Isabel Edgar

𝐖𝐡𝐚𝐭 𝐌𝐚𝐠𝐧𝐮𝐬 𝐂𝐚𝐫𝐥𝐬𝐞𝐧, 𝐅𝐨𝐫𝐦𝐮𝐥𝐚 𝐎𝐧𝐞 𝐃𝐫𝐢𝐯𝐞𝐫𝐬, & 𝐍𝐢𝐜𝐨𝐥𝐚𝐢 𝐓𝐚𝐧𝐠𝐞𝐧 𝐂𝐚𝐧 𝐓𝐞𝐚𝐜𝐡 𝐘𝐨𝐮 𝐀𝐛𝐨𝐮𝐭 𝐑𝐢𝐬𝐤-𝐓𝐚𝐤𝐢𝐧𝐠 𝐢𝐧 𝐈𝐧𝐯𝐞𝐬𝐭𝐢𝐧𝐠. Nicolai Tangen manages $2T as CEO of the Norwegian Sovereign Wealth Fund. And 𝘰𝘯𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵 𝘪𝘮𝘱𝘰𝘳𝘵𝘢𝘯𝘵 𝘭𝘦𝘴𝘴𝘰𝘯𝘴 he shares isn’t about valuation or portfolio construction. 𝘐𝘵’𝘴 𝘢𝘣𝘰𝘶𝘵 𝘱𝘴𝘺𝘤𝘩𝘰𝘭𝘰𝘨𝘺. Here’s the insight: “𝙈𝙤𝙨𝙩 𝙥𝙚𝙤𝙥𝙡𝙚 𝙩𝙖𝙠𝙚 𝙡𝙚𝙨𝙨 𝙧𝙞𝙨𝙠 𝙬𝙝𝙚𝙣 𝙩𝙝𝙚𝙮 𝙡𝙤𝙨𝙚 𝙢𝙤𝙣𝙚𝙮. 𝙀𝙫𝙚𝙣 𝙩𝙝𝙤𝙪𝙜𝙝 𝙮𝙤𝙪 𝙨𝙝𝙤𝙪𝙡𝙙 𝙩𝙖𝙠𝙚 𝙩𝙝𝙚 𝙨𝙖𝙢𝙚 𝙖𝙢𝙤𝙪𝙣𝙩 𝙤𝙛 𝙧𝙞𝙨𝙠.” ___ ♟️ Think about Magnus Carlsen. After losing a chess match, does he become more conservative in the next game? Tangen asked him directly. The answer was fascinating — and counterintuitive. Carlsen actually takes more risk in the next game. Not less. He doesn’t retreat into protection mode — he leans in. That’s what separates the greatest chess player in the world from everyone else. While most players pull back after a loss, 𝐂𝐚𝐫𝐥𝐬𝐞𝐧 𝐫𝐞𝐜𝐚𝐥𝐢𝐛𝐫𝐚𝐭𝐞𝐬 𝐚𝐧𝐝 𝐚𝐭𝐭𝐚𝐜𝐤𝐬. 🏎️ Formula One drivers face the same battle. After a crash or a poor race, the instinct is to brake earlier, to be more cautious, to protect what’s left. But the great ones — Senna, Schumacher, Hamilton — understood that the 𝐧𝐞𝐱𝐭 𝐫𝐚𝐜𝐞 𝐝𝐞𝐦𝐚𝐧𝐝𝐬 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐜𝐨𝐦𝐦𝐢𝐭𝐦𝐞𝐧𝐭, 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐜𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐞𝐝 𝐚𝐠𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧. The moment you let a loss change your baseline risk tolerance, you’ve already lost the next race before it starts. ___ Tangen sees the same pattern in investing — and it’s one of the most costly psychological traps an investor can fall into. When you lose money, the instinct is to conclude that you took too much risk. So you pull back. You sit in cash. You wait for more certainty. But what actually happened is that you’ve applied the wrong mental model. You’ve confused the outcome with the process. A loss doesn’t mean your risk calibration was wrong — markets are volatile, conditions change, and even the best decisions don’t always produce the best short-term results. By pulling back, you rob yourself of the very opportunities that could make you whole — and then some. The lesson for investors is clear: Your risk framework should be anchored to process and fundamentals — not to your last investment. A great business bought at a reasonable valuation doesn’t become a worse investment because the market went against you last month. The wind direction changed. That’s all. ___ 🎙️ The Knowledge Project Podcast | Nicolai Tangen (02/17/2026)

@yianisz 666

$CIFR is the one the market is misreading. Everyone sees “former $BTC miner” and stops there. That’s outdated. This is now a contracted AI infra landlord with ~$9B+ in long-term hyperscaler deals: AWS, Google-backed revenue, 10–15 year terms. That’s real cash flow visibility. and here’s the key difference vs $IREN: No ATM. Funding = project finance + lease-backed debt. It’s boring… and that’s exactly why it works. Trading ~0.4x backlog while others get premium multiples. I’m not saying it’s risk-free.. execution still matters. But this smells like early-stage repricing, not a crowded trade. $NBIS is the leader. Full-stack, software + infra, already proving it.. You always want the leader and the emerging winner..

@yianisz 22 333

I sold $PLTR at ~$200 after riding it from $7. No regrets. Now 35% off highs = reset, not broken. Time to get back on the ship.

@yianisz 111

@socialwithaayan 111

MIT's Nobel Prize-winning economist just published a model with one of the most alarming conclusions in the AI literature so far. If AI becomes accurate enough, it can destroy human civilization's ability to generate new knowledge entirely. Not gradually degrade it. Collapse it. The paper is called AI, Human Cognition and Knowledge Collapse. Authors: Daron Acemoglu, Dingwen Kong, and Asuman Ozdaglar. MIT. Published February 20, 2026. Acemoglu won the Nobel Prize in Economics in 2024. He is not a doomer blogger. He is the most cited economist of his generation, and his models tend to be taken seriously by the people who set policy. Here is the argument in plain terms. Human knowledge is not just a collection of facts stored in individuals. It is a living system that requires continuous reproduction. People learn things. They apply them. They teach others. They build on prior work to generate new work. The entire engine of science, medicine, technology, and innovation runs on this cycle of active human cognition. What happens when AI provides personalized, accurate answers to every question people would otherwise have to learn themselves? Individually, each person is better off. They get correct answers faster. They make fewer errors. Their immediate outcomes improve. But they stop doing the cognitive work that sustains the collective knowledge base. Acemoglu's model shows this produces a non-monotone welfare curve. Modest AI accuracy: net positive. AI helps at the margin, humans still do enough learning to sustain collective knowledge, everyone gains. High AI accuracy: net catastrophic. AI is accurate enough that learning yourself feels unnecessary. Human learning effort collapses. The knowledge base that AI was trained on is no longer being refreshed or extended. Innovation stalls. Then stops. The model proves the existence of two stable steady states. A high-knowledge steady state where human learning and AI assistance coexist productively. A knowledge-collapse steady state where collective human knowledge has effectively vanished, individuals still receive good personalized AI recommendations, but the shared intellectual infrastructure that enables new discoveries is gone. And the transition between them is not gradual. It is a threshold effect. Below a certain level of AI accuracy, society stays in the high-knowledge equilibrium. Above that threshold, the system tips. And once it tips, the collapse is self-reinforcing. Because the people who would have learned the things that would have pushed the frontier forward never learned them. And the AI cannot push the frontier on its own. It can only recombine what humans already knew when it was trained. The dark irony at the center of the model: The AI does not fail. It keeps giving accurate, personalized, useful answers right through the collapse. From the individual's perspective, nothing looks wrong. You ask a question, you get a correct answer. But the collective capacity to ask questions nobody has asked before, to build the frameworks that generate new knowledge rather than retrieve existing knowledge, that capacity is quietly disappearing. Acemoglu has been the most prominent mainstream economist skeptical of transformative AI productivity claims. His prior work found that AI's actual measured productivity gains were much smaller than the technology industry projected. This paper is a different kind of warning. Not that AI will fail to deliver promised gains. But that if it succeeds too completely, it will undermine the human cognitive infrastructure that makes long-run progress possible at all. The welfare effect is non-monotone. That is the sentence worth sitting with. Helpful until it is not. Beneficial until it crosses a threshold. And past that threshold, the same accuracy that made it so useful is precisely what makes it devastating. Every student who uses AI instead of working through a problem is a data point. Every researcher who uses AI instead of developing intuition is a data point. Every generation that grows up with accurate AI answers and no incentive to develop deep domain knowledge is a data point. Individually rational. Collectively catastrophic. Acemoglu proved this is not just a cultural concern or a vague anxiety about screen time. It is a mathematically coherent equilibrium that a sufficiently accurate AI system will push society toward. And there is no visible warning sign before the threshold is crossed.

@hyic_sol 111

Never Give Up On Crypto 📉💯