Even if you’re only mildly interested in the economics of book publishing, this is interesting.
Zack Kanter, a startup founder contributing to Techcrunch has clearly studied Amazon in detail. As a result, his analysis is better and more subtle than the usual Amazon guff. The idea of using an external market to avoid internal bloat is fascinating. Compare and contrast with companies that want to “outsource”.
In the 10+ years since AWS’s debut, Amazon has been systematically rebuilding each of its internal tools as an externally-consumable service. A recent example is AWS’s Amazon Connect – a self-service, cloud-based contact center platform that is based on the same technology used in Amazon’s own call centers. Again, the ‘extra revenue’ here is great – but the real value is in honing Amazon’s internal tools.
If Amazon Connect is a complete commercial failure, Amazon’s management will have a quantifiable indicator (revenue, or lack thereof) that suggests their internal tools are significantly lagging behind the competition. Amazon has replaced useless, time-intensive bureaucracy like internal surveys and audits with a feedback loop that generates cash when it works – and quickly identifies problems when it doesn’t. They say that money earned is a reasonable approximation of the value you’re creating for the world, and Amazon has figured out a way to measure its own value in dozens of previously-invisible areas.
But this much is obvious – we all know about AWS. The incredible thing here is that this strategy – in one of the most herculean displays of effort in the history of the modern corporation – has permeated Amazon at every level. Amazon has quietly rolled out external access in nooks and crannies across their entire ecosystem, and it is this long tail of external service availability that I think will be nearly impossible to replicate.
Edge talks to Ross Anderson, professor of security engineering at Cambridge University, and one of the founders of the field of information security economics:
Meanwhile, in society at large, what we have seen over the past fifteen years is that crime has gone online. This has been particularly controversial in the UK. Back in 2005, the then Labour government struck a deal with the banks and the police to the effect that fraud would be reported to the banks first and to the police afterwards. They did this quite cynically in order to massage down the fraud figures. The banks went along with it because they ended up getting control of the fraud investigations that were done, and the police were happy to have less work for their desk officers to do.
For a decade, chief constables and government ministers were claiming that “Crime is falling, we’re doing a great job.” Some dissident criminologists started to say, “Hang on a minute. Crime isn’t actually falling, it’s just going online like everything else.” A year and a half ago, the government started publishing honest statistics for the first time in a decade. They found, to their disquiet, that online and electronic crime is now several times the rate of the traditional variety. In fact, this year in Britain we expect about one million households will suffer a traditional property crime like burglary or car theft, and somewhere between three and four million—probably nearer four million—will suffer some kind of fraud, or scam, or abuse, almost all of which are now online or electronic.
From the point of view of the police force, we got policy wrong. The typical police force—our Cambridgeshire constabulary, for example, has one guy spending most of his time on cybercrime. That’s it. When we find that there’s an accommodation scam in Cambridge targeting new students, for example, it’s difficult to get anything done because the scammers are overseas, and those cases have to be referred to police units in London who have other things to do. Nothing joins up and, as a result, we end up with no enforcement on cybercrime, except for a few headline crimes that really annoy ministers.
We’ve got a big broken area of policy that’s tied to technology and also to old management structures that just don’t work. In a circumstance like this, there are two options for someone like me, a mathematician who became a computer scientist and an engineer. You can either retreat into a technical ghetto and say, “We will concentrate on developing better tools for X, Y, and Z,” or you can engage with the broader policy debate and start saying, “let’s collect the evidence and show what’s being done wrong so we can figure out ways of fixing it.”
Dan Kaplan at Exponents looks at Twitter:
Indeed, one particularly interesting section of Israeli society provides a unique laboratory for how to live a contented life in a post-work world. In Israel, a significant percentage of ultra-orthodox Jewish men never work. They spend their entire lives studying holy scriptures and performing religion rituals. They and their families don’t starve to death partly because the wives often work, and partly because the government provides them with generous subsidies. Though they usually live in poverty, government support means that they never lack for the basic necessities of life.
That’s universal basic income in action. Though they are poor and never work, in survey after survey these ultra-orthodox Jewish men report higher levels of life-satisfaction than any other section of Israeli society. In global surveys of life satisfaction, Israel is almost always at the very top, thanks in part to the contribution of these unemployed deep players.
You don’t need to go all the way to Israel to see the world of post-work. If you have at home a teenage son who likes computer games, you can conduct your own experiment. Provide him with a minimum subsidy of Coke and pizza, and then remove all demands for work and all parental supervision. The likely outcome is that he will remain in his room for days, glued to the screen. He won’t do any homework or housework, will skip school, skip meals and even skip showers and sleep. Yet he is unlikely to suffer from boredom or a sense of purposelessness. At least not in the short term.
Hence virtual realities are likely to be key to providing meaning to the useless class of the post-work world. Maybe these virtual realities will be generated inside computers. Maybe they will be generated outside computers, in the shape of new religions and ideologies. Maybe it will be a combination of the two. The possibilities are endless, and nobody knows for sure what kind of deep plays will engage us in 2050.
In any case, the end of work will not necessarily mean the end of meaning, because meaning is generated by imagining rather than by working. Work is essential for meaning only according to some ideologies and lifestyles. Eighteenth-century English country squires, present-day ultra-orthodox Jews, and children in all cultures and eras have found a lot of interest and meaning in life even without working. People in 2050 will probably be able to play deeper games and to construct more complex virtual worlds than in any previous time in history.
Consider the conditions that allow for tacit collusion. First, the market is concentrated and hard for others to enter. The petrol stations on the Vineyard were cut off from the mainland. Second, prices are transparent in a way that renders any attempt to steal business by lowering prices self-defeating. A price cut posted outside one petrol station will soon be matched by the others. And if one station raises prices, it can always cut them again if the others do not follow. Third, the product is a small-ticket and frequent purchase, such as petrol. Markets for such items are especially prone to tacit collusion, because the potential profits from “cheating” on an unspoken deal, before others can respond, are small.
Now imagine what happens when prices are set by computer software. In principle, the launch of, say, a smartphone app that compares prices at petrol stations ought to be a boon to consumers. It saves them the bother of driving around for the best price. But such an app also makes it easy for retailers to monitor and match each others’ prices. Any one retailer would have little incentive to cut prices, since robo-sellers would respond at once to ensure that any advantage is fleeting. The rapid reaction afforded by algorithmic pricing means sellers can co-ordinate price rises more quickly. Price-bots can test the market, going over many rounds of price changes, without any one supplier being at risk of losing customers. Companies might need only seconds, and not days, to settle on a higher price, note Messrs Ezrachi and Stucke.
THE EFFECT I cut a deck of cards a couple of times, and you glimpse flashes of several different cards. I turn the cards facedown and invite you to choose one, memorize it and return it. Now I ask you to name your card. You say (for example), “The queen of hearts.” I take the deck in my mouth, bite down and groan and wiggle to suggest that your card is going down my throat, through my intestines, into my bloodstream and finally into my right foot. I lift that foot and invite you to pull off my shoe and look inside. You find the queen of hearts. You’re amazed. If you happen to pick up the deck later, you’ll find it’s missing the queen of hearts.
Teller (the smaller, quieter half of Penn & Teller) gives the best explanation of a magic trick I’ve seen.
In the Uber model (and many other gig economy platform models) the platform agencies are the ones who collect the cash first and then distribute it back (less fees) to drivers on a weekly basis.
While many might deem this an innocuous difference, it isn’t really. The platforms gain a significant cash-flow advantage over the drivers as a consequence. The arrangement also entirely flips the power and credit risk distributions on their head. It is not the drivers, under the model, who are purchasing services from third-party dispatch platforms, it is by all logical means the platforms who are purchasing services from drivers.
Facebook has publicly acknowledged that its platform has been exploited by governments seeking to manipulate public opinion in other countries – including during the presidential elections in the US and France – and pledged to clamp down on such “information operations”.
In a white paper authored by the company’s security team and published on Thursday, the company detailed well-funded and subtle techniques used by nations and other organizations to spread misleading information and falsehoods for geopolitical goals. These efforts go well beyond “fake news”, the company said, and include content seeding, targeted data collection and fake accounts that are used to amplify one particular view, sow distrust in political institutions and spread confusion.
“We have had to expand our security focus from traditional abusive behavior, such as account hacking, malware, spam and financial scams, to include more subtle and insidious forms of misuse, including attempts to manipulate civic discourse and deceive people,” said the company.
In its effort to clamp down on information operations, Facebook suspended 30,000 accounts in France before the presidential election. The company said it was a priority to remove suspect accounts with high volumes of posting activity and the biggest audiences.
The company also explained how it monitored “several situations” that fit the pattern of information operations during the US presidential election. The company detected “malicious actors” using social media to share information stolen from other sources such as email accounts “with the intent of harming the reputation of specific political targets”. This technique involved creating dedicated websites to host the stolen data and then creating social media accounts and pages to direct people to it.
Some progress at last. They now appear to recognize that they’ve been used. As @Pinboard noted, the 3 threats the doc outlines—Content Creation, False Amplification, and Targeted Data Collection—are literally the Facebook business model.
The people who work on News Feed aren’t making decisions that turn on fuzzy human ideas like ethics, judgment, intuition or seniority. They are concerned only with quantifiable outcomes about people’s actions on the site. That data, at Facebook, is the only real truth. And it is a particular kind of truth: The News Feed team’s ultimate mission is to figure out what users want — what they find “meaningful,” to use Cox and Zuckerberg’s preferred term — and to give them more of that.