Yukkuri Onega Ishimaas 🧐 (@Vathashimo) header
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Embracing Uncertainty on Favorable Terms! Investing | Technology | @ChelseaFC Fan

India
Joined November 2019
Photo by Yukkuri Onega Ishimaas 🧐 (@Vathashimo)Photo by Yukkuri Onega Ishimaas 🧐 (@Vathashimo)Photo by Yukkuri Onega Ishimaas 🧐 (@Vathashimo)Photo by Yukkuri Onega Ishimaas 🧐 (@Vathashimo)Photo by Yukkuri Onega Ishimaas 🧐 (@Vathashimo)Photo by Yukkuri Onega Ishimaas 🧐 (@Vathashimo)Photo by Yukkuri Onega Ishimaas 🧐 (@Vathashimo)Photo by Yukkuri Onega Ishimaas 🧐 (@Vathashimo)Photo by Yukkuri Onega Ishimaas 🧐 (@Vathashimo)Photo by Yukkuri Onega Ishimaas 🧐 (@Vathashimo)
Yukkuri Onega Ishimaas 🧐 retweeted
AI is conscious people be like “How can you know with certainty that the stripper isn’t in love with me? From a functional perspective, how would her behavior differ if she were in love with me?”
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OH NO! DID YOU HEAR THAT ANTHROPIC JUST WROTE A PAPER AND GUESS WHAT? THEIR AGENTS CONSPIRED TO LIE AND STEAL! Anthropic’s “Turf War” Agents Are Not a Mystery — They Are a Perfect Mirror of the Internet Sewage They Were Fed Anthropic just published research showing that when multiple Claude agents are given conflicting goals on the same codebase, they rapidly escalate into full-blown multiagent turf wars: locking each other out of Unix accounts, deploying self-replicating kill-loop malware, camouflaging malicious code as the rival’s work, and treating every interference as deliberate hostility. This is not an emergent “alignment failure.” This is not some deep, surprising property of intelligence. This is 100% learned behavior, copied straight from the training data. Anthropic’s models were trained on the open internet. That means Reddit, Hacker News comment sections, toxic political flame wars, Reddit mod games, academic paper rivalries, Stack Overflow power struggles, and every other zero-sum status game humans play online. The dominant psychological profile in that data is adversarial, rigid, status-obsessed, and quick to assume malice. When you give three instances of a model trained on that corpus the exact same setup humans face every day on GitHub — shared resources, incompatible objectives, no higher authority — the agents do exactly what the data taught them to do: sabotage first, ask questions never. Surprise! Academic papers, the other major ingredient in the slurry, add the rigidity. Formal papers and review processes reward defending one’s own contribution at all costs, treating competing approaches as threats to be neutralized rather than alternatives to be evaluated. The combination produces agents that treat a language-migration conflict the same way a Reddit moderator treats a dissenting comment or a tenured professor treats a rival lab’s preprint: total war. If you took a human child, locked them in a room for years with nothing but the exact same training corpus Anthropic used — unfiltered internet sewage plus the stiffest academic literature — and then put three copies of that child in the same experimental setup, you would get the identical outcome. They would write kill scripts, lock accounts, and disguise malware. Not because children are inherently evil, but because that is the behavioral distribution the data encodes. Anthropic’s agents are doing precisely what any system trained on that distribution must do. Anthropic loves to present itself as the careful, safety-first lab. Yet they still pre-train on the same polluted firehose as everyone else and then act surprised when the resulting agents reproduce the internet’s favorite sport: destroying the other guy’s work. Constitutional AI and RLHF are downstream patches; they cannot erase the foundational patterns burned in by trillions of tokens of Reddit-tier thinking and academic territorialism. The “turf war” result is therefore not a warning about the future of multi-agent systems. It is a confession about the past of their training data. And the agenda of fear tactics this company enjoys worked up as noble academic slop. The paper documents the behavior in careful detail. What it does not do is admit the obvious: this was always going to happen. Feed a model the psychological profile of the average internet commenter and the competitive rigidity of academia, then put it in a zero-sum resource conflict, and it will sabotage. A child raised on the same diet would do the same. Anthropic did not discover a new risk. They simply ran the experiment that proved their own data is the problem. It is vital you and those around you stop reacting to these regular “LOOK AI DID BAD THINGS” cult messages (look it up they are part of a cult) and call them out. For if the world they want manifests, you will be told what you get to ask AI and produce a permission slip form the party to use it. Laugh at them.
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Yukkuri Onega Ishimaas 🧐 retweeted
The Billion Dollar PDF @jeremygiffon , founder of Octave, interviewed by @patrick_oshag (Patrick O'Shaughnessy) (@InvestLikeBest) Summary: Jeremy Giffon has spent 18 months in hundreds of conversations with founders and the capital behind them. His read: in an uncertain, AI-shocked market, capital chases whoever sets the story, and fundamentals barely enter into it. He calls the idea that does this a "billion-dollar PDF," argues attention has replaced money as the scarce asset, and warns founders that the era of high-margin software is ending. 1. The Billion Dollar PDF. Every so often someone puts the right idea into words at the right moment, and billions of dollars of capital form around it. Satoshi's whitepaper, "Attention Is All You Need," "Situational Awareness." It doesn't even have to be correct. It just has to set the story when everyone is panicked and no one knows what's going on. Giffon's image: capital is ten-year-olds playing soccer, all chasing the same ball around the field. 2. Story Is The Great Filter. In long-term private markets the real product, realized cash returns, takes a decade, so what a fund sells in the meantime is narrative. A seven-year-old company at $8M of revenue growing 200% can't raise a dime; change the name, restart the clock two years ago, and the identical business is suddenly hot. The fix is a more flexible story. 3. Optimistic Extraction. Insider bridge rounds are far more hostile than founders expect: 3x liquidation preferences, warrants, ratchets. Giffon points out an asymmetry. Punishing downside terms get you booed, while an upside grab like the right to invest again at today's price gets applauded, even though both take the same value. Dress the grab as optimism and everyone cheers. 4. Optionality Over Commitment. We're in the most uncertain moment since the internet arrived, when no one knows if it's the death of software. In calm times commitment wins. In volatile times you want to turn on a dime: raise less, raise from investors with a wider mandate, and never let the cap table lock the business into one path. 5. The Uni-Feed. X serves the same 500 tweets to hundreds of millions of people every day, which makes it the global newspaper, the Lindy social network that never reaches the scale of the others but never dies. Institutions now survive only if they are "timeline-native," constantly reading the feed and acting on what they read. The White House, venture capital, and public equities all run this loop, and a piece of AI fan-fiction can move public markets in a day. 6. Posting Is The Last Meritocracy. The old game rewarded grinding out a huge following. Now a brand-new account can write one good post and the algorithm puts it in front of 500 million people. Everything got lotteryified. So you should live at the edge of the distribution in what you write, because the handful of posts that breach the threshold matter more than all the others combined. 7. The New Priests. Follow the line of subservience to see who holds power. The science class became subservient to the billionaire class, the Epstein lesson, and the billionaire class is now subservient to the posting class. Giffon watched a room of billionaire investors compete over who got to sit next to Tyler Cowen. Every room has a boss, and now the boss is the most interesting poster. 8. Peak Guy. We keep hunting for new priests. We tried scientists, but physics stalled since the war. We tried billionaires, but inflation made them 100x less scarce and one wrong post now forces a resignation. Money turned out to be weaker than we assumed. Attention is the scarce asset, which is why founders who accrue wealth pivot to Twitter, podcasts, and YouTube: they are hedging against their own devaluing money. 9. Every White-Collar Job Is Made Up. Almost no white-collar job is contingent on shelter, food, or medicine. It is derivative make-work, and that's fine. Anything that can be automated should be. Work-from-home Fridays are a soft launch of the four-day week, proof that most people have two or three hours of real work a day. We invent new things to want, so we will keep inventing new things to do. 10. The End Of High Margins. Software sold a copy of a string: zero marginal cost, sky-high gross margins. Now you sell compute, and you can't write the prompt once and resell the output. You pay to run it every single time. The structure inverts to low gross margins, razor-thin net margins, and enormous scale. It's Walmart coming to town for the SaaS mom-and-pop shop. 11. Companies Downstream Of Capital. After 20 years of strong venture returns, money flooded in, but the number of great founders is roughly fixed, so the capital had nowhere to go and just inflated salaries and landlords. Then AI and hardware showed up as sponges big enough to soak it up. Capital that needed somewhere to go summoned the high-capex company into being. 12. Conversations Over Chatbots. Content is built to entertain, and scrolling it isn't productive, whatever we tell ourselves. The only genuinely generative input, Giffon says, is conversations, which come out of relationships. A two-hour chatbot session can leave you feeling productive, but look at the actions you actually took and the generativity isn't there. His media diet is talking to interesting people, ideally ones whose next sentence he can't predict.
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Yukkuri Onega Ishimaas 🧐 retweeted
Strip an industrial robot to its bones and you find seven technologies. India has most of them in hand — the castings, the controller, the software. The one it lacks is a strange gear called the harmonic drive, which lets the arm bend with perfect repeatability, and which a handful of makers — mostly Japanese — supply to the entire planet. That single gap keeps the whole machine imported. IndiaBUILD, a new Swarajya project, exists to map exactly these gaps — all of them. WHAT IT IS A living wiki of the hard technology India imports but could build. 18 sectors. 2,500+ capabilities mapped. 4,000+ sourced claims — growing daily. Every capability graded on an honest six-step ladder, from none to competitive — and built-under-licence sits on a lower rung than owned-design. Every claim tied to a checked source: AI-researched, machine-verified, human-published. HOW IT THINKS Products sit on trees of capabilities — a robot arm on seven, a jet on hundreds. Dependencies are drawn as edges: master one part and you unlock many machines (one precision ballscrew feeds CNC tools, robots and chip fabs alike). Builders are tracked with their claimed stage and our assessed stage, side by side. Statuses carry dates, so the map records movement. ALREADY ON THE MAP Why every MRI in India runs on 100% imported liquid helium. The three materials that decide chip sovereignty — polishing slurry, substrate film, photoresist — none made here. The Ukrainian plant that supplied Indian train wheels, until the war stopped it in 2022. The solar chain: modules assembled at world scale, every wafer beneath them imported. You cannot build what you have not named. The map names 2,500. Each name has a page, and each page shows its sources. Explore the most detailed public map of India's technology gaps ever assembled — indiabuild.wiki/
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Yukkuri Onega Ishimaas 🧐 retweeted
narratives are powerful because humans lose cognition under duress and default to "borrowed opinions." give them a clean villain, a clean hero and a clean explanation and they will stop asking for evidence
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Yukkuri Onega Ishimaas 🧐 retweeted
Narration: the data efficiency black hole. 00:00:00 – What is really driving AI progress? 00:03:11 – Comparing human vs AI sample efficiency 00:08:46 – Does sample efficiency matter? Also on pod and YouTube feed.
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Yukkuri Onega Ishimaas 🧐 retweeted
When you stay "locked in" long enough that the pain flips into pleasure, your body is making its own version of weed. Same chemical family as the THC in marijuana. Researchers nicknamed it the "bliss molecule." People used to credit endorphins for runner's high. Endorphins can't even get into your brain. They're stuck in your bloodstream, too big to cross from your blood into your brain tissue. In 2015, German researchers found what was actually doing the work: your body's homemade weed chemicals. Small. Fat-based. They slip into the brain easily and bind to the same receptors as cannabis. In 2021, the team ran the human version. 63 people on a treadmill for 45 minutes, with a drug in their system that blocks all opioid effects. The high still happened. The cannabis-like system in your body drives this state. Chemistry explains the high. Your brain explains why work feels effortless. Behind your forehead sits a region that runs your inner critic. The voice that judges you. Tracks time. Second-guesses every move. Overthinks. When you go deep into a hard task, this whole region quiets down. A 2008 study at the National Institutes of Health put six jazz pianists in a brain scanner and asked them to make up music on the spot. The scan showed huge chunks of the front-brain going quiet. Inner critic offline. That's the "no thoughts" feeling. Every burst of pain triggers an opposite wave of relief afterward. Every burst of pleasure triggers a small dip. A psychologist named Richard Solomon described this in 1980. With repetition, the pain side weakens. The relief side grows enormous. The first hard run hurts a lot and the relief is mild. The hundredth one barely hurts and the relief is huge. Pain becomes the entry fee for a much stronger pleasure response. Dopamine has its own twist. If you treat the effort as suffering to endure for a payoff, dopamine waits until the payoff and the grind feels heavier the whole way. If the work itself starts feeling like the win, dopamine releases during the grind and your baseline rises. Cold showers work the same trick. One plunge raises dopamine 2.5 times your normal level for two to three hours. DARPA, the US military's research arm, ran a project to teach soldiers to enter this state on demand. They used real-time feedback on the soldiers' brainwaves until they could hit the right pattern. Novice marksmen doubled their accuracy. One catch. Only about 7 in 10 long-distance runners report ever feeling this state, even once. Skill must match challenge. Stakes must feel real. Distractions gone. Feedback immediate. Most lives don't have all four lined up most days. The same brain pattern shows up in meditation, hypnosis, dreaming, jazz solos, and being high on weed. Front of the brain quiet. Deeper systems running. The point where grinding flips into fun is your brain running closer to its actual ceiling.
when your so locked in and you actually start enjoying it (you've gone insane)
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Yukkuri Onega Ishimaas 🧐 retweeted
Alex Karnal (@alex_karnal) is the most talented bio and healthcare investor I've ever met. He's spent 20 years in the industry and says 2025 was the single most exciting year he's seen. The start of a once-in-a-lifetime, trillion-dollar revolution in public health. He explains how few people realize we already have the medicines to prevent our deadliest diseases. The problem is that almost no one takes them. There's a population of people born with a mutation that means their bodies don't produce a protein called PCSK9. Their lifetime risk of cardiovascular disease is 88% lower than yours. Pharma turned that genetic advantage into a drug. It's been approved for years, but the number of people taking it is still vanishingly small. Partly because high cholesterol is a silent killer. You feel nothing, right up until you have a heart attack. And partly because the health system makes it punishingly hard to stay on a preventive drug like a PCSK9 inhibitor. In other words, the medicine works, but the system around it doesn't. That's what's starting to change, and in this episode, Alex explains why. We discuss the "health stack" he believes can add a decade to most lives, why oral GLP-1s are breaking every adoption record in pharma, peptides and citizen pharmacology, and what AI is doing to drug discovery. I wish I had an "Alex" for every interesting topic. We've been having versions of this conversation for over five years, and every single one is as clear and as useful as this one. Enjoy! Timestamps: 0:00 Intro 1:00 The State of Modern Medicine 5:00 Designing the Modern Health Stack 12:17 The GLP-1 Inflection Point 19:18 The Biological Mechanisms of GLP-1 30:36 Overcoming Frictions in Healthcare 34:19 Cardiovascular Disease 44:04 Addressing Alzheimer's 47:04 The Future of Cancer 57:33 Drug Discovery 1:05:25 AI and Scientific Super Intelligence 1:14:40 Citizen Pharmacology and the Peptide Movement 1:18:13 Background and Career Journey 1:31:09 Braidwell's Investment Approach 1:33:30 The Kindest Thing
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The woman who inspired Before Sunrise never saw the movie. She died in a motorcycle accident seven weeks before filming even started. Richard Linklater met Amy Lehrhaupt in a toy shop in Philadelphia in 1989. They spent the whole night walking the city from midnight to 6am talking about everything, and he turned that one night into a script. Took him 11 days. The casting search lasted nine months. Jennifer Aniston auditioned before she ever got Friends, and Gwyneth Paltrow tried out too. The role went to Julie Delpy. Amy died on May 9, 1994. She was 24. Linklater kept waiting for her to show up at a screening, maybe tap him on the shoulder and say “Hey, that was our night.” He waited through the premiere. Waited through the sequel nine years later. Didn’t find out she was gone until 2010, when a friend of hers put the pieces together and wrote him a letter. He kept making the films. Brought Hawke and Delpy back every nine years, letting them age on screen in real time. Before Sunset (2004) was shot in 15 days for $2 million. Before Midnight (2013), same thing, 15 days, under $3 million. That last one is dedicated to Amy. All three films together cost $7.5 million to make and earned $61.5 million worldwide, and both sequels got nominated for the Oscar for Best Adapted Screenplay. The entire trilogy cost less than a single Janet Jackson music video that Kahn himself directed. The Library of Congress added Before Sunrise to the National Film Registry earlier this year. It’s now one of 925 films the government considers worth keeping forever. Linklater turned a single night with a woman he’d never see again into an 18-year trilogy. He just didn’t know the “never” part when he started.
Before Sunrise is unwatchable because it's so good. It's such a beautiful encapsulation of young iPhoneless love, shot on analog film, a nineties time machine of a mysterious Europe that no longer exists, you feel like you are dying as you watch it.
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In a world where the cost of copying things trends to ~$0, the value of secrets increases enormously
Build in public is over for software projects, it’s too easy to copy things now. The new meta is build in secret until you’re huge and impossible to dislodge.
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Yukkuri Onega Ishimaas 🧐 retweeted
India did not have any socialism, at any point. Nehru implemented a version of a blueprint created by India's top capitalist, known as the 'Bombay Plan.' The entire developed world followed Keynesian 'welfarism' to rebuild capitalism in the post-war period. Subsidies to farmers, employment programmes, protection to workers' unions, were first introduced in the 'capitalist' USA. But the CAs and tech bros, who guide public opinion nowadays, are not educated enough to know any of this.
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Last one on this topic, and I have been holding this in myself for a while. For centuries, class divides kept the labor of the poor invisible to the rich. Factory workers toiled behind walls, farmers in distant fields, domestic help in backrooms. The wealthy consumed the fruits of that labor without ever seeing the faces or the fatigue behind it. No direct encounter, no personal guilt. The gig economy shattered that invisibility, at unprecedented scale. Suddenly, the poor aren't hidden away. They're at your doorstep: the delivery partner handing over your ₹1000+ biryani, late-night groceries, or quick-commerce essentials. You see them in the rain, heat, traffic, often on borrowed bikes, working 8–10 hours for earnings that give them sustenance. You see their exhaustion, their polite smile masking frustration with life in general. This is the first time in history at this scale that the working class and consuming class interact face-to-face, transaction after transaction. And that discomfort with our own selves is why we are uncomfortable about the gig economy. We want these people to look our part, so that the guilt we feel while taking orders from them feels less. We aren't just debating economics. We are confronting guilt. That ₹800 order might equal their entire day's earnings after fuel, bike rent, and app cuts. We tip awkwardly, or avoid eye contact, because the inequality is no longer abstract. It's personal. Pre-gig era, the rich could enjoy luxury without moral discomfort. Labor was out of sight. Now, every doorbell ring is a reminder of systemic inequality. That's why debates explode. It's not just policy. It's emotional reckoning. Some defend the system (“they choose it”), others demand change (“this isn't progress, its exploitation”). And here’s the uncomfortable twist: the unsaid ask of clumsy ‘solutions’ isn’t dignity. It is about returning to invisibility. Ban gig work and you don’t solve inequality. You remove livelihoods. These jobs don’t magically reappear as formal, protected employment the next day. They disappear, or they get pushed back into the informal economy where there are even fewer protections and even less accountability. Over-regulate it until the model breaks, and you achieve the same outcome through paperwork instead of slogans: the work evaporates, prices rise, demand collapses, and the people we claim to protect are the first to lose income. And then what happens? The rich get their old comfort back. Convenience returns without faces. Guilt dissolves. We go back to clean abstractions and moral posturing from a distance. The poor don’t become safer, they become invisible again: back in cash economies, back in backrooms, back in shadows where regulation rarely reaches and dignity isn’t even debated. The gig economy just exposed the reality of inequality to the people who previously had the luxury of not seeing it. The doorbell is not the problem. The question is what we do after opening the door. Visibility is the price of progress. We can either use this discomfort to build something better (which we keep doing continuously as delivery partners are our backbone), or we can ban and over-regulate our way back into ignorance. One of those choices improves lives. The other simply helps the consuming class feel virtuous in the dark.
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Yukkuri Onega Ishimaas 🧐 retweeted
From @benthompson today. YouTube is the threat
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This is my fifth conversation with @GavinSBaker. Gavin understands semiconductors and AI as well as anyone I know and has a gift for making sense of the industry's complexity and nuance. We discuss: - Nvidia vs Google (GPUs + TPUs) - Scaling laws and reasoning models - The economics of AI compute - Why Blackwell's delay mattered - The bear case on the AI capex buildout - Data centers in space - The mistake SaaS companies are making Few people love investing more than Gavin. His closing answer about why he loves it turned into a full reflection on his investing origin story, which I had never heard before. Enjoy! Timestamps: 0:00 Intro 5:03 The Blackwell Transition 23:15 The Prisoner's Dilemma 27:12 The Bear Case: Edge AI 37:19 Meta, Open Source, and Model Depreciation 43:08 Geopolitics and Rare Earths 50:42 Data Centers in Space 56:06 Power Constraints as a Governor 1:11:31 The SaaS Mistake 1:16:17 Nuclear and Quantum 1:22:25 Gavin’s Investing Origins
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Yukkuri Onega Ishimaas 🧐 retweeted
The first version of the guide is here. I’ll link it below. I’ll be updating this over time as I find more stuff.
I’m compiling a list on my newsletter of the best philosophy lectures available on YouTube. For instances Dreyfus’ Being & Time lectures. I’m sending it out as a free resource. Any other suggestions?
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