Cultivating Range: Lessons for Startups in a Wicked World

Introduction

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.

4 Waves of AI – And why they matter

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:

  1. Internet AI
  2. Business AI
  3. Perception AI
  4. 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.

Do you know your dependencies?

A contributor on GitHub finds an abandoned, but popular JS library and commits code that targets a Bitcoin wallet made by a particular company. Hundreds of other libraries use this library making this vulnerability affect thousands of applications since it is a transitive dependency.


Photo by Bryson Hammer on Unsplash

NPM (and npmjs.com) provide a valuable service in hosting JavaScript dependencies. By blindly upgrading to latest version of libraries, developers can open themselves to malicious attacks similar to those described below.

I would recommend developers understand how npm’s package lock mechanism works. This will ensure that your dependencies are reproducible and force the use of known and trusted modules instead of downloading the latest version.

This is not a problem just with the JavaScript eco-system. Python (via pip or conda) and Java (via maven & gradle) have similar issues. However my, totally subjective and un-scientific, observation is that JavaScript libraries tend to have way more dependencies (see the “left-pad” debacle for example)..

Ars Technica has a good write up about this particular issue: https://arstechnica.com/information-technology/2018/11/hacker-backdoors-widely-used-open-source-software-to-steal-bitcoin/