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Barbie’s opening weekend was $337M. That’s nearly 2x Oppenheimer’s $174M. Warner Brother's insane ”Operation Barbie Summer” is a big part of that success. These are 50 of the team's most amazing marketing stunts: 1. 3D ad in front of Burj Khalifa

I worked at Epic Games for two years. This is real, and the strategy behind it is smarter than most people realize. Tim Sweeney has spent nearly two decades buying North Carolina forest land. 50,000+ acres across 15 counties. He’s now one of the largest private landowners in the state. The purchases started in 2008, right after the real estate collapse wiped out developers who had been planning golf resorts and luxury communities on biodiverse wilderness. Sweeney paid $15 million for Box Creek Wilderness, a 7,000-acre stretch in the Blue Ridge foothills containing 130+ rare and threatened species. Developers had owned 5,000 of those acres before the crash. He bought them for conservation prices when nobody else was bidding. He runs the acquisitions through an LLC called “130 of Chatham.” He buys the land, holds it for years, then either donates it to the U.S. Fish and Wildlife Service, sells it at a discount to state parks, or hands it to land trusts. In 2021, he donated 7,500 acres in the Roan Highlands to the Southern Appalachian Highlands Conservancy. Largest private land donation in North Carolina history. The part people miss: he told the News & Observer that since 2021, land got too expensive to keep buying. So he shifted focus to converting his existing 50,000 acres into permanent conservation status. He’s locking the land into legal structures that make development impossible regardless of who owns it in the future. A billionaire worth roughly $6 billion is spending tens of millions acquiring wilderness specifically during economic downturns, then giving it away or placing it under permanent legal protection. The land will outlast him, Epic Games, and Fortnite. That’s the part that separates Sweeney from billionaires who write checks to get their name on a building. The building depreciates. The forest compounds.

The math on this image is insane. New Horizons transmitted at 2,000 bits per second from 3 billion miles away. Slower than a 1990s dial-up modem. It took 16 months to download all the flyby data. The spacecraft had to hit a target box 100km wide, arriving within 150 seconds of schedule, after 9 years of flight. Miss it and the preloaded observation commands point at empty space. Ten days before arrival, the spacecraft crashed and went into safe mode. Engineers had 72 hours to restore everything. The probe is now 5 billion miles out, still whispering data back to Earth. We got 50 gigabits of Pluto photos using technology slower than your phone’s bluetooth.

This isn’t a random scientist who got lucky. Mariano Barbacid discovered the first human oncogene in 1982. He isolated H-RAS from bladder cancer cells and proved a single point mutation could trigger cancer. That finding launched the entire field of molecular oncology. KRAS mutations cause 90% of pancreatic cancers. For 43 years, oncologists called KRAS “undruggable” because the protein had no obvious binding pocket. Barbacid spent the last decade using genetically engineered mice to systematically test every node in the KRAS signaling pathway, looking for combinations that would work without killing the patient. The triple therapy blocks KRAS three ways at once: the main growth signal, the escape routes through EGFR and HER2, and the stress-response backup through STAT3. Cut the engine, seal the exits, disable the emergency system. Tumors vanished in mice and didn’t return for 200+ days after treatment stopped. Pancreatic cancer has a 13% five-year survival rate. 8% for the ductal adenocarcinoma type this therapy targets. Most patients live one year after diagnosis. The catch: this is preclinical. Human trials are 3+ years away. One of the drugs, RMC-6236, might get approved this year, but the full triple combination has regulatory hurdles. Still. The man who discovered human oncogenes in 1982 may have just figured out how to eliminate the cancer those genes cause. That’s a 43-year arc from first principles to potential cure. Science rarely works this clean.

Ring paid somewhere between $8 and $10 million for a 30-second Super Bowl spot to tell 120 million viewers that their cameras now scan neighborhoods using AI. The math is wild. Ring has roughly 20 million devices in American homes. Search Party is enabled by default. The opt-out rate on default settings in consumer tech is historically around 5%. So approximately 19 million cameras are now running AI pattern matching on anything that moves past your front door. Today the target is dogs. The same infrastructure already handles “Familiar Faces,” which builds biometric profiles of every person your camera sees, whether they know about it or not. Ring settled with the FTC for $5.8 million after employees had unrestricted access to customers’ bedroom and bathroom footage for years. They’re now partnered with Flock Safety, which routes footage to local law enforcement. ICE has accessed Flock data through local police departments acting as intermediaries. Senator Markey’s investigation found Ring’s privacy protections only apply to device owners. If you’re a neighbor, a delivery driver, a passerby, you have no rights and no recourse. This tells you everything about Amazon’s actual product. The customer paid for the camera. The customer pays the electricity. The customer pays the $3.99/month subscription. And Amazon gets a surveillance grid that would cost tens of billions to build from scratch, with an AI layer activated by default, and a law enforcement pipeline already connected. They wrapped all of that in a lost puppy commercial because that’s the only version of this story anyone would willingly opt into.

A YouTuber with 38 million subscribers just beat the entire Hollywood studio system at its own game, and he did it with 80,000 gallons of fake blood and a submarine made of painted wood. Mark Fischbach played a $6 indie horror game on his channel in 2022. The game was Iron Lung, developed by one guy, David Szymanski, in his spare time. It had no windows, no enemies you could see, just a convict trapped in a submarine navigating an ocean of blood. The entire thing took about 75 minutes to beat. Fischbach saw something the game industry didn’t. The constraint was the feature. A single claustrophobic set. One man losing his mind. Sound design carrying all the horror that visuals couldn’t. This wasn’t a limitation to work around. This was a low-budget filmmaker’s dream. So he did what studios would never greenlight. He wrote the script himself, directed it himself, acted in it himself, edited it himself, and paid for the whole thing out of pocket. No studio. No distribution deal. No marketing budget. Then the gatekeepers showed up. When he tried to get theaters, they told him the demand wasn’t there. So he posted about it. His fans called every independent theater chain in the country. Theaters started complaining to the distributor that they were getting too many calls. Within weeks, AMC, Cinemark, and Regal announced they’d carry it. The film went from “maybe 60 theaters” to over 4,000 screens worldwide. Presales crossed $5 million before opening day. $8.9 million on Friday. $17 million projected by Sunday. A self-distributed movie by a YouTuber, beating initial projections by 70%. The studio model is built on the assumption that audiences need to be told what to watch. Fischbach proved that 38 million subscribers is its own distribution network. The audience was already waiting. He just had to make something worth waiting for.

Let me explain exactly why every new subdivision in America looks like the top photo, because the math is wild. A mature tree increases a home's value by 7 to 19 percent. On a $400,000 house, that's $28,000 to $76,000. A single shade tree produces the cooling equivalent of ten room-size air conditioners running 20 hours a day. One tree on the west side of a house cuts energy bills by 12 percent within 15 years. The bottom photo is worth more, costs less to live in, and sells faster. This has been documented by the University of Washington, Clemson, Michigan State, and the USDA. The data is not in dispute. Removing those trees saves the builder roughly $5,000 per lot. Concrete trucks need twice the dripline radius of every standing tree. Utility trenches need flat ground. A bulldozer flattens 200 lots in an afternoon. Preserving trees adds weeks and thousands per home. So the developer pockets $5,000 in savings and the buyer eats $50,000 in lost value for the next two decades. The person making the decision and the person paying for it have never been in the same room. The Woodlands, Texas is the proof of what happens when they are. George Mitchell bought 28,000 acres of Houston timberland in 1974 and preserved 28% as permanent green space. He forced McDonald's to build behind the tree canopy. That McDonald's became one of the highest-volume locations in Texas. The first office building, designed to reflect the surrounding forest so you couldn't see it from the street, leased completely. The Woodlands median home price today: $615,000. Katy, a comparable Houston suburb that clear-cut: $375,000. Named #1 community to live in America two years running. Fifty years of data. The trees are worth more than removing them saves. Developers clear-cut anyway because they sell the house once and leave. You live in it for 30 years.

The math on this project should mass-humble every AI lab on the planet. 1 cubic millimeter. One-millionth of a human brain. Harvard and Google spent 10 years mapping it. The imaging alone took 326 days. They sliced the tissue into 5,000 wafers each 30 nanometers thick, ran them through a $6 million electron microscope, then needed Google’s ML models to stitch the 3D reconstruction because no human team could process the output. The result: 57,000 cells, 150 million synapses, 230 millimeters of blood vessels, compressed into 1.4 petabytes of raw data. For context, 1.4 petabytes is roughly 1.4 million gigabytes. From a speck smaller than a grain of rice. Now scale that. The full human brain is one million times larger. Mapping the whole thing at this resolution would produce approximately 1.4 zettabytes of data. That’s roughly equal to all the data generated on Earth in a single year. The storage alone would cost an estimated $50 billion and require a 140-acre data center, which would make it the largest on the planet. And they found things textbooks don’t contain. One neuron had over 5,000 connection points. Some axons had coiled themselves into tight whorls for completely unknown reasons. Pairs of cell clusters grew in mirror images of each other. Jeff Lichtman, the Harvard lead, said there’s “a chasm between what we already know and what we need to know.” This is why the next step isn’t a human brain. It’s a mouse hippocampus, 10 cubic millimeters, over the next five years. Because even a mouse brain is 1,000x larger than what they just mapped, and the full mouse connectome is the proof of concept before anyone attempts the human one. We’re building AI systems that loosely mimic neural networks while still unable to fully read the wiring diagram of a single cubic millimeter of the thing we’re trying to imitate. The original is 1.4 petabytes per millionth of its volume. Every AI model on Earth fits in a fraction of that. The brain runs on 20 watts and fits in your skull. The data center required to merely describe one-millionth of it would span 140 acres.

She wasn't acting. And Ledger knew it. Heath Ledger refused to do the full Joker in rehearsals. No voice, no laugh, no mannerisms. Christian Bale confirmed Ledger only turned the character on when cameras rolled. The cast had no idea what was coming. This is the party scene. Ledger is holding a knife to Gyllenhaal's face telling a fake story about his scars. Gyllenhaal couldn't maintain eye contact. She was genuinely trying to pull away from him. She was silently looking at Nolan to stop the scene. Ledger saw her break eye contact and improvised the line "Look at me." Four syllables that turned a scripted scene into something nobody on set could control. Michael Caine forgot his lines the first time he saw Ledger in full Joker. A 75-year-old actor with 130 films on his résumé, and his brain locked up. Caine wrote in his memoir last year that Ledger was "a lovely guy, very gentle and unassuming" between takes. Skateboarded around set. Then the camera turned on and everyone on the crew froze. The film made $1 billion. Ledger won a posthumous Oscar, only the second actor in history to do so. He died six months before the movie opened. He was 28. The performance that redefined what a villain could be in a studio film was built on a simple trick: never let your scene partners rehearse against the real thing. When they finally see it, you get something a director can't manufacture. Actual fear on actual faces. That's what Nolan saw through the monitor. And that's why he didn't say cut.

If you pitched this as a screenplay, every studio would reject it for being too unrealistic. A 28-year-old Vietnamese developer living with his parents programs a game over a single holiday weekend in his bedroom. He uses free ad monetization because he hopes to make a few hundred dollars a month. The game sits untouched in the App Store for eight months. Then something inexplicable happens. With zero marketing spend, the game goes viral. Within weeks it tops the charts in 102 countries, hits 50 million downloads, and starts generating $50,000 per day in pure ad revenue. He’s making $18 million annualized from a game he built in three days. So what does he do? He kills it. Not because Apple forced him. Not because Nintendo sued him. Not because a competitor acquired him. He kills it because he felt guilty that people were too addicted. The guilt was ruining his sleep. He tweeted “I cannot take this anymore” and pulled it the next day. Within hours, people listed phones with the game on eBay for $99,900. Fans sent him death threats demanding he put it back. His only response was “And I still make games.” The part nobody talks about: he turned down every acquisition offer afterward. A game generating $1.5 million per month, and a solo developer in Hanoi said no to everyone because he refused to compromise his independence. Every founder in Silicon Valley talks about “mission over money.” Dong Nguyen actually did it, and the internet tried to destroy him for it.

Let me explain exactly why VLC is free despite 6B downloads, because no one seems to get it. VLC doesn’t make money because making money would destroy the only thing that made it reach 6 billion downloads in the first place. The player grew through a specific distribution loop: tech-savvy users install it once, it works perfectly on every weird video file they throw at it, and they recommend it to everyone forever. IT departments deploy it across entire companies. A Reddit comment from 2009 still drives downloads in 2025 because the answer never changed. That recommendation engine dies the second ads appear. Not slowly. Immediately. The users who drive VLC’s distribution are the exact people who understand what ads mean. Your incentives just switched from “make the best player” to “maximize impressions.” They see it, stop recommending it, and your growth engine shuts off. Run the actual numbers. VLC gets maybe 50 million active users daily across 6 billion total downloads. Typical video player ad rates run $1-3 CPM. Even if you served ads on every playback session, you’re looking at maybe $50-150 million annually at absolute peak optimistic assumptions. Sounds like a lot until you realize what Kempf actually traded it for. VLC reaching 6 billion people made Kempf the person who built the infrastructure everyone depends on. He runs a video consulting business. He built dav1d, an AV1 codec that powers modern streaming. Being “the guy who kept VLC free” opens every door in video technology. Clients pay him to solve problems because he proved he optimizes for quality over quick monetization. “Former ad-supported media player executive” gets you exactly zero of that leverage. The people celebrating Kempf’s ethics are missing the calculation. He didn’t sacrifice millions for principles. He rejected $150M in highly uncertain ad revenue to build permanent positioning worth multiples of that in everything else he touches. VLC free generates more value for Kempf than VLC monetized ever could. The trade was never even close.

Twitter revealed its algorithm to the world. But what does it mean for you? I spent the evening analyzing it. Here’s what you need to know:

Everyone’s missing the real story here. Meta’s Ray-Ban glasses need human data annotators to train the AI. When you say “Hey Meta” and ask the glasses to analyze something, that video gets sent to Meta’s servers, then routed to Sama, a subcontractor in Nairobi, Kenya. Workers there manually label objects in your footage. They see everything you recorded, intentionally or not. 7 million pairs sold in 2025 alone. Every single pair generates training data that flows through human eyes in Kenya. Workers told Swedish journalists they see people undressing, using bathrooms, having sex, and accidentally filming bank card details. One worker said “we see everything, from living rooms to naked bodies.” Meta’s automatic face anonymization is supposed to protect people in the footage. Workers say it fails in certain lighting. Faces that should be blurred are sometimes fully visible. The person you recorded without knowing? A stranger in Nairobi can identify them. Buried in Meta’s terms of service is one sentence doing enormous legal work: the company reserves the right to conduct “manual (human) review” of your AI interactions. That’s the legal cover for routing intimate footage from Western homes to a $2/hour labor force operating under NDAs, office surveillance cameras, and a strict no-questions policy. Workers say if you raise concerns about what you’re seeing, you’re fired. This is the same company, Sama, that TIME exposed in 2023 for paying Kenyan workers $2/hour to label graphic content for OpenAI while being billed at $12.50/hour per worker. Workers described the experience as torture. Sama ended that contract, then pivoted to labeling Meta’s glasses footage. Same workforce. Same rates. Meta markets these glasses as “designed with your privacy in mind.” The privacy design is a tiny LED light on the frame that most people don’t notice. The data pipeline behind it routes your bedroom footage to a contractor with a documented history of worker exploitation, failed anonymization, and union-busting lawsuits. And the next generation of these glasses? Meta is planning to add facial recognition. The same system that can’t reliably blur faces in training data wants to start identifying them on purpose. The LED light on the frame is doing about as much for your privacy as the terms of service nobody reads.

The new sphere in Las Vegas is the coolest building I’ve ever seen. It features 580K square feet of fully-programmable LED lighting to produce life-like images visible from miles away. And the story behind it is wilder than the videos it creates: A THREAD 1/16

The air traffic controller cleared the fire truck onto the runway. Seconds later, the same controller screamed “stop, stop, stop.” The plane was doing 93 to 105 mph. Both pilots are dead. Everyone will frame this as controller error. One controller was simultaneously managing a United flight that aborted takeoff after an anti-ice warning, dispatching a fire truck across an active runway, and sequencing an inbound Air Canada landing at highway speed. At 11:40 PM. On a mandatory overtime shift at a facility that has been understaffed for years. A system that assigns one person that workload will produce exactly this outcome. The only variable is when. The FAA is short approximately 3,000 controllers. The headcount dropped 13% from 2010 to 2024 while flight volume rose 10%. Over 40% of the FAA’s 290 terminal facilities are understaffed. The New York TRACON, which manages the most congested airspace in America across LaGuardia, JFK, and Newark, has been chronically below target. Newark was operating at 59% of its staffing goal. LaGuardia handles 900 flights a day. The hiring pipeline is broken at every stage. Only 2% of applicants complete the full process. Training takes up to 6 years. The FAA Academy in Oklahoma City is a bottleneck, with roughly 35% of trainees washing out. Congress blocked legislation to build a second academy. In one recent hiring cycle, the FAA brought on 1,512 candidates and lost 1,300 in the same window. Net gain: around 160 controllers for an entire country. Three things need to happen and everyone who can make them happen has known for years. Congress needs to fund and authorize a second FAA training academy. One facility in Oklahoma City cannot produce enough controllers for 900 million annual passengers. Members of Congress from Oklahoma have actively blocked this. That needs to end yesterday. The FAA needs to cut certification time. Six years from application to fully certified controller is absurd. The agency’s own data shows tower simulators reduce certification time by 27%. They’ve installed them at 95 facilities. That should be every facility, and the simulated hours should count toward more of the certification requirement. The FAA needs to stop plugging staffing gaps with mandatory overtime. Controllers at understaffed facilities are working six-day weeks rotating between morning, mid, and night shifts. The NTSB has flagged fatigue repeatedly. The controller last night was managing overlapping emergencies during a nighttime operation. Overtime is not a staffing plan. It’s a countdown to the next runway collision. The controller said “I messed up” to a Frontier pilot who watched the whole thing. The pilot responded “No man, you did the best you could.” One of them is right. The answer determines whether this happens again.

This is the coldest life advice you’ll hear

Barbie’s opening weekend was $337M. That’s nearly 2x Oppenheimer’s $174M. Warner Brother's insane ”Operation Barbie Summer” is a big part of that success. These are 50 of the team's most amazing marketing stunts: 1. 3D ad in front of Burj Khalifa

Sam Altman said people saying “please” and “thank you” to ChatGPT costs OpenAI tens of millions of dollars a year in compute. 67% of Americans do it anyway. Run the math on why. A 2024 Waseda University study tested LLM responses across politeness levels in English, Chinese, and Japanese. Impolite prompts produced measurably worse outputs: more bias, more errors, more refusals. Moderate politeness consistently beat both extremes. The mechanism makes sense once you see it. Polite prompts pattern-match to higher-quality training data. When you write “Could you help me structure this analysis?”, the model pulls from professional, well-reasoned text. When you write “give me the answer,” it pulls from Reddit. Google DeepMind’s Murray Shanahan explained it simply: the model is role-playing a smart intern. Treat the intern like a colleague, you get colleague-quality work. Bark orders, you get minimum-viable compliance. Now look at the cost side. OpenAI handles over a billion queries daily. Each GPT-4 query uses roughly 2.9 watt-hours, ten times a Google search. But OpenAI just raised $40 billion at a $300 billion valuation. Tens of millions in politeness tokens is a rounding error on a rounding error. 67% of users do it anyway, and 55% of them say it’s because it’s “the right thing to do.” They’re maintaining a behavioral habit that governs every other interaction in their life. The parent who teaches their kid to say please to Alexa isn’t doing it for Alexa. They’re doing it because the alternative is raising someone who learns that being rude gets faster results. Telling 900 million people to stop saying thank you so OpenAI can save 0.01% of operating costs is the most engineer-brained optimization take on the internet. You’re training yourself to treat every interaction as a transaction. And that habit doesn’t stay in the chat window.

"Our marketing team is just me and ~40 AI agents." -Million-dollar founder with no marketing team Want the template? Retweet + Reply and I'll DM you.

Let me explain exactly why every new subdivision in America looks like the top photo, because the math is wild. A mature tree increases a home's value by 7 to 19 percent. On a $400,000 house, that's $28,000 to $76,000. A single shade tree produces the cooling equivalent of ten room-size air conditioners running 20 hours a day. One tree on the west side of a house cuts energy bills by 12 percent within 15 years. The bottom photo is worth more, costs less to live in, and sells faster. This has been documented by the University of Washington, Clemson, Michigan State, and the USDA. The data is not in dispute. Removing those trees saves the builder roughly $5,000 per lot. Concrete trucks need twice the dripline radius of every standing tree. Utility trenches need flat ground. A bulldozer flattens 200 lots in an afternoon. Preserving trees adds weeks and thousands per home. So the developer pockets $5,000 in savings and the buyer eats $50,000 in lost value for the next two decades. The person making the decision and the person paying for it have never been in the same room. The Woodlands, Texas is the proof of what happens when they are. George Mitchell bought 28,000 acres of Houston timberland in 1974 and preserved 28% as permanent green space. He forced McDonald's to build behind the tree canopy. That McDonald's became one of the highest-volume locations in Texas. The first office building, designed to reflect the surrounding forest so you couldn't see it from the street, leased completely. The Woodlands median home price today: $615,000. Katy, a comparable Houston suburb that clear-cut: $375,000. Named #1 community to live in America two years running. Fifty years of data. The trees are worth more than removing them saves. Developers clear-cut anyway because they sell the house once and leave. You live in it for 30 years.

The air traffic controller cleared the fire truck onto the runway. Seconds later, the same controller screamed “stop, stop, stop.” The plane was doing 93 to 105 mph. Both pilots are dead. Everyone will frame this as controller error. One controller was simultaneously managing a United flight that aborted takeoff after an anti-ice warning, dispatching a fire truck across an active runway, and sequencing an inbound Air Canada landing at highway speed. At 11:40 PM. On a mandatory overtime shift at a facility that has been understaffed for years. A system that assigns one person that workload will produce exactly this outcome. The only variable is when. The FAA is short approximately 3,000 controllers. The headcount dropped 13% from 2010 to 2024 while flight volume rose 10%. Over 40% of the FAA’s 290 terminal facilities are understaffed. The New York TRACON, which manages the most congested airspace in America across LaGuardia, JFK, and Newark, has been chronically below target. Newark was operating at 59% of its staffing goal. LaGuardia handles 900 flights a day. The hiring pipeline is broken at every stage. Only 2% of applicants complete the full process. Training takes up to 6 years. The FAA Academy in Oklahoma City is a bottleneck, with roughly 35% of trainees washing out. Congress blocked legislation to build a second academy. In one recent hiring cycle, the FAA brought on 1,512 candidates and lost 1,300 in the same window. Net gain: around 160 controllers for an entire country. Three things need to happen and everyone who can make them happen has known for years. Congress needs to fund and authorize a second FAA training academy. One facility in Oklahoma City cannot produce enough controllers for 900 million annual passengers. Members of Congress from Oklahoma have actively blocked this. That needs to end yesterday. The FAA needs to cut certification time. Six years from application to fully certified controller is absurd. The agency’s own data shows tower simulators reduce certification time by 27%. They’ve installed them at 95 facilities. That should be every facility, and the simulated hours should count toward more of the certification requirement. The FAA needs to stop plugging staffing gaps with mandatory overtime. Controllers at understaffed facilities are working six-day weeks rotating between morning, mid, and night shifts. The NTSB has flagged fatigue repeatedly. The controller last night was managing overlapping emergencies during a nighttime operation. Overtime is not a staffing plan. It’s a countdown to the next runway collision. The controller said “I messed up” to a Frontier pilot who watched the whole thing. The pilot responded “No man, you did the best you could.” One of them is right. The answer determines whether this happens again.

I worked at Epic Games for two years. This is real, and the strategy behind it is smarter than most people realize. Tim Sweeney has spent nearly two decades buying North Carolina forest land. 50,000+ acres across 15 counties. He’s now one of the largest private landowners in the state. The purchases started in 2008, right after the real estate collapse wiped out developers who had been planning golf resorts and luxury communities on biodiverse wilderness. Sweeney paid $15 million for Box Creek Wilderness, a 7,000-acre stretch in the Blue Ridge foothills containing 130+ rare and threatened species. Developers had owned 5,000 of those acres before the crash. He bought them for conservation prices when nobody else was bidding. He runs the acquisitions through an LLC called “130 of Chatham.” He buys the land, holds it for years, then either donates it to the U.S. Fish and Wildlife Service, sells it at a discount to state parks, or hands it to land trusts. In 2021, he donated 7,500 acres in the Roan Highlands to the Southern Appalachian Highlands Conservancy. Largest private land donation in North Carolina history. The part people miss: he told the News & Observer that since 2021, land got too expensive to keep buying. So he shifted focus to converting his existing 50,000 acres into permanent conservation status. He’s locking the land into legal structures that make development impossible regardless of who owns it in the future. A billionaire worth roughly $6 billion is spending tens of millions acquiring wilderness specifically during economic downturns, then giving it away or placing it under permanent legal protection. The land will outlast him, Epic Games, and Fortnite. That’s the part that separates Sweeney from billionaires who write checks to get their name on a building. The building depreciates. The forest compounds.

The math on this project should mass-humble every AI lab on the planet. 1 cubic millimeter. One-millionth of a human brain. Harvard and Google spent 10 years mapping it. The imaging alone took 326 days. They sliced the tissue into 5,000 wafers each 30 nanometers thick, ran them through a $6 million electron microscope, then needed Google’s ML models to stitch the 3D reconstruction because no human team could process the output. The result: 57,000 cells, 150 million synapses, 230 millimeters of blood vessels, compressed into 1.4 petabytes of raw data. For context, 1.4 petabytes is roughly 1.4 million gigabytes. From a speck smaller than a grain of rice. Now scale that. The full human brain is one million times larger. Mapping the whole thing at this resolution would produce approximately 1.4 zettabytes of data. That’s roughly equal to all the data generated on Earth in a single year. The storage alone would cost an estimated $50 billion and require a 140-acre data center, which would make it the largest on the planet. And they found things textbooks don’t contain. One neuron had over 5,000 connection points. Some axons had coiled themselves into tight whorls for completely unknown reasons. Pairs of cell clusters grew in mirror images of each other. Jeff Lichtman, the Harvard lead, said there’s “a chasm between what we already know and what we need to know.” This is why the next step isn’t a human brain. It’s a mouse hippocampus, 10 cubic millimeters, over the next five years. Because even a mouse brain is 1,000x larger than what they just mapped, and the full mouse connectome is the proof of concept before anyone attempts the human one. We’re building AI systems that loosely mimic neural networks while still unable to fully read the wiring diagram of a single cubic millimeter of the thing we’re trying to imitate. The original is 1.4 petabytes per millionth of its volume. Every AI model on Earth fits in a fraction of that. The brain runs on 20 watts and fits in your skull. The data center required to merely describe one-millionth of it would span 140 acres.

Twitter revealed its algorithm to the world. But what does it mean for you? I spent the evening analyzing it. Here’s what you need to know:

A city on the Moon will cost somewhere between $100B and $500B, require thousands of Starship flights, and demand a decade of nonstop construction in a place where the temperature swings 400°C between day and night, the dust cuts through metal seals like sandpaper, and a single cracked habitat window means everyone inside is dead in about 90 seconds. Musk just announced SpaceX is doing it anyway. Here’s the actual engineering path. You build at the south pole. Specifically the rims and floors of craters like Shackleton and Cabeus, where temperatures in permanent shadow drop below -230°C. NASA estimates 600 million metric tons of water ice are buried in these craters under about 40 cm of dry regolith. That water becomes your oxygen supply, your drinking water, your radiation shielding, and 78% of your rocket propellant by mass. The crater rims get near-continuous sunlight for solar power. You build where the resources are. Getting there is where it gets wild. Every Starship lunar mission requires 10-15 tanker flights to fill 1,200 tons of propellant in Earth orbit before the ship can even leave. One cargo delivery to the lunar surface burns through roughly 12 Starship launches. Starship V3 lands 100 metric tons per trip. The Moon is 2 days away with launch windows every 10 days. Mars gets one window every 26 months with a 6-month flight. That 13x iteration advantage is why Musk pivoted. The first 20-30 landings are all cargo. No humans. You’re sending solar arrays for the crater rims targeting 100+ kW continuous, nuclear fission reactors for the 14-day lunar night, ISRU rigs that mine ice from regolith and electrolyze it into hydrogen and oxygen, pressurized hab modules, and autonomous rovers that 3D-print structures from lunar soil using concentrated solar heat. Each landed Starship also stays as a permanent building. 50 meters tall, 9 meters wide, 1,100 cubic meters of pressurized volume. The ISS has 916 cubic meters and took 13 years to assemble. Three Starships on the surface already exceed that. The economics flip the moment you start producing oxygen on the Moon. You stop shipping 78% of your propellant from Earth. Tanker flights per mission drop from 15 to about 4. Every ton produced locally frees up mass budget on the next inbound Starship for more construction equipment, food systems, and mining hardware. The base starts building the base. That’s what “self-growing” means. Compound logistics where each delivery makes the next delivery cheaper. 2027: first uncrewed Starship lunar landing. SpaceX told investors March 2027. 2028-2030: cargo buildup, 30-50 deliveries, all robotic, ISRU prototypes go operational. 2030-2032: first crews arrive, probably 6-12 people, 6-month rotations, running equipment maintenance and scaling propellant production. 2033-2035: permanent population hits 50-100, propellant depot goes up in low lunar orbit so arriving ships refuel before descent. 2035 onward: population grows past 100, agricultural modules come online, the base becomes partially self-sustaining. The unsolved problems are real. Lunar dust is electrostatically charged and sharp as broken glass. It shreds seals, clogs machinery, and embeds in lung tissue. Nobody has a long-duration fix. Radiation on the surface runs 200x Earth’s dose. Regolith shelters and water shielding help but add enormous construction overhead. The 14-day night drops temperatures to -173°C and kills all solar power, and the only flight-ready nuclear reactors produce 1-10 kW, far below what a growing base demands. What years of 1/6 gravity do to human bone density and cardiovascular systems is completely unknown. SpaceX is valued at a trillion dollars and just told investors the Moon comes first. They’re betting that proving lunar logistics at commercial cadence builds the playbook for Mars. The Moon is a 2-day test lab with a 12-day resupply cycle. Mars is a 6-month voyage with a 2.5-year wait if anything breaks. It makes sense.

Red Robin is a case study in how to kill a restaurant chain from the inside out. In 2015, the stock hit $92.90 per share. Revenue peaked in 2017 at $1.4 billion across 573 locations. Families loved the place. Bottomless fries. Birthday parties. “Gourmet” burgers when that word still meant something in casual dining. The brand had real equity. Then management panicked about rising minimum wages and made the single worst decision in the company’s history: they fired all the bussers. January 2018. CEO Steve Carley cut bussers across every location, eliminated expeditors, and replaced kitchen managers with generic “back-of-house” roles. The logic was pure spreadsheet thinking. Labor costs were rising, so remove labor. The savings looked great in quarterly earnings. The second and third order effects were catastrophic. Tables stopped getting cleared. Wait times ballooned. Walkaways increased 85% year over year. 75% of the dine-in traffic loss came during peak hours, the exact window when the restaurant makes money. Ticket times out of the kitchen jumped a full minute on average. Customers who waited 20 minutes for a table and another 20 for a burger stopped coming back. Red Robin’s own CEO at the time, Denny Marie Post, admitted the damage was self-inflicted. And here’s the compounding problem. While Red Robin was gutting its own service model, it simultaneously launched a “Tavern Double” value menu at $6.99 to drive traffic. Orders of the cheap burgers jumped from 9% to 15% of all orders, which cratered the average check. So Red Robin was now serving worse food, slower, in a dirtier restaurant, at a lower price point. That combination is how you enter a death spiral. Meanwhile, 16% of locations were in malls. Mall traffic was already declining. Those locations saw 5.5% sales drops versus 3% at standalone stores, dragging the whole system down. Management acknowledged the problem quarter after quarter and did nothing about it for years. Five CEOs in 10 years. Think about that. The one leader who provided stability, Michael Snyder, was with the chain from 1979 to 2005. After that, it was a revolving door. Every new CEO launched a new turnaround plan. Every plan was abandoned by the next CEO. The North Star plan. The First Choice plan. New menu rollouts. Loyalty program reboots. None of it addressed the core issue: they’d trained an entire generation of customers to think of Red Robin as the place where the service is terrible. The contrast with Chili’s makes the failure even clearer. Kevin Hochman took over Chili’s in 2022 and did the opposite of what Red Robin did. He simplified the menu, invested in operations, launched a $10.99 “3 for Me” deal that went viral on TikTok, and let the food speak for itself. Chili’s just posted 31% same-store sales growth. Red Robin’s comparable revenue was down 1.2% for all of 2024. Both chains were in roughly the same position three years ago. One chain invested in the customer experience. The other spent a decade cutting it. Red Robin’s $65M market cap and Chili’s $3.3B market cap tell you which approach works. The stock went from $92 to $3.61. That’s what happens when you optimize for the quarterly earnings call instead of the customer walking through the door.

Bezos: “Somebody needs to make a list where they rank people by how much wealth they’ve created for other people — instead of the Forbes list where it ranks you by your own wealth. Amazon’s market cap is $2.3 trillion today. I own about $200 billion-ish of it. So if you take $2.3 trillion and subtract out the piece I kept for myself, then I’ve created something like $2.1 trillion of wealth for other people. That should put me pretty high on some kind of list. And that’s a better list — how much wealth have you created for other people?”

Noam Chomsky on ChatGPT: He’s not wrong.

Google’s single data center in Council Bluffs, Iowa consumed 1 billion gallons of fresh water in 2024. One facility. One year. Enough to supply every home in Iowa for five days. The reason they need fresh water is pure chemistry. Evaporative cooling towers work by running water over hot surfaces and letting it evaporate. 80% of the water a data center pulls in literally vanishes into the atmosphere as steam. You can’t recycle steam. The remaining 20% becomes concentrated mineral waste. Calcium, magnesium, silica. Every cycle through the cooling loop makes the water more corrosive. After enough passes, it starts clogging pumps and eating through heat exchangers. Multi-million dollar equipment destroyed by limescale. Recycled wastewater carries even more of these minerals from the start. You could treat it, but less than 1% of U.S. water is recycled. Most cities don’t even have separate pipes to deliver reclaimed water to industrial customers. A data center wanting to use recycled water would essentially need to build its own treatment plant on site. Meanwhile, municipal potable water costs almost nothing. So they just drink from the tap. Across all its data centers, Google used 8.1 billion gallons in 2024, nearly double what it used three years earlier. The company claims its water stewardship projects “replenished” 4.5 billion gallons. Those projects aren’t even in the same watersheds where they’re pulling the water. Same playbook as carbon offsets. Consume locally, offset globally, call it sustainable. The trajectory is the real story. U.S. data center water consumption could quadruple by 2028. That’s 68 billion gallons for cooling alone, before the 211 billion gallons consumed indirectly through electricity generation. Two-thirds of new data centers since 2022 are being built in regions already facing water scarcity. Nobody’s asking why they use fresh water. They’re asking what happens to the towns sharing a water main with a facility that drinks like 50,000 people showed up overnight.

this is an insane chart

Ring paid somewhere between $8 and $10 million for a 30-second Super Bowl spot to tell 120 million viewers that their cameras now scan neighborhoods using AI. The math is wild. Ring has roughly 20 million devices in American homes. Search Party is enabled by default. The opt-out rate on default settings in consumer tech is historically around 5%. So approximately 19 million cameras are now running AI pattern matching on anything that moves past your front door. Today the target is dogs. The same infrastructure already handles “Familiar Faces,” which builds biometric profiles of every person your camera sees, whether they know about it or not. Ring settled with the FTC for $5.8 million after employees had unrestricted access to customers’ bedroom and bathroom footage for years. They’re now partnered with Flock Safety, which routes footage to local law enforcement. ICE has accessed Flock data through local police departments acting as intermediaries. Senator Markey’s investigation found Ring’s privacy protections only apply to device owners. If you’re a neighbor, a delivery driver, a passerby, you have no rights and no recourse. This tells you everything about Amazon’s actual product. The customer paid for the camera. The customer pays the electricity. The customer pays the $3.99/month subscription. And Amazon gets a surveillance grid that would cost tens of billions to build from scratch, with an AI layer activated by default, and a law enforcement pipeline already connected. They wrapped all of that in a lost puppy commercial because that’s the only version of this story anyone would willingly opt into.

The new sphere in Las Vegas is the coolest building I’ve ever seen. It features 580K square feet of fully-programmable LED lighting to produce life-like images visible from miles away. And the story behind it is wilder than the videos it creates: A THREAD 1/16