Will it be is a different thing though. And if it’s not, who exactly is accountable?
With funds and portfolio managers that run them, there’s a clear accountability model (if the fund sucks, the manager loses their job and the company loses credibility)
With AI agents doing the management, who is accountable when the fund sucks? If it’s the customer, we’ve moved accountability from someone who at least in theory, knows what they’re doing to someone who has little to no clue.
You have to be accountable for what you have the model do on your behalf. I hear what you're saying, but there are also issues with the hedge fund accountability model. There are certainly swaths of fund managers who are only there because they got lucky or had the right pedigree, and more that are better traders but never became a fund manager because they got unlucky or had other passions.
An individual investor can invest with their risk appetite on their time horizon and not be subject to Citadel's "5% draw down in a quarter and you're fired" culture which can be toxic to returns over time.
Per a report that came out the other day, the GitHub move to Azure has been slowed down (i.e. I don't think it's done). But maybe you have newer/better info than me
Yeah, that and Microsoft has been slow to move the infrastructure to something that scales better to handle that load.
The more surpassing part is that Microsoft hasn't figured out a way to manage/contain the AI-sourced traffic better so it doesn't create all this noisy neighbor problems for non-AI usage/users.
Github's core platform doesn't really make that separation, anything a human can leverage on github an AI agent can as well, just faster and with heavier usage. End of day agents and humans are using the same services.
Sure, still need to enable access the same info but feels like bucketing the clients into
bucket1 = clients that were working just fine before (users and whatever automation they had in place)
bucket2 = ai clients that contributed to, if not flat out caused, the scale problems
then slowing down/limiting the bucket2 clients while keeping the bucket1 clients rolling as-is, is both doable and keeps existing customers happy while the underlying infra gets scale/perf improvements needed to support ai clients at scale.
> Every use of AI for these robs the employee culture of a genuine trust building moment.
Spot on.
The erosion of communication and relationships between people in the workplace (or even outside it) that AI contributes to is something that we don't talk about nearly enough. Society today has already suffered greatly in these areas thanks to social media, and AI just makes it worse.
People (in general) are really struggling to understand when/how to use AI to be more productive and happier (and imo there is a way to do it, by offloading the grunt work to AI). With the constant rush and jamming of AI down everyone's throats though, its hard to be able to take that step back and think "is this use of AI making me happier/more productive".
Yes. The role of good management here cannot be understated. Good management (all the way up) is the difference between saying "be more productive, here's an AI subscription" and people understanding what types of usage are actually wanted and useful.
As it is now, with just the vague handwaving many managers are doing, people are hearing "You should reach for AI immediately anytime you get an input that you technically can paste into the AI" - so we can't be mad at them if they're just doing what they think they're being told to do.
I was a bit annoyed with an IC recently because he would just respond to peoples messages with "Heres with Claude says".
He didn't understand why I was so annoyed because we have been pushing them to use AI for everything.
I had to explain to him, its like when google first came about, yes you can just send people Let me Google that for you links, or you can respond politely and reasonably to build a relationship. In the end I don't think he got it and it's something I'll need to continue working on with him.
Where are you guys working where people are doing this? I work in a company where leadership is also ramming AI down everyone's throats, but I don't recall ever getting copy/pastes from LLM as responses to E-mails or chats. My biggest problem is people not reading/answering their E-mails and chats at all, or finally getting back to me long after the due date of whatever I'm asking about. Which is a different workplace comms problem altogether.
Design docs on the other hand have been fully taken over by the slop machine. They all kind of look the same now, and give off that familiar "I didn't write it so you might as well not read it" vibe.
I was working in an env where I started to suspect that people I was chatting with were using LLMs. These were people that didn't want to talk to me either way, so there was not much lost here. I suspected that, because the technical expertise they were showcasing when responding to messages would evaporate when talkin f2f
Norms surrounding the use of LLMs are in the process of being established, it's a new frontier. Many people rely on these signals over common sense. The feedback loop will lead to corrections in time, for now people are sussing out where the boundaries of appropriate-use are. Corp/gov policy is still lagging as well.
I really am not a big fan of this... Hand-waving, I guess? Around this problem. Saying "well the norms are still being established" feels kind of like a "well don't really get mad at the people doing it, they're still trying to figure out the boundaries of acceptable use" kind of thing to me. People should already know that this kind of behavior is unacceptable. The fact that they don't is very, very telling and says a lot about the people doing it IMO.
They should, but they (some) don't. As with most things, it's more attributable to ignorance than malice. Not much I want to do with what this "tells" me.
Maybe. It doesn't help that a lot of corporations are pushing their employees into dark patterns around LLMs. That in turn informs their own personal use of LLMs outside the workplace
> This sounds like an attempt to rationalize the fact that your business isn't that effective, otherwise adding more people would result in making more money.
Yes, or that businesses are expecting a slow down in the economy that hinders their ability to sell (i.e. their customers are going to cutback on spending)
This was the case last year (or maybe it was the year before) where technology companies saw their customers reducing spend and tightening belts.
The current economy feels hard to figure out, in that the market keeps going up but so is inflation and the struggle of the everyday American at least.
Perhaps that is leading technology companies to be more conservative in how much they produce.
But if we're assuming that AI is highly effective, shouldn't that lead to short-term growth in the economy as inflation drops? Shouldn't people then be expected to spend even more?
+1 to all of this. The challenge can be staying focused and thinking when the AI assistant is (1) moving very fast and (2) often times doing multiple things at the same time.
I know I have struggled to keep up, and fall into the trap of approving things (either commands or recommendations) without taking the time to really process and think about them.
It's a bit like the age old problem of "it's super easy to ask questions, and can be super hard to answer many of them". So the economy of the conversation gets out of whack fast.
From reading the text of the article, and the direct quotes, I'm also unclear on why they booed him.
My guess is because of what he's done, or at least perceived to have done, in the area of AI. Because what he said (at least to me) didn't seem boo-worthy, but in the context of who is saying it, I can see it.
Put another way, if someone that the audience liked said the same things, its not clear the person would get booed.
Relatable! Or at least making me feel dumb (at times). Things that help me feel smarter are
* actually writing more on my own - created a personal blog just to get myself to write more
* upleveling my thinking - think more about problems and framing
* leverage my experience - guide (or sometimes force) the AI assistant to leverage my experience to avoid problems
* learning new things - rather than let AI just replace things I can do, I use AI to help me learn new things/technology faster than I would have pre-AI
I wonder lately, doesn't that all new knowledge push out the old knowledge? As in new things replace old things we know. I don't know any studies on this but do we have infinite capacity for knowledge?
What about retaining it? I catch myself asking AI wondering about random things that pop into my head, reading it, maybe using that knowledge once and later no longer remembering what it was.
Maybe if you use that knowledge in practice from the get go but projects get so complicated sometimes it seems like there is not enough space in my brain for things AI is learning me.
Knowledge memory doesn't really work that way, it is more like that it is constantly fading unless re-imprinted by use and learning new things is just imprinting new knowledge on top. The new knowledge will form connections with the old knowledge which will help keep some of it from fading, but not all.
Another way of looking at what you said is that the practicing the new knowledge takes the place of practicing the old knowledge. So it isn't the knowledge that is replaced, but the learning (imprinting).
New knowledge doesn't necessarily push out old knowledge, and we probably don't have infinite capacity for knowledge. That being said, at least in my experience, the time when new pushes out old is when old is less useful than new.
Retaining (again just speaking for myself) requires actually using / applying the knowledge at some point within some timeframe of learning it. Otherwise yeah it fades to the point of disappearing over time.
Will it be is a different thing though. And if it’s not, who exactly is accountable?
With funds and portfolio managers that run them, there’s a clear accountability model (if the fund sucks, the manager loses their job and the company loses credibility)
With AI agents doing the management, who is accountable when the fund sucks? If it’s the customer, we’ve moved accountability from someone who at least in theory, knows what they’re doing to someone who has little to no clue.