". The same is true of the task used in the "legal analysis" study, which involved drafting contracts or complaints. This seems exactly like the type of cookie cutter tasks that the article describes would become like cashiers and have their wages stagnate. Meanwhile the studies with negative results were far more realistic and measured actual results. Otis et al 2023 measured profits and revenue of actual Kenyan SMBs. Roldan-Mones measured debate performance as judged by humans.
Okay, well the majority of this "recent" evidence agrees with the pre-existing evidence that inequality is reduced.
> "Debating points" is uncommon?
Yes. That is nobody's job. Maybe every now and then you might need to come up with some arguments to support a position, but that's not what you get paid to do day to day.
> You're also conveniently omitting "investment decisions" and "profits and revenue", which basically everyone is trying to optimize.
Very few people are making investment decisions as part of their day to day job. Hedge funds may experience increasing inequality, but that kinda seems on brand.
On the other hand "profits and revenue" is not a task.
> You might be tempted to think "Coding efficiency" represents a high complexity task, but the abstract says the test involved "Recruited software developers were asked to implement an HTTP server in JavaScript as quickly as possible". The same is true of the task used in the "legal analysis" study, which involved drafting contracts or complaints.
These sound like real tasks that a decent number of people have to do on a regular basis.
> Meanwhile the studies with negative results were far more realistic and measured actual results. Otis et al 2023 measured profits and revenue of actual Kenyan SMBs. Roldan-Mones measured debate performance as judged by humans.
These sound like niche activities that are not widely applicable.
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.)
It's free for everyone with a phone or a laptop.
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
Once I have the MVP working, I will be working on publishing as a means to dogfood the tool. So, check back soon!
What you could do: sign in using one of the OAuth methods, go to the user page and then go to the feedback section. Let me know in a message your email and I'll ping you once the blog is setup.
Sorry it is primitive at this stage but I'm prioritizing MVP before marketing.
We use the same analogy for the last 20 years or so. Provisioning 150 cattle servers take 15 minutes or so, and we can provision a pet in a couple of hours, at most.
[0]: https://www.engineyard.com/blog/pets-vs-cattle/
*: Engine Yard post notes that Microsoft's Bill Baker used the term earlier, though CERN's date (2012) checks out with our effort timeline and how we got started.
this tweet by Tim Bell seems to indicate shared credit with Bill Baker and Randy Bias
https://x.com/noggin143/status/354666097691205633
@randybias @dberkholz CERN's presentation of pets and cattle was derived from Randy's (and Bill Baker's previously).
Before you call this pattern silly, consider that the fairly normal plural “Unices” is by analogy with Latin plurals in -x = -c|s ~ -c|ēs, where I’ve expanded -x into -cs to make it clear that the Latin singular comprises a noun stem ending in -c- and a (nominative) singular ending -s, which does exist in Latin but is otherwise completely nonexistent in English. (This is extra funny for Unix < Unics < Multics.) Analogies are the order of the day in this language.
Also, probably because of approaching graybeard territory, Thinking about boxen of VAXen running UNIXen makes me feel warm and fuzzy. :D
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.
I don't have a crystal ball and I can't predict the actual future. But I can see the list of potential futures and I can assign likelihoods to them. And among the potential futures is one where the need for humans to fix the problems created by poor AI coding agents dwindles as the industry completely reshapes itself.
I just wouldn't want to be responsible for servicing a guarantee about the reliability of early cars.
And I'll feel no sense of vindication if I do get that support case. I will probably just sigh and feel a little more tired.
So consider differing perspectives. Like a teenage kid that is hanging around the stables, listening to the veteran coachmen laugh about the new loud, smoky machines. Proudly declaring how they'll be the ones mopping up the mess, picking up the stragglers, cashing it in.
The career advice you give to the kid may be different than the advice you'd give to the coachman. That is the context of my post: Andrew Ng isn't giving you advice, he is giving advice to people at the AI school who hope to be the founders of tomorrow.
And you are probably mistaken if you think the solution to the problems that arise due to LLMs will result in those kids looking at the past. Just like the ultimate solution to car reliability wasn't a return to horses but rather the invention of mechanics, the solution to problems caused by AI may not be the return to some software engineering past that the old veterans still hold dear.
Things change, and that's ok. I guess I just got lucky so far that this thing I like doing just so happens to align with a valuable skill.
I'm not arguing for or against anything, but I'll miss it if it goes away.
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.
[0] https://arxiv.org/pdf/2403.15498v2
[1] https://github.com/adamkarvonen/chess_gpt_eval
Insane that the model actually does seem to internalize a representation of the state of the board -- rather than just hitting training data with similar move sequences.
...Makes me wish I could get back into a research lab. Been a while since I've stuck to reading a whole paper out of legitimate interest.
(Edit) At the same time, it's still worth noting the accuracy errors and the potential for illegal moves. That's still enough to prevent LLMs from being applied to problem domains with severe consequences, like banking, security, medicine, law, etc.
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?
There are lies, statistics and goddamn hallucinations.
AI, in all its glory, is seen as an extension of that. A deterministic thing which is meticulously crafted to provide an undisputed truth, and it can't make mistakes because computers are deterministic machines.
The idea of LLMs being networks with weights plus some randomness is both a vague and too complicated abstraction for most people. Also, companies tend to say this part very quietly, so when people read the fine print, they get shocked.
I think it's just that LLMs are modeling generative probability distributions of sequences of tokens so well that what they actually are nearly infallible at is producing convincing results. Often times the correct result is the most convincing, but other times what seems most convincing to an LLM just happens to also be most convincing to a human regardless of correctness.
> In computer science, the ELIZA effect is a tendency to project human traits — such as experience, semantic comprehension or empathy — onto rudimentary computer programs having a textual interface. ELIZA was a symbolic AI chatbot developed in 1966 by Joseph Weizenbaum and imitating a psychotherapist. Many early users were convinced of ELIZA's intelligence and understanding, despite its basic text-processing approach and the explanations of its limitations.
(To be fair, in many cases, I'm not terribly interested in learning the details of their field.)
We are barely talking modern media literacy, and now we have machines that talk like 'trusted' face to face humans, and can be "tuned" to suggest specific products or use any specific tone the owner/operator of the system wants.
Highly educated professionals in my experience are often very bad at applied epistemology -- they have no idea what they do and don't know.
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
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.
You absolutely can. This is exactly what SWE-Bench[0] measures, and I've been amazed at how quickly AIs have been climbing those ladders. I personally have been using Warp [1] a lot recently and in quite a lot of low-medium difficulty cases it can one-shot a decent PR. For most of my work I still find that I need to pair with it to get sufficiently good results (and that's why I still prefer it to something cloud-based like Codex [2], but otherwise it's quite good too), and I expect the situation to flip over the coming couple of years.
[0] https://www.swebench.com/
[1] https://www.warp.dev/
[2] https://openai.com/index/introducing-codex/
[0] https://www.tbench.ai/
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)
To clarify, I mean that the AI tools can help you get things done really fast but they lack both breadth and depth. You can move fast with them to generate proofs of concept (even around subproblems to large problems), but without breadth they lack the big picture context and without depth they lack the insights that any greybeard (master) has. On the other hand, the "engineering" side is so much more than "things work". It is about everything working in the right way, handling edge cases, being cognizant of context, creating failure modes, and all these other things. You could be the best programmer in the world, but that wouldn't mean you're even a good engineer (in real world these are coupled as skills learned simultaneously. You could be a perfect leetcoder and not helpful on an actual team, but these skills correlate).
The thing is, there will never be a magic button that a manager can press to engineer a product. The thing is, for a graybeard most of the time isn't spent around implementation, but design. The thing is, to get to mastery you need experience, and that experience requires understanding of nuanced things. Things that are non-obvious. There may be a magic button that allows an engineer to generate all the code for codebase, but that doesn't replace engineers. (I think this is also a problem in how we've been designing AI code generators. It's as if they're designed for management to magically generate features. The same thing they wish they could do with their engineers. But I think the better tool would be to focus on making a code generator that would generate based on an engineer's description.
I think Dijkstra's comments apply today just as much as they did then[0]
[0] On the foolishness of "natural language programming" https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...
Edit: Geometrically, I agree an extraloper could run on secants (or radii) but they're not allowed to strike a chord.
But getting back to generative text, I feel that "tangent" is more appropriate ;-)
Intraloper, weirdly enough, is a word in use.
In any single sentence context they cannot refer to the same relationships, and that which they are not is precisely the domain of the other word: they are true antonyms.
External relationships are a thing, which are in neither of those domains.
An intramural activity is not the same thing as an intermural activity, but the overwhelming majority of activities which are not intramural activities are not intermural activities either, most are extramural activities.
I'm exter mad about that.
> 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.
Does it solve the problem? As long as it isn't prohibitively costly in terms of time or resources, then the rest is really just taste. As a user I have no interest whatsoever if your code is "idiomatic" or "modular" or "functional". In other industries "quality" usually means free of defects, but software is unique in that we just expect products to be defective. Your surgeon operates on the wrong knee? The board could revoke the license, and you are getting a windfall settlement. A bridge design fails? Someone is getting sued or even prosecuted. SharePoint gets breached? Well, that's just one of those things, I guess. I'm not really bothered that AI is peeing in the pool that has been a sewer as long as I can remember. At least the AI doesn't bill at an attorney's rate to write a mess that barely works.
Below average people can use AI to get average results.
I suppose it's all a matter of what one is using an LLM for, no?
GPT is great at citing sources for most of my requests -- even if not always prompted to do so. So, in a way, I kind of use LLMs as a search engine/Wikipedia hybrid (used to follow links on Wiki a lot too). I ask it what I want, ask for sources if none are provided, and just follow the sources to verify information. I just prefer the natural language interface over search engines. Plus, results are not cluttered with SEO ads and clickbait rubbish.
Also I think what you're saying is a direct contradiction of the parent. Below average people can now get average results; in other words: The LLM will boost your capabilities (at least if you're already 'less' capable than average). This is a huge benefit if you are in that camp.
But for other cases too, all you need to know is where your knowledge ends, and that you can't just blindly accept what the AI responds with. In fact, I find LLMs are often most useful precisely when you don’t know the answer. When you’re trying to fill in conceptual gaps and explore an idea.
Even say during code generation, where you might not fully grasp what’s produced, you can treat the model like pair programming and ask it follow-up questions and dig into what each part does. They're very good at converting "nebulous concept description" into "legitimate standard keyword" so that you can go and find out about said concept that you're unfamiliar with.
Realistically the only time I feel I know more than the LLM is when I am working on something that I am explicitly an expert in, and in which case often find that LLMs provide nuance lacking suggestions that don’t always add much. It takes a lot more filling in context in these situations for it to be beneficial (but still can be).
Take a random example of nifty bit of engineering: The powerline ethernet adapter. A curious person might encounter these and wonder how they work. I don't believe an understanding of this technology is very obvious to a layman. Start asking questions and you very quickly come to understand how it embeds bits in the very same signal that transmits power through your house without any interference between the two "types" of signal. It adds data to high frequencies on one end, and filters out the regular power transmitting frequencies at the other end so that the signal can be converted back into bits for use in the ethernet cable (for a super brief summary). But if want to really drill into each and every engineering concept, all I need to do is continue the conversation.
I personally find this loop to be unlike anything I've experienced as far as getting immediate access to an understanding and supplementary material for the exact thing Im wondering about.
But that would shift the average up.
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?
I think a lot of these upskilling utilities will come down to delivery format. If you use a chat that gives you answers, don't expect to get better at that topic. If you use a tool that forces you to come up with answers yourself and get personalized validation, you might find yourself leveling up.
Disagree. It's only the illusion of a personal ceiling raise.
---
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'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.
>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.
Will some LLMs have ads? Sure, especially at a free tier. But I bet the option to pay $20/month for ad-free LLM usage will always be there.
$20 month won't get you much, if you're paying above what it costs to run the "AI", and for what? Answers that are in the ballpark of suspicious and untrustworthy?
Maybe they just need to keep spending until all the people who can tell slop from actual knowledge are all dead and gone.
edit: this comment was posted tongue-in-cheek after my comment reflecting my actual opinion was downvoted with no rebuttals:
https://news.ycombinator.com/item?id=44749957
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.
Think of how a similar chart for chess/go/starcraft-playing proficiency has changed over the years.
There will come a time when the hardest work is being done by AI. Will that be three years from now or thirty? We don't know yet, but it will come.
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 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”.
But once you know basics, LLMs are really good to deepen the knowledge, but using only them is quite challenging. But as a complementary tool I find them excellent.
AI also enables much more efficient early stage idea validation, the point at which ideas/projects are the least anchored in established theory/technique. Thus AI will be a great aid in idea generation and early stage refinement, which is where most novel approaches stall or sit on a shelf as a hobby project because the progenitor doesn't have enough spare time to work through it.
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.
It is a false confidence generator
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.
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...
I believe hallucinations are partly an artifact of imperfect model training, and thus can be ameliorated with better technique.
Smaller models may hallucinate less: https://www.intel.com/content/www/us/en/developer/articles/t...
The RAG technique uses a smaller model and an external knowledge base that's queried based on the prompt. The technique allows small models to outperform far larger ones in terms of hallucinations, at the cost of performance. That is, to eliminate hallucinations, we should alter how the model works, not increase its scale: https://highlearningrate.substack.com/p/solving-hallucinatio....
Pruned models, with fewer parameters, generally have a lower hallucination risk: https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00695.... "Our analysis suggests that pruned models tend to generate summaries that have a greater lexical overlap with the source document, offering a possible explanation for the lower hallucination risk."
At the same time, all of this should be contrasted with the "Bitter Lesson" (https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson...). IMO, making a larger LLMs does indeed produce a generally superior LLM. It produces more trained responses to a wider set of inputs. However, it does not change that it's an LLM, so fundamental traits of LLMs - like hallucinations - remain.
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?
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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
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.
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".