That really depends on who is the one that benefits from automation. Companies automate support systems in order to keep their support staff small, because apparently for many of them it is more profitable to frustrate their customers with crappy support than to pay more support staff to do a better job.
In addition to your last paragraph: lots of things that we used to do the less efficient way had side-benefits that were not immediately obvious, probably because they compounded over time. Now that we're not doing them anymore, we notice all kinds of widespread societal problems (in particular among young people) that come up that were never there before.
True. People don't do it though, because keeping skills sharp and using them takes effort, and we have a predisposition to be as efficient as possible with how we spend our effort; if there's an easier way to do it in our awareness, we will naturally gravitate towards that. LLMs are often a universal crutch or swiss-army-knife that significantly take away workload for many abstract tasks, so all kinds of atrophy in abstract thinking is to be expected.
However, when looking at muscle, once you have it you don't need to use it as much in order to maintain it. I wonder if the same is true for skills; in that case, some kind of regiment where you still use the skill you delegate once a week or so could maybe help with avoiding this loss of skill for most part.
“ However, when looking at muscle, once you have it you don't need to use it as much in order to maintain it”
No.. this depends on how much muscle you have. The appropriate comparison is mass and density of knowledge/understanding vs muscle. There’s not a chance in hell you will retain mass and dense muscle without pushing the body hard. Just in the same way you will not retain very deep understanding of things unless a) you’ve been reciting it for over 10 yrs b) you go back and push the understanding continuously for it to remain as part of your being
Building muscle is much harder than maintaining muscle.
And if you went 3 years without exercising, you'll be able to get your muscles back much quicker than had you never had the muscle before.
It's pretty comparable to skills. You don't need to practice as hard to maintain a skill than you do to build it. And if you let the skill atrophy, it's much easier to recover the skill compared to building it from scratch.
> And if you went 3 years without exercising, you'll be able to get your muscles back much quicker than had you never had the muscle before.
This very much depends on age. I went on statins about 18 months, which destroyed about 15lbs of muscle over the course of a year (160->145). Along with that muscle loss came about a halving or more of the weights I could lift in any given exercise. I interpreted the "do you have any weakness on this medication" question as inability to function levels of weakness, it wasn't until I showed my training logs to my physician that she asserted that I was having weakness.
It's been a year since I went off them and I'm still lifting barely what I could in high school. I'm exploring some different training plans, but AFAIK, there isn't much research into if different weight/volume breakdowns work better for older guys.
I’ve got 20 inch lean arms - I know far more about muscle building and retention than you. I train just as hard to maintain them as I did to get them there.
The people who say “oh it’s easy to maintain” LOL it’s easy to maintain 16 inch arms.
Chiming into this little tiff to say I think bulk muscle is a bad analogy in the first place. It’s more akin to a muscle memory/skill. Something like golf is a better analogy. If you took any golfer, at any level, and had them refrain from golfing for 3 years. I feel pretty confident asserting they would all perform worse than they had. Their skill is diminished.
They would also likely get that skill back faster than a brand new golfer.
I noticed it myself with cycling. Took 8 years off the bike, when I started up again I was nearly back to my old FTP in about 2 months despite starting from basically zero. Muscle memory is real, where I am now as a returning cyclist would take a pure beginner cyclist at least 4+ months to get to, fitness wise.
That said, you do have to work somewhat hard to maintain. With cycling, just 2 weeks off the bike is enough to see a VO2 max drop of anywhere from 4 to 7%. After just 4 weeks, your glycogen storage capacity decreases and you start rapidly losing fitness. After 2 months, you are basically now out of shape.
Detraining happens faster than most people think. And therein lies the danger with over reliance on LLMs for your cognitive skills. Detraining there happens just as fast, skills atrophy in a matter of weeks, not months or years.
People could also regain some cognitive skill back rather fastr when they worked to regain it. But the issue is, many people just lack the motivation to do so. If you golf or cycle, it's likely a passion or hobby. Most people don't view their cognitive health this way, they view it as work. It's why most people don't read much after their schooling, learning and being smart was only ever an ends to a means (diploma, job, money, etc).
I think part of the problem is also that many people simply work too hard or have too much going on in their lives to have any kind of cognitive energy left for this sort of maintenance work, even when they reason/plan that it is useful. This also seems to be encouraged somehow (by society?), to keep going like a freight train, or maybe it doesn't get discouraged enough (i.e. it doesn't get recognized as a problem).
My experience as a parent to an only-child has shown me there's just zero boredom or tolerance of boredom. Any pause or void needs to be filled with something. Any time my son says "I'm bored" my default response has become "awesome", "you're lucky", "I wish I had time to be bored" along with other quips like "boredom is a life skill". Of course, I see this same phenomenon in adults as well. So my rebuttal is that most people have much more free time than they think, it's just a matter of prioritization.
To maintain the muscle you have, you only need about 1/3rd of your normal workouts. It can be retained with 1-2 workouts per week. I imagine the same would be for something you've learned. If you've already put in the effort to learn it, reviewing it ~1x per week is probably enough. During the accumulation phase though - whether it be muscle or learning a new skill - once a week is definitely not enough.
Yes, this is my experience for muscle at least. I used to work out 3-4 times a week, maybe a little more sometimes. Lately due to circumstances, I've been doing smaller workouts about 1-2 times a week. I've lost some finesse, but my muscle mass has remained roughly the same.
Also like some people hinted at this in sibling threads, I think it's different between purely abstract skills, and skills that involve muscle memory. For instance, I could probably stop using my bicycle for a very long time, and still not unlearn how to use it, or learn it again really quickly. Maybe it is because abstract skills are inherently more complex and require more cognitive effort and connections to knowledge overall, and are therefore more fragile.
Money is how you test whether someone is currently in privileged circumstances (be it their own doing or not), not whether they are good at argumentation or decision-making.
I think you can quantify the amount of creative expression you engage in by looking at all the decision points in the creative process where you are directly involved in making the decision. For an LLM prompt, that is going to be fairly limited by definition. I suppose the quality can be measured then by how novel and effective the output/approach of each decision is then, how much impact is made.
The amount of creative expression does not necessarily correlate with impact. Something can be created with nearly zero creative expression, that ends up making a significant impact. In that case you are more of a director than an artist I suppose, in that you direct the high-level process and only make decisions there. You can call it creative thinking in the same way a good businessman makes smart high-level decisions and then delegates what is downstream to others, with decisions being optimized for impact.
I think you can be creative "within a frame" in that sense, e.g. creative in the way you wield an LLM for instance, which is on a different scale compared to being creative on the piano roll with how you organize and brainstorm your melodies. It's just a different skill set at a different granularity altogether. But the one thing that I think holds, is that higher level methods have less creative expression by definition, because you are delegating more decisions to other faculties; you are seeing less of the "creator" in the work.
Actually, in the specific case of a 3D program it's the current generation of LLM's complete lack of ability in spatial reasoning that prevents them from "understanding" what you want when you ask it to e.g. "make a camera that flies in the direction you are looking at".
It necessarily has to derive it from examples of cameras that fly forward that it knows about, without understanding the exact mathematical underpinnings that allow you to rotate a 3D perspective camera and move along its local coordinate system, let alone knowing how to verify whether its implementation functions as desired, often resulting in dysfunctional garbage. Even with a human in the loop that provides it with feedback and grounds it (I tried), it can't figure this out, and that's just a tiny example.
Math is precise, and an LLM's fuzzy approach is therefore a bad fit for it. It will need an obscene amount of examples to reliably "parrot" mathematical constructs.
> "make a camera that flies in the direction you are looking at"
That's not the task of a renderer though, but its client, so you're talking past your parent comment. And given that I've seen peers one-shot tiny Unity prototypes with agents, I don't really believe they're that bad at taking an educated guess at such a simple prompt, as much as I wish it were true.
You're right. My point was more that LLMs are bad at (3D) math and spatial reasoning, which applies to renderers. Since Unity neatly abstracts the complexity away of this through an API that corresponds well to spoken language, and is quite popular, that same example and similar prototypes should have a higher success rate.
I guess the less detailed a spec has to be thanks to the tooling, the more likely it is that the LLM will come up with something usable. But it's unclear to me whether that is because of more examples existing due to higher user adoption, or because of fewer decisions/predictions having to be made by the LLM. Maybe it is a bit of both.
A couple of other people have expressed similar sentiments here, and I think it's the truth. You have to be in a position to give before you can sustain it reliably and/or reap the benefits from it.
Often though, this position is highly subjective and mental in nature. A homeless man could willingly give his food away, and still somehow be fine with that, if he believes that things will be fine regardless somehow (perhaps he has an alternative source of food, or sincerely doesn't think that skipping food once will set him back forever). At the other hand, someone with a difficult and tedious job that pays well may not feel like they have the time or energy to give without necessarily receiving anything in return, even though they may objectively be in a much better overall position for it.
I guess altruism necessarily requires some other essential basic needs to be in abundance first before it can overflow.
What makes you think that AI cannot become significantly better than humans at "understanding" and modelling the world? If the AI is always more likely to be right than you or me due to being able to take more variables/knowledge into account by default, then why ever listen to a human, or even to yourself when it comes to an economic decision?
My honest and rather pessimistic take is that in the long-term any craft that purely lives in the abstract is likely to be doomed.
It's not that it won't be better at understanding, it's that there's too many possibilities to understand. This is true for humans too, but I can use the output to make money in a particular scenario.
Take even 1 simple example - software applications on a smart watch. How many dimensions of reality are relevant? Maybe I'm a busy person, so I need a personal assistant for my calendar. Maybe my wife needs access too. Maybe I'm a bird watcher and I'd like to track the birds I see. Maybe I'm a bird researcher and those observations need to integrate with my research.... ad nauseum forever.
AI will write all the code, and make all the meaningful decisions, but the backstop of the whole thing has to be some non-virtual reality with a paying user, otherwise there is no value to extract.
I personally only care about the outcome, I don't even really care if I understand how anything else works, or any of the decisions made. My dollars go in, working code comes out to suit me.
I agree with your overall perspective here. You need the human in the loop to ground the request/direction in a reality with human needs, but that's about it.
What I was getting at is that nothing stops you from asking AI what would be the next best smartwatch app to build, and based on all its aggregated knowledge and other inputs (e.g. search) it has, it can potentially make a better estimation than you or any human of a product that would sell.
Of course whether that is actually true depends on how well its training data is able to model/mimic reality, and how grounded its inputs (e.g. internet) are. You can always help it a bit by steering it into the right direction, providing additional grounding. Was mainly wondering for how long this "additional" guidance would be a necessity, fearing that it won't be for as long as we think.
That makes sense. Some skills just have more utility than others. There are skills that are universally relevant (e.g. general problem solving), and then there are skills that are only relevant in a specific time period or a specific context.
With how rapidly the world has been changing lately, it has become difficult to estimate which of those more specific skills will remain relevant for how long.
That's a nice anecdote, and I agree with the sentiment - skill development comes from practice. It's tempting to see using AI as free lunch, but it comes with a cost in the form of skill atrophy. I reckon this is even the case when using it as an interactive encyclopedia, where you may lose some skill in searching and aggregating information, but for many people the overall trade off in terms of time and energy savings is worth it; giving them room to do more or other things.
If the computer was the bicycle for the mind, then perhaps AI is the electric scooter for the mind? Gets you there, but doesn't necessarily help build the best healthy habits.
Trade offs around "room to do more of other things" are an interesting and recurring theme of these conversations. Like two opposites of a spectrum. On one end the ideal process oriented artisan taking the long way to mastery, on the other end the trailblazer moving fast and discovering entirely new things.
Comparing to the encyclopedia example: I'm already seeing my own skillset in researching online has atrophied and become less relevant. Both because the searching isn't as helpful and because my muscle memory for reaching for the chat window is shifting.
It's a servant, in the Claude Code mode of operation.
If you outsource a skill consistently, you will be engaging less with that skill. Depending on the skill, this may be acceptable, or a desirable tradeoff.
For example, using a very fast LLM to interactively make small edits to a program (a few lines at a time), outsources the work of typing, remembering stdlib names and parameter order, etc.
This way of working is more akin to power armor, where you are still continuously directing it, just with each of your intentions manifesting more rapidly (and perhaps with less precision, though it seems perfectly manageable if you keep the edit size small enough).
Whereas "just go build me this thing" and then you make a coffee is qualitatively very different, at that point you're more like a manager than a programmer.
> then perhaps AI is the electric scooter for the mind
I have a whole half-written blog post about how LLMs are the cars of the mind. Massive externalities, has to be forced on people, leads to cognitive/health issues instead of improving cognition and health.
I’ve also noticed that I’m less effective at research, but I think it’s our tools becoming less effective over time. Boolean doesn’t really work, and I’ve noticed that really niche things don’t surface in the search results (on Bing) even when I know the website exists. Just like LLMs seem lazy sometimes, search similarly feels lazy occasionally.
This is the typical arrogance of developers not seeing the value in anything but the coding. I've been hands on for 45 years, but also spend 25 of those dealing with architecture and larger systems design. The actual programming is by far the simplest part of designing a large system. Outsourcing it is only dumbing you down if you don't spend the time it frees up to move up the value chain.
Talk about arrogance, Mr 45 years of experience. Ever thought that there might be people under skyscraper that is your ego? I’m pretty sure majority of tech workers aren’t even 45 years old. Where are they supposed to learn good design when slop takes over? You’ve spent at least 20 years JUST programming, assuming you’ve never touched large scale design before last 25 years. Simplest part my ass.
> Ever thought that there might be people under skyscraper that is your ego?
I do, which is exactly why I found the presumption that not spending your time doing the coding is equivalent to a disability both gross and arrogant.
> Where are they supposed to learn good design when slop takes over?
You're not learning good architecture and systems design from code. You learn good architecture and systems design from doing architecture and systems design. It's a very different discipline.
While knowing how to code can be helpful, and can even be important in narrow niches, it is a very minor part of understanding good architecture.
And, yes, I stand by the claim the coding is by far the simplest part, on the basis of having done both for longer than most developers have been doing either.
> And, yes, I stand by the claim the coding is by far the simplest part, on the basis of having done both for longer than most developers have been doing either.
"I reckon this is even the case when using it as an interactive encyclopedia".
Yes, that is my experience. I have done some C# projects recently, a language I am not familiar with. I used the interactive encylopedia method, "wrote" a decent amount of code myself, but several thousand lines of production code later, I don't I know C# any better than when I started.
OTOH, it seems that LLMs are very good at compiling pseudocode into C#. And I have always been good at reading code, even in unfamiliar languages, so it all works pretty well.
I think I have always worked in pseudocode inside my head. So with LLMs, I don't need to know any programming languages!
It's even more general than that: LLMs seem to be exceedingly good at translating bodies of text from one domain to another. The frontier models also have excellent natural language translation capabilities, far surpassing e.g. Google translate.
In that sense, going from pseudocode to a programming language is no different from that, or even translating a piece of code from one programming language to another.
I agree. I also don't think forcing yourself to be an organizer is necessarily a solution to fixing the loneliness, as it also just requires a certain passion. In my experience, some people love organizing things, others just really hate it. I am in that last camp, after having organized quite a lot. For me, simply participating with things that are organized by others has done me much more good. Of course, that still requires being in a state of mind where you are able to take initiative with signing up for such group activities.
In addition to your last paragraph: lots of things that we used to do the less efficient way had side-benefits that were not immediately obvious, probably because they compounded over time. Now that we're not doing them anymore, we notice all kinds of widespread societal problems (in particular among young people) that come up that were never there before.
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