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"With each interaction, Alexa is training to hear better, to interpret more precisely, to trigger actions that map to the user's commands more accurately, and to build a more complete model of their preferences, habits and desires. What is required to make this possible? Put simply: each small moment of convenience - be it answering a question, turning on a light, or playing a song - requires a vast planetary network, fueled by the extraction of non-renewable materials, labor, and data. The scale of resources required is many magnitudes greater than the energy and labor it would take a human to operate a household appliance or flick a switch. A full accounting for these costs is almost impossible, but it is increasingly important that we grasp the scale and scope if we are to understand and govern the technical infrastructures that thread through our lives. III The Salar, the world's largest flat surface, is located in southwest Bolivia at an altitude of 3,656 meters above sea level. It is a high plateau, covered by a few meters of salt crust which are exceptionally rich in lithium, containing 50% to 70% of the world's lithium reserves. 4 The Salar, alongside the neighboring Atacama regions in Chile and Argentina, are major sites for lithium extraction. This soft, silvery metal is currently used to power mobile connected devices, as a crucial material used for the production of lithium-Ion batteries. It is known as 'grey gold.' Smartphone batteries, for example, usually have less than eight grams of this material. 5 Each Tesla car needs approximately seven kilograms of lithium for its battery pack. 6 All these batteries have a limited lifespan, and once consumed they are thrown away as waste. Amazon reminds users that they cannot open up and repair their Echo, because this will void the warranty. The Amazon Echo is wall-powered, and also has a mobile battery base. This also has a limited lifespan and then must be thrown away as waste. According to the Ay

Some national chains, both retail and restaurants, are closing outlets in New York City, which are struggling more than their branches elsewhere.

You probably interact with fifty to a hundred machine learning products every day, from your social media feeds and YouTube recommendations to your email spam filter and the updates that the New York Times, CNN, or Fox News decide to push, not to mention the hidden models that place ads on the websites you visit, and that redesign your 'experience' on the fly. Not all models are created equal, however: they operate on different principles, and impact us as individuals and communities in different ways. They differ fundamentally from each other along dimensions such as alignment of incentives between stakeholders, "creep factor", and the nature of how their feedback loops ope !L

"n a recent article in the New York Times, the development economist Seema Jayachandran discusses three studies that used Randomised Controlled Trials (or RCTs) to understand the benefits of enhancing the self-worth of poor people. Despite wide differences in context, all the cases explore the viability of 'modest interventions' to 'instill hope' in marginalised communities, concluding that 'remarkable improvements' in the quest for poverty reduction are possible."

In the not-too-distant past, narratives were set more or less consensually by the New York-based media establishment (assisted by its Washington-based enablers). But as Martin Gurri and Bruno Maçães have shown, the narrative-setting days of elite media are now over, and we live in a world of fractured narratives proffered by Extremely Online factions that interpret reality-or jettison it entirely, in favor of constructing their own "unreality" (Maçães' phrase)-primarily through the lens of their own self-justifying and unfalsifiable narratives.

The starting point is to note that, for people to be held responsible for their actions, they have to know about certain features of the world. In many cases, even this minimal condition for blameworthiness isn't satisfied. For example, Chow would have struggled to predict that the rise of ridesharing apps would crater the market for taxi medallions in New York City - but so, too, did most of us. By their very nature, technological disruptions are difficult to foresee; if they were easy to predict, early investors in these technologies wouldn't get so rich. Such a low bar for blameworthiness seems too harsh to be plausible; how can any of us be blamed for failing to spot trends that almost no one was able to see, despite the significant material incentives for doing so?