Refined Youtube Quotes

The data that drives algorithms isn't just a few numbers now. It's monstrous tables of millions of numbers, thousands upon thousands of rows and columns of numbers....Matrices are created and refined by computers endlessly churning through Big Data's records on everyone, and everything they've done. No human can read those matrices, even with computers helping you interpret them they are simply too large and complex to fully comprehend. But the computers can use them, applying the appropriate matrix to show us the appropriate video that will eventually lead us to make an appropriate purchase. We are not living in "The Matrix," but there's still a matrix controlling us. What does this have to do with the rabbit hole of conspiracy theories? It has everything to do with it. These algorithms are quickly becoming the primary route down the rabbit hole. To a large extent this has already happened, but it's going to get far, far worse. Tufekci described what happened when she tried watching different types of content on YouTube. She started watching videos of Donald Trump rallies. 'I wanted to write about one of [Donald Trump]'s rallies, so I watched it a few times on YouTube. YouTube started recommending to me, and autoplaying to me, white supremacist videos, in increasing order of extremism. If I watched one, it served up one even more extreme. If you watch Hilary Clinton or Bernie Sanders content, YouTube recommends and autoplays [left-wing] conspiracy videos, and it goes downhill from there." Downhill, into the rabbit hole....Without human intervention the algorithm has evolved to a perfect a method of gently stepping up the intensity of the conspiracy videos that it shows you so that you don't get turned off, and so you continue to watch. They get more intense because the algorithm has found (not in any human sense, but found nonetheless) that the deeper it can guide people down the rabbit hole, the more revenue it can make.

David versus Goliath Asymmetry lies at the heart of network-based competition. The larger or smaller network will be at different stages of the Cold Start framework and, as such, will gravitate toward a different set of levers. The giant is often fighting gravitational pull as its network grows and saturates the market. To combat these negative forces, it must add new use cases, introduce the product to new audiences, all while making sure it’s generating a profit. The upstart, on the other hand, is trying to solve the Cold Start Problem, and often starts with a niche. A new startup has the luxury of placing less emphasis on profitability and might instead focus on top-line growth, subsidizing the market to grow its network. When they encounter each other in the market, it becomes natural that their competitive moves reflect their different goals and resources. Startups have fewer resources—capital, employees, distribution—but have important advantages in the context of building new networks: speed and a lack of sacred cows. A new startup looking to compete against Zoom might try a more specific use case, like events, and if that doesn’t work, they can quickly pivot and try something else, like corporate education classes. Startups like YouTube, Twitch, Twitter, and many other products have similar stories, and went through an incubation phase as the product was refined and an initial network was built. Trying and failing many times is part of the startup journey—it only takes the discovery of one atomic network to get into the market. With that, a startup is often able to start the next leg of the journey, often with more investment and resources to support them. Contrast that to a larger company, which has obvious advantages in resources, manpower, and existing product lines. But there are real disadvantages, too: it’s much harder to solve the Cold Start Problem with a slower pace of execution, risk aversion, and a “strategy tax” that requires new products to align to the existing business. Something seems to happen when companies grow to tens of thousands of employees—they inevitably create rigorous processes for everything, including planning cycles, performance reviews, and so on. This helps teams focus, but it also creates a harder environment for entrepreneurial risk-taking. I saw this firsthand at Uber, whose entrepreneurial culture shifted in its later years toward profitability and coordinating the efforts of tens of thousands. This made it much harder to start new initiatives—for better and worse. When David and Goliath meet in the market—and often it’s one Goliath and many investor-funded Davids at once—the resulting moves and countermoves are fascinating. Now that I have laid down some of the theoretical foundation for how competition fits into Cold Start Theory, let me describe and unpack some of the most powerful moves in the network-versus-network playbook.

One of the most important – and sudden – changes in politics for several decades has been the move from a world of information scarcity to one of overload. Available information is now far beyond the ability of even the most ordered brain to categorise into any organising principle, sense or hierarchy. We live in an era of fragmentation, with overwhelming information options. The basics of what this is doing to politics is now fairly well-trodden stuff: the splintering of established mainstream news and a surge of misinformation allows people to personalise their sources in ways that play to their pre-existing biases.5 Faced with infinite connection, we find the like-minded people and ideas, and huddle together. Brand new phrases have entered the lexicon to describe all this: filter bubbles, echo chambers and fake news. It’s no coincidence that ‘post-truth’ was the word of the year in 2016. At times ‘post-truth’ has become a convenient way to explain complicated events with a simple single phrase. In some circles it has become a slightly patronising new orthodoxy to say that stupid proles have been duped by misinformation on the internet into voting for things like Brexit or Trump. In fact, well-educated people are in my experience even more subject to these irrationalities because they usually have an unduly high regard for their own powers of reason and decision-making.* What’s happening to political identity as a result of the internet is far more profound than this vote or that one. It transcends political parties and is more significant than echo chambers or fake news. Digital communication is changing the very nature of how we engage with political ideas and how we understand ourselves as political actors. Just as Netflix and YouTube replaced traditional mass-audience television with an increasingly personalised choice, so total connection and information overload offers up an infinite array of possible political options. The result is a fragmentation of singular, stable identities – like membership of a political party – and its replacement by ever-smaller units of like-minded people. Online, anyone can find any type of community they wish (or invent their own), and with it, thousands of like-minded people with whom they can mobilise. Anyone who is upset can now automatically, sometimes algorithmically, find other people that are similarly upset. Sociologists call this ‘homophily’, political theorists call it ‘identity politics’ and common wisdom says ‘birds of a feather flock together’. I’m calling it re-tribalisation. There is a very natural and well-documented tendency for humans to flock together – but the key thing is that the more possible connections, the greater the opportunities to cluster with ever more refined and precise groups. Recent political tribes include Corbyn-linked Momentum, Black Lives Matter, the alt-right, the EDL, Antifa, radical veganism and #feelthebern. I am not suggesting these groups are morally equivalent, that they don’t have a point or that they are incapable of thoughtful debate – simply that they are tribal.

Fortunately, Google found product/ market fit by refining Overture’s advertising auction model. Google’s AdWords product was so much better at monetizing search through its self-service, relevance-driven, auction system that by the time those competitors managed to play catch-up, Google had amassed the financial resources that allowed it to invest whatever was necessary to maintain product superiority. Google doesn’t always get product/ market fit right (and if it had run out of money before hitting upon AdWords, the search business might have died before ever achieving that fit). This is a reflection of its very intentional product management philosophy, which relies on bottom-up innovation and a high tolerance for failure. When it works, as in Gmail, which was a bottom-up project launched by Paul Buchheit, it can produce killer products. But when it fails, it results in killed products, as demonstrated by projects like Buzz, Wave, and Glass. To overcome this risk of failure, Google relies on both its financial strength (which comes from its high gross margins, among other things) and a willingness to decisively cut its losses. For example, when Google bought YouTube (which had clearly achieved product/ market fit), it was willing to abandon its own Google Video service, even though it had invested heavily in that product. Other massively successful companies take a very different approach. In contrast to Google, where new ideas can come from anywhere in the company and there are always many parallel projects going on at the same time, Apple takes a top-down approach that puts more wood behind fewer arrows. Apple keeps its product lines small and tends to work on a single major product at a time. One philosophy isn’t necessarily better than the other; the important thing is simply to find that product/ market fit quickly, before your competition does.