Rereading The Long Tail

This was officially launch week for The Umlaut, a new online magazine that my friends Jerry Brito and Eli Dourado have started. There are five of us who will be regular writers for it. For my first piece, I thought it might be fun to go back and re-examine The Long Tail almost seven years after it was published.

The Long Tail had a big impact on the conversation around new media at the time, and was very personally significant. The original article was published in October of 2004, a mere month before I began blogging. Trends in new media were a fascination for me from the beginning, so I kept up with Chris Anderson’s now-defunct Long Tail blog religiously.

A 19-year-old and a tad overenthusiastic, I strongly believed that the mainstream media was going the way of the dinosaur and would be replaced by some distributed ecosystem of mostly amateur bloggers. In short, I thought the long tail was going to overthrow the head of the tail, and that would be that. Moreover, I thought that all content would eventually be offered entirely free of charge.

That was a long time ago now, and my views have evolved in some respects, and completely changed in others. I think that the head of the tail is going to become larger, not smaller, and professionals are here to stay–as I elaborate on here. However, I do think that the growth of the long tail will be very culturally significant.

When I began rereading The Long Tail, I expected to find a clear argument from Anderson that he thought the head of the tail would get smaller relative to the long tail. Instead, he was frustratingly vague on this point. Consider the following quote:

What’s truly amazing about the Long Tail is the sheer size of it. Again, if you combine enough of the non-hits, you’ve actually established a market that rivals the hits. Take books: The average Barnes & Noble superstore carries around 100,000 titles. Yet more than a quarter of Amazon’s book sales come from outside its top 100,000 titles. Consider the implication: If the Amazon statistics are any guide, the market for books that are not even sold in the average bookstore is already a third the size of the existing market—and what’s more, it’s growing quickly. If these growth trends continue, the potential book market may actually be half again as big as it appears to be, if only we can get over the economics of scarcity.

Let us unpack this quote a little.

First, Anderson is offering the fact that more than 25% of Amazon’s book sales occur outside of its top 100,000 titles as evidence of the revenue potential for the long tail. But this is very flawed conceptually. At the time of the book’s publication, Amazon sold some 5 million books. If nearly all of the additional revenue beyond the top 100,000 titles was encompassed by the following 100,000 titles, then 4% of Amazon’s titles account for nearly all of its book revenues. And there is good reason to believe that that is exactly how the distribution played out, back then and now.

The fact that 200,000 is a larger number than 100,000 is indeed a significant thing; it shows the gains that a company can make from increasing their scale if they are able to bring down costs enough to do so. But to  claim that this is evidence of the commercial potential of the long tail is flat out wrong. We’re still talking about a highly skewed power law distribution–in fact, an even more skewed power law distribution, as we used to speak of 20% of books accounting for 80% of the revenue, and here we are talking about 4% of the books accounting for something on the order of 99% of the revenue.

This argument appears several times throughout the book, in several forms. At one point he talks about how the scaling up of choices makes the top 100 inherently less significant. Which is true, but it does not make the head of the tail any less significant; it just means that there are a larger quantity of works within that head.

Second, this bit about “if only we can get over the economics of scarcity.” Anderson argues, repeatedly, that mass markets and big blockbusters are an artifact of a society built on scarcity, and the long tail is a creation of the new economics of abundance. This is wrong to its core.

As I argue in my first piece at The Umlaut, we have been expanding the long tail while increasing the head of the tail since the very beginning of the Industrial Revolution. Scale in the upward direction fuels scale in the outward direction. Consider Kevin Kelly’s theory of 1,000 true fans, the paradigm of the long tail success.

Assume conservatively that your True Fans will each spend one day’s wages per year in support of what you do. That “one-day-wage” is an average, because of course your truest fans will spend a lot more than that.  Let’s peg that per diem each True Fan spends at $100 per year. If you have 1,000 fans that sums up to $100,000 per year, which minus some modest expenses, is a living for most folks.

Now ask yourself: how do we get to a world where someone can make a living by having 1,000 true fans, or fewer? Or 1,000 more modest fans, or fewer?

One way we get to that world is through falling costs. If we assume a fixed amount that some group of fans is willing to pay for your stuff, then progress is achieved by lowering the cost of producing your stuff.

Another way is for everyone to get wealthier, and thus be able to be more effective patrons of niche creators. If I make twice as much this year as I did last year, then I can afford to spend a lot more above and beyond my costs of living.

Another conceivable way is sort of a combination of the first two–falling costs for the patrons. If I make as much in nominal terms as I did last year, but my costs of living fall by half, then it is effectively the same as though I had doubled my income.

Put all three of these trends together and you have perfectly described the state of material progress since the onset of the Industrial Revolution. Huge breakthroughs in our productive capacities have translated into a greater ability to patronize niche phenomena.

Obviously the personal computer and the Internet have taken this trend and increased its scale by several orders of magnitude–especially in any specific area that can be digitized. But that doesn’t mean we’ve entered a new era of abundance. The economics are the same as they have always been. The frontier has just been pushed way, way further out.

Moreover, the blockbuster is not an artifact of scarcity. Quite the opposite. The wealthier and more interconnected we are, the taller the “short tail” can be. In my article, I mention the example of Harry Potter, which was a global hit on an unprecedented scale (this Atlantic piece estimates the franchise as a whole has generated something like $21 billion). Hits on that scale are rare, giving us the illusion at any given moment that they are a passing thing, a relic of a bygone era of mass markets. But the next Harry Potter will be much, much bigger than Harry Potter was, because the size of the global market has only grown and become more connected.

Consider Clay Shirky’s observation that skew is created when one person’s behavior increases the probability that someone else will engage in that behavior “by even a fractional amount”. His example involves the probability that a given blog will get a new reader, but it extends to just about every area of human life. And the effect he describes, but does not name, is the network effect–one additional user of Facebook increases the probability that they will gain yet another one, one additional purchaser of a Harry Potter book increases the probability that yet another person will purchase it.

And we know, from the diffusion of innovations literature, that there comes a certain point at which one additional person increases the probability by a lot more than a fractional amount. As Everett Rogers put it:

The part of the diffusion curve from about 10 percent adoption to 20 percent adoption is the heart of the diffusion process. After that point, it is often impossible to stop the further diffusion of a new idea, even if one wished to do so.

Now, if network effects are what create skew in the first place, and we are living in the most networked age in history, how plausible does Anderson’s argument seem that the head of the tail will be of decreasing significance because of new networks?

What Does He Really Think?

Part of what’s frustrating about the book is that Anderson doesn’t really make a solid claim about how big he thinks the head of the tail is going to be relative to the tail. He provides some facts that are erroneous to answering this question, such as the Amazon statistic described above. In some places he seems like he’s saying the head will be smaller:

The theory of the Long Tail can be boiled down to this: Our culture and economy are increasingly shifting away from a focus on a relatively small number of hits (mainstream products and markets) at the head of the demand curve, and moving toward a huge number of niches in the tail. In an era without the constraints of physical shelf space and other bottlenecks of distribution, narrowly targeted goods and services can be as economically attractive as mainstream fare.

The long tail is going to be “as economically attractive” as the head of the tail. That’s what he’s saying, right? If so, then he is wrong, for the reasons described above.

But maybe that isn’t what he’s saying. Consider:

This is why I’ve described the Long Tail as the death of the 80/20 Rule, even though it’s actually nothing of the sort. The real 80/20 Rule is just the acknowledgment that a Pareto distribution is at work, and some things will sell a lot better than others, which is as true in Long Tail markets as it is in traditional markets. What the Long Tail offers, however, is the encouragement to not be dominated by the Rule. Even if 20 percent of the products account for 80 percent of the revenue, that’s no reason not to carry the other 80 percent of the products. In Long Tail markets, where the carrying costs of inventory are low, the incentive is there to carry everything, regardless  of the volume of its sales. Who knows—with good search and recommendations, a bottom 80 percent product could turn into a top 20 percent product.

Here he seems to be saying that the 80/20 Rule will always remain true, but that shouldn’t stop us from realizing how important the long tail is in our lives, and how much more important it will be in the future as we get ever more diversity of choices in the relatively niche. Moreover, companies should continue to extend their long tail offers because, at any moment, one of them might suddenly jump to the head of the tail. So a Kindle book that’s only selling a handful per year may suddenly go viral and make Amazon a ton of money.

If that’s what he believes, then he is correct. But the mixture of the bad accounting of the sort in the top 100,000 books example above, statements such as the one quoted above about what “the theory of the Long Tail can be boiled down to”, and this last quote about the 80/20 rule, force me to conclude that Anderson’s thinking is simply muddled on this particular point.

Credit Where Credit is Due

Finally, if there’s one thing that I think we can all agree with Anderson on, it is that the expansion of the long tail has greatly increased the quality of our lives. Whether it’s people like Scott Sigler who has managed to make a living from his fans, or the passionate community of a small subreddit, there is an ever expanding virtual ocean of choices in the long tail today.

Chris Anderson argued that the fact that something is not a hit of the blockbuster variety does not mean it is a miss. There are some things that are much more valuable to a small group of people than they are to everyone else, thereby precluding their ability to become a blockbuster. There are also some things that might be equally appealing to the same number of people as a blockbuster, but they simply were not lucky enough to be among the few that won that particular lottery.

All of us live in both the head of the tail and the long tail, and I’m glad that Anderson convinced so many of the value of the latter.

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Adam Gurri

Adam Gurri works in digital advertising and writes for pleasure on his spare time. His present research focuses on the ethics of business and work, from the perspective of virtue and human flourishing.

19 thoughts on “Rereading The Long Tail”

  1. I completely disagree with the key arguments of your post. First, a math mistake: you say that the top 500k books sold by Amazon represent 1% of their 5 million books sold, that should be 10%.

    You claim that the head of the tail is getting bigger. All the evidence here contradicts you, from TV network viewership to album sales (you had to sell 10 million albums to be no. 1 in 2000, that was down to 3 million by 2008) to the disintegrating radio and newspaper businesses. The long tail is killing the head of the tail and the death blow is coming.

    Now, the reason it hasn’t happened yet is because while distribution may not be scarce any more, marketing has only gotten more expensive, now that there are a million options to choose from. Recommendation systems need to be created to solve this problem and the day they get good enough, the head of the tail gets chopped off. The one super-hit you mention, Harry Potter, is an artifact of the antiquated distribution system of paper books and movie theaters still being in place, which can also use the internet to generate additional mass marketing… for now. Recommendation systems will kill advertising- well, it will be one of many factors in the death of ads, like ad-blockers or the rise of paid models- then these recent super-hits will disappear.

    Another big mistake you make is claiming that we still have the long/head tail distribution we’ve always had, only that the negligible sales in the long tail has gotten much longer. That is plain wrong. It is clear that a century ago, before the rise of broadcast tech like radio and TV, the content market was highly fragmented, with thousands of local theaters and newspapers. The 20th century made the head of the tail huge, three broadcast TV networks killing off hundreds of local theaters. The fact that small newsletters or ham radio groups also came into existence in this rapidly growing communications market is washed away by the much greater rise of mass media. Well, what we’re seeing now is exactly the opposite, as the mass media dies off and top radio shows that used to get 5-7 million listeners are regularly replaced by top podcasters who get maybe 200-500k listeners and newspaper readers die off every year (literally die off, apparently the joke in the newspaper industry is that every time a senior citizen dies, there goes a newspaper subscriber, never to return), replaced by blog readers.

    And the benefit of this longer tail is not just that any small creator can snowball into a hit, though that does happen, but that people can find material that is better suited to them. Each of those blogs, podcasts, online videos has a niche audience that will like it better than a bland, mass media concoction aimed at the lowest common denominator and once you can easily match each creator with his 1000 fans (hence the need for good recommendation systems), mass media is obsoleted.

    Now, recommendation systems are not easy: it’s a complex interplay of human input and machine intelligence, but I’m confident something workable can be built fairly soon, that will be many times better than what we have today. Once that happens, bye, bye, head of the tail, 🙂 well, at least any kind of silly 20/80 rule, more like 20/30 or some such much smaller number for the hits.

    1. Thanks for the correction–I’ve edited the post to reflect the right math. Embarrassing!

      You are of course entitled to your opinion, but I don’t think you’re right. Of course, it all is a matter of how you do the accounting. The fact of the matter is, the long tail of video is longer than ever, right? With things such as YouTube and Vimeo. If you count all of the videos on the Internet that people could be watching, then 1% of all videos available to people really do account for 99% of the viewing–whether they are viewing it on broadcast, Cable, Netflix or Hulu; the professionally produced stuff still accounts for an overwhelming supermajority of what people watch. And of the non-professional stuff that people watch, only a tiny fraction of it account for a supermajority of views there, too.

      Same thing for app downloads and app revenue—we have more mobile apps than ever, but nearly all of them get almost no downloads. A tiny minority like Angry Birds ends up getting tens of millions while the vast majority only make peanuts, if anything.

      And the Harry Potter effect is undeniable. The fact that markets are global and interconnected makes the potential upside for a single work much much larger. But there’s a very low probability that any given work will hit all of that upside; it’s a volatile thing. The next Harry Potter may not come for many years, but do you doubt that it is possible? And that the upside will be even bigger by the time the next one comes?

      Thanks for your comment.

      1. The fact that there are now millions of videos online doesn’t necessarily mean that “1% of all videos available to people really do account for 99% of the viewing.” It’s possible that the distribution has gone from 20/80 on TV to 20/50 for online video, while the tail got much longer. In other words, it’s possible for the distribution to get both flatter and longer, simply getting longer doesn’t imply that the head got compressed much more. I suspect the distribution got both flatter and longer, but that is based on the various stats I linked to above and my anecdotal perusal of various sites: I don’t have definitive stats, but then, I suspect you don’t either.

        What is “professionally produced?” The top podcasters are often one-man operations that started up without any corporate backing, startups if you will. The notion of professional production is increasingly meaningless: was House of Cards not professional because it’s only available online? To the extent the distribution online is still 20/50, ie the top 20% get 50% of the sales or views, and not the 20/30 or lower which I forecast, I gave the reason why: we still use extremely crude methods to find what we want.

        My family has a satellite TV hookup at home, with hundreds of channels available, but how do you ever know what to watch? It would take you 20 minutes just to flip through all the channels. The entire user interface for video is broken. We’ve taken channel flipping, which was okay when we had a couple of channels or even a couple dozen, and extended it beyond where it makes any sense whatsoever. And this is just one of the ways in which current “content discovery” is completely broken. There are companies working on this, whether Google TV or Apple TV or Netflix, and recommendation systems are a large part of the solution.

        My point is that Angry Birds and Harry Potter are the last giant wave of the old, about-to-be-obsoleted system, driven by marketing rather than consumer choice. With recommendation systems, marketing and advertising die off. As for your questions, anything is possible, another Harry Potter will just be far more unlikely. The upside always gets larger, but if the probability is getting much smaller much faster than that, who cares? Obviously this new system isn’t here yet, so what I’m saying is not obvious to everyone, but I think you can see the contours of what’s coming if you look closely enough. 😉

        1. I strongly recommend you read this essay by Clay Shirky: http://www.shirky.com/writings/herecomeseverybody/powerlaw_weblog.html

          And reconsider your position. Network scientists (like Duncan Watts) who have been studying this subject for some time come to the same conclusion that I’m coming to here. If there’s any preferential attachment–that is, if, say, one person viewing a video increases the probability that another person will view that video, even by a very small amount–then you will get skew. The greater the marginal probability increase of getting one more viewer, the greater the skew.

          In a world where one person viewing something increases the chances that they will share it on facebook, and that increases the chances that THEIR friends will reshare it—how exactly do you think these probabilities play out?

          My recent Umlaut piece goes into this further, from a different angle: http://theumlaut.com/2013/03/18/filter-bubbles-versus-viral-meme-why-we-have-more-common-ground-than-ever-before/

          1. Oh, I’m pretty sure I read that piece years ago. Typical Shirky, who I rarely read, because the few pieces of his I’ve read I find are invariably obvious or deeply stupid. Take, for example, his takedown of micropayments, which is deeply idiotic.

            There is no doubt that there are herding effects in anything humans do, but we now have brand spanking new technology to counteract those effects. A search engine is a fundamentally new technology, which finds web pages with exactly the keywords of our choice. Now, the search results are still collaboratively determined, based on what other sites linked to for those keywords and what other search users clicked on before, but it is a complex interplay of machine intelligence and human input that yields vastly more places to go than anything that came before, because of the millions of keyword combinations we can choose from and those always mutating list of search results. In other words, whatever Duncan Watts may have found in his test networks or Facebook is completely irrelevant in much more complex networks.

            As for your recent piece, do you really think the “Daily Me” or filter bubble are here yet? Because what I see today is painfully crude, people being drowned in crap posts by their friends on Facebook or the silly memes that you highlight on twitter, precisely because the techies have built the easy part of mass distribution on the internet but have done almost nothing about recommendation, ie filtering. Of course, the latter is harder, maybe that has something to do with it. 😉

            My fundamental thesis is that we’ve greatly increased distribution but done little to improve discovery (google aside 🙂 ), so of course there are a few bigger viral hits, like the Gangnam Style video. Once we get a handle on discovery, by implementing recommendation systems or what you might call filters, there will still be the possibility of viral hits, but the probability drops like a stone. It seems to me that is where we disagree, because you don’t seem to think filters can work? You seem to keep ignoring my argument about recommendation systems.

            btw, you missed one last detail with your math, there aren’t 500k titles in the top 4% anymore.

          2. Whoops, missed that second 500K. Thanks.

            You think new technology counteracts that effect, and that is a bold (empirically unsupported) claim. The effects described by Shirky (based on the work of network scientists) apply to linking as well—there is a highly skewed head of the tail that gets the supermajority of links on the web. Thus, Google’s algorithm by its nature is also skewed.

            Recommendation systems _exacerbate_ skew, because they are always based on other, similar’s people’s preferences. Think Amazon—“People who bought X also bought Y”. In other words, when people went on to buy Y, they increased the probability that yet more people would buy Y—thus, preferential attachment.

            In fact, the only way big data can be useful at all in finding people’s preferences is if their preferences are similar to other people’s in some way! If everyone were a unique set of preferences, Google would be unable to find anything for anyone because all previous data would be irrelevant to each case. In so much as filters and recommendation systems are useful, it is because we resemble other people—and thus, other people’s choices will influence the choices of people who use recommendation systems, and thus, you will have preferential attachment and huge skew effects.

          3. Ah, I think I see the point of confusion. You think web linking and Facebook likes and Amazon’s “X bought Y” are good recommendation systems. Hardly, that’s like saying Altavista was a good search engine. 😉 Of course my claim isn’t empirically supported yet, the technology hasn’t been built yet! 😀 Google’s algorithm is only skewed if you’re not searching for something specific or unpopular: type “rereading the long tail” into google and see what pops up, doesn’t matter much how many people linked to your post if it’s the only one with that title. 😉

            Simple recommendation systems may exacerbate skew, because they are based on an extremely crude filter, like Digg’s was. Finding others with preferences similar to yours is only one way of filtering, the human input I mentioned earlier. There are other ways that are based more on machine intelligence, say recommending a comedy to you because the machine notices that most of your previous viewings were labeled as comedies.

            “In so much as filters and recommendation systems are useful, it is because we resemble other people,” in a word, no. Resembling other people will be one input, but it will not be the only input.

            First, underlying all this is is a fundamental philosophical question: how different are we really? I argue that we are very different and that mass media masked that only by creating lowest-common denominator content that could appeal to everybody on some low level, then stuffed the few channels with those few choices. That is not the case anymore. Think of the multitude of blogs and podcasts and online forums that people frequent today. There are still more popular blogs and less popular ones, but the distribution is almost certainly flatter than what came before. And that’s just with increased distribution, discovery hasn’t gotten much better at all, yet.

            Now that I can choose a handful of highly-focused podcasts, I actually listen to an audio podcast like Econtalk, whereas I almost never used to listen to the radio before, because I found it all to be mass-produced crap. Everyone has niches like this that they fall into, whether it’s french cooking blogs or motorcycle forums. You posit two binary extremes as the only possibilities: that we’re all either completely unique snowflakes, with nothing in common with anyone else, or we’re fairly similar, thus many of us liking the viral hits. I’d argue instead that we’re a collection of small tribes, not in the conventional sense, but in the sense of splintering into the audience for the french cooking blogs and economics podcasts I mentioned before. So yes, there is scope for finding similar interests, but it is intrinsically limited by the small pools of people with the same specific interests.

            Second, recommendations won’t just be done by friends or similar people but by others who will categorize or tag material based on who is likely to like it. So these will be people who might have nothing similar to you, but simply know what you’re likely to like. Then, the machine might even extrapolate based on their tags and make recommendations with little human input, the way some music services currently try to recommend songs based on machine-analyzing the songs for songs with similar tone or tempo, ie with no human input. All these methods, manual or human, will be inputs to the final recommendation decision.

            The gist is that recommendation systems much more complex than what we’ve built so far are on the horizon and they will invariably be based on much more complex algorithms than Digg’s stupid one, which link gets the most upvotes. Arguably that’s why Digg went out of business, because they were too thick to progress past that extremely simple filter to build more complex ones. Arguing that such complex recommendation systems, which will fully take our preferences and differences into account, will still recommend the same crap playing on a top 40s radio station is silly in my view.

          4. 1. Your argument boils down to the following: we have never seen a filtering or recommendation system in the real world that contradicts your claims, but you believe one is coming. Forgive me if I don’t find that persuasive.

            Addendum: “There are other ways that are based more on machine intelligence, say recommending a comedy to you because the machine notices that most of your previous viewings were labeled as comedies.”

            There is no Platonic form of comedy. Categories are defined because more than one person is likely to understand what is implied by the label. In other words, something is defined as a comedy in Netflix because they believe that people who like X set of things are likely to seek them out under the heading of comedy. In other words, labels are only useful to the extent that people have similar expectations; to the extent that we are the same.

            2. I have not argued that it is black and white, unique or totally the same. If that is what I have communicated, I apologize for making my argument badly.

            What I believe is that we all walk in both worlds; there’s a small set of things at the head that nearly all of us have in common, and we all have a lot of things in the long tail that we are into as well. The differences between us come from the differences in what stuff we mine from the long tail, and the similarities are reflected in the same stuff we’re all looking at from the head of the tail.

            I believe that for a long time now, we’ve been getting more of both. More common ground, and more diversity, at the same time. We’re getting to a point where there’s a teeny tiny number of things that nearly everyone has seen, and that scales down fractally (so that even something like EconTalk stands at the head of the tail within the niche of podcast listening, or at least closer to the head than the vast majority of podcasts out there).

            I think the majority of the stuff your or I consume on a daily basis is in the long tail. BUT the majority of views, links, money, etc, are still going to the head of the tail, because while a few components of your content diet and mine are in the content diets of a huge majority of people, while the rest of our content diet may be more niche.

            Does that make sense? Not sure I explained that well.

          5. Did you mean “backs up my claims?” If there’s nothing that contradicts my claims, then it certainly is possible, no? I did mention google search as a very complex recommendation system. It takes your human input, the keywords that you type in, combines it with the collective human input of the link structure of the web and the click history of previous google users, and also factors in the machine input of which pages actually contain those exact keywords. My information diet has gotten more diverse because of google search, mostly because I was searching for very specific info that google’s machines found in some blog post somewhere, which then occasionally becomes a blog I follow. I don’t use any social networks, so my new sources come either from links from the blogs I already read or google search, more often the latter.

            In any case, if we only found arguments persuasive because they had been amply demonstrated, there would be need to argue. We could just wait till it’s all been done. 😉 We do need to theorize at some point.

            Why does it matter that most of us would label a comedy a comedy or that stoners all find Pineapple Express to be hilarious? I never said that there aren’t similarities, only that there are many more non-stoners who would find Pineapple Express to be a bore. Also, my point wasn’t that machines would become artificially intelligent and categorize movies as comedies by themselves, but that some humans would label the comedies and then the machine would recognize that you watch a lot of movies labeled as comedies and factor that into its next recommendation. As always, it’s a mix of human and machine input, I’ve stated this many times.

            Yes, most of us do partake from the head and the tail, this argument is about how much we’ll partake in each going forward. I see the head getting chopped off, you apparently don’t. Econtalk may be within the head of the distribution for podcasts, but with about 30-50k listeners, it is much less than a top podcast like Carolla’s, which he’s said before had about 200-300k listeners, or a popular radio show like Stern’s, which had millions of listeners all over the country, ie the head has already gotten much smaller, just because of increased distribution online.

            I agree that the head of the tail still gets the majority of the views, where we seem to disagree is about how the head is evolving and whether it can survive, especially once good recommendation systems are here. Would you still call it a head when the top 20% gets only 30% of the views/sales? I’d call that getting chopped off, and that’s the prediction I made earlier.

            As for whether you explained it well, it is difficult to talk about this topic because we’re talking about how a distribution will evolve, without using any precise charts or equations to describe it. So we’re using qualitative words or simple metrics like 20/80 to fuzzily describe something more precise: that often descends into confusion. I’ve tried to be more precise, for example, when I said the distribution will look more like 20/30 a couple comments back.

          6. Time will tell, but I 100% disagree with you about the head getting chopped off. I stand by my claim that it will get more skewed rather than less, and I think all the research on this (from network scientists all the way back to Mr. Vilfredo Pareto :D) backs me up.

            But we will see. I’d prefer to live in the world you’re describing, I just do not think it exists, and don’t think it’s possible that it ever would.

          7. In any case, thank you for the engaging discussion. Going to digest your arguments now, as I don’t think I have much more useful to say at this moment 🙂

          8. I suddenly remembered some data from when I used to read Technorati’s state of the blogosphere every year. Check out the one from 2009: http://technorati.com/social-media/article/day-4-blogging-for-profit/

            Relevant quote:

            “The average annual blogger revenue is more than $6,000. However, this is skewed by the top 1% of bloggers who earn $200k+. Among active bloggers that we surveyed, the average income was $75,000 for those who had 100,000 or more unique visitors per month (some of whom had more than one million visitors each month). The median annual income for this group is significantly lower — $22,000”

            That’s what I call skew! Do you really think it has grown any _less_ skewed now that we have even more bloggers and the top bloggers have even more traffic?

          9. I have no idea if blogger “skew” has grown or receded since 2009. We are still in the early days of blogging and the early days of any movement are always chaotic. And we still don’t have widespread recommendation systems, other than the simple one of some bloggers linking to other bloggers, so anything is possible till they get here.

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