This is the ultimate problem with AI in academia. We all inherently know that “no pain no gain” is true for physical tasks, but the same is true for learning. Struggling through the new concepts is essentially the point of it, not just the end result.
Of course this becomes a different thing outside of learning, where delivering results is more important in a workplace context. But even then you still need someone who does the high level thinking.
It's not a perfect analogy though because in this case it's more like automated driving - you should still learn to drive because the autodriver isn't perfect and you need to be ready to take the wheel, but that means deliberate, separate practice at learning to drive.
I think that's a bit of a myth. The Greeks and Romans had weightlifting and boxing gyms, but no forklifts. Many of the most renowned Romans in the original form of the Olympics and in Boxing were Roman Senators with the wealth and free time to lift weights and box and wrestle. One of the things that we know about the famous philosopher Plato was that Plato was essentially a nickname from wrestling (meaning "Broad") as a first career (somewhat like Dwayne "The Rock" Johnson, which adds a fun twist to reading Socratic Dialogs or thinking about relationships as "platonic").
Arguably the "meritocratic ideal" of the Gladiator arena was that even "blue collar" Romans could compete and maybe survive. But even the stories that survive of that, few did.
There may be a lesson in that myth, too, that the people that succeed in some sports often aren't the people doing physical labor because they must do physical labor (for a job), they are the ones intentionally practicing it in the ways to do well in sports.
They don’t go to the gym, they don’t have the energy; the job shapes you. More or less the same for the farmers in the family.
Perhaps this was less so in the industrial era because of poor nutrition (source: Bill Bryson, hopefully well researched). Hunter gatherer cultures that we still study today have tremendous fitness (Daniel Lieberman).
We may not have any evidence that they had forklifts but we also can't rule out the possibility entirely :)
Why do you think that? It's definitely true. You can observe it today if you want to visit a country where peasants are still common.
From Bret Devereaux's recent series on Greek hoplites:
> Now traditionally, the zeugitai were regarded as the ‘hoplite class’ and that is sometimes supposed to be the source of their name
> but what van Wees is working out is that although the zeugitai are supposed to be the core of the citizen polity (the thetes have limited political participation) there simply cannot be that many of them because the minimum farm necessary to produce 200 medimnoi of grain is going to be around 7.5 ha or roughly 18 acres which is – by peasant standards – an enormous farm, well into ‘rich peasant’ territory.
> Of course with such large farms there can’t be all that many zeugitai and indeed there don’t seem to have been. In van Wees’ model, the zeugitai-and-up classes never supply even half of the number of hoplites we see Athens deploy
> Instead, under most conditions the majority of hoplites are thetes, pulled from the wealthiest stratum of that class (van Wees figures these fellows probably have farms in the range of ~3 ha or so, so c. 7.5 acres). Those thetes make up the majority of hoplites on the field but do not enjoy the political privileges of the ‘hoplite class.’
> And pushing against the ‘polis-of-rentier-elites’ model, we often also find Greek sources remarking that these fellows, “wiry and sunburnt” (Plato Republic 556cd, trans. van Wees), make the best soldiers because they’re more physically fit and more inured to hardship – because unlike the wealthy hoplites they actually have to work.
( https://acoup.blog/2026/01/09/collections-hoplite-wars-part-... )
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> Many of the most renowned Romans in the original form of the Olympics and in Boxing were Roman Senators
In the original form of the Olympics, a Roman senator would have been ineligible to compete, since the Olympics was open only to Greeks.
My favorite historic example of typical modern hypertrophy-specific training is the training of Milo of Croton [1]. By legend, his father gifted him with the calf and asked daily "what is your calf, how does it do? bring it here to look at him" which Milo did. As calf's weight grew, so did Milo's strength.
This is application of external resistance (calf) and progressive overload (growing calf) principles at work.
[1] https://en.wikipedia.org/wiki/Milo_of_Croton
Milo lived before Archimedes.
... it's a calf, dad, just like yesterday
People used to get strong because they had to survive. They stopped needing strength to survive, so it became optional.
So what does this mean about intelligence? Do we no longer need it to survive so it's optional? Yes/No informs on how much young and developing minds should be exposed to AI.
Like many educational tests the outcome is not the point - doing the work to get there is. If you're asked to code fizz buzz it's not because the teacher needs you to solve fizz buzz for them, it's because you will learn things while you make it. Ai, copying stack overflow, using someone's code from last year, it all solves the problem while missing the purpose of the exercise. You're not learning - and presumably that is your goal.
Here's the thing -- I don't care about "getting stronger." I want to make things, and now I can make bigger things WAY faster because I have a mech suit.
edit: and to stretch the analogy, I don't believe much is lost "intellectually" by my use of a mech suit, as long as I observe carefully. Me doing things by hand is probably overrated.
If you weren't even "clever enough" to write the program yourself (or, more precisely, if you never cultivated a sufficiently deep knowledge of the tools & domain you were working with), how do you expect to fix it when things go wrong? Chatbots can do a lot, but they're ultimately just bots, and they get stuck & give up in ways that professionals cannot afford to. You do still need to develop domain knowledge and "get stronger" to keep pace with your product.
Big codebases decay and become difficult to work with very easily. In the hands-off vibe-coded projects I've seen, that rate of decay was extremely accelerated. I think it will prove easy for people to get over their skis with coding agents in the long run.
That's kinda how I see vibe coding. It's extremely easy to get stuff done but also extremely easy to write slop. Except now 10x more code is being generated thus 10x more slop.
Learning how to get quality robust code is part of the learning curve of AI. It really is an emergent field, changing every day.
With all respect, that's nonsense.
Absolutely no one gains more than a superficial grasp of a skill just by observing.
And even with a good grasp of skills, human boredom is going to atrophy any ability you have to intervene.
It's why the SDCs (Tesla, I think) that required the driver to stay alert to take control while the car drove itself were such a danger - after 20+ hours of not having to to anything, the very first time a normal reaction time to an emergency is required, the driver is too slow to take over.
If you think you are learning something reviewing the LLM agent's output, try this: choose a new project in a language and framework you have never used, do your usual workflow of reviewing the LLMs PRs, and then the next day try to do a simple project in that new language and framework (that's the test of how much you learned).
Compare that result to doing a small project in a new language, and then the next day doing a different small project in that same language.
If you're at all honest with yourself, or care whether you atrophy or not, you'd actually run that experiment and control and objectively judge the results.
I'd agree, if my goal was "to be a great and complete coder."
I don't. I want just enough to build cool things.
Now, that's just me.
That being said, I'd also venture to say that your attitude here might be a tad dinosaurish. I like it too, but also, know that to a large extent, especially in the market -- this "quality" that you're striving for here may just not happen.
Let's not mince words here, what you mean is that you don't care to learn about a craft. You just want to get to the end result, and you are using the shiny new tool that promises to take you from 0 to 100% with little to no effort.
In this way, I'd argue what you are doing is not "creating", but engaging in a new form of consumption. It used to be you relied on algorithms to present to you content that you found fun, but the problem was that algorithm required other humans to create that content for you to later consume. Now with LLMs, you remove the other humans from the loop, and you can prompt the AI directly with exactly what you wish to see in that moment, down to the fine grained details of the thing, and after enough prompts, the AI gives you something that might be what you asked for.
You are rotting your brain.
In true HN fashion of trading analogies, it’s like starting out full powered in a game and then having it all taken away after the tutorial. You get full powered again at the end but not after being challenged along the way.
This makes the mech suit attractive to newcomers and non-programmers, but only because they see product in massively simplified terms. Because they don’t know what they don’t know.
You need to be strong to do so. Things of any quality or value at least.
Thinking through the issue, instead of having the solve presented to you, is the part where you exercise your mental muscles. A good parallel is martial arts.
You can watch it all you want, but you'll never be skilled unless you actually do it.
Or "An [electric] bicycle for the mind." Steve Jobs/simonw
There is more than one kind of leverage at play here.
That's the job of the backhoe.
(this is a joke about how diggers have caused quite a lot of local internet outages by hitting cables, sometimes supposedly "redundant" cables that were routed in the same conduit. Hitting power infrastructure is rare but does happen)
Regardless of whose fault it was, the end result was the bucket snagged the power lines going into the datacentre and caused an outage.
Unless you happen to drive a forklift in a power plant.
> expose millions to fraud and theft
You can if you drive forklift in a bank.
> put innocent people in prison
You can use forklift to put several innocent people in prison with one trip, they have pretty high capacity.
> jeopardize the institutions of government.
It's pretty easy with a forklift, just try driving through main gate.
> There is more than one kind of leverage at play here.
Forklifts typically have several axes of travel.
The activity would train something, but it sure wouldn't be your ability to lift.
There are enthusiasts who will spend an absolute fortune to get a bike that is few grams lighter and then use it to ride up hills for the exercise.
Presumably a much cheaper bike would mean you could use a smaller hill for the same effect.
If you practice judo you're definitely exercising but the goal is defeating your opponent. When biking or running you're definitely exercising but the goal is going faster or further.
From an an exercise optimization perspective you should be sitting on a spinner with a customized profile, or maybe do some entirely different motion.
If sitting on a carbon fiber bike, shaving off half a second off your multi-hour time, is what brings you joy and motivation then I say screw it to further justification. You do you. Just be mindful of others, as the path you ride isn't your property.
Now compare this to using the LLM with a grammar book and real world study mechanisms. This creates friction which actually causes your mind to learn. The LLM can serve as a tool to get specialized insight into the grammar book and accelerate physical processes (like generating all forms of a word for writing flashcards). At the end of day, you need to make an intelligent separation where the LLM ends and your learning begins.
I really like this contrast because it highlights the gap between using an LLM and actually learning. You may be able to use the LLM to pass college level courses in learning the language but unless you create friction, you actually won’t learn anything! There is definitely more nuance here but it’s food for thought
[0] https://eazypilot.com/blog/automation-dependency-blessing-or...
I think forklifts probably carry more weight over longer distances than people do (though I could be wrong, 8 billion humans carrying small weights might add up).
Certainly forklifts have more weight * distance when you restrict to objects that are over 100 pounds, and that seems like a good decision.
So the idea is that you should learn to do things by hand first, and then use the powerful tools once you're knowledgeable enough to know when they make sense. If you start out with the powerful tools, then you'll never learn enough to take over when they fail.
Indeed, usually after doing weightlifting, you return the weights to the place where you originally took them from, so I suppose that means you did no work at in the first place..
It's the whole "journey vs destination" thing.
Currently AI seems to be the rocket you strap to your back as you put on VR glasses and enjoy the entertainment. You'll get there fast or blow up in the middle.
The True Artisanal Coders are the ones running the whole way, enjoying the scenery and the physical conditioning they get.
And there are people in between with bikes, cars etc. (different stages of AI use)
Analogies are fun =)
But if you're a junior developer, you won't be able to gain the most vital skills.
OK but then why even use Python, or C, or anything but Assembly? Isn't AI just another layer of value-add?
We have a decent sized piece of land and raise some animals. People think we're crazy for not having a tractor, but at the end of the day I would rather do it the hard way and stay in shape while also keeping a bit of a cap on how much I can change or tear up around here.
There has to be a base of knowledge available before the student can even comprehend many/most open research questions, let alone begin to solve them. And if they were understandable to a beginner, then I’d posit the LLM models available today would also be capable of doing meaningful work.
Unfortunately, many sdevs don't understand it.
I wouldn't want to write raw bytes like Mel did though. Eventually some things are not worth getting good at.
Forklift operators don't lift things in their training. Even CS students start with pretty high level of abstraction, very few start from x86 asm instructions.
We need to make them implement ALU's on logical gates and wires if we want them to lift heavy things.
Well, whether we like it or not, we are all eventually going to find out if "developing a product that adds value to its users" can be done when you have no more skill than aforementioned users.
Skills atrophy is a real thing.
