AI로 모든 것을 자동화했더니 직원이 세 배로 늘었다

You prompt AI to do something, it blows your mind, you feel inadequate, you feel like,"Oh my god, this thing is going to take my job." AI에게 뭔가를 시키면, 깜짝 놀랄 일이 일어나요. 부족함을 느끼고, 이런 생각이 들죠, '오 맙소사, 이 And then it stops working and it looks back at you and says,"What should I do next?" 그러다가 AI가 멈추고 당신을 돌아보며 묻죠, '다음에 뭘 해야 하나요?' The further away an agent gets from a human, the less valuable it is. 에이전트가 인간에게서 멀어질수록, 그 가치는 줄어들어요. If you just ride the models, you're going to be fine. 모델의 흐름을 잘 타면, 분명히 잘 될 거예요. If you care about leading a really ambitious life, I truly think that this is going to make that more possible for more people. 정말 야심찬 삶을 살고 싶다면, 이게 오히려 그 꿈을 더 쉽게 이루게 해줄 거라고 진심으로 생각해요. Every is the only subscription you need to stay at the edge of AI. Every는 AI의 최전선에 머물기 위해 필요한 유일한 구독 서비스예요. If you care about being on top of the latest models and using latest tools, you have to subscribe to Every to separate out the signal from the noise. 최신 모델과 최신 도구를 파악하고 최전선에 있고 싶다면, 잡음 속에서 신호를 걸러내기 위해 Every를 구독해야 해요. Go to every.to/subscribe today. 오늘 every.to/subscribe로 가보세요. So, we are here uh because we're going to flip the script a little bit. 자, 저희가 여기 온 건, 이번에 좀 방식을 바꿔볼 거거든요. I am going to be interviewing Dan Sick. 제가 Dan Shipper를 인터뷰할 거예요. uh about the piece that he published yesterday, May 21st. 어, 그가 어제, 5월 21일에 발행한 글에 대해서요. Uh and we're going to try to understand why he wrote it, what's underneath his reasoning for it. 어, 그가 왜 그걸 썼는지, 그 논리의 밑바탕에 뭐가 있는지 이해해보려고 해요. There's going to be some conflict. 좀 갈등이 생길 것 같아요. I'm going to I'm going to fight with him on it. 제가 그와 좀 싸울 거예요. Let's fight. 한번 싸워봅시다. and see, you know, bringing some of my opinions, which are more or less aligned, but uh trying to understand does this is this piece going to reflect the future in 10 years, in 5 years? 그리고 제 의견도 좀 가져왔는데, 대체로 비슷하지만, 어, 더 깊이 이해해보려고요. And who are you again? 그리고 당신은 누구죠? Um I'm Brandon. 음, 저는 Brandon이에요. I'm our COO. 저는 COO예요. And that's it. 그게 다예요. So, the piece is called After 그래서 그 글의 제목은 After예요. And it it comes from this feeling that I have. 그리고 그건, 제가 느끼는 감정에서 나온 거예요. And there's a video about this, and there's there's a piece, but just for people who who have not seen either of those things. 이에 대한 영상도 있고, 글도 있는데, 아직 보지 못한 분들을 위해 간단히 설명하자면요. It comes from this feeling that I have that at Every, we are as AI native, as agent native, as it as you as it gets, you know, if you swing a stick around in our Slack, you're you're as likely to hit a human as you are an Every에서 우리가 AI 네이티브이고, 에이전트 네이티브이고, 그것만큼은 분명하다는 느낌에서 나왔어요. [snorts] [콧웃음] Everyone's using Claude Code and codex and all these tools to do their job every day. 모두가 Claude Code와 Codex, 그리고 이런 도구들을 매일 업무에 쓰고 있어요. Um and yet it feels like there's more human work to do than ever. 어, 그런데도 해야 할 인간적 업무가 그 어느 때보다 더 많아진 것 같아요. And in fact like since the GPT-3 days like we've grown from four people to like 30 people and we're hiring more now. 사실 GPT-3 시절부터 우리는 4명에서 30명 정도로 성장했고, 우리는 And so it came from me looking at that and then looking at the environment and being like what's going on because the whole information environment if you look at Dario is out there saying like half of entry-level white-collar jobs may be wiped out. 그래서 그걸 보면서, 또 주변 환경을 보면서 도대체 무슨 일이 일어나고 있는 건지 생각하게 됐어요. Even even people like um like Ken Griffin from Citadel is like you can tell he just had this moment where someone showed him an AI doing like an advanced data or finance question and he was like holy [ __ ] like that's what I would pay PhDs to do for me and it just did it. Ken Griffin 같은 사람도, Citadel의, 딱 이런 순간을 맞이한 것 같았어요. And I feel like I'm watching a lot of people who maybe don't have a ton of experience with agents and don't have a ton of experience with the curve of improvement that we've been riding for the last like three three and a half years hit it for the first time and then come to all these conclusions about 에이전트나 이런 것들에 경험이 많지 않은 분들이 많은데, 그들을 보면서 느끼는 게 있어요. oh my god like all work is going away we're not going to have jobs and I'm just like sitting here being like actually your intuitions when you first see a technology like this are usually very 오 맙소사, 모든 일이 사라지는 거야, 직업이 없어지는 거야 하는데, 저는 그냥 여기 앉아서 And we've seen a lot over and over again over the years that 그리고 우리는 수년에 걸쳐 반복적으로 봐왔어요. Every is a very good bellwether for where things are going because it's a it's a group of early adopters we have people in doing all sorts of work internally at Every and if something works here it there's a good bet that it's going to spread to other other places other other businesses that are that are adjacent to ours. Every는 앞으로의 흐름을 잘 보여주는 좋은 지표예요. 얼리어답터들의 그룹이거든요. And so when I look around at Every I I see so much automation and I also see way more human work. 그래서 Every를 살펴보면, 엄청난 자동화도 보이고, 동시에 훨씬 더 많은 인간의 업무도 보여요. So I was really um the the whole piece is saying here's the current state of work with agents. 그래서 저는 정말, 그 글 전체가 에이전트와 함께하는 현재의 업무 상태를 담고 있어요. And then pulling apart that paradox and sort of explaining why does why does more automation mean more work? 그리고 그 역설을 분석하면서 왜 더 많은 자동화가 더 많은 일을 만들어내는지 설명하는 거예요. Yeah, when I read the piece it was there wasn't like an explicit call to action in it, but I sort of felt this call to action of like there is actually a massive amount of hope right now in a world that is filled with a lot of doomers. 네, 제가 그 글을 읽었을 때 명시적인 행동 촉구는 없었지만, 그래도 뭔가 And um and this is why. 그래서요. Um 음 [snorts] [콧웃음] but I am going to come out of the gate and ask you a devil's advocate question. 하지만 저는 바로 악마의 변호인 질문을 드리려고 해요. Which is a couple hours before you publish this piece the CEO of ClickUp came out with this long tweet about why he fired 8,000 people and 3,000 people some 당신이 이 글을 발행하기 몇 시간 전에 ClickUp CEO가 긴 트윗을 올렸거든요. What? 뭐라고요? I don't I don't think it was 8,000. 8,000명이었다고는 생각 안 해요. I was 20,000 people. 20,000명이었어요. What I mean 제 말은 [laughter] [웃음] I think it was like 3,000. 3,000명 정도였던 것 같아요. an entire economy. 경제 전체나 다름없네요. like 22% of of his workforce. 직원의 약 22% 정도요. I don't think it was in the thousands, but yes, it was it was a 수천 명이라고는 생각 안 해요. 하지만 맞아요, 그건 it was a lot of his workforce. 직원의 상당수였어요. Yeah. 네. Yeah. 네. So my question to you is um in a business like every we're growing super fast. 그래서 질문이 있어요. Every 같은 회사에서, 우리는 엄청나게 빠르게 성장하고 있잖아요. Um what you wrote makes a lot of sense to me. 어, 당신이 쓴 것들이 저한테는 많이 공감이 돼요. And what you wrote theoretically makes a ton of sense in that AI is not autonomous right now. 당신이 이론적으로 쓴 것들이 정말 말이 되는데, AI가 지금 자율적이지 않다는 거잖아요. It has to be told what to do and then has to be checked. 뭘 해야 할지 알려줘야 하고, 그 다음에 확인을 해야 하는 거고. We need to have that that sandwich that you described in in the piece. 당신이 그 글에서 묘사한 그 샌드위치 구조가 필요하다는 거죠. But in a business that is 8,000 people, 10,000 people that is mature and has built ways of managing like SOPs for managing their business does this manifesto and this thesis still hold true? 하지만 8,000명, 10,000명 규모의 성숙한 기업에서 오랫동안 특정 방식으로 관리해왔다면 It's a 그건 It's a really good question. 정말 좋은 질문이에요. Um 음 Are there a couple 몇 가지 There are a couple different questions here. 여기에 몇 가지 다른 질문들이 있어요. The first thing I want to do is like lay out the argument. 제가 먼저 하고 싶은 건 논거를 정리하는 거예요. Why does Why does automation make more work? 왜 자동화가 더 많은 일을 만들어낼까요? I'm sure many people listening to this also haven't read it. 이걸 듣는 많은 분들도 아직 못 읽으셨을 것 같아요. So take a second to explain that in detail. 그러니 잠깐 자세히 설명해주세요. I will do that. 그렇게 할게요. So basically, the idea is the way that AI works and the way it functions in the workplace is AI makes yesterday's expert competence cheap. 기본적으로, AI가 작동하는 방식과 직장에서 기능하는 방식은 이래요. AI는 과거의 전문가 능력을 만들어요. And by that I mean AI is trained on all of our outputs, all of the code and the writing and the design and and decision-making and everything that's ever been written. 그 말은, AI가 우리의 모든 결과물, 즉 코드, 글, 디자인 등 모든 것을 학습한다는 거예요. And it makes that available to everyone for very cheap. 그리고 그것을 매우 저렴하게 모두에게 제공해요. So uh you can 그래서, 당신은 So anyone now with a prompt can use yesterday's competence to solve a programming problem, build an app, or write a uh write a piece like I did, write a report, or uh or, you know, make a YouTube thumbnail. 이제 누구든 프롬프트 하나로 어제의 전문 역량을 활용해서 프로그래밍 문제를 해결하고, 뭔가를 만들 수 있어요. And the interesting thing is that when you do that when when expert competence is available for cheap, it gets really widely adopted. 그리고 흥미로운 점은, 전문적 역량이 저렴하게 제공될 때 무슨 일이 일어나냐면 So everyone starts to do it. 모두가 그걸 하기 시작해요. Everyone starts to like, you know, we see this internal 모두가 하기 시작하죠. 우리 내부에서도 이런 게 보여요. Everyone's making pull requests 모두가 풀 리퀘스트를 올리고 있어요. [ __ ] [비프음] this is crazy. 이거 말도 안 돼요. Yeah. 네. And and and like I'm making pull requests and ops people are making pull requests and you know, engineers are like writing essays and you know, there's all this line crossing basically for non-experts to do the thing that experts used to do. 그리고, 저도 풀 리퀘스트를 올리고, 운영팀 사람들도 올리고, 엔지니어들도 올리고 있어요. And that feels very threatening to experts. 그게 전문가들한테는 정말 위협적으로 느껴지죠. They're like,"Well, what's my job going to be now?" 그들은 이렇게 생각해요, '이제 내 역할이 뭐지?' Mhm. 맞아요. And [snorts] what's interesting about that is because these tools are trained on outputs, are trained on yesterday's data the stuff that they do is uh with with default prompt with is it uh the stuff that they do with a default prompt all looks kind of similar and is all kind of right for the current situation, but it's like not actually totally right. 그리고 [콧웃음] 흥미로운 건, 이 도구들이 결과물로 학습되고, 결과물로 훈련되기 때문에 And so what happens is you sort of like flood the zone with tons of stuff that's like close, but not quite right. 그래서 비슷하지만 딱 맞지는 않는 것들로 가득 차버려요. And then you need to basically like and and well 그러면 기본적으로 해야 하는 건, 그리고, 음 There's a 그게 있어요. There's a lot of that at every too. Every에서도 그런 일이 많아요. There's a lot of 정말 많아요. There's a lot of people doing what seems like great work and then you go under the hot under the hood and you're like, this isn't quite right. 대단한 작업처럼 보이다가 막상 뚜껑을 열어보면, 어 Maybe like the expert should do it. 아마도 전문가가 해야 할 것 같아요. Yeah. 네. Yeah. 네. Yeah. 네. Exactly. 정확해요. Uh me for example. 어, 제 경우를 예로 들면요.