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Loonshots - Safi Bahcall

Review by Ron Immink




“Loonshots” is a very different book about innovation and organisational design. Closer to Taleb and “Antifragile”  than “Creative construction“. Also reminds me of “The day after tomorrow“. Solid, fluid and superfluid as key concepts to manage your innovation portfolio.

Managing superfluid

Particularly superfluid is difficult. Those are the loonshots you need, but loonshots by their nature are completely and utterly unpredictable. However, the most important breakthroughs come from loonshots, widely dismissed ideas whose champions are often written off as crazy. Without loonshots companies (and empires) will eventually die.

Phase transition

Loonshots shares the lessons and learning of how to manage innovation from a loonshot perspective. It as with startups and biology, you need lots of weird ideas, and there is no way of predicting which ones will succeed. It is all to do with phase transition. Organisations work in the same way and when you start to understand why teams suddenly turn, you can start to control those transitions. The same way as temperature controls the boiling and freezing of water. Solid, fluid, superfluid.


Think of the water molecules in the tub as a platoon of cadets running randomly around a practice field. When the temperature drops below freezing, it’s as if a drill sergeant blew a whistle and the cadets suddenly snapped into formation. The rigid order of the solid repels the hammer. The chaotic disorder of the liquid lets it slip through. Systems snap when the tide turns in a microscopic tug-of-war. Binding forces try to lock water molecules into rigid formation. Entropy, the tendency of systems to become more disordered, encourages those molecules to roam. As temperature decreases, binding forces get stronger and entropy forces get weaker. When the strengths of those two forces cross, the system snaps. Water freezes.

Competing forces

All phase transitions are the result of two competing forces, like the tug-of-war between binding and entropy in water. When people organise into a team, a company, or any kind of group with a mission they also create two competing forces, two forms of incentives. We can think of the two competing incentives, loosely, as stake and rank. When groups are small, for example, everyone’s stake in the outcome of the group project is high. The perks of rank, job titles or the increase in salary from being promoted, are small compared to those high stakes. As teams and companies grow larger, the stakes in outcome decrease while the perks of rank increase. When the two cross, the system snaps. Incentives begin encouraging behaviour no one wants.


The bad news is that phase transitions are inevitable. All liquids freeze. No group can do both at the same time, because no system can be in two phases at the same time. One molecule can’t transform solid ice into liquid water by yelling at its neighbours to loosen up a little. When the density exceeds a critical threshold, the system will flip from the smooth-flow to the jammed-flow state.

Control parameters

Identifying control parameters is the key to changing when systems will snap: when solids will melt, when traffic will jam, or when teams will begin rejecting loonshots. As the temperature of water falls, molecules vibrate more slowly until they reach a critical temperature, at which point their binding energy exceeds their entropy, and they crystallise into the rigid order of ice. That’s the liquid-to-solid phase transition.

Live on the edge

To nurturing loonshots, you need to live on the edge of a phase transition: the unique conditions under which two phases can coexist. Engineering serendipity, create the opportunity and space to explore the bizarre and creating a dynamic equilibrium between loose and structure. The magic is in the network, a shared purpose and the loose connections.

The rules

Do not undertake a program unless the goal is manifestly important and its achievement nearly impossible
If anything is worth doing, it’s worth doing to excess.
Shelter radical ideas

There is a need for separating and sheltering radical ideas—the need for a department of loonshots run by loons, free to explore the bizarre. Where failure is accepted and expected. The breakthroughs that change the course of science, business, and history, fail many times before they succeed. Sometimes they survive through the force of exceptional skill and personality. Sometimes they survive through sheer chance. In other words, the breakthroughs that change our world are born from the marriage of genius and serendipity.

Create a loonshot structure

There is a pervasive myth of the genius-entrepreneur who builds a long-lasting empire on the back of his ideas and inventions. Rather than champion any individual loonshot, the secret is to create an outstanding structure for nurturing many loonshots. Rather than focus on being a visionary innovator, be a careful gardener. Getting the touch and balance right requires a gentle helping hand to overcome internal barriers, the hand of a gardener rather than the staff of a Moses. The tips (not that dissimilar to “Zone to win“):

Separate the phases

The goal of phase separation is to create a loonshot nursery. The nursery protects those embryonic projects. It allows caregivers to design a sheltered environment where those projects can grow, flourish, and shed their warts. Tailor the tools to the phase

Love your artists and soldiers equally

Surviving those journeys requires passionate, intensely committed people—with very different skills and values. Artists and soldiers. Manage the transfer, not the technology. Manage the balance between loonshots and franchises—between scientists exploring the bizarre and soldiers assembling munitions; between the blue-sky research of Bell Labs and the daily grind of telephone operations. Rather than dive deep into one or the other, focus on the transfer between the two. Intervene when the balance breaks down. Keeping the forces in balance is so difficult because loonshots and franchises follow such different paths.

Beware the False Fail

False Fail—a result mistakenly attributed to the loonshot but actually a flaw in the test. We will see the False Fail over and over, both in science and in business. There are many reasons projects can die: funding dwindles, a competitor wins, the market changes, a key person leaves. But the False Fail is common to loonshots. Skill in investigating failure not only separates good scientists from great scientists but also good businessmen from great businessmen.

Create project champions
Fragile projects need strong hands. Great project champions are much more than promoters. They are bilingual specialists, fluent in both artist-speak and soldier-speak, who can bring the two sides together.

Listen to the Suck with Curiosity

Listening to the Suck with Curiosity (LSC), overcome the urge to defend and dismiss when attacked LSC means not only listening for the Suck and acknowledging receipt but also probing beneath the surface, with genuine curiosity, why something isn’t working, why people are not buying. It’s hard to hear that no one likes your baby. It’s even harder to keep asking why.

Ferocious attention to detail

Ferocious attention to scientific detail, or artistic vision or engineering design, is one tool, tailored to the phase, that motivates excellence among scientists, artists, or any type of creative.

Beware of the Moses trap

When ideas advance only at the pleasure of a holy leader—rather than the balanced exchange of ideas and feedback between soldiers in the field and creatives at the bench selecting loonshots on merit—that is exactly when teams and companies get trapped.

The types of loonshots

There are two types: P and S.

 - With P-type loonshots, people say, “There’s no way that could ever work” or “There’s no way that will ever catch on.” And then it does.
 - With S-type loonshots, people say, “There’s no way that could ever make money.” And then it does.
 - Deaths from P-type loonshots tend to be quick and dramatic. A flashy new technology appears (streaming video), it quickly displaces what came before (rentals), champions emerge (Netflix, Amazon), and the old guard crumbles (Blockbuster).
 - Deaths from S-type loonshots tend to be more gradual and less obvious. It took three decades for Walmart to dominate retail and variety stores to fade away. S-type loonshots are so difficult to spot and understand, even in hindsight, because they are so often masked by the complex behaviours of buyers, sellers, and markets.


The book covers mindset, and there the book reminds me of “The algorithmic leader“.The difference between a system mindset and outcome mindset. Teams with an outcome mindset, analyse why a project or strategy failed. Teams with a system mindset, probe the decision-making process behind a failure. How did we arrive at that decision? System mindset means carefully examining the quality of decisions, not just the quality of outcomes. Which is why probing wins, critically, is as important, if not more so, as probing losses. Failing to analyse wins can reinforce a bad process or strategy. Always ask how the decision-making process can be improved

Examples of randomness

The book is full of examples and what it illustrates is the sheer randomness, the messiness, the serendipity of breakthrough ideas and companies. Bell lab, Genentech, Pixar, Star Wars, James Bond, IKEA but it also points out one organisation that has been at the forefront of many breakthroughs, which is DARPA. Since 1958, this one two-hundred-person research group, deep inside a massive organisation, has spun out the internet, GPS, carbon nanotubes, synthetic biology, pilotless aircraft (drones), mechanical elephants, the Siri assistant in iPhones, and more. Never underestimate the importance of government on innovation and technology development.

Lessons from DARPA

The DARPA’s principles are elevated autonomy and visibility; a focus on the best external rather than internal

 - DARPA is run like a loose collection of small startups, with no career ladder. Their employee badges are printed with an expiration date.
 - DARPA’s structure has eliminated the benefit of spending any time on politics, of trying to sound smart in meetings and put down your colleagues by highlighting the warts in their nutty loonshots so that you can curry favour and win promotions.
 - DARPA managers are broadly known in their community. They are granted authority to choose their projects, negotiate contracts, manage timelines, and assign goals. The combination of visibility and autonomy creates a powerful motivating force: peer pressure.
 - In DARPA recognition from peers is a form of intangible or soft equity. It can’t be measured through stock price or cash flows. But it can be just as strong a motivator, or even stronger, as both a carrot and a stick.


Your company

You won’t apply the same way to every company (most companies are not faced with problems that might be solved by a giant nuclear suppository). But every organisation can find opportunities to increase autonomy, visibility, and soft equity. Some additional tips:

Beware of the skill fit

Match employees and projects and ensure optimal skill-fit. Poor project–skill fit can also result from an overmatch: skills so far above project needs that the employee has maxed out what he or she can contribute. Employees who are not stretched by their assigned projects have little to gain from spending more time on them. How much might politics decrease and creativity improve if rewards for teams and individuals were closely and skillfully matched to genuine measures of achievement?

Watch the incentives

“Powerful” has an interesting perspective on incentives.  Competitors in the battle for talent and loonshots may be using outmoded incentive systems. Bring in a specialist in the subtleties of the art—a chief incentives officer. Companies with outstanding chief incentives officers—experts who understand the complex psychology of cognitive biases, are skilled in using both tangible and intangible equity, and can spot perverse incentives—are likely to do a better job than their competitors in attracting, retaining, and motivating great people. In other words, they will create a strategic advantage.

Management span

Wider spans (15 or more direct reports per manager) encourage looser controls, greater independence, and more trial-and-error experiments. Which also leads to more failed experiments. Narrower spans (five or fewer per manager) allow tighter controls, more redundancy checks, and precise metrics. When we assemble planes we want tight controls and narrow spans. When we invent futuristic technologies for those planes, we want more experiments and wider spans.
A wide management span helps nurture loonshots: it encourages constructive feedback from peers. Peers, rather than authority.

Disruptive innovation

The author makes a good point about loonshot versus disruptive. A loonshot refers to an idea or project that most scientific or business leaders think won’t work, or if it does, it won’t matter (it won’t make money). It challenges conventional wisdom. Whether a change is “disruptive” or not, on the other hand, refers to the effects of an invention on a market. Early-stage projects in rapidly evolving markets behave like a leaf in a tornado. You wouldn’t put a lot of faith in guessing where that leaf might end up. It’s easy to point to technologies that disrupted a market in hindsight, once the leaf has landed.

Embrace the crazies

Look for the innovation outliers (read “Quirky” and the “Rebel talent“. Perhaps everything that you are sure is true about your products or your business model is right, and the people telling you about some crazy idea that challenges your beliefs are wrong. But what if they aren’t? Wouldn’t you rather discover that in your own lab or pilot study, rather than read about it in a press release from one of your competitors? How much risk are you willing to take by dismissing their idea?


You want to design your teams, companies, and nations to nurture loonshots, in a way that maintains the delicate balance with your existing business, so that you avoid ending up like the Chinese and Roman Empire or most of the companies in your sector in the near future. See loonshots as insurance for day after tomorrow.



The Algorithmic Leader - Mike Walsh 

Review by Ron Immink



How do you make decisions? I am taking a bet that you spend very little time thinking about thinking or how and why you make the decisions you make. Let alone how you can use AI to do that even better and consistent.



Once you have read “Principles“, you should be looking at your business in a different way. Before “Principles” there was “Making money is killing your business“. Your business as an engine or combination of (decision making) processes. That is why I started to read “The Algorithmic leader”. Managing and optimising your decision making as a leader, using and applying machine learning.



Algorithms are not some computational incantation that somehow bring machines to life. They are more like a recipe for baking a cake: a step-by-step process (mixing ingredients) to solve a problem The very concept of an algorithm predates the modern computer by several thousand years; it can be traced back to some of the greatest minds of the ancient world who also used algorithms to think through difficult challenges.

Deep learning
With the advent of deep learning, computers can train themselves on datasets that contain millions of inputs and outputs, evolving as they do so. Being smart when machines are smarter than you require you to become something new. You can safely assume that if something can be automated, it will be—if not by you, then almost certainly by one of your competitors.
And unlike a human being, an algorithm will come to the same conclusions every single time, whether it is Monday morning or Friday afternoon, cold or hot, or after the algorithm has handled thousands of similar cases.
Live management
Soon algorithms will shape almost every aspect of our lives. They will determine our right to enter a country (US Customs now requires you to share your social media handles on request), buy train tickets (as happens with the social credit system in China), or shop at Amazon (Amazon can fire you as a customer if you start returning too many high-value items). Our refrigerators will keep track of what we eat, our toilets will report on our physical health, or our shoes will vibrate to alert us to walk in the direction of something we might find personally interesting.
35,000 decisions a day
It has been estimated that an adult might make around 35,000 decisions every single day. Your kids are unlikely to be in that position, given that an increasing number of their choices are being automated for them. Once you have experienced a world where your decisions are handled for you by smart algorithms, there is no going back. Those born after 2007, is the first not only to have had access to smartphones from birth but also to have been completely immersed in algorithmic platforms.
The next big shift in interface design is the move toward more natural interactions. Our bodies are becoming interfaces. Whether it be smart speakers or sensors, smart tattoos or augmented reality glasses, we are learning to sense and respond to data in a more intuitive way. The more natural the interface, the more likely we are to start forgetting about the algorithmic machinery hard at work in the background. Instead of automation creating more standardised experiences, your future customers will expect you to leverage machine learning to create more natural, personalised, human-level interactions. Human-level interfaces that understand our natural speech, recognise our faces, respond to our emotional states, and even track our gestures will be useful. They will allow us to effortlessly communicate our intentions and accomplish our objectives without resorting to command interfaces and workflows. Algorithms and data make “anticipatory design” possible. 
When algorithms become deeply embedded in our daily lives, they have the potential to greatly influence how we behave. If we reach that point, we will no longer be able to easily discern how much of our memory, experiences, tastes, or even our own identity is native to us and how much is merely the technological extensions of ourselves.
Optimal design of your business
So the question to ask is; if an AI were to design an organisation optimised for machine learning, how would that company operate? If you could start your business again with a clean sheet of paper and the ability to leverage AI, algorithms, and automation, what would you do differently? You need to become an algorithmic leader. The job of an algorithmic leader is not to work. Their real job is to design work. Here are the tips from the book to become one:
  • Put data at the heart of the business
  • Build a central data lake
  • Map the way you make decisions and solve problems
  • Adapt your decision making, management style, and creative output to the complexities of the machine age
  • Map your connections and relationships
  • Connect people, partners, and platforms
  • Avoid digital incrementalism.
  • Apply first principles thinking
  • Move beyond true and false
  • Break problems down into a series of smaller, more manageable problems (decomposition)
  • Separate the strategy (how to approach a problem) from the execution (crunching the data)
  • Apply probabilistic thinking or think like a gambler
  • Augment your intelligence
  • Track the results of your cognitive systems
  • Compare your decisions against people who are making decisions in a wide variety of areas
  • Keep meetings brief
  • Conduct decision audits
  • Principles rather than processes are what matter
  • Focus on the exceptions
Imagine what services might be made possible? The best way to imagine a future shaped by AI is not to focus on machines and their current capabilities but to think about the potential interactions between algorithms, human behaviour, and identity. Imagine a future without your company in it. If firms only exist to either reduce or eliminate transaction costs, what if technology like blockchain could do this more effectively? Would the firm as a concept still need to exist in its current form? In the future, some firms may operate without people or locations. They will be “decentralised autonomous organisations” based entirely on smart contracts that react to data and other algorithms. Imagine what life might be like in ten years. Build a picture of what life might be like in thirty years. He then asks himself what technologies, business models, and infrastructure might, therefore, need to be in place in fifteen years, ten years, five years, and next year.
The book is full of examples
Publicis, a multinational marketing company, has already started using algorithms to organise and assign its 80,000 employees, including account managers, coders, graphic designers, and copywriters. Whenever there is a new project or client pitch, the algorithm recommends the right combination of talent for the best possible result.
IBM has also applied for a patent for a system that monitors its workforce with sensors that can track pupil dilation and facial expressions and then use data on an employee’s sleep quality and meeting schedule to deploy drones to deliver a jolt of caffeinated liquid, so its employees’ workday is undisturbed by a coffee break.
Rolls Royce builds a virtual copy of every engine they make, combining data insights from throughout the business with design and manufacturing data, resulting in a perfect digital twin of their underlying physical asset. The Rolls-Royce Trent engine is an early example of a digital twin. A digital twin is a digital model of a physical object or process that allows you to optimise its performance. You could build a digital twin of a manufacturing line or a factory, a self-driving car, or even just a small component in a larger system, for example.
Google spent two years studying 180 of its teams. It called the study Project Aristotle. The researchers discovered that the best teams at Google exhibit a range of soft skills or “group norms”: equality, generosity, curiosity about teammates’ ideas, empathy, and emotional intelligence.
For Bezos, there are two categories of decisions. Type 1 decisions are the mission-critical, high-impact choices that influence higher-level strategy and can determine your future; Type 2 decisions are the lower-stakes choices that can be reversed if need be. You are better off acting on or even automating Type 2 decisions as quickly as possible. Amazon’s senior leaders typically leave all Type 2 decisions to the teams and individuals below them so they themselves can focus on Type 1 decisions.
Rakuten created its own algorithmic brain trust. Every quarter, all the senior leaders gather to discuss data.
When you arrive for an appointment at Forward, you are scanned by a full-body scanner that checks multiple physical attributes and downloads the diagnostic data collected by your wearable devices about your physical activity levels and heart rate.
A bank started to track who was talking to whom in the company, and what the social network looked like. Then, using just those metrics, they tried to predict subjective and quantitative ratings of performance for different employees as well as self-reported job satisfaction levels. The data from even these simple employee interactions proved to be an incredibly accurate predictor of performance.
IBM has also applied for a patent for a system that monitors its workforce with sensors that can track pupil dilation and facial expressions and then use data on an employee’s sleep quality and meeting schedule to deploy drones to deliver a jolt of caffeinated liquid, so its employees’ workday is undisturbed by a coffee break.
About decision making
Good decisions should be difficult. Their difficulty reflects the challenging nature of the topics and issues they relate to; they require strong cognitive skills if you are to arrive at a good solution. If your decision doesn’t seem difficult to make, you are probably not asking the right questions.
About decentralised control
The British Empire, expanded globally over a hundred-year period until it managed over 20 per cent of the land on the planet without any direct control from the centre. Buccaneers, tradesmen, and merchant seamen were given lots of delegated decision making in a highly self-organised fashion. When messages went from being transported by ships, with a send and receive time of six months, to a mere six minutes, everything fell apart. Because, as Parsa explained with a wry grin, the centre suddenly started to micromanage.
About the law
Under the General Data Protection Regulation, which came into effect in 2018, the European Union requires companies to be able to explain a decision made by one of its algorithms. Which means there will be lawsuits that require you to reveal the human decisions behind the design of your AI systems, what ethical and social concerns you took into account, and how well you monitored the results of those systems for traces of bias or discrimination. Document your decisions carefully and make sure you understand, or at the very least trust, the algorithmic processes at the heart of your business. If an AI causes a deadly mistake, who is ultimately culpable: the programmer who designed the algorithm, the data scientist who chose the training data, the safety engineer who failed to intervene, or you, the business leader who approved the optimisation target?
About work
In the future, you will be either working for the algorithm or if you are lucky, on the algorithm. What happens when more and more people are employed as part of a transient workforce governed by algorithms? For example, courier nomads may have the latest holographic phones, bone-conduction headphones, and low-latency, augmented reality eyewear, but make no mistake, they will be the physical manifestation of a new algorithmic underclass. Even within organisations, you will find growing inequality and a widening gap between top executives and an outer fringe of transient workers.
The algorithmic leaders
  • Focus on their future customers, not their existing ones
  • Design their operating model for multipliers, not margins
  • Analyse problems from first principles, not by analogy
  • Seek to be less wrong with time, rather than always being right
  • Humanise and complexify, rather than standardise and simplify
  • Are guided by user empowerment, rather than mere regulatory compliance
  • Ask whether they have the right approach, rather than whether they are getting results
  • Manage by principles, rather than processes
  • Believe that they should automate and elevate, rather than automate and decimate
  • Transform for purpose, not just profit


The book is a harder version of “The new leadership literacies“. Just add algorithmic to the skill set. Combine it with some meditation and mindfulness to make sure you stay on the right side of manipulation.