[orev]

It’s like weightlifting: sure you can use a forklift to do it, but if the goal is to build up your own strength, using the forklift isn’t going to get you there.

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.

[frankc]

I think this is a pretty solid analogy but I look at the metaphor this way - people used to get strong naturally because they had to do physical labor. Because we invented things like the forklift we had to invent things like weightlifting to get strong instead. You can still get strong, you just need to be more deliberate about it. It doesn't mean shouldn't also use a forklift, which is its own distinct skill you also need to learn.

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.

[WorldMaker]

> people used to get strong naturally because they had to do physical labor

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.

[thesz]

Weightlifting and weight training was invented long before forklifts. Even levers were not properly understood back then.

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.

[SecretDreams]

> people used to get strong naturally because they had to do physical labor.

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.

[bryan2]

You don’t need to be strong to operate a forklift but you definitely need to be able to write the simple code to be a SWE.

[hennell]

>if the goal is to build up your own strength I think you missed this line. If the goal is just to move weights or lift the most - forklift away. If you want to learn to use a forklift, drive on and best of luck. But if you're trying to get stronger the forklift will not help that goal.

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.

[jrm4]

I like this analogy along with the idea that "it's not an autonomous robot, it's a mech suit."

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.

[orev]

The point of going to school is to learn all the details of what goes into making things, so when you actually make a thing, you understand how it’s supposed to come together, including important details like correct design that can support the goal, etc. That’s the “getting stronger” part that you can’t skip if you expect to be successful. Only after you’ve done the work and understand the details can you be successful using the power tools to make things.

[bccdee]

> Everyone knows that debugging is twice as hard as writing a program in the first place. So if you're as clever as you can be when you write it, how will you ever debug it? — The Elements of Programming Style, 2nd edition, chapter 2

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.

[bitwize]

No, it's not a mech suit. A mech suit doesn't fire its canister rifle at friendly units and then say "You're absolutely right! I should have done an IFF before attacking that unit." (And if it did the engineer responsible should be drawn and quartered.) Mech-suit programming AI would look like something that reads your brainwaves and transduces them into text, letting you think your code into the machine. I'd totally use that if I had it.

[quinnjh]

This analogy works pretty well. Too much time doing everything in it and your muscles will atrophy. Some edge cases will be better if you jump out and use your hands.

[lelanthran]

> 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.

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.

[wrs]

OK, it’s a mech suit. The question under discussion is, do you need to learn to walk first, before you climb into it? My life experience has shown me you can’t learn things by “observing”, only by doing.

[jplusequalt]

>Here's the thing -- I don't care about "getting stronger."

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.

[ljm]

If all I know is the mech suit, I’ll struggle with tasks that I can’t use it for. Maybe even get stuck completely. Now it’s a skill issue because I never got my 10k hours in and I don’t even know what to observe or how to explain the outcome I want.

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.

[PKop]

> I want to make things

You need to be strong to do so. Things of any quality or value at least.

[storystarling]

The mech suit works well until you need to maintain stateful systems. I've found that while initial output is faster, the AI tends to introduce subtle concurrency bugs between Redis and Postgres that are a nightmare to debug later. You get the speed up front but end up paying for it with a fragile architecture.

[b112]

If observing was as good as doing, experience would mean nothing.

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.

[xnx]

> "it's not an autonomous robot, it's a mech suit."

Or "An [electric] bicycle for the mind." Steve Jobs/simonw

[treetalker]

Misusing a forklift might injure the driver and a few others; but it is unlikely to bring down an entire electric grid, expose millions to fraud and theft, put innocent people in prison, or jeopardize the institutions of government.

There is more than one kind of leverage at play here.

[pjc50]

> Misusing a forklift might injure the driver and a few others; but it is unlikely to bring down an entire electric grid

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)

[yetihehe]

> but it is unlikely to bring down an entire electric grid

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.

[arghwhat]

I do appreciate the visual of driving a forklift into the gym.

The activity would train something, but it sure wouldn't be your ability to lift.

[Lerc]

A version of this does happen with regard to fitness.

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.

[randysalami]

A use case I’ve been working through is learning a language (not programming). You can use LLMs to translate and write for you in another language but you will not be able to say, I know that language, no matter how much you use the LLM.

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

[boilerupnc]

I feel like the aviation pilot angst captured by "automation dependency" and the fears around skills loss is another great analogy. [0]

[0] https://eazypilot.com/blog/automation-dependency-blessing-or...

[ModernMech]

I've been showing my students this video of a robot lifting weights to illustrate why they shouldn't use AI to do their homework. It's obvious to them the robot lifting weights won't make them stronger.

https://www.youtube.com/watch?v=Be7WBGMo3Iw

[hyperpape]

How seriously do you mean the analogy?

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.

[burkaman]

I think it's a good analogy. A forklift is a useful tool and objectively better than humans for some tasks, but if you've never developed your muscles because you use the forklift every time you go to the gym, then when you need to carry a couch up the stairs you'll find that you can't do it and the forklift can't either.

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.

[_flux]

You're making the analogy work: because the point of weightlifting as a sport or exercise is to not to actually move the weights, but condition your body such that it can move the weights.

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..

[antod]

I feel we are on the cusp of a new era... Civil Engineering bridge analogies about to be replaced by forklift analogies.

[volkanvardar]

I think a better analogy is a marathon. If you're training for a marathon, you have to run. It won't help if you take the car. You will reach the finish line with minimal effort, but you won't gain any necessary muscles.

[theshrike79]

But if your goal is to get from A to B, car is more efficient.

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 =)

[TuringNYC]

> 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.

OK but then why even use Python, or C, or anything but Assembly? Isn't AI just another layer of value-add?

[saulpw]

No, because AI is not deterministic. All those other tools are intentionally isomorphic with machine code, even if there's a lot of optimization going on under the hood. AI may generate code that's isomorphic with your prompt, but it also may not. And you have no way of telling the difference besides reading and understanding the code.

[_heimdall]

The real challenge will be that people almost always pick the easier path.

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.

[kwikiel]

Wondering why the obvious solution isn’t applied here - instead of giving already well known problems that have been solved thousand times give students open research opportunities- stuff which is on the edge of being possible, no way to cheat with Ai. And if Ai is able to solve those - give harder tasks

[imadethis]

The same reason we give beginner math students addition and subtraction problems, not Fermat’s last theorem?

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.

[rienbdj]

Coincidentally, this is why Duolingo doesn’t work. They need to make it easy/low friction to keep you engaged but if it’s not hard you won’t be learning much.

[stackedinserter]

Unlike weightlifting, the main goal of our jobs is not to lift heavy things, but develop a product that adds value to its users.

Unfortunately, many sdevs don't understand it.

[burkaman]

Yes but the goal of school is to lift heavy things, basically. You're trying to do things that are difficult (for you) but don't produce anything useful for anyone else. That's how you gain the ability to do useful things.

[lelanthran]

> the main goal of our jobs is not to lift heavy things, but develop a product that adds value to its users.

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.

[MyHonestOpinon]

Thanks for the analogy. But I think students may think to themselves:"Why do I need to be stronger if I can use a forklift?"

[fragmede]

Same reason to still memorize basic math in the time of calculators. gotta learn the fundamentals and you're not always going to have one.

[MarsIronPI]

Hey, I guess that means there's less competition for me. I don't mind.

[wklm]

I like the weightlifting parable!