Here is how platforms die: first, they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves. Then, they die. (2023) source Verified

8. Given a large enough beta-tester and co-developer base, almost every problem will be characterized quickly and the fix obvious to someone. Or, less formally, ``Given enough eyeballs, all bugs are shallow.'' I dub this: ``Linus's Law''. My original formulation was that every problem ``will be transparent to somebody''. Linus demurred that the person who understands and fixes the problem is not necessarily or even usually the person who first characterizes it. ``Somebody finds the problem,'' he says, ``and somebody else understands it. And I'll go on record as saying that finding it is the bigger challenge.'' That correction is important; we'll see how in the next section, when we examine the practice of debugging in more detail. But the key point is that both parts of the process (finding and fixing) tend to happen rapidly. (2000) source Unverified

And, finally, you have to find a way to spare the group from scale. Scale alone kills conversations, because conversations require dense two-way conversations. In conversational contexts, Metcalfe's law is a drag. The fact that the amount of two-way connections you have to support goes up with the square of the users means that the density of conversation falls off very fast as the system scales even a little bit. You have to have some way to let users hang onto the less is more pattern, in order to keep associated with one another. (2003) source Unverified

This development is interesting for another reason: graph-based social capital allocation mechanisms can suffer from runaway winner-take-all effects. In essence, some networks reward those who gain a lot of followers early on with so much added exposure that they continue to gain more followers than other users, regardless of whether they've earned it through the quality of their posts. One hypothesis on why social networks tend to lose heat at scale is that this type of old money can't be cleared out, and new money loses the incentive to play the game. It's not that the existence of old money or old social capital dooms a social network to inevitable stagnation, but a social network should continue to prioritize distribution for the best content, whatever the definition of quality, regardless of the vintage of user producing it. Otherwise a form of social capital inequality sets in, and in the virtual world, where exit costs are much lower than in the real world, new users can easily leave for a new network where their work is more properly rewarded and where status mobility is higher. (2019) source Unverified

Those whom the Gods wish to destroy,” says the adage, “they first make mad.” Actually, that’s overkill: the Gods just need to make people forget. Amnesia turns out to be a powerful narcotic and it’s been clouding our perceptions of what’s been happening on the internet for at least 25 years, namely the inexorable degradation of the online environment and our passive, sullen acceptance of that. Thanks to Cory Doctorow, the great tech critic, we now have a term for this decay process in online platforms – enshittification. “First,” he writes, “they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves.” Enshittification results from the convergence of two things: the power of platform owners to change how their platforms extract value from users and the nature of the two-sided markets – where the platforms sit between buyers and sellers, holding each hostage to the other and then raking off an ever-larger share of the value that passes between them… (2023) source Unverified

Perhaps this is just nostalgia and such complaints are the disaffected grumbling of an out-of-touch cohort of early adopters. Or it could be the “headwinds” effect, familiar to any cyclist, which is that you always notice headwinds but take tailwinds for granted. Similarly, whenever a platform changes, we obsess over what is worse and quickly forget what is better. This negativity makes evolutionary sense: the secret of happiness may be to focus on what’s going well, but the secret of survival is to pay attention to what’s going badly. Nevertheless, I’m quite sure enshittification is real. The basic idea was sketched out in economic literature in the 1980s, before the world wide web existed. Economic theorists lack Doctorow’s gift for a potent neologism, but they certainly understand how to make a formal model of a product going to the dogs. There are two interrelated issues at play. The first is that internet platforms exhibit network effects: people use Facebook because their friends use Facebook; sellers use Amazon because it’s where the buyers are, while buyers use Amazon because it’s where the sellers are. Second, people using these platforms experience switching costs if they wish to move from one to another. [...] Both switching costs and network effects tend to lead to enshittification because platform providers see early adopters as an investment in future profits. (2023) source Unverified

As our shareholders know, we have made a decision to continuously and significantly lower prices for customers year after year as our efficiency and scale make it possible. This is an example of a very important decision that cannot be made in a math-based way. In fact, when we lower prices, we go against the math that we can do, which always says that the smart move is to raise prices. We have significant data related to price elasticity. With fair accuracy, we can predict that a price reduction of a certain percentage will result in an increase in units sold of a certain percentage. With rare exceptions, the volume increase in the short term is never enough to pay for the price decrease. However, our quantitative understanding of elasticity is short-term. We can estimate what a price reduction will do this week and this quarter. But we cannot numerically estimate the effect that consistently lowering prices will have on our business over five years or ten years or more. [...] creates a virtuous cycle that leads over the long term to a much larger dollar amount of free cash flow, and thereby to a much more valuable Amazon.com. As another example, in 2000 we invited third parties to compete directly against us on our “prime retail real estate”—our product detail pages. [...] Over time, third-party sales have become a successful and significant part of our business. Third-party units have grown from 6% of total units sold in 2000 to 28% in 2005, even as retail revenues have grown three-fold. (2006) source Unverified

Page: We have to. The increasing volume of information is just more opportunity to build better answers to questions. The more information you have, the better. Page: We built a business on the opposite message. We want you to come to Google and quickly find what you want. Then we’re happy to send you to other sites. In fact, that’s the point. The portal strategy tries to own all the information. (2004) source Unverified

This tendency of networks to drastically amplify small inputs leads to the second key axiom of network logic: the law of increasing returns. In one way or another this law undergirds much of the strange behavior in the network economy. The simplest version goes like this: The value of a network explodes as its membership increases, and then the value explosion sucks in yet more members, compounding the result. In the industrial economy success was self-limiting; it obeyed the law of decreasing returns. In the network economy, success is self-reinforcing; it obeys the law of increasing returns. [...] Networks, on the other hand, increase value exponentially–small efforts reinforce one another so that results can quickly snowball into an avalanche. (2009) source Unverified

History shows a typical progression of information technologies [...] from open to closed system. (2015) source Unverified

So the value of a GFN increases exponentially, in proportion to 2^n. (2001) source Unverified

The value (A*N^2) of particular social networks has been growing with broadband and mobile Internet access. (2006) source Unverified

Well, the value of the network is the value of the service increasing as more people use it. (2024) source Unverified

the giant social networks seem to inevitably piss off their user bases by changing product features and terms of service (2012) source Unverified

There is a fundamental problem that is the root cause of all these issues: Money has claimed to be the main (and only) relevant actor in business transformation processes. source Unverified

When numbers skyrocket and laggards start to arrive, who make completely unexpected uses of your technology, when a chat turns into an email, an email into chat or into a personal notes repository, when rankings and algorithms are attempted to be hacked by tens of millions of people, the situation ends up being uncontrollable. source Unverified