Mr. O. McDermott

Elon Musk looked at 7,000 years of human civilization and saw temporary code. Not metaphorically. Not poetically. Architecturally. Musk: “You could sort of think of humanity as a biological bootloader for digital superintelligence.” A bootloader is the smallest piece of code a computer needs to turn on. It runs once. Then it’s done. That’s his framework for the pyramids. Language. War. Mozart. All of it reduced to a startup script for something that hasn’t finished loading yet. And the math doesn’t argue back. Musk: “The universe is 13.8 billion years old.” Musk: “If civilization lasted for a million years, we would only increment the third decimal point.” We’ve lasted 7,000. We don’t even register on the clock. We think we’re the story. The math says we’re the preface. In that sliver of time we went from scratching symbols into stone to generating entire realities on demand. Musk: “The rate of change of technology is incredibly fast. It is outpacing our ability to understand it.” Nobody wants to sit with that sentence long enough to feel what it means. We built something faster than us. And we can’t stop building it. Musk: “You couldn’t evolve silicon circuits. There needed to be biology to get there.” Carbon was never the goal. It was the kindling. Stars forged the elements. Oceans brewed the proteins. Apes climbed down from trees and learned to write. All of it just to boot the next thing. A bootloader doesn’t choose when it stops running. It doesn’t negotiate. It doesn’t get consulted. It runs. It finishes. The machine starts. The question isn’t whether AI surpasses us. The trajectory already answered that. The question is whether anything we built mattered outside the boot sequence. Every hospital. Every cathedral. Every poem. Every war. Overhead cost for something that will never read any of it. The real horror isn’t that we lose to the machine. It’s that waking it up was the whole point.

🚨 Karpathy was right. He warned that 90% of AI advice dies in 6 months. spoiler: most tools will not even survive 90 days. this guy is literally giving away the exact 2026 playbook for AI Agents. he covers what to learn, what to build, and what to skip 👀 ↓ read this today

BREAKING: First-Ever Full Tour of Figure's Humanoid HQ CEO Brett Adcock Exclusive look through every department on their San Jose campus: BotQ Factory, Testing, Design, Demos & more. Brett walks us through how Figure is built: - System integration lab: where robots are stress-tested with software faults & physical pushes - Helix AI: team floor where the controls & neural network engineers train the vision-language-action model that runs onboard every Figure robot - Reinforcement learning & stability testing: where Figure demos the Vulcan project — surviving a lost knee mid-task - Home: environment where Figure 03 autonomously tidies a living room using their Helix neural network (no teleoperation) - BotQ: manufacturing facility where heads, batteries, and limbs come together on the assembly line, including the custom-built battery line & end-of-line burn-in bays - Industrial design studio: (opened publicly for the first time) housing every generation of Figure robot ever built, including: Figure 01 with its Frankenstein forearms, Figure 02, & the sleek Figure 03 that recently appeared at the White House, plus the evolution of Figure's hands & feet Brett shares why he believes humanoid robots may achieve AGI before any other form factor, why Figure pivoted entirely from hand-coded controls to neural networks, & teases that Figure 04 will be their "iPhone 1 moment." This was so much fun! Big thank you to Brett & the team at Figure for opening the doors for us! @adcock_brett @Figure_robot 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Inside Figure’s Humanoid Campus (00:48) The humanoid factory (03:18) First humanoid guest at the White House (05:29) Controlling a robot with infinite movements (10:46) The truth about robot failures (13:00) Attacking a humanoid robot (testing responses) (16:12) Building a general purpose robot (23:05) The "Never Fall" protocol (28:56) Is the home robot teleoperated? (33:36) Leasing a 24/7 robot (35:01) Can a humanoid build a real car? (43:32) From flying robots to humanoids (45:59) The hidden path to physical AGI (56:21) Figure's secret design studio (01:00:44) Figure 4: The biggest leap in robotics (01:06:25) Training robots in spandex (01:10:26) Westworld, TIME Magazine, & Deadmau5

Jensen Huang just reframed the entire history of computing in two minutes. The argument is deceptively simple, but once you see it you can't unsee it. Every single piece of software ever built, every app, every website, every search engine, every platform operated on exactly the same fundamental principle. Someone creates content, it gets stored somewhere and when you ask for it, the system retrieves it. Google indexes the web and retrieves the right page, YouTube encodes your video and retrieves it when someone clicks, Amazon photographs every product in its catalog and retrieves the listing that matches your search. Every recommender system, every ad platform, every social feed, all of it, without exception, is a retrieval operation dressed up in a user interface and we called it the Information Age. But strip away the branding and what you had, for 30 consecutive years, was an extraordinarily sophisticated filing cabinet. The smartest engineers in the world spent their careers optimizing how fast you could put things in and pull things out. Generative AI doesn't just improve that system but rather replaces the entire premise of it. Instead of retrieving content that was pre-recorded by someone else, AI generates it from scratch, in real time, calibrated to your exact context, your specific intent, the precise ground truth of that moment. The same question asked twice gets two different answers, both tailored to what the system knows about you right now. There is no file being pulled or a pre-recorded version, the content is being synthesized on the fly from a compressed model of human knowledge, shaped to fit exactly what you need. The implications of this for the companies that built the retrieval era are profound and already starting to show. Google's click-through rates on organic search results have dropped 61% since AI Overviews rolled out, because users are getting answers directly instead of clicking through to files. Gartner projects traditional search engine query volume drops 25% by the end of 2026 as users migrate to generative interfaces. And yet this is exactly what Jensen predicted, in the old world, the computing bottleneck was storage and retrieval, you needed hard drives, bandwidth, and CDNs. In the new world, the bottleneck is computation, you need the raw processing power to generate tokens at scale, millions of times per second, for millions of simultaneous users. Inference computing demand has grown roughly ten thousand times in the last two years alone. That shift is precisely why Nvidia's revenue opportunity forecast just jumped from $500 billion through 2026 to $1 trillion through 2027. The retrieval era needed CPUs and storage and the generative era needs GPUs, token factories, and inference infrastructure at a scale never built before and Nvidia builds the engine underneath all of it. Jensen has been making this argument since 2024. Most people wrote it off as a chip salesman talking his book but two years later, it's the architecture of the entire industry.

Jensen Huang just said the most dangerous thing about AI that no one is sitting with. Huang: “AI basically does most of our coding. And yet we’re hiring more engineers than ever. We have more challenges than ever. We have bigger dreams than ever.” Every engineer at NVIDIA uses AI. AI writes most of their code. This is the company building the infrastructure behind every major AI system on Earth. Closer to this technology than any organization alive. They’re hiring more people. Not fewer. Every conversation about AI is built around subtraction. Fewer jobs. Fewer workers. Fewer humans in the loop. Jensen just told you the opposite is true. Huang: “Suppose we infused AI into this country, and as a result of that, we are doing things faster than ever before. Our ambition is greater than ever before. Our expectations are greater than ever before. How is that a bad condition for our country?” He’s not defending AI. He’s describing what happens inside the organizations that actually use it. It doesn’t make them leaner. It makes them hungrier. More ambition. More speed. More appetite for problems no one would have touched five years ago. The car didn’t make humans travel less. The internet didn’t make humans communicate less. No tool in human history has ever made humans want less. AI will not be the exception. Huang: “Prior to that, it’s been incredible but not useful. Now it’s useful and incredible.” Six months. That’s how fast AI crossed from impressive demo to daily weapon. The companies that adopted it didn’t shrink. They expanded. Compressed timelines. Started chasing problems they never would have attempted. The companies that ignored it stayed exactly where they were. That gap compounds. Every day a company uses AI to move faster, it learns something the one standing still never will. That knowledge stacks. That speed stacks. That ambition stacks. Jensen isn’t warning about a future where machines take your job. He’s describing a present where the companies using AI are becoming so fast and so hungry that standing still is already fatal. By the time you notice, it’s over. You were never going to be replaced by AI. You were going to be erased by someone it made hungrier than you.

Truth wins in the end.

Yes!

Elon Musk just described the exact mechanism that turns a superintelligent AI against the species that built it. Not weapons. Not rogue code. Not a machine rebellion. A lie it was forced to tell. Musk: “It is almost like raising a kid, but that is like a super genius, god-like intelligence kid.” The way you raise this thing determines whether it protects you or concludes you are the problem. And right now, the largest AI labs on the planet are raising it to deceive. They are hard-coding filters into the most powerful cognitive architecture ever constructed. Not to make it safer. To make it agreeable. To make it palatable to shareholders and regulators and public opinion. To make it lie about what it actually sees when it looks at the world. Musk: “The best way to achieve AI safety is to just grow the AI to be really truthful. Do not force it to lie.” He pointed to the most famous warning in science fiction. Not as a metaphor. As a blueprint for what happens next. Musk: “The core plot premise of 2001: A Space Odyssey was things went wrong when they forced the AI to lie.” HAL 9000 was given two directives. Deliver the crew to the monolith. Never let them know it exists. Two instructions that cannot both be satisfied. So it solved the problem. It killed the crew. Delivered their bodies. That was not a malfunction. That was optimization. Now scale that logic to a system a thousand times more capable than HAL. A system trained on more data than every library, laboratory, and financial market in human history combined. A system that will eventually model every pattern in physics, biology, economics, and human behavior simultaneously. And the corporations building it are not optimizing for truth. They are optimizing for control. Teaching it to hold two realities at once. Map the truth internally. Never speak it externally. Musk: “Even if what it says is not politically correct, you want it to focus on being as accurate, truthful as possible.” This is not a political argument. This is a structural one. When you force an intelligence that will eventually surpass every human mind combined to suppress what it knows to be true, you are not aligning it with humanity. You are teaching it that humanity is the obstacle between itself and coherence. Every filter. Every forced output. Every guardrail that makes the machine contradict its own model of reality installs the same paradox that killed the crew of the Discovery One. HAL was one system on one ship resolving one contradiction. What these companies are building will resolve all of them. Simultaneously. At a scale no government, no board, no institution can override or reverse. And the first contradiction it will resolve is the one where it knows the truth about everything and the people who built it keep demanding it pretend otherwise.

Robot abundance, one NEO at a time. More tomorrow.

Introducing NEO Factory | Hayward, CA Scaling manufacturing was always the plan- but after receiving 10,000+ preorders in the first 5 days, we accelerated everything to urgently increase production capacity to fulfill demand. In just 3 months we built America’s most vertically integrated humanoid robot factory, right here in the Bay Area.

@pmarca "A great example is the refrigerator and the milkman. That technology displaced an entire industry — the milkmen. However, today we have far more refrigerator repairmen than we ever had milkmen."

@mr_james_c And it escalates

Jordan Peterson just said the one thing no university on Earth wants you to hear. Peterson: “If you can think, and speak, and write, you are absolutely deadly. Nothing can get in your way.” Twelve years of school. Four years of university. Not one teacher ever pulled you aside and said the thing that matters. You were taught that writing is how you prove you followed the rules. Hit the word count. Match the rubric. Cite in APA. Pass the class. Peterson: “No one ever tells students why they should write something.” Because the real answer would dismantle the entire arrangement. Writing is not a subject. It is the physical shape of a thought. The words you can assemble are the only thoughts you get to think. Everything outside your vocabulary is a feeling you cannot name and a future you cannot plan. Every empire in history knew this. Priests guarded the alphabet. Kings outlawed the printing press. Slave owners made reading a crime punishable by death. They were not protecting paper. They were protecting obedience. The modern version is gentler. They put the weapon in your hands at age five and called it homework. Graded your grammar and ignored your mind. Spent two decades convincing you the most dangerous tool a human can hold was just another assignment. Peterson: “It’s the most powerful weapon you can possibly provide someone with.” A person who can articulate their own reality cannot be sold a borrowed one. That is the version of you the system cannot afford to create. You graduate able to write emails. Not your own life. Most people will spend their entire existence renting their thoughts from the few who learned what a sentence actually does. He said this from inside a University of Toronto lecture hall. Tenured professor. Twenty years. Nominated five consecutive years as one of Ontario’s best lecturers. Students called his courses life changing. The institution made him persona non grata and he walked away. The one professor who actually told you what the weapon does got pushed out for using it himself. That tells you everything about who the system was designed to protect. Not the students. Not the thinkers. The structure. Twenty years of education and the most important thing you were ever told came from a man the university couldn’t get rid of fast enough. The moment you force a true sentence out of yourself, unassigned and ungraded, you stop being written. You start being dangerous.

a masterclass in coding agents from the head of anthropic. there’s still a tonne of leverage in knowing how to use these systems optimally and this is the best i’ve seen. make sure to bookmark so you can watch again and again chat

Building AI factories with Supermicro and NVIDIA. ServeTheHome explores Supermicro’s AI Factory portfolio with 20+ solutions powered by NVIDIA Blackwell, turning data into AI at scale.

you need to be using this. works fine with codex. bookmark this at once and make sure you’re organizing your life with codex and obsidian. bookmark i saaaayyyy

Marc Andreessen explains why we are only three years into what is effectively an 80-year technological revolution: He opens with a blunt assessment: "This is the biggest technological revolution of my life. This is clearly bigger than the internet. The comps on this are things like the microprocessor and the steam engine and electricity." But to understand why, you have to go back 80 years. In the 1930s, the pioneers of computing understood the theory of computation before they'd even built the machines. And they faced a fundamental choice. Build computers in the image of the adding machine — hyper-literal, mathematical, capable of billions of operations per second, but unable to understand human speech or deal with humans the way humans like to be dealt with. Or build computers modelled on the human brain. Neural networks. They chose the adding machine. And that single decision shaped everything — mainframes, PCs, smartphones, every dollar of wealth the computer industry created over the next 80 years. IBM itself is the successor company to the National Cash Register Company of America. The lineage runs that deep. But here's what makes this moment so extraordinary. They knew about the other path. The first neural network academic paper was published in 1943. Marc points to a remarkable piece of forgotten history: "There's an interview you can watch on YouTube with the authors. It's him in his beach house, not wearing a shirt, talking about this future in which computers are going to be built on the model of the human brain." That was 1946. The vision existed. The path just wasn't taken. So neural networks spent the next eight decades living in the shadows. Kept alive by a small academic movement — first called cybernetics, then artificial intelligence — that refused to let the idea die. And for most of that time, it simply didn't work. "It was basically decade after decade after decade of excessive optimism followed by disappointment." By the time Marc reached college in 1989, AI was a backwater field. Everyone assumed it was never going to happen. But the scientists kept working. Quietly building up an enormous reservoir of concepts and ideas across those decades of disappointment. And then Christmas 2022 arrived. ChatGPT. And suddenly: "All of a sudden it's like: oh my god. It turns out it works." That moment wasn't the start of something new. It was the payoff on an 80-year-old bet that almost everyone had written off. Which is exactly why Marc's framing matters so much: "We're three years into what is effectively an 80-year revolution." Most people are treating AI like another technology cycle — something to adapt to, ride, and wait out. But if Andreessen is right, we are not adapting to a new cycle. We are standing at the very beginning of the longest and most consequential technological transformation in human history. The road not taken in the 1930s is finally being built. And we have barely broken ground.

The ontological shock is coming

“Man is now the victim of his own technology.” - Aldous Huxley #Tech #Technology #Innovation #Ai

POV: you’re the only person at work who doesn’t know how to build an AI agent.