TED READS Presented Orally / Adapted for Print COLLABORATIVE CONSUMPTION & ONLINE CREATION
TED R E A DS
Presented Orally / Adapted for Print
COLLABORATIVE CONSUMPTION & ONLINE CREATION
How the rise of the sharing economy is changing the way we create and consume.
CO NT EN TS
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How Cognitive Surplus Will Change the World
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Massive-Scale Online Collaboration
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The New Open-Scource Economics
Clay Shirky - June 2010
Luis von Ahn - April 2011
Yochai Benkler - April 2008
This is Part I of a booklet series exploring common themes and ideas presented on the world-renowned TED stage by some of the world’s greatest minds. TED is a nonprofit devoted to Ideas Worth Spreading, usually in the form of short, powerful talks (18 minutes or less). TED began in 1984 as a conference where Technology, Entertainment and Design converged, and today covers almost all topics—from science to business to global issues—in more than 100 languages, which are then made available for view, free, at TED.com.
H OW C O G N I T I V E S U R P LU S W I L L C H A N G E T H E WO R L D Clay Shirky - June 2010
Clay Shirky’s work focuses on the rising usefulness of networks, using decentralized technologies such as peer-to-peer sharing, wireless, software for social creation, and open-source development. New technologies are enabling new kinds of cooperative structures to flourish as a way of getting things done in business, science, the arts and elsewhere, as an alternative to centralized and institutional structures, which he sees as self-limiting. In his writings and speeches he has argued that “a group is its own worst enemy.” Clay Shirky argues that the history of the modern world could be rendered as the history of ways of arguing, where changes in media change what sort of arguments are possible—with deep social and political implications. Shirky is an adjunct professor in New York University’s graduate Interactive Telecommunications Program, where he teaches a course named “Social Weather.” He’s the author of several books, including Cognitive Surplus: Creativity and Generosity in a Connected Age and Here Comes Everybody: The Power of Organizing Without Organizations.
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The story starts in Kenya in December of 2007, when there was a disputed presidential election, and in the immediate aftermath of that election, there was an outbreak of ethnic violence. And there was a lawyer in Nairobi, Ory Okolloh—who some of you may know from her TEDTalk—who began blogging about it on her site, Kenyan Pundit. And shortly after the election and the outbreak of violence, the government suddenly imposed a significant media blackout. And so weblogs went from being commentary as part of the media landscape to being a critical part of the media landscape in trying to understand where the violence was. And Okolloh solicited from her commenters more information about what was going on. The comments began pouring in, and Okolloh would collate them. She would post them. And she quickly said, “It’s too much. I could do this all day every day and I can’t keep up. There is more information about what’s going on in Kenya right now than any one person can manage. If only there was a way to automate this.”
1:01
And two programmers who read her blog held their hands up and said, “We could do that,” and in 72 hours, they launched Ushahidi. Ushahidi—the name means “witness” or “testimony” in Swahili—is a very simple way of taking reports from the field, whether it’s from the web or, critically, via mobile phones and SMS, aggregating it and putting it on a map. That’s all it is, but that’s all that’s needed because what it does is it takes the tacit information available to the whole population—everybody knows where the violence is, but no one person knows what everyone knows—and it takes that tacit information and it aggregates it, and it maps it and it makes it public. And that, that maneuver called “crisis mapping,” was kicked off in Kenya in January of 2008.
1:91
And enough people looked at it and found it valuable enough that the programmers who created Ushahidi decided they were going to make it open source and turn it into a platform. It’s since been deployed in Mexico to track electoral fraud. It’s been deployed in Washington D.C. to track snow cleanup. And it’s been used most famously in Haiti in the aftermath of the earthquake. And when you look at the map now posted on the Ushahidi front page, you can see that the number of deployments in Ushahidi has gone worldwide, all right? This went from a single idea and a single implementation in East Africa in the beginning of 2008 to a global deployment in less than three years.
2:33
Now what Okolloh did would not have been possible without digital technology. What Okolloh did would not have been possible without human generosity. And the interesting moment now, the number of environments where the social design challenge relies on both of those things being true. That is the resource that I’m talking about. I call it cognitive surplus. And it represents the ability of the world’s population to volunteer and to contribute and collaborate on large, sometimes global, projects. Cognitive surplus is made up of two things. The first, obviously, is the world’s free
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time and talents. The world has over a trillion hours a year of free time to commit to shared projects. Now, that free time existed in the 20th century, but we didn’t get Ushahidi in the 20th century. 3:29
That’s the second half of cognitive surplus. The media landscape in the 20th century was very good at helping people consume, and we got, as a result, very good at consuming. But now that we’ve been given media tools —the Internet, mobile phones—that let us do more than consume, what we’re seeing is that people weren’t couch potatoes because we liked to be. We were couch potatoes because that was the only opportunity given to us. We still like to consume, of course. But it turns out we also like to create, and we like to share. And it’s those two things together—ancient human motivation and the modern tools to allow that motivation to be joined up in large-scale efforts—that are the new design resource. And using cognitive surplus, we’re starting to see truly incredible experiments in scientific, literary, artistic, political efforts. Designing.
4:25
We’re also getting, of course, a lot of LOLcats. LOLcats are cute pictures of cats made cuter with the addition of cute captions. And they are also part of the abundant media landscape we’re getting now. This is one of the participatory— one of the participatory models we see coming out of that, along with Ushahidi. Now I want to stipulate, as the lawyers say, that LOLcats are the stupidest possible creative act. There are other candidates of course, but LOLcats will do as a general case. But here’s the thing: The stupidest possible creative act is still a creative act. Someone who has done something like this, however mediocre and throwaway, has tried something, has put something forward in public. And once they’ve done it, they can do it again, and they could work on getting it better.
5:17
There is a spectrum between mediocre work and good work, and as anybody who’s worked as an artist or a creator knows, it’s a spectrum you’re constantly struggling to get on top of. The gap is between doing anything and doing nothing. And someone who makes a LOLcat has already crossed over that gap. Now it’s tempting to want to get the Ushahidis without the LOLcats, right, to get the serious stuff without the throwaway stuff. But media abundance never works that way. Freedom to experiment means freedom to experiment with anything. Even with the sacred printing press, we got erotic novels 150 years before we got scientific journals.
6:98
So before I talk about what is, I think, the critical difference between LOLcats and Ushahidi, I want to talk about their shared source. And that source is design for generosity. It is one of the curiosities of our historical era that even as cognitive surplus is becoming a resource we can design around, social sciences are also starting to explain how important our intrinsic motivations are to us, how much we do things because we like to do them rather than because our boss told us to do them, or because we’re being paid to do them.
6:34
This is a graph from a paper by Uri Gneezy and Aldo Rustichini, who set out to test, at the beginning of this decade, what they called “deterrence theory.” And deterrence theory is a very simple theory of human behavior: If you want somebody to do less of something, add a punishment and they’ll do less of it. Simple, straightforward, commonsensical—also, largely untested. And so they went and studied 10 daycare centers in Haifa, Israel. They studied those daycare centers at the time of highest tension, which is pick-up time. At pick-up time the teachers, who have been with your children all day, would like you to be there at the appointed hour to take your children back. Meanwhile, the parents—perhaps a little busy at work, running late, running errands—want a little slack to pick the kids up late.
7:18
So Gneezy and Rustichini said, “How many instances of late pick-ups are there at these 10 daycare centers?” Now they saw -- and this is what the graph is, these are the number of weeks and these are the number of late arrivals—that there were between six and 10 instances of late pick-ups on average in these 10 daycare centers. So they divided the daycare centers into two groups. The white group there is the control group; they change nothing. But the group of daycare centers represented by the black line, they said, “We are changing this bargain as of right now. If you pick your kid up more than 10 minutes late, we’re going to add a 10 shekel fine to your bill. Boom. No ifs, ands or buts.”
7:97
And the minute they did that, the behavior in those daycare centers changed. Late pick-ups went up every week for the next four weeks until they topped out at triple the pre-fine average, and then they fluctuated at between double and triple the pre-fine average for the life of the fine. And you can see immediately what happened, right? The fine broke the
8:88
The explanation of human behavior that we inherited in the 20th century was that we are all rational, self-maximizing actors, and in that explanation—the daycare center had no contract—should have been operating without any constraints. But that’s not right. They were operating with social constraints rather than contractual ones. And critically, the social constraints created a culture that was more generous than the contractual constraints did. So Gneezy and Rustichini run this experiment for a dozen weeks—run the fine for a dozen weeks—and then they say, “Okay, that’s it. All done; fine.” And then a really interesting thing happens: Nothing changes. The culture that got broken by the fine stayed broken when the fine was removed. Not only are economic motivations and intrinsic motivations incompatible, that incompatibility can persist over long periods. So the trick in designing these kinds of situations is to understand where you’re relying on the economic part of the bargain—as with the parents paying the teachers—and when you’re relying on the social part of the bargain, when you’re really designing for generosity.
10:04
This brings me back to the LOLcats and to Ushahidi. This is, I think, the range that matters. Both of these rely on cognitive surplus. Both of these design for the assumption that people like to create and we want to share. Here is the critical difference between these: LOLcats is communal value. It’s value created by the participants for each other. Communal value on the networks we have is everywhere—every time you see a large aggregate of shared, publicly available data, whether it’s photos on Flickr or videos on Youtube or whatever. This is good. I like LOLcats as much as the next guy, maybe a little more even, but this is also a largely solved problem. I have a hard time envisioning a future in which someone is saying, “Where, oh where, can I find a picture of a cute cat?”
11:01
Ushahidi, by contrast, is civic value. It’s value created by the participants but enjoyed by society as a whole. The goals set out by Ushahidi are not just to make life better for the participants, but to make life better for everyone in the society in which Ushahidi is operating. And that kind of civic value is not just a side effect of opening up to human motivation. It really is going to be a side effect of what we, collectively, make of these kinds of efforts. There are a trillion hours a year of participatory value up for grabs. That will be true year-in and year-out. The number of people who are going to be able to participate in these kinds of projects is going to grow, and we can see that organizations designed around a culture of generosity can achieve incredible effects without an enormous amount of contractual overhead—a very different model than our default model for large-scale group action in the 20th century.
12:08
What’s going to make the difference here is what Dean Kamen said, the inventor and entrepreneur. Kamen said, “Free cultures get what they celebrate.” We’ve got a choice before us. We’ve got this trillion hours a year. We can use it to crack each other up, and we’re going to do that. That, we get for free. But we can also celebrate and support and reward the people trying to use cognitive surplus to create civic value. And to the degree we’re going to do that, to the degree we’re able to do that, we’ll be able to change society.
12:43
Thank you very much. (Applause)
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culture of the daycare center. By adding a fine, what they did was communicate to the parents that their entire debt to the teachers had been discharged with the payment of 10 shekels, and that there was no residue of guilt or social concern that the parents owed the teachers. And so the parents, quite sensibly, said, “10 shekels to pick my kid up late? What could be bad?” (Laughter)
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M AS S IVE- S C A L E ON L IN E C O LL A B O R AT I O N Luis von Ahn - April 2011
Louis von Ahn is an associate professor of Computer Science at Carnegie Mellon University, and he’s at the forefront of the crowdsourcing craze, building systems that combine humans and computers to solve large-scale problems that neither can solve alone. His work takes advantage of the evergrowing Web-connected population to acheive collaboration in unprecedented numbers. His projects aim to leverage the crowd for human good. His company reCAPTCHA, sold to Google in 2009, digitizes human knowledge (books), one word at a time. His new project is Duolingo, which aims to get 100 million people translating the Web in every major language.
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How many of you had to fill out some sort of web form where you’ve been asked to read a distorted sequence of characters like this? How many of you found it really, really annoying? Okay, outstanding. So I invented that. (Laughter) Or I was one of the people who did it. That thing is called a CAPTCHA. And the reason it is there is to make sure you, the entity filling out the form, are actually a human and not some sort of computer program that was written to submit the form millions and millions of times. The reason it works is because humans, at least non-visually-impaired humans, have no trouble reading these distorted squiggly characters, whereas computer programs simply can’t do it as well yet. So for example, in the case of Ticketmaster, the reason you have to type these distorted characters is to prevent scalpers from writing a program that can buy millions of tickets, two at a time.
0:39
CAPTCHAs are used all over the Internet. And since they’re used so often, a lot of times the precise sequence of random characters that is shown to the user is not so fortunate. So this is an example from the Yahoo registration page. The random characters that happened to be shown to the user were W, A, I, T, which, of course, spell a word. But the best part is the message that the Yahoo help desk got about 20 minutes later: “Help! I’ve been waiting for over 20 minutes, and nothing happens.” (Laughter) This person thought they needed to wait.
1:13
CAPTCHA Project is something that we did here at Carnegie Melllon over 10 years ago, and it’s been used everywhere. Let me now tell you about a project that we did a few years later, which is sort of the next evolution of CAPTCHA. This is a project that we call reCAPTCHA, which is something that we started here at Carnegie Mellon, then we turned it into a startup company. And then about a year and a half ago, Google actually acquired this company.
1:35
So this project started from the following realization: It turns out that approximately 200 million CAPTCHAs are typed everyday by people around the world. When I first heard this, I was quite proud of myself. I thought, look at the impact that my research has had. But then I started feeling bad. See here’s the thing, each time you type a CAPTCHA, essentially you waste 10 seconds of your time. And if you multiply that by 200 million, you get that humanity as a whole is wasting about 500,000 hours every day typing these annoying CAPTCHAs. So then I started feeling bad. (Laughter)
2:07
And then I started thinking, well, of course, we can’t just get rid of CAPTCHAs, because the security of the Web sort of depends on them. But then I started thinking, is there any way we can use this effort for something that is good for humanity? So see, here’s the thing. While you’re typing a CAPTCHA, during those 10 seconds, your brain is doing something amazing. Your brain is doing something that computers cannot yet do. So can we get you to do useful work for those 10 seconds? Another way of putting it is, is there some humongous problem that we cannot yet get computers
2:41
So what you may not know is that nowadays while you’re typing a CAPTCHA, not only are you authenticating yourself as a human, but in addition you’re actually helping us to digitize books. So let me explain how this works. So there’s a lot of projects out there trying to digitize books. Google has one. The Internet Archive has one. Amazon, now with the Kindle, is trying to digitize books. Basically the way this works is you start with an old book. You’ve seen those things, right? Like a book? (Laughter) So you start with a book, and then you scan it.
3:07
Now scanning a book is like taking a digital photograph of every page of the book. It gives you an image for every page of the book. This is an image with text for every page of the book. The next step in the process is that the computer needs to be able to decipher all of the words in this image. That’s using a technology called OCR, for optical character recognition, which takes a picture of text and tries to figure out what text is in there. Now the problem is that OCR is not perfect. Especially for older books where the ink has faded and the pages have turned yellow, OCR cannot recognize a lot of the words. For example, for things that were written more than 50 years ago, the computer cannot recognize about 30 percent of the words. So what we’re doing now is we’re taking all of the words that the computer cannot recognize and we’re getting people to read them for us while they’re typing a CAPTCHA on the Internet.
3:90
So the next time you type a CAPTCHA, these words that you’re typing are actually words that are coming from books that are being digitized that the computer could not recognize. And now the reason we have two words nowadays instead of one is because, you see, one of the words is a word that the system just got out of a book, it didn’t know what it was, and it’s going to present it to you. But since it doesn’t know the answer for it, it cannot grade it for you. So what we do is we give you another word, one for which the system does know the answer. We don’t tell you which one’s which, and we say, please type both. And if you type the correct word for the one for which the system already knows the answer, it assumes you are human, and it also gets some confidence that you typed the other word correctly. And if we repeat this process to like 10 different people and all of them agree on what the new word is, then we get one more word digitized accurately.
4:31
So this is how the system works. And basically, since we released it about three or four years ago, a lot of websites have started switching from the old CAPTCHA where people wasted their time to the new CAPTCHA where people are helping to digitize books. So for example, Ticketmaster. So every time you buy tickets on Ticketmaster, you help to digitize a book. Facebook: Every time you add a friend or poke somebody, you help to digitize a book. Twitter and about 350,000 other sites are all using reCAPTCHA. And in fact, the number of sites that are using reCAPTCHA is so high that the number of words that we’re digitizing per day is really, really large. It’s about 100 million a day, which is the equivalent of about two and a half million books a year. And this is all being done one word at a time by just people typing CAPTCHAs on the Internet. (Applause)
5:17
Now of course, since we’re doing so many words per day, funny things can happen. And this is especially true because now we’re giving people two randomly chosen English words next to each other. For example, we presented this word. It’s the word “Christians”; there’s nothing wrong with it. But if you present it along with another randomly chosen word, bad things can happen. So we get this: “bad christians”. But it’s even worse, because the particular website where we showed this actually happened to be called The Embassy of the Kingdom of God. (Laughter) Oops. (Laughter)Here’s another really bad one: “damn liberal” on JohnEdwards.com. (Laughter) So we keep on insulting people left and right everyday.
6:05
Now, of course, we’re not just insulting people. See here’s the thing, since we’re presenting two randomly chosen words, interesting things can happen. So this actually has given rise to a really big Internet meme that tens of thousands of people have participated in, which is called CAPTCHA art. I’m sure some of you have heard about it. Here’s how it works. Imagine you’re using the Internet and you see a CAPTCHA that you think is somewhat peculiar, like “invisible toaster”. Then what you’re supposed to do is you take a screen shot of it and you draw something that is related to it.
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to solve, yet we can split into tiny 10-second chunks such that each time somebody solves a CAPTCHA they solve a little bit of this problem? And the answer to that is “yes,” and this is what we’re doing now.
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(Laughter) That’s how it works. There are tens of thousands of these. 7:11
So this is the favorite thing that I like about this whole project. This is the number of distinct people that have helped us digitize at least one word out of a book through reCAPTCHA: 750 million, which is a little over 10 percent of the world’s population, has helped us digitize human knowledge. And it is numbers like these that motivate my research agenda. So the question that motivates my research is the following: If you look at humanity’s large-scale achievements, these really big things that humanity has gotten together and done historically—like for example, building the pyramids of Egypt or the Panama Canal or putting a man on the Moon—there is a curious fact about them, and it is that they were all done with about the same number off people. It’s weird; they were all done with about 100,000 people. And the reason for that is because, before the Internet, coordinating more than 100,000 people, let alone paying them, was essentially impossible. But now with the Internet, I’ve just shown you a project where we’ve gotten 750 million people to help us digitize human knowledge. So the question that motivates my research is, if we can put a man on the Moon with 100,000, what can we do with 100 million?
8:14
So based on this question, we’ve had a lot of different projects that we’ve been working on. Let me tell you about one that I’m most excited about. This is something that we’ve been semi-quietly working on for the last year and a half or so. It hasn’t yet been launched. It’s called Duolingo. Since it hasn’t been launched, shhhhh! (Laughter) Yeah, I can trust you’ll do that. So this is the project. Here’s how it started. It started with me posing a question to my graduate student, Severin Hacker. By the way, you did hear me correctly; his last name is Hacker. So I posed this question to him: How can we get 100 million people translating the Web into every major language for free?
9:05
Okay, so there’s a lot of things to say about this question. First of all, translating the Web. So right now the Web is partitioned into multiple languages. A large fraction of it is in English. If you don’t know any English, you can’t access it. But there’s large fractions in other different languages, and if you don’t know those languages, you can’t access it. So I would like to translate all of the Web, or at least most of the Web, into every major language.
8:94
Now some of you may say, why can’t we use computers to translate? Why can’t we use machine translation? Machine translation nowadays is starting to translate some sentences here and there. Why can’t we use it to translate the whole Web? Well the problem with that is that it’s not yet good enough and it probably won’t be for the next 15 to 20 years. It makes a lot of mistakes. Even when it doesn’t make a mistake, since it makes so many mistakes, you don’t know whether to trust it or not.
9:37
So we need people to translate the whole Web. So now the next question you may have is, well why can’t we just pay people to do this? We could pay professional language translators to translate the whole Web. We could do that. Unfortunately, it would be extremely expensive. For example, translating a tiny, tiny fraction of the whole Web, Wikipedia, into one other language, Spanish. Wikipedia exists in Spanish, but it’s very small compared to the size of English. It’s about 20 percent of the size of English. If we wanted to translate the other 80 percent into Spanish, it would cost at least 50 million dollars—and this is at even the most exploited, outsourcing country out there. So it would be very expensive. So what we want to do is we want to get 100 million people translating the Web into every major language for free.
10:19
Now if this is what you want to do, you pretty quickly realize you’re going to run into two pretty big hurdles, two big obstacles. The first one is a lack of bilinguals. So I don’t even know if there exists 100 million people out there using the Web who are bilingual enough to help us translate. That’s a big problem. The other problem you’re going to run into is a lack of motivation. How are we going to motivate people to actually translate the Web for free? Normally, you have to pay people to do this. So how are we going to motivate them to do it for free? Now when we were starting to think about this, we were blocked by these two things. But then we realized, there’s actually a way to solve both these problems with the same solution. There’s a way to kill two birds with one stone. And that is to transform language translation into something that millions of people want to do, and that also helps with the problem of lack of bilinguals, and that is language education.
So it turns out that today, there are over 1.2 billion people learning a foreign language. People really, really want to learn a foreign language. And it’s not just because they’re being forced to do so in school. For example, in the United States alone, there are over five million people who have paid over $500 for software to learn a new language. So people really, really want to learn a new language. So what we’ve been working on for the last year and a half is a new website—it’s called Duolingo—where the basic idea is people learn a new language for free while simultaneously translating the Web. So basically they’re learning by doing. The way this works is whenever you’re a just a beginner, we give you very, very simple sentences. There’s, of course, a lot of very simple sentences on the Web. We give you very, very simple sentences along with what each word means. And as you translate them, and as you see how other people translate them, you start learning the language. And as you get more and more advanced, we give you more and more complex sentences to translate. But at all times, you’re learning by doing.
12:01
Now the crazy thing about this method is that it actually really works. First of all, people are really, really learning a language. We’re mostly done building it, and now we’re testing it. People really can learn a language with it. And they learn it about as well as the leading language learning software. So people really do learn a language. And not only do they learn it as well, but actually it’s way more interesting. Because you see with Duolingo, people are actually learning with real content. As opposed to learning with made-up sentences, people are learning with real content, which is inherently interesting. So people really do learn a language.
12:30
But perhaps more surprisingly, the translations that we get from people using the site, even though they’re just beginners, the translations that we get are as accurate as those of professional language translators, which is very surprising. Now of course, we play a trick to make the translations as good as professional language translators. We combine the translations of multiple beginners to get the quality of a single professional translator.
13:13
Now even though we’re combining the translations, the site actually can translate pretty fast. So let me show you, this is our estimates of how fast we could translate Wikipedia from English into Spanish. Remember, this is 50 million dollarsworth of value. So if we wanted to translate Wikipedia into Spanish, we could do it in five weeks with 100,000 active users. And we could do it in about 80 hours with a million active users. Since all the projects that my group has worked on so far have gotten millions of users, we’re hopeful that we’ll be able to translate extremely fast with this project.
13:84
Now the thing that I’m most excited about with Duolingo is I think this provides a fair business model for language education. So here’s the thing: The current business model for language education is the student pays, and in particular, the student pays Rosetta Stone 500 dollars. (Laughter) That’s the current business model. The problem with this business model is that 95 percent of the world’s population doesn’t have 500 dollars. So it’s extremely unfair towards the poor. This is totally biased towards the rich. Now see, in Duolingo, because while you learn you’re actually creating value, you’re translating stuff—which for example, we could charge somebody for translations. So this is how we could monetize this. Since people are creating value while they’re learning, they don’t have to pay their money, they pay with their time. But the magical thing here is that they’re paying with their time, but that is time that would have had to have been spent anyways learning the language. So the nice thing about Duolingo is I think it provides a fair business model—one that doesn’t discriminate against poor people.
14:41
So here’s the site. Thank you. (Applause) We haven’t yet launched, but if you go there, you can sign up to be part of our private beta, which is probably going to start in about three or four weeks. By the way, I’m the one talking here, but actually Duolingo is the work of a really awesome team, some of whom are here.
15:09
Thank you. (Applause)
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TH E NEW OPEN- S OURC E E C O N O M I C S Yochai Benkler - April 2008
Larry Lessig calls law professor Yochai Benkler “the leading intellectual of the information age.” He studies the commons—including such shareable spaces as the radio spectrum, as well as our shared bodies of knowledge and how we access and change them, proposing that volunteer-based projects such as Wikipedia and Linux are the next stage of human organization and economic production. His most recent writings (including his 2006 book The Wealth of Networks) discuss the effects of net-based information production on our lives and minds and laws. He’s the Berkman Professor of Entrepreneurial Legal Studies at Harvard, and faculty co-director of the Berkman Center for Internet and Society (home to many of TED’s favorite people).
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One of the problems of writing, and working, and looking at the Internet is that it’s very hard to separate fashion from deep change. And so, to start helping that, I want to take us back to 1835. In 1835, James Gordon Bennett founded the first mass-circulation newspaper in New York City. And it cost about 500 dollars to start it, which was about the equivalent of 10,000 dollars of today. By 15 years later, by 1850, doing the same thing—starting what was experienced as a mass-circulation daily paper—would come to cost two and a half million dollars. 10,000, two and a half million, 15 years. That’s the critical change that is being inverted by the Net. And that’s what I want to talk about today, and how that relates to the emergence of social production.
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Starting with newspapers, what we saw was high cost as an initial requirement for making information, knowledge and culture, which led to a stark bifurcation between producers—who had to be able to raise financial capital, just like any other industrial organization—and passive consumers that could choose from a certain set of things that this industrial model could produce. Now, the term “information society,” “information economy,” for a very long time has been used as the thing that comes after the industrial revolution. But in fact, for purposes of understanding what’s happening today, that’s wrong. Because for 150 years, we’ve had an information economy. It’s just been industrial, which means those who were producing had to have a way of raising money to pay those two and a half million dollars, and later, more for the telegraph, and the radio transmitter, and the television, and eventually the mainframe. And that meant they were market based, or they were government owned, depending on what kind of system they were in. And this characterized and anchored the way information and knowledge were produced for the next 150 years.
2:16
Now, let me tell you a different story. Around June 2002, the world of supercomputers had a bombshell. The Japanese had, for the first time, created the fastest supercomputer—the NEC Earth Simulator—taking the primary from the U.S., and about two years later—this, by the way, is measuring the trillion floating-point operations per second that the computer’s capable of running—sigh of relief: IBM [Blue Gene] has just edged ahead of the NEC Earth Simulator. All of this completely ignores the fact that throughout this period, there’s another supercomputer running in the world—SETI@home. Four and a half million users around the world, contributing their leftover computer cycles, whenever their computer isn’t working, by running a screen saver, and together sharing their resources to create a massive supercomputer that NASA harnesses to analyze the data coming from radio telescopes.
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What this picture suggests to us is that we’ve got a radical change in the way information production and exchange is capitalized. Not that it’s become less capital intensive—that there’s less money that’s required—but that the ownership of this capital, the way the capitalization happens, is radically distributed. Each of us, in these advanced economies, has one of these, or something rather like it—a computer. They’re not radically different from routers inside the middle of the network. And computation, storage and communications capacity are in the hands of practically every connected
4:14
What this means is that for the first time since the industrial revolution, the most important means, the most important components of the core economic activities—remember, we are in an information economy—of the most advanced economies, and there more than anywhere else, are in the hands of the population at large. This is completely different than what we’ve seen since the industrial revolution. So we’ve got communications and computation capacity in the hands of the entire population, and we’ve got human creativity, human wisdom, human experience—the other major experience, the other major input—which unlike simple labor—stand here turning this lever all day long—is not something that’s the same or fungible among people. Any one of you who has taken someone else’s job, or tried to give yours to someone else, no matter how detailed the manual, you cannot transmit what you know, what you will intuit under a certain set of circumstances. In that we’re unique, and each of us holds this critical input into production as we hold this machine.
5:25
What’s the effect of this? So, the story that most people know is the story of free or open source software. This is market share of Apache Web server—one of the critical applications in Web-based communications. In 1995, two groups of people said, “Wow, this is really important, the Web! We need a much better Web server!” One was a motley collection of volunteers who just decided, you know, we really need this, we should write one, and what are we going to do with what—well, we’re gonna share it! And other people will be able to develop it. The other was Microsoft.
6:04
Now, if I told you that 10 years later, the motley crew of people, who didn’t control anything that they produced, acquired 20 percent of the market and was the red line, it would be amazing! Right? Think of it in minivans. A group of automobile engineers on their weekends are competing with Toyota. Right? But, in fact, of course, the story is it’s the 70 percent, including the major e-commerce site—70 percent of a critical application on which Web-based communications and applications work is produced in this form, in direct competition with Microsoft. Not in a side issue—in a central strategic decision to try to capture a component of the Net. Software has done this in a way that’s been very visible, because it’s measurable. But the thing to see is that this actually happens throughout the Web.
6:99
So, NASA, at some point, did an experiment where they took images of Mars that they were mapping, and they said, instead of having three or four fully trained Ph.D.s doing this all the time, let’s break it up into small components, put it up on the Web, and see if people, using a very simple interface, will actually spend five minutes here, 10 minutes there, clicking. After six months, 85,000 people used this to generate mapping at a faster rate than the images were coming in, which was, quote, “practically indistinguishable from the markings of a fully-trained Ph.D.,” once you showed it to a number of people and computed the average.
7:44
Now, if you have a little girl, and she goes and writes to—well, not so little, medium little—tries to do research on Barbie. And she’ll come to Encarta, one of the main online encyclopedias. This is what you’ll find out about Barbie. This is it, there’s nothing more to the definition, including, “manufacturers”—plural—“now more commonly produce ethnically diverse dolls, like this black Barbie.” Which is vastly better than what you’ll find in the encyclopedia.com, which is Barbie, Klaus. (Laughter) On the other hand, if they go to Wikipedia, they’ll find a genuine article—and I won’t talk a lot about Wikipedia, because Jimmy Wales is here—but roughly equivalent to what you would find in the Britannica, differently written, including the controversies over body image and commercialization, the claims about the way in which she’s a good role model, etc.
8:42
Another portion is not only how content is produced, but how relevance is produced. The claim to fame of Yahoo! was, we hire people to look—originally, not anymore—we hire people to look at websites and tell you—if they’re in the index, they’re good. This, on the other hand, is what 60,000 passionate volunteers produce in the Open Directory Project, each one willing to spend an hour or two on something they really care about, to say, this is good. So, this is the Open Directory Project, with 60,000 volunteers, each one spending a little bit of time, as opposed to a few hundred fully paid employees. No one owns it, no one owns the output, it’s free for anyone to use and it’s the output of people acting out of social and psychological motivations to do something interesting.
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person—and these are the basic physical capital means necessary for producing information, knowledge and culture, in the hands of something like 600 million to a billion people around the planet.
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9:31
This is not only outside of businesses. When you think of what is the critical innovation of Google, the critical innovation is outsourcing the one most important thing—the decision about what’s relevant—to the community of the Web as a whole, doing whatever they want to do: so, page rank. The critical innovation here is instead of our engineers, or our people saying which is the most relevant, we’re going to go out and count what you, people out there on the Web, for whatever reason—vanity, pleasure—produced links, and tied to each other. We’re going to count those, and count them up. And again, here, you see Barbie.com, but also, very quickly, Adiosbarbie.com, the body image for every size. A contested cultural object, which you won’t find anywhere soon on Overture, which is the classic market-based mechanism: whoever pays the most is highest on the list.
10:29
So, all of that is in the creation of content, of relevance, basic human expression. But remember, the computers were also physical. Just physical materials—our PCs—we share them together. We also see this in wireless. It used to be wireless was one person owned the license, they transmitted in an area, and it had to be decided whether they would be licensed or based on property. What we’re seeing now is that computers and radios are becoming so sophisticated that we’re developing algorithms to let people own machines, like Wi-Fi devices, and overlay them with a sharing protocol that would allow a community like this to build its own wireless broadband network simply from the simple principle: When I’m listening, when I’m not using, I can help you transfer your messages; and when you’re not using, you’ll help me transfer yours. And this is not an idealized version. These are working models that at least in some places in the United States are being implemented, at least for public security.
11:36
If in 1999 I told you, let’s build a data storage and retrieval system. It’s got to store terabytes. It’s got to be available 24 hours a day, seven days a week. It’s got to be available from anywhere in the world. It has to support over 100 million users at any given moment. It’s got to be robust to attack, including closing the main index, injecting malicious files, armed seizure of some major nodes. You’d say that would take years. It would take millions. But of course, what I’m describing is P2P file sharing. Right? We always think of it as stealing music, but fundamentally, it’s a distributed data storage and retrieval system, where people, for very obvious reasons, are willing to share their bandwidth and their storage to create something.
12:23
So, essentially what we’re seeing is the emergence of a fourth transactional framework. It used to be that there were two primary dimensions along which you could divide things. They could be market based, or non-market based; they could be decentralized, or centralized. The price system was a market-based and decentralized system. If things worked better because you actually had somebody organizing them, you had firms, if you wanted to be in the market—or you had governments or sometimes larger non-profits in the non-market. It was too expensive to have decentralized social production, to have decentralized action in society. That was not about society itself. That was, in fact, economic.
13:05
But what we’re seeing now is the emergence of this fourth system of social sharing and exchange. Not that it’s the first time that we do nice things to each other, or for each other, as social beings. We do it all the time. It’s that it’s the first time that it’s having major economic impact. What characterizes them is decentralized authority. You don’t have to ask permission, as you do in a property-based system. May I do this? It’s open for anyone to create and innovate and share, if they want to, by themselves or with others, because property is one mechanism of coordination. But it’s not the only one.
13:45
Instead, what we see are social frameworks for all of the critical things that we use property and contract in the market: information flows to decide what are interesting problems; who’s available and good for something; motivation structures—remember, money isn’t always the best motivator. If you leave a $50 check after dinner with friends, you don’t increase the probability of being invited back. And if dinner isn’t entirely obvious, think of sex. (Laughter)
14:15
It also requires certain new organizational approaches. And in particular, what we’ve seen is task organization. You have to hire people who know what they’re doing. You have to hire them to spend a lot of time. Now, take the same problem, chunk it into little modules, and motivations become trivial. Five minutes, instead of watching TV? Five minutes I’ll spend just because it’s interesting. Just because it’s fun. Just because it gives me a certain sense of meaning, or, in places that are more involved, like Wikipedia, gives me a certain set of social relations.
14:89
So, a new social phenomenon is emerging. It’s creating, and it’s most visible when we see it as a new form of competition. Peer-to-peer networks assaulting the recording industry; free and open source software taking market share from Microsoft; Skype potentially threatening traditional telecoms; Wikipedia competing with online encyclopedias. But it’s also a new source of opportunities for businesses. As you see a new set of social relations and behaviors emerging, you have new opportunities. Some of them are toolmakers. Instead of building well-behaved appliances—things that you know what they’ll do in advance—you begin to build more open tools. There’s a new set of values, a new set of things people value. You build platforms for self-expression and collaboration. Like Wikipedia, like the Open Directory Project, you’re beginning to build platforms, and you see that as a model. And you see surfers, people who see this happening, and in some sense build it into a supply chain, which is a very curious one. Right?
15:96
You have a belief: stuff will flow out of connected human beings. That’ll give me something I can use, and I’m going to contract with someone. I will deliver something based on what happens. It’s very scary—that’s what Google does, essentially. That’s what IBM does in software services, and they’ve done reasonably well.
16:12
So, social production is a real fact, not a fad. It is the critical long-term shift caused by the Internet. Social relations and exchange become significantly more important than they ever were as an economic phenomenon. In some contexts, it’s even more efficient because of the quality of the information, the ability to find the best person, the lower transaction costs. It’s sustainable and growing fast.
16:40
But—and this is the dark lining—it is threatened by—in the same way that it threatens—the incumbent industrial systems. So next time you open the paper, and you see an intellectual property decision, a telecoms decision, it’s not about something small and technical. It is about the future of the freedom to be as social beings with each other, and the way information, knowledge and culture will be produced. Because it is in this context that we see a battle over how easy or hard it will be for the industrial information economy to simply go on as it goes, or for the new model of production to begin to develop alongside that industrial model, and change the way we begin to see the world and report what it is that we see.
16:98
Thank you. (Applause)
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