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    "text": " What? Oh, come on. That's okay. Okay, what I'd like to do is I'd like to get the slackers up here, up into this portion of the chairs. I have got some t-shirts that I'm going to give away. We're going to do a Q&A session and hopefully we're going to have a little bit of fun with this. Everybody looks bored. It's not going to be as bad as you think. Here is the mandatory personal anecdote. Okay, I'm in New Jersey right now. Yeah. So I'm on the plane, right? And we're getting ready to take off. And I've got two guys behind me. And I've got the cute girl next to him. And there's a guy over here that looks kind of goofy. And there's another girl next to him, right? So we're waiting to take off and I'm thinking these two don't know each other. These two don't know each other. We're headed to Vegas. There's all kinds of possibilities here, right? Then the dumb guy gets up, taps the girl on the shoulder and he says, Hey, would you mind moving over so that I can sit with my friend? That's when I knew that I had computer geeks behind me. True story. And somewhere I was hoping that there would be a guy here whose face would turn really red. Anyway, so they're behind me and they start talking geek speak. They start talking about XML and security and all this stuff, which past a certain point you get kind of tired of it. Then I got a surprise. These guys are all in their 20s. They're clean cut. They seem really driven by what they're doing, which is typical for DEF CON, I think. And then they're going through resumes and the one guy looks at a resume and he says, This guy's got a lot of technical knowledge. And the other guy looks and he says, Yeah, I interviewed him, but he doesn't have any business knowledge and he doesn't know what is of value and what is not. And that surprised me. That's what I look for now. I look for what things have value. I think we're in the midst of a change in the industry where we focus on what is of value rather than the mechanics of building things. So that's what our focus is going to be. It's going to be on keeping people employed through the industry because I personally don't believe that the shakeout is over yet. This is my other personal anecdote, which I decided was too egotistical. Why should you listen to me? I don't have a black t-shirt on, so maybe you shouldn't. What we're going to do, we're going to go through and we're going to hit three basic areas. We're going to lay out a couple simple business concepts, which are very common in MBA programs. They're common in large companies. We're going to lay those theories out. We're going to drive them down into a technical implementation that gives us concrete results that we can make decisions from. And then towards the end of the presentation, what I'm going to do is I'm going to throw out some strategic concepts to you, which are going to be a little bit radical, I think. We'll test it. We'll see. This is DEF CON. Maybe it won't be radical. Maybe nobody will care. But there's some ideas that I think that people can play around with and they might be able to turn them into additional concrete implementations in companies, things like that. Okay. Everybody watches the news, right? I took this a month ago and this is already out of date because there's already like a dozen more I could add on here. Layouts are still going on. People are still being laid off left and right. The crash is not over yet. Historically, industrial revolutions like this have seen a shakeout of approximately 30% of the peak population of the leading technologies. What we've seen in IT right now is about a 15% shakeout. So personally, I think that you're looking at another wave that will be equivalent to the last four years. How many people here have been laid off or out of work in the last five years? Yeah. And you know what? For every hand that's here, there's a guy that isn't here because he's not in the industry anymore. He got washed out. My goal here is to give people a little bit of knowledge so that they can make decisions that will keep them employed or it will let them make a decision to leave the industry now before they waste a lot of time on it. So either way, if that's the outcome here, I'm happy with either outcome. The time and effort that you put out to learn things is finite. If you learn something that has no value, you're fucked. Right? Your time is gone. You can't get it back. People are not going to pay you for it. Now, the skew here in DEF CON is a little bit different because you guys are in the security industry and I think you've got a substantially better chance of staying employed over the next four or five years than business. If you're in business software, I think you're going to be in trouble. IT market changes constantly, everybody knows that. What we're going to look at is we're going to try and focus on what kind of skills that we should be learning to stay employed. How many people know what a meme is? That's good. The first question I got asked when I walked in was, what is a meme? And I thought, oh my god, I should have put that up. A meme is an idea it's a mind virus that passes through a population. And typically, if you want to be completely theoretical about it, it starts with a single person, that person talks to two more, those two people talk to four people, four people talk to eight, so it's typically an exponential function. And it looks like an exponential function, a hyperbolic curve, until it reaches about the 50% mark of reaching the entire population and then, because you're getting people that already know it, you're talking to people, oh yeah, I already know that, I already know that, it starts tapering down and you get a drop-off as you reach market saturation. And that's typically known as an S-curve. How many people know the concept of an S-curve? Okay, that's good. Here's a picture of it. I start with a single point, it goes through a rapid run-up, it reaches the 50% point, and then I reach market saturation. This is a fundamental concept that you can apply in a lot of different situations, a surprising number. And it's simple, it's a simple concept. Ultimately, this is our goal. We want to eliminate getting into new technologies when they're first introduced because there's a very high failure rate. I don't want to be here. I don't want to be in the high-risk area. I don't want to be in the low-reward area because that's a technology on its way out and I can't make any money in it. What I want to do is I want to identify an area as it's ramping up, but I want to get it before it's at the 50% mark so I can make the most money off of it. Right here, I can make a decision that, yeah, this is probably going to stick around, this is probably going to be a viable technology, and I can ride the highest part of the wave off, which is the optimum profit point because other people are not paying attention and you're already in the market to learn these things. You're already in the market to sell your services in this. This is a simple concept. Does everybody grasp this? Can I get a show of hands where everybody thinks that this is... do you think it's important? The reason I care about an S-curve is it has predictive power into the future. How many people here know calculus? Oh, there you go. So you know this already. As this is ramping up, my rate of change is constantly increasing and I can measure that by doing empirical analysis on certain things. And I can see that the saturation is... or the propagation of the mean is rising. Once it reaches the 50% mark, where roughly I have 50% of the people know the mean's already propagated, it changes, I reach the inflection point and the rate of change begins decreasing. So if I can measure the rate of change and see that it's increasing, I know that I'm still on the pre-50% mark. Afterwards, I know that I'm after the 50% mark. So this is really what we look for. We look for a rate of change. Okay, peak oil. How many know about peak oil? Oh my god, now I'm stunned. Just as a quick overview, peak oil is the idea that oil resources are finite in the world. And there was a guy named Hubert which, in the 50s, he did basically an S-curve analysis at the rate at which they were finding oil drilling holes, you know, that they were making discoveries. And he graphed it down on a chart and he was able to see that in the United States that peak oil production would occur around 1970. And he was right within about a year. And he predicted it 15 to 20 years before it happened. This is all numbers off the top of my head, so I didn't actually expect to talk about this. What is probably very likely going to happen over the next few years is you're going to see peak oil occur worldwide. There's a lot of talk about it. If you do a search on the internet, Google peak oil, you'll get all kinds of information. Environmentalists have been talking about it for a long time. I'm not an environmentalist, but I understand the concept of how Hubert did the analysis, and I personally believe that this is very likely to happen. I think you're going to see peak oil happen within the next five years. And that's why you see oil prices being driven up. It's because there's a lot of guys like me sitting at companies like J.P. Morgan, and they're doing the same analysis, and they're saying, peak oil is probably going to happen. We need to get our money into oil now and make money on that. Oil prices at $60, right? Everybody thinks it's speculation? I don't think so. This is why you care. You can make money. Or you can avoid losing money, which depends on if you're risk averse or not. This is another example of an S-curve. How many people know Harry Dent? Yeah, that's not surprising. Harry Dent wrote a book in 1989 which was called The Great Boom Ahead, where he predicted that there would be a rapid IT boom, which would essentially look like an S-curve, and that's exactly what we saw from 1990 to 2000. We saw the entire information technology sector boom hugely beyond what anybody imagined, except Harry Dent did predict it. I don't buy a lot of his other theories. I do believe he was right. I mean, you can't deny that he was right about what he claimed. He made a second prediction in his book, which is that there will be a subsequent wave of S-curve technologies, which will build upon the infrastructure that was created in the first S-curve. That's where we're at right now. There's an infrastructure in place. There's networks everywhere. There's wireless networks, there's standards, there's TCP, IP, all the acronyms, all those things. It's too hard for me to remember. But ultimately, if you want to be looking where you should be, you don't want to be here anymore. This is foundational stuff that's in place, that's standard. It's not interesting to work on. It's maintenance work, and it doesn't pay a lot of money, and that's why you see a lot of things being outsourced now. It's stuff that's duplicatable, that we can send somewhere where there's a different kind of culture that adapts to those kind of things. What you want to be looking for is technologies that build on the existing infrastructure that was created in the last ten years. And I goofed up because I was going to give away a t-shirt if somebody told me what peak oil was and I blew it. Okay, MemMiner. This is a little tool that I made, which I've been doing this for about thirteen years, and basically it assumes that a frequency count, a word count on the internet is roughly equivalent to an S-curve growth of that technology. That's a big assumption, I know. And how many math people do we have in here? Oh, thank God. I don't have to worry about all the criticism. You get a ton of criticism. From my standpoint, I'm very pragmatic. I don't want to sit down and do a lot of analysis about probability and this and that. I just want a tool, a rough cut tool that will give me a way to make a decision quickly and easily without having to do a lot of analysis and a lot of guessing and things like that. And that's what this does. In my mind, I think of it as a data miner that anyone can implement and use in a small business and compete pretty well against large companies that have substantially more resources and better ideology, better methodology of how they do predictions. It's a simple little program. I'll go through and do it. I don't consider... does anybody want to see a demo of it? Do you need to see a Java code of it? How about if I do it at the end? I don't want to break out of this thing right now. But to me, it's really simple. It really is. It took me about 4 or 5 days to write it. Anybody could write it with basic programming knowledge. The key to it is the business concepts behind it and the application of those things. Okay, here's an example of one of the first graphs that I ran after I built the tool. This is the first T-Cert question. Can anybody tell me what this pattern is? What's driving this pattern? That's true. Okay, this is a graph made off of the keyword of work, but can you tell me why it's so rhythmic? Exactly. People don't talk about work on Saturday and Sunday. That's exactly right. So, T-Cert. I don't want to take those home. Saturday, Sunday. Monday, Tuesday, Wednesday, Thursday, Friday. And look, Wednesday really is the hump day. This is a small example that there really is data that you can extract out of the Internet. I use Deja News right now because Deja News, I've used it for several years because it gives me a time history and I don't have to previously know about a concept. You know, Deja News is being recorded all the time. So, let's say RSS comes along. And I'm not paying attention. And a year later I decide, oh, I should have been paying attention. I want to see what RSS is doing. Deja News has a history that I can go back and I can backfill it. You can see this graph. The date here is April 2002. I ran this graph two years ago. Okay, I was able to backfill it and figure out what was going on. The search engines, I have done pieces to do the same kind of analysis against the search engines, against Google, against Yahoo, a couple of the others, but they're very highly skewed to the present. You can recreate, you can make a crude approximation of the same data that you can get out of Deja News, but it's much less granular and it's just, you've got to do manipulations, it's really goofy. The search engines are very highly skewed to the present. You can't backfill data without doing a lot of analysis for more than 90 days or maybe a year. That's another key, using Deja News. Using some kind of pre-recorded data that had no knowledge of the fact that a trend was going on. Here's Easter Bunny, same thing. You can see every year, along comes Easter, people talk about Easter Bunny. The data is there waiting to be pulled out, you just have to know how to do it. Here's Comdex. Now, Comdex got cancelled in 2004, right? Actually, this isn't my new graph. Comdex got cancelled right here. If I had had this graph last year, like I did, is there any doubt in my mind that Comdex is going to get cancelled? There's not a lot of doubt. It was a predictable event. The fact that it got cancelled in 2005, same thing, a predictable event. You could have made money on it if you'd gone to the bookies and said, I think Comdex is going to be cancelled. Very clear trend, starting right here. Each successive high is lower. Does anybody do technical analysis of stocks? This is a very similar analogy. Each successive high never reaches the previous high, so we're clearly in a downtrend. Now, here's a graph. I went back later. You know, I'm not... I don't care about money a lot, but I wish I had. I went back and I retrofitted stuff on here, and I did keyword searches against stock prices of the bubble. The green is the stock price of Siebel. The blue is the frequency word count out of Deje News at the same time period, and what I did was I matched the amplitudes. You can see there's a... for Siebel, there's a very tight correlation. I mean, this is like a 90... you know, for the statistical people, this is close to a 90% correlation. Now, in this particular example, the word count does lag the actuality, so maybe it doesn't help you so much, but that's not always the case. Let's take a look at Palladium. Now, this is the one I really wish I had done in 2000. You can see clearly that there are rumors about Palladium's price spiking almost two years before it actually happens. Okay, right here. Although, if you really want to get technical and drive it back, you can say that maybe this was the peak, but that's still a year. A year before Platinum peaked, it was... you could have figured out that there was a good chance that Platinum was going to spike. And also, if you take a look, that when Platinum collapses, the keyword told you that it was going to collapse before it happened almost a year ahead of time. That's money. There's money right there. There are large companies that are doing all this analysis right now. The fact that they're doing this analysis... okay, you know, there's guys like me floating all around. I'm not really a large corporation guy, but there are guys in those corporations right now that are doing this and they understand all these concepts. And they've been piling onto the internet over the last five years and what they've done is they've cut the margin of where you can make the money. The latency is shrinking over the last four or five years. This is a very high latency a year. That's amazing to me. If you look at some of the later graphs that happened two or three years later, the latency has shrunk to like three months, four months, things like that. Sometimes it's down to like a month. Here's another example. Applied materials. There was a latency. Applied materials was clearly in an uptrend years before the price spike. And the keyword count told you that it was collapsing before the stock collapsed by about a quarter. And if you look right here, you can see that now the latency has disappeared out of it. There's a latency here in 2000 and as we go along, right about here, now there's no latency anymore. That's an excellent question. I've got a couple of other graphs that will show that it's very hard to determine and that's where there really is. There is no money to be made in mechanical skills. You can't make money in those things, especially in a global market. You have to move into something that requires some kind of artistic ability. And this particular mechanism right here does require a fair amount of interpretation and analysis that sometimes you're wrong. I mean, it's just the way it goes. In the case of Palladium, there were rumors flowing out of Russia that they were having problems and that they weren't going to make deliveries. So in that case, it was the lack of metal that drove the price spike. But you're absolutely right. There are cases where the rumor precedes and sometimes, you know, I'm not so sure that there isn't guys at some of the companies that are actually spiking the internet now in order to create an artificial demand in order to offload their stock or whatever it is. So that's something you've got to be careful of. I didn't run Enron. I didn't have any interest in Enron. I just looked at the dot com boom. But you're more than happy if you want the source code, you're more than happy to run Enron. Okay, JBuilder. How many programmers do we have in here? How many JBuilder programmers? Ah, interesting. You guys are all assembly, low level, right? What kind of languages do we have in here? Well, JBuilder, that's okay. This is a graph of JBuilder and it basically, you can see an S curve right here. The inflection point is roughly right here. And typically what you see, at the inflection point, your rate of change is infinite. As you're switching over from a rising to a falling rate of change, there's a spike there of infinite demand, theoretically. And you can see it reflected in this graph very clearly. There's a huge spike right at the time that JBuilder is undergoing its inflection point. And by about this time right here, you should have been out of JBuilder and you should have been doing something else. Although it's only been in the past year that it finally came to fruition. So there was a lag time of about two years before JBuilder finally died off and Borland had to admit it. No, you have to have a keyword that has uniqueness. Well, you have to do qualifiers. And I've got a couple other graphs in there further on where you'll see that, like ColdFusion. ColdFusion is a perfect one. I ran a graph on ColdFusion because I saw an article on it and there is some evidence to support that ColdFusion is real. So I ran a graph and I got this really strange graph that didn't make any sense to me. And about three days later, after I published it, I found out, oh right, it's ColdFusion the programming language. So it kind of made me look like a fool. So what I did for that graph was I added a new qualifier, ColdFusion Physics. And that gave me a much better picture of what was really going on. So yeah, you've got to be careful about keyword picks, which I have a little bit later on. Delphi. Now this is a real life example of something that I did in my own life. I got into Delphi around 95. I did programming on it. I wrote the wave up. And about 98 it was clear to me that this peak was over and I needed to do something different. So right in here I started learning Java. I went to Sun's conference. I started learning a whole different ideology and methodology of the way I did programs. And I switched over here and I missed the crash. There's a lot of Delphi programmers. Delphi is an extremely good language. I like it a lot. It's the best thing I ever used. I can't make money in it anymore. And there's a lot of ex-Delphi programmers that found that out the hard way. This, for some reason, I put this up and this is like the single most red graph on my site. The guys are just fanatical about scripting languages. It's very strange. Here you see Perl. Very clear rise. Very clear fall. Should have got out of it right around here. Maybe. Maybe should have. PHP. Same thing. Rises. Inflection points are roughly out here. It's not a true S-curve. It's not identical. But in real life that never happens because you get all kinds of variations. You just have to kind of guesstimate between peak and fall. And then Python and Ruby. Python, this doesn't show enough detail, but Python to me is the choice out of these four. If you were going to have to do a project right now I would choose Python. Assuming I could fit it into the requirements of the project. I mean you always get languages that fit a certain set of things. And you can't just choose a language in order to boost your employability. You have to look at what you're doing for the customer. There's also a very interesting thing that happens in the past year in that all scripting languages have suddenly got a high level of interest out of nowhere. And I don't know what drives that. YMAX. How many people know YMAX? YMAX is going to be big. I believe YMAX is going to be a big technology. YMAX basically replaces 802.11b with the same imagine 802.11b with a 30 mile range. And also I think, you guys can correct me I'm far from perfect. You can correct me if I'm wrong. But I believe that YMAX already has permission to be able to tap into cell phone networks. So you're going to see a huge amount of grassroots competition against the major wireless carriers from people setting up their own shops in YMAX. I can easily imagine someone getting a few million dollars of capital, dumping a few YMAX things in the center of maybe 30 cities and just cutting the hell out of Verizon's market or Singular's market. Blogs. This is an interesting one. This is a little bit more This is a question. This is a t-shirt question. You can see pretty clearly here. This is the moving average. You got to do a moving average. There's too many spikes that happen on the internet. Too many weird nuances. But basically by looking at a moving average you can get an idea. And we can see that blogs are clearly rising since 2002. And it was probably a good technology to get on around January 2004, which a lot of people did. Did anybody read the article that was in USA Today or one of the major papers about two months ago where they said that 27% of the public does not know what a blog is. Did anybody read that? Now tie those two numbers together. I have an inflection graph here, right? It hasn't reached a peak point yet. I know it's an exponential function and I know that 27% of the people have not been exposed to the meme yet. What kind of thing can I derive from that? I can make a prediction from that. T-shirt. Anybody that can tell me gets a t-shirt. Anybody that makes a good guess gets a t-shirt. Can you give me more detail? I don't want to go down the calculus path. It's too much stuff for me. I took what I needed from it and I threw the rest away. Anybody else? Down there. Black. Black shirt. It's going to peak within the next year. T-shirt. It's not going to be a big money maker. What you'll see is you'll see a lot of hysteria in the media over the next year and then that'll be it. It'll have peaked and it'll be ramping down. That doesn't mean you can't make money in it. All it is is an indicator. There are technologies that have peaked that you can still make money in. That's one thing I don't want to tell you is that if it goes through an inflection point and it peaks that you can't make money in anymore it's a warning indicator that tells you you need to look at the surroundings you need to look at your competition, you need to look at your skill set and make a knowledgeable decision about what you're going to do. You can make money in post like J2EE. I don't know, probably nobody here has read my site. I put a controversial article out about 3 months ago that got a lot of Java people riled up which basically says that J2EE is about to go through its inflection point. That it's peaked out as a technology. I have no interest in moving on to another technology right now because I still make money in it and I'll probably ride it for 3 or 4 more years but if I were an entry level person getting into the market I don't think I would choose J2EE because there's already going to be a bunch of guys like me that have experience and knowledge that are going to compete against you and the market isn't expanding anymore. Now, it's time for the half time here. How many people think so far that this has been more interesting than you thought it would be? Oh, that's so gratifying. I mean, I kind of took this as a lark. I sent in like a basic program, you know, basic speech People that have looked on the disk realize now that it's like about one third of what I'm doing right now. It was like a perfunctory effort and then I decided a couple days ago, well, I really need to do something serious on this. So that's nice. That's what was my aim was to get, to have people interested and have some kind of value out of this presentation. Wi-Fi phone. How many people know Wi-Fi phone? That's another big one. It's going to be tied into WiMAX, I think. Okay, so we already talked about this. Inflection point tends to be at about the 50% saturation point. That's not always true. That's just a rule of thumb. It's very easy to get and we'll look at another graph here later on where you get a quick ramp up but the infrastructure is not in place to support a technology. There's two technologies that tried to go through inflection points. They tried to go through a growth phase and the support just wasn't there. One of them is Bluetooth. I was here two years ago and there was a lot of Bluetooth fervor. There was a lot of Bluetooth hacking, things like that. So I went back and I did a graph on Bluetooth and I was surprised to see that Bluetooth tried to take off three or four years ago and it died out because there wasn't a critical mass to make it go. And then within the last two years it's picked up a lot. So a lot of times you'll see new technologies sputter and try and start and then they'll die out because there's some missing factor there. Service-oriented architecture is another one. It came out around 2000, 2001, the buzzword, all that, and it tried to take off and it died. But for the past 17 or 18 months it's been rising steadily. It's now something that organizations either understand and feel that they need or there's some critical component there that makes it viable. We already did the blogging. RSS feeds. This is very highly correlated to blogs. Same difference. By doing a search on and you can see here I used a qualifier. RSS RDF. I don't know if everybody can see that or not. People in back probably can't. So I used a qualified word here because RSS wasn't a good search. And now we're going to talk about the problems with this. The biggest single problem that I run into is that I'm part of the meme. How do I know to search for something? How do I know about RSS? I read it in an article. I'm part of the meme propagation and statistically I will learn about the meme at about the 50% mark which means I can't make any money in it. So I've got to figure out a way to track the meme before it gets to the 50% mark. I don't want to be like everybody else and be the average. So that's one problem with the methodology I'm using right now. What it requires is me to be reading articles all the time I have to spend a lot of time trying to be at the forefront of technology when there are easier ways to do it. It is subject to manipulation although not anywhere near as much as you would think. When you do a search off Deja News you are doing a search against hundreds of thousands and millions of people. It's very hard to inject a meme and create an artificial result. I know because I've tried. It's not statistically rigorous. There's a lot of criticism from people that are mathematicians. I've heard a lot of criticism about it and it is kind of flaky but it's worked well for me for 13 years. It's helped me make a lot of decisions where I could have easily gone off onto a bad path and ended up in another industry. Maybe that would be good, maybe that would be bad but that's not the path I chose. It works very well for technical trends. It does not work as well for social trends. It doesn't work for things that have lifetimes of 20 years or 30 years. It works very well for a range of 4 to 5 years and we'll look at why in a few minutes. If I were serious about using this as a technology to drive my company and make decisions with, I would throw away the current methodology of using Deja News. I would start capturing RSS feeds and I would build my own database and that will allow me to capture trends as they happen in real time and I guarantee you that there's people out there right now that are doing it. I did it. I wrote a little piece about 6 months ago. It has some technical problems but you can easily get around them if you spend enough time on it. I have a lot of other things to do but there is a lot of value to be done in applying this methodology to pulling down real time RSS feeds, building a database and looking at rates of change that are happening in keywords. I believe there's a lot of value there. We already talked about skewing factor. One of the problems is you lose abstract relationships with the way you store the RSS. There's some things there but they can be gotten around. Okay, so now we did the business process. We did a tactical implementation that gives us real decision making tools. Now what we're going to do is we're going to push this back up into a strategic framework that will let us make decisions about more than just about what technology I choose. And this, I admit, this is going to be kind of flaky. One of the big justifications for Moore's Law people like to quote Moore's Law. Everybody knows Moore's Law, right? How many people don't know Moore's Law? It's a trade off. I can't let Moore's Law go. It gets quoted a lot because it's used as a justification that the number of transistors will double every year so there will always be an IT industry that will continue booming. And I would agree with that if there were no other factors. People have a finite amount of time and effort to spend on anything. Geeks, techno geeks, they like technical things, they like trends, they like detail, but ultimately people are social animals. They will spend time on things that they enjoy or things that they're forced to do. Other things they will throw away in favor of things that they enjoy or things that they're forced to do. And this is part of the reason why the Memminer works off of Deja News. People talk about things that they enjoy or that they're forced to do. For the average person, they spend their time sleeping, working, I'm glad I got a laugh, that's good, shopping, eating, having sex. The range of that variance is not a lot from person to person on average. So if I want somebody to read the internet two hours a day and I get a certain level of growth, now to double that, I've got to have them read the internet four hours a day, it's not going to happen. They've got other things they want to go do. They want to go eat, they want to go have sex, they want to go visit Las Vegas. So there is a fundamental limiting factor in the growth of the internet based on what people's time is. There's other factors on top of that. Now for some people, you do get a wide variance. For instance... But I don't think we're going to see a society that looks like this. Now we're going to look at Deja News again, and what we're going to do is we're going to invert. What I was looking for was uniqueness, right? What we're going to look for now is the ether of Deja News. We're going to look for the rate of growth of Deja News abstracted of all uniqueness. What I'm going to do is I'm going to use keywords that have no identifying ability, that are used in anything, anytime. This, have, one, all, them, will, other. That comes out of the top number of people that do crypto analysis. E-T-A-O-N-R-I-S-H-D, right? That's what you do a frequency analysis to be able to crack codes that are not well done. Well, you can do the same thing with keywords. What I did was I went through the top 100 keywords and I picked out ones that were fairly generic, and then I did a search and you can see very clearly exponential growth in Deja News. This is one reason why the MemMiner works is because over any small period like from here to here, it looks linear. There's not skew being introduced by Deja News into the uniqueness of a meme. There's a little bit, but not enough to matter. Okay, so this is what the ether of Deja News looks like. The transport. If you do a really detailed search, you can pick out events that cause excitement on the internet. And these are admittedly, I guessed at them, I think I'm right on most of them. JFK crash, a lot of people can't see this, but there's a spike right here. Y2K causes a spike, there's a spike here I didn't hunt down. Bush election 2000, that definitely caused a controversy. 9-11 causes a big spike. What's interesting about 9-11 is that if you do keyword searches on other terms, you get spikes. If you look for death around that time period, you get a spike. If you look for peace, you get a spike. What you don't get a spike in... Okay, remember, we're talking about people that have finite bandwidth, right? They're using more bandwidth to talk about 9-1-1, they're using more bandwidth to talk about death, they're using more bandwidth to talk about peace. What is being cut out of the equation to free up that bandwidth for those things? Can anybody give me an idea or a guess? It took me weeks to figure this out. No, there's, well, there's a particular thing that it cuts into, and I think it'll make sense to everybody once they hear. No. Work, who said work? T-shirt. Work goes down. And I remember 9-1-1, what did I do on 9-1-1? We all sat around talking, we didn't do any work. Now, here's the bad news. Deja News gets very flaky after 2004. I got a couple theories about it. I have three theories about it. One is the shift of blogging, clearly. What people were using Deja News for, they now use blogging for. But that does not account for the rate of change that you see here. This is almost instantaneous. You see the moving average here, but the actual numbers, it changes within like two months, and I know that's not happening. Blogging is taking away chunks. Google changes their methodology, their search methodology. If I go back and I run a query two or three months later, it changes. They're doing subtle shifts in the way they do things, and it shows up. And I do think that there's some kind of error in the way that they're manipulating or accounting for things after 2004. But I think a lot of what you're seeing is the inflection point in the internet itself. If you read enough articles, you see a couple articles came up in the past year or two that talk about a slowing rate of growth in the internet. I think that that's what you're seeing. I think you're seeing a slowing rate of growth in the internet, and it will impact all the other industries that are associated with that, mostly consumer-related industries. Chip industry, business software, it won't affect niche markets so much. And it probably won't affect security very much. And now, we're going to take a look at a couple concepts to make strategic decisions about how you're going to spend your resources. Very abstract concept. What caused the IT boom? If I were to create a market basket of all the items that create a cost of how I get information, chips cost me money, people cost me money, I have to develop methodologies, there's a lot of communications costs, I could create a market basket of the cost of information, assign it to a unit of information, and I would be able to get a rough number that each unit of information cost me a certain amount of money. Now, up until 2000, everybody was making money, right? That's because I have a price of information. I'm selling that information to somebody somewhere and I'm making a profit margin on it. As we get down to 2000, cost of information is not decreasing like it used to, and companies that are used to this curve, you know, they get into this mindset of seeing this, they overran it and they went into debt and they went out of business. Some companies veered off to the side and adapted and survived. But ultimately, if the cost of information stops falling, there are fundamental changes in the marketplace that companies are going to have to adapt to. And ultimately what you have to do, because market forces force you to adapt to a pricing model that the public sets, or governments, you have to find some way to drop your cost of information. And that's why you see outsourcing over the last 3 or 4 years. Companies have to. They have no choice. Otherwise they'll go out of business, because there is a ceiling on what they can charge. That's a very strategic concept. It sets the stage. You can't do anything with it directly. It's not a tool. And this is another picture that shows you roughly the same thing from an economic standpoint. As a market matures, your marginal cost of processing stuff rises. Your marginal return falls. You eventually reach a point where there is no profit margin, as you pick up all the easy things. What that means is that there is money in hard things. And the same difference. Now this is a theory of my own, just from watching the market. Watching the IT market for the last 10 or 15 years, the market rewarded diversity. As I created programs that did more and more stuff like Word, it does all the bells and whistles and stuff, I got a bigger and bigger share of the market, and people bought my product. But once I reached the point, the inflection point, the inflection point punishes diversity, and it rewards cost-cutting. And that's the point that we're at right now, for consumer-related industries. Like I said, there are niche markets that don't get impacted this way right now. So, from my standpoint, this is what I see. I see the up-cycle favoring diversity, and everyone pursuing that, and they get into a mindset over the last 20 years that diversity, diversity, diversity, I want to create a diverse product and get as much market share as possible. We've seen a fundamental change around 2000, which now punishes diversity and rewards cost. And most people's minds are still in this state. I don't know if I should go into this or not. This is the single most read picture on my site. And it surprised me a little bit. In a nutshell, diversity functions on contexts that are mismatched. If I had two clones, and they had identical information sets, and they know the exact same information, and they've had the exact same experiences, their communications and teamwork will be perfect. There will be no miscommunications, correct? As those clones have different life experiences, their contexts will shift apart, and there will be a set in the middle that they share, but there will be unique features that they each have, and then you'll start getting miscommunications. If I wanted to cut my cost of information in teamwork or software development, what I would do is I would try to get contexts to be as matched as possible within a team framework. Like I said, this is very high level, and there's some other things I played around with it, but I put it in there just to maybe get people thinking about ways that they could drive their teams or their software development towards a competitive methodology, and what you have to focus on is reducing transaction costs internally in your organization. There's a couple other things here. I'm sitting in a microbrewery, and I realized, wow, microbreweries are a lot like the software industry in certain ways. The company that I work for right now basically resells IBM software, but what we do is we tweak it to a very minimum level, five or ten percent, and match it to that set of customers that are willing to deal with changing their business processes to map to what the software does, and that's a very efficient model that I think can last over time. I don't think a lot of very customized software, it's going to have a very hard time surviving in business software over the next ten years. And we'll let that go. I'll go ahead and I'll take some questions. The question is, do I think that the difference between the dichotomy between diversity and cost-cutting is driving some of the open source movement? I don't know. I don't... I've looked at the open source movement and I still honestly don't have an opinion about it one way or the other. I can't get a reading on it out of the graphs that I've run. I can't speak to it one way or the other. I haven't done any analysis of that, but I would say companies that are doing Six Sigma are probably unless they're in like a financial industry, I think they're probably at a competitive disadvantage. I think they're producing quality that the market isn't going to reward. That's a guess off the top of my head. Yeah, you can use the search engines. You can get a lot of the same data out of Google. It comes out in a different format and you've got to massage it and you've got to work with it and it's very crude. It doesn't give you the same detail. But yeah, you can pull it out of other sources. I used to use Monster. I used to run queries against Monster. I used to use Dice. I used to use Miscellaneous Jobs News Group for people that remember that far back. No, I haven't done anything on security because that's not my field. One more. A lot of what you just saw, not in so much detail. There's more business here than I have on my website because I haven't updated for a while. There's a lot more graphs that show you more detail in different industries like religion. I did some queries on religion, social trends, things like that. So you can get an idea of different things. And actually, I did mean to bring that up. I'm not out of time yet, but we can look at a couple of those real quick. Or maybe not. There's no doubt in my mind after watching what's going on in the marketplace, there are a lot of people that are smarter than me. MBA program teaches all these things. I haven't been through MBA. I did a little bit of an MBA, but they teach you these kind of concepts and there are guys out there that are applying them. I have no doubts. You can see it in the way the information has been changing over the last five years. There's someone that's taken a chunk, taken a slice of the profit margin out of it. Let's see, what can we look at? Oh, here's saving J2E. If you go through here, you can see that J2E looks a lot like it's topping out. Yeah, these are good right here, Republicans. Does anybody care about the federal deficit? You bet people care. Whether the media tells you they care or not, it's being talked about a lot. Trade deficit. Does anybody care about the trade deficit? Media doesn't talk about it, but people are talking about it. You can get a lot of interesting information that runs counter to what the media is telling you. As a closing comment, I'll tell you that when you see Alan Greenspan talking on TV, don't listen to his words anymore. Watch the beads of perspiration on his forehead. That's what matters. And that's it. I got two more t-shirts up here. Two more t-shirts if people want to nab them. Oh, am I out of time? Do I have like two more minutes? Real meme. R-E-A-L-M-E-M-E. Yeah, yeah, that's what I'm doing right now. I'm going to run it. I got like a minute. It's a very simple program. Send me an email and I'll send you the base to it. I'm not going to support it because it's got a couple bugs in it that I work around because I don't want to spend time on it. But we'll run one real quick here that might be funny. No, I think there's profit. I think ultimately what you're going to be driven into is, at least in the United States, I think you're going to be driven into niche markets. In a single phrase. Niche markets that are supported by small industries that have high profit margins that are willing to pay for some level of customized software development. But that's a trend that's going to run over, I think, five to ten years. It's hard to say. I do think, for anybody that cares, I do think J2EE has peaked. And if I were entering the market, I don't think I would put any effort into trying to learn J2EE as an enterprise system. I think there's alternatives that are on the horizon. I think your time would be better spent in other things that are built upon the infrastructure of the existing S-curve that's already run. Oh, I was going to mention Newbridge. The Newberry guys in there. The Newberry guys in there that are running that that tracker, that wireless tracker, that's an example of a technology that's built on a foundation that's been built over the last ten years. That's the kind of thing I would look at. Okay. I look for it. I see it. It comes up in different places in different ways. It's intuitive. It's from seeing it so often. It happens around 2004, 2005. Gosh, I'm really sorry here. One more minute. Well, if I have a good net connection and I'm running it threaded, probably 20, 30 seconds. Yeah, I have done run RFID and that's on my site. It's in an uptrend. It's in a pre-inflection state. Definitely worth putting money into. Hmm? That's a whole other session. That's a political session. No, I don't blame you. Anyway, here's Paris Hilton. She's still got some life there. I'm surprised. I didn't expect it. Okay, thank you."
}