인공 지능: 에이전트, 일자리, 주식 시장에 미치는 영향

인공지능, 특히 인공지능의 발전과 일자리 및 주식 시장에 대한 잠재적 영향이 입력 스크립트의 주요 초점입니다.

### AI의 미래: 산업과 인력의 변화
* 🤖 인공지능(AI)은 **2030년까지 전 세계 경제에 약 15조 7천억 달러를 추가**할 것으로 예상되는 AI 및 로봇공학 등의 분야에서 산업을 혁신할 것으로 보입니다.
* 🏦 AI 기술은 이미 **음악과 동영상 제작부터 복잡한 데이터 분석과 예측**에 이르기까지 다양한 분야에서 활용되고 있습니다.
* 🧠 AI에서 머신러닝은 **데이터에 대한 모델을 학습**시켜 예측을 수행하는 핵심 하위 분야이며, **딥러닝은 인공 신경망을 사용**하여 인간의 두뇌를 시뮬레이션하는 고급 기술입니다.
* 🤖 AI의 다음 영역은 클라우드에서 연중무휴로 서로 통신하고 작업을 수행할 수 있는 전문 모델인 **'에이전트'의 개발**입니다.
* 🌐 전 구글 CEO 에릭 슈미트는 5년 이내에 협업하고 소통할 수 있는 **'에이전트'가 등장**해 과학, 의학 및 기타 분야에서 새로운 돌파구를 마련할 수 있을 것으로 예측합니다.
* 💼 AI는 새로운 일자리를 창출할 것으로 예상되는 동시에 기존의 많은 역할을 자동화할 것으로 예상되며, **가까운 미래에 40~50%의 일자리가 자동화될 수 있을 것**으로 추정됩니다.
* 📊 AI가 주식 시장에 미칠 잠재적 영향에 대한 논의가 활발하게 이루어지고 있으며, **일부에서는 몇몇 대기업이 AI 기술을 통해 권력을 강화**하고 시장을 지배할 수 있다고 추측하고 있습니다.
* 🤔 연사는 **AI가 경제와 고용 시장에 미칠 수 있는 잠재적 영향과 관련된 위험을 완화하기 위해 투자를 다각화**할 것을 권장합니다.

there's a lot of questions here, and now we get into the questions of Science Fiction. I'm sure the three things I've named are happening because that work is happening now, but at some point these systems will get powerful enough that you'll be able to take the agents, and they'll start to work together. So, there is one technology out there that promises to change our lives forever, and that technology is AI. AI refers to the simulation of intelligence in machines that can think and learn. But you do believe it's going to change the world. I believe it's going to change the world more than anything in the history of mankind, more than electricity. It's already in our smartphones, it's in Tesla's full self-driving, it's already allowing non-musicians to create music, non-videographers to create cinematic videos. It can create apps and websites, come up with recipes, do your taxes, analyze complex data, and make predictions. And pretty soon, it promises to dream up new cures and drugs for diseases, all by

itself, and thanks to a video from Jeff Sue that I recently watched, I just learned that artificial intelligence is actually an entire field of study all by itself, just like physics. And within artificial intelligence as a study, there's a subfield called machine learning, in the same way that thermodynamics is a subfield within physics. And within the field of machine learning, there's something called Deep learning, which can be broken down into discriminative models, generative models, and language learning models. Tools like Chad GPT and Google's Gemini are a combination of language learning models and generative models, and this industry is becoming extremely valuable altogether. Fields like Ai and Robotics are expected to add around $15.7 trillion to the global economy by the year 2030, but it can also cost as many as 50% of jobs to be lost to automation. Some people think AI is about to transform our lives mostly for the better, and then there's some people that think this is just another marketing

gimmick by the corporations to artificially inflate their stock prices by promising us a technology that's actually really far away. Now what I think is most interesting though is what the former CEO of Google just said about it in an interview, and he said that in 5 years time we'll create what are called agents, and those agents will be able to talk to other agents. At which point, when we don't understand what we're doing, you know what we should do? Pull the plug, literally unplug the computer. And I just want to know what happens to the idea of investing if just a handful of companies come together to consolidate and end up running the entire world with this technology. What happens to the global stock market? That's what I want to help explain in today's video, and a whole lot more, and show you what I think is really going on. So with that said, let's get into it. Hi, my name is on J. Hope you're doing well. Come for the finance and stay for AI. Um

you know, I think AI will probably, like, most likely, sort of lead to the end of the world. But in the meantime, all right, so I think artificial intelligence is extremely misunderstood. So first, I want to explain exactly how the technology works, and I want to give credit to Jeff Sue for making an amazing breakdown of this. I'll leave a link to his video down below. Now, at the center of artificial intelligence is something called machine learning, which is actually pretty simple. All it does is it takes a bunch of data and it trains a program to create a model. Once it creates a model, you can give it a completely new set of data, and with it, the model will be able to find patterns and make predictions. I predict that if I do enough card tricks, you might subscribe someday. Never mind, I need new data. Now, there's two different kinds of models in machine learning. There's supervised models and unsupervised models. Supervised models use data that is labeled, and the example Jeff shows in his video is how much someone might

leave a tip for depending on the order if it was picked up, which are the blue dots, or delivered, which are the yellow dots. If you have both sets of data and each is labeled, you can make predictions about the next order. So when you get another order, depending on what type it is, the model will be able to predict the tip, or vice versa. Pretty easy. Now, an unsupervised model works the exact same way, but it uses data that's not labeled. And this is how we can predict someone's career trajectory based on income versus time. So, if we take the amount of years someone spends at a given job versus what their income is at any given time, even though the data is not labeled, meaning we don't know much about the person or their job title, what this model can do now is make predictions. If, for example, someone works for a company for a short amount of time but they have a higher income, chances are they'll be on the fast track to success. But if their income falls in the second half below a certain threshold in relation to

the years they've worked then they're not basically unsupervised models. Take a huge amount of unlabeled data and they try to find new patterns. But within machine learning, there's also a special learning process and it's called Deep learning. It uses a different method that's trying to simulate the human brain using artificial neural networks. All right, so here's my silly analogy. Deep learning takes a small amount of data that's labeled and it applies it to a huge amount of unlabeled data. So in John's original example, A Bank might use deep learning to figure out which of its transactions may be fraudulent. Since a bank can't look at every single transaction that people make, instead it can label a smaller set of transactions as fraudulent or not. And then using that newly trained model, it can organize the rest of the data automatically. And that's deep learning. And banks are using this technology right now. And I think the most interesting technology that AI is working on today, something that we're about to have in our lives pretty soon, is something called the agents A.

Smith, agent Smith. I wish I was joking, but I'm not. So, speaking of harnessing the power of AI, I'm super excited to announce the partner of today's video that's making waves in integrating AI into everyday tech: Asus and their new Asus Vivobook S15. The Asus Vivobook S15 is the inaugural Asus NextGen AI PC featuring cutting-edge AI capabilities that I found incredibly useful. I've been able to enhance my productivity and efficiency with the 45-watt Qualcomm Snapdragon X Elite processor, which has handled even the most demanding tasks, and it's been a game-changer. It has features like AI-enhanced connectivity. It has full I/O ports, including USB 4, USB 3, HDMI 2.1, a Micro SD card reader, and audio jack for connectivity anywhere. It has a 70Wh battery that can last up to 18 hours. It's super slim at just 0.58 in, and it comes in at a little over 3 lb. But my favorite features are the AI-driven co-pilot key and the RGB keyboard.

With just the click of a button, the Asus Vivobook S15 becomes an instant AI powerhouse. It's like having a personal assistant at my fingertips all the time. The Live Caption feature, for example, translates Zoom calls and videos automatically in real time. Co-Creator allows me to draw whatever I want and brings it to life with AI images. Windows Studio Effects improves my lighting and blurs out my background during video calls. Asus' two-way AI noise cancellation also isolates my voice when I'm in my Zoom meetings, and AI-powered visuals look amazing on the 3K 120Hz Asus Lumina OLED display with an 89.4% screen-to-body ratio for an immersive experience. The Asus Vivobook S15 combines AI with elegance, intelligence, and incredible performance. This is my first Asus co-pilot plus PC, and I'm super excited to integrate AI into my everyday life into a laptop that I can carry with me anywhere. So thank you, Asus, for sponsoring this segment of my video.

the product link is down below. And now let's get back to it. Now, this next part is where AI becomes science fiction becomes reality. It's really exciting, but it's also kind of scary. Let me show you an interview with Eric Schmidt, the former CEO of Google. He said there's three things happening right now that will profoundly change the world: the context window, agents, and text-to-action. The first one is the context window. The context window refers to how much text an AI can keep in mind or reference at any given time. So when we ask it a question, it understands what we mean and it can build on top of it. And this year, people are inventing a context window that is infinitely long. And this is very important because it means that you can take the answer from the system and feed it in and ask it another question. Let's say I want a recipe to make a drug or something. They say, What's the first step? And it says, Buy these materials. So then you say, Okay, I bought these materials. Now what's my next step? And then it says:

buy a mixing pan, and then the next step is: how long do I mix it for? You see, it's a recipe that's called Chain of Thought reasoning, and it generalizes really well. We should be able, in 5 years for example, to be able to produce a thousand-step recipes to solve really important problems in science, in medicine, in Material Science, climate change, that sort of thing. Now, the second profound change is the creation of the agents. Now, agents are just models that specialize in very specific data. An agent can be understood as a large language model that knows something new or has learned something. So, an example would be: read all of chemistry, learn something about chemistry, have a bunch of hypotheses about chemistry, run some tests in a lab about chemistry, and then add that to your agent. These agents are going to be really powerful, and it's reasonable to expect that agents will be not only will there be a lot of them, and I mean Millions, but there'll be like the equivalent of GitHub for agents. There'll be lots of lots of Agents running around.

so just imagine that these agents are experts experts in medicine law Athletics nutrition any industry, and all the knowledge that we possess about it will be condensed into these agents that people can just use and talk with. And then there's the third profound change, which is text action, and that's asking these agents to do whatever it is people want, and they will do this in the cloud in the background 24/7. You add it all up though, and you get something that looks kind of like science fiction. Can you imagine having programmers that actually do what you say you want and it does it 24 hours a day, and strangely these systems are good at writing codes such as language like python. You put all that together, and you've got infinite context window, the ability for agents, and then the ability to do this programming. Now this is very interesting. What then happens, there's a lot lot of questions here, and now we get into the questions of Science Fiction. I'm sure the three things I've named are happening because that work is happening now, but at some

point these systems will get powerful enough that you'll be able to take the agents, and they'll start to work together. Right, so your agent and my agent, and her agent, and his agent will all combine to solve a new problem. At some point, people believe that these agents will develop their own language. It's really a problem when agents start to communicate in ways and doing things that we as humans do not understand. That's the limit in my view. So it's exactly when these agents start collaborating with each other and saying things that we don't fully understand is when we should stop this whole experiment. But also kind of sounds like science fiction that's so far away. So my question is, how many decades away is this really a reasonable expectation? Is we'll be in this new world within 5 years, wow, not 10. And the reason is there's so much money. I think there's every reason to think that some version of what I'm saying will occur within 5 years, and maybe sooner. Now that you

of understand how this technology Works. How it reasons, and how fast it's growing, and exactly when we'll be living in The Matrix. Let's talk about some of the real-world challenges of this technology and what it will actually do to jobs. So not everyone agrees exactly how many jobs will be lost or created, but let me share with you some numbers that have come out from a lot of different studies. The World Economic Forum, for example, which is where global leaders come together every year, estimated that AI and automation will displace more than 85 million jobs by the year 2025. According to MIT and Boston University, AI will replace as many as 2 million manufacturing workers by 2025 as well. The McKenzie Global Institute report reported that on a worldwide level, 14% of the entire population of Earth will have to change their careers at some point, and 87% of companies have admitted that they have a skills gap when it comes to AI technology. And it's not just all these random studies and corporations saying all of this.

also an agency from within the United States government. The Bureau of Labor Statistics is reporting that between 40 to 50% of jobs will be automated in just a couple years. So a lot of jobs will go away, and unfortunately, people are just not prepared for it. The incomes that will be affected most are the white-collar jobs that make $8,000 a year, according to Nexford University. And the jobs that will be most affected by this are people in customer service, receptionists, accountants, bookkeepers, salespeople, research and analysis, warehouse work, insurance underwriting, and people working within retail. In other words, jobs that are either physically or mentally repetitive, especially ones where you have to make a decision based on analyzing some set of data or some numbers. But there will also be new jobs that will be created, like AI managers, because you can't lose your job to AI if your job is to manage AI. But even those people could lose their jobs, thanks to agents whose specialty might be to manage other agents and AI systems. But the good news is that same world

Economic Forum study also predicted that 97 million new jobs will be created. So if you're still in school, the jobs I think that will be safest are in the trades like plumbers, electricians, mechanics, engineers, barbers, landscapers, trainers, teachers, and performers. But don't be a performer unless you have no choice like me. Complex manual labor won't be replaced until we have a breakthrough in robotics. And then it would have to become so cheap that it makes more economic sense to replace the workers with robots. But that probably won't happen soon because we just don't have the technology to do that yet. And what we do have is super expensive. Which also means people in the civil services like police officers and firefighters will be safe. As well as people in the medical industry like doctors, nurses, veterinarians, lawyers, and unfortunately the politicians will be safe as well. Now the most profound question that I personally have is what does this technology mean for the idea of investing. When we invest, we put our

money into companies that use it to solve the global problems of today. They create new technologies and products that will help us, which in return makes them more profitable and their stock prices go up, and it makes us money. But what happens when the last creation we ever need to make becomes reality? What happens if just a couple corporations band together and use their technology and these AI agents to be able to solve any problem that they want? At that point, do we really, really need thousands upon thousands of specialized companies solving all these different problems, or does the stock market consolidate into a handful of companies that become a lot more valuable than the rest? I have a tinfoil hat theory that the stock market thinks that's exactly what will happen, and why I think this is because last year there was a headline that the top seven tech stocks returned 92% for the entire stock market's performance, and today out of the top 500 companies, the top 10 accounted for 27% of the index. Now, some years that number is lower, but

some years it's even higher, but over the long term, that number has been growing. 10 years ago, for example, the top 10 companies represented just 14% of the index, roughly half of what it is today. Just to put all this in context, for every $100 I put into the S&P 500 Index, $27 of that $100 goes towards these top 10 stocks. The other $73 gets shared amongst $490 stocks, which is kind of interesting. So it seems to me that the stock market is making this prediction that this is what's going to happen potentially in the future, which is why so much of this money is being concentrated in the top 10, presumably because they have the best chance of figuring it all out. So taking all of that into context, the question is, should I just sell everything and then chase the top 10 stocks? And for me personally, no, the answer is I'll continue to dollar-cost average into the index because presumably if the market consolidates into fewer and fewer companies, if my theory is correct and in

the future, there will be less stocks to pick from than there is today. Then the S&P 500 Index, by design, should figure out how to adjust for it by allocating the money in different ways proportionally to these companies' successes. That's why for me, diversifying is the best way to go, but buying individual stocks is a lot more risky, especially with the pace of AI's development. Of course, some people also say that it's all just hype in marketing, that these companies are running out of data to train these models on, and it's just a way to boost their stock prices. Based on all the things that I've seen, I don't think that's the case, but I don't know. That's why I diversify, but I'd love to hear your thoughts. I hope you have a wonderful rest of your day. Smash the like button, subscribe if you haven't already, don't forget to grab your free stocks. Links are down below, and I go track them automatically with a spreadsheet. Link down below in my

patreon, thank you so much for watching this video. I'd love to see you back here next week. I'll see you soon. Bye-bye. [Music]

How Ai Is About To Transform The World’s Economy

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I'm like Harry Potter - if he was a financial minimalist, did card tricks and worried about money. Biz inquiries: andrei@majikh.com


Andrei Jikh

AI는 이미 다양한 산업 분야에서 그 영향력을 발휘하고 있으며 2030년까지 세계 경제에 수조 달러를 기여할 것으로 예상되는 등 전 세계에 큰 영향을 미칠 것으로 전망됩니다.

머신러닝과 딥러닝을 포함한 이 기술은 음악, 동영상, 앱, 웹사이트 등을 제작하는 데 적용되면서 빠르게 발전하고 있으며, 동시에 일자리 대체에 대한 우려도 제기되고 있습니다.

AI의 발전은 소수의 기업이 기술을 통제할 가능성, 글로벌 주식 시장에 미치는 영향 등 AI의 잠재적 혜택과 그 힘의 증가에 따른 윤리적 영향에 대한 논쟁을 촉발시켰습니다.

예측에서 다양한 AI 모델과 딥러닝의 역할

지도 모델은 레이블이 지정된 데이터를 사용하여 결과를 예측하는 반면, 비지도 모델은 레이블이 지정되지 않은 데이터에서 패턴을 찾습니다.

딥러닝은 레이블이 지정된 데이터와 레이블이 지정되지 않은 데이터를 사용하여 인간의 두뇌를 시뮬레이션하여 예측을 수행합니다.

"에이전트"라는 특별한 AI 기술은 생산성과 효율성을 향상시키기 위해 Asus Vivobook S15와 같은 일상적인 기술에 통합되고 있습니다.

아수스 비보북 S15: AI 기술의 강자

아수스 비보북 S15는 실시간 번역을 위한 라이브 캡션과 AI 생성 이미지를 위한 코크리에이터와 같은 기능을 통해 버튼 클릭 한 번으로 즉각적인 AI 성능을 제공합니다.

또한 3K 120Hz 아수스 루미나 OLED 디스플레이의 AI 기반 비주얼과 양방향 AI 소음 제거 기능으로 몰입감 넘치는 경험을 제공합니다.

에릭 슈미트의 인터뷰에서는 무한히 긴 컨텍스트 창과 전문 에이전트 생성 등 AI가 다양한 분야의 문제 해결에 혁명을 일으키며 세상에 가져올 중대한 변화에 대해 강조합니다.

다양한 산업 분야의 지식을 압축하고 클라우드에서 24시간 연중무휴로 업무를 수행하는 첨단 AI 에이전트가 공상 과학 소설처럼 개발되고 있습니다.

이러한 에이전트들은 궁극적으로 협업을 통해 자체 언어를 개발할 것으로 예상되어 미래에 대한 우려를 낳고 있습니다.

이 기술의 급속한 성장은 이러한 새로운 세상이 5년 이내에 현실화될 수 있음을 시사하며, AI와 자동화로 인해 수백만 개의 일자리가 사라지고 인력의 기술 격차가 크게 벌어질 수 있습니다.

자동화가 고용 시장에 미치는 영향

미국 노동통계국은 향후 몇 년 안에 일자리의 40~50%가 자동화될 것이며, 연봉 8,000달러 정도의 화이트칼라 일자리가 가장 큰 영향을 받을 것으로 예측하고 있습니다.

하지만 이로 인해 AI 관리자와 같은 새로운 일자리도 창출될 것입니다.

이러한 점을 고려할 때 무역업, 복잡한 육체 노동, 공무원, 의료 산업 분야의 직업은 안정적으로 유지될 가능성이 높습니다.

현재 S&P 500 지수에서 상위 10개 기업이 차지하는 비중은 10년 전보다 훨씬 높아졌습니다.

상위 10개 종목에 자금이 집중되면서 모든 종목을 매도하고 이들 상위 기업에만 투자해야 하는지에 대한 의문이 제기되고 있지만 여전히 분산 투자 전략이 최선의 방법으로 여겨지고 있습니다.

AI의 급속한 발전과 개별 주식 투자에 대한 영향도 투자자들의 관심사로 떠오르고 있습니다.