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- Hacker News
- Beautifully written essay.
The benefits of serendipity are intractable by design whereas throughput gains can be readily measured (or at least we think so).
The OP notes a point - "who did you think with?" -- maybe there are points based on some social graph, that adds weightage to a particular output?
Edit : Other posters have said it is AI generated, I did not catch it, but felt the point was well-made. I dont know the author, but lets assume that he is not a good writer but had these valuable insights, did this AI generation not add value here?
by foolinaround - > Beautifully written essay.
Agreed! Great flow, structure, no BS, really good!
I would wager this didn't use LLM help to be written. Though if I'm wrong I'd really like to know and would be very impressed. (And if I'm right then the article is an illustration of its own message. You don't learn how to write such essays without having practised it for many years without opting for the easy way of letting the LLM do this for you.)
by teiferer - FWIW, this same thing is happening in regular corporate America too. I have all kinds of non-CPA, non-finance background people telling me how I should treat financial transactions, without the nuance of GAAP or how an auditor would handle their reasoning. Everyone has an opinion on what our tech teams should/could allow or enable or create, without understanding the security implications and support it would require or what other projects are already working on.
It’s good in a way, because you can certainly challenge people’s answer when an idea gets shot down. But it’s also a bit exhausting that everyone is challenging everything all the time.
by conductr - This is the exact same thing that we have been seeing in open source software.
The amount of OSS is exploding, but the community part of it is not
by maddiaa0 - I'm seeing this on a current academic research project as well- it's like throwing fuel on the fire for all the best and worst parts of working with researchers.
First: it's definitely become harder to get people to integrate their work and use standard tools. We're exploring creating AI skills etc for our tooling, simply because the actual audience we need to convince isn't in the room. If the LLM doesn't echo our recommendations, people will go with whatever one off script their personal bad idea bear churns out. Many research software errors are edge cases (scaling, numerical errors, etc), and "works on my computer" syndrome is endemic in the literature. The field has made big strides in enabling computational reproducibility, but lately it feels like we're set to lose ground again.
Second: LLMs have a bias to action, and can easily bury the user reporting on whatever. I've seen multiple seminars recently where the Q&A devolves to "Q: What are the implications of this finding? A: I don't know, this is just presenting a report on the results".
It seems that humans are still figuring out how to maintain agency and steer the chatbot to the big picture, and unreviewable science is just as likely an outcome as unreviewable code... but with far less automated tooling to help guide the process.
Historically, PIs nominally guided the big picture, and the entity who did the work was a participant in the review process; now students are having to look at projects from a new angle with their own (invisible) chatbot underlings. I suspect that the solution will involve a combination of technological change, capturing common expert review checks, and really adjusting the kinds of skills that trainees are expected to have early on.
by abought - For software developers, the equivalent is writing software without beta testers. If you don’t seek out users, you will likely build something nobody else wants.
And, that’s what I do. I’m retired and I use AI to build websites for myself. If anyone else ends up using it, that will be a happy side-effect.
If we want to do collaboration, we are going to need to do it intentionally rather than relying on side effects like conversations with cab drivers.
by skybrian - Counterpoint: most Open Source projects are (or started off as) programs people built for themselves, and anyone else ending up using them was a happy side-effect.by Xirdus
- I think this notion of friction vs frictionless is one of the key features of this time in history.
To my eye, the whole line from personal technology in the 80s -> through UX on the internet -> through app platform lock-in -> through subscription-centric "ownership" have been providing us all with the promise that we can buy our way into less friction. The next device or subscription solves problems or enables us one way or another. We see this line of history, draw it into the future and see a utopian future with zero friction.
Zero friction meaning zero difference between how you want to feel about the world and what you get out of it. More feeling empowered or satisfied or comfortable or right.
The parts of our culture that embrace friction are the slow parts - grousing with neighbors, weeding a garden, letting the other car go first. These are the parts where we meet with the reality of other people or nature. Maybe there's a tool or framework or gadget that could help, but we know and accept that there is something immutable outside of ourselves and we have to find a way to flow with it.
I'm getting older, I'm finally starting to see the limits of pursuing a frictionless life.
by chairleader - This resonates with me so much.
One thing I personally have noticed is that very result-oriented people seem to be vastly more optimistic about AI since for them it’s all about getting from A to B as quickly as possible, and AI broadly seems to promise that.
Conversely, the people who find joy in what they do, and see the journey and its frustrations as fulfilling and full of learning, tend to be more pessimistic or disillusioned.
(sample size maybe N ~8-10, all SWE)
by alex_suzuki - The notion that "every human interaction is worth preserving" is probably not right, and Waymos are showcasing this. As the author mentions, many people don't enjoy the forced small talk with Uber drivers. Interacting with people from different backgrounds and perspectives is important, but I doubt the forced interactions like taxi rides are where we were getting those until day.
- I think you're missing the point, which is not that every human interaction is worth preserving, but that convenience has a hidden cost.
People would rather not spend time and effort to help their neighbors.
People would rather not pick up trash they see on the side of the road.
People would rather not take accountability.
If these things become the default action, then you've traded something for the convenience, and that something might be non obvious until you feel it is missing.
The neighbors one, I think a lot of people will admit a certain loneliness and desire to have community back. I don't think a lot of people will actually give up the inconvenience for it though, the sedative nature of convenience is one of its core problems. The number of people I hear say with conviction they can't imagine living anywhere other than CA because they couldn't stand the weather anywhere else...
by spyckie2 - Completely agree. I think there is a danger that we will loose important interactions because we don't need them, but there's also a chance that we skip unimportant ones and focus on important ones.
If being in a Waymo alone causes you to be more comfortable to chat with people on the phone, it can even have a positive effect, for example.
by ddosmax556 - I think the problem is the same as with any technology: used correctly it adds value, used incorrectly it subtracts from it.
LLMs can massively speed up a process, but without control they can turn into social "sources of truth." A research process has well-defined, well-founded phases: you delimit the topic, search for sources, evaluate whether they're suitable, review their content, and place them on the map of the subject. The more sources, the greater the knowledge, and the better the final result — mental, or in the form of a report — is built. For that you have to read, and reread, and think, connect, relate, and conclude.
AI can do all of those steps faster than a human. But if we let it do the entire job on its own, its own way, with no checks at each stage, the conclusions can end up distorted. If we know what we want it to do and how we want it done, and we put the mechanisms in place to enforce that, the result is different — better, more reliable. It's worth remembering: it's just a tool, nothing more.
The wording of this comment in English has been corrected with AI, I don't have enough fluency to express myself clearly, but I do review the final result. In this case it got the verb tenses wrong, I saw it clearly, but the AI didn't understand it, it took me several instructions to explain it so it would understand. It's a tool, without supervision it can lead to problems, but it has expanded the world for a lot of people.
by Cribalis - I wonder if AI is making us dumber
I can have AI pull some analysis - sql query, crunch some numbers and bam, some precise sounding number.
But then I question it, and then it says "you are right, I didn't check that"
How did I know what to question? By doing the friction work of querying myself, calculating myself, and then getting questions from leadership.
I worry about the younger generation. They spit out Claude results fast, people question it and they say "ok I'll take that back to Claude".
I suppose they'll eventually learn to do their own sense checking because they still have the friction of presenting to others.
And maybe my model is a thing of the past. Everyone is now going to be a manager (of agents) fresh out of school. They aren't doing the work anymore but they will need to set clear goals, validate work and hold agents accountable.
by xivzgrev - Oh it is, no question. All the people who come to AI with years of expertise are feeling it - what happens to all the people who never get the chance to develop it?by mpalmer
- Pre-LLMs: anyone can generate ideas, the implementation is what matters
Post-LLMs: anyone can generate implementations, the ideas are what matter
by TaupeRanger - AI is extremely useful, but it’s also extremely easy to fool yourself into thinking you understand what is going on without really understanding. This is often true with the code, but also for math and science concepts, etc.
Not many professions are formally trained to be cognizant of this lack of understanding, and how to confront it.
Usage of AI in collaborative settings is an amplifier of these issues, especially if someone doesn’t realize they don’t understand: they couldn’t teach or explain the concepts they use, or be forced to work with them malleably in a way that an expert or researcher would.
If you are cognizant of your lack of understanding, you can remedy it by slowing down and teaching yourself. This is required to make better use of AI in the domain of interest!
But you can’t have all things at once: you can’t move at speed with AI, collaborate effectively, and understand what is going on as an expert would. It is not physically possible for a human brain.
by mccoyb - > But you can’t have all things at once: you can’t move at speed with AI, collaborate effectively, and understand what is going on as an expert would. It is not physically possible for a human brain.
Very well put. You can pick one or two, but not all three.
by kaffekaka - > But you can’t have all things at once: you can’t move at speed with AI, collaborate effectively, and understand what is going on as an expert would. It is not physically possible for a human brain.
I think the article's point is more nuanced. Short term, the human brain as the "conductor" can keep up and an expert can see whether the machine did a good job and course correct if necessary. Over time though that degrades more and more.
by teiferer - I work at an intersection of tech, applied research, and science.
Something I’ve noticed in collaboration that does occur is an increased confidence in people outside their domains to say things with conviction. I have people who have limited experience with software pushing out layers and layers of abstracted code that’s fairly sophisticated but often misguided in intent who will say what they’re doing is correct, with conviction.
I also hear a lot more questioning people in their domains and challenging opinions, then hearing what I can only imagine are fragmented pieces of conversations they had with an LLM thinking through some argument. Then there’s silence when you discuss shortcomings, then they come back later with their memorized fragments of what you said, combined with memorized fragments of the LLM response to the argument.
It’s occurring, a lot more. People are treating their LLMs in collaboration as a source of truth and using then to focus on their specific path or goals they think or have bias towards going down, vs just opening discussing things, considering tradeoffs from experts multiple disciplines weigh in on and then taking an approach that everyone finds most agreeable.
It’s making me want to be a lot less collaborative with such individuals. I don’t want to sit around and refute Claude text outputs all day.
by Frost1x - What do you suggest these people do instead? I’ve been frustrated by this recently: I pivoted to a new subfield and am working on stuff that I would love to dive deep into and really learn what is going on so I can speak intelligently about the tradeoffs etc. But that would take a long time, and I have tasks that I should get done. So I have found myself working with a pretty vague understanding that, when pressed by coworkers, quickly finds its limits. Then I go back and try to deepen my understanding enough to cover those limits. But because I’m not working with each detail of the problem, implementing line by line with time to think about what’s happening, there just isn’t time for me to learn this unfamiliar topic. But I would love to, and I would enjoy the work much more if I could. So what am I supposed to do?by pastel8739
- > It’s making me want to be a lot less collaborative with such individuals.
I have the same conclusion and am getting a growing list of mental blacklisted people who I just sort of ignore or greatly discount their efforts.
I suppose I always did this if people were time wasters but what used to be a tiny list of “idiots” is now getting much, much bigger. Hopefully it doesn’t grow to everyone I work with like an AI Nothing.
by prepend - Idk, seems like overconfidence outside your domain really got going in the '00s and is largely perpetrated by SWEs.
Something about making a lot of money and living in a world of abstractions really seems to fuel our sense of overconfidence in our abilities in other areas.
by 01100011 - "I also hear a lot more questioning people in their domains and challenging opinions, then hearing what I can only imagine are fragmented pieces of conversations they had with an LLM thinking through some argument. Then there’s silence when you discuss shortcomings, then they come back later with their memorized fragments of what you said, combined with memorized fragments of the LLM response to the argument."
We hired this guy recently.
by oniony - This resonates a lot with me. I'm the CTO of a software factory-ish company. I get daily emails from clients running our decisions through LLMs and asking for ridiculous stuff that would multiply the implementation cost for them, just because Claude said so. No, your 3 users app doesn't need to be SOC2 compliant. No, of course we don't have triple zone redundancy while you're paying a 50 USD AWS bill.
Most of the time, these are just concerns triggered by chatting by an over-zealous LLM; in the worst cases, they are demands.
I'm usually able to clear concerns and disarticulate the LLM by explaining to customers how much more expensive everything would be if we did things like that. But it feels so frustrating, it's like you have to prove yourself everytime and justify every decision. These interactions have made me question my future in the industry, if I'm willing to keep dealing with these situations. I'm trying to foster patience in my life to cope with this.
by jcmontx - I also work at an intersection of tech, applied research and science. My experience has been different. It's been surprising how controlled my collaborators/coworkers have been with their reliance on AI.
Sure, they use it a lot, but despite the stereotypes against physicists in some of the replies to you, they have shown a good ability to catch themselves before leaning too hard on what an LLM tells them about things they are not experts in, and I haven't had any interactions where I felt like I was just arguing with a meat proxy.
Maybe it's because in the environment I'm in; it's relatively easy to just ask someone who is an expert in the topic for their advice.
My feeling is that this kind of overconfidence outside of one's domain is largely a thing for people with little "physical reality" experience. When all you deal with is the very flexible digital world, it becomes very easy to ignore how deep the knowledge and intuition goes in things that are directly constrained by reality. I consider myself to have been in this category too (CE background), though I have been making efforts to improve.
by hgoel - Yup, the arrogance when barging into unfamiliar domain that was previously reserved mainly to physics grads seems to have spread everywhere. I also had people opining on my expert area via their LLMs and the problem is they can't even ask the model the right question, let alone evaluate the nuance of their answer.by varjag
- Wow, oddly similar thing happened to me. Anecdotally, I was working with a non-developer. She was building a reporting dashboard with Claude. I pointed out that the system she built was using the filesystem to store one line notes about the report, and I said "I would probably tell the agent to use a database for that, but it's not the end of the world if it works". And she replied "Claude said the developer's right, a database would be a better fit..."
So, a non-developer, who has no understanding of the underlying systems they are working with, fact-checked a developer with years of experience.
I'm not against fact checking, but it absolutely felt like a punch in the gut coming from a non-dev. There was no "trust" there. She couldn't rely on my expertise, she had to go back to her "source of truth" (Claude) and asked for a second opinion on something that she wouldn't be able to verify.
This is why collaboration exists. I trust my colleagues to give me insight+advice on things that I do not know. I don't ask AI to vet their decisions, because at some level there are judgement calls, and I WANT to trust them because it makes my life easier.
Treating AI as if the output is factual is just a misuse of the tools, I think. It's significantly more effective when given expert direction and the output can be verified by... an expert.
- This article is written by an LLM, by the way. ("Quietly" is... as Claude might put it... "often the quiet tell".)by meowface
- This rings more and more true every day: https://samkriss.substack.com/p/if-you-let-ai-do-your-writin...by AlexandrB