in pioneering technical practices, and brand recognition from a “hot” company, we were the “anti‐pattern.” The desirable startup teams in 2011 were young, White, male engineers from prestige programs. We were definitely outsiders … and that gave us an important insight. From our vantage point, we could see the flaws in the assumed benefit of homogeneous teams, and we could access networks of knowledge and talent that were ignored by the mainstream of Silicon Valley.15 That didn't guarantee success, however – we had to turn our advantage into a sustainable company.
Our Move to the Edge started with several hypotheses:
Building a diverse company is a long series of investments resulting in a network of trust.
Diverse teams outperform nondiverse teams, especially around innovation, decision‐making, and accounting for risks.
Diversity and inclusion initiatives must be central to the core operating model of the company. DEI initiatives that are only marketing or human resource initiatives will fail.
There are skilled, motivated, and high‐integrity people from diverse backgrounds who live outside the San Francisco Bay area.
Those were our beliefs – now we had to test them with data. I started writing articles on using networks to find diverse candidates, in order to attract like‐minded employees. I found research from academics like Dr. Kathy Williams,16 demonstrating that companies with diverse boards and executive teams outperform nondiverse ones on a variety of metrics, including stock price valuation on the public markets.17 We built a network of relationships by attending dozens of events, meetups, and gatherings focused on diversity and inclusion, and learned how we could help one another. I joined a new group called Black Founders and helped start the N.O.D., a social gathering for Black men in tech, finance, and legal fields.
Each of these actions was small, but we followed a principle of improving by marginal gains. We gained insights that validated or invalidated these hypotheses. Over time, we felt more equipped to place bigger bets and make bigger commitments. In 2016, we joined the first startup cohort of Project Include, a data‐driven DEI initiative anchored at the Kapor Center.18 We validated that DEI initiatives weren't successful as just HR or marketing initiatives. We sharpened our perspective, learning that the best results come when the CEO is directly involved, either hands‐on or as an executive sponsor. Cultural change requires the CEO to set, model, and reinforce purpose and accountability.
Finally, there was the well‐documented, cross‐industry data on pay disparities. We surmised that if there is a pay disparity between a Black woman and a White man hired on the same day for the same job, they could perform equally well, earn the same percentage salary increase, yet in five years, have a substantial difference in their compensation. How long do you think that high‐performing Black woman will remain at your company after she finds out that she is being paid less for the same job? We saw that even well‐intentioned companies fell into that trap. If we wanted to attract and retain people from diverse backgrounds at Truss, we believed salary transparency was a concrete initiative to address this complex problem.
But we didn't know the answer, so we started on the journey using what became the framework for Move to the Edge, Declare It Center. I'll return to the specific methods in the Salary Transparency Case Study in Chapter 2. However, it is time to examine the ways we can make poor decisions around complex problems, despite our best intentions.
Defeating the Defaults: Making Decisions Under Uncertainty
When I wrote the July 2016 letter that opened the book, I was struggling with two significant issues: complexity and uncertainty. The issue of racial justice is complex, and I felt deeply uncertain about how to address it with my company. My “I don't know” moment was how my employees and the larger public would respond as I revealed how vulnerable I felt. There were significant risks if I got the message wrong or created unintended consequences. I decided to show up and speak up, and my message resonated – not just with my employees but also with a larger readership when I published it in Forbes. I've also witnessed leaders, including myself, making poor decisions when confronted with complexity and uncertainty, especially during the summer of 2020. I got curious about how complexity and uncertainty affects decision‐making, and it became an important building block for the Move the Edge, Declare It Center framework.
If you searched “decision‐making under uncertainty” on the internet, you would find thousands of academic citations across the fields of organizational behavior, neuroscience, behavioral economics, and computer science. In addition to the academic work, the topic shows up in the theater (from Hamlet to Hamilton) and even in your decision about which line to get in at the grocery. Suffice to say that a full literature review is way beyond the scope of my brain, your attention, and this book.
Over the course of my career, I have experimented with diverse research, tools, and practices in order to solve complex problems under uncertainty. I learned, through good and bad decisions, to cultivate reliable practices that help make better – though not perfect – decisions in uncertain times. There are four key principles that anchor these practices, and they animate the rest of this book.
Our Brains: Wired for Shortcuts
The first three principles have to do with our brains. First, we're less rational than we think. I used to have endless arguments with economists in grad school, where the dominant paradigm was based on the assumption that humans are rational actors. In contrast, my personal experience reflected theories in social psychology that people use simplifying shortcuts, social cues, and responses to their own emotional states in order to make decisions that might be considered irrational. For example, the collected works of Daniel Kahneman and Amos Tversky, detailed in Thinking, Fast and Slow, not only begat the field of behavioral economics and won Kahneman a Nobel Prize but also opened the door to more accurate ways of thinking about decision‐making that assume that humans are full of predictable biases, illusions, and preferences.
Second, our brains are wired to make systematic shortcuts in decision‐making. Kahneman distinguishes two types: System 1 and System 2 thinking. System 1 thinking is fast.19 It's generally unconscious, automatic, and effortless. The brain in System 1 mode assesses the situation, acts without conscious control, and responds. Roughly 98 percent of our thinking occurs here. System 2, on the other hand, is slow. It is conscious, deliberate, requires effort, and is rational. The brain in System 2 mode seeks anomalies, missing information and consciously makes decisions – 2 percent of our thinking occurs here. This is purposeful – imagine having to think about contracting and lowering our diaphragm in order to inhale every single breath.20
Unfortunately, System 1 (fast) can interfere with or influence System 2 (slow) with numerous cognitive biases without our conscious knowledge. For example, confirmation bias is the tendency to search, interpret, and recall information that confirms one's prior beliefs or preconceptions.21 For example, a 2021 study of soccer commentators showed that they are six times more likely to associate attributes of physical power to players of darker skin tone than to lighter skin tone.22 This is an example of how the
