Part-Maven Part-Maverick

Every time you hand a task to AI, you make two decisions.The first is visible: get this done. The second is invisible: stop getting better at this.Most people only see the first one.Today, practically all writing about AI asks what the technology will do next. Eric So, who teaches AI at MIT, asks a question with a longer shelf life: given what we already know about how humans think, learn, and compete, what happens when you place a machine that thinks into that environment?His new book, The Collision: What AI Does to Us, makes almost no predictions about AI. It takes decades of established science about people and treats AI as a new variable in their environment. That choice is why the ideas hold up. Below are the models from our two hours together that stayed with me, especially the ones that run against intuition.1. The Dream Team FallacyThe intuition is simple. Take an elite human, add an elite machine, and you get the best of both. A doctor with AI should beat a doctor without it, and a strong analyst with a strong model should beat either one working alone.The evidence says otherwise. Study after study finds that the combination often performs worse than the human alone or the machine alone. Eric’s explanation is that the two do not add together like ingredients in a recipe. They combine like chemicals in a reaction, where each one changes how the other behaves.The main mechanism is anchoring. The picture people have in mind is two independent thinkers stress-testing each other. What happens in practice is that the machine speaks first, the human latches onto its answer, and the rest of the session is spent adjusting around it. The human stops contributing an independent view, which was the whole point of including the human.The practical lesson: decide in advance which work goes to the human alone, which to the machine alone, and which to both. When you do combine them, form your own view before you look at the machine’s.2. The RatchetYour brain is about 2 percent of your body mass and burns about 20 percent of your energy. Evolution built you to cut that cost whenever something offers to carry it, which makes a machine that thinks for you the most tempting offer your brain has ever received.Now add competition. You do not need to see a colleague use AI. You only need to suspect it, and the suspicion pulls you in. Your use then pulls them in further. This is a prisoner’s dilemma, and the mechanism that keeps it moving is a ratchet: each turn is easy to make and very hard to reverse.The subtle part is what the ratchet does to institutions. Eric described colleagues attending seminars on how to make their assignments “AI-proof.” Each redesign assumes every student will use AI, which quietly removes the option of doing the work unassisted. Once the system is redesigned around universal use, the choice to abstain no longer appears on the menu at all.3. Capability Has No Shelf LifeMost organizations treat skill as inventory. You build it once, store it, and retrieve it when needed. Eric calls this assumption one of the most common and costly errors he sees in business.The brain works differently. People who learned to juggle grew measurable brain matter in the regions responsible for hand-eye coordination within weeks, and within a few months of stopping, that growth receded. Skill is a state you maintain through use, and the brain reclaims what you stop using.AI makes this dangerous because the output stays high while the capability underneath it drains. The essay still reads well and the code still runs, so the gauge reads full right up to the moment you need the skill and find it gone. Eric admitted on air that after thirty years of programming, he now mostly types instructions into an interface. He also named a loss he suspects but has not yet tested: thinking through a problem without a machine to argue with.4. Work Is a Chain, and the Cheap Link Holds It TogetherAI is good at tasks. Jobs are sequences of tasks. The pitch deck is one link, and presenting it, defending it under questioning, and adjusting it in the room are the links that follow. Outsource the first link and you weaken every link downstream, because the understanding you would have built while making the deck is the understanding you need when someone challenges it.The same logic applies to junior roles. Many firms now use AI as a reason to hire fewer junior people, because their grunt work looks easy to automate. That grunt work is how senior people get made. A tennis academy that stopped junior players from hitting against the wall, because wall practice produces no trophies, would have no champions in ten years.Rory Sutherland’s hotel doorman makes the same point from a different angle. A consultant sees a salary for opening a door and recommends cutting it. The consultant does not see the greeting, the help with luggage, the security, or the impression of quality that justified the room rate. The cost appears on a spreadsheet. The value was never written down.5. The Skill-Biased ParadoxEconomists call AI a skill-biased technology. Its benefits go mostly to people who already have expertise, because they know which questions to ask and can tell a good answer from a plausible one.The paradox is in where that expertise came from. Eric built his through years of formative struggle before generative AI existed, and he doubts he would ask the same questions today if he had skipped that struggle. Today’s experts are the last generation trained without the shortcut. The tool amplifies exactly the expertise it makes harder to build, and that leaves every organization with an open question: where will the next generation of experts come from?6. When Polish Is Free, Signals CollapseA tailored cover letter used to tell an employer that a candidate cared enough to do the work. Now anyone can produce one in seconds, so it signals nothing. Eric pointed to research showing what employers do next: they fall back on older signals, such as which university you attended and whom you know.The people who lose are the capable outsiders, whose effort was the only signal they had. Ramanujan reached Hardy through a cold letter full of mathematics. In today’s inbox, filled with polished messages that cost nothing to write, it is worth asking whether that letter would get read.7. Guard or CedeThe useful question is which work AI should do, and the answer depends on who you are. Eric gives coding to AI because his value lives in the questions he asks as a researcher. A security engineer who did the same would be giving away the core of the job. Eric calls the parts of your work that define your value your signature thinking skills. Guard those, and let the machine have the rest.One subtle point he added after finishing the book: even “harmless” uses carry a cost. Asking AI for a first outline or a brainstorm anchors your thinking before you have explored the problem yourself. The blank page is uncomfortable, and some of its value lies in that discomfort.The System Gambit is a rigorous guide to the one strategic move that most investors and operators never make: This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mavenmaverick.substack.com

Joseph Moore is a national bestselling author, historian, and investor whose relentless optimism, academic research, and self-experimentation with history’s wildest financial strategies made him financially independent in his mid-40s. His new book “leaves you believing in the American Dream again.” He teaches readers how to avoid the scams we keep falling for, what history says about crypto, stocks, and real estate (hint: it isn’t what they tell you), and offers 25 lessons that really worked (and 7 that didn’t) to get ahead in America.History tells you what doesn’t work, not what works. It shows what did not happen and why. Anything else is to try to predict what will happen is overfitting: porting a rule that worked in one environment into another where the conditions are very different.Financial advice is environment-specific. Whether to save, spend, hold cash or hold property depends on the context you are operated in. Advice often outlives the conditions that made it effective, so inherited wisdom is often entirely wrong.Passive income is a macro artifact. It works only without inflation. Once inflation is normalized, passive pays only negligibly. Anything paying well demands active work or carries speculative risk.Assets that decay are like bonds with entropy. Anything physical has a maintenance cost that eats the yield. Thus value comes from actively improving it, and not from passively owning it.Net worth is a meaningless number. It has at most 3 uses: settling an estate, assessing credit, signalling status. Treating it as wealth or money results in bad policy and bad decisions.Fast time and slow time. Financial time decouples from clock time and runs at different speeds for different people. Most of life is slow. Fast time rewards concentration or punishes it. The work of slow time is building something that survives fast time and is positioned to be rewarded by it, instead of punished by it.Technology is revolutionary only when it forces a new system. Plugged into the old paradigm it yields marginal efficiency, which competes away because anyone can buy the same tool. Gains persist only where you hold leverage or proprietary asymmetry.More on proprietary asymmetry in my book The System Gambit:Limit, concentrate, then diversify. Narrow into your comparative advantage. Get good enough to see where the paradigm is unoptimized. Diversify to protect what you made. Remember that diversification preserves wealth, but it does not create it.Retirement saving is not wealth building. Confusing them substitutes preservation with growth. Investment in your own capability compounds faster than any regular contribution to a savings plan can.Define enough. Without a ceiling, capacity keeps buying returns you no longer need and that are likely to put you in trouble.Attitude is a structural input. Optimism plus a saving habit predicts financial outcomes better than inheritance or income.Thanks for reading Part-Maven Part-Maverick! Subscribe for free to receive new posts. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mavenmaverick.substack.com

Uncertainty is about unknown probabilities. Doubt is about interpretation: two people can face the same facts and reach different conclusions because context, experience, and identity shape what the facts mean.That distinction matters because most systems train people to treat not knowing as weakness. School rewards fast right answers. Work rewards confidence and speed. But real judgment requires something else: sit with ambiguity, generate options, test assumptions, revise.Intuition belongs in that process, but only as a hypothesis. Good decision-makers do not stop at their first instinct. They ask: what would change my mind? where could this fail? what evidence is missing?The practical rule from the episode: slow down early to move faster later. Testing, feedback, and extra discussion are not overhead; they prevent expensive mistakes. The firefighter equipment example made this concrete: technically sound gear failed in use because nobody tested it under real conditions.The AI takeaway is similar. Automate formalized work. Keep human judgment where the problem is ambiguous, cross-functional, or strategic. Speed only helps if you’re doing the right thing.The hardest challenge is scale: how do you preserve personalized judgment and feedback across many students or employees?Bidhan’s shift toward oral exams instead of hackable papers shows one answer: design for real understanding, not just output.Mental model: use structure for routine work, doubt for ambiguous work, and intuition as a starting point, not as a verdict.The System Gambit is a rigorous guide to the one strategic move that most investors and operators never make:Thanks for reading Part-Maven Part-Maverick! Subscribe for free to receive new posts. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mavenmaverick.substack.com

The System Anti-GambitWhen paradigms break, the biggest threat to an investor or operator is their own most sensible instinct. How to tell a real structural move from a panic reflex?When India liberalised in 1991, it made the sensible bet. Decades of Nehruvian investment had left the country with graduates, so India sold their brainpower to the world through IT and knowledge services: hire a young graduate for a couple of thousand dollars a year, give him a month of standardised training on a campus in Mysore or Chennai, bill his time to a Fortune 500 company, and keep a twenty to twenty-five per cent operating margin. It worked for three decades and earned three or four hundred billion dollars of foreign exchange. It was a system gambit, and it paid off for a generation.AI is close to purpose-built to take it apart. When Saurabh Mukherjea came on my podcast to discuss his book Breakpoint, this was the case he kept returning to. The Fortune 500 client no longer cares how many people you deploy. It names a fixed price for a CRM system and leaves the method to you, and the method that wins now is two senior people and AI rather than a floor of graduates billed by the hour. Smaller Indian IT firms have already started to shut down.The move that built the industry is now the move dismantling it.In The System Gambit I call the winning move a system gambit and its shadow the system anti-gambit. The system anti-gambit is what you do when the ground shifts and you answer by doing more of what used to work. It is the most rational-feeling response available, and that is what makes it dangerous.The party most exposed to the system anti-gambit is the one currently winning. Sit at the top of a paradigm and your entire apparatus, your training, your hiring, your metrics, your instinct for what good looks like, is tuned to that paradigm, so when it shifts your immune response is to intensify the very thing that put you on top.Germany won the World Cup in 2014 by doing the classical things well. Find the children early. Drill them. Make them fit and disciplined. Then Germany kept doing exactly that while France, Spain and Morocco moved to fluid roles, fluid formations, and, in Morocco’s case, a platform model that pulled world-class players from its diaspora toward one clear mission. Watching Germany play France today, Saurabh said, is like watching teams from different planets. The 2014 title was the setup for the decline that followed.India produces eight to nine million graduates a year, close to the population of Germany every decade. Graduate unemployment runs between thirty and forty per cent, while the rate for non-graduates is around three. The degree now predicts unemployment. The old script, study hard, earn the degree, work hard, land the job, arrive at a middle-class life, has inverted. And the sensible-sounding instruction, send more young people to university, is the system anti-gambit at national scale. Arguing against university feels wrong. That feeling is the trap.AI sharpens all of this through one property. The transforming technologies of the past forced a firm to change how it operated before it could use them at all, and that friction is exactly what made them worth capturing. AI runs the other way. It bolts onto whatever you already do, which means the gains from bolting it on are available to anyone with a credit card and an API key, and anything available to everyone earns nothing that lasts.This is why the most common reaction I meet, the senior executive who asks me how to learn prompting, is the system anti-gambit in a new form. It carries an industrial-era instinct, learn a defined new skill, tick the box, feel safe, into a situation where the field itself has changed. Sangeet Paul Choudary makes the point in Reshuffle, because the game has changed underneath you, and learning to dribble faster is no help once the court has become a rink.If the standardised skill is losing its value, where the value flows next depends on what a system actually does. In the book I separate four modes of controlling any system, and they stack into a ladder. The lowest is regulation, the thermostat that holds a variable inside a band. Above it is coordination, the traffic light that keeps the flows from colliding without telling anyone where to go. Above that is orchestration, the conductor who plays no instrument yet makes a written score sound like music. At the top is governance, the captain of a ship who, with a storm ahead and no way to know the outcome, decides whether to stop, turn, reroute, or sail straight through. AI is already good at the thermostat, and it is climbing into the traffic light and the scored parts of the orchestra. What it cannot do is the captain’s judgement under genuine uncertainty. So value drains out of the lower rungs and pools at governance.That ladder explains the value migrations Saurabh tracks as an investor. Index funds turned asset management, the discretionary stock-picking that Saurabh and I both do for a living, into something close to a regulated utility, and the market answered by paying far higher multiples for wealth managers, whose real job is the ungovernable part, sitting with a frightened client and talking them out of selling at the bottom.The same logic is draining broadcast and print into YouTube and OTT, where what survives is the customised and the emotionally specific rather than the templated segment. Anything that is cut, paste, and repeat is being repriced towards zero, while anything that turns on reading a particular person in a particular moment is gaining.Saurabh reframes it as three fluidities:* whether a person’s skills are fluid or fixed,* whether their role is fluid or fixed, and* whether the organisation around them is rigid or fluid.The industrial revolution, from Adam Smith’s pin factory to Ford’s line at River Rouge, made all three as static as it could, deliberately. The coming years will hurt mainly because most firms are still built that way while the world has started paying for the opposite.None of this tells you which specific move to make, and it should not, because there is no formula that guarantees the result. But you can test a candidate move before the market passes its verdict. There are three checks.The first check is whether the move builds a self-improving loop. If each turn of the wheel makes the next turn better, you own something that compounds. If it does not, you have swapped an old asset for a newer one, and a newer asset is not a position.The second check is path dependence. A real structural position cannot be reached by a richer competitor writing a cheque, because that competitor would have to run the loop from the start rather than buy their way to your thousandth iteration.This is what makes Bajaj Finance so hard to copy. Bajaj has collected granular data on Indian borrowers at the point of sale, down to whether a doctor is a cardiologist or a dentist, trained at AIIMS or an ordinary college, practising in an elite Mumbai suburb or the boondocks, and that data lets it price risk for doctors better than anyone and hold the largest medical loan book in the country. If Saurabh and I raised money tomorrow to rebuild that dataset, we could not, because the signal was gathered over years at the core bottleneck by an agent standing at the checkout. That is a moat. A skill anyone can buy with an API key never becomes one.The third check is the one people skip, and the one I rate highest. I call it management-logic antagonism. It means doing the new thing in a way that runs against the logic of the old system, so the incumbent cannot copy you without dismantling itself. Ukraine is the best illustration.On paper, a country attacked by a neighbour with ten times its economy and the largest nuclear arsenal in the world should fold in days. Ukraine survived by refusing to fight the industrial war Russia had prepared for, the war of feeding more recruits into the line, and in… This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mavenmaverick.substack.com

Matthew Stafford is a VC and the co-author of “Find Your 9others: The questions to ask yourself as you start up and scale up” and co-founded 9others. You can follow him here on LinkedIn.In this episode, Matthew Stafford shares how defining your intellectual challenge can help overcome envy and focus on personal puzzles to solve, leading to greater fulfillment and success.Key Topics:The concept of intellectual challenge as a puzzle to solveHow envy distracts from personal goalsStrategies for focusing on your own puzzleThe importance of clarity in personal growthMaintaining motivation through puzzle-solvingTakeaways:Identifying your intellectual challenge helps you stay focused.Envy often distracts from personal progress.Focusing on your own puzzle leads to fulfillment.Clarity about your challenge boosts motivation.Consistently solving your puzzle builds success.Thanks for reading Part-Maven Part-Maverick! Subscribe for free to receive new posts and support my work.The launch discount of my book The System Gambit will expire today, so grab your copy right away:If these insights resonate with you, feel free to share the post:To know what comes next on Part-Maven Part-Maverick:* Subscribe here for early access to future episodes* For more of my thoughts, follow me on LinkedIn* Get my book The System Gambit about how to find leverage to unlock compounding value* Get my book Data Impact for a pragmatic take on data-driven value creation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mavenmaverick.substack.com

Most of our education systems are designed for a different era: built on fear, conformity, and a focus on standardized metrics. But what if true learning isn’t about fitting into a box, but about daring to step out of it?Kathrin Höckel’s extraordinary journey from classical dressage to Arctic expeditions reveals the secret sauce: learning as an adventure rooted in responsibility, freedom, and mastery.My new book The System Gambit shows how you can compound your career or business:In Brief:Learn why education rooted in fear fosters passivity and how embracing responsibility and adventure can unlock true learning. Discover the importance of environments, mastery, and lifelong curiosity in shaping resilient, responsible humans ready for the complexities of tomorrow.Learning as an Adventure Inspired by Extreme SituationsHave you ever wondered why the education system feels disconnected from real life?Why do students often emerge from school ill-prepared for actual challenges?These questions are at the core of a profound conversation with Kathrin Herkel, an educator and explorer who believes that true learning happens when we leave the familiar and confront the unknown. In this post, we’ll explore how education can evolve from a fear-driven, conservative system into a dynamic, adventure-based journey that fosters responsibility, curiosity, and resilience, qualities essential for thriving in a rapidly changing world.The Root of Education’s Failures: Fear and ConservatismEducation today is often perceived as broken, primarily because it clings to traditional structures rooted in fear. Kathrin Herkel points out that at every level, from policymakers to teachers, fear of making mistakes hampers innovation. Schools and teachers are wary of experimenting, fearing that deviations might threaten their jobs or reputation. Parents, similarly, prefer to play it safe, resisting change that might temporarily lower performance metrics or disrupt routines.This inertia results in an education system that prioritizes standardization and predictability. As Kathrin emphasizes, “The system is conservative by nature, and that inertia feeds into its resistance to change.” Consequently, students are rarely challenged to explore their identity, take responsibility, or develop the resilience needed to navigate the uncertainties of life. Instead, they are often passive recipients of knowledge, rather than active participants in their growth.Learning as an Adventure: Lessons from Extreme SituationsKathrin’s unique perspective is shaped by her extraordinary adventures, biking across the US and Russia, trail running in Kenya, and experiencing the Arctic in a hut above the North Pole. She underscores that these extreme experiences strip away comfort and force individuals to confront their limits, revealing their true character.She introduces the concept of “learning from an adventure,” where stepping into unfamiliar, even dangerous situations, is the best teacher of resilience, responsibility, and decision-making. For example, in the Arctic, she learned that “in a place where nobody can hide their character, authenticity shines through.” These environments demand that one acts with responsibility, calmness under pressure, and adaptability, traits that are often absent in traditional education settings.The Power of Responsibility and Freedom in EducationA recurring theme in Kathrin’s philosophy is the vital link between responsibility and freedom. Her schooling experience, especially at a Steiner school, allowed her to choose her own learning path at 15. This approach instilled a deep sense of ownership and intrinsic motivation. She argues that responsibility fosters freedom because it teaches individuals to own their choices and outcomes.She advocates for an educational paradigm that reintroduces responsibility early, encouraging students to make decisions, face consequences, and develop self-awareness. The metaphor of the Mustang is instructive: “You’re born wild and free, then tamed and broken, and once you understand discipline, you are truly free.” Only through this cycle, free exploration, controlled challenge, and responsible ownership, can learners truly thrive.The Role of a Master: Learning from Those Who Have Walked the PathKathrin’s journey highlights the importance of learning from masters, mentors who embody mastery, responsibility, and authenticity. Whether in dressage or extreme expeditions, working with a master involves surrendering ego and aspiring toward an ideal. These relationships demand humility, patience, and a willingness to learn from someone who is a step ahead.She underscores that these models of mastery are present in many cultures, think of the sensei in Japan, gurus in India, or classical trainers in Europe. The profound value lies in observing how masters live and act, internalizing their principles, and ultimately evolving into masters ourselves.Aesthetic and Spiritual DimensionsA striking insight from Kathrin involves the environment’s aesthetics and spiritual undercurrents. She describes her school as a space of beauty, coherence, and purpose, an environment that reflects and nurtures the spirit of learning. Conversely, many conventional schools resemble prisons or military camps, sterile, ugly, and utilitarian.This aesthetic dimension affects students’ emotional states and their willingness to engage. “If environments are beautiful and aligned with values like harmony and responsibility, they inspire pride and responsibility,” she notes. Her school’s focus on integral education, covering physical, emotional, mental, and spiritual aspects, demonstrates that education is about more than acquiring facts. It’s about cultivating human flourishing.From Formal Pedagogy to Authentic LearningKathrin criticizes mainstream pedagogy for its transactional, bureaucratic nature, target-driven, algorithmic, and dogmatic. She argues for a shift from “dog school” training that enforces compliance to an approach that values intensity, responsibility, and authentic mastery.She emphasizes that real learning involves self-awareness, deliberate practice, and relationship with mentors. The emphasis should be on fostering curiosity, judgment, and the ability to operate above the algorithm, skills that AI is unlikely to replace.The Continuous Journey: Learning as a Lifelong AdventureEducation, in Kathrin’s view, does not end at graduation. We are all lifelong learners, especially as AI and changing technologies reshape work and society. She advocates for “learning from an adventure,” where each experience deepens our self-knowledge and broadens our horizon.She introduces the idea of “operating above the algorithm,” being a human who combines mastery, curiosity, and responsibility to navigate a world of automation. The goal is to be not just a passive user of technology but an active shaper of our future.Thanks for reading Part-Maven Part-Maverick! Subscribe for free to receive new posts. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mavenmaverick.substack.com

Erik Lie is the author of “Catching Cheats: Everyday Forensics to Unmask Business Fraud”. You can follow him here on LinkedIn.Erik uncovering financial fraud patterns, insider trading, and the importance of forensic analysis in detecting deception in markets and corporate behavior.This episode will help you stay alert, apply basic forensic analysis to your investments and help you protect your portfolio from fraud and losses.Key TopicsPatterns in stock price movements before and after corporate eventsThe role of insider trading and its detectionThe impact of regulations like Sarbanes-Oxley on fraud preventionTechniques for forensic analysis of financial dataBehavioural insights into why executives commit fraudThanks for reading Part-Maven Part-Maverick! Subscribe for free to receive new posts.My new book The System Gambit just launched:If these insights resonate with you, feel free to share the post:To know what comes next on Part-Maven Part-Maverick:* Subscribe here for early access to future episodes* For more of my thoughts, follow me on LinkedIn* Get my book The System Gambit about how to find leverage to unlock compounding value* Get my book Data Impact for a pragmatic take on data-driven value creation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mavenmaverick.substack.com

In this insightful interview, Alexander Hoare shares the secrets behind the 350-year-old family bank's success, emphasizing the importance of purpose, simplicity, and long-term thinking. Discover how their contrarian approach to growth, technology, and relationships creates a resilient and enduring business model.Key Topics Long-term family business successContrarian approach to growth and technologyRole of unlimited liability in decision-makingImportance of relationships over transactionsSimplicity and diseconomies of scale in banking Long-term stewardship and legacyThe concept of obliquity and its application in businessBuilding and maintaining trust and reputationImpact investing and societal contributionThe importance of culture and values in bankingChapters00:00 The Unconventional Success of C. Hoare & Co.11:40 Contrarian Approaches in Banking16:33 The Legacy and Governance of C. Hoare & Co.21:20 The Role of Partners and Succession Planning31:22 Unlimited Liability: A Unique Perspective39:21 Consequences of Limited Liability44:19 Simplicity vs. Complexity in Business49:19 Building Relationships Over Transactions57:47 The Power of Word of Mouth01:14:54 The Banker's Banker: Trust and Reputation01:21:20 Investing in Meaningful Impact01:28:45 Trust as an Uncommoditizable Asset01:33:14 Making Your Own Luck: The Role of Judgment01:38:49 The Importance of Staff and Culture01:43:12 Quality Over Quantity: The Data Dilemma01:48:24 The Value of a Large Family Network01:52:49 Navigating Wealth: Parenting and Money01:57:40 Environmental Stewardship: The Seagrass Initiative02:04:51 Faith, Family, and the Inner Life02:19:22 The Emotional Aspects of Money and PhilanthropyMy new book The System Gambit has C. Hoare & Co. as a case study:In 2008, as the world’s largest banks collapsed or were nationalised, one smaller bank on Fleet Street had the opposite problem: too much money was arriving at its door.C. Hoare & Co., founded in 1672 and older than both the Bank of England and the United States, came through the crisis with barely a scratch, its deposits swelling as frightened money looked for somewhere run by prudent people with a clean balance sheet.For decades the bank had been doing the very things every consultant and textbook had told its eleventh-generation partner not to do.As Alexander Hoare recalls in Impact Banker, “many people told me we were mad to carry on in the modern litigious world with unlimited liability, but in 2008 when the Great Financial Crash hit, people looked around for honest bankers with a clean and conservative balance sheet, and the deposits piled up.”In the year conventional wisdom was put to the ultimate test, the man who had ignored it for decades was suddenly the most sought after.Alexander Hoare is a nonconformist, and a cheerful one.“It has pleased me,” he writes, “to do many things wrong from a conventional point of view.” He kept unlimited liability when the whole City fled to limited liability. He keeps his bank deliberately small while everyone worships scale. He turns away profitable customers, has “never aimed to maximize profits,” and once sold a good, growing business in order to become smaller.He quips that his job “is to keep the family poor.” And he states his conviction clearly:“I see maximizing short term profits as the wide gate to failure.”The word nonconformist is apt. In England a nonconformist was someone who declined to worship at the established church, not from revolt but from conviction, and many of Britain’s most durable enterprises were built by people of that temperament.Alexander calmly declines the established church of contemporary finance: scale at all costs, short-term shareholder-value maximisation, limited liability, and the eternal myopia about the next quarter.It would be easy to dismiss this as nostalgia. But examined closely, nearly every one of his tenets is the same structural principle: he re-couples a feedback loop the industry has, for short-term greed, deliberately decoupled.* Action to consequence.* Price to value.* The firm to its community and society.* The present to the future.Decoupling those reinforcing feedback loops window-dresses the numbers in the short-term but re-coupling them is better for the business, and over three and a half centuries, have shown it is also what better-for-the-world looks like.1st tenet: actions have consequencesBegin with the nonconformity that sounds most reckless.Every partner of C. Hoare & Co. stands to lose everything they own if the bank fails: homes, savings, including their children’s inheritance. To most financiers this is complete lunacy; the entire architecture of the limited-liability corporation exists to prevent it.Alexander inverts the frame. The natural order is for actions to have consequences; it is in fact the contemporary arrangement that is the aberration from historical pattern.Until Big Bang in the late 1980s nearly every City firm traded on unlimited liability, and “my word is my bond” was an enforcement mechanism, not just a slogan. What followed its abolition was an era in which executives could half-bankrupt a country and walk away unscathed. Unlimited liability, by contrast, “focusses the family on a culture of excellence, not scale.”Through a first principles systems thinking lens the argument is obvious.Any successful system needs feedback: consequences must return to the actor and shape the next action. Limited liability severs that loop, capping the decision-maker’s downside at zero while leaving the upside unbounded.A diversified shareholder optimises the ensemble average across many companies; while a partner with unlimited liability and a more than 350-year time horizon must optimise the time average, the single path of history where ruin is permanent.A bet with a glorious expected value and a small chance of ruin is excellent for the ensemble and fatal for whoever gets ruined. This is why the book’s risk philosophy is so unfashionably simple: “banking is quite straight-forward if you merely avoid the mad and the bad.”2nd tenet: perpetuate, do not maximiseThe mission statement the older partners first mocked is seven words long:“to perpetuate a profitable family business.”It “does not say anything about conquering or winning,” and its ambition is bounded: “we want to survive as a family business for perpetuity. This means being just big enough, not vast.”To the modern mind this is a category error: surely the purpose of an enterprise is to grow?Here Alexander deploys the most counterintuitive finding of his forty years, borrowed from John Kay’s notion of obliquity:“We have never aimed to maximize profits or to grow dramatically, and it turned out that not aiming at these things can be very effective at delivering them.”Kay’s cautionary tale is ICI, which adopted shareholder-value maximisation as its explicit goal and destroyed itself pursuing it. Aim at excellent banking and profit arrives as a by-product; aim at profit directly and you strip the institution that produces it.Donella Meadows identified the goal of a system as one of its highest leverage points:change the goal and everything downstream follows.Set it to maximise short-term profits and the system rationally destroys its future to grow the current quarter; set it to perpetuate and any gain that imperils survival is no gain, because the future is the whole point.Extraction generates large early terms in a series that is then truncated. This is best illustrated by the average lifespan of an S&P 250 company falling from 60 years in the past to just 15 years now. The perpetuator generates decent terms forever, and a series that never terminates beats any short burst that does.Eric Ries, in Incorruptible, names the force this resists: financial gravity, the pull on every company exposed to the capital markets toward short-term extraction at the expense of mission and survival.3rd tenet: small is beautiful, & KISS principleEarly on, Alexander adopted “two handy man… This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mavenmaverick.substack.com

Robin Kermode, is a top executive communication coach and a veteran actor, he is also the author of “Speak: So Your Audience Will Listen - 7 Steps to Confident and Successful Public Speaking”. You can follow him here on LinkedIn.Robin Kermode shares expert insights on effective communication, overcoming nerves, and engaging audiences. Discover practical techniques for public speaking, body language, voice modulation, and crafting simple, impactful messages.Key Topics:The importance of authenticity and simplicity in speechTechniques to manage nerves and anxietyThe role of body language and voice modulationCrafting messages with clear intention and empathyThe importance of emotional connection and storytellingRobin Kermode:https://robinkermode.comhttps://en.wikipedia.org/wiki/Robin_K...https://www.imdb.com/de/name/nm0449418/https://www.amazon.com/Robin-Kermode-...Ritavan:https://www.amazon.com/dp/B0GY8J23SKhttps://www.amazon.com/Data-Impact-bu...https://ritavan.com/If these insights resonate with you, feel free to share the post:ShareTo know what comes next on Part-Maven Part-Maverick:Subscribe here for free for early access to future episodesFor more of my thoughts, follow me on LinkedInGet my book The System Gambit for finding leverage to unlock compounding value This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mavenmaverick.substack.com

John Brandon Elam with Adam DeJans Jr., is the bestselling author of “The Decision Factory: A Novel about Decisions Under Uncertainty”. You can follow him here on LinkedIn.In this insightful interview, John Brandon Elam discusses the importance of decision-making in organizations, the limitations of traditional planning, and how to leverage decision systems and policies for better business outcomes.Drawing from his book and influences like Warren Powell and the Phoenix Project, he offers practical frameworks for understanding and improving decision quality.Takeaways:Decisions as the core of enterprise controlLimitations of traditional planning and forecastingDecision systems and policies in organisationsMeasuring decision quality and trade-offsInfluences from Warren Powell and the Phoenix ProjectIf these insights resonate with you, feel free to share the post:ShareTo know what comes next on Part-Maven Part-Maverick:Subscribe here for early access to future episodesFor more of my thoughts, follow me on LinkedInGet my book Data Impact for a pragmatic take on data-driven value creation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit mavenmaverick.substack.com