51–60 of 97 posts
Aligned with the theory of Bullshit Jobs - LLMs expose the fact that the white collar work most of us have been doing at this point were actually bullshit. When LLMs "fake" work, it actually hides the reality that there was no meaningful work here in the first place.
With AI, we‘re cargo-culting understanding. We‘re reproducing the surface of having understood something, but we‘re robbing ourselves the time and effort to truly do it.
i'd say it's been a huge distraction for him and the obsession over using LLM for Big Wikiz hasn't yielded anything near what he thought the tech was for. few occasions now he's learned the hard way how imperfect the technology is.
between that and everyones grand visions for agentic workflows i've mostly just receded into being one of the few who is still regularly delivering stuff. i'm using AI to speed my delivery up quite a bit, i'm just not wasting my time taking it on some big grand adventure. the irony that a lot of people pushed back on companies who wanted to implement chat bots and they spend most of their credits/tokens making their own chat bots by collecting six trillion .md files and adding skill files.
my real takeaway is this: i've come to reason that there is some sort of loss in actual real institutional knowledge when we attempt to take shortcuts to growing the breadth of our own knowledge. i don't mean "hey claude give me some examples of how companies typically design x to solve for y" or "golang is new to me, what are the benefits of a compiled language versus something that requires a runtime going".
no, i'm talking about these kinds of questions:
"/somePersonalBigWikiProjectInvokedBySkill.md claude review our current tooling and infrastructure, how can we 5x our deployment speed, then search the web for and put a proposal together to get it implemented at the organization and include a 5 year cost benefit analysis and ... "
i look around and it feels like everyone is nerfing themselves. that latter question? people are just sending claude proposals left and right. my eyes have completely glazed over. is it really that hard to do some digging yourself? we're already ceding the ability to just go grab an architect or senior engineer and ask him what he thinks about how will fit with the broader suite of technologies and visions on the horizon. we're just skipping the pieces where we do a little discovery together and work together on an outcome. we're walking away with surface level understanding of many things.
this clearly has visible impacts on how we engage with each other, there's something there that I'm noticing and don't have the words for. it's mostly that people are less able to explain what it is they're talking about when pressed for deeper details, but also everyone's behavior is now different because AI sort of... makes them feel like they have definitive answers/strategies and they're no longer willing to have their ideas challenged. they no longer see that as a learning experience, a chance to learn from someone who has wisdoms who is already a walking wikipedia on something. the perfect technology for people who hate when someone with way more experience than them says "maybe not a good idea and here's why"
i've met some interesting people who are just... walking encyclopedias on some or many domains. incredibly smart people who have so much knowledge and wisdom and so many years of experience not just with tech but with people and failures and successes. i don't doubt for a second that the human brain is capable of holding an unbelievable index of information in a natural way that marries well with decision making processes that come from experience. i'm not sure what gap people are trying to close building themselves some proverbial great library here, but i would encourage people to just sit back and trust that their brain is still one of the greatest technologies at their disposal.
This is an already apparent problem in academia, though not for the reasons the article suggests. It is not so much that the "tells" of a poor quality work are vanishing, but that even careful scrutiny of a work done with AI is going to become too costly to be done only by humans. One only has so much time to read while, say, in economics journals, the appendices extend to hundreds of pages. Would love to hear if oth…
"How do you know the output is good without redoing the work yourself?" Verifying the correctness of solutions is often much easier than finding correct solutions yourself. Examples: Sudoku and most practical problems in just about any field. - "The training doesn't evaluate 'is the answer true' or "is the answer useful.'" Lets pretend RLVF does not exist to give this argument a chance. Then, while the training loop…
>Have you even tried using these systems to produce valuable work? How could this possibly be your conclusion after having tried them?
The operative words there are used to, as opposed only able to. The conclusion isn't derived from using the tools, it's from observing how other people tend to use them.
It's a weird space in middle management where all of the incentives other than true competency in the role push you to abstract the knowledge work that you're managing, and that abstraction seems to well describable in embedding space.
With AI, we‘re cargo-culting understanding. We‘re reproducing the surface of having understood something, but we‘re robbing ourselves the time and effort to truly do it.
AI can do things on its own, without you understanding them yes. But if you are trying to understand something well, there is no better tool for helping you than AI.
Could not disagree more.
The best way to understand something deeply is to practice it. AI is anti-practice. It's like trying to learn something by following a YouTube video step by step. It has an outcome and it feels productive but it's not going to stick in your head at all. It's not practice
"How do you know the output is good without redoing the work yourself?" Verifying the correctness of solutions is often much easier than finding correct solutions yourself. Examples: Sudoku and most practical problems in just about any field. - "The training doesn't evaluate 'is the answer true' or "is the answer useful.'" Lets pretend RLVF does not exist to give this argument a chance. Then, while the training loop…
In order to verify correctness you need to understand what correctness is in context, which is actually pretty hard to do if you can't actually find correct solutions yourself, or even if you can but haven't bothered to do so
The article asserts that the quality of human knowledge work was easier to judge based on proxy measures such as typos and errors, and that the lack of such "tells" in AI poses a problem. I don't know if I agree with either assertion… I've seen plenty of human-generated knowledge work that was factually correct, well-formatted, and extremely low quality on a conceptual level. And AI signatures are now easy for people…
It was and still is a negative filter, not a positive one. Meaning it is easy to reject work because there typos and basic factual errors, absence of them is not a good measure of quality. Typically such checks is the first pass not the only criteria. It is valuable to have this, because it the work passes the first check then it easier to identify the actual problems. Same reason we have code quality, lint style fix…
Earlier quoted context omitted.
AI can do things on its own, without you understanding them yes. But if you are trying to understand something well, there is no better tool for helping you than AI.
> But if you are trying to understand something well, there is no better tool for helping you than AI Could not disagree more. The best way to understand something deeply is to practice it. AI is anti-practice. It's like trying to learn something by following a YouTube video step by step. It has an outcome and it feels productive but it's not going to stick in your head at all. It's not practice
am I losing out on something by not having to spend hours clicking through redundant parts of a large codebase to get a concrete answer on something? doesn't feel like it