 In this video, really, what it's really about is you have to take into consideration the implications of automation. Hi, this is Chichu. Welcome back to the channel and welcome back to our little discussion we're having regarding personal finance. Now what I want to do in this video is sort of continue on with where we left off in the previous video. In the previous video what we did was talk about one of the most important things we have to keep in mind. When we're thinking about investing in anything, maybe investing our time and our energy or finances into something. Which is basically taking into account our timeline, our time frame. And we sort of focused in on Wall Street talking about the stock market specifically. And the reason I did that was because when it comes to stock, when it comes to Wall Street, we can look at different time frames. Look at different timelines. We start off talking about months and then we sort of went into years and possibly decades. And then we took our timeline here and we narrowed it down to days, minutes and seconds and milliseconds when it comes to high frequency trading. And the thing we have to really appreciate regarding that discussion is the only reason we're able to talk about trading, talk about the sort of business decision that you can make on the second and millisecond fraction of a second level is because of technology. For us, we would not be able to talk about seconds or even minutes really trading stocks on a minute basis if it wasn't for computer powers, right? For computer technology. And that's one thing I want to expand on right now specifically related to how computers are bringing about something that's been happening for a little bit of time right now. Basically the concept of automation and how automation is going to play out in our current economic system. And automation is something that all of us, it really doesn't make a difference which field, what business you're in. If you already have a business or if you're planning on going to school in a certain field to get a certain degree to enter that industry or start your own business, you have to take into account the role of automation and how that's going to play out in the near future. Because it's already playing out in certain industries and it's slowly being rolled out in other industries. And to have a really good appreciation of how this is going to play out. What I want to do is just read you a quote just to kick things off. And this is a quote that you've heard me read before and it's a quote from Stephen Hawkins and I sort of put out a video of a reading of some quotes and it was more politically oriented. But there was three quotes in there from three different people which we can think about of being very relevant in our current economic system as well. And politics, economics, again, is the same beast, right? But when it comes to technology, these three quotes, one was Stephen Hawkins which we're going to read. The other one was Carl Sagan and the third one was Jacob Appelbaum. Those three things are related to what we're going to talk about right now which is basically computer technology and in a more focused way automation, right? So quoting Stephen Hawkins from Black Holes and Baby Universes, page 28, it was a speech given in Spain in 1989. Quote, if we accept that we cannot prevent science and technology from changing our world, we can at least try to ensure that the changes they make are in the right directions. In a democratic society, this means that the public needs to have a basic understanding of science so they can make informed decisions and not leave them in the hands of experts. And that quote has a political overtone to it as the speech was given. But the first part of that quote, if we accept that we cannot prevent science and technology from changing our world, we can only direct those changes. And that's something we have to really appreciate and we can sort of work within those changes that are happening and use those changes to our advantage. So that's the thing we have to keep in mind. No matter what you're doing, no matter where you think society is going in the future and what you're investing your time, your energy, your money on, you have to take into account the concept of automation. Really, this is huge. And the reason I'm emphasizing this is a lot of people are not thinking about this. And one of the things we should keep in mind when it comes to technology and how technology rolls out in our societies, and technology is not just computer powers. Technology has been being rolled out in our societies throughout decades, throughout centuries, throughout millennia. What we're talking about right now specifically is computer power, processing power. Because the invention of the wheel was technology that changed the economic system at the time. The invention of fire was new technology that changed that economic system. Gutenberg's printing press was technology that was introduced in an economic system that changed that economic system. Those things took years for them to play out, right? What's happening with processing power with computers? The time frame, right, that new technology, how fast that's changing our society is becoming shorter and shorter, right? And that's specifically something related to computers. We haven't seen these types of changes coming to play this fast previously in history. And if you want to get a feel for, just for your own reference, if you want to get a feel for how technology changes certain industries, there are certain industries that end up rolling out new technology faster than other industries. When it came to computers, there were three or four industries that really rolled out computer power faster than other industries. The adult entertainment industry may be gambling or other things. They were one of the first industries that really took advantage of personal computers and the internet and streaming videos and stuff like that as secure communication, right? Another industry that exists everywhere around the world is the illicit industry, I guess, illicit system of doing business, which is anything that's really, well, used your imagination. Any type of illicit business really started rolling out computer technology quite rapidly, right? And those are two things that I don't think we're going to talk too much about when it comes to personal finance. But one of the other industries that has rolled out technology in a way that in certain parts was decades ahead of other industries, and we're sort of seeing some of that stuff being rolled out now, is the finance sector of our economic system, which is one of the reasons why we started talking about the stock market thing, you know, talking about shorter time frame of doing trades, because the only reason that we can do trades on this level is because of computers, because of technology, right? And what we're going to do is sort of expand on the topic of Wall Street, the stock market trading a little bit further, where I'm going to read you a couple of short segments from an article which sort of gives us a history of how computers came into play in finance when it comes to trading stocks. And it's a fantastic little history, and keep this in mind when we're reading that, is that finance, Wall Street was one of the first places that we saw computing power change that industry. And what we're seeing right now play out in our current economic system when it comes to Wall Street, Main Street and Wall Street, and everything that's happened the last 10, 15 years, right, with the economic crisis and the financialization and how our current economic system is laid out. I don't want to go too deep into that, we will later and talk about some of the pitfalls of that and some of the excerpts from this article. And this article is Must Read, and I'll tell you what it is right now before we go on any further. It's an article by Martin Armstrong, and it's called Behind the Curtain, the Full Monty. And if you've read this, you'll know why this is important. If you haven't read this, it's pretty important to read if you want to have a good appreciation for how Wall Street works and how stock trading works and how our current system works when it comes to financializing or commodifying everything. But before we get into this, what I want to do is talk about how we can think about how computers play out in our current economic system. And one of the things we have to appreciate is computers are a disruptive innovation. What we talked about initially in the first set of videos when we talked about disruptive animation, differential accumulation and we did a maximizing revenue problem. Disruptive innovation is basically technology that's come into play where it's forcing industries to change their business models to use this technology to improve their business practices to improve profitability, I guess, or it's technology that is coming into play which is going to make certain industries completely obsolete. And we've had this happen throughout history throughout decades, centuries, millennia, right? When it comes to invention of fire or discovery of fire invention of the wheel, to the printing press, to electricity, to computers, right? And one thing we have to have another appreciation for, some of those technologies took a long time for them to roll out in our economic system. Computers is not taking a long time to roll out in our economic system. Personal computers did. The internet to a certain degree, based on what's happening right now, how fast things are moving along right now even the internet took a long time to filter within our society, right? Because if you think about it, personal computers really came into play in the late 1970s, early 1980s, right? Mid-1980s, the internet came into play in the late 1980s, early 1990s, right? Our ability to process big data came into play in the late 1980s, mid-1980s, late 1990s, early 2000s and our ability to do secure high-speed communication online, maybe streaming or banking, really didn't come into effect until the early 2000s to the mid-2000s. And what we're seeing right now coming to play in the last 10 years or so is automation. And it's going to change things on the same level, if not greater, as did personal computers, as did the internet, as did our ability to do processing of big data, as did our ability to do secure high-speed communication online. The big difference is automation is assumed and what's happening right now is that it's going to take away more job than it's going to create because with previous technology, what's happened is most other disruptive innovation that has come into our economic systems, they've created more jobs than they've taken away, right? The invention of the wheel created more jobs than it took away. Initially, certain people lost their jobs, they were carrying things, now you could roll things, right? The invention of electricity initially took away a certain number of jobs, but it created a huge number of jobs. The reason we had so much growth within our societies is because of electricity, right? And computers, you can think about it the same way, personal computers initially took away a certain number of jobs, very few, but they introduced a tremendous number of jobs, right? The internet took away a certain number of jobs but introduced a huge number of jobs, right? The ability to process big data created a lot of jobs. The ability to communicate at high speed created a lot of jobs. It took away a certain number of jobs, right? When it comes to mainstream media, right? When it comes to entertainment, when it comes to reporting news, right? There's a lot of industries that have collapsed because of high speed secure communication, right? Because of our ability to stream video and share information, right? So there's a lot of industries that have been hurt because of this, but there's a lot of growth that's also happened. When it comes to automation, the way it seems to be playing out, it's taking away more jobs than it's creating and that's going to be a huge problem for us, for the short term anyway, right? Because any business that we're thinking about investing in, any business that we're thinking about starting, any field that we plan on studying to be able to get a career, to be able to get jobs in generating common manager personal finances is going to be affected with automation, okay? And the way you think about automation is the following. And the best way to think about it is think about it in a way of how we end up making decisions, right? The way we end up making decisions is we look at a situation, we decide, you know, all the content, what the important variables are from that situation and based on our experiences, our education and our society, you know, what we've been exposed to, we make a certain decision based on what we're experiencing, what we're seeing and we decide to do something, right? That's the way a human being basically ends up making decisions, right? So basically what we do is we have a certain amount of input, right? So whenever we look at a situation, and this is pretty important, it's something that I sort of got a full appreciation for as I got older is what I see happening may be different than what you see happening. When I look at a situation, what I deem to be important may be different than what you deem to be important, right? What your mind, your experiences, your knowledge considers to be the most important variables in a certain situation, right? When I look at something, I'm looking at my room, I might look at the plants first and consider those to be the most important thing in this room, right? I might be looking at my bookshelf and thinking that the books are the most important things. You might be looking at the situation and you might look at the albums and say, hey, there's albums here. The albums are the most important thing, right? So data, when we acquire data, right away when we're acquiring it, there's a certain filter being put on where we're considering certain things to be important or certain things to be worth noting, right? And that's the same way computers to a certain degree work. When we're entering data into computers, into programs, really, code, we decide what the variables are, what's important, what's the data being put in, right? And what we do with that data, so when I look at a situation, I end up processing that information based on, again, my experiences, my education. And that part is called the processing part. Computers do the same thing. They take all of this information and they process it. And based on that processing, based on the code that was written, they make decisions, as do we, right? We look at the situation, we decide what the important information is in that situation. Based on our experiences and our education, our knowledge, we make a decision, right? We take into account all the variables, whatever we could make a note of. We decide what we're giving weight to, right? One of these things that we give weight to, what we think is important, maybe a combination of three things here, right? And then based on our processing, we make a decision and that's our output. And let's call that what to do, right? What to do. This is basically the same way computers work. We feed them data based on the code. They make it, you know, they process the information and they give us an output. This is playing out in automation in a huge way, in a huge way, right? Maybe from self-driving cars to just simple robots and factories or automated tellers in grocery stores, right? Or fast food chains or restaurants and whatnot, right? So this system, this process is having tremendous impact on our current economic system, right? Because one of the things that's happened is we've been able to input more and more data into programs and because our processing speed has reached the level where we can process all that data, we're able to create basically neural networks, AI, to make decisions for us or to automate things, right? And this is something for one to understand, wasn't in play a few years ago. A few years ago we had a bigger role to play here than we did here. And if you've been following our blog in 2006, I wrote a little article talking about data and processing and how that's playing out on a political front and how that could play out on a political front. I call that article anomalies, prisons and geophysics, how governments use data and how to stop them on a political front anyway if you don't agree with what's happening. But we did a reading of that as well. I sort of did a soft spoken reading of that because I think that's extremely important. And I've been following sort of this progression of processing large data for a while now. And in that piece, we sort of mentioned that good data is extremely important. So if the data being entered into the software, into the code is not accurate, it's not taken into account some of the most important variables, right? Then that's a place where we have a weak leak in the chain, right? Things can seriously go wrong, right? We might have forgotten to account for a certain variable, right? And the processing is basically the interpreter, the code written to be able to take this data, process it to give us an output. So keep in mind that there's two places where this system is dependent on, right? Basically almost 100%. One is the accuracy of the data and the magnitude of the data, how large the data is because right now in our current economic system, something that has tremendous value that didn't really have this much value in the past is big data. So data now has become something that is worth a lot, right? So the amount of data that we have is extremely important, the accuracy of the data and the code, which is basically your expertise. Whoever is working here to write the code to be able to do the processing may it be more hands-on, us writing the code and picking the important variables from the data set or letting a neural network make that decision for us. One of the places where I can think this is happening when it comes to a supervised learning is I had a conversation with one of my students who is very active online. My student brought this up and we're talking about YouTube videos and how censorship is coming into play on some platforms, right? And the platform that you're watching right now is definitely coming into play, right? And what's happened is Google, YouTube has hired thousands of people and they're basically getting their employees to look at videos, even segments and flagging them and they're also put out apps where they're getting you, the viewer, to flag. And that option has been available to flag certain videos, certain content, right? And when I was talking to my student, this subject came up, censorship and stuff like this and different platforms, video sharing, the discussion of video, different video sharing platforms came about and how there are certain monopolies of play right now. And my student mentioned that what Google, YouTube, Alphabet, Inc. is doing is basically running a program in the background which is monitoring all the activity, the human beings sitting there flagging video or sending comments or doing whatever it is, their contribution, whatever it is they think requires censoring or is inappropriate content. The program is monitoring that information, the time, code, when they did it, how long they watched it, which part of the video that appeared, the length of the video who's putting it out, where it's being put out from, the amount of data I can't even imagine what they're collecting and how the process is going. But what they're doing is they have computers right now learning from that behavior and in the near future they're not going to require any more employees to do that censoring, to do that monitoring because they're going to get the program, the code, right? The algorithms to do that work for them, right? So all of those jobs at Google, at other tech companies where they're sitting there going through the content to see what's appropriate and what's not appropriate based on their business model because they have the right to do this, right? It's their business model, it's their platform. They can decide themselves who they want their audience to be, right? So they're going to fine tune that and get some kind of output and they're going to decide which industry they want to function in, right? Is it maybe just the adult industry or they want to be family oriented or they want to be more geared towards younger audience, right? It's their business decision. So they're running software and that software is learning from the behavior of their employees and the behavior of their customers, right? You, me, viewing YouTube videos and giving them our feedback, right? So all of those jobs are going to disappear. So if anybody's working in that field where they're watching videos you know, flagging videos, that's most likely the short term thing unless they're willing to work their way up the ladder because what's going to happen? The majority of that work is going to be automated, right? And the only human part of that interaction, that decision making is going to come into play in the management level, right? And that's one place where, you know, on a short term automation is going to play. Automation for sure is going to play in self-driving cars. It is going to come into play in self-driving cars, right? And incorporate that, sort of think about that in the same breath as, you know, different types of apps that have already come into play that completely changed our certain industries when it comes to ride-sharing apps, apps like Uber or taxi service apps and stuff like this, right? I've sort of, I've been following that aspect of our economic system for a while. I started talking with taxi cab drivers, you know, about 10 years ago or so. Basically I was asking them how their computer systems were coming into play and I sort of got involved in this in the early 2000s where taxi cabs were introducing new technology where they were, it wasn't dispatch calling in who wanted rides where it was just popping up on, you know, the little network, I forget what the systems were. This was in 2000s. There were a couple of companies in Vancouver that were introducing this software and hardware as well into four taxi cabs. So I knew some people involved in the industry, so I followed that a little bit. And they were rolling this out fairly rapidly. So I've been tracking the system a while. So, you know, I've been talking with taxi cab drivers for 10, 15 years or so. And one thing that I noticed was when Uber ride sharing technology apps were coming into play, there were some taxi cab drivers, people working in that industry that really didn't appreciate the full impact of what was happening. And previously the way it worked was if you wanted to drive a taxi in my area anyway, you need to be licensed. And those licenses, the government in general would sell as raffles and would sell them for a few thousand dollars, five to ten thousand dollars. And because they were limited, sort of an arbitrary scarcity was put into the system based on licensing introduced by government. In the aftermarket, those licenses were selling for a lot of money. They were selling upwards of, you know, I talked with some of the information that I got. Initially they were going for double, triple, they were going for 80,000, 100,000, 150,000, 200,000, 250,000 dollars. So if you were able to get your hands on a license to drive a taxi, you could have sold that license for, you know, $150,000, $200,000, $250,000. But what happened with apps, ride sharing apps when they came into play, the cost of those licenses plummeted to a level where last time I talked to someone it was last year, about a year ago or so. So this is a year outdated. And they said that those people who had the licenses, who had bought those licenses to drive a taxi cab and had paid, you know, $100,000, $150,000, $200,000, right? Couldn't even sell those licenses for $20,000 now, right? Or $15,000. I think the lowest I heard was one person was having a hard time selling at anywhere between five to ten thousand dollars. So if you did take into consideration how not automation, just apps, technology, computer power was going to play out in your industry. And ten years ago, you bought a taxi cab license for $200,000 and you couldn't even sell it now for, let's say, $20,000. You just lost 90% of your investment, right? So you really have to take that into consideration. So, you know, what did that app do? That app basically, I don't know if it's taken away more jobs than created more jobs. My guess is created more jobs because a lot of people doing, you know, ride sharing, using ride sharing apps to pick up customers, maybe through Uber. I'm not sure where the technical term is for specifically Uber type of software or ride sharing apps or software, right? I'm pretty sure there's more people that are driving Uber cars right now or using those kinds of apps to generate secondary income than there were tax cab drivers, right? But one place, you know, the next step for this could be possibly, you know, if you merge that technology with automation when it comes to self-driving cars, which will come into effect, you know, we're basically one step away from, you know, a company coming into play where they can buy a whole bunch of self-driving cars and go back to getting a license from a municipality, right? To be able to function as a company where these self-driving cars using apps now become your taxis, right? So what you could do is use an app, call a taxi, and a self-driving car would come and pick you up and take you to where you were going, right? Completely automating that whole process, eliminating the need for taxi cab drivers and eliminating the jobs. I don't want to say the need, but how lucrative it is to be a taxi cab driver, or how lucrative it is to be a ride-sharing driver with companies like Uber and whatnot, right? So all of a sudden, automation will make all those jobs disappear, but will it create a lot more jobs? That's something that's still in debate, right? Again, it's something you're going to have to consider. It's something in your industry that might come into play, right? 3D printing is another one. A lot of industries might be immune to automation, but a lot of industries will not be immune to automation. In some places, that stuff is going to roll out a lot faster than others, right? There's automation being rolled out in construction right now, right? There's 3D printers, large-scale 3D printers that are printing structures, right? How viable is that? At some point, it's going to be viable. On a smaller scale anyway, we'll see that rolling out. Is it a good idea to go into... to stay in the bank teller business or on a retail level where you're a teller or where you're working at a restaurant where you're taking orders? Because what's happening right now is there's a lot of automation coming into play when it comes to grocery stores, when it comes to certain types of fast-food restaurants, right? They're introducing automatic tellers, and some places are sort of forcing their customers to use automatic tellers, right? If you go to certain places where there's a few automatic tellers and when it comes to human beings working the register, they only have a couple of people working there and the line-ups are really big to check out your stuff, right? So some people are being forced out of frustration to use automated machines to do their shopping, right? Is that a good thing or a bad thing? You'll have to consider, right, that we'll see how it plays out. It might free people up to do more things, to get into different fields, right? Another example I have is in the past and the last few decades what we've seen was basically industry factories moving their production chain, right? Where they're making materials to countries where there was cheap labor, right? Maybe to Asia, to South America, to Central America, to Africa, to different parts of the world other than the Western world where labor was cheap, right? So there's a lot of factories being built in a huge part of the world, right? They were hiring a lot of people for fairly cheap and those jobs were basically jobs that people were losing in the Western world, right? Now, was that positive or negative? There was a lot of jobs, a lot of wealth created in certain countries and there was a certain number of jobs being lost in certain countries. Now, the number of jobs being lost I think was less than the number of jobs that were being created, right? There was a lot of inequality being created in the same process, right? People losing their jobs in the Western world that were extremely well-paying jobs. All of a sudden we're having a hard time finding new jobs to go to, right? Because they've been working in a certain field for 5, 10, 15 years, 20 years and they had to be re-educated, right? But what's happening right now with automation is a lot of factories, not a lot but we're seeing the beginning stages of it. Some of those industries are now bringing those factories back to the Western world, right? So there's going to be a lot of jobs being lost in a lot of the countries that were hiring people on the cheap, right? Where labor wages were really low, where environmental regulations didn't really exist There were no labor laws, there were no unions, right? So it was really cheap to make materials there But what's happening right now, some of those businesses, some of those industries are now moving those factories back to the Western world One of them that recently, last year, announced that there were a German company that were going back to Germany, was Adidas, right? Last year, 2016, they mentioned that they're bringing a factory back to Germany from, I believe it was China or Asia anyway and they're going to start producing textiles, shoes in Germany closer to the point of purchase, right? And that's something to consider as well because that's going to have a huge effect for businesses, right? You don't have to pay for those transportation costs and your carbon footprint becomes less But there was a catch in that announcement The factory coming back is almost going to be fully automated So there isn't going to be any factory jobs being created there There's going to be a lot of jobs being lost in the Asian area wherever they were set up, right? There's going to be a lot of jobs lost there But there's going to be an equivalent number of jobs created in Germany on the factory level anyway, right? On the factory floor And there's definitely not going to be more jobs created, right? And that's the role of automation And that's the role it's going to have in our society So in this video, really what it's really about is you have to take into consideration the implications of automation It's huge It's going to play out in so many different fields in so many different fields And there are certain fields that are going to get a boom that are going to get a huge boost and they're already seeing a huge boost, huge demand, right? I don't know if it's if they're going to create as many jobs as are being lost because of automation I don't know if they're going to give birth to new industries while they are, right? New businesses And those businesses are going to create more jobs that are being lost One place that's seeing a huge growth is education What we're doing right now because a lot of people that are losing their jobs they're going to have to be retrained They're going to have to acquire new tools to be able to find something else that they're either passionate about to go into or they can find jobs in, right? Because they have to manage their personal finances, right? Education is 100% Another industry that's seeing a huge boost, huge growth is the healthcare industry in the large part There's two reasons for that One of them is aging population in the west Another one is our life... lifestyles, right? There's a huge number of jobs being created in my area Anyway, when it comes to massage therapists when it comes to kinesiologists when it comes to chiropractors when it comes to Pilates instructors physical fitness instructors, right? So there's a lot of jobs being created there So there's a whole bunch of secondary tertiary areas There's a ripple effect throughout our economic system, right? Sort of a long-winded discussion of how technology is playing out the role of automation is playing out but this is such a broad, broad, broad topic and it covers so many different industries so many different places this is playing out and one thing I want to do now is sort of read to you this little segment from this article by Martin Armstrong Martin E. Armstrong and he wrote this in 2010 and it's going to give us a pretty good idea sort of feel of how technology as disruptive innovation played out when it comes to finance when it comes to the stock market when it comes to Wall Street and this is going to continue to change have a huge effect in our economic system because this introduction of personal computing into our economic system into Wall Street, into the stock market really changed the game and it had a domino effect, a ripple effect throughout our societies of the way how directly related to the 2018 2008 financial crisis directly related to how Wall Street, the banking industry had a finance industry is working right now how Wall Street is functioning right now and how our political system is functioning right now it's a little history on how computers changed the game when it came to Wall Street came to the stock market and I found it incredibly intriguing very fascinating and again I highly recommend it it's by Martin E. Armstrong behind the curtain the full Monty it's available online I found it online anyway as PDF and other formats you can download it, print it or just read it directly online I printed it because I read this a while ago and I made some notes and it's well worth reading if you want to know how the economic system works how finance works but let me read you this part and this is from page 15 of the PDF that I downloaded and the area that's in it's called the age of computers just on top there the age of computers then I'm going to read you a little segment a little paragraph I'm going to read a couple other segments here and then I'm going to move on to a couple of other areas okay so quote in 1985 the Supreme Court ruled in a major case, low versus sec, the Securities Exchange Commission number 472 USA 181 1985 that held the publishing of analysis was protected by the First Amendment that did not require to be regulated by the sect and what that means basically the First Amendment in the United States is that freedom of expression freedom to express yourself to have your opinion right and there's a little bit more to that but this is what it's really related to so basically the ruling in 1985 said that the government couldn't prevent people from looking at companies doing their own analysis and writing articles and giving a buy or sell recommendation and talking about that business that was protected on the First Amendment right so once a law, a rule and a political realm was clarified all of a sudden the doorway was opened where it gave birth to a whole new industry and thanks to personal computing processing speed right that in turn changed our economic system right so the two played together right you know the question of chicken or da which gave birth to the first my belief is personal computers gave birth to the law being clarified because what was happening was and you can read this there's a build up to this taking excerpts from this but what was happening is some people were using computer computer programs to write business analysis and selling that analysis to certain investors and those investors were making business decisions that forced this clarification in the law right so in 1995 the Supreme Court said publishing analysis was protected in the First Amendment and we skip a few paragraphs and a few lines just the first sentence of one paragraph says this everyone was rushing out and buying IBM desktop computers and try to create models so in Wall Street those people that were involved in finance were going out and buying computers and using this processing power to create models on certain companies we skip a paragraph a couple of paragraphs next paragraph the greatest problem that Wall Street ran into with their attempt to model the markets you have a huge gap between the trader and the programmer they do not even speak the same language what the trader is trying to explain the program is then trying to write in computer language it is not easy the trader does not comprehend how a computer operates so he skip such basic steps that the programmer not understanding trading cannot fill in the gap so basically what he is saying Martin Armstrong is saying is that there was a gap between the data and the processing the computer programmers were writing code the traders were giving them but the traders did not understand how the programmers were working and the programmers did not really appreciate all the nuances in trading they were not taking into account all the variables here and it goes into some detail about the 1987 collapse in the stock market and how that played out and how certain people were blaming computers the introduction of personal computers and all these different types of analysis coming up for causing the 1987 stock market collapse and he continues on in the next segment and just one little sentence and one paragraph over the next 8 to 9 years computer models were getting more sophisticated but at the same time more myopic and dangerous we skip a sentence or two the collapse of long term capital management quote again the collapse of long term capital management illustrated the danger between merging the fields of experience with no practical risk management what was happening was twofold it was a blending of manipulation insider info and sophisticated computer models that did not take into consideration what happens when the market goes into total illiquidity and liquidity in the markets is huge liquidity basically means how fast can you sell something can you change from one asset value to another from stocks to cash and from cash to something else so basically what Armstrong is saying is the computer models here didn't take into account what happens when there are dramatic changes happening in the stock market where all of a sudden the market is not liquid the programmers didn't take that into consideration you can think about that as an anomaly, an asymptote when it came to talking about certain types of functions we talked a little bit about this in the language of mathematics what happens when we had an asymptote and basically in the 1987 crash the stock market had an asymptote and it collapsed there were safety net setups and it recovered but not after a lot of people lost a lot of money both noobs, people that were new in the industry and experts and people blamed computers blamed analysts for causing that crash he goes on saying that computer models were telling people to sell and it was the analysts that didn't take into consideration a lot of variables what was happening in the markets that didn't sell so all of a sudden the difference between prices was huge and it continues on we skip a few pages and it goes into a section called the birth of derivatives the birth of derivatives and what we're going to do is read just a little segment here and I'm going to read you a little segment here actually we're going to read this whole paragraph and I'm going to read you this segment and here's the thing computers when they came into personal computers when they came to play in the stock market they created a certain number of jobs they created a whole new industry financial advisors personal financial advisors in a big way they existed before but that really gave birth to something else and they also gave birth to derivatives and derivatives are basically secondary markets where you can trade one of the first order derivatives that we have are call and put options and call and put options in the stock market are basically you buy the rights to sell something at a certain price for a certain period of time or you buy the rights to buy something at a certain price for a certain period of time the first one is called puts the first and the second one is called calls and it's basically betting on another bet it's risky and there are multiple derivatives in play so what's happened is computer technology processing abilities Moore's law their ability to process faster and faster doubling of processing ability every 18 months since the 1960s up to this point 20 years ago basically gave birth to derivatives and that created a whole bunch of jobs again right and was basically considered to be disruptive innovation in industry that had to adjust in Wall Street that had to adjust to this new system and one thing one thing I should mention is pretty important to note that I don't think actually talks about it here but some of these computer models on Wall Street that people were trading on a millisecond level they're not really thinking about long term they're not running models that are looking at a company thinking about what this company is going to be doing 5 years from now 10 years from now they were looking at the millisecond scale so they were looking at being able to make profits on very short term with high frequency we sort of mentioned that in the previous video how you could make a lot of money your investment would be worth a lot if you were able to decrease your time frame so what they were really doing was not making money in the rise and value of that stock but they were making money in the arbitrage and the difference between the selling and the buying price and the computer technology that's what computer power allowed people to do within finance for the stock market so let's continue on with this little reading from this important must read if you're involved in the stock market behind the curtain the full montage by Martin Armstrong okay coding again the birth of derivatives in so far as the financial markets was concerned came after the turning of our economic confidence model model in 1985 that's when the second ruling happened right continue coding from there onward there was a mad rush to bring in computers and create models Princeton economics was very well known behind the public that's what we've seen we were primary institutional advisory for the big corporates and banks around the world I would rarely grant interviews with the American press for clients had long made it clear that they were paying the big bucks for us paying the big bucks to us and they did not want to see the same forecast given out for free on the front pages of Wall Street Journal so everyone knew we had sophisticated computer models long before anyone else and I have been told this perhaps then fueled the rush to get into the field I am not sure that is 100% correct but there was a mad dash to suddenly get sophisticated by the 1987 crash the press was blaming somehow computer trading portraying computers were trading on their own right so in 1987 because of the 1987 crash people playing computers for putting in trades selling independently independently right causing a flash crash and we've seen that happen in the stock market before we saw that happen in 2008 we saw that happen recently-ish we've seen that happen in Bitcoin it was a flash crash in Bitcoin cryptocurrencies really across the board in about a year and a half when the price went from $250 down to $150 for a few seconds a few minutes and then came back up again right so people were blaming code programs for the flash crash coding again the last little section I'm going to quote quote the truth of the matter is that all the firms were trying to use computers not to in any any way forecast the future but to create a way to exploit the differences and arbitrage the markets as a whole right so again computer programs were not trying to forecast that a certain company stock was going to collapse or some kind of news was coming out that the value of their stock was going to depreciate what the computer programs were doing was they had written code where they were buying and selling at a high frequency and trying to make money on the differences right we're sort of connected to differential accumulation right differential accumulation says if you can collect at a higher percentage it's going to accumulate assets accumulate get a rate of return at a higher rate than the norm than the averages then you're in the money and that's what computer programs are trying to do and one of the ways you can do that is look for higher yield look for higher returns over a certain period of time or you can decrease your time frame right and make a smaller return but that return becomes compounded over a long period of time your gains right your rate of return accumulation of power is huge okay and that's what computer programs were doing and that's to a certain degree what computer programs in large part are doing right now in the markets and not just in these markets not just in Wall Street in trading stocks but in other markets as well and that's I'm not sure how a neural network's artificial intelligence for lack of a better word are going to come into play in this if you like your animation there's just taking a little tangent but if you like your animation there's an episode from Cowboy Beepop where they actually is it Cowboy Beepop I believe it's Cowboy Beepop but I think it's Cowboy Beepop where the bounty hunters, it's basically Cowboy Beepop is a story of futuristic story of bounty hunters and in this one episode they're looking for this one person and they end up I'm giving spoilers here but they end up finding the person that they're looking for and it ends up that the person is dead but they're one of the richest people in the galaxy in the world because what they've done is set up some kind of program that has been buying and selling commodities I believe it was gold or anything buying and selling on the markets based on the code and the code was running after they were dead so the program was continuously communicating assets it was a nice episode it was a cool episode and it sort of touches on neural networks and supervised learning and deep learning and whatever you want to call this how automation, technology computer power is changing our world changing our current economic system and that's something we have to keep in mind when it comes to automation and what we're going to plan for the future that's sort of the basis of what I wanted to cover in this video I know we touched on a lot of stuff and I really it really goes back to Stephen Hawking's quote right if we accept that we cannot prevent science and technology from changing our world we can at least try to ensure that the changes they make are in the right direction directions for us personally for our community for our family, for our society for our investments however whatever level you want to think about wherever you want to take this that's basically the gist of it and what I want to do from here in the next video is sort of expand on this topic of trading of what we give sort of value to where we want to function and you know overlap that with technology and what we're going to do in the next video is talk about currencies and trade basically and what we personally are deciding to give value to and that's huge that's playing on a huge way within our society and it goes hand in hand with computer technology and stuff ok that's about it for now I'll see you guys in the next video