I read most of “Playground” in a rattly old plane as it shook and juddered over the Atlantic and then the vast emptiness of Russia before landing in New Delhi. I finished the book in a crowded airport, in tears and in awe of what Richard Powers has achieved.
Playground has a beautiful cover
The novel weaves together an exploration of friendship and the games people play with one another, a hypnotic love letter to the ocean, and a deep meditation on technology and meaning. Like memory itself, the story refuses to follow straight lines. Instead, it spirals and circles, guided by a narrator whose version of events becomes increasingly complex and layered as the story unfolds.
At its heart are four people – Todd, Rafi, Ina, and Evie. Todd and Rafi both call Chicago home, but they might as well be from different planets. Todd is wealthy, white, and obsessed with computers; Rafi is poor, African American, and a precocious reader. What bridges their worlds is a shared love of games – chess, Go, and eventually the intricate game of their own peculiar friendship. When they meet Ina in college, their duo becomes a trio, and their lives become permanently entangled in ways that echo across decades.
In contrast stands Evie – a scientist and pioneering diver whose sections contain the book’s most luminous writing. Through her eyes, we discover coral reefs, sunken ships, and manta rays in passages that evoke pure wonder about the ocean’s depths. While others build virtual worlds, Evie explores an actual one, until all four lives ultimately converge on the Pacific island of Makatea – a place strip-mined for phosphate in the 20th century and slowly being reclaimed by jungle. The island stands as a testament to both human intervention and nature’s resilience.
Threading through these human stories runs the history of modern technology and machine learning, embodied in Todd’s journey. He transforms his obsession with computers and gaming into a wildly successful social platform that crosses Reddit with Facebook. But as his success peaks, tragedy strikes – a debilitating neurological disease that leads him to narrate his story to an AI assistant before memory fails. This creates layers of uncertainty about perception and reality that build toward a wonderful (and slightly puzzling) final act that questions what it means to be alive and how technology might reshape our understanding of consciousness and truth.
As a technologist, I found “Playground” to be a powerful lens for examining both my relationship with technology and my feelings about the natural world as we venture deeper into the Anthropocene. The book doesn’t choose sides. Instead, it shows us how the awe inspired by a coral reef and the possibilities of artificial intelligence can coexist, each raising questions about consciousness and reality that the other helps us explore.
If only I could write like Mr Powers
There’s still so much to process in this book. Like the games its characters play, each move reveals new possibilities, new uncertainties to consider. And I’m nowhere near done processing.
In times of rapid change, fiction serves as a reflective lens, casting light on current anxieties and offering insights beyond simple commentary. “The Mountain in the Sea” by Ray Nayler navigates the complex relationship between humans and technology.
But Nayler’s work goes further. While the book revolves around first contact with a civilization of Octopii, it delves into the nature of consciousness. It critiques our relentless drive to build, optimize, and consume. Nayler raises pertinent questions about loneliness, isolation, and the role of technology in our lives.
In the pages of “The Mountain in the Sea,” these themes come alive through well-realized characters and intricate plotlines, providing a vital tool for understanding our relationship with the worlds we live in – social, internal, external, and digital.
There are three PoV characters – Ha Nguyen is a scientist who has spent years studying Cephalods – the family of animals that include Octopus, Squid, and Cuttlefish. The second character is a hacker, Rustem, who specializes in breaking AIs. The third is a young Japanese man, Eiko, who, through a series of unfortunate events, ends up a slave aboard an AI-powered fishing vessel.
Each character in the book deals with loneliness and isolation and has somewhat awkward if dependent, relationships with technology.
In general, AI, or the nature of intelligence, is a key theme that runs through the various plot lines of the book. Ha Nguyen and her team try to make sense of the culture and symbolic language of the Octopus civilization. Eiko has to deal with a murderous and indifferent AI driven by optimization algorithms built to maximize the amount of protein the ship hauls from the depleted oceans.
While I picked up the book because of the striking cover and because I love First Contact books – I read it in a couple of sittings because of the underlying themes of our relationship and dependence on technology and what it does to us and the world around us resonated deeply with me. As someone excited about technology’s promises and challenges, this book prompted me to consider where our pursuit of innovation is taking us.
For example, people in “The Mountain..” have AI companions called point-fives. These companions form relationships but do not make any demands on their human owners. They give, but they do not take. There is only one point five instead of two “people” in a relationship. Hence the moniker.
The loneliness of people in this world is mollified by technology, but it is not solved. The only way is through genuine contact, through a process of both taking and giving.
I spend a lot of time working on and thinking about systems that would save time, optimize workflows, and make more money. Despite the potential for disruption and displacement, I welcome new technology like Generative AI.
But, there are clearly issues and risks in the somewhat reckless attitude to embracing technology. Threats not just to our environment but also to society and to ourselves.
“The Mountain in the Sea” is a cautionary tale and a story of hope. Each character’s arc in the novel is discovery and possible redemption. This book had me thinking long and hard about where our obsession with optimization and technology is taking us.
“A Philosophy of Software Design” by John Ousterhout is a short and thought-provoking book about practical software development.
Key Concept
The book starts with a bold claim – the most critical job of a software engineer is to reduce and manage complexity.
Mr. Ousterhout defines complexity as “anything related to the structure of a software system that makes it hard to understand and modify the system.”
This definition serves as a motivating principle for the book. The author explores where complexity comes from and how to reduce it in a series of short chapters, which often include real-world code examples.
My well-thumbed copy of the book
Summary
The book starts with identifying the symptoms of complexity:
The difficulty in making seemingly simple changes to a system.
Increasing cognitive load – i.e., a developer’s ability to understand a system’s behavior.
The presence of “Unknown unknowns” – undocumented and non-obvious behavior.
Mr. Ousterhout states that there are two leading causes of complexity in a software system:
Dependencies – A given piece of code cannot be understood or modified in isolation
Obscurity – When vital information is not apparent. Obscurity arises due to a need for more consistency in how the code is written and missing documentation.
To reduce complexity, a developer must focus not only on writing correct code (“Tactical Programming”) but also invest time to produce clean designs, and effective comments and fix problems as they arise (“Strategic Programming”).
The book provides several actionable approaches to reducing complexity.
Some highlights:
Modular design can help encapsulate complexity, freeing developers to focus on one problem at a time. It is more important for a module to have a simple interface than a simple implementation.
Prevent information leakage between modules and write specialized code that implements specific features (once!).
Functions (or modules) should be deep – and developers should prioritize sound design over writing short and easy-to-read functions.
Consider multiple options when faced with a design decision. Exploring non-obvious solutions before implementing them could result in more performant and less complex code.
Writing comments should be part of the design process, and developers should use comments to describe things that are not obvious from the code.
The book concludes with a discussion of trends in software development, including agile development, test-driven development, and object-oriented programming.
Conclusion
“A Philosophy of Software Design” is an opinionated and focused book. It provides a clear view of the challenges of writing good code, which I found valuable.
Mr. Ousterhout provides actionable advice for novice and experienced developers by focusing on code, comments, and modules.
However, the book is also relatively low-level. The book contains little discussion around system design, distributed systems, or effective communication (outside of good code and effective comments).
While books such as “The Pragmatic Programmer” provide a more rounded approach to software engineering, I admire that Mr. Ousterhout sticks to the core concepts in his book.
Artificial Intelligence – A Guide For Thinking Humans
Introduction
Melanie Mitchell’s book “Artificial Intelligence – A Guide for Thinking Humans” is a primer on AI, its history, its applications, and where the author sees it going.
Ms. Mitchell is a scientist and AI researcher who takes a refreshingly skeptical view of the capabilities of today’s machine learning systems. “Artificial Intelligence” has a few technical sections but is written for a general audience. I recommend it for those looking to put the recent advances in AI in the context of the field’s history.
Key Points
“Artificial Intelligence” takes us on a tour of AI – from the mid-20th century, when AI research started in earnest, to the present day. She explains, in straightforward prose, how the different approaches to AI work, including Deep Learning and Machine Learning, based approaches to Natural Language Processing.
Much of the book covers how modern ML-based approaches to image recognition and natural language processing work “under the hood.” The chapters on AlphaZero and the approaches to game-playing AI are also well-written. I enjoyed these more technical sections, but they could be skimmed for those desiring a broad overview of these systems.
This book puts advances in neural networks and Deep Learning in the context of historical approaches to AI. The author argues that while machine learning systems are progressing rapidly, their success is still limited to narrow domains. Moreover, AI systems lack common sense and can be easily fooled by adversarial examples.
Ms. Mitchell’s thesis is that despite advances in machine learning algorithms, the availability of huge amounts of data, and ever-increasing computing power, we remain quite far away from “general purpose Artificial Intelligence.”
She explains the role that metaphor, analogy, and abstraction play in helping us make sense of the world and how what seems trivial can be impossible for AI models to figure out. She also describes the importance of us learning by observing and being present in the environment. While AI can be trained via games and simulation, their lack of embodiment may be a significant hurdle towards building a general-purpose intelligence.
The book explores the ethical and societal implications of AI and its impact on the workforce and economy.
What Is Missing?
“Artificial Intelligence” was published in 2019 – a couple of years before the explosion in interest in Deep Learning triggered due to ChatGPT and other Large Language Models (LLMs). So, this book does not cover the Transformer models and Attention mechanisms that make LLMs so effective. However, these models also suffer from the same brittleness and sensitivity to adversarial training data that Ms. Mitchell describes in her book.
Ms. Mitchell has written a recent paper covering large language models and can be viewed as an extension of “Artificial Intelligence.”
Conclusion
AI will significantly impact my career and those of my peers. Software Engineering, Product Management, and People Management are all “Knowledge Work.” And this field will see significant disruption as ML and AI-based approaches start showing up.
It is easy to get carried away with the hype and excitement. Ms. Mitchell, in her book, proves to be a friendly and rational guide to this massive field. While this book may not cover the most recent advances in the field, it still is a great introduction and primer to Artificial Intelligence. Some parts of the book will make you work, but I still strongly recommend it to those looking for a broader understanding of the field.
Managing and supporting a team is a difficult job. A manager is often a coach, disciplinarian, a surrogate parent, and cheerleader – all rolled into one. I am always on the lookout for ways to be a better supporter of my teams. Over the last few days I discovered a fortunate intersection in my interests in sport and in management.
I am a fan of the Arsenal football club. Like many other Arsenal fans, I have been watching and enjoying the Amazon Prime show “All or Nothing: Arsenal,” which follows Arsenal through the 2021 – 2022 season. We get a close look at how Arsenal’s manager Mike Arteta works with his players and his management team and motivates them over a challenging 45-game season.
At 38 years old, Arteta is currently the youngest manager in the English Premier league. He has been at the helm since 2019. The Arsenal squad also has the youngest average age in the Premier League – this season, the first team averages just 25.2 years old.
Arteta’s reign has seen the club slump to 8th place in the 2019 and 2020 seasons before having a marked improvement in form to finish 5th in 2021.
Arteta comes across as an intense, detail-oriented and hands-on manager. I realized that Arteta’s approach to management was something I had come across before. It is strikingly similar to that described in Radical Candor by Kim Scott – one of my favorite books on building high-performance teams.
In this post, I will summarize the Radical Candor approach through the lens of Arteta’s unique take on people management.
What is Radical Candor?
Radical Candor is a book by Kim Scott published in 2017. It focuses on creating a culture of guidance, building an effective and cohesive team, and driving results collaboratively.
The book’s central thesis is that effective leadership requires direct, clear, truthful, and kind feedback, even when difficult. Scott believes getting to know each person in your team personally is essential to understanding their desires and motivations.
The book offers tactical and strategic advice to leaders on building high-performing teams in an open, healthy, and productive environment. I strongly recommend Radical Candor for those looking for an authentic and modern approach to people management.
We see Arteta speaking candidly and passionately with his players throughout the season. He is generous in his feedback when things go well. When things go poorly, Arteta is direct, passionate, and emotional. While he doesn’t mince words, he doesn’t humiliate his players in the dressing room or in front of the media.
Caring Personally while Challenging Directly
The 2X2 below shows “Radical Candor” as giving feedback by caring personally while challenging directly. It also covers some dysfunctional ways of giving feedback – obnoxious aggression, ruinous empathy, and manipulative insincerity.
From Radical Candor by Kim Scott
Ted Lasso aside, football managers are not known for their empathy. Indeed, the likes of Sir Alex Ferguson are revered for their ability to drive performance through aggression and intimidation. Ferguson’s proverbial “hairdryer treatment” would probably end up in the “Obnoxious Aggression” quadrant above.
While he is partial to the odd F-bomb, Arteta’s open displays of emotion and vulnerability inspire his players, as seen in this clip. At the end of a run of poor results in April at Crystal Palace and Brighton, we see a manager who cares about the results and is passionate about wanting to make things better. He calls out a lack of intensity from his players and gives specific feedback on the training pitch and in the dressing room.
This combination of caring personally and directly challenging poor performance is right out of the Radical Candor playbook.
Building Resilience Through Trust
The Radical Candor approach is built on a foundation of trust. Trust is difficult to gain and easy to lose. The key to building trust is to be transparent and authentic, clear and concise in communication, and consistent in your actions.
Arteta calls out his “non-negotiables” in explaining his management philosophy: respect, commitment, and passion. Throughout the show, we see Arteta embodying these values.
This results in significant friction with his star player Pierre-Emerick Aubameyang who does not meet Arteta’s high expectations around discipline and accountability. Aubameyang is the club captain and is a popular member of the squad.
Arteta ends up stripping Aubameyang from the captaincy of the team. This could have destabilized the team, but it seems to have the opposite effect. Arteta does not criticize Aubameyang, and his team is made aware of how important trust and accountability are to their manager. By showing consistency in his actions and clarity in his communication, Arteta builds trust and resilience, resulting in outstanding results on the pitch in the second half of the season.
Managing Rockstars and Superstars
In Radical Candor, Scott describes Rockstars as stable employees who are happy and effective in their roles. These are folks who are aware of their talents and limitations and can consistently perform at a high level. On the other hand, Superstars are on a steep career trajectory and can be change agents. They are ambitious and want new opportunities. A high-performing team usually has both rockstars and superstars.
Given his young team, Arteta works with plenty of players on steep growth trajectories. Bukayo Saka, Emile Smith Rowe, and Eddie Nketiah are all young and eager to learn and perform at the highest level. However, he also has players like Rob Holding and Mohammad Elneny. While experienced pros, they have specific roles and are not guaranteed a place in the starting lineup. Holding and Elneny are the rocks (and Rockstars) that provide a stabilizing influence in the dressing room and on the pitch while laying a foundation for the more flamboyant players up front.
As a manager, Arteta has to ensure that the players like Holding and Elneny feel valued and are ready to perform when called upon while the ambition and talents of the young Gooners are nurtured. You can see this come together towards the end of the season. Holding and Elneny perform well after being called into the starting eleven after injuries. He also gives the ambitious Nketiah an extended run. He repays his faith by scoring five goals in the last seven games. Arteta and his team need to understand each player’s mentality and ensure they feel motivated to perform when needed.
Conclusion
All or Nothing is entertainment and has been edited to push a narrative and maximize engagement. Mikel Arteta has come under intense criticism for being uncompromising and stubborn at times – especially with how he has managed high-profile players like Aubameyang and Mesut Ozil. But, the little glimpse we get in the documentary shows a young manager trying to build a successful team.
Plenty of books like Radical Candor have come out of Silicon Valley, and the content often reflects the author’s experience working in technology companies. The strength of a book, especially in the crowded management genre, is how applicable the message is across different domains.
Managing Arsenal presents quite different challenges from managing a software engineering team. However, I hope the lessons of Radical Candor and All or Nothing are valuable to managers looking to build and support a high-performance team.
I read Morgan Housel’s “The Psychology of Money” towards the end of last year. I found it an insightful book that took a more personal and nuanced look at money and building wealth. It is not a “how to get rich quick book.” Its core advice is to take advantage of compounding and take a reasonable approach to risk — hardly rocket science. However, it explores some of the more common pitfalls and anti-patterns when people think of money. While I would strongly recommend everyone to read the book — it is fantastic, here is a quick summary of my notes from reading (and enjoying) the book. I hope you find it helpful!
A more personal view of money
People think of money as an abstract. We think about and are taught about money like we are taught physics. We assume that money is governed by rules and laws. Yet, psychology, with its study of emotions and nuance, may offer a better way to think about money.
Most people make financial decisions by taking the information that they have access to and plugging it into their mental model of how the world works. But these mental models are driven profoundly by personal experience.
Mr. Housel’s book takes a personal and intimate approach to understand how money works and illuminates some of the difficulties we face when making money decisions.
How to get rich and stay rich
Compound growth is the key to growing wealth. There is plenty of material available that describes viable strategies for becoming wealthy. However, Mr. Housel states that there is only one way to stay wealthy — “some combination of frugality and paranoia.”
If one can stick around for a long time without wiping out or being forced to give up, the power of compounding comes into play and helps generate wealth.
The key to a successful investment strategy is to not risk what you have and need for what you don’t have and don’t need.
The importance of sensible optimism
Successful investors take an optimistic view that, in the long run, the odds are in their favor, and over time things will balance out to a good outcome even if what happens in between is filled with misery.
But the optimism must be balanced with a healthy dose of paranoia. This means accepting nuance and understanding that the key to exploiting long-term optimism is survival.
It is critical not to get swept up in short-term momentum or get giddy about short-term gains or losses. The most effective long-term strategy is to not get overly influenced by short-term events.
Understanding wealth
When most people think about becoming a millionaire, they think of the ability to spend a million dollars. However, the true meaning of wealth is the ability to deploy money towards living a life that lets you do what you want, when you want, with who you want, where you want, for as long as you want. So, true wealth is financial assets that haven’t yet been converted into consumption.
The ability to save is also critical to building wealth. Savings are a hedge against life’s inevitable ability to surprise the hell out of you at the worst possible moment. The most potent way of increasing savings is not to raise your income but to raise your humility.
Making reasonable financial decisions
Financial decisions making is thought of as making coldly rational decisions in the light of available information and knowledge of the past. However, history is primarily the study of unanticipated events.
Therefore, relying on history as an unassailable guide to the future is risky. It is important to consider the past but to look at it in terms of generalities.
So, one must not be overly influenced by history and take a reasonable and pragmatic approach when making financial decisions. Having savings gives a buffer to absorb short-term volatility. Having a realistic and flexible approach to financial decisions makes it likely to stick with your investment strategy in the long run.
The role of skill and of luck
Money constantly changes returns. If an asset has momentum, a group of short-term traders will assume it will keep moving up. We have seen this play out in recent times with the GameStop saga.
It is not an unreasonable strategy for the short term. Executing such short term strategy doesn’t really require much skill but does need some luck in timing the strategy just right. Plenty of traders both lost and made huge amounts of money trying to time their GameStop trade. It was all about momentum.
The mistake we are susceptible to is focusing solely on what we want to do and have the ability to do. We ignore the plans and skills of others whose decisions might affect our outcomes. We also focus too much on the causal role of skill and neglect the role of luck. This makes us overly confident in our beliefs.
Lessons from Maria Konnikova’s The Biggest Bluff: How I Learned to Pay Attention, Master Myself and Win
The Biggest Bluff by Maria Konnikova
Maria Konnikova is a journalist, writer, and professional poker player. I came across her an interview with her on the excellent Knowledge Project podcast. She intrigued me enough to want to know more about her journey, so I picked up her book, “The Biggest Bluff: How I Learned to Pay Attention, Master Myself and Win.”
The Biggest Bluff is Ms. Konnikova’s account of going from being a complete poker novice to a tournament-winning pro. The book is not a “how-to” guide to making millions in Vegas. It is instead a meditation on learning, paying attention, and making decisions.
I enjoyed following Ms. Konnikova on her journey. Here are some things I took away from the book.
Paying Attention to the Present
Poker is a game of simple rules but complex behaviors. Success relies on luck and the ability to understand and predict what other players on the table might do. Ms. Konnikova had to pay attention to the cards on the table and how the other players had played throughout the day and tried and figured out what their tells were.
She also had to learn to pay attention to herself and identify when she was fatigued, and take appropriate action when going off course.
John Von Neumann describes poker as the perfect game of incomplete information. But, by paying attention, it is possible to identify when emotions get in the way of sound decision-making and to try and predict your competitors’ actions and consequences.
In life, just like in poker, paying attention to the present is table stakes.
Intuition vs. Process
Ms. Konnikova is dismissive of intuition or “gut feeling.” She says we have intuitions all the time, but we are terrible at telling the right ones from wrong. She suggests that we trust our intuition only if we are an expert in the area.
As a novice poker player, she had to work hard to identify and suppress false confidence. She did this by learning to distinguish the action and the outcome from the thought process. In the short term, it didn’t matter if she won or lost a hand provided she was thinking through things correctly. In the long run, this focus on process meant that she would have better inputs and eventually the right conclusion with more experience.
I agree with the author that we are terrible at linking outcome to process. Luck, both good and bad, always adds noise. But by having a thought-through strategy, we can avoid false confidence and learn to avoid the pitfalls of relying on unreliable intuitions.
Avoiding going Full Tilt
In poker parlance, “tilting” is when a player lets irrelevant emotion cloud their thinking. You start tilting when another player or an aggravating circumstance gets under your skin and makes you emotional.
As one of very few professional female poker players, Ms. Konnikova dealt with misogynistic behavior from her fellow players. From being called “little girl” to being propositioned on the poker table — these unpleasant experiences did end up getting under her skin and affected her game.
She came up with techniques to become mindful of her emotions. She wanted to experience them but be self-reflective and not let them affect her thought process.
Humans are emotional. We experience life through emotions and can never be purely rational. Ms. Konnikova says the key is to identify irrelevant feelings and develop strategies to ignore them — avoiding going full tilt.
Making Good Decisions
Poker forces a player to place a monetary value on the opinions driving decisions at the table. Having a flawed decision-making process makes going broke a likely outcome.
As she became a better poker player, Ms. Konnikova became less confident in her opinions. This may seem counter-intuitive — surely becoming more experienced means becoming more confident in your opinions! But Ms. Konnikova made better decisions when she forced herself to question her assumptions. Her decision-making process relied on paying attention, not relying on flawed intuition, and having a well-practiced process.
Judging the success or otherwise of a decision-making process is more straightforward in poker than in real life. If you lose money consistently, you might want to either stop playing or take a close look at how you are playing. Judging success in other domains may not be easy, but having a clear decision-making process remains crucial.
Conclusion: Less Certainty, More Inquiry
We often end up making decisions on auto-pilot. We take received wisdom and our intuitions for granted. When bad things happen, we attribute them to bad luck, crappy circumstances, or other external factors.
But, as Ms. Konnikova’s mentor advises her, it is better to be less certain about things and always inquire, ask questions, and to think through things for yourself.
To have any chance of success in complex domains, it is essential to be aware of blind spots, pay attention to what is happening, and have a deliberate and well-understood decision-making process.
The Biggest Bluff is an entertaining, well-written, and thought-provoking book. Ms. Konnikova’s journey pushed me to take a closer look at how I make decisions and to ponder where my blind spots lay.
I recently read David Epstein’s book Range: Why Generalists Triumph in a Specialized World. The book focuses on how to cultivate broad thinking strategies to learn effectively. Epstein’s focus is on individuals. As I made my way through the book, I saw that the points made in this book apply equally well to teams.
Range by David Epstein
I work with and advise early stage technology startups. I learnt a lot while reading “Range”. In this post, I explore how the lessons from “Range” can benefit technology startups or teams looking to launch a new product.
Thriving in Wicked Environments
Epstein introduces the concept of Kind and Wicked environments. A chessboard is a kind environment: the rules are clear, and actions are deterministic. Strategies that work in one situation should work well in similar cases. However, in the real world there are feedback loops and second-order consequences that are difficult to predict. It is a rapidly changing Wicked environment. Strategies that worked well in the past can stop working due to changes to the external environment or the market’s reaction to your previous actions.
We see this pattern repeatedly in the world of startups. Ideas that seem destined for success fail because they attempt to solve a problem that is no longer important or serve a market that no longer exists.
To thrive in a Wicked environment, a team may need to take conceptual knowledge from one problem domain and apply it to an entirely new one. The ability to think broadly and to be able to deploy flexible solutions to complex problems could be the difference between a successful product launch and complete failure.
Creating Innovative Products Through Analogical Thinking
Epstein describes Analogical Thinking as —
“The practice of recognizing conceptual similarities in multiple domains or scenarios that may seem to have little in common on the surface.”
Barriers to entry in the information economy are low. While anyone can launch a software product or service, successful companies frequently bring together ideas from different fields to build a compelling product.
Uber brought together logistics, mapping, mobile experiences, and access to an entirely new labor market to create a transformational service. Snowflake’s recent success is another example of a business built on the convergence of industry and technology trends. They successfully executed a simple, in hindsight, idea — cloud-only data warehouses.
Building a Successful Team
In Superforecasting, Philip Tetlock quotes the Greek poet Archilochus: “the fox knows many things, but the hedgehog knows one big thing.” Hedgehogs are specialists — they love to focus on one problem and usually work within their specialty’s confines. Foxes tend to work across various disciplines and work under ambiguity and contradictory conditions.
Epstein cites Tetlock’s research in forecasting and shows that in the face of uncertainty, individual breadth is critical. Similarly, teams that were open-minded and embraced a wide range of experience outperformed teams of narrow specialists.
A Team of Foxes may be more effective in a startup
Early-stage teams need to be open-minded and willing to change their assumptions and pivot when circumstances demand it. As a company matures, it may become useful to include specialists to refine a product and idea. However, having too many specialists at an early stage could lead to tunnel vision.
Choosing a Technology Stack
Gunpei Yokoi was a legendary video game designer at Nintendo. He designed the Game Boy. In Range, Epstein talks about Yokoi’s concept of “Lateral Thinking with Withered Technology.”
The heart of his philosophy was putting cheap, simple technology to use in ways no one else considered. If he could not think more deeply about new technologies, he decided, he would think more broadly about old ones.
You can still see this philosophy in play at Nintendo today.
The Nintendo Gameboy — A Lateral Application of Withered Technology
This lesson is of particular importance for startups with technical founders. It is tempting to be on the cutting edge of technology. But few customers will pay to use a product because it uses a fashionable technology stack. The ability of the company to solve the customer’s problem is way more important.
It may be more productive and faster to build a product using battle-tested, well-understood technology that is quickly and cheaply available. Just like Nintendo, a startup must cultivate a relentless focus on delighting the customer. Technology choices should come second.
Deploying Data Carefully
Startups are encouraged to be data-driven. They optimize for metrics such as customer behavior metrics, sales funnels, infrastructure costs, etc. The danger for the startup here is relying too much on data to make decisions without considering the market or whether the data is relevant to the vision of the company. As Epstein says — the critical question to ask is:
‘Is this the data that we want to make the decision we need to make?’
A dogmatic data-driven approach may lead to doing the same thing in response to the same challenges over and over until the behavior becomes so automatic that it is no longer recognized as a situation-specific tool.
An over-reliance on data can lead to actions that may improve the metrics the team relies on, but may not help the company in the long run to achieve their strategic objectives.
Making the most of External Advisors
Formal or informal advisors can play a critical role to the founding team in a startup. The most effective advisors are outsiders who may be removed from the company’s problem but may help reframe the problem that unlocks the solution.
Epstein notes —
‘A key to creative problem solving is tapping outsiders who use different approaches so that the “home field” for the problem does not end up constraining the solution.’
An outside advisor may offer solutions to a problem the founding team may not even consider because they are too close to the problem.
Knowing when to Give Up
Thirty percent of startups will go under within two years. Fifty percent will fail within five. Running out of money is the most common reason for failure. If a startup keeps trying to execute the same plan despite not gaining traction, it will fail.
Startup culture venerates hard work and not giving up. But here, Epstein provides an essential quote from Seth Godin:
‘We fail when we stick with tasks we don’t have the guts to quit.’
The best, most thought-through plan may fail when it comes up against external conditions — like a global pandemic. Persevering through difficulty can be a competitive advantage, but knowing when to quit can also be a significant strategic advantage. As a startup, it is vital to define and understand the conditions in which it is clear that Plan A has failed, and it is time to try something else.
Conclusion
Building and running a startup is exciting, scary, and can be extremely challenging. It rewards being able to adapt to complex, changing environments. It is vital to pick the right problem to solve, identify the correct tools to solve the problem, and build a team that learns how to make the most of diverse skill sets. Leveraging data and being metric driven can help guide, but must not constrain decision making. Leaning on external advisors and investors is essential to help keep the team grounded and provide different perspectives to solve tricky problems.
Finally, success is not just about persevering through difficult times; it also involves knowing when to quit and when to pivot. A battle may be won simply by disengaging at the right time.
Range is a fantastic book and one that I strongly recommend. The lessons in the book are important not just for individuals but also for teams.
I can’t open a newspaper or visit my friendly local bookstore without coming across a think piece about why AI is a *BIG DEAL* and how it changes everything. The tone of most of the material that I have come across is aptly summed up in this classic xkcd panel.
Classic xkcd panel on AI
In January 2019, I read Kai-Fu Lee’s fantastic book “AI Super-Powers: China, Silicon Valley, and The New World Order.” Mr. Lee is a thoughtful, even-handed guide to what is going on in the field of Artificial Intelligence (specifically Machine Learning) and how it may impact our future. The book is also an eye-opening account of the Chinese startup eco-system — but perhaps more on that another day.
Early in the book, Mr. Lee talks about how the spread of AI is happening in four waves. These waves are:
Internet AI
Business AI
Perception AI
Autonomous AI
Let’s take quick a look at each of these waves.
Internet AI
We deal with Internet AI every time we shop online, scroll through our social media feeds or Google something. From AI Superpowers:
Internet AI is mainly about using AI algorithms as recommendation engines: systems that learn our personal preferences and then serve up content hand-picked for us.
Examples of Internet AI include online advertising optimization, personalized news feeds, and algorithmic content recommendation.
Business AI
Advances in machine learning have allowed businesses to take advantage of labeled, structured data that resides in data repositories and train algorithms to outperform humans on clearly defined optimization tasks. Some examples here include automated credit scoring, fraud detection, algorithmic trading, and supply chain optimization. While not the most exciting topic, in the short term, Business AI has the potential to have a significant impact in the way we work and more potently, what *types of work* make sense to automate.
Business AI is about optimising and generating value from structured data.
Business AI has the potential to make what were once stable professions like accountancy, insurance, and medicine obsolete in their current form. It also has the potential to generate vast and lucrative new opportunities. More on this later.
Perception AI
Perception AI is about the “Digitisation of the physical world.” It is about using real-world data captured from IoT devices, cameras, smartphones, and other devices to blur the lines between the online and offline worlds. We already see applications of facial recognition and machine translation technology enhance offline experiences such as shopping and travel as well as enrich experiences such as education.
Perception AI is about blurring the lines between the online and offline world
Augmented reality (AR) devices and applications increase merging of the offline and online world. Perception AI also has worrying implications around surveillance, privacy and data protection.
Autonomous AI
Autonomous AI represents the culmination of the three preceding waves of AI. What was once science fiction is slowly becoming mundane. Autonomous AI is about fusing the ability to optimize from extremely complex datasets and integrate them with powerful sensory abilities resulting in machines that can understand and shape the world around them.
Autonomous AI results in machines that can understand and shape the world around them.
We already see some limited applications of Autonomous AI in the fields of self-driving cars, automated factories and pollinators.
What does it all mean?
Ben Evans, a partner at the storied VC firm Andreessen Horowitz, talks a little about the implications of advances in AI in the November 2018 presentation “The End of the Beginning”. He says:
“Tech is building different kinds of businesses, and so will take different shares of that opportunity, but more importantly change what those industries look like.“
He says further that a combination of high internet penetration, changing consumer expectations and a general “unbundling” of supply chains are creating business models that in turn are enabled and accelerated by AI. The breaking apart of tightly coupled logistics supply chains is just one example of this phenomenon.
At my work with Jeavio’s portfolio companies, I can already see this in action. We support entrepreneurs who are working in diverse fields such as customer experience analytics, construction and high tech agriculture. In each of these various fields, we see applications of Business AI that have the potential to disrupt existing models and generate tremendous value.
In my previous career working in high-frequency algorithmic trading, I have seen technology disrupt financial markets. Advances in AI are now doing the same in a wide variety of fields.
While AI cannot by itself generate new business models, it is already a potent force multiplier, which when deployed effectively, can increase efficiency and help businesses capture more value. We may not worry about our Robot Overlords just yet; we should keep an eye on the disruption and opportunities presented by the four waves of AI.
I had a target of reading 50 books in 2016. I didn’t quite manage to make it. I managed to only read 24. A dismal performance indeed. I read a couple of wonderful books and discovered some great new writers. I also managed to break out (well a little bit) from the cozy comforts of science fiction.
