Bad Religion: A review of Work Pray Code

When I worked as a professor at the University of Nebraska—Lincoln, after being there for a few months, during a conversation with the chair of the computer science department he asked me “have you found a church community yet?” I had not. I had, however, found a synagogue. The choice wasn’t difficult: there were only two. Nobody asked me a question like that after I moved to San Jose, which describes itself as the heart of Silicon Valley.

Why is Silicon Valley so non-religious is the question that sociologist Carolyn Chen seeks to answer here. As a tenured faculty member at UC Berkeley, Chen is a Bay Area resident herself. Like so many of us here, she’s a transplant: she grew up in Pennsylvania and Southern California, and first moved to the area in 2013 to do research on Asian religions in secular spaces.

Chen soon changed the focus of her research from Asian religions to the work culture of tech companies. She observes that people tend to become less religious when they move to the area, and are less engaged in their local communities. Tech work is totalizing, absorbing employees entire lives. Tech companies care for many of the physical needs of their employees in a way that companies in other sectors do not. Tech companies provide meditation/mindfulness (the companies use these terms interchangeably) to help their employees stay productive, but it is a neutered version of the meditation of its religious, Buddhist roots. Tech companies push up the cost of living, and provide private substitutes for public infrastructure, like shuttle busses.

Chen tries to weave these threads together into a narrative about how work substitutes for religion in the lives of tech workers in Silicon Valley. But the pieces just don’t fit together. Instead, they feel shoehorned in to support her thesis. And that’s a shame, because, as a Silicon Valley tech worker, many of the observations themselves ring true to my personal experience. Unlike Nebraska, Silicon Valley really is a very secular place, so much so that it was a plot point in an episode of HBO’s Silicon Valley. As someone who sends my children to religious school, I’m clearly in the minority at work. My employer provides amenities like free meals and shuttles. They even provide meditation rooms, access to guided meditations provided by the Mental Health Employee Resource Group, and subscriptions to the Headspace meditation app. The sky-high cost of living in Silicon Valley is a real problem for the area.

But Chen isn’t able to make the case that her thesis is the best explanation for this grab bag of observations. And her ultimate conclusion, that tech companies behave more and more like cults, just doesn’t match my own experiences working at a large tech company in Silicon Valley.

Most frustratingly, Chen doesn’t ever seem to ask the question, “are there other domains where some of these observations also hold?” Because so much of the description of the secular and insular nature of Silicon Valley tech workers applies to academics, the culture that Chen herself is immersed in!

Take this excerpt from Chen:

Workplaces are like big and powerful magnets that attract the energy of individuals away from weaker magnets such as families, religious congregations, neighborhoods, and civic associations—institutions that we typically associate with “life” in the “work-life” binary. The magnets don’t “rob” or “extract”—words that we use to describe labor exploitation. Instead they attract the filings, monopolizing human energy by exerting an attractive rather than extractive force. By creating workplaces that meet all of life’s needs, tech companies attract the energy and devotion people would otherwise devote to other social institutions, ones that, traditionally and historically, have been sources of life fulfillment.

Work Pray Code, p197

Compare this to an excerpt from a very different book: Robert Sommer’s sardonic 1963 book Expertland (sadly, now out of print), which describes itself as “an unrestricted inside view of the world of scientists, professors, consultants, journals, and foundations, with particular attention to the quaint customs, distinctive dilemmas, and perilous prospects”.

Experts know very few real people. Except for several childhood friends or close relatives, the expert does not know anybody who drives a truck, runs a grocery store, or is vice-president of the local Chamber of Commerce. His only connection with these people is in some kind of service relationship; they are not his friends, colleagues, or associates. The expert feel completely out of place at Lion’s or Fish and Game meeting. If he is compelled to attend such gatherings, he immediately gravitates to any other citizen of Expertland who is present… He has no roots, no firm allegiances, and nothing to gain or lose in local elections… Because he doesn’t vote in local elections, join service clubs, or own the house he lives in, outsiders often feel that the expert is not a good citizen.

Expertland pp 2-3

Chen acknowledges that work is taking over the lives of all high-skilled professionals, not just tech workers. But I found work-life balance to be much worse in academia than at a Silicon Valley tech company! To borrow a phrase from the New Testament, And why beholdest thou the mote that is in thy brother’s eye, but considerest not the beam that is in thine own eye?

Software engineering in-the-large: the coordination challenge

Back when I was an engineering student, I wanted to know “How do the big companies develop software? How does it happen in the real world?”

Now that I work at a company that has to do large-scale software development, I understand better why it’s not something you can really teach effectively in a university setting. It’s not that companies doing large-scale software development are somehow better at writing software than companies that work on smaller-scale software projects. It’s that large-scale projects face challenges that small-scale projects don’t.

The biggest challenge at large-scale is coordination. My employer provides a single service, which means that, in theory, any project that anyone is working on inside of the company could potentially impact what anybody else is working on. In my specific case, I work on delivery tools, so we might be called upon to support some new delivery workflow.

You can take a top-down command-and-control style approach to the problem, by having the people at the top attempting to filter all of the information to just what they need, and them coordinating everyone hierarchically. However, this structure isn’t effective in dynamic environments: as the facts on the ground change, it takes too long for information to work its way up the hierarchy, adapt, and then change the orders downwards.

You can take a bottoms-up approach to the problem where you have a collection of teams that work autonomously. But the challenge there is getting them aligned. In theory, you hire people with good judgment, and provide them with the right context. But the problem is that there’s too much context! You can’t just firehose all of the available information to everyone, that doesn’t scale: everyone will spend all of their time reading docs. How do you get the information into the heads of the people that need it? becomes the grand challenge in this context.

It’s hard to convey the nature of this problem in a university classroom, if you’ve never worked in a setting like this before. The flurry of memos, planning documents, the misunderstandings, the sync meetings, the work towards alignment, the “One X” initiatives, these are all things that I had to experience viscerally, on a first-hand basis, to really get a sense of the nature of the problem.

The ambiguity of real work

All ambiguity is resolved by actions of practitioners at the sharp end of the system.

Dr. Richard I. Cook, How Complex Systems Fail

There’s a wonderful book by the late urban planning professor Donald Schön called The Reflective Practitioner: How Professionals Think in Action. In the first chapter, he discusses the “rigor or relevance” dilemma that faces educators in professional degree programs. In the case of a university program aimed at preparing students for a career in software development, this is the “should we teach topological sort or React?” question.

Schön argues that the dilemma itself is a fundamental misunderstanding of the nature of professional work. What it misses is the ambiguity and uncertainty inherent in the work of professional life. The “rigor vs relevance” debate is an argument over the best way to get from the problem to the solution: do you teach the students first principles, or do you teach them how to use the current set of tools? Schön observes that a more significant challenge for professionals is defining the problems to solve in the first place, since an ill-defined problem admits no technical solution at all.

In the varied topography of professional practice, there is a high, hard ground where practitioners can make effective use of research-based theory and technique, and there is a swampy lowland where situations are confusing “messes” incapable of technical solution. The difficulty is that the problems of the high ground, however great their technical interest, are often relatively unimportant to clients or to the larger society, while in the swamp are the problems of greatest human concern.

His use of the term “messes” evokes Russell Ackoff’s use of the term in his paper The Future of Operational Research is Past:

Managers are not confronted with problems that are independent of each other, but with dynamic situations that consist of complex systems of changing problems that interact with each other. I call such situations messes. Problems are abstractions extracted from messes by analysis; they are to messes as atoms are to tables and chairs. We experience messes, tables, and chairs; not problems and atoms

To take another example from the software domain. Imagine that you’re doing quarterly planning, and there’s a collection of reliability work that you’d like to do, and you’re trying to figure out how to prioritize it. You could apply a rigorous approach, where you quantify some values in order to do the prioritization work, and so you try to estimate information like:

  • the probability of hitting a problem if the work isn’t done
  • the cost to the organization if the problem is encountered
  • the amount of effort involved in doing the reliability work

But you’re soon going to discover the enormous uncertainty involved in trying to put a number on any of those things. And, in fact, doing any reliability work can actually introduce new failure modes.

Over and over, I’ve seen the theme of ambiguity and uncertainty appear in ethnographic research that looks at professional work in action. In Designing Engineers, the aerospace engineering professor Louis Bucciarelli did an ethnographic study of engineers in a design firm, and discovered that the engineers all had partial understanding of the problem and solution space, and that their understandings also overlapped only partially. As a consequence, a lot of the engineering work that was done actually involved engineers resolving their incomplete understanding through various forms of communication, often informal. Remarkably, the engineers were not themselves aware of this process of negotiating understandings of the problems and solutions.

The famous Common Ground and Coordination in Joint Activity paper by Gary Klein, Paul Feltovich, and David Woods, makes explicit the role that ambiguity plays in human coordination and communication.

You’ll sometimes hear researchers who study work talk about the process of sensemaking. For example, there’s a paper by Sana Albolino, Richard Cook, and Micahel O’Connor called Sensemaking, safety, and cooperative work in the intensive care unit that describes this type of work in an intensive care unit. I think of sensemaking as an activity that professionals perform to try to resolve ambiguity and uncertainty.

(Ambiguity isn’t always bad. In the book On Line and On Paper, the sociologist Kathryn Henderson describes how engineers use engineering drawings as boundary objects. These are artifacts are that are understood differently by the different stakeholders: two engineers looking at the same drawing will have different mental models of the artifact based on their own domain expertise(!). However, there is also overlap in their mental models, and it is this combination of overlap and the fact that individuals can use the same artifact for different purposes that makes it useful. Here the ambiguity has actual value! In fact, her research shows that computer models, which eliminate the ambiguity, were less useful for this sort of work).

As practitioners, we have no choice: we always have to deal with ambiguity. As noted by Richard Cook in the quote that opens this blog post, we are the ones, at the sharp end, that are forced to resolve it.