# Context ## Why am I writing this [I'm in the arena](https://x.com/karanmariojude/status/2001153938155024685?s=20). That's the difference between this and most personal investment writing - I'm sharing what I'm actually doing with my own capital, not selling any insights from the sidelines. In 2025, I've realized that I really enjoy investing too. In addition to building and operating companies, I like investing. I like investing as I like learning and applying on the learning via doing, and then self-reflecting, to then repeat. As I step into a new journey together with the [Utopia Capital](https://www.utopia-capital.co/) team and [The Studio](https://www.utopia-studio.co/), and with great things happening at [Navon](https://www.navonworld.com/), I wanted to take stock of my fascination with investing, and share my performance and unique perspectives with you. I continue to be led by [[A Sense of Wonder]], constantly optimizing for interestingness and collecting [[First Principles and Mental Models MoC|principles]] whilst doing, I think I have a special vantage point that few out there have, because of 3 reasons: ## Where I'm standing I'm lucky to have had the opportunity to work with the smartest scientists, engineers and entrepreneurs on the planet. From London and Germany to Seoul and Tokyo - over the past 5 years, I've been to on average 25 different places every year at least. This has helped me build a well-rounded world-view, and develop incredible breadth plus depth into some of the most important topics of our times, namely [[Artificial Intelligence]], [[Quantum Tech MOC|Quantum Technologies]] and [[010 Venture Building|Company Building]]. ## How I'm curating For the past few years, I've also been evolving how I [[Information Evolution, Personal Knowledge & Collective Intelligence|curate information and capture knowledge]] through using Obsidian. This [[090 Personal Knowledge Management - PKM|personal knowledge management]] has allowed me to form a system, where I can get the basics right and share the wider context around something I've thinking about. Something that is really hard to do i.e. get the basics right / just get it. And I know that being able to do that well, and correctly is super valuable. So I continue to hone that. ## What I'm championing I strongly believe that [[What makes Entrepreneurs Entrepreneurial|entrepreneurship]] is at the core of pushing humanity forward and an equalizer, which is a medium for doing good. This, combined with my affinity for the global south, has helped me anchor where I want to channel my energy for the next decade: helping foster entrepreneurship at scale across the global south - both through building and investing. Given this, I want to ***"invest in public"*** - sharing what I'm actually doing with my own capital and why. I feel that taking real risk with my own money holds a lot more weight than a newsletter with paid insights. So, here I will share my observations and select investment plays/positions. --- # Observations: Where We Are and Where We're Going I travel constantly - 25 cities a year on average. I read voraciously. I talk to founders, scientists, and operators across continents. From all of this, seven patterns keep emerging: ## One: Capital is flowing to physical assets and infrastructure is highly geo-politically competitive **Critical Fact:** Global infrastructure spending reached $4.5 trillion in 2025, while AI data centers alone consumed 300+ terawatt-hours - more electricity than all of Argentina. The things that actually keep civilization running - ***energy, water, minerals, manufacturing*** - are getting harder to produce. Everyone talks about the AI boom like it's ethereal, but I see something ***profoundly physical.*** A physical world fueled by growth CAPEX that needs to be [[Maintenance CapEx|maintained]]. > [[Data Centers could make or break electricity affordability|Data Centers]] are drawing gigawatts. The copper is measured in thousands of tons. The cooling water in teraliters. The future runs on [[Steel MoC|steel]], not only software. I can't unsee a pattern too: when everything financializes, **scarcity retreats from paper to physical.** You can print liquidity, but you can't print lithium. The intangible economy anchors in tangible foundations. In an age of infinite code and finite copper, **alpha returns to the things we can touch, measure, and build upon.** > This is what I call **The Great Inversion**: capital migrating from financial to physical assets - a sort of regime change. ![[Screenshot 2025-12-29 at 16.21.02.png]] The world's defining rivalry is being fought through **transistors, transducers, and term sheets, not tanks and troops.** This is a contest of ideology AND infrastructure where America rules the realm of bits while **China consolidates dominion of atoms.** America maintains unrivaled ability to summon liquidity and scale. They've birthed nearly **7,000 AI startups since 2013**, drawing nearly $500B in private investment. Today they count nearly **700 unicorns worth over $2.5 trillion** combined. But software supremacy rests precariously on hardware dependency. China's [[Periodic Table Leverage]] gives them a near-monopoly over refined rare earths, the ultimate chokepoint in geopolitical alchemy. Beijing deploys **300,000 industrial robots yearly**, mechanizing the global factory floor while Washington debates industrial policy. There are signs of optimism in the US as the Pentagon and Treasury move fast to be more competitive. Building [[Navon Sovereign Vaults|sovereign solutions]] and [infrastructure intelligence](https://www.utopia-studio.co/infrastructure-intelligence) for emerging economies becomes a key competitive vector in this new geopolitical order. ![[Screenshot 2025-12-29 at 16.21.13.png]] ## Two: Central banks are abandoning the dollar for gold and bitcoin **Critical Fact:** Central bank gold reserves hit 24% of global reserves in Q1 2025 - the highest in 30 years - while the dollar's share of global reserves fell below 58% for the first time since 1995. Here's what I'm watching: markets no longer mirror the world, they project belief. And right now, belief in fiat is cracking. This isn't panic - it's a systematic repricing of risk in real time. [[Gold Investment Thesis|Gold]] is gleaming not because the metal changed, but because faith in dollars did. At $4,546/oz, gold is replacing paper as the reserve currency of choice. Central banks are buying hand over fist - Russia, China, India, Turkey, Poland - all net accumulating while quietly reducing dollar holdings. When [[Navigating the Crisis Cycle - Tariffs, Uncertainty, and the Price of Risk|equity risk premiums spike and treasuries see flight-to-safety flows]], gold absorbs the uncertainty. The message is clear: sovereignty means owning assets no government can print away. Bitcoin is following the same pattern, just faster. It functions as the monetary mirror image of sovereign debt: finite where debt is infinite, transparent where money printing is opaque, immune to decree where dollars bend to policy. El Salvador started it. Now we're seeing sovereign wealth funds and corporate treasuries allocate. [[MicroStrategy Bitcoin Strategy - A Gamechanger or Risky Bet|MicroStrategy's playbook]] is being copied across boardrooms. ![[Screenshot 2025-12-29 at 16.19.36.png]] But there's a third force most miss: [[Unravelling Stablecoins - a short journey through modern digital dollars|stablecoins]]. Over **$160B in on-chain digital dollars** now exist - essentially dollar deposits living on blockchains. They're [[Stable Coins|digitizing the dollar]] faster than any government program could. Fiat-backed stablecoins dominate (94% market share), but they're just the beginning. We're watching centuries of banking evolution play out in compressed time: from simple IOUs to fractional reserves to complex lending protocols. These aren't replacing the dollar - they're extending it globally, making it more accessible and programmable. ![[Screenshot 2025-12-29 at 16.19.25 1.png]] The dollar still dominates trade settlement, but its role as store of value is being quietly dismantled. When central banks diversify reserves, corporations add bitcoin to balance sheets, and millions transact in stablecoins daily, they're not speculating. They're hedging against the endgame of monetary expansion. ![[Screenshot 2025-12-29 at 16.19.54.png]] ![[Screenshot 2025-12-29 at 16.19.45.png]] The trend is irreversible. Every basis point the dollar loses in reserve status is a vote of no confidence that compounds over time. Related: [[Stable Coins]], [[Navigating the Crisis Cycle - Tariffs, Uncertainty, and the Price of Risk]] ## Three: The S&P 500 will deliver a lost decade for passive investors **Critical Fact:** The top 10 S&P 500 companies now represent 35% of the index's market cap, while 60% of constituents face structural displacement from AI automation. I have a contrarian take that will make index fund evangelists uncomfortable: [the S&P 500 is likely a bad idea for the next 10 years](https://x.com/karanmariojude/status/1990893681662898407?s=20). The index is dominated by companies facing structural headwinds. The service economy - **consulting, enterprise software, financial intermediation** - is getting automated away. The winners of the last cycle (FAANG+) are over earning and overvalued relative to the competitive pressures they face. Meanwhile, the physical economy - energy, materials, infrastructure, manufacturing - trades at decade lows despite being structurally undersupplied. The Great Inversion means the next decade's winners won't be in the S&P 500's top 10. They'll be the companies building the **atoms economy**: modular data centers, semiconductor fabs, uranium mines, defense primes, industrial automation. The index is looking backward. Alpha is looking forward. ## Four: Wealth and talent are migrating from old financial centers to new ones **Critical Fact:** London IPO proceeds hit a 30-year low while Middle East sovereign wealth funds deployed $150B+ in 2024 - more than all European venture capital combined. [[Geopolitical Capital Flows|London's capital-formation]] pace is sliding, with IPO proceeds at their lowest in 30 years. Meanwhile, Abu Dhabi, Dubai, and Qatar are becoming testbeds for a new model of state capitalism: nimble, cash-rich, future-obsessed. They're absorbing the financial and human capital London loses, making this inversion as symbolic as it is structural. But the shift goes deeper than Middle East sovereign wealth. [[How Global Power is being Reshaped|Economic power is decentralizing]]. By 2030, emerging markets will contribute **over 60% of global industrial production**, up from just 20% in 2000. This isn't just about China - India, Indonesia, Vietnam, and others are rapidly expanding their industrial and technological footprints. Intra-emerging market trade is surging, often **bypassing the U.S. dollar entirely**, reflecting a new self-sustaining growth model. Technology and human capital are the new currency. China now accounts for **28% of the world's top AI talent**. The Asia-Pacific's free trade agreement covers **30% of global GDP**. Infrastructure projects like the Central Eurasian Corridor and Pan-Asia high-speed rail are binding these economies closer together. The old Western-centric trade routes are being redrawn. ![[Screenshot 2025-12-29 at 16.17.06.png]] Heat, wealth, and talent move along gradients. Europe's old hubs are cooling and their gravitational pull weakening, while new centers combine capital surplus with strategic ambition where mass liquidity meets manic velocity. [Programmable rails for money movement and economic coordination](https://www.utopia-studio.co) will define how these new financial centers compete globally. We're entering an era of multi-polarity. The future belongs to those who adapt to this decentralization. Related: [[How Global Power is being Reshaped]], [[China - Risky Bet or Unique Opportunity]] ## Five: Venture capital has become a game of mega managers and mega deals **Critical Fact:** 43% of all LP capital flows to just 10 mega managers, while 41% of all VC dollars chase 10 companies - 8 of them AI-focused. We've seen 43% of all venture capital raised by LPs flow to just 10 managers, while 41% of VC dollars went into 10 anointed companies, eight of them AI-focused. The venture market has become a tale of **megas and minnows.** But there's a crisis beneath this concentration: **over 1,400 private unicorns are stuck**, unable to exit. The IPO market has frozen - fewer than **100 tech companies went public between 2020 and 2024**. The FTC has shut down M&A. VC funds are now **returning less capital to LPs than they're raising** - the most negative cash flows in venture history. Bridge rounds are surging as startups scramble for survival. The "triple threat" now matters: growth, profitability, and riding AI trends. Financial discipline replaced "growth at all costs." AI remains the bright spot, but even there, concentration is dangerous - most capital chasing the same 8-10 companies while thousands fight for scraps. The days of easy exits are over. What comes next belongs to those willing to adapt. Related: [[State of the Unicorn Economy - Thomas Laffont]], [[Venture Capital]], [[Investment Thesis Plays]] ## Six: AI is dismantling the service economy faster than expected **Critical Fact:** Consulting giants (Accenture, Capgemini, Gartner) down 30%+ in 2025 while AI coding assistants now write 40% of code at leading tech companies. ![[Screenshot 2025-12-29 at 16.15.47.png]] The first fracture is running through the service economy even as the S&P 500 climbs to record highs. [[AI Capex Super-Cycle|AI]] is mowing down the middlemen: Accenture, Capgemini, Gartner - consulting empires that monetized human repetition - are facing obsolescence, down 30%+. AI is proving to be a **deflationary scythe** through white-collar work. ![[Screenshot 2025-12-29 at 16.15.25 1.png]] But here's what's actually happening: [[The Misleading Allure of Anthropomorphizing AI|AI doesn't think - it processes and predicts]]. These systems excel at **pattern recognition**, not understanding. They're crushing consulting because consulting work was already pattern-based: take client data, match it to frameworks, output recommendations. The jobs falling first aren't the ones requiring real reasoning - they're **repetitive cognitive tasks at scale** we mistakenly called "knowledge work." This creates a paradox for AI startups. Growth is explosive but fragility is hidden. [[AI era Defensibility|Speed wins the start, but defensibility wins the war]]. Most AI companies are building in the "bailey" - fast distribution, viral growth, brand momentum. Few are building the "motte" - network effects, workflow embedding, switching costs. The winners will be those who layer defenses while others chase growth metrics. Google did it. Groupon didn't. ![[Screenshot 2025-12-29 at 16.15.00 1.png]] In an age where AI commoditizes cognitive labor, **trust and brand become foundational**, not soft advantages. The real moat isn't the model - it's the embedded workflows, the collaborative memory, the agent-to-agent networks that make switching impossible. Capital seeks [[Physical Assets Revival|physical refuge]] as volatility rises. Gold is up 70%+ YTD, trading at $4,546/oz and outperforming major indices. > In an age of digital abundance, scarcity still shines. Related: [[The Misleading Allure of Anthropomorphizing AI]], [[AI era Defensibility]], [[7 Powers]] ## Seven: The AI capex boom will end in oversupply and commoditized compute **Critical Fact:** Hyperscalers spending $400B+ in AI capex while competing for the same market - if compute commoditizes as predicted, most will never earn returns to justify the spend. All the large frontier lab companies are acting individually rational yet collectively irrational. Each is chasing 100% market share while together spending over 400% in capex. If Google offers Gemini free (as they did Google Docs two decades ago) or an open-source model offers compelling good-enough performance, competitors may never grow revenues to compensate for their build-outs. The [[AI Capex Super-Cycle|AI capex super-cycle]] will be gloriously useful for society and brutally destructive for many investors funding it. History rhymes with fiber, colo, and early cloud. Most **gross margin accrues to the toll-keepers**: Nvidia and the hyperscalers. ![[Screenshot 2025-12-29 at 16.14.05.png]] But here's the counterintuitive part: [compute will become dirt cheap](https://x.com/modestproposal1/status/1804213365171957941?s=20). The oversupply is inevitable. When frontier labs are burning $400B+ in capex chasing the same market, the laws of supply and demand assert themselves. Compute commoditizes. The winners won't be the builders of compute but the orchestrators of it - those who can route, optimize, and extract value from abundant, cheap compute resources. The outcome may be familiar: **misallocation on a monumental scale, over-investment followed by surplus.** Users surely win, investors may scatter, but the infrastructure nonetheless compounds. ![[Screenshot 2025-12-29 at 16.12.26.png]] This is why I'm super excited about [Swarmone](https://medium.com/@karanmjpinto/why-im-excited-about-swarmone-and-why-enterprises-should-be-too-4f46e5f38b58) - AI infrastructure orchestration at scale. Related: [[Data Center MoC]], [[Modular Data Center Design Principles]],[Navon](https://www.navonworld.com/), [[From Code to Currency - How Crypto and AI Are Rewiring Digital Power]] --- # Alpha Areas ## What I'm Looking For [[The Evolving Entrepreneur|Special Entrepreneurs]] who work on high-probability [[Pattern breaking ideas]] with [[Founder Led Not Founder Lost - Why Ownership Matters|operational]] and [[Level 5 Leadership|quality]] leadership that find scalable business models and drive breakthrough yet sustainable growth. I've always been the underdog. The outsider. The odds against me. But that's taught me to look where others aren't looking, and to bet on leaders with resiliency, [[High Agency]], and [[Time in the Market]]. I prefer opportunities fewer people are watching. That's where the alpha is - still early, still mispriced. Yes, it's unsettling, obscure, scary. But I've found calm in this chaos. Here is where I also find there is an interesting merge between venture, private equity, hedge fund and value investments. I feel this blur will only increase over time with AI, its democratization and application across industries. ## How am I gauging these opportunities? Intuition. That's the honest answer. Yes, I look at the data, charts, financial reports. But the final decision? Intuition. It's something Thrive Capital talks about too - the merger of quantitative rigor and gut feel. ## Where am I seeing alpha areas of opportunity? Alpha seems to hide at the edges, where systems break, borders blur, and the future forces mark-to-market discipline. Alpha lives in sectors undergoing rapid existential change, in assets escaping traditional finance gravity, and in companies crawling through the valley of venture death to emerge as indispensable infrastructure. Here are seven alpha areas I am watching closely: ## One: Defense & Aerospace Transformation The [[Defense Supply Chain Fragmentation|postwar West]] traded discipline for diffusion. Defense and aerospace supply chains that won wars have splintered into a democratized archipelago of mom-and-pop machine shops. [[Fertilisers]] plants replaced munitions factories, and by-products of Chinese cotton shirts now feed Europe's explosives industry. > What was resilient interdependence became strategic vulnerability. Reshoring for the U.S. seems more about survival than a slogan. > The U.S. builds fewer than six [[US Shipbuilding - A crisis under the surface|commercial ships]] yearly while China built the majority of new deadweight tonnage last year. Europe, jolted awake by Ukraine, is rearming on a scale unseen in half a century. New primes are rising, new multi-layered air defenses are being designed for drone-saturated skies, and new supply chains are being built to last beyond the current crisis. ![[Screenshot 2025-12-29 at 16.31.15.png]] Warfare itself is mutating. Tanks die to **cheap quadcopters**; anti-tank missiles give way to **anti-drone jammers**. Unmanned systems fight below a thousand feet while AI fuses the battlespace above, compressing decision time from minutes to milliseconds. From Mosul to Mariupol: learn fast or learn late. > The new race isn't for territory or ideology but for adaptation: the ability to evolve at the speed of threat. > ![[Screenshot 2025-12-29 at 16.30.51.png]] The moral dimension is key: **deterrence through precision.** Higher technical resolution yields higher moral resolution: a big, credible arsenal you ideally never use. Making defense cool attracts top-grade talent from big tech and old primes alike. [Predictive and autonomous systems for critical infrastructure](https://www.utopia-studio.co/sovereign-systems) operations will define the next generation of defense capabilities across both intelligence and security. Related: [[AI Defense]], [[Autonomous AI Agents - The rise, potential and challenges]], [[U.S. Government Accelerates Transition to Quantum-Resistant Cybersecurity]] ## Two: Healthcare & Biotech Biotech sits at a rare intersection: **valuations at decade lows, technology quality at decade highs.** > The disconnect between capital cycle and capability curve is an arbitrage opportunity. AI lets startups do more with less, using [[Large Language Model - LLMs|LLMs]] to underwrite biology, focus on single targets, and reach early clinical milestones with less capital. Yet the half-life of a good idea has collapsed since Chinese labs can read a Nature paper and start clinical trials before the original team finishes hiring. ![[Screenshot 2025-12-29 at 16.35.10.png]] The next U.S. edge won't be cost or speed but **complexity**: ventures so technically hard and infrastructure-intensive that only frontier builders dare attempt them. Examples include pig-to-human organ xenotransplantation, single-cell perturbation genomics, and [[Biodefense Imperative|AI-driven biodefense]]. ![[Screenshot 2025-12-29 at 16.34.53.png]] ## Three: Biology's Compiler Phase Biology has crossed the threshold from observation to orchestration. The code of life - once read, now written - has entered a compiler phase where the **boundary between wet lab and cloud dissolves.** ![[Screenshot 2025-12-29 at 16.40.48.png]] > A decade ago protein folding was the frontier; now the question is how fast we can design, synthesize, and test new and more complex interacting biological machines. The **codebase of nature is being forked in real time.** ![[Screenshot 2025-12-29 at 16.41.25.png]] The old deterrence model (slow defense versus fast offense) fails when the pace of invention quickens. The most credible antidote is speed itself: **a civilization whose countermeasures iterate as fast as threats mutate.** This will need close collaboration between government and life-science companies creating a rapidly responsive biodefense ecosystem. ![[Screenshot 2025-12-29 at 16.40.58.png]] Related: [[TechBio MoC]], [[Drug Discovery]], [[Digitalisation of Biology]] ## Four: AI & Computation Stack At the capex layer, scale compounds. At the application edge, **brand and habit may win but switching costs remain low.** ![[Screenshot 2025-12-29 at 17.01.34.png]] The stack is cohering into **vertical utilities by craft** (code, support), with each automating a specialized labor market rather than merely a software niche. [Purpose-built software for high-stakes vertical domains](https://www.utopia-studio.co) will capture value where horizontal platforms can't deliver the precision required. ![[Screenshot 2025-12-29 at 16.46.04.png]] Ironically in this world of "artificial" intelligence, the only scarce resource that still decides outcomes is **human intelligence.** Talent raids, acqui-hires, "Poachapallooza 2025", wunderkinds deemed barely old enough to drink or rent a car; all proof that while capital builds capacity, **people still build winners.** ![[Screenshot 2025-12-29 at 16.45.54.png]] We're seeing evolution beyond scale: Japan's approach shows an **archipelago of smaller, evolving intelligences** - systems that learn by recombination, like coral reefs of code. The next frontier may not be larger models but living ones: **adaptive, self-correcting, communal.** The future of intelligence may belong not to empires of scale but to **ecosystems of emergence.** ## Five: Generative AI Maturation Generative AI tools began as toys and have matured into instruments for serious imagination. They're no longer rivals to creativity but partners in production. But the shift goes deeper: [[AI usage is now a baseline expectation|AI usage is now baseline expectation]], not competitive advantage. As Tobi Lutke put it, **"you have to keep running just to stay still."** Stagnation is slow-motion failure. ![[Screenshot 2025-12-29 at 17.07.15.png]] > Every tedious edit a machine absorbs is air for an artist's intuition. What matters next is control. The true artist doesn't seek random outputs but refined ones, capable of being shaped and rehearsed like any craft. ![[Screenshot 2025-12-29 at 17.04.49.png]] This is where [our Creatives pod](https://www.utopia-studio.co) is exploring the frontier of human-AI collaboration. The heart of which will be [tacit knowledge](https://www.stripe.press/tacit) and [wonderism](https://x.com/ConjectureInst/status/2004501749348852070?s=20). The interface layer is evolving fast. [[Intimate Interfaces and Sensory AI|Technology is becoming personal]] - multi-sensory devices that don't just react but anticipate. Gadgets that see, hear, touch, and orchestrate actions across your day without you lifting a finger. The winners will be systems that feel effortless, understanding each user intimately. We're entering an Agent Economy where value lies in how well these systems handle life's complexity. ![[Screenshot 2025-12-29 at 17.05.19.png]] But here's the paradox: while AI democratizes creation, [[A short note on IP|IP protection becomes more critical]], not less. Without IP moats, there's no justification for the capital and risk needed to commercialize breakthroughs. No one funds what everyone can instantly copy. Even Musk, who "open sourced" Tesla patents, holds 3,000+ patents and protects SpaceX trade secrets like state secrets. An early Math Olympiad genius is creating an "applied AI lab" building the first true autonomous engineer. The [[AI coding tool|AI software engineer]] doesn't just autocomplete lines; it reasons, debugs, and improves itself. We're watching the boundary between user and system collapse. The next leap won't come from faster typing but from code that wakes up tomorrow wiser than yesterday. ![[Screenshot 2025-12-29 at 17.05.03.png]] Related: [[Artificial Intelligence]], [[AI Agents Stack]], [[AI usage is now a baseline expectation]], [[Intimate Interfaces and Sensory AI]], [[A short note on IP]] ## Six: Physical Intelligence & Robotics For decades intelligence **lived weightless**: text in a window, answers in the cloud. Now it's acquiring mass. ![[Screenshot 2025-12-29 at 17.11.02.png]] But this shift builds on decades of work I know well: [[Climate Modelling MoC|simulating the physical world]] in software. We've spent years mastering [[CFD|computational fluid dynamics]], [[Climate Modelling 101|atmospheric modeling]], [[Seismic Data Processing|seismic processing]] - teaching computers to predict how air flows, how weather evolves, how earth moves. These models discretize the messy continuous physics of reality into computational grids we can solve. Physical intelligence is the next step. We've moved from **simulating physics to embodying it**. Watching a robot fold a t-shirt, clumsily but autonomously, is witnessing **embodied intelligence** learn friction, gravity, and grace. The miracle isn't that machines are imitating humans but that they're beginning to share our constraints - the same physical laws we've been modeling for decades. ![[Screenshot 2025-12-29 at 17.11.25.png]] > Every gesture becomes data, every mistake a moment of learning, every repetition a rehearsal. ![[Screenshot 2025-12-29 at 17.11.33.png]] When robots learn from their own motion, labor itself becomes self-improving: a kind of infinite apprenticeship where the factory learns as fast as it produces. Early demos look humble (linen creased, mugs toppled) but the trajectory is unmistakable: code becoming corporeal. ![[Screenshot 2025-12-29 at 17.11.48.png]] What [[CFD|computational fluid dynamics]] did for simulating airflow, these physical models will do for dexterity and navigation. We're moving from software that **predicts** the world to systems that **participate** in it. ![[Screenshot 2025-12-29 at 17.12.03.png]] ![[Screenshot 2025-12-29 at 17.12.16.png]] Related: [[AI x Robotics Flow]], [[Autonomous Agents]], [[CFD]], [[Climate Modelling MoC]] ## Seven: Memory & Edge Computing Shift A decade ago, engineers were playing "Grand Theft Auto" with lidar feeds, training cars in silico on unreleased Nvidia chips. Back then Nvidia was worth $15 billion and Intel $150 billion. That was the pair trade of the century as Nvidia sits at $4.5 trillion market cap today. The same inversion is **coming for memory with SK Hynix, Samsung, and Micron** set to become the soul of the new machine. If 50% of AI inference moves on-device using flash memory AND demand stays high for [[hbm|high-bandwidth memory]] attached to GPUs, it may upend consensus assumptions of infinite demand for data centers and power plant build outs. That future would be propitious for edge inference chip investments. Related: [[Chip Innovation x Scaling]], [[Chiplets]], [[Scaling Edge Physical AI - Bridging Cloud Intelligence with Real-World Systems]], [[Nvidia-Groq - Inference Disaggregation Play]] --- # Investment Plays [I'm sharing this as I am the man in the arena](https://x.com/karanmariojude/status/2001153938155024685?s=20) - taking real risk with my own money, not theorizing from the sidelines. The wins feel great. The losses teach me more. Below, I've noted select investments, both good and bad that I've made in the public markets in the past year, average returns have been 180%: ## One: [[Iris Energy - IREN|Iris Energy - $IREN]]: +400% This has been one of my best positions. Bitcoin mining company that pivoted into AI compute infrastructure at exactly the right time. I saw the convergence early: stranded energy + high-performance computing + modular data centers. [I laid out the thesis here](https://x.com/karanmariojude/status/1888264845998408151?s=20). The stock went from single digits to where it is now. Sometimes you get the timing right. I have an unfair knowledge advantage here, through the work done at [Navon](https://www.navonworld.com/) - and I feel that plays that seem so obvious because of insider knowhow, give me an edge and a margin of safety of sorts. ## Two: [[Japanese Semiconductors Play|Japanese Chips/Semis]]: +80% Post visiting Japan, I noted some [interesting observations](https://x.com/karanmariojude/status/1903476004124770808?s=20) and [[Japan Reflections Mar 25|reflections]]. This informed my investment into the Japanese semiconductor supply chain - Advantest, Shin-Etsu, SUMCO. These are upstream oligopolies that make the equipment and materials everyone else needs. While the world continues chasing NVIDIA, I wanted to find plays that are unknown but essential. Japan's manufacturing discipline combined with semiconductor shortage economics was obvious in hindsight. ## Three: [[Gold Investment Thesis|Gold]]: +40% Been long gold and gold equities through the entire macro uncertainty. [Posted about it here](https://x.com/karanmariojude/status/1947052025369248205?s=20). When central banks are net buyers and geopolitical fragmentation accelerates, gold isn't barbarous relic - it's insurance. Up 40% and still holding. And this is the most stable and risk free part of my portfolio. ## Four: Estée Lauder: +44% Beauty and luxury consumption thesis in emerging markets. Sometimes the best tech investment is no tech at all. I have followed Michael Burry in on this one, all credit to him. ## Five: [[Fannie Mae and Freddie Mac]]: +75% on both Long the preferred shares. [Wrote about it here](https://x.com/karanmariojude/status/1953926694122688897?s=20). Still playing out but the risk/reward on government-sponsored enterprises exiting conservatorship was too compelling to ignore. ## Six: [[Sable Offshore Corp - SOC|Sable Offshore]]: -73% This one hurts. Offshore oil development play in California. [Called it here](https://x.com/karanmariojude/status/1959044355035209956?s=20) and [here](https://x.com/karanmariojude/status/1885646883348979905?s=20). The thesis was sound: stranded assets trading at huge discounts to NAV, permitting risk overpriced. But I underestimated California's political gridlock and the timeline for regulatory approval. Down 73%. Still holding because the asset value is real, but the path to monetization is longer and messier than I thought. This is what you pay for being early, and I could be wrong here too, but it just shows how important timing is with certain plays. You might feel like if I don't enter into a position now, that it is truly now or never, but actually it's fine to find favorable entry points and not overpay or rush into positions. The rushing rarely is worth it. ## Seven: [[Uranium Investment Thesis|Uranium]]: +65% Through my work at Accenture, I spent some time learning about Nuclear at Hinkley Point C, and this is where my knowledge come from. I'm long Nuclear and think it just needs a rebrand to something like Elemental Energy. Nuclear is a very credible low-carbon way in which we could meet AI data center power demands at scale. See: [[SMRs x Data Center Opportunities]] ## Compounding Engines I've got a number of plays that are invest and forget, and I will be writing more about these in the near future. These cover names such as Fairfax Financial, Markel Group, Howard Hughes, Pershing Square, Scottish Investment Trust, Röko, Topicus.com, HEICO, Lifco, Halma, QXO, [[Constellation Software]] and Judges Scientific. I also dived deeper into **[[Atlas Capco - ATKLY|Atlas Copco]]**: Industrial automation and compressed air systems, which is of this nature - [Thread here](https://x.com/karanmariojude/status/1878052050463813995?s=20). The boring businesses that enable manufacturing matter more than ever. I'm also finding my own version of Mohnish Pabrai's ["Circle of Wagons" approach](https://wagonsfund.b-cdn.net/Pabrai%20Wagons%20Fund%20Investor%20Presentation.pdf) - concentrated bets on compounding opportunities in Turkey, India, car dealerships, coal businesses, and homebuilders. There's elegance in simplicity and conviction. --- # Key Learnings Every month, I force myself to reflect on what's working and what isn't. I document everything in [[Investing Learnings]] to maintain [[Investing Discipline]]. Here are the principles that keep emerging: 1. **Buy when fear is in the air, not greed** - Have the courage to look crazy. Luck favors the prepared and brave. [[Navigating the Crisis Cycle - Tariffs, Uncertainty, and the Price of Risk|Navigating crisis is inevitable.]] 2. **Think 18 months ahead, not in the present** - Markets discount the future, not today. See: [[Five year psychological bias]] 3. **Act boldly when the odds are in your favor** - Position size matters ALOT. 4. **Flexibility is an edge** - Rigid philosophies break under change. Markets evolve. Your framework should too. 5. **Build nimble decision-making muscles** - Speed trumps complexity. The ability to act fast on high-conviction ideas beats perfect analysis every time. Investing success is more process than luck. But you need both. 6. **We should favor process over impulse, structure over spasm** - The goal is to minimize both errors of omission (missed opportunities) and errors of commission (bad![[Screenshot 2025-12-29 at 17.16.48.png]] positioning or sizing). 7. **Know [[When to sell]]** - This might be the hardest part. Selling winners too early leaves alpha on the table. Holding losers too long compounds losses. The discipline is knowing which is which - and that distinction changes as facts change. See [[When to Sell - Lessons from Investment Legends]] for frameworks from Buffett, Munger, and others who've mastered this art. A big part of what made these investors great was spotting when they were wrong quicker. Successful public market stock picking isn't just picking winners. It also means picking out the losers in your portfolio. The greatest advantage in public markets is "You can sell". But you have to know when to sell. 8. **Operate in your circle of competence** - Depth beats breadth. I know energy infrastructure, semiconductors, physical systems. I stay there. 9. **Ignore earnings, focus on liquidity** - Cash flow and balance sheet strength matter more.![[Screenshot 2025-12-29 at 17.17.21.png]] 10. **Youth and inexperience can be strategic advantages** - You're not anchored to old mental models. 11. **Believe before others understand** - Being early requires [[Conviction|conviction]] when consensus doesn't exist. Imagining your own counterfactual keeps you steady when facing adversity.![[Screenshot 2025-12-29 at 17.17.34.png]] 12. **Avoid stupidity, get the basics right** - [[Carlo Cipolla's Stupidity Principle|Carlo Cipolla's insight]] remains relevant here: humanity's most underestimated force is stupidity. Not malice or greed, but the consistent capacity to harm themselves and others without gain. The most dangerous actors take value down while blundering confidently into ruin. This is why we must build [[Anti fragility|antifragile]] decision-making systems and heuristics that make seizing positive opportunities easier while striking against flawed thinking.![[Screenshot 2025-12-29 at 17.17.28.png]] 13. **We consistently overvalue how proprietary a technology can be while undervaluing how important founders are** - When we study the S-curves of venture capital waves - PCs in the 1970s, biotech in the 1980s, media/telecom/internet in the 1990s, physical/material sciences in the 2000s, this pattern emerges.![[Screenshot 2025-12-29 at 17.17.43.png]] ![[Screenshot 2025-12-29 at 17.18.00.png]] --- # Building for the Future De novo company creation isn't separate from investing for me - it's portfolio construction through another lens. The question I keep asking: how do I get better at founding companies so contrarian that no one else is building them? This is where thinking about [[The Company as a Machine for Doing Stuff]] becomes critical. A company isn't just a collection of people and capital. It's a machine - a system of processes, incentives, and feedback loops designed to convert inputs (capital, labor, ideas) into outputs (products, services, value). The best companies are machines that improve themselves: they learn, adapt, and compound over time. (we can all learn from [[Operating Principles for Growth - Mark Leonard & Constellation Software|Mark Leonard]]) When you're building from scratch, you have the luxury of designing the machine correctly from day one. No legacy systems. No organizational debt. Just pure intentionality about how the machine should work. This is where I'm incredibly excited about [The Studio](www.utopia-studio.co) - building [sovereign digital, compute, and energy infrastructure](https://www.utopia-studio.co/sovereign-systems) that the world actually needs. Whether it's [industrial decarbonization through intelligent operating systems](https://www.utopia-studio.co/decarb-industry), [programmable rails for money movement](https://www.utopia-studio.co/flow-rails), or [predictive autonomous systems for critical infrastructure](https://www.utopia-studio.co/sovereign-systems), the opportunity to build companies from scratch around The Great Inversion thesis is what gets me out of bed. > The future belongs to those who build it. And right now, that means building in atoms, not just bits. Related: [[Venture Building Manifesto]], [[Pattern breaking ideas]], [[010 Venture Building]], [[Operating Principles]] --- Tags: #deeptech #kp #systems #investing