presentation. This is the wrap-up of a special broadcast of the Cube Silicon Angle Wikibon's exclusive coverage of HP's moonshot announcement. We're here to extract a signal from those. We were here all day at the announcement, following the webcast, breaking down all the analysis, talking to all the special guests, all the executives, John and Dave hitting them up with the questions, and now here to review and break down and conclude this special broadcast we have Dave Vellante of wikibon.org and Stu Miniman of wikibon.org who's in the house as well. Guys, let's quickly moderate this through. I'll moderate this guy. I want to get your opinion. As analysts, obviously we've been covering this and you guys have been we've been covering the HP moonshots at the beginning as well as other initiatives around this. I want to get your take and start with Dave. Your take of today in summary, what do you think what happened? Yeah well I think again HP is the leader in servers and they've just put down thrown on the gauntlet and said this is our new server platform. I actually got a little heat you know from people saying that this is ultimately going to eat blades and there's no question in my mind it is. HP is going to slot in virtually every type of processor into this new format and they're going to drive lower power, better packaging, greater flexibility, you know better workload tuning. This is the future of HP server platform. There's no question in my mind about it. I think they were very careful at the original HP moonshot launch because they didn't want to you know cannibalize their existing business. That's a messaging optical issue though. That's not an assumption. It was just more they don't want to focus on that but that's the reality. Oh it's true and because they have to be careful because they got to ramp this thing up and they've still got existing products to sell and you know so you know that's the balancing act that they have to play but I think the gloves are off and they're going for it and they're going to you know transition to this new environment. Now I think it's obviously not going to happen overnight. They're going to start with these specialized workloads but I'm reasonably impressed with the ecosystem partners that they have here. These guys are actually building products. They're the real deal. They have the real deal. And they let's face it we saw them on the cube today that every one of the ecosystem partners had a prop. They were showing the capabilities and you know they want to be next in line to be able to participate in this new market. So those are all good good fundamental trends. I think the question is how far into the hyperscale market can HP go. Is HP really truly you know going to compete and grab share there or is this more about trickling a little bit into the hyperscale market and bringing that capability or proxy for that capability into the traditional enterprise. And I think initially it's more the latter than it is really driving the hyperscale market. Stu Miniman of Wikibon. Obviously you know a little bit about a convergent infrastructure. You've been covering for a long time and previously in your career at EMC Office of the CTO you know what's going on under the hood down lower in the stack. What's your take on the announcement. Obviously you've been following some of the silicon that they've been partnering with and some of the software and Intel's got some other Adam announcements. What's your take and then just what's your analysis. So John I think similar to what Dave said you know HP really knows that enterprise market. They were first with the x86. And if you look at some of these these trends like software defined networking and where they're bringing these hyperscale solutions in it is tied more to the enterprise. So while most of the SDN players out there are looking for those service providers and the big web scale companies HP is trying to hit the enterprise market because that's where they live today. So the question is can they really crack some of those big accounts. We were a little surprised to see Intel as the strong partner here. And we said if you know HP really wanted to take that moonshot further and go after some of these newer applications you know maybe it should be ARM. And of course ARM's here as a partner. We expect many of those solutions to come in the second half of this year. But Intel HP so HP has a relationship with Intel going back many many generations of computer industry. Right. So maybe it's a pressure there. I understand. But if we talk about taking the you know mobile platform and bringing it to this generation Intel is not a major player. ARM is the player. Yeah. David Floyer wrote that you know Intel's in big trouble if you look at it. Don't. Yeah. Of course our CTO from from Wikibon you know it was written a post saying if you look at this trend and if you look at people like Microsoft and Apple if they port to ARM you know Intel's in trouble. So you know it's good for Intel to be here. Premier's you know partner with HP they've got a long relationship. They've got the atom they just announced their next generation ship of atom that they're pushing here. But you know if this is a true pivot point might it be something other than Intel. So exciting technologies definitely system. Some things changing in the market. So let me ask just to ask you we know this year we've been we had a martini on the cube at the end world software defined networking is very geeky. It's a lot of specific things that define what in open flow and on SD software defined networking their position. This is software defined servers. Obviously the loose definition I want to get your take on that. I mean that's not a bad thing but you know they got to be careful not to to get step in the hype factor here. They could you know there is some good stuff that we drilled that down the queue but I want to get your take on that I'll see software defined networking purists are saying whoa this is not so far to find anything. So I think that the change that we need in the industry is rather than taking general processors and trying to over optimize and have my people and software trying to make things fit the application. If you look at what Facebook is doing with open compute. They are saying we here's the kind of five to ten models and that's what we're doing. Now moonshots doing something different starting with the application it's really hyper designing those for the environment so software is leading what's happening but I'm not sure if it's software defined. There's lots of flexibility in the way moonshots build lots of different cartridges that can go in and through the software we can manage and change and optimize what's going on here. But you know I'm I'm not sold on software defined servers as as a story there but it's still early in that that's just market tech. Well so that I mean you know the fact is that nobody's really doing software defined the true hyperscales guys are doing software defined but you know he's talking software defined on the on the on the enterprise side. I mean there are some examples you know you have certain file systems that are doing scale out but in general you know none of the big whales are really driving it. I mean you've seen some some pre announcements but John what's your take on all this. On what moonshot and HP's positioning. Yeah yeah absolutely. I think it's an amazing move for HP obviously they have a lot of expertise in server. I think one you know one of the things that they were trying to do in the webcast which I kind of didn't like because this made me kind of you know a little bit but they don't tell you went back and it's like oh yeah we did the Unix servers and was kind of a historical walk down memory lane. HP's got a boatload of expertise in the server business. This is no question about it. So for them to make this shift over to this way is not groping for a market. They definitely have the chops in the server space so you know super impressed by that I think Wall Street is all is all wet on HP and I think that's why everyone was watching the webcast. I think smart money knows HP's not only well be on a turnaround relative to this area but they have a chance to pull it off so that's that's kind of my take on that. The other thing that I find really super interesting Dave is that the market place of software defined meaning the world of flash what flash memories doing in Linux kernels software developers guys who have a computer science degree whether it's low level programming down to the hardware level or just lamb stack developers these guys are powering the next generation sets of solutions from software to service to platforms of service so the cloud market the mobile market are developing huge amounts of developer expertise and those developers are the key to success in the infrastructure has been lagging behind you've seen that fumbling of the cloud and what Amazon has done is absolutely taken that market share Amazon.com went from what I used to call a junkyard for cloud Bill Jerome has now grown up with right scale and others to have a really really compelling obviously public cloud but they are aggressively incredibly getting into the enterprise to shadow IT day we've talked about this Amazon is absolutely kicking some serious but right now and I think everyone's on notice and I think that is driving a lot of change because they are the infrastructure for mobile they're also the infrastructure for a lot of you know developers so you know the issue with Amazon is people are afraid of Amazon enterprise customers are afraid of Amazon and Amazon's got competition so I think it's an exciting time I think this is a good move for HP I think it's going to be one of those things that's going to make or break the company because I think again I think it's a solid bet because of their expertise so I look at that yes so John and what one of the things we've been tracking you talked about converged infrastructure that the thing that will move converged infrastructure from evolutionary to truly revolutionary is that programmability and scale out as really the architecture that it's built on typical to converge infrastructure are not really built for scale and they're not programmable they're more of a black box solutions solutions like Natanix a little bit what IBM's trying to do with pure systems and now Moonshot do take that next step to truly make a scale out architecture that have some program you know Dave's too I think you're totally right on that one I think Wikibon has the most research out there just a little prop to you guys but Dave and I have talked to some of the top executives including a fusion IO violin and other flash guys Veriden and others and you have as well scale out open source absolutely is the game right now and I don't care what anyone says I'm completely convinced I'm seeing too many data points scale up commercial software is slowly and quickly eroding and you're going to see Oracle out there all these guys out there EMC HP IBM all the traditional vendors Dave love to get your take on this have lived a great life very prosperous life selling gear on a stand-up vertically you know scale up infrastructure with commercial software licenses VMware Pat Gelsinger he's he's not someone who's estranged this marketplace he's moving off the licensing with the hypervisor moving quickly to different value activities so so clearly scale out commodity or industry standard hardware is the game one of our guests today said you got a disk drive and then you got you know a NASA's 10x or yeah yeah 10x and then a hand is like a hundred X is an officer's math was quite right but but the the point that you're making is a good one for years server and storage vendors and networking vendors have been marking up commodity components now they've done that on the basis of hey you know those commodity components that you buy at fries aren't enterprise ready we make them enterprise ready well how do they make them enterprise ready to make it through software the problem is they've done that through not really software but through middleware that's tightly embedded and integrated with the hardware it doesn't really scale well and this whole notion of software defined in the scale out business and the hyper scale is all about commoditizing that infrastructure and again Jeff hammer Barker statement about I was just sick when I was at Facebook of putting all this money into the container and so the idea is if you can make the hardware as standard as possible and as low cost and defriiled as possible and add value through software then you can drive business value through things like algorithms and processes and apply people to create more value and that's where I think this industry is headed so this is why I come back to HP there's I think they've got an excellent opportunity here to be a proxy for hyper scale in the traditional data center because the traditional data center is just not going to all go to the cloud they're not going to disappear overnight they need companies like HP and others to provide that level of infrastructure that allows them to partially replicate what the hyper scale guys are doing I think Google and Facebook are harbingers of the future I don't think they're outliers okay Dave and Stu I want to ask final question as we wrap up the day kind of just get your thoughts together around what you think is really happening but while you're doing that I want to ask you a quick question on the front end of that is Davis to obviously HP this is not the first time I asked Jim Ganti there's not the first time they've done something compelling from a tech announcement standpoint I mean there's a HP's a lot of pretty amazing technology products that ended up into a cul-de-sac of no market obviously this market is a market opportunity with low power and high performance so the question is good market opportunity they still got competition how do they pull it off talk about the execution what does HP need to do to do this answer that question and then let's wrap up your thoughts for me but I think the first I mean they have positioned you know initially around these these edge applications and I think they got to pick those off and deliver number one number two is they've got to continue to enable other you know players I mean having Intel you know as the the the mainstay for the initial system is interesting but I think it's much more interesting if we can get some of these other ecosystem suppliers in here now we saw those guys today it's to me it's you got to start shipping those third is you've got to have the software infrastructure built up around it it takes a company like HP to actually provide you know that level of leadership and so those are really the three things that I'm looking for in terms of seeing how successful this is going to be and I think they've got a great shot at it so what do you think about the execution they have to do so Dave you mentioned some of those early applications if you look at big data if we look at the government these are relatively low margin environments and there's a lot of specialization required one of the real concerns if we look at HP is only three percent of the revenues today is done with software so they really have to build that ecosystem and rely on that ecosystem and how much of the ecosystem is going to say okay I want to go with HP so that's up their game on software yeah absolutely and this is the software defined server that we've had to see some software love the quote from the AMD guy software turns on the hardware everything's software defined so okay final packaging of this event Dave I want you to wrap this thing up for the folks summarize real quickly your thoughts impressions and what do you think is going to happen post post this announcement well John I think you know I'm honored that we were here at the original moonshot announcement outside Bill and Dave's office and the HP allowed us to come in and independently report on what's going on here at the moonshot announcement and so I think that we are seeing a major change a major shift in server infrastructure I think it's it's very consistent with the hyperscale trends we've been we've been covering this again what we cover at Wikibon and Silicon angle it's big data and it's software led infrastructure those are the two big giant trends that are being driven by cloud mobile social big data I mean everybody's sort of hopping on that but underneath that that nuance piece really is what's happening in the hyperscale with infrastructure and what's happening with software led infrastructure on top of it are really to drive new levels of value so I'm very excited to see how this thing progresses and as I say I think HP's got a really excellent shot at making it happen. Stu anything to add? No I think Dave summed it up real well it's still John as you said early innings for this technology at this point but you know big announcements expected in the second half as the partner goes from kind of one solution to lots of options and you know well still we'll be looking for your analysis obviously we know that you know you're in the lower end of the stack relative to some of the things going on conversion for structure you would put out some of the best research early on with David Floyer on this area we'll watch you. My take on kind of the wrap up here is I think what's interesting is I've sort of said my thoughts on how I feel at the announcement I think it's a home run ridiculously amazing numbers on the energy savings that alone should just tsunami of units will go out the door on that one point but the software defined piece the software led the compatibility with the future architecture of how people are going to build software I think is really going to be the key that everyone's overlooking. The other thing that I will say Dave and Stu is that I'm really surprised that Intel's got the lead position here on the announcement and I think what's exciting is HP's not picking winners here they're just probably because of their relationship with Intel and their performance with Adam but if you look at the other ecosystem partners it's a jump ball in my mind I don't think there's any decision at this point I think this product is going to be so compelling that TI arm everyone else is going to have a horse in this in this game Dave and I think there's plenty of courses so there's a lot of fertile ground for these horses to run you're going to have different tracks and different use cases of I think purpose built custom solutions I think so I like that the ecosystem's got a lot of vendors in there that are going to be built in product I think it's still going to be a jump ball. I want to add one more thing I mean the companies in this business and the enterprise business need to figure out how to make money at low cost with lower margins and that's something that HP has always done pretty well at and I think that it's going to be really interesting to see how others respond and I think software led is one way but there's a lot of talk about that I want to see more people put their money with them out this. Well that's going to be a wrap for us here for the special edition for the cube long day excellent day with HP again a lot of competition is still out there see what they can do with it I want to thank Dave Vellante for helping us out here at the cube as well as Kenny and Mick out there on the board appreciate it and all the folks back home at Silicon Angle popping out those stories Markers and Hopkins Kristen Nicole Mike Wheatley and the whole team great job and also thank HP for supporting us to come out here and be part of their great launch Silicon Angles independent coverage this event we're continuing to do more free research at wikibon.org go to wikibon.org and look for to me most seminal research on software led infrastructure we're calling it software led mainly because we believe it's not yet defined and networking servers storage they're converge they're converging fast it's software based and HP's product fits right at the wheelhouse of some things we talking about and I'll see Jeff Kelly with big data check out his new report he's leading the market and go to SiliconAngle.com is for the reference point for tech innovation covering the latest in emerging technologies and the data center and the enterprise and we're excited to be here and that's going to be a wrap for the cube thanks guys appreciate it and we'll see you next time look for silicon angle wikibon's the cube at the next event


Hey guys, welcome back to our channel. My name is Andrea. And I'm Howard. And we are with Food and Mentory Adventures in Food. Our channel is all about the food. We do taste tests, recipes, grocery hauls, and what's for dinner. And we are here today with a taste test from Patty LaBelle. She has a line of frozen foods. I've only seen them at Walmart. I haven't seen them anywhere else. But we have three items that we're going to try today. She has more that's out there, but they weren't available in our Walmart. So we'll go ahead and get started with the Black Eyed Peas. Okay, this is what it looks like. So they come in these trays that you can either microwave or bake in the oven. So everything that I'm showing you today, I baked in the oven. So this is a 20 ounce package, which is a pound and four ounces. And it is Black Eyed Peas with smoked turkey, sweet onions, and garlic. I didn't add any seasonings to it. We are tasting it as is. So it smells good. It does really smell good. So you ready? Sure. It has the smoked turkey and all that stuff in there. Cheers. It's good. It does have a good flavor. Yeah, you can taste the turkey. Yeah, you can taste that smokiness. I don't really taste any onions, sweet onions, but I do taste the garlic. That's pretty good. I like it. I like it too. I would definitely purchase this again. The picture though is a little different. See those big chunks of of sausage or whatever it is, turkey. We don't have a big chunk like that. Yeah, the turkey is actually chopped up very, very tiny. Little red things. That's the turkey. So yeah, the picture doesn't look exactly like, you know, the entree, but it's okay. And like in the picture also on the box, you can see like bell pepper, it looks like an onion, but you can't really see that in that little tray. But anyway, it's good. Good. It's really good. And it's soul food. I forgot to mention that. So okay, so the next thing that we are going to try is her mac and cheese and it says it's made with cheddar, Monterey Jack and real butter. Yummy, yummy. Want to show them? Okay, this is what the like here. Yeah, so it comes in a tray that's very similar to the Black Eyed Peas, but this is one pound 14 ounces. And again, I cooked it the same way on the tray for an hour. Again, you can make it in the microwave if you want, but I just prefer to cook things in the oven. So ready? Here we go. And I love baked macaroni and cheese. This looks good. Okay, here is my fork full. Man, it's dripping everywhere.  Here's mine. Ready? Yep, let's go. Good. That is good. It's a cheddar. You can definitely taste the cheddar. It definitely tastes like a baked macaroni and cheese, but it's not to me super cheesy. Do you think it's like really, really cheesy? I wouldn't say super, super cheesy, but it's cheesy and I do like it. I think it takes good. Yeah, I think it takes good. I would, you know, if I didn't feel like making macaroni and cheese, I probably would purchase this, but I will say, let's see if I can show it to you. Now, when I make macaroni and cheese, there's usually not that much. I can't show you. There is a ton of grease at the bottom and you can't see it. I'm sorry from the angle of the camera, but there is a ton of grease in the bottom of this casserole dish. And I don't normally have that when I make my own homemade mac and cheese. So I'm not sure what that's about. And this has been sitting for a while. Let me see if I can show you.  You can see it dripping from my fork. Well, you could see it dripping from his fork and then there's all that grease in there. I mean, there's tons and this is just a little bit. So I don't know where all that grease came from. The cheese. Yeah, but it's not normally in mine. I don't know. So, okay, last thing we're going to try is her cornbread. And it says fully prepared batter ready to bake. So again, this came like, again, in the same type of tray. You want to show it to them. So it came in the same type of tray and all you do is just stick it in the oven and you bake it. I started cutting it. You want to finish cutting it? Here we go. It says no artificial flavors or colors. No high fructose corn syrup. And then like I said, all I did is bake it for 35 minutes. And this is what it looks like. And I personally like, oh, and it's sweet cornbread. I personally like cornbread a lot. So curious to try this one looks like kind of dense. Ready? Cheers. It's dense. I don't think you'd like it like this. I don't. It's super dense. If you like jiffy cornbread, it is not the consistency of jiffy cornbread whereas jiffy cornbread is to me a little bit lighter. This is very, very heavy. It's dense. I don't mind it. It's a little dense, but I don't mind at all. It's kind of sweet, which I prefer. And between this and jiffy, I may take this one. Yeah. And so between this and jiffy, I would definitely take jiffy. If you like a heavier, kind of, it almost tastes kind of wet. It's very heavy, very dense. It is sweet. I couldn't even describe it as cake-like because it's just so dense. But I could be a little sweeter though. You think so? I like sweet. Yeah. You like super sweet cornbread. I think this is fine. What would you say is your favorite entree? Oh, the mac and cheese. I'll take the mac and cheese. I mean, I did like the black-eyed peas, but I'd take the mac and cheese. So I would probably agree with you. Now, normally when I eat black-eyed peas or really any type of peas, I normally eat them with rice. So I plan on having some white rice with these black-eyed peas, but they're really good. I mean, I'm really surprised for frozen food how good the flavor really is. So I would definitely repurchase these black-eyed peas again. I would definitely probably repurchase the mac and cheese. Not the cornbread. I would not repurchase the cornbread. I wish I could have found some of her other entrees because I think she has some type of greens. I don't know if they're collard or mustard, but I would definitely, if I see them, I would definitely try them because I was pretty impressed. I would try too. Okay. So that's it for our taste test. We hope you guys like this. If you did, please make sure to give us a thumbs up. And if you haven't subscribed to our channel, we certainly hope that you'll do so. And we'll see you guys next time. Bye.


Live from San Juan, Puerto Rico. It's theCUBE, covering Blockchain Unbound. Brought to you by Blockchain Industries. Hello and welcome to our special on the ground coverage in Puerto Rico. It's theCUBE covering Blockchain Unbound. This is the industry gathering from all the global industry leaders, emerging entrepreneurs, financers, investors and cryptocurrency, Blockchain, the decentralized internet, part of our continuing coverage. My next guest is Jim Calhoun, is the founder of CoinBot, an innovative entrepreneurial project that turned into something really growing in large. Great to have you on. Thanks for joining. Thanks for having me. So CoinBot, first take a minute to explain what it is. It's a really clever way that you guys built a product for yourselves, and it kind of went crazy. Yes. And it's going great. So talk about what is CoinBot, how you guys got there, and what's going on with it right now. So CoinBot is a chatbot you can message on Facebook or Slack or Telegram that gives you real-time cryptocurrency and fiat prices. So basically what that means is it's basically an AI you can message. It'll tell you Bitcoin's price or Ethereum's price.  And you're like, how much is that in dollars? You can just send out the CoinBot. It'll do the calculation for you and give you the right price. So Jim, the number one question I want to ask you is everyone's going to, it's like Alexa or Siri for pricing. Is it going to go up? Hey CoinBot, is Bitcoin rising or falling? I mean, technically you're not there yet, but I mean, you can almost imagine the dots connecting with the data. Yeah. So we get a lot of analytics and data on our backend. The way CoinBot works is we're individually hooked into each exchange. And so a lot of developers find that interesting. They want access to our API and a lot of traders find that interesting as far as the data. So like you said, we can pick up on trends. We can make some positions ourselves sometimes. And a lot of people are interested in that. So it's a free service for users. Yes. So you go to CoinBot.co, CoinBot.co, it's free, but the analytics is really the compelling thing that's an opportunity. So is there an API to the data? Yes. Traders, developers, can you play with the data? A lot of people party with the data. So we're working on our API and access to our data. Right now, if you sign up for a newsletter on our website, we'll email you when it's available and you can sign up and get early access. Okay, so we were talking before we came on that side project for friends. Yes. That's kind of how Uber started, by the way. We've got a couple of town cars and next to you know, it's Uber. So in a way, then it kind of went mainstream. Talk about what happened and where is it now? So it's funny, I've been trading crypto for years and before that I was in the stock market. So a few friends of mine, we were all in the stock market trading and we said, let's meet up and talk about some trading strategies. And again, this is like in the standard stock market. So we meet at a coffee shop, we sit down, we talk about stocks for about three minutes and then crypto, Bitcoin comes up and the next three hours dominate or dominated by crypto. So we start talking about crypto currencies, this and that. And so I created a Slack group with us. It's about, I think it was about eight or nine of us at the time. I created a Slack group and in the Slack group, I noticed people were asking, what's the price of this or who's longing Ethereum, who's shorting Bitcoin? And I would go to Google, find the prices or go to the exchanges, find the prices, copy and paste it into the chat room. And one day I just thought to myself, this would be so much easier if there was a bot or something in the chat room that can do this on its own. So that's how CoinBot was born. It's basically a group of crypto hedge fund managers, crypto miners, a tight-knit group of crypto entrepreneurs and CoinBot lived there for about a year. And one of the people in the group is an, he's also in a lot of other crypto groups. And he asked me, he was like, well, I'd like to use CoinBot in these other groups. I mean, have you guys thought about releasing it? So we released it. We got 2,000 users right off the bat. It kind of exploded like wildfire. We've had exponential growth. I think we're at 25,000 users now, almost at 30,000 just this week. And we have a lot of, I think we get 12,000 queries per day minimum. How do people get it? How do people work with you guys? So let's just say I want to put it on silkenangle.com or thecube.net, our sites. How do I share it? If I'm a developer, is there, have you guys thought that through? What's the current situation with the product? I know you just gave it for free. Yes. So how do I integrate it into my workflow via API or? So why not? We could chat after this if you'd like. But right now, if you get in contact us from our website, you can email us directly. Those emails go to me, actually. And you can ask for API access. We'll ask a couple of questions. What's your project? What are you looking to do? What kind of endpoints or data are you looking for? And if it's the right fit, then we'll approve you. So far, we have a handful of developers who've built some products based on our API. One example is CoinView app. And another example is Edgeablock wants to incorporate CoinBots API into their curriculum. And the Slack group, the original nine guys or so, or gals, was that all here in Puerto Rico? Was it around the world? Around the world. And where do you live? You're living in Los Angeles. Los Angeles, okay. So you're in LA. So any involvement with Puerto Rico here, or is it just here for the conference? So Puerto Rico, I love Puerto Rico. The more I, so it's my first time here, the more I'm learning about it and the more I'm realizing there's huge opportunities here. I'd be looking to probably start something out here soon. And yeah, so. Are you gonna move here? I'm considering it, honestly. I don't know, now that I've said that publicly, I guess I might have to stick to my word, but I'm considering it really. Awesome. And let's talk about how we can help spread the love for people who want to promote its CoinBot.co. Any other things you want to say about the project? We're on Facebook. That's our newest launch. That was a month ago. Facebook Messenger. Facebook Messenger. So you can search Ask CoinBot, or just search CoinBot, we're the first one to come up. We're on there. Other platforms, they mentioned Telegram. Telegram, we're going to be on Discord next. And we've got a lot of requests for Amazon Echo and Google Home. Yeah, yeah, Echo's a key. Yeah. We got some contacts at Amazon, we can help you out there. Okay, awesome.  We love the maturation of going on in the ecosystem. Your thoughts on the good, what you're concerned about, and what you hope gets better fast. Yes. I hope we get some more clarity on regulations, so especially when it comes to the ICOs, the initial coin offerings, it's a huge gray area, and everybody, of course, anybody like myself and like yourself, we want to follow the rules, we want to do things properly, but it's hard when there are no rules, necessarily, or the rules are very early. So I think 2018 is going to be the year of clarity on regulation, and I think that's where Puerto Rico comes in and plays a major role as being a more innovative space to do those types of things. I think also being in crypto for a long time as a trader, I think the old days are over, if that makes sense. Getting rich off the massive rise in three days. Yeah. That's over. I think those glory days, that's the thing of the past now, but this is a good thing, because I think it'll bring more volume, more stability, and more people will start getting involved in it. What do you say to the people out there that are getting into the trading? I hear a lot of parents of their kids that are my age and have kids that are in high school or college, they're letting them play with some money, and they're learning how fast they can lose money. So as an experienced trader, certainly in crypto, which is kind of wild west, it's a big wave coming, your advice to folks that want to dabble, put their toe on the water, whether it's learning or and or, trying to make a career out of it, what's your advice? This is good. This is a great question actually. So when I give talks at different conferences about Coinbot, I'd say half the people who come up to me after are developers, but the other half are people who are completely new to crypto. And they say, I love your product because it's simple, it's not complicated, it's an easy way for me to get started. So that's part of the reason why we're partnering with EduBlock here in Puerto Rico, the education, the blockchain education group, because we know that our product is good for beginners. And what I've learned when I first got into crypto, I was in high school, I bought a Bitcoin for 150 bucks, and but because it was 150 bucks, it was a little amount of money, but because it was in there, I found myself constantly checking the prices. And as I was checking prices, I started getting sucked into it more and then I started educating myself and learning more from there. So I think if your kids are really into this, give them a hundred bucks or however much you can spare or put $100 in yourself too with your kids and you'll see you'll start getting sucked in. You can message Coinbot, you can check the prices on other apps and you'll start getting sucked into it. It's almost uncontrollable. It's addictive, it's fun. Again, I've always said, we've reported in our show and our sites, this is the biggest wave in the computer revolution ever.  This wave is here. And it's moving really, really fast compared to those. And the ecosystem is forming really fast. You got funding coming in. What's your take on the ecosystem? Obviously it's super fast. What's your view early still, but still growing fast? What's the sentiment? That's a big question. I think it's growing extremely fast. And I think that's a good thing because I think it's going to be a lot of ups and downs. So the quicker we can go through the ups, the quicker we can go through the downs. If that makes sense, I know that's abstract. Basically what I mean is I think that there's going to be some booms and there's going to be a lot of busts, especially this year when it comes to the alt coins. I think we'll start seeing some coins maybe die out. And I think that's a good thing because it's like a natural process of selection, I guess you can say. Yeah, have allusion Darwinism for the crypto world. The ICO boom obviously has been a great run for people looking to raise money, but we've been reporting obviously, and we also have tokenized our business as well, not yet ICOed or maybe not even do it, but the point is utility token, if you don't have a utility and you're raising money, you now a security token. So nice movement on the ICO front, that should clarify kind of the good, bad, and the ugly, the scams from the legit deals. So give people more time to get their utility up and running. What do you see the role of the utility token as security token emerges as the preferred way to finance news ventures? Okay, so I think I'm actually, I like the direction of security tokens. If only we can figure out a way for many people to participate as opposed to just accredited investors. So I know there's a lot of types of securities. There's your standard CF crowdfunding raise and your reggae, your reggae plus, there's a handful of different directions you can go with security tokens. And we looked into this with CoinBot as well. We've been talking about doing an ICO and we were torn between utility or security. I think 2018 may be the year of security tokens, maybe more towards July, towards the end of the year. But I think it's a good direction to go in. It legitimizes our industry. It legitimizes the technology we're building. And as long as we can get all the participants as many people involved as possible and we're not excluding people who would otherwise not be an accredited investor or have the opportunities that the accredited investors have, I think it's a good thing. That's awesome. Question again on the institutional side. I had a chance to interview the folks, ex Goldman Sachs guys in charge of Block Tower and you got Polychain kind of two different makeup of kind of hedge funds, hedge funds and crypto. I mean, come on, this is happening, right? I mean, a good thing. Money's coming into the market. How does that all fit in your mind? How does it all tie together? I think the institutions are really banking on the technology of blockchain itself. I'm not sure where they are as far as trading the actual currencies, but I know they like the technology and to me that's all that's needed. In my mind, that certainty that this industry is going to be the absolute foundation of the future of technology. Well, you're a great walking example of what we see in the ecosystem that is unprecedented. That is someone who's developed, who also makes money with coins. So your developer, you're seeing the emergence of people like building products, making money on trades and then starting hedge funds. A lot of people who are starting financing organizations are entrepreneurs and crypto traders and investors. So interesting dynamic because they're not the old model of venture capital where they're just business guys investing in ponies, the hot techies, it's kind of a new cultural shift. This is significant. A lot of different dynamics are at play here. They kind of know what's going on. So interesting to see what the outcome will be. Yeah, a lot of entrepreneurs in this space come from the front lines. So they got their start trading a couple coins, built some personal wealth from there and expanded on that and learned more. And now I see people, especially in Puerto Rico, the entrepreneurs I've met since I've been here, the native Puerto Ricans here have done amazing things coming from the very, very, coming from zero and building themselves up and then building a company and helping others and helping each other on the island is extremely inspiring. So that's why I'm excited to maybe set something up here and possibly move. And it's an opportunity for a new kind of democracy, total decentralized, no central trust issues, complete transparency, but also security with the immutable blockchain, great opportunity. Thanks for spending the time and chatting with us here in Puerto Rico. Congratulations on CoinBot, check it out. I mean, to me, it's like the Siri, it's the Alexa of pricing, a lot of opportunities and we'll keep in touch. John Furrier here in Puerto Rico on the ground for cryptocurrency, blockchain, decentralized applications, all the future of work and how society works. We are here with Blockchain Unbound. Thanks for watching.


Hi, I'm Zor. Welcome to Neuzart Education. We are continuing talking about relationship between the heat and as a form of energy actually. And in this particular case, we will consider ideal gas and its kinetic characteristics, which will be eventually related to heat and temperature actually. So, today's lecture is kinetics of ideal gas. Now this lecture is part of the course called Physics 14, presented on Unison.com. I do suggest you to watch this lecture from the website because it contains notes for each lecture and all lectures are actually presented in a logical sequence. Plus on the same website you will have mass for teens, which is a prerequisite course. So, I do suggest you to use the website rather than YouTube or any other source where you can just come up, come to accidentally basically find this particular lecture there. Now it's part of the course that's very important and the course should be taken sequentially. And by the way, the site is completely free. It has no strings attached. You don't even have to sign on unless you would like to take exams, which are also provided on that course, on that website. Alright, so let's talk about kinetics of ideal gas. Now we will talk about this in stages. First we will consider only one molecule of gas. Then we will consider an ensemble of molecules which are moving in a similar direction. And then we will go to a concept of ideal gas. So it will be stage by stage. And let's talk about one particular molecule which is enclosed. Let's say these are coordinates and we have somewhere a cubical reservoir with gas. Which has lengths of each h, l. So it's l by l by l. l is the length of the side of the h. Now let's consider one molecule. So this is x, this is y, and this is z. And let's consider one particular molecule which travels from this side, which is parallel to yz plane, to this side, which is also parallel to yz. So it's only within the direction of x coordinate. So it goes back and forth elastically reflecting from the walls. Well, in some way if you have one particular molecule that might be the case if it goes exactly in this particular direction. So this is one very, very simple model of one particular molecule of gas which is moving between two opposite walls of this cubical reservoir perpendicularly to the walls, to the opposite walls. It reflects exactly along the same trajectory. So what happens in this particular case with this molecule? Let's just think about it. Our purpose is to express the pressure which gas exerts on the walls of the reservoir in terms of kinetic characteristics like mass and the speed of the molecule. So what happens in this particular case? Well, the molecule goes into this direction and momentarily exerts certain pressure. Now the wall reflects it back and while it's traveling there is no pressure basically. Now, we used to have some kind of a concept of a pressure like for instance some kind of an object is lying on the table. It has certain weight and certain area on which it touches the table. And what we do, if you would like to know what's the pressure we basically divide the force which is the weight by the area. So that's the pressure each square meter or square inch or whatever matrix we are using. So pressure is basically the force per unit of area. Now in this particular case the force is variable. So during this moment of reflection the force basically is exerted because the way how the wall reflects the molecule which means acts against the molecule the molecule according to the third Newton's law is pressing the wall and that's what pressure actually is. So how can we actually talk about the pressure in this particular case? Well, a very reasonable suggestion is to average the pressure. So if during certain amount of time the pressure exists and then during certain other amount of time while the molecule is flying between the walls pressure does not exist all we have to do we have to average the force during that period of time. Now if we consider that there is certain average of this force what does this particular force cause? Well, it causes the reflection. So if my molecule of the mass m goes into this direction this is the vector then after reflection the same molecule goes with minus v. So if positive direction is towards the x then this is direction this is the moment of the molecule which goes towards this wall and this is the moment when it goes back. So let's establish a period of time. Let's consider the molecule is touching this left wall and from this moment it goes this way and then goes back. This is a period so to speak. It's like a pendulum goes back and forth, back and forth. So this is the period during which we would like to average our force because this is actually a repeated period because the next period it will be exactly the same. So that's why it makes sense actually to consider one period of time which is a real period it's a periodic function if you wish. The force is a periodic function of time and the period is the time during which the molecule goes back and forth. Let's call this period tau. Now what exactly tau is? Well, we know the speed, well we assume we know the speed. So it goes from one wall to another that's L and then back so it's 2L. So if we get 2L and divide by absolute value of the speed that would be our time during which the molecule does this repeated movement. It's a period. Now during this time again force is variable. However, let's just think about the variable force as acting to change the moment from this to this. So in absolute terms the moment is changed from mv to minus mv to minus mv which means the moment is changed by 2mv. Well I'm talking about absolute value right now not as a vector. So this is the change of the moment. So the force which was actually exerted during a very small amount of time but if you are averaging for the whole period this would be the change which this force actually did which means that the impulse of the force should be equal to the change of momentum. This is the impulse of the force again. This is not now a vector. This is absolute value of this force. So if we would like to know the average force then we have to multiply this average force by the time and that gives me the change of momentum. Now in practical life this force is actually zero while the molecule is flying in between the walls and it's something really strong during the reflection and then again it's zero while it's flying this way. But if you would like to average the force then the average force times average by time. I'm talking about the time. So averaging the force by time should give exactly the same result. So this is average force. Now why is it making sense actually to talk about the average? If we are talking about gas obviously there are more than one molecule and since each of them is doing something during some small period of time but then another molecule does something similar which means reflection then it does make sense because there are many molecules so that's why the average does make sense in this case. So this is the way how I can basically establish the force. So let's just skip these absolute value things so whenever I don't really use the vector it means it's a scalar. It's an absolute value. So 2m times the speed is equal to force times my time which is 2l divided by v from which f is equal to 2 is out so it's m v square divided by l. Now this is average force. Again not the momentarily exerted force because sometimes it's zero sometimes it's something which is the average force during this period of time and the period makes sense again because everything is repeated after this period. All right now. So what is the pressure? Well this force actually is exerted for the whole area. The whole area is l square so the pressure which is equal to f divided by l square is equal to m v square divided by l cube. Now what is l cube? That's the volume of the reservoir, right? So it's m v square divided by volume. So I used volume also v but the speed is v so it's much easier to distinguish them. So this is the speed this is the volume and it can be expressed differently. What is m v square? m v square divided by 2 is kinetic energy, right? So we will have 2 kinetic energy of the molecule divided by volume. This is actually a very interesting formula. The pressure which molecule exerts against the wall is equal to energy divided by volume. This is actually the energy of this cube. Okay that's my first step. Now I will try to go from this step to slightly more complex one and then to ideal gas. All right so let's just wipe out this. We will preserve our formula. The pressure equals m v square divided by volume or 2 e kinetic divided by volume. And the rest we will erase. Now my next level of complexity is when instead of one molecule which goes between these two opposite walls along the x direction I will have many molecules which are going exactly parallel course. All of them. So let's say we have n molecules. Okay now what happens in this particular case? All right each molecule will exert certain pressure, right? So the molecule number i will exact this pressure. Volume is exactly the same thing by the way. Now if I would like to know the entire pressure of all these molecules which are exerting against the wall I will have to summarize it, right? So I will have to summarize. So this is the total pressure. Now if m is constant which means all the molecules have the same mass then I can express it slightly differently. So I will put m divided by wall outside and instead of summarizing all my speeds in square I will rather do this and multiply by m. Now what is this? This is average of the square of the speed. So this is therefore n times m divided by volume and times average speed average. So square of the speed average. By the way, square and then average is not exactly the same as average squared. Just as an example if you have 2 and 4 their average is 3 and 3 square is 9 but 2 square plus 4 square divided by 2 is 4 and 16 is 20 that's 10. So they are not the same. So now I'm talking about not average speed squared I'm talking about average of squared speed. That's quite different. And if you wish in slightly different form it's n times, well actually I need to multiply 2 times n times average kinetic energy divided by volume. Because m and v square average is basically average kinetic energy of all the molecules. Now this form of the same basically equation has a slightly better way of interpreting this. You see if all our molecules are different then I have to put i here, right? In this case this formula actually still holds because this would be 2 times average energy, kinetic energy. And this one where m is outside of the averaging is only for cases where all the masses are exactly the same. Alright so we have come up to a conclusion this. So in this particular case when we have n molecules so let me put it here. If we have n molecules then the pressure is equal to n times m times square average of the squares of the speeds of the molecules or 2 and average of kinetic energy divided by volume. And now we are ready for the third step when we will introduce ideal gas and we will try to do very similarly to get the formula for a pressure in case of ideal gas. Now what is ideal gas? Well it's molecules which are absolutely chaotically moving in all the directions. All the collisions between the molecules are 100% elastic. They don't interact in any other way like magnetic forces or gravity forces etc. Nonexistent. Only kinetic aspects of their movement is taken into consideration. And obviously reflection of all the walls is also completely elastic. So the only difference is that instead of parallel moving we have moves in all different directions. So one goes this way, this way, this way, this way etc. All different directions. Now let's consider. We know that the speed as a vector can be represented as sum of 3 speeds along 3 coordinates. That's a vector equation. Now, if you have a movement which consists of 3 different movements along the x, along the y and along the z then if I would like to know what is the pressure on the, let's say this same wall which I was considering before on this side of the cube I only have to consider the pressure of the x components of all these molecules. So if they are moving in 3 different directions towards the wall, towards the upper wall and towards the side wall or a back wall, whatever it is this is the side, this is the back. So side wall I'm interested in and I'm not interested in top I'm interested in back. So I have to only take into consideration the x, which means if I would like to know the pressure on my wall, this one I have to take px which is equal to m vix2 divided by 2 this is the pressure of the molecule number i so molecule number i against the wall which I'm talking about this side wall along the x direction is this one. Now the total pressure on this wall would be a sum of px which is m let me put m inside so you will have the case with why did I put 2? It's my inertia, it's volume m vi2 divided by volume that's my total pressure which is equal to absolutely the same as before m times number of molecules times vx this is x again my problem square divided by volume right? So that's my pressure on the x on the side along the x along the x axis so I am dependent on the average of all the different molecules all of them but only on their component of the speed which is directed towards the x along the x axis now what is it? well that's not easy to determine basically because all the molecules are moving in all the different directions but don't forget that we are averaging this thing now let's talk about this equation again now from the solid geometry from here you obviously can it's a three-dimensional Pythagorean theorem okay so again if you have a vector you represent this vector as sum of this, this and this right? so along the... if you consider this triangle this is x, this is y, and this is z it's a right triangle this is the right angle, right? so it's a square of this is equal to square of this which is vz squared plus square of this and square of this along... according to the Pythagorean theorem it's vx squared plus v plus vy squared so that's why we have this three-dimensional equivalent of Pythagorean theorem now next consideration is all the molecules are moving completely chaotically in all the direction which means all directions are exactly the same so if I will take average of this it should be average of this plus this, plus this and since we are talking about averaging which means sum of all these different v-axis divided by the number of molecules they should be the same because all the directions are exactly the same from our perspective number of molecules which are moving into this direction is exactly the same as moving into that direction into this direction and their components which means if we are averaging them should be exactly the same which means it's the same as 3vx squared or if you wish 3vy squared or 3vz squared same thing which means in our case this average of vx squared average of vx squared should be equal to one third of v squared average right? what follows is that I would like actually this formula to be dependent not on the x component of the speed but on the speed itself so it's equal to 2m well, wait, it was 2 so far it's mn divided by 3v squared average volume that's what it is, right? because vx squared average is one third of v squared average or equals to in terms of kinetic energy it's n e kinetic energy divided by 3 volume so this is the real formula when all the molecules are moving chaotically now this, forget about this this is only an artificial case when all the molecules are moving in the same direction and this is not the case obviously so this is the real formula which basically tells what is the pressure of the ideal gas on its wall now it's really kind of understandable in many cases it's proportional to the number of molecules well, that's understandable the more molecules you put the greater pressure on the wall now, if the molecules are having more kinetic energy obviously it should produce more pressure either because of the mass because maybe the mass of the molecule is big but the same speed it hits harder which means the pressure should be harder if you're bombarding the wall with heavier molecules now, even if the molecules have a certain thick mass but their speed is increasing then, and this is an interesting thing now the pressure is increasing as a square of the average speed of all the molecules right? so that's what we came up with and obviously it depends on the volume because if you have N divided by wall by volume, this is a density of the molecule so you can have this new density of the molecules number of molecules per unit of volume so it's 2 it's 2-3 it's 2-3 N E kinetic average where N is the density of the molecules the number of molecules per unit of volume and again, obviously the greater the density of the molecules the stronger the pressure and with kinetic energy we already spoken about so this is the main formula this is the result of whatever we were trying to come up today now, this formula is a little bit more universal than if we will take as M and then average of the squares of the speed because this formula encompasses the case of different masses of the different molecules, right? so that's why this is a very important formula from which we will do at the next lecture we will do some consequences actually because kinetic energy obviously is related to a temperature as I was talking about in the previous lecture so my next step for the next lecture would be to connect the pressure the density or the volume or whatever and the temperature but that will be the next lecture so meanwhile I do suggest you to read a very detailed explanation of everything which I have talked about today in the notes for the lecture on theunisort.com so this is the very important part I was trying to explain basically what I was talking about in written form with certain formulas and I hope that after this explanation the written part would be maybe better understood so I do suggest you to read the written part of this of this lesson, of this lecture on unisort.com so other than that that's the end of it, thank you very much and good luck


Okay. Thanks very much, and especially to John and the other organizers for having me. So I'm going to give a fairly broad brush interpretation of Mexico's experience with trade liberalization and try to draw some general lessons, especially for Vietnam. I should say that this talk is out of the ordinary for me for a couple of reasons. One is it's much broader brush than talks I usually give. I'm usually interested in papers about the effect of X on Y. I were trying to be very, very precise about what X is doing to Y. So, and it's also out of the ordinary for me. I'm not making any claim to novelty as you'll see as I go. I'm relying heavily on other ideas of others and in some case, quite old ideas. Okay. So why is Mexico possibly an interesting example here, especially for Vietnam, but other countries? So it clearly differs in many ways, but it's illustrative arguably of issues that Vietnam and other countries will face in their future growth as they move into what I think was middle income status and the puzzle of how to keep moving after you reach middle income status. And this is my first time in Vietnam. I don't know so much, but Vietnam has, as other countries, has been signing a number of different trade agreements with the US. There was a WTO accession with Japan. There's agreements through ASEAN, and there's pending agreements with the EU and the TPP, the Trans-Pacific Partnership. So arguably the sort of issues we're going to be talking about are ones that are directly relevant to Vietnam now. With the advantage though that in Mexico, NAFTA is now 20 years old. Liberalization is essentially 30 years old in Mexico. And so we have a chance to take the sort of broad view of what's happened there. Okay. So again, sort of very, very broad view of what's happened. Between 85 and 94, so following macro crisis in the early 80s, Mexico undertook a quite ambitious program of liberalization. So in part that included trade liberalization. So Mexico unilaterally joined the GATT in 85. That continued in 1994 as often NAFTA's often seen as sort of a further commitment to this broader program of liberalization. There was private citizen of state owned enterprises. There was liberalization of investment regime. And there was a general reduction of the role of the state in the economy. So Mexico, I guess the point here in sort of motivating what I'm going to say later is that Mexico was trying to do this all by the book. Basically was doing liberalization, kind of a poster child for liberalization. And the people who are advocating these reforms are quite confident that they would lead to rising average incomes and growth. And the fact is that Mexico's growth over the last 30 years has been quite disappointing. So I'm borrowing some slides from, this is from Gordon Hansen has a piece in the Journal of Economic Literature in 2010. Rafael Bergoying this morning had a graph that looked something similar to this. So basically he's comparing just normalizing in 1980 GDP per capita to be zero. And then looking, comparing to a number of different middle income countries, there are going to be three of these slides. So here's comparing to Latin America. You can see that Chile is just vastly outperformed Mexico. Mexico is the green here. Mexico is closer to Argentina and Brazil. But I think the key point there is Argentina and Brazil have significantly more heterodox policy packages. And so this is not what the advocates of reform would have predicted back when Mexico was undertaking these reforms. The only country that Mexico is convincingly beating is Venezuela, which is not an enormous mark of distinction. If you look at Asian countries, again, so Mexico is down here, so Malaysia, Indonesia, and Thailand are vastly outperforming Mexico. Mexico is down here with the Philippines. Or also if you look at a number of countries in Eastern Europe, again, Mexico was green. You have Turkey, Hungary, Bulgaria are outperforming. And even Romania, which has had a mixed history, is sorry, doing better than Mexico. Okay, now there are obviously a number of reasons why Mexico's growth may have been disappointing, a number of that have been emphasized. So there's monopolies and inefficient regulations. So this is a paper, Jim Heckman is a co-author, underdeveloped credit markets. Santiago Levy is here in the conference, has emphasized informality and evasion and how the policy regime may foster those. Corruption is obviously a problem, and more is violence is obviously a problem. All these likely played a role, I don't want to discount them. But I do want to focus on trade and what I'm going to call the links between the pattern of specialization and innovation. Okay, so I'm going to talk about the intra, what I'm going to call inter and intersexual resource shifts. Looking at, at, at, at sectoral reallocation in Mexico. I'm going to try and relate that to, and then relate that to the sort of overall rate of innovation. Okay, now, I guess already a caveat about this. So evaluating the effect of trade policy is, is, is extremely challenging. It's challenging in many situations, but especially challenging in Mexico here, partly because NAFTA, which was undertaken and were implemented in January 1994, was followed very quickly by, by a major pace of devaluation and the pace of crisis, started in December 1994 and continued into 1995. As, and Kruger and others have noted that the, the, the nominal devaluation was just much, much larger than the tariff changes under, under, under NAFTA. There may also have been sort of lagging effects of this mid-80s liberalization of Mexico, which was in many ways probably larger than, than the, than the changes under NAFTA, both in, in terms of tariff changes, but also in the removal of non-tariff barriers, etc. So essentially, I'm going to lump all these things together. I'm going to look broadly at what we think this opening of the Mexican economy, which had been quite closed prior to 1905, is now quite open at broadly what that opening has, has done to, to, to sectoral shifts. Okay, so here, so I'm using data from the Mexican industrial census supplemented by data from a 1999 survey called the NST, sort of a special survey where we can also on manufacturing firms. So the, here on the X axis, you just have the share of workers with more than, greater than or equal to 12 years of education as a measure of skill intensity by, by industry. Each dot is a four digit NAICS industry, so that's the industrial classification used in North America. And then we have the change in log employment between 1988 and 1998, the, the census that are every five years, 88, 93, 98, etc. Okay, I'm focusing, you can see there's highlighting the size of these, of these symbols is, is just the size of the sector. I'm going to focus on a few. These, these red diamonds are in apparel and textile products. The, the blue triangles are transportation equipment, which is largely auto and auto parts. And there's also various electronics industries are the, are the green squares. And then we have other, other industries. Okay, so what message are we taking from this? This is basically just saying that from, at least from 88 to 98 was this period of initial period of liberalization. What you see is that there's a, there's a sort of roughly downward sloping relationship. That's saying that the industries that grew the most were the ones that were least skill intensive, which is basically exactly what we'd expect from a standard Hector, Hector-Olean type model. That Mexico should integrating with the United States. That was by far the major, major trade partner. You'd expect Mexico to specialize in, in unskilled labor intensive activities, and that's more or less, more or less what happened. I've also done work, other work, sort of looking at effects within industries. There's sort of differential effects within industries. There's some pressure to upgrade within an industry. But for the most part, I'd, I'd even argue, arguing against my previous work, the first order effect is just the sectoral shifts in the direction that we'd expect from the, from a standard Hector-Olean type trade model. Okay, if you look at capital intensity, so if you just take, this is just the capital labor ratio. This is also from the economic censuses. There you can see the relationship even more starkly. Okay, so it looks like the least capital intensive industries are the ones that between 1988 and 1998 are growing the fastest. Okay, then what happened in Mexico? So this is now from, graph is in every way similar, except now we're going from 1998 to 2008. And you can see basically those industries, so compared to this one, and especially that big square, that's, that's apparel, that big diamond, I mean, just tanked, just really didn't do well at all from 1998 to 2008. We'll talk a little bit about why that is. Okay, but you can see that, that these, here, these sectors in general, sort of at the low skill ends, had very underwhelming growth over the later period. If you look at the capital intensity, you see something, see something similar. Now part of what's going on there, which John alluded to, is what's happening in the McElodora sector. So the McElodora's are these sort of assembly sector, they are sometimes built in bond plants where they get relief from import duties. If goods are subsequently exported, you have to be a McElodora, you have to register with the government, and then you have to follow certain rules and provide data to the government on a monthly basis. So here, the main McElodora sectors are apparel, electrical and electronic equipment, and then transportation equipment, which is mainly auto parts. Here, I'm using two different sources of data. The top one would be all, it's from the economic censuses, where we only have data every five years, and that's employment, total employment in the sector. And then for the McElodora's, we actually have yearly data, or you could even get monthly data if you wanted. So that's the following. So that's saying, so here we have total employment apparel, and here we have McElodora in apparel. And the key thing to notice here is you have a big boom. So McElodora employment in general, in particular apparel employment, is often credited with sort of getting Mexico out of the pace of crisis and the crisis in the mid-90s, okay? So clearly, employment grew very quickly at sort of what people were expecting, and advocates of reform were quite excited about that, and there's no question that that helped cushion the Mexican economy during this big crisis. Here you can see, and then what happened is, so basically following 2000, you see a major decline, okay? So a big part of that was that big diamond that looks so low in the second graph when looking cross-sector was, in part, this apparel sector has just taken a major hit. Electrical and electronic equipment, this is slightly misleading in the sense that here I only have data every five years, and so it looks like McElodora employment exceeds total employment. That's obviously not what's going on. There would be a bump there if I could see what's happening in between years. But there again, you can see pretty strong growth between 95 and 2000. That's actually, sorry, 93 and 98, and then stagnation afterwards with a little bit of recovery, so not quite as severe as an apparel, but anyway, certainly in this period there's not very strong growth in employment. The one sector sort of McElodora that seems to be doing okay is transportation equipment, so auto parts. So many of those auto parts factories are integrated geographically into a North American production system for autos, and so that sector has been more, I'd say, more robust growth. What can we say about this, about the McElas relative to the non-McElas? So here this is comparing, so we're going to have McElodora sector versus non-McElas, and then within the non-McElodora sector, we're going to compare non-exporters and exporters. This is just sort of means, and this is from this 1999 NSTIC survey which has the advantage, unlike other Mexican datasets that it, or the sort of the high-frequency Mexican datasets that it combines McElas and non-McElas in the same dataset. So what do we see here? So the McElas tend to be large. They obviously export almost all of their output, although they don't have to. They're highly, high share of foreign ownership. Now, okay, if we're getting here, you can see for a capital labor ratio, which is here, so much, much lower than even the non-exporters in the non-McElas sector. So they're clearly focused in labor-intensive activities. When you look at the share with greater than or equal to 12 years of schooling, again, that share is much lower. The percentage blue collar is higher than in the non-McElas sector, the sort of non-exporters or exporters in the non-McElodora sector. Years of schooling is also lower. So what's clear is not surprising to anybody, but this tends to be the least skilled sort of phase of the production process that McElodoras are specializing in. Interestingly, I guess giving the full picture here, is their wages are not necessarily lower. You can see the wages are closer here to the exporter wages in both cases, which is interesting. Partly that's because McElas tend to be focused in the northern part of Mexico, where wages are a bit higher than in the center in the south, but it's also, I think even when you look within the north, wages are reasonably high relative to the non-exporting non-McElodora sectors. Okay, so what's my basic message so far in terms of just what happened? So from 88 to 98, manufacturing specialized in less capital, less skill-intensive activities, both across sectors and within sectors, in the sense that there was a big shift towards this McElodora type production. From 98 to 2008, these sectors and sub-sectors tended to stack, okay? And so then, now as I mentioned before, there's some sort of other effects within industry that partly, then in my own work I've tried to emphasize, but I think when you look at this broad story of what was Mexico's response, I think the first two points are really the first order facts. Now, okay, so the question is what happened? I'd say probably the most common explanation for what happened is that Mexico, well, that's China entered. And so people in Mexico often think of this as we had bad luck, right? This program would have worked, liberalization was a good idea. It just so happened that we liberalizing at the same time that China entered the world trading system. And in particular, this is a more severe problem for Mexico, but for other countries in part because the pattern of specialization, the goods that Mexico was exporting to the US are highly correlated, essentially with the goods that China is exporting to the US. Okay, so there's been various work on that. I have a graphic and show if you want. That's clearly true. And I don't want to say there's no truth to this story. There clearly is a lot of truth to this story. So there's a number of papers. This Uttara-Zuiz paper is looking at the effect on Mexican microdures of Chinese competition in the US market. They had an impact. This is a graduate student of mine who's just finishing, who's looking at using an approach of Otto Dornan Hansen to look at local labor market effects in Mexico of Chinese competition. He's finding strong effects that regions in Mexico that had a lot of firms that were competing directly with Chinese firms tend to see lower employment growth. There are a number of different papers in the trade literature. This Hansen and Robertson are saying, oh, so looking sort of slightly more structural approaches, looking at the effect of China on Mexico. Okay, what I'd like to argue though, and here slightly more speculative than the sorts of arguments I usually make. But what I'd like to argue is that I think that Mexico would have had problems even if China had not entered. So we don't observe that counterfactual, obviously. But I think there's something to support this story and I'm going to tell you why. Okay, and this is a very, again, here's this very old fashioned idea. Going back to prebish and then sort of formalized by Matsuyama, Hansen and Rodriguez Clair, sorry, and this is not Harrison and Rodriguez Clair in a handbook of development economics sort of literature view make this point and summarize it well, but basically different activities are associated with different inherent rates of innovation and productivity growth. And liberalization, which may bring about sort of reallocation to sectors that have a static, when I think of as a static comparative advantage, may affect the rate of long-term growth and innovation. Okay, so it's a very simple idea, but I happen to think that it applies quite directly to the Mexican experience. Okay, so let me show you a little bit of evidence of that. The, I'm going to use a measure of R&D from this NSDIC survey, this sort of, which I mentioned before. So the survey asked, this is in 1999, and asked, since 1997, has this established an undertaken R&D. And I like the survey a lot because it used a quite broad notion of what R&D is. It's not just patenting, not just something new to the world. But you can see it says, if yes, if they answered yes, they said, what did the R&D principally consist of, which could be design of new products, process improvements, product quality improvements, design, improvement in manufacture of machinery, equipment, or other. So it's quite broad view. Basically, anything new or innovative, sort of include what I think of as upgrading, not necessarily coming up with something patentable. So I like that, it's not perfect, but it's not bad as a first pass. We're just going to code that as zero one about whether the firm had any R&D or not, okay? And then I'm going to take an average at the sector level, this four to the sector level, and plot that using a plot similar to the ones I showed before, okay? So this is just a cross-section, I just have one year of data for this. I don't have it in the other years of the, or at least the previous years of the NSD. So I can't do it before and after. But anyway, in 1999, so what does this look like? Recall, this is this big red diamond. So recall what we said is that in that 88 to 98 period, the sectors that were growing fast were the ones down here, the low skill intensive, okay, and low capital intensive. Those sectors are sort of fairly systematically doing less upgrading than the more skill intensive or more, if you do more capital intensive, it's even stronger, more capital intensive sectors, okay? So again, the same thing, you see that the sectors out here are doing more of this R&D, the sectors down here, which were the ones that the Mexican economy was specializing in, were doing less, okay? So that, I mean, mechanically is going to lead to less R&D overall than would have taken place in the absence of those sectoral shifts. Another way of seeing this, you could see, so this is just R&D, this is looking at McElias, this is kind of, again, giving you, make sure you get the full view R&D intensity by sector. So if you look at all, I guess, all manufacturing for the non-exporters or the exporters of McElias, so McElias are sort of intermediate between the non-exporters and the exporters. It's not the case that McElias are worse than the non-exporters, but they're sort of intermediate. Now, in part, that's because, so in apparel, actually, McEliadores are sort of comparable to exporters. But the apparel sector overall has less R&D or less upgrading than other sectors, and that's partly what's bringing this number down. Okay, a couple of other ways of showing this, just to, if you don't like my, that R&D measure from the Anastique. This is from a book by Laderman, Maloney, and Cervéna at the World Bank. So if you look at, this is just patents per million workers. And here they've got Latin American, the Caribbean, sort of on average, versus Mexico, okay? And you can see there, so in the 60s, Mexico has actually outperformed on this alternative measure, outperforming the rest of Latin American Caribbean. By 1995 to 2000, it's actually significantly underperforming, because in terms of patents, okay? They have some other comparisons. Korea is obviously a star here, and here, notice that the Y-axis are changing, but the patents for workers are significantly below also other high income countries. Another way of looking at a simple way, and again, this is from downloading from the World Development Indicators, just the R&D spending as a fraction of GDP. You have Chile and China at about 0.65%. Korea obviously is a star up there with the US and Canada, but Mexico is significantly below, okay? Which doesn't say there are other countries, if you look at Argentina or look at Brazil, they're also down there with Mexico. But Mexico's being outperformed by Chile and China, okay? So summing up, what it seems to me is that integration led Mexico to specialize in less capital and skill-intensive activities, both across and within sectors. These sectors tend to be less innovative. This did not have to be true. I wasn't actually convinced that this would be true when I first did those plots of the innovation rate or the upgrading rate against capital-intensive or skill-intensity. But it turned out to be, the correlation appears to be quite robust. So these sector shifts tended to dampen the overall rate of innovation in the economy. What if China had not entered? So we don't, again, observe the counterfactual. But my sense is that even if there hadn't been China, there would have been other countries moving up the quality ladder and competing with Mexico. So that would have been Malaysia, Thailand, Indonesia, maybe even Vietnam, would have been entering. So you can't not innovate and not have productivity growth for long before you're out-competed by other countries, okay? So more research is needed and needless to say. If I'd been asked by Tony Addison yesterday what graduate students should work out, I'd say go out and figure out what works and what doesn't work in industrial policy. So we don't have a lot of evidence about that. Or we don't have a lot of what are called sort of rigorous econometric evidence about what works and doesn't work. But the pattern suggests that there may be this trade-off between what I call static, allocative efficiency, and long-term productivity growth. So clearly there was some benefit in a static sense of reallocation towards sectors in which Mexico had a competitive advantage, right? But it may lead to lower growth in the long run. And so now that we're in the long run, arguably, that may be what we're seeing. And so maybe that trade-off needs to be taken into account now when you start thinking about entering the liberalized trade regime. So liberalization alone may not be enough to bring about sustained growth. That's another way of saying that, saying that same point. I think that there's a strong case which Justin and John and many others have made also for interventions to promote the sorts of activities that generate innovation and productivity growth, both across and within sectors. Underlying that, there's an argument about externalities. Okay, so you somehow firms have to not be taking into account the effect of their own investments in innovation. I'm working on other, have other work, especially in Pakistan with some soccer ball producers, which I'm happy to tell you about at length during a break or something, about that working on that. Anyway, so some caveats. And this is why this relates to another point that John made about trade policy versus industrial policy. So these interventions do not have to be. So if we agree that there's a case for intervention to subsidize innovative activities, it's not clear these interventions have to be at the border. Okay, so there's a well-established principle that it's actually better to address a market failure or something like this at the source. In this case, you'd go to the innovative activities and subsidize them directly in some sense. And obviously, WTO accession limits the ability to conduct industrial policy through tariffs or other restrictions at the border. So we should, in that sense, I think my argument is really about industrial policy rather than trade policy. The other caveat, which is again not a new point to make, is that it's true that government officials have no special knowledge about which sectors or firms or ideas or technologies are likely to be successful in the future, okay? So many of the best-intentioned officials may be less well-informed than the business people in the sector, although they have different incentives and different objective functions arguably, but they may have less information. Okay, so I don't know how much I've got, a minute or two minutes or something, zero. Okay, let me do at least the next two slides, and then maybe we'll stop. So one common approach, which is that later Manamaloni have a new book, which is arguing is that what you do is you do broad-based policies which provide broad support for innovative activities without having to pick which particular firm or sector is going to be successful. That seems to me like a very good idea. So you've got infrastructure or technical vocational education, or another one would be removal of restrictions on imports of high quality or high tech imported inputs. That seems like no brainer, but that would be in the spirit of this argument of doing things that broadly will support innovation. Normally these policies along these lines are already in place in Vietnam, which we can talk more, but I don't have very much time, so I won't do that. Now, so the other alternative would be targeted policies. Now, targeted policies as we all know are inherently more subject to corruption or capture or plain old, you know, sort of mis-targeting, but if implemented well, they can be very effective. So again, this is, I was reading up on this in preparation for this talk, but so Vietnam has this law on corporate income tax and law 31 where we have a sense of preferential tax treatment for investments in a number of areas. And so this is all very much in the spirit of trying to promote these innovative activities. So in high technology, scientific research, technology development, I mean, set our high grade steel, energy saving products, et cetera, et cetera. Okay. So I think, you know, I formulated the interpretation of the mixing experience before finding out what Vietnam was doing, but I think from the mixing experience, you'd have to say these are the sorts of, we do want to think about these sorts of policies. Obviously, they're caveats. I'm sure they're ways that they could be implemented better, but there's, I'd say that there's a strong case for these sorts of policies. It's not a blanket case for it. Blanket endorsement of intervention. Obviously, there are many interventions that produce more distortions and rent seeking than growth, but I think that the fact that, so not that there should be trade policy to promote innovative activities, but the fact that Vietnam is liberalizing trade means that direct support for innovative activities may be more salient, or maybe even more necessary now than before. Okay. So go out, the graduate students either go out there and tell us what works and what doesn't work in trying to evaluate some of these policies. I have one idea that we could talk about, but maybe I'll stop and I'll let you ask me about that in the question and answer. Thanks.