We can’t circumvent the work needed to train our minds

I was talking with somebody about their migration recently [0], and we got to speculating about AI and how it might have helped. There were basically 2 paths:

- Use the AI and ask for answers. It'll generate something! It'll also be pleasant, because it'll replace the thinking you were planning on doing.

- Use the AI to automate away the dumb stuff, like writing a bespoke test suite or new infra to run those tests. It'll almost certainly succeed, and be faster than you. And you'll move onto the next hard problem quickly.

It's funny, because these two things represent wildly different vibes. The first one, work is so much easier. AI is doing the job. In the second one, work is harder. You've compressed all your thinking work, back-to-back, and you're just doing hard thing after hard thing, because all the easy work happens in the background via LLM.

If you're in a position where there's any amount of competition (like at work, typically), it's hard to imagine where the people operating in the 2nd mode don't wildly outpace the people operating in the first, both in quality and volume of output.

But also, it's exhausting. Thinking always is, I guess.

[0] Rijnard, about https://sourcegraph.com/blog/how-not-to-break-a-search-engin...

> You have to remember EVERYTHING. Only then you can perform the cognitive tasks necessary to perform meaningful knowledge work.

You don't have to remember everything. You have to remember enough entry points and the shape of what follows, trained through experience and going through the process of thinking and writing, to reason your way through meaningful knowledge work.

> You have to remember EVERYTHING. Only then you can perform the cognitive tasks necessary to perform meaningful knowledge work.

If humans did not have any facilities for abstraction, sure. But then "knowledge work" would be impossible.

You need to remember some set of concrete facts for knowledge work, sure, but it's just one—necessary but small—component. More important than specific factual knowledge, you need two things: strong conceptual models for whatever you're doing and tacit knowledge.

You need to know some facts to build up strong conceptual models but you don't need to remember them all at once and, once you've built up that strong conceptual understanding, you'll need specifics even less.

Tacit knowledge—which, in knowledge work, manifests as intuition and taste—can only be built up through experience and feedback. Again, you need some specific knowledge to get started but, once you have some real experience, factual knowledge stops being a bottleneck.

Once you've built up a strong foundation, the way you learn and retain facts changes too. Memorization might be a powerful tool to get you started but, once you've made some real progress, it becomes unnecessary if not counterproductive. You can pick bits of info up as you go along and slot them into your existing mental frameworks.

My theory is that the folks who hate memorization are the ones who were able to force their way through the beginner stages of whatever they were doing without dull rote memorization, and then, once there, really do not need it any more. Which would at least partly explain why there are such vehement disagreements about whether memorization is crucial or not.

I've been having conversations about this topic with friends recently and I keep coming back to this idea that most engineering work, which I will define as work that begins with a question and without a clear solution, requires a lot of foundational understanding of the previous layer of abstraction. If you imagine knowledge as a pyramid, you can work at the top of the pyramid as long as you understand the foundation that makes up your level, however to jump a level above or below that would require building that foundation yet again. Computer science fits well into this model where you have people at many layers of abstractions who all work very well within their layer but might not understand as much about the other layers. But regardless of where you are in the pyramid, understanding ALL the layers underneath will lead to better intuition about the problems of your layer. To farm out the understanding for these things will obviously end up having negative impact not just on overall critical thinking, but on the way we intuit how the world works.

I am sympathetic to memory-focused tools like Anki and Zettelkasten (haven't used the latter myself, though) but I think this post is a bit oversimplified.

I think there are at least two models of work that require knowledge:

1. Work when you need to be able to refer to everything instantly. I don't know if this is actually necessary for most scenarios other than live debates, or some form of hyper-productivity in which you need to have extremely high-quality results near-instantaneously.

(HN comments are, amusingly, also an example – comments that are in-depth but come days later aren't relevant. So if you want to make a comment that references a wide variety of knowledge, you'll probably need to already know it, in toto.)

2. Work when you need to "know a small piece of what you don't remember as a whole", or in other terms, know the map, but not necessarily the entire territory. This is essentially most knowledge work: research, writing, and other tasks that require you to create output, but that output doesn't need to be right now, like in a debate.

For example, you can know that X person say something important about Y topic, but not need to know precisely what it was – just look it up later. However, you do still need to know what you're looking for, which is a kind of reference knowledge.

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What is actually new lately, in my experience, is that AI tools are a huge help for situations where you don't have either Type 1 or Type 2 knowledge of something, and only have a kind of vague sense of the thing you're looking for.

Google and traditional search engines are functionally useless for this, but asking ChatGPT a question like, "I am looking for people that said something like XYZ." This previously required someone to have asked the exact same question on Reddit/a forum, but now you can get a pretty good answer from AI.

> If you can’t produce a comprehensive answer with confidence and on the whim the second you read the question, you don’t have the sufficient background knowledge.

While the article makes some reasonable points, this is too far gone. You don't need to know how to "weigh each minute spend on flexibility against the minutes spent on aerobic capacity and strength" to put together a reasonable workout plan. Sure, your workouts might not be as minmaxed as they possibly could be, but that really doesn't matter. So long as the plan is not downright bad, the main thing is that you keep at it regularly. The same idea extends to nearly every other domain, you don't need to be a deep expert to get reasonably good results.

The actual central point is that the brain requires conditioning via experience. That shouldn't be controversial, and I can't decide if the general replies here are an extended and ironic elaboration of his point or not.

If you never memorize anything, but are highly adept at searching for that information, your brain has only learned how to search for things. Any work it needs to do in the absence of searching will be compromised due to the lack of conditioning/experience. Maybe that works for you, or maybe that works in the world that's being built currently, but it doesn't change the basic premise at all.

It’s the same with math. A lot of people say they don’t need to be able to do basic arithmetic because they can use a calculator. But I think that you can process the world much better and faster if at a minimum you have some intuition about numbers and arithmetic.

It’s the same with a lot of other things. AI and search engines help a lot but you are at an advantage if at least you have some ability to gauge what should be possible and how to do it.

I agree with the point being made, even if it is taken to an extreme. I would say you don't need to remember everything, but you do need to have been exposed to it. Not knowing what you don't know is a huge handicap in knowledge work.

“Try to learn something about everything and everything about something.”

Before the internet we asked people around us in our sphere. If we wanted to know the answer to a question, we asked, they made up an answer, and we believed it and moved on.

Then the internet came, and we asked the internet. The internet wasn't correct, but it was a far higher % correct than asking a random person who was near you.

Now AI comes. It isn't correct, but it's far higher % correct than asking a random person near you, and often asking the internet which is a random blog page which is another random person who may or may not have done any research to come up with an answer.

The idea that any of this needs to be 100% correct is weird to me. I lived a long period in my life where everyone accepted what a random person near them said, and we all believed it.

The author makes a lot of bold claims and I don’t take his main one serious re: remembering everything. I think he’s being intentionally hyperbolic. But the gist is sound to me, if you can put one together. He needs an editor.

> To find what you need online, you require a solid general education and, above all, prior knowledge in the area related to your search. > > [...] > > If you can’t produce a comprehensive answer with confidence and on the whim [...] you don’t have the sufficient background knowledge. > > [...] > > This drives us to one of the most important conclusions of the entire field of note-taking, knowledge work, critical thinking and alike: You, not AI, not your PKM or whatever need to build the knowledge because only then it is in your brain and you can go the next step. > > [...] > > The advertised benefits of all these tools come with a specific hidden cost: Your ability to think. [This passage actually appears ahead of the previous one–ed.]

This is best read alongside: https://news.ycombinator.com/item?id=45154088

Descartes' brief rules for the direction of the mind [1] is pertinent here, as it articulates beautifully what it means to do "thinking" and how that relates to "memory".

Concepts have to be "internalized" into intuition for much of our thinking, and if they are externalized, we become a meme-copy machine as opposed to a thinking machine.

[1] https://en.wikipedia.org/wiki/Rules_for_the_Direction_of_the...