The Kraft Foods Company presents Willard Waterman as The Great Gilder Sleeve. The Great Gilder Sleeve is brought to you transcribed by The Kraft Foods Company. Twenty years ago, The Kraft Foods Company introduced a wonderful new salad dressing, a superbly smooth, delicious tasting salad dressing called Miracle Whip. Miracle Whip was so remarkably good that it soon became the most popular salad dressing ever created. Now Miracle Whip outsells the next twenty leading brands of salad dressing combined, and good cooks everywhere depend on it to make their salads better tasting. To bring out the best in your salads, use the one and only Miracle Whip salad dressing. It's after dinner at The Great Gilder Sleeve's house, and the evening paper has been divided between the water commissioner and his nephew. Lee Roy has the sports section in the comics, and the great man has the financial section, which carries the crossword puzzle on the back. Lee Roy. Yeah? What's an eight letter word for an African anteater? That's hard work. Oh yes. They eat ants. I know, Lee Roy. I'll bet you don't know how to spell it. Why don't you work your own crossword puzzle? Lee Roy, I'm just testing you to see what you've learned at school. Ah! Yes, yes. Oh boy. Hey, else look here in the paper. Let's go fishing at Grass Lake tomorrow. All right. Oh, look at the big fish that was caught. A 90 pound swordfish. Swordfish in Grass Lake? Let me see. Lee Roy, this wasn't caught in Grass Lake. It was caught in Florida. Well sure, but it shows they're biting. Oh my. Let's go on tomorrow morning, six o'clock. We'll have a lot of fun. Six o'clock. Mr. Banks took big fish in last Saturday. Well, how many swordfish did they catch? Well, they didn't catch anything, but they went fishing. Lee Roy, a fish hasn't been caught in Grass Lake in three years. Besides, you have to practice the piano tomorrow morning. Oh gosh, can a little kid have one day off? I practice every day of the week and two hours every Saturday. Faithfully. Faithfully. I have reason to doubt young man that you even touched the piano last Saturday. Well sure I did. I must have. Let's not fear, Lee Roy. I didn't hear you strike a note, Saturday. Well, when it's time for my piano lessons, you always leave home. I'm a busy man. But I happen to see your music teacher on the street and from her report. I don't think you went near the piano last Saturday. Well, she's prejudiced. I put out good money for your music lessons. A small fortune for a very small return. Yeah, why don't I do something nice for you and give up the piano? You will not. I'm going to find out if you practice. Birdie, step in here a minute, Birdie. You can't tell fibs around here and get away with them, Lee Roy. This is out there. Well, I've got a little boy full of fibs in here. Birdie, Lee Roy thinks he should go fishing tomorrow. Now tell me, did he practice the piano last Saturday morning? I'm usually busy in the kitchen on Saturday mornings, Mr. Guilty. Yeah, I know, Birdie. But you can hear Lee Roy playing in the kitchen. You can hear that touch all over the house. Did you hear it? Well... Yes or no, Birdie. Well, I usually have the windows open on Saturday mornings. Lots of ambiguous noises coming in and out. Birdie, did you hear the piano? Well, I heard something. You did? Music of some kind. Of course, it could have been old clothes, man. Yeah, all right, Birdie. It's very evident Lee Roy did not practice his piano. What do you think I should do with a boy like this? I think you should take him fishing. Oh, my goodness. Sure, why? Well, I'm not taking you fishing until you learn to tell the truth. For Lee Roy. He's in the doghouse tonight. Oh?  He does look unusually subdued for Lee Roy. Is the Lord of the Manor in? He went upstairs to shave. He should be down any minute. I thought Gildy and I might have a game of checkers tonight. He has a date, Judge. Oh? Yes. He's going out on the town while Lee Roy stays in the doghouse. Yeah.  Well, perhaps I can change his mind. Oh, so it's you, Judge. Yeah. I thought we might have a fast game of checkers. Whenever I play you, it's a fast one. Yes, yes. Judge, I don't have time to play checkers tonight. So I see. You did a nice job of shaving, Gildy. It smells nice, too. Don't go sniffing, you old goat. But Bayrom's not for your benefit, believe me. Sorry, I can't play checkers with you. Good night. Well, as long as you're leaving, I'll walk away as with you. I'm just going over to Miss Tuttle. Well, I'll walk that far away. I don't think that I should go in, though. I don't either. Good night, Bertie. Good night. Good night, Lee Roy. Lee Roy, say good night. What seems to be the matter, Lee Roy? Good morning. You bet I won't. Well, why don't you take him, Gildy? It's none of your business, Horace. Or do you think you'll be too tired to get up and go fishing in the morning? After all the work you've done, I'm not taking him for a very good reason, judge. I caught him in a lie about practicing his piano. Lee Roy? A lie? Yes.  It was just a little white one. Well, little white ones grow into big ones. If I've told you once, I've told you a hundred times. Anybody who lies is bound to get into trouble. And he's in trouble. Oh, that's right.  I think I'll go to bed. Oh, Lee Roy, don't go to bed feeling sorry for yourself. I'm not feeling sorry for myself. This hurts me more than it does you. Why go to bed so early? I'll stay and play a game of checkers. Good idea. Go to bed, Lee Roy, and say, hear any more about fishing.  Yes, sir. Good. What is it now, Lee Roy? Good night, Lee Roy. Let's get out of here, Judge. A girl's house at 8.30 in the evening. But Grace and I couldn't find anything to talk about. I guess I still have little Lee Roy on my mind. He looked so heartbroken when he went to bed. Well, Pee-Bee's still open. Maybe he'll cheer me up. Hello, Pee-Bee. Yeah, I don't know anything young about fishing. What can I do for you this evening? You can give me a cup of coffee, Pee-Bee. Yeah, yeah. Black. Yeah, yeah. Thank you. Now you can pour me another cup. Yeah, man, another one. That's what I said. Yeah, yeah. Sometimes I don't know which holds the most. You or you're ready for it. Well, I feel like downing a lot of coffee tonight.  Your girlfriend sent you home with curfew? I left of my own accord, Pee-Bee.  Now, Pee-Bee, don't you start picking on me. I've already been raked over the coals by the judge and Bertie. You don't say. I came in here hoping you'd cheer me up. Well, as long as you keep buying coffee, I'll do my best. Let's see now. Here's a new songbook I picked up for us Johnny boys. Oh? I got it at the diner. It cost a quarter.  That's what I'm trying to say. Here's Sonny Boy. Sonny Boy?  Little Leroy? Well, bring the book up to the next meeting, Pee-Bee. I always liked Sonny Boy. Climb upon my knee, Sonny Boy. Climb upon my knee, Sonny Boy. You were only three, Sonny Boy. You've no way of knowing. I've no way of showing what you mean to me, Sonny Boy. Pee-Bee, stop. What's the matter, Mr. Goodman? That's so sad. I thought I sang it very well. That's not what I meant. Good night, Pee-Bee. Are you going home so early? It's carefully nine o'clock. I know, but I have to get up at six o'clock in the morning. Six o'clock? What for? To take Leroy fishing. Don't rock the boat while I'm reading. What are you reading? This paper that Bertie wrapped our lunch in. She would wrap it in the wanted section. Too bad. I don't know. Here's a place where we can buy patience on the way home. Let's go, Leroy. Let's go. We've been out here three hours and nothing's happened. But turn on how fish are. Aren't they traveling school? They'll be by pretty soon. Yes, yes. My next getting sunburned. Let's go home. Aw, what if you turn it on in the boat? I did that when my nose turned red. Now let's pull up anchor. Everybody else has left the lake. It's probably out of joy riding. You know, aren't any girls in the boat? It could be the Game Warden. Hacker, we haven't any fish to show him. I hope he stops. Maybe he can convince you there aren't any fish in this lake. Yeah, it's the warden all right. They always make a lot of noise around fishing. Now, Betty says, what are you doing? Fishing? Fishing? Sonny boy? Sonny boy. No. He said he was fighting yesterday, but I know they're here. Yes, yes. I suppose you have your licenses. Well, the boy doesn't need one. He's only 12, but the size is 16. Yeah. He'll stand up in the boat for the warden. Never mind. How about you? Me?  You're out here? I just rode my nephew out. Yeah? Yeah. I've heard that story before. It's no story. Besides, you ought to be ashamed to sell licenses to fish in this swamp. Now let's not get tough with the Game Warden, big boy. Well, let's not get tough with me either. I happen to be Throckport and Pete Jonas leave the water commissioner. I don't care if you're King Neptune. You know you have to have a license to fish. I'm not fishing. There's nothing I like better than the slap of fine on guys who get tough with the Game Warden. And there's nothing I like better than the turn off the water of guys who get tough with the water commissioner. Where do you live? I have my own well. Just don't let me catch you fishing. I'm not fishing. I've heard that story before. Well, that doesn't leave you. We're pulling up anchor. We're going home. Okay. Bye! Leave you. What did I say about telling Pete? Whoops! Pull it up! We're going by. Okay. Give me that pole. Okay. I'm going by. Get in, then. There's one waiting on your other line. Back in just a minute. 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It's creamy thick and velvety smooth because Miracle Whip is blended thoroughly with special beaters. So many people like Miracle Whip so much it's become the most popular salad dressing ever created and actually outsells the next 20 leading brands of salad dressing combined. Try it. See how much more delicious your salads can be when you make them with America's favorite salad dressing, Miracle Whip. Let's get back to things in Summerfield. A car is pulling up in front of the Great Gilbert Sleeves House occupied by one sunburned uncle, one happy nephew, and eight very surprised fish. Fish and aunts, I had a wonderful time. That's fine, Leroy. You see this big one? He's the one who took the lion away. Let's not talk about it. Let's just forget the whole thing. Boy, it's great big bass and I caught every one of them. Let's not gloat, Leroy. Oh, I'm sorry you didn't have a license, John. That's all right. Now, just along for the ride. Let's get out, Leroy, and take your fish with you. Aw, but you didn't catch any. You can have them. What? Come back here and clean these. It's my piano lesson. Big brown eyes. You'll take your taken with them. You will. I never saw such big ones. Is that a flounder, see? No, they're all bass. I just sat on this one. Taking with them? You know, I don't have time for that. Take them along, Birdie, and we'll have them for dinner. Oh, we'll have them for dinner, all right. Just as soon as you get them cleaned. I caught the fish. Now it's up to somebody else. This calls for a little cooperation. Well, you can count on Birdie to cooperate. Say, nobody can cook fish any better than Birdie can. Anytime you get them cleaned. Yeah, I know Birdie, but... That's one thing I'll say for me. Nobody can cook fish any better than Birdie. Anytime you get them cleaned. Birdie, that's not cooperating. Mr. Giffley, when I cook steak, I don't dress no sin. You know who takes care of that? Mr. Kansas City? They've got to be... That's right. They've got to be cleaned before they cook. And I said, ready to cook them, just edit them. Because that's one thing you can count on from Birdie. Call operation. Who, operation? Well, maybe I can give them to a neighbor. Still, as Birdie said, there's a beautiful string of fish. Naturally, I wouldn't have my picture taken with them, but I may just go downtown for a minute and show them to the boys. Right, how's business? Well, I can't kick, Commissioner. How about a haircut today? Oh, thanks. What do you think of these? Hey, bass. They're beautiful. Yeah, quite a string. Catch all those yourself? Well, true sportsman doesn't like to gloat. But who do you suppose caught them? Gosh, why didn't you ask me to go? Well, I took the boy along. You took the kid out. That's great stuff, Commissioner. Did he catch any? Oh, yes. How many? Well, as a matter of fact, he caught exactly this many. Well, I'll be done. I didn't know they were biting. Yeah, you've got to know how to catch employed.  But you're going to take them home now? Well, I had them home, but nobody was very interested in them. It wasn't. Hey, I got an idea. Lovey's out of town. Why don't we have a fish fry over at the house tonight? Fish fry? Sure. Invite all the Jolly Boys over. It's Saturday night. We'll throw a wing ding. Yeah, it sounds great. I'll go around and invite the Jolly Boys. Okay. While you clean the fish. Wait a minute. Commit. You're handy with a razor, Floyd. You can scale them in a jiffy. Me? A licensed barber? Scraped fish? What do you want me to do? Lose my professional standing? But Floyd? Kind of a clientele that people think I got. Come in here and find fish scales all over the floor. Well, they have to be clean. And the man who furnishes the fish shouldn't have to clean them. Okay. You caught them. I furnished the house. Let somebody else clean them. How about the peeve? The peevee? Yeah, he's got the equipment there at the soda fountain. I bet he could clean them between customers. I don't know if peeve will go for that, Floyd. Oh, you can convince him. Commit. Come on. I'll show you and the fish out the door. It should have a lot of fun at Floyd's tonight if I can ever get these fish clean. Yeah, better keep them behind my back until I find out how the peevee feels about such things. Hello, peevee. Hello, Mr. Jonathan. Beautiful day. And so? Yeah, it's been beautiful all day. No, it's great. You bet. There's one. Birds have been singing. Fish have been biting. I've been too busy to notice. What can I do for you, Mr. Jonathan? Well, nothing. peevee, you know I went out to Grass Lake this morning. Any luck? Any luck. Peevee, take a look at these. Yeah. Do you ever see a more beautiful string of fish? Never in my drugstore. Did you catch all those, Mr. Jonathan? Did I catch them? Well, here they are. My, my. The Jolly Boys are going to have a big fish fry tonight. I'm furnishing the fish and Floyd's furnishing the house. I guess you'd like to come. Yes, you would. Great. Then I imagine you'd like to cooperate with us and clean the fish. Well, no, I wouldn't say that. I'm telling you what I wouldn't do, you know. I'll furnish the coax. Who's going to clean the fish, Peevee? Have you tried Chief Gates down at the jail? He's a nice, amiable Jolly Boy, the chief. Say, he might clean them. He probably has an empty cell you can use. I don't imagine the prisoners are in any position to object. Yeah, good idea, Peevee. Bring the coax and we'll see you at Floyd's tonight. Yeah, well, Mr. Jonathan. Are you surprised that Leroy caught those fish? What I can do is clean them after you've caught them. Well, you must be quite a fisherman to pull in big ones like these. Well, you got to know how to do it. How'd you happen to go out, Commissioner? Oh, Leroy wanted to go and I nearly didn't take him. Discipline. Nothing serious, I hope. No, no.   Sometimes they can grow pretty big. Oh, yes. Well, gentlemen, the game warden. What are you doing here in the cell, Chief? Cleaning fish. How are you, George? Fine, fine. Just came in to report a few game law violations. Well, hello, Water Commissioner. Hello, Mr. Warden. Hey, that's a beautiful string of bass you caught there, Chief. Well, I'd like to say I caught them, but that wouldn't be the honest thing to say. Mr. Gildersleeve here caught them. Gildersleeve? Mm-hmm. Oh, oh. I'd love to catch up with a guy who gets tough with the game warden. Hmm. The idea. A city official fishing without a license. What's this, Commissioner? Come to think of it, Chief, I didn't really catch these things. But I thought you said that you... I didn't say I caught them, Chief. I just said I didn't. Uh, little Leroy caught them. I heard that story before. Well, he did. Water Commissioner, I'd like to ask you a few questions in the Chief's office. Now, wait a minute. Chief, you believe me? Mr. Gildersleeve, do you realize what an embarrassing position you placed me in? But the Chief of Police cleaning illegal things. I wasn't even fishing. Well, Leroy, he'll tell you the truth. All right, I'll call the boy. I want this cleared up as much as you do. No, I'm innocent. I've got a witness. You better have followers. This is Mr. Gates. Oh, hi, Chief. Uh, there seems to be a little discussion about who caught those beautiful fish. Yeah? At first, your uncle led me to believe that he caught them. He did. Now then, who really caught them? Not those fish and you know it. Well, sure. I'll be with us again in just 30 seconds. To keep fresh fruit from turning dark before you can serve it in your favorite fruit salad, do this. Cut it at the last possible minute, then immediately dip it into orange or lemon juice. That will help make your fruit salad attractive. And to make sure that salad tastes as good as it looks, make it with Miracle Whip salad dressing. Miracle Whip has a wonderful peppy flavor, a special flavor that belongs to Miracle Whip alone. Try it. Enjoy better tasting salads with the one and only Miracle Whip salad dressing. Hey! You nearly gave me a fine, Leroy. Remember, always tell the truth. I will, uncle. Yes, sir. You can't go wrong if you tell the truth.  I think we should all toast Leroy, the mighty fisherman. Yeah, that's a good idea. No, wait, gentlemen. I propose that we sing a song for our honored guest and provider, young Isaac Walton, who fared forth with rod and reel. No, judge. I thought you said a song, not a speech. How about he's a jolly good fellow. Okay. We'll do the singing. Do you want all the credit? Here we go. Me, me, me. Good night, folks. Patrick, Kim Christie, and Dick LeGrand. Musical composition by Jack Meakin. This is John Heaston saying good night for the Craft Foods Company, makers of the famous line of craft-quality food products. Be sure to listen in next Wednesday and every Wednesday throughout the summer for the further adventures of the great Gildersleeve. Sandwich lovers, here's how to make some of the most delicious sandwiches you've ever tasted. Just spread that bread with miracle sandwich spread. Miracle sandwich spread is made by Craft, from America's favorite salad dressing, miracle whip, and spicy relishes to give you a spread with a wonderful tang. Use miracle sandwich spread along with the meat or cheese sandwich filling you like best, or for the quickest, easiest, thriftiest sandwich you could want, use it alone between slices of bread. Tomorrow, get miracle sandwich spread. Enjoy the best of Groucho on NBC.


The history of U.S. venture capital is a lens through which we can view the dynamics of technological innovation. Now there's a certain heroic myth about venture capital, usually propagated by venture capitalists, as the unique engine for driving innovation and consequent economic growth. These lectures will put that myth into context. As we'll see, the economic impact and financial success of venture capital has depended on two institutions that exist before and beyond the activity of venture capitalists themselves and of the entrepreneurs whom they back. First, a mission-driven, economically engaged state and second, episodic waves of financial speculation. Let's begin by recognizing the economic significance of venture capital over recent decades. Almost 50% of all U.S. public companies founded since 1995 have had venture capital backing. These companies accounted for more than 75% of the market capitalization of the stock market as a whole and more than 50% of the net income of public companies. But note especially that they were responsible for more than 85% of all R&D spending. And the total amount and share of R&D has risen monotonically over this period. Now let's explore the domain where venture capitalists operate. We are in the world of Keynesian uncertainty and Zach Hauser's unique extension of it. Three outstanding scholars of venture capital, William Kerr, Ramana Nanda and Matt Rhodescroft have appropriately framed the challenge facing entrepreneurs and the VCs who back them as experimentation. Experimentation by venture capitalists is characterized by an enormous skew in the outcomes. Many failures, a few huge successes. Experimentation at the level of the economy as a whole has systemic consequences. At the level of the individual venture, each investment is a unique experiment dependent on idiosyncratic factors. The authors provide an illustration, a misleading illustration in fact, of the value of an experiment. Here they lay out how discovering that the likelihood of success for the venture is 10%, not 1%, then conducting an experiment that costs less than the increase in the expected value of the venture is worthwhile. But of course, the illustration is misleading. Who constructed the specified distribution of expected returns? A different way to think about the problem is in terms of, first, the scale of the addressable market. Is it bigger than a bread box? Second, what can be learned at what cost by conducting the experiment? The excessive precision of the example distorts the useful underlying message. Innovation proceeds by trial and error and error and error at both the systemic, economy-wide level and at the level of the individual venture. Standard contracts between venture capitalists and entrepreneurs map to that process of experimentation. This is the canonical venture capital model. Staged investments, funding forward for a reasonably short period of time. Multiple investors per stage checking on each other's judgment. Appropriate for funding experiment at the frontier, but this model necessarily generates governance issues and conflicts of interest that can compromise the process. You have multiple sets of investors who have different cost bases on their investments, who are in different internal funding cycles with respect to their own venture capital funds, and may indeed be short of money when the next round is required or when the choice has to be made between continuing to back the venture and declaring victory by selling it for what can be obtained at the time. Seen in the realm of such infamous unicorns as Uber and WeWork, the potential for conflict both amongst investors and between investors and the founding entrepreneur, those conflicts are built into the model. Each round of investment, the venture is exposed to capital market risk to whether in fact the capital markets will be excited about funding new ventures. And building a new business in part by acquisition is very difficult to execute. You can't go to the owner of a big company and say, we want to buy this asset from you, but sit tight. Don't do anything. We'll be back to do a deal as soon as we raise our next round. Finally, Curran Nanda and Rhodes Krop accurately characterize the factors that render a technological opportunity appropriate for venture capital, capital efficiency, and the potential for big returns in a short period of time. Now this is good as far as it goes, but it does not go far enough. It explains the retreat of venture capital from renewable energy, but it does not explain the continuing commitment of venture capital to biotech investing, where total cash required is enormous and the cost of experimentation rises exponentially. This is the biotech paradox which we will shortly explore. It also does not address the correlation between the limited scope of venture capital investing and the prior focus of state investments in research and development. This is the second stylized fact about venture capital that has been almost completely ignored in the academic literature. Venture capital investment has been closely mapped to prior investments in research by the state. This is critical for understanding the dynamics of the innovation economy. So biotech is second only to information technology, representing consistently 20 to 25% of venture capital investments. The maturation of the digital revolution has been confusing categories since about 2010. For example, Uber was classified as, quote, social platform software, unquote, and so in IT. But Airbnb was classified as other restaurants, hotels and leisure, which rolls up to consumer goods and recreation, which rolls up to other. Now, the venture investing and entrepreneurial landscape in IT and beyond IT has been transformed by the availability of free open source software tools plus computing resources in the cloud that are available to be rented on a pay-as-you-go basis, radically reducing the cost of developing and deploying software. Consumer-focused software benefits as much as enterprise software. The cost of initial development has been reduced to the opportunity cost of undergraduates' times. In fact, I always tell my students, they don't need their parents' credit cards to fund their startup. They can do it themselves without any help from anybody else. And the response of venture capitalists has been appropriate. Think about a venture capital investment as represented in exchange of two different securities. The startup issues a real equity instrument, typically convertible preferred stock, to the venture capitalist and gets cash back. But the venture capitalist, in addition to the cash, issues a virtual call option on time and energy. Spray and pray delays the venture capitalist issuance of that option until the entrepreneurs offer evidence of traction from the first small commitment. Now, biotech is different from this kind of startup software company. There's very high technology risk and a very long time to generate any cash flow, let alone cash flow that will cover all the expenses of the venture. Longer than any venture capitalist fund life, which is only 10 to 12 years. In the face of these facts, why have there been such large and persistent investments in biotech? Explanation, tactical immediate explanation, almost continuous access to the public market for initial public offerings. And here is the data. Since 2004, there have been more pharma biotech IPOs than software IPOs every year but 2011 and 2012. Since 2014, pharma and biotech, plus healthcare devices and services, dominate venture capital backed IPOs. But why have public market investors bought biotech IPOs with such regularity? Gary Pizzano of the Harvard Business School offers an explanation from behavioral finance. Investor expectations are anchored on past winners. But there's another explanation that actually derives from mainstream finance theory. Given the lack of market risk, it is distinctively possible, uniquely possible, to evaluate the potential return of a biotech investment at the time of startup. This explanation of the biotech part paradox suggests that market risk dominates technology risk in the calculus of venture capitalists. There are multiple technical alternatives for addressing any perceived market opportunity. But if the perception of opportunity is false and no market exists for any of the alternatives, the return is zero. Note that this explanation is dependent on the existence of an inefficient market characterized by third party financing of demand that enables a knowable, a quasi vertical demand curve. So don't get carried away with reliance on the theory of efficient markets. Investor motivation is reinforced by, first, the low and declining productivity of big pharma's research efforts. Increased acquisitions by big pharma of biotech startups as a result. So investors in a biotech IPO can also speculate on acquisition before having to find out whether or not the venture succeeds in its own terms. A recent study of MIT's Venture Monitoring Service emphasizes the salience of market risk. Experts evaluate ventures submitted to the service and indicate whether they are interested in providing mentoring. Their indication of interest reflects a, quote, subjective evaluation of the venture. Only 4.4% of ventures gain an indication of interest. 54% of entrepreneurs pursue their venture and 28% of total ventures achieve commercialization. So how well the positive evaluations match commercial success? The accuracy of experts evaluation is high when assessing technical risk is what matters. The accuracy of an expert's evaluation disappears when it is market risk that really matters. Away from information technology in biotech, venture capital has struggled. Silicon is the only material in whose commercialization venture capitalists successfully invested. But that was under the direct and massive sponsorship of the United States Department of Defense, as we shall see in lecture four. With respect to the most strategically needed innovations, clean renewable energy, Hargedon and Kenny provide a comprehensive obituary. In addition to the enormous required scale of investment, they point to the value embedded in the existing energy system and the extent to which that value is reflected in and protected by regulation. In addition, any energy investments conventional and unconventional are exposed to extreme commodity price risk. No surprise that venture capital has been missing in action. Compared to the billions of dollars invested in IT and software and in biotech, these numbers are miserably low. It's no surprise because the US government is also missing in action, failing to provide the support of a politically legitimized mission, very much unlike China, as we shall see in the last lecture. Finally, let's turn to the problematic relationship between the venture capitalist and the entrepreneur. Some payers agent who loses other people's money. In a canonical paper, Bob Hall and Susan Woodward argue that the venture capitalist not only starts with the gold, but in the vast majority of cases ends with the gold. They visualize here the enormous skewin returns from venture capital back startups. 75% of all startups generate zero value to the entrepreneur. Returns to the entrepreneur greater than $100 million, the home run, account for only 2% of all venture capital back startups. From this analysis, they suggest that most entrepreneurs face an unattractive prospect. It appears that the entrepreneur bears the risk. The certainty equivalent return that they cite is a function of the entrepreneur's salary from secure employment in a big company, the initial wealth that she starts with, and his or her risk aversion. But recently, Mike Uens of MIT and his co-authors, Ramana Nanda and Chris Stanton of the Harvard Business School, have questioned Hall and Woodward's analysis, arguing that success in hitting milestones and reducing uncertainty increases CEO compensation substantially, shifting the calculus in favor of the entrepreneur and rendering it far more reasonable for entrepreneurs to accept the startup challenge. Entrepreneurs may be driven, they may be difficult, they may be willful, but they're not necessarily crazy. A recent comprehensive survey of venture capitalists offers insight into how VCs themselves characterize their work and the sources of success and failure. The core issue is the importance of selection, choosing which ventures to back, versus treatment, the contribution the venture capitalist makes to the success or not of the venture. First, selection. The venture capitalist network is a critical resource and it is effective when it is embedded in a specific investment domain, such as enterprise software or particular types of biotechnology. Waiting for the knock on the door ensures failure. Look at the last row in the slide. Across all the categories of venture capitalists, the ratio of closed deals, closed investment deals to the total of all deals looked at is only about three to five percent. Now much debate takes place about whether the quality of the horse, the business idea embodied by the venture and the technology that drives it, is more important than the jockey, the entrepreneurial team in charge. The survey results are unequivocal. It's the jockey that matters. The team matters. Here's the data. The team matters most for early stage investors. It matters less for late stage investors when it becomes more possible to evaluate business accomplishments. The team matters less for healthcare investors. What matters there is the technical promise of the project. For success, the team dominates. Compare the credit given to the team, 94 to 97 percent as an important factor and about 50 percent as the most important factor with the credit given to my, my own contribution. 20 to 30 percent is important and never the most important factor. That result, that result gives real credibility to this survey. One additional survey result illuminates the irrelevance of much modern finance theory to venture capital that limited extent to which venture capitalists actually try to forecast cash flows. And of course, it's forecast cash flows that are discounted to calculate that famous net present value, the fundamental value of the investment. 31 percent of early stage venture capitalists do not forecast at all. In fact, you could ask the real question. What do the 69 percent think they're accomplishing? And note that fewer than 30 percent of ventures actually meet their projections. For all the emphasis on the entrepreneurial team, there is evidence that venture capitalists do contribute above and beyond selecting better ventures to back. The authors of this paper matched venture capital backed firms with non VC backed firms and followed their progress. Prior to venture capital investment, the two samples matched. Same employment growth, virtually identical. But over the next 10 years, the VC backed firms had double the employment growth of non VC backed firms. VC backed firms grew their patent stock by 11 times versus only 4.4 times for matched non venture capital backed firms. And high quality venture capitalists matter more. You're a high quality venture capitalist at the number of deals you're involved in over the entire sample period is in the 90th percentile of the distribution over the total number of deals by venture capitalists. Over 10 years from funding, employment and firms backed by high quality venture capitalists grow by about 400 percent versus 320 percent in firms backed by low quality venture capitalists. Firms backed by high quality venture capitalists grow their quality adjusted patents 50 times over 10 years from first funding firms backed by low quality venture capitalists grow their patent stock by only 19 times. So the venture capitalists deserve the big bucks. The subject of the next lecture is evaluating venture capital performance. We're going to dig deep into answering that question.


What's going on everybody? Welcome back to another video. Today, we're going to be taking a look at the machine learning specialization on Coursera. Andrew Ng needs no introduction, but if you have not heard of him, Andrew Ng is the co-founder of Coursera, the very platform that we're looking on today. He was also the co-founder and the head of Google Brain.  I mentioned Andrew Ng because he is the creator of this machine learning course, but he created it back in 2012. But this one that we're going to be looking at today is a revamped, updated, and more enhanced version of the one that he originally created. And we're going to be diving into the courses so you can see what you're actually going to get if you decide to take this course, but from everything that I've seen is absolutely phenomenal. One of the biggest upgrades that they made from the original course is that they changed the programming language that they used in it. Originally, they were using a programming language called Octave and now they're using Python.  Now I'm a data analyst and my audience is mostly data analysts as well. So you may be wondering why we're even looking at machine learning specializations because that's more data science. Well, one of the biggest things that I usually tell people is that you may not use machine learning as a data analyst, but you're going to be working with people who do use it. And so understanding how it works, understanding the terminology behind it can really help you go a long way. And it really helped me out about two years ago when I was on the data science team and they were talking about models and all these different things that they were doing. And I felt like I actually knew what they were talking about and I could kind of keep up with them. It was really beneficial to at least know the basics of machine learning. Now, before we jump onto my screen and start taking a look at this specialization, I want to give a huge shout out to the sponsor of this video. And that is Coursera. Coursera is one of the best platforms just about anything data related. So if you want to learn SQL all the way to machine learning, you can do that on Coursera. Or if you want to become a data analyst, a data scientist, or a data engineer, you can do all of your learning right here on Coursera. It has been one of my favorite platforms to learn on for the past five years since I became a data analyst and I still love it today. Thank you again to Coursera for sponsoring this video. And without further ado, let's jump on my screen and take a look. As you can see on my screen, this is the machine learning specialization. It is brought to you by deeplearning.ai and Stanford, both things that are associated with Andrew Ng. Now, there are a few other instructors on here, but Andrew is the one who's actually doing almost every single one of the videos, goes through all the math, walks or everything. And I mentioned him specifically because he's just such a huge name in machine learning and artificial intelligence. He's just incredibly popular. And, you know, the original course that he did back in 2012 was like a staple in the machine learning community. I mean, just about everybody who got started walked through it. And it's just because he's so smart and he talks about it so well. So if you haven't taken his original one, this new one is just enhanced. It just goes above and beyond without original one did. So if you haven't taken it, then this is like the time to take it.  It's amazing. And so with that being said, let's jump into it. So it just came out like a couple weeks ago from when I'm recording this, like the middle of June is when it came out. And you're going to learn a ton of stuff in here. Right down here, you're going to build machine learning models with NumPy and Scikit Learn, building, training. You work mostly with supervised learning. Andrew goes a little bit into the unsupervised learning as well. But he'll even tell you he primarily is looking at linear and logistic regression, those type of machine learning models. So that this is like, I would say the nuts and bolts of what you're going to learn. He teaches you about the best practices for machine learning development, how to train neural networks using TensorFlow and TensorFlow is fantastic. You've never used it. And he goes really in depth into all these things, like none of these are just like brushed off or like the basics. He goes like really in depth in almost all these things, as well as recommendation systems and how they work and the math behind them and everything. It's really fascinating. Now, one thing I want to mention before we get into like the actual course is that he goes into a lot of math. If you don't know the math behind this stuff, that is okay. In fact, it's pretty common for most people who take this to not understand the math behind it. And he talks about that in one of his videos, he's like, Hey, listen, the math behind this is really important, but it doesn't mean you have to go and be able to do the math, just maybe understand the some of the concepts. And it's true, a lot of this math is really advanced. And so, again, that's why it's like, if you aren't going into machine learning, and you don't need to know all this math, still walking through these videos and understanding these concepts, I mean, it is phenomenal. Let's go down really quickly to the courses. There's three courses. The biggest one is the supervised machine learning regression and classification, again, that's all supervised machine learning. The second course is the advanced learning algorithms. This one's very hands on, so it's a lot of building the models, understanding how to train your models and things like that. And then we have the third course, which this one has not been released yet as of when I'm recording it, although it may be when I release this video, it may be available. But this goes into the unsupervised learning, recommenders and reinforcement learning. So, again, the bulk of what the original course and this is built on and what you are going to learn is the supervised machine learning, the logistic and linear regression. That is what this is primarily focused on. But the third course is on kind of the other things that are still popular, just not as heavily used, like unsupervised learning and recommendation systems, recommenders. So, let's go and take a look at this first course and see what is actually in there. All right, if we take a look at week one, this is basically Andrew kind of walking you through the basics. Andrew is like the ideal professor. I mean, just look at him. He's just like the happiest guy. And you can tell he just loves what he does. He's just like that professor in college that you want to take his course or his class over and over again, but you already passed it. He was such a good teacher. That's how Andrew is this entire time. He's just a fantastic teacher and he teaches it extremely well. So, in this first week, you're going to go through the supervised and unsupervised learning. Just what is it? How does it work and the math behind it? And then he's going to introduce Jupiter notebooks. Now, most of these videos are exactly like how he has them right here. So, he's talking and then he'll have some type of kind of whiteboard where he's talking over it and reading through it, asking questions. It's very interactive. And let me find one really quickly because he's going to go into a lot of the math as well. So, let me find the math really quickly. I'll kind of show you how he does that also. So, this is a good example of how he teaches the math in his course. So, he usually has some type of visualization. Then he has the equation that we're looking at. And typically the equation is looks really complex. Like this one to me, I would not understand it at all if I was just taking a look at it. But Andrew has a really unique way of just breaking things down, helping you understand it and kind of showing you usually on some type of visualization how it actually works in machine learning. So, if you're not a math person and you don't care about the math behind it, you know, you don't have to skip these sections because they're just so good. You'll feel like you understand it because Andrew just teaches it really well. But this is still week one. And so, in this one, you're learning how to train models with gradient descent and kind of understanding what gradient descent is. Along the way, you have these quizzes. All really great. I can't speak highly enough of this course. It's just absolutely phenomenal. In week two, we have multiple linear regression, which is something he mentions in the first one. But then you actually learn how to implement it and use it in week two. And we also go into gradient descent and practice. So, it kind of shows you how it's actually used and not just, you know, what it is like he did in the second week. And then week three, we have a lot of logistic regression. It's basically the entire week. This is what you cover. So, you have classification with logistic regression, cost function with logistic regression, gradient descent for logistic regression. And then at the very end, you have the problem with overfitting. Now, this one was actually, the math was the most interesting in this section to me in this first couple of weeks. Because this one, the visual is just phenomenal about how overfitting actually occurs and how to fix it using math. I thought it was just extremely interesting. So, again, if you're not a math person, you don't have to take it. But I love the math behind this overfitting stuff. It was extremely interesting. Now, let's take a look at the advanced learning algorithms course. And I think this is the one that most people are going to really enjoy, especially if you want to get more hands on. So, in this course, in the first week, we're going to be looking at neural networks, and then we'll actually be implementing it using TensorFlow. And so, in this first week, we look at neural networks intuition. So, what is neural networks? How is it used? Let's look at the math behind it. That's kind of what you're going to be looking at. And then we have neural network models. And then we have the TensorFlow implementation. This is where they actually walk you through how to set up TensorFlow, how to actually use it to create your first neural network. And right down here, we have neural network implementation in Python. Again, super happy that they're using Python. I think that's just a huge upgrade from the previous course, is that a lot of people and companies are using Python for machine learning. And so, I think it just obviously shows that they're keeping up with things, they're improving and making things better, which is just better for everyone in the long run. In week two, we're still looking at neural networks. Again, this stuff is a lot more of the advanced, as the course says, it's a lot more of the advanced stuff. So, it's not as introductory like the first course where he's kind of walking through, here's what machine learning is, here's how it's used. These are like, let's dive into the math, let's look how to implement, let's start using it. It goes from beginner to advanced, I think pretty quickly, at least advanced for me. So, it's kind of like a diving head first into this stuff and just really trying to learn it as you go. So, we have these activation functions. We also have multi-class classification and neural networks and how those work together. Now, in week three, he kind of takes a step back and he's like, whoa, I know I said let's just dive in and learn it and get our hands dirty, but we kind of need to take a step back and learn a little bit more so that when you're actually using this, you don't just dive in and make a bunch of mistakes. So, here are some of the things that you should look at. So, we look at some of his advice for applying machine learning on what models to use in evaluating how, if it's good or not, we also look at bias and variance and then the machine learning development process and how to actually implement your machine learning models once you've actually developed them. And then in the very last week, you learn about decision trees and a few different variations and ways to do them. So, you have decision trees and kind of what they are. And on the surface, they're pretty simple, but they get pretty advanced. That's why it's in this advanced course. But we have the decision tree section right there. Then we kind of look at how to use them, how to implement them. And then he walks you through how to actually set it up in the code. And then at the very bottom, we have a few different options, things like multiple decision trees, sampling with replacement, random forest algorithm and XGBoost. XGBoost is all over. I see it everywhere. So, again, these are things that if you're not going to be using them, if you're not going to be implementing these things, these are really great things to just know what they are and how they work so that if you are working with somebody who's using these, you can at least understand them and work with them instead of just being confused and asking a million questions. So, the third and final course is the unsupervised learning recommenders and reinforcement learning. Again, this has not been released yet. It's not released until July 19th, which if you're watching it after July 19th, there you go. It's already out there, but I plan on releasing this at the first week of July. So, as of when I'm releasing this, this most likely will not be out yet. But we can get a sneak peek really quickly into what you're going to be learning in here. So, you're going to be learning how to use unsupervised learning techniques for unsupervised learning, including clustering and anomaly detection, building a deep reinforcement learning model, and building recommender systems with collaborative filtering approach and a content-based deep learning method. So, there you go. Again, we just don't have it yet, so I can't dive into it. But it's going to be phenomenal, and I'm going to take the whole thing. Again, I am not a machine learning expert, but it has really helped me work within my team or my old team when I was working with the data scientist to really understand this stuff. So, if you're somebody who wants to get into machine learning, 10 out of 10, 100% you need to take this course. And I don't say that for every course. Usually there's a lot of qualifiers. It's like, if this, if that, I think that if you have a pulse and you are even remotely interested in machine learning, you should take this course. It is by far hands down one of the best courses that you will ever take on machine learning. And I've taken several other ones, and I've learned a lot more about the implementation and how to actually create it and do that. But in this one, you're going to learn some of that as well. We're also going to learn a lot of the math behind it, how it actually works. And for the original course back in 2012, I mean, that thing was a staple. Every machine learning person, every data scientist had taken it because it was just phenomenal. And this just builds on it and makes it better. So, if you've gotten this far and you are interested in this at all, even in the slightest, I think you should absolutely take it again. I would not be saying that if I did not believe it. It's just phenomenal. It's one of the best courses on machine learning that you'll find out there anywhere. So with that being said, I hope that this video was helpful. I hope that you're as excited about this as I am. I think it's just a fantastic reboot and upgrade of the original course. It's just really great to see them continuing to improve and make things better over time. So thank you again. And if you liked this video, be sure to like and subscribe below. I'll see you in the next video.