Jin S. Chun (@jsc43) profile picture
Los Angeles, CA
Joined March 2009
Photo by Jin S. Chun (@jsc43)
Jin S. Chun retweeted
A San Francisco startup says it just turned mercury into real gold, and their own math says gold is one of the cheapest things their machine can make. Aim it at a different metal and it makes a cancer drug ingredient worth tens of billions of dollars a gram. Here's how it works. When you fuse two kinds of hydrogen, deuterium and tritium, every single reaction kicks out a neutron moving at 14.1 MeV, which is absurdly fast. Fire one into mercury-198 and it knocks another neutron loose on the way through, so two come out and you're left with mercury-197. Give that a few days and it decays into gold-197, the only stable kind of gold there is. The whole trick needs neutrons above about 6 MeV, and regular nuclear reactors barely make those, while fusion makes one every time it fires. Now swap the mercury for radium-226. Same reaction, one neutron in and two out, and you end up with radium-225, which decays into actinium-225. Novartis is building its next prostate cancer drug around actinium-225, and it's so scarce it sells by the millicurie, roughly $1,200 to $2,500 apiece. Price it by the gram and you're into the tens of billions of dollars, for a material so radioactive nobody could safely hold a gram of it anyway. Marathon's team actually put a dollar value on a single fusion neutron in a paper this year. Use it to make electricity and it's worth about $3 × 10⁻²⁰. Gold comes in at $2.6 × 10⁻²⁰, basically a wash. Molybdenum-99, which goes into about 80% of nuclear medicine scans, is worth $2.6 × 10⁻¹⁷ a neutron. Actinium-225 is worth about $10⁻¹⁰, around 3 billion times what the electricity's worth. That's why fusion people should care. Every fusion company you've heard of is chasing electricity, and selling electricity only pays once the plasma puts out something like 10 to 22 times the energy you pump in to heat it. The best result so far, at JET in England, was about a third. Add gold on the side and Marathon's math says you break even around 3x. For molybdenum-99, the number is 0.001x. So a machine that gives back one-thousandth of the energy you feed it can still make money, as long as the neutrons hit the right metal. By their numbers, a 3 megawatt machine running way below breakeven could cover the whole world's supply of molybdenum-99, which today comes from splitting uranium in a handful of research reactors. Today's experimental fusion machines already beat the gain that job needs. Their second post basically says this out loud. Marathon now describes itself as an isotope company for radiopharma. Their chart shows roughly 390 targeted radiotherapy trials. Pluvicto, the prostate cancer drug Novartis launched at $42,500 a dose, did $1.9 billion in sales last year. Gold still matters to them, just later. It's about a $700 billion a year market, and Marathon's paper figures it could soak up around 2 terawatts of fusion capacity, which is roughly the scale where fusion actually moves the grid. So the plan is to sell cancer isotopes out of small machines now and use gold to pay for the giant ones, years before electricity can pay for itself.
Last month, we demonstrated high-rate alchemy: turning mercury into real, stable gold, beating CERN’s record by a factor of 10. Last week, we enriched isotopes in the world’s most powerful plasma centrifuge, beating the Soviet record that stood for 40 years. Now, we’re building a fusion reactor in San Francisco to achieve record power density.
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Jin S. Chun retweeted
🇮🇷 Something extraordinary is happening in the Strait of Hormuz Iran is still attacking ships, yet roughly 10.3 MILLION barrels of crude oil per day now appear to be making it through At the same time, according to TankerTrackers founder Samir Madani, Iran's own major crude exports have effectively collapsed. So how the hell is everyone else's oil getting out? An entire “Arab dark fleet” has emerged: more than 100 tankers operating largely without AIS, shuttling oil from Gulf ports into the Gulf of Oman. There, they transfer their cargo at sea to other tankers unwilling to risk entering Hormuz themselves. And the money being made is INSANE!! A tanker that might previously have cost around $50,000 a day to charter can now command roughly $1.25 MILLION A DAY. Meanwhile, Samir says satellite imagery shows essentially no major Iranian crude loading activity at Kharg Island. Before the war, Iran was exporting around 1.6 million barrels of crude per day. Today, Samir says Iran can still move smaller quantities of refined products and LPG through alternative routes. But the crude? “There’s nothing happening right now in Iran. It’s just flat.” That leaves us with one of the strangest realities of the Hormuz war: Iran can threaten the world's most important oil chokepoint. But right now, the oil still flowing through it largely belongs to everyone else.
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Jin S. Chun retweeted
Norway 🇳🇴 put Chinese 🇨🇳 EVs in a mine so the cars could not “see” the outside world. 🚘 They still tried to call home. 🛜 Researchers found data moving with the car switched off. About 90% of the traffic from the car they tore down went to China. Same country now selling cheap EVs into Canada. 🇨🇦 Cameras. Mics. Always-on modems. PRC intelligence law. This is not a green-car debate. ♻️ It is a rolling sensor debate. 🛰️ Ottawa wants the cheap sticker price. Beijing wants the feed. #cdnpoli #CCP
Researchers in Norway hacked Chinese-made EVs and found data still flowing home with the cars switched off. Sam Cooper explains to Stephen LeDrew that 90% of it went back to China. #EVSecurity #DataPrivacy #CyberSecurity #Cdnpoli @Stephen_Ledrew #thenewsforumca #stephenledrew
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Jin S. Chun retweeted
From 1973 to 1986, Walmart reported negative free cash flow for 14 consecutive years. Its stock returned 33% annually over that period, three times the return of the S&P 500. @mjmauboussin’s framework for understanding free cash flow. 10 lessons for investors: 1. A company’s value is the present value of its future free cash flow. Free cash flow is profit after taxes minus investments in growth, primarily capital expenditures. An investor’s job is to find the company that will ultimately generate the most FCF. But that FCF will not always be positive along the way. 2. Negative FCF is not always bad. If a company invests at a return above its cost of capital, it creates value. People love posting charts showing negative FCF at the hyperscalers. What matters is the return those investments will generate over the following years. 3. The Walmart lesson. From 1973 to 1986, Walmart had negative FCF for 14 consecutive years because it invested aggressively in expansion. Its return on invested capital averaged about 18%, well above its cost of capital. The stock returned 33% annually, three times the return of the S&P 500. Negative FCF was a sign of growth, not weakness. 4. Focus on investment returns, not the minus sign. The key question is whether revenue and profit are keeping pace with capital expenditures. If profit grows faster than investment, value is being created. If it falls behind, cash is being destroyed. 5. Watch return on incremental invested capital (ROIIC). It measures the return generated by each new dollar invested. If ROIIC is above the company’s current average return on capital, its overall return should rise. If it is lower, the average should fall. Hyperscalers’ combined ROIIC is currently near its peak at more than 35%, compared with a cost of capital of about 8%. 6. Subtract stock-based compensation. Paying employees with shares is effectively a combination of issuing stock and paying wages. If SBC is properly deducted from operating cash flow, the reported cash flow of large technology companies falls by 10% to 20%. Many investors ignore this and overstate the true figure. 7. A mature company can return to growth. A sharp increase in capital expenditures can move a business back to an earlier stage of its life cycle. Alphabet, Meta, and Oracle moved from “maturity” to “growth” after accelerating data center construction. This is not deterioration. It is a new investment phase. 8. The current decline in hyperscaler cash flow is expected to be temporary. Combined FCF for Amazon, Alphabet, Microsoft, Meta, and Oracle falls from $170 billion in early 2024 to negative $265 billion in 2027. Consensus then expects it to recover to ~$505 billion by 2030. Returns on capital remain above the cost of capital throughout this period. Microsoft is the only hyperscaler expected to maintain positive FCF across the entire forecast horizon. 9. Returns arrive with a delay, so patience matters. Amazon CEO Andy Jassy explained it directly. During periods of rapid growth, capital expenditures rise faster than revenue. This weakens near-term cash flow. The returns appear a few years later, after the new capacity is operating and generating revenue. Investors who focus only on current cash flow see the worst part of the cycle. 10. Consensus forecasts are not facts. The further out the forecast, the less reliable it becomes. Watch how estimates change. Are revenue and profit expectations rising as quickly as capital expenditure forecasts? The differences can be enormous. The 2027 EBIT forecast for Micron was raised by $192 billion, while Nvidia’s was raised by $231 billion. Their capital expenditure forecasts increased much less. That is a sign that investors expect those investments to generate strong returns. $MSFT $META $AMZN $ORCL $SPCX $NVDA $MU
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Jin S. Chun retweeted
Sta Asia: "European refiners face complete cargo cancellations under term contracts for the upcoming month, sending physical benchmark crude soaring past US$130 a barrel as buyers scramble for scarce Atlantic replacements"
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Some random thoughts on the lithium market as i sip my morning coffee! The problem is that at the moment, there are only high quality assets that are operating. There's not a healthy spread of producers across the cost curve. There are no mid to low cost producers that can suddenly be turned on. And that's largely due to the recent rapid recovery in the lithium price and only the ones that survived the last down cycle staying online. Even the expansions, P2000 from PLS for example, which has existing infrastructure and is lower risk, is going to take several years to come online. The issue with this is, once the market swings into a deficit (like we are in now), due to the lack of quality assets that are able to come online, the tonnes come from marginal, high cost, price sensitive projects. This creates major instability and is very different to how most mature commodity markets behave (copper, Iron etc.). When the price rises, the extreme marginal tonnes rush in, when it softens slightly these tonnes exit. So we are just continually adding and removing unstable high cost tonnes. There are four main restarts/starts that are set to take place this year. DVP's pioneer dome, MIN's Bald Hill, Ngungaju and Core lithium's Finniss project. They could do a combined total of ~34kt this year. Pioneer Dome for example is going to be a DSO project, which means they aren't processing the ore themselves, they are selling ore ~1.2% Li2O grade. Just in June this year, they announced the FID for the start. The price assumption they've used is US$26,000/t China. Now the problem with this is, due to the recent cyclical price dip, the futures is now at US$22,000/t. If the lithium price keeps falling, it's possible that a large majority of these restarts (~34kt of LCE) won't go ahead. So if you take out 34kt of LCE, you're just getting higher lithium prices and repeating the same cycle. The faster the market grows, those marginal tonnes become an increasingly smaller proportion of total demand, making each cycle harder to balance than the last. Therefore, the price needs to go higher and stay there to incentivise new low cost projects to come online (excluding some low risk expansions from existing mines). If it doesn't, then prices could boom back to 2022 levels due to the combination of the pool of readily available high cost projects becoming increasingly scarce and the lag to build new mines. Also the fear of Jianxiawo being turned on is totally nonsensical. It's just an excuse to help drive the lithium price down. Why do we care so much about Jianxiawo coming back online (which would do about ~25kt this year), but nobody batted an eye lid when Bald Hill, Finniss, Pioneer dome and Ngungaju said they'd come online this year (which is higher at ~34kt this year). Also, as I've said many times before, even if it does come back online, due to the rapid growth of the market, it won't have the same effect as it did last time it was operating. That lever has been pulled. Cheers for reading!☕️
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NVIDIA CEO Jensen Huang said the “ChatGPT moment for general robotics” is coming. The biggest winners may not be the robot makers. They may be the companies building what’s inside the robot. Robotics ecosystem: • AI Brains → $NVDA $QCOM • Sensors & Perception → $OUST $CGNX $ALGM $VPG • Edge AI Inference → $AMBA $CEVA $LSCC • Motors & Motion → $NJDCY $RBC $RRX $AME • Precision Joints → 6324.T 6481.T $ALNT • Power Electronics → $NVTS $TXN $WOLF $RNECY $IFNNY $STM $MPWR $ON • Energy & Rare Earths → $ENS $MP $USAR $LYSCF $UUUU If you’re interested in Physical AI and robotics, I shared my top 5 robotics stocks and a deeper dive here: 10xdisruptivestocks.substack…
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Jin S. Chun retweeted
A Norwegian neuroscientist spent 20 years proving that the act of writing by hand changes the human brain in ways typing physically cannot, and almost nobody outside her field has read the paper. Her name is Audrey van der Meer. She runs a brain research lab in Trondheim, and the paper that closed the argument was published in 2024 in a journal called Frontiers in Psychology. The finding is brutal enough that it should have changed every classroom on Earth. The experiment was simple. She recruited 36 university students and put each one in a cap with 256 sensors pressed against their scalp to record brain activity. Words flashed on a screen one at a time. Sometimes the students wrote the word by hand on a touchscreen using a digital pen, and sometimes they typed the same word on a keyboard. Every neural response was recorded for the full five seconds the word stayed on screen. Then her team looked at the part of the data most researchers had ignored for years, which is how different parts of the brain were communicating with each other during the task. When the students wrote by hand, the brain lit up everywhere at once. The regions responsible for memory, sensory integration, and the encoding of new information were all firing together in a coordinated pattern that spread across the entire cortex. The whole network was awake and connected. When the same students typed the same word, that pattern collapsed almost completely. Most of the brain went quiet, and the connections between regions that had been alive seconds earlier were nowhere to be found on the EEG. Same word, same brain, same person, and two completely different neurological events. The reason turned out to be something nobody had really paid attention to before her work. Writing by hand is not one motion but a sequence of thousands of tiny micro-movements coordinated with your eyes in real time, where each letter is a different shape that requires the brain to solve a slightly different spatial problem. Your fingers, wrist, vision, and the parts of your brain that track position in space are all working together to produce one letter, then the next, then the next. Typing throws all of that away. Every key on a keyboard requires the exact same finger motion regardless of which letter you are pressing, which means the brain has almost nothing to integrate and almost no problem to solve. Van der Meer said it plainly in her interviews. Pressing the same key with the same finger over and over does not stimulate the brain in any meaningful way, and she pointed out something that should scare every parent who handed their kid an iPad. Children who learn to read and write on tablets often cannot tell letters like b and d apart, because they have never physically felt with their bodies what it takes to actually produce those letters on a page. A decade before her, two researchers at Princeton ran the same fight using a completely different method and ended up at the same answer. Pam Mueller and Daniel Oppenheimer tested 327 students across three experiments, where half took notes on laptops with the internet disabled and half took notes by hand, before testing everyone on what they actually understood from the lectures they had watched. The handwriting group won by a wide margin on every question that required real understanding rather than surface recall. The reason was hiding in the transcripts of what the two groups had actually written down. The laptop students typed almost word for word, capturing more total content but processing almost none of it as they went, while the handwriting students physically could not write fast enough to transcribe a lecture in real time, which forced them to listen carefully, decide what actually mattered, and put it in their own words on the page. That single act of choosing what to keep was the learning itself, and the keyboard had quietly skipped the choosing and skipped the learning along with it. Two studies. Two countries. Same answer. Handwriting makes the brain work. Typing lets it coast. Every note you have ever typed instead of written went into your brain through a thinner pipe. Every meeting, every book highlight, every idea you captured on your phone instead of on paper was processed at half depth. You did not forget those things because your memory is bad. You forgot them because typing never woke the part of the brain that would have made them stick. The fix is the thing your grandmother already knew. Pick up a pen. Write the thing down. The slower road is the faster one.
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Jin S. Chun retweeted
🚨 LEOPOLD ASCHENBRENNER IS OFFICIALLY BETTING BILLIONS THAT THE AI HARDWARE BOOM HAS PEAKED. The exOpenAI researcher who was fired for warning that China could steal their AI models then turned $225 million into $5.5 billion in 12 months just filed his Q1 2026 13F with the SEC. One quarter ago he had $5.5 billion in disclosed equity exposure. As of March 31, 2026 that number is $13.67 billion. The portfolio nearly tripled in a single quarter across 42 positions. He initiated $7.46 billion in put options against every major semiconductor company between January 1 and March 31, 2026. None of these positions existed in his Q4 2025 filing. - SMH VanEck Semiconductor ETF PUT: $2.04 billion - Nvidia PUT: $1.57 billion - Oracle PUT: $1.07 billion - Broadcom PUT: $1.01 billion - AMD PUT: $969 million - Micron PUT: $583 million - Taiwan Semiconductor PUT: $535 million - ASML PUT: $494 million - Intel PUT: $159 million For the past 18 months Aschenbrenner was betting only on electricity, memory, compute, and physical data center infrastructure. That made him one of the best performing fund managers in the world. And his long stock book still reflects that exact same thesis. - Bloom Energy: $878 million - SanDisk: $724 million - CoreWeave: $556 million - IREN: $401 million - Core Scientific: $389 million - Applied Digital: $320 million - Riot Platforms: $142 million - CleanSpark: $104 million - Solaris Energy: $62 million - T1 Energy: $43 million - Bitfarms: $38 million - Bitdeer: $29 million - Power Solutions: $26 million - WhiteFiber: $20 million - Babcock and Wilcox: $19 million - SharonAI: $18 million - ProPetro: $13 million - Hive Digital: $6 million He is also running call options on specific names at the same time as his puts, which means he is not simply betting against semiconductors everywhere. - Micron CALL: $422 million - SanDisk CALL: $388 million - Taiwan Semiconductor CALL: $354 million - CoreWeave CALL: $140 million - Bloom Energy CALL: $55 million This means he believes the companies supplying power, storage, and compute to the AI industry still have years of growth ahead of them. But the chip companies that Wall Street has been buying for the past two years at record valuations have already priced in everything good that is going to happen to them. The man who has been right about every major AI trade for the past 18 months is now betting that the biggest names in semiconductors are about to fall. If his track record means anything, the chip stocks Wall Street has been buying for the past two years may be in serious trouble.
Readers added context they thought people might want to know
The values for put options in 13F filings (e.g., $2.04B SMH PUT) represent the notional value of the underlying securities controlled by the options, not the premium paid or amount invested. netacn.com/faq
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Jin S. Chun retweeted
Yesterday @coinbase experienced a multi-hour service disruption affecting trading, exchange access, and balance updates. Here's our initial read from Coinbase engineering on what happened, how we recovered, and what we're addressing. At approximately 23:50 UTC on 2026-05-07, our monitoring detected cascading quote failures from internal services that triggered multiple Sev1 incidents that engineering immediately began investigating. Customer-facing impacts included spot trading, Prime, International and derivative exchanges. Root cause: a thermal event (cooling system failure) inside a subset of racks within a single building in AWS us-east-1. We run a primary replica of our exchange infrastructure in a single zone, consistent with industry standards to reduce latency. To prepare for failures like this, we maintain a distributed standby, but during this incident, failures in the primary zone that were designed to be isolated were not, extending the duration of our outage. The failure cascaded down two paths: 1. Multiple hardware components beneath our exchange’s matching engine failed, requiring recovery and failover 2. Distributed Kafka clusters that manage messaging across Coinbase systems failed to remain available, also requiring partition failovers to new hardware brokers with many TiBs of data After isolating the incident: automated tooling drained ~10 Kubernetes clusters worth of related workloads out of the affected zone to stabilize internal services. Most services were back to normal within ~30 minutes of diagnosis. The two things we couldn't automatically drain: the exchange (dedicated hardware and storage) and Kafka (managed service that was designed to be resilient to this, with unique problems). The exchange matching engine is the core system responsible for processing orders and maintaining order books. It is a distributed cluster and requires quorum to safely elect a leader and continue processing trading activity. During the incident, infrastructure-level constraints in the affected datacenter left only a subset of nodes healthy, preventing the cluster from reaching quorum. As a result, trading across Retail, Advanced, and Institutional exchanges were blocked. Recovery required our oncall and engineering teams to execute our disaster recovery plan, restore quorum safely, and validate system health under constrained infrastructure conditions. The team built, tested, deployed, and validated the fix while continuing to manage the broader incident. Kafka recovery was a much larger scale operation. Our primary managed Kafka partitions process many terabytes of data daily and are designed with resiliency guarantees for uninterrupted operation during a datacenter failure just like this. In this case, those guarantees failed and required manual recovery. We again relied on disaster recovery procedures to recover stuck partitions onto new hardware (brokers) that enabled us to safely bring x-service messaging back online across Coinbase. During the lag, customers saw delayed balance streams which resolved automatically once replication caught up. No data lost. Once the engine came back up as part of our standard runbooks, we re-opened markets carefully: all products to cancel-only mode first, audited product states, then moved all markets to auction mode, before restoring trading on Coinbase Exchange. What went right: the team. Incident response across the company came together within minutes, followed well-rehearsed playbooks and used secure automation tooling to recover all services. We have a strong, senior team at Coinbase that worked through rare failure modes to recover all services. To our customers: losing access to your account, even temporarily, is unacceptable. We know that. We're sorry, and we’ll publish a full root cause analysis in the coming weeks 🙏
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Jin S. Chun retweeted
The Data Center Empire in Malaysia 1. Singapore continues to be the second-largest importer of Nvidia GPUs every month since the beginning of 2026. In April alone, Singapore imported over $2B worth of chips.” 2. Since the beginning of the year, Singapore and Malaysia have imported roughly $13B to $15B worth of Nvidia GPUs (if we try to isolate the noise), equal to up to 20% of Nvidia’s total revenue for the upcoming Q1 2026, based on the $78B guidance. 3. Most of the chips imported into Singapore continue by land to Johor, one of the fastest-growing, if not the fastest-growing, data center regions in the world right now. 4. In four months, Singapore and Malaysia imported enough chips to accommodate 0.5GW–0.7GW-powered data centers. 5. According to Nvidia, almost all sales to Singapore and Malaysia are to “U.S.-headquartered” companies. 6. As of today, Nvidia continues to aggressively deny any signs of smuggling or misuse of its chips under AI chip export controls.
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Jin S. Chun retweeted
If Jensen converts to being Korean can Nvidia finally go up again?
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Interactive Brokers just allowed trading on Korean stocks for the first time in history. Korean stocks are dirt cheap, bombed out and completely undervalued and overlooked. I am going to find the best ones and invest in them. Follow along if you want cheap Korean stocks.
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Jin S. Chun retweeted
$SIVE – If you are wondering if it is too late to invest, it isn’t and here is why. Goldman Sachs just unveiled their Optical Communications industry map for the AI infrastructure buildout. The full ecosystem laid out – IC Design, Materials, Optical Components, Packaging and Testing. Guess what? $SIVE is not on the map, yet. The highlights from Goldman: TAM growing from $15B to $154B. Scale-up optical interconnects within racks and super-nodes are the real driver. NVIDIA’s GPU clusters jumping from 72 to 576 GPUs by 2027-2028, with interconnect dollar value per computing unit going from $315K to $9.4B. A 29x increase per unit. Silicon photonics penetration jumping from 6% in Q1 2024 to 46% by Q4 2028. CPO commercialization starting in 2026. The shift from copper to optical is structural and accelerating. In the laser/light source segment, Goldman maps $LITE, $COHR, $AVGO, $AAOI – the obvious ones. Sivers is nowhere to be seen. $SIVE powers Jabil’s 1.6T LRO architecture. Win Semi is their confirmed foundry partner. They sit upstream of the entire optical interconnect stack with InP lasers everyone needs. When Goldman publishes the next iteration of this map, $SIVE will most likely be listed on the US Nasdaq and big enough for institutional coverage. Which means institutions will try to shake the ground just so they can catch up. This report is extremely bullish for the entire photonics ecosystem. Being early pays. Having conviction pays even more.
Goldman Sachs JUST announced: "AI optics industry will 10X until 2028." (April 2026 AI Optical Report) If you want to be a millionaire in 2 years, buy these stocks on the next 10% dip: 1. Poet Technologies $POET 2. AXT Inc. $AXTI 3. Applied Optoelectronics $AAOI 4. Lumentum Holdings $LITE 5. Coherent $COHR 6. MACOM $MTSI All my buy and sell signals in Discord @ stockwhale.vip.
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Strategic governance matters #sgf
Every time I post about this company on X, the replies come fast and they come ugly. “Boomer brand.” “Going to zero.” “Who even uses these products anymore?” I have been called an idiot more times on this one name than any other idea I have shared publicly in the last two years. I love it. There is a principle that has guided value investors since Benjamin Graham first wrote it down: the market is not always right, and the times it is most wrong are precisely the times when everyone agrees on the narrative. When a stock has nearly 14% of its float sold short, when the comment section fills with contempt, when the sell-side has a consensus “Hold” and the price is near a multi-year low, that is not evidence the thesis is wrong. That is evidence that expectations have been reset to a level where almost any improvement becomes a positive surprise. Here is what I will tell you in the free section, and it is already more than most people following this story have bothered to figure out. This company is being valued by almost everyone, including the sell-side analysts who cover it daily, using a completely wrong enterprise value. Not slightly off. Not a rounding error. The number most investors are looking at is roughly five times higher than the real number. The reason is a balance sheet technicality that takes about twenty minutes to work through, and almost nobody has done it. When you do the work and strip out the non-recourse financing subsidiary that has nothing to do with the core operating business, you find a company trading at an enterprise value of roughly one billion dollars on a business that has historically generated hundreds of millions in annual EBITDA and billions in revenue. You also find something else. A new CEO who just bought stock in the open market with his own money. A board member who did the same. Three hundred and forty-seven million dollars in share buybacks completed in 2025 alone, retiring eleven percent of shares outstanding in a single year. An investor day coming in May that could be the most important single catalyst this company has seen in a decade, specifically because management has signaled the return of the one product that dealers loved, that entry-level riders needed, and that the previous CEO killed for reasons that had more to do with his own preferences than with any rational business logic. The previous CEO is gone. He was pushed out after a proxy fight. The new man came from a consumer brand background, flew to Milwaukee, sat with dealers, and listened. His first moves were to stop doing the things that were destroying the business and start doing the things that the people actually closest to the product had been begging for. Meanwhile, there is a pension fund sitting on the balance sheet that is overfunded by nearly half a billion dollars. There are four owned manufacturing facilities plus a corporate headquarters building that would take hundreds of millions of dollars to replace. There is over a billion dollars in net cash at the parent company, completely separate from the financing subsidiary, that most investors do not realize is there because they are reading the consolidated statements without doing the work of separating the pieces. Nearly 14% of the float is sold short. That is not a warning sign. That is a coiled spring. I am a member of the Ben Graham school. I do not buy on hope. I buy on math. The math here, when you do it correctly, is among the most compelling I have seen in years on a company of this size, this brand recognition, and this operating history. The full analysis is below for paid subscribers. It covers the balance sheet deconsolidation in detail, the real enterprise value, the valuation case, every catalyst I can see between now and year-end, and what I think this is worth on a conservative recovery scenario.
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Jin S. Chun retweeted
When she hears you have exposure to SpaceX ahead of the IPO (it’s a triple-layered SPV that you paid 20% upfront and 30% carried interest without knowing who ultimately owns the shares)
BREAKING: Musk's SpaceX confidentially files for IPO
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Jin S. Chun retweeted
Google just released a warning for cryptocurrency that the number of qubits required to break ECDSA is 20x less than previously thought. They have proof. They’re (strongly?) recommending crypto upgrade to post-quantum by 2029 now. 4 years!? Timelines are accelerating rapidly.
Many are wondering "what Google saw" that caused them to revise their post-quantum cryptography transition deadline to 2029 last week. It was this: research.google/blog/safegua…
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Jin S. Chun retweeted
SUMMARY OF THE BUSINESS INSIDER INTERVIEW WITH $NBIS CEO ARKADY VOLOZH Q: What are the key bottlenecks in AI infrastructure right now? A: Nebius calls them the “four Cs”: capacity, capital, chips, and customers. On capacity: “The physical world simply cannot build data centers fast enough.” The real bottleneck is the supply chain, especially transformers and power equipment. Nebius is targeting more than 3GW of contracted power by 2026. On capital: “Capturing just 10% of the AI infrastructure market requires around $400 billion.” The scale of funding has fundamentally changed. On chips: “A year ago, it was GPUs. Now the constraints go deeper into the silicon.” The bottleneck has shifted toward memory shortages. On customers: “Demand vastly outpaces the supply we can build.” Demand is no longer a question. Q: Why does Nebius build its own stack instead of leasing or using third parties? A: “Think about Nebius as a fourth hyperscaler. You do not achieve that by acting as a hardware wholesaler.” Nebius builds everything itself, from servers to racks. “We design our own servers and racks. This allows us to bypass middlemen and capture that margin ourselves.” “Look at the alternative. Companies lease data center shells, buy pre-assembled racks, and sacrifice margin at every step.” Q: How does Nebius think about the AI stack and where value is created? A: Nebius is focused on owning the entire stack. “We control our cost structure from the concrete up to the software.” The stack is broken into five layers: Layer 1: Land, power, physical infrastructure Layer 2: Compute hardware “By building our own racks, we save 15% to 20% and deliver more compute per unit of power.” Layer 3: Bare metal Layer 4: Multi-tenant cloud Layer 5: Services and inference (including Token Factory) “Most companies operate at one or two layers and pay a premium to middlemen. We own the entire stack.” Q: Does being a new company affect how Nebius builds at scale? A: “Nebius is a new company, but our team has decades of experience building infrastructure at hyperscale.” They handle everything internally. “We know how to buy the land, get the permits, and contract the power. We do not outsource these problems.” They also emphasize working directly with local communities to keep projects on track. Q: What are “dark GPUs” and why do they matter? A: “Dark GPUs are idle compute capacity.” Customers are paying for GPUs they cannot fully utilize due to poor orchestration. “We built our cloud for AI engineers and manage orchestration so customers always know their exact available capacity.” Q: How do large deals with companies like Meta and Microsoft fit into the strategy? A: “Our core product is our multi-tenant AI cloud.” These deals are not the core business. “Large contracts with Meta and Microsoft are fuel.” “They allow us to build faster, create a foundation for infrastructure, and give us more options to raise capital.”
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Jin S. Chun retweeted
⭕️I wonder how Iranian monarchists feel about President Trump returning the three islands (Abu Musa, Greater Tunb and Lesser Tunb) to the UAE. ⭕️The three small islands sit right at the entrance to the Strait of Hormuz. The last Shah ordered the Navy to occupy them in 1971. ⭕️The UAE has claimed them ever since, especially since Arab tribes inhabited the Greater Tunb and the others. ⭕️By returning them to the UAE, the US can establish a military base there and control the Hormuz Strait for decades to come.
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