Category: Book Review

It is so good to see Michael Lewis back to writing books that don’t have an agenda. I have read every book Lewis ever wrote and, while I am generally politically aligned, wasn’t excited when he turned to writing about climate change, the disfunction of the Trump administration, and Covid. Going Infinite is a triumphant return to his typical style of telling the story of moment in time that’s difficult for most of us to understand through the lens of an individual who’s emblematic of that time.

In this case the story is of Sam Bankman-Fried (SBF). The story is as entertaining as it is insightful; both giving us amusing anecdotes about the antics of the world’s youngest self-made billionaire and helping shed light on the moment that made his rise and fall possible. It’s a breezy day-long read and I recommend it to everyone.

The thing that struck me most about it is how naive it’s possible to be when you’ve been right so often early in your life. SBF made some brilliant (and notably lucky) moves early in his career and then rose to a point where he must of thought he could never be wrong. Imagine sitting down at a blackjack table, betting $100, winning 20 times in a row, and betting everything you had each time. Now you have over $100M and more than likely a belief that you could never be wrong. You’re no more likely to make good decisions than you were 20 minutes ago, but you’re climbing life’s scoreboard and it appears you’re a genius.

Anyway, that’s what I kept thinking about as I read the book.

I’ve always found Gene Kim’s work interesting and his new book is no exception.  When I read The Pheonix Project I was a relatively new Dev Manager and getting my first taste of IT Operations.  It allowed me to see the problem I was given in an entirely new light.  Kim solved that problem and moved on to bigger challenges (along with collaborator Stephen Spears), culminating in this book which tackles a much more sophisticated problem; how to make an entire Organization more effective.  While the evolution of Kim’s thinking has advanced a bit faster than my career, this book is much more relevant to me (while running/leading an organization of 100+) than another DevOps book would have been.  It was fun to take inspiration at this new stage of my career from the same guy I took inspiration from in the last phase.

I enjoyed the way this book was laid out.  Each primary topic had a section for theory, a section for examples, and a section for a deep case study.  It made it easy to navigate the book the way I wanted to.  The book was also centered around two core ways of looking at an organization that can be stated and understood simply.  The concepts aren’t simple of course, and each requires another hundred pages to expand.  One aspect of these expansions that I found extremely captivating was the authors’ comparisons with known management systems and an exploration of why people think they will work and then why they often don’t.  This lead to one of my favorite sections of the book, where they break down why using escalation and expediting is not as effective as having iterative, modularized, linear work assignments.

The first way of looking at the organization that Kim explorers is thinking of the organization in Layers:

1. Layer One is the technical object and the people doing the “line” work.
2. Layer Two is the tooling and the people who maintain the tooling that allows the “line” work to proceed.
3. Layer Three is the social circuitry that links Layer One, Layer Two, your customers, your management, other parts of your organization, etc…

I’ve started incorporating this concept into my retrospectives.  When we don’t get an outcome we want, I’m having the team think about which level we think can be adjusted.

The second way the authors break down the organization is into three categories of how work can be improved; Simplification (breaking down big problems and giving teams/people something they can solve), Slowification (finding the right time/place to solve problems (for example moving  an issue into a lab or testing a change with a small population), and amplification (making sure problems are well known and discussed).  Obviously there are dozens of techniques for each of these and the book goes through many of them, but I love the simplicity of the overall solution.

I highly recommend this book for anyone who leads (even if they don’t manage) a team of more than ~15 that is managed on outcomes.  It’s less of an answer key with specific tips, and more of a guide to understanding why some things work and others don’t.

If you do pick it up, make sure to read Appendix B on leadership!

This book didn’t change my life like how discovering an agile methodology for software development or reading Mindset did, but it is a great book if you need a subtle reminder on how to build a good company or a good team. Essentially, Sinek applies the concept of a “growth mindset” to companies in a way that’s easy to understand and very motivating.

Sinek asks readers to recognize that business is not a “finite game” like a game of football where there is a prescribed end and a declared winner. It is an “infinite game” where you can’t “win”, you can just hope to leave the company you worked for better than it was when you started.

Sinek creates five steps for building a company or team ready for the infinite game. I’m going to list them all here, but I really think that 1 and 4 are the core. Of course, Sinek had to write a whole book, and I only have to write this blog post:

1. Advance a Just Cause – For you and your company to function in an infinite game, you must have a just cause worthy of an infinite pursuit, otherwise, you will revert to measuring yourself against quarterly outputs. Sinek makes a good observation that America succeeds because the founding fathers sought to establish a country that would seek to protect “inalienable rights” rather than one that sought to lower the taxes that Britain had levied on the colonies, and that is why we are still in the infinite game.
2. Build Trusting Teams – Sinek argues that you need to build trust amongst your teams. Individuals need to believe that they are being looked after beyond finite results.
3. Worthy Rival – The argument is that we need to understand what our competition is doing. Not to identify weaknesses and beat them in finite ways, but to understand our strengths and weaknesses and refine our just cause.
4. Existential Flexibility – We need to understand that the way that we apply our just cause today may not be the best or only way to apply it tomorrow. When we see opportunities to improve on how we perform our just cause, we must be ready to move to it. Sinek provides the example of Walt Disney realizing that he could also use Theme Parks in addition to movies to create a joyous departure from reality.
5. Courage to Lead – The argument here is that you  must have the courage to make tough decisions consistent with your just cause even if those trying to apply finite game measurements are resistant.

Overall, I think the book is worth a quick read especially if you have recently taken over a new team or are considering a pivot.

The word “optimist” in the subtitle is very well earned here. Friedman’s book explains why/how someone can hope that the same technologies and macro-trends that are leading to hyper-nationalism, extreme divisiveness, and massive pollution might actually be harnessed for good. He tells that story with his typical great story telling and insightful anecdotes.

As usual I found a bunch of tidbits interesting and had a few realizations while reading the book that I’ll just list here:

• One of the points that’s made over and over again that resonated with me is how fast technology is moving and how slow our institutions (particularly laws) are adapting to it. Especially since those technologies are contributing to the gridlock that’s keeping us from effectively regulating them (let alone adopting them for public purposes).
• I found interesting that Friedman suggests his hometown in Wisconsin as a place well suited to adapt to the new world, mostly because of the strong community and the small size enabling a single suburb to really make it theirs. I understood the argument, but couldn’t help thinking that I’m glad I live in New York City… the combination of wealth and bright people I think will help push our leaders to adopt new advances more quickly in spite of our large size and less than community feel.
• We’ve all heard Moore’s law, but I thought this explanation of just how fast computing power has grown to be compelling: “If a 1971 Volkswagen Beetle improved at the same rate as microchips did… In 2015 that Beetle could go 300,000 mph, get 2,000,000 mpg, and would cost $0.04.”
• He makes a compelling argument for us entering the “cognitive” era of computing. While I still think this will be slower to take hold than people expect, it is fascinating that we have enough compute power now to just throw a bunch of data at the cloud and let the computers sort out if it means anything and what it means (as opposed to old school computing where you gave it an algorithm to make sense of the data with).
• Since the book is optimitistic it makes the point that while artificial intelligence will do a lot of what we do for jobs today, that is likely to actually lead to more jobs. He brings up the example of automation in the textile industry. It actually caused MORE people to be employed making clothes because the price dropped so far that individuals owned more (far more) than one set of clothes.

Jeff Bezos built a company that’s worth about the same on the stock market ($1.67T) as the entire circulating supply of Pakistani Ruppees ($1.68T). That means you could BARELY trade every Pakistani Rupee in existence for all the stock in Amazon. I guess I’ll read some of his wisdom and see what he has to say.

I found the book pretty interesting. It’s not exactly a page turner and there are parts that are a little repetitive (the guy has clearly practiced telling his life story), but you can leaf through those parts quickly and be back in good material.

It starts with a republishing of all of his shareholder letters as CEO of Amazon (the last one you’ll have to find on the internet until they publish a new edition). They’re a fantastic read. It’s almost a retelling of the whole internet. From an optimistic Bezos reporting that Amazon had “established long-term relationships with many strategic partners, including America Online, Yahoo!, Excite, Netscape, GeoCities, AltaVista, @Home, and Prodigy.” in 1997. To a one word title of “Ouch” in 2001 after he saw some 90% of the value of Amazon wiped out. To explanations of how Amazon refuses to take a short-term perspective, even in a world where quarterly earnings are analyzed so heavily. To discussions of how, being a big company allows it to make big bets like the Fire Phone (dud) and AWS (clear winner) that it might not always win. To the mentality of “customer first” that Bezos uses to push employees because customer loyalty only lasts until your competition shows your customer something they never knew they wanted.

The remainder is a collection of Bezos’ speeches. This got repetitive with stories of how he admired his grandfather for being resourceful on a remote farm and sticking up for his mom when she was a pregnant high school. You should feel free to skip around, but don’t miss the speech on Blue Origin (his space company). The company is endeavoring to build the “infrastructure” for future generations to reinvent industries in space. He talks about the need to have big Lunar landers to send enough equipment to the moon so that the natural resources on the moon can start to be used for construction of more items in space (this makes sense since it’s much more efficient than trying to launch HEAVY things out of earth’s gravitational pull). It’s a fascinating way to choose to spend a personal fortune that is roughly 2,000,000 times larger than the average American’s.

Because of where I am professionally (with Kyndryl about to break off of IBM and provide me the opportunity to really grow the size, number of services, and the value we can provide the customers of my Cloud Advisory Service practice), I found the 2016 shareholder letter the most impactful. He talks about techniques for always staying in Day 1 because “Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it’s always day 1.” Resisting the urge to just get what you can from customers and instead keep innovating for customers, is the energy we’re going to need at Kyndryl.

I rarely bother to post books I read for pleasure on the blog, but I thought that a lot of the people that I know professionally may be interested in this one. I started reading it in November when we were just starting to be able to think about what a post-Trump America might look like. After four years of hating the news so much I rarely watched it, I was trying to remember what it felt like when I had agreed to let an Obama staffer live in my extra bedroom when the 2008 campaign was tight in Pennsylvania. This book did not restore that optimism.

The book is simultaneously interesting and boring because President Obama really dives in to telling about what his goals were, what challenges he met, and how he and the team dealt with them. The book would be more inspirational, and more of a page turner, if he left out those details. It would also feel like a commercial for his presidency instead of a memoir. If you’re in the mood to hear the details of the political process and how Obama and team tried to manipulate it, you’ll enjoy this book. If you’re looking for a compelling narrative that’s fun to read, there’s an abridged version.

One last recommendation. Definitely get the audiobook, he reads it himself and I think being able to hear his emotions and emphasis while he reads is worth the slower pace.

If you’ve read much of Gene Kim’s work you’ve heard of the transformation that HP Printers made to their firmware development process. Kim cites it so frequently because it is an example of one of the oldest software development groups around making software that’s some of the hardest software to test (printer emulators anyone?) transforming from a waterfall development process to an agile one. Gruver and Mouser are the executives behind that transformation and this book is full of great lessons that everyone who’s had an IT department for longer than Amazon’s been a bookstore can learn from.

Let me give you three of my favorite things about this book, and I encourage you to read it and come up with your own:

1. In the very first chapter they talk about how many companies make the mistake of trying to start an enterprise agile transformation by just having one team transform. It doesn’t work because, at least in most companies, a meaningful development team can’t go to production on its own. You have that one transformed team running scrums and using whiteboards full of sticky notes to deliver code to an integration test environment where it will sit for 6 months. The key is to find ways to iteratively improve the whole organization (or at least ALL the parts that have to develop together) towards agile.
2. Having worked in technology all my life, I had never really considered why software development should be managed so much differently then your other cost centers. I really liked their point that what makes software development so hard to plan for is that it’s hard to see and often changes from looking 80% done to looking destined for the trash heap overnight. That ever-changing aspect is also what’s great about software. It’s hard to predict where you’ll be in 6 months in a software dev project, but it’s much much easier to change your mind today about where you want to be tomorrow. Doing agile software development makes so much sense because it’s just using the inherent strengths of software rather than trying to mold it in to a waterfall techniques.
3. Finally, this book isn’t afraid to make the tough point that executive teams must understand technical concepts like continuous delivery to be successful. They can’t just be great people managers or inspirational leaders. They have to know when to make exceptions to the “no branches” rule and when to require automated tests. These are things that reasonable development managers can disagree on, but a knowledgeable executive must set a standard for.

I confess, Value Stream Mapping as a technique is something I already do with clients and I was reading this book mostly to make sure that I could say that I did (and now it’s on the Internet!). I will say that after being taught value stream mapping primarily as it relates to the DevOps movement and CI/CD pipelines, I was surprised to see how general the approach is. It seems like it would be really valuable for enterprises to do in areas outside of manufacturing (it’s based on Lean and started as one of the Toyota techniques) and IT. Maybe someday I’ll focus more on my MBA and less on my Comp Sci background and actually use it that way.

In this book “review”, I thought I’d spend some time in this blog post talking about the nuggets throughout the book that are a bit different when our team looks at creating a value stream to find opportunities for IT Automation. Feel free to leverage this in your business, or give us a call and we can help you get started!

First of all, we are usually doing a couple Value Stream Maps in our spare time in the first week of an Ansible Tower Foundation engagement. While also installing Ansible, connecting it to our client’s Git, Active Directory, Credential Management, etc… Consequently they are usually a lot less involved. This different results in a few big changes:

1. The scope has to be different. The book talks about focusing on customer value streams being from “ring to ring” (from the phone call to order to the ring of the cash register). We still like to focus on this, but we often take IT Operations customers ring (a ServiceNow request to get a new VM) to service fulfillment (a notification to the user that they can log in).
2. The session itself is shorter. I like to try to keep it to either one hour or two depending on the complexity. This allows for nice crisp time keeping. 10 minutes to intro the concept, 20 minutes for current state, 10 minutes to brainstorm opportunities for improvement, 20 minutes for future state.
3. I think the charter becomes MORE important when you want to do a short session. It is important to have your sponsor identify the limitations of the exercise and the precise scope. This will limit wasteful conversation during the workshop.

Since our focus is typically on finding out how best to improve an IT Operations value stream via automation, we are looking for slightly different things in the value stream map:

1. We do not limit ourselves in looking only for automation opportunities. We are looking for all manner of improvements… avoiding batching, identifying opportunities to run in parallel, etc… It’s not our goal to fix those as part of the project (though IBM has some excellent process consultants). It’s merely our goal to avoid the investment of automating a process that shouldn’t exist at all or is not a bottleneck.
2. The Gemba walk is usually not a physical one. I like to have actual operators walk an actual ticket on screen where possible. In longer running processes it may be constructive to view several tickets in different phases.
3. We use all of the resulting automation “kaizens” to start our clients’ automation backlog. Because of the value stream mapping we can be clear about just how much value can be expected out of each automation.

Overall, I definitely recommend reading the book. Even if you’re only planning to use the technique for CI/CD or IT Operations value streams. It will give you a lot of perspective on the history of the technique and its uses in the wider enterprise, in addition to arming you with the vocabulary you need to leverage it for your use case.

If you’re going to be working in technology for more than 10 years, you should read this book. It’s written by a set of economists associated with the Creative Destruction Lab at the University of Toronto. As you might imagine from an incubator with that name, they have made many investments in Machine Learning companies. The observations in the book are less about how machine learning works (though there is obviously some introduction required) and more about the implications for the wider economy.

As the title suggests, they believe that the current advancements in artificial “intelligence” are not related to general intelligence, but are making advancements in computers’ ability to distill lots of data about what humans have done to make a prediction. For example, Google’s image recognition doesn’t actually “recognize” images, it is only able to say, “based on other sets of pixels that people have identified as tables, I think that there is a 98% chance that is a photo of a table.” This key assumption then allows them to predict how further advancements in these prediction machines will impact the kinds of companies, jobs, social programs, etc… that will go up (or down) in economic value.

As they put it, “Economics offers clear insights regarding the business implications of cheaper prediction. Prediction machines will be used for traditional prediction tasks (inventory and demand forecasting) and new problems (like navigation and translation). The drop in the cost of prediction will impact the value of other things, increasing the value of complements (data, judgement, and action) and diminishing the value of substitutes (human prediction).”

The remainder of the book explores logical deductions from that fact. Some of the more interesting of which are:

• Some areas of prediction only become real game changers when the machines reach near 100% certainty. For example, imagine if when Amazon made a recommendation they could be 98% sure that you’d want to buy the product… would they send automatically send the product to a distribution center near your house? Maybe just send it straight to you and let you return one product in fifty that they’re actually wrong about.
• The value of data is going up quickly.
• Machines and humans have distinct strengths… humans tend not to be thorough while machines can’t discover independently a new type of data that should be incorporated. The two will be paired with machines doing the prediction and humans doing the judgement.
• They identify 6 types of risks associated with Prediction Machines. The most interesting of which is that on things like driving an airplane, humans are getting less and less skilled at it while auto-pilot does more.