What We're Reading: October 2020

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Ad Tech Could Be the Next Internet Bubble Hwang’s new book, Subprime Attention Crisis, lays out the case that the new ad business is built on a fiction. Microtargeting is far less accurate, and far less persuasive, than it’s made out to be, he says, and yet it remains the foundation of the modern internet: the source of wealth for some of the world’s biggest, most important companies, and the mechanism by which almost every “free” website or app makes money. If that shaky foundation ever were to crumble, there’s no telling how much of the wider economy would go down with it. Stop Explaining Black Box Machine Learning Model for High Stakes Decisions and Use Interpretable Models Instead Rather than trying to create models that are inherently interpretable, there has been a recent explosion of work on ‘explainable ML’, where a second (post hoc) model is created to explain the first black box model. This is problematic. Explanations are often not reliable, and can be misleading, as we discuss below. If we instead use models that are inherently interpretable, they provide their own explanations, which are faithful to what the model actually computes. How Data Literally Saves Lives (Audio) On this episode, Craig and Cindi discuss the life- and cost-saving benefits of leveraging data to make the best choices available, how moving from financial services into healthcare has given Craig a more holistic view of what's possible with data, why an individual should never stop learning and broadening their skills at any age, and establishing beneficial relationships with vendors that make you partners in each others' success. The End of Cloud Computing (Audio) Everything that’s popular in technology always gets replaced by something else. The beauty of the business is that things actually go away, and part of our job as investors is to look not at where the puck is today, but where the puck is going to be in the future, typically five or 10 years out. It takes a long time for companies to build up and you want to hit that puck where it intends to be. It’s actually a very simple exercise that I would encourage you all to do if you ever want to predict the future. Subtract something that’s important today and fill it with something else, and you’ll start to think out of the box, as opposed to sequentially. I believe that if you subtract something, you can actually fill it with other dimensions that you might not think about otherwise. I call it my Forrest Gump Rule of Investing. It’s so simple: you just subtract something and replace it with something else. About six months ago, I started to think, “Well, what happens when cloud computing goes away? It’s so popular now, how could it go away?” Yet, I think it’s actually happening right under our noses, and let me explain why I think that’s occurring. Emerging Architectures for Modern Data Infrastructure And yet, despite all of this energy and momentum, we’ve found that there is still a tremendous amount of confusion around what technologies are on the leading end of this trend and how they are used in practice. In the last two years, we talked to hundreds of founders, corporate data leaders, and other experts – including interviewing 20+ practitioners on their current data stacks – in an attempt to codify emerging best practices and draw up a common vocabulary around data infrastructure. This post will begin to share the results of that work and showcase technologists pushing the industry forward. Good Intentions, Bad Inventions: The Four Myths of Healthy Tech The heart of this misunderstanding is biological determinism, which suggests that our “Paleolithic” brains cannot resist “God-like” technology, placing too much power in the hands of tech companies to both create and destroy our capacity for attention. But attention is not a fixed biological entity, it is a value-laden social category; people stop using social media of their own volition all the time. Current approaches to improving digital well-being also promote tech solutionism, or the presumption that technology can fix social, cultural, and structural problems. At their core, these approaches lack empirical evidence to support them. We want to replace these myths with new evidence-based narratives that shift the conversation toward agency and equity. Why Our Leaders Fail Us and Then Save Us: The Preventable Problem Paradox During the earlier stages of your career, it makes sense for you to go in and solve whatever problem your organization is facing. And while that is an eminently reasonable starting point, it’s a terrible ending point. Because, after multiple years of doing this, many leaders convince themselves that problem solving is their job. They come into the office, look for the “problem of the day”, and then get to work. And having successfully solved today’s problem, they go back home satisfied about “a job well done”, within an environment that rewards the perception of speed rather than true velocity and progress. By contrast, if you want to be a true and unselfish leader of people, to do what’s right for your organization, its customers and stakeholders, you need to be the captain who proactively avoids the iceberg, and not the one who unwittingly hits it and then heroically attempts to rescue everyone.

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