December 2023

So far this year I’ve published 52 articles at ClearPurpose.media. These can be grouped into four categories:

• Founder to CEO
• Category Making
• Book Reviews
• Miscellaneous Observations

Let me cover each of these topics separately:

Founder to CEO

In 2022 I began a series on the transition from startup founder to CEO of a growing enterprise. I wrapped up this series at the beginning of the year with four final articles:

Cash Matters! (2023–01–23)

Where Does Cash Come From? (2023–01–31)

Founder to CEO: Chief Cash Officer (2023–02–13)

Founder to CEO: A Recap (2023–02–20)

Category Making

The discipline of creating a new category also continued to be a major focus of mine in 2023 with 15 new articles on the topic:

Defining a New Category (2023–03–10)

Creating the Un-Carrier Category (2023–05–01)

How to Launch a New Category (2023–05–08)

Making the Case for Something Different (2023–05–15)

Your Category’s Grand Entrance (2023–05–22)

Owning Your Category (2023–05–29)

How Nvidia Created the GPU Category (2023–07–04)

When It Takes Collaboration to Create a Category (2023–09–05)

Collaborating to Create the Commercial Internet Category (2023–09–12)

A Coalition Creates the Competitive Communications Carrier Category (2023–09–28)

Non-Aggression as the Key to Establishing the Frame Relay Category (2023–10–11)

Nextel Helped Work Get Done (2023–10–24)

How Tech Giants Teamed Up to Establish 4G (2023–11–7)

How Salesforce Collaborated with Startups to Create the SaaS Category (2023–11–28)

How Tech Titans Teamed To Create the Gen-AI Category (2023–12–05)

Book Reviews

This year I wrote 29 book reviews. That included a burst of reviews written in the middle of the year revisiting classic business books I’ve read in prior years but had never reviewed. If you’re looking for your next book to read, I recommend quickly purusing this list — there’s something for almost everyone here:

Book Brief: Unleash Your Cash Flow Mojo (2023–01–10)

Book Brief: Financial Intelligence (2023–01–18)

Book Brief: Fall in Love with the Problem, Not the Solution (2023–02–08)

Book Brief: The Ecosystem Economy (2023–03–14)

Book Brief: Unwired (2023–03–28)

Book Brief: Soul Work (2023–04–03)

Book Brief: Switch (2023–04–10)

Book Brief: Unconventional Business (2023–04–24)

Book Brief: The Unconventional Leader (2023–06–05)

Book Brief: The Design of Business (2023–06–12)

Book Brief: REWIRED (2023–06–20)

Book Brief: Strategy in the Digital Age (2023–06–27)

Book Brief: Leadership Not By The Book (2023–07–11)

Book Brief: Business Model Generation (2023–07–19)

Book Brief: The Lean Startup (2023–07–25)

Book Brief: Wireless Nation (2023–07–31)

Book Brief: Madison Avenue Makeover (2023–08–07)

Book Brief: Good to Great (2023–08–15)

Book Brief: Going On Offense (2023–08–22)

Book Brief: Every Good Endeavor (2023–08–29)

Book Brief: There’s No Such Thing As Business Ethics (2023–09–19)

Book Brief: Whatever You Do (2023–10–17)

Book Brief: Playing to Win (2023–10–04)

Book Brief: Startup Nation (2023–10–31)

Book Brief: Product Roadmaps Relaunched (2023–11–14)

Book Brief: Behind the Cloud(2023–11–21)

Book Brief: The Agile Pocket Guide (2023–12–13)

Book Brief: The Signal and the Noise (2023–12–19)

Miscellaneous Observations

Surprisingly, there were only three articles this year that didn’t fit into one of the above categories:

My 10 Most Referenced Books (2023–01–02)

The Semiconductor Industry 101 (2023–03–23)

 “Ready Golf” (2023–04–17)

To see a more complete representation of my writing on different topics, visit my website.

I welcome any input on topics I should cover heading into 2024!

The Signal and the Noise by Nate Silverexplains how data is used to make predictions in many different fields; why some of those have failed to show meaningful advances in accuracy; why a few have; and what we can learn from both the successes and failures. The book is not an easy read. There’s nothing in the book that is overly hard to understand, at least not when Silver explains it. The problem is that he covers such a broad landscape and he does so in such great detail that the overall experience is somewhat overwhelming.

Even with being more than a decade old, The Signal and the Noise is still worth reading. There are a few helpful over-arching concepts that Silver introduces that will continue to be relevant, a bunch of real-world examples that demonstrate those concepts, and a few general suggestions that will continue to be helpful as experts in any field seek to use data to make hard predictions about the future.

In my full review linked below, I briefly describe three over-arching concepts: signal/noise, uncertainty/probability/confidence, and Bayes theorem. If you want to know what each of these concepts reference, click the link below. These three concepts are foundational to the book, but they are mostly presented through a series of detailed examples from different industries. The author has professionally applied data analysis in a variety of different fields. He started his career as a pricing analyst for KPMG, built a system to predict the performance of Major League Baseball players (which he sold to Baseball Prospectus), became a professional gambler, accurately predicted the 2008 presidential and Senate elections, and turned that into a successful business called FiveThirtyEight (which he sold to Disney/ESPN/ABC). 

Each chapter in The Signal and the Noise digs into a different domain and the challenges and opportunities for applying data analysis to make accurate predictions in that domain. I can’t possibly do justice in summarizing these detailed analyses, but I can tell you the domains covered: economic forecasting, political punditry, baseball analysis, weather forecasting, earthquake prediction, health epidemic management, chess and poker playing, stock market investing, climate change warning, geo-political intelligence.

The final chapter in The Signal and the Noise is titled “Conclusion” and helps the reader to pull together the important lessons taught throughout the book. “This book is less about what we know than about the difference between what we know and what we think we know. It recommends a strategy so that we might close that gap. The strategy requires one giant leap and then some small steps forward. The leap is into the Bayesian way of thinking about prediction and probability.” The small steps are best summarized by the sub-section titles that follow in the chapter: think probabilistically, know where you’re coming from, try, and err.

Read my full review of The Signal and the Noise here.

The Agile Pocket Guide by Peter Saddington is a helpful guide for experienced software developers who have been asked to lead their first Agile project.

The book is roughly broken into four parts. 

Chapters 1–4 teach general principles that will serve the leader well and each chapter ends with a list of three “Leader Questions” aimed at helping the reader internalize the lessons taught.

Chapters 5–16 get more into the actual tools and processes used in Scrum. The chapters include these tools, processes, and concepts, and sometimes include examples from the author’s experience. Each chapter ends with a list of three questions for either the leader or the entire team.

Chapters 17–24 get into the dynamics of the Agile team (and its leader) interacting with other parts of the business. Each chapter ends with a specific “Example Case” from the author’s personal experience.

Chapters 25–28 deal with Kaizen at the personal, team, product, and cultural levels. Kaizen is a Japanese term roughly meaning “continuous improvement”. Each of these chapters is primarily a long list of things that leaders, teams, and organizations can focus on to integrate the Kaizen concept into how they operate.

Bottom line, The Agile Pocket Guide is written by an experienced Agile practitioner based on coaching he’s provided to many who are new to the framework. It serves as a broad-ranging introduction to many of the terms, concepts, and practices that a leader of an Agile team will need to learn to become successful. If you’ve been asked to lead an Agile team, this short book might prove helpful to you.

Read the full review here.

OpenAI has been in the news lately for how its unique corporate governance model and conflicting agendas caused a bitter split. In contrast, the company’s foundation in 2015 was the result of a unique collaboration between some of the most powerful people in the tech industry working together with a clear shared purpose. In 8 short years OpenAI has dramatically demonstrated to the world the capabilities of Artificial Intelligence (AI), created the Generative AI category, and captured $tens of billions in value.

The article linked below tells this story. Here’s a little teaser…

In the years leading up to the formation of OpenAI, the stage was being set for the emergence of Artificial General Intelligence (AGI). Leading technology thinkers began to both get excited about the potential benefits of these advances and become worried about their potential negative implications. In December 2015 Elon Musk, Sam Altman, Reid Hoffman, Peter Thiel, Greg Brockman, Jessica Livingston, AWS, and Infosys committed $1B to form OpenAI, a non-profit artificial intelligence research company whose goal was to “advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.” The company promised to share their work with the world. Thanks to the unique combination of individuals involved, the organization’s mission, and the funds available to pay competitive salaries, OpenAI was able to recruit top AI researchers.

During 2016 and 2017, the OpenAI team made significant progress in their research. As promised, the products of these efforts were released as Open Source software. They also shared their research extensively through published papers. In 2017, the Google Brain team published a paper titled “Attention is All You Need” which introduced the concept of Generative Pre-Trained Transformers (GPTs). The OpenAI team also began working on GPTs.

While OpenAI had done a great job of building one of the most impressive research teams in the world, they were competing to retain those employees with companies like Google who could offer stock-based compensation. In March 2019, OpenAI announced that they were shifting from a non-profit to a “capped profit” model in order to be able to take direct investments to fund their computing needs. In July of that year Microsoft announced a $1B investment in the company as part of a broader partnership.

Most new categories involve a new approach to solving an old problem. The world embraces the new category because the new approach dramatically improves on old approaches, is radically different — not just better (and therefore is defensible), can easily be adopted by the target users, and finds some way to get users’ attention. 

The Generative AI (Gen-AI) category solves the very old and very broad problem of creating something new. Humans by their very nature are creative. God made us that way. Over the years, men have invented tools to help those being creative. But the greatest tool we’ve had in the creative process is that of collaboration — working with other humans to make our creative works better.

As standalone creators, Gen-AI bots are mediocre at best, but they truly shine when collaborating with a flesh-and-blood human creator. The problem that this new category solves is that it provides an always-available, untiring, patient, affordable, infinitely knowledgeable, reasonably skilled (and yet still imperfect) collaborator for our creative endeavors.

So, what did OpenAI do to create the category? Category creation typically follows three major steps: 1) Define the problem, the solution, and the category. 2) Launch the category, often with a “lightning strike”. 3) Own the category by continuing to lead in capability, mind-share, and market-share.

Read the full story here.