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  • Sangeet Paul Choudary — Reshuffle
    Who Wins When AI Reshuffles, Reshapes, and Restacks the Economy?War is the most ruthless form of competition and the best metaphor for what’s happening in today’s AI-fueled economy. As Sangeet Paul Choudary explains in his new book Reshuffle, the French generals after World War I weren’t foolish. They were simply brilliant in the wrong paradigm. They built the Maginot Line: 450 miles of bunkers, turrets, and underground fortresses. But when German forces launched the blitzkrieg, they bypassed it entirely.The Maginot Line didn’t fail because it was weak. It failed because it answered the wrong question.That’s exactly what most companies are doing with AI today: treating it as a high-tech fortification. Bolting it onto legacy workflows and rebrand yourself as “AI-powered”. The real edge doesn’t come from slapping LLMs onto old pipes. It comes from ripping the system open, mapping its friction points, nodes, feedback loops, and asking: What happens when AI changes how this system works altogether?SaaS companies are especially guilty here. Add a chatbot to your product, build some automation into your backend, and suddenly you're “AI-first”? It’s the most ridiculous form of window-dressing, a short-term rebrand. And worse, it’s shortchanging customers. These superficial upgrades don’t solve deeper problems of coordination or deliver system-level value. They just inflate price tags with zero structural change.Most business leaders unfortunately still start with the tech, not the terrain. They chase the hottest models, slap them onto dusty workflows, and hope for ROI. That’s like adding turbochargers to a horse cart and expecting lift-off. Sure, you’ll get more speed, but you’re definitely not leaving the ground.Forget ownership. That’s yesterday’s source of power. In the agrarian age, it was land. In the industrial era, it was factories. In the 20th century, it was IP. Today, power flows to those who coordinate what they don’t own—supply chains, developer ecosystems, user networks, fragmented data.AI isn’t just a better tool. It’s a system reshaper. It’s a chance to rewrite the rules of engagement. And the real advantage lies not in automating tasks, but in orchestrating systems. But you’ll never see that if you treat it merely as a plug & play tool.The magic happens when you use AI to solve coordination problems:• What are the decision bottlenecks?• Which handoffs introduce delays?• Where does misaligned data cause friction?Solve those, and you don’t just cut costs. You unlock new possibilities.This Substack is reader-supported. To receive new posts and support my work, consider becoming a free or paid subscriber.Think Blitzkrieg, not Maginot. Germany didn’t win by building bigger tanks, they won by syncing infantry, armor, air cover, and supply chains into one blistering advance. It wasn't just a change in technology, but a change in military doctrine. AI works the same way in modern business: sure, it can automate invoice approval or customer chat. But its strategic superpower is dismantling coordination barriers.In Reshuffle, Sangeet Paul Choudary argues that AI’s true story is one of system evolution, not tool installation or upgrade. Sustainable advantage grows from system‑first thinking, relentless focus on coordination, and unleashing compounding and cascading effects to change your scope.This cascading dynamic is the untold tale of global trade’s second wave, and the real story of AI today. The first wave of breakthroughs—faster GPUs, bigger models—grab headlines. The second wave, of unlocking coordination will reshapes economies.Want proof? Look at logistics giants that use AI to coordinate fleets in real time, rerouting trucks, drivers, and dock slots with relentless precision. They didn’t just save money, they invented on‑demand supply chains that redefined customer expectations.AI isn’t the answer to an isolated task. It is the lever that shifts entire systems from static panels to adaptive networks.Ignore this, and you’ll build digital Maginot Lines: sleek dashboards, fancy bots, and shiny analytics that fail to move the needle when the real world flips the script. Understand this, and you’ll orchestrate an AI-powered Blitzkrieg, where synchronization becomes your moat.The takeaway from Reshuffle?Sustainable advantage starts with systems, not tech stacks.It flows from orchestration, not automation.It’s earned by redefining the game, not by playing it slightly better.Rethink the system you’re operating in, from first principles. Then play to win.Thanks for reading! This post is public so feel free to share it.Watch the full video episode:Follow Sangeet Paul Choudary on LinkedIn: https://www.linkedin.com/in/sangeetpaulBuy Reshuffle by Sangeet Paul Choudary: https://www.amazon.com/Reshuffle-wins-restacks-knowledge-economy-ebook/dp/B0DTKW6NQV/Follow Sangeet on Substack:This Substack is reader-supported. To receive new posts and support my work, consider becoming a free or paid subscriber.How I Apply This to “AI“ Right NowHere’s a summary of the key points I learned and will apply to my work:We tend to overvalue AI’s automation capabilities because we undervalue coordination’s role in economic evolution. While efficiency is AI’s visible benefit, the hidden reward lies in gaining control over how others align with your system.Coordination—which hinges on five structural levers: representation, decision-making, execution, composition, and governance—drives productivity by allowing components to specialize and scale in unison.If AI is added without changing coordination mechanisms, you end up building leverage for the AI vendor rather than for yourself. So, instead of asking, “What can’t AI do?” ask, “What does AI break?” The next major opportunity arises from the constraints AI introduces, not from the tasks it hasn’t yet mastered.Reskilling alone falls short when AI restructures the systems that once valued those skills. AI introduces new choke points where contextual value concentrates—often beyond traditional roles. In a world of abundant answers, value shifts toward asking the right questions and identifying the right signals, making curiosity and curation increasingly vital human traits.Economic value hinges more on scarcity and relevance than on mere skill or effort. Jobs, therefore, exist to manage constraints rather than execute tasks, and roles are valuable when they resolve the system’s most limiting factors.To operate ‘above the algorithm’ is to shape the system; to operate ‘below’ it is to be shaped by it. AI intensifies the divide between tool users and those who build integrated solutions around them. Strategic advantage now resides with solution architects, not just skilled operators.Historically, autonomy and coordination have been at odds in organizations: more autonomy reduced alignment, while more coordination slowed execution. AI shifts this dynamic—greater autonomy enhances coordination and vice versa—generating a flywheel of value creation.Thanks for reading! This post is public so feel free to share it.Next on Part-Maven Part-Maverick, we will discuss with Chris Reiter the challenges that the world’s 3rd largest economy Germany faces and the implications for the future.This Substack is reader-supported. To receive new posts and support my work, consider becoming a free or paid subscriber.Subscribe to be first to know when the episode drops: https://www.youtube.com/@SLASOGFor more of my thoughts, follow me on LinkedIn: https://www.linkedin.com/in/rritavan/Get my book Data Impact for a pragmatic take on data-driven value creation for business: https://www.amazon.com/Data-Impact-businesses-LEVERAGE-SIMPLIFY/dp/178133921X/Thanks for reading! This post is public so feel free to share it. Get full access to Part-Maven Part-Maverick at mavenmaverick.substack.com/subscribe
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  • Alex Edmans — May Contain Lies
    What is a lie?I always thought that a lie is a bold and shameless falsehood. It turns out that a lie is just a complement of truth. And given the high bar of truth, anything may contain lies. That’s in fact the title of Alex Edmans’ book: May Contain Lies.Lies are everywhere; they come wrapped in facts, backed by data, supported by studies, or amplified by authoritative voices. And they feel true, because they flatter what we already believe.I spent my teenage years devouring behavioural economics books. Daniel Kahnemann, Dan Ariely, Richard Thaler, V Raghunathan. This resulted in a vast zoo of behavioural biases that I always tried to keep in mind when thinking things through. That was rather unwieldy, because it is hard to go through an extensive checklist of biases for every thought or decision.In May Contain Lies, Alex Edmans argues that when one already has a point of view on something, then Confirmation Bias is a huge danger, and otherwise, when one doesn’t have a point of view, then Black and White Thinking is lurking around. Confirmation Bias is a cognitive bias where people tend to search for, interpret, favor, and recall information in a way that confirms their pre-existing beliefs or assumptions, while giving less attention to or dismissing contradictory evidence.Black-and-White Thinking, also called all-or-nothing thinking or dichotomous thinking, is a cognitive bias where people view situations, people, or events in extreme, either/or terms, ignoring any middle ground or nuance.Alex Edmans argues that by being aware of just these 2 biases, one can improve critical thinking across the board. That sort of parsimony is rare and enlightening — and boy, do I love the Pareto principle!Reality is not a flat surface where our thoughts wander. The geometry of thought has some steep slopes shaped by Confirmation Bias and Black and White Thinking. Being aware of this nature of our minds makes the self-correcting nature of critical thinking essential for not slipping into a vortex of lies.A useful framework Alex Edmans introduces is The Ladder of Misinference:* A statement is not fact: it may not be accurate.* A fact is not data: it may not be representative* Data is not evidence: it may not be conclusive* Evidence is not proof: it may not be universalWhat May Contain Lies by Alex Edmans taught me more than anything else is that truth-seeking is a mindset, a process, and a way of life.And given how easy it has become to generate text/multimedia, the price of creating misinformation is tending towards zero. And with that, the need and ability to think critically is becoming increasingly priceless.Watch the full video episode:Follow Alex Edmans on LinkedIn: https://www.linkedin.com/in/aedmans/Find out more about May Contain Lies: https://maycontainlies.comBuy May Contain Lies by Alex Edmans: https://www.amazon.com/May-Contain-Lies-Statistics-Biases_And/dp/0520405854How I Apply This to “AI“ Right NowTo bring greater clarity to the current “AI“ landscape, here’s a summary of how I applied what I learnt.1. Uncover vested interestsAnyone selling “AI” or to “AI” is probably bullish due to their incentive to sell.Any investor in “AI” companies that has deployed lots of capital and needs growth.2. Ignore extreme sensational positions that ignore nuanceHypists: The world has changed dramatically because “AGI“ has been achieved.Doomers: the world will end because of “AI“ or “AGI“.3. Be honest about confirmation bias and its originsI am trained as a mathematician with a research specialisation in machine learning. Over a decade, I have operated by training and deploying learning algorithms and am thus sceptical that just scaling compute and data will result in “AGI“4. List all nuanced points of view and explore them with curiosity and deliberate open-mindednessI make an effort to gather a wide range of perspectives, especially those that challenge my views. I approach them with curiosity, not defensiveness, and remind myself that no one has a monopoly on truth.5. Articulate the smallest set of perspectives that are exhaustive and as orthogonal as possibleI try to reduce the noise by mapping out a minimal set of distinct and independent perspectives, those that together cover the landscape without too much overlap. This avoids drowning in details and helps sharpen thinking. Something similar to a Principal Component Analysis in mathematics.6. Reflect and do thought experiments to simulate in my mind the nuanced implications of each point of viewI deliberately imagine how the world might look in each scenario. I ask myself: What changes about how value is created? What changes about how value is captured? This mental simulation forces me to think through second-order effects, not just headlines.7. Try to identify gaps in knowledge or insightAfter going through this process, I focus on spotting my blind spots.Where am I the most certain or uncertain?What areas feel underexplored?Where do experts seem to disagree sharply, or where am I relying on assumptions that I haven't fully tested?Next on Part-Maven Part-Maverick, we will discuss with Sangeet Paul Choudary how value is created in the age of AI, who captures it, and what the implications are for businesses and us as humans.This Substack is reader-supported. To receive new posts and support my work, consider becoming a free or paid subscriber.Subscribe to be first to know when the episode drops: https://www.youtube.com/@SLASOGFor more of my thoughts, follow me on LinkedIn: https://www.linkedin.com/in/rritavan/Get my book Data Impact for a pragmatic take on data-driven value creation for business: https://www.amazon.com/Data-Impact-businesses-LEVERAGE-SIMPLIFY/dp/178133921X/ Get full access to Part-Maven Part-Maverick at mavenmaverick.substack.com/subscribe
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