Labriant @Labriant0

Michael Jackson said he didn't write Billie Jean, he just got out of its way, and Thriller still sold about 70,000,000 copies. The claim sounds mystical, but it describes a real working method. He set 1 clear target, a song with a great bass hook, then stopped forcing it and let it arrive days later. That is deliberate preparation followed by deliberate absence, and it is how many creative people actually work. The goal is fixed in advance, and the solution is allowed to show up on its own. His advice on dancing carries the same idea. Thinking is the mistake, so you drop the mind and become the sound. Skilled performers know that conscious control slows them down once the skill is trained. Practice is where you think, and the stage is where you stop. Notice the shyness as well. When the interviewer pushed him to demonstrate, he deflected, called himself shy, and only then showed it. That fits the pattern: he is at ease inside the work and uneasy about explaining it, because explaining is a thinking task and the work is not. There is a caution here. Saying the music wrote itself also removes the credit from the person and the years behind it. A hook found in a car on Ventura Boulevard came after a lifetime of training that made the idea recognizable when it appeared. Inspiration only lands on a prepared mind. The skill is knowing when to stop trying.

@vladuah interesting thinks of how they did tooo much money

Beyoncé, who earned an estimated $87,000,000 in 2010, was offered total privacy for 1 price, going back to being poor, and she couldn't say yes. What stands out is how early she had already worked out the price of fame. Most people learn that cost after years of it. She talks about it like a contract she signed on purpose, with a fee paid in privacy and a return collected in venues and income. A young star who thinks in terms and trade-offs has stopped being surprised by fame, and that is rare. Her hesitation over the offer is the strongest signal. She believes she would have succeeded anyway, so the deal wasn't really about money. She says she would no longer be herself, which means she has fused her identity with the career. That is both her drive and her exposure, because when who you are depends on the work, you can't separate the two even for peace. Her read on money is just as precise. It doesn't buy happiness, it removes worry, and she says it from having lived through the stressful years. Young people who have just made it tend to treat money as a prize. She treats it as a floor. Fame is a bill you accept once and keep paying.

Mike Tyson earned at least $400,000,000, went bankrupt owing $23,000,000, and still can't say what he was trying to fill. The useful part is the order of it. He doesn't say he found what fixed him. He says he knows what he doesn't want anymore. Real change usually starts by elimination, long before a person can name a goal. Buying satisfaction fails for a mechanical reason. Each purchase relieves the feeling briefly, then it returns and the price goes up. The bankruptcy fits that loop: the spending never fixed the feeling, so it never had a ceiling. Look at how he talks about the late 90s. He asks what he was angry about and what was wrong with him, and still has no answer. Rage was a signal nobody was reading, and fame only made it louder by removing every outside reason to blame. Someone who claims the hole is fixed is usually still filling it. Someone who admits he doesn't know what he wants has at least stopped spending on the wrong answer. Money can buy everything except the answer to what was missing.

Mukesh Ambani says Reliance would be 10 times bigger if he could start again at 25. The 10x matters less than who says it. A founder at this scale, speaking to Rajdeep Sardesai in 2017, is pointing at his own younger self as the missing ingredient. The limit was never capital or market size. It was his appetite for risk and his speed at that age. Read it as a statement about time. At 25 you have energy, few obligations, and a low cost of being wrong. Once a company is huge, every bet carries more weight, so the same founder moves slower. Size makes caution rational, and caution is what caps growth. The 10x is a feeling of regret, not a forecast. Nobody can prove the counterfactual. But the instinct behind it is useful: the price of early hesitation gets paid for decades. For anyone under 30, the lesson is blunt. The cheapest time to take a big risk is now, because the price only goes up. Ambition has a shelf life, and it is shorter than the company's.

Steve Jobs handed Steve Wozniak $350 for a job Atari paid him about $5,000 for, and Woz found out 10 years later. The money was never the real transaction. Jobs was the only one talking to Atari, so he was the only one who knew the number, and whoever holds the client sets the price. The skill Woz brought was rare, but the information advantage belonged to Jobs, and he used it against his own partner. The lie was also badly priced. Woz says he would have given up the whole payment if Jobs had just asked. Jobs paid about $4,650 of trust to avoid a conversation that would have cost him nothing. That makes the Kottke story more than a coincidence. When the stakes grew from a $5,000 bonus to Apple stock, he made the same choice, and Woz had to fix it with his own shares. The pattern is that Jobs treats a friend's contribution as a cost to minimize, not a stake to honor. The genius question is a false choice. Hard bargaining is a skill, but hiding the number from a partner is a decision. You can win the deal and still lose the person.

Mark Zuckerberg was worth $16,000,000,000 and still took a $5,950,000 mortgage at 1.05%. Paying cash isn't free, because it pulls that money out of anything that could earn more. Buffett put about $30,000 into a $150,000 house in 1971 and kept the rest free for Berkshire shares. At 1.05%, the bar for beating the loan sat near the floor. A 30-year fixed rate works like an option: if rates rise, yours stays, and if they fall, you refinance. The catch is that payments come due even when investments drop. Debt is cheap until the day your investments aren't.

Elon Musk says AI will out-think all of humanity combined in maybe 5 or 6 years, and he calls that fine if humans come along. His scale is share of total intelligence, not jobs. On that scale humans shrink to a rounding error whatever we do, so the question shifts from stopping AI to keeping a seat. He names the goal as spreading consciousness and intelligence into the future, not saving 1 species. That makes Mars insurance for intelligence itself. The 5 to 6 year figure is a hedged guess, so the timeline is a bet and the priority is the real claim. He isn't asking if AI wins. He's asking who rides along.

Tesla spent 9 years building a flying Roadster, and the only passenger on its first flight was nobody. The demo used 10 nozzles and compressed air, and the car flew with nobody inside. That proves a controlled burst, not a ride. The real product stays on the ground: 60 mph in 0.81 seconds, thanks to fans that press the car onto the road. Production starts in 2028, the flying version has no road approval, yet shares rose the next morning. The demo paid before the car did. Flight sells the dream. Grip sells the car.

@vladuah all words of trump is bullshit

Eric Weinstein spoke to about 50 insiders about UFOs, and he still refuses to say aliens are real. His claim is smaller than the headlines, and that is why it is harder to dismiss. He points to secret programs with almost no oversight, officially denied, that exist on paper. Proving that needs documents, not a spacecraft. It splits into 2 questions. Do these programs exist? That can be checked. Are aliens behind them? Nobody has shown that. The bolder stories, like underwater bases or threats against a whistleblower, are testimony, not proof. Even if there are no aliens, a program with no oversight and a budget nobody can see is a real problem. The secrecy is the story, and the aliens are the distraction.

Meta may spend up to $100,000,000,000 on AI, and the bill arrives long before any proof of payback. The money goes into 5 things: computing power, data centers, custom chips, top talent, and new models. Meta is planning for several gigawatts of capacity, a long-term commitment with years of spending before the payoff is clear. The return depends on products built on top: assistants that keep people in its apps, sharper ads, and easier shopping. The hurdle is steep. A simple 10-year payback on $100,000,000,000 means $10,000,000,000 a year before costs, and at $200,000,000,000 that doubles. These are illustrations, not forecasts. The test is not how many chips Meta buys. It is whether revenue grows faster than the cost of compute.

RJ Mitte played the scene where Walter Jr. calls 911 on his own father by replaying real memories on command. He says he has a semi-photographic memory: he closes his eyes, goes back to a moment of hardship, and carries that feeling into the scene. On set, he tossed his co-star twice before the choreography worked, and treats every prop, even his crutches, as an extension of himself. He draws on his own father, who he says is his opposite. He argues anger and sadness differ depending on whether they come from love or hate. He leaves it all on set.

Alexandr Wang left MIT at 19 to sell the data layer every AI model would need and became the world's youngest self-made billionaire at 24. In 2016 a 19-year-old dropped out of MIT to co-found Scale AI. He skipped the contest to train the next famous model and aimed at the bottleneck no one else owned: turning raw human judgment into the labeled, ranked, and evaluated data that advanced systems require to improve. Most founders chased the visible product. Wang built the invisible input. The loop compounded fast. Cleaner data produced stronger models, stronger models raised the cost of bad data, and every new competitor increased demand for the same scarce resource Scale already organized. The advantage was not better algorithms. It was owning the scarce step everyone else treated as a commodity. In June 2025 Meta committed $14,300,000,000 and valued Scale above $29,000,000,000. Wang joined Meta's AI team. The number attached to the company, not the founder.

Alexandr Wang says first-time founders should do every job themselves, then learn to stop. His advice for a first company: hold a strong view on how every detail should work. Scale AI was built that way, and it succeeded because of that control. The cost was becoming the bottleneck, since every decision ran through him. At Meta he couldn't repeat it. He is not an AI researcher or a product designer, and the timeline was too short. So his job became hiring the best people in every field and giving them room to shape the work. Control builds the first company. Hiring builds the second.

Alexander Wang revealed DeepSeek holds 50,000 banned H100 chips it cannot discuss. Alexander Wang said the United States led for the past decade. On Christmas Day roughly a month ago Chinese lab DeepSeek released its model while others celebrated the holiday, then followed with a second release. Export controls were supposed to stop the highest-powered NVIDIA chips from reaching China. Wang stated DeepSeek already has about 50,000 H100s, more than outsiders expected, and cannot talk about the stockpile. Those controls will still constrain what the lab can build next. Wang showed the ban arrived after the chips were already inside.

SpaceX will capture an asteroid with more gold than Earth's crust and land Optimus robots carrying American flags on Mars within 8 years. Starship parks Psyche in geosynchronous orbit over an American Pacific atoll. No humans within 50 miles. Optimus robots extract the metals. Delivery to Earth is free though some burns up on entry. Bezos said 15 years ago Earth gets zoned residential as heavy industry moves to space. Vast distances make emissions irrelevant. The same fleet lands on Mars in 8 years max. Optimus robots deploy solar panels, batteries, compute, and Starlinks for 4K video before humans arrive. SpaceX moves the factories off the planet.

Alexandr Wang's company paid American contributors about $500,000,000 last year to teach AI what it gets wrong. Scale AI runs a platform called Outlier, which he describes as Uber for AI: algorithms need data, and a network of people across 9,000 US towns produce and correct it for pay. A nurse, for example, catches an AI telling someone to ignore stomach pain that could be appendicitis, and her correction feeds back into the model. Spam is filtered through peer review and automated checks. Experts are wanted in anything, from medicine to train tracks. AI is a race on 3 fronts: chips, data, and algorithms. The cleanest data wins.