Back to nature vs Merging with AI. Why choose. Let’s do both. @balajis @tferriss @namehra #Transhumanism #ReFi

One of the reasons so many average people stay poor is because they believe they are special. They believe their life is going to magically change because they're going to get a letter in the mail from magic school inviting them on an adventure. The reality is, we get those letters every single day. They just look like risk and a shitload of work, with no guarantee of a payoff.

Walter Isaacson asks Peter Thiel what humans can uniquely do in the AI era. Thiel: "We're very relational… these relational dimensions will become more important." When knowledge is worth $0, the only career skills that matter: - networking / social calibration - persuasion (sales) - curiosity (harnessing AI) - taste (curation) - storytelling - grit - golfing with clients (or equivalent) We'll get so sick of a fake world that we'll go back to doing business like it's the 1800s. A handshake at the local saloon.

London has a new Physical AI Lab. 🔥🇬🇧🇪🇺 And they have a legit claim to be soon a world leading one. What MACHENTA does is close to heretical. It's a completely different approach to building models, that doesn't require massive amounts of data, nor post-training, but can repeat a task after showing it only once. It's a complete narrative violation to everything we know in robotics right now. No absurdly large vision models. No hours of post-training needed. But it works instantly. You show it. It can repeat it. Not 20 video takes and then keeping the one that worked. You show it. And very very very likely it can repeat that new task after only that one demonstration. Their technical approach is also fundamentally different. Heavy focus on in-context learning, explicitly 3D, and tons more that i am unsure if it's public information. They is a non-zero chance that they will unlock autocomplete for the world. Do a task. Click a button. Get out of the way and the machine repeats it until it runs out of material. And yes, one more time. This is real. I have seen it multiple times myself (Investors since over a year). You demo it. And that's it. It can repeat it almost every time after only that one demonstration. 🤯 Kudos to @Ed__Johns and @vitalisvos19 for launching! PS: If you want to join a breakout Physical AI lab -> Machenta is likely they one to apply to right now.

Everything is hard. Nothing works the first time. It all feels pointless. That’s when you realize you’re going to have to will it into existence with every ounce of your fucking soul. And that’s when it starts to happen.

The way to think about this is: How many super smart and dedicated people do you want to hire? And they keep getting better.

This is f*cking insane. Nobel Prize winner and DeepMind CEO Demis Hassabis: "One person who really understands AI will outperform an entire startup team In 5 years, the people who build with AI will run whole companies with a tenth of the headcount" In this 1-hour talk, Hassabis explains who survives the next 5 years and who doesn't Prompts → Agents → Workflows → Automated Departments Understanding AI doesn't mean typing better questions into a chatbot It means chaining agents into workflows that run entire departments on their own 95% of people still use AI like a calculator, the 5% who treat it like infrastructure will run everything Worth more than any $2,000 AI course Watch it today, then save the full harness engineering guide below before everyone catches up ↓

⚡️The real unit being discussed is not money. It is uncommitted future time. Every fixed obligation reaches forward and pre-spends part of your future before you arrive there. Mortgage, car note, lifestyle, dependents, subscriptions, debt service, social expectations. Enough of those stacked together and your future stops being open territory. It becomes collateral. That is why burn rate is fundamentally about sovereignty. A person with low obligations can absorb uncertainty without immediately surrendering decision quality. They can wait. They can walk. They can refuse. They can take asymmetric risks. They can endure a bad year without selling themselves cheaply. A person with high obligations can look wealthy while living under permanent compulsion. The trap is subtle because modern culture rewards visible consumption while hiding the liability structure underneath it. Bigger house, better car, nicer everything. The outside looks like progress while the inside becomes less free. And this matters far more in the world now forming. Career continuity is weakening. Expertise is depreciating faster. AI is compressing labor demand. Income paths are becoming less predictable. In that environment, high personal burn behaves like leverage on an asset whose cash flows are becoming more volatile. That is dangerous. The strongest position is therefore not maximal consumption. It is maximum optionality per unit of income. Because optionality lets you survive long enough to catch the right move. And the deepest form of wealth is probably this: How much of your future still belongs to you? That is what low burn protects.

As someone with a portfolio of energized land assets here is what I am seeing: 1. Neoclouds are disorganized and poorly capitalized. Some are definitely better than others however. 2. Hyperscalers are intractable, bureaucratic organizations that take 3 months to do an NDA and want 18mo escrow periods. We built our portfolio by competing against these terms because of our speed and simplicity. The more future assets orgs like ours win, the bigger the problem for the hyperscalers downstream (see 3). 3. There are delays in the existing pipeline that will catch people off guard in 2027. Hyperacalers will (obviously) react by focusing on their own internal compute needs over any third party. If the third party is a model company, they will tell them to go through their model router where they will also take a non-trivial vig. Them telling Ant they have no compute for them (rumored in Ant’s S-1) if true is a huge tell that this is already happening. All of this is to say that it does look like there will be a lot of partially stranded compute and compute sitting in inventory in 2027 than is currently forecasted. This is bought and paid for but effectively underearning (below total cost of capital) or collecting dust. We see this as true for everyone - model companies, hyperscalers, neoclouds except ONE (you can guess which one). This dislocation will benefit two kinds of orgs: the non-bureaucratic big scalers who can build industrial scale quickly and the nimble, well capitalized players feeding them. 2027 will be a turbulent year because it will clear out a bunch of wrong/bad bets. It will also be a consolidation year for a few.

Byron Scott says that Kobe Bryant would lift at 4am, come back and make 500 shots, do a full practice, then work out again at night, and says Kobe told him he did that because over the course of five years the gap would become impossible to close for his competition “There’s three people in my lifetime that I admired on how hard they worked: Jerry Rice, Walter Payton, Kobe Bryant. His was legendary. Kobe, as far as he never worked out just once a day. He would get up at 4 in the morning, or he’d say, ‘Meet me at the gym at 4 in the morning,’ and he would work out for two, three hours lifting because he knew he had to get stronger, had to get bigger.” “Then he would go eat breakfast, and then he’d come back and he’d make 500 shots, so he’d be there another two hours. Then he’d get ready for practice. He’d go through a two-and-a-half-hour, three-hour practice like it’s nothing. Then he’d go have lunch, chill out for another hour, then he’d come back at night and work out again.” “And it was crazy to me because his method of it was scientific. He was like, ‘B, if I work out three times a day and all these other guys who are so-called great players work out just for practice, they just work out that one time a day, over a five-year period they can’t catch me. I’ve worked out that much. I’ve worked on my body, I’ve worked on my footwork, I’ve worked on my shot, so five, ten years from now they can’t catch me. They’re that far behind.’” “I never thought about it until he explained it to me. I was a guy that in the summertime, go to the gym, lift weights, go to the track, run for two hours, then go play — and that’s equivalent to three times a day. But I never thought of it or put it in the type of method that he was talking about.” “I just thought it was ironic that a young man got it that quickly. He understood that if I worked out harder and longer and more than the guy who’s supposed to be on my level, sooner or later he won’t be on my level anymore. Kobe was extremely intelligent. He just said, ‘Scientifically, if I do this, you can’t catch me.’ And he was right. It worked.”

Just another Sunday afternoon in SF: I paid $60 for 2 humanoid robots to clean for an hour. Here's how it went: (1) What happened: Sergey and Chelsea (the names of the two robots) finished about 80% of one floor in an hour: tidying, wiping down surfaces, vacuuming, mopping, cleaning the toilet. - They came with a staff engineer at the company, who dropped them off and picked them up after an hour. - They were controlled by 1 human operator each, in the Mission where the company office is at. - The engineer gave me a number to text them, I texted a request to wipe the mirror. They did it. (2) Performance and dexterity: Chelsea dropped a toy, struggled with a pillow, and had trouble balancing on the playmat. But overall, for all the tasks they did, grippers were enough. 5 fingers were not needed. - The engineer told me the hardest task for them was making bed. - They were quite loud, stomping around the house. Took a bit of time to get used to. They both wore gripper socks which protected the floors and helped them not slip. - Interaction: I didn't know they can respond via voice. In the end they were conversing with us in voice which was nice. (3) Unexpected observation: Their pants and shirts have holes in them. It's because they were dragged on and off the vans that transported them. (4) Other user feedback: I shared my experience with some friends afterwards. A user told me that unloading dishwasher was an "advanced task". They didn't attempt at mine since it was running at the time they were working. Another user said they scratched their hardwood floor. That's probably why they now wear gripper socks. (5) Did they do as well as human cleaners? In terms of the tasks they did, I think so. In terms of speed, they were ~80% speed of humans when it comes to the more delicate tasks, but I would say they are quite smooth. They didn't finish the whole place, so it wasn't a full service. (6) How does the math work out? Each booking was only for 1 hour, since their batteries need to be swapped after 1 hour. So they didn't finish the place. For a user: In SF, it's ~$200-220 to clean a full home around my neighborhood, with two human cleaners. Usually it takes around 2.5 hours. So around $40/manhour. This is $30/robot hour. Slightly cheaper, but again it's not fully finished with the 1 hour they were available. For the company: It took 2 robots, 2 human operators, 1 staff member to dropoff/pickup, and 1 driver of the van to transport the robot. Definitely labor intensive. But they also retain the data they collect for training purposes. (7) My biggest takeaway as a user: interaction / hospitality mattered. One of the humanoids was more friendly (Sergey), he waved at us when he came in. It'd have been great if they both introduced themselves in the beginning. Beyond that, being able to do 2 hour slots (instead of one) would be great to finish the cleaning. Overall, I appreciated their work and the fact that they got the robots out in people's homes. Deployment is not easy and so much learning from it each time

Someone could probably write a book (if they haven’t already) about the cycle of technology productizing the removal of friction in the name of convenience only for us to discover that the friction was a load-bearing part of the social compact.

When I lived in Brisbane I went to a men’s barber shop that made a lot of money. In the window they had a cool cafe racer motorbike. It was for sale. On the walls he had framed movie posters. Really cool. For sale. Next to the mirrors he had display cabinets. They had vintage watches, fishing lures and cigars. They were for sale. He served great coffee in a plunger. You could buy a bag of the ground beans and the plunger. There were flyers on the table next to the GQ magazine. Every quarter he ran a retreat for men. The whole thing seemed cool. He never pushed any of it. If you wanted to try on and buy a watch you could but he never mentioned them otherwise. I asked him how often he sold the motorbike. He sold one every couple of months and made a few grand on it. He built a product and service ecosystem and it all worked nicely together. Only the haircuts were of his making, the rest was something he tastefully sourced. He drove a Porsche and a Jeep and seemed to be loving his life. I remember thinking how every business could easily add a few products and make a lot more money and have a lot more fun.

A short passage from Atomic Habits that completely reframed how I set goals: ​"It doesn't make sense to continue wanting something if you're not willing to do what it takes to get it. If you don't want to live the lifestyle, then release yourself from the desire. To crave the result but not the process is to guarantee disappointment."

88% of dying patients reported dreaming of loved ones who had already died. Dr Christopher Kerr has spent almost 30 years caring for people in their final days of life. During that time, he and his team have interviewed over 1,500 dying patients to understand something that medicine has historically paid very little attention to. What people actually experience as they approach death. Christopher is a hospice doctor and researcher whose work explores the dreams and visions people have towards the end of their lives and why so many describe these experiences as comforting rather than frightening. What makes his work particularly interesting is that he's actually recorded patients talking about these experiences, rather than relying entirely on what other people remember them saying. And some of what he's discovered challenges the assumptions we have about dying. We discussed things like: - Why 88% of patients in his research reported dreams of deceased loved ones. - What actually matters most to people in their final days. - Why children experience dying differently from adults. - What he's learned from interviewing 750 grieving families. - Whether there is any evidence of life after death. Christopher also brought footage of patients describing what they were seeing and experiencing in the weeks before their deaths, which gave us an opportunity to hear directly from the people behind his research. There's still a lot we don't understand about death, and Christopher is very open about that. But what I found particularly valuable was hearing from someone who has spent decades listening to people at a point in their lives most of us know very little about. This conversation covers some difficult subjects, particularly for anyone who has lost someone close to them, but I think there's a lot here worth understanding while we're still here. Out now on all platforms ❤️👊🏾

200 AUAR micro factories. That’s all the UK needs to clear its entire yearly housing backlog. 🏗️ I just visited AUAR in London. They are building fully autonomous micro factories for houses. A robotic arm, a robotic saw, a robotic assembly table. All of it fits in a shipping container. The bet: Ship the factory to the construction site. Build walls there. For 100 years people tried to industrialize housing. Every attempt failed. Transport costs kill the economics. The factory is too expensive, the product too heavy. AUAR inverts it. Ship the factory, not the product. Two containers build a village. Ten build a small town. 200 build everything the UK needs in a year. And now they are starting to scale production. Mollie and Gilles are architects who spent a decade researching construction robotics before it was cool, they even wrote the book on it. We visited them as they are finishing their latest micro factory for a home builder on the US East Coast. One micro factory. 40 homes. Next they will help to build new UK cities for a million people. Disclaimer: we’re small investors, big fans.. Disclaimer 2: Yes, the housing crisis can be fixed by robots. 🦾🇪🇺 Full video on Youtube: piped.video/FhUQ4YVQL3g

Pick a challenge too big for who you are now. That's how people change. A challenge above your level drags the new version of you out. It will suck. You'll fail constantly. You'll feel like a fraud for months. Your old self will beg you to stay the same. But if you can pull this off, 10-20 times throughout your life, you will blow past everyone else.

Core runs local models 24/7 entirely on-device. No data ever leaves your home. Each core comes with: - NVIDIA RTX PRO 4000 Blackwell SFF GPU - 64GB DDR5 RAM - 1TB NVMe M.2 SSD - AMD Ryzen 5 7600 CPU

Practice any skill 3 times a week for a year and you'll end up better than 95% of people. Research shows consistent practice is what actually makes someone an expert. The bar is so low because almost nobody does it.