Chapter 8 explains why there has been so much enthusiasm for integrating AI into multiple dimensions of the hiring process, from resume screening to interview bots, despite these endeavors being marred by fundamental flaws, including, in some cases, integrating bias, unreliable pseudoscientific methods, and dehumanizing interactions. In addition to analyzing the incentives that have motivated companies to use flawed, innovative tools, we provide a road map for how to develop and use responsible AI upgrades in the hiring process.

In Chapter 9, we argue that cybersecurity professionals embody the ideal of the careful, systematic upgrading that Move Slow and Upgrade has been advocating for. Unlike the flashy innovations that we’ve criticized in earlier chapters, cybersecurity professionals focus on making small, proven improvements through such practices as privacy by design and zero-trust architecture. Recognizing that no single change can solve complex problems, they layer multiple safeguards while acknowledging that human behavior – from falling for scams to knowingly taking risks – is often the main vulnerability. By discussing how cybersecurity teams do quality work, we aim to offer important lessons about how other industries might benefit from adopting an upgrading mindset.

Chapter 1, “Introduction,” welcomes you to day 1 of your new life as an upgrader. In this introduction we not only provide an overview of the book’s thesis for better problem solving and more effective technological advancement, we start to draw out the contours of what it means to be an Upgrader. This loosely nit cohort of changemakers has rejected both the inadequacy of the status quo and the destructiveness of innovation. Here, you’ll begin to learn what it means to solve some of the most urgent problems facing our society today through the lens of upgrades. We’ll also begin to provide examples of ways that innovations have fallen flat, or blown up completely, in the past. Crucially, upgraders aren’t just backseat drivers in the journey of social change, they are forward looking experts who are often able to see the dangers of pending innovations before they occur. In your life as an upgrader, you’ll not only be able to avoid these missteps yourself, but you’ll be able to see the innovation traps so many others are poised to fall into.

Chapter 3 dives deep into the beating heart of cryptocurrency, the paradoxical technology that has made early adherents billions, while adding nothing of real value to society. By any measure, crypto has failed at its stated goal: creating a better financial system. Looking to Bitcoin, we show how the core innovation – a distributed encrypted database – makes a terrible payment system, with slow, expensive, uncorrectable transactions. But crypto enthusiasts ignore more than a decade of failure, doubling down on grandiose claims about solving everything from financial inclusion to corporate governance while ignoring the far easier, low-tech solutions to these very real needs. We include an interview with an early supporter of the massive crypto currency Ethereum, who came to see how crypto became “just a tool for the wealthy to become wealthier” rather than fulfilling its promise of financial inclusion for the world’s 1.7 billion unbanked people.

Chapter 4 critically examines the fact that sometimes innovations not only fail to solve crucial problems, but are the problem itself. Specifically, it explains why Ring doorbell exemplifies the threat of home surveillance innovation. The billion-dollar Amazon subsidiary sold millions of Americans on the promise of security via surveillance without any credible evidence that its system works. But rather than encouraging people to adopt proven security upgrades, such as better locks and secure package drops, Ring wins customers by making its digital innovation seem essential amid a climate of rising fear. By fighting against boring yet effective alternatives, Ring’s anxiety-inducing features have further normalized intensive networked surveillance and helped turn innocuous neighborly interactions into potential threats.

In Chapter 10, we conclude with an overview of the broader themes seen throughout this work, showcasing the tell-tale signs of innovation failure. These patterns go to the core of our work, lessons learned from past innovations that can help us to avoid repeating similar mistakes in the future. No one, not even the cagiest upgrader, is going to be able to predict every new technology that will succeed or flop. But with this mindset, you can avoid some of the more obvious traps that investors, politicians, and the public continue to fall for, while valuing the evidence-based alternatives we so often neglect.

In Chapter 7, “Upgrades in the Age of Generative AI,” we consider the hype around generative AI tools, like ChatGPT, and explain how the razzle-dazzle has captured the public’s imagination, even as the technology hasn’t come close to being artificial general intelligence—the goal companies like OpenAI aspire for. While tech giants race to develop generative AI products, we emphasize that they currently are sophisticated pattern-matching systems that simulate intelligence without truly understanding it. Analyzing both negative (political campaigns) and positive (the possibility of helping doctors communicate more empathetically over patient portals) examples, we offer recommendations for spotting uses of generative AI to avoid and how technological upgrades can be carefully and ethically integrated into communication systems to improve human welfare.

St Andrews Agreement; DUP; Ian Paisley; Arlene Foster;Martin McGuinness; Renewable Heat Initiative (RHI);dissident republicans; flags protest; ColumEastwood; Mike Nesbitt