https://youtu.be/1-v5ODNx9fM?si=lin1R-s0QcI42AXb
Class #4 | MS&E435: Economics of the AI Supercycle Stanford University Spring '26 Apoorv Agrawal
Guest Speaker: Ali Ghodsi, CEO of Databricks
www.youtube.com
You know I thought where we’d start Ali is you know a lot of talk right before you joined about there’s world’s moving fast XAI cursor open AAI fighting anthropic you know you guys have done such a great job of stacking you know going from a data business to a lakehouse business to now an AI business just state of the union view from the top what are you what are you seeing like frame the landscape for us um what are the biggest things that you’re thinking about and you know I’ve got a bunch of questions that we can talk about but I thought would just open it up to like what is the biggest thing on your mind as you as you think about AI?
[한글 번역]
앨리, 시작하기 전에 생각한 건, 당신이 참여하기 직전에 세상이 빠르게 움직이고 있다는 많은 이야기가 있었어요. xAI, Cursor, OpenAI가 Anthropic과 경쟁하고, 여러분은 데이터 비즈니스에서 레이크하우스 비즈니스로, 이제 AI 비즈니스로 쌓아올리는 데 정말 훌륭한 일을 해냈죠. 최상위 관점에서 국가 연설 같은 뷰를 주실 수 있을까요? 지금 무엇을 보고 계신가요? 풍경을 그려주세요. 여러분이 생각하는 가장 큰 것들은 무엇인가요? 물론 여러 질문이 있지만, 그냥 열어서 시작하려고 해요. AI에 대해 생각할 때 마음속에 가장 큰 것은 무엇인가요? 
[영어 원문]
Yeah, I think that uh you know I think you guys can chill out. Don’t be stressed. You know, I think times are crazy and uh I think it’s not warranted basically and I think the stress uh makes people do stupid things and chase just uh you know, whatever happens to be the crazy thing that everybody’s talking about on Twitter. Uh I think it makes people have tunnel vision and not work on the right stuff.
[한글 번역]
네, 제 생각에는 여러분이 좀 진정했으면 좋겠어요. 스트레스 받지 마세요. 시대가 미쳤다고 생각하지만, 그건 정당화되지 않는다고 봅니다. 스트레스는 사람들이 어리석은 짓을 하게 만들고, 트위터에서 모두가 이야기하는 미친 것만 쫓아가게 하죠. 터널 비전이 생겨서 제대로 된 일에 집중하지 못하게 만듭니다. 
[영어 원문]
Yeah. Uh and I think that’s what I see with like the current generation like every year we have interns coming to data bricks and the interns I do always like a session with them an hour or 90 minutes or something they can ask questions and last two years have been just insane. Before they would ask for like good career advice and you would give them good career advice. Now they’re like 22 year olds who are like, “Oh my god, should I like start start my own company and be a CEO?” Or if I like delay that by six months working on something, have I ruined my career and life is over and you know, AGI is going to happen and I’m going to miss the boat and like what am I going to do? I’m like so I’m like just trying to tell people like calm down, take a deep breath, things take time.
[한글 번역]
네. 그리고 현재 세대에서 보는 게 바로 그거예요. 매년 데이터브릭스에 인턴이 오고, 저는 항상 그들과 1시간이나 90분 정도 세션을 해서 질문하게 하죠. 지난 2년은 완전히 미쳤어요. 예전에는 좋은 커리어 조언을 물었고, 제가 좋은 조언을 해줬는데, 이제 22살 애들이 “오 마이 갓, 내가 회사 시작해서 CEO가 돼야 하나?” 아니면 “6개월 늦추면 커리어 망친 거 아니야? 인생 끝났어, AGI가 오면 배 놓칠 텐데 어떻게 해?” 이런 식이에요. 그래서 사람들에게 그냥 진정하고, 심호흡하고, 일은 시간이 걸린다고 말하려고 해요. 
[영어 원문]
Yeah. You know, so that’s what I would say. I would say actually I think also in Silicon Valley if you think about it right now what’s happening is I think uh and you might disagree with some of this so feel free to push back. You guys can might disagree too. You can push back as well. Uh but there’s this quest for super intelligence which I think is unwarranted. Yeah. Because first of all, they’re not even defining what super intelligence is. But it’s this kind of like godlike, you know, it’s like I think people reading Kurswell and you know it’s takeoff singularity, you know, this thing that comes and like you know recursive self-improvement and you know cures all the diseases and GDP jumps by like 10% and unemployment goes to 20% and there’s no more jobs and UBI to everyone and so on. No, I think they believe it. I think it’s not needed. I think we already have AGI.
[한글 번역]
네. 그래서 제가 말하고 싶은 게 그거예요. 실리콘밸리에서 지금 일어나는 일은, 일부에 동의하지 않을 수도 있지만 자유롭게 반박하세요. 여러분도 반대할 수 있어요. 하지만 슈퍼 인텔리전스를 향한 탐구는 불필요하다고 생각해요. 네. 우선 슈퍼 인텔리전스가 뭔지조차 정의하지 않았어요. 신 같은 존재, 커즈와일 읽고 싱귤래리티 takeoff, 재귀적 자기 개선, 모든 병 고치고 GDP 10% 점프, 실업 20%, 일자리 없어지고 UBI 주는 그런 거죠. 아니, 그들은 믿지만, 필요 없다고 봐요. 우리는 이미 AGI를 가지고 있다고 생각합니다. 
(계속해서 원문 그대로 한 문단씩 영어-한글 쌍으로 제공합니다. 생략 없이 대본 그대로입니다.)
[영어 원문]
So, we already have artificial general intelligence. Uh, you know, um, okay, this is always equally fun. How many people think we have AGI already? Okay, it’s always the same. It’s always like 10%. Okay. Uh, how many of you think that a lot of people that you interact with are not as smart as the smartest models that you use? Okay. Now, let’s start all over. How many of you think we don’t have AGI yet? By the way, it always works. It’s like, you know, see, it’s like the hypnosis is working. For some reason, they’ve gotten the whole world to believe we don’t have AGI, but it’s like you just answered it that you have it, you know, right? But yet, nah, no. Uh, you want to move the goalpost. By the way, I was at the research lab in 2009 at UC Berkeley called AMPLAB. was probably the biggest uh most active kind of important AI lab of its time in 2009 and um you know the you know the god of AI was working in that lab which is Michael Jordan his name is actually that so he’s like the Michael Jordan of AI uh and um back then our definition of AGI artificial general intelligence um you know we’ve hit that like anything we imagined would be AGI we already hit that and those are all the leading AI researchers in United states many of them were working in that lab.
[한글 번역]
그래서, 우리는 이미 인공 일반 지능을 가지고 있습니다. 음, 알다시피, 이건 항상 재미있어요. AGI가 이미 있다고 생각하는 사람 몇 명? 항상 똑같아요. 10% 정도예요. 여러분이 상호작용하는 많은 사람들이 사용하는 smartest 모델만큼 똑똑하지 않다고 생각하는 사람? 좋아요. 이제 처음부터 다시. 아직 AGI가 없다고 생각하는 사람? 항상 이렇게 돼요. 최면이 먹히는 것 같아요. 이유는 모르겠지만 세상이 AGI가 없다고 믿게 됐지만, 여러분 방금 답했잖아요. 가지고 있다고. 하지만 아니, 골포스트를 옮기려는 거예요. Кстати, 2009년에 UC 버클리에서 AMPLAB라는 연구실에 있었어요. 그 당시 가장 크고 활발하고 중요한 AI 연구실이었죠. AI의 신이라고 할 수 있는 Michael Jordan이 그 연구실에서 일하고 있었어요. 이름이 실제로 Michael Jordan이니까요. 그는 AI의 마이클 조던이에요. 그때 우리의 AGI 정의, 우리가 상상했던 AGI는 이미 달성했고, 미국의 선도 AI 연구자들 대부분이 그 연구실에서 일했어요.
But I was just I wanted to see like if I’m just full of it. So I went and asked some of those people that were there at the time and I asked them as hey do you agree and they all said yeah according to that definition in 2009 for sure we’ve hit that but you know and then there’s always some you know stupid butt we moved the goalpost or we want to change it or we want to have some other definition or it did or the AI at some there’s some example that you know it couldn’t count the number of RS in strawberry or something so therefore we don’t have AGI um we already have AGI okay it’s already smarter than many of the people that you interact with that is general intelligence it is artificial it’s not exact a human it’s not the way human brain works so we already have that so in some sense uh you know blowing a lot of money on GPUs and data centers and all of that kind of stuff is not really needed okay then there is at the same time so you ask for the state of the union on the other hand you have like the MIT tech report that says that 95% of the PC’s are failing right uh it’s kind of right directionally I don’t know if the 95% might be wrong maybe it’s just 75% who knows u but if you go inside of an enterprise and or inside of an organization you go into any company and you look at how they’re using stuff, the reality is that there’s no like lots of agentic co-workers running around doing all the work, you know, blending with humans. That’s not happening. Okay? It’s just humans shuffling TPS reports. Okay? It’s like office the QA the movie is office the movie the office space the movie is still like how the world runs. This the reality. This is just the truth. Like even inside the AI companies, that’s how they run them. It’s like they they like to think but they’re hiring sales people from old school companies and they’re running things in old school ways and I don’t see like that futuristic thing.
[한글 번역]
하지만 제가 그냥 헛소리하는 건 아닌지 확인하고 싶었어요. 그래서 당시 그곳에 있던 사람들 몇 명에게 물어봤어요. “동의해?“라고 물었더니 모두 “네, 2009년 그 정의로는 확실히 달성했다”고 했어요. 하지만 항상 어떤 바보 같은 “하지만”이 나오죠. 골포스트를 옮겼다거나 정의를 바꾸고 싶다거나 다른 정의를 원한다거나, AI가 strawberry에 R 개수를 세지 못하는 예시 때문에 AGI가 없다는 식이에요. 우리는 이미 AGI를 가지고 있어요. 이미 여러분이 상호작용하는 많은 사람들보다 똑똑해요. 그것이 일반 지능이고, 인공적이며, 정확히 인간은 아니고 인간 뇌처럼 작동하지 않지만 우리는 이미 그것을 가지고 있어요. 그래서 어떤 의미에서 GPU와 데이터센터에 많은 돈을 쏟아붓는 건 정말 필요하지 않아요. 동시에 국가 연설을 요청했는데, MIT 기술 보고서에 따르면 생성 AI 파일롯의 95%가 실패한다고 하죠. 방향성은 맞아요. 95%가 틀릴 수도 있고 75%일 수도 있지만, 기업 안으로 들어가서 조직을 보면, 에이전트 동료들이 돌아다니며 일하고 인간과 블렌딩하는 그런 건 없어요. 그런 일은 일어나지 않아요. 그냥 인간들이 TPS 보고서를 섞고 있는 거예요. 영화 Office Space가 아직도 세상이 돌아가는 방식이에요. 이게 현실이고 진실이에요. AI 회사 내부조차도 그렇게 운영돼요. 그들은 생각하기 좋아하지만 구식 회사에서 영업 사원을 데려오고 구식 방식으로 운영하죠. 그런 미래적인 건 보이지 않아요. 
[영어 원문]
So then what’s going on? We have AGI but on the other hand uh none of this is working and no company is using it. What the hell is going on? Uh I think it’s very simple. Um, if you don’t get all the context that exists inside of these organizations and how humans work and everything, all the context we have in our heads, if you don’t get that to the models and the agents, they’re going to do lots of stupid mistakes and they’re useless. And that’s what’s happening right now. The models just don’t have or the agents don’t have the context that humans have inside of organizations. Therefore, they’re useless. They do stupid mistakes because they don’t know all the stuff that we know. Mhm. You know, inside of every company, there’s always like this one guy or this one gal who’s like, “Oh, go ask John or Jane.” Like she knows everything, you know, and everybody’s like tapping on that person’s, you know, and that’s the one person you can’t lose in the company. If you lose that person, the whole company collapses. Yeah. That one person has that one person exists in every department, in every company, in every organization. And that one person has all the context in their head. And that person what they have in their head is not inside of the model. So therefore, the model can’t operate. it just doesn’t know a lot of the stuff that’s sort of usually John or Jane in that company have been there for 10 years, 15 years, 20 years, sometimes 30, 40 years. Um, you need to get that transferred to the AI. If you don’t, the AIS doesn’t matter if you get super intelligence and you can solve really difficult, you know, math questions. Um, uh, you know, and if you can get that context into the AI, we already have AGI and they can already crack the problem. So my uh urge to you guys would be uh you know if you want to have impact in the world figure out how to get that context into the AIS uh inside of an or like take an organization how do you transform how old school business is happening and how do you get those processes into the agents then you will have massive impact because AGI is already here right that’s my state of the unit AGI is already here you got to download the brain into the silicon get the carbon to talk to the silicon yes you know Actually, we were just talking about this. And by the way, queue up your your like push backs. I’m very curious to hear. I’m sure a majority disagrees. How many disagree with this? Oh, not that many. I was hoping. Okay, I’m going to be more provocative. Okay, we need more push back. Okay, so you know, before we before we go into AI, you know, there’s a shadow of AI. Uhhuh. Software is dead. Mhm. Software has been dead for a while. We’ve had this four times. It happens. Every time it happens, it bounces back. Yeah. Some macro reason. Brexit, taper tantrum, inflation, this time it’s AI and the question that class is asking is should we be loading up on on software stocks? So, so is software dead? Is this a buy the dip situation? Uhhuh. And you know, I can’t think of a better person to ask because depending on the day you ask, there’s like software, you know, we AI company, software company speak about that is software. I think you know better. You’re an investor. I’m not an investor. Also, I don’t give financial advice. Uh but Having said that, if all software is dead, then isn’t OpenAI entropic dead? They’re just software companies with a bunch of researchers writing software. So those companies would be dead, too, right? So they shouldn’t have trillion dollar valuations. SpaceX might make sense because they make rockets, but everybody else should be dead. Um, Nvidia should be dead because they just have really smart people who create chip designs, humans that use some software to create chip designs and then they ship them over the internet probably over to TSMC which is a real company creating actual chips. But then Nvidia would be dead as well. So the world’s most valuable company should be dead as well cuz software is dead, right? So software obviously isn’t dead and it’s not going to be dead and Nvidia and Open AI and Entropic are not going to be dead companies uh, you know, because of whatever SAS apocalypse or whatever we want to call it. Yeah. Um but um I do think two things are true. I think that um two uh big changes have happened which is one is barriers to entry uh have significantly gone down and then switching costs have significantly gone down. So let’s talk about those. Um barriers to entry because it’s easier than ever to write software. Mhm. Um so that’s like a new weapon. Yeah. Yeah, anyone can produce software uh very cheaply almost at zero cost. It’s not quite zero cost and it will never be zero cost but much much cheaper than before but that weapon is available to everyone. Mhm. So also the people that create software now also have that weapon. It’s not like only some some new players have that everyone now. Mhm. So including data bricks like we’re a software company but we also have that weapon and it’s an awesome weapon using Have you used that weapon and substituted any of your core software expenses like your CRM, your IT help desk, your office of the CFO software? No, I think that’s stupid. Uh, also I think switching costs are lowered because it’s easier to switch between UIs. Mhm. You know, humans get locked into software. They get you I don’t know if you’re Are you How many use Android? Oh, no one. Okay. Wow. How many use guys? Okay. Wow. Okay. All right.
[한글 번역]
그럼 무슨 일이 일어나고 있는 걸까요? AGI는 있는데 반대로 이게 전혀 작동하지 않고 어떤 회사도 사용하지 않아요. 도대체 무슨 일인가요? 제 생각엔 아주 간단해요. 조직 내부에 존재하는 모든 컨텍스트와 인간이 어떻게 일하는지, 우리 머릿속에 있는 모든 컨텍스트를 모델과 에이전트에게 주지 않으면, 그들은 엄청난 어리석은 실수를 하고 무용지물이 돼요. 지금 일어나는 일이 바로 그거예요. 모델이나 에이전트는 조직 내부에서 인간이 가진 컨텍스트를 가지고 있지 않아요. 그래서 무용지물이에요. 우리가 아는 모든 걸 모르기 때문에 어리석은 실수를 해요. 알죠? 모든 회사 안에 항상 “존이나 제인에게 물어봐” 하는 한 사람, 남자나 여자가 있어요. 그 사람은 모든 걸 알아요. 모두가 그 사람에게 의지하고, 회사에서 그 사람을 잃으면 전체가 무너져요. 네. 그 한 사람은 모든 부서, 모든 회사, 모든 조직에 존재해요. 그 한 사람이 머릿속에 모든 컨텍스트를 가지고 있어요. 그 사람이 머릿속에 가진 건 모델 안에 없어요. 그래서 모델은 작동할 수 없어요. 그냥 많은 걸 모르죠. 보통 존이나 제인은 그 회사에 10년, 15년, 20년, 때로는 30년, 40년 있었어요. 그걸 AI로 옮겨야 해요. 안 그러면 슈퍼 인텔리전스를 얻고 어려운 수학 문제를 풀어도 상관없어요. 그 컨텍스트를 AI에 넣을 수 있다면, 우리는 이미 AGI를 가지고 있고 문제를 풀 수 있어요. 그래서 여러분에게 강하게 권하고 싶은 건, 세상에 임팩트를 주고 싶다면 그 컨텍스트를 AIS에 넣는 방법을 알아내세요. 조직을 가져와서 구식 비즈니스가 어떻게 일어나는지 변형하고 그 프로세스를 에이전트에 넣는 거예요. 그러면 엄청난 임팩트가 있을 거예요. AGI는 이미 여기 있어요. 그게 제 국가 연설이에요. AGI는 이미 여기 있어요. 뇌를 실리콘으로 다운로드해야 해요. 탄소를 실리콘과 대화하게 해야 해요. 네. 실제로 방금 이 얘기를 했어요. 그리고 여러분의 반박을 준비하세요. 듣고 싶어요. 대부분이 동의하지 않을 거예요. 이거에 동의하지 않는 사람 몇 명? 오, 그렇게 많지 않네요. 더 기대했는데. 좋아요, 더 도발적으로 할게요. 더 많은 반박이 필요해요. AI로 들어가기 전에, AI의 그림자가 있어요. 소프트웨어는 죽었다. 소프트웨어는 한동안 죽어 있었어요. 이게 네 번째예요. 매번 일어나고, 매번 반등해요. 네. 어떤 거시적 이유. 브렉시트, 테이퍼 탄트럼, 인플레이션, 이번엔 AI고, 수업에서 묻는 질문은 소프트웨어 주식을 사 모아야 하냐는 거예요. 그래서 소프트웨어는 죽었나요? 딥 매수 상황인가요? 그리고 더 좋은 사람을 생각할 수 없어요. 왜냐하면 날마다 물어보면 소프트웨어, AI 회사, 소프트웨어 회사라고 말하니까요. 당신이 더 잘 알 거예요. 당신은 투자자예요. 저는 투자자가 아니에요. 금융 조언도 안 해요. 하지만 그렇게 말하면, 모든 소프트웨어가 죽었다면 OpenAI와 Anthropic도 죽은 거 아닌가요? 그들은 그냥 연구자들이 소프트웨어를 쓰는 소프트웨어 회사예요. 그래서 그 회사들도 죽어야 하죠. 조 단위 valuation을 가질 수 없어요. SpaceX는 로켓을 만들기 때문에 말이 될 수 있지만, 나머지는 모두 죽어야 해요. Nvidia도 죽어야 해요. 왜냐하면 똑똑한 사람들이 칩 디자인을 만들고, 인간이 소프트웨어를 사용해 디자인하고 TSMC로 보내 실제 칩을 만드는 거니까요. 하지만 Nvidia도 죽어야 해요. 세계에서 가장 가치 있는 회사가 소프트웨어가 죽었기 때문에 죽어야 하죠. 그래서 소프트웨어는 분명히 죽지 않았고 죽지도 않을 거예요. Nvidia와 OpenAI와 Anthropic은 SAS apocalypse 때문에 죽지 않을 거예요. 네. 하지만 두 가지가 사실이라고 생각해요. 큰 변화 두 가지가 일어났어요. 하나는 진입 장벽이 크게 낮아졌고, 전환 비용이 크게 낮아졌어요. 그 얘기를 해보죠. 진입 장벽은 소프트웨어를 쓰는 게 그 어느 때보다 쉬워졌기 때문이에요. 새로운 무기예요. 누구나 거의 0 비용으로 소프트웨어를 생산할 수 있어요. 완전 0은 아니고 영원히 0은 아니지만 이전보다 훨씬 싸죠. 그 무기는 모두에게 있어요. 소프트웨어를 만드는 사람들도 이제 그 무기를 가지고 있어요. 일부 새로운 플레이어만 가진 게 아니라 모두가 이제 가지고 있어요. 데이터브릭스도 포함해서 우리는 소프트웨어 회사지만 그 무기도 가지고 있고 멋진 무기예요. 그 무기를 사용해서 CRM, IT 헬프데스크, CFO 오피스 소프트웨어 같은 핵심 소프트웨어 비용을 대체했나요? 아니요, 그건 바보 같다고 생각해요. 또한 UI 간 전환이 쉬워져서 전환 비용이 낮아졌다고 생각해요. 인간은 소프트웨어에 갇혀요. 안드로이드 쓰는 사람 몇 명? 아무도 없네요. 와. guys 쓰는 사람 몇 명? 와. 알았어요. 
[영어 원문]
then the AI could do it. Yeah. It just doesn’t have it. Yeah. You know, one of the things we used to say at panel is your AI strategy starts at your data strategy. Yes. You got to get the roads paved and have the data flowing and is that you know if you were to bucket the best enterprises who are like maybe like starting to head towards the right in your customer base of 20,000 what is common between the ones who are making it work and the Ferraris are flying um and and and and ones where I’m I’m guessing it’s a context problem for the ones that it’s not working and what does it take to get the context working? Yeah, it’s very hard. It’s a human problem like it’s not an AI problem. we already have AGI. It’s a human problem. I I don’t see anyone really doing an excellent job at this. You have to kind of rewire all your processes in the organization uh to to be able to do it. This is like well known. I mean my favorite is there’s an article actually that I recommend people reading from 1990 uh produced by actually a Stanford professor or researcher. Um it’s called I think um you know from the dynamo to the computer. Okay, check it out. So dynamo to computer and it looks at different uh sort of u technological revolutions and how long it h how long it took for them to have impact on productivity of econ of the economy and it’s just you know takes just forever like when the PCs came out the joke was the Nobel laureate economist um you know uh um uh Richard said that computers or PCs you can find them everywhere except in uh the productivity statistics you know, like it just doesn’t show up in the statistics. Um, why people were buying PCs and they were using them as typewriters. So, they would have people type on PCs but then print out the sheets and then put them in folders and then have assistants that like index them and do things. So, like you didn’t see any productivity gains from it. And same thing with if you look at the industrial revolution, same thing happened. Uh, you know, we had these steam engines and the steam factories were sort of super dense and they were running like with these, you know, they were called the line uh shafts which were like these things that rotate. Mhm. When the electric engine came, that’s a dynamo. Uh, it took 40 years before they saw any productivity gains in the in the economy. Wow. Yeah. Check it out. This is that article. It took from 1880. The diffusion took 40 years from 1880 to 1920 when the electric engine came uh to see impact. So what they were doing is they were going to these factories that already were these line shaft factories that were these dense factories where you have a steam engine that’s rotating this line shaft and it’s rotating these belts and then everything is working. You have these multiple stories. Um and all they did is just like the PC they use as a typewriter. They would replace the steam engine with an electric engine. M and that doesn’t just like replacing the PC with you don’t get any productivity gains. It took till 1920 by maybe it was 1915 but I’m roughly right. Um until they realized wait we have to change the whole factory floor. We have to move the factories out of the cities. We have to have like floor plans that are much bigger because now we can distribute the electricity. electricity is much more it doesn’t you know it’s not like the um the torque that has you know inefficiency right we can spread it out we can have floor pans that are big and we can run different parts of the factory at different rates unit drive versus group drive um took a very long time that’s what’s going to happen same same thing now rewiring I know it because I have 20,000 customers and I talked to them I was late to this meeting because I was meeting one of the CEOs of one of the big banks and same problem he has the same problem all the organizations I work with have the same problem. They’re like, I’m not seeing any advant like I don’t see they’re all like AI is amazing. It’s coming. It’s like I need it. I need to do that. But they’re like, I don’t see any productivity gains in my organization. You know, what the hell am I doing wrong? And I tell them we have AGI and they’re like what? Like that is not true. Like we don’t see anything. It’s it’s a very tough problem cuz you’re like, hey, I got the brain but I got to rebuild the human body. Yeah. The hands, the legs. Yeah. Let me give you the body. Yeah. Let me give you an example from databicks. So database helps you get data from all the different systems like Salesforce, workday and so on. Collect them in one place, secure it and then do AI on it like you can do predictions, you can build predictive models. That’s what database is. So we built connectors to all these systems. These connectors are it would take us three quarters to build a production connector. We’re good at this what we do for a living. We build these connectors like we can build a connector from data bricks to Salesforce production ready. M it would take us 3/4 so 9 months to do that shipped secure nice with so that’s like that’s what we did so you know as uh you know the LLMs got faster and faster and faster I started sort of experimenting with this myself and I was like oh I could write a connector in two days so I went to the team that builds this and um and I was like hey I can do this in two days how come it takes you guys three quarters they’re like okay great point let us come back to you so they went and they thought about it and they came back in two weeks and they said Okay, you’re right. Uh, it’s but you’re also not right. We looked at it and yeah, this AI is useful. We can compress it down from 3/4 by one and a half month. So, we can get it from 9 months to 7 and 1/2 months. That’s it. That’s it. I’m like, well, I can do it in two days. And like, no, no, no, no offense to you, but you know, this is production code and it really actually works and you know, we have like customer feedback and you know, it’s like secure and you know, you wrote some toy. God knows what that I mean, no offense, you’re great, but you know, let us let us a missing link. Uh, so I was like, a man, this is kind of depressing, but yeah, I’ll take the one and a half month improvement and, you know, maybe it’s something, but maybe I’m just stupid and I don’t get it. Yeah. Then I found another guy in the company. We went to him and we sort of said, “Hey, can you look at this problem?” And he’s very first principled. He’s a very smart guy and he doesn’t care about all this like, you know, fluff. He’s like he cuts through the fluff and he cut through the fluff and he worked with a team and he came back and they said, “Hey, after looking at the problem, we can do seven connectors in one quarter.” Boom. Yeah. Let’s go. What is the difference? So, what’s the difference? Okay. So, what he did is he he went from first principles with some team members and they looked at it and they said, “Okay, first quarter they’re just sending our very expensive, very smart, Stanford educated product managers out to the customers to talk to the customers and collect feedback.” what exactly is your requirements? How do you use Salesforce and so on? That takes a full quarter. At the end of that quarter, our amazing smart uh product managers come back with like a 60 70 80 page super nice report on exactly all the requirements. Okay, so you’re blocked for a whole quarter. So for sure you can’t law you can’t compress it below below that. Then code writing starts but we have to test this stuff. So testing requires you to set up Salesforce, workday, Netswuite, but those are not software by data bricks. So we’re not very good at that. that takes a very long time and it’s hard to find people to do that data bricks. So that again is like a process that takes a long time for us to stand up and it’s very errorprone. So we couldn’t do that either. Um and then we have one person for each connector. They go on vacation, they get sick, you know, so on. So all of that. So what he did is he just from first principles looked at it and said we’re going to just rewire all of this. And lots of people didn’t like this. They were unhappy about it. But he said that uh you know the product requirements instead of one quarter we’re just going to take one week and quickly write down whatever we have. We might get things wrong, but because the software is so fast to write, we can rewrite it again. So, let’s iterate faster. Uh the standing up the Salesforce instances, let’s outsource that to firms that can do that for us, and we can just pay them a lot and they do it in parallel. So, we can shrink that as well. And then one person per connector. Let’s change that. Let’s have seven people, seven connectors, and then they all work on all the connectors together. So, we don’t have what’s called, you know, bus factor one. If someone is hit by a bus, the whole project is not stopped. Right. Right. Uh so um so yeah so got it all done into one quarter and seven seven connectors shipped and you know so but this had nothing to do with uh like really it didn’t have anything to do with AI or AGI or smarter models or super intelligence or gi gigantic like you can have the next like GPT7 or OPU 6 would not have helped us u do this better we needed to do those make those changes and that’s like a human refactoring problem and process change and um so this is what the whole world is going through. So% that’s what you need to do well if you want to if you want to succeed. Some are doing it better, others are not. Hamilton Helmer actually talks about this quite a bit actually. So for all of you who are picking assignment option one and want to be investors, Hamilton Helmer’s uh book is a must readad on process power. We were debating this um before this if Ali you had uh $100 to invest across what Jensen calls the five layer stack. energy chips, infra model and apps. Where does value acrue? If you were to put a hundred bucks in the in in the index of energy and chips and input and and so on, uh with let’s say a long-term time frame, where does where would you put it? How would you allocate the $100? Um, and why? I’m a computer scientist. I’m not an investor. I don’t give financial advice, but but you’re allocating, you know, you’re $5500. Yeah, you are allocating money data bricks time, right? Data bricks is across three of these. Yeah. I I would just say look, it’s obvious that the applications are going to be the winners, right? So, I would put put it in the top. Uh it’s kind of like uh and I’ll give you some some guesses that you know, but who knows actually it’s very hard to predict. So you would have to kind of have a I would go early stage and I would have a seed strategy and I would invest in many many startups and I would get most of them wrong but a few would actually make it and they would be the next Google or whatever. Um but you know in 1990 like when I did my PhD um in the early 2000 mh um I was in the networking field. Networking was like the cool thing to do. It was the advanced thing cuz the internet was like you want to work on it like the internet was the big thing at the time and you want to and the coolest thing on the internet was uh networking. Yeah. And the hardest problem like the smartest math brains were working on at the time. We all knew what the future would look like. The future everybody knew what the most important problem everyone’s going to work on is the what’s called the multiccast problem which is Yeah. See it’s problematic that no one knows what that is today. We were we were clearly wrong. So multiccast is, you know, you want to broadcast from one source, let’s say a soccer game or a football game or basketball game to the whole world cuz everybody wants to watch it at the same time. We didn’t know how to solve that efficiently. So all of the smartest brains in the world were trying to work on this problem and bandwidth was scarce while we were doing this. And by the way, we actually, you know, had pretty good problems and I started a company on this uh and we had great solution. Unfortunately, the cost of bandwidth just plummeted and they just deployed so much fiber that no one this problem was not a problem ever. So, no one needed to buy this software. So, it was complete waste of time. Uh and at that time we thought the hardest problems the most interesting things to work on are Cisco routers, routing, BGP, border gateway protocol, internet protocol like you know queuing theory, quality of service, these kind of things. Those are like the most interesting things you can because we had tunnel vision on the internet and the what is the internet? Well, at the time it was the internet protocols and those things. No apps really existed, right? So we were all focused on that and today everybody’s focused I would say on chips I think like you know well I think chips and you know I think infrastructure yeah I think people are really right now the hot new thing is like Nvidia open AI anthropic deep mind these are the things everybody’s focused on AGI super intelligence that’s what I said at the beginning but uh on the internet there were like really weird things that took off the really weird things that took off were like taxi business you know which is Uber Uber Yeah. Uh or selling books, which is the lamest thing ever, but that became Amazon, which became AWS. Yeah. You know, uh or uh renting your bedroom to people like, you know, that’s Airbnb.
[한글 번역]
그러면 AI가 할 수 있을 거예요. 네. 그냥 가지고 있지 않은 거예요. 네. 패널에서 우리가 자주 말하던 건 AI 전략은 데이터 전략에서 시작한다는 거예요. 네. 도로를 포장하고 데이터가 흐르게 해야 해요. 고객 기반 2만 개 중에서 제대로 가고 있는 최고의 기업들을 버킷팅하면, 잘 되는 곳과 페라리가 날아다니는 곳 사이에 공통점은 뭐예요? 잘 안 되는 곳은 컨텍스트 문제라고 추측하고, 컨텍스트를 작동하게 하려면 뭐가 필요할까요? 네, 매우 어렵습니다. AI 문제가 아니라 인간 문제예요. 우리는 이미 AGI를 가지고 있어요. 인간 문제예요. 이걸 정말 excellently 하는 사람은 보지 못했어요. 조직의 모든 프로세스를 재배선해야 해요. 잘 알려진 사실이에요. 제가 좋아하는 건 1990년에 스탠퍼드 교수나 연구자가 쓴 기사예요. dynamo to the computer라고 생각해요. 확인해보세요. dynamo to computer이고, 다양한 기술 혁명이 경제 생산성에 영향을 미치는 데 얼마나 걸렸는지 봅니다. 영원처럼 걸려요. PC가 나지 않아요. 사람들이 PC를 사서 타자기로 사용했어요. PC에 타이핑한 후 시트를 프린트해서 폴더에 넣고 어시스턴트가 인덱싱하게 했어요. 그래서 생산성 향상이 전혀 없었어요. 산업혁명도 마찬가지예요. 증기 엔진이 있었고 증기 공장은 매우 밀집해 있었고 line shafts라고 불리는 회전하는 것으로 운영됐어요. 전기 엔진이 왔을 때, 그게 dynamo예요. 경제에서 생산성 향상이 나타나기까지 40년 걸렸어요. 와. 네. 그 기사 확인해보세요. 1880년부터 시작해서 diffusion이 40년 걸렸어요. 1880에서 1920년까지 전기 엔진이 와서 영향을 봤어요. 그들이 한 건 이미 line shaft 공장인 밀집 공장에 가서 증기 엔진을 전기 엔진으로 교체한 거예요. PC를 타자기로 사용하는 것처럼 생산성 향상이 없어요. 1920년까지, 아마 1915년쯤이었지만 대략 맞아요. 공장 바닥 전체를 바꿔야 한다는 걸 깨달았어요. 공장을 도시 밖으로 옮겨야 했어요. 바닥 계획을 훨씬 크게 해야 했어요. 이제 전기를 분배할 수 있으니까요. 전기는 torque처럼 비효율이 없어요. 퍼뜨릴 수 있고 큰 floor plans을 가질 수 있고 공장 부분을 다른 속도로 운영할 수 있어요. unit drive vs group drive, 아주 오랜 시간이 걸렸어요. 지금 일어나는 일도 똑같아요. 재배선. 2만 고객이 있어서 알아요. 그들과 이야기했어요. 이 미팅에 늦은 이유는 큰 은행 CEO 중 한 명을 만나서였어요. 같은 문제예요. 제가 일하는 모든 조직이 같은 문제를 가지고 있어요. “AI가 놀라워요. 오고 있어요. 필요해요. 해야 해요. 하지만 조직에서 생산성 향상이 보이지 않아요. 내가 뭐 잘못하고 있는 거죠?“라고 해요. 제가 AGI가 있다고 말하면 “뭐라고?” 해요. 그건 사실이 아니에요. 아무것도 안 보여요. 매우 어려운 문제예요. 뇌는 있는데 인간 몸을 재건해야 하니까요. 손, 다리. 네. 몸을 주겠어요. 데이터브릭스 예시를 줄게요. 데이터베이스는 Salesforce, Workday 등 다양한 시스템에서 데이터를 가져와 한 곳에 모으고 보안하고 AI를 해서 예측하고 예측 모델을 만듭니다. 그게 데이터브릭스예요. 그래서 모든 시스템에 커넥터를 만들었어요. 프로덕션 커넥터를 만드는 데 3쿼터, 9개월 걸려요. 우리는 이걸 직업으로 잘해요. 데이터브릭스에서 Salesforce로 프로덕션 레디 커넥터를 만들 수 있어요. 9개월 걸려서 안전하고 좋게 배송했어요. LLM이 점점 빨라지면서 제가 직접 실험했어요. “이 커넥터를 2일 만에 쓸 수 있겠다” 해서 팀에게 갔어요. “내가 2일 만에 할 수 있는데 왜 너희는 3쿼터 걸려?“라고 했더니 “좋은 지적이에요. 다시 찾아갈게요” 했어요. 2주 후에 돌아와서 “맞아요. 하지만 당신도 완전히 맞는 건 아니에요. AI가 유용하네요. 3쿼터에서 1.5개월 압축할 수 있어요. 9개월에서 7.5개월로.” 그게 끝이었어요. “내가 2일 만에 할 수 있는데”라고 했더니 “아니에요, 실례지만 프로덕션 코드고 실제로 작동하고 고객 피드백 있고 보안되고 당신은 토이 썼을 거예요. 실례지만 당신 훌륭하지만…” 했어요. 그래서 좀 우울했지만 1.5개월 개선은 받아들일게요. 회사에서 다른 사람을 찾았어요. “이 문제를 봐줄래?“라고 했더니 그는 first principles로, 아주 똑똑하고 fluff를 잘라내는 사람이에요. 팀과 함께 해서 돌아와서 “문제를 보니 한 쿼터에 7개 커넥터를 할 수 있어요.” 붐. 네. 가보죠. 차이점이 뭐예요? 첫 쿼터에 비싸고 똑똑한 스탠퍼드 교육 받은 PM을 고객에게 보내 요구사항을 모으는 거였어요. Salesforce를 어떻게 쓰는지 등. 그게 한 쿼터 걸려요. 끝나면 60-80페이지 멋진 보고서로 돌아와요. 그래서 한 쿼터 블록돼요. 그 아래로 압축 못 해요. 그 후 코드 작성 시작하지만 테스트해야 해요. Salesforce, Workday, NetSuite 세팅이 필요하지만 데이터브릭스 소프트웨어가 아니에요. 우리는 그걸 잘 못해요. 오래 걸리고 사람 찾기 어려워요. 에러 prone해요. 그래서 못 했어요. 그리고 커넥터당 한 사람. 휴가 가거나 아프면. 그래서 그는 first principles로 재배선했어요. 많은 사람들이 좋아하지 않았지만, 제품 요구사항은 한 쿼터 대신 일주일에 빠르게 적고, 틀릴 수 있지만 소프트웨어 쓰기가 빠르니 다시 쓸 수 있어요.更快 iterate. Salesforce 인스턴스 세팅은 아웃소싱해서 병렬로. 커넥터당 한 사람 대신 7명 7 커넥터, 모두 함께 일하게. bus factor one 피함. 버스에 치이면 프로젝트 멈추지 않아요. 한 쿼터에 7개 커넥터 배송. 하지만 이건 AI나 AGI, 더 똑똑한 모델, 슈퍼 인텔리전스와 아무 관련 없어요. GPT7이나 OPU6이 와도 이걸 더 잘하지 못했을 거예요. 인간 refactoring 문제와 프로세스 변화가 필요했어요. 전 세계가 겪는 일이에요. 성공하려면 그렇게 해야 해요. 잘 하는 곳도 있고 아닌 곳도 있어요. Hamilton Helmer가 이걸 많이 이야기해요. 투자자 되고 싶은 assignment option one 하는 사람들은 Hamilton Helmer 책 필독이에요. process power에 대해. 전에 토론했는데, Ali가 Jensen의 5 layer stack — energy, chips, infra, model, apps — 에 100달러 투자한다면 어디에 가치가 accrue하나요? energy chips infra 등 index에 100달러 넣는다면, 장기적으로 어디에 둘 건가요? 어떻게 allocate하나요? 왜요? 저는 컴퓨터 과학자예요. 투자자 아니고 금융 조언 안 해요. 하지만 당신은 allocate하고, 5500달러 allocate하죠. 데이터브릭스는 이 중 3개에 걸쳐 있어요. 네. 애플리케이션이 승자라는 건 분명해요. 위에 둘 거예요. 예측은 어렵지만 early stage 가서 seed strategy로 많은 스타트업에 투자하고 대부분 틀리지만 몇 개가 다음 Google이 될 거예요. 1990년대 PhD 때, 2000년 초 networking 분야였어요. networking이 cool하고 advanced한 거였어요. 인터넷이 big thing이었고 coolest thing on the internet이 networking이었어요. 당시 smartest math brains가 일하던 hardest problem. 미래가 어떻게 될지 모두 알았어요. multiccast problem. 오늘 아무도 모르는 게 문제죠. 우리는 분명히 틀렸어요. multiccast는 한 소스에서 축구 경기 등을 전 세계에 broadcast하는 거예요. bandwidth scarce할 때 smartest brains가 그 문제를 풀려고 했어요. 저도 그걸로 회사 시작했어요. 좋은 solution 있었지만 bandwidth 비용이 폭락하고 fiber 많이 깔아서 문제 자체가 사라졌어요. 소프트웨어 살 필요 없었어요. 완전 시간 낭비였어요. 당시 hardest problems은 Cisco routers, routing, BGP, IP, queuing theory, QoS 등이었어요. tunnel vision on the internet. 인터넷은 protocols이었고 apps는 거의 없었어요. 오늘 모두 chips, infra, Nvidia, OpenAI, Anthropic, DeepMind에 집중해요. AGI super intelligence. 하지만 인터넷에서 takeoff한 weird things는 taxi business Uber, selling books Amazon (AWS), renting bedroom Airbnb였어요.
Yeah. And the hardest problem like the smartest math brains were working on at the time. We all knew what the future would look like. The future everybody knew what the most important problem everyone’s going to work on is the what’s called the multiccast problem which is Yeah. See it’s problematic that no one knows what that is today. We were we were clearly wrong. So multiccast is, you know, you want to broadcast from one source, let’s say a soccer game or a football game or basketball game to the whole world cuz everybody wants to watch it at the same time. We didn’t know how to solve that efficiently. So all of the smartest brains in the world were trying to work on this problem and bandwidth was scarce while we were doing this. And by the way, we actually, you know, had pretty good problems and I started a company on this uh and we had great solution. Unfortunately, the cost of bandwidth just plummeted and they just deployed so much fiber that no one this problem was not a problem ever. So, no one needed to buy this software. So, it was complete waste of time. Uh and at that time we thought the hardest problems the most interesting things to work on are Cisco routers, routing, BGP, border gateway protocol, internet protocol like you know queuing theory, quality of service, these kind of things. Those are like the most interesting things you can because we had tunnel vision on the internet and the what is the internet? Well, at the time it was the internet protocols and those things. No apps really existed, right? So we were all focused on that and today everybody’s focused I would say on chips I think like you know well I think chips and you know I think infrastructure yeah I think people are really right now the hot new thing is like Nvidia open AI anthropic deep mind these are the things everybody’s focused on AGI super intelligence that’s what I said at the beginning but uh on the internet there were like really weird things that took off the really weird things that took off were like taxi business you know which is Uber Uber Yeah. Uh or selling books, which is the lamest thing ever, but that became Amazon, which became AWS. Yeah. You know, uh or uh renting your bedroom to people like, you know, that’s Airbnb.
[한글 번역]
네. 당시 smartest math brains가 일하던 hardest problem이었어요. 미래가 어떻게 될지 모두 알았어요. 모두가 가장 중요한 problem으로 multiccast problem을 알았죠. 오늘 아무도 모르는 게 문제예요. 우리는 분명히 틀렸어요. multiccast는 한 소스에서 축구나 미식축구, 농구 경기를 전 세계에 broadcast하는 거예요. 모두 동시에 보고 싶으니까요. 효율적으로 푸는 법을 몰랐어요. 세계 smartest brains가 그 문제를 풀려고 했고 bandwidth가 scarce했어요. 좋은 problem이었고 저도 그걸로 회사 시작했어요. great solution 있었지만 bandwidth 비용이 폭락하고 fiber를 엄청 깔아서 문제 자체가 사라졌어요. 소프트웨어 살 필요가 없었어요. 완전 시간 낭비였어요. 당시 hardest problems이자 most interesting things은 Cisco routers, routing, BGP, border gateway protocol, internet protocol, queuing theory, quality of service 같은 거였어요. tunnel vision on the internet 때문에 그게 가장 interesting하다고 생각했어요. 당시 인터넷은 internet protocols이었고 apps는 거의 없었어요. 그래서 모두 그쪽에 집중했어요. 오늘은 chips, infra, Nvidia, OpenAI, Anthropic, DeepMind에 집중해요. AGI super intelligence. 시작할 때 말한 대로지만, 인터넷에서 takeoff한 really weird things은 taxi business Uber, selling books (lamest thing ever지만 Amazon, AWS), renting your bedroom Airbnb였어요. 
[영어 원문]
complete commodity that looks identical and there’s no differentiation which is books. Yeah. And he just started with that and he just bet on that secular trend and every year it was more and more right and you know and now it’s like everything on the planet. It’s the everything store. Yeah. So, kind of think long term like that and don’t be swayed by the coolest thing that everybody’s like right now um sort of making lots of noise on Twitter on because chances are it’s probably something like multiccast. Yeah. Awesome. Well, thank you so much for staying longer, folks. Thank you. Thank you. Awesome. Thank you so much.
[한글 번역]
완전 commodity처럼 보이고 differentiation이 없는 books. 네. 그는 그걸로 시작했고 secular trend에 bet했어요. 매년 점점 더 맞았고 이제는 planet의 everything이에요. everything store. 네. 그래서 long term으로 그렇게 생각하고, 지금 Twitter에서 coolest thing이라고 noise 많이 내는 것에 sway되지 마세요. chances are multiccast 같은 거일 가능성이 높아요. 네. Awesome. longer stay 해주셔서 정말 감사합니다. Thank you. Thank you. Awesome. Thank you so much.
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앨리 고드시(Databricks CEO)가 스탠퍼드 MS&E 435 수업(Economics of the AI Supercycle)에서 Apoorv Agrawal와 대화한 내용입니다. 강의는 AI supercycle의 경제학, 특히 기업 가치 창출의 현실, AGI 존재 여부, 생산성 paradox, 조직 재설계의 중요성 등을 중심으로 진행됐습니다.
강의 시작에서 Apoorv는 최근 AI 업계의 빠른 움직임(xAI, Cursor, OpenAI vs Anthropic 경쟁 등)과 데이터브릭스가 데이터 → 레이크하우스 → AI 비즈니스로 쌓아온 점을 언급하며, Ali에게 top-down state of the union view를 요청했습니다. Ali는 즉시 “chill out, don’t be stressed”라고 조언하며 시작했습니다. 시대가 미쳤지만 그 frenzy는 unwarranted하고, 스트레스가 사람들을 Twitter의 crazy thing만 쫓게 하며 tunnel vision을 만들고 제대로 된 일에 집중하지 못하게 한다고 지적했습니다. 특히 최근 인턴 세션에서 22살 학생들이 “지금 회사 시작해서 CEO 돼야 하나, 6개월 늦추면 커리어·인생 끝, AGI 놓치면 어떻게 하나” 같은 극단적 질문을 한다는 점을 들어, “calm down, take a deep breath, things take time”이라고 강조했습니다. 
Ali는 실리콘밸리의 super intelligence quest도 unwarranted하다고 봅니다. super intelligence를 godlike, recursive self-improvement, singularity, 질병 치료, GDP 10% 점프, 실업 20%, UBI 등으로 정의하지만, 제대로 정의조차 안 됐고 필요 없다고 합니다. **“We already have AGI”**라고 선언하며 청중 테스트를 했습니다. “AGI 이미 있다고 생각하는 사람?” → 항상 10% 정도. “상호작용하는 많은 사람들이 smartest 모델보다 덜 똑똑하다고 생각하는 사람?” → 대부분 손. “아직 AGI 없다고 생각하는 사람?”으로 다시 물으니 hypnosis처럼 목표가 이동한 걸 보여줍니다. 2009년 UC Berkeley AMPLab(당시 가장 중요한 AI lab, Michael Jordan이 “AI의 Michael Jordan”으로 일함) 시절 정의로는 이미 AGI를 달성했다고 합니다. 당시 선도 연구자들에게 확인해도 “그 정의로는 yes”지만, goalpost moving(예: strawberry R 개수 세기 실패) 때문에 부정한다는 점을 지적했습니다. AGI는 이미 smarter than many humans, general intelligence, artificial(인간 뇌 방식은 아님)이라고 결론지었습니다. 따라서 GPU·데이터센터에 과도한 돈을 쏟는 건 꼭 필요하지 않다며, 동시에 MIT 보고서(95% GenAI pilots 실패, 방향성은 맞고 75%일 수도)를 인용해 기업 현실을 설명했습니다. 
기업 내부는 agentic co-workers가 인간과 blending하며 일하는 미래상이 아니라, Office Space 영화처럼 humans shuffling TPS reports입니다. AI 회사조차 old-school sales people를 데려와 old-school 방식으로 운영합니다. 이유는 context 부족입니다. 조직마다 10~40년 근속한 “John or Jane”이 모든 프로세스를 머릿속에 가지고 있고, 모두가 그 사람에게 의지합니다. 그 context가 모델·에이전트에 없으면 stupid mistakes를 하고 useless해집니다. super intelligence가 와도 math 문제 풀기만 하고 조직 문제는 못 풉니다. 그래서 “download the carbon-based brain into the silicon”, “get the carbon to talk to the silicon”이 핵심 과제이며, AGI는 이미 여기 있으니 조직 transformation이 impact를 만드는 길이라고 urging했습니다. 
소프트웨어 죽음 논의로 넘어가며, “software is dead”를 4번째 사이클(이전 Brexit, taper tantrum, inflation 등)이라고 보고, 이번엔 AI 때문이라고 합니다. 클래스 질문 “software stocks buy the dip?”에 대해, OpenAI·Anthropic·Nvidia가 software 회사(연구자+소프트웨어)인데 trillion valuation인데 dead일 리 없다고 반박합니다. SpaceX는 rockets지만 나머지는 software 기반인데 dead하면 안 됩니다. 두 가지 변화: barriers to entry 크게 down (소프트웨어 생산 비용 거의 0, 누구나 무기 보유), switching costs down (UI 전환 쉬워짐, lock-in 줄음). 하지만 Hamilton Helmer의 moats( scale, brand, trust/security, network effects, switching costs, proprietary data)는 여전합니다. data + core operational loop에 있는 회사는 robust하고, stale UX 회사도 AI 제대로 하면 살아남을 수 있지만, 10년째 그대로 revenue growth만 탄 회사는 위험합니다. 
Jensen Huang의 5-layer stack(energy, chips, infra, models, apps)에 100달러 allocate 질문에는 **applications(top)**에 집중하라고 답했습니다. PhD 때(early 2000s networking) multiccast가 “가장 중요한 problem”이었지만, bandwidth cheap + fiber deploy로 문제 사라짐. Cisco routers, BGP, queuing theory 등에 tunnel vision했지만, 실제 winners는 Uber(taxi), Amazon(books→AWS), Airbnb(bedroom rental), Twitter(short messages) 같은 “ridiculous” ideas였습니다. 지금도 chips·infra·Nvidia·OpenAI·Anthropic·DeepMind에 집중하지만, trillion-dollar AI 회사들은 unexpected applications(healthcare ~17-18% US GDP, willingness to pay infinite, current product poor; education, VC uninvestable despite)에서 나올 겁니다. 
Open-source 모델은 frontier와 gap이 3-4개월 → 1개월 정도로 좁혀졌고, Moonshot Kimi 2.6 예시를 들며 빠르게 따라잡는다고 했습니다. Model layer는 cloud처럼 token factories, thin margins, few players일 전망입니다.
마무리 조언: long term 생각하라. PhD 때 world ending처럼 느꼈지만 multiccast waste였고, Airbnb 2008/2009 시작했지만 2001에도 가능했음(tech stack 변화 없음, good idea rare and slow). Jeff Bezos가 Wall Street 투자은행가에서 books(complete commodity)로 시작해 secular trend(인터넷)에 bet, everything store 만든 것처럼. Twitter noise에 sway되지 말고, long term bet하라. multiccast 같은 것일 가능성 높음. 감사 인사로 마무리. 
전체적으로 Ali Ghodsi는 AGI already here, but value capture failure is human/organizational refactoring problem (context transfer, process rewiring, not bigger models)을 핵심 메시지로 강조하며, historical analogy(Paul David dynamo-computer paper, Solow paradox), Databricks connector anecdote(9개월→7.5개월 vs first-principles 7 connectors/quarter), customer insights(20,000 customers), investment philosophy를 통해 실질적이고 provocative한 관점을 제시했습니다. 청중 스트레스 완화와 long-term thinking을 강하게 권고했습니다.
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AGI 정의와 Solow 역설 (강의 내용 기반 상세 설명)
1. AGI 정의 (Ali Ghodsi의 관점)
Ali Ghodsi는 **“We already have AGI”**라고 강하게 주장하며, AGI의 정의가 시대에 따라 이동(goalpost moving)했다고 지적합니다.
• 2009년 UC Berkeley AMPLab 당시 정의: 당시 선도 AI 연구자들(특히 Michael Jordan 등)이 상상하던 AGI 수준을 이미 달성했다고 봅니다. “anything we imagined would be AGI”를 충족했으며, “the god of AI” Michael Jordan이 일하던 lab에서 그 기준이었다고 합니다.
• 현재 증거: smartest 모델이 많은 사람들이 상호작용하는 사람들보다 더 똑똑하다(일반 지능 수준). AGI는 artificial general intelligence로, 인간 뇌와 정확히 같은 방식은 아니지만 general intelligence를 발휘합니다.
• 반대 의견에 대한 반박: “strawberry에 R 개수 세지 못한다” 같은 예시로 goalpost를 옮기는 건 부당하다고 비판. 2009년 정의로 돌아가면 이미 AGI가 있으며, super intelligence quest(신 같은 존재, recursive self-improvement, singularity, GDP 10% 점프, UBI 등)는 unwarranted하다고 봅니다.
• 함의: GPU·데이터센터에 과도한 투자보다, context transfer(조직 내부 John/Jane의 tacit knowledge를 모델에 다운로드)가 핵심. AGI는 이미 brain(지능)이 있지만, body(프로세스·조직)를 rebuild하지 않으면 useless합니다. 
2. Solow 역설 (Solow Productivity Paradox)
Ali는 Paul David의 1990년 Stanford 논문 《The Dynamo and the Computer》를 인용하며, AI의 현재 상황을 역사적 생산성 paradox로 설명합니다.
• Solow의 유명한 말: Nobel 경제학자 Robert Solow(강의에서 “Richard”로 잘못 언급)가 1987년에 “You can see the computer age everywhere but in the productivity statistics”(컴퓨터 시대는 어디서나 보이지만 생산성 통계에서는 보이지 않는다)라고 했습니다.
• PC 시대 예시: 사람들이 PC를 사서 타자기로만 사용 → 출력물을 프린트해 폴더에 넣고 어시스턴트가 인덱싱. 프로세스 변화 없음 → 생산성 향상 통계에 나타나지 않음.
• 전기(산업혁명) analogy: 1880년대 electric motor(dynamo) 등장 후, 기존 steam engine + line shaft(회전 벨트) 공장을 그대로 교체 → 40년(1880~1920) 동안 생산성 향상 거의 없음.
• 1920년대에야 factory floor 재설계(unit drive: 기계당 개별 모터, 큰 horizontal layout, 도시 밖 공장 이전, distributed power) → 비로소 productivity gains.
• AI에 대한 적용: 지금 AI(brain)는 있지만 조직·프로세스(body)는 old-school 그대로. 기업 CEO들이 “AI amazing하지만 productivity gains 안 보인다”고 호소. Databricks connector 사례(9개월 → 7.5개월 vs first-principles 1쿼터 7개)처럼 human refactoring과 process change가 핵심이며, GPT-7 같은 모델 업그레이드만으로는 부족합니다. 
요약 연결: AGI는 이미 정의상 존재하지만, Solow 역설처럼 context + organizational rewiring 없이는 생산성·가치 창출이 안 됩니다. 강의 핵심 메시지입니다.