AI를 둘러싼 과대 광고와 기술에 미치는 영향

### AI의 과대 광고와 현실: 비판적 분석
* 🤖 AI를 둘러싼 과대 광고에도 불구하고 AI 기술의 개선 속도는 일부에서 주장하는 것처럼 빠르지 않을 수 있으며, 연사는 **"GPT-3.5에서 GPT-4.0으로 발전하는 속도는 분명히 매우 느렸다"**고 주장했습니다.
* 💼 연사는 대기업들이 AI에 막대한 투자를 하는 동기에 대해 의문을 제기하며, 이러한 투자는 **수익 보장보다는 다음 큰 것을 놓칠까 봐 두려워서**일 수 있다고 말합니다.
* 📉 마이크로소프트가 스마트폰 산업을 놓친 것과 같은 역사적 사례는 **신흥 기술에 투자하지 않을 경우 발생할 수 있는 잠재적 위험**을 설명하는 데 사용됩니다.
* 🎩 이 연사는 가짜 데모와 과장된 주장을 포함한 AI 과대광고의 사례를 강조하며 **테슬라의 자율주행 데모와 같은 역사적 사례**와 비교합니다.
* 🧠 연사는 작업을 자동화하고 효율성을 향상시킬 수 있는 AI의 잠재력을 인정하면서도 **현재 AI의 능력은 아직 여러 측면에서 인간의 지능에 미치지 못한다**고 주장합니다.
* 🎓 연사는 AI가 고용 시장에 미칠 영향에 대한 추측성 예측에 근거해 과감한 진로 결정을 내리지 말라고 조언하며 **수학 및 프로그래밍과 같은 기초 기술의 지속적인 가치**를 강조합니다.
* 🔮 연사는 AI의 잠재력을 과대포장하는 것을 경계하는 한편, **"AI는 약간의 개선을 가져올 것"**이지만 극적인 과대광고에 부응하지 못할 수도 있다고 말하며 AI가 중요한 변화를 가져올 수 있는 능력을 인정하면서 마무리합니다.
* 🌐 이 분석은 AI 기술의 현재 상태와 미래 궤적에 대한 비판적 관점을 제공하며, **기존의 통념에 이의를 제기**하고 그 기능과 한계에 대한 균형 잡힌 시각을 유지하는 것이 중요하다는 점을 강조합니다.

AI의 극적인 과대 광고가 기대에 부응하지 못할 수도 있지만, 일부 사람들에게는 꼭 그럴 필요는 없습니다.

it seems like it's just a 1% improvement. This is actually a 10x improvement to go. There's a new virus running around, have you heard about it? It's called hype. It's as old as human history, and some of the symptoms include rabidly mentioning the term AI. AI first, generative AI, third AIying AI, finally rigorously tested AI with AI, and making verifiably false claims. Google's best Gemini demo was faked, that's their headline. I want to try to address some of your anxieties, but at the same time I'm not an expert. I'm not following every cutting-edge research paper, but I would argue two things. Sometimes when people spend all their time focusing on every tiny little detail, it prevents them from zooming out and looking at the bigger picture. And also, most of the conclusions I'm going to make in this video are actually in line with the leading AI researcher at Meta and many other intelligent folks who do happen to be experts. So perhaps there is some truth to what I'm about to

say now I can't predict the future. I can't tell you exactly where the tech job market is going, but I'll try to provide some historical context, give a lot of facts, and to reach my conclusions, I'll make a series of thought experiments. Some of the things on my list to address are fake demos, why is every big company investing in AI? It must mean something, right? A lot about human behavior, actually, and also with so many unknowns in a changing world, how do us humans make big life decisions like which college major to pursue or which career path to transition into. Pretty much everything I'll talk about today centers around one thing: technology changes a lot, but there's one thing throughout all of human history that doesn't change, and that's human nature. Obviously, all of this started in 2022 when Chach PT was released. At that point, I think two things were immediately clear to anyone who is paying attention: one, this is a revolutionary technology completely unlike the web 3 hype; two, this technology was going to be slow.

moving it wasn't going to change the world overnight, and I publicly made these claims the day after GPT-4 was released. Make no mistake, this technology will revolutionize the world. It's already doing that, but I don't think it's quite as powerful as the hype is making it out to be. I even questioned Sam Altman and OpenAI's ethics, and do you know what people said to me? They said I was coping. They even said I was a Google fanboy. I don't know where they got that idea, but more recently, people are starting to agree with me. It's been revealed that OpenAI has made employees sign illegal non-disclosure agreements. They're promoting a culture of lying. What data was used to train Sora? We used publicly available data and licensed data, so videos on YouTube. I'm actually not sure about that and this weird thing where Sam Altman is scanning people's eyes in exchange for some useless cryptocurrency. For better or for worse, I've been pretty good at avoiding this sort of herd mentality.

honestly, it didn't really bother me when people said I was wrong. Now who knows, all of my predictions could turn out to be wrong. Everything could change overnight, but I'm going to explain why that's unlikely. These big tech companies can't all be stupid. Why would they be pouring billions of dollars into AI and chips? Firstly, I agree they're not stupid, but that definitely doesn't mean they haven't been wrong before. And second, the question of why is kind of hard to answer, so let's do a thought experiment to answer the question. Let me ask you a question: if you were Google, what would you do? You have the choice of investing in AI or skip it. In the case of investing, it's possible that one, it pays off. AI hype was actually well worth it. The second case is that Google loses money. They lose their investment. It wasn't worthwhile. Believe it or not, from Google's perspective, either of these two outcomes doesn't matter because if the investment pays off, Google will make a lot of money. If it doesn't pay off

Google will make a lot of money. The last thing Google would want is to skip the investment, and then somebody else entirely disrupts Google's business. Google is a monopoly. They have so much money they don't know what to do with it, and they sure as hell aren't going to give it to their employees. So the case that every company is trying to avoid right now is the case of Microsoft in the late 2000s. Steve, let me ask you about the iPhone and the zoom if, if I may. $500 fully subsidized with a plan. I said, That is the most expensive phone in the world, and it doesn't appeal to business customers because it doesn't have a keyboard, which makes it not a very good email machine. Now with hindsight, are there things I'd do differently? Of course. Like what? Come on, Charlie. I probably would have started as doing hardware earlier so that we could have been more effective in the phone business. I mean, this is kind of like an IQ test. Microsoft missed out on smartphones and missed out on a trillion

dollar industry. That is the case that Google and every big tech company is trying to avoid. Maybe these investments will pay off eventually, but right now the only company that's actually making money from AI is NVIDIA. They're the ones making the profit. It's not surprising because they're the ones selling the shovels. It's not proven yet that those shovels can actually find gold, and it might never be proven. History repeats itself. This has all happened before in different ways. Please tell me you don't have amnesia. Please tell me you didn't forget the Euphoria of 2021 when every company was just hiring as much as they possibly could. Why do you think they did it? Because we're in a new world. We will need as many Engineers. Technology is everything now, and if we don't hire a bunch of people, our competitors will, and then we'll fall behind. And what happened after that? Not just Google, not just Amazon, not just meta, not just Microsoft. All these big tech companies were dead wrong, and they laid off thousands of

people, you have to understand that hype is a force of nature. It's the most powerful marketing tool that's ever existed. Let's talk Devon AI for a second. I made a video talking about it right after the announcement. I thought the founders were super smart, but also that I'm not worried about Devon because these people are clearly fighting an uphill battle. I don't know a lot about it. The benchmarks say that this is more effective at software engineering tasks than the other LLMs. I wonder if that's because it just puts a few things together, like it has its own capabilities to, like, do research, like go on Google, browse Stack Overflow, and run code and execute code, and they just put those pieces together more cohesively than GPT, obviously, in a short amount of time. They didn't create their own complex LLM. I'm pretty sure they're using one of the existing LLMs. I didn't closely examine the evidence or anything like that. It seemed kind of obvious to me. A few months later, people, people are

realizing they were fooled by the hype. Devon is extremely overrated, at least for now. If you don't believe me, maybe you'll believe this guy. He basically proved that at this point, Devon AI is more useless than a freshman CS student who's one week into their first programming course. More recently, they've kind of pulled back their claims. Okay, for now we're just going to start with some code migrations here and maybe some code refactoring over here. Now, can you think of another company who heavily exaggerated their claims and still turned out to be pretty successful? Well, I can think of a few dozen, but the most notable example would probably be Tesla. I've patiently been waiting for the day that I can just sit in a car and have it drive me somewhere. Tesla has been selling full self-driving software for a long time, and I'll say this gently, but I think I have very different definitions of full self-driving. But it doesn't matter, does it? People buy the hype. It's not going to be long before Tesla

cars actually are a good investment. Your Tesla will appreciate in value. You'll be able to use it as a robo taxi when you're not driving it. Again, it's not true, but it probably worked in raising the hype for Tesla. It's less likely that Devon will replace software engineers, and more likely that it'll make the founders and investors a lot of money. And there are many times where people blurred the lines between hype and fraud. Notable examples include thonos and FTX. Very recently, there's been a bunch of fake demos. The Sora video was fake. It was created largely by a video studio. The Google Gemini demo was fake. So why did companies do this? Because it works. Devon AI is valued at $2 billion. That's a lot for something that's currently just an OpenAI wrapper, especially when OpenAI itself is valued at $80 billion. But to go back to answer the question of why build the hype, let's think about it from a numerical standpoint. If I have a company that's

valued at $2 billion. That's incredibly valuable. Because if I need to hire somebody, I'll give them a salary and all. Also, just throw in, hey, why not 100K in equity in my company. You can't sell it yet, it's just paper money for now, but hey bro, it's 100K. Don't pay attention to the fact that my company is not profitable now and it might never be. My company is worth 2 billion. This especially applies to startups and non-public companies, but it also obviously applies to publicly traded companies. Short-term stock gains are good for them because they can then issue shares at a higher valuation. It's basic math. Now, when it comes to the rate of improvement of these llms, throwing better chips at them will help. Throwing better high-quality data at them will help. And definitely, another research breakthrough like the transformer architecture will also help. But it could also be possible that computers are just incompatible for the level of intelligence that many people are expecting them to have. Isn't it possible?

that a paradigm shift might be necessary. Who knows what it could be? It could be quantum computers, or it could be some kind of organic material. So here's how I think about this: it's impossible to predict what the rate of improvement of AI is going to be, but we do have a few data points already. It's been almost 2 years since ChatGPT was released, and quite frankly, from GPT-3.5 to GPT-4.0, the rate of improvement has obviously been very slow. Now, who knows when GPT-5 releases, it could be a dramatic improvement just like how GPT-3.5 was. And of course, the bigger question isn't what's going to happen in 2 years, it's what's going to happen in 10 or 20 years. Is the rate of improvement going to look like this, or is it going to look like this? Well, we obviously don't know. But if you're good at anything, you know that as you get better and better, it becomes harder and

harder to improve. This is analogous to so many things. An example I think is really interesting is distributed systems. If you have a system with 99% availability and now you want to push that system to 99.9% availability, it seems like it's just a 1% improvement. How hard could it be? But that's a very common misconception. It's not a 1% improvement. This is actually a 10x improvement to go from 99 to 99.9 because the failure rate with a system that's up 99% of the time is just 1% of the time, whereas with a system up 99.9% of the time, the failure rate would be 0.1%. There's 10 times fewer failures. And of course, we know with AI, when it works, people are fine, like they're impressed. But when it fails, that really matters. When it comes to Tesla, if your car fails even one time, it could be game over. This needs to go down as small as possible. So in fact, we're not trying to go to 99.9%, but I want it to see it. I want my

Tesla to go as far as it could possibly go. It'll never be a 100. Even humans aren't that good. But I think it's becoming clear that the rate of improvement eventually is going to dramatically slow down. It's just not quite clear what the bottleneck is just yet. I have so many examples I could share with you. I'll share a couple very quickly. One, Tesla faked a self-driving demo in 2016. Why would they do such a thing? Companies want you to think that they're better than they are. They're improving more quickly than they are because if you buy at the bottom, well, eventually it's going to be up here. And even if it never gets there, well, you already bought the product. If I was a taxi driver and I quit my job over something Tesla or Elon Musk said, I would feel kind of like a jackass. Fool me once, shame on you. Fool me twice, shame on me. So when Elon says we're going to have artificial general intelligence.

within 2 years, I'm a little hesitant, and I'm more inclined to listen to the experts. It's not like Elon makes claims purely from an emotional perspective; I'm sure he's always driven by facts and logic and research. I have a relatively simple way of making tough decisions given so many unknown variables, and the first thing is it's not proven right now that software developer jobs are being automated. Who knows, that could change overnight, and even if it were to start happening, it still wouldn't be proven that we don't know if the code quality will suffer so much and there will be so many outages that it wasn't worth it. It wasn't worth saving a little bit of money in the first place. And second is more of a human aspect: how can you live with regret if you were to drop out of your major right now or transition into a completely different career because you're predicting developers will be automated within a few years, and it turns out that that doesn't happen.

happen. Can you live with that? How would that make you feel? Obviously, consider the alternative. You change paths right now and you get a step ahead. Well, what exactly are you getting a step ahead on? I don't know what exactly you're planning on doing. Becoming an accountant, a lawyer, a physician? Because if developers get automated, which jobs aren't going to be automated? There is some aspect of at least we're all in this together. I would not make any drastic decisions, especially when it comes to education. Guys, guys, we have calculators. Why are we still teaching kids math? We have voice-to-text engines now. Nobody needs to know how to read and write. I'm sure all the cutting-edge researchers right now have very poor math skills and very poor reading and writing skills. The logic that goes into programming can be applied in so many areas, especially business. That's why some of the best business leaders today are technical founders. Honest to God, I think I owe most of my success to the education I received early in my life, teaching me math, physics, and eventually

programming now. Of course, some things are really complex like architectural details, but some things are simple. It's obvious that human brains are fundamentally different than the most advanced AI today. Humans only need at most a couple dozen hours to fully learn how to drive medium, medium left, well, at least most of us. Small children only need to see an animal a few times, like a cat, to be able to identify it in the future. This is just not true with AI today. And I'm also saying that AI is going to make some improvements. It's going to automate many things. It's not, in my opinion, going to live up to this dramatic hype that we've seen, but it doesn't need to live up to the hype to make some people a lot of money. If you wish to make an apple pie from scratch, you must first invent the universe. Thank you very much.

This video will change your mind about the AI hype

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AI 과대 광고의 영향과 GPT-4의 진화

연사는 AI에 대한 과대광고에 대해 언급하며 더 큰 그림을 보고 역사적 맥락을 고려하는 것이 중요하다고 강조합니다.

GPT-4의 혁신적인 속도에 대해 회의적인 입장을 표명하고 OpenAI에 대한 윤리적 우려를 제기합니다.

연사는 AI 과대광고에 대해 반대 입장을 취하며 주요 AI 기업의 윤리에 의문을 제기하고 집단 사고방식을 피해야 할 필요성을 강조합니다.

AI와 칩에 대한 빅테크의 투자 이면의 합리성

대기업들은 결과에 관계없이 상당한 수익을 가져다줄 것이라는 믿음으로 AI와 칩에 수십억 달러를 투자하고 있습니다.

이는 스마트폰 산업에서 도태되고 뒤처질 수 있다는 두려움에서 비롯된 것입니다.

그러나 현재 AI로 수익을 창출하는 주요 기업은 NVIDIA이며, 다른 기업들은 아직 그 결실을 기다리고 있습니다.

기술의 과대 광고의 힘: Devon AI 사례

Devon AI는 처음에는 소프트웨어 엔지니어링 작업을 위한 강력한 도구로 과대 포장되었지만 이후 과대 평가된 것으로 드러났습니다.

이 회사의 과장된 주장은 테슬라의 완전 자율 주행 소프트웨어와 같이 성공했지만 과대 포장된 다른 벤처 기업을 떠올리게 합니다.

가짜 데모를 포함한 기술 업계에서 과대 광고는 20억 달러에 달하는 Devon AI의 가치 평가에서 알 수 있듯이 수익성 있는 전략으로 입증되었습니다.

AI 기업은 현재 수익성보다는 잠재력을 기준으로 수십억 달러의 가치를 평가받고 있습니다.

AI의 개선 속도는 예측할 수 없으며, 더 높은 수준의 인텔리전스로의 전환은 상당한 도전 과제입니다.

AI를 크게 개선하는 것은 분산 시스템의 가용성을 높이는 과제와 비슷하며, 작은 비율의 증가도 상당한 도약을 의미합니다.

테슬라와 자동화의 미래: 비판적 분석

테슬라의 개선 속도가 느려질 것으로 예상되면서 향후 테슬라의 발전에 대한 의문이 제기되고 있습니다.

2016년 자율주행 시연과 같이 과장된 주장을 하는 패턴은 Tesla의 약속에 대한 신뢰성에 대한 우려를 불러일으킵니다.

발표자는 엘론 머스크의 대담한 예측에 주저함을 표하며 감정적인 주장보다는 전문가의 의견에 의존할 필요가 있다고 강조합니다.

교육과 고용 시장에 미치는 AI의 영향

인공지능이 많은 직업을 자동화할 것으로 예상되면서 수학이나 작문과 같은 분야에서 교육의 관련성에 대한 의문이 제기되고 있습니다.

하지만 프로그래밍에서 배우는 논리와 문제 해결 능력은 다양한 분야, 특히 비즈니스에서 유용하게 쓰입니다.

인공지능의 발전에도 불구하고 인간 두뇌의 고유한 학습 속도와 능력은 인공지능과 차별화됩니다.