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.

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.

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.

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.

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.

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.

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.

The amount of OSS is exploding, but the community part of it is not

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.

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?

The fact that work is retreating into private chat sessions with AI agents is a loss; the idea of bringing those agentic collaborators out into public spaces to collaborate with teams is interesting.

the driver was, for many of us on many days, the last stranger we were obliged to encounter. The last person from outside our bubble – professionally, politically, socially – with whom we had an unchosen conversation. The last reliable source of a view we did not ask for.

We need more encounters like these in our human lives. Burst our little bubbles every now and then.

People become islands working on something without the bigger picture.

This is very dangerous.

- AI companies and the rich have increasingly pushed an agenda on the populace that most people didn't want, and the foundation of work in AI was formed by theft.

- Theft of creative work from millions of people, and a selective enforcement of law and compromise of investigatory authorities has demonstrated the law, morals, and the welfare of most people doesn't matter for people in power.

- This is the real "less collaborative" nature of the world where we find ourselves in. Add to that the increasing push for war by our leaders and rich too, people often shielded from the consequences, and you have a net less collaborative world.

It actually isn't even AI, but the same people pushing it are the same ones compromising collaboration for most people.

So close!

Yet the piece literally says

    Two people who had crossed continents to talk to each other were quietly delighted by a technology whose central feature is that you don’t have to talk to anyone

This has been going on for quite some time, you can find lots of people talking about things like disappearing "third place/space"'s [0]. The trend seems to be fewer people know or interact with their neighbors, something I've been guilty of as well. We have unlimited opportunities for both entertainment and pursuing our interests. COVID also was like pouring gas on this with people isolating and some people never "reintegrated" afterwards. A general lack of community is the root issue from my perspective but how to best encourage/build that is something I struggle with.

I've seen some theorize the need for everything to be in service of growing the economy, everything needing to be turned into a side-hustle or monetized in some way is to blame. I can believe that, so often things feel like a zero-sum game and the fatigue from that is real. I can't tell you how many times I've started writing software (before or after LLMs) and found myself lost (in the very early stages) thinking about monetization or thinking about "how will this scale?", "How could I make this configurable for people who want to use this differently than I do?", etc, all _before_ there is a viable piece of software to use. Once I realize what I'm doing it's maddening. It's the "planning for how to handle 1M simultaneous users when you don't have 1".

LLMs have let me break from that thinking, design software just for my needs, which is freeing in many ways and I love what I've been able to do but also isolating. More and more I find myself wanting to fork "ideas" not code. Why would I want to try to participate in someone else's vibe/llm-assisted project instead of just making my own? I feel that pull and I also feel like it's wrong or misguided.

I have no answers here, just noting I'm seeing and experiencing this and I don't know how to stop it.

I'll end with a quote that Aaron Sorkin has written into multiple of his TV shows:

    It seems to me that more and more we've come to expect less and less from each other, and I think that should change.

    — Aaron Sorkin

Also, Waymo is an automated taxi service, so it's an exteremly misleading analogy. Taxis existed before Waymo.

Otherwise, all this technology will totally isolate us.

the Waymo effect is what happens when a technology removes the friction of dealing with another human being

Some mistake, I think. Removing that friction would ease the interaction. This effect eliminates it.

From knowing the area and telling me the Alamo location has moved (and not updating their address on their official website!); to coming back and returning an item I forgot to retrieve; these incidents have helped me so much; not to mention occasionally having deep and meaningful conversations that enriched my life and perspectives.

I’ve had some occasional bad experiences too; but that’s life. I took a Waymo twice, and I still go for Uber or Didi every time.

The benefits are diffuse and deferred: the driver was, for many of us on many days, the last stranger we were obliged to encounter. The last person from outside our bubble – professionally, politically, socially – with whom we had an unchosen conversation. The last reliable source of a view we did not ask for.

This is a very strange take, to me.

I have conversations with cashiers, bartenders, servers, baristas, the random person in line in front of me or behind me, the person next to me on my flight (if they seem open to it), barbers, you name it.

I've had a handful of interesting conversations with cab drivers, but it's a tiny proportion of all my conversations with people "outside my bubble".

So I'm not really worried about this. The idea that cab drivers are somehow the last vestige of someone outside of your bubble is... bizarre to me.

The flip side is the people with the biggest problems with AI are those that survive by imposing themselves on others in unnecessary ways, which is why so many bureaucrats are so enthused about regulating it.

And AI precisely builds upon that large availability of our common data. That's a modern enclosure movement, at least it would be but fortunately, the Communist Chinese are there to enforce sharing.

collaboration adds overhead, but expands what's possible. need to think bigger to continue to see the benefits