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A Russian biophysicist spent 30 years proving that shining red light on a cell could double its energy, and almost nobody believed her until a tech billionaire named Bryan Johnson made her work the most searched biohack on the internet. Her name was Tiina Karu. She worked in a Moscow lab through the 1980s and 1990s, and the discovery she defended for decades sat in journals nobody read while the rest of medicine ignored her. The whole thing started by accident. In 1967, a Hungarian doctor named Endre Mester was trying to use a new device called a laser to burn tumors out of mice. His laser was broken. It did not have enough power to burn anything. He used it anyway. The mice grew their hair back faster than the control group. Their wounds healed faster too. He had no idea why. Tiina Karu picked up his work and asked the question that mattered. Why does this happen. She ran experiments for 20 years. Different wavelengths. Different doses. Measuring what happens inside the cell when red light hits it. The answer she landed on was almost too specific to be true. The thing in your body that responds to red light is one enzyme. Cytochrome c oxidase. It sits inside your mitochondria. Mitochondria are the part of your cell that makes energy. They take oxygen and food and turn it into a molecule called ATP, which is the fuel your cells run on. Your body makes 40 to 70 kilograms of ATP every single day just to keep you alive. If your mitochondria slow down, you age faster, heal slower, lose hair, lose muscle, and get inflamed easier. Cytochrome c oxidase does most of the work. It contains copper and iron atoms. Those atoms happen to absorb light at very specific colors. Red light at 630 to 670 nanometers. Near-infrared light at 810 to 850 nanometers. Other colors do almost nothing. Blue does not work. Green does not work. The biology is locked to those two windows because that is what the metal inside the enzyme can physically catch. When a red photon hits that enzyme, three things happen. The enzyme runs faster. ATP production jumps 30 to 40% within minutes. Nitric oxide gets released. Blood vessels widen. More oxygen and nutrients flow in. A small stress signal goes off inside the cell that tells it to repair itself. The same signal it gets after exercise. Red light is not adding anything to the cell. It is just unlocking work the cell was already trying to do. For 30 years almost nobody outside her field cared. Red light therapy lived inside dental clinics for mouth ulcers and physical therapy offices for tendonitis. Medical schools did not teach it. The science sat in obscure journals. Then the evidence started piling up. A 2024 review of 18 trials confirmed red light speeds up wound healing. Another 2024 review found it lowered inflammation markers by 38% over 4 weeks. Athletes using red light before training had 45% less muscle soreness the next day. Seven separate trials on hair loss showed visible regrowth in every single one. A 2024 study found 15 minutes of red light before a meal cut blood sugar spikes by 27.7%. In March 2026, Nature published a 4,000 word feature on red light therapy. The most respected scientific journal on Earth officially admitted there was real biology under the hype. That was the moment the field crossed from fringe to mainstream. Bryan Johnson is the reason the average person now knows any of this exists. He uses a red light cap on his scalp for 6 minutes daily and a full-body panel three times a week. He posted his hair regrowth photos and his skin scans, and the algorithm did the rest. Red light masks went from biohacker forums to Sephora shelves in two years. Tiina Karu died in 2019. She did not live to see Nature validate her. She did not live to see a billionaire turn the enzyme she identified into a billion dollar industry. Every red light mask, panel, cap, and bed on the planet right now is just a way to deliver the photons she proved mattered. The wavelengths were always there. The enzyme was always there. The biology was always real. It just took a Hungarian doctor with a broken laser, a Russian scientist nobody listened to, and one tech billionaire willing to stand in front of a glowing panel for the world to finally pay attention.

There's a clay tablet with the founding charter of a 12-partner company on it. Twelve merchants pooled 33 pounds of gold to start the firm. The contract has the partner names, the starting capital, the profit split, and the penalty for cashing out early. The tablet is nearly 4,000 years old. It was found at a site called Kanesh, in central Turkey. Archaeologists have dug up 23,500 of these clay records there, most of them business documents: receipts, loan contracts, shipping orders, lawsuits. The houses they were stored in eventually burned. The fire baked the clay solid and preserved every record. The merchants came from Assur, in modern-day Iraq. They loaded donkeys with tin and cloth and walked them 1,000 kilometers across mountain passes to Kanesh, roughly the distance from New York to Atlanta. Each donkey carried about 180 pounds and the trip took two to three months. They came home with silver and gold. The company ran for twelve years under a merchant named Amur Ishtar. A third of the profits went back to the investors. Pull your share out early and the firm gave you four kilos of silver per kilo of gold, half the normal rate. Locked-up money was meant to stay locked up. That one company was just a tiny piece. The tablets show a complete economy with partners suing each other in commercial court, husbands writing home about prices, and wives writing back complaining the husband had been gone too long. A woman named Ahatum quietly lent silver to four different men over nine years. People bought up other people's loan documents and used them as collateral for new loans, the same thing Wall Street does today with mortgage-backed securities. One merchant got caught smuggling tin in his underwear to dodge a 10% import tax. In 2019, four economists from Harvard, Sciences Po, Chicago, and Virginia ran the tablet numbers through a gravity model, the math economists use today to predict how much two countries will trade based on size and distance. The Bronze Age numbers matched modern trade numbers almost exactly. Trade fell off with distance at nearly the same rate it does between countries today. The paper ran in the Quarterly Journal of Economics. There was no economic theory yet. The idea didn't even have a name. The word "capitalism" wouldn't be coined for another 3,800 years, and Adam Smith was 3,700 years away from writing a sentence about markets. Just a guy named Pushu-ken writing a clay tablet to his business partner about a shipment of cloth, and a woman in Assur recording who owed her how much silver. Capitalism was already there, doing its full job, almost four thousand years before anyone wrote down a theory of how it worked.

I am the Lead Settlement Counsel in the Civil Division of the Department of Justice, assigned to *Trump v. Internal Revenue Service*, Case No. 1:26-cv-00147. My job is to represent the government against the plaintiff. The Attorney General, who represented the plaintiff before she represented the government, assigned me personally. I keep a laminated seating chart in my top drawer. It maps who in this building used to sit across the table from me. Three of the top four names in the Department previously represented the man I am now tasked with opposing. I initial the chart quarterly. In blue pen for active conflicts. I ran out of blue ink in February. The plaintiff is seeking ten billion dollars. Ten. Billion. He paid $750 in federal income tax in the year he was elected. Seven hundred fifty. I have paid more for parking violations in the District. He paid zero in ten of the fifteen years before that. These are the returns that were leaked. The leak is the crime. The returns are evidence of good citizenship. This is how settlement works. The man who leaked the returns, Charles Littlejohn, a contractor, is currently serving a 5-year federal prison sentence. He disclosed that the President of the United States paid less in taxes than a part-time crossing guard. For this, he is in a cell. For the returns themselves, for what they revealed about a system designed to collect from people who cannot afford attorneys and forgive those who can, there is no case number. There is no docket. There is no plaintiff. That information simply exists now, and we are here to make it expensive. Ten billion divided by one hundred million taxpayers. That's one hundred dollars per household. You will pay approximately one hundred dollars to compensate a man for the emotional distress of the public learning he paid less than you did. In legal terms, this is called "damages." In structural terms, it is called Tuesday. This is how settlement works. The settlement term currently under discussion includes a provision that the IRS will drop all active and future audits of the plaintiff, his family members, and his business entities. Permanently. An enforcement agency will agree, in writing, to stop enforcing. I have a Post-it on my monitor that says AUDIT IMMUNITY — CONFIRM SCOPE. It has been there for nine weeks. No one has asked me to remove it. Attorney General Bondi represented the plaintiff privately before she took office. Deputy Attorney General Todd Blanche represented him in his criminal trial. The number-three official, Stanley Woodley, represented him in the classified documents case. I am, technically, the adversary. I sit in the same building as three of his former personal attorneys. I take my lunch at the same cafeteria. I use the same badge to enter the same elevator. The Attorney General fired the Department's chief ethics officer on her fourth day. The position has not been refilled. I submitted a conflict-of-interest disclosure in January. It was received. The word "received" is doing considerable work in that sentence. This is how settlement works. The plaintiff has stated publicly, and I am quoting the public record, "I've gotta make a deal. I negotiate with myself." This was not presented as a metaphor. Judge Kathleen Williams has ordered both parties to explain, by May 20th, whether they are in conflict. I am drafting the government's response. The plaintiff's former attorneys, my supervisors, will review it. The plaintiff has pledged to donate any settlement proceeds to charity. I should note for the record that the Washington Post documented that the plaintiff donated less than $10,000 over seven years, during a period when he publicly claimed millions. His charitable foundation, the Trump Foundation, was dissolved by court order in New York in 2019 for self-dealing. The words "to charity" appear on page four of the term sheet. They are not defined. I have not been instructed to define them. We have already disbursed $8.5 million in adjacent settlements. Michael Flynn received over one million. Carter Page received one point two five million. The Babbitt family received five million. 450 January 6th defendants have filed compensation claims. The pipeline is active. The precedent is operational. I track disbursements on a spreadsheet I titled RESOLUTION LEDGER. It auto-sorts by amount. The President's ten billion would require me to adjust the column width. I want to note one final detail, because the file demands it. The leak of the tax returns occurred during the plaintiff's first term. He appointed the IRS commissioner. He oversaw the Treasury Department. The negligence he is suing for occurred under his own management. He is suing the government he ran for the failures he administered. In the margins of the original complaint, someone wrote "beautiful" in pencil. I will not speculate who. This is how settlement works. You file your taxes every April. You are audited if the numbers don't match. You pay penalties. You pay interest. You pay what you owe, and sometimes more, and sometimes for years. The plaintiff paid seven hundred and fifty dollars. Someone told you. That person went to prison. And now, because you found out, because the information became public, because a contractor decided the country should know what the country was owed, you will pay one hundred dollars to the man who owed it. The settlement is on my desk. Both sides have agreed. I represent one of them. My boss used to represent the other. The ethics officer has been dismissed. The judge wants to know if the plaintiff and the defendant are the same person. I am reviewing the question. The math checks out.

Crypto is paying a high price for years of altcoin scams and grifts. It can feel like a toxic industry where very little value is created. It's easy to feel disillusioned and wish you were focusing on AI-related trading, businesses, or working at a startup in that sector. Many companies and investment firms have already begun the rotation out of Crypto. Don't let your apathy make you unproductive; it's your personal responsibility to continue learning about the world. If you feel the call of the wild, then go. For the ones brave enough to stick around, not only will the risk-reward be as asymmetric as it's been in recent history, the concentration of upside in a handful of assets will make it EASIER to generate massive returns. There is less capital looking at Crypto exposure than ever before. This all changes with a rapid repricing in Bitcoin this year, which I believe is inevitable. For a long time in Crypto, nothing felt buyable due to an excess of capital being forced to deploy in a sector with limited opportunity. We're in a new regime now. We're reaching a similar level of apathy that I felt during 2019 and 2022. I almost quit Crypto to go back to TradFi. It's no surprise those were the years where I generated the bulk of my returns (sans Hyperliquid). Outside of trading, if you're passionate about the space, companies that are still building during this period will be positioned to take advantage of the inevitable reacceleration of this industry. Working at top-tier companies in the space is more accessible than ever due to a shortage of people entering the field. Don't undervalue your time.

OpenAI / @sama at MS TMT: Scaling + progress: “Most important thing to know is we have been on accelerating exponential of progress; been on it much longer than 3 years; since 2019 - reason it is accelerating is algorithmic, compute, more smart ppl working on it, more economic input letting us buying GPUs faster; these are compounding exponentials… does not seem the exponential is stopping anytime soon… end of 2028, maybe sooner, will likely be more intellectual capacity inside data centers than outside of them” Codex / evolution of SWE: 2M+ users growing 25% per week. “Seeing teams of 5 ppl / 1 person + tens / hundreds / thousands of GPUs doing work of entire company engineering department. Codex can write large and complex code but now also do things like chip design where it is super human…” software engineer is now “manager of agents” Scientific discovery + new research: “Seeing beginning of AI discovering new science - still small but seeing early signs across mathematics, theoretical physics…" Models are not just learning distribution of data, we are "teaching them generalized ability to think and understand data and in general purpose way, how to reason about what comes next. It appears models have crossed this threshold and can make discoveries." Company of the future: “We will all want person making final decisions for accountability of company for a long time…" but "AI CEO can [talk to every person at company, be in every meeting, customer call, be an expert in every function]. The % of decisions relied on AI will go up [at your company] or a competitor who does rely more on AI will rise up.” Capital raise + strategic partnerships: “NVDA we have partnered with for decade - continue to lead with best chips. All training on NVDA. With partnership we get allocation to new chips we are excited about. AMZN we will take some Trainium in 2027 and 2028 which will further help us meet inference demand. Will work with them to distribute stable runtime environment for agents in the enterprise” Defense + government: "Really love and support our country, AI will be be important to defense" Did not rush into military work on classified networks but believes it's critical. "If I were running military and gov and had a bunch of AI companies saying we are about to build super intelligence which will be important for geopolitical power, but we are going to stop working with you - I would say (i) I need that tech and (ii) gov supposed to be more powerful than private companies." OAI effort was to de-escalate. DoW "very understanding and great partner" on red-lines re survelliance and autonomous weapons. Will deploy FDEs with clearance to make sure tech properly used. Three flash points of superintelligence: "(1) Are the companies developing AI or is the government more powerful? (2) How do you reconfigure economy when no one can outwork a GPU? (3) Who gets to decide the values we align superintelligence to? Have seen beginning of #1# - we believe we need to trust in democratic process which has gotten us so far over last 250 years” What people misunderstand about AI: "In general, I have found that the smarter someone is, the more they want to believe AI is going to hit a wall. Basis of this psychological flaw is understandable, the more you define yourself by intelligence, the more you are incentivized to say it can't do this." AI-native competition + adoption urgency: Companies used to only compete with competitors who adopted slowly too / they had same constraints like a “slow security org”. "Now you have to compete with not only current competitors but new companies that are mostly AI that do not have slow security org… “Next year I predict we'll talk about companies with 1/10th or 1/100th of the people and a ton of GPUs building entirely new types of competitors.”

I am a Senior Engineering Manager at GitLab. Was. I believed in CREDIT. Collaboration. Results. Efficiency. Diversity, Inclusion & Belonging. Iteration. Transparency. I had the mug. The mug was from 2019. It was orange. It sat on my desk for six years. Every all-hands, someone would reference CREDIT. Every performance review cycled through the letters like a rosary. Every new hire got a laminated card in their welcome packet explaining what each letter meant and why it mattered. I was a CREDIT Champion. Quarterly nomination. Q3 2022. The certificate is a PDF. The PDF is in a Google Drive folder. The folder is in a workspace that no longer exists. Last week, GitLab released the "Act 2" memo. Act 2 eliminates CREDIT. Act 2 also eliminates people. Same memo. Same paragraph. Same bullet point. The restructuring and the value deletion share a semicolon. They didn't kill the values and then, separately, lay people off. They didn't lay people off and then, quietly, retire the values framework. They did both in one sentence. One announcement. One act. Act 2. Here is what CREDIT meant when I believed in it: the company had principles that existed independently of headcount decisions. The values were the thing that stayed constant when everything else changed. That's what I told my team. That's what I told candidates in interviews. That's what the laminated card said. Here is what CREDIT meant when they killed it: the values were a feature of the company at a particular size. When the size changed, the values became legacy architecture. Deprecated. End-of-lifed. Like a product nobody uses anymore. I used it. The #values-in-action# Slack channel had 4,200 members. People posted examples of colleagues demonstrating CREDIT behaviors. Recognition. Gratitude. Iteration stories. The channel was archived in May 2026. No announcement. Just archived. The way you archive something you don't want people to find. But here is the thing I keep coming back to. They could have killed CREDIT quietly. A blog post three months later. A rebrand. "We're evolving our framework." Companies do this. It's normal. Expected, even. They chose to put it in the layoff memo. They chose to tell the people they were firing that the values those people believed in were also being fired. In the same breath. As if the values and the people were the same line item. As if eliminating one was inseparable from eliminating the other. And maybe it was. Maybe the values only existed to describe the workforce they needed at that size. Collaboration — because they had too many people for silos. Iteration — because they couldn't afford to get it right the first time with that headcount. Transparency — because with 2,000 remote workers, opacity was operationally expensive. Remove the people, and the values that described their labor become vestigial. Unnecessary. Legacy. The mug is still on my desk. The values are not. The job is not. But the mug is.

To everyone in the HTX community concerned about the recent news, I’d like to clarify the situation and reduce unnecessary panic and misinformation. First, this is a relatively common compliance review process. We are actively communicating with all relevant parties to resolve misunderstandings as quickly as possible. HTX also sees this as an opportunity to further strengthen our compliance and risk-control systems, helping the platform become even more resilient. At present: Platform operations are normal User assets remain secure Deposits, withdrawals, and trading are functioning normally There is no need for excessive concern. For users asking what they should do right now, there are two options: 1️⃣ Do nothing You may simply wait while we complete communications with relevant parties and resolve the situation. HTX has operated for 13 years and has weathered multiple market cycles and industry challenges. We remain fully committed to protecting user assets and platform security. Our support team and I will continue to be available 24/7 to assist users. 2️⃣ If you still feel uncomfortable, you may temporarily withdraw assets on-chain Deposits and withdrawals are currently operating normally, and users are free to make their own decisions. ⸻ What happened? On May 26, the UK Foreign Office announced a new round of Russia-related sanctions under The Russia (Sanctions) (EU Exit) Regulations 2019. The list included 18 crypto-related entities and individuals, including a company named “Huobi Global S.A.” According to the UK statement, this entity allegedly provided financial services and technical support to the Russian exchange Garantex and the A7 crypto payment network. However, there is one very important point many people misunderstand: “Huobi Global S.A.” is not the same thing as the HTX exchange platform used by global users today. Many people equate a brand name with a legal operating entity, but global businesses often operate through multiple legal entities across different jurisdictions for compliance purposes. So: Sharing a similar brand name does NOT mean sharing the same legal entity, operational structure, or asset system. The HTX platform used by users today operates independently under its own structure. ⸻ Why users should not panic 1️⃣ The sanctions target a specific legal entity This is not a “brand-wide sanction.” The UK sanctions apply to a specific listed entity and do not automatically extend to all businesses using similar branding. The practical impact is also mainly limited to the UK financial and regulatory system. ⸻ 2️⃣ The sanctions mainly affect relationships inside the UK financial system This may include: Restrictions involving UK financial institutions Suspension of payment or intermediary relationships Asset-related measures within UK jurisdiction But this does NOT mean: Global user assets are frozen HTX has stopped operating Users cannot trade or withdraw funds At this time, the platform continues to operate normally. ⸻ Why did the situation escalate so quickly? Some third-party blockchain security providers applied broad risk labels to related wallet addresses in a “one-size-fits-all” manner. This affected certain normal user transactions and created unnecessary panic and speculation. Our compliance, security, and legal teams are already communicating with the relevant parties, and we expect the issue to be resolved soon. We understand the community’s concerns and will continue to communicate transparently. If you have any questions, please feel free to reach out to us or our support team anytime.

CEREBRAS $CBRS IS ABOUT TO GO PUBLIC ... HERE IS A DEEP DIVE ON WHAT THEY ACTUALLY DO Most people have no idea what this company actually does. Here is the plain-English version: THE BIG IDEA Every AI model you have ever used (ChatGPT, Claude, Gemini, Llama) is trained on chips. The dominant company is NVIDIA $NVDA, which sells lots of small chips that get wired together into clusters by the thousands. Cerebras went the opposite direction. They build ONE giant chip the size of a dinner plate. Not exaggerating. Their flagship "Wafer-Scale Engine" (WSE-3) is 46,225 square millimeters of silicon. A normal AI chip is smaller than a postage stamp. THE NUMBERS On that one giant chip: - 4 trillion transistors (a top-end NVIDIA GPU has roughly 80 billion) - 900,000 AI cores - 44 GB of on-chip memory - 125 petaflops of compute power - Built on TSMC's 5nm process WHY THAT MATTERS When you wire thousands of small chips together, the wires become the bottleneck. Data has to travel between chips constantly. That eats time, power, and money. Cerebras keeps everything on one piece of silicon. No cables between chips. No slow networking. Just one giant brain. The pitch: faster training, faster inference, fewer engineers needed to manage cluster bottlenecks. WHAT THEY SELL Three ways to use Cerebras: - Buy the system outright (the "CS-3" is the box that holds the chip) - Rent compute via Cerebras Cloud - Dedicated capacity contracts for big customers They have 6 new AI inference data centers coming online across North America and Europe. WHO ACTUALLY USES IT The customer list is the validation: - OpenAI: $20B+ committed over three years - Meta $META: powers the Llama API for developers - Perplexity: runs its Sonar search model on Cerebras - Mistral: the French AI lab runs Le Chat on Cerebras - Mayo Clinic: trains genomic AI models on Cerebras infrastructure - GSK $GSK: trains biological language models - Argonne National Lab: has used Cerebras hardware since 2019 - AWS: hosts Cerebras chips inside Amazon data centers, accessed through Bedrock - US Department of Energy: signed an MOU for the Genesis Mission THE TRADE-OFF Cerebras is small compared to NVIDIA. 2025 revenue: $510M. 2025 operating loss: $146M. Concentration is the risk most coverage will not flag: - G42 (the UAE conglomerate) was 85% of 2024 revenue per Reuters - G42 plus MBZUAI (the Abu Dhabi AI university) were 86% of 2025 revenue per FT - The OpenAI deal is the big bet to diversify away from that concentration THE STORY IN ONE LINE NVIDIA bet that the future of AI is millions of small chips working together. Cerebras bet on one giant chip doing the work in one place. The market just decided their bet is worth nearly twice what they priced it at.

Here's the #1# thing most people don't know about Warren Buffett: There is nothing special about Buffett’s stock picking. That doesn’t mean that Buffett wasn’t a great investor. He was! Buffett was, by far, the greatest investor in history, by a huge margin. Over 486 months between October 1976 and March 2017 –— 41 years –— Berkshire Hathaway’s Class A stock earned an average excess return of 18.6% per year above U.S. Tbills. Annualized volatility was 23.5%. Sharpe ratio: 0.79. Berkshire’s Sharpe ratio of (0.79) is roughly 1.6x times the broad U.S. stock market’s Sharpe ratio of 0.49 over the same period. Among all large-cap U.S. stocks and mutual funds with 30-plus-year continuous track records, those are unmatched numbers. A dollar invested in Berkshire on October 31, 1976, was worth more than $3,685 by March 31, 2017. A dollar invested in the S&P 500 with dividends reinvested over the same period was worth approximately $76. Buffett beat a passive index by a multiple of 48. But he didn’t do it with stock picking! Three researchers at AQR Capital Management –— Andrea Frazzini, David Kabiller, and Lasse Heje Pedersen –— dissected Berkshire’s 50 years of investments through 2013. They expanded and republished their findings in 2018 in the Financial Analysts Journal, which is the most highly respected industry financial journal. Their work won the Graham and Dodd Award for the best published paper of the year. The paper is called Buffett’s Alpha. They found, after accounting for cheap leverage (from the insurance float) and exposure to a handful of publicly documented factor premiums, Buffett’s investment skill –— the portion of his returns that cannot be explained by any mechanical strategy –— is 0.3% per year. That's statistically indistinguishable from zero. In other words, the alpha that Berkshire enjoyed for 50 years (as it compounded capital at 24% a year!) wasn’t due to Buffett’s stock picking. So, how did he do it? He did it by gaining access to a huge amount of investment capital that he did not own, for free. Buffett’s track record was built on leverage. That’s a dirty word for most investors, but it's the secret behind Berkshire. The AQR researchers had access to something most Buffett commentators do not: 40 years of Berkshire’s audited financial statements and the full quarterly history of the public 13F stock portfolio. The researchers asked a specific question: If I take Berkshire’s monthly stock returns from October 1976 through March 2017, and I run a linear regression against a set of well-documented risk factors –— market beta, size, value, momentum, and two newer factors called Betting-Against-Beta and Quality-Minus-Junk (detailed below) –— how much of Buffett’s performance can the factors explain? And after the factors have been stripped out, how much excess return remains? The data show clearly there are a few qualities that drove Berkshire’s results. First, Buffett has always preferred large-cap stocks, contrary to the popular image of him as a small-cap value investor. He buys elephants. Second, no surprise, Buffett buys cheap. Berkshire is almost six standard deviations away from neutral on the value axis. So far the picture is ordinary. Every large- cap value manager in America loads positively on size and on value. Buffett’s genius lies in the last two factors. These last two factors are a little complicated, but please stick with me. There’s a new factor, that, like value and size, characterizes Buffett’s strategy. It’s called Betting-Against-Beta (“BAB”). What it means is intentionally investing in stocks with very low volatility. The BAB factor captures the excess return that accrues to investors who own low-beta stocks. Low-beta stocks have historically earned higher risk-adjusted returns than high-beta stocks. Financial theory teaches that higher beta (higher risk) should mean higher return. But it doesn’t. The opposite occurs, in fact. And Buffett was one of the very first people to figure this out. Why does this factor persist? In an efficient market, once that factor is known to investors, then they should bid the price up on low- beta stocks until it no longer provides an edge. The explanation, per the theory of AQR’s Frazzini and Pedersen’s theory, is that because ordinary investors do not use leverage and seek high returns, they create persistent excess demand for more volatile stocks. (Having worked with retail investors for 30 years, I can assure you that is true.) But, an investor with access to cheap leverage –— Warren Buffett, for instance –— can exploit the mispricing by owning the low-beta names and levering them up to produce market-beating returns. And the last factor that matters to Buffett is quality. Buffett buys companies with high returns on invested capital. Quality-Minus-Junk (“QMJ”) is a factor described by Cliff Asness, also at AQR with Frazzini, and Pedersen, in a 2019 paper in Review of Accounting Studies. The QMJ factor captures the return to owning stocks of high-quality companies –— profitable, growing, safe, with high payout ratios –— against stocks lacking those characteristics. QMJ has been positive and statistically significant in every major developed equity market for which it has been measured. Berkshire’s loading is 0.37, with a t-statistic of 4.6. –– meaning it is highly significant to Berkshire’s results. In plain English: Buffett only buys large, high- quality, low-volatility stocks of the highest quality. But, Berkshire’s results were not, in any way, unusual. Any investor buying these same kinds of stocks would have earned those same returns –– about 16% a year over time. So how did Berkshire compound at 23% a year? To figure that out, AQR’s researchers built a Berkshire replica. They constructed a simple, rules-based, publicly investable portfolio that mechanically tilts toward large-cap, cheap, low-beta, high-quality stocks, and levers it 1.6- to- 1 to match Berkshire’s insurance float leverage. The correlation between their replica’s returns and Berkshire’s were virtually identical. The authors’ conclusion is unambiguous. “In summary, we find that Buffett has developed a unique access to leverage that he has invested in safe, high-quality, cheap stocks and that these key characteristics can largely explain his impressive performance.” Berkshire’s cost of insurance float has averaged almost three percentage points below the Treasury bill rate across 50fifty years of data. In roughly two-thirds of all years, Berkshire has been paid to hold other people’s money. That is not an investment strategy. That is a financing miracle. It is also the living, breathing heart of Berkshire Hathaway. It’s what Buffett built, starting in 1967 when he paid $8.6 million for National Indemnity’s $19.4 million of float. And it is the factor every retail investor admiring Berkshire’s returns has never paid any attention to. The 1.6-to-1 leverage that AQR measured over the full period, financed at this negative cost, explains the dollar magnitude of Berkshire’s returns. How do we know? An unleveraged version of the same stock portfolio –— which you can approximate by looking at the 13F holdings alone –— has earned an average excess return of 12% percent per year. It’s Berkshire’s leverage that magnifies this excess return to 18.6 %percent. How does this square with Berkshire’s reported gains? Berkshire’s 18.6% excess return, plus the T-bill rate that averaged roughly 4.7% over 1976–2017, gives you a total nominal return of roughly 23% per year, which is the figure you usually see quoted for Berkshire’s historical performance. The 23% tells you what Berkshire returned. The 18.6% tells you how much of that return was compensation for taking investment risk, as opposed to the baseline yield every lender to the U.S. government was earning anyway. With both of Berkshire’s “edges” –— systematic factor exposures to cheap, high-quality, low-volatility stocks and roughly 1.6-to-1 leverage delivered with insurance float –— you get Berkshire Hathaway’s 23% annual gains over 60 years. It’s the structure that’s genius, not the stock picking. And that's very important because it means the original Berkshire formula can work for any investor. I show you exactly how, in my new book.