Artificial Intelligence (AI) is poised to change the world far beyond the innovations brought by electricity. AI technology, already present in our smartphones and pioneering self-driving vehicles, is making it easier for individuals to create music, videos, and apps, and will soon be envisioning cures for diseases. The underpinnings of AI revolve around machine learning, particularly supervised and unsupervised models, and deep learning, which mimics human brain processes. This technological field, especially when considering its subfields like machine learning and deep learning, is projected to create massive economic value by 2030, despite the risk of significant job Displacement due to automation.

The advances in AI are not without controversy, with some viewing these as potentially transformative and others as exaggerated promises by corporations. Former Google CEO Eric Schmidt highlights key AI advancements such as context windows, agents, and text-to-action models, essentially forming a framework for futuristic AI capabilities. These advancements suggest a future where specialized AI agents could collaborate and communicate autonomously to solve complex problems. The discourse around investing in this rapidly evolving landscape also raises questions about the future role of stock markets.

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Key Vocabularies and Common Phrases:

1. Simulation [ˌsɪmjʊˈleɪʃən] - (n.) - Imitating a real-world process or system over time.

AI refers to the Simulation of human intelligence in machines that can think and learn.

2. Displacement [dɪsˈpleɪsmənt] - (n.) - The enforced departure of people from their homes, typically due to external factors.

AI and automation will displace more than 85 million jobs by the year 2025.

3. Trajectory [trəˈdʒɛktəri] - (n.) - The path followed by a projectile or an object moving under the action of given forces.

This is how we can predict someone's career Trajectory based on income versus time.

4. Paradigm [ˈpærəˌdaɪm] - (n.) - A typical example or pattern of something, a model.

Supervisor models are a Paradigm of how machine learning structures data.

5. Profound [prəʊˈfaʊnd] - (adj.) - Having deep insight or understanding.

There's three things happening right now that will profoundly change the world.

6. Consolidate [kənˈsɒlɪdeɪt] - (v.) - To combine multiple elements into a single, more effective or coherent whole.

...come together to Consolidate and end up running the entire world with this technology?

7. Autonomous [ɔːˈtɒnəməs] - (adj.) - Having freedom to govern itself or control its own affairs.

...could collaborate and communicate autonomously to solve complex problems.

8. Intriguing [ɪnˈtriːɡɪŋ] - (adj.) - Arousing curiosity or interest; fascinating.

The industry is becoming extremely valuable altogether and arguably Intriguing.

9. Hypothetical [ˌhaɪpəˈθɛtɪkəl] - (adj.) - Based on or serving as a hypothesis.

And at that point, does the stock market Consolidate into a handful of companies...?

10. Speculative [ˈspekjʊlətɪv] - (adj.) - Engaged in, expressing, or based on conjecture rather than knowledge.

At some point, people believe that these agents will develop their own language.

AI's Impact on Jobs, Markets, and Future Trends Unveiled

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 human 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 Chapt and Google. 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 five 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? 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 Andre Jick. Hope you're doing well. Come for the finance and stay for Aih. 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 nothing. 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 automagically. 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.

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 five years, for example, to be able to produce 1000 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 and 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 code, 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 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, it 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 five years, not tend. 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 five years and maybe sooner, now that you kind 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. And according to MIT and Boston University, AI will replace as many as 2 million manufacturing workers by 2025 as well.

The McKinsey Global Institute 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. It's 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 $80,000 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 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 ten 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. Ten years ago, for example, the top ten companies represented just 14% of the index, or roughly half of what it is today.

Just to put all this in context, for every $100 I put into the s and p 500 index, 27 of that 100 goes towards these top ten 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 ten, presumably because they have the best chance of figuring it all out.

So, taking all of that in context, the question is, should I just sell everything and then chase the top ten 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, than the S and P 500 index.

Bye. 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 and 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. And 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 the spreadsheet linked down below on 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.

Technology, Innovation, Science, Eric Schmidt, Machine Learning, Investing